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Background: The visual-aural-read/write-kinesthetic (VARK) questionnaire is a pre-eminent, accessible, and rigorously validated instrument that facilitates students in discerning their learning predilections. Nonetheless, the interrelation between VARK learning styles and scholastic achievement remains largely uncharted. Visual learners excel with diagrams, graphs, and flowcharts; auditory learners prefer aural information; read/write learners engage with textual material; and kinesthetic learners thrive through experiential learning and practical applications. This investigation endeavors to elucidate the correlation between predominant learning styles and the academic performance of 1st-year medical students in both theoretical and practical contexts. Aims and Objectives: The aims and objectives of the study are to scrutinize the correlation between distinct learning modalities (VARK) and academic performance among 1st-year medical students at GMERS Medical College. Materials and Methods: Subsequent to ethical sanction, 1st-year MBBS students from the 2021 cohort were solicited to partake in this cross-sectional, educational institution-based, temporally constrained study. Students who withheld consent were excluded. The investigation was executed in three phases. In the initial phase, the VARK 8.1 questionnaire was disseminated to 189 students to ascertain their learning styles, concomitant with the collection of demographic data. The VARK questionnaire encompasses 16 multiple-choice questions, each corresponding to a specific sensory learning modality. The modality with the highest score was designated as the student's preferred learning style. In the second phase, preliminary examination scores (theoretical and practical) for physiology, anatomy, and biochemistry were recorded. In the third phase, direct, in-depth interviews were conducted to rectify any data discrepancies or ambiguities regarding learning style preferences. Results: Among the participants, 61 out of 104 males and 51 out of 85 females exhibited a predilection for the kinesthetic learning style as their unimodal preference. The second most prevalent preference among males was visual, whereas females favored aural learning. Merely two males and no females identified as read/write learners. Correlation coefficient analysis revealed a robust association between visual learning scores and academic performance across both genders. High achievers predominantly favored the visual modality. Conversely, the aural and read/write modalities exhibited weak or negligible correlation with academic performance. Despite the prevalence of the kinesthetic modality among both male and female students, it only demonstrated a tenuous association with biochemistry scores. For male students, a moderate correlation was observed in anatomy and biochemistry. Conclusion: This study intimates that the preponderance of students prefers the kinesthetic learning style. Nevertheless, there exists a significant correlation between visual learning styles and academic performance in both male and female students. [ABSTRACT FROM AUTHOR]

Introduction: Gestational Diabetes Mellitus (GDM) affects maternal placental blood flow. As a compensatory mechanism for placental haemodynamic changes, blood flow is redistributed from peripheral vessels to the brain. This re-distribution can be evaluated using doppler Ultrasonography (USG) measurements of the umbilical arteries and foetal middle cerebral arteries. Aim: To compare doppler flow parameters {Pulsatility Index (PI), Resistance Index (RI), Peak Systolic Velocity (PSV), Systolic- To-Diastolic (S/D)} of the foetal Middle Cerebral Artery (MCA) in pregnancy with gestational diabetes versus normal pregnancy, and to compare pregnancy outcomes in GDM versus non-GDM women. Materials and Methods: A prospective cohort study was conducted from November 2019 to November 2021 at the Department of Obstetrics and Gynaecology, Department of Radiology, and Department of Paediatrics in Tertiary care centre of New Delhi, India. All pregnant women attending the Antenatal Care Outpatient Department (ANC OPD) upto 34 weeks with an Oral Glucose Tolerance Test (OGTT) ≥140 mg/dL were defined as cases, and those with OGTT <140 mg/dL were taken as controls. All patients underwent doppler USG after 34 completed weeks, and doppler flow parameters (PI, RI, PSV, S/D ratio) were noted. Patient outcomes were recorded, including maternal outcomes such as mode of delivery, pregnancy-induced hypertension, polyhydramnios, stillbirth, and Intrauterine Death (IUD). Foetal outcomes such as APGAR (Activity Pulse Grimace Appearance Respiration) score, hypoglycemia, hyperbilirubinemia, admission to the Neonatal Intensive Care Unit (NICU), Respiratory Distress Syndrome (RDS), and neonatal death were also documented. Appropriate statistical tests were used for the statistical analysis (Chi-square test, Fisher's exact test and Wilcoxon- Mann-Whitney test). Results: The mean age of GDM and non-GDM women was 27.40±3.57 years and 26.20±3.29 years, respectively. Among GDM women, 14 patients (31.1%) were primigravida, while there were 15 patients (33.3%) who were primigravida among non-GDM women. MCA PI was significantly higher in cases (1.79±0.36) compared to controls (1.65±0.19) (p-value=0.023), whereas MCA PSV was significantly lower in cases (45.86±2.17) than in controls (50.10±1.62) (p-value=0.001). Pregnancyinduced hypertension was observed in 28.9% of GDM women and 6.7% of non-GDM women, with the difference being statistically significant (p-value=0.006). There was a statistically significant difference between APGAR scores at 1 minute (p-value=0.030). However, there was no statistically significant difference in the APGAR scores at 5 minutes (p-value=0.242). Conclusion: Foetal MCA PI on doppler velocimetry was significantly higher in the GDM group, and MCA PSV was significantly lower in the study group, while MCA RI and MCA S/D ratio were not significantly different. Maternal complications were more common in GDM compared to non-GDM. [ABSTRACT FROM AUTHOR]

Since the outbreak, the novel coronavirus (COVID-19) has infected more than 180 million people and has taken a toll of 3.91 million lives globally as of June 2021. This virus causes symptoms like fever, cold, and fatigue, and can develop into Pneumonia which can be detected using chest X-rays (CXRs). Therefore, early detection of COVID-19 can help get early medical attention. However, a sudden rise in the number of cases in many countries caused by COVID waves increases the burden on their testing facilities. As a result, they sometimes fail to perform enough testing to contain the spread. This work proposes a deep learning model to detect COVID-19 and Pneumonia based on CXRs. The dataset for our COVID model contains a total of 3,400 CXRs images of COVID-19 patients and 3,400 normal CXRs. The dataset for our Pneumonia model contains 1,300 CXR images of Pneumonia patients and 1,300 normal CXRs. We use convolutional neural network provided by TensorFlow to build our model, which gave 94.17% and 93.55% accuracy for COVID model and Pneumonia model, respectively. Finally, we deployed our model on the web and added a web tracker, which gives us the cases, deaths, and recoveries state-wise and nationwide. [ABSTRACT FROM AUTHOR]