An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users.

In work-from-home (WFH) situation due to coronavirus (COVID-19) pandemic, the handheld device (HHD) users work in awkward postures for longer hours because of unavailability of ergonomically designed workstations. This problem results in different type of musculoskeletal disorders (MSDs) among the HHD users. An integrated multi-criteria decision-making approach was offered for identifying the risk level of MSDs among HHD users. A case example implemented the proposed approach in which, firstly, the best-worst method (BWM) technique was used to prioritize and determine the relative importance (weightage) of the risk factors. The weightages of the risk factors further used to rank the seven alternatives (HHD users) using Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) technique. The outcomes of the BWM investigation showed that the three most significant risk factors responsible for MSDs are duration of working, poor working posture and un-ergonomic design. The outcome of the VIKOR technique exhibited that computer professionals were at the highest risk among all users. The risk factor priority must be used for designing a working strategy for the WFH situation which will help to mitigate the risks of MSDs.

Effect of physical activity intervention on the musculoskeletal health of university student computer users during homestay.

Objectives. The coronavirus outbreak delivered the condition of dying from infection and forced people (especially university student computer users) to perform all working and non-working activities during homestay. In this situation, device usage for a longer duration is mainly responsible for work-related health issues. This study aims to discover the effect of a physical activity intervention (PAI) on computer users' musculoskeletal health during homestay. Methods. The investigation was performed on 40 university student computer users. To measure body discomfort before and after using the PAI, the body part discomfort scale of Corlett and Bishop was applied. Results. After implementing the PAI, the musculoskeletal disorder (MSD) decrement in major body regions was reported as wrist/forearm (from 8.17 ± 1.45 to 4.57 ± 1.10), lower back (from 8.01 ± 1.42 to 4.40 ± 1.14), elbow (from 7.57 ± 1.71 to 3.49 ± 1.13) and neck (from 7.40 ± 1.71 to 4.02 ± 0.81). Conclusions. PAI significantly decreased the discomfort among users in various body regions. This research suggested that PAIs may reduce the risk of MSDs in the long term for different body regions.

Effect of Concomitant Tuberculosis Infection on COVID-19 Disease in Children: A Matched, Retrospective Cohort Study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has had devastating effects on the health of millions globally. Patients with tuberculosis (TB) are a vulnerable population. There is paucity of data to assess association between the 2 diseases in Pediatric population. OBJECTIVE: To elucidate the effect of concomitant TB on clinical course of pediatric COVID-19 disease. METHODS: Retrospective matched cohort study was conducted at dedicated tertiary COVID-19 hospital in India. All consecutive patients aged <18 y admitted with COVID-19 were line listed. Patients with current or recently diagnosed TB were included. Consecutive age and sex matched COVID-19 patients with no history of TB were included as controls. Medical records were retrieved, clinical data entered in pre-determined proforma. RESULTS: During study period, 327 pediatric COVID-19 patients were admitted. Study group included 17 patients with TB. These patients, tended to be referred from other hospitals, be sicker, had lower SpO2 at arrival and higher severity of COVID-19 as compared to controls (All P < 0.05). They required more mechanical ventilation, had longer length of stay and worse outcome. CONCLUSION: COVID-19 may secondarily affect and modify the course of TB in children. Given the high case fatality rate in this association and potentially treatable nature of TB, attention of the policy makers is drawn to this. NAME OF IEC COMMITTEE: Maulana Azad Medical College and Associated Hospital Institutional Ethics Committee. IEC no: F.1/IEC/MAMC/(80/8/2020/No274). Dated 9 November 2020. TRIAL REGISTRATION: CTRI/2021/02/031197 [Registered on: 10 February 2021].

Risk factors of musculoskeletal symptoms among mobile device users during work from home.

Objectives. Coronavirus (COVID-19) is affecting people throughout the world. People are forced to conduct various activities at home using mobile devices (MDs) as a result of the outbreak. In this case, prolonged use of MDs is the major cause for work-related health problems. Methods. We used systematic cluster random sampling to sample a diverse group of Indians from India's various states. Subjects filled out a questionnaire with questions about their demographics, MD usage and musculoskeletal symptoms (MSSs) faced. The relationship between MSSs and various factors was investigated using χ2 and binomial logistic regression analysis. Results. An online survey yielded 720 responses. More than half of employees registered MSSs in their upper body regions. Age, gender and MD usage were correlated with MSSs in various body regions. According to the binomial logistic regression findings, gender was significantly linked to MSSs in each body region. Conclusions. Results show that MDs can be used effectively in intermediate leisure activities if they are used in accordance with their basic needs.

Do Elevated Levels of Inflammatory Biomarkers Predict the Risk of Occurrence of Ischemic Stroke in SARS-CoV2 ?: An Observational Study.

INTRODUCTION: Stroke, a dreaded complication of SARS-CoV2, has been reported in 0.9 to 5% of SARS-CoV2 patients. There are concerns that SARS-CoV2 infection has a significant independent association with acute ischemic stroke, even in the absence of conventional cerebrovascular risk factors. Whether elevated levels of inflammatory biomarkers have predictive value in the occurrence of stroke in SARS-CoV2 is poorly understood. AIM: To profile the characteristics of SARS-CoV2 positive patients with ischemic stroke (COVID-Stroke) and to identify the significance of elevated IBMs in the prediction of ischemic COVID-stroke. MATERIALS AND METHODS: Clinical characteristics, stroke risk factors, laboratory parameters- including levels of inflammatory biomarkers, and outcome of SARS-CoV2 patients with stroke (n=60) were collected. SARS-CoV2 RT- PCR positive age, gender, and pulmonary severity matched non-stroke patients were taken as controls (n = 60). Binary multivariate logistic regression analysis was used to find the predictors of ischemic COVID-stroke. RESULTS: D-dimer > 441.8 ng/mL, LDH> 395U/L, ESR >19 mm/h and CRP> 0.2 mg/dL were independently found to be very strong predictors of occurrence of ischemic COVID-stroke (p < 0.001 for each). On multivariate analysis, D-dimer > 441.8 ng/mL, ESR > 19 mm/h, and RDW > 16.1% were found to be the most strong predictors of the occurrence of ischemic COVID-stroke. Conventional CVD risk factors- higher age (> 60years), presence of diabetes mellitus, and hypertension were not found to be significant predictors in multivariate analysis. CONCLUSION: In SARS-CoV2 patients, D-dimer elevated beyond 441.8 ng/mL, ESR greater than 19 mm/h, and RDW widened more than 16.1% were the strongest predictors of the occurrence of ischemic stroke. This is the first study that attempts to find cut-off levels of IBMs in the prediction of ischemic COVID-stroke.

COVID-19: Automatic detection from X-ray images by utilizing deep learning methods.

In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible.

Cardiology and COVID-19: do we have sufficient information?

COVID-19 caused by severe acute respiratory syndrome coronavirus 2, which originated in Wuhan (China), transformed into a worldwide pandemic. The short span associated with the spread of the virus and its varied manifestations presents a steep learning curve for many clinicians on the front-line of treatment. Cardiology is one such affected area. This paper details the signs and symptoms of cardiovascular disease resulting from COVID-19, including its proposed pathophysiology, signs and symptoms, treatments and outcomes under investigation. The consensus is that COVID-19 patients with cardiovascular injury have a shorter duration from symptom onset to deterioration, higher mortality and higher prevalence in older populations. Diagnosis and intervention for patients with underlying cardiovascular comorbidities is critical.

Neurological Symptoms as Initial Manifestation of Covid-19 - An Observational Study.

BACKGROUND: Respiratory system involvement and fever are considered as a cardinal manifestation of Covid-19 infection for the screening of case detection. We (India) are into the fourth month of Covid-19 and cases are still rising, this could mean that fever and respiratory symptoms may not be the only initial symptoms. Therefore, we intend to investigate whether neurological symptoms can precede the cardinal symptoms. METHODS: Totally, 391 Covid-19 RTPCR positive hospitalized patients were enrolled. All included subjects were presented with a questionnaire pertaining to systemic symptoms. For analysis of the chronology of symptoms, the study population was sub-grouped according to onset of their systemic involvement e.g., (1) Fever (2) Respiratory symptoms (3) Neurological symptoms (4) Gastrointestinal symptoms. RESULTS: New-onset neurological symptoms were found in 106 (27.1%) out of 391 patients irrespective of their chronology to the onset of other symptoms. Of these 106 patients, altered taste (33.1%), altered smell (24.5%), and headache (22.6%) were the most common neurological symptoms. However, 38 (9.7%) subjects recognized neurological symptoms, as the initial manifestation of their illness. Mean duration of neurological symptoms before the onset of respiratory symptoms or fever was 2 ± 1.57 days. CONCLUSION: New-onset headache, altered taste, and smell were the most common neurological symptoms. In the context of the current pandemic, a high index of suspicion should be kept in patients presenting with these symptoms even in the absence of fever and respiratory symptoms. To the best of our knowledge, this is the first study from India comparing chronology of neurological symptoms with cardinal symptoms.