Nevin Manimala

JMIR Form Res. 2026 May 14;10:e91260. doi: 10.2196/91260.

ABSTRACT

BACKGROUND: Difficulty understanding speech in noisy environments is a primary challenge of hearing impairment, inadequately addressed by hearing aids alone. While auditory training can enhance selective attention and speech perception, current digital programs face poor user adherence and lack realistic 3D spatial audio.

OBJECTIVE: This pilot study evaluated the feasibility, usability, and preliminary efficacy of ARIA (Augmented Reality Immersive Auditory training), a handheld mobile intervention that provides gamified at-home auditory training to middle-aged adults via earbud-delivered spatial audio.

METHODS: In this single-arm, pre-post-follow-up pilot study, 11 adults (mean age 53.0, SD 3.0 y) with functional hearing not requiring amplification completed a 4-week at-home training program using ARIA on provided devices (iPhone 14 Pro, AirPods Pro 2). Speech-in-noise perception was assessed via the Korean Matrix Sentence Test at baseline, 4 weeks, and 8 weeks at 3 signal-to-noise ratios (SNRs; 0 dB, -6 dB, and -9 dB, respectively). Feasibility, usability (System Usability Scale), user experience (Player Experience of Need Satisfaction), in-game performance, and qualitative feedback were collected.

RESULTS: Protocol completion was 100% (11/11), demonstrating technical feasibility. Exploratory efficacy analyses revealed statistically significant speech-in-noise improvements posttraining across all conditions (0 dB: t10=3.43, P=.02; -6 dB: t10=5.34, P<.001; -9 dB: t10=4.34, P=.004). Gains were maintained at the 8-week follow-up. In-game localization improvements correlated significantly with speech perception gains at -6 dB SNR (ρ=0.639; P=.03) and -9 dB SNR (ρ=0.612; P=.045). User experience showed mixed results: the mean System Usability Scale score was 70.2 (SD 19.6; range 47.5-92.5), reflecting substantial individual differences in usability perception. While 72% (n=8) reported difficulties with the augmented reality (AR) environmental setup, 63% reported genuine mastery-driven engagement with core gameplay. Thematic analysis revealed a dissociation between peripheral usability challenges (setup friction, “homework” characterization due to protocol structure) and successful engagement with the training paradigm itself.

CONCLUSIONS: This pilot demonstrated the feasibility of AR-based audio-motor training for at-home delivery and revealed encouraging preliminary efficacy signals, warranting progression to controlled efficacy trials. Formative findings identified specific usability refinements needed for broader implementation, particularly streamlining AR setup while preserving the core gameplay elements that successfully fostered competence and engagement. These insights provide clear guidance for platform optimization and randomized controlled trial design.

PMID:42133934 | DOI:10.2196/91260

Pharm Dev Technol. 2026 May 14:1-23. doi: 10.1080/10837450.2026.2673037. Online ahead of print.

ABSTRACT

Topical administration of non-steroidal anti-inflammatory drugs (NSAIDs) is often limited by poor skin permeability and a short duration of action. Solid lipid nanoparticles (SLNs) represent a promising carrier system capable of enhancing dermal drug delivery while sustaining local therapeutic effects. The present study aimed to formulate and optimize ketoprofen (KP)-loaded SLNs to enhance the drug’s therapeutic efficacy in topical inflammatory conditions. Modeling and optimization of formulation components were performed using a mixture design (MD) approach. SLNs were prepared using two methods: hot melting and solvent evaporation. The prepared formulations were characterized in terms of particle size, zeta potential, entrapment efficiency, and in vitro drug release profiles. Pharmacodynamic evaluation was conducted in rats using the carrageenan-induced paw edema model and compared with a marketed formulation (FASTUM® gel 2.5%).The optimized SLNs exhibited a particle size of 51.9 ± 4.55 nm, a polydispersity index (PDI) of 0.398 ± 0.02, and a zeta potential of -14.2 ± 0.61 mV, indicating acceptable colloidal stability. The optimized KP-SLN formulation produced a significant reduction in paw edema volume (57.65%) in pre-treated rats (P < 0.05), along with significant decreases in inflammatory mediators prostaglandin E₂ (PGE₂) and tumor necrosis factor-α (TNF-α) levels by 55.6% and 58.4%, respectively, compared with the carrageenan control group. Furthermore, the SLN-based gel demonstrated markedly higher bioadhesion (+81%) and a two-fold increase in permeation flux compared with the pure drug gel.Overall, the optimized ketoprofen SLN gel achieved enhanced bioadhesion, skin permeation, and anti-inflammatory efficacy, confirming the potential of lipid nanoparticle-based systems for topical NSAID delivery. This strategy provides a rational, statistically optimized platform for improving localized therapy while minimizing systemic adverse effects.

PMID:42133922 | DOI:10.1080/10837450.2026.2673037

J Med Internet Res. 2026 May 14;28:e84844. doi: 10.2196/84844.

ABSTRACT

BACKGROUND: Machine learning (ML) and deep learning (DL) show promise for fall risk prediction, but prior reviews focused mainly on real-time fall detection, in-hospital falls, or conventional statistical models. The performance of ML-DL-based models for predicting future falls in community-dwelling older adults remains unclear.

OBJECTIVE: This study aimed to review ML-DL studies for predicting future falls among community-dwelling older adults and meta-analyze discrimination where feasible.

METHODS: Six databases were searched from inception to September 23, 2024, with updates on August 31, 2025, and February 28, 2026. We included longitudinal studies developing or validating ML-DL models to predict future falls in community-dwelling adults aged ≥60 years and excluded real-time detection, simulated or no fall, and inpatient studies. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Areas under the curve (AUCs) were meta-analyzed using Hartung-Knapp-Sidik-Jonkman random-effects models with 95% CIs. Heterogeneity, 95% prediction intervals (PIs), sensitivity analyses, and subgroup analyses were conducted.

RESULTS: After screening 10,253 records, 28 (0.3%) studies were included; 18 (64.3%) focused on general older adults. Prediction horizons ranged from 3 months to 7 years, and fall incidence ranged from 1.6% to 46.6%. Twenty-three (82.1%) studies applied ML, and 5 (17.9%) studies used DL. Input modalities included text (n=18, 64.3%), sensor (n=5, 17.9%), image (n=1, 3.6%), and multimodal data (n=4, 14.3%). Common predictors included age, sex, fall history, depression, and basic daily activities. Only one model underwent external validation. Calibration reporting was sparse. All models were rated at high risk of bias. Ten models were meta-analyzed, yielding a pooled AUC of 0.79 (95% CI 0.69-0.87) with extreme heterogeneity (τ2=0.64; τ=0.80; I2=99.8%; Q=4128.99). The confidence-distribution bootstrap PI was 0.20 to 0.99, indicating substantial uncertainty in expected performance across new populations. Subgroup analyses indicated moderation by sample size and population type, with higher discrimination in specific populations than in general samples; however, the specific population subgroup included only 2 studies. Although all participants were community dwelling, some cohorts were recruited through clinically enriched pathways rather than general community sampling.

CONCLUSIONS: ML-DL models show potential for identifying community-dwelling older adults at elevated future fall risk; however, wide PIs, limited external validation, and high risk of bias suggest real-world performance may be optimistic. The pooled AUC should be interpreted as a summary of reported discrimination under study-specific conditions, predominantly from internally validated, high-risk-of-bias models, rather than as a robust estimate of transportable real-world performance. This review extends prior reviews by focusing on community-dwelling settings and by integrating PROBAST, Hartung-Knapp-Sidik-Jonkman meta-analysis, PIs, and modality-specific synthesis to evaluate both discrimination and uncertainty. Findings support the use of ML-DL models for proactive fall prevention while emphasizing the need for validation and context-specific implementation.

PMID:42133917 | DOI:10.2196/84844

Neurology. 2026 Jun 9;106(11):e214908. doi: 10.1212/WNL.0000000000214908. Epub 2026 May 14.

ABSTRACT

OBJECTIVES: The HERMES-24 score recently demonstrated high accuracy for outcome prediction after large vessel occlusion (LVO) treatment in late time window patients from randomized clinical trials. In this study, we externally validate the score in a real-world patient cohort.

METHODS: Data from German Stroke Registry patients with LVO treated with endovascular therapy beyond 6 hours from symptom onset or last seen well were used. We performed a complete case analysis, excluding functionally dependent patients (premorbid modified Rankin Scale [mRS] >2/>3 for prediction of mRS ≤2/≤3, respectively). We assessed the HERMES-24 score for 90-day mRS prediction using bootstrap resampling and the c-statistic.

RESULTS: The analyzed cohort comprised 2,117 patients (mean age 74 ± 13.3 years; 55.4% female; median admission NIH Stroke Scale (NIHSS) 14 (Q1-Q3: 9-18)). The HERMES-24 score achieved an area under the curve (AUC) of 0.876 (95% CI 0.859-0.889) for mRS ≤2 and 0.856 (95% CI 0.837-0.875) for mRS ≤3. Subgroup analysis for mRS ≤2 prediction showed lower performance in patients with NIHSS <18 (AUC 0.850, 95% CI 0.832-0.870).

DISCUSSION: In our real-world cohort of late time window patients with LVO, the HERMES-24 score showed good discriminative performance, supporting its cautious clinical applicability, considering its lower performance than in trial populations, especially in patients with lower baseline NIHSS scores.

PMID:42133912 | DOI:10.1212/WNL.0000000000214908

J Med Internet Res. 2026 May 14;28:e78321. doi: 10.2196/78321.

ABSTRACT

BACKGROUND: Previous research has demonstrated that the use of continuous glucose monitoring (CGM) can improve glycemic control in people with type 2 diabetes when used regularly alongside a digital diabetes self-management education and support (DSMES) program. However, to date, there is limited evidence showing the benefits of a digitally delivered DSMES program combined with real-time CGM for adults with type 2 diabetes.

OBJECTIVE: The objective of this study is to evaluate the impact of a DSMES program coupled with CGM on hemoglobin A1c (HbA1c) and CGM-derived glycemic measures compared to usual care for adults with type 2 diabetes over 6 months.

METHODS: Participants with type 2 diabetes and HbA1c of 8% or higher (64 mmol/mol) who were not using mealtime bolus insulin (aged 26-83 y; mean HbA1c 9.6%, SD 1.4% [mean 81.2 mmol/mol, SD 15.8 mmol/mol]) were randomly assigned to a digital DSMES+CGM integrated solution (n=51) or usual care (n=49) for 6 months. The primary outcome was HbA1c. The secondary outcomes were CGM-derived glycemic measures, including glucose management indicator, percent time in range 70 to 180 mg/dL, percent time above range (>180 mg/dL), percent time below range (<70 mg/dL), and mean glucose. Linear mixed effects models were used for intention-to-treat analyses.

RESULTS: HbA1c was lower among the intervention group versus the usual care group at 3 months (difference=-0.7%, 95% CI -1.4% to -0.1% or difference=-8.1 mmol/mol, 95% CI -15.5 to -0.7 mmol/mol; P=.03) and at 6 months (difference=-0.6%, 95% CI -1.4% to 0.2% or difference=-6.9 mmol/mol, 95% CI -15.7 to 1.9 mmol/mol; P=.12) but only reached statistical significance at 3 months. CGM-derived glycemic measures, including glucose management indicator (difference=-0.9%, 95% CI -1.7% to -0.1%; P=.03), time in range (difference=14.6%, 95% CI 1.0% to 28.2%; P=.04), time above range (difference=-14.9%, 95% CI -29.0% to -0.9%; P=.04), and mean glucose (difference=-36.4 mg/dL, 95% CI -70.0 to -2.9 mg/dL; P=.03), also significantly improved for the intervention group versus the usual care group at 6 months.

CONCLUSIONS: The combination of digital DSMES+CGM is effective for supporting adults with type 2 diabetes in managing their condition and has the potential to reduce the risk of long-term health complications.

PMID:42133904 | DOI:10.2196/78321

JCO Precis Oncol. 2026 May;10(5):e2500597. doi: 10.1200/PO-25-00597. Epub 2026 May 14.

ABSTRACT

PURPOSE: Oligometastatic disease (OMD) is an intermediate state of metastatic disease in which metastasis-directed therapy (MDT) may improve outcomes. The classification of OMD is inconsistent, typically defined by number of metastases without considering tumor biologic characteristics. To help optimize patient selection for MDT, we analyzed integrated genomic sequencing results from patients with metastatic non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS: Patients with metastatic NSCLC who had molecular sequencing through a validated institutional assay (MSK-IMPACT) were included. The number and location of metastases were manually annotated (assigned 1-10 and >10). Individual gene variant scores were analyzed by gene length and patient mutation burden. Analysis was completed in R and included maximally selected rank statistics, agglomerative hierarchical clustering, and the chi-square test for independence.

RESULTS: In total, 844 patients had clinical data, tissue sequencing, and annotated imaging for analysis. Of these, 635 had >10 metastases (75.2%), and 209 had 1-10 metastases (24.8%). The cutpoint that maximized overall survival (OS) was four metastases, and six mutation signatures were identified. For patients with 1-4 metastases, TERT and KMT2D had inferior OS, while for those with ≥5 metastases, EGFR, ALK, and TBX3 had superior OS.

CONCLUSION: The cutpoint that maximized difference in OS was four metastases, but incorporating genetic alteration information modified this criterion. These findings were proof of principle that integrating multimodal data beyond number of lesions can better identify patients with metastatic NSCLC who may be candidates for MDT.

PMID:42133899 | DOI:10.1200/PO-25-00597

JCO Precis Oncol. 2026 May;10(5):e2501125. doi: 10.1200/PO-25-01125. Epub 2026 May 14.

ABSTRACT

PURPOSE: Given the paucity of data regarding circulating tumor DNA (ctDNA) as a modality to guide diagnosis and treatment for children with advanced solid tumors, we conducted a prospective study to characterize the frequency and clinical impact of diagnostic and targetable genomic variants identified by clinical ctDNA testing.

METHODS: Our study cohort included 36 children and young adults with advanced solid tumors enrolled in the prospective, multicenter molecular profiling GAIN study. Tumor samples underwent next-generation sequencing, and serial blood samples were analyzed by the FoundationOne Liquid Companion Diagnostic ctDNA test. We evaluated the prevalence of ctDNA in our cohort using ctDNA tumor fraction (TF), the prevalence of genomic variants in ctDNA compared with those in tumor tissue stratified by ctDNA TF, the association between ctDNA TF and radiographic disease response, and the clinical impact of ctDNA testing.

RESULTS: In our cohort, 67% of patients had at least one liquid biopsy sample with detectable ctDNA by TF. Most patients (72%) had detection of a clinically relevant genomic alteration in at least one ctDNA sample. Most (85%) short variants and translocations identified in tissue were present in ctDNA when TF ≥ 1%, with variation by tumor type. We demonstrated statistically significant differences in absolute ctDNA TF changes among patients with radiographic disease progression (mean, +12.2%, standard deviation, 18.7%) and response (mean, -23.4%, standard deviation, 30.6%), P < .001. ctDNA results affected the clinical care of 26% of patients.

CONCLUSION: ctDNA is prevalent across a wide range of advanced pediatric solid tumors, identifies clinically important variants, changes with radiographic disease burden, and has a clinical impact.

PMID:42133898 | DOI:10.1200/PO-25-01125

JCO Precis Oncol. 2026 May;10(5):e2500500. doi: 10.1200/PO-25-00500. Epub 2026 May 14.

ABSTRACT

PURPOSE: Immune response to tumors defines patient outcomes and our goal was to identify where tumors lie on the self/nonself axis and if they exploit selfness to subvert immune destruction.

MATERIALS AND METHODS: A new measure for quantifying selfness was developed using transcriptomes based on a selfness model stemming from thymic education. The thymus-likeness score (TLS) was validated on data from the Human Protein Atlas and then used to interrogate tumor recognizability across 32 solid tumor cohorts from The Cancer Genome Atlas (TCGA). A robust statistical pipeline was devised to identify links between the TLS and disease outcomes such as immune infiltration and patient survival.

RESULTS: We first developed a novel selfness score (TLS) for estimating the T-cell recognizability in any sample, given its transcriptome. Using the TLS, we interrogated tumor recognizability across 32 solid tumor cohorts from TCGA and found that there is a consistent increase in selfness in tumors compared with their adjacent nontumor tissues. In our pan-cancer analysis, we found an unexpected inverse association between TLS and immune infiltration of macrophages, dendritic cells, CD8 T cells, and Treg cells. We found the selfness, when combined with the immune infiltration, to correlate with patient survival in multiple cohorts.

CONCLUSION: The TLS is one of the first models for quantifying tumor recognizability from an immune perspective. The increase in selfness in tumors, along with its inverse relationship with immune infiltration, and links to survival hints at the presence of an immune sculpting selection mechanism that selects for two types of tumors: (1) easily recognizable, but poorly infiltrated and (2) heavily infiltrated, but poorly recognizable. Our results provide a new direction for investigating tumor evolution trajectories.

PMID:42133896 | DOI:10.1200/PO-25-00500

JMIR Form Res. 2026 May 14;10:e85703. doi: 10.2196/85703.

ABSTRACT

BACKGROUND: Tobacco, alcohol, and illicit drug use continue to pose substantial public health challenges in China. Although public service announcements (PSAs) are widely used for prevention, little is known about how these messages are constructed or the extent to which they draw on established health communication theories.

OBJECTIVE: This exploratory study aimed to characterize the design features of anti-substance use PSAs in China, assess their use of constructs from the extended parallel process model (EPPM) and the elaboration likelihood model (ELM), and compare patterns across anti-substance use PSAs.

METHODS: We conducted a content analysis of 89 publicly available anti-substance use PSAs produced in mainland China. Messages were identified via major Chinese video platforms and institutional websites and then screened using predefined eligibility criteria. Variables captured message source, intended audience, framing, substance depiction, cultural appeals, and EPPM and ELM components. Frequencies and proportions were calculated, and χ2 tests were used to examine differences by PSA type. To account for multiple comparisons, P values were adjusted using the Holm-Bonferroni correction.

RESULTS: Most PSAs did not identify a target audience (54/89, 60.7%), and public security departments were the most common sponsors (n=37, 41.2%), while none were sponsored by public health agencies. Theory use was selective: response efficacy (n=63, 70.8%) and perceived severity (n=55, 61.8%) appeared more often than self-efficacy (n=45, 50.6%) and perceived susceptibility (n=34, 38.2%); peripheral cues (n=79, 88.8%) were more common than central route cues (n=16, 18%). Differences across PSA types were observed in sponsorship, message features, and theoretical constructs. After adjustment for multiple comparisons, associations involving sponsoring organizations (public security departments and Chinese media) and perceived susceptibility remained statistically significant (all adjusted P=.01). Antidrug PSAs were predominantly associated with public security sponsorship, whereas antialcohol and antitobacco PSAs were more frequently linked to Chinese media sources. Perceived susceptibility cues were more common in antismoking PSAs than in antidrug PSAs, while other differences in framing, substance cues, cultural appeals, and ELM or EPPM constructs were not statistically significant after adjustment.

CONCLUSIONS: Anti-substance use PSAs in China were characterized by limited audience segmentation and uneven use of theory-based persuasive strategies. Observed differences across alcohol-, tobacco-, and drug-focused messages suggest that PSA design may be shaped not only by partial application of communication theory but also by institutional influences and substance-specific contexts. These findings highlight the need for more context-sensitive and theory-informed approaches to anti-substance use PSA design in China.

PMID:42133889 | DOI:10.2196/85703

JMIR Res Protoc. 2026 May 14;15:e89056. doi: 10.2196/89056.

ABSTRACT

BACKGROUND: Neighborhood revitalization is a process through which land use rezoning and capital investment can spur new resources, such as access to healthful food and amenities for physical activity. While revitalization efforts may promote cardiovascular health, their benefits may not be distributed equally across sociodemographic groups.

OBJECTIVE: The objective of the study is to apply a socioecological framework that uses a multimethod approach incorporating quantitative data (longitudinal electronic health records and cross-sectional surveys) and qualitative data (longitudinal “walk-a-long” interviews) to examine the short-term effect of neighborhood land use rezoning and revitalization efforts on cardiovascular disease (CVD), CVD-related health behaviors, and access to and utilization of health care. System science methods, namely microsimulation modeling and system dynamics modeling, will be used to assess the long-term effects of land use rezoning policy and revitalization efforts on cardiovascular health and ways to sustain priority health equity goals in revitalized neighborhoods.

METHODS: We leverage a land use rezoning initiative in the Bronx, New York, where a largely commercial area is being rezoned along with capital investments to expand healthful neighborhood resources. Using electronic health records from a single hospital system, we will follow cohorts of midlife and older adults (≥50 y) residing in both the rezoned area and a comparison area. We will assess clinically measured incident CVD and other CVD risk factors to evaluate changes in cardiovascular health over time. In parallel, we will conduct a cross-sectional survey and a purposive sampling of patients for in-person “walk-a-long” qualitative interviews to understand how residents perceive neighborhood access to healthful resources after land use rezoning. To estimate long-term effects, we will use a validated microsimulation model to project CVD outcomes and costs. Finally, we will use system dynamics modeling to integrate quantitative and qualitative findings to inform future revitalization and public health strategies.

RESULTS: Midlife and older adult patients (N=10,813) in the intervention area and the comparison area will be followed for approximately 7 years following land use rezoning and revitalization efforts to compare CVD risk between neighborhoods. The cross-sectional survey (n=300) and qualitative assessment (n=36) will increase understanding of perceptions of access to healthful resources and related health behaviors among residents. Systems science approaches will estimate long-term CVD risk and related costs associated with revitalization efforts. An advisory committee of clinical and community stakeholders will assist in interpreting results and developing dissemination strategies for their constituents. This study was funded from January 2023 until December 2026.

CONCLUSIONS: This study uses a socioecological framework to provide a novel, transferable method for evaluating the impact of neighborhood revitalization efforts on cardiovascular health by combining methods to examine short- and long-term effects across individual, neighborhood, and structural (system) levels over time. Findings will inform policies aimed at reducing CVD through equitable urban revitalization.

PMID:42133886 | DOI:10.2196/89056