Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

Purpose: In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective: In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method: We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results: We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion: By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea.

Dyspnea is one of the most common symptoms of many respiratory diseases, including COVID-19. Clinical assessment of dyspnea relies mainly on self-reporting, which contains subjective biases and is problematic for frequent inquiries. This study aims to determine if a respiratory score in COVID-19 patients can be assessed using a wearable sensor and if this score can be deduced from a learning model based on physiologically induced dyspnea in healthy subjects. Noninvasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea was also performed for blind comparison. The learning model was built from the self-reported respiratory features of 32 healthy subjects under exertion and airway blockage. A high similarity between respiratory features in COVID-19 patients and physiologically induced dyspnea in healthy subjects was observed. Learning from our previous dyspnea model of healthy subjects, we deduced that COVID-19 patients have consistently highly correlated respiratory scores in comparison with normal breathing of healthy subjects. We also performed a continuous assessment of the patient's respiratory scores for 12-16 h. This study offers a useful system for the symptomatic evaluation of patients with active or chronic respiratory disorders, especially the patient population that refuses to cooperate or cannot communicate due to deterioration or loss of cognitive functions. The proposed system can help identify dyspneic exacerbation, leading to early intervention and possible outcome improvement. Our approach can be potentially applied to other pulmonary disorders, such as asthma, emphysema, and other types of pneumonia.

Evaluation of medical decision errors during the transition period to telemedicine

The context of the Coronavirus pandemic has fundamentally changed the way we approach medical services. Beyond setting up new technological possibilities, it has propelled telemedicine to become a reality, bringing undeniable practical benefits. The questions that arise are both justified and worrying: can digitalization replace a direct interpersonal relationship that involves a physical examination, while preserving the quality of the medical act and the degree of patient satisfaction? Isn't there a risk that the digitization of the medical record will cancel out the deep human character of classical medicine that has evolved since the time of Hippocrates? Should the implementation of telemedicine be "the state-of-art" of modern medicine, in accordance with the co-evolution of digital technology? It is hard to believe that once used in this period, telemedicine will be abandoned. However, telemedicine must be analyzed not only in the short term but also in the long term, in order to be able to evaluate both its usefulness and possible deficiencies.

Simple Symptom-Based Prediction of COVID-19: A Single-Center Study of Outpatient Fever Clinic in Japan.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) symptoms are not fully understood in non-hospitalized individuals in Japan, and COVID-19 differentiation by symptoms alone remained challenging. Therefore, this study aimed to examine COVID-19 prediction from symptoms using real-world data in an outpatient fever clinic. METHODS: We compared the symptoms of COVID-19-positive and negative patients who visited the outpatient fever clinic at Imabari City Medical Association General Hospital and tested for COVID-19 from April 2021 to May 2022. This retrospective single-center study enrolled 2,693 consecutive patients. RESULTS: COVID-19-positive patients had a higher frequency of close contact with COVID-19-infected patients compared with COVID-19-negative patients. Moreover, patients with COVID-19 had high-grade fever at the clinic compared with patients without COVID-19. Additionally, the most common symptom in patients with COVID-19 was sore throat (67.3%), followed by cough (62.0%), which was approximately twice as common in patients without COVID-19. COVID-19 was more frequently identified in patients having a fever (≥37.5℃) with a sore throat, a cough, or both. The positive COVID-19 rate reached approximately half (45%) when three symptoms were present. CONCLUSION: These results suggested that COVID-19 prediction by combinations of simple symptoms and close contact with COVID-19-infected patients might be useful and lead to recommendations for testing of COVID-19 in symptomatic individuals.

AIM in Alternative Medicine

Alternative medicine (AM) is one of the medical fields that use more natural and traditional therapies for disease diagnosis and treatment, in which traditional Chinese medicine (TCM) now has been recognized as one of the main approaches of AM. As a clinical and evidencedriven discipline with long histories, AM is also heavily relied on in the utilization of big healthcare and therapeutic data for improving the capability of diagnosis and treatment. In particular, artificial intelligence (AI) has been widely adopted in AM to deliver more practical and feasible intelligent solutions for clinical operations since 1970s. This chapter summarizes the main approaches, related typical applications, and future directions of AI in AM to give related researchers a brief useful reference. We find that although AM has not been widely used in clinical practice internationally, the AI studies showed abundant experiences and technique trials in expert system, machine learning, data mining, knowledge graph, and deep learning. In addition, various types of data, such as bibliographic literatures, electronic medical records, and images were used in the related AI tasks and studies. Furthermore, during this COVID-19 pandemic era, we have witnessed the clinical effectiveness of TCM for COVID-19 treatment, which mostly was detected by real-world data mining applications. This indicates the potential opportunity of the booming of AI research and applications in various aspects (e.g., effective clinical therapy discovery and network pharmacology of AM drugs) in AM fields. © Springer Nature Switzerland AG 2022.

Expert recommendations for clinical diagnosis and treatment of coronavirus disease 2019 induced neurological diseases

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly infectious and pathogenic. Although it mainly involves the respiratory system, it can also invade the nervous system and lead to a variety of neurological symptoms and diseases, further complicating the clinical conditions of the patients. In order to assist physicians and neurologists in understanding the pathogenesis, clinical features, diagnostic procedures, therapeutic principles, and clinical outcomes of the diseases, the experts of Neurology Branch of Chinese Medical Doctor Association wrote this expert recommendation based on present research articles and clinical practices about the epidemiology, clinical symptoms, diagnostic algorithms, treatment and prognosis of neurological diseases caused by coronavirus disease 2019, in order to provide reference for clinical diagnosis and treatment.

Artificial intelligence in COVID-19

The COVID-19 pandemic has taken the world by storm, placing healthcare systems around the globe under immense pressure. The exceptional circumstance has made the scientific community turn to artificial intelligence (AI), with hopes that AI techniques can be used in all aspects of combating the pandemic, whether it is in using AI to uncover sequences in the genomic code of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) virus for the purposes of developing therapeutics, such as antivirals, antibodies, or vaccines, or using AI to provide (near-) instantaneous clinical diagnosis techniques by way of analysis of chest X-ray (CXR) images, computed tomography (CT) scans or other useful modalities, or using AI for as a tool for mass population testing by analyzing patient audio recordings. In this chapter, we survey the AI research literature with respect to applications for COVID-19 and showcase and critique notable state of the art approaches. © 2023 Elsevier Inc. All rights reserved.

A New Pneumonia Detection Model Based on Transformer with Improved Self-Attention Mechanism

Clinical diagnosis based on computed tomography (CT) could be used, as part of diagnosis standard of COVID-19 pneumonia. Addressing the problem that accuracy of CT-based traditional pneumonia classification diagnosis models is relatively low when employed for classification of community-acquired pneumonia (CP), COVID-19 pneumonia (NCP) and normal cases, a new network model is proposed which combines application of Swin Transformer and multi-head axial self-attention (MASA) mechanism, to analyze CT images and make intelligence-assisted diagnosis. The method in detail is to partially replace traditional multi-head self-attention (MSA) mechanism in encoders of Swin Transformer by MASA. The improved model is applied to train and test on commonly used pneumonia CT dataset CC-CCII. The results show that the proposed network outperforms traditional networks ResNet50 and Vision Transformer in indicators of accuracy, sensitivity and F1-measure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Inflammatory Bowel Disease: Clinical Diagnosis and Pharmaceutical Management.

Inflammatory bowel disease has an enormous impact on public health, medical systems, economies, and social conditions. Biologic therapy has ameliorated the treatment and clinical course of patients with inflammatory bowel disease. The efficacy and safety profiles of currently available therapies are still less that optimal in numerous ways, highlighting the requirement for new therapeutic targets. A bunch of new drug studies are underway in inflammatory bowel disease with promising results. This is an outlined guideline of clinical diagnosis and pharmaceutical therapy of inflammatory bowel disease. Outline delineates the overall recommendations on the modern principles of desirable practice to bolster the adoption of best implementations and exploration as well as inflammatory bowel disease patient, gastroenterologist, and other healthcare provider education. Inflammatory bowel disease encompasses Crohn's disease and ulcerative colitis, the two unsolved medical inflammatory bowel disease-subtypes condition with no drug for cure. The signs and symptoms on first presentation relate to the anatomical localization and severity of the disease and less with the resulting diagnosis that can clinically and histologically be non-definitive to interpret and establish criteria, specifically in colonic inflammatory bowel disease when the establishment is inconclusive is classified as indeterminate colitis. Conservative pharmaceuticals and accessible avenues do not depend on the disease phenotype. The first line management is to manage symptoms and stabilize active disease; at the same time maintenance therapy is indicated. Nutrition and diet do not play a primary therapeutic role but is warranted as supportive care. There is need of special guideline that explore solution of groundwork gap in terms of access limitations to inflammatory bowel disease care, particularly in developing countries and the irregular representation of socioeconomic stratification with a strategic plan, for the unanswered questions and perspective for the future, especially during the surfaced global COVID-19 pandemic caused by coronavirus SARS-CoV2 impacting on both the patient's psychological functioning and endoscopy services. Establishment of a global registry system and accumulated experiences have led to consensus for inflammatory bowel disease management under the COVID-19 pandemic. Painstakingly, the pandemic has influenced medical care systems for these patients. I briefly herein viewpoint summarize among other updates the telemedicine roles during the pandemic and how operationally inflammatory bowel disease centers managed patients and ensured quality of care. In conclusion: inflammatory bowel disease has become a global emergent disease. Serious medical errors are public health problem observed in developing nations i.e., to distinguish inflammatory bowel disease and infectious and parasitic diseases. Refractory inflammatory bowel disease is a still significant challenge in the management of patients with Crohn's disease and ulcerative colitis. There are gaps in knowledge and future research directions on the recent newly registered pharmaceuticals. The main clinical outcomes for inflammatory bowel disease were maintained during the COVID-19 pandemic period.

Intelligent Pneumonia Detection Model of Focal Transformer and Multi-scale-input SPD

The clinical diagnosis results based on lung X-rays provide important evidence in the COVID-19 pneumonia diagnosis process and for some other disease. However, due to the similarity of the lesions among many types of pneumonia displayed by X-rays, and due to the huge amount of X-ray readings of a doctor's daily work, traditional reading and identification method purely by human have problems of diagnosis mistakes, missed diagnosis and huge time consumption. Therefore, an intelligent detection model of pneumonia with multi-scale-input Focal Transformer integrated with SPD module is proposed to automatically identify various types of pneumonia including COVID-19 pneumonia. The method can pay attention to the multi-scale characteristic features of pneumonia lesions, and then make improved classification among COVID-19 pneumonia, cases with lung opacity, viral pneumonia and normal cases, providing stronger support for radiologists in medical diagnosis. The experiment results show that the proposed model has advantages in comparison to the traditional network models ResNet-50 and Swin Transformer in aspects of accuracy, recall, F1-Measure and other indicators. © 2022 IEEE.

Covid-19 classification using CNN with radiography image data set

COVID-19 is a novel coronavirus disease that has been reported in Wuhan, China since late December 2019 and has subsequently spread around the world. In severe cases of illness, there may be no option but to die due to substantial alveolar damage and progressive respiratory failure. Testing with RT-PCR, for instance, is the gold standard for clinical diagnosis, but it is possible for the tests to produce false negatives. Further, the lack of resources for conducting RT-PCR testing may deter the next clinical decision and treatment under the pandemic situation. As a result, chest CT imaging has become a valuable tool for diagnostic and prognostic purposes in COVID-19 patients. Detection of COVID-19 early enables the development of prevention plans and a disease control plan. Through this experimentation, the main objective is to utilize transfer learning to leverage pre-trained weights from CNNs. We propose the ResNet50 architecture based on the ImageNet pre-trained weights to detect the Covid-19. The proposed model is evaluated on X-ray images of COVID-19 chests and on images taken with a Computerized Tomography scanner. Using the 746 images of covid and non-covid patient datasets are bifurcated into train and test datasets for training and validate our model and achieved 84.90 % model accuracy. The Accuracy, precision, recall and F1-Scores are presented along with the receiver operating characteristic (ROC) curve, the precision-recall curve, the average prediction, and the confusion matrix of three distinct models. © 2022 IEEE.

COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis.

In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures.

Comparison of clinician diagnosis of COVID-19 with real time polymerase chain reaction in an adult-representative population in Sweden.

BACKGROUND: Due to the high transmissibility of SARS-CoV-2, accurate diagnosis is essential for effective infection control, but the gold standard, real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is costly, slow, and test capacity has at times been insufficient. We compared the accuracy of clinician diagnosis of COVID-19 against RT-PCR in a general adult population. METHODS: COVID-19 diagnosis data by 30th September 2021 for participants in an ongoing population-based cohort study of adults in Western Sweden were retrieved from registers, based on positive RT-PCR and clinician diagnosis using recommended ICD-10 codes. We calculated accuracy measures of clinician diagnosis using RT-PCR as reference for all subjects and stratified by age, gender, BMI, and comorbidity collected pre-COVID-19. RESULTS: Of 42,621 subjects, 3,936 (9.2%) and 5705 (13.4%) had had COVID-19 identified by RT-PCR and clinician diagnosis, respectively. Sensitivity and specificity of clinician diagnosis against RT-PCR were 78% (95%CI 77-80%) and 93% (95%CI 93-93%), respectively. Positive predictive value (PPV) was 54% (95%CI 53-55%), while negative predictive value (NPV) was 98% (95%CI 98-98%) and Youden's index 71% (95%CI 70-72%). These estimates were similar between men and women, across age groups, BMI categories, and between patients with and without asthma. However, while specificity, NPV, and Youden's index were similar between patients with and without chronic obstructive pulmonary disease (COPD), sensitivity was slightly higher in patients with (84% [95%CI 74-90%]) than those without (78% [95%CI 77-79%]) COPD. CONCLUSIONS: The accuracy of clinician diagnosis for COVID-19 is adequate, regardless of gender, age, BMI, and asthma, and thus can be used for screening purposes to supplement RT-PCR.

Automated System for Multiplexing Detection of COVID-19 and Other Respiratory Pathogens.

OBJECTIVE: Infectious diseases are global health challenge, impacted the communities worldwide particularly in the midst of COVID-19 pandemic. The need of rapid and accurate automated systems for detecting pathogens of concern has always been critical. Ideally, such systems shall detect a large panel of pathogens simultaneously regardless of well-equipped facilities and highly trained operators, thus realizing on-site diagnosis for frontline healthcare providers and in critical locations such as borders and airports. METHODS & RESULTS: Avalon Automated Multiplex System, AAMST, is developed to automate a series of biochemistry protocols to detect nucleic acid sequences from multiple pathogens in one test. Automated processes include isolation of nucleic acids from unprocessed samples, reverse transcription and two rounds of amplifications. All procedures are carried out in a microfluidic cartridge performed by a desktop analyzer. The system was validated with reference controls and showed good agreement with their laboratory counterparts. In total 63 clinical samples, 13 positives including those from COVID-19 patients and 50 negative cases were detected, consistent with clinical diagnosis using conventional laboratory methods. CONCLUSIONS: The proposed system has demonstrated promising utility. It would benefit the screening and diagnosis of COVID-19 and other infectious diseases in a simple, rapid and accurate fashion. Clinical and Translational Impact Statement- A rapid and multiplex diagnostic system proposed in this work can clinically help to control spread of COVID-19 and other infectious agents as it can provide timely diagnosis, isolation and treatment to patients. Using the system at remoted clinical sites can facilitate early clinical management and surveillance.

Deep Learning Based COVID-19 Detection: Challenges and Future Directions

Coronavirus (COVID-19) is an ecumenical pandemic that has affected the whole world drastically by raising a global calamitous situation. Due to this pernicious disease, millions of people have lost their lives. The scientists are still far from knowing how to tackle the coronavirus due to its multiple mutations found around the globe. Standard testing technique called Polymerase Chain Reaction (PCR) for the clinical diagnosis of COVID-19 is expensive and time consuming. However, to assist specialists and radiologists in COVID-19 detection and diagnosis, deep learning plays an important role. Many research efforts have been done that leverage deep learning techniques and technologies for the identification or categorization of COVID-19 positive patients, and these techniques are proved to be a powerful tool that can automatically detect or diagnose COVID-19 cases. In this paper, we identify significant challenges regarding deep learning-based systems and techniques that use different medical imaging modalities, including Cough and Breadth, Chest X-ray, and Computer Tomography (CT) to combat COVID-19 outbreak. We also pinpoint important research questions for each category of challenges. IEEE

Detection of Bovine Coronavirus by Conventional Reverse Transcription Polymerase Chain Reaction

Bovine coronavirus (BCoV) is an economically significant cause of enteric and respiratory diseases in cattle throughout the world. BCoV is a known cause of neonatal calf diarrhea, winter dysentery in adult cattle, and respiratory disorders in cattle of all ages. In this chapter, we describe a simple and efficient protocol for total nucleic acids extraction to be used in conventional RT-PCR assay. This is a technique used routinely in our virology laboratory to detect BCoV from stool and nasopharyngeal samples of cattle. Copyright © Springer Science+Business Media New York 2016.

Real-Time RT-PCR Detection of Equine Coronavirus

Equine coronavirus (ECoV) is a recently identified equine virus, involved mainly in enteric infections. Since the ECoV discovery in 1999, only two real-time RT-PCRs have been developed for viral identification. In this chapter we describe a one-step real-time RT-PCR that has been routinely used in our laboratory for ECoV detection from fecal and respiratory samples. Copyright © Springer Science+Business Media New York 2016.

Comparison of clinical characteristics and outcome measures of PCR-positive and PCR-negative patients diagnosed as COVID-19: Analyses focusing on the older adults.

PURPOSE: While the definitive diagnosis of COVID-19 relies on PCR confirmation of the virus, the sensitivity of this technique is limited. The clinicians had to go on with the clinical diagnosis of COVID-19 in selected cases. We aimed to compare PCR-positive and PCR-negative patients diagnosed as COVID-19 with a specific focus on older adults. METHODS: We studied 601 hospitalized adults. The demographics, co-morbidities, triage clinical, laboratory characteristics, and outcomes were noted. Differences between the PCR (+) and (-) cases were analyzed. An additional specific analysis focusing on older adults (≥65 years) (n = 184) was performed. RESULTS: The PCR confirmation was present in 359 (59.7 %). There was not any difference in terms of age, sex, travel/contact history, hospitalization duration, ICU need, the time between first symptom/hospitalization to ICU need, ICU days, or survival between PCR-positive and negative cases in the total study group and older adults subgroup. The only symptoms that were different in prevalence between PCR-confirmed and unconfirmed cases were fever (73.3 % vs. 64 %, p = 0.02) and fatigue/myalgia (91.1 % vs. 79.3 %, p = 0.001). Bilateral diffuse pneumonia was also more prevalent in PCR-confirmed cases (20 % vs. 13.3 %, p = 0.03). In older adults, the PCR (-) cases had more prevalent dyspnea (72.2 % vs. 51.4 %, p = 0.004), less prevalent fatigue/myalgia (70.9 % vs. 88.6 %, p = 0.002). CONCLUSION: The PCR (+) and (-) cases displayed very similar disease phenotypes, courses, and outcomes with few differences between each other. The presence of some worse laboratory findings may indicate a worse immune protective response in PCR (-) cases.

A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity.

Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewater samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target nonstructural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship between COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.

Smartphone-Based Multiplexed Biosensing Tools for Health Monitoring.

Many emerging technologies have the potential to improve health care by providing more personalized approaches or early diagnostic methods. In this review, we cover smartphone-based multiplexed sensors as affordable and portable sensing platforms for point-of-care devices. Multiplexing has been gaining attention recently for clinical diagnosis considering certain diseases require analysis of complex biological networks instead of single-marker analysis. Smartphones offer tremendous possibilities for on-site detection analysis due to their portability, high accessibility, fast sample processing, and robust imaging capabilities. Straightforward digital analysis and convenient user interfaces support networked health care systems and individualized health monitoring. Detailed biomarker profiling provides fast and accurate analysis for disease diagnosis for limited sample volume collection. Here, multiplexed smartphone-based assays with optical and electrochemical components are covered. Possible wireless or wired communication actuators and portable and wearable sensing integration for various sensing applications are discussed. The crucial features and the weaknesses of these devices are critically evaluated.