ABSTRACT
In response to the need for rapid and accurate COVID-19 diagnosis during the global pandemic, we present a two-stage framework that leverages pseudo labels for domain adaptation to enhance the detection of COVID-19 from CT scans. By utilizing annotated data from one domain and non-annotated data from another, the model overcomes the challenge of data scarcity and variability, common in emergent health crises. The innovative approach of generating pseudo labels enables the model to iteratively refine its learning process, thereby improving its accuracy and adaptability across different hospitals and medical centres. Experimental results on COV19-CT-DB database showcase the model's potential to achieve high diagnostic precision, significantly contributing to efficient patient management and alleviating the strain on healthcare systems. Our method achieves 0.92 Macro F1 Score on the validation set of Covid-19 domain adaptation challenge.
Subject(s)
COVID-19ABSTRACT
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyse the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt50 as the strong feature extractor, initializing it with pretrained weights which have COVID-19-specific prior knowledge. Our model achieves a Macro F1 Score of 0.94 on the validation set of the 4th COV19D Competition Challenge $\mathrm{I}$, surpassing the baseline by 16%. This indicates its effectiveness in distinguishing between COVID-19 and non-COVID-19 cases, making it a robust method for COVID-19 detection.
Subject(s)
COVID-19ABSTRACT
In the context of continued spread of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and the emergence of new variants, the demand for rapid, accurate, and frequent detection is increasing. Moreover, the new predominant strain, Omicron variant, manifests more similar clinical features to those of other common respiratory infections. The concurrent detection of multiple potential pathogens helps distinguish SARS-CoV-2 infection from other diseases with overlapping symptoms, which is significant for providing tailored treatment to patients and containing the outbreak. Here, we report a lab-on-a-chip biosensing platform for SARS-CoV-2 detection based on the subwavelength grating micro-ring resonator. The sensing surface is functionalized by specific antibody against SARS-CoV-2 spike protein, which could produce redshifts of resonant peaks by antigen-antibody combination, thus achieving quantitative detection. Additionally, the sensor chip is integrated with a microfluidic chip featuring an anti-backflow Y-shaped structure that enables the concurrent detection of two analytes. In this study, we realized the detection and differentiation of COVID-19 and influenza A H1N1. Experimental results indicate that the limit of detection of our device reaches 100 fg/ml (1.31 fM) within 15 min detecting time, and cross-reactivity tests manifest the specificity of the optical diagnostic assay. Furthermore, the integrated packaging and streamlined workflow facilitate its use for clinical applications. Thus, the biosensing platform presents a promising approach for attaining highly sensitive, selective, multiplexed, and quantitative point-of-care diagnosis and distinction between COVID-19 and influenza.
ABSTRACT
The COVID-19 pandemic that is still prevalent around the globe each day consumes massive disposable face masks and consequently lays a heavy burden on waste management. Meanwhile, the incineration of these medical wastes further escalates the already overwhelming carbon emission that leads to global warming and climate change. To offer a potential solution addressing medical waste and CO2 emission challenges, we herein develop a synthetic protocol to upgrade face masks into Ni, N-doped graphene (Ni–N-C) sheet catalysts for selectively reducing CO2 into CO electrochemically. The high specific surface area and the uniform dispersion of Ni active sites of the catalyst derived from a regular disposable face mask enable a near-unity CO Faradaic efficiency (FE) at the current density of 200 mA cm−2. This study offers outside-of-the-box thinking to address environmental issues by turning medical wastes into CO2 reduction catalysts. Supplementary Information The online version contains supplementary material available at 10.1007/s10008-023-05444-7.
ABSTRACT
The COVID-19 pandemic that is still prevalent around the globe each day consumes massive disposable face masks and consequently lays a heavy burden on waste management. Meanwhile, the incineration of these medical wastes further escalates the already overwhelming carbon emission that leads to global warming and climate change. To offer a potential solution addressing medical waste and CO2 emission challenges, we herein develop a synthetic protocol to upgrade face masks into Ni, N-doped graphene (Ni-N-C) sheet catalysts for selectively reducing CO2 into CO electrochemically. The high specific surface area and the uniform dispersion of Ni active sites of the catalyst derived from a regular disposable face mask enable a near-unity CO Faradaic efficiency (FE) at the current density of 200 mA cm-2. This study offers outside-of-the-box thinking to address environmental issues by turning medical wastes into CO2 reduction catalysts. Supplementary Information: The online version contains supplementary material available at 10.1007/s10008-023-05444-7.
ABSTRACT
Objectives: This study aims to evaluate the effect of coronavirus disease 2019 (COVID-19) on the long-term risk of digestive diseases in the general population. Design: Large-scale population-based cohort study based on a prospective cohort. Setting: UK Biobank cohort linked to multiple nationwide electronic health records databases. Participants: The cohort consisted of 112,311 individuals who survived the initial 30 days following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, as well as two control groups: a contemporary group (n = 359,671) without any history of COVID-19, and a historical control group (n = 370,979) that predated the COVID-19 outbreak. Main outcome measures: Main outcomes were predefined digestive diseases. Hazard ratios and corresponding 95% confidence intervals (CI) were computed utilizing the Cox regression models after inverse probability weighting. Results: Compared with the contemporary control group, patients with previous COVID-19 infection had higher risks of digestive diseases, including functional gastrointestinal disorders (hazard ratios [HR] 1.95 (95% CI 1.62 to 2.35)); peptic ulcer disease (HR 1.27 (1.04 to 1.56)); gastroesophageal reflux disease (GERD) (HR 1.46 (1.34 to 1.58)); inflammatory bowel diseases (HR 1.40 (1.02 to 1.90)); gallbladder disease (HR 1.28 (1.13 to 1.46)); severe liver disease (HR 1.46 (1.12 to 1.90)); non-alcoholic liver disease (HR 1.33 (1.15 to 1.55)); and pancreatic disease (HR 1.43 (1.17 to 1.74)). The risks of GERD were stepwise increased with severity of the acute phase of COVID-19 infection. The results were consistent when using the historical cohort as the control group. Conclusions: Our study provides important insights into the association between COVID-19 and the long-term risk of digestive system disorders. COVID-19 patients are at a higher risk of developing gastrointestinal disorders, with stepwise increased risk with the severity and persisting even after one year follow-up.
Subject(s)
Coronavirus Infections , Peptic Ulcer , Gastroesophageal Reflux , Liver Diseases, Alcoholic , Liver Diseases , Pancreatic Diseases , COVID-19 , Gallbladder Diseases , Gastrointestinal Diseases , Inflammatory Bowel DiseasesABSTRACT
Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg). Concurrently, multi-layer perceptron (MLP) network has regained popularity among researchers due to their comparable results to ViT, albeit with the exclusion of the resource-intensive self-attention module. In this work, we propose a novel permutable hybrid network for Vol-MedSeg, named PHNet, which capitalizes on the strengths of both convolution neural networks (CNNs) and MLP. PHNet addresses the intrinsic isotropy problem of 3D volumetric data by employing a combination of 2D and 3D CNNs to extract local features. Besides, we propose an efficient multi-layer permute perceptron (MLPP) module that captures long-range dependence while preserving positional information. This is achieved through an axis decomposition operation that permutes the input tensor along different axes, thereby enabling the separate encoding of the positional information. Furthermore, MLPP tackles the resolution sensitivity issue of MLP in Vol-MedSeg with a token segmentation operation, which divides the feature into smaller tokens and processes them individually. Extensive experimental results validate that PHNet outperforms the state-of-the-art methods with lower computational costs on the widely-used yet challenging COVID-19-20 and Synapse benchmarks. The ablation study also demonstrates the effectiveness of PHNet in harnessing the strengths of both CNNs and MLP.
Subject(s)
COVID-19ABSTRACT
In the context of continued spread of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and the emergence of new variants, the demand for rapid, accurate, and frequent detection is increasing. Besides, the new predominant strain, Omicron variant, manifests more similar clinical features to those of other common respiratory infections. The concurrent detection of multiple potential pathogens helps distinguish SARS-CoV-2 infection from other diseases with overlapping symptoms, which is significant for patients to receive tailored treatment and containing the outbreak. Here, we report a lab-on-a-chip biosensing platform for SARS-CoV-2 detection based on subwavelength grating micro-ring resonator. The sensing surface is functionalized by specific antibody against SARS-CoV-2 spike protein, which could produce redshifts of resonant peaks by antigen-antibody combination, thus achieving quantitative detection. Additionally, the sensor chip is integrated with a microfluidic chip with an anti-backflow Y-shaped structure that enables the concurrent detection of two analytes. In this study, we realized the detection and differentiation of COVID-19 and influenza A H1N1. Experimental results show that the limit of detection of our device reaches 100 fg/mL (1.31 fM) within 15 min detecting time, and cross-reactivity tests manifest the specificity of the optical diagnostic assay. Further, the integrated packaging and streamlined workflow facilitate its use for clinical applications. Thus, the biosensing platform offers a promising solution to achieve ultrasensitive, selective, multiplexed, and quantitative point-of-care detection of COVID-19.
Subject(s)
COVID-19 , Respiratory Tract InfectionsABSTRACT
To help optimize online learning platforms for in-service teachers’ professional development, this study aims to develop an instrument to assess the quality of this type of platforms on teacher satisfaction. After reliability and validity tests and expert empowerment, the 27-item instrument was formed. Based on the information systems (IS) success model, this instrument was designed to measure teacher perceptions of the quality of online learning platforms from three dimensions, namely, content quality, technical quality, and service quality. Moreover, the developed instrument was used to analyze the effects of the National Teacher Training Platform amid the COVID-19 outbreak in China. The findings revealed that the improvement of the platform’s style, tool function, operating efficiency, and teaching methods could enhance teachers’ experience of online training.
ABSTRACT
Objectives To prospectively investigate the associations of habitual fish oil use with Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, hospital admission, or mortality with Corona Virus Disease-19 (COVID-19) in a large-scale cohort. Design Prospective population-based cohort study. Setting UK Biobank. Participants A total of 110 440 participants aged 37 -73 years who completed a questionnaire on supplement use, which included fish oil at baseline were enrolled between 2006 and 2010 and followed up until 2022. Main exposure All participants filled out questionnaires about the habitual use of supplements, including fish oil. Main outcome measures SARS-CoV-2 infection, COVID-19 hospital admission and COVID-19 mortality. Results At baseline, 29 424 (26.6%) of the 110 440 participants reported habitual use of fish oil supplements. The multivariable adjusted hazard ratios for habitual users of fish oil versus non-users were 0.95 (0.93 to 0.98) for SARS-CoV-2 infection among participants with follow-up time less than 12.1 years but no significant associations were observed for participants with follow-up time more than 12.1 years. For COVID-19-related outcomes, the hazard ratios were 0.79 (95% confidence interval 0.71 to 0.88) for COVID-19 hospital admission and 0.72 (0.60 to 0.87) for COVID-19 mortality. For COVID-19-related outcomes, the association seemed to be stronger among those with longstanding illness. The Cox proportional hazard analysis after propensity-score matching yielded consistent results. Conclusions Habitual fish oil supplement is associated with a lower risk of hospital admission and mortality with COVID-19, but not associated with SARS-CoV-2 infection in the population with more than 12.1 years of follow-up.
Subject(s)
COVID-19 , Virus Diseases , Severe Acute Respiratory SyndromeABSTRACT
Objectives To assess the association of habitual glucosamine use with coronavirus 2 (SARS-CoV-2) infection, hospital admission, or mortality with Corona Virus Disease-19 (COVID-19) in a large population based cohort. Design Population based, prospective cohort study. Setting UK Biobank. Participants Participants with complete information on habitual glucosamine use and SARS-CoV-2 infection or COVID-19-related outcomes were included. These participants were registered from 2006 to 2010, followed up until 2022 and participated in SARS-CoV-2 tests between 2020 and 2022. Main outcome measures SARS-CoV-2 infection, COVID-19 hospital admission, and COVID-19 mortality. Results At baseline, 20,118 (15.9%) of the 126,518 participants reported as habitual glucosamine users. During the median follow-up 12.16 years, there were 53,682 cases of SARS-CoV-2 infection, 2,120 cases of COVID-19 hospital admission and 548 cases of COVID-19 mortality. The multivariate adjusted hazard ratios of habitual glucosamine users to non-users were 1.02 (95% confidence interval [CI] 0.99 to 1.05) for SARS-CoV-2 infection, 0.73 (95% CI 0.63 to 0.85) for COVID-19 hospital admission, and 0.74 (95% CI 0.56 to 0.98) for COVID-19 mortality. The Cox proportional hazard analysis after propensity-score matching yielded consistent results. Conclusions Habitual glucosamine use seems to be associated with a lower risk of hospital admission and mortality with COVID-19, but not the risk of SARS-CoV-2 infection.
Subject(s)
COVID-19 , Virus DiseasesABSTRACT
Foodborne diseases are an increasing concern to public health;climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an “exposure–sensitivity–adaptability” vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases.
ABSTRACT
Objective Concerns have been raised about the widespread use of proton pump inhibitors (PPIs), and current findings linking the regular use of PPIs to respiratory infections remain inconsistent. Our study aims to evaluate whether PPI use increases the risk of pneumonia, influenza, and COVID-19. Method The presented study included 160,923 eligible participants from the UK Biobank (mean age 56.5 years, 53% women). Cox proportional hazards regression and propensity score-matching analyses were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). Effect modifications by stratifications, including indications and CYP2C19 phenotypes were tested. Results The regular use of PPIs was associated with increased risks of developing pneumonia (hazard ratio [HR] 1.42, 95% confidence interval [CI] 1.26-1.59) and influenza (HR 1.31, 95% CI 1.11-1.55). However, the risk of COVID-19 infection among regular PPI users was not significantly increased (HR 1.05, 95% CI 0.95-1.16). The burden was more notably observed in patients without indications of PPI use (HR 1.52, 95% CI 1.33-1.73 for pneumonia; HR 1.36, 95% CI 1.12-1.64 for influenza). The risk for pneumonia was higher among the CYP2C19 rapid and ultrarapid metabolizers (HR 1.45, 95% CI 1.22-1.73, P for interaction < 0.001). The propensity score-matching analyses yielded similar trends. Conclusions The regular use of PPIs is associated with increased susceptibility to pneumonia and influenza, but not COVID-19 infection. The risks are even higher among recipients without main indications. Our study highlights the appropriate use and de-prescribing of PPIs according to indications and CYP2C19 phenotypes for patients and clinical practitioners.
Subject(s)
COVID-19 , Respiratory Tract Infections , PneumoniaABSTRACT
Purpose>Social shopping platforms have flourished by using multiple social shopping features, yet little is known about how the combination of these features affects purchase intention, particularly in terms of the product itself. The purpose of the paper is to draw on the concept of social shopping feature richness, adopting a formative approach on the survey used, and endeavors to reveal the concept's impact on consumers' buying intention from a product perspective.Design/methodology/approach>Building on mental accounting and signaling theories, a theoretical model is proposed and empirically evaluated with 356 samples collected using a questionnaire survey.Findings>The results suggest that social shopping feature richness promotes consumers' consumption by providing information signals to satisfy acquisition utility and transaction utility. Specifically, social shopping feature richness enhances perceived product quality, while decreasing negative perceptions regarding price. Moreover, perceived product quality and perceived price significantly influence buying intention through the mechanism of perceived value.Originality/value>The authors' study highlights the role of the combination of functionally diverse social shopping features on product sales for social shopping platforms.
ABSTRACT
Currently, in terms of reducing the infection risk of the COVID-19 virus spreading all over the world, the development of touchless blood pressure (BP) measurement has potential benefits. The pulse transit time (PTT) has a high relation with BP, which can be measured by electrocardiogram (ECG) and photoplethysmogram (PPG). The ballistocardiogram (BCG) reflects the mechanical vibration (or displacement) caused by the heart contraction/relaxation (or heart beating), which can be measured from multiple degrees of the body. The goal of this study is to develop a cuffless and touchless BP-measurement method based on a commercial weight scale combined with a PPG sensor when measuring body weight. The proposed method was that the PTTBCG-PPGT was extracted from the BCG signal measured by a weight scale, and the PPG signal was measured from the PPG probe placed at the toe. Four PTT models were used to estimate BP. The reference method was the PTTECG-PPGF extracted from the ECG signal and PPG signal measured from the PPG probe placed at the finger. The standard BP was measured by an electronic blood pressure monitor. Twenty subjects were recruited in this study. By the proposed method, the root-mean-square error (ERMS) of estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 6.7 ±1.60 mmHg and 4.8 ±1.47 mmHg, respectively. The correlation coefficients, r2, of the proposed model for the SBP and DBP are 0.606 ±0.142 and 0.284 ±0.166, respectively. The results show that the proposed method can serve for cuffless and touchless BP measurement.
ABSTRACT
Purpose. The purpose was to explore the relationship between monocyte-to-lymphocyte ratio (MLR) and the severity of spinal tuberculosis. Methods. A total of 1,000 clinical cases were collected, including 496 cases of spinal tuberculosis and 504 cases of nonspinal tuberculosis. Laboratory blood results were collected, including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cells (WBC), hemoglobin (HGB), platelets (PLT), neutrophil count, percentage of neutrophils, lymphocyte count, percentage of lymphocytes, monocyte count, percentage of monocytes, MLR, platelets -to- monocyte ratio (PMR), platelets -to- lymphocyte ratio (PLR), neutrophil -to- lymphocyte ratio (NLR), and platelets -to- neutrophil ratio (PNR). The statistical parameters analyzed by the Least Absolute Shrinkage and Selection Operator (LASSO) and receiver-operating characteristic (ROC) curves were used to construct the nomogram. The nomogram was assessed by C-index, calibration curve, ROC curve, and decision curve analysis (DCA) curve. Results. The C-index of the nomogram in the training set and external validation set was 0.801 and 0.861, respectively. Similarly, AUC was 0.801 in the former and 0.861 in the latter. The net benefit of the former nomogram ranged from 0.1 to 0.95 and 0.02 to 0.99 in the latter nomogram. Furthermore, there was a correlation between MLR and the severity of spinal tuberculosis. Conclusion. MLR was an independent factor in the diagnosis of spinal tuberculosis and was associated with the severity of spinal tuberculosis. Additionally, MLR may be a predictor of active spinal tuberculosis.
ABSTRACT
Unemployment can have devastating effects on people?s psychological and social wellbeing. The effects of unemployment can be exacerbated during the COVID-19 pandemic due to the lack of control over one?s life and the loss of social connectedness. Through a survey of 480 unemployed workers, this study examined how emotion-focused coping using video game can affect the workers? wellbeing and reemployment. The findings showed that escapism was associated with decreased wellbeing, which reduced job-search efficacy and behaviors. However, when video game playing was viewed as a source of self-determination, it can support the unemployed workers? intrinsic needs of autonomy and relatedness, which improved their wellbeing, their job-search efficacy, and job-search behaviors. Further comparison of effects between gender, age, race, and income found that unemployed workers who made lower to medium income were more likely to seek escapism through games compared to female unemployed workers.
ABSTRACT
Serological assays are indispensable tools in public health. Presently deployed serological assays, however, largely overlook research progress made in the last two decades that jeopardize the conceptual foundation of these assays, i.e., antibody (Ab) specificity. Challenges to traditional understanding of Ab specificity include Ab poly-specificity, and most recently non-reproducible Ab-probe interactions (NRIs). Here, using SARS-CoV-2 and 4 common livestock viruses as a test bed, we developed a new serological platform that integrates recent understanding about Ab specificity. We first demonstrate that the response rate (RR) from a large sized serum pool (~100) is not affected by NRIs or by non-specific Ab-probe interactions (NSIs), so RR can be incorporated into the diagnostic probe selection process. We subsequently used multiple probes (configured as a “protein peptide hybrid microarray”, PPHM) to generate a digital microarray index (DMI), and finally demonstrate that DMI-based analysis yields an extremely robust probabilistic trend that enables accurate diagnosis of viral infection that overcomes multiple negative impacts exerted by NSI/NRI. Thus, our study with SARS-CoV-2 confirms that the PPHM-RR-DMI platform enables very rapid development of serological assays that outperform traditional assays (for both sensitivity and specificity) and supports that the platform is extendable to other viruses.
ABSTRACT
Studies are needed to evaluate the safety and effectiveness of mRNA SARS-CoV-2 vaccination during pregnancy, and the levels of protection provided to their newborns through placental transfer of antibodies. We evaluated the transplacental transfer of mRNA vaccine products and functional anti-SARS-CoV-2 antibodies during pregnancy and early infancy in a cohort of 20 individuals vaccinated during pregnancy. We found no evidence of mRNA vaccine products in maternal blood, placenta tissue, or cord blood at delivery. However, we found time-dependent efficient transfer of IgG and neutralizing antibodies to the neonate that persisted during early infancy. Additionally, using phage immunoprecipitation sequencing, we found a vaccine-specific signature of SARS-CoV-2 Spike protein epitope binding that is transplacentally transferred during pregnancy. In conclusion, products of mRNA vaccines are not transferred to the fetus during pregnancy, however timing of vaccination during pregnancy is critical to ensure transplacental transfer of protective antibodies during early infancy