Nevin Manimala

J Gen Intern Med. 2026 Feb 17. doi: 10.1007/s11606-026-10257-1. Online ahead of print.

ABSTRACT

BACKGROUND: Most people with opioid use disorder (OUD) do not receive evidence-based treatment. To increase treatment rates, primary care clinics may choose to implement risk prediction tools available in the electronic health record (EHR) to identify patients with a high risk of OUD or overdose.

OBJECTIVE: To externally validate Epic’s cognitive computing model to predict the Risk of Opioid Abuse or Overdose (referred to as the Opioid Risk Score; ORS) in three large integrated health systems.

DESIGN: Prospective cohort study secondary to an ongoing clinical trial.

PARTICIPANTS: Patients (N = 704,764) aged 18-75 who had a primary care encounter during the study period (April 2021-December 2022) and did not have an OUD diagnosis at index.

MAIN MEASURES: Data were extracted from the EHR. The index date was defined as the first date within the study period where the patient met eligibility criteria and had an ORS calculated by the EHR. The binary outcome variable was whether the patient was diagnosed with OUD or experienced an opioid overdose within 12 months of the index date.

KEY RESULTS: Most patients were classified as low risk on ORS (99.6%). Few patients experienced an OUD diagnosis or overdose in the 12-month follow-up period (0.3%). The model correctly classified 185 of 2362 patients who experienced an event (sensitivity 0.0783, 95% CI 0.0675, 0.0892) and 699,926 of 702,406 patients who did not experience an event (specificity 0.9965, 95% CI 0.9963, 0.9966). Few patients with high ORS experienced the event (PPV 0.0694, 95% CI 0.0598, 0.0791). The model had excellent discrimination (c-statistic = 0.815) but was poorly calibrated, underestimating risk for patients who experienced the outcomes.

CONCLUSIONS: Epic’s ORS demonstrated excellent discrimination but very low sensitivity across three large integrated health systems. Health systems should exercise caution before implementing vendor risk prediction models without validating their use in their patient populations.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:41703383 | DOI:10.1007/s11606-026-10257-1

Neuropsychopharmacol Rep. 2026 Mar;46(1):e70098. doi: 10.1002/npr2.70098.

ABSTRACT

M. K. Trivedi, A. Branton, D. Trivedi, S. Mondal and S. Jana, “Assessment of Cognitive-Motor Functions in Adults With Perceived Neuropsychological Problems Using NIH Toolbox After Remote Biofield Energy Treatment as Non-Pharmacological Intervention: A Randomized Double-Blind Placebo Controlled Trial,” Neuropsychopharmacology Reports 44, no. 4 (2024): 749-761, https://doi.org/10.1002/npr2.12482. The above article, published online on 13 September 2024 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Tsuyoshi Miyakawa; the Japanese Society of Neuropsychopharmacology; and John Wiley & Sons Australia, Ltd. The retraction has been agreed upon as the study’s design, methods, results and conclusions are essentially the same as another article published elsewhere by the same author group in the same year, without any attribution to that article. Furthermore, the study contains physiologically implausible data and statistical anomalies. The editors consider the results and conclusions of this article to be invalid. The authors do not agree with the retraction.

PMID:41703379 | DOI:10.1002/npr2.70098

Psychon Bull Rev. 2026 Feb 17;33(3):68. doi: 10.3758/s13423-025-02821-3.

ABSTRACT

Threat detection is compromised across the schizophrenia spectrum, often revealed by paranoia and delusions. Threat difficulties extend to nonclinical populations with liability toward schizophrenia. A key source of these difficulties may be due to hyper-sensitivity to social stressors in real-world environments. In a large, nonclinical sample (N = 161), we measured the influence of social context to threat detection in social interactions. Social interactions were captured in naturalistic videos and validated as threatening or nonthreatening. Deep learning models were employed to re-render the videos by parsing different amounts of social context depicted in these interactions. Then, we measured how threat detection was influenced by individual variability in schizotypal and autistic traits as a function of social context. Individuals with high schizotypal traits showed reduced threat discrimination ability in the presence of more social context, but better threat detection when the interactions were primarily reduced to body kinematics. The effect was more pronounced in individuals higher on suspicious tendencies and odd belief traits in schizotypy, and social communication traits in the autism spectrum. These results suggest that disruptions from social context may underlie threat detection difficulties across the schizophrenia spectrum.

PMID:41703359 | DOI:10.3758/s13423-025-02821-3

Ann Biomed Eng. 2026 Feb 17. doi: 10.1007/s10439-026-04022-y. Online ahead of print.

ABSTRACT

AI is widely recognized as a tool that biomedical scientists, engineers and clinicians can, and should, use. However, what do we mean by a tool? I take the example of convolutional neural networks that learn latent statistical associations from images, but those associations can be used to different ends. I focus on two different uses in the field of medical diagnostics, what I call human-AI “outskilling” and human-AI “newskilling”. Outskilling is a prosthetic human-AI activity to outperform human capacities (in Greek: prosthesis, adding) in tasks that experts can nevertheless perform well. I study computer-aided diagnostics (CADx) to detect polyps as an example of AI outskilling, which carries the risk of deskilling without a proven gain in meaningful outcomes. I term the second use “newskilling,” a human-AI activity that brings forth something new (in Greek: poiesis) by using latent statistical associations to discover variables that human inference cannot detect. I study the example of AI deriving clinically relevant variables from retinal fundus images to derive “retinal age gaps” as an example of human-AI newskilling. There are two major conclusions based on this distinction: the design of AI uses, and the discernment of how and when to use them.

PMID:41703356 | DOI:10.1007/s10439-026-04022-y

Inflamm Res. 2026 Feb 17;75(1):37. doi: 10.1007/s00011-025-02137-x.

ABSTRACT

BACKGROUND: Acute kidney injury (AKI), characterized by rapid renal dysfunction and high mortality, is critically driven by ferroptosis, an iron-dependent form of cell death. While PTEN-induced kinase 1 (PINK1) and sirtuin 3 (SIRT3) are implicated in mitochondrial homeostasis and ferroptosis regulation, their mechanistic interplay in AKI remains unclear. This study investigated the role of emodin, a natural anthraquinone, in alleviating AKI via SIRT3-mediated PINK1 deacetylation and ferroptosis suppression, focusing on mitochondrial integrity, transferrin (TF) interaction, and redox balance.

MATERIALS AND METHODS: Male C57BL/6 mice (n = 6/group), PINK1⁻/⁻, and SIRT3⁻/⁻ mice were pretreated with emodin (40-160 mg/kg, 3 days) before LPS-induced AKI (15 mg/kg). Human renal tubular HK-2 cells were treated with emodin (10-40 µg/ml) and Erastin (0.4 µM, 24 h). Assays included RNA sequencing, immunoprecipitation-mass spectrometry (IP-MS), histopathology (H&E/PAS/PB-DAB staining), ROS/Fe²⁺/GSH quantification, and immunoblotting. Statistical analysis used ANOVA and Student’s t-test.

RESULTS: Emodin reduced serum creatinine and urea in AKI mice, alongside decreased tubular injury and apoptosis. RNA-seq identified ferroptosis as the central pathway, with emodin upregulating PINK1 expression. IP-MS revealed emodin disrupted PINK1-TF binding via SIRT3-mediated deacetylation, reducing Fe²⁺ accumulation and restoring GPX4 levels. In SIRT3⁻/⁻ and PINK1⁻/⁻ models, emodin’s protective effects were abolished, confirming pathway dependency.

CONCLUSION: Emodin mitigates AKI by activating the SIRT3/PINK1 axis, suppressing ferroptosis through cytoplasmic PINK1 deacetylation and TF interaction disruption. These findings highlight SIRT3/PINK1 as a therapeutic target and emodin as a potential agent for AKI management.

PMID:41703345 | DOI:10.1007/s00011-025-02137-x

Forensic Sci Int Genet. 2026 Feb 4;84:103441. doi: 10.1016/j.fsigen.2026.103441. Online ahead of print.

ABSTRACT

This study reports novel 21 aSTR (autosomal Short Tandem Repeats) allele frequencies from 538 individuals, as well as 11 triallelic profiles, representing seven Bantu-speaking groups in Southern Africa (Ndebele, Pedi, Phuthi, Tsonga, Sotho, Swati, and Xhosa). These data contributed to a comprehensive representation of the Southern Bantu (SB). The defined SB reference database was evaluated for various forensic uses and applications: extant diversity, population structure, adequacy of alternative reference databases, and continental biogeographical ancestry prediction. Different analytical methods-including summary statistics, multivariate analyses (Multidimensional Scaling, MDS; Discriminant Analysis of Principal Components, DAPC), and Bayesian clustering-detected continental structure, identifying four major clusters: Southern, Eastern, Western, and Horn of Africa. This observation motivated the evaluation of two practical applications of this information: one methodological (alternative reference frequency database) and one predictive (biogeographic assignment). The adequacy of alternative reference databases for representing SB populations-STRidER South Africa, STRidER Africa, African American, and global datasets-was assessed by comparing reciprocal allelic coverage and shifts in random match probabilities (RMPs). Of the databases tested, the STRidER Africa database provided the closest representation of the SB. Population-level analyses evidenced the need for a stratification correction (θ = 0.005 or 0.01) for SB populations. Intracontinental biogeographic prediction was assessed using an XGBoost machine learning classification model across four major African regions. The model’s predictive balanced accuracy ranged from 80 % to 94 % across African regions (94 % for the Horn of Africa, 87 % for Southern Africa, 84 % for Western Africa, and 80 % for Eastern Africa). The accuracy and limitations of this practice are discussed, along with its ethical implications. The assessment of reference databases can be extended to more general applications across Africa.

PMID:41702037 | DOI:10.1016/j.fsigen.2026.103441

JMIR Mhealth Uhealth. 2026 Feb 17;14:e66435. doi: 10.2196/66435.

ABSTRACT

BACKGROUND: As the world’s population ages, the prevalence of chronic low back pain (CLBP) is increasing, placing a substantial burden on individuals and health care systems. Mobile health (mHealth) apps offer a potentially scalable solution to support self-management, but little is known about how, why, for whom, and under what circumstances such tools work in real-world settings.

OBJECTIVES: This study aimed to test and refine 3 program theories-developed through a previous realist review-on how mobile apps support CLBP self-management. The goal was to understand the key contextual factors and mechanisms that influence when and why a digital self-management intervention may succeed or fail.

METHODS: A realist evaluation was conducted using one-on-one telephone interviews with 9 participants who had used the Curable app for 3 months to self-manage their CLBP. Realist interviews followed a teacher-learner cycle to explore, test, and refine the program theories. Abductive and retroductive analysis was used to develop context-mechanism-outcome configurations (CMOCs), which were synthesized into refined theories of digital self-management in chronic pain.

RESULTS: A total of 20 CMOCs were constructed, supporting 3 overarching program theories centered on empowerment, self-management burden, and timing. First, the app was empowering when it offered credible and accessible knowledge that helped participants understand their pain, build confidence, and reduce reliance on health care providers. However, engagement depended on individual beliefs and expectations: those with strong biomedical views struggled to connect with the app’s psychosocial framing. Second, while the app could ease the burden of self-management by offering support between appointments, it could also increase burden during flare-ups, when users lacked the capacity to engage. Features such as proactive content delivery and low-demand interfaces were viewed as essential for continued use. Third, timing emerged as a key factor. Early introduction was beneficial for some, but others needed to first accept the chronicity of their condition before they were ready to engage with self-management tools. Trust in the source recommending the app also influenced engagement. While clinician endorsement was often valued-especially early in the self-management journey-participants who had experienced unmet needs or disillusionment in clinical encounters reported that peer recommendations or nonclinical sources held greater weight. This highlights the importance of aligning recommendations with individuals’ evolving relationships with authority and trust.

CONCLUSIONS: Mobile apps like Curable (Curable Inc) can support empowerment and continuity of care in CLBP, but their success depends on personalization, timing, and relational dynamics. To prevent feelings of abandonment, such tools should be introduced as an adjunct to-rather than a replacement for-ongoing clinical support.

PMID:41701989 | DOI:10.2196/66435

Am J Trop Med Hyg. 2026 Feb 17:tpmd250567. doi: 10.4269/ajtmh.25-0567. Online ahead of print.

ABSTRACT

Post-discharge mortality (PDM), defined as deaths that occur in the weeks and months after hospital discharge, remains a critical, yet under-recognized, contributor to high childhood mortality rates in sub-Saharan Africa. However, a comprehensive understanding of effective interventions to prevent PDM is lacking. The aim for the present study was to evaluate the efficacy of published interventions to prevent PDM among neonates and children aged 0-18 years in sub-Saharan Africa. A systematic review was conducted to assess the efficacy of interventions for preventing PDM. The CABI Global Health, Cochrane Reviews, Cochrane Trials, ProQuest Dissertations and Theses, Embase, PubMed, and Web of Science databases were searched without language restriction. Publications that involved interventions for preventing PDM, included children, and were conducted in sub-Saharan Africa were included in the present study. Of 4,893 publications screened, 17 were included, with 12,938 participants in total (10.6% experienced PDM). The most common interventions included supplemental feeding programs, kangaroo mother care, antibiotic use, and micronutrient supplementation. Effectiveness varied within and between intervention types. Only two interventions resulted in statistically significant reductions in PDM: vitamin A supplementation for children with pneumonia (hazard ratio: 0.51; 95% CI: 0.29-0.90; low quality of evidence) and linkage to services for children with sickle cell disease (adjusted hazard ratio: 0.26; 95% CI: 0.08-0.83; low quality of evidence). No single intervention type provided consistent benefits across studies. Most interventions targeted children with specific diagnoses; however, some strategies addressed social determinants of health. Future research must prioritize cost-effective, scalable strategies across diverse sub-Saharan African settings to accelerate the prevention of PDM among children.

PMID:41701981 | DOI:10.4269/ajtmh.25-0567

JMIR Form Res. 2026 Feb 17;10:e82356. doi: 10.2196/82356.

ABSTRACT

BACKGROUND: Allogeneic hematopoietic stem cell transplant (HCT) is a complex but essential treatment for malignant and nonmalignant conditions, requiring strict posttransplant adherence to immunosuppressant medications to prevent complications such as graft-versus-host disease. Adolescents and young adults undergoing HCT face unique challenges, including balancing growing independence with ongoing reliance on care partners, often parents. Medication adherence in this group is often suboptimal, and few interventions address adolescent and young adult-care partner dyads. To address this gap, we aim to develop a mobile health (mHealth) app that engages both the patients and care partner to improve adherence.

OBJECTIVE: As formative research for early-stage intervention development, this study aimed to (1) explore current HCT medication adherence strategies and challenges; (2) understand attitudes toward digital technology, including dyadic perspectives on app use to support adherence; and (3) assess adolescent and young adult-care partner relationships, including views on care partner involvement. This process was intended to inform the design of a relevant, user-centered mHealth app.

METHODS: Eligible participants included adolescents and young adult patients aged 12-39 years and primary care partners, such as parents, involved in medication management. Participants were recruited from a large academic medical center through direct outreach and electronic health records. Data collection involved 2 focus groups (6 dyads and 2 additional adolescents and young adults), 4 individual interviews (2 patients and 2 care partners), and 6 dyadic interviews. Semistructured sessions (in person or virtual) gathered feedback on medication adherence practices and app design preferences. All sessions were audio recorded with consent and professionally transcribed. Qualitative data were analyzed systematically: transcripts were deidentified, coded using both inductive and deductive strategies, and themes were refined through team consensus. Patterns were organized into major themes, and representative quotations were selected to illustrate findings. Data management was facilitated by NVivo (version 13; Lumivero) software.

RESULTS: We included 28 participants (15 adolescents and young adults and 13 care partners). The median age of adolescents and young adults was 18 (range 13-39) years and 53% (8/15) were female. Adolescents and young adults were 47% (7/15) White, 40% (6/15) Black, and 13% (2/15) mixed race. Care partners’ median age was 48 (range 36-72) years, with 92% (12/13) female and 77% (10/13) White. Three principal themes emerged: (1) existing reminders and organizational tools are often insufficient for consistent adherence; (2) adherence barriers are multifaceted, often involving autonomy vs care partner support; and (3) both adolescents and young adults and care partners showed strong interest in a dyadic digital health intervention to foster collaboration and support shared adherence goals.

CONCLUSIONS: This formative study highlights the complex dynamics of medication adherence in adolescent and young adult-care partner dyads and supports the need for a dyadic mHealth app to enhance adherence, collaboration, and relationship quality.

PMID:41701968 | DOI:10.2196/82356

J Med Internet Res. 2026 Feb 17;28:e78731. doi: 10.2196/78731.

ABSTRACT

BACKGROUND: Medication adherence is vital for older adults living with type 2 diabetes mellitus (T2DM), but it remains low and needs improvement. Current interventions have limited effectiveness, while video-based interventions show promising potential for enhancing adherence.

OBJECTIVE: To evaluate the impact of the “Diabetes Little Helper” video intervention, developed based on the information-motivation-behavioral skills model, on improving medication adherence in older adults living with T2DM in Henan.

METHODS: This parallel-group randomized controlled trial was conducted in 2 hospitals in Zhengzhou, involving 68 patients from each hospital. The intervention group (IG) received standard care plus the video intervention for one month, while the control group (CG) received only standard care. The primary outcome was medication adherence, and secondary outcomes included medication knowledge, attitude, behavior, belief, and social support. Data were collected at baseline, postintervention, and at 3-month follow-up. Intention-to-treat analysis and the last observation carried forward method were applied for missing data, with the generalized estimating equation model used for effect assessment (P<.05 considered statistically significant).

RESULTS: The average age of participants in the IG was 67.5 (SD 8.0) years, while in the CG, the average age was 66.0 (SD 7.0) years. Sex distribution was nearly identical, with 51.5% (n=35) of participants in the IG and 50.0% (n=34) in the CG being male. After the intervention, the IG and CG had retention rates of 95.6% (n=65) and 97.1% (n=66), respectively. At the 3-month follow-up, the retention rates for the IG and CG were 92.6% (n=63) and 92.2% (n=62), respectively. Both postintervention (β=4.956, 95% CI 3.702-6.210, P<.001) and at the 3-month follow-up (β=3.691, 95% CI 2.379-5.003, P<.001), medication adherence score for the IG was significantly higher than that for the CG. In addition, the IG showed significant improvements in secondary outcome, with medication knowledge (β=11.592, 95% CI 6.923-16.260, P<.001), attitude (β=5.467, 95% CI 4.531-6.763, P<.001), behavior (β=4.176, 95% CI 3.220-5.133, P<.001), and belief (β=2.882, 95% CI 1.990-3.775, P<.001) compared with the CG postintervention. However, there was no statistically significant difference in the secondary outcome of social support (β=0.008, 95% CI -1.834 to 2.011, P=.928).

CONCLUSIONS: The Diabetes Little Helper video intervention effectively improved medication adherence in older adults living with T2DM in Henan, highlighting the potential of digital health tools for managing chronic diseases in older adult populations.

TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2400083282; https://www.chictr.org.cn/showprojEN.html?proj=214847.

PMID:41701967 | DOI:10.2196/78731