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Aim Method Results Conclusion To explore perceived expressed emotion in the south Asian context for individuals with a first episode of psychosis (FEP).Semi‐structured interviews were conducted with 16 service users experiencing a FEP to understand their experience of expressed emotion (EE) from their caregivers. Interviews were analysed using inductive thematic analysis.Four main categories were identified: connection and support, understanding and awareness, boundaries and independence and context and influence. Factors influencing perceived expressed emotion such as acceptance, acculturation, warmth and expressions of love, communication and family values were identified. Findings highlight south Asian's experiences of being cared for, and their perception of EE, including warmth and connection as a strength and resource.The findings shed light on culturally specific EE within the context of FEP that can be considered when working with south Asian communities within early intervention services. Findings highlight the impact of navigating and negotiating bicultural identities and generational differences in EE in the British south Asian context. [ABSTRACT FROM AUTHOR]

Context Identification of insulin resistance (IR) in South Asians, who are at a higher risk for type 2 diabetes, is important. Lack of standardization of insulin assays limits the clinical use of insulin-based surrogate indices. The lipoprotein insulin resistance index (LP-IR), a metabolomic marker, reflects the lipoprotein abnormalities observed in IR. The reliability of the LP-IR index in South Asians is unknown. Objective We evaluated the predictive accuracy of LP-IR compared with other IR surrogate indices in South Asians. Methods In a cross-sectional study (n = 55), we used calibration model analysis to assess the ability of the LP-IR score and other simple surrogate indices (Homeostatic Model Assessment of Insulin Resistance, Quantitative insulin sensitivity check index, Adipose insulin resistance index, and Matsuda Index) to predict insulin sensitivity (SI) derived from the reference frequently sampled intravenous glucose tolerance test. LP-IR index was derived from lipoprotein particle concentrations and sizes measured by nuclear magnetic resonance spectroscopy. Predictive accuracy was determined by root mean squared error (RMSE) of prediction and leave-one-out cross-validation type RMSE of prediction (CVPE). The optimal cut-off of the LP-IR index was determined by the area under the receiver operating characteristic curve (AUROC) and the Youden index. Results The simple surrogate indices showed moderate correlations with SI (r = 0.53-0.69, P <.0001). CVPE and RMSE were not different in any of the surrogate indices when compared with LP-IR. The AUROC was 0.77 (95% CI 0.64-0.89). The optimal cut-off for IR in South Asians was LP-IR >48 (sensitivity: 75%, specificity: 70%). Conclusion The LP-IR index is a simple, accurate, and clinically useful test to assess IR in South Asians. [ABSTRACT FROM AUTHOR]

The term 'trailblazer' is often used casually nowadays, but in the early twentieth century, there was a woman who epitomized it by shattering barriers at every turn. Dr. Muthulakshmi Reddy who holds many firsts to her name was an outstanding Indian woman of her time. She was an eminent medical practitioner who wore multiple hats throughout her life as an educator, a lawmaker, and a social reformer. She sacrificed her lifetime for women's upliftment and children especially the underprivileged. She played a pivotal role in establishing one of India's largest cancer institutes as a 'mission' to provide treatment among all sections of people regardless of their socio-economic background. The main purpose of this article is to highlight the indisputable contribution of Dr. Muthulakshmi Reddy in the fields of medicine, education, law, and much more., Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, R et al.)

Purpose: Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research., Methods: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States., Results: A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents., Conclusions: A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models., (Published by Oxford University Press 2023.)