Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model incorporating machine learning (ML) for assessing kidney fibrosis severity using biochemical markers.

This multi-center retrospective study included 598 patients with kidney fibrosis from four hospitals. A training cohort of 360 patients from Shanghai Tongji Hospital was used to develop a predictive nomogram and ML model, with fibrosis severity classified as mild or moderate-to-severe based on Banff scores. Logistic regression identified key predictors, which were incorporated into a nomogram and ML model. An external validation cohort of 238 patients from three additional hospitals was used for model evaluation.

Serum creatinine (Scr), estimated glomerular filtration rate (eGFR), parathyroid hormone (PTH), brain natriuretic peptide (BNP), and sex were identified as independent predictors of kidney fibrosis severity. The nomogram demonstrated superior discriminative ability in the training cohort (AUC: 0.89, 95% CI: 0.85-0.92) compared to eGFR (AUC: 0.83, 95% CI: 0.78-0.87) and Scr (AUC: 0.87, 95% CI: 0.83-0.91). Among ML models, the Random Forest (RF) model achieved the highest AUC (0.98). In external validation, the nomogram and RF models maintained robust performance with AUCs of 0.86 and 0.79, respectively.

This study presents a validated, noninvasive, multi-center Scr-based machine learning model for assessing kidney fibrosis severity in CKD. The integration of a clinical nomogram and ML approach offers a novel, practical alternative to biopsy for dynamic fibrosis evaluation.

This retrospective cohort study aimed to define the optimal Regions of Homozygosity (ROH) size cut-offs for prediction of morbidity, based on 13 483 Chromosomal Microarray Analyses (CMA). Receiver operating characteristic (ROC) curves were generated, and area under the curve (AUC) was used to assess the predictive capability of total ROH percentage (TRPS), ROH number and ROH segment size in distinguishing between healthy (n=6,196) and affected (n=6,839) cohorts. The metrics were examined for telomeric and interstitial segments, distinct TRPS categories, and across different ancestral origins. ROH segments were identified in 13 035 samples (96.7%), encompassing 66 710 ROH segments. Significant differences in TRPS and ROH segment size were observed between healthy and affected cohorts (p=0.012 and p < 0.001, respectively). However, no clinically significant thresholds could be established based on ROC curves for TRPS and ROH number per sample, as well as for ROH size (AUC 0.64, 0.55, and 0.62, respectively, Figure 1). The same was noted for telomeric versus interstitial locations, various origins, and subcategories of TRPS. In conclusion, this study highlights the complexity of ROH interpretation and emphasizes the importance of tailored reporting strategies in clinical practice. Our findings underscore the need for context-specific reporting guidelines and further research, particularly in consanguineous populations.

The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes.

The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles.

Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles.

This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.

BackgroundExtent of resection (EOR), including gross total resection (GTR), is one of the most important factors in predicting overall survival (OS) in IDH-wild type (IDH-WT) glioblastoma patients. Although GTR represents the complete resection of all visible contrast-enhancing parts of the tumor, imaging predictors of achieving this extent still need to be better understood.PurposeTo assess the impact of preoperative imaging phenotypes as defined by the VASARI feature set and tumoral volumetry to determine predictors of GTR in patients with IDH-WT glioblastoma.Material and MethodsThis retrospective, single-center study analyzed imaging characteristics based on the VASARI features in the preoperative scans of IDH-WT glioblastoma patients. Volumetric analysis was performed to determine associations with clinical outcomes. Univariate analysis was used to determine the association of VASARI features with GTR. A multivariate analysis model was used to determine predictors of GTR.ResultsGTR was achieved in 79/144 (54.8%) patients, near total resection in 15 (10.4%), and subtotal resection in 50 (34.7%) patients. Our results showed non-eloquent tumor regions (55% vs. 35%; P = 0.04) and thick margin of enhancement (56.1% vs. 43.9%; P = 0.04) were associated with GTR and ependymal extension (37% vs. 63%; P = 0.02). Deep white matter invasion (36.3% vs. 63.7%; P = 0.03) was significantly associated with non-gross total resection. Lower tumoral volumes were also associated with gross total resection (P < 0.01). After performing multivariate analysis, the thickness of the tumoral enhancing margins was correlated with GTR with an OR of 1.57 (95% CI=1.1-2.23). Furthermore, the volume of the enhancing component was significantly different according to EOR with a calculated OR of 0.95 (95% CI = 0.92-0.97; P < 0.01).ConclusionImaging characteristics on standard-of-care MRI can predict the rate of GTR in patients with IDH-WT glioblastomas. The thickness of enhancing margins predicts GTR after multivariate analysis. A diagnostic model that includes a combination of the discriminating depicted features on MRI and brain tumor volumetrics has an acceptable diagnostic performance with a specificity >90%.

Cardiac rehabilitation (CR) has been proven to reduce mortality and morbidity in patients with cardiovascular disease. Machine learning (ML) techniques are increasingly used to predict healthcare outcomes in various fields of medicine including CR. This systemic review aims to perform critical appraisal of existing ML-based prognosis predictive model within CR and identify key research gaps in this area.

A systematic literature search was conducted in Scopus, PubMed, Web of Science, and Google Scholar from the inception of each database to January 28, 2024. The data extracted included clinical features, predicted outcomes, model development, and validation as well as model performance metrics. Included studies underwent quality assessments using the IJMEDI and Prediction Model Risk of Bias Assessment Tool checklist.

A total of 22 ML-based clinical models from 7 studies across multiple phases of CR were included. Most models were developed using smaller patient cohorts from 41 to 227, with one exception involving 2280 patients. The prediction objectives ranged from patient intention to initiate CR to graduate from outpatient CR along with interval physiological and psychological progression in CR. The best-performing ML models reported area under the receiver operating characteristics curve between 0.82 and 0.91, with sensitivity from 0.77 to 0.95, indicating good prediction capabilities. However, none of them underwent calibration or external validation. Most studies raised concerns about bias. Readiness of these models for implementation into practice is questionable. External validation of existing models and development of new models with robust methodology based on larger populations and targeting diverse clinical outcomes in CR are needed.

Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children's health and quality of life. Machine learning, an advanced data analysis technique, has shown great potential in healthcare applications in recent years. This systematic review aims to assess the application of ML techniques in pediatric asthma exacerbation and explore their effectiveness and potential value.

Studies from four electronic databases, including PubMed, EBSCO, Elsevier, and Web of Science, from Jan 2000 to Jan 2025, were searched. Studies applying the ML methods for pediatric asthma exacerbation and published in English were eligible. The risk of bias and applicability of the included studies was assessed using the Effective Public Health Practice Project (EPHPP) quality assessment tool.

A total of 23 studies were selected for inclusion in this review, covering different ML models such as decision trees, neural networks, and support vector machines. These studies focused on analyzing risk factors for asthma exacerbation, diagnosing and predicting, optimizing and allocating healthcare resources, and comprehensive asthma management. The results show that ML techniques have significant advantages in the application of pediatric asthma exacerbation and in the provision of personalized health care.

ML techniques show great promise for application in pediatric asthma exacerbations. With further research and clinical validation, these techniques are expected to provide strong support for diagnosis, personalized treatment, and long-term management of pediatric asthma exacerbation.

Not applicable, Prospero registration number CRD42024559232.

The application of artificial intelligence (AI) in laboratory medicine will revolutionize predictive modeling using clinical laboratory information. Machine learning (ML), a sub-discipline of AI, involves fitting algorithms to datasets and is broadly used for data-driven predictive modeling in various disciplines. The majority of ML studies reported in systematic reviews lack key aspects of quality assurance. In clinical laboratory medicine, it is important to consider how differences in analytical methodologies, assay calibration, harmonization, pre-analytical errors, interferences, and physiological factors affecting measured analyte concentrations may also affect the downstream robustness and reliability of ML models. In this article, we address the need for quality improvement and proper validation of ML classification models, with the goal of bringing attention to key concepts pertinent to researchers, manuscript reviewers, and journal editors within the field of pathology and laboratory medicine. Several existing predictive modeling guidelines and recommendations can be readily adapted to the development of ML models in laboratory medicine. We summarize a basic overview of ML and key points from current guidelines including advantages and pitfalls of applied ML. In addition, we draw a parallel between validation of clinical assays and ML models in the context of current regulatory frameworks. The importance of classification performance metrics, model explainability, and data quality along with recommendations for strengthening journal submission requirements are also discussed. Although the focus of this article is on the application of ML in laboratory medicine, many of these concepts extend into other areas of medicine and biomedical science as well.

The incidence of depression among adolescents has risen significantly over the past decade. Emotional dynamics, including variability, instability, and inertia of positive affect (PA) and negative affect (NA), are potential risk factors for depressive psychopathology. However, limited longitudinal evidence exists on how these emotion dynamics relate to depression, particularly in collectivistic cultural contexts, where emotional expression and regulation are shaped by social and familial expectations. This study aimed to investigate the longitudinal associations between emotion dynamics-variability, instability, and inertia of PA and NA-and subsequent depressive symptoms among Chinese adolescents.

Data were collected from middle school students in Taizhou, China, between November 2021 and April 2022. Participants completed baseline surveys and experience sampling assessments, reporting their emotional states ten times daily over five consecutive weekdays. Depressive symptoms were assessed using the Children's Depression Inventory (CDI), while emotional dynamics (variability, instability, and inertia) were derived from the experience sampling data. Logistic regression models were employed to examine whether emotion dynamics predicted depressive symptoms at 1-month and 3-month follow-ups.

A total of 448 participants completed all study procedures and were included in the analysis. Emotional variability and instability in PA and NA were longitudinally associated with depressive symptoms at both 1-month and 3-month follow-ups. After controlling for mean affect levels, PA variability and instability, but not NA, were uniquely linked to depressive symptoms. Emotional inertia showed no significant association with subsequent depressive symptoms. Emotional variability and instability in PA and NA also predicted the development of new symptoms in adolescents without baseline depression (n = 372).

Emotional variability and instability of PA and NA were longitudinally associated with changes in depressive symptoms and the development of new symptoms among adolescents. These emotion dynamics provide insights into real-world emotional processing and offer important targets for adolescent depression interventions.

Pain assessment is essential in nursing care. The Numerical Rating Scale (NRS) is widely used but may not fully capture pain's multidimensional nature. The Pain Assessment in Advanced Dementia (PAINAD) scale is reliable for assessing pain in cognitively impaired patients. This study aims to evaluate the validity of the Italian version of PAINAD (PAINAD-IT) for postoperative pain assessment in geriatric patients with femur fractures.

This study employs the PAINAD-IT, which was translated and validated for the Italian context by Costardi et al. (2007). Face and content validity (I-CVI and S-CVI) for non-cognitively impaired patients were evaluated by experts. Pain assessments were conducted at rest (T0) and during movement (T1). Convergent validity was tested using Spearman correlation, discriminant validity with the Wilcoxon test, and inter-rater reliability with Cohen's kappa. Sensitivity and specificity were calculated.

I-CVIs were ≥0.90 and S-CVI was 0.96. 75 patients were included. Cohen's kappa was 0.918 at T0 and 0.881 at T1. Both PAINAD and NRS detected a significant increase in pain from T0 to T1 (Wilcoxon p < 0.001). Sensitivity was 26 % and specificity was 99 % for PAINAD-IT scores ≥3.

PAINAD showed strong reliability and correlation with NRS, effectively distinguishing between rest and pain stages, these results suggest that PAINAD-IT may be a useful tool for pain assessment in geriatric patients operated for femur fracture. PAINAD-IT scores ≥3 may suggest severe pain. Further multi-centre studies with larger sample sizes are needed to fully validate PAINAD-IT for postoperative pain assessment in geriatric patients with.

This letter addressed the impactful study by Zhong et al, which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy. By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia, the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care. This correspondence highlighted the clinical significance, emphasizing its potential to optimize patient outcomes through tailored interventions. Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB) infection, remains a significant public health threat. The timeliness, portability, and capacity of nanopore sequencing for diagnostics can aid in early detection and drug susceptibility testing (DST), which is crucial for effective TB control. This study synthesized current evidence on the diagnostic accuracy of the nanopore sequencing technology in detecting MTB and its DST profile. A comprehensive literature search in PubMed, Scopus, MEDLINE, Cochrane, EMBASE, Web of Science, AIM, IMEMR, IMSEAR, LILACS, WPRO, HERDIN Plus, MedRxiv, and BioRxiv was performed. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Pooled sensitivity, specificity, predictive values (PV), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated. Thirty-two studies were included; 13 addressed MTB detection only, 15 focused on DST only, and 4 examined both MTB detection and DST. No study used Flongle or PromethION. Seven studies were eligible for meta-analysis on MTB detection and five for DST; studies for MTB detection used GridION only while those for DST profile used MinION only. Our results indicate that GridION device has high sensitivity [88.61%; 95% CI (83.81-92.12%)] and specificity [93.18%; 95% CI (85.32-96.98%)], high positive predictive value [94.71%; 95% CI (89.99-97.27%)], moderately high negative predictive value [84.33%; 95% CI (72.02-91.84%)], and excellent DOR [107.23; 95% CI (35.15-327.15)] and AUC (0.932) in detecting MTB. Based on DOR and AUC, the MinION excelled in detecting pyrazinamide and rifampicin resistance; however, it underperformed in detecting isoniazid and ethambutol resistance. Additional studies will be needed to provide more precise estimates for MinION's sensitivity in detecting drug-resistance, as well as DOR in detecting resistance to pyrazinamide, streptomycin, and ofloxacin. Studies on detecting resistance to bedaquiline, pretomanid, and linezolid are lacking. Subgroup analyses suggest that overall accuracy of MTB detection tends to be higher with prospective study design and use of standards other than CSTB (Chinese national standard for diagnosing TB). Sensitivity analyses reveal that retrospective study design, use of GridION, and use of Illumina whole-genome sequencing (WGS) decrease overall accuracy in detecting any drug-resistant MTB. Findings from both types of analyses, however, should be interpreted with caution because of the low number of studies and uneven distribution of studies in each subgroup.

Esophageal cancer (EC), the fifth most common cause of cancer-related mortality in China, poses a significant threat to public health. Among the pathological types, esophageal squamous cell carcinoma (ESCC) is predominant, comprising approximately 90 % of cases. Screening is crucial for early detection, diagnosis and treatment, thereby reducing ESCC mortality. This study aimed to develop a rapid, accurate, and cost-effective method based on near-infrared (NIR) spectroscopy combined with aquaphotomics for ESCC screening. NIR spectra were obtained from plasma samples of both healthy controls and ESCC patients. Subsequently, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were utilized to identify the water matrix coordinates (WAMACS), thereby delineating the water absorption spectrum pattern (WASP) and constructing an aquagram. The results showed that the PLS-DA screening test model demonstrated high accuracy and precision rates of 95.12 % and 97.10 %, respectively, along with sensitivity and specificity rates of 97.10 % and 84.62 %. The area under the curve (AUC) achieved 0.9064. Aquaphotomic analysis revealed that the WASP of the healthy group predominantly exhibited strong absorption in regions indicative of strong hydrogen bonds (1460 nm, 1480 nm, 1494 nm), while the WASP of the ESCC group showed strong absorption in regions associated with strong hydrogen bonds, weak hydrogen bonds and free water, especially the regions of weak hydrogen bonds (1434 nm) and free water (1390 nm) were significantly different from those of the healthy group. The findings indicated that the rapid screening model for ESCC, integrating NIR spectroscopy with aquaphotomics, is both effective and feasible, with the WASP presenting as a potentially valuable biomarker for ESCC screening.

The aim of this study was to evelop a predictive model, estimating the probability of an in-hospital fall using previously identified associated words, and word combinations in daily nursing records. To assess the difference in discriminatory ability between the predictive model and currently used screening questions.

Hospital falls are a persistent challenge. Identifying patients at high risk before fall incidents occur is essential to optimize preventive measures and reduce the burden on nursing staff.

Words from daily nursing records were used as predictive variables to construct and validate the model. The DeLong's test was used to determine statistical differences between the developed model and the current screening questions.

A total of 3255 consecutive admissions of patients aged 70 and over were included, of whom 110 experiences a fall. Upon internal validation, the predictive text model demonstrated moderate discriminatory ability (AUC-ROC 0. 737 (CI 95 % 0. 683-0.791)) and good calibration across a range of the risk groups. Compared to the screening questions (AUC-ROC 0.603 (CI 95 % 0.555-0.652)) the text model (AUC-ROC 0.734 (CI 95 % 0.679-0.788)) showed significantly better discriminatory ability (DeLong's - 3.93, p ≤0.001).

Daily nursing records can be used to estimate the probability of in-hospital falls. A text-based predictive model outperforms the currently employed screening questions and provides insights for the efficient use of fall prevention interventions. Further research should focus on improving the accuracy and external validation of the model and implementation strategies.

Understanding patterns of bacterial carriage and otitis media (OM) microbiology is crucial for assessing vaccine impact and informing policy. The microbiology of OM can vary with geography, time, and interventions like pneumococcal conjugate vaccines (PCVs). We evaluated the microbiology of nasopharyngeal and middle ear effusions in children living in Western Australia, 11 years following the introduction of PCV13.

Children undergoing surgery for recurrent acute OM and/or chronic OM with effusion (cases), and children undergoing surgery for non-infectious reasons (controls), were recruited. Nasopharyngeal swabs and middle ear effusions (MEE - cases only) were collected, and quantitative PCR applied for detection of Haemophilus influenzae, Moraxella catarrhalis, Streptococcus pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus and Streptococcus pyogenes. S. pneumoniae-positive MEE were serotyped by culture.

Nasopharyngeal swabs from 166 children under 5 years of age (123 cases, 43 controls) and MEE from 103 cases (93 with bilateral effusion - 196 MEE samples) were collected between September 2022 to December 2023. Nasopharyngeal carriage of H. influenzae was more common and density 10 times higher in cases compared to controls (84.2% H. influenzae carriage-positive cases versus 48.9% of controls, p=0.024; mean DNA concentration of 1.8 pg/µL versus 0.13 pg/µL, p=0.037). S. pneumoniae was more commonly carried in cases (not significant), and carriage density was higher in cases compared to controls (mean pneumococcal DNA concentration of 0.4 pg/µL versus 0.09 pg/µL, p=0.049). M. catarrhalis carriage and carriage density were similar between cases and controls (82.1% versus 76.7%). Carriage of 2 or more otopathogen species was common (80% of swabs). In the MEE, H. influenzae predominated (53% PCR-positive) followed by M. catarrhalis (31%), S. pneumoniae (22%), S. aureus (6%), S. pyogenes (2%) and P. aeruginosa (2%). Polymicrobial infection was identified in 26% of effusions. Of the S. pneumoniae PCR-positive MEE, 14 specimens from 11 children were culturable and all serotypes were non-PCV13 types.

The aetiology of recurrent and/or chronic OM in children continues to be primarily associated with H. influenzae. These data highlight the need for a concerted effort to develop effective preventative strategies for H. influenzae, most notably, nontypeable (NTHi). Higher valency PCVs may impact on pneumococcal OM.

Basal cell carcinoma (BCC) is the most common form of skin cancer, originating from basal cells in the skin's outer layer. It frequently arises from prolonged exposure to ultraviolet (UV) radiation from the sun or tanning beds. Although BCC rarely metastasizes, it can cause significant local tissue damage if left untreated. Early detection is essential to prevent extensive damage and potential disfigurement. The United States Preventive Services Task Force (USPSTF) currently remains uncertain about the benefits and potential harms of routine skin cancer screenings in asymptomatic individuals. This paper evaluates the accuracy of predicting BCC using patients' medical histories to address this uncertainty and support early detection efforts.

We analyzed the medical histories of 405,608 patients, including 7733 with BCC. We categorized 25,154 diagnoses into 16 body systems based on the hierarchy in the Systematized Nomenclature of Medicine (SNOMED) ontology. For each body system, we identified the most severe condition present. Logistic Least Absolute Shrinkage and Selection Operator (LASSO) regression was then employed to predict BCC, using demographic information, body systems, and pairwise and triple combinations of body systems, as well as missing value indicators. The dataset was split into 90% for training and 10% for validation. Model performance was evaluated using McFadden's R 2 , Percentage Deviance Explained (PDE), and cross-validated with the area under the receiver operating characteristic curve (AUC).

Diagnoses related to the Integument system showed an 8-fold higher likelihood of being associated with BCC compared to diagnoses related to other systems. Older (age from 60 to 69) white individuals were more likely to receive a BCC diagnosis. After training the model, it achieved a McFadden's R 2 of 0.286, an AUC of 0.912, and a PDE of 28.390%, reflecting a high level of explained variance and prediction accuracy.

This study underscores the potential of LASSO Regression models to enhance early identification of BCC. Extant medical history of patients, available in electronic health records, can accurately predict the risk of BCC. Integrating such predictive models into clinical practice could significantly improve early detection and intervention.

In autopsy practice, the thickness of ventricular walls is one of the parameters used to identify cardiac hypertrophy. The presented study aimed to compare ante- and postmortem measurements of ventricular wall thickness, (i) to determine a postmortem standardized localization and dissection method for ventricular wall measurements, and (ii) to determine the ability of postmortem measurements in recognition of antemortem hypertrophy. A single-center prospective study was conducted at the Institute of Legal Medicine in Hamburg, Germany. Sixty hearts were dissected alternating by the inflow-outflow or short-axis method, and the ventricular walls were measured at different locations and compared with the echocardiographic values of the end-diastolic phase during life of these individuals. The results showed measurement differences between the autoptic and echocardiographic values-for the left ventricle between 3.3 and 5.2 mm, for the right ventricle between 0.2 and 1.1 mm, and for the septum between 1.3 and 1.4 mm. Diagnostic performance of recognizing antemortem hypertrophy with postmortem measurement was poor, except for measuring the right ventricle and septum with the short-axis method (area under the ROC curve of 0.72 and 0.82, respectively). According to the results, cardiac changes may occur postmortem and need to be considered when used for diagnosing cardiac pathology. The postmortem diagnosis of left or right ventricular hypertrophy should always be made in conjunction with other, particularly cardiac, autopsy findings. An autoptic diagnosis of hypertrophy solely by a ventricular wall thickness > 15 mm or > 5 mm alone is not sufficient.

Consensus agreement exists regarding the management of Anderson and D'Alonzo type 1 and type 3, however management of type 2 odontoid fractures remains largely controversial. Though displaced type 2 odontoid fractures are generally considered operative, in the appropriate patient, the parameters that define "displacement" and their relation to fracture stability and outcomes, are poorly defined in the literature. Sagittal fracture displacement, sagittal fracture angulation, fracture comminution, and presence of local cervical deformity impact surgical decision-making, but the effect each characteristic has on clinical decisions has yet to be defined.

Our goal in this study is to 2-fold: (1) define agreement among spine surgeons, as it relates to 5 type 2 odontoid parameters: presence of local cervical deformity, presence of fracture comminution, sagittal displacement >5 mm, sagittal angulation >11 degrees, and clinical management, (2) assess the impact each variable has on the likelihood of surgical indication.

Radiographic analysis study of spine surgeons assessing odontoid fracture morphology and clinical management, using retrospectively collected imaging data.

Patients 65 years or older, treated within the military health system, with type 2 odontoid fractures and CT imaging.

Our outcome measures of interest in this study are (1) agreement among spine surgeons, as it relates to radiographic measurements, and (2) the relative impact, measured by odds ratio, that each radiographic parameter has on clinical decision making, METHODS: We queried the Military Health System (MHS) for all type 2 odontoid fractures, between 2016 and 2023, which resulted in 441 patients, of which 37 had viewable CT scans. Six spine surgeons, 3 orthopedic-trained spine surgeons and 3 neurosurgery-trained spine surgeons, reviewed 37 sagittal CT scans of acute type 2 odontoid fractures and recorded "0' or "1" for the presence of local cervical deformity, presence of fracture comminution, sagittal displacement >5 mm, sagittal angulation >11 degrees and surgical (1) or nonsurgical management (0). We performed an interrater reliability analysis using Fleiss' kappa coefficient to assess agreement among raters and binary multivariate regression analysis to quantify the effect of each variable on eventual clinical management.

Among all spine surgeons, there was substantial agreement with sagittal angulation measurements (k=0.69, p<.01), moderate agreement with sagittal displacement measurements (k=0.55, p<.01 and comminution (k=0.40, p<.01), and fair agreement regarding surgical decision-making (k=0.262, p<.000001). Subspecialty subgroup analysis demonstrated slight agreement with operative management in, both, orthopedic-trained spine surgeons (k=0.10, p>0.05) and neurosurgery-trained spine surgeons (k=0.02, p<.05). Binary univariate regression analysis identified each variable as significantly associated with surgical management. Binary multivariate regression analysis indicated sagittal displacement (OR=21.3, [9.19-54.0, 95% CI]), fracture comminution (OR=6.29, [1.84-23.1, 95% CI]), and local cervical deformity (OR=11.0, [3.87-35.2, 95% CI]), as independently associated with surgical management. ROC and AUC analysis identified sagittal displacement as an excellent predictor (AUC=0.96 [0.903-1.00]) relative to surgical management, while the combinations of deformity and comminution (AUC=0.78, [61.99-94.57]), deformity and angulation (AUC=0.79, [64.94-93.50]), and angulation and comminution (AUC=0.75, [56.61-93.96]), represent fair predictors of surgical management.

Our findings quantify the magnitude of surgical agreement of type 2 odontoid fractures among spine surgeons. Our analysis identified displacement >5mm, angulation >11 degrees, and presence of local cervical deformity as independent variables associated with a higher likelihood of surgical management in patients with type 2 odontoid fractures. Though there is general agreement upon radiographic assessment, there lacks a corresponding agreement in clinical management. This indicates the need for prospective studies that identify predictive preoperative characteristics that correlate with optimal postoperative patient-related outcomes.

The usefulness of the 6-Minute Walk Test (6MWT) in Interstitial Lung Disease (ILD) has been proven. This test assesses the 6-Minute Walk Distance (6MWD), Oxygen Saturation (SpO2) and Chronotropic Response (CR). We aimed to develop an index, the Distance-Saturation-Chronotropic Response (DSC) index and to analyze its relevance in the evaluation of functional capacity and prognosis of patients with ILD.

A retrospective study including 101 ILD patients was conducted. Data collected were results of Pulmonary Functional Tests (PFTs) and 6MWT. We developed a staging system called DSC index and divided it into 3 items (minimal SpO2, 6MWD and CR). Points are assigned to each item ranging from 0 to 2. The scores of each item are summed to obtain the DSC score. The maximal score is 6. To evaluate the reliability of the DSC in assessing functional impact, we analyzed correlations of DSC index with PFTs results and Gender-Age-Physiology (GAP) index. In addition, Receiver Operating Characteristic (ROC) curves were plotted for DSC index and its components, taking a GAP stage ≥ 2 as reference.

The DSC index was correlated with respiratory function and GAP score. This correlation was greater than those of PFTs results and GAP score with each component of the DSC taken independently. The ability of DSC to discriminate patients with a GAP stage ≥ 2 was better than that obtained for each 6MWT parameter.

The DSC index could be considered a practical tool for global assessment of functional capacity and prognosis in patients with ILD.

The Society for Maternal-Fetal Medicine's (SMFM) diagnostic criteria for fetal growth restriction (FGR) recently added abdominal circumference (AC) <10th percentile to estimated fetal weight (EFW) <10th percentile; however, its prediction of neonatal morbidity is unknown. Our objective was to compare the two definitions for their prediction of composite neonatal morbidity.

Secondary analysis of the Fetal Growth Study-Singletons, 2009-2013. The last ultrasound (mean 36.9 ± 2.3 weeks) was included from non-anomalous fetuses. Composite neonatal morbidity was the primary outcome: metabolic acidosis, neonatal intensive care unit stay >3 days, significant respiratory morbidities, seizures, hyperbilirubinemia requiring exchange transfusion, intrapartum aspiration, necrotizing enterocolitis, hypoglycemia, hypoxic ischemic encephalopathy, periventricular leukomalacia, sepsis, retinopathy of prematurity, or neonatal death. The secondary outcome was small for gestational age (SGA). Logistic regression modeled the association of each FGR definition with outcomes, and receiver operating characteristic area under the curve (AUC) assessed predictive ability.

Of 2400 eligible individuals, 135 (5.6%) neonates had composite neonatal morbidity, and 245 (10%) were SGA. At the last ultrasound, 181 (7.5%) had FGR based on EFW alone (original definition) and 215 (9.0%) had FGR based on a small EFW or AC (expanded definition) (P < .0001). Both definitions had poor discrimination for composite neonatal morbidity (original: AUC 0.52, 95% confidence interval [CI] 0.49-0.54; expanded: AUC 0.51, 95% CI, 0.48-0.54). Both had acceptable discrimination of SGA (original: AUC 0.70, 95% CI 0.67-0.73; expanded: AUC 0.71, 95% CI 0.68-0.75).

Adding AC <10th percentile to the EFW <10th percentile definition of FGR significantly increased the incidence of FGR but did not improve the prediction of neonatal morbidity in a low-risk population. The SMFM guideline for FGR should be adopted with caution.

Background/Objectives: Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with its global prevalence projected to rise sharply as diabetes incidence increases. Early detection and timely management are critical to reducing DR-related vision loss. Optical Coherence Tomography Angiography (OCTA) now enables non-invasive, layer-specific visualization of the retinal vasculature, facilitating more precise identification of early microvascular changes. Concurrently, advancements in artificial intelligence (AI), particularly deep learning (DL) architectures such as convolutional neural networks (CNNs), attention-based models, and Vision Transformers (ViTs), have revolutionized image analysis. These AI-driven tools substantially enhance the sensitivity, specificity, and interpretability of DR screening. Methods: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. Results: A total of 32 studies were included in this systematic review. In comparison to conventional ML techniques, our results indicated that DL algorithms significantly improve the accuracy, sensitivity, and specificity of DR screening. Multi-branch CNNs, ensemble architectures, and ViTs were among the sophisticated models with remarkable performance metrics. Several studies reported that accuracy and area under the curve (AUC) values were higher than 99%. Conclusions: This systematic review underscores the transformative potential of integrating advanced DL and machine learning (ML) algorithms with OCTA imaging for DR screening. By synthesizing evidence from 32 studies, we highlight the unique capabilities of AI-OCTA systems in improving diagnostic accuracy, enabling early detection, and streamlining clinical workflows. These advancements promise to enhance patient management by facilitating timely interventions and reducing the burden of DR-related vision loss. Furthermore, this review provides critical recommendations for clinical practice, emphasizing the need for robust validation, ethical considerations, and equitable implementation to ensure the widespread adoption of AI-OCTA technologies. Future research should focus on multicenter studies, multimodal integration, and real-world validation to maximize the clinical impact of these innovative tools.

The relationship between disulfidptosis and rheumatoid arthritis (RA) remains unclear. We aimed to identified biomarkers disulfidptosis-related in RA and revealed potential targeted drugs. Two microarray datasets (GSE93272, GSE45291) related to RA were downloaded from the Gene Expression Omnibus (GEO) database. Disulfidptosis-related genes(DRGs) were extracted from FerrDb database. GSE93272 was used to identify DRGs, and GSE45291 was used to verify results. Multivariate Cox regression analysis was used to identify candidate disulfidptosis-associated hub genes. The differentiated values of DRGs were determined by receiver operator characteristic (ROC) monofactor analysis to judge their potential quality as biomarkers. RT-qPCR were used to validate the expression of hub genes. Additionally, we analyzed the connection between the hub genes and the filtration of immune cells in RA. We made predictions about the miRNAs, TFs and possible drugs that regulate the hub genes. Subsequently, molecular docking was carried out to predict the combination of drugs with hub targets. Finally, molecular dynamics simulation was conducted to further verify the findings. Oxoacyl-ACP Synthase Mitochondrial(OXSM) was identified as a biomarker with high diagnostic value, and an RA diagnostic model based on OXSM for a single gene was constructed. The model showed high accuracy in distinguishing RA and healthy controls (AUC = 0.802) and was validated by external datasets, showing excellent diagnostic power (AUC = 0.982). Twelve potential drugs against RA were recognized by comparative toxicogenomics database (CTD). Molecular docking results showed that ICG 001 had the highest binding affinity to OXSM, and molecular dynamics simulations confirmed the stability of this complexes. Furthermore, CIBERSORT analysis showed a significant correlation between immune cell infiltration and OXSM, and a regulatory network of TFs-gene-miRNAs comprising 8 miRNAs and 34 TFs was identified. Finally, the RT-qPCR results showed that OXSM was significantly increased in the peripheral blood of RA patients compared with healthy controls, consistent with the bioinformatics analysis. These studies suggest that OXSM may be a potential biomarker and therapeutic target for diagnosing RA, and ICG 001 may be a potential drug for RA. These findings may provide new avenues for the effective diagnosis and treatment of RA.

Anaphylaxis is a rapidly onset, life-threatening hypersensitivity reaction, and in some patients, a biphasic reaction may develop following initial treatment. This study aims to investigate the prognostic value of the peripheral perfusion index (PPI) in predicting biphasic reactions among patients presenting to the emergency department with anaphylaxis.

The study is prospective and single-centered. A total of 104 patients aged 18 years and older, diagnosed with anaphylaxis in the emergency department, were included. PPI values, along with other vital signs, were measured at 0, 10, 20, and 30 min, as well as after symptom resolution. All patients were observed for a minimum of 6 h to monitor for the development of biphasic reactions.

The median PPI value at the 0-min mark was 2.20 (IQR, 1.52-3.67), while the median PPI value after symptom resolution was 4.20 (IQR, 3.10-6.35). A biphasic reaction occurred in 10.6 % of patients. Among patients who developed a biphasic reaction, PPI values at 0, 10, 20, and 30 min were significantly lower compared to those who did not (p < 0.05). In ROC analysis, a PPI cutoff of ≤2.17 for predicting biphasic reactions yielded 57 % sensitivity and 91 % specificity (AUC = 0.75).

PPI may serve as an accessible and cost-effective test in emergency departments for continuous monitoring of patients diagnosed with anaphylaxis, allowing for assessment of treatment response, early detection of biphasic reactions, and risk evaluation.

Cognitive impairment (CI) in bipolar disorder (BD) significantly impacts overall functioning and quality of life. A better understanding of the neurobiological mechanisms associated with CI is needed. Studies on neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, have revealed promising findings related to retinal thickness alterations using optical coherence tomography (OCT). Similarly, retinal differences between healthy controls and individuals with BD or schizophrenia have been described. This study explores the utility of OCT in discerning retinal changes possibly associated with CI in BD to enhance our understanding of the biological markers of BD and provide additional information to neuropsychological testing.

Optical coherence tomography (OCT) was employed to measure retinal thickness in the macular ganglion cell layer (GCL), inner plexiform layer (IPL), retinal nerve fiber layer (RNFL), and peripapillary RNFL (pRNFL) in 50 individuals with bipolar disorder (BD). Associations with cognitive impairments were analyzed using cross-validated Random Forest models.

The analysis revealed significant associations between retinal thinning in various segments of the macular GCL, IPL, and RNFL and cognitive impairment (CI) in BD, with particular relevance to executive function deficits (AUC>0.8).

Thinning of the GCL, IPL, and RNFL was significantly associated with worse cognitive performance in individuals with BD. Similar patterns have been observed in schizophrenia, highlighting an innovative and promising field for research and clinical application.

Oral cavity cancer is associated with high morbidity and mortality, particularly with advanced stage diagnosis. Oral cavity cancer, typically squamous cell carcinoma (OSCC), is often preceded by oral potentially malignant disorders (OPMDs), which comprise eleven disorders with variable risks for malignant transformation. While OPMDs are clinical diagnoses, conventional oral exam followed by biopsy and histopathological analysis is the gold standard for diagnosis of OSCC. There is vast heterogeneity in the clinical presentation of OPMDs, with possible visual similarities to early-stage OSCC or even to various benign oral mucosal abnormalities. The diagnostic challenge of OSCC/OPMDs is compounded in the non-specialist or primary care setting. There has been significant research interest in technology to assist in the diagnosis of OSCC/OPMDs. Artificial intelligence (AI), which enables machine performance of human tasks, has already shown promise in several domains of medical diagnostics. Computer vision, the field of AI dedicated to the analysis of visual data, has over the past decade been applied to clinical photographs for the diagnosis of OSCC/OPMDs. Various methodological concerns and limitations may be encountered in the literature on OSCC/OPMD image analysis. This narrative review delineates the current landscape of AI clinical photograph analysis in the diagnosis of OSCC/OPMDs and navigates the limitations, methodological issues, and clinical workflow implications of this field, providing context for future research considerations.

Tick-borne diseases are among the major widespread emerging zoonotic diseases, and their circulation in the environment is influenced by a broad range of abiotic and biotic factors, including the abundance of vectors and vertebrate hosts. In this study, we estimated the prevalence of tick-borne pathogens and the impact of wildlife head count on their circulation in a lowland natural area in northwestern Italy. We collected ticks and camera trap pictures from 14 sampling points every 2 weeks for 1 year and identified pathogens through molecular analyses: Babesia capreoli, B. microti-like, Borrelia burgdorferi sensu lato (s.l.), Rickettsia of the spotted fever group (SFG), Theileria capreoli, and Anaplasma phagocytophilum. We modeled the presence of B. capreoli, B. microti-like, B. burgdorferi s.l., and SFG Rickettsia on head counts of wild ungulates and mesocarnivores. We tested a global model including all collected ticks, as well as a model focusing solely on Ixodes ricinus nymphs, the species, and the developmental stage most associated with zoonotic infection risk. The highest prevalence was obtained for B. microti-like (13%) and SFG Rickettsia (11%), and, for most pathogens, no differences were detected among tick species and their developmental stages. Mesocarnivores showed an additive effect on B. microti-like and B. burgdorferi s.l., while wild ungulates, non-competent for transmission of our target pathogens, showed a dilutive effect. These findings confirm the circulation of relevant tick-borne pathogens in the study area and show the use of camera trap data in predicting tick-borne pathogens' risk by targeting host species which may have an indirect impact and are more easily addressed by monitoring and control strategies.

Traditional assessments of femoral bone shape are inaccessible and do not adequately describe the full complexity of concave bone shape. We aimed to develop and validate an ultrasound-based statistical shape model (SSM) and derived bone shape score (B-Score) to quantify femoral trochlear bone shape morphology associated with anterior cruciate ligament reconstruction (ACLR).

Cross-sectional investigation involving 20 individuals with and 28 individuals without a history of ACLR. Bilateral ultrasound images of the femoral trochlear groove were acquired and analyzed. Both the SSM and B-Score were validated using 5-fold cross-validation, assessing reconstruction and classification accuracy, respectively.

In held out test data, the SSM captured over 99% of the bone shape variance with minimal reconstruction error (RMSE = 0.027 ± 0.004 mm). On test data, the B-Score accurately quantified bone shape associated with ACLR, demonstrating high accuracy (92.42%), sensitivity (97.37%), specificity (85.71%), and AUROC (0.95). A B-Score threshold of 1.41 standard deviations from the mean healthy bone shape was identified for classifying ACLR history.

The ultrasound-based SSM and derived B-Score provide a valid and accessible method for quantifying femoral trochlear bone shape changes post-ACLR. This approach offers potential for early detection of bone shape changes associated with disease and injury, improving long-term outcomes for ACLR patients. Future research should focus on enhancing model generalizability and assessment of bone shape changes longitudinally.

This study investigated the application of sparsity regularization methods to improve the classification of leukemia subtypes using high-dimensional gene expression data. Multinomial logistic regression models with the sparsity methods of Ridge, Lasso, and Elastic Net regularizations were employed to address overfitting and dimensionality issues while enhancing model interpretability. The study used a leukemia cancer dataset from the Curated Microarray Database (CuMiDa), which included gene expression data for 16,383 genes across 281 samples representing seven different types of leukemia. The statistical metrics of Accuracy, Kappa statistics, AUC, and F1-score were used to measure the models' implementation. Besides, the effectiveness and ability of each method in gene selection and dimensional reduction of the models were discussed. Elastic Net regularization was a better technique than the Ridge and Lasso based on overall classification performance; it also reached the highest accuracy along with Kappa values. On the other hand, both Lasso and Elastic Net were making more effective feature selections, creating sparse models that could efficiently discriminate leukemia subtypes. In this way, the results highlighted that the inclusion of sparsity regularization could enhance knowledge and accuracy in such a challenging task of subclass leukemia classification, enabling much more tailored treatments.

Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. This study employs machine learning (ML) to classify α-thalassemia traits based on gender and CBC, exploring the effects of grouping silent- and non-carriers.

The dataset includes 288 individuals with suspected α-thalassemia from Sri Lanka. It was classified using eleven discriminant formulae and nine ML models. Outliers were removed using Mahalanobis distance, and resampling was conducted with the synthetic minority oversampling technique (SMOTE) and SMOTE-nominal continuous (NC). The Mann-Whitney U test handled feature extraction and class grouping. ML performance was evaluated with eight criteria.

The Ehsani formula achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.66 by grouping silent- and non-carriers. The convolutional neural network (CNN) without feature extraction demonstrated better performance, with an accuracy of 0.85, sensitivity of 0.8, specificity of 0.86, and ROC-AUC of 0.95/0.93 (micro/macro). Performance was maintained even without preprocessing.

ML models outperformed classical discriminant formulae in classifying α-thalassemia using sex and CBC features. A larger dataset could enhance ML model generalization and the impact of feature extraction. Grouping silent- and non-carriers improved ML results, especially with resampling. The silent carriers were not separable from non-carriers regarding the available features.

The Maternal-Fetal Medicine Units Network calculator for vaginal birth after cesarean (VBAC) probability was updated to exclude a race and ethnicity variable, but its impact on marginalized groups is unknown. We assessed the tool with attention to birth history and body mass index (BMI) in a predominately Hispanic cohort and examined the possible effect of discouraging labor after cesarean (LAC) with a low score.

We conducted a retrospective cohort study of LACs by patients who entered spontaneous or induced labor with a live, nonanomalous fetus in cephalic presentation between 2012 and 2022. Observed VBAC rates were compared with the mean predicted VBAC probability derived from the calculator. Areas under the curve (AUCs) were calculated for the entire cohort and for individuals with LAC with and without prior vaginal birth. A z-test was used to determine the significance between VBAC rates in 4 BMI categories. The impact of discouraging LAC with VBAC probability below 70% was examined.

A total of 400 people experienced 507 LACs, with 417 (82.2%) resulting in VBAC compared with a mean predicted probability of 71.2%. The AUC for all LACs was 0.76 (95% CI, 0.71-0.81), whereas the AUC for LACs with prior vaginal birth was 0.70 (95% CI, 0.56-0.85) and without was 0.60 (95% CI, 0.52-0.67). Observed VBAC rates exceeded predicted rates for individuals with overweight and obese BMIs: <25 (79.6% vs 75.6%; P = .520), 25 to 30 (83.2% vs 71.9%; P = .007), 30 to 40 (82.7% vs 70.0%; P = .004), and ≥40 (82.8% vs 58.3%; P = .040). Discouraging LAC below 70% probability might have prevented 71 unsuccessful LACs, 160 initial VBACs, and 57 subsequent VBACs, decreasing the VBAC rate to 39.4% (200/507).

In a predominately Hispanic cohort, the updated calculator underestimated VBAC potential for people with no vaginal birth history and/or an elevated BMI. Discouraging LAC based on low VBAC probability may prevent both initial and subsequent VBACs.

Circadian rhythms influence various physiological and behavioral processes such as sleep-wake cycles, hormone secretion, and metabolism. In Drosophila, an important set of circadian output neurons is called pars intercerebralis (PI) neurons, which receive input from specific clock neurons called DN1. These DN1 neurons can further be subdivided into functionally and anatomically distinctive anterior (DN1a) and posterior (DN1p) clusters. The neuropeptide diuretic hormones 31 (Dh31) and 44 (Dh44) are the insect neuropeptides known to activate PI neurons to control activity rhythms. However, the neurophysiological basis of how Dh31 and Dh44 affect circadian clock neural coding mechanisms underlying sleep in Drosophila is not well understood. Here, we identify Dh31/Dh44-dependent spike time precision and plasticity in PI neurons. We first find that a mixture of Dh31 and Dh44 enhanced the firing of PI neurons, compared to the application of Dh31 alone and Dh44 alone. We next find that the application of synthesized Dh31 and Dh44 affects membrane potential dynamics of PI neurons in the precise timing of the neuronal firing through their synergistic interaction, possibly mediated by calcium-activated potassium channel conductance. Further, we characterize that Dh31/Dh44 enhances postsynaptic potentials in PI neurons. Together, these results suggest multiplexed neuropeptide-dependent spike time precision and plasticity as circadian clock neural coding mechanisms underlying sleep in Drosophila.

This study aimed to develop a predictive model for mortality outcomes among pediatric trauma patients using machine learning (ML) algorithms.

We extracted data on a cohort of pediatric trauma patients (18 years and younger) from the National Trauma Data Bank (NTDB). The main aim was to identify clinical and physiologic variables that could serve as predictors for pediatric trauma mortality. Data was split into a development cohort (70%) to build four ML models and then tested in a validation cohort (30%). The area under the receiver operating characteristic curve (AUC) was used to assess each model's performance.

In 510,381 children, the gross mortality rate was 1.6% (n = 8,250). Most subjects were male (67%, n = 342,571) and white (62%, n = 315,178). The AUCs of the four models ranged from 92.7 to 97.7 with XGBoost demonstrating the highest AUC. XGBoost demonstrated the highest accuracy of 97.7%.

Machine learning algorithms can be effectively utilized to build an accurate pediatric mortality prediction model that leverages variables easily obtained upon trauma admission.

Canine vector-borne diseases (CVBDs) are illnesses caused by pathogens transmitted by blood-feeding arthropods such as ticks and mosquitoes. Many CVBDs, including dirofilariosis, anaplasmosis, and ehrlichiosis, are globally distributed and may cause a variety of clinical signs in dogs. Several CVBD agents are zoonotic, making epidemiological surveillance a joint veterinary and public health effort. In this study, we determined the seropositivity of four pathogens from dogs on Saipan, Northern Mariana Islands, a US Commonwealth located in the western Pacific Ocean.

Blood samples (n = 443) were collected from client-owned, owner surrendered, and shelter dogs that participated in an island-wide spay-and-neuter event in 2023. All samples were assessed using a commercial, point-of-care enzyme-linked immunosorbent assay (ELISA) test (SNAP® 4Dx® Plus, IDEXX Laboratory, Westbrook, Maine, USA) to detect the Dirofilaria immitis antigen and antibodies against Ehrlichia spp., Anaplasma spp., and Borrelia burgdorferi sensu lato. Risk factors were assessed for each pathogen through a univariate analysis, followed by a multivariable logistic regression.

Overall, 66.1% (n = 300/443) of the dogs tested positive for at least one pathogen, with the highest prevalence observed for Ehrlichia spp. (58.0%; n = 246/443), followed by Anaplasma spp. (43.1%; n = 184/443) and D. immitis (14.8%; n = 63/443). Among the dogs with a single pathogen detected (30.9%; n = 137/443), Ehrlichia spp. was most prevalent (64.9%; n = 89/137), followed by Anaplasma spp. (23.3%; n = 32/137) and D. immitis (11.6%; n = 16/137). For co-detection of two or more pathogens (36.7%; n = 163/443), Ehrlichia spp. + Anaplasma spp. presented the highest frequency (70.5%; n = 115/163), followed by Ehrlichia spp. + D. immitis (6.7%; n = 11/163), Anaplasma spp. + D. immitis (3.6%; n = 6/163), and Ehrlichia spp. + Anaplasma spp. + D. immitis (19.0%; n = 31/163). Age (P = < 0.001), residing district (P = 0.001), and ownership status (P = < 0.001) were significantly associated with D. immitis positive status in a univariable analysis. Age (P = < 0.001), residing district (P = 0.177), and ownership status (P = 0.014) were significant in a univariable analysis with Ehrlichia spp. as an outcome. Finally, Anaplasma spp. had a significant association with ownership status (P = < 0.001) as a risk factor in a univariable analysis.

This study shows high seropositivity for CVBPs in a dog population living in a poorly studied area. The results of this study suggest that strategies for the prevention and control of these CVBDs should be reinforced on the Island of Saipan.

Skin corrosion assessment is an essential toxicity end point that addresses safety concerns for topical dosage forms and cosmetic products. Previously, skin corrosion assessments required animal testing; however, differences in skin architecture and ethical concerns regarding animal models have fostered the advancement of alternative methods such as in silico and in vitro models. This study aimed to develop deep learning (DL) models based on recurrent neural networks (RNNs) for classifying skin corrosion of chemical compounds based on chemical language notation, molecular substructure, physicochemical properties, and a combination of these three properties called conjoint fingerprints. Simple RNN, long short-term memory, bidirectional long short-term memory (BiLSTM), gated recurrent units, and bidirectional gated recurrent units models, along with 11 molecular features, were employed to generate 55 RNN-based models. Applicability domain and permutation importance analysis were exploited for additional trustable prediction and explanation ability of the models, respectively. Our findings indicate that BiLSTM with conjoint features of MACCS keys and physicochemical descriptors is the most effective model with 84.3% accuracy, 89.8% area under the curve, and 57.6% Matthews correlation coefficient for the external test performance. Furthermore, our model accurately predicted the skin corrosion toxicity of all new and unseen compounds beyond our test set, highlighting prominent classification performance compared to existing skin corrosion models. This finding will contribute to the utilization of DL and conjoint characteristics of molecular structure to enhance the model's predictive capability for skin toxicity assessment.

Melioidosis is a zoonotic disease that is caused by Burkholderia pseudomallei, which is a serious public health and safety risk. In order to explore the global animal melioidosis risk distribution and its dynamic response to future climate scenarios, we collected global data about reported animal incidence sites. Data regarding the density of Burkholderia pseudomallei in the environment were created by collecting and sorting information about the Burkholderia pseudomallei occurrence sites in contaminated air, soil, and water. Combined with bioclimatic variables, the maximum entropy (MaxEnt) niche was modeled for global animal melioidosis. The findings indicate that under current bioclimatic conditions, global animal melioidosis risk regions are concentrated between 30° S and 30° N, with high-risk areas being mainly in Central America, the northern part of South America, and eastern and southern India, among others. Most countries will expand their risk regions under future climatic scenarios. Melioidosis risk expanding towards higher northern latitudes has led to new epidemic areas. In addition, the melioidosis risk area will contract in some areas. Therefore, we have provided a basis for global melioidosis surveillance and propose feasible strategies for prevention and control in high-risk regions, which will help countries to carry out targeted surveillance and prevention to reduce risks and losses.

Preoperative intramuscular fat (IMF) is a strong predictor of tendon failure after a rotator cuff repair. Due to the contemporary labor intensive and time-dependent manual segmentation required for quantitative assessment of IMF, clinical implementation remains a challenge. The emergence of accurate three-dimensional evaluation of the rotator cuff may permit implementation with greater inter-rater reliability than common subjective scales (eg, Goutallier classification (GC)). Here, we developed and validated a convolutional neural network (CNN) model for auto-segmentation of the shoulder on Dixon magnetic resonance imaging. Also, we aimed to assess the agreement among GC, two-dimensional (2D), and 3D IMF, including their discriminatory ability for the identification of muscles above an IMF threshold shown to negatively impact surgical outcomes (ie, GC ≥ 3).

This study retrospectively obtained fat-water Dixon shoulder magnetic resonance imagings between March 2023 and March 2024 to develop and validate a CNN model for the segmentation of individual rotator cuff muscles and surrounding tissues. The CNN model was trained using a modified U-Net architecture (n = 80) and tested on an external dataset (n = 25). Accuracy was primarily evaluated using the Dice Similarity Coefficient (DSC) compared to manual segmentation. Reliability was evaluated by the intraclass correlation coefficient (ICC2,1) and discriminatory ability was evaluated by the area under the receiver operating characteristic curve.

The model after training (37 male and 43 female, mean age = 55.8 ± 15.6 years) and testing (15 male and 10 female, mean age = 56.6 ± 19.7 years) produced DSCs of ≥0.89 except for teres minor (DSC = 0.86 ± 0.03). The model demonstrated excellent reliability for volume (ICC2,1 ≥ 0.93) and good to excellent reliability for IMF (ICC2,1 ≥ 0.80), with the exceptions of teres major volume (ICC2,1 = 0.82, 95% CI: 0.63-0.92, P < .001) and subscapularis IMF (ICC2,1 = 0.55, 95% CI: 0.22-0.77, P < .001). 3D IMF but not 2D IMF was associated with GC for the supraspinatus, subscapularis, and infraspinatus (U ≥ 4.02, P < .045). The proposed CNN model's IMF outputs produced excellent discriminatory capability of muscles above the IMF threshold shown to negatively impact outcomes (receiver operating characteristic curve ≥0.93).

The development of a CNN model allows for efficient, accurate segmentation of muscle and bone, enabling reliable evaluation of muscle quality. The model demonstrates that 2D evaluation of IMF is insufficient for differentiating between rotator cuff muscles on either side of a clinically meaningful IMF threshold on the GC scheme, whereas 3D IMF shows excellent discriminant validity across all rotator cuff muscles.

The objective of the study was to investigate the value of platelet distribution width (PDW) as a prognostic biomarker by comparing PDW between COPD patients with pulmonary hypertension (PH) (PASP 50 mmHg) and those without PH (PASP < 50 mmHg) and to explore its diagnostic and predictive value in this population. A retrospective study of 270 COPD patients in Liaocheng People's Hospital (July 2018 to July 2019) was done by dividing them into two groups according to pulmonary artery systolic pressure (PASP): COPD-only (PASP <50 mmHg) and COPD with PH (PASP ≥50 mmHg). Routine blood tests, C-reactive protein, pulmonary function tests, echocardiography, Chronic Obstructive Pulmonary Disease Assessment Questionnaire (CAT), Clinical COPD Questionnaire were performed. PDW was higher in COPD with PH group than COPD-only group. After adjusting for confounders including age, smoking history, CAT scores, white blood cell count, PDW, and NT-proBNP in COPD with PH, PDW was positively correlated with various parameters. PDW can diagnose COPD with PH, also prognostic value and cardiovascular distinction in these patients. The study concluded that PDW is a prognostic marker, reflecting pulmonary and cardiovascular physiology in COPD patients with PH. It can be used for early diagnosis, risk stratification, and individualized management for this high-risk population.

Many fall risk assessment tools exist. However, few of these fall risk assessment tools have been tested in the acute rehabilitation setting. The purpose of our study was to compare the accuracy of the Hendrich II Fall Risk Model (HIIFRM) and Sunnyview Test Scale in predicting falls. We also identified factors associated with falls in the rehabilitation patient.

In this retrospective cohort study, we extracted electronic health record data from two acute inpatient rehabilitation units and compared the predictive validity of the HIIFRM and the Sunnyview Test Scale.

Our sample included 134 fallers and 1,667 nonfallers. The HIIFRM and the Sunnyview Test Scale had similar predictive performance with area under the receiver operating characteristic curve (AUC) of .62 and .60, respectively.

The HIIFRM and the Sunnyview Test Scale had poor performance (AUC < .70) predicting falls in this acute rehabilitation setting. Using a fall risk assessment tool alone does not consider unique risk factors and makes implementation of individualized prevention interventions challenging. Nurses need a framework to use individualized factors to determine high fall risk. Further research is needed to clarify variables specific to the inpatient rehabilitation population.

Current fall risk assessment tools are inadequate in the inpatient rehabilitation setting; an individualized fall prevention plan is recommended to ensure patient safety.

Infections caused by extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) are increasing in the United States. Although many risk factor scoring tools exist, many are specific to bloodstream isolates and may not represent all patient populations. The purpose of this study was to create and validate an institution-specific scoring tool for select ESBL-E of non-urinary origin based on previously identified risk factors.

This retrospective, case-control analysis included inpatient adults at an academic medical center from July 2021 through August 2023 with a documented ESBL-E or non-ESBL-E infection of non-urinary origin. Patients with ESBL-E isolates were matched in a 1:1 ratio to non-ESBL-E isolates by organism and specimen type. Points for each risk factor were assigned by dividing their respective regression coefficient by half of the smallest regression coefficient and rounding to the nearest integer (prior ESBL-E within the past 12 months: 6 points, urinary catheter: 3 points, central venous catheter: 2 points, cirrhosis: 2 points). Sensitivities, specificities, positive predictive values (PPV), and negative predictive values (NPV) were calculated for each score, and discriminatory power was assessed via the receiver operating characteristic (ROC)-area under the curve (AUC).

Of the 1139 identified cultures, 140 patients met the criteria for inclusion into the ESBL-E case arm, thus 140 patients with non-ESBL-E cultures were matched as controls. Baseline characteristics were relatively similar between the groups. A score of 0 was associated with low risk of ESBL-E (PPV 0.31, NPV 0.36), whereas scores between 2 and 5 were considered moderate risk (PPV 0.56, NPV 0.55), and scores ≥6 were associated with high risk (PPV 0.91, NPV 0.56). The ROC curve AUC was 0.705.

The majority of ESBL-E risk factor scoring tools are specific to isolates causing bloodstream infections. This institution-specific scoring tool may be used to tailor empiric antimicrobial regimens and decrease unnecessary exposure to carbapenems in non-ESBL-E infections of non-urinary origin.

Perinatal mental health disorders are common complications of pregnancy and the postpartum period. The value of screening for their early detection is well-recognized, but to-date, research-validated mental health measures for postpartum women in Malta are lacking. In this prospective cross-sectional study, we assessed the validity of the Edinburgh Postnatal Depression Scale (EPDS), an EPDS subscale (EPDS-3A), and the Generalised Anxiety Disorder-7 item (GAD-7) as screening measures for postpartum depression and anxiety. The optimal cut points were calculated.

243 randomly selected women from birth to 12 months postnatally self-completed the EPDS and the GAD-7. For women scoring ≥10 in at least one of the questionnaires, the Mini International Neuropsychiatric Interview (MINI) was applied to confirm or refute a diagnosis of depression and/or anxiety disorder based on DSM-5 criteria. Total EPDS, EPDS-3A and GAD-7 scores were analysed against MINI outcomes using receiver operator curve (ROC), and area under curves (AUCs) were determined. Sensitivity, specificity, positive and negative predictive values, likelihood ratios and Youden's indices were calculated across a range of cut-off values.

Both the EPDS and GAD-7 had significant AUCs (>0.8) and Youden's indices (>0.6), contrary to the EPDS-3A. When screening for postnatal depression with the EPDS, the optimal cut-off is 11/12 (sensitivity 75 %; specificity 87.6 %). For postnatal anxiety, the recommended GAD-7 cut-off is 8/9 (sensitivity 79.2 %; specificity 85.3 %).

Both the EPDS and GAD-7 are valid screening measures for postpartum depression and anxiety, respectively. These findings can inform the implementation of postpartum screening programs to improve maternal healthcare in Malta.

Over 25 % of bipolar disorder (BD) patients are misdiagnosed with major depressive disorder (MDD). An urgent need exists for a biomarker to differentiate BD from MDD. Various manifestations and intensities of maladaptive guilt processing might uniquely contribute to the pathogenesis of BD compared to MDD.

This study adopted a first-person perspective guilt-provoking functional magnetic resonance imaging (fMRI) task, respectively induced by painful and ambiguous scenarios in 111 individuals: 35 with remitted MDD, 38 with remitted bipolar I disorder (BD-I), and 38 matched controls.

A significant interaction between group and sense of agency in predicting guilt ratings for ambiguous, rather than painful, scenarios. The association between sense of agency and guilt was significant in MDD but not in BD-I patients or controls. Activation in the dorsomedial prefrontal cortex (dmPFC), pregenual anterior cingulate cortex (pgACC), and right inferior parietal lobule (IPL) was higher in BD-I than MDD subjects in response to ambiguous scenarios, whereas these were comparable to painful ones. The correlation between guilt ratings and activation in the dorsal anterior cingulate cortex (dACC) to ambiguous scenarios was significant in MDD, but not in BD-I or controls. The results of the multivariate pattern classification analysis showed that in the ambiguous scenarios, the accuracy of using brain activation patterns in the dmPFC, pgACC, and IPL to distinguish between participants with MDD and BD-I was 70.0 %, 71.5 %, and 68.7 %, respectively. An additional test of the classification model, employing a combined mask of the three ROIs to distinguish between the two mood disorders in ambiguous scenarios, achieved an accuracy of 78.6 % and an AUC value of 0.84.

Subjective reports and neural correlates of guilt in ambiguous social situations, as well as a sense of agency, could provide to be a potential biomarker to help distinguish between BD-I and MDD even in the remitted stage.

To evaluate and compare the accuracy of detection methods for the diagnosis of secondary caries around direct restorations in posterior teeth.

Accuracy parameters including sensitivity, specificity, diagnostic odds ratio (DOR), area under curve (AUC), and partial AUC (pAUC) are generated from studies assessing the accuracy of detection methods for secondary caries.

Publications from PubMed, Web of Science, Scopus, Medline, EMBASE and Cochrane Library databases.

This review included 25 studies evaluating visual examination (V(laboratory); n = 9 & V(clinical); n = 2), tactile examination (T; n = 3), intra-oral radiography (IR; n = 14), cone-beam computed tomography (CBCT; n = 4), quantitative light-induced fluorescence (QLF; n = 4), laser fluorescence (LF; n = 8) and digital imaging fiber-optic transillumination (DIFOTI; n = 1). The pooled sensitivity [95 % Confidence Interval, CI] and specificity [95 % CI] of detection methods for secondary caries were 0.60[0.45-0.73] and 0.67[0.53-0.78] for V(laboratory); 0.82[0.23-0.99] and 0.77[0.15-0.98] for V(clinical); 0.31[0.25-0.39] and 0.95[0.78-0.99] for T; 0.59[0.52-0.66] and 0.82[0.75-0.88] for IR; 0.61[0.48-0.73] and 0.82[0.64-0.92] for CBCT; 0.71[0.64-0.78] and 0.51[0.40-0.62] for QLF; 0.57[0.43-0.71] and 0.81[0.76-0.85] for LF; and 0.63[0.47-0.76] and 0.95[0.90-0.98] for DIFOTI. DOR values [95 % CI] of the secondary caries detection methods were V(laboratory)-2.88[2.18-3.80]; V(clinical)-16.66[3.84-72.28]; T-6.36[1.12-36.28]; IR-6.55[3.44-12.46]; CBCT-6.18[1.42-26.91]; QLF-2.25[1.39-3.63]; LF-4.86[2.40-9.82]; and DIFOTI-30.00[11.94-75.36], respectively. Respective AUC (pAUC) were V-0.645(0.535); T-0.379(0.315); IR-0.767(0.693); CBCT-0.887(0.820); QLF-0.581(0.633) and LF-0.828(0.590). AUC values were not available for DIFOTI and V(clinical).

Among the seven types of detection method for secondary caries diagnosis, none of the detection methods demonstrate satisfactory accuracy in detecting secondary caries around direct restorations in posterior teeth.

This systematic review provides insights for the clinician and researcher in selecting the clinical detection method for secondary caries diagnosis and facilitates clinical decision making.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills. Timely detection of these symptoms can facilitate early intervention, potentially slowing disease progression and enabling appropriate treatment and care.

The RADAR-AD study was designed to evaluate the accuracy and validity of multiple RMTs in detecting functional decline across various stages of AD in a real-world setting, compared to standard clinical rating scales. Our approach involved a univariate analysis using Analysis of Covariance (ANCOVA) to analyze individual features of six RMTs while adjusting for variables such as age, sex, years of education, clinical site, BMI and season. Additionally, we employed four machine learning classifiers - Logistic Regression, Decision Tree, Random Forest, and XGBoost - using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs.

The ANCOVA results indicated significant differences between healthy and AD subjects regarding reduced physical activity, less REM sleep, altered gait patterns, and decreased cognitive functioning. The machine-learning-based analysis demonstrated that RMT-based models could identify subjects in the prodromal stage with an Area Under the ROC Curve of 73.0 %. In addition, our findings show that the Amsterdam iADL questionnaire has high discriminatory abilities.

RMTs show promise in AD detection already in the prodromal stage. Using them could allow for earlier detection and intervention, thereby improving patients' quality of life. Furthermore, the Amsterdam iADL questionnaire holds high potential when employed remotely.