Clinical manifestations and outcomes of COVID-19 in maintenance hemodialysis patients of a high infectious epidemic country: a prospective cross-sectional study.

Introduction: The outbreak of COVID-19 poses great challenges for patients on maintenance haemodialysis. Here, we reported the clinical characteristics and laboratory features of maintenance haemodialysis (MHD) patients with COVID-19 in Bangladesh. Methods: Altogether, 67 MHD patients were enroled in the study from two dedicated tertiary-level hospitals for COVID-19 after the prospective cross-sectional execution of selection criteria. Data were collected from medical records and interviews. Different statistical analysis was carried out in the data analysis. Results: The mean age was 55.0±9.9 years, with 40 males (59.7%). The mean dialysis duration was 23.4±11.5 months. The most common symptoms were fever (82.1%), cough (53.7%), and shortness of breath (55.2%), while the common comorbid condition was hypertension (98.5%), followed by diabetes (56.7%). Among MHD patients, 52.2% to 79.1% suffered from severe to critical COVID-19, 48 patients (71.6%) had 26-75% lung involvement on high resolution computed tomography of the chest, 23 patients (34.3%) did not survive, 20 patients (29.9%) were admitted to ICU, and nine patients (13.4%) needed mechanical ventilation. Patients who did not survive were significantly older (mean age: 63.0 vs. 50.86 years, P=0.0001), had significantly higher cardiovascular risk factors (69.6% vs. 43.2%, P=0.04), severe shortness of breath (82.6% vs. 40.9%, P=0.0001), and longer hospital stays (mean days: 17.9 vs. 13.0, P=0,0001) compared to the survivor group. The white blood cell count, C-reactive protein, lactate dehydrogenase, pro-calcitonin, and thrombocytopenia were significantly (P<0.0001) higher, while the albumin level was significantly lower (P=0.0001) in non-survivor compared to patients who survived. Conclusion: Maintenance haemodialysis patients had severe to critical COVID-19 and had a higher risk of non-survival if they were older and had comorbidities such as hypertension and diabetes. Therefore, MHD patients with COVID-19 need close monitoring to improve their outcomes.

Toxicological effect of sumithion pesticide on the hematological parameters and its recovery pattern using probiotic in Barbonymus gonionotus.

The experiment was conducted to clarify sumithion induced hematoxicity in silver barb (Barbonymus gonionotus) through in vivo exposures (25 % and 50 % of LC50 of sumithion) and subsequent recovery patterns using normal and probiotic treated feed were also assessed. Three treatments each incorporating three replications were used in the experiment for different days (1, 7, 14, 21, and 28). Treatment T1 was control (0 mg/L), and two concentrations, such as 2.61 mg/L (25 % of 96 h LC50), 5.21 mg/L (50 % of 96 h LC50) were used as Treatment T2 and T3, respectively. After 28 days of exposure to pesticide half of the fishes of T2 and T3 were reared in sumithion free water with normal (T2N, T3N) and probiotic treated feed (T2P, T3P). The median lethal concentration (50 %) for 96 h was 10.42 mg/L. In pesticide-treated groups, values of each hematological parameter (blood glucose, red blood cell, hematocrit, and hemoglobin) decreased but prevalence and severity of micronucleus and white blood cells increased significantly (p< 0.05) with concentration and time duration. Other blood indices including mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC) were correspondingly changed in comparison to the control. In the recovery experiment, the silver barb recovered spontaneously, but the recovery rate was significantly higher in probiotic treated groups than normally treated groups in time and duration reliant fashion. In conclusion, persistent sublethal dosages of sumithion caused hematological abnormalities in silver barb. Probiotic supplement can recover the damage but only 28 days of recovery is not enough to recover the total alterations.

Scalable Telehealth Services to Combat Novel Coronavirus (COVID-19) Pandemic.

An ongoing pandemic, the novel coronavirus disease 2019 (COVID-19) is threatening the nations of the world regardless of health infrastructure conditions. In the age of digital electronic information and telecommunication technology, scalable telehealth services are gaining immense importance by helping to maintain social distances while providing necessary healthcare services. This paper aims to review the various types of scalable telehealth services used to support patients infected by COVID-19 and other diseases during this pandemic. Recently published research papers collected from various sources such as Google Scholar, ResearchGate, PubMed, Scopus, and IEEE Xplore databases using the terms "Telehealth", "Coronavirus", "Scalable" and "COVID-19" are reviewed. The input data and relevant reports for the analysis and assessment of the various aspects of telehealth technology in the COVID-19 pandemic are taken from official websites. We described the available telehealth systems based on their communication media such as mobile networks, social media, and software based models throughout the review. A comparative analysis among the reviewed systems along with necessary challenges and possible future directions are also drawn for the proper selection of affordable technologies. The usage of scalable telehealth systems improves the quality of the healthcare system and also reduces the infection rate while keeping both patients and doctors safe during the pandemic.

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic.

During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.