Analysis of spontaneous individual case safety reports reported at adverse drug reaction monitoring centre: tertiary care teaching hospital in South India

Background: Drugs are double edged weapons, they are used in treatment of the patients but in return can harm as well. The safety of drug prescribing has become a need of the hour topic in medicine. Safety monitoring of patients via Pharmacovigilance tool has become an integral part of pharmacotherapy. This study has been undertaken to analyze the various individual case safety reports including the Special situation cases of medicational error and over dose and to promote the reporting of adverse drug reactions (ADRs) among the healthcare professionals (HCPs).Methods: A retrospective non-interventional observational study was done for indexed period of six months at AMC-PvPI under Osmania Medical College and General Hospital. The reported individual case safety reports (ICSRs) are evaluated on basis of demographics of age and gender, seriousness criteria, outcome parameters and causality assessment of suspected drug (s) and suspected ADR/AE (s) as per the ICH guidelines and WHO causality assessment scale.Results: A total of 177 ICSRs are reported out of that 137 were ADRs, 36-medication error cases and 4-cases of over dose. The incidence of ADRs in females are high compared with males was identical. The occurrence of ADRs in adult patients (61%) was significantly higher than other age groups. Of total ADRs, most of them were with analgesics (26%) and highly reported system organ classification was CNS. Overall, 79% patients were recovered from ADRs.Conclusions: The results depicted an insight to the HCPs on the importance of monitoring and reporting of ICSRs. Our study results emphasized need to roll out a pharmacovigilance practice tool to ensure the safe use of drugs for better Pharmacotherapy and development of pharmacogenomic studies

Cross sectional study on different doses of acenocoumarol with INR in a tertiary care hospital

Background: It is of high value to be assess the relationship between doses of Acenocoumarol and the INR values to offer better patient care. Since Acenocoumarol is a commonly used drug with a narrow therapeutic range it is essential to monitor the variations encountered in response to it to avoid drastic complications and to provide better health care. Aim: The aim of this study is to compare the INR values with different doses of Acenocoumarol, to compare the association of dose of Acenocoumarol with their respective INR and to find out the occurrence of bleeding with different doses of Acenocoumarol.Methods: The study was conducted in a Tertiary care hospital. 40 patients taking Acenocoumarol were recruited in the study. Relevant details like age, weight, dose of Acenocoumarol, INR and other concomitant drugs were obtained in a prospective manner. Correlation of dose of Acenocoumarol with respective INR was done by simple linear regression.Results: The relationship between dose and INR was analyzed using Simple linear regression and the scatter plot revealed no significant correlation between the dose and INR values. There is a lot of inter-individual variability in the dose response and thereafter the INR values.Conclusions: The dose of Acenocoumarol cannot predict INR values. Patient can ideally be started treatment on a low dose of Acenocoumarol and based on the INR values, dose can be titrated. There is a need for consideration of other factors which influence the dose and INR values.

BIG DATA ANALYTICS IN PHARMACOVIGILANCE - A GLOBAL TREND

Big data analysis has enhanced its demand nowadays in various sectors of health-care including pharmacovigilance. The exact definition of big data is not known to many people though it is routinely used by them. Big data refer to immense and voluminous computerized medical information which are obtained from electronic health records, administrative data, registries related to disease, drug monitoring, etc. This data are usually collected from doctors and pharmacists in a health-care facility. Analysis of big data in pharmacovigilance is useful for early raising of safety alerts, line listing them for signal detection of drugs and vaccines, and also for their validation. The present paper is intended to discuss big data analytics in pharmacovigilance focusing on global prospect and domestic country-India

A cross-sectional, questionnaire-based study on knowledge, attitude, and practice of pharmacovigilance among post-graduates at a tertiary care teaching hospital, Telangana

Background: Pharmacovigilance is the process of drug safety monitoring that improves patients' quality of life through the collection and analysis of Adverse Drug Reactions (ADRs). In our state, most of the ADRs are reported by a spontaneous reporting system of individual cases from health care professionals to Adverse Drug Reaction Monitoring Centre (AMC) under the Pharmacovigilance Programme of India (PvPI). Post-graduates (PGs) play a vital role in reporting ADRs as they are in personal evidence with all events after drug administration. The main objective of our study is to evaluate the Knowledge, Attitude, and Practice of Pharmacovigilance among post-graduates.Methods: The present study was a cross-sectional questionnaire-based study on knowledge, attitude, and practice (KAP) of Pharmacovigilance among 150 post-graduates at a tertiary care teaching hospital, Telangana. The statistical analysis was done using Statistical Package for Social Sciences (SPSS) version 25 software.Results: The results showed that there is relatively less knowledge among postgraduates. Attitude and practice-based questions evidenced a paradigm shift towards the construction of an organized Pharmacovigilance system. This study also highlights the under-reporting and the interventions needed to improve spontaneous reporting of ADRs.Conclusions: The knowledge of Pharmacovigilance with a positive attitude and practice among post-graduates is essential for reporting ADRs and reducing under-reporting.ng

Floral biology studies in wild melon [Cucumis melo L. ssp. agrestis (Naudin) Pangalo var. agrestis Naudin]

Studies on floral morphology, phenology and biology of wild melon revealed that the ratio of staminate and pistillate flowers was 3.40:1. The longevity of the male flowers were between 5 and 6 days, whereas, female flowers between 6 and 7 days. Anthesis was observed from 4.00 am to 10.00 am, while, the anther dehiscence started from 5.00 am which was continued to 7.00 am. The peak anthesis was observed from 8.00 am to 9.00 am and anther dehiscence from 6.00 am to 6.30 am. Freshly opened flowers showed pollen viability up to 98.35%, decreased upon closure and crashed to 17.48% in 3 days. Pollen germination was occurred after 15 minutes of incubation and continued up to 24 h of incubation. The stigma receptivity lasts from one to two days of anthesis. Major pollinator of wild melons observed was honey bee, mostly visited between 9:00 am to 6:00 pm

Crystal structure of the complete integrin αVβ3 ectodomain plus an α/β transmembrane fragment

We determined the crystal structure of 1TM-αVβ3, which represents the complete unconstrained ectodomain plus short C-terminal transmembrane stretches of the αV and β3 subunits. 1TM-αVβ3 is more compact and less active in solution when compared with ΔTM-αVβ3, which lacks the short C-terminal stretches. The structure reveals a bent conformation and defines the α–β interface between IE2 (EGF-like 2) and the thigh domains. Modifying this interface by site-directed mutagenesis leads to robust integrin activation. Fluorescent lifetime imaging microscopy of inactive full-length αVβ3 on live cells yields a donor–membrane acceptor distance, which is consistent with the bent conformation and does not change in the activated integrin. These data are the first direct demonstration of conformational coupling of the integrin leg and head domains, identify the IE2–thigh interface as a critical steric barrier in integrin activation, and suggest that inside-out activation in intact cells may involve conformational changes other than the postulated switch to a genu-linear state

Antenatal dexamethasone for early preterm birth in low-resource countries

BACKGROUND: The safety and efficacy of antenatal glucocorticoids in women in low-resource countries who are at risk for preterm birth are uncertain. METHODS: We conducted a multicountry, randomized trial involving pregnant women between 26 weeks 0 days and 33 weeks 6 days of gestation who were at risk for preterm birth. The participants were assigned to intramuscular dexamethasone or identical placebo. The primary outcomes were neonatal death alone, stillbirth or neonatal death, and possible maternal bacterial infection; neonatal death alone and stillbirth or neonatal death were evaluated with superiority analyses, and possible maternal bacterial infection was evaluated with a noninferiority analysis with the use of a prespecified margin of 1.25 on the relative scale. RESULTS: A total of 2852 women (and their 3070 fetuses) from 29 secondary- and tertiary-level hospitals across Bangladesh, India, Kenya, Nigeria, and Pakistan underwent randomization. The trial was stopped for benefit at the second interim analysis. Neonatal death occurred in 278 of 1417 infants (19.6%) in the dexamethasone group and in 331 of 1406 infants (23.5%) in the placebo group (relative risk, 0.84; 95% confidence interval [CI], 0.72 to 0.97; P=0.03). Stillbirth or neonatal death occurred in 393 of 1532 fetuses and infants (25.7%) and in 444 of 1519 fetuses and infants (29.2%), respectively (relative risk, 0.88; 95% CI, 0.78 to 0.99; P=0.04); the incidence of possible maternal bacterial infection was 4.8% and 6.3%, respectively (relative risk, 0.76; 95% CI, 0.56 to 1.03). There was no significant between-group difference in the incidence of adverse events. CONCLUSIONS: Among women in low-resource countries who were at risk for early preterm birth, the use of dexamethasone resulted in significantly lower risks of neonatal death alone and stillbirth or neonatal death than the use of placebo, without an increase in the incidence of possible maternal bacterial infection.Fil: Oladapo, Olufemi T.. Organizacion Mundial de la Salud; ArgentinaFil: Vogel, Joshua P.. Organizacion Mundial de la Salud; ArgentinaFil: Piaggio, Gilda. Organizacion Mundial de la Salud; ArgentinaFil: Nguyen, My-Huong. Organizacion Mundial de la Salud; ArgentinaFil: Althabe, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Metin Gülmezoglu, A.. Organizacion Mundial de la Salud; ArgentinaFil: Bahl, Rajiv. Organizacion Mundial de la Salud; ArgentinaFil: Rao, Suman P.N.. Organizacion Mundial de la Salud; ArgentinaFil: de Costa, Ayesha. Organizacion Mundial de la Salud; ArgentinaFil: Gupta, Shuchita. Organizacion Mundial de la Salud; ArgentinaFil: Shahidullah, Mohammod. No especifíca;Fil: Chowdhury, Saleha B.. No especifíca;Fil: Ara, Gulshan. No especifíca;Fil: Akter, Shaheen. No especifíca;Fil: Akhter, Nasreen. No especifíca;Fil: Dey, Probhat R.. No especifíca;Fil: Abdus Sabur, M.. No especifíca;Fil: Azad, Mohammad T.. No especifíca;Fil: Choudhury, Shahana F.. No especifíca;Fil: Matin, M.A.. No especifíca;Fil: Goudar, Shivaprasad S.. No especifíca;Fil: Dhaded, Sangappa M.. No especifíca;Fil: Metgud, Mrityunjay C.. No especifíca;Fil: Pujar, Yeshita V.. No especifíca;Fil: Somannavar, Manjunath S.. No especifíca;Fil: Vernekar, Sunil S.. No especifíca;Fil: Herekar, Veena R.. No especifíca;Fil: Bidri, Shailaja R.. No especifíca;Fil: Mathapati, Sangamesh S.. No especifíca;Fil: Patil, Preeti G.. No especifíca;Fil: Patil, Mallanagouda M.. No especifíca;Fil: Gudadinni, Muttappa R.. No especifíca;Fil: Bijapure, Hidaytullah R.. No especifíca;Fil: Mallapur, Ashalata A.. No especifíca;Fil: Katageri, Geetanjali M.. No especifíca;Fil: Chikkamath, Sumangala B.. No especifíca;Fil: Yelamali, Bhuvaneshwari C.. No especifíca;Fil: Pol, Ramesh R.. No especifíca;Fil: Misra, Sujata S.. No especifíca;Fil: Das, Leena. No especifíca

Methotrexate Induced Toxic Epidermal Necrolysis: A Case Report

Methotrexate continues to be one of the most widely used systemic immunosuppressive agents in dermatology. We describe a case of low-dose methotrexate (MTX) toxicity in a patient with chronic plaque psoriasis occurring during long-standing methotrexate therapy. This case report emphasizes the fact that co-medications like NSAIDS may exacerbate MTX toxicity. This patient had a rare methotrexate-induced toxic epidermal necrolysis including cutaneous ulceration. Hence, careful consideration of concomitant medication is required to avoid drug interactions for safe long-term methotrexate treatment

A Comprehensive Survey on Face Quality Detection in a Video Frame

The correctness of the generated face data, which is impacted by a number of variables, significantly affects how well face analysis and recognition systems perform. By automatically analysing the face data quality in terms of its biometric value, it might be able to identify low-quality data and take the necessary action. With a focus on visible wavelength face image input, this study summarises the body of research on the evaluation of face picture quality. The use of DL-based methods is unquestionably expanding, and there are major conceptual differences between them and current approaches, such as the inclusion of quality assessment in face recognition models. In addition to image selection, which is the topic of this article, face picture quality assessment can be used in a wide range of application scenarios. The requirement for comparative algorithm assessments and the difficulty of creating Deep Learning (DL) techniques that are intelligible in addition to providing accurate utility estimates are just a few of the issues and topics that remain unanswered. For each frame, the suggested method is compared to traditional facial feature extraction, and for a collection of video frames, it is compared to well-known clustering algorithms