The study aimed to evaluate the diagnostic accuracy of chest CT for COVID-19 pneumonia in resource-limited Gaza. It compared CT performance to RT-PCR and examined how CT severity scores and interobserver agreement influence diagnostic accuracy, reproducibility, and clinical utility for early detection and triage.

A retrospective analysis was performed on 252 consecutive patients diagnosed with COVID-19 pneumonia between September 2020 and June 2021 at three major governmental hospitals across the Gaza Strip. Chest CT scans were compared to RT-PCR as the gold standard for diagnosis. CT severity scores were calculated using a 25-point system, and interobserver agreement was assessed using kappa statistics. Sensitivity, specificity, and predictive values were calculated for various threshold levels.

Among the 252 patients included in the study, the mean age was 56.81 ± 11.34 years, with 113 males and 139 females. The diagnostic sensitivity of chest CT was 91.4%, with a specificity of 76.4%. The highest accuracy was observed with a severity score threshold of ≥ 15, with a Youden index of 0.630. Interobserver agreement was excellent (kappa = 0.87) for ground-glass opacities and consolidation. The NPV was 71.2%, indicating the need for supplementary RT-PCR testing in low-prevalence cases.

Chest CT is a reliable diagnostic adjunct for COVID-19 pneumonia, especially in Gaza's severely resource-limited setting, where CT was more accessible than RT-PCR. A CT severity score threshold of ≥ 15 offers an optimal balance of sensitivity and specificity. These findings highlight the practical role of CT imaging in pandemic response in resource-constrained environments.

The COVID-19 pandemic has underscored the urgent need for rapid, accurate, and non-invasive diagnostic methods. This study explores the potential of beryllium-doped C60 fullerene as a sensor for detecting COVID-19 via isobutyric acid (ISO-But), a biomarker found in the breath of infected individuals. By employing Density Functional Theory (DFT), we analyze the electronic and structural properties of pristine and metal-doped C60 fullerenes (Beryllium (Be) and Calcium (Ca)), focusing on their interactions with isobutyric acid. Our findings reveal that BeC59, combined with isobutyric acid, displays a colorimetric response within the visible spectrum, indicating its suitability for point-of-care diagnostics. With rapid recovery and strong interaction properties, this sensor design promises to advance non-invasive COVID-19 detection, making it accessible and feasible for real-time applications.

The urgent need for sensitive, rapid, and reliable diagnostic methodologies to control and prevent life-threatening pandemic infectious disease, such as COVID-19, remains a critical priority. Timely and on-site detection of viral pathogens is essential for effective disease management and mitigation of societal disruptions. Recent advancements in optical diagnostic methods have positioned them at the forefront of healthcare diagnostics, offering high sensitivity and specificity as viable alternatives to conventional techniques such as the Polymerase Chain Reaction (PCR), which often suffer from time delays and limited accessibility in resource-constrained environments. This review elucidates the potential of various optical diagnostic techniques, highlighting their advantages over traditional methods. It encompasses a range of optical modalities, including fluorescence-based approaches, Raman spectroscopy (RS), Plasmonic techniques (e.g., surface plasmon resonance (SPR), localized SPR, (LSPR), surface-enhanced Raman spectroscopy (SERS), and surface-enhanced fluorescence (SEF)), super resolution microscopies (SRMs), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), and integrated platforms such as waveguides and molecularly imprinted polymer (MIP)-based biosensors. Additionally, the evolution of novel biosensors, particularly 5th and 6th generation biosensors, in healthcare and the challenges related to these technologies were discussed. This studies reviewed aims to advance the development of portable, sensitive, specific, and cost-effective point-of-care (POC) diagnostic devices for the rapid detection of viral pathogens.

The traditional molecular-based identification (TMID) technique of new infections from genome sequences (GSs) has made significant contributions so far. However, due to the sensitive nature of the medical data, the TMID technique of transferring the patient's data to the central machine or server may create severe privacy and security issues. In recent years, the progression of deep federated learning (DFL) and its remarkable success in many domains has guided as a potential solution in this field. Therefore, we proposed a dependable and privacy-preserving DFL-based identification model of new infections from GSs. The unique contributions include automatic effective feature selection, which is best suited for identifying new infections, designing a dependable and privacy-preserving DFL-based LeNet model, and evaluating real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Our proposed model has an overall accuracy of 99.12% after independently and identically distributing the dataset among six clients. Moreover, the proposed model has a precision of 98.23%, recall of 98.04%, f1-score of 96.24%, Cohen's kappa of 83.94%, and ROC AUC of 98.24% for the same configuration, which is a noticeable improvement when compared to the other benchmark models. The proposed dependable model, along with empirical results, is encouraging enough to recognize as an alternative for identifying new infections from other virus strains by ensuring proper privacy and security of patients' data.

Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severity requires a radiologist with significant expertise. This study introduces a pioneering utilization of deep learning (DL) for evaluate COVID-19 severity using lung CT images, presenting a novel and effective method for assessing the severity of pulmonary manifestations in COVID-19 patients. Inception-Residual networks (Inception-ResNet), advanced hybrid models known for their compactness and effectiveness, were used to extract relevant features from CT scans. Inception-ResNet incorporates the dilated mechanism into its ResNet component, enhancing its ability to accurately classify lung involvement stages. This study demonstrates that dilated residual networks (dResNet) outperform their non-dilated counterparts in image classification tasks, as their architectural designs allow the systems to acquire comprehensive global data by expanding their receptive fields. Our study utilized an initial dataset of 1548 human thoracic CT scans, meticulously annotated by two experienced specialists. Lung involvement was determined by calculating a percentage based on observations made at each scan. The hybrid methodology successfully distinguished the ten distinct severity levels associated with COVID-19, achieving a maximum accuracy of 96.40%. This system demonstrates its effectiveness as a diagnostic framework for assessing lung involvement in COVID-19-affected individuals, facilitating disease progression tracking.

This investigation attempts to propose a novel Wavelet and Local Binary Pattern-based Xception feature Descriptor (WLBPXD) framework, which uses a deep-learning model for classifying chronic infection amongst other infections. Chronic infection (COVID-19 in this study) is identified via RT-PCR test, which is time-consuming and requires a dedicated laboratory (materials, equipment, etc.) to complete the clinical results. X-rays and computed tomography images from chest scans offer an alternative method for identifying chronic infections. It has been demonstrated that chronic infection can be diagnosed from X-ray images acquired in a real-world setting. The images are transformed using the discrete wavelet transform (DWT), combined with the local binary pattern (LBP) technique. Pre-trained deep-learning models, such as AlexNet, Xception, VGG-16 and Inception Resnet50, extract the features. Subsequently, the extracted features are fused using feature-fusion approaches and subjected to classification. The AlexNet, in conjunction with the DWT model, produced 99.7% accurate results, whereas the AlexNet and the LBP model produced 99.6% accurate results. Therefore, the proposed method is efficient as it offers a better detection accuracy and eventually enhances the scope of early detection, thus assisting the clinical perspectives.

Severe acute syndrome coronavirus 2 can invade a variety of tissues, including the testis. Even though this virus is scarcely found in human semen polymerase chain reaction tests, autopsy studies confirm the viral presence in all testicular cell types, including spermatozoa and spermatids.

To investigate whether the severe acute syndrome coronavirus 2 is present inside the spermatozoa of negative polymerase chain reaction-infected men up to 3 months after hospital discharge.

This cross-sectional study included 13 confirmed moderate-to-severe COVID-19 patients enrolled 30-90 days after the diagnosis. Semen samples were obtained and examined with real-time polymerase chain reaction for RNA detection and by transmission electron microscopy.

In moderate-to-severe clinical scenarios, we identified the severe acute syndrome coronavirus 2 inside spermatozoa in nine of 13 patients up to 90 days after discharge from the hospital. Moreover, some DNA-based extracellular traps were reported in all studied specimens.

Although severe acute syndrome coronavirus 2 was not present in the infected men's semen, it was intracellularly present in the spermatozoa till 3 months after hospital discharge. The Electron microscopy (EM) findings also suggest that spermatozoa produce nuclear DNA-based extracellular traps, probably in a cell-free DNA-dependent manner, similar to those previously described in the systemic inflammatory response to COVID-19. In moderate-to-severe cases, the blood-testes barrier grants little defence against different pathogenic viruses, including the severe acute syndrome coronavirus 2. The virus could also use the epididymis as a post-testicular route to bind and fuse to the mature spermatozoon and possibly accomplish the reverse transcription of the single-stranded viral RNA into proviral DNA. These mechanisms can elicit extracellular cell-free DNA formation. The potential implications of our findings for assisted conception must be addressed, and the evolutionary history of DNA-based extracellular traps as preserved ammunition in animals' innate defence might improve our understanding of the severe acute syndrome coronavirus 2 pathophysiology in the testis and spermatozoa.

The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds.

By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions.

The experimental results demonstrate that a set of acoustic parameters of cough sounds, e.g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general automatic COVID-19 detection model performs significantly above chance level, i.e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201).

Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models.

Epidemiological and clinical features of COVID-19 have been reported, but risk factors need to be determined to predict the course of the disease. In our study, we aimed to determine the effect of uric acid level on the severity of the disease. COVID-19 patients who received inpatient treatment between March 11, 2020 and May 30, 2020, and whose uric acid level was measured were included in the study. Demographic characteristics, comorbidities, symptoms, clinical course, laboratory parameters, and treatments were recorded. The study included 83 patients, of these 43 (52%) were males. The mean age was 59±17.1 years. The mean uric acid and albumin levels of the patients who needed oxygen were lower than in those who did not need oxygen (p=0.471 and p=0.057, respectively). There was a significant relationship between the presence of hypouricemia and mortality (p=0.019). In addition, the mean uric acid levels of patients who needed intensive care or died were lower than the mean uric acid levels of patients who did not need intensive care or who lived (p=0.665 and p=0.241, respectively). Oxygen need, intensive care need, and low uric acid level were found to be associated with increased length of hospital stay (p=0.00, p=0.001, p=0.012, and r=-0.276, respectively). Our study results suggest that uric acid levels may be associated with disease severity in the course of COVID-19.

Purpose of this article is to study the possible direct antiviral effect of "Armenikum" on SARS-CoV-2, conduct an in vitro study on the SARS-CoV-2 encephalomocarditis virus, and an in vivo study on the Syrian hamster model. Human coronavirus SARS-CoV-2 (delta strain) was used as the virus. Two groups of four-specimen hamsters were used to study the therapeutic activity of the drug during 48 h after infecting. One group of hamsters served as positive control and was infected with the virus at a similar dose as experimental one and was used as a control of pathology induced by the viral infection till the end of the experiment. Another group of hamsters (four of them) was injected physiological solution and was used as a control. The Syrian hamsters underwent a clinical blood test and computed tomography. "Armenikum" in the form of an injection has a significant antiviral effect on the human coronavirus SARS-CoV-2, credibly reducing the titers of the virus and the time of its elimination from the Syrian hamsters, significantly mitigating the viral infection. "Armenikum" in the form of an injection drug almost completely removes the pathological effect of the virus in the lungs of the hamsters.

In December 2019, a number of subjects presenting with an unexplained pneumonia-like illness were suspected to have a link to a seafood market in Wuhan, China. Subsequently, this illness was identified as the 2019-novel coronavirus (2019-nCoV) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the World Committee on Virus Classification. Since its initial identification, the virus has rapidly sperad across the globe, posing an extraordinary challenge for the medical community. Currently, the Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is considered the most reliable method for diagnosing SARS-CoV-2. This procedure involves collecting oro-pharyngeal or nasopharyngeal swabs from individuals. Nevertheless, for the early detection of low viral loads, a more sensitive technique, such as droplet digital PCR (ddPCR), has been suggested. Despite the high effectiveness of RT-PCR, there is increasing interest in utilizing highly trained dogs and electronic noses (eNoses) as alternative methods for screening asymptomatic individuals for SARS-CoV-2. These dogs and eNoses have demonstrated high sensitivity and can detect volatile organic compounds (VOCs), enabling them to distinguish between COVID-19 positive and negative individuals. This manuscript recapitulates the potential, advantages, and limitations of employing trained dogs and eNoses for the screening and control of SARS-CoV-2.

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network's determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region of interest (ROI) cropping, and data augmentation steps to enhance classification accuracy. Later, a multi-layer Gradient CAM (ML-Grad-CAM) algorithm is integrated to generate a class-specific saliency map for improved visualization in CXR images. We also define and calculate a Severity Assessment Index (SAI) from the saliency map to quantitatively measure infection severity. The trained model achieved an accuracy score of 96.44% for the three-class CXR classification task, i.e., COVID-19, pneumonia, and normal (healthy patients), outperforming many existing techniques in the literature. The saliency maps generated from the proposed ML-GRAD-CAM algorithm are compared with the original Gran-CAM algorithm.

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework's effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.

This review explores imaging's crucial role in acute Coronavirus Disease 2019 (COVID-19) assessment. High Resolution Computer Tomography is especially effective in detection of lung abnormalities. Chest radiography has limited utility in the initial stages of COVID-19 infection. Lung Ultrasound has emerged as a valuable, radiation-free tool in critical care, and Magnetic Resonance Imaging shows promise as a Computed Tomography alternative. Typical and atypical findings of COVID-19 by each of these modalities are discussed with emphasis on their prognostic value. Considerations for pediatric and immunocompromised cases are outlined. A comprehensive diagnostic approach is recommended, as radiological diagnosis remains challenging in the acute phase.

Amidst the COVID-19 pandemic, the Polytechnic University of Setúbal (IPS) used its expertise in molecular genetics to establish a COVID-19 laboratory, addressing the demand for community-wide testing. Following standard protocols, the IPS COVID Lab received national accreditation in October 2020 and was registered in February 2021. With the emergence of new SARS-CoV-2 variants and safety concerns for students and staff, the lab was further challenged to develop rapid and sensitive diagnostic technologies. Methodologies such as sample-pooling extraction and multiplex protocols were developed to enhance testing efficiency without compromising accuracy. Through Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) analysis, the effectiveness of sample pooling was validated, proving to be a clear success in COVID-19 screening. Regarding multiplex analysis, the IPS COVID Lab developed an in-house protocol, achieving a sensitivity comparable to that of standard methods while reducing operational time and reagent consumption. This approach, requiring only two wells of a PCR plate (instead of three for samples), presents a more efficient alternative for future testing scenarios, increasing its throughput and testing capacity while upholding accuracy standards. The lessons learned during the SARS-CoV-2 pandemic provide added value for future pandemic situations.

Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.

A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.

In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.

RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

The first reports of viruses in psittacine birds date back to the early 1970s. Here, we elucidate the differences among these previous studies and the advances achieved. The objective of this study was to carry out a comprehensive review using both scientometric and systematic methods to analyze the evolution of published studies on viruses in psittacine birds up to 2022. The search descriptors "virus", "diagnosis", and "Psittaciformes" were used to find the articles of interest for this study. A total of 118 articles were manually selected, and the scientometric data were organized using the software VOSviewer® version 1.6.18. The present review revealed that: (i) on average, 2.5 articles/year on the diagnosis of viral infection in psittacine birds were published since 1975; (ii) the most productive research groups are concentrated in three countries: Australia, the United States, and Germany; (iii) the most important virus in psittacine birds is the Circovirus, which causes psittacine beak and feather disease; (iv) the diagnostic method of choice is polymerase chain reaction (PCR); and (v) the most studied psittacine birds were those in the Psittacidae family that were kept in captivity.

SARS-CoV-2 is usually diagnosed from naso-/oropharyngeal swabs which are uncomfortable and prone to false results. This study investigated a novel diagnostic approach to Covid-19 measuring volatile organic compounds (VOC) from patients' urine.

Between June 2020 and February 2021, 84 patients with positive RT-PCR for SARS-CoV-2 were recruited as well as 54 symptomatic individuals with negative RT-PCR. Midstream urine samples were obtained for VOC analysis using ion mobility spectrometry (IMS) which detects individual molecular components of a gas sample based on their size, configuration, and charge after ionization.

Peak analysis of the 84 Covid and 54 control samples showed good group separation. In total, 37 individual specific peaks were identified, 5 of which (P134, 198, 135, 75, 136) accounted for significant differences between groups, resulting in sensitivities of 89-94% and specificities of 82-94%. A decision tree was generated from the relevant peaks, leading to a combined sensitivity and specificity of 98% each.

VOC-based diagnosis can establish a reliable separation between urine samples of Covid-19 patients and negative controls. Molecular peaks which apparently are disease-specific were identified. IMS is an additional non-invasive and cheap device for the diagnosis of this ongoing endemic infection. Further studies are needed to validate sensitivity and specificity.

Artificial intelligence (AI) based models are explored increasingly in the medical field. The highly contagious pandemic of coronavirus disease 2019 (COVID-19) affected the world and availability of diagnostic tools high resolution computed tomography (HRCT) and/or real-time reverse transcriptase-polymerase chain reaction (RTPCR) was very limited, costly and time consuming. Therefore, the use of AI in COVID-19 for diagnosis using cough sounds can be efficacious and cost effective for screening in clinic or hospital and help in early diagnosis and further management of patients.

To develop an accurate and fast voice-processing AI software to determine voice-based signatures in discriminating COVID-19 and non-COVID-19 cough sounds for screening of COVID-19.

A prospective study involving 117 patients was performed based on online and/or offline voice data collection of cough sounds of COVID-19 patients in isolation ward of a tertiary care teaching hospital and non-COVID-19 participants using a smart phone. A website-based AI software was developed to identify the cough sounds as COVID-19 or non-COVID-19. The data were divided into three segments including training set, validation set and test set. A pre-processing algorithm was utilized and combined with Short Time Fourier Transform feature representation and Logistic regression model. A precise software was used to identify vocal signatures and K-fold cross validation was carried out.

A total of 117 audio recordings of cough sounds were collected through the developed website after inclusion-exclusion criteria out of which 52 have been marked belonging to COVID-19 positive, while 65 were marked as COVID-19 negative/unsure /never had COVID-19, which were assumed to be COVID-19 negative based on RT-PCR test results. The mean and standard error values for the accuracies attained at the end of each experiment in training, validation and testing set were found to be 67.34%±0.22, 58.57%±1.11 and 64.60%±1.79 respectively. The weight values were found to be positive which were contributing towards predicting the samples as COVID-19 positive with large spikes around 7.5 kHz, 7.8 kHz, 8.6 kHz and 11 kHz which can be used for classification.

The proposed AI based approach can be a helpful screening tool for COVID-19 using vocal sounds of cough. It can help the health system by reducing the cost burden and improving overall diagnosis and management of the disease.

Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers.

Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score.

A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers.

Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the "KAIZEN checklist", which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models' AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.

Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables-gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.

This article aims to synthesize the existing literature on the implementation of public policies to incentivize the development of treatments for rare diseases, (diseases with very low prevalence and therefore with low commercial interest) otherwise known as orphan drugs. The implementation of these incentives in the United States (US), Japan, and in the European Union (EU) seems to be related to a substantial increase in treatments for these diseases, and has influenced the way the pharmaceutical research & development (R&D) system operates beyond this policy area. Despite the success of the Orphan Drug model, the academic literature also highlights the negative implications that these public policies have on affordability and access to orphan drugs, as well as on the prioritization of certain disease rare areas over others. The synthesis focuses mostly on the United States' Orphan Drug Act (ODA) as a model for subsequent policies in other regions and countries. It starts with a historical overview of the creation of the term "rare diseases", continues with a summary of the evidence available on the US ODA's positive and negative impacts, and provides a summary of the different proposals to reform these incentives in light of the negative outcomes described. Finally, it describes some key aspects of the Japanese and European policies, as well as some of the challenges captured in the literature related to their impact in Low- and Middle-Income Countries (LMICs).

Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test (nasopharyngeal swab) to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing.

The polymerase chain reaction (PCR) technique is one of the most potent tools in molecular biology. It is extensively used for various applications ranging from medical diagnostics to forensic science and food quality testing. This technique has facilitated to survive COVID-19 pandemic by identifying the virus-infected individuals effortlessly and effectively. This review explores the principles, recent advancements, challenges, and alternatives of PCR technique in the context of COVID-19 and fungal infections. The introduction of PCR technique for anyone new to this field is the primary aim of this review and thereby equips them to understand the science of COVID-19 and related fungal infections in a simplistic manner.

Pathogenesis of Coronavirus disease 2019 (COVID-19) has been associated with dysregulation of both adaptive and innate immune systems. Hence, we determined the contribution of inflammasome in the nasopharyngeal epithelial cells isolated from COVID-19 subjects to disease pathogenesis and outcomes. Epithelial cells from 150 COVID-19 patients and 150 healthy controls were yielded through nasopharyngeal swab sampling. Patients were categorized into three groups of those with clinical presentations/need hospitalization, with clinical presentations/no need hospitalization and cases without clinical symptoms/no need hospitalization. Finally, the transcriptional amount of inflammasome related genes were assessed in the nasopharyngeal epithelial cells using qPCR. There was a significant upregulation of nod-like receptor (NLR) family pyrin domain containing 1 (NLRP1), nod-like receptor (NLR) family pyrin domain containing 3 (NLRP3), Apoptosis-associated speck-like protein containing a CARD (ASC) and Caspase-1 mRNA expressions in patients compared to controls. NLRP1, NLRP3, ASC and Caspase-1 were upregulated in epithelial cells of patients with clinical symptoms/need hospitalization and cases with clinical symptoms/no need hospitalization when compared to controls. There was a correlation between expression of inflammasome-related genes and clinicopathological features. Abnormal expression of inflammasome-related genes in the nasopharyngeal epithelial cells obtained from COVID-19 patients may be of prognostic value to determine the intensity of the disease's outcomes and requirement for alternative supports in hospitals.

Lung Ultrasound (LUS) is a reliable, radiation free and bedside imaging technique to assess several pulmonary diseases. Although the diagnosis of COVID-19 is made with the nasopharyngeal swab, detection of pulmonary involvement is key for a safe patient management. LUS is a valid alternative to explore, in paucisymptomatic self-presenting patients, the presence and extension of pneumonia compared to High Resolution Computed Tomography (HRCT) that represent the gold standard. This is a single-centre prospective study with 131 patients enrolled. Twelve lung areas were explored reporting a semiquantitative assessment to obtain the LUS score. Each patient performed reverse-transcription polymerase chain reaction test (rRT-PCR), hemogasanalysis and HRCT. We observed an inverse correlation between LUSs and pO2, P/F, SpO2, AaDO2 (p value < 0.01), a direct correlation with LUSs and AaDO2 (p value < 0.01). Compared with HRCT, LUS showed sensitivity and specificity of 81.8% and 55.4%, respectively, and VPN 75%, VPP 65%. Therefore, LUS can represent an effective alternative tool to detect pulmonary involvement in COVID-19 compared to HRCT.

RT-PCR is the leading method used in the diagnosis of COVID-19, caused by 2019-nCoV. CT applications also provide a fast and easy diagnosis for detecting pneumonia caused by the SARS-CoV-2 virus. The current study, aimed to compare the lung involvement of vaccinated (two-dose CoronaVac) and unvaccinated patients in the early stage of COVID-19 disease.

In the current retrospective study, which included patients diagnosed with RT-PCR COVID-19 positivity (n=651) between 01 July 2021-15 September 2021, patient information was obtained from the authorized hospital of the pandemic. Data included patients' chest CT scans and whether patients had been vaccinated (two-dose CoronaVac) information.

The ratio of vaccination with double-dose CoronaVac in positive patients was 74.3%. The ratio of patients with normal lung appearance was 61.8%. It was determined that the ratio of involvement in both lungs of patients who were vaccinated with a double dose was significantly lower than the ratio of involvement in patients who were never vaccinated (p <0.001).

In this study, it was determined that pneumonia cases were less common in individuals vaccinated with double-dose CoronaVac. In this study, it was also determined that the protection of the vaccine was higher in females than in males and that the protection of the double-dose CoronaVac vaccine was higher in the 50-60 age group compared to 60 older patients.

Following the global spread of COVID-19, scientists and engineers have adapted technologies and developed new tools to aid in the fight against COVID-19. This review discusses various approaches to engineering biomaterials, devices, and therapeutics, especially at micro and nano levels, for the prevention, diagnosis, and treatment of infectious diseases, such as COVID-19, serving as a resource for scientists to identify specific tools that can be applicable for infectious-disease-related research, technology development, and treatment. From the design and production of equipment critical to first responders and patients using three-dimensional (3D) printing technology to point-of-care devices for rapid diagnosis, these technologies and tools have been essential to address current global needs for the prevention and detection of diseases. Moreover, advancements in organ-on-a-chip platforms provide a valuable platform to not only study infections and disease development in humans but also allow for the screening of more effective therapeutics. In addition, vaccines, the repurposing of approved drugs, biomaterials, drug delivery, and cell therapy are promising approaches for the prevention and treatment of infectious diseases. Following a comprehensive review of all these topics, we discuss unsolved problems and future directions.

To compare the diagnostic effectiveness of chest computed tomography (CT) utilizing a single- versus a dual-reviewer approach in patients with pneumonia secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we conducted a retrospective observational study of data from a cross-section of 4809 patients with probable SARS-CoV-2 from March to November 2020. All patients had a CT radiological report and reverse-transcription polymerase chain reaction (PCR) results. A dual-reviewer approach was applied to two groups while conducting a comparative examination of the data. Reviewer 1 reported 108 patients negative and 374 patients positive for coronavirus disease 2019 (COVID-19) in group A, and 266 negative and 142 positive in group B. Reviewer 2 reported 150 patients negative and 332 patients positive for COVID-19 in group A, and 277 negative and 131 positive in group B. The consensus result reported 87 patients negative and 395 positive for COVID-19 in group A and 274 negative and 134 positive in group B. These findings suggest that a dual-reviewer approach improves chest CT diagnosis compared to a conventional single-reviewer approach.

The purpose of this research is to evaluate the accuracy of AI-assisted quantification in comparison to conventional CT parameters reviewed by a radiologist in predicting the severity, progression, and clinical outcome of disease. The current study is a cross-sectional study that was conducted on patients with the diagnosis of COVID-19 and underwent a pulmonary CT scan between August 23th, 2021 to December 21th, 2022. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), and consolidation were visually evaluated. CT severity score was calculated according to a semi-quantitative method. In addition, AI based quantification of GGO and consolidation volume were also performed. 291 patients (mean age: 64.7 ± 7; 129 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume percentage (40.6%± 11.9%versus 21.7%± 8.8%, p ˂0.001) as well as consolidation volume percentage (4.8% ± 2% versus 1.9% ± 1%, p < 0.001). Among imaging parameters, consolidation volume percentage and the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.91, p < 0.001). According to multivariate regression, consolidation volume was the strongest predictor for disease progression. In conclusion, the consolidation volume measured on the initial chest CT was the most accurate predictor of disease progression, and a larger consolidation volume was associated with a poor clinical outcome. In patients with COVID-19, AI-assisted lesion quantification was useful for risk stratification and prognosis evaluation.

Since the COVID-19 pandemic outbreak, over 760 million confirmed cases and over 6.8 million deaths have been reported globally, according to the World Health Organization. While the SARS-CoV-2 virus carried by COVID-19 patients can be identified though the reverse transcription-polymerase chain reaction (RT-PCR) test with high accuracy, clinical misdiagnosis between COVID-19 and pneumonia patients remains a challenge. Therefore, we developed a novel CovC-ReDRNet model to distinguish COVID-19 patients from pneumonia patients as well as normal cases. ResNet-18 was introduced as the backbone model and tailored for the feature representation afterward. In our feature-based randomized neural network (RNN) framework, the feature representation automatically pairs with the deep random vector function link network (dRVFL) as the optimal classifier, producing a CovC-ReDRNet model for the classification task. Results based on five-fold cross-validation reveal that our method achieved 94.94%, 97.01%, 97.56%, 96.81%, and 95.84% MA sensitivity, MA specificity, MA accuracy, MA precision, and MA F1-score, respectively. Ablation studies evidence the superiority of ResNet-18 over different backbone networks, RNNs over traditional classifiers, and deep RNNs over shallow RNNs. Moreover, our proposed model achieved a better MA accuracy than the state-of-the-art (SOTA) methods, the highest score of which was 95.57%. To conclude, our CovC-ReDRNet model could be perceived as an advanced computer-aided diagnostic model with high speed and high accuracy for classifying and predicting COVID-19 diseases.

In addition to efficacious vaccines and antiviral therapeutics, reliable and flexible in-home personal use diagnostics for the detection of viral antigens are needed for effective control of the COVID-19 pandemic. Despite the approval of several PCR-based and affinity-based in-home COVID-19 testing kits, many of them suffer from problems such as a high false-negative rate, long waiting time, and short storage period. Using the enabling one-bead-one-compound (OBOC) combinatorial technology, several peptidic ligands with a nanomolar binding affinity toward the SARS-CoV-2 spike protein (S-protein) were successfully discovered. Taking advantage of the high surface area of porous nanofibers, immobilization of these ligands on nanofibrous membranes allows the development of personal use sensors that can achieve low nanomolar sensitivity in the detection of the S-protein in saliva. This simple biosensor employing naked-eye reading exhibits detection sensitivity comparable to some of the current FDA-approved home detection kits. Furthermore, the ligand used in the biosensor was found to detect the S-protein derived from both the original strain and the Delta variant. The workflow reported here may enable us to rapidly respond to the development of home-based biosensors against future viral outbreaks.

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.

Early diagnosis of SARS-CoV-2 is fundamental to reduce the risk of community transmission and mortality, as well as public sector expenditures. Three years after the onset of the SARS-CoV-2 pandemic, there are still gaps on what is known regarding costs and cost drivers for the major diagnostic testing strategies in low- middle-income countries (LMICs). This study aimed to estimate the cost of SARS-CoV-2 diagnosis of symptomatic suspected patients by reverse transcription polymerase chain reaction (RT-PCR) and antigen rapid diagnostic tests (Ag-RDT) in Mozambique. We conducted a retrospective cost analysis from the provider's perspective using a bottom-up, micro-costing approach, and compared the direct costs of two nasopharyngeal Ag-RDTs (Panbio and Standard Q) against the costs of three nasal Ag-RDTs (Panbio, COVIOS and LumiraDx), and RT-PCR. The study was undertaken from November 2020 to December 2021 in the country's capital city Maputo, in four healthcare facilities at primary, secondary and tertiary levels of care, and at one reference laboratory. All the resources necessary for RT-PCR and Ag-RDT tests were identified, quantified, valued, and the unit costs per test and per facility were estimated. Our findings show that the mean unit cost of SARS-CoV-2 diagnosis by nasopharyngeal Ag-RDTs was MZN 728.00 (USD 11.90, at 2020 exchange rates) for Panbio and MZN 728.00 (USD 11.90) for Standard Q. For diagnosis by nasal Ag-RDTs, Panbio was MZN 547.00 (USD 8.90), COVIOS was MZN 768.00 (USD 12.50), and LumiraDx was MZN 798.00 (USD 13.00). Medical supplies expenditures represented the main driver of the final cost (>50%), followed by personnel and overhead costs (mean 15% for each). The mean unit cost regardless of the type of Ag-RDT was MZN 714.00 (USD 11.60). Diagnosis by RT-PCR cost MZN 2,414 (USD 39.00) per test. Our sensitivity analysis suggests that focussing on reducing medical supplies costs would be the most cost-saving strategy for governments in LMICs, particularly as international prices decrease. The cost of SARS-CoV-2 diagnosis using Ag-RDTs was three times lower than RT-PCR testing. Governments in LMICs can include cost-efficient Ag-RDTs in their screening strategies, or RT-PCR if international costs of such supplies decrease further in the future. Additional analyses are recommended as the costs of testing can be influenced by the sample referral system.

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) has infected millions of individuals and continues to be a major health concern worldwide. While reverse transcription-polymerase chain reaction remains a reliable method for detecting infections, limitations of this technology, particularly cost and the requirement of a dedicated laboratory, prevent rapid viral monitoring. Antigen tests filled this need to some extent but with limitations including sensitivity and specificity, particularly against emerging variants of concern. Here, we developed aptamers against the SARS-CoV-2 Nucleocapsid protein to complement or replace antibodies in antigen detection assays. As detection reagents in ELISA-like assays, our DNA aptamers were able to detect as low as 150 pg/mL of the protein and under 150 k copies of inactivated SARS-CoV-2 Wuhan Alpha strain in viral transport medium with little cross-reactivity to other human coronaviruses (HCoVs). Further, our aptamers were reselected against the SARS-CoV-2 Omicron variant of concern, and we found two sequences that had a more than two-fold increase in signal compared to our original aptamers when used as detection reagents against protein from the Omicron strain. These findings illustrate the use of aptamers as promising alternative detection reagents that may translate for use in current tests and our findings validate the method for the reselection of aptamers against emerging viral strains.

During the waves of the coronavirus disease (COVID-19) pandemic, emergency departments were overflowing with patients suffering with suspected medical or surgical issues. In these settings, healthcare staff should be able to deal with different medical and surgical scenarios while protecting themselves against the risk of contamination. Various strategies were used to overcome the most critical issues and guarantee quick and efficient diagnostic and therapeutic charts. The use of saliva and nasopharyngeal swab Nucleic Acid Amplification Tests (NAAT) in the diagnosis of COVID-19 was one of the most adopted worldwide. However, NAAT results were slow to report and could sometimes create significant delays in patient management, especially during pandemic peaks. On these bases, radiology has played and continues to play an essential role in detecting COVID-19 patients and solving differential diagnosis between different medical conditions. This systematic review aims to summarize the role of radiology in the management of COVID-19 patients admitted to emergency departments by using chest X-rays (CXR), computed tomography (CT), lung ultrasounds (LUS), and artificial intelligence (AI).

The most grievous threat to human health has been witnessed worldwide with the recent outbreak of Corona virus disease 2019 (COVID-19). There is mounting evidence available regarding theconnect of COVID -19 and oral cavity, particularly periodontal disease. The current review provides an update on the diagnostic potential of dental calculus and how this bio resource may help in providing us huge amount of diagnostic regarding the causative virus. Contemporary standard method of diagnosis via nasopharyngeal swabs (NPS) is tedious, may enhance the risk of aerosol contamination by inducing sneezing and detects the presence of active infection only.However,dental calculus being a mineralized deposit serves as a reservoir for biomoleculesand provides detection of past SARS CoV2 infection. Further, the abundance of information that can be obtained from this remarkable mineralized deposit on teeth regarding the viral genome, its evolution and interactions with the oral microflora shall enhance the understanding of the viral disease process and its connection with the periodontal disease. Additional diagnostic information, which may be obtained from this simple bio reservoir can complement the contemporary diagnostic strategies adopted in the management of COVID-19pandemic and enhance our existing knowledge for developing improvised novel approaches to mitigate the effects of mutated variants of the infectious agent.

Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79-13.2 mGy, 0.90-4.40 mGy, and 0.20-0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66-6.60 mSv, 0.50-0.80 mGy, and 0.39-0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2-4, whereas ULD reduced the dose reduction by a factor of 8-13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.