To construct a diabetic retinopathy (DR) prediction nomogram based on optical coherence tomography angiography (OCTA) parameters. Ophthalmologists can then use this nomogram to assess the risk of early-stage DR.

In this retrospective study, patients with type 2 diabetes mellitus who completed DR screening were enrolled and divided into training and validation sets. Fifteen parameters, including OCTA parameters, axial length (AL), age, and sex, were selected via least absolute shrinkage and selection operator (LASSO) in the training set. The chosen parameters were used to construct the model. Model performance was evaluated for both the training and validation sets via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A corresponding nomogram was created.

A total of 464 eyes from 464 patients were divided into a training set (324, 69.83%) and a validation set (140, 30.17%). The superficial parafoveal capillary density (CD), deep parafoveal CD, foveal CD in the 300 µm-wide area surrounding the foveal avascular zone (FD- 300 area), AL, and patient ages were included in the final model. The area under curve of the model was 0.825 in the training set and 0.831 in the validation set. The calibration curves showed good alignment between the actual and predicted outcomes in both datasets. DCA demonstrated that the nomogram was clinically useful.

A model with good performance for predicting DR via OCTA parameters was developed. The superficial parafoveal CD, deep parafoveal CD, and FD- 300 area were important predictive parameters in this model. The corresponding nomogram may serve as a convenient tool for early DR risk prediction and lay the foundation for developing OCTA-based automated diagnostic software for early DR detection.

Exploring the expression of Mitochondrial Permeability Transition Dependent Necrosis lncRNA in bladder cancer and elucidate their precise function within the tumor microenvironment and impact on prognosis.

We employed a comprehensive bioinformatics approach to investigate the function and influence of lncRNA in bladder cancer. Gene expression data, clinical data, and mutation data of bladder cancer are obtained from TCGA database.

We developed a new prognostic model incorporating 6 lncRNAs. The predictive efficacy of this model for bladder cancer prognosis was validated. Furthermore, we investigated the influence of model on the tumor microenvironment and drug sensitivity.

This study presents a novel prognostic framework for bladder cancer that holds great potential for enhancing prognostic prediction accuracy and optimizing treatment strategies for patients with this disease.

Postoperative delirium (POD) is a severe complication following cardiac surgery and is associated with increased morbidity and mortality. The impact of intra- and early postoperative factors on the occurrence of POD following cardiac surgery remains controversial. To close this gap, we investigated intra- and early postoperative factors and their predictive values for POD.

We performed a prospective observational study that aimed to FIND DElirium RIsk factors (FINDERI) for patients undergoing elective cardiac surgery. POD was assessed using the Confusion Assessment Method algorithm. Intra- and early postoperative factors were extracted from electronic medical records and reviewed by cardiac surgeons. To identify potential predictors of POD, we used univariate and multivariate logistic regression along with machine learning (ML) with ten-fold cross-validation.

In our study cohort of 490 patients, 106 screened positive for POD (21.6%). In the multivariate analysis, we found a positive association between POD occurrence and age ( P < 0.001), duration of surgery ( P = 0.027), combined (versus isolated) surgical procedures ( P = 0.024), opening of the cardiac chambers ( P = 0.046), and ventilation time ( P < 0.001). The ML-based decision tree identified a two level-algorithm including ventilation time and aortic cross-clamping time, with an AUC of 0.7116 ( P = 0.0002) in the validation set. In the ML-based LASSO regression analysis, we identified ventilation time, administration of erythrocyte concentrates (EC), and usage of cardiopulmonary bypass (CPB) as predictors of POD, with an AUC of 0.7407 ( P < 0.0001) in the validation set.

The results of this analysis highlight the associations between ventilation time, aortic cross-clamping time, administration of EC, and usage of CPB and POD. Additionally, they suggest that the optimization of surgical protocols has the potential to reduce POD risk in individuals undergoing cardiac surgery.

We interrogated two large national databases to explore the underlying mechanisms and institutional effects of the known association of enhanced survival with a higher lymph node yield (LNY) in non-metastatic colon cancer.

Clinical and pathological data for stage I-III colon adenocarcinomas were extracted from the CORECT-R (England, 2010-2020) and SEER database (USA, 2000-2020). A lymph node (LN) cut-off for the lack of clinically significant increase in nodal positivity was identified. A multivariable Cox-regression model was developed to study the effect of LNY on overall survival. Furthermore, institutional variations in LNY and their impact on survival were explored.

Patients were retrospectively included from the CORECT-R (n = 84,116) and SEER (n = 287,974) databases. No significant increase in nodal positivity was noted after a LN cut-off of 9. However, improved survival was noted in node-negative and node-positive cancers beyond this cut-off. A 1% risk-reduction concerning overall survival was reported for every node counted. We identified ten outlying institutions across England with an observed LNY greater or less than the expected, with no impact on overall survival.

We advocate incorporating LNY into patient and clinician discussions as a surrogate marker of tumour biology and prognosis rather than using LNY as a quality indicator.

This study sought to quantify uptake rates of non-invasive prenatal screening for de novo single-gene disorders (NIPS-SGD) in pregnant subjects whose reproductive partner is of advanced paternal age (APA) and to determine individual parameters associated with higher test uptake rates. A cross-sectional study was performed of pregnant subjects who received prenatal genetic counseling at a mid-size tertiary care center and were offered NIPS-SGD for APA (defined as 45 or older at delivery) between June 1, 2017 and April 13, 2021. We included non-anomalous, singleton gestations who conceived without donor egg/sperm. The outcome was measured as subjects who elected to do NIPS-SGD versus subjects who declined. Multivariable logistic regression was used to develop a model to discriminate between those who opted for testing and those who did not. 186 subjects were offered NIPS-SGD and met inclusion criteria. 70 had testing and 116 declined. Overall uptake rate was 38%. Several individual parameters were associated with higher test uptake including utilization of other screenings such as NIPS for aneuploidy (OR 3.4), carrier screening (OR 7.0) and invasive diagnostic testing (OR 8.4), presence of reproductive partner (OR 4.3), medicaid insurance (OR 2.6), and counseling at an offsite location (OR 2.0). AUC for the final regression model predicting NIPS-SGD uptake was 0.79. Based on this study, subjects who are information seekers and who opt for other prenatal screenings are more likely to pursue NIPS-SGD for de novo conditions associated with APA.

Cancer-related pain is a common and distressing symptom in patients with malignant tumors, significantly affecting quality of life. Transdermal fentanyl is a convenient opioid option for patients with intestinal obstruction or difficulty swallowing; however, some patients do not experience adequate pain relief. Predicting transdermal fentanyl analgesic effectiveness is crucial to optimize pain management.

This study aimed to develop a predictive model for transdermal fentanyl effectiveness in cancer patients.

Clinical data from adult cancer pain patients at Chongqing University Cancer Hospital were analyzed (January 2020-December 2022). Logistic regression and feature selection were applied, followed by developing nine predictive models using Logistic Regression, Random Forest (RF), and Extreme Gradient Boosting. The receiver operating characteristic (ROC) curves, the Youden index, and the Brier score were used to evaluate the performance of the model. Cross-validation and SHapley Additive exPlanations (SHAP) analysis were used for validation and feature interpretation.

Among 151 patients, 27.2% reported ineffectiveness of transdermal fentanyl. Logistic regression identified key factors of NRS, transdermal fentanyl dosage, BMI, and ALT. Among the nine models, RF Model 8 exhibited the best performance, achieving a ROC-AUC of 0.984 (95% CI: [0.968, 0.999]). This performance was further validated by the confusion matrix metrics and visualization results. The SHAP analysis highlighted BMI, lower doses, NRS, and ALT as predictors of transdermal fentanyl ineffectiveness.

The Random Forest model offers a valuable tool for predicting the effectiveness of transdermal fentanyl in cancer pain patients, supporting the refined assessment and management of pain.

Sepsis, a critical condition caused by a dysregulated host response to infection, has high morbidity and mortality rates. Timely diagnosis and treatment are vital for improving patient outcomes. This study explores the potential role of CXCL5 in the diagnosis, severity assessment, and prognosis of sepsis.

We included 147 sepsis patients, 50 patients with systemic inflammatory response syndrome (SIRS) and 120 healthy controls. Serum CXCL5 levels, inflammation scores (APACHE II, SOFA), and other laboratory indicators were recorded. Univariate and multivariate logistic regression analyses were conducted to assess the relationship between CXCL5 and sepsis diagnosis, severity, and prognosis. A prognostic nomogram was constructed and evaluated using receiver operator characteristic curves, calibration curves, and clinical decision curves.

Serum CXCL5 levels in sepsis patients were significantly higher than those in patients with SIRS and healthy controls. CXCL5 was identified as a risk factor for sepsis diagnosis. CXCL5 levels were significantly elevated in patients with septic shock (P = 0.04) and in deceased patients compared to survivors (P < 0.001). The prognostic model, incorporating CXCL5, lactate, APACHE II scores, C-reactive protein levels, and respiratory rate, demonstrated high predictive accuracy with an area under the curve of 0.873. Calibration and decision curve analyses demonstrated the model's good predictive performance and potential clinical value.

Serum CXCL5 concentration is a promising biomarker for enhancing the diagnostic accuracy and prognostic evaluation of sepsis. The constructed multivariate prediction model offers new insights into sepsis prognosis, but its direct application in clinical practice requires further validation.

The ability to represent and infer accurately others' mental states, known as Theory of Mind (ToM), has been theorised to be associated with metacognitive ability. Here, we considered the role of metacognition in mental state inference through the lens of a recent theoretical approach to explaining ToM, the 'Mind-space' framework. The Mind-space framework posits that trait inference, representation of the qualities of the mind giving rise to the mental state, is important in forming accurate mental state inferences. We tested a potential role for metacognition in facilitating optimal weighting of trait inferences, as well as several theoretical predictions regarding factors associated with the accuracy of trait inference and confidence in those trait inferences. Participants completed a judgement-of-confidence task in the trait inference domain alongside the Interview Task, a recently-developed task for assessing the accuracy of trait and mental state inferences. A simple relationship in which increased metacognitive sensitivity is associated with increased accuracy of mental states inferences was not found. However, when predicting trial-level performance, confidence in trait inference was shown to modulate the effect of trait inference accuracy on mental state inference accuracy. This effect was greater in magnitude with lower metacognitive sensitivity, i.e., when confidence is more likely to be misplaced. Furthermore, participants' trait inference ability was associated with the accuracy of their understanding of the average mind. In addition, the accuracy of specific trait inferences was predicted by the participant's similarity to the target, but this similarity benefit was reduced in participants whose self-perception was inaccurate. Reported confidence in a given trait inference was also predicted by participant-target similarity, such that participants showed greater overconfidence in judgements made about similar targets. This overconfidence effect was larger when self-perception was more erroneous. Results support several theoretical claims made by the Mind-space theory, and further elucidate the processes underlying accurate mental state inference.

Long-term care trajectories typically vary in care types, transitions and durations. Understanding these patterns can help optimize care provision. This study aimed at (1) identifying formal long-term care trajectories of severely disabled older individuals, and (2) assessing the relationships between individual characteristics and these trajectories.Nationwide administrative data from the Centre for Care Needs Assessment were used. The sample included all 140,973 individuals older than 65 and with institutional care entitlements for somatic or psychogeriatric problems on July 1, 2015. The study period was from January 2010 to June 2019. Latent class growth analysis was performed to derive distinct care trajectories. Multinomial logistic regression analysis was used to examine the relationships between individual characteristics and these trajectories.Five trajectories were identified, varying in complexity and duration of needs in long-term care. Two were characterized by mostly mild (somatic) institutional care and three by (psychogeriatric) institutional care with increasing duration and intensity. Men and younger individuals were more likely to require intensive (psychogeriatric) institutional care.This study highlighted clear divergences among formal long-term care trajectories of severely disabled older individuals. Though less generous entitlement criteria may result in relatively lower expenditures in formal (institutional) care, they also imply increased use of alternatives, such as informal and community care. These insights can be used to tailor the provision of long-term care services in the future, and inform policy related to the financial sustainability of long-term care systems.

Adverse childhood experiences (ACEs) are a possible pathway through which poor family finances influences adolescents' behavioral and mental health problems. However, the interrelationship between family finances, ACEs, and behavioral and mental health outcomes in adolescence has received little attention. We aimed to (1) document exposure to family-based ACEs by perceived family finances (PFF), (2) examine how PFF relates to cumulative and pattern-based approaches to ACEs, and (3) assess the direct and interactive associations between PFF and ACEs in relation with behavioral and mental health problems.

Data stem from the 2017-2019 nationwide Ungdata surveys of adolescents aged 13-15 (n = 12,560). Family-based ACEs were measured by 6 items covering parental alcohol use and intoxication, and intra-familial violence and fighting. The family-based ACEs were used both as a cumulative index score and examined through latent class analysis (LCA) to identify patterns of exposures. Cluster robust linear regression analyses were used to examine additive and interactive associations between PFF, family-based ACEs, and behavioral and depressive symptoms.

Poor (compared to not poor) PFF was significantly associated with more depressive symptoms and behavioral problems. Cumulative and pattern based approaches to family-based ACEs partially and similarly attenuated the association between PFF and mental health outcomes. Mainly, however, PFF and family-based ACEs were independently associated with mental health outcomes and did not interact.

Poor family finances and family-based ACEs co-occur, and both have strong associations with depressive symptoms and behavioral problems in adolescence.

Resting respiratory sinus arrhythmia (RSA) and family cohesion may jointly influence children's empathy. Resting RSA has been found to exhibit an inverted U-shaped relation with empathy, and family cohesion is a psychosocial correlate of children's empathy that may moderate this relation. The present study recruited 182 first-grade children as participants (87 girls, Mage = 7.02, SDage = 0.36). Parents completed questionnaires to assess family cohesion and children's empathy. The SOMNOtouch RESP device was utilized to collect resting electrocardiogram data and calculate resting RSA. The results showed that family cohesion significantly moderated the inverted U-shaped curve between resting RSA and children's empathy. High family cohesion served as a protective factor in mitigating the negative impact of high resting RSA on empathy but could not buffer the negative effect of extremely low resting RSA on empathy among children. The findings suggest a role for high family cohesion in promoting empathy in children with moderate to high resting RSA.

Laboratory studies have demonstrated that manual dexterity decreases with increasing cold, which may adversely affect performance. Dexterity may be impaired by cooling of the hand, cooling of the lower motor neurons, and cognitive impairment. Wetsuits are commonly used in open-water swimming and are mandated in some situations. This study investigates the effects of cold-water wetsuited swimming on dexterity.

Five male and 4 female trained swimmers were recruited for this cohort crossover study. Following dual-energy X-ray absorptiometry scans to determine body composition, they swam in a freshwater lake on 7 occasions with water temperatures between 24.5 °C and 8.4 °C. Dexterity was measured preswim and postswim with a "nut-washer-bolt assembly time test" and cognition with a Stroop test. Core and peripheral body temperatures were continuously monitored. Effects were analyzed by linear mixed-model regression.

Pre-post swim difference in time to complete the nut-bolt assembly increased as water temperatures decreased (1.0 s, 95% CI, 0.5-1.5 per 1 °C, P < .0001; R2 = .456), to a maximum of 14.7 seconds (95% CI, 3.3-26.0). This represented a 47.5% increase in assembly time from 24.5 °C to 8.4 °C, which we consider to be of practical significance. Decreased dexterity was associated with decreased forearm and scapular temperature and decreased cognitive function. Body composition did not affect dexterity, cognitive function, or body temperature during swims. Water temperature did not affect swim speed.

Despite the use of wetsuits, manual dexterity decreased with cold-water swimming. Swimmers, triathletes, and event organizers should consider the implications for safety, performance, and equipment utilization.

In light of the increasing importance for measuring myelin g ratios - the ratio of axon-to-fiber (axon + myelin) diameters in myelin internodes - to understand normal physiology, disease states, repair mechanisms and myelin plasticity, there is urgent need to minimize processing and statistical artifacts in current methodologies. Many contemporary studies fall prey to a variety of artifacts, reducing study outcome robustness and slowing development of novel therapeutics. Underlying causes stem from a lack of understanding of the myelin g ratio, which has persisted more than a century. An extended exploratory data analysis from first principles (the axon-fiber diameter relation) is presented herein and has major consequences for interpreting published g ratio studies. Indeed, a model of the myelin internode naturally emerges because of (1) the strong positive correlation between axon and fiber diameters and (2) the demonstration that the relation between these variables is one of direct proportionality. From this model, a robust framework for data analysis, interpretation and understanding allows specific predictions about myelin internode structure under normal physiological conditions. Further, the model establishes that a regression fit to g ratio plots has zero slope, and it identifies the underlying causes of several data processing artifacts that can be mitigated by plotting g ratios against fiber diameter (not axon diameter). Hypothesis testing can then be used for extending the model and evaluating myelin internodal properties under pathophysiological conditions (forthcoming). For without a statistical model as anchor, hypothesis testing is aimless like a rudderless ship on the ocean.

This study evaluates the potential of dual-energy CT (DECT) for preoperative prediction of tumor budding (TB) and lymphovascular invasion (LVI) in colon cancer.

This prospective study enrolled 153 patients (mean age 61.33 years ± 0.88) with pathologically confirmed colon cancer. All participants underwent arterial and venous phase DECT scans within one week before surgery. Two radiologists independently analyzed the images, assessing tumor location, clinical N stage (cN stage), iodine concentration (IC), effective atomic number (Z-eff), and dual-energy index (DEI). The normalized iodine concentration (nIC) was obtained by comparing measured IC to the abdominal aortic IC. Logistic regression identified independent risk factors for high-grade TB and LVI positivity. The Akaike Information Criterion guided model selection, and the area under the curve (AUC) was calculated. Bootstrap validation with 1000 iterations was used for internal validation.

Tumor location and cN stage were identified as independent risk factors for high-grade TB, and nICA tumor and cN stage for LVI positivity. The optimal model for predicting high-grade TB included tumor location, cN stage, and DEIV tumor, with an AUC of 0.763 (sensitivity: 75.0%; specificity: 64.7%) and a mean AUC of 0.712. Similarly, the model for LVI positivity included nICA tumor, cN stage, and nICA peripheral fat, with an AUC of 0.811 (sensitivity: 71.7%; specificity: 76.6%) and a mean AUC of 0.814.

DECT could consistently quantify colon cancer characteristics, and DECT-based models performed well in the preoperative prediction of TB and LVI.

New indicators of potential human immunodeficiency virus (HIV) transmission are being actively explored. We aim to categorical testing of the viral load (VL) of persons living with HIV (PLWH) in order to explore new indicators to measure the intensity of the epidemic and the effectiveness of the response in the community.

A dynamic cohort study was conducted in Yining to monitor the VL of all persons living with HIV from 2017 to 2019. Different population VL (PVL) surrogate indicators were measured and the strength of the associations of different PVL surrogates with HIV incidence, antiretroviral therapy (ART) coverage, virus unsuppression, and viremia prevalence was assessed. PVL surrogate indicators were used to describe the current status of HIV transmission in different populations and communities.

All PVL indicators decreased from 2017 to 2019 (P < 0.05). Arithmetic mean community viral load (CVL) (r = 1.000, P = 0.006) and geometric mean CVL (r = 1.000, P = 0.001) were positively associated with HIV incidence, ART coverage and viral unsuppression (P < 0.05). CVL was higher in the male, ≤ 25 years of age, primary school or below, other household registration, other medical insurance types, other source of sample, nonmarital and noncommercial heterosexual contact, and nonmarital and commercial heterosexual contact subgroups. Community-based cross-sectional analyses showed that CVL in community 10 was positively correlated with viral unsuppression rate and viremia prevalence but negatively correlated with ART coverage rate, suggesting that the community was a hotspot for HIV epidemics.

CVL can be used as an indicator for assessing HIV transmission and identifying high-risk populations and hotspot communities.

This study aimed to evaluate and compare the predictive performance of negative lymph nodes (NLN), lymph node ratio (LNR), and N stage in pancreatic ductal adenocarcinoma (PDAC) among patients with ≤ 12 retrieved lymph nodes and those with > 12 retrieved lymph nodes. Moreover, the association between the three nodal staging systems and survival was also explored. Clinical data on patients diagnosed with PDAC between 2004 and 2020 were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression was performed to identify independent predictors of cancer specific survival (CSS) and overall survival (OS). Survival probability was calculated and compared by the Kaplan-Meier method and log rank test. Akaike information criterion (AIC) and Harrell's C-index were used to evaluate the prognostic ability of each nodal staging system. All three lymph node staging systems were independent predictors of CSS and OS. A higher NLN, a lower N stage, and a lower LNR were associated with improved survival. Compared with N stage, LNR staging performed better with a lower AIC and higher C-index for predicting the prognosis regardless of the sufficiency of retrieved lymph nodes, while NLN staging performed poorly in both the training and validation set. Subgroup analyses showed that the NLN successfully predicted survival outcomes in both lymph node-positive and node-negative patients. LNR demonstrated better predictive performance in PDAC patients regardless of the sufficiency of retrieved lymph nodes. Notably, for stage N0 disease, NLN was a more important prognostic predictor. The combination of LNR and NLN may offer more precise information on lymph node staging than the current staging system.

Longitudinal cognitive changes in Parkinson's disease (PD) exhibit considerable heterogeneity.Predicting cognitive trajectories in early PD patients can improve prognostic counseling and guide clinical trials.

This study included 337 early PD patients with 6-year follow-up in the Parkinson's Progression Markers Initiative (PPMI) database.Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) to identify subtypes of longitudinal cognitive trajectories, and a nomogram predictive model was constructed using baseline clinical variables.

The 337 PD patients had a mean age of 61.0 years, mean disease duration of 0.55 years, and mean MoCA score of 27.1 points. Latent class mixed models (LCMM) identified two longitudinal cognitive subtypes: cognitive stable (276 cases, 81.9%) and cognitivel deteriorating (61 cases, 18.1%). The cognitively deteriorating subtype presented poorer baseline cognition, older age, and more severe motor and non-motor symptoms. On biomarkers, the cognitively deteriorating subtype revealed higher serum NFL levels and lower mean striatum DAT uptake. Six baseline clinical variables (age, Letter Number Sequencing score, Symbol Digit Modalities Test score, Benton Judgment of Line Orientation Test score, Hopkins Verbal Learning Test-Revised score, and REM Sleep Behavior Disorder) were selected to construct the nomogram predictive model which achieved an AUC of 0.92.The calibration curve demonstrated high consistency between predicted and observed probabilities.The predictive model has potential utility in disease-modifying clinical trials by pre-screening patients at high risk for cognitive deterioration.

This study identified two longitudinal cognitive subtypes: cognitive stable and cognitive deterioration within 6-year follow-up, and eighteen percent of early PD patients shared the cognitive deterioration subtype The predictive model, incorporating six baseline variables could estimate the risk of longitudinal cognitive deterioration in PD.

One of the key strategies to achieve the sustainable development goal by reducing maternal deaths below 70 per 100,000 is improving knowledge of obstetric danger signs (ODS). However, mothers' knowledge of ODS is low in general and very low in rural settings, regardless of local and national efforts in Ethiopia. Further, there is significant variation of ODS knowledge among women from region to region and urban/rural settings. Most studies are limited only to northern Ethiopia and focused on individual-level determinants. Thus, this study aimed to assess the individual and community-level determinants of knowledge of obstetrics danger signs among women who delivered in the last 12 months in the northern zone of the Sidama region, Ethiopia.

We conducted a community-based cross-sectional study from October 21 to November 11, 2022. A multi-stage sampling procedure was utilized to select study participants. A structured and pretested questionnaire was utilized to collect data. Open Data Kit (ODK) smartphone application was used to collect data at women's homes. A multi-level mixed-effects negative binomial regression model was used to control effects of clusters and confounders.

The overall response rate of this study was 99.12%. The proportion of knowledge of obstetrics danger sign was 22.3% (95% CI: 18.7, 25.9). Government-employed women [adjusted incidence ratio (AIR) = 1.37; 95% CI: 1.20, 1.56], women who had exposure to mass media (AIR = 1.16; 95% CI: 1.08, 1.25), women who had received model family training (AIR = 1.15; 95% CI: 1.10, 1.25), autonomous women (AIR = 1.34; 95% CI: 1.25, 1.46), women who had faced health problems during pregnancy (AIR = 1.21; 95% CI: 1.11, 1.32), and urban women (AIR = 1.22; 95% CI: 1.09, 1.62) were determinants positively affect knowledge of ODS.

Only one in five women has good knowledge of ODS in the study setting. Urban residence, mass media exposure, receiving model family training, facing health problems during pregnancy, and women's autonomy were the main determinants of knowledge of ODS. Any intervention strategies should focus on multi-sectorial collaboration to address determinants of knowledge of ODS at the individual and community level. Reinforcing the existing model family training, particularly focusing on rural women and women who denied autonomy in decision-making about health care, should be considered. Awareness creation should be increased about ODS through mass media exposure.

The aim of this study was to adapt the Children Participation Questionnaire (CPQ) into Turkish culture and evaluate its psychometric properties in typically developing children aged 4-6 years. This study involved 235 typically developing children and 61 children with autism spectrum disorder (ASD). Parents completed the Turkish CPQ (CPQ-T) and demographic form. The adaptation process followed the World Health Organization's guidelines. Confirmatory factor analysis (CFA) was conducted, and construct validity was assessed using the Mann-Whitney U test to compare participation levels between typically developing children and those with ASD. Reliability was measured through Cronbach's alpha and test-retest reliability, with agreement assessed using Intraclass Correlation Coefficient (ICC) and McDonald's ω. Sensitivity and specificity were determined using ROC curves. The study demonstrated significant differences in participation levels between the study group and children with ASD across all domains (p < 0.001). Confirmatory factor analysis indicated a good model fit (CMIN/DF = 2.692, GFI = 0.862, IFI = 0.932). The CPQ showed excellent internal consistency (Cronbach's alpha = 0.978) and test-retest reliability (ICC = 0.943 to 0.983). Sensitivity and specificity analyses yielded high values, with Area Under the Curve (AUC) ranging from 0.956 to 0.978. The Turkish version of the CPQ is a valid and reliable tool for assessing child participation, effectively distinguishing between typically developing children and those with ASD. This study contributes significantly to the limited literature on the CPQ, highlighting its utility in clinical and research settings for evaluating children's participation.

To explore the diagnostic performance of the Node-RADS scoring system on preoperative assessment of mesorectal lymph node metastasis (LNM) status in rectal cancer, in comparison with the ESGAR category and size of lymph node (LN).

Preoperative clinical and MRI data of 154 rectal adenocarcinoma patients treated with radical resection surgery were retrospectively analyzed. The differences in the clinical, pathological and imaging characteristics between the pN- and pN + groups were surveyed. The correlations of Node-RADS score and ESGAR category to pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological LNM were compared among individual approaches. A nomogram combined the imaging and clinical features was also established and evaluated.

Significant differences in CEA, tumor maximum diameter, tumor location, LN short-axis diameter, Node-RADS score and ESGAR category were found between the pN- and pN + groups. Node-RADS correlated significantly with pN stage, LNM number, and LNR (r = 0.665, 0.685, and 0.675, p < 0.001). Node-RADS had the highest AUC (0.862) for predicting pN + status, surpassing ESGAR (AUC = 0.797, p = 0.040) and LN size (AUC = 0.762, p = 0.015). The nomogram had the best diagnostic performance (AUC = 0.901), significantly outperforming Node-RADS alone (p = 0.037).

The Node-RADS scoring system is comparable to the ESGAR category and surpasses short-axis diameter in preoperatively predicting LNM in rectal cancer. Integrating imaging and clinical features will lead to an enhancement in diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

To evaluate preferences for key attributes of injected or infused preventive migraine treatments and assess heterogeneity in preferences among Canadian participants with migraine.

Current treatment options for migraine prevention differ in their attributes, including mode of administration, efficacy, and dosing frequency; preferences for such attributes can vary among patients. With the advent of new therapies, evidence demonstrating patient preferences for injected or infused preventive migraine treatments is necessary.

Canadian adults self-reporting a diagnosis of migraine completed a cross-sectional, internet-based survey that included a discrete choice experiment. Participants were presented with attributes of preventive migraine treatments, including speed of onset, durability of efficacy, mode of administration, administration setting, and dosing frequency. Latent class analysis (LCA) was used to identify subgroups of patients who differed in their treatment preferences.

In total, 200 participants completed the survey. Participants' treatment preferences were most sensitive to improvements in the durability of effectiveness from "wears off 2 weeks before next dose" to "does not wear off before the next dose" (absolute difference in weights = |-0.95 to 1.07| = 2.02) and improvements from "cranial injections" to "intravenous infusions" (|-1.04 to 0.58| = 1.62); participants equally preferred self-injection and intravenous infusion from a health-care provider (mean weight = 0.58 and 0.47, respectively) as a route of administration over cranial injections (mean weight = -1.04). Three subgroups were identified with LCA: group one (n = 103) prioritized fast-acting and durable therapies, group two (n = 54) expressed aversion to cranial injections, and group three (n = 43) favored treatments administered in a health-care provider setting.

In this sample of Canadian adults with migraine, we showed that durability of effectiveness and mode of administration are key attributes influencing patient preferences for preventive migraine treatments; however, certain groups of patients may differ in their treatment priorities. Our results highlight the need for patient-provider discussions regarding treatment attributes and consideration of patients' preferences when selecting a preventive migraine treatment.

Adolescents with congenital heart disease (CHD) have elevated risk for acquired cardiovascular complications, increasing their vulnerability to e-cigarette-related health harms. Impulsivity and risky decision-making have been associated with adolescent substance use, but the relationships between these factors and e-cigarette-related outcomes among cardiovascular at-risk adolescents with CHD are unknown. This cross-sectional study aimed to (a) determine the associations of impulsivity and risky decision-making with e-cigarette-related outcomes (i.e. susceptibility, ever use, perceptions of harm and addictiveness) via variable-oriented analysis (logistic regression), (b) identify groups of adolescents with similar profiles of impulsivity and risky decision-making via exploratory person-oriented analysis (latent profile analysis; LPA), and (c) examine differences on e-cigarette-related outcomes between profile groups. Adolescents aged 12 to 18 years with CHD (N = 98) completed a survey assessing impulsivity facets (Short UPPS-P) and e-cigarette-related outcomes and were administered a risky decision-making task (Iowa Gambling Task, Version 2; IGT2). In variable-oriented analyses, impulsivity facets (negative urgency, positive urgency, lack of premeditation) but not risky decision-making were associated with e-cigarette susceptibility and ever use. The exploratory LPA identified two groups with similar patterns of responding on the Short UPPS-P and IGT2 labeled "Low Impulsivity" and "High Impulsivity," which were primarily characterized by significant differences in negative and positive urgency. Adolescents in the High Impulsivity group had increased odds of e-cigarette susceptibility but not ever use compared to the Low Impulsivity group. This work indicates that strategies to prevent e-cigarette use among adolescents with CHD may be enhanced by addressing impulsivity, particularly negative and positive urgency.

The application of artificial intelligence (AI) in radiation therapy holds promise for addressing challenges, such as healthcare staff shortages, increased efficiency and treatment planning variations. Increased AI adoption has the potential to standardise treatment protocols, enhance quality, improve patient outcomes, and reduce costs. However, drawbacks include impacts on employment and algorithmic biases, making it crucial to navigate trade-offs. A discrete choice experiment (DCE) was undertaken to examine the AI-related characteristics radiation oncology professionals think are most important for adoption in radiation therapy treatment planning.

Radiation oncology professionals completed an online discrete choice experiment to express their preferences about AI systems for radiation therapy planning which were described by five attributes, each with 2-4 levels: accuracy, automation, exploratory ability, compatibility with other systems and impact on workload. The survey also included questions about attitudes to AI. Choices were modelled using mixed logit regression.

The survey was completed by 82 respondents. The results showed they preferred AI systems that offer the largest time saving, and that provide explanations of the AI reasoning (both in-depth and basic). They also favoured systems that provide improved contouring precision compared with manual systems. Respondents emphasised the importance of AI systems being cost-effective, while also recognising AI's impact on professional roles, responsibilities, and service delivery.

This study provides important information about radiation oncology professionals' priorities for AI in treatment planning. The findings from this study can be used to inform future research on economic evaluations and management perspectives of AI-driven technologies in radiation therapy.

This commentary discusses opportunities for advancing the field of developmental psychopathology through the integration of data science and neuroscience approaches. We first review elements of our research program investigating how early life adversity shapes neurodevelopment and may convey risk for psychopathology. We then illustrate three ways that data science techniques (e.g., machine learning) can support developmental psychopathology research, such as by distinguishing between common and diverse developmental outcomes after stress exposure. Finally, we discuss logistical and conceptual refinements that may aid the field moving forward. Throughout the piece, we underscore the profound impact of Dr Dante Cicchetti, reflecting on how his work influenced our own, and gave rise to the field of developmental psychopathology.

The removal of dye molecules in alkaline environments is an issue that should receive increased attention. In this study, the interaction mechanism between polydopamine-modified multiwalled carbon nanotubes (P-MWCNTs) and multiwalled carbon nanotubes (MWCNTs) with the cationic dye methylene blue (MB) in alkaline environments was explained in depth by adsorption, spectroscopy, and density functional theory (DFT). The mechanism of action and dominant forces between the adsorbent and adsorbate were analyzed graphically by introducing energy decomposition analysis (EDA) and an independent gradient model (IGM) into the DFT calculations. In addition, the force distribution was investigated through an isosurface. Moreover, batch adsorption studies were conducted to evaluate the performance of MWCNTs and P-MWCNTs for MB removal in alkaline environments. The maximum MB adsorption capacities of the MWCNTs and P-MWCNTs in solution were 113.3 mg‧g-1 and 230.4 mg‧g-1, respectively, at pH 9. The IGM and EDA showed that the better adsorption capacity of the P-MWCNTs originated from the enhancement of the electrostatic effect by the proton dissociation of polydopamine. Moreover, the adsorption of MB by MWCNTs and P-MWCNTs in alkaline environments was governed by dispersion and electrostatic effects, respectively. Through this study, it is hoped that progress will be made in the use of DFT to explore the mechanism of adsorbent-adsorbate interactions.

Given the rising burden of heart failure (HF), stratification of patients at increased risk for adverse events is critical. We aimed to compare the predictive value of various maximal and submaximal cardiopulmonary exercise test (CPET) variables for adverse events in patients with HF.

A total of 237 patients with HF (66 (58-73) yr, 30% women, 70% HF with reduced ejection fraction) completed a CPET and had 5 yr of follow-up. Baseline characteristics and clinical outcomes (all-cause mortality, major adverse cardiovascular events, and cardiovascular-related hospitalization) were extracted from electronic patient files. Receiver operating characteristics curves for maximal (e.g., peak V̇O 2 ) and submaximal CPET variables (e.g., VE/V̇CO 2 slope, cardiorespiratory optimal point (COP), V̇O 2 at anaerobic threshold) were compared using the Akaike Information Criterion (AIC) method, whereas their calibration was assessed.

One hundred three participants (43%) reached the composite endpoint, and 55 (23%) died. Percent predicted peak V̇O 2 was the best predictor for adverse outcomes (AIC: 302.6) followed by COP (AIC: 304.3) and relative peak V̇O 2 (mL·(kg·min) -1 , AIC: 304.4). Relative peak V̇O 2 (AIC: 217.1) and COP (AIC: 224.4) were also among the three best predictors for mortality, together with absolute peak V̇O 2 (mL·min -1 , AIC: 220.5). A good calibration between observed and predicted event rate was observed for these variables.

Percent predicated and relative peak V̇O 2 had the best predictive accuracy for adverse events and mortality, but the submaximal COP had a noninferior predictive accuracy for adverse events in patients with HF. These findings highlight the potential of submaximal exercise testing in patients with HF.

There is increasing interest in examining the development of frontal EEG power in relation to self-regulation in early childhood. However, the majority of previous studies solely focuses on the brain's alpha rhythm and little is known about the differences between young boys and girls. The aim of the current study was therefore to gain more insight into the neural mechanisms involved in the emergence of self-regulation. The sample consisted of 442 children and data were collected at approximately 5 months, 10 months, and around 3 years of age. Latent growth curve models indicated that,while the neurobiological foundations of self-regulation are established during infancy,it is the maturation of the frontal alpha rhythm that contributes to variations in both observed and parent-reported self-regulation. In addition, it appears that boys might have a greater reliance on external regulation than girls during early childhood, as evident by higher scores of girls on both measures of self-regulation. More insight into the role of external regulators in brain maturation can help to implement interventions aimed at establishing bottom-up self-regulatory skills early in life, in order to provide the necessary foundations for the emergence of top-down self-regulatory skills in the preschool period.

To examine associations among the time and content of rehabilitation treatment with self-care and mobility functional gain rate for adults with acquired brain injury.

Retrospective cohort study using electronic health record and administrative billing data.

Inpatient rehabilitation unit at a large, academic medical center.

Adults with primary diagnosis of stroke, traumatic brain injury, or nontraumatic brain injury admitted to the inpatient rehabilitation unit between 2012 and 2017 (N=799).

Not applicable.

Gain rate in self-care and mobility function, using the Functional Independence Measure. Hierarchical regression models were used to identify the contributions of baseline characteristics, units, and content of occupational therapy, physical therapy, and speech-language pathology treatment to functional gain rates.

Median length of rehabilitation stay was 10 days (interquartile range, 8-13d). Patients received an mean of 10.62 units of therapy (SD, 2.05) daily. For self-care care gain rate, the best-fitting model accounted for 32% of the variance. Occupational therapy activities of daily living units were positively associated with gain rate. For mobility gain rate, the best-fitting model accounted for 37% of the variance. Higher amounts of physical therapy bed mobility training were inversely associated with mobility gain rate.

More activities of daily living in occupational therapy is associated with faster improvement on self-care function for adults with acquired brain injury, whereas more bed mobility in physical therapy was associated with slower improvement. A potential challenge with value-based payments is the alignment between clinically appropriate therapy activities and the metrics by which patient improvement are evaluated. There is a risk that therapists and facilities will prioritize activities that drive improvement on metrics and deemphasize other patient-centered goals.

Housing is a critical social determinant of children's health. While individual pathways between housing and health are well evidenced, there has been less attention on the co-occurrence of housing disadvantages. We aim to identify typologies of children's housing disadvantage and describe the health inequities they generate. A cross-sectional latent class analysis (LCA) was conducted on 4355 Australian children aged 4 to 5 years who participated in the Longitudinal Study of Australian Children. Nine binary indicators-dwelling tenure, external condition, affordability, stability, crowding, cleanliness, fuel poverty, and noise exposure-were used to generate latent housing typologies. Generalized linear models with robust standard errors described disparities in quality of life, injury, disability, and health service use across these housing typologies. Four distinct housing typologies were identified in this cohort: "good condition, affordable, and secure" (60%); "insecure" (11%); "crowded" (24%); and "unsuitable" (5%). Unsuitable housing, characterized by crowding, poor external conditions, and noise exposure, was more likely to be occupied by single-parent families, low-income households, and be headed by parents with low levels of education. Children in unsuitable housing lagged on quality of life indicators at preschool age (e.g., by 8.0 points on emotional functioning, 95% CI - 10.6, - 5.5), and underutilized primary healthcare services (e.g., prevalence ratio 0.76 for GP services, 95% CI 0.67, 0.87), compared to children in good quality housing. This finding supports the case for early intervention strategies that account for children's housing circumstances.

Consistent with reporting standards for structural equation modelling (SEM), model fit should be evaluated at two different levels, global and local. Global fit concerns the overall or average correspondence between the entire data matrix and the model, given the parameter estimates for the model. Local fit is evaluated at the level of the residuals, or differences between observed and predicted associations for every pair of measured variables in the model. It can happen that models with apparently satisfactory global fit can nevertheless have problematic local fit. This may be especially true for relatively large models with many variables, where serious misspecification is indicated by some larger residuals, but their contribution to global fit is diluted when averaged together with all the other smaller residuals. It can be challenging to evaluate local fit in large models with dozens or even hundreds of variables and corresponding residuals. Thus, the main goal of this tutorial is to offer suggestions about how to efficiently evaluate and describe local fit for large structural equation models. An empirical example is described where all data, syntax and output files are freely available to readers.

Understanding the temporal relationship between key events in an individual's infection history is crucial for disease control. Delay data between events, such as infection and symptom onset times, is doubly censored because the exact time at which these key events occur is generally unknown. Current mathematical models for delay distributions are derived from heuristic justifications. Here, we derive a new model for delay distributions, specifically for incubation periods, motivated by bacterial-growth dynamics that lead to the Burr family of distributions being a valid modelling choice. We also incorporate methods within these models to account for the doubly censored data. Our approach provides biological justification in the derivation of our delay distribution model, the results of fitting to data highlighting the superiority of the Burr model compared to currently used models when the mode of the distribution is clearly defined or when the distribution tapers off. Under these conditions, our results indicate that the derived Burr distribution is a better-performing model for incubation-period data than currently used methods, with the derived Burr distribution being 13 times more likely to be a better-performing model than the gamma distribution for Legionnaires' disease based on data from a known outbreak.

Rationale: Because of the effects of aging on the respiratory system, it is conceivable that the association between driving pressure and mortality depends on age. Objectives: We endeavored to evaluate whether the association between driving pressure and mortality of patients with acute respiratory distress syndrome (ARDS) varies across the adult lifespan, hypothesizing that it is stronger in older, including very old (⩾80 yr), patients. Methods: We performed a secondary analysis of individual patient-level data from seven ARDS Network and PETAL Network randomized controlled trials ("ARDSNet cohort"). We tested our hypothesis in a second, independent, national cohort ("Hellenic cohort"). We performed both binary logistic and Cox regression analyses including the interaction term between age (as a continuous variable) and driving pressure at baseline (i.e., the day of trial enrollment) as the predictor and 90-day mortality as the dependent variable. Measurements and Main Results: On the basis of data from 4,567 patients with ARDS included in the ARDSNet cohort, we found that the effect of driving pressure on mortality depended on age (P = 0.01 for the interaction between age as a continuous variable and driving pressure). The difference in driving pressure between survivors and nonsurvivors significantly changed across the adult lifespan (P < 0.01). In both cohorts, a driving pressure threshold of 11 cm H2O was associated with mortality in very old patients. Conclusions: Data from randomized controlled trials with strict inclusion criteria suggest that the effect of driving pressure on the mortality of patients with ARDS may depend on age. These results may advocate for a personalized age-dependent mechanical ventilation approach.

Agricultural intensification has led to significant declines in beneficial insect populations, such as pollinators and natural enemies, along with their ecosystem services. The installation of perennial flower margins in farmland is a popular agri-environmental scheme to mitigate these losses, promoting biodiversity, pollination, and pest control. However, outcomes can vary widely, and recent insights into flower margins in an agricultural context suggest that management could be an important contributor to this variation. This study evaluated two mowing management regimes: the new "three-strip management" method with uneven, curved mowing lines and regular phased mowing as a control method. During the third year of application, we evaluated the effects on the alpha diversity indices of pollinators and natural enemies, as well as plant-pollinator visitation networks. Curved three-strip management significantly increased the abundance of all pollinator groups (+44%) and natural enemies (+50%), and the taxonomic richness and diversity of pollinators, especially for rarer solitary bees. Floral diversity was also higher, with more unique plants blooming in early spring and late summer, generating more unique plant-pollinator interactions (+54%) and a positive impact on multiple network-level properties. Our findings provide new evidence that nature-based management methods can be a win-win solution, creating high-quality habitats that enhance the insect diversity of various groups, support associated ecosystem services, and help restore overall farmland biodiversity.

Insulin resistance (IR) is considered a major driver of the pathophysiology of polycystic ovary syndrome (PCOS), mediating the progression of hyperandrogenism and metabolic and reproductive dysfunction in patients with PCOS. Early detection of the risk of concurrent IR is essential for women with PCOS. To address this need, this study developed a predictive nomogram for assessing the risk of IR in women with PCOS, aiming to provide a tool for risk stratification and assist in clinical decision-making.

Patients with untreated PCOS-IR diagnosed in a single-center retrospective cohort study from January 2023 to December 2023 were included for nomogram construction and validation. The area under the ROC curve (AUC), calibration curve, Hosmer-Lemeshow (H-L) goodness-of-fit test, and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and clinical decision performance. A risk stratification model based on the nomogram was then developed.

A total of 571 patients were included in the study; 400 patients enrolled before September 2023 were divided into the training and validation sets, and 171 patients enrolled later were used as the external validation set. The variables identified by logistic regression and the random forest algorithm-body mass index (BMI, OR 1.43), triglycerides (TG, OR 1.22), alanine aminotransferase (ALT, OR 1.03), and fasting plasma glucose (FPG, OR 5.19)-were used to build the nomogram. In the training, internal validation, and external validation sets, the AUCs were 0.911 (95% CI 0.878-0.911), 0.842 (95% CI 0.771-0.842), and 0.901 (95% CI 0.856-0.901), respectively. The nomogram showed good agreement between predicted and observed outcomes, and patients were categorized into low-, medium-, and high-risk groups based on their scores.

Independent predictors of untreated PCOS-IR risk were incorporated into a nomogram that effectively classifies patients into risk groups, providing a practical tool for guiding clinical management and early intervention.

Research on early risk factors for disruptive behaviors (DBs: hyperactivity-impulsivity/HI, non-compliance/NC, or physical aggression/PA) has predominantly focused on individual DBs in silos (i.e., HI, NC, or PA) or the broader category of externalizing, reporting mostly common risk factors among them. However, studies addressing DB comorbidity, i.e., the simultaneous occurrence of more than one DB, showed differences in risk factors among DB comorbid profiles. Aiming to clarify this discrepancy, the present study compared the early risk factors associated with different longitudinal patterns (i.e., trajectories) of single-DBs (HI, NC, PA) with risk factors associated with monomorbid (HIonly, NConly, PAonly) and comorbid (HI + NC, NC + PA, HI + NC + PA) joint-DBs trajectories during the preschool period.

In a population-based birth cohort (N = 2045), parents' pre-conception characteristics, pregnancy and perinatal conditions, and age 5 months child and family characteristics were used to compare children following single-DB and joint-DBs high trajectories to children following low or moderate trajectories. The DB trajectories were derived from mother ratings at ages 1½, 2½, 3½, 4½, and 5 years.

More risk factors were identified for single-DB high trajectories than for joint-DBs high trajectories. On average, children on a single-DB high trajectory shared only 44.2% of their risk factors with children on a related joint-DBs high trajectory. Moreover, high trajectories of single-DBs shared a larger proportion of their risk factors than did high trajectories of joint-DBs. The findings show that categories of DBs include different subgroups of children based on their comorbidity patterns across DBs, which are differentially linked to early risk factors.

Addressing comorbidity when investigating early risk factors of preschool DBs may improve our understanding of the etiological processes leading to these distinct but related behaviors and increase our ability to intervene upstream to prevent the earliest forms of potentially life-altering psychopathological conditions.

Increasing knowledge of obstetric danger signs (ODS) and encouraging birth preparedness and complication readiness (BPCR) practices are strategies to increase skilled maternal health service utilization in low-income countries. One of the methods to increase mothers' knowledge about ODS and promote BPCR practice is through health education intervention (HEI). However, the effect of context-specific community-based health education led by women's groups on these outcomes has yet to be comprehensively studied, and the existing evidence is inconclusive. Thus, we aimed to evaluate the effect of a context-specific community-based HEI led by women's groups on mothers' knowledge regarding ODS and BPCR practices in southern Ethiopia.

An open-label, two-arm parallel group cluster-randomized controlled trial was conducted from January to August 2023 on pregnant women from 24 clusters (kebeles) (12 interventions and 12 controls) in the northern zone of the Sidama region. The Open Data Kit smartphone application was utilized to collect data. The intention-to-treat analysis was used to compare outcomes between groups. We fitted multilevel mixed-effects modified Poisson regression with robust standard error to account for between and within cluster effects.

One thousand and seventy pregnant women (540 in the intervention and 530 in the control clusters) responded to this study, making the overall response rate 95.02%. Excessive vaginal bleeding (94.3% in the interventional group vs. 88.7% in the control group) was the commonest ODS mentioned during childbirth. Overall, 68.7% of women in the intervention group and 36.2% of mothers in the control group had good knowledge of ODS (P-value < 0.001). Saving money and materials (97.1% in the interventional group vs. 92.7% in the control group) was the most frequently practiced BPCR plan. Overall, 64.3% of women in the intervention group and 38.9% of mothers in the control group practiced BPCR (P-value < 0.001). HEI significantly increased overall knowledge of ODS (adjusted risk ratio [ARR]: 1.71; 99% CI: 1.14-2.57) and improved overall BPCR practice (ARR: 1.55; 99% CI: 1.02-2.39).

A community-based HEI led by women's groups improved mothers' knowledge regarding ODS and BPCR practices in a rural setting in southern Ethiopia. Interventions designed to increase women's knowledge of ODS and improve BPCR practice must implement context-specific, community-based HEI that aligns with World Health Organization recommendations.

NCT05865873.

Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment. This will result in a more accurate (less biased) estimate of the treatment effect.

Pervasive structural violence causes higher organ failure rates among Black Americans and excess Black potential deceased organ donors. Underuse of Black donors would exacerbate organ shortages that disproportionately harm Black transplant candidates. This study investigates racial differences in transit between distinct donation steps among 132 968 potential donors across 557 hospitals and 6 Organ Procurement Organizations (OPOs) from 2015 through 2021. Multilevel multistate modeling with patient covariates and OPO random effects shows adjusted likelihoods (95% confidence interval [CI]) of non-Black versus Black patients transitioning from OPO referral to approach: odds ratio (OR) 1.23 (95% CI 1.18, 1.27), approach to authorization: OR 1.64 (95% CI 1.56, 1.72), authorization to procurement: OR 1.08 (95% CI 1.02, 1.14), and procurement to transplant: OR 0.99 (95% CI 0.93, 1.04). Overall organ utilization rates for Black, Latino, White, and other OPO referrals were 5.88%, 8.17%, 6.78%, and 5.24%, respectively. Adjusting for patient covariates and hospital and OPO random effects, multilevel logistic models estimated that compared with Black patients, Latino, White, and other patients had ORs of organ utilization of 1.82 (95% CI 1.61, 2.04), 3.19 (95% CI 2.91, 3.50), and 1.25 (95% CI 1.06, 1.47), respectively. Nationwide in 2022, donor conversion disparities likely lost more than 1800 donors-70% of whom would have been Black. Achieving racial equity for transplant candidates will require reducing racial disparities in organ donation.

Post-traumatic stress disorder (PTSD) is a major psychiatric health issue among intensive care unit (ICU) survivors with post-intensive care syndrome (PICS). Although early PTSD intervention has been demonstrated to decrease the risk of progression from acute to chronic PTSD, information on the progression trajectory of short-term PTSD symptoms and modifiable risk factors in PICS patients is limited. This study aimed to explore the clinical progression trajectories of short-term PTSD symptoms and the associated factors in PICS patients by conducting a prospective longitudinal observational study.

This study was conducted at a tertiary hospital in China. The impact of event scale-revised was used to collect data on the PTSD symptoms of patients at 1, 2, 3, and 4 months post-discharge from the ICU. The latent growth mixture model was used to construct trajectory models for PTSD symptoms and multivariate logistic regression was used to determine the factors associated with the trajectories.

A total of 130 ICU survivors with PICS completed the 4-month short-term follow-up. Our results showed that PTSD symptoms in PICS patients manifested as three trajectories, namely, moderate chronic (n = 17, 13.1%), recovery (n = 25, 19.2%), and resilience (n = 88, 67.7%). Compared with the resilience trajectory, age and female were identified as risk factors for the moderate chronic trajectory, while prolonged ICU stay was a risk factor for the recovery trajectory.

Our study showed that short-term PTSD symptoms in PICS patients manifested as moderate chronic, recovery, and resilience trajectories. Additionally, our results showed that PTSD screening should be conducted for critically ill patients, especially younger, female, or long-term ICU patients, immediately after their discharge from the ICU.

Low-fat dietary (LFD) pattern refers to a dietary structure with reduced fat intake. The aim was to investigate the association between LFD pattern and risk of head and neck cancer (HNC).

Data were derived from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. LFD score was used to assess adherence to an LFD pattern, with higher scores indicating greater adherence. Cox regression was used to evaluate the association between LFD score and risk of HNC and its subtypes. To visualize the trend in risk of HNC and its subtypes with changing LFD score, restricted cubic spline plots were utilized. A series of subgroup analyses were conducted to identify potential confounders. Sensitivity analyses were performed to assess the robustness of the results.

Among 98,459 participants of PLCO trial, 268 cases with HNC were identified during an average of 8.8 years of follow-up. In the fully adjusted model, participants in the highest compared with the lowest quartiles of LFD score had a lower risk of HNC (HR Q4 vs. Q1: 0.60; 95% CI: 0.40-0.90; P for trend = 0.026) and larynx cancer (HRQ4 vs. Q1: 0.46; 95% CI: 0.22-0.96; P for trend = 0.039). The restricted cubic spline plots demonstrated a linear dose-response relationship between the LFD score and the risk of HNC and its subtypes (all P for nonlinearity > 0.05). The primary association remained robust in the sensitivity analysis.

Our findings suggest that adherence to an LFD pattern may lower the risk of HNC in the US population.

Research links life stressors, including acute, chronic, and early life stress, to the development of ruminative brooding. However, singular forms of life stress rarely occur in isolation, as adolescents typically encounter stressors that vary on important dimensions (e.g., types, timings, quantities) across development. The current study employs latent profile analysis (LPA) to identify natural clusters of life stress that, over time, may be differently associated with ruminative brooding. Evaluations of episodic, chronic, and early life stress were conducted with community-recruited mid-adolescents (N = 241, Mage = 15.90 years, 53% female) and their parents using the UCLA Life Stress Interview and lifetime adversity portions of the Youth Life Stress Interview. Analyses identified four distinct patterns: low stress, high peer stress, moderate home / family stress, and multifaceted / high school stress. Adolescents in the high peer stress and moderate home / family stress profiles were at highest risk for developing a brooding style over time. Despite high overall levels of stress, teens in the multifaceted / high school stress profile were at not at elevated risk for developing a brooding style. Findings demonstrate the utility of person-centered approaches to identify patterns of stress exposure that heighten risk for brooding over time.

Generalized linear mixed models (GLMMs) have great potential to deal with count data in single-case experimental designs (SCEDs). However, applied researchers have faced challenges in making various statistical decisions when using such advanced statistical techniques in their own research. This study focused on a critical issue by investigating the selection of an appropriate distribution to handle different types of count data in SCEDs due to overdispersion and/or zero-inflation. To achieve this, I proposed two model selection frameworks, one based on calculating information criteria (AIC and BIC) and another based on utilizing a multistage-model selection procedure. Four data scenarios were simulated including Poisson, negative binominal (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB). The same set of models (i.e., Poisson, NB, ZIP, and ZINB) were fitted for each scenario. In the simulation, I evaluated 10 model selection strategies within the two frameworks by assessing the model selection bias and its consequences on the accuracy of the treatment effect estimates and inferential statistics. Based on the simulation results and previous work, I provide recommendations regarding which model selection methods should be adopted in different scenarios. The implications, limitations, and future research directions are also discussed.

Noisy time-series data-from various experiments, including Förster resonance energy transfer, patch clamp, and force spectroscopy, among others-are commonly analyzed with either hidden Markov models or step-finding algorithms, both of which detect discrete transitions. Hidden Markov models, including their extensions to infinite state spaces, inherently assume exponential-or technically geometric-holding time distributions, biasing step locations toward steps with geometric holding times, especially in sparse and/or noisy data. In contrast, existing step-finding algorithms, while free of this restraint, often rely on ad hoc metrics to penalize steps recovered in time traces (by using various information criteria) and otherwise rely on approximate greedy algorithms to identify putative global optima. Here, instead, we devise a robust and general probabilistic (Bayesian) step-finding tool that neither relies on ad hoc metrics to penalize step numbers nor assumes geometric holding times in each state. As the number of steps themselves in a time-series are a priori unknown, we treat these within a Bayesian nonparametric (BNP) paradigm. We find that the method developed, BNP Step (BNP-Step), accurately determines the number and location of transitions between discrete states without any assumed kinetic model and learns the emission distribution characteristic of each state. In doing so, we verify that BNP-Step can analyze sparser data sets containing higher noise and more closely spaced states than otherwise resolved by current state-of-the-art methods. What is more, BNP-Step rigorously propagates measurement uncertainty into uncertainty over state transition locations, numbers, and emission levels as characterized by the posterior. We demonstrate the performance of BNP-Step on both synthetic data as well as data drawn from force spectroscopy experiments.

To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI.

A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort.

Based on multivariate logistic regression, clinical dementia rating and Alzheimer's disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341).

The present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

The rapid expansion of Uber Technologies, Inc.'s ride-sharing, courier service, and food delivery system and e-hailing applications has been transforming the logistics network and urban mobility around the world. We aimed to evaluate the impact of the Uber system on traffic injury (TI) mortality during its implementation in Brazilian capital cities. A quasiexperimental design of interrupted time series was used. The monthly mortality rates for TI standardized by age were analyzed. The date of availability of the Uber app, specific to each capital, was considered the start date. Data from the Brazilian Mortality Information System and the Brazilian Institute of Geography and Statistics were used. For the data analysis, from an interrupted time-series design, autoregressive integrated moving average (ARIMA) models with a transfer function were fitted. In 92.6% (n = 25) of Brazilian capitals, there was no impact of Uber system implementation, 12 months after the start of its activities, on TI mortality. A reduction in mortality from this cause was observed after the system was implemented in Belo Horizonte and Rio de Janeiro. The impact on TI mortality was progressive and continuous in both. More studies are needed to establish the factors associated with the inequalities observed in the impact of Uber system implementation between different locations and the heterogeneity of effects.