Mohamed S. Ahmed

Objective physical-function assessment may help personalize supportive care across chimeric antigen receptor (CAR) T-cell therapy, yet rehabilitation evidence remains dispersed. We narratively synthesized adult hematologic CAR T-cell reports identified through a reproducible PubMed search, targeted source verification, conference and registry surveillance, and reference tracking through 7 August 2026. Studies used the six-minute walk test, sit-to-stand tests, gait speed, Timed Up and Go, grip strength, balance testing, Section GG, and the Functional Independence Measure. Prehabilitation and ward-based rehabilitation appeared feasible. Six-minute walk distance was preserved overall in one acute cohort, although cytokine release syndrome and neurotoxicity were associated with worse change. Retrospective studies linked poor endurance, falls, balance or strength deficits, and assistive-device use with hospitalization, neurotoxicity, or mortality. Selected patients improved during inpatient rehabilitation, while recommended postacute services were not consistently received. The evidence is heterogeneous, predominantly single-center, and insufficient for causal or eligibility thresholds. Objective functional phenotyping may nevertheless provide a complementary patient-level data layer for matching assessment, mobility, referral, and recovery pathways to treatment phase and clinical context.

Immune checkpoint inhibitors (ICIs) have translated the scope of cancer therapy, but their immune-restorative mechanism can lead to immune-related adverse events (irAEs), which can affect several organs with varying severity. Early identification of patients at risk of irAEs is prudent to inform clinical decisions in precision oncology and remains a challenge as current clinical and biomarker methods still lack predictive accuracy. Artificial Intelligence (AI) presents a promising strategy for improving early detection, risk stratification as well as monitoring of irAEs. This narrative review summarizes the current AI modalities for detecting and predicting irAEs risk occurring with ICI therapy, including clinical Machine Learning models, radiomics-based approaches, natural language processing (NLP) systems and the integration of modalities with multimodal AI frameworks. Clinical Machine Learning models demonstrate moderate predictive performance whereas radiomics-derived modeling appears promising for pneumonitis. NLP and language models have achieved higher accuracy for retrospective irAEs detection. Multimodal AI applications offer theoretical potential through diverse data integration that captures the complex biology of irAEs; however, current evidence is limited. All these modalities face limitations of inadequate sample sizes, retrospective design, heterogeneous outcome definitions, class imbalance, and insufficient external validation. AI-based models have significant potential for personalized immunotherapy monitoring but require prospective multicenter validation, standardized datasets and clinically interpretable frameworks prior to implementation. Future advances in multimodal modeling can also enable precise prediction and early detection of irAEs, ultimately improving the safety and effectiveness of cancer immunotherapy.

Background Opioid overdoses in the USA have increased to unprecedented levels. Administration of the opioid antagonist naloxone can prevent overdoses. Objective This study was conducted to reveal the pharmacoepidemiologic patterns in naloxone prescribing to Medicaid patients from 2018 to 2021 as well as Medicare in 2019. Design Observational pharmacoepidemiologic study Setting US Medicare and Medicaid naloxone claims Intervention The Medicaid State Drug Utilisation Data File was utilised to extract information on the number of prescriptions and the amount prescribed of naloxone at a national and state level. The Medicare Provider Utilisation and Payment was also utilised to analyse prescription data from 2019. Outcome measures States with naloxone prescription rates that were outliers of quartile analysis were noted. Results The number of generic naloxone prescriptions per 100 000 Medicaid enrollees decreased by 5.3%, whereas brand naloxone prescriptions increased by 245.1% from 2018 to 2021. There was a 33.1-fold difference in prescriptions between the highest (New Mexico=1809.5) and lowest (South Dakota=54.6) states in 2019. Medicare saw a 30.4-fold difference in prescriptions between the highest (New Mexico) and lowest states (also South Dakota) after correcting per 100 000 enrollees. Conclusions This pronounced increase in the number of naloxone prescriptions to Medicaid patients from 2018 to 2021 indicates a national response to this widespread public health emergency. Further research into the origins of the pronounced state-level disparities is warranted.