A data-driven approach utilizing a raw material database and machine learning tools to predict the disintegration time of orally fast-disintegrating tablet formulations

Orally fast-disintegrating tablets (OFDTs) have seen a significant increase in popularity over the past decade, becoming a rapidly expanding sector in the pharmaceutical market. The aim of the current study is to use machine learning (ML) methods to predict the disintegration time (DT) of OFDTs. In this study, we have developed seven ML models using the TPOT AutoML platform to predict the DT of OFDTs. These models include the decision tree regressor (DTR), gradient boost regressor (GBR), random forest regressor (RFR), extra tree regressor (ETR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and deep learning (DL). The results indicate that ML methods are effective in predicting the DT, especially with ETR. However, after fine-tuning the deep neural network with a 10-point cross-validation scheme, the DL model showed superior performance with an NRMSE of 6.2% and an R2 of 0.79. The key factors influencing the DT of OFDTs were identified using the SHAP method

Synthesis and Antimicrobial Activity of Novel 3-[1-(3-nitrophenyl)-ethyl]-1-(indole-1-yl) Substituted Aryl/alkyl-phosphinoyl/thiophosphinoyl/ selenophosphinoyl-1H-indole Derivatives

Syntheses of novel 3-[1-(3-nitrophenyl)-ethyl]-1-(indole-1-yl) substituted aryl/alkyl phosphinoyl/thiophosphinoyl/selenophosphinoyl-1H-indole derivatives were accomplished in two steps. The synthetic route involves the cyclisation of equimolar quantities of 3-[1H-3-indolyl(3-nitrophenyl)methyl]-1H-indole with dichlorophenyl phosphine/ethyldichlorophosphite in the presence of triethylamine in dry acetonitrile at room temperature. These compounds were further converted to the corresponding oxides, sulphides and selenides by reacting them with hydrogen peroxide, sulphur and selenium, respectively. The structures of the novel products were established by elemental analyses, IR, 1H, 13C and 31P NMR and mass spectroscopy. They were screened for antibacterial and antifungal activity against Staphylococcus aureus/Klebsiella pneumoniae and Pellicularia solmanicolor/Macrophomina phaseolina, respectively.Keywords: Bisindolylalkanes, alkyl/aryl phosphorodichloridates, antimicrobial activit

Survey Among Medical Students During COVID-19 Lockdown: The Online Class Dilemma

Background: In view of COVID-19 lockdown in India, many colleges started online classes. This study aimed to evaluate the attitudes of, and the factors affecting, medical students attending online classes during lockdown. Methods: We designed an online questionnaire with open-ended, close-ended, and Likert scale questions. Links to the questionnaires were shared with the medical students who have attended at least one online class during the COVID-19 lockdown period. Respondents were 1061 participants from 30 medical colleges from the states of Kerala and Tamil Nadu in India. Results: The majority of students – 94% (955/1016) – used smartphones to attend online classes. ZOOM/ Skype – by 57.1% (580/1016) – and Google platforms – by 54.4% (553/1016) – were commonly used. Learning at leisure – 44.5% (452/1016) – was the top reason why students liked online classes, whereas network problems – 85.8% (872/1016) – was the top reason why students disliked them. Lack of sufficient interaction – 61.1% (621/1016) was another reason why students disliked online learning. More than half the participants – 51.7% (526/1016) – did not want to continue online classes after COVID-19 lockdown. More students – 55% (558/1016) – favored regular classes than online classes. Conclusion: Students in our survey did not seem favorably disposed to online classes. Network problems experienced by students should be addressed. Furthermore, teachers should try to make the classes more interactive and educational institutions should address the problems pointed out by the students in order to make online classes more effective in the future

Burden and predictors of hypertension in India: results of SEEK (Screening and Early Evaluation of Kidney Disease) study

Background: Hypertension (HTN) is one of the major causes of cardiovascular morbidity and mortality. The objective of the study was to investigate the burden and predictors of HTN in India. Methods: 6120 subjects participated in the Screening and Early Evaluation of Kidney disease (SEEK), a community-based screening program in 53 camps in 13 representative geographic locations in India. Of these, 5929 had recorded blood pressure (BP) measurements. Potential predictors of HTN were collected using a structured questionnaire for SEEK study. Results: HTN was observed in 43.5% of our cohort. After adjusting for center variation (p < 0.0001), predictors of a higher prevalence of HTN were older age ≥40 years (p < 0.0001), BMI of ≥ 23 Kg/M2 (p < 0.0004), larger waist circumference (p < 0.0001), working in sedentary occupation (p < 0.0001), having diabetes mellitus (p < 0.0001), having proteinuria (p < 0.0016), and increased serum creatinine (p < 0.0001). High school/some college education (p = 0.0016), versus less than 9th grade education, was related with lower prevalence of HTN. Of note, proteinuria and CKD were observed in 19% and 23.5% of HTN subjects. About half (54%) of the hypertensive subjects were aware of their hypertension status. Conclusions: HTN was common in this cohort from India. Older age, BMI ≥ 23 Kg/M2, waist circumference, sedentary occupation, education less, diabetes mellitus, presence of proteinuria, and raised serum creatinine were significant predictors of hypertension. Our data suggest that HTN is a major public health problem in India with low awareness, and requires aggressive community-based screening and education to improve health

Burden of typhoid and paratyphoid fever in India.

BACKGROUND: In 2017, more than half the cases of typhoid fever worldwide were projected to have occurred in India. In the absence of contemporary population-based data, it is unclear whether declining trends of hospitalization for typhoid in India reflect increased antibiotic treatment or a true reduction in infection. METHODS: From 2017 through 2020, we conducted weekly surveillance for acute febrile illness and measured the incidence of typhoid fever (as confirmed on blood culture) in a prospective cohort of children between the ages of 6 months and 14 years at three urban sites and one rural site in India. At an additional urban site and five rural sites, we combined blood-culture testing of hospitalized patients who had a fever with survey data regarding health care use to estimate incidence in the community. RESULTS: A total of 24,062 children who were enrolled in four cohorts contributed 46,959 child-years of observation. Among these children, 299 culture-confirmed typhoid cases were recorded, with an incidence per 100,000 child-years of 576 to 1173 cases in urban sites and 35 in rural Pune. The estimated incidence of typhoid fever from hospital surveillance ranged from 12 to 1622 cases per 100,000 child-years among children between the ages of 6 months and 14 years and from 108 to 970 cases per 100,000 person-years among those who were 15 years of age or older. Salmonella enterica serovar Paratyphi was isolated from 33 children, for an overall incidence of 68 cases per 100,000 child-years after adjustment for age. CONCLUSIONS: The incidence of typhoid fever in urban India remains high, with generally lower estimates of incidence in most rural areas. (Funded by the Bill and Melinda Gates Foundation; NSSEFI Clinical Trials Registry of India number, CTRI/2017/09/009719; ISRCTN registry number, ISRCTN72938224.)

Epidemiology and risk factors of chronic kidney disease in India – results from the SEEK (Screening and Early Evaluation of Kidney Disease) study

Background: There is a rising incidence of chronic kidney disease that is likely to pose major problems for both healthcare and the economy in future years. In India, it has been recently estimated that the age-adjusted incidence rate of ESRD to be 229 per million population (pmp), and >100,000 new patients enter renal replacement programs annually. Methods: We cross-sectionally screened 6120 Indian subjects from 13 academic and private medical centers all over India. We obtained personal and medical history data through a specifically designed questionnaire. Blood and urine samples were collected. Results: The total cohort included in this analysis is 5588 subjects. The mean ± SD age of all participants was 45.22 ± 15.2 years (range 18–98 years) and 55.1% of them were males and 44.9% were females. The overall prevalence of CKD in the SEEK-India cohort was 17.2% with a mean eGFR of 84.27 ± 76.46 versus 116.94 ± 44.65 mL/min/1.73 m2 in non-CKD group while 79.5% in the CKD group had proteinuria. Prevalence of CKD stages 1, 2, 3, 4 and 5 was 7%, 4.3%, 4.3%, 0.8% and 0.8%, respectively. Conclusion: The prevalence of CKD was observed to be 17.2% with ~6% have CKD stage 3 or worse. CKD risk factors were similar to those reported in earlier studies. It should be stressed to all primary care physicians taking care of hypertensive and diabetic patients to screen for early kidney damage. Early intervention may retard the progression of kidney disease. Planning for the preventive health policies and allocation of more resources for the treatment of CKD/ESRD patients are imperative in India

NetPath: a public resource of curated signal transduction pathways

NetPath, a novel community resource of curated human signaling pathways is presented and its utility demonstrated using immune signaling data

Human Protein Reference Database—2009 update

Human Protein Reference Database (HPRD—http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system—Human Proteinpedia (http://www.humanproteinpedia.org/)—through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15 000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome

Protocol for establishing a model for integrated influenza surveillance in Tamil Nadu, India

The potential for influenza viruses to cause public health emergencies is great. The World Health Organisation (WHO) in 2005 concluded that the world was unprepared to respond to an influenza pandemic. Available surveillance guidelines for pandemic influenza lack the specificity that would enable many countries to establish operational surveillance plans. A well-designed epidemiological and virological surveillance is required to strengthen a country’s capacity for seasonal, novel, and pandemic influenza detection and prevention. Here, we describe the protocol to establish a novel mechanism for influenza and SARS-CoV-2 surveillance in the four identified districts of Tamil Nadu, India. This project will be carried out as an implementation research. Each district will identify one medical college and two primary health centres (PHCs) as sentinel sites for collecting severe acute respiratory infections (SARI) and influenza like illness (ILI) related information, respectively. For virological testing, 15 ILI and 10 SARI cases will be sampled and tested for influenza A, influenza B, and SARS-CoV-2 every week. Situation analysis using the WHO situation analysis tool will be done to identify the gaps and needs in the existing surveillance systems. Training for staff involved in disease surveillance will be given periodically. To enhance the reporting of ILI/SARI for sentinel surveillance, trained project staff will collect information from all ILI/SARI patients attending the sentinel sites using pre-tested tools. Using time, place, and person analysis, alerts for abnormal increases in cases will be generated and communicated to health authorities to initiate response activities. Advanced epidemiological analysis will be used to model influenza trends over time. Integrating virological and epidemiological surveillance data with advanced analysis and timely communication can enhance local preparedness for public health emergencies. Good quality surveillance data will facilitate an understanding outbreak severity and disease seasonality. Real-time data will help provide early warning signals for prevention and control of influenza and COVID-19 outbreaks. The implementation strategies found to be effective in this project can be scaled up to other parts of the country for replication and integration