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Introduction: The lesser trochanter profile (LTP) method is an intraoperative fluoroscopic technique that can assess the femoral version and limit malrotation. The purpose of this study was to directly assess the accuracy and reliability of the LTP method, as well as determine the incidence of malrotation produced by this technique. Methods: Three groups of observers (fellowship-trained orthopedic surgeons, orthopedic residents, and medical students) utilized the LTP method to replicate pre-imaged rotation angles on a cadaveric femur bone. Recorded outcomes include rotational error and number of attempts. Accuracy and interobserver reliability were assessed by rotational error and the interclass correlation coefficient (ICC), respectively. Results: Accuracy was within 3° for all three groups. ICC between each group was greater than 0.99. There was no statistical difference between the accuracy of fellowship-trained surgeons, orthopedic residents, and medical students. Medical students on average required more attempts to obtain their final image compared to fellowship-trained surgeons. There was no statistical difference in the number of attempts between residents and fellowship-trained surgeons. Conclusion: None of the LTP measurements were greater than 15°, the clinical threshold for malrotation. The average error of the observers was less than 3°, demonstrating that the LTP is an effective method of assessing the femoral version. There was no statistically significant difference between the observers, indicating that this technique is reliable and easy to use. Ultimately, the LTP method is easily reproducible for surgeons to avoid femoral malrotation.

The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset ( $ \lt$ 200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R ^2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.

Introduction: Hyperinsulinemia in the absence of impaired glucose tolerance and normal HbA1c is considered indicative of pre-diabetes. Very few Indian studies have focused on hyperinsulinemia particularly in young adults. The present study aimed to determine whether hyperinsulinemia may be present despite HbA1c being normal., Methods: This was a cross-sectional study conducted on adolescents and young adults aged 16-25 years living in Mumbai, India. The participants attended various academic institutions and were those who underwent screening as the first step of a clinical trial for studying the efficacy of almond intake in prediabetes., Results: Among this young population (n=1313), 4.2% (n=55) of the participants were found to be prediabetic (ADA criteria) and 19.7% of them had HbA1c levels between 5.7%-6.4%. However, almost, 30.5% had hyperinsulinemia inspite of normal blood glucose levels and normal HbA1c. Among those with HbA1c<5.7 (n=533), 10.5% (n=56) participants had fasting insulin>15 mIU/L and a higher percentage (39.4%, n=260) had stimulated insulin above 80 mIU/L. These participants had higher mean anthropometric markers than those with normal fasting and/or stimulated insulin., Conclusion: Hyperinsulinaemia in the absence of impaired glucose tolerance and normal HbA1c may provide a much earlier indicator of detection for risk of metabolic disease and progression to metabolic syndrome and diabetes mellitus., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Vaidya, Desai, Moitra, Salis, Agashe, Battalwar, Mehta, Madan, Kalita, Udipi and Vaidya.)

Background & Objectives: Polycystic Ovary Syndrome (PCOS) is becoming an area of global and national health concern. It requires a life cycle approach from adolescence to menopause. To comprehensively address the wide spectrum of this disorder, a multidisciplinary model of care was established for women with PCOS in a government setting in India with an objective to screen and manage multifaceted manifestations of PCOS and to diagnose and treat associated comorbidities such as metabolic syndrome, dermatologic manifestations and psychological issues., Methods: A model of integrated multidisciplinary PCOS clinic was implemented for services and research at ICMR-National Institute for Research in Reproductive and Child Health (NIRRCH), Mumbai Maharashtra, India. This is a one-stop holistic centre for managing menstrual, cosmetic, infertility, obesity, metabolic and psychological concerns of women affected with PCOS. Two hundred and twenty six women diagnosed with PCOS using the Rotterdam criteria were screened for metabolic comorbidities with anthropometry, ultrasonography, hormonal and biochemical tests and for psychological problems. Analysis was performed using SPSS version 19.0., Results: Mean body mass index (BMI) was 26.1 kg/m 2 , higher for Asians. Hirsutism was observed in 53.6 per cent of women. Metabolic syndrome was seen among 35.3 per cent and non-alcoholic fatty liver in 18.3 per cent. Psychological issues such as anxiety and depression were identified in majority of the women 31.4 per cent of women could achieve pregnancy at the end of one year of multidisciplinary management., Interpretation & Conclusions: The results of the present study suggest that an integrated multidisciplinary approach led to the early identification and treatment of comorbidities of PCOS, especially metabolic syndrome. There is hence an urgent need to implement multidisciplinary PCOS clinics in government health facilities.