Adherence to Infection Prevention and Control Measures Among Health-Care Workers Serving in COVID-19 Treatment Centers in Punjab, Pakistan.

OBJECTIVE: Infection prevention and control (IPC) measures are easily adoptable activities to prevent the spread of infection to patients as well as among health-care workers (HCWs). METHODS: This cross-sectional study evaluated the adherence to IPC measures among HCWs working at coronavirus disease 2019 (COVID-19) treatment centers in Punjab, Pakistan. HCWs were recruited by means of convenient sampling through Google Form® using the World Health Organization risk assessment tool. All data were analyzed using SPSS 20. RESULTS: A total of 414 HCWs completed the survey (response rate = 67.8%), and majority of them were males (56.3%). Most of the HCWs were nurses (39.6%) followed by medical doctors (27.3%). Approximately 53% reported insufficiency of personal protective equipment (PPE), 58.2% did not receive IPC training and 40.8% did not have functional IPC team at their health facilities. The majority of HCWs (90%) used disposable gloves and N95 facemasks while interacting with COVID-19 patients. Nearly 45% used protective face shields and gowns before providing care to their patients. Hand hygiene practices while touching, and performing any aseptic procedure was adopted by 70.5% and 74.1% of HCWs, respectively. CONCLUSIONS: In conclusion, the adherence to IPC measures among Pakistani HCWs working in COVID-19 treatment centers is good despite the limited availability of PPEs. Their practices can be optimized by establishing institutional IPC teams, periodic provision of IPC training, and necessary PPE.

Psychological Effects of COVID-19 among Healthcare Workers and How They Are Coping: A Web-based, Cross-sectional Study during the First Wave of COVID-19 in Pakistan.

OBJECTIVE: To ascertain the psychological impacts of COVID-19 among the Pakistani healthcare workers (HCWs) and their coping strategies. METHODS: This web-based, cross-sectional study was conducted among HCWs (N=398) from Punjab province of Pakistan. The generalized anxiety scale (GAD-7), patient health questionnaire (PHQ-9) and Brief-COPE were used to assess anxiety, depression and coping strategies, respectively. RESULTS: The average age of respondents was 28.67 years (SD=4.15), with the majority of medical doctors (52%). The prevalence of anxiety and depression were 21.4% and 21.9%, respectively. There was no significant difference in anxiety and depression scores among doctors, nurses and pharmacists. Females had significantly higher anxiety (p=0.003) and depression (p=0.001) scores than males. Moreover, frontline HCWs had significantly higher depression scores (p=0.010) than others. The depression, not anxiety, score were significantly higher among those who did not receive the infection prevention training (p=0.004). Most frequently adopted coping strategy were religious coping (M=5.98, SD=1.73), acceptance (M=5.59, SD=1.55) and coping planning (M=4.91, SD=1.85). CONCLUSION: A considerable proportion of HCWs are having generalized anxiety and depression during the ongoing COVID-19 pandemic. Our findings call for interventions to mitigate mental health risks in HCWs.

Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics.

Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.