Coronavirus disease 2019 (COVID-19) is a newly emerged disease that has become a global public health concern as it rapidly spread around the world. The etiologic agent responsible for this disease has been named as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses as it shows similar genomic features to that of SARS-CoV which caused a pandemic in 2002. This disease first appeared in Hubei province of China and it follows human-to-human transmission but the path this virus took to set up human infection remains a mystery. By 17 April 2020, globally there have been 2,074,529 confirmed cases with 139,378 deaths because of COVID-19. SARS-CoV-2 shows several similarities with SARS?CoV, and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) with its clinical presentations. This can vary from asymptomatic infection to severe disease and mortality. Real-time reverse-transcription polymerase chain reaction (rRT-PCR) screening is considered as the standard laboratory test for the diagnosis of COVID-19. There is no proven antiviral agent against SARS-CoV-2 so the treatment for COVID-19 is symptomatic, aiming for the management of the symptoms and prevention of the complications. The outbreak of COVID-19 has led to the implementation of extraordinary public health measures throughout the world. Numerous antiviral compounds used to treat other infections are being clinically researched to find possible treatment. Similarly, the traditional public health outbreak response strategy of isolation, quarantine, social distancing and community containment has been implemented in multiple countries and has played an important role in the prevention of new outbreaks. This review aims to enhance our understanding of COVID 19.Keywords: Coronavirus disease 2019; COVID-19; SARS-CoV-2; novel coronavirus 2019; severe acute respiratory syndrome-2

Background: Postpartum depression is a type of mental disorder associated with childbirth during pregnancy or within the first postpartum year. It is reported as a common psychological health problem affecting 10-15% of women worldwide. The duration of postpartum depression frequently depends on its severity and the time of initiation of treatment. This study assessed depression and its associated factors among postpartum period women of Godavari municipality, Lalitpur, Nepal.Methods: A community-based cross-sectional study was conducted using Edinburg Postpartum Depression Scale among 195 mothers who were within six months of the postpartum period. The chi-square and logistic regression were applied to establish the association between postpartum depression and associated factors. Results: Out of the total 195 postpartum women, 37(19%) women suffered from depression and out of those women 2.1% had suicidal thoughts. Among the associated factors, education, occupation, the intent of pregnancy, family support and pregnancy-related problems/complications were found to be significantly associated with Postpartum depression (p<0.05). Conclusions: Nearly one-fifth postpartum women suffered from some type of depression. It is one of the public health concerns which directly or indirectly corresponds to the socio-economic condition of the women. The improved education and economic status of women, intention of pregnancy, family care and support during pregnancy and the postpartum period and early diagnosis and management of health problems could reduce the magnitude of the postpartum depression. Keywords: Associated factors of postpartum depression; edinburg postpartum depression scale; Nepal; postpartum depression.

ObjectivesTo estimate the prevalence of anxiety and depression and identify the associated factors among people with type 2 diabetes mellitus (T2DM) visiting diabetes clinics of Pokhara Metropolitan, Nepal.DesignCross-sectional study.SettingThree diabetes clinics in Pokhara Metropolitan, Nepal, from May to July 2021.Participants283 people with T2DM visiting selected diabetes centres of Pokhara Metropolitan.Outcome measuresAnxiety and depression were the outcome measures. Face-to-face interviews were conducted using a structured questionnaire comprising information related to participants’ sociodemographic profile and several factors along with Hospital Anxiety and Depression-Anxiety subscale and Patient Health Questionnaire-9 to assess the levels of anxiety and depression, respectively. Pearson’s Χ2tests and binary logistic regression were performed to examine association between dependent and independent variables at 5% level of significance.ResultsThe prevalence of anxiety and depression was 31.4% (95% CI 26.2% to 37.5%) and 36.4% (95% CI 30.8% to 42.0%), respectively. Anxiety was found to be associated with a lower level of perceived social support (adjusted OR (AOR) 2.442, 95% CI 1.020 to 5.845), multiple complications (AOR 2.758, 95% CI 1.015 to 7.334) and comorbidities (AOR 2.110, 95% CI 1.004 to 4.436), severe COVID-19 fear (AOR 2.343, 95% CI 1.123 to 4.887) and sleep dissatisfaction (AOR 1.912, 95% CI 1.073 to 3.047). Economical dependency (AOR 1.890, 95% CI 1.026 to 3.482), no insurance (AOR 2.973, 95% CI 1.134 to 7.093), lower perceived social support (AOR 2.883, 95% CI 1.158 to 7.181), multiple complications (AOR 2.308, 95% CI 1.585 to 6.422) and comorbidities (AOR 2.575, 95% CI 1.180 to 5.617), severe COVID-19 fear (AOR 2.117, 95% CI 1.009 to 4.573), alcohol use (AOR 2.401, 95% CI 1.199 to 4.806) and sleep dissatisfaction (AOR 1.995, 95% CI 1.093 to 3.644) were found to be associated with depression.ConclusionThis study showed high prevalence levels of anxiety and depression among people with T2DM. Strengthening social support and focusing on people with diabetes suffering from comorbidity and complications could help to reduce their risk of mental health problems.

The deterioration of surface water quality occurs due to the presence of various types of pollutants from human activities such as agriculture, industry, construction, deforestation, etc. Thus, the presence of various pollutants in water bodies can lead to deterioration of both surface water quality and aquatic life. Conventional surface water quality assessment methods are widely performed using laboratory analysis, which are labour intensive, costly, and time consuming. Moreover, these methods can only provide individual concentration of surface water quality parameters (SWQPs), measured at monitoring stations and shown in a discrete point format, which are difficult for decision-makers to understand without providing the overall patterns of surface water quality. To such problem, Remote Sensing has been a blessing because of its low cost, spatial continuity and temporal consistency. The relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. This study aims to develop techniques for estimating the concentration of both optical and non-optical SWQPs from Satellite Imagery (Landsat8) which supports coastal studies and mapping the complex relationship between satellite multi-spectral signature and concentration of SWQPs. It will also focus on classifying the most significant SWQPs that contribute to both spatial and temporal surface water quality. In contrast to traditionally performed surface water quality assessment methods, this research project will be focused on identifying such parameters incorporating the new and evolving machine intelligence that is Artificial Intelligence (AI). Significant number of samples have to be collected along with the GPS data which is used to model the relationship. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) will be developed to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. The study area chosen for this research is Bijayapur River of distance approximately 10 km flowing above, through and down the Pokhara city. The sole purpose of this research is to examine the water quality before it flows through the city and analysing after it passes through the city.

The whole world struggles to combat COVID-19 pandemic forcing nations to take extreme measures in an attempt to prevent outbreaks and save lives. It has been noted that COVID-19 has established itself as a risk factor for psychological distress among the population of different subgroups. There are several factors such as uncertainties, controversies, misinformation, social isolation, stigma, and discrimination which are escalating the risk of massive mental distress among the public. Nepal is trying to increase its effort to combat COVID-19 by adopting community containment measures, but the mental health of the frontline health workforce, service providers, and the general public seems to be highly overshadowed. In this aspect, this paper aims to shed light on different aspects of mental health issues emerging in Nepal during the time of COVID-19 lockdown, along with some of its potential contributing factors. Nepal lacks adequate infrastructure and human resource to provide mental healthcare services effectively in case of any massive mental distress. In this state of resource deprivation, providing education and training regarding psychosocial issues to health system leaders, first responders, and health care professionals could be a key to address the population need at present and to prevent further complications.