Sociodemographic factors associated with vaccine hesitancy in the South Asian community in Canada.

OBJECTIVE: South Asians represent the largest non-white ethnic group in Canada and were disproportionately impacted by the COVID-19 pandemic. We sought to determine the factors associated with vaccine hesitancy in South Asian Canadians. METHODS: We conducted a cross-sectional analysis of vaccine hesitancy using data collected at the baseline assessment of a prospective cohort study, COVID CommUNITY South Asian. Participants (18 + years) were recruited from the Greater Toronto and Hamilton Area in Ontario (ON) and the Greater Vancouver Area in British Columbia (BC) between April and November 2021. Demographic characteristics and vaccine attitudes measured by the Vaccine Attitudes Examination (VAX) scale were collected. Each item is scored on a 6-point Likert scale, and higher scores reflect greater hesitancy. A multivariable linear mixed effects model was used to identify sociodemographic factors associated with vaccine hesitancy, adjusting for multiple covariates. RESULTS: A total of 1496 self-identified South Asians (52% female) were analyzed (mean age = 38.5 years; standard deviation (SD): 15.3). The mean VAX score was 3.2, SD: 0.8 [range: 1.0‒6.0]. Factors associated with vaccine hesitancy included: time since immigration (p = 0.04), previous COVID-19 infection (p < 0.001), marital status (p < 0.001), living in a multigenerational household (p = 0.03), age (p = 0.02), education (p < 0.001), and employment status (p = 0.001). CONCLUSION: Among South Asians living in ON and BC, time since immigration, prior COVID-19 infection, marital status, living in a multigenerational household, age, education, and employment status were associated with vaccine hesitancy. This information can be used to address vaccine hesitancy in the South Asian population in future COVID-19 waves or pandemics.

RéSUMé: OBJECTIF: Les Asiatiques du Sud, qui représentent le plus grand groupe ethnique non-blanc au Canada, ont été démesurément touchés par la pandémie de COVID-19. Nous avons cherché à déterminer les facteurs associés à l'hésitation vaccinale chez les Canadiennes et les Canadiens asiatiques du Sud. MéTHODE: Nous avons mené une analyse transversale de l'hésitation vaccinale à l'aide des données collectées durant l'évaluation préliminaire d'une étude de cohorte prospective du nom de COVID CommUNITY South Asian. Les personnes participantes (18 ans et plus) ont été recrutées dans la région du grand Toronto et de Hamilton, en Ontario, et dans la région du Grand Vancouver, en Colombie-Britannique, entre avril et novembre 2021. Le profil démographique et les attitudes face aux vaccins, mesurées selon l'échelle Vaccine Attitudes Examination (VAX), ont été obtenus. Chaque élément a été noté selon une échelle de Likert en 6 points (plus la note est élevée, plus l'hésitation vaccinale est importante). Un modèle linéaire multivarié à effets mixtes a servi à identifier les facteurs sociodémographiques associés à l'hésitation vaccinale, en rajustant les données pour tenir compte de plusieurs covariables. RéSULTATS: En tout, 1 496 personnes s'identifiant comme étant Asiatiques du Sud (dont 52 % de femmes) ont été analysées (âge moyen = 38,5 ans; écart-type [S] : 15,3). La note VAX moyenne était de 3,2, S : 0,8 [intervalle : 1,0‒6,0]. Les facteurs associés à l'hésitation vaccinale étaient : le temps écoulé depuis l'immigration (p = 0,04), une infection antérieure par la COVID-19 (p < 0,001), l'état matrimonial (p < 0,001), le fait de vivre dans un ménage multigénérationnel (p = 0,03), l'âge (p = 0,02), l'instruction (p < 0,001) et la situation d'emploi (p = 0,001). CONCLUSION: Chez les Asiatiques du Sud vivant en Ontario et en Colombie-Britannique, le temps écoulé depuis l'immigration, une infection antérieure par la COVID-19, l'état matrimonial, le fait de vivre dans un ménage multigénérationnel, l'âge, l'instruction et la situation d'emploi étaient associés à l'hésitation vaccinale. Ces informations peuvent être utilisées pour aborder l'hésitation vaccinale dans la population asiatique du Sud lors de vagues de COVID-19 ou de pandémies futures.

A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness.

OBJECTIVES: We evaluated methods for preparing electronic health record data to reduce bias before applying artificial intelligence (AI). METHODS: We created methods for transforming raw data into a data framework for applying machine learning and natural language processing techniques for predicting falls and fractures. Strategies such as inclusion and reporting for multiple races, mixed data sources such as outpatient, inpatient, structured codes, and unstructured notes, and addressing missingness were applied to raw data to promote a reduction in bias. The raw data was carefully curated using validated definitions to create data variables such as age, race, gender, and healthcare utilization. For the formation of these variables, clinical, statistical, and data expertise were used. The research team included a variety of experts with diverse professional and demographic backgrounds to include diverse perspectives. RESULTS: For the prediction of falls, information extracted from radiology reports was converted to a matrix for applying machine learning. The processing of the data resulted in an input of 5,377,673 reports to the machine learning algorithm, out of which 45,304 were flagged as positive and 5,332,369 as negative for falls. Processed data resulted in lower missingness and a better representation of race and diagnosis codes. For fractures, specialized algorithms extracted snippets of text around keywork "femoral" from dual x-ray absorptiometry (DXA) scans to identify femoral neck T-scores that are important for predicting fracture risk. The natural language processing algorithms yielded 98% accuracy and 2% error rate The methods to prepare data for input to artificial intelligence processes are reproducible and can be applied to other studies. CONCLUSION: The life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. When applying artificial intelligence methods, input data must be prepared optimally to reduce algorithmic bias, as biased output is harmful. Building AI-ready data frameworks that improve efficiency can contribute to transparency and reproducibility. The roadmap for the application of AI involves applying specialized techniques to input data, some of which are suggested here. This study highlights data curation aspects to be considered when preparing data for the application of artificial intelligence to reduce bias.

Prioritizing sexual and reproductive health research and care for people with cystic fibrosis: A 2023 workshop report from the Cystic Fibrosis Foundation Sexual Health, Reproduction, and Gender (SHARING) Research Working Group.

BACKGROUND: To address sexual and reproductive health (SRH) concerns among people with cystic fibrosis(PwCF), the CF Foundation created the Sexual Health, Reproduction, and Gender Research (SHARING) Working Group. This report summarizes CF community SRH research priorities and workshop discussions/future study planning. METHODS: Pre-workshop, we distributed a community prioritization survey on CF SRH research/care. During the workshop, we used results and reviewed existing research to establish research priorities and design studies to address identified knowledge gaps. RESULTS: A total of 303 respondents (85 % PwCF, 15 % caregivers) completed the survey. Highly-rated SRH topics were: 1) effects of CF modulator therapy on sex hormones; 2) effects of sex hormones on CF; 3) fertility; 4) pregnancy; and 5) SRH/mental health. Twenty-four workshop participants established the need for further research on sex hormones and CF, optimizing SRH care provision, and fertility/ART. CONCLUSION: SRH is an important and emerging area in CF and thoughtful consideration of community perspectives can ensure that future research is relevant and responsive.

Engaging Participants Through Hybrid Community-Centered Approaches: Lessons Learned During the COVID CommUNITY Public Health Research Program.

Community-centered research studies can improve trust, cultural appropriateness, and accurate findings through meaningful, in-depth engagement with participants. During the COVID-19 pandemic, researchers shifted to implement pandemic-specific guidelines on top of already existing safety practices; these adjustments gave insight into bettering the structure of forthcoming research studies. At the Population Health Research Institute (PHRI)/McMaster University, the COVID CommUNITY study staff took field notes from their experience at the Ontario (ON) and British Columbia (BC) sites navigating an observational prospective cohort study during the pandemic. These field notes are outlined below to provide insight into culturally responsive, trust-centered, and communication-focused strategies used to improve hybrid research. A significant challenge the team overcame was obtaining blood sample collections by executing socially distanced sample collections outside of participants' homes, coined "Porch Pickups." Data collection was made more accessible through phone surveys and frequent virtual contact. To enhance recruitment strategies for sub-communities of the South Asian population, staff focused on cultural interests and "gift-exchange" incentives. Cultural awareness was prioritized through correct name pronunciation, conducting data collection in participant preferred languages, and using flexible approaches to data collection. These strategies were developed through weekly team meetings where improvement strategies were discussed, and concerns were addressed in real-time.

New advances in type 1 diabetes.

Type 1 diabetes is an autoimmune condition resulting in insulin deficiency and eventual loss of pancreatic ß cell function requiring lifelong insulin therapy. Since the discovery of insulin more than 100 years ago, vast advances in treatments have improved care for many people with type 1 diabetes. Ongoing research on the genetics and immunology of type 1 diabetes and on interventions to modify disease course and preserve ß cell function have expanded our broad understanding of this condition. Biomarkers of type 1 diabetes are detectable months to years before development of overt disease, and three stages of diabetes are now recognized. The advent of continuous glucose monitoring and the newer automated insulin delivery systems have changed the landscape of type 1 diabetes management and are associated with improved glycated hemoglobin and decreased hypoglycemia. Adjunctive therapies such as sodium glucose cotransporter-1 inhibitors and glucagon-like peptide 1 receptor agonists may find use in management in the future. Despite these rapid advances in the field, people living in under-resourced parts of the world struggle to obtain necessities such as insulin, syringes, and blood glucose monitoring essential for managing this condition. This review covers recent developments in diagnosis and treatment and future directions in the broad field of type 1 diabetes.

Exploring Attitudes Toward "Sugar Relationships" Across 87 Countries: A Global Perspective on Exchanges of Resources for Sex and Companionship.

The current study investigates attitudes toward one form of sex for resources: the so-called sugar relationships, which often involve exchanges of resources for sex and/or companionship. The present study examined associations among attitudes toward sugar relationships and relevant variables (e.g., sex, sociosexuality, gender inequality, parasitic exposure) in 69,924 participants across 87 countries. Two self-report measures of Acceptance of Sugar Relationships (ASR) developed for younger companion providers (ASR-YWMS) and older resource providers (ASR-OMWS) were translated into 37 languages. We tested cross-sex and cross-linguistic construct equivalence, cross-cultural invariance in sex differences, and the importance of the hypothetical predictors of ASR. Both measures showed adequate psychometric properties in all languages (except the Persian version of ASR-YWMS). Results partially supported our hypotheses and were consistent with previous theoretical considerations and empirical evidence on human mating. For example, at the individual level, sociosexual orientation, traditional gender roles, and pathogen prevalence were significant predictors of both ASR-YWMS and ASR-OMWS. At the country level, gender inequality and parasite stress positively predicted the ASR-YWMS. However, being a woman negatively predicted the ASR-OMWS, but positively predicted the ASR-YWMS. At country-level, ingroup favoritism and parasite stress positively predicted the ASR-OMWS. Furthermore, significant cross-subregional differences were found in the openness to sugar relationships (both ASR-YWMS and ASR-OMWS scores) across subregions. Finally, significant differences were found between ASR-YWMS and ASR-OMWS when compared in each subregion. The ASR-YWMS was significantly higher than the ASR-OMWS in all subregions, except for Northern Africa and Western Asia.

Frequency of dysnatremia in patients admitted with COVID-19 infection and its prognostic implication.

OBJECTIVE: We aimed to investigate the frequency of dysnatremia among patients admitted with COVID-19 infection and its association with inpatient mortality. METHODS: This retrospective longitudinal study was conducted for 12 weeks. Serum sodium levels were recorded at admission, during the hospital stay, and within 48 hours of discharge or death. Logistic regression was used to determine the predictors of mortality. RESULTS: This study included 574 patients (69.7% men, age 55.6 ± 14.4 years). On admission, mean sodium was 135.9 ± 6.4 mEq/L; 39% had hyponatremia and 4.7% had hypernatremia. During admission, hypernatremia increased to 18.8%; maximum sodium in patients who survived was 140.6 ± 5.0 mEq/L versus 151.0 ± 9.9 mEq/L in those who died. The final sodium was 145.4 ± 9.4 mEq/L in patients who died versus 137.7 ± 3.7 mEq/L in those who survived (odds ratio [OR]: 1.22, 95% confidence interval [CI]: 1.13-1.32). Other predictors of mortality included ischemic heart disease (OR: 3.65, 95% CI: 1.39-9.61), acute kidney injury (OR: 6.07, 95% CI: 2.39-15.42), invasive ventilation (OR: 28.4, 95% CI: 11.14-72.40), and length of stay (OR: 0.91, 95% CI: 0.86-0.97). CONCLUSION: Hypernatremia was frequently observed in patients who were critically ill and died and may be considered a predictor of mortality in COVID-19 infection.

Synthesis and HDAC1 inhibitory activity of a novel series of coumarin-based amide derivatives for treatment of cancer.

Background: Histone deacetylases (HDACs) play a vital role in the epigenetic regulation of transcription and expression. HDAC1 overexpression is seen in many cancers. Methodology: The authors synthesized and evaluated 27 novel coumarin-based amide derivatives for HDAC1 inhibitory activity. The compounds were screened at the US National Cancer Institute, and 5k and 5u were selected for five-dose assays. Compound 5k showed GI50 values of 0.294 and 0.264 µM against MOLT-4 and LOX-IMVI, respectively; whereas 5u had GI50 values of 0.189 and 0.263 µM, respectively. Both derivatives showed better activity than entinostat and suberoylanilide hydroxamic acid. Compound 5k exhibited an IC50 value of 1.00 µM on ACHN cells. Conclusion: Coumarin derivatives exhibited promising HDAC1 inhibitory potential and warrant future development as anticancer agents.

A Roadmap to Artificial Intelligence (AI): Methods for Designing and Building AI ready Data for Women's Health Studies.

Objectives: Evaluating methods for building data frameworks for application of AI in large scale datasets for women's health studies. Methods: We created methods for transforming raw data to a data framework for applying machine learning (ML) and natural language processing (NLP) techniques for predicting falls and fractures. Results: Prediction of falls was higher in women compared to men. Information extracted from radiology reports was converted to a matrix for applying machine learning. For fractures, by applying specialized algorithms, we extracted snippets from dual x-ray absorptiometry (DXA) scans for meaningful terms usable for predicting fracture risk. Discussion: Life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. For applying AI, data must be prepared optimally to reduce algorithmic bias. Conclusion: Algorithmic bias is harmful for research using AI methods. Building AI ready data frameworks that improve efficiency can be especially valuable for women's health.

Oral administration of Nigella sativa oil attenuates arsenic-induced redox imbalance, DNA damage, metabolic distress, and histopathological alterations in rat intestine.

BACKGROUND: Exposure to arsenic, a widespread environmental toxin, produces multiple organ toxicity, including gastrointestinal toxicity. Nigella sativa (NS) has long been revered for its numerous health benefits under normal and pathological states. In view of this, the present study attempts to evaluate the protective efficacy of orally administered Nigella sativa oil (NSO) against arsenic-induced cytotoxic and genotoxic alterations in rat intestine and elucidate the underlying mechanism of its action. METHODS: Rats were categorized into the control, NaAs, NSO, and NaAs+NSO groups. After pre-treatment of rats in the NaAs+NSO and NSO groups daily with NSO (2 ml/kg bwt, orally) for 14 days, NSO treatment was further continued for 30 days, with and without NaAs treatment (5 mg/kg bwt, orally), respectively. Various biochemical parameters, such as enzymatic and non-enzymatic antioxidants, carbohydrate metabolic and brush border membrane marker enzyme activities were evaluated in the mucosal homogenates of all the groups. Intestinal brush border membrane vesicles (BBMV) were isolated, and the activities of membrane marker enzyme viz. ALP, GGTase, LAP, and sucrase were determined. Further, the effect on kinetic parameters viz KM (Michaelis-Menten constant) and Vmax of these enzymes was assessed. Integrity of enterocyte DNA was examined using the comet assay. Histopathology of the intestines was performed to evaluate the histoarchitectural alterations induced by chronic arsenic exposure and/or NSO supplementation. Arsenic accumulation in the intestine was studied by inductively coupled plasma-mass spectroscopy (ICP-MS). RESULTS: NaAs treatment caused substantial changes in the activities of brush border membrane (BBM), carbohydrate metabolism, and antioxidant defense enzymes in the intestinal mucosal homogenates. The isolated BBM vesicles (BBMV) also showed marked suppression in the marker enzyme activities. Severe DNA damage and mucosal arsenic accumulation were observed in rats treated with NaAs alone. In contrast, oral NSO supplementation significantly alleviated all the adverse alterations induced by NaAs treatment. Histopathological examination supported the biochemical findings. CONCLUSION: NSO, by improving the antioxidant status and energy metabolism, could significantly alter the ability of the intestine to protect against free radical-mediated arsenic toxicity in intestine. Thus, NSO may have an excellent scope in managing gastrointestinal distress in arsenic intoxication.

A culturally tailored personaliseD nutrition intErvention in South ASIan women at risk of Gestational Diabetes Mellitus (DESI-GDM): a randomised controlled trial protocol.

INTRODUCTION: South Asians are more likely to develop gestational diabetes mellitus (GDM) than white Europeans. Diet and lifestyle modifications may prevent GDM and reduce undesirable outcomes in both the mother and offspring. Our study seeks to evaluate the effectiveness and participant acceptability of a culturally tailored, personalised nutrition intervention on the glucose area under the curve (AUC) after a 2-hour 75 g oral glucose tolerance test (OGTT) in pregnant women of South Asian ancestry with GDM risk factors. METHODS AND ANALYSIS: A total of 190 South Asian pregnant women with at least 2 of the following GDM risk factors-prepregnancy body mass index>23, age>29, poor-quality diet, family history of type 2 diabetes in a first-degree relative or GDM in a previous pregnancy will be enrolled during gestational weeks 12-18, and randomly assigned in a 1:1 ratio to: (1) usual care, plus weekly text messages to encourage walking and paper handouts or (2) a personalised nutrition plan developed and delivered by a culturally congruent dietitian and health coach; and FitBit to track steps. The intervention lasts 6-16 weeks, depending on week of recruitment. The primary outcome is the glucose AUC from a three-sample 75 g OGTT 24-28 weeks' gestation. The secondary outcome is GDM diagnosis, based on Born-in-Bradford criteria (fasting glucose>5.2 mmol/L or 2 hours post load>7.2 mmol/L). ETHICS AND DISSEMINATION: The study has been approved by the Hamilton Integrated Research Ethics Board (HiREB #10942). Findings will be disseminated among academics and policy-makers through scientific publications along with community-orientated strategies. TRIAL REGISTRATION NUMBER: NCT03607799.

Performance of a modified fracture risk assessment tool for fragility fracture prediction among older veterans living with HIV.

OBJECTIVE: Fragility fractures (fractures) are a critical outcome for persons aging with HIV (PAH). Research suggests that the fracture risk assessment tool (FRAX) only modestly estimates fracture risk among PAH. We provide an updated evaluation of how well a 'modified FRAX' identifies PAH at risk for fractures in a contemporary HIV cohort. DESIGN: Cohort study. METHODS: We used data from the Veterans Aging Cohort Study to evaluate veterans living with HIV, aged 50+ years, for the occurrence of fractures from 1 January 2010 through 31 December 2019. Data from 2009 were used to evaluate the eight FRAX predictors available to us: age, sex, BMI, history of previous fracture, glucocorticoid use, rheumatoid arthritis, alcohol use, and smoking status. These predictor values were then used to estimate participant risk for each of two types of fractures (major osteoporotic and hip) over the subsequent 10 years in strata defined by race/ethnicity using multivariable logistic regression. RESULTS: Discrimination for major osteoporotic fracture was modest [Blacks: area under the curve (AUC) 0.62; 95% confidence interval (CI) 0.62, 0.63; Whites: AUC 0.61; 95% CI 0.60, 0.61; Hispanic: AUC 0.63; 95% CI 0.62, 0.65]. For hip fractures, discrimination was modest to good (Blacks: AUC 0.70; 95% CI 0.69, 0.71; Whites: AUC 0.68; 95% CI 0.67, 0.69]. Calibration was good in all models across all racial/ethnic groups. CONCLUSION: Our 'modified FRAX' exhibited modest discrimination for predicting major osteoporotic fracture and slightly better discrimination for hip fracture. Future studies should explore whether augmentation of this subset of FRAX predictors results in enhanced prediction of fractures among PAH.

Challenges Training Women in Neurology.

The challenges that current women neurologists in training face remain similar to a certain extent to those experienced by their women neurologists' mentors. The key challenge that often coincides with residency or fellowship is childbearing. This has the potential to prolong training time and delaying graduation. Women neurologists in training face additional challenges of juggling between family responsibilities--as caregivers--and professional roles.

Protein Phosphatase 2A Regulates Phenotypic and Metabolic Alteration of Microglia Cells in HFD-Associated Vascular Dementia Mice via TNF-α/Arg-1 Axis.

Protein phosphatase 2A (PP2A), the activity of which is dictated by the composition of its regulatory subunit, is strongly related to the progression of neurodegenerative disease. The potential role of PP2A on the phenotypic transition of microglial cells under obese conditions is poorly explored. An understanding of the role of PP2A and identification of regulatory subunits contributing to microglial phenotypic transitions in obese condition may serve as a therapeutic target for obesity-associated neurodegeneration. C57BL/6 mice were exposed to obese-associated vascular dementia conditions by performing unilateral common carotid artery occlusion on obese mice of microglial polarization and PP2A activity using flow cytometry, real-time PCR, western blotting, and immunoprecipitation enzymatic assay, followed identifications of PP2A regulatory subunits using LCMS and RT-PCR. Chronic HFD feeding significantly increased the populations of infiltrated macrophages, showing a high percentage of CD86+ in VaD mice, and the expression of pro-inflammatory cytokines, and we observed that PP2A modulates metabolic reprogramming of microglia by regulating OXPHOS/ECAR activity. Using Co-IP and LCMS, we identified the six specific regulatory subunits, namely PPP2R2A, PPP2R2D, PPP2R5B, PPP2R5C, PPP2R5D, and PPP2R5E, that are associated with microglial-activation during obesity-associated-VaD. Interestingly, pharmacological up-regulation of PP2A more significantly suppressed the expression of TNF-alpha than other pro-inflammatory-cytokines and increased the expression of Arginase-1, suggesting that PP2A modulates microglial-phenotypic transitions through TNF-α/Arg-1 axis. Our present findings demonstrate microglial polarization in HFD associated with VaD, and point towards a therapeutic target by providing specific PP2A regulatory-subunits implicated in microglial activation during obesity-related-vascular-dementia.

Decreased hospital readmissions after programmatic strengthening of an outpatient parenteral antimicrobial therapy (OPAT) program.

Objective: To determine whether a structured OPAT program supervised by an infectious disease physician and led by an OPAT nurse decreased hospital readmission rates and OPAT-related complications and whether it affected clinical cure. We also evaluated predictors of readmission while receiving OPAT. Patients: A convenience sample of 428 patients admitted to a tertiary-care hospital in Chicago, Illinois, with infections requiring intravenous antibiotic therapy after hospital discharge. Methods: In this retrospective, quasi-experimental study, we compared patients discharged on intravenous antimicrobials from an OPAT program before and after implementation of a structured ID physician and nurse-led OPAT program. The preintervention group consisted of patients discharged on OPAT managed by individual physicians without central program oversight or nurse care coordination. All-cause and OPAT-related readmissions were compared using the χ2 test. Factors associated with readmission for OPAT-related problems at a significance level of P < .10 in univariate analysis were eligible for testing in a forward, stepwise, multinomial, logistic regression to identify independent predictors of readmission. Results: In total, 428 patients were included in the study. Unplanned OPAT-related hospital readmissions decreased significantly after implementation of the structured OPAT program (17.8% vs 7%; P = .003). OPAT-related readmission reasons included infection recurrence or progression (53%), adverse drug reaction (26%), or line-associated issues (21%). Independent predictors of hospital readmission due to OPAT-related events included vancomycin administration and longer length of outpatient therapy. Clinical cure increased from 69.8% before the intervention to 94.9% after the intervention (P < .001). Conclusion: A structured ID physician and nurse-led OPAT program was associated with a decrease in OPAT-related readmissions and improved clinical cure.

Design, synthesis and evaluation of 5-chloro-6-methylaurone derivatives as potential anti-cancer agents.

A series of novel 5-chloro-6-methylaurone derivatives (6a-p) were synthesized and characterized by various spectroscopic techniques. The synthesized compounds were tested for anticancer activity against 60-human cancer cell line panel derived from nine cancer types at NCI, Bethesda, USA. Among the synthesized compounds, six compounds (6e, 6f, 6h, 6i, 6k and 6 m) exhibited growth inhibition and cytotoxic activity against various human cancer cell lines in one-dose data. The most potent compound among the series, 6i was active against 55 out of 60 human cancer cell lines. Compound 6i showed remarkable % growth inhibition and cytotoxicity against various cancer cell lines exhibiting % GI in the range 36.05-199.03. The compound 6i was further evaluated for five dose assay and exhibited GI50 1.90 µM and 2.70 µM against melanoma and breast cancer cell lines respectively. Further evaluation of 6i for five-dose assay exhibited a diverse spectrum of anti-cancer activity towards all the 60 human cancer cell line panel with the selectivity index ratio ranging 0.854-1.42 and 0.66-1.35 for GI50 and TGI respectively. Based on one-dose and five-dose data compound 6i was further evaluated for cell apoptosis against MDA-MB-468 breast cancer cell line and was found to induce early apoptosis in cells explaining its mode of action. The in-silico studies for the synthesized compounds as LSD1 inhibitors (2H94) have shown better docking score and binding energy comparable to vafidemstat. All the compounds followed Lipinski rule of five. These findings concluded that the compound 6i could lead to the development of a promising therapeutic anticancer agent.

Prescribing practices of Hormone Therapy for Menopausal females by OBGYN of Pakistan.

Objectives: To assess the prescribing practice of obstetricians and gynecologists (OBGYN) regarding Menopausal Hormone therapy (MHT) for menopausal females and assess the knowledge regarding indications for prescribing MHT and identify the barriers to HRT. Methods: This was a cross-sectional study conducted from May 2021 to December 2021. The participants were OBGYN experts (MCPS, FCPS, MRCOG) and senior experts. The tool was formulated after looking at contemporary literature and then validated by experts for face, content and construct validity. It was piloted and hence fourth given to study participants after approval by IRB of JSMU. Data was analyzed by SPSS version 22. Mean and Standard Deviation of categories in Likert scale were calculated. Results: Majority of the participant gynecologists prescribed HRT for Hot flushes and vaginal dryness having the highest mean scores nearing 4 on a Likert scale of (1-5). The prescription for other menopausal symptoms was observed to be less. The highest score for category of women in whom hormone therapy is specifically justified was "Premature ovarian failure" (4.37) followed by "Hysterectomy with bilateral oophorectomy before the age of 50" (4.23). Conclusion: Pakistani gynecologists are more cautious in their management strategies concerning MHT. Most of the gynecologists showed good and up to date information while prescribing MHT however knowledge for preventing fractures, alleviating anxiety/depression and weight gain was less among the gynecologist of Pakistan. We recommend refresher courses and online webinars for updated information on menopause and its management.

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care.

Introduction: A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data. Method: This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction. Results: The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration. Discussion: Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.