Nevin Manimala

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

JMIR Res Protoc. 2026 Feb 17;15:e76027. doi: 10.2196/76027.

ABSTRACT

BACKGROUND: The loss of tooth structure in endodontically treated teeth compromises their structural integrity and increases their vulnerability to fractures. To strengthen these teeth, post and core systems must be used. Among post materials, glass fiber has become more and more common because of it advantageous mechanical and aesthetic qualities. However, the effectiveness of customized glass fiber posts in enhancing fracture resistance compared to prefabricated posts remains a subject of ongoing debate.

OBJECTIVE: This systematic review primarily focuses on the fracture resistance of endodontically treated teeth reinforced with customized glass fiber posts. It aims to assess how well they function in terms of resilience, flexibility, and failure patterns in comparison to prefabricated fiber posts and other post materials.

METHODS: To locate relevant studies published up to 2025, a comprehensive literature search will be conducted through various materials available electronically on websites such as Google Scholar, Web of Science, PubMed, Semantic Scholar, and Scopus. The selection criteria will include randomized controlled clinical trials that evaluate fracture resistance of customized glass fiber posts in teeth that have had endodontic treatment. The research will be screened using predetermined standards for inclusion and exclusion. Quality assessment and collection of data will be performed strictly, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. To examine the results quantitatively, a meta-analysis will be conducted, if at all possible, with a random effects model for heterogeneity. Statistical methods will be used for evaluating the effectiveness of custom glass fiber posts, alternative post materials, and prefabricated posts.

RESULTS: By comparing the mean differences in fracture resistance of teeth treated endodontically using various post systems, the effectiveness of customized glass fiber posts will be assessed. Other aspects to be examined include general biomechanical performance, failure mechanisms, and stress distribution. This study will use a random effects model to estimate the combined effect size measurements and the corresponding 95% CIs. We anticipate that the data synthesis will be conducted between February and March 2026 for this systematic review and will be finished by April or July 2026.

CONCLUSIONS: Customized glass fiber posts may show promise in strengthening teeth that have undergone endodontic treatment by lowering the risk of failure and increasing fracture resistance. Even if there is current evidence that they are more effective than prefabricated posts, we need to synthesize the quality of the evidence. Therefore, this review aims to evaluate whether customized glass fiber posts effectively enhance fracture resistance in endodontically treated teeth.

PMID:41701956 | DOI:10.2196/76027

JCO Oncol Pract. 2026 Feb 17:OP2501029. doi: 10.1200/OP-25-01029. Online ahead of print.

ABSTRACT

PURPOSE: Healthy lifestyle behaviors, including regular exercise and balanced nutrition, affect quality of life in cancer survivors. However, barriers to adopting these behaviors across the cancer continuum are poorly understood. We evaluated barriers to adopting healthy lifestyle behaviors in a national cohort of patients and survivors of cancer.

METHODS: ASCO distributed an online survey to adult patients with cancer in the United States from March to June 2020. In this secondary analysis, we evaluated the perceived barriers to incorporating healthy lifestyle behaviors after cancer diagnosis and how they differ by disease stage and treatment status. Descriptive statistics summarized barriers to lifestyle changes. Comparisons across disease stage and treatment status were assessed using Rao-Scott chi-square tests, and weighted multivariable logistic regression modeling identified associated factors.

RESULTS: Of 2,419 survey respondents, 1,987 were included in this analysis and grouped by disease stage: early stage on-treatment (n = 461), early stage post-treatment (n = 916), and metastatic on-treatment (n = 610). The most common barriers to healthy behaviors included lack of energy (58.7%) and physical limitations (52.0%), with significant differences (P < .05) in barriers related to time, logistics, cancer effects, and physical limitations across disease stages and treatment groups. Among those not meeting lifestyle guidelines, lack of motivation (51.3%-62.0%) and lack of energy (59.5%-77.0%) were major barriers, with barriers related to time, cancer effects, and physical limitations varying significantly by disease stage and treatment status.

CONCLUSION: Patients with cancer face multiple barriers to adopting healthy lifestyle behaviors after diagnosis. Targeted interventions based on disease stage and treatment status may help address these challenges.

PMID:41701954 | DOI:10.1200/OP-25-01029

JMIR AI. 2026 Feb 17;5:e75064. doi: 10.2196/75064.

ABSTRACT

BACKGROUND: The impact of surgical complications is substantial and multifaceted, affecting patients and their families, surgeons, and health care systems. Despite the remarkable progress in artificial intelligence (AI), there remains a notable gap in the prospective implementation of AI models in surgery that use real-time data to support decision-making and enable proactive intervention to reduce the risk of surgical complications.

OBJECTIVE: This scoping review aims to assess and analyze the adoption and use of AI models for preventing surgical complications. Furthermore, this review aims to identify barriers and facilitators for implementation at the bedside.

METHODS: Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we conducted a literature search using IEEE Xplore, Scopus, Web of Science, MEDLINE, ProQuest, PubMed, ABI, Embase, Epistemonikos, CINAHL, and Cochrane registries. The inclusion criteria included empirical, peer-reviewed studies published in English between January 2013 and January 2025, involving AI models for preventing surgical complications (surgical site infections, and heart and lung complications or stroke) in real-world settings. Exclusions included retrospective algorithm-only validations, nonempirical research (eg, editorials or protocols), and non-English studies. Study characteristics and AI model development details were extracted, along with performance statistics (eg, sensitivity and area under the receiver operating characteristic curve). We then used thematic analysis to synthesize findings related to AI models, prediction outputs, and validation methods. Studies were grouped into three main themes: (1) duration of hypotension, (2) risk for complications, and (3) decision support tool.

RESULTS: Of the 275 identified records, 19 were included. The included models frequently demonstrated strong technical accuracy with high sensitivity and area under the receiver operating characteristic curve, particularly among studies evaluating decision support tools. However, only a few models were adopted routinely in clinical practice. Two studies evaluated the clinicians’ perceptions regarding the use of AI models, reporting predominantly positive assessments of their usefulness.

CONCLUSIONS: Overall, AI models hold potential to predict and prevent surgical complications as the validation studies demonstrated high accuracy. However, implementation in routine practice remains limited by usability barriers, workflow misalignment, trust concerns, and financial and ethical constraints. The evidence included in this scoping review was limited by the heterogeneity in study design and the predominance of small-scale feasibility studies, particularly for hypotension prediction. Future research should prioritize prospectively validated models that use other physiologic features and address clinicians’ concerns regarding generalizability and adoption.

PMID:41701931 | DOI:10.2196/75064