COVID -19 Predictions using Transfer Learning based Deep Learning Model with Medical Internet of Things

Early detection of COVID-19 may help medical expert for proper treatment plan and infection control. Internet of Things (IoT) has vital improvement in many areas including medical field. Deep learning also provide tremendous improvement in the field of medical. We have proposed a Transfer learning based deep learning model with medical Internet of Things for predicting COVID-19 from X-ray images. In the proposed method, the X ray images of patient are stored in cloud storage using internet for wide access. Then, the images are retrieved from cloud and Transfer learning based deep learning models namely VGG-16, Inception, Alexnet, Googlenet and Convolution neural Network models are applied on the X-rays images for predicting COVID 19, Normal and pneumonia classes. The pre-trained models helps to the effectiveness of deep learning accuracy and reduced the training time. The experimental analysis show that VGG -16 model gives accuracy of 99% for detecting COVID19 than other models. © 2023 Sunarno Basuki and Perdinanto.

Health informatics system using machine learning techniques

A global pandemic is the cause of concern for humanity. The data collection and their analytics are a critical part of research and clinical studies for decision-making activities in the healthcare sector. Healthcare informatics systems and analytics (HCI&A) is a rapidly emerging technology in the medical domain that could be explored for analyzing pandemics like coronavirus disease 2019 (COVID-19). The ethical, legal, and privacy issues to be considered during data collection for research activities. Data governance and data stewardship are required to be addressed during interoperability and interpretation while sharing and reusing the data in collaborative research. The sharing of comprehensive records of clinical data collected by EHRs, also known as electronic health records, to be stored and analyzed on a time-to-time basis. The emerging area of information technology, represented by big data and artificial intelligence (AI) technology, has been widely studied in recent circumstances like COVID-19 for pandemic management. The possibility of using machine learning is explored for better predictive diagnostics and treatment. This chapter discusses the application of artificial intelligence in pandemic management including prevention, diagnosis, treatment, and also critical policy decisions in the COVID-19 pandemic. The methods to collect the digital data of health records are categorized along with few constraints as most of the electronic records related to clinical and epidemiological data are obtained through a shared database such as national and international collaborative informatics infrastructure. The necessity of digital technologies for pandemic emergencies including medical infrastructure reorganization and data workflow model is highlighted. A comparative study of different machine learning models is discussed in the subsequent sections. The digital healthcare informatics envisage a decentralized network architecture and better privacy and security such as blockchain and heterogeneous data collection with machine learning capability are also emphasized. © 2022 River Publishers.

Immunity benefits of yoga and physical exercises in the era of COVID-19 Pandemic

Purpose. The novel coronavirus is the recently emerged disease of the respiratory system for which various national and international research agencies are putting joint efforts towards finding a permanent cure. Recently, the vaccine against coronavirus has been designed by various pharmaceutical agencies that are currently undergoing clinical trials. Since vaccines prevent infection by strengthening the defense system of the body, we proposed that yoga and physical exercises could act as an integrative approach to synergize the immunogenic response of the coronavirus vaccine. Yoga and physical exercises are already known to boost immunity against several other infections. Materials and Methods. In the present review article, we aimed towards exploring the role of yoga and physical exercise as an immunity booster against coronavirus infection. Being India is a low-income country, yoga and physical exercises could be an excellent cost-effective strategy that could be administrated along with vaccine trials to enhance immunity against virus infection. Results. In the present review, we analyze the studies conducted to date focusing on finding the role of yoga and physical exercises to prevent coronavirus infection. We also described the potential exercises, which are already known to enhance the immunity of the body by particularly targeting respiratory disease. Conclusion. The present review article will help in providing the health agencies potential targets, which could further be explored to established a standard exercise module to enhance the vaccine-mediated immunity against coronavirus infection. © Karuppasamy Govindasamy, Chandrababu Suresh, Mithin Anand, Saran KS, Mou Pramanik, Dilpreet Kaur, Imen Achouri, Hiba Boughanmi.

Efficacy of Convalescent Plasma Therapy in COVID-19: A Systematic Review

A type of new-coronavirus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) was isolated. The first death caused by this virus occurred on January 9, 2020, in Wuhan and since then >600 million cases and more than six million deaths have occurred worldwide. This disease is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as the 2019 novel coronavirus (2019-nCoV). The disease is mainly spread between people through respiratory droplets that come from coughing and sneezing.There is currently no treatment that has been shown to be both safe and effective for patients who are suffering from life-threatening complications due to severe acute respiratory syndrome, coronavirus 2 (SARS-CoV-2). Convalescent plasma has showed promise as a treatment for adults infected with SARS-CoV 2, but it is not without its downsides. Convalescent immune plasma is plasma that has been obtained from a person who has recovered from an infection and developed antibodies after the infection has been cleared up. The maximum plasma dose that should be administered during convalescence is 5 mL/KgBW. It is advised that patients who are severely ill or in critical condition receive this plasma, but other sites also encourage using it for preventative purposes. Copyright © 2022, Anka Publishers. All rights reserved.

Cytokinesas Predictor of Severity in Covid-19 Patients: A Literatur Review

The 2019 novel coronavirus, also known as coronavirus 2 (SARS-CoV-2) or 2019-nCoV, is an enveloped virus with a positive-sense single-stranded RNA that causes a disease known as Coronavirus Disease 2019. (COVID-19). As an immunology response, cells produce soluble proteins called cytokines during inflammatory or immune reactions. Cytokines are intercellular messengers that regulate local and generalized inflammation in response to external antigens or wounds, which control the healing process.In patients with severe COVID-19, an intense cytokine reaction might developed, which known as a cytokine storm.We aim to review the role of cytokines in COVID-19. Method(s): The full-text English articles served as the source for the data that was collected for this systematic review. The objective of this research was to ascertain the cytokine levels present in individuals who were diagnosed with Covid-19. Pubmed and Google Scholar are the two databases that were employed during the production of this essay. The PICO analysis was used to include Covid-19 patients, and the cytokine patient served as the index. This analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) paradigm, in which the researchers originally entered keywords into each database. Result(s): 1022 articles from the Googlescholar database and 244 articles from Pubmed journals were identified in the last 3 years, and 4 cross-sectionalarticles are eligible for this study. These studies show that cytokines were significantly elevated in COVID-19 patients and showed the severity of the condition. Several cytokines, G-CSF, HGF, IL-10, IL-18, M-CSF, and SCGF-beta were found correlated with COVID-19 severity. Blood levels of IL-6, IL-8, and TNF-alpha were substantial and independent predictors of patient survival (P 0.0001, P = 0.0205, and P = 0.0140, respectively). Conclusion(s): There are several cytokines involved in the pathophysiology of COVID-19. Elevation of these cytokines were related to disease severity, worse disease progression and development of organ damage. Copyright © 2022, Anka Publishers. All rights reserved.

Handwashing Action Detection System for an Autonomous Social Robot (preprint)

Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene. An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit. In this article, we present a human action recognition system, which is part of the vision system of a social robot platform, to assist children in developing a correct handwashing technique. A modified convolution neural network (CNN) architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with a VGG-16 architecture as the backbone is trained and validated on an augmented dataset. The modified architecture generalizes well with an accuracy of 90% for the WHO-prescribed handwashing steps even in an unseen environment. Our findings indicate that the approach can recognize even subtle hand movements in the video and can be used for gesture detection and classification in social robotics.

Admission Neutrophil-lymphocyte Ratio for Prediction of Severity and Mortality in COVID Patients: A Systematic Review and Meta-analysis

Aims and objectives: Multiple studies have suggested that neutrophil-lymphocyte ratio (NLR) derived from differential white cell count might be a useful marker for COVID-related disease severity and mortality. We conducted a systematic review and meta-analysis and investigated if the same can be predicted with on-admission NLR values and also evaluated the prognostic significance of NLR on disease outcomes in patients with COVID-19. Materials and methods: We searched PubMed, EMBASE, MEDLINE, and SCOPUS databases for published articles in peer-reviewed journals from 01 March 2020 and 01 March 2021. Meta-analysis was performed to determine the pooled standardized mean difference (SMD) for the mean values of NLR. A random-effects meta-regression was performed for the following potential confounders: age, gender, and comorbidities. Results: After study screening, systematic review included 68 studies comprising 15,818 patients in total, 2260 with severe disease and 1198 patients with mortality outcomes. The meta-analysis showed significant difference in mean NLR between severe and non-severe patients {SMD was 2.88 (95% CI: 2.32 to 3.44)} and between survivors and non survivors {SMD was 7.89 (95% CI: 3.37 to 12.42)}. Both outcomes were heterogeneous (Q = 1912.85, P < 0.0001, tau2 = 3.14, I2 = 98.35% and Q = 5898.15, p < 0.0001, tau2 = 116.65, I2 = 99.92% for severity and mortality, respectively). Meta-regression analysis showed that the association between NLR values on admission and severity in COVID-19 patients was not influenced by age (p = 0.893), cardiovascular diseases (p = 0.259), diabetes mellitus (p = 0.545), or hypertension (p = 0.104). Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients and is not affected by age or comorbidities. Further high-quality studies are needed to confirm these findings. Results2: After study screening, systematic review included 68 studies comprising 15,818 patients in total, 2260 with severe disease and 1198 patients with mortality outcomes. A summary receiver operating characteristic (SROC) curve to determine a pooled estimate of the prognostic accuracy of NLR for severity showed that the pooled sensitivity, specificity and AUC were 80.2% (95% CI: 74.0-85.2%), 75.8% (95% CI: 71.3-79.9%), and 0.833, respectively, with the pooled diagnostic odds ratio of 13.63 (95% CI: 9.71-19.02). According to the Q-test, the true outcomes appeared to be heterogeneous for both severity and mortality (Q = 1912.85, P < 0.0001, tau2 = 3.14, I2 = 98.35% and Q = 5898.15, p < 0.0001, tau2 = 116.65, I2 = 99.92%, respectively). The same was done for NLR and mortality showed the pooled sensitivity, specificity, and diagnostic odds ratio were 78.8% (95% CI: 73.5-83.2%), 73.0% (95% CI: 68.4-77.1%), and 11.483 (95% CI: 7.814-16.875), respectively, with AUC of 0.820. Meta-regression analysis showed that the association between NLR values on admission and severity in COVID-19 patients was not influenced by age (p = 0.893), cardiovascular diseases (p = 0.259), diabetes mellitus (p = 0.545), or hypertension (p = 0.104).

Case of Suspected SARS-CoV-2 Vaccine-induced Immune Thrombotic Thrombocytopenia: Dilemma for Organ Donation.

Several vaccines were developed and rolled out at an unprecedented rate in response to the coronavirus disease-2019 (COVID-19) pandemic. Most vaccines approved globally by WHO for emergency use to combat the pandemic were deemed remarkably effective and safe. Despite the safety, rare incidences of vaccine-induced thrombosis and thrombocytopenia (VITT), sometimes known as vaccine-induced prothrombotic thrombocytopenia (VIPIT), have been reported. We report a case of young female with prothrombotic conditions and suspected VITT who developed catastrophic cerebral venous sinus thrombosis (CVST) and progressed to brain death. We highlight hurdles of organ retrieval from a brain-dead patient with suspected SARS-CoV-2 vaccine-induced immune thrombotic thrombocytopenia. There is limited data and lack of substantial evidence regarding transplantation of organs from brain-dead patients with suspected VITT. How to cite this article: Tiwari AM, Zirpe KG, Gurav SK, Bhirud LB, Suryawanshi RS, Kulkarni SS. Case of Suspected SARS-CoV-2 Vaccine-induced Immune Thrombotic Thrombocytopenia: Dilemma for Organ Donation. Indian J Crit Care Med 2022;26(4):514-517.

Timing of Invasive Mechanical Ventilation and Mortality among Patients with Severe COVID-19-associated Acute Respiratory Distress Syndrome.

BACKGROUND: Severe acute respiratory distress syndrome associated with coronavirus disease-2019 (COVID-19) (CARDS) pneumonitis presents a clinical challenge as regards to the timing of intubation and ambiguity of outcome. There is a lack of clear consensus on when to switch patients from trials of noninvasive therapies to invasive mechanical ventilation. We investigated the effect of the timing of intubation from the time of admission on the clinical outcome of CARDS. AIM AND OBJECTIVE: The aim and objective was to analyze the effect of timing of intubation early (within 48 hours of admission to critical care unit) versus delayed (after 48 hours of admission to critical care unit) on mortality in severe CARDS patients. MATERIALS AND METHODS: A retrospective observational study performed in a 28-bedded COVID-19 intensive care unit of a tertiary care hospital in Pune, India. All patients admitted between April 1, 2020, and October 15, 2020, with confirmed COVID-19 (RT-PCR positive) requiring mechanical ventilation were included in the study. RESULTS: The primary outcome was in-hospital mortality. Among 2,230 patients that were admitted to the hospital, 525 required critical care (23.5%), invasive mechanical ventilation was needed in 162 patients and 147 (28%) of critical care admission were included in the study cohort after exclusion. Seventy-five patients (51%) were intubated within 48 hours of critical care admission (early group) and 72 (48.9%) were intubated after 48 hours of critical care admission (delayed group). With regards to the total of 147 included patients; male patients were 74.1% with a median age of 59 years (interquartile range, 51-68 years). Diabetes (44.9%) and hypertension (43.5%) were the most common comorbidities. Higher admission acute physiology and chronic health evaluation II scores and lower absolute lymphocyte count were observed in patients intubated within 48 hours. The early intubated group had a mortality of 60% whereas the same was observed as 77.7% in delayed intubation group, and this difference was statistically significant (p = 0.02). CONCLUSION: Current study concludes that early intubation is associated with improved survival rates in severe CARDS patients. HOW TO CITE THIS ARTICLE: Zirpe KG, Tiwari AM, Gurav SK, Deshmukh AM, Suryawanshi PB, Wankhede PP, et al. Timing of Invasive Mechanical Ventilation and Mortality among Patients with Severe COVID-19-associated Acute Respiratory Distress Syndrome. Indian J Crit Care Med 2021;25(5):493-498.

Patient satisfaction with telehealth in fpmrs during the COVID-19 pandemic

Objective: SinceMarch 2020, the COVID-19 pandemic has catalyzed rapid integration of telemedicine services into clinical practice. Our primary aim was to assess patient satisfaction with telehealth care in Female Pelvic Medicine and Reconstructive Surgery (FPMRS). Our secondary aim was to assess patient access to technology for telehealth visits. Methods: This was an IRB-approved, single-institution, survey study of a convenience sample of patients presenting for telehealth visits within the Division of FPMRS from July 22, 2020 to January 15, 2021. We invited new and established patients to complete a single survey regarding reason for visit, overall satisfaction, access to technology, previous use of telemedicine, and preference for future visits. We present data as mean ± standard deviation or proportion. Results: Of 227 patients offered the survey, 142 (62.6%) responded;84 (59.2%) completed the survey following a video visit, and 58 (40.9%) completed the survey following a telephone visit. Respondents had a mean age of 51.5 ± 15.4 years, and most were Non-Hispanic White (70.4%) and had at least a Bachelor's degree (64.8%). The most common primary diagnoses were sexual dysfunction (26.1%), overactive bladder (21.8%), and urinary incontinence (14.1%). Most patients in both the video and phone groups were completely satisfied (62.4% and 56.0%, respectively) or moderately satisfied (24.4% and 39.3%, respectively). Patient-reported advantages of telehealth included saving travel time (93.0%), waiting room time (59.2%), parking fees (50.0%), time off work (43.7%), and public transit fees (22.5%). Patients also reported greater overall convenience (60.6%) and feeling that they had more time with the provider (21.8%). Patient-cited disadvantages were concern about the provider's ability to make a diagnosis (69.0%) or see something of concern remotely (64.1%). Only 5.6% of patients expressed concern about difficulty building rapport with a provider virtually. Regarding access to telehealth visits, 8.5% cited a poor internet connection and 7.0% felt that setting up a virtual platform could be challenging. In anticipation of post-COVID FPMRS visits, most respondents (79.6%) preferred a combination of virtual and in-person visits. Only 2.1% indicated a preference for all future visits to be virtual, while 10.6% preferred only inperson visits, and 7.7% did not express a preference. Among respondents who preferred at least some virtual visits, 73.3% preferred video, whereas 25.9% preferred telephone visits. Conclusions: This study demonstrates high patient satisfaction with virtual telehealth visits at our FPMRS Division during the COVID-19 pandemic. Saving travel time and overall convenience were themost highly cited advantages of telehealth by patients. Patients prefer receiving a combination of telehealth and in-person visits post-pandemic. This study underscores an important role for telehealth in future FPMRS practice and should inform future studies to explore which conditions, visit types, and patient characteristics are best served by virtual versus in-person visits.

A Retrospective Analysis of Risk Factors of COVID-19 Associated Mucormycosis and Mortality Predictors: A Single-Center Study.

Background Mucormycosis has been identified with increasing frequency in patients with coronavirus disease 2019 (COVID-19). Aims We aimed to determine the in-hospital outcome of patients with COVID-19 associated mucormycosis (CAM). Materials and methods This was a single-center, retrospective, observational study. We included patients diagnosed with CAM from a tertiary care hospital in Pune, India. Clinical, laboratory, and in-hospital outcomes were noted. We analyzed factors associated with in-hospital mortality. Results Between February 2021 and June 2021, we identified 84 patients of CAM. The mean age was 49.3 ± 12.1 years. Of the included patients, 64.3% had diabetes mellitus, and 83.3% had received steroids. Mucormycosis was diagnosed after a median of 11 days from the COVID-19 diagnosis. Orbital and central nervous system (CNS) involvement was seen in 29.8% and 23.8% of patients, respectively. During a mean hospital stay of 12.5 ± 8.5 days, 15.5% of patients died. Compared to survivors, the presence of chronic kidney disease (CKD) (p<0.0001), orbital involvement (p=0.039), use of tocilizumab (p<0.0001), and development of renal dysfunction during hospitalization (p<0.0001) were seen in a significantly higher proportion of nonsurvivors. The proportion of patients with diabetes, those receiving steroids, and mean glycosylated hemoglobin (HbA1c) levels did not differ significantly in survivors and nonsurvivors. Conclusion In-hospital mortality in CAM is relatively lower in our institution. CKD, orbital involvement, use of tocilizumab, and renal dysfunction during hospital stay were found to be strong predictors of mortality.

Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert's pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual's clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.

Impact of Nonpharmacological Public Health Interventions on Epidemiological Parameters of COVID-19 Pandemic in India.

Background Public health interventions are epidemiologically sound and cost-effective methods to control disease burden. Non-pharmacological public health interventions are the only mode to control diseases in the absence of medication. Objective To find the impact of public health interventions on the epidemiological indicators of disease progression. Methods This is a secondary data analysis done on COVID-19 data. The median doubling time and R0 were calculated for a rolling period of seven days. Interventions were scored from zero to three with an increasing level of stringency. Multivariate linear regression was performed to find the role of individual interventions on R0 and the median doubling time. Results The highest intervention score was reported in the lockdown phase, which gradually decreased to the lowest level of 22. The R0 values settled to a level of 1.25, and the median doubling time increased to 20 days at the end of the study. Public awareness and public health laws were found to be related to both R0 and the median doubling time in the pre-lockdown phase only. Conclusion The implementation of interventions at the ground level is one of the key factors in the success of public health interventions. Post implementation, poor effectiveness of many interventions is evident from the study. Further, studies related to the sequence of interventions are required to further analyze the poor effect of the interventions.

A comprehensive review on plasmonic-based biosensors used in viral diagnostics.

The proliferation and transmission of viruses has become a threat to worldwide biosecurity, as exemplified by the current COVID-19 pandemic. Early diagnosis of viral infection and disease control have always been critical. Virus detection can be achieved based on various plasmonic phenomena, including propagating surface plasmon resonance (SPR), localized SPR, surface-enhanced Raman scattering, surface-enhanced fluorescence and surface-enhanced infrared absorption spectroscopy. The present review covers all available information on plasmonic-based virus detection, and collected data on these sensors based on several parameters. These data will assist the audience in advancing research and development of a new generation of versatile virus biosensors.

Conditions for a Second Wave of COVID-19 Due to Interactions Between Disease Dynamics and Social Processes

In May 2020, many jurisdictions around the world began lifting physical distancing restrictions against the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This gave rise to concerns about a possible second wave of coronavirus disease 2019 (COVID-19). These restrictions were imposed in response to the presence of COVID-19 in populations, usually with the broad support of affected populations. However, the lifting of restrictions is also a population response to the accumulating socio-economic impacts of restrictions, and lifting of restrictions is expected to increase the number of COVID-19 cases, in turn. This suggests that the COVID-19 pandemic exemplifies a coupled behavior-disease system where disease dynamics and social dynamics are locked in a mutual feedback loop. Here we develop a minimal mathematical model of the interaction between social support for school and workplace closure and the transmission dynamics of SARS-CoV-2. We find that a second wave of COVID-19 occurs across a broad range of plausible model input parameters governing epidemiological and social conditions, on account of instabilities generated by behavior-disease interactions. The second wave tends to have a higher peak than the first wave when the efficacy of restrictions is greater than 40% and when the basic reproduction number R0 is less than 2.4. Surprisingly, we also found that a lower R0 value makes a second wave more likely, on account of behavioral feedback (although a lower R0 does not necessarily cause more infections, in total). We conclude that second waves of COVID-19 can be interpreted as the outcome of non-linear interactions between disease dynamics and social behavior. We also suggest that further development of mathematical models exploring behavior-disease interactions could help us better understand how social and epidemiological conditions together determine how pandemics unfold. © Copyright © 2020 Pedro, Ndjomatchoua, Jentsch, Tchuenche, Anand and Bauch.