Pathogenic infection characteristics and risk factors for bovine respiratory disease complex based on the detection of lung pathogens in dead cattle in northeast China.

Bovine respiratory disease complex (BRDC) involves multiple pathogens, shows diverse lung lesions, and is a major concern in calves. Pathogens from 160 lung samples of dead cattle from 81 cattle farms in northeast China from 2016 to 2021 were collected to characterize the molecular epidemiology and risk factors of BRDC and to assess the major pathogens involved in bovine suppurative or caseous necrotizing pneumonia. The BRDC was diagnosed by autopsy, pathogen isolation, PCR, or reverse transcription-PCR detection, and gene sequencing. More than 18 species of pathogens, including 491 strains of respiratory pathogens, were detected. The positivity rate of bacteria in the 160 lung samples was 31.77%, including Trueperella pyogenes (9.37%), Pasteurella multocida (8.35%), Histophilus somni (4.48%), Mannheimia haemolytica (2.44%), and other bacteria (7.13%). The positivity rate of Mycoplasma spp. was 38.9%, including M. bovis (7.74%), M. dispar (11.61%), M. bovirhinis (7.94%), M. alkalescens (6.11%), M. arginini (0.81%), and undetermined species (4.68%). Six species of viruses were detected with a positivity rate of 29.33%, including bovine herpesvirus-1 (BoHV-1; 13.25%), bovine respiratory syncytial virus (BRSV; 5.50%), bovine viral diarrhea virus (BVDV; 4.89%), bovine parainfluenza virus type-3 (BPIV-3; 4.28%), bovine parainfluenza virus type-5 (1.22%), and bovine coronavirus (2.24%). Mixed infections among bacteria (73.75%), viruses (50%), and M. bovis (23.75%) were the major features of BRDC in these cattle herds. The risk analysis for multi-pathogen co-infection indicated that BoHV-1 and H. somni; BVDV and M. bovis, P. multocida, T. pyogenes, or Mann. haemolytica; BPIV-3 and M. bovis; BRSV and M. bovis, P. multocida, or T. pyogenes; P. multocida and T. pyogenes; and M. bovis and T. pyogenes or H. somni showed co-infection trends. A survey on molecular epidemiology indicated that the occurrence rate of currently prevalent pathogens in BRDC was 46.15% (6/13) for BoHV-1.2b and 53.85% (7/13) for BoHV-1.2c, 53.3% (8/15) for BVDV-1b and 46.7% (7/15) for BVDV-1d, 29.41% (5/17) for BPIV-3a and 70.59% (12/17) for BPIV-3c, 100% (2/2) for BRSV gene subgroup IX, 91.67% (33/36) for P. multocida serotype A, and 8.33% (3/36) for P. multocida serotype D. Our research discovered new subgenotypes for BoHV-1.2c, BRSV gene subgroup IX, and P. multocida serotype D in China's cattle herds. In the BRDC cases, bovine suppurative or caseous necrotizing pneumonia was highly related to BVDV [odds ratio (OR) = 4.18; 95% confidence interval (95% CI): 1.6-10.7], M. bovis (OR = 2.35; 95% CI: 1.1-4.9), H. somni (OR = 8.2; 95% CI: 2.6-25.5) and T. pyogenes (OR = 13.92; 95% CI: 5.8-33.3). The risk factor analysis found that dairy calves <3 mo and beef calves >3 mo (OR = 5.39; 95% CI: 2.7-10.7) were more susceptible to BRDC. Beef cattle were more susceptible to bovine suppurative or caseous necrotizing pneumonia than dairy cattle (OR = 2.32; 95% CI: 1.2-4.4). These epidemiological data and the new pathogen subgenotypes will be helpful in formulating strategies of control and prevention, developing new vaccines, improving clinical differential diagnosis by necropsy, predicting the most likely pathogen, and justifying antimicrobial use.

An updated review of SARS-CoV-2 detection methods in the context of a novel coronavirus pandemic.

The World Health Organization has reported approximately 430 million confirmed cases of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), worldwide, including nearly 6 million deaths, since its initial appearance in China in 2019. While the number of diagnosed cases continues to increase, the need for technologies that can accurately and rapidly detect SARS-CoV-2 virus infection at early phases continues to grow, and the Federal Drug Administration (FDA) has licensed emergency use authorizations (EUAs) for virtually hundreds of diagnostic tests based on nucleic acid molecules and antigen-antibody serology assays. Among them, the quantitative real-time reverse transcription PCR (qRT-PCR) assay is considered the gold standard for early phase virus detection. Unfortunately, qRT-PCR still suffers from disadvantages such as the complex test process and the occurrence of false negatives; therefore, new nucleic acid detection devices and serological testing technologies are being developed. However, because of the emergence of strongly infectious mutants of the new coronavirus, such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529), the need for the specific detection of mutant strains is also increasing. Therefore, this article reviews nucleic acid- and antigen-antibody-based serological assays, and compares the performance of some of the most recent FDA-approved and literature-reported assays and associated kits for the specific testing of new coronavirus variants.

Automatic detection of indoor occupancy based on improved YOLOv5 model.

Indoor occupancy detection is essential for energy efficiency control and Coronavirus Disease 2019 traceability. The number and location of people can be accurately identified and determined through classroom surveillance video analysis. This information is used to manage environmental equipment such as HVAC and lighting systems to reduce energy use. However, the mainstream one-stage YOLO algorithm still uses an anchor-based mechanism and couples detection heads to predict. This results in slow model convergence and poor detection performance for densely occluded targets. Therefore, this paper proposed a novel decoupled anchor-free VariFocal loss convolutional network algorithm DFV-YOLOv5 for occupancy detection to tackle these problems. The proposed method uses the YOLOv5 algorithm as a baseline. It uses the anchor-free mechanism to reduce the number of design parameters needing heuristic tuning. Afterwards, to reduce the coupling of the model, speed up the model's convergence ability, and improve the model detection performance, the detection head is decoupled based on the YOLOv5 model. It can resolve the conflict between classification and regression tasks. In addition, we use the VariFocal loss to assign more weights to difficult data points to optimize the class imbalance problem and use the training target q to measure positive samples, treating positive and negative samples asymmetrically. The total loss function is redesigned, the L 1 loss is increased, and the ablation experiment verifies the effect of the improved loss. By applying a hybrid activation function of the sigmoid linear unit and rectified linear unit, we improved the model's nonlinear representation and reduced the model's inference time. Finally, a classroom dataset was constructed to validate the occupancy detection performance of the model. The proposed model was compared with mainstream target detection models regarding average mean precision, memory allocation, execution time, and the number of parameters on the VOC2012, CrowdHuman and self-built datasets. The experimental results show that the method significantly improves the detection accuracy and robustness, shortens the inference time, and proves the practicality of the algorithm in occupancy detection compared with the mainstream target detection model and related variants of the model.

On the Frontlines: The Impact of the COVID-19 Pandemic on Social Workers' Well-Being.

The crisis created by the spread of COVID-19 brought increasing needs and referrals to social welfare services in many countries. However, at the same time, social services suffered from staff cutbacks and service closures, resulting in significant workload increases to address the hardships associated with the pandemic. This article investigates the impact of the COVID-19 pandemic on Israeli social workers' well-being, using a mixed-methods design with a sample of 2,542 licensed social workers. Findings show that over 70 percent of social workers suffered from at least one health problem related to their work. Path analysis findings indicated that social workers who experienced greater service restrictions reported a greater decrease in job satisfaction and experienced higher levels of stress and work-related problems. Machine learning emotion-detection analysis revealed that the pandemic affected their lives, causing feelings of fear, frustration, and sadness. This article demonstrates how social workers whose work was characterized by greater service restrictions were less satisfied with their jobs, more stressed, and experienced greater job-related health problems, and concludes with a discussion of the implications for social work practice in times of crisis.

A deep learning approach for classification of COVID and pneumonia using DenseNet-201.

In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.

COVID-19 diagnostics: Molecular biology to nanomaterials.

The SARS-CoV-2 pandemic has claimed around 6.4 million lives worldwide. The disease symptoms range from mild flu-like infection to life-threatening complications. The widespread infection demands rapid, simple, and accurate diagnosis. Currently used methods include molecular biology-based approaches that consist of conventional amplification by RT-PCR, isothermal amplification-based techniques such as RT-LAMP, and gene editing tools like CRISPR-Cas. Other methods include immunological detection including ELISA, lateral flow immunoassay, chemiluminescence, etc. Radiological-based approaches are also being used. Despite good analytical performance of these current methods, there is an unmet need for less costly and simpler tests that may be performed at point of care. Accordingly, nanomaterial-based testing has been extensively pursued. In this review, we discuss the currently used diagnostic techniques for SARS-CoV-2, their usefulness, and limitations. In addition, nanoparticle-based approaches have been highlighted as another potential means of detection. The review provides a deep insight into the current diagnostic methods and future trends to combat this deadly menace.

A hybrid LBP-DCNN based feature extraction method in YOLO: An application for masked face and social distance detection.

COVID-19 is an ongoing pandemic and the WHO recommends at least one-meter social distance, and the use of medical face masks to slow the disease's transmission. This paper proposes an automated approach for detecting social distance and face masks. Thus, it aims to help the reduction of diseases transferred by respiratory droplets such as COVID-19. For this system, a two-cascaded YOLO is used. The first cascade detects humans in the environment and computes the social distance between them. Then, the second cascade detects human faces with or without a mask. Finally, red bounding boxes encircle the people's images that did not follow the rules. Also, in this paper, we propose a two-part feature extraction approach used with YOLO. The first part of the proposed feature extraction method extracts general features using the transfer learning approach. The second part extracts better features specific to the current task using the LBP layer and classification layers. The best average precision for the human detection task was obtained as 66% using Resnet50 in YOLO. The best average precision for the mask detection was obtained as 95% using Darknet19+LBP with YOLO. Also, another popular object detection network, Faster R-CNN, have been used for comparison purpose. The proposed system performed better than the literature in human and mask detection tasks.

Third wave of COVID-19: mathematical model with optimal control strategy for reducing the disease burden in Nigeria.

The study of COVID-19 pandemic which paralyzed global economy of countries is a crucial research area for effective future planning against other epidemics. Unfortunately, we now have variants of the disease resulting to what is now known as waves of the pandemic. Several mathematical models have been developed to study this disease. While recent models incorporated control measures, others are without optimal control measures or demographic parameters. In this study, we propose a deterministic compartmental epidemiological model to study the transmission dynamic of the spread of the third wave of the pandemic in Nigeria, and we incorporated optimal control measures as strategies to reduce the burden of the deadly disease. Specifically, we investigated the transmission dynamics of COVID-19 model without demographic features. We then conducted theoretical analysis of the model with and without optimal control strategy. In the model without optimal control, we computed the reproduction number, an epidemiological threshold useful for bringing the third wave of the pandemic under check in Nigeria, and we proofed the disease stability and conducted sensitivity analysis in order to identify parameters that can impact the reproduction number tremendously. In a similar reasoning, for the model with control strategy, we check the necessary condition for the model. To validate our theoretical analyses, we illustrated the applications of the proposed model using COVID-19 data for Nigeria for a period when the country was under the yoke of the third wave of the disease. The data were then fitted to the model, and we derived a predictive tool toward making a forecast for the cumulative number of cases of infection, cumulative number of active cases and the peak of the third wave of the pandemic. From the simulations, it was observed that the presence of optimal control parameters leads to significant impact on the reduction of the spread of the disease. However, it was discovered that the success of the control of the disease relies on the proper and effective implementation of the optimal control strategies efficiently and adequately.

Mass spectrometry detection of monkeypox virus: Comprehensive coverage for ranking the most responsive peptide markers.

The recent and sudden outbreak of monkeypox in numerous non-endemic countries requires expanding its surveillance immediately and understanding its origin and spread. As learned from the COVID-19 pandemic, appropriate detection techniques are crucial to achieving such a goal. Mass spectrometry has the advantages of a rapid response, low analytical interferences, better precision, and easier multiplexing to detect various pathogens and their variants. In this proteomic dataset, we report experimental data on the proteome of the monkeypox virus (MPXV) recorded by state-of-the-art shotgun proteomics, including data-dependent and data-independent acquisition for comprehensive coverage. We highlighted 152 viral proteins, corresponding to an overall proteome coverage of 79.5 %. Among the 1371 viral peptides detected, 35 peptides with the most intense signals in mass spectrometry were selected, representing a subset of 13 viral proteins. Their relevance as potential candidate markers for virus detection by targeted mass spectrometry is discussed. This report should assist the rapid development of mass spectrometry-based tests to detect a pathogen of increasing concern.

Simultaneously ultrasensitive and quantitative detection of influenza A virus, SARS-CoV-2, and respiratory syncytial virus via multichannel magnetic SERS-based lateral flow immunoassay.

Respiratory viruses usually induced similar clinical symptoms at early infection. Herein, we presented a multichannel surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-based LFA) using high-performance magnetic SERS tags for the simultaneous ultrasensitive detection of respiratory viruses, namely influenza A virus (H1N1), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and respiratory syncytial virus (RSV) in biological samples. As-prepared magnetic SERS tags can directly enrich and capture target viruses without pretreatment of samples, avoiding the interference of impurities in the samples as well as improving the sensitivity. With the capture-detection method, the detection limits of the proposed assay reached 85 copies mL-1, 8 pg mL-1, and 8 pg mL-1 for H1N1, SARS-CoV-2 and RSV, respectively. Moreover, the detection properties of the proposed method for target viruses in throat swab samples were verified, suggesting its remarkable potential for the early and rapid differential diagnosis of respiratory viruses.

Influence of the COVID-19 Pandemic on the Prevalence Pattern of Allergens.

INTRODUCTION: The effect of the COVID-19 pandemic on allergic diseases is not certain, as people's living habits and the environment have been affected by the pandemic. The present study described the influence of the COVID-19 pandemic on the allergen sensitization rate in patients with allergic diseases in central China. The results provide reliable epidemiological data for the prevention and control of allergic diseases during the COVID-19 epidemic. METHODS: Data were collected from a total of 6,915 patients with symptoms of allergic diseases who visited the Third Xiangya Hospital of Central South University in China for allergen testing from January 1, 2018, to December 31, 2021. Patients were divided into a children group (<14 years old), youth group (15∼44 years old), middle-aged group (45∼59 years old), and elderly group (>60 years old). Immunoblotting was used to detect 20 serum allergen-specific IgE (sIgE) antibodies in patient serum samples. We compared the positive rates of various allergens in different age and sex groups before and during the COVID-19 epidemic, and the prevalence data of sIgE sensitization were analysed. RESULTS: Among the 6,915 patients with symptoms of allergic diseases, 2,838 (41.04%) patients were positive for at least one of the allergens. The top three positive rates of inhaled allergens were Dermatophagoides farinae (1,764 cases, 25.51%), Dermatophagoides pteronyssinus (1,616 cases, 23.37%), and house dust (645 cases, 9.33%). The top three positive rates of food allergens were eggs (686 cases, 9.92%), milk (509 cases, 7.36%), and crabs (192 cases, 2.78%). The total positive rate of allergens was higher in men (46.99%) than in women (37.30%). Compared to 2 years before the COVID-19 epidemic, the rate of sensitization to indoor inhalant allergens increased, but outdoor inhalant allergens showed no significant change. The positive rates of milk and eggs peaked during the outbreak of COVID-19 (2020) then declined in 2021. The total positive rate of allergens was higher in males than females before and during the COVID-19 epidemic, but more allergens were different between males and females during the pandemic. Compared to middle-aged and older adults, the children and youth groups were more susceptible to allergic diseases, and they exhibited an increasing positive rate for most common allergens, especially indoor inhalant allergens, during the COVID-19 epidemic than before the pandemic. CONCLUSION: D. pteronyssinus and D. farinae are the most common allergens in South China. Under the background of normalization of epidemic prevention, indoor inhaled allergens should be first in the prevention and control of allergic diseases, and a combination of various indoor cleaning measures should be used to improve the efficiency of interventions.

A novel stock counting system for detecting lot numbers using Tesseract OCR.

Counting stock is one of the warehouse's methods for preventing insatiable stock. Moreover, it could help the company forecast how many products they need to store and predict the replenished goods for customers. However, stock count in the medical business, which sells specialized medical equipment, needs more focus on, because it uses to treat the patient. So that lack of inventory should not happen. In a normal situation, stock count at some hospitals is quite hard for salespeople, especially hospitals in upcountry that far away. During the COVID-19 situation, many limits need to be strict. At this point, it causes a shortage of goods in many hospitals. In this paper, we represent how computer vision can help this process. When the hospital's officer sends images of stock to our system. The system will recognize the quantity and lot number of goods that remain in the hospital. Therefore, salespeople can decrease the times to visit hospitals. The result showed that for text detection and text recognition in a specific use case. Our prototype system achieves 84.17% in accuracy.

CRISPR/Cas12a-Enabled Multiplex Biosensing Strategy Via an Affordable and Visual Nylon Membrane Readout.

Most multiplex nucleic acids detection methods require numerous reagents and high-priced instruments. The emerging clustered regularly interspaced short palindromic repeats (CRISPR)/Cas has been regarded as a promising point-of-care (POC) strategy for nucleic acids detection. However, how to achieve CRISPR/Cas multiplex biosensing remains a challenge. Here, an affordable means termed CRISPR-RDB (CRISPR-based reverse dot blot) for multiplex target detection in parallel, which possesses the advantages of high sensitivity and specificity, cost-effectiveness, instrument-free, ease to use, and visualization is reported. CRISPR-RDB integrates the trans-cleavage activity of CRISPR-Cas12a with a commercial RDB technique. It utilizes different Cas12a-crRNA complexes to separately identify multiple targets in one sample and converts targeted information into colorimetric signals on a piece of accessible nylon membrane that attaches corresponding specific-oligonucleotide probes. It has demonstrated that the versatility of CRISPR-RDB by constructing a four-channel system to simultaneously detect influenza A, influenza B, respiratory syncytial virus, and SARS-CoV-2. With a simple modification of crRNAs, the CRISPR-RDB can be modified to detect human papillomavirus, saving two-thirds of the time compared to a commercial PCR-RDB kit. Further, a user-friendly microchip system for convenient use, as well as a smartphone app for signal interpretation, is engineered. CRISPR-RDB represents a desirable option for multiplexed biosensing and on-site diagnosis.

Recent updates on liposomal formulations for detection, prevention and treatment of coronavirus disease (COVID-19).

The unprecedented outbreak of severe acute respiratory syndrome-2 (SARS-CoV-2) worldwide has rendered it one of the most notorious pandemics ever documented in human history. As of November 2022, nearly 626 million cases of infection and over 6.6 million deaths have been reported globally. The scientific community has made significant progress in therapeutics and prevention for the management of coronavirus disease (COVID-19), including the development of vaccines and antiviral agents such as monoclonal antibodies and antiviral drugs. Although many advancements and a plethora of positive results have been obtained and global restrictions are being uplifted, obstacles in efficiently delivering these therapies, such as their rapid clearance, suboptimal biodistribution, and toxicity to organs, have yet to be addressed. To address these drawbacks, researchers have attempted applying nanotechnology-based formulations. Here, we summarized the recent data about COVID-19, its emergence, pathophysiology and life cycle, diagnosis, and currently-available medications. Subsequently, we discussed the progress in lipid nanocarriers, such as liposomes in infection detection and control. This review provides critical insights into the design of the latest liposomal-based formulations for tackling the barriers to detecting, preventing, and treating SARS-CoV-2.

Low-Power Negative-Differential-Resistance Device for Sensing the Selective Protein via Supporter Molecule Engineering.

Van der Waals (vdW) heterostructures composed of atomically thin two-dimensional (2D) materials have more potential than conventional metal-oxide semiconductors because of their tunable bandgaps, and sensitivities. The remarkable features of these amazing vdW heterostructures are leading to multi-functional logic devices, atomically thin photodetectors, and negative differential resistance (NDR) Esaki diodes. Here, an atomically thin vdW stacking composed of p-type black arsenic (b-As) and n-type tin disulfide (n-SnS2 ) to build a type-III (broken gap) heterojunction is introduced, leading to a negative differential resistance device. Charge transport through the NDR device is investigated under electrostatic gating to achieve a high peak-to-valley current ratio (PVCR), which improved from 2.8 to 4.6 when the temperature is lowered from 300 to 100 K. At various applied-biasing voltages, all conceivable tunneling mechanisms that regulate charge transport are elucidated. Furthermore, the real-time response of the NDR device is investigated at various streptavidin concentrations down to 1 pm, operating at a low biasing voltage. Such applications of NDR devices may lead to the development of cutting-edge electrical devices operating at low power that may be employed as biosensors to detect a variety of target DNA (e.g., ct-DNA) and protein (e.g., the spike protein associated with COVID-19).

Healthcare system engagement and algorithm-identified cancer incidence following initiation of a new medication.

PURPOSE: Implausibly high algorithm-identified cancer incidence within a new user study after medication initiation may result from increased healthcare utilization (HU) around initiation ("catch-up care") that increases diagnostic opportunity. Understanding the relationships between HU prior to and around initiation and subsequent cancer rates and timing is important to avoiding protopathic bias. METHODS: We identified a cohort of 417 458 Medicare beneficiaries (2007-2014) aged ≥66 initiating an antihypertensive (AHT) after ≥180 days of non-use. Initiators were stratified into groups of 0, 1, 2-3, and ≥4 outpatient visits (OV) 60-360 days before initiation. We calculated algorithm-identified colorectal cancer (aiCRC) rates stratified by OVs and time since AHT initiation: (0-90, 91-180, 181-365, 366-730, and 731+ days). We summarized HU -360/+60 days around AHT initiation by aiCRC timing: (0-29, 30-89, 90-179, and ≥180 days). RESULTS: AiCRC incidence (311 per 100 000 overall) peaked in the first 0-90 days, was inversely associated with HU before initiation, and stabilized ≥180 days after AHT initiation. Catch-up care was greatest among persons with aiCRCs identified <30 days in follow-up. Catch-up care magnitude decreased as time to the aiCRC date increased, with aiCRCs identified ≥180 days after AHT initiation exhibiting similar HU compared with the full cohort. CONCLUSION: Lower HU before-and increased HU around AHT initiation-seem to drive excess short-term aiCRC incidence. Person-time and case accrual should only begin when incidence stabilizes. When comparison groups within a study differ by HU, outcome-detection bias may exist. Similar observations may exist in other settings when typical HU is delayed (e.g., cancer screening during SARS-CoV-2).

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning.

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.

A portable, low-cost and high-throughput electrochemical impedance spectroscopy device for point-of-care biomarker detection

This paper presents a portable, fast and accurate electrochemical impedance spectroscopy (EIS) device with 8-well interdigitated electrode chips for biomarker detection. The design adopts low crest factor multisine signal synthesis at low frequencies (<1 kHz) and single-tone signals at high frequencies (>1 kHz), which significantly increases measurement speed without sacrificing accuracy. In addition, the low excitation amplitude of 10 mV preserves impedance linearity and protects the biosamples. The system achieved an average magnitude accuracy error of 0.30% in the frequency range of interest and it requires only 0.46 s to scan 28 frequency points from 10 Hz to 1 MHz. Experiments were conducted to test the capability to detect antibodies against SARS-CoV-2. Gold nanoparticles bound with protein G (GNP-G) were employed as the conjugated secondary antibody probe to detect anti-SARS-CoV-2 IgG in serum. A highly statistical significance (p = 7×10−6) could be found in the impedance data at 10 kHz. The impedance magnitude alteration caused by the GNP-G of the positive and negative groups were 27.2%±13.6% and 4.1%±1.7%, respectively. The results imply that the proposed system enables rapid COVID-19 antibody biomarker detection. Moreover, the EIS system and GNPs have the potential to be modified to detect other biomarkers. © 2022 The Author(s)

Molecularly imprinted polymer-based nanodiagnostics for clinically pertinent bacteria and virus detection for future pandemics

There seems to be a growing curiosity for utilizing MIPs to recognize molecules that can be applied in numerous fields, such as biomimetic antibodies, detection of viruses and bacteria, the broad range of sensing devices, etc., owing to its scalability and economic viability. MIPs have higher thermal and chemical stability, delivering a promising potential for recognizing bacteria and viruses. The bacteria and virus imprinted polymer exhibit elongated product life-time, reproducible fabrication, robustness, reusability, sensitivity, and high target selectivity. Moreover, the MIPs could give consistent screening along with negligible false positive/negative outcomes, which is vital for the control and prevention of viral and bacterial infections. In the viral and bacterial imprinting process, critical aspects, such as compositional complexity, fragility, solubility, and target size, should be systematically evaluated and analytically considered. Although, the application of MIPs has a number of drawbacks and challenges that require solving to develop sensing platforms with high specificity and sensitivity for clinical application. In the present review, current progress and advancement regarding the reasoning and applications of MIPs as recognition molecules in various biosensors for detecting bacteria and viruses and its existing noteworthy challenges along with future perspectives are also reflected.

The application of nano-biosensors in the detection of 2019-nCoV

The pandemic of 2019 novel coronavirus (2019-nCoV) infection since 2020 caused Coronavirus Disease 2019 (COVID-19) leads the serious threaten to global public health. It is urgent to diagnose COVID-19, guide epidemiological measures, control the infection rates, research/develop the antiviral treatment and promote the vaccine research. The application of nano-material based biosensors (the nano-biosensors) has achieved the high-performance detection of a variety of biomarkers due to their small device size, label free detection, high sensitivity, good specificity, short detection time, and has been considered as great potential to become a point-of-care testing tool for detecting 2019-nCoV. Therefore, by summarizing the working principle and classification of nano-biosensors, and focusing on the research progress of nano-biosensors in the detection of 2019-nCoV reported in the recent years, our review provides the challenges and future development prospects of the nano-biosensor in clinical laboratory.