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Developing a Novel Mobile App to Support HIV Testing and Pre-Exposure Prophylaxis Uptake Among Men Who Have Sex With Men: Formative and Technical Pilot Study

Developing a Novel Mobile App to Support HIV Testing and Pre-Exposure Prophylaxis Uptake Among Men Who Have Sex With Men: Formative and Technical Pilot Study

Eligible participants included cisgender men who were aged 15 to 24 years, self-reported being HIV negative or HIV status unknown at screening (technical pilot only), not currently taking Pr EP (technical pilot, as participants are testing an app, which assists with Pr EP uptake), fluent in English, owned an i OS or Android mobile phone, and had self-reported evidence of being at risk for HIV acquisition (detailed risk criteria are described in the study by Liu et al [26]).

Albert Y Liu, Thiago S Torres, Cat-Dancing Alleyne, Janie Vinson, Kelly Bojan, Pedro Alonso Serrano, Temitope Oyedele, Elizabeth Enriquez-Bruce, Patricia Emmanuel, Jeb Jones, Kathryn E Muessig, Susan P Buchbinder, Patrick Sullivan, Lisa B Hightow-Weidman, Hyman Scott

JMIR Hum Factors 2025;12:e62830

Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation

Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation

In addition, Liu et al [24] reported that preoperative NT-pro BNP levels can more effectively predict postoperative AKI in patients undergoing noncardiac surgery, further corroborating the prognostic utility of NT-pro BNP. Given that our study cohort consisted of patients with CHD, we included relevant pharmacological agents in the predictive model. Results revealed that antiplatelet therapy ranks first in importance in both the XGBoost and DT models.

Yike Li, Mingyang Xiao, Yaqian Li, Lulu Lv, Shanshan Zhang, Yuhui Liu, Juan Zhang

JMIR Med Inform 2025;13:e72349