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Objectives: To determine the prevalence of diabetic peripheral neuropathy (DPN) and associated risk factors among patients with type 2 diabetes mellitus (T2DM). Study Design: Cross Sectional Study. Setting: Outpatient Department Combined Military Hospital, Abbottabad. Period: From Jan 2017 to May 2017. Material & Methods: A total of 270 patients with T2DM and age greater than 30 were included in the study. Peripheral diabetic neuropathy was assessed by using Neuropathy Symptoms Score (NSS) and Neuropathy Disability Score (NDS). Data was collected by using a self structured questionnaire and analyzed by using SPSS software version 20. Results: Majority (60%) of patients were male; male to female ratio being 1:1.5. Mean age was 62.26±11.38 years and the most frequent age group was 60-69 years (30.37% of patients). Overall prevalence of DPN was 36.7 % (n=99). Gender, age, educational status, duration of diabetes, HbA1c level, hypertension and mode of treatment were found to be significantly associated with DPN. Conclusion: Diabetic peripheral neuropathy is common entity seen in 36.7% patients with type 2 diabetes and is associated with several demographic and clinical factors.

Supplemental material, sj-doc-1-npx-10.1177_1934578X221086988 for Antiviral Potential of Indian Medicinal Plants Against Influenza and SARS-CoV: A Systematic Review by Bharat Krushna Khuntia, Vandna Sharma, Mohit Wadhawan, Varun Chhabra, Bharatraj Kidambi, Shubhangi Rathore, Aman Agrawal, Amirtha Ram, Sahar Qazi, Shaban Ahmad, Khalid Raza and Gautam Sharma in Natural Product Communications

Supplemental material, sj-docx-2-npx-10.1177_1934578X211056753 for Ayurvedic Medicinal Plants Against COVID-19: An In Silico Analysis by Bharat Krushna Khuntia, Vandna Sharma, Sahar Qazi, Soumi Das, Shruti Sharma, Khalid Raza and Gautam Sharma in Natural Product Communications

Coronavirus disease 2019 (COVID-19) has been declared as pandemic which took the lives of more than 500 thousand people till mid of 2020 worldwide. Since the coronavirus is highly contagious in nature, COVID-19 is spreading rapidly despite observing social distancing and taking other recommended precautionary measures. Computational intelligence, a powerful tool that mimics human intelligence and learns specific tasks using data, is widely deployed to combat the COVID-19. This chapter briefly covers the computational intelligence methods and its applications in the surveillance, prevention, prediction, and diagnosis of COVID-19. Further, the limitation of current systems and prospects are also discussed.

Remote health monitoring for patients is increasing with the advancements of various types of health related mobile applications. The vital signs such as pulse rate, blood pressure and temperature are the basic parameters, used for monitoring a patient’s health. The designed healthcare kit consists of different sensors that are used for sensing and monitoring a patient’s health. The data from healthcare kit is transmitted via the internet to save in a cloud-based server, which helps to monitor the consistent health situation of a candidate. The sensing information will be collected constantly over specified intervals of time and will be used to aware the patient about any concealed problem to endure possible diagnosis. The higher and lower range of temperature, blood pressure and heartbeat can be defined by the doctor according to the patient’s health. Afterward, the system starts monitoring the patient and sends alert to the concerned doctor as soon as the sensing parameters cross the defined limits. The recorded values are transmitted via internet to the cloud server and a notification in the form of a tweet is also displayed on the doctor’s twitter account and as well as the patient’s account.

The objective of this research was to enhance adsorption capacity of Acacia nilotica (keekar) sawdust for the abatement of chromium bearing wastewater and to investigate the effect of process parameters on adsorption capacity. The sawdust was activated by acid wash and functionalized subsequently with formaldehyde. Functionalization of activated sawdust raised its chromium removal efficiency of almost 10% as compared to its adsorption removal efficiency of HCl treated sawdust in a batch adsorption study. Adsorption kinetic data provided better fitting with pseudo second order model. Maximum adsorption capacity calculated through the best fitting Langmuir model was 6.34 mg·g− 1 and 8.2 mg·g− 1 for HCl treated and formaldehyde functionalized sawdust adsorbents, respectively. The adsorption of Cr(VI) was endothermic when studied by varying temperature from 20 °C to 50 °C for both activated and functionalized adsorbents.