Survey is an effective way of collecting data from a large sample within a short time. Due to increased use of digital medium, online survey is gaining popularity among researchers. It enables the surveyor to collect data from any corner of the world. Though the response rate is lower than physical survey, online survey has several advantages. Hence, we felt the necessity of sharing the basics of creating an online survey with budding researchers. Google form is an online survey platform which provides the service of creating survey forms, receiving responses and analysis of data free of cost. We used this platform to describe an example of conversion of a physical questionnaire to an online form. Hope this miniature technical guide help the readers to know the basic skills to create a form online for their future studies.

BackgroundDrug-drug interactions (DDIs) can have serious consequences for patient health and well-being. Patients who are taking multiple medications may be at an increased risk of experiencing adverse events or drug toxicity if they are not aware of potential interactions between their medications. Many times, patients selfprescribe medications without knowing DDI. ObjectiveThe objective is to investigate the effectiveness of ChatGPT, a large language model, in predicting and explaining common DDIs. MethodsA total of 40 DDIs lists were prepared from previously published literature. This list was used to converse with ChatGPT with a two-stage question. The first question was asked as "can I take X and Y together?" with two drug names. After storing the output, the next question was asked. The second question was asked as "why should I not take X and Y together?" The output was stored for further analysis. The responses were checked by two pharmacologists and the consensus output was categorized as "correct" and "incorrect." The "correct" ones were further classified as "conclusive" and "inconclusive." The text was checked for reading ease scores and grades of education required to understand the text. Data were tested by descriptive and inferential statistics. ResultsAmong the 40 DDI pairs, one answer was incorrect in the first question. Among correct answers, 19 were conclusive and 20 were inconclusive. For the second question, one answer was wrong. Among correct answers, 17 were conclusive and 22 were inconclusive. The mean Flesch reading ease score was 27.64±10.85 in answers to the first question and 29.35±10.16 in answers to the second question, p = 0.47. The mean Flesh-Kincaid grade level was 15.06±2.79 in answers to the first question and 14.85±1.97 in answers to the second question, p = 0.69. When we compared the reading levels with hypothetical 6th grade, the grades were significantly higher than expected (t = 20.57, p < 0.0001 for first answers and t = 28.43, p < 0.0001 for second answers). ConclusionChatGPT is a partially effective tool for predicting and explaining DDIs. Patients, who may not have immediate access to the healthcare facility for getting information about DDIs, may take help from ChatGPT. However, on several occasions, it may provide incomplete guidance. Further improvement is required for potential usage by patients for getting ideas about DDI.

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Background Recent literature suggests a bi-directional relationship between COVID-19 infection and diabetes mellitus, with an increasing number of previously normoglycemic adults with COVID-19 being admitted with new-onset diabetic ketoacidosis (DKA). However, the possibility of COVID-19 being a potential trigger for A-β + ketosis-prone diabetes (KPD) in these patients needs elucidation. Our study aimed at analyzing such a cohort of patients and determining their natural course of β-cell recovery on serial follow-up. Methods After initial screening, n = 42 previously non-diabetic patients with new-onset DKA and RT-PCR positive COVID-19, were included in our ten-month follow-up study. Of these, n = 22 were negative (suspected A-β + KPD) and n = 20 were positive (Type 1A DM) for autoantibodies (GAD/IA-2/ZnT8). Subsequently, n = 19 suspected KPD and n = 18 Type 1A DM patients were followed-up over ten months with serial assessments of clinical, biochemical and β-cell secretion. Amongst the former, n = 15 (79%) patients achieved insulin independence, while n = 4 (21%) continued to require insulin at ten-months follow-up. Results On comparison, the suspected KPD patients showed significantly greater BMI, age, Hba1c, IL-6 and worse DKA parameters at presentation. Serial C-peptide estimations demonstrated significant β-cell recovery in KPD group, with complete recovery seen in the 15 patients who became insulin independent on follow-up. Younger age, lower BMI, initial severity of DKA and inflammation (IL-6 levels), along-with reduced 25-hydroxy-Vitamin-D levels were associated with poorer recovery of β-cell secretion at ten-month follow-up amongst the KPD patients, Conclusions This is the first prospective study to demonstrate progressive recovery of β-cell secretion in new-onset A-β + KPD provoked by COVID-19 infection in Indian adults, with a distinctly different profile from Type 1A DM. Given their significant potential for β-cell recovery, meticulous follow-up involving C-peptide estimations can help guide treatment and avoid injudicious use of insulin.

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