Nevin Manimala

Front Surg. 2025 Aug 12;12:1613915. doi: 10.3389/fsurg.2025.1613915. eCollection 2025.

ABSTRACT

BACKGROUND AND OBJECTIVES: Objectively studying patient outcomes following surgery has been an important aspect of evidence-based medicine. The current gold-standard-patient reported outcomes measures-provides valuable information but have subjective biases. Smartphones, which passively collect data on physical activity such as daily steps, may provide objective and valuable insight into patient recovery and functional status. This study aims to provide a methodological guide for data collection and analysis of smartphone accelerometer data to assess clinical outcomes following surgery.

METHODS: Patient health metrics-namely daily steps, distance travelled, and flights climbed-were extracted from patient smartphones using easy-to-download applications. These applications upload the data that smartphone accelerometers passively collect daily to a HIPAA compliant encrypted server while de-identifying the patient’s personal health information. Patients were consented in multiple settings-synchronously during clinical visits or asynchronously over the phone-and could be enrolled during the initial pre-operative visit or well after the surgery. With the patient data acquired, the peri-operative window of selection is determined based on the needs to the study. The timeseries data is then statistically normalized to account for individual baselines and smoothened over a 14-day moving average to minimize noise. Mathematical analysis can be harnessed to study quantifiable recovery and decline periods, which provide continuous and nuanced insight into patient’s health throughout their spine disease and treatment course. Additionally, integrating clinical variables permits computational machine models capable of predicting patient trajectories and guiding clinical decisioning.

CONCLUSION: Smartphones offer a new metric for studying patient well-being and outcomes after surgery. The research with them is in its nascent stages but further studies can potentially revolutionize our understanding of spinal disease.

PMID:40874243 | PMC:PMC12378811 | DOI:10.3389/fsurg.2025.1613915

Bioinform Adv. 2025 Aug 9;5(1):vbaf189. doi: 10.1093/bioadv/vbaf189. eCollection 2025.

ABSTRACT

MOTIVATION: The growing availability of single-cell RNA sequencing (scRNA-seq) data highlights the necessity for robust integration methods to uncover both shared and unique cellular features across samples. These datasets often exhibit technical variations and biological differences, complicating integrative analyses. While numerous integration methods have been proposed, many fail to account for individual-level covariates or are limited to discrete variables.

RESULTS: To address these limitations, we propose scINSIGHT2, a generalized linear latent variable model that accommodates both continuous covariates, such as age, and discrete factors, such as disease conditions. Through both simulation studies and real-data applications, we demonstrate that scINSIGHT2 accurately harmonizes scRNA-seq datasets, whether from single or multiple sources. These results highlight scINSIGHT2’s utility in capturing meaningful biological insights from scRNA-seq data while accounting for individual-level variation.

AVAILABILITY AND IMPLEMENTATION: The scINSIGHT2 method has been implemented as a R package, which is available at https://github.com/yudimu/scINSIGHT2/.

PMID:40874236 | PMC:PMC12380451 | DOI:10.1093/bioadv/vbaf189

Clin Epidemiol. 2025 Aug 22;17:693-705. doi: 10.2147/CLEP.S521309. eCollection 2025.

ABSTRACT

BACKGROUND: In the presence of competing risks, when the baseline risk is unclear, if only the sub-distribution hazard ratio (SHR) is reported in the results, which is related to the cumulative incidence function, the survival disparity of events of interest between groups cannot be clarified. In contrast, the difference in restricted mean time lost (RMTLd), which is the difference in the areas under the cumulative incidence between two groups, can well compensate for the deficiencies of SHR and explain the effects on a time scale, facilitating clinical interpretation and communication.

METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to collect information on female patients with locally advanced breast cancer diagnosed between 2010 and 2015. The prognostic factors of breast cancer death were evaluated considering competing risk. Univariable and multivariable analyses were conducted to get SHR and RMTLd.

RESULTS: SHR can indicate the direction of prognostic factors, while RMTLd can quantify prognostic effects and provide time-scale interpretation. For instance, in adjuvant radiotherapy, the SHR showed a protective effect, which can be quantified as an average increase of 4.15 months in survival time.

DISCUSSION: In the presence of competing risks, the combined use of absolute measure RMTLd can more intuitively explain the prognostic effect, which is convenient for clinical practice and communication.

PMID:40874217 | PMC:PMC12380099 | DOI:10.2147/CLEP.S521309

SAGE Open Med. 2025 Aug 25;13:20503121251345598. doi: 10.1177/20503121251345598. eCollection 2025.

ABSTRACT

INTRODUCTION: Preconception care involves measures to enhance a woman’s physical, psychological, and nutritional health before pregnancy. Despite various observational studies assessing healthcare practitioners’ knowledge of preconception care in East Africa, the overall pooled knowledge level remains unclear, and the studies often report inconsistent associated factors. This systematic review and meta-analysis aimed to determine the aggregated knowledge of preconception care among healthcare providers in East Africa and identify influencing factors.

METHOD: We searched studies using PubMed, Scopus, Embase, and Google Scholar that were published between January 01, 2018 and November 30, 2024. This study used the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. The quality of studies was evaluated using the modified Newcastle-Ottawa quality assessment tool. The data were extracted by two authors independently using Microsoft Excel and analyzed by Stata version 17. A random effects model was applied to calculate the pooled level of knowledge and its associated factors. The International Prospective Register of Systematic Review registration number for the review was CRD42024608878.

RESULTS: A total of 12 studies comprising 4892 participants were involved in this meta-analysis. The pooled knowledge of preconception care among healthcare providers was 56% (95% CI: 45%-66%). This study showed that gender (odds ratio (OR) = 1.35), educational level (OR = 3.52), monthly salary (OR), work experience (OR = 1.77), Internet access (OR = 3.41), ever read the preconception care guideline (OR = 2.77), having Smartphone (OR = 1.70), working institution (OR = 2.05), Training on HIV testing and management (OR = 4.28), training on providing alcohol or tobacco cessation service (OR = 1.14), the presence of a library in a working health facility (OR = 1.98), taking training on preconception care education and counseling (OR = 3.44) were significant factors associated with knowledge of preconception care.

CONCLUSION: The findings indicate that healthcare providers in East Africa have limited knowledge of preconception care. Gender, educational level, monthly salary, previous work experience, internet connection, awareness of preconception care policy, smartphone possession, type of work schedule, prior HIV testing, and management training, library access in healthcare facilities, and involvement in preconception care training meetings and counseling sessions are significant factors of the knowledge of preconception care among healthcare providers.

PMID:40874216 | PMC:PMC12378543 | DOI:10.1177/20503121251345598

J Hepatocell Carcinoma. 2025 Aug 22;12:1893-1904. doi: 10.2147/JHC.S536877. eCollection 2025.

ABSTRACT

BACKGROUND: The tumor immune microenvironment (TME) plays a key role in the development of hepatocellular carcinoma (HCC). As the important components of TME, interleukin-2 (IL-2) mediates immune responses by specifically binding to the interleukin-2 receptor (IL-2R). This study aimed to explore the role of IL-2R in HCC development and provided possible clinical implications in HCC prognosis and treatment.

METHODS: The IL-2R genetic data were acquired from publicly available TCGA and CCLE databases. Data processing and analysis, including construction of the prognostic model and evaluation of immune status in HCC, were performed on Xiantao platform by using statistical methods including the Wilcoxon test, Cox regression analysis, correlation analysis. GEPIA2 was used to explore the relationship between IL-2R genes expression and clinical stages, while genetic variations in IL-2R subunits in HCC were determined using cBioPortal. The IL-2Rα co-expression gene analysis was conducted on the LinkedOmics database. Enzyme-linked immunosorbent assay (ELISA), colorimetric method, and flow cytometric method were used to analyze peripheral blood samples from patients with HCC.

RESULTS: A prognostic risk model was established by incorporating IL-2Rα, IL-2Rβ, and IL-2Rγ expression. The infiltration levels of B cell memory, T cell regulatory cells (Tregs), and immune checkpoints (PDCD1, CTLA4, CD274 and TIGIT) were significantly elevated in high-risk group of the risk model. Additionally, sIL-2Rα levels were positively correlated with tumor-specific growth factor (TSGF) and Tregs in the peripheral blood of HCC patients.

CONCLUSION: The prognostic risk model based on IL-2R subunits may play a role in the regulation of immune function within the HCC tumor microenvironment. Besides, IL-2Rα may act as a more important role in HCC development among the three IL-2R subunits. Further research will be needed to verify these initial findings. Overall, these results may provide important insights in clinical prognosis and therapeutic strategies for HCC.

PMID:40874213 | PMC:PMC12379987 | DOI:10.2147/JHC.S536877

Clin Interv Aging. 2025 Aug 22;20:1293-1304. doi: 10.2147/CIA.S532913. eCollection 2025.

ABSTRACT

INTRODUCTION: The Integrated Theory of Health Behavior Change (ITHBC) offers a structured framework for promoting sustained health behavior change through cognitive beliefs, self-regulation, and social facilitation. However, its application in geriatric oncology remains unexplored.

METHODS: This quasi-experimental study enrolled 291 older adult patients who underwent radiotherapy at the Jiangsu Cancer Hospital. Patients hospitalized from July to December 2024 (n=146) received ITHBC-guided multidisciplinary nursing intervention, while those treated from January to June 2024 (n=145) received conventional individualized nursing care. Key outcomes, including disease cognition, self-management efficacy, and quality of life, were assessed at baseline and five months post-intervention using validated instruments. Statistical analyses included t-tests, ANCOVA, and effect-size calculations.

RESULTS: After 5 months, the intervention group showed significantly greater improvements in disease cognition (Δ=+23.5 vs +16.4), self-management efficacy (Δ=+10.63 vs +3.77), and quality of life scores (Δ=+22.07 vs +6.98), all P < 0.001. The effect size for disease cognition was 1.32 (95% CI: 1.08-1.56).

DISCUSSION: These findings confirm the efficacy of the ITHBC-based nursing model in enhancing cognitive, behavioral, and psychosocial outcomes in older patients undergoing radiotherapy. Structuring geriatric oncology care around behavioral theories, such as ITHBC, yields measurable benefits and supports its broader application in nursing interventions.

PMID:40874210 | PMC:PMC12379990 | DOI:10.2147/CIA.S532913

Clin Interv Aging. 2025 Aug 22;20:1305-1313. doi: 10.2147/CIA.S527660. eCollection 2025.

ABSTRACT

BACKGROUND: Enhanced Recovery After Surgery (ERAS) has been extensively applied across numerous surgical specialties. However, there remains a paucity of research regarding the implementation of ERAS in advanced age patients (≥75 years) who undergo InterTAN nail surgery for intertrochanteric femoral fractures (ITF). This study aimed to assess if our ERAS protocol improves satisfaction and clinical outcomes in such patients.

METHODS: This was a retrospective cohort study included advanced age patients who underwent InterTAN nail surgery. The ERAS group included patients who underwent surgery between January 2022 and December 2024, while the non – ERAS group consisted of those who had the same surgery between January 2019 and December 2023. Demographics, comorbidities, surgical details, ERAS compliance, outcomes, complications, and length of stay (LOS) were evaluated.

RESULTS: A total of 144 patients were included in the ERAS group and 135 in the non – ERAS group. Analysis of demographic data showed no statistically significant intergroup differences. ERAS compliance was 100%. There were no significant differences between the ERAS and non – ERAS groups in terms of operative side, anesthesia type, operating time, intraoperative blood loss, and postoperative Visual Analogue Scale scores. Moreover, 30 – day follow – up revealed no significant differences in readmission rates and mortality between the two groups. However, the LOS was significantly shorter in the ERAS group (5.68±2.34 days vs 6.54±2.04 days in the non – ERAS group; p = 0.03). The overall complication rate was also significantly lower in the ERAS group (10/144 vs 23/135; P < 0.01).

CONCLUSION: In this cohort of advanced age patients with ITF managed via our ERAS program, it was evidenced that this program is safe and can effectively reduce the LOS and the incidence of complications.

PMID:40874207 | PMC:PMC12380002 | DOI:10.2147/CIA.S527660

Open Forum Infect Dis. 2025 Aug 15;12(8):ofaf496. doi: 10.1093/ofid/ofaf496. eCollection 2025 Aug.

ABSTRACT

BACKGROUND: Amid the opioid epidemic in the United States, hepatitis C virus (HCV) infections are rising, with one-third of individuals with infection unaware due to the asymptomatic nature. This study aimed to develop and validate a machine learning (ML)-based algorithm to screen individuals at high risk of HCV infection.

METHODS: We conducted prognostic modeling using the 2016-2023 OneFlorida+ database of all-payer electronic health records. The study included individuals aged ≥18 years who were tested for HCV antibodies, RNA, or genotype. We identified 275 features of HCV, including sociodemographic and clinical characteristics, during a 6-month period before the test result date. Four ML algorithms-elastic net (EN), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN)-were developed and validated to predict HCV infection. We stratified patients into deciles based on predicted risk.

RESULTS: Among 445 624 individuals, 11 823 (2.65%) tested positive for HCV. Training (75%) and validation (25%) samples had similar characteristics (mean, standard deviation age, 45 [16] years; 62.86% female; 54.43% White). The GBM model (C statistic, 0.916 [95% confidence interval = .911-.921]) outperformed the EN (0.885 [.879-.891]), RF (0.854 [.847-.861]), and DNN (0.908 [.903-.913]) models (P < .0001). Using the Youden index, GBM achieved 79.39% sensitivity and 89.08% specificity, identifying 1 positive HCV case per 6 tests. Among patients with HCV, 75.63% and 90.25% were captured in the top first and first to third risk deciles, respectively.

CONCLUSIONS: ML algorithms effectively predicted and stratified HCV infection risk, offering a promising targeted screening tool for clinical settings.

PMID:40874186 | PMC:PMC12378832 | DOI:10.1093/ofid/ofaf496

Open Forum Infect Dis. 2025 Aug 18;12(8):ofaf499. doi: 10.1093/ofid/ofaf499. eCollection 2025 Aug.

ABSTRACT

Among patients with blastomycosis (n = 281), coccidioidomycosis (n = 1920), and histoplasmosis (n = 2180), 62% experienced diagnostic delays (mean 29 days). Patients who experienced delays incurred average excess healthcare costs of up to $15 648 (95% confidence interval: $8600-$22 695) compared with those without a delay. Earlier diagnosis may help reduce excess costs.

PMID:40874182 | PMC:PMC12378736 | DOI:10.1093/ofid/ofaf499

J Neuroophthalmol. 2025 Aug 28. doi: 10.1097/WNO.0000000000002393. Online ahead of print.

ABSTRACT

BACKGROUND: Multimodal artificial intelligence (AI) models have recently expanded into video analysis. In ophthalmology, one exploratory application is the automated detection of extraocular movement (EOM) disorders. This proof-of-concept study evaluated the feasibility of using Gemini 2.0 to recognize EOM abnormalities, identify the affected eye, and recognize specific movement limitations from publicly available, real-world clinical videos.

METHODS: We retrospectively collected 114 YouTube videos of EOM disorders, including cranial nerve (CN) palsies, internuclear ophthalmoplegia (INO), supranuclear disorders, nystagmus, and ocular myasthenia gravis (MG), alongside 15 control videos demonstrating normal EOMs. Videos were trimmed to include only the pertinent clinical examinations, and audio was removed to avoid diagnostic cues. Using a standardized zero-shot prompt, Gemini 2.0 analyzed each video via the Google AI Studio platform. Gemini 2.0 was evaluated based on its ability to provide the correct diagnosis, identify the affected eye, and recognize the specific movement limitation (if any). Descriptive statistics, Spearman correlations, and comparative analyses were used to assess performance.

RESULTS: Gemini 2.0 correctly identified the primary diagnosis in 43 of 114 videos, yielding an overall diagnostic accuracy of 37.7%. Diagnostic performance varied by condition, with the highest accuracies observed in third nerve palsy (81.1%), INO (80.0%), sixth nerve palsy (66.7%), and ocular MG (20.0%), whereas normal EOMs were correctly classified in 93.3% of cases. In misclassified cases, the correct diagnosis appeared in the differential diagnosis in 15.5% of instances. Laterality was correctly identified in 26.5% of eligible cases overall, 73.1% among correctly diagnosed cases vs. 9.6% in misclassified ones. Similarly, movement limitations were accurately identified in 30.3% of eligible cases overall, with a marked increase to 88.5% accuracy in correctly diagnosed cases compared to 9.6% in misclassified cases. Longer videos moderately correlated with longer processing time (ρ = 0.55, P < 0.001). Significant correlations were observed between correct diagnosis and correct laterality identification (ρ = 0.45, P < 0.001), correct diagnosis and correct movement limitation identification (ρ = 0.61, P < 0.001), and laterality and movement limitation (ρ = 0.51, P < 0.001). Processing time averaged 11.0 seconds and correlated with video length (ρ = 0.55, P < 0.001).

CONCLUSIONS: This proof-of-concept study demonstrates the feasibility of using Gemini 2.0 for automated recognition of EOM abnormalities in clinical videos. Although performance was stronger in overt cases, overall diagnostic accuracy remains limited. Substantial validation on standardized, clinician-annotated datasets is needed before clinical application.

PMID:40867040 | DOI:10.1097/WNO.0000000000002393