Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis.

BACKGROUND: Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain. OBJECTIVE: This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events. METHODS: Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. RESULTS: A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%). CONCLUSIONS: Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability. TRIAL REGISTRATION: Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867.

Saliva Is a Sensitive and Accessible Sample Both for SARS-CoV-2 Detection and for the Evaluation of Treatment Effectiveness in Follow-Up Studies.

Despite emerging evidence indicating that molecular SARS-CoV-2 tests performed on saliva have diagnostic sensitivity and specificity comparable to those observed with nasopharyngeal swabs (NPSs), most in vivo follow-up studies on the efficacy of drugs against SARS-CoV-2 have been performed on NPSs, not considering saliva as a possible alternative matrix. For this reason, in this study, we used, in parallel, saliva and NPS samples for the detection of SARS-CoV-2 by real-time RT-PCR in patients receiving Tixagevimab/Cilgavimab, Nirmatrelvir/Ritonavir, or Sotrovimab as a treatment against SARS-CoV-2. Our results showed a good correlation between the NPS and saliva samples for each drug; moreover, comparable changes in the cycle threshold (Ct) levels in saliva and NPSs were observed both 7 days and 30 days after treatment, thus confirming that the saliva represents a good matrix for in vivo follow-up studies verifying the effectiveness of treatments against SARS-CoV-2.

CAFES: Chest X-ray Analysis using Federated Self-supervised Learning for Pediatric COVID-19 Detection.

Chest X-rays (CXRs) play a pivotal role in cost-effective clinical assessment of various heart and lung related conditions. The urgency of COVID-19 diagnosis prompted their use in identifying conditions like lung opacity, pneumonia, and acute respiratory distress syndrome in pediatric patients. We propose an AI-driven solution for binary COVID-19 versus non-COVID-19 classification in pediatric CXRs. We present a Federated Self-Supervised Learning (FSSL) framework to enhance Vision Transformer (ViT) performance for COVID-19 detection in pediatric CXRs. ViT's prowess in vision-related binary classification tasks, combined with self-supervised pre-training on adult CXR data, forms the basis of the FSSL approach. We implement our strategy on the Rhino Health Federated Computing Platform (FCP), which ensures privacy and scalability for distributed data. The chest X-ray analysis using the federated SSL (CAFES) model, utilizes the FSSL-pre-trained ViT weights and demonstrated gains in accurately detecting COVID-19 when compared with a fully supervised model. Our FSSL-pre-trained ViT showed an area under the precision-recall curve (AUPR) of 0.952, which is 0.231 points higher than the fully supervised model for COVID-19 diagnosis using pediatric data. Our contributions include leveraging vision transformers for effective COVID-19 diagnosis from pediatric CXRs, employing distributed federated learning-based self-supervised pre-training on adult data, and improving pediatric COVID-19 diagnosis performance. This privacy-conscious approach aligns with HIPAA guidelines, paving the way for broader medical imaging applications.

An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease.

The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.

Evaluation of Binding and Neutralizing Antibodies for Inactivated SARS-CoV-2 Vaccine Immunization.

The circulating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variant presents an ongoing challenge for surveillance and detection. It is important to establish an assay for SARS-CoV-2 antibodies in vaccinated individuals. Numerous studies have demonstrated that binding antibodies (such as S-IgG and N-IgG) and neutralizing antibodies (Nabs) can be detected in vaccinated individuals. However, it is still unclear how to evaluate the consistency and correlation between binding antibodies and Nabs induced by inactivated SARS-CoV-2 vaccines. In this study, serum samples from humans, rhesus macaques, and hamsters immunized with inactivated SARS-CoV-2 vaccines were analyzed for S-IgG, N-IgG, and Nabs. The results showed that the titer and seroconversion rate of S-IgG were significantly higher than those of N-IgG. The correlation between S-IgG and Nabs was higher compared to that of N-IgG. Based on this analysis, we further investigated the titer thresholds of S-IgG and N-IgG in predicting the seroconversion of Nabs. According to the threshold, we can quickly determine the positive and negative effects of the SARS-CoV-2 variant neutralizing antibody in individuals. These findings suggest that the S-IgG antibody is a better supplement to and confirmation of SARS-CoV-2 vaccine immunization.

Facile construction of nanocubic Mn3[Fe(CN)6]2@Pt based electrochemical DNA sensors for ultrafast precise determination of SARS-CoV-2.

Owing to the high mortality and strong infection ability of COVID-19, the early rapid diagnosis is essential to reduce the risk of severe symptoms and the loss of lung function. In clinic, the commonly used detection methods, including the computed tomography (CT) and reverse transcription-polymerase chain reaction (RT-PCR), are often time-consuming with bulky instruments, which normally require more than one hour to report the results. To shorten the analytical period for testing the COVID-19 virus (SARS-CoV-2), we proposed an ultrafast and ultrasensitive DNA sensors to achieve an accurate determination of the DNA sequence by the RNA reverse transcription (rtDNA) of the SARS-CoV-2. A nanocubic architecture of the MnFe@Pt crystals was constructed to integrate both electrocatalysis and conductivity to greatly improve the biosensing performance. After the immobilization of a specific capture and report DNA on above nanocomposite, the rtDNA can be rapidly caught to the DNA sensor to form a double-helix structure, thus generating the current signal change. Within only 10 min, the as-prepared DNA sensors exhibited ultralow detection limit (1 × 10-20 M) and wide linear detection range, together with an outstanding selectivity among various interfering substances.

Developing a Deep Neural Network model for COVID-19 diagnosis based on CT scan images.

COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty in network training and high computation rates. Hence, in this study, we developed the 6-layer Deep Neural Network (DNN) model for COVID-19 diagnosis based on CT scan images. The proposed DNN model is generated to improve accurate diagnostics for classifying sick and healthy persons. Also, other classification models, such as decision trees, random forests and standard neural networks, have been investigated. One of the main contributions of this study is the use of the global feature extractor operator for feature extraction from the images. Furthermore, the 10-fold cross-validation technique is utilized for partitioning the data into training, testing and validation. During the DNN training, the model is generated without dropping out of neurons in the layers. The experimental results of the lightweight DNN model demonstrated that this model has the best accuracy of 96.71% compared to the previous classification models for COVID-19 diagnosis.

FP-CNN: Fuzzy pooling-based convolutional neural network for lung ultrasound image classification with explainable AI.

The COVID-19 pandemic wreaks havoc on healthcare systems all across the world. In pandemic scenarios like COVID-19, the applicability of diagnostic modalities is crucial in medical diagnosis, where non-invasive ultrasound imaging has the potential to be a useful biomarker. This research develops a computer-assisted intelligent methodology for ultrasound lung image classification by utilizing a fuzzy pooling-based convolutional neural network FP-CNN with underlying evidence of particular decisions. The fuzzy-pooling method finds better representative features for ultrasound image classification. The FPCNN model categorizes ultrasound images into one of three classes: covid, disease-free (normal), and pneumonia. Explanations of diagnostic decisions are crucial to ensure the fairness of an intelligent system. This research has used Shapley Additive Explanation (SHAP) to explain the prediction of the FP-CNN models. The prediction of the black-box model is illustrated using the SHAP explanation of the intermediate layers of the black-box model. To determine the most effective model, we have tested different state-of-the-art convolutional neural network architectures with various training strategies, including fine-tuned models, single-layer fuzzy pooling models, and fuzzy pooling at all pooling layers. Among different architectures, the Xception model with all pooling layers having fuzzy pooling achieves the best classification results of 97.2% accuracy. We hope our proposed method will be helpful for the clinical diagnosis of covid-19 from lung ultrasound (LUS) images.

Household conditions, COVID-19, and equity: Insight from two nationally representative surveys.

Background: With people across the United States spending increased time at home since the emergence of COVID-19, housing characteristics may have an even greater impact on health. Therefore, we assessed associations between household conditions and COVID-19 experiences. Methods: We used data from two nationally representative surveys: the Tufts Equity Study (TES; n = 1449 in 2021; n = 1831 in 2022) and the Household Pulse Survey (HPS; n = 147,380 in 2021; n = 62,826 in 2022). In the TES, housing conditions were characterized by heating/cooling methods; smoking inside the home; visible water damage/mold; age of housing unit; and self-reported concern about various environmental factors. In TES and HPS, household size was assessed. Accounting for sampling weights, we examined associations between each housing exposure and COVID-19 outcomes (diagnosis, vaccination) using separate logistic regression models with covariates selected based on an evidence-based directed acyclic graph. Results: Having had COVID-19 was more likely among people who reported poor physical housing condition (odds ratio [OR] = 2.32; 95% confidence interval [CI] = 1.17-4.59; 2021), visible water damage or mold/musty smells (OR = 1.50; 95% CI = 1.10-2.03; 2022), and larger household size (5+ versus 1-2 people; OR = 1.53, 95% CI = 1.34-1.75, HPS 2022). COVID-19 vaccination was less likely among participants who reported smoke exposure inside the home (OR = 0.53; 95% CI = 0.31-0.90; 2022), poor water quality (OR = 0.42; 95% CI = 0.21-0.85; 2021), noise from industrial activity/construction (OR = 0.44; 95% CI = 0.19-0.99; 2022), and larger household size (OR = 0.57; 95% CI = 0.46-0.71; HPS 2022). Vaccination was also positively associated with poor indoor air quality (OR = 1.96; 95% CI = 1.02-3.72; 2022) and poor physical housing condition (OR = 2.27; 95% CI = 1.01-5.13; 2022). Certain heating/cooling sources were associated with COVID-19 outcomes. Conclusions: Our study found poor housing conditions associated with increased COVID-19 burden, which may be driven by systemic disparities in housing, healthcare, and financial access to resources during the COVID-19 pandemic.

Momentum contrast transformer for COVID-19 diagnosis with knowledge distillation.

Intelligent diagnosis has been widely studied in diagnosing novel corona virus disease (COVID-19). Existing deep models typically do not make full use of the global features such as large areas of ground glass opacities, and the local features such as local bronchiolectasis from the COVID-19 chest CT images, leading to unsatisfying recognition accuracy. To address this challenge, this paper proposes a novel method to diagnose COVID-19 using momentum contrast and knowledge distillation, termed MCT-KD. Our method takes advantage of Vision Transformer to design a momentum contrastive learning task to effectively extract global features from COVID-19 chest CT images. Moreover, in transfer and fine-tuning process, we integrate the locality of convolution into Vision Transformer via special knowledge distillation. These strategies enable the final Vision Transformer simultaneously focuses on global and local features from COVID-19 chest CT images. In addition, momentum contrastive learning is self-supervised learning, solving the problem that Vision Transformer is challenging to train on small datasets. Extensive experiments confirm the effectiveness of the proposed MCT-KD. In particular, our MCT-KD is able to achieve 87.43% and 96.94% accuracy on two publicly available datasets, respectively.

GIONet: Global information optimized network for multi-center COVID-19 diagnosis via COVID-GAN and domain adversarial strategy.

The outbreak of coronavirus disease (COVID-19) in 2019 has highlighted the need for automatic diagnosis of the disease, which can develop rapidly into a severe condition. Nevertheless, distinguishing between COVID-19 pneumonia and community-acquired pneumonia (CAP) through computed tomography scans can be challenging due to their similar characteristics. The existing methods often perform poorly in the 3-class classification task of healthy, CAP, and COVID-19 pneumonia, and they have poor ability to handle the heterogeneity of multi-centers data. To address these challenges, we design a COVID-19 classification model using global information optimized network (GIONet) and cross-centers domain adversarial learning strategy. Our approach includes proposing a 3D convolutional neural network with graph enhanced aggregation unit and multi-scale self-attention fusion unit to improve the global feature extraction capability. We also verified that domain adversarial training can effectively reduce feature distance between different centers to address the heterogeneity of multi-center data, and used specialized generative adversarial networks to balance data distribution and improve diagnostic performance. Our experiments demonstrate satisfying diagnosis results, with a mixed dataset accuracy of 99.17% and cross-centers task accuracies of 86.73% and 89.61%.

COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images.

BACKGROUND: The medical profession is facing an excessive workload, which has led to the development of various Computer-Aided Diagnosis (CAD) systems as well as Mobile-Aid Diagnosis (MAD) systems. These technologies enhance the speed and accuracy of diagnoses, particularly in areas with limited resources or remote regions during the pandemic. The primary purpose of this research is to predict and diagnose COVID-19 infection from chest X-ray images by developing a mobile-friendly deep learning framework, which has the potential for deployment in portable devices such as mobile or tablet, especially in situations where the workload of radiology specialists may be high. Moreover, this could improve the accuracy and transparency of population screening to assist radiologists during the pandemic. METHODS: In this study, the Mobile Networks ensemble model called COV-MobNets is proposed to classify positive COVID-19 X-ray images from negative ones and can have an assistant role in diagnosing COVID-19. The proposed model is an ensemble model, combining two lightweight and mobile-friendly models: MobileViT based on transformer structure and MobileNetV3 based on Convolutional Neural Network. Hence, COV-MobNets can extract the features of chest X-ray images in two different methods to achieve better and more accurate results. In addition, data augmentation techniques were applied to the dataset to avoid overfitting during the training process. The COVIDx-CXR-3 benchmark dataset was used for training and evaluation. RESULTS: The classification accuracy of the improved MobileViT and MobileNetV3 models on the test set has reached 92.5% and 97%, respectively, while the accuracy of the proposed model (COV-MobNets) has reached 97.75%. The sensitivity and specificity of the proposed model have also reached 98.5% and 97%, respectively. Experimental comparison proves the result is more accurate and balanced than other methods. CONCLUSION: The proposed method can distinguish between positive and negative COVID-19 cases more accurately and quickly. The proposed method proves that utilizing two automatic feature extractors with different structures as an overall framework of COVID-19 diagnosis can lead to improved performance, enhanced accuracy, and better generalization to new or unseen data. As a result, the proposed framework in this study can be used as an effective method for computer-aided diagnosis and mobile-aided diagnosis of COVID-19. The code is available publicly for open access at https://github.com/MAmirEshraghi/COV-MobNets .

Bright photon upconversion in LiYbF4:Tm3+@LiYF4 nanoparticles and their application for singlet oxygen generation and in immunoassay for SARS-CoV-2 nucleoprotein.

Photon upconversion is an intensively investigated phenomenon in the materials sciences due to its unique applications, mainly in biomedicine for disease prevention and treatment. This study reports the synthesis and properties of tetragonal LiYbF4:Tm3+@LiYF4 core@shell nanoparticles (NPs) and their applications. The NPs had sizes ranging from 18.5 to 23.7 nm. As a result of the energy transfer between Yb3+ and Tm3+ ions, the synthesized NPs show intense emission in the ultraviolet (UV) range up to 347 nm under 975 nm excitation. The bright emission in the UV range allows for singlet oxygen generation in the presence of hematoporphyrin on the surface of NPs. Our studies show that irradiation with a 975 nm laser of the functionalized NPs allows for the production of amounts of singlet oxygen easily detectable by Singlet Oxygen Sensor Green. The high emission intensity of NPs at 800 nm allowed the application of the synthesized NPs in an upconversion-linked immunosorbent assay (ULISA) for highly sensitive detection of the nucleoprotein from SARS-CoV-2, the causative agent of Covid-19. This article proves that LiYbF4:Tm3+@LiYF4 core@shell nanoparticles can be perfect alternatives for the most commonly studied upconverting NPs based on the NaYF4 host compound and are good candidates for biomedical applications.

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network.

This study proposes a deep-learning-based solution (named CapsNetCovid) for COVID-19 diagnosis using a capsule neural network (CapsNet). CapsNets are robust for image rotations and affine transformations, which is advantageous when processing medical imaging datasets. This study presents a performance analysis of CapsNets on standard images and their augmented variants for binary and multi-class classification. CapsNetCovid was trained and evaluated on two COVID-19 datasets of CT images and X-ray images. It was also evaluated on eight augmented datasets. The results show that the proposed model achieved classification accuracy, precision, sensitivity, and F1-score of 99.929%, 99.887%, 100%, and 99.319%, respectively, for the CT images. It also achieved a classification accuracy, precision, sensitivity, and F1-score of 94.721%, 93.864%, 92.947%, and 93.386%, respectively, for the X-ray images. This study presents a comparative analysis between CapsNetCovid, CNN, DenseNet121, and ResNet50 in terms of their ability to correctly identify randomly transformed and rotated CT and X-ray images without the use of data augmentation techniques. The analysis shows that CapsNetCovid outperforms CNN, DenseNet121, and ResNet50 when trained and evaluated on CT and X-ray images without data augmentation. We hope that this research will aid in improving decision making and diagnostic accuracy of medical professionals when diagnosing COVID-19.

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images.

Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches.

Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis.

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019, and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by ground glass opacity, a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo-lesion generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo-COVID-19 images. The pairs of normal and pseudo-COVID-19 images were then used to train an encoder-decoder architecture-based U-Net for image restoration, which does not require any labeled data. The pretrained encoder was then fine-tuned using labeled data for COVID-19 diagnosis task. Two public COVID-19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis, and the accuracy of the proposed method outperformed the supervised model pretrained on large-scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively.

SARS-CoV-2 antigen detection by saliva; an alternative to nasopharyngeal specimen: A cross-sectional study.

Background and Aims: Saliva samples are less invasive and more convenient for patients than naso- and/or oropharynx swabs (NOS). However, there is no US Food and Drug Administration-approved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapid antigen test kit, which can be useful in a prolonged pandemic to reduce transmission by allowing suspected individuals to self-sampling. We evaluated the performances of High sensitive AQ+ Rapid SARS-CoV-2 Antigen Test (AQ+ kit) using nasopharyngeal swabs (NPs) and saliva specimens from the same patients in laboratory conditions. Methods: The real-time reverse transcription-polymerase chain reaction (rRT-PCR) test result was used for screening the inrolled individuals and compared as the gold standard. NP and saliva samples were collected from 100 rRT-PCR positives and 100 negative individuals and tested with an AQ+ kit. Results: The AQ+ kit showed good performances in both NP and saliva samples with an overall accuracy of 98.5% and 94.0%, and sensitivity of 97.0% and 88.0%, respectively. In both cases, specificity was 100%. AQ+ kit performance with saliva was in the range of the World Health Organization recommended value. Conclusion: xOur findings indicate that the saliva specimen can be used as an alternative and less invasive to NPs for quick and reliable SARS-CoV-2 antigen detection.

Heat stroke management during the COVID-19 pandemic: Recommendations from the experts in Japan (2nd edition).

Both coronavirus disease 2019 (COVID-19) and heat stroke have symptoms of fever or hyperthermia and the difficulty in distinguishing them could lead to a strain on emergency medical care. To mitigate the potential confusion that could arise from actions for preventing both COVID-19 spread and heat stroke, particularly in the context of record-breaking summer season temperatures, this work offers new knowledge and evidence that address concerns regarding indoor ventilation and indoor temperatures, mask wearing and heat stroke risk, and the isolation of older adults. Specifically, the current work is the second edition to the previously published guidance for handling heat stroke during the COVID-19 pandemic, prepared by the "Working group on heat stroke medical care during the COVID-19 epidemic," composed of members from four organizations in different medical and related fields. The group was established by the Japanese Association for Acute Medicine Heatstroke and Hypothermia Surveillance Committee. This second edition includes new knowledge, and conventional evidence gleaned from a primary selection of 60 articles from MEDLINE, one article from Cochrane, 13 articles from Ichushi, and a secondary/final selection of 56 articles. This work summarizes the contents that have been clarified in the prevention and treatment of infectious diseases and heat stroke to provide guidance for the prevention, diagnosis, and treatment of heat stroke during the COVID-19 pandemic.