Stem cell-derived pancreatic beta cells: a step closer to functional diabetes treatment?

ABSTRACT: Diabetes remains one of the significant health struggles worldwide, leading to disability and mortality. There are several types, with Types 1 and 2 representing the majority of cases. Pancreatic beta cells play a key role in glucose control through the secretion of insulin. Insulin, a key player, is under-secreted in diabetes due to autoimmune destruction of the beta cells in type 1 diabetes and exhaustion of beta cell secretion in type 2. Hence, insulin plays a central role in the management of both conditions. Lifestyle modifications and pharmacological agents are significant components of managing diabetes, although they come with limitations; hence, the exploration of stem cells in diabetes management. A thorough literature search was conducted across several databases, identifying randomized and non-randomized controlled trials that utilized stem cells in patients with diabetes. The intervention details, primary and secondary outcomes, key findings, and safety profiles were documented and discussed. We explored key protocols and methods for generating pancreatic beta-like cells from stem cells, as well as the role of specific molecules and pathways in stem cell differentiation. In this paper, we also discuss preclinical animal studies, explore the challenges of immunogenicity, and address ethical concerns that limit the implementation of stem cells in clinical practice. A comparative analysis was conducted to evaluate conventional insulin therapy, islet transplantation, and stem cell-based approaches. Although stem cells represent a potentially valuable direction, their application clinically remains mainly experimental, and future studies should incorporate larger cohorts, diverse populations with varying comorbidities, and extended follow-up periods to better ascertain their long-term efficacy and safety. CLINICAL TRIAL NUMBER: Not applicabl

Standardization of Myasthenia Gravis Outcome Measures in Clinical Practice. A Report of the MGFA Task Force

INTRODUCTION/AIMS: Myasthenia gravis (MG) specific outcome measures are being used in clinical trials to evaluate therapeutic effectiveness. These validated tools are also becoming a necessity in clinical practice, with payors in the US market often requiring them to be used to monitor disease state. There is considerable variation and subjectivity regarding their use. This study aimed to develop consensus-based recommendations for the standardization of MG specific outcome measures in clinical practice. METHODS: A panel of 10 US-based MG specialists developed consensus-based recommendations based on three rounds of formal voting using the UCLA-RAND appropriateness method after surveying myasthenia gravis clinicians and developing a focus group. RESULTS: Twenty one expert consensus statements based on six themes were developed following clinician survey result review and focus group theme development. Some key recommendations include: the MGFA Clinical Classification assesses disease at that examination and should be updated at intervals of 3-6 months to reflect current clinical status. MGFA PIS represents the overall clinical judgment of the evaluator without the requirement for a defined change in scores on any outcome measure. Patient-reported items, such as MG-ADL and MGC, should be referenced to the previous 1 week to optimize recall. Additional recommendations include scoring outcome measures in the presence of co-morbidity, scoring specific physical exam findings, and clarification regarding the administration of outcome measures. DISCUSSION: This method provided expert consensus-based recommendations for the use of MG-specific outcome measures and exam findings to help standardize how they are used in clinical practice

Perception and Attitude towards Fertility and Fertility Preservation Options in Parents of Children with Turner Syndrome: A Qualitative Survey Study

STUDY OBJECTIVE: Turner Syndrome (TS) is a genetic condition often characterized by ovarian insufficiency and infertility. Fertility preservation discussions are recommended early in care, but few studies have explored parental perspectives. Our objective was to assess the knowledge, perceptions, and attitudes regarding fertility and fertility preservation among parents of young children with TS. METHODS: An anonymous bilingual survey (English/Spanish) was completed by parents of children under 12 years with TS seen at a multidisciplinary clinic (April 2022 -September 2023). Quantitative data were analyzed using descriptive statistics and Fisher\u27s exact test. Open-ended responses were analyzed using conventional content analysis to identify recurring themes. RESULTS: Forty-five parents (82% mothers) completed the survey, with 60% identifying as non-Hispanic white (NHW), 20% Hispanic, 11% Black, and 9% other. All were aware of the association of infertility with TS. NHW respondents were more likely to have a higher income, educational status and private insurance (p\u3c0.05). We found differences by race/ethnicity regarding the value of biological parenthood and openness to fertility preservation options as well as factors that contribute to these decisions. Cost emerged as a major consideration across all income groups. Despite high counseling rates (\u3e90%), only 60% of parents recalled fertility discussions. Themes from open-ended responses emphasized cost, procedural risk/benefit, child autonomy, divergent opinions on timing of counseling, and a desire for more information. CONCLUSION: This study highlights the varied perspectives and priorities voiced by parents of young children with TS regarding fertility preservation and can inform fertility counseling practices by providers

Machine learning approach for dosage individualization of azithromycin in children with community-acquired pneumonia

AIMS: The uncertainty about the efficacy and safety of currently used azithromycin dosing regimens in children warrants individualized therapy. The area under the plasma concentration-time curve over 24 h (AUC) of azithromycin correlates best with its effectiveness. The aim of this study was to evaluate the ability of machine learning (ML) to predict the AUC of azithromycin in children with community-acquired pneumonia. METHODS: Various ML algorithms were used to build ML models based on simulated pharmacokinetic profiles from a published population pharmacokinetic model. A priori-ML model predicted AUC using patients\u27 characteristics and after the trough concentration (C) became available, a posteriori-ML model was built for improved prediction. Statistical methods and pharmacodynamic (PD) evaluation methods were used to evaluate the ML model\u27s predictive accuracy in a real-world study. ML-optimized doses were evaluated by calculating the probability of PD target attainment in virtual trials compared with guideline-recommended doses. RESULTS: The AUC can be predicted by priori-ML model using the CatBoost algorithm with dosing regimen and two covariates as predictors (weight, alanine aminotransferase) before initial administration. A posteriori-ML model using CatBoost algorithm was built with adding C as a predictor. In real-world validation, the mean absolute prediction error of the priori-ML and posteriori-ML models was less than 30%. The accuracy (determining whether the PD target is met) of the priori-ML model was 76.3%, whereas that of the posteriori-ML model increased to 90.4%. CONCLUSIONS: ML models were established to predict the AUC of azithromycin successfully and could be used for individual dose adjustment in children before treatment and after obtaining C

Assessing the Adherence of ChatGPT Chatbots to Public Health Guidelines for Smoking Cessation: Content Analysis

BACKGROUND: Large language model (LLM) artificial intelligence chatbots using generative language can offer smoking cessation information and advice. However, little is known about the reliability of the information provided to users. OBJECTIVE: This study aims to examine whether 3 ChatGPT chatbots-the World Health Organization\u27s Sarah, BeFreeGPT, and BasicGPT-provide reliable information on how to quit smoking. METHODS: A list of quit smoking queries was generated from frequent quit smoking searches on Google related to how to quit smoking (n=12). Each query was given to each chatbot, and responses were analyzed for their adherence to an index developed from the US Preventive Services Task Force public health guidelines for quitting smoking and counseling principles. Responses were independently coded by 2 reviewers, and differences were resolved by a third coder. RESULTS: Across chatbots and queries, on average, chatbot responses were rated as being adherent to 57.1% of the items on the adherence index. Sarah\u27s adherence (72.2%) was significantly higher than BeFreeGPT (50%) and BasicGPT (47.8%; P\u3c.001). The majority of chatbot responses had clear language (97.3%) and included a recommendation to seek out professional counseling (80.3%). About half of the responses included the recommendation to consider using nicotine replacement therapy (52.7%), the recommendation to seek out social support from friends and family (55.6%), and information on how to deal with cravings when quitting smoking (44.4%). The least common was information about considering the use of non-nicotine replacement therapy prescription drugs (14.1%). Finally, some types of misinformation were present in 22% of responses. Specific queries that were most challenging for the chatbots included queries on how to quit smoking cold turkey, ...with vapes, ...with gummies, ...with a necklace, and ...with hypnosis. All chatbots showed resilience to adversarial attacks that were intended to derail the conversation. CONCLUSIONS: LLM chatbots varied in their adherence to quit-smoking guidelines and counseling principles. While chatbots reliably provided some types of information, they omitted other types, as well as occasionally provided misinformation, especially for queries about less evidence-based methods of quitting. LLM chatbot instructions can be revised to compensate for these weaknesses

The gateway effect of cigarette, e-cigarette, cigar, and alcohol use vs. Cannabis use

BACKGROUND: While the gateway hypothesis suggests that using tobacco and alcohol increases likelihood of initiating cannabis, cannabis use may precede and increase other substance use. We examined gateway effects of cigarettes, e-cigarettes, cigars, and alcohol on cannabis use, and reverse associations. METHODS: We analyzed 2023 survey data from 4,031 US young adults (M = 26.29, 60 % female, 19 % Hispanic, 14 % Black, 14 % Asian). Discrete-time survival analysis assessed hazards of initiating cannabis based on self-reported age of initiating other substances, and vice versa. Time(age)-lagged predictors indicated whether participants had initiated the other substances by one year younger, accounting for sociodemographics; state non-medical cannabis laws; lifetime depression, anxiety, or attention deficit disorder [ADD]) diagnoses; and personality characteristics. RESULTS: Lifetime use was: 68 % for cannabis, 45 % cigarettes, 49 % e-cigarettes, 31 % cigars, and 85 % alcohol. Past-year cigarette, e-cigarette, cigar, and alcohol initiation increased hazards of initiating cannabis (adjusted Hazard Ratio, aHR = 3.78, 95 %CI = 3.39-4.22; aHR = 2.17, 95 %CI = 1.86-2.53; aHR = 2.90, 95 %CI = 2.45-3.43; aHR = 3.41, 95 %CI = 3.11-3.75, respectively). Past-year cannabis initiation increased hazards of other substance initiation (cigarettes: aHR = 3.51, 95 %CI = 3.11-3.96; e-cigarettes: aHR = 3.73, 95 %CI = 3.34-4.17; cigars: aHR = 3.66, 95 %CI = 3.20-4.18; alcohol: aHR = 3.07, 95 %CI = 2.73-3.45). Associations were generally stronger when initiation occurred at ages 5-18 vs. \u3e 18. Depression predicted cannabis initiation; anxiety and ADD predicted e-cigarette initiation. Certain personality characteristics were protective against initiation (agreeableness and conscientiousness for each, openness for cigarettes and cigars, emotional stability for cannabis, cigarettes, and cigars); extraversion increased hazards of initiating cannabis and e-cigarettes. CONCLUSIONS: Interventions should target underlying mechanisms influencing the use of various substances, such as mental health and personality characteristics, especially among adolescents

Preoperative Risk Assessment for Lumbar Fusion in Patients With Diabetes: Data-Driven Stratification of HbA1c and Same Day Glucose Levels that Predict 90-Day Complication Rates

STUDY DESIGNS: Retrospective Database Analysis. OBJECTIVES: Pre-operative glycemic control in diabetic patients undergoing lumbar fusion (LF) is essential for evaluating complication risk. However, current thresholds for preoperative HbA1c and same-day-glucose (SDG) are either non-specific or have low predictive power. This study uses HbA1c and SDG to provide data-driven risk stratification for 90-day major and wound complications in LF patients. METHODS: Using a national database, patients undergoing LF from 2013-2022 with a recorded preoperative HbA1c and SDG level were included for analysis. Multiple HbA1c and SDG strata were identified using stratum specific likelihood ratio analysis (SSLR). Each stratum was then propensity-score matched to the lowest strata and compared using risk ratios. Significance level was set at a P-value \u3c0.05. RESULTS: 12,026 patients met inclusion criteria. For 90-day major complications, SSLR identified 3 predictive HbA1c (4.5-5.4, 5.5-7.9, and 8.0+) and SDG strata (60-159, 160-239, and 240+). Following propensity-matching, the 90-day major complication risk sequentially increased for HbA1c: 5.5-7.9 (1.69; P = 0.001; 95% CI 1.24-2.30), 8.0+(2.31; P \u3c 0.001; 95% CI 1.56-3.43). Following propensity-matching, the SDG strata similarly demonstrated sequentially increasing 90-day major complication risk: 160-239 (1.34; P \u3c 0.001; 95% CI 1.18-1.54), 240+ (1.64; P \u3c 0.001; 95% CI 1.31-2.05). Matched analysis demonstrated a higher relative-risk of 90-day wound complications for the 8.0+ HbA1c strata (2.23; P = 0.001; 95% CI 1.37-3.63) compared to the HbA1c 4.5-5.4 strata. No other strata were identified that predicted differences in 90-day wound complications. CONCLUSIONS: This study identified data-driven HbA1c and SDG strata that better risk-stratify 90-day major complications following LF. Instead of current single-value thresholds, these multiple strata may be utilized for better preoperative guidance

Histology-Specific Treatment Strategies and Survival Prediction in Lung Cancer Patients with Spinal Metastases: A Nationwide Analysis

BACKGROUND/OBJECTIVES: Spinal metastases are a common and severe complication of lung cancer, particularly in small cell lung cancer (SCLC), and are associated with poor survival. Despite advancements in treatment, optimal management strategies remain unclear, with significant differences between non-small cell lung cancer (NSCLC) and SCLC. This study evaluates treatment patterns, survival outcomes, and prognostic factors in lung cancer patients with spinal metastases, integrating deep learning survival prediction models. METHODS: This retrospective cohort study analyzed the National Cancer Database (NCDB) to identify NSCLC and SCLC patients diagnosed with spinal metastases. Demographics and treatment modalities were analyzed and adjusted for age, sex, and comorbidities. Kaplan-Meier analysis and Cox proportional hazards models assessed overall survival (OS). Five advanced survival prediction models estimated 1-year and 10-year mortality, with feature importance determined via permutation analysis. RESULTS: Among 428,919 lung cancer patients, 5.1% developed spinal metastases, with a significantly higher incidence in SCLC (13.6%) than in NSCLC (5.1%). SCLC patients had poorer OS. Radiation therapy alone was the predominant treatment, and stereotactic body radiation therapy (SBRT) predicted better short- and long-term survival compared to other radiation techniques. High-dose radiation (71-150 Gy BED) improved OS in NSCLC, while reirradiation benefited NSCLC but had a limited impact in SCLC. SurvTrace demonstrated the highest predictive accuracy for 1-year and 10-year mortality, identifying age, radiation dose, reirradiation, and race as key prognostic factors. CONCLUSIONS: The management of spinal metastases requires a histology-specific approach. Radiation remains the primary treatment, with SBRT predicting better short- and long-term survival. High-dose radiation and reirradiation should be considered for NSCLC, while the benefits are limited in SCLC. These findings support histology-specific treatment strategies to improve survival of patients with metastatic lung cancer to the spine