Nevin Manimala

J Am Acad Orthop Surg Glob Res Rev. 2025 Jul 17;9(7). doi: 10.5435/JAAOSGlobal-D-25-00195. eCollection 2025 Jul 1.

ABSTRACT

BACKGROUND: Climate change is a global health emergency, with substantial carbon emissions coming from health care. This study compares the carbon footprint of in-person versus virtual orthopaedic care at a large, urban, academic healthcare system.

METHODS: Data were abstracted from the billing and claims database for orthopaedic clinic visits from 2018 to 2023 at a large, urban, academic medical center and its suburban satellite clinics. Carbon footprint per in-person visit was determined by combining emissions from supplies, facility energy use, and patient travel. The reduction in emissions of virtual visits was calculated compared with if all visits occurred in person.

RESULTS: Overall, 508,394 orthopaedic clinic visits (94.3% in-person, 5.7% virtual) were recorded. The average in-person visit resulted in 7.12 vs. 0.026 kg CO2e for the average virtual visit. Actual carbon emissions were estimated to be 3,411,206 kg CO2e compared with 3,714,565 kg if all visits occurred in person (8.2% reduction). Most emissions (99.8%) were attributed to patient travel, with 0.2% coming from supplies and <0.1% from facility energy use. The peak of the COVID-19 pandemic in 2020 saw the greatest reduction in carbon emissions at 19.5%, with emissions increasing each year thereafter (8.3% reduction in 2023).

CONCLUSION: The carbon footprint of clinic-based orthopaedic care is large and can be reduced by transitioning from in person to virtual care. Although virtual orthopaedic care has limitations, the environmental benefits are clear. Further research into virtual outpatient orthopaedic care should consider environmental impacts in addition to safety, effectiveness, and patient satisfaction.

PMID:40680267 | DOI:10.5435/JAAOSGlobal-D-25-00195

J Am Acad Orthop Surg Glob Res Rev. 2025 Jul 2;9(7). doi: 10.5435/JAAOSGlobal-D-24-00263. eCollection 2025 Jul 1.

ABSTRACT

INTRODUCTION: To assess ballistic proximal femur fracture outcomes in comparison with proximal femur fractures sustained by blunt mechanisms. We hypothesized that ballistic proximal femur fractures would have higher rates of infection, nonunion, and compartment syndrome than nonballistic fractures.

METHODS: A retrospective cohort was collected from the electronic medical record of a single, Level I, trauma center over a 10-year period (2013 to 2022) using Current Procedural Terminology codes. All consecutive adult patients with ballistic proximal third femur fractures (femoral neck, intertrochanteric, subtrochanteric) managed with surgical fixation were identified. A comparison group of proximal femur fractures sustained by nonballistic mechanisms was collected from consecutive patients in a 3-year period (2020 to 2022), creating a 2:1 nonballistic-to-ballistic fracture ratio. Exclusion criteria consisted of younger than 18 years or older than 65 years, primary fixation of total/hemi hip arthroplasty, primary pathologic fractures, and fractures across existing prosthesis. The primary outcomes measured include concomitant genitourinary injury, computed tomographic angiography with abnormality, vascular injury requiring repair, soft-tissue reconstruction, thigh compartment syndrome, length of stay, fracture-related infection, revision surgery to promote bone healing, and implant failure.

RESULTS: A total of 411 patients were included with 137 (33%) sustaining ballistic proximal femur fractures. Most blunt fractures were closed (86.8%), whereas most ballistic fractures were Gustilo Anderson type 1 open fractures (81.7%). The individuals in the ballistic cohort were more likely to have vascular injury requiring surgical intervention (8.8% vs. 1.1%, P < 0.001), computed tomographic angiography with abnormality (10.9% vs. 1.1%, P < 0.001), compartment syndrome (7.3% vs. 0.7%, P < 0.001), concomitant GU injury (12.4% vs. 1.8%, P < 0.001), and deep vein thrombosis (5.1% vs. 1.5%, P = 0.048).

CONCLUSION: Ballistic proximal femur fractures are associated with a higher risk of developing complications associated with trauma to nearby vascular structures and concomitant genitourinary structures. The rates of infection, revision surgery to promote bone healing, and implant failure were similar between the ballistic and nonballistic proximal femur fractures.

PMID:40680257 | DOI:10.5435/JAAOSGlobal-D-24-00263

JMIR Form Res. 2025 Jul 18;9:e74411. doi: 10.2196/74411.

ABSTRACT

BACKGROUND: Digital interventions have been proposed as a solution to meet the growing demand for mental health support. Large language models (LLMs) have emerged as a promising technology for creating more personalized and adaptive mental health chatbots. While LLMs generate responses based on statistical patterns in training data rather than through conscious reasoning, they can be designed to support important psychological processes. Prospection-the ability to envision and plan for future outcomes-represents a transdiagnostic process altered across various mental health conditions that could be effectively targeted through such interventions. We designed “Future Me,” an LLM-powered chatbot designed to facilitate future-oriented thinking and promote goal pursuit using evidence-based interventions including visualization, implementation intentions, and values clarification.

OBJECTIVE: This study aims to understand how users engage with Future Me, evaluate its effectiveness in supporting future-oriented thinking, and assess its acceptability across different populations, with particular attention to postgraduate students’ stress management needs. We also seek to identify design improvements that could enhance the chatbot’s ability to support users’ mental well-being.

METHODS: In total, 2 complementary studies were conducted. Study 1 (n=20) examined how postgraduate students used Future Me during a single guided session, followed by semistructured interviews. Study 2 (n=14) investigated how postgraduate students interacted with Future Me over a 1-week period, with interviews before and after usage. Both studies analyzed conversation transcripts and interview data using thematic analysis to understand usage patterns, perceived benefits, and limitations.

RESULTS: Across both studies, participants primarily engaged with Future Me to discuss career or education goals, personal obstacles, and relationship concerns. Users valued Future Me’s ability to provide clarity around goal-setting (85% of participants), its nonjudgmental nature, and its 24/7 accessibility (58%). Future Me effectively facilitated self-reflection (80%) and offered new perspectives (70%), particularly for broader future-oriented concerns. However, both studies revealed limitations in the chatbot’s ability to provide personalized emotional support during high-stress situations, with participants noting that responses sometimes felt formulaic (50%) or lacked emotional depth. Postgraduate students specifically emphasized the need for greater context awareness during periods of academic stress (58%). Overall, 57% of requests occurred outside office hours, dropping from 40 on day 1 to 12 by day 7.

CONCLUSIONS: Future Me demonstrates promise as an accessible tool for promoting prospection skills and supporting mental well-being through future-oriented thinking. However, effectiveness appears context-dependent, with prospection techniques more suitable for broader life decisions than acute stress situations. Future development should focus on creating more adaptive systems that can adjust their approach based on the user’s emotional state and immediate needs. Rather than attempting to replicate human therapy entirely, chatbots like Future Me may be most effective when designed as complementary tools within broader support ecosystems, offering immediate guidance while facilitating connections to human support when needed.

PMID:40680255 | DOI:10.2196/74411

J Gerontol A Biol Sci Med Sci. 2025 Jul 18:glaf157. doi: 10.1093/gerona/glaf157. Online ahead of print.

ABSTRACT

BACKGROUND: Aim of the study was a comparative analysis of different epigenetic clocks with regard to their ability to predict a future onset of the Metabolic Syndrome (MetS). In addition, cross-sectional relationships between epigenetic age measures and MetS were investigated.

METHODS: MetS was diagnosed in participants of the Berlin Aging Study II at baseline (n = 1,671, mean age 68.8 ± 3.7 years, 51.6% women) and at follow-up (n = 1,083; 7.4 ± 1.5 years later). DNA methylation age acceleration (DNAmAA) was calculated for a total of ten epigenetic clocks at baseline. In addition, DunedinPACE, a DNAm-based measure of the pace of aging, was calculated. The relationship between MetS, DNAmAA and DunedinPACE was investigated by fitting regression models adjusted for potential confounders and calculating receiver operating characteristic statistics.

RESULTS: Among all biomarkers investigated, DunedinPACE was the only DNAm-based predictor that was significantly associated with incident MetS at follow-up on average 7.4 years later (OR: 9.84, p = 0.028). Logistic regression models predicting MetS that either included solely clinical parameters or solely epigenetic clock estimates (DNAmAA) or DunedinPACE revealed that GrimAge DNAmAA had an area under the curve most comparable to the model considering clinical variables only. Cross-sectional differences between participants with and without MetS remained statistically significant for DunedinPACE only after covariate adjustment (baseline: β = 0.042, follow-up: β = 0.031, p < 0.0001 in both cases).

CONCLUSION: Comparison of epigenetic clocks in relation to MetS showed strong and consistent associations with DunedinPACE. Our results highlight the potential of using certain DNAm-based measures of biological ageing in predicting the onset of clinical outcomes, such as MetS.

PMID:40680238 | DOI:10.1093/gerona/glaf157

J Med Internet Res. 2025 Jul 18;27:e76215. doi: 10.2196/76215.

ABSTRACT

BACKGROUND: Machine learning (ML) models may offer greater clinical utility than conventional risk scores, such as the Thrombolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) risk scores. However, there is a lack of knowledge on whether ML or traditional models are better at predicting the risk of major adverse cardiovascular and cerebrovascular events (MACCEs) in patients with acute myocardial infarction (AMI) who have undergone percutaneous coronary interventions (PCI).

OBJECTIVE: The aim of this study is to systematically review and critically appraise studies comparing the performance of ML models and conventional risk scores for predicting MACCEs in patients with AMI who have undergone PCI.

METHODS: Nine academic and electronic databases including PubMed, CINAHL, Embase, Web of Science, Scopus, ACM, IEEE, Cochrane, and Google Scholar were systematically searched from January 1, 2010, to December 31, 2024. We included studies of patients with AMI who underwent PCI, and predicted MACCE risk using ML algorithms or conventional risk scores. We excluded conference abstracts, gray literature, reviews, case reports, editorials, qualitative studies, secondary data analyses, and non-English publications. Our systematic search yielded 10 retrospective studies, with a total sample size of 89,702 individuals. Three validation tools were used to assess the validity of the published prediction models. Most included studies were assessed as having a low overall risk of bias.

RESULTS: The most frequently used ML algorithms were random forest (n=8) and logistic regression (n=6), while the most used conventional risk scores were GRACE (n=8) and TIMI (n=4). The most common MACCEs component was 1-year mortality (n=3), followed by 30-day mortality (n=2) and in-hospital mortality (n=2). Our meta-analysis demonstrated that ML-based models (area under the receiver operating characteristic curve: 0.88, 95% CI 0.86-0.90; I²=97.8%; P<.001) outperformed conventional risk scores (area under the receiver operating characteristic curve: 0.79, 95% CI 0.75-0.84; I²=99.6%; P<.001) in predicting mortality risk among patients with AMI who underwent PCI. Heterogeneity across studies was high. Publication bias was assessed using a funnel plot. The top-ranked predictors of mortality in both ML and conventional risk scores were age, systolic blood pressure, and Killip class.

CONCLUSIONS: This review demonstrated that ML-based models had superior discriminatory performance compared to conventional risk scores for predicting MACCEs in patients with AMI who had undergone PCI. The most commonly used predictors were confined to nonmodifiable clinical characteristics. Therefore, health care professionals should understand the advantages and limitations of ML algorithms and conventional risk scores before applying them in clinical practice. We highlight the importance of incorporating modifiable factors-including psychosocial and behavioral variables-into prediction models for MACCEs following PCI in patients with AMI. In addition, further multicenter prospective studies with external validation are required to address validation limitations.

PMID:40680235 | DOI:10.2196/76215

Physiol Rep. 2025 Jul;13(14):e70463. doi: 10.14814/phy2.70463.

ABSTRACT

Selective androgen receptor modulators (SARMs) are important hypertrophic molecules that are potential treatments for many types of myopathy and osteopathy. This study aimed to determine if the SARM RAD140 had additive effects on muscle hypertrophy when combined with the functional overloading (FO) of the plantaris muscle. Male, Sprague-Dawley rats (n = 10 rats/group) were randomly selected into one of four treatment groups: (1) RAD140-FO, (2) RAD140-Control, (3) Vehicle-Control, or (4) Vehicle-FO. RAD140 groups received drugs through drinking water, and control groups received only Vehicle (methylcellulose). Standard rat chow and water were provided ad libitum. Muscle weights of the triceps-surae group and muscle fiber cross-sectional area (CSA) were measured. Muscle weight analysis showed a marked increase in RAD140-FO groups but was not statistically different from the Vehicle-FO group. CSA results indicated similar findings; however, RAD140-Control showed significantly elevated CSA compared to Vehicle-Control. The tibial microarchitecture was analyzed using micro computed tomography. RAD140 did not impact cortical or trabecular bone structural morphometric properties following 14 days of treatment. The data presented here show the potential of RAD140 to stimulate muscle hypertrophy in young healthy rats.

PMID:40680216 | DOI:10.14814/phy2.70463

Aging Ment Health. 2025 Jul 18:1-12. doi: 10.1080/13607863.2025.2532658. Online ahead of print.

ABSTRACT

OBJECTIVES: The aim of this study was to assess the effectiveness of a dementia awareness and stigma reduction program among the Arab minoritized population in Israel. Specifically, we examined changes in knowledge, stigma, perceived susceptibility, and support sources following community-based informational sessions conducted by trained Arab student facilitators.

METHODS: A mixed-methods approach was used. A pre-post design with 1349 participants was employed for the quantitative component, assessing changes in subjective and objective knowledge, stigma, perceived susceptibility, and support sources. The qualitative component included in-depth interviews with 40 student facilitators to explore their experiences and insights. Data were analyzed using descriptive statistics, regression analyses, and thematic content analysis.

RESULTS: The findings revealed significant increases in dementia knowledge, positive emotional reactions, and use of formal support sources. However, no significant changes were observed in perceived susceptibility, negative emotional reactions, or behavioral discrimination. Qualitative findings highlighted facilitators’ role in challenging misconceptions, fostering engagement, and addressing cultural barriers to dementia discussions.

CONCLUSION: The program effectively enhanced dementia knowledge and encouraged community dialogue but had limited impact on deep-seated stigma. Future initiatives should incorporate sustained interventions and culturally tailored messaging to further promote dementia awareness and reduce stigma in minoritized communities.

PMID:40680202 | DOI:10.1080/13607863.2025.2532658

Am J Respir Crit Care Med. 2025 Jul 18. doi: 10.1164/rccm.202505-1102LE. Online ahead of print.

NO ABSTRACT

PMID:40680196 | DOI:10.1164/rccm.202505-1102LE

Am J Respir Crit Care Med. 2025 Jul 18. doi: 10.1164/rccm.202505-1235LE. Online ahead of print.

NO ABSTRACT

PMID:40680193 | DOI:10.1164/rccm.202505-1235LE

J Med Internet Res. 2025 Jul 18;27:e75701. doi: 10.2196/75701.

ABSTRACT

BACKGROUND: In today’s digital landscape, social media proliferation offers easier access to others’ information and social activities but also introduces challenges such as social media fatigue (SMF). Previous studies have linked the fear of missing out (FoMO) to SMF; however, the directionality of this relationship remains unclear.

OBJECTIVE: This study aimed to explore the relationship between FoMO and SMF among college students and examine whether a mutually predictive relationship exists between them.

METHODS: This study adopted a longitudinal research design, administering questionnaires at two distinct time points (ie, T1 and T2) separated by a two-month interval. At T1, the questionnaire included demographic variables of the research subjects (student ID, name, gender, etc.), the Fear of Missing Out Scale, and the Social Media Fatigue Scale. At T2, the questionnaire consisted only of collecting demographic information (student ID and name) for matching, along with the same two scales. Following data collection, the datasets from the two time points were matched based on the demographic information; only successfully matched data were included in the final analyses. Subsequently, descriptive statistics and correlation analyses of FoMO and SMF at T1 and T2 were conducted using SPSS (version 26.0). Finally, a cross-lagged panel analysis was conducted using the FoMO and SMF at T1 and T2 to examine the autoregressive and cross-lagged relationships between the variables over time.

RESULTS: A total of 862 valid questionnaires were matched across the two data collection steps. Correlation analysis showed that FoMO at T1 was positively correlated with SMF at T1 (r=0.340; P<.001) and FoMO at T2 (r=0.332; P<.001) and SMF at T2 (r=0.229; P<.001). FoMO at T2 was positively correlated with SMF at T1 (r=0.217; P<.001) and T2 (r=0.417; P<.001). SMF at T1 and T2 were also positively correlated (r=0.425; P<.001). The cross-lagged regression results indicated that using the autoregressive path, FoMO at T1 positively predicted FoMO at T2 (β=0.300; P<.001), and SMF at T1 positively predicted SMF at T2 (β=0.351; P<.001). Additionally, FoMO at T1 positively predicted SMF at T2 (β=0.067; P=.003), and SMF at T1 positively predicted FoMO at T2 (β=0.156; P<.001).

CONCLUSIONS: There is a bidirectional relationship between FoMO and SMF among college students, suggesting a mutual influence over each other and that this relationship perpetuates a negative cycle. These findings further extend existing research and provide insights for developing mental health programs for college students.

PMID:40680183 | DOI:10.2196/75701