METHODS: This study aims to develop a highly accurate and lightweight model for automatically predicting and classifying KOA through knee X-ray imaging. We propose a deep learning model named OA-MEN, which integrates a hybrid model combining ResNet and MobileNet feature extraction with multi-scale feature fusion. This approach ensures enhanced extraction of semantic information without losing the advantages of large feature maps provided by high image resolution in lower layers of the network. This effectively expands the model's receptive field and strengthens its understanding capability. Additionally, we conducted unseen-data tests and compared our model with widely used baseline models to highlight its superiority over conventional approaches.
RESULTS: The OA-MEN model demonstrated exceptional performance in tests. In the unseen-data test, our model achieved an average accuracy (ACC) of 84.88% and an Area Under the Curve (AUC) of 89.11%, marking improvements over the best-performing baseline models. These results showcase its improved capability in predicting KOA from X-ray images, making it a promising tool for assisting radiologists in diagnosis and treatment selection in clinical settings.
CONCLUSION: Leveraging deep learning for osteoarthritis classification guarantees heightened efficiency and accuracy. The future goal is to seamlessly integrate deep learning and advanced computational techniques with the expertise of medical professionals.
MATERIAL AND METHODS: A total of 34 chronic renal disease patients (stage 3 and 4) were recruited in a randomized controlled trial. Handgrip exercise was performed for 8 weeks in the intervention group. Handgrip-strength measurement and distal forearm cephalic vein diameter of a non-dominant hand with and without tourniquet was recorded (measurement is taken 1 cm proximal to the radial styloid).
RESULTS: After 8 weeks, the mean cephalic vein diameter in the intervention group increased from 1.77 and 1.97 mm to 2.15 and 2.43 mm, without and with a tourniquet, respectively (p < 0.05). There is also a significant change in the mean diameter of distal forearm cephalic vein (p < 0.05) in the intervention group when measured in both the absence (mean change 0.39 ± 0.06 mm vs 0.01 ± 0.02 mm) and the presence of tourniquet (mean change 0.47 ± 0.07 mm vs 0.01 ± 0.01 mm).
CONCLUSION: These findings suggest that non-invasive handgrip exercise can increase in the diameter of the distal forearm cephalic vein, thereby increasing the rate of successful arteriovenous fistula creation.
METHODS: PubMed, Scopus, Web of Science, and Embase databases were systematically searched from their inceptions until 10 December 2019 for randomized controlled trials (RCTs) comparing individuals who underwent resistance training and control participants. We applied a random-effects model to calculate the weighted mean difference (WMD).
RESULTS: 33 trials reported IGF-1 level as an outcome measure. The pooled estimate demonstrated a significant increase in IGF-1 (WMD: 10.34 ng/ml, 95 % CI: 4.93, 15.74, p = 0.000, I2 = 90.3 %) after resistance training compared with the control group. Subgroup analysis demonstrated that the increase in IGF-1 levels following resistance training was only statistically significant in treatment duration ≤16 weeks (WMD: 8.04 ng/ml), participants aged more than 60 years old (WMD: 9.84 ng/ml); and in women (WMD: 17.27 ng/ml). Subsequent analysis of the relationship between participants' age with plasma IGF-1 alterations revealed a U shape correlation in non-liner dose response, in which resistance training resulted in a declined IGF-1 level up to 40 years of age. Beyond 40 years old, the IGF-1 level was increased following resistance training.
CONCLUSION: We have successfully demonstrated that resistance training was associated with an increased IGF-1 level among those who received the training for ≤16 weeks, among participants older than 60 years old, and among women. Further studies are warranted to clarify the mechanisms underlying the influence of resistance training on IGF-1.
METHODS: We obtained information on medication use and cancer diagnosis from National Health and Nutrition Examination Survey participants. After propensity score matching, we conducted survey-weighted multivariate logistic regression and restricted cubic spline analysis to assess the observed correlation between medication use and cancer while adjusting for multiple covariates. We also performed MR analysis to investigate causality based on summary data from genome-wide association studies on medication use and cancers. We performed sensitivity analyses, replication analysis, genetic correlation analysis, and reverse MR analysis to improve the reliability of MR findings.
RESULTS: We found that the use of agents acting on the renin-angiotensin system was associated with reduced risk of prostate cancer (odds ratio (OR) = 0.42; 95% confidence interval (CI) = 0.27-0.63, P
OBJECTIVE: To investigate the associations of ACEs with changes in epigenetic age acceleration (EAA), a biomarker associated with various health outcomes in middle-aged adults, in a population with balanced race and sex demographics.
DESIGN, SETTING, AND PARTICIPANTS: Data for this cohort study were from the Coronary Artery Risk Development in Young Adults (CARDIA) study. Participants in CARDIA underwent 8 follow-up exams from baseline (year 0 [Y0]; 1985-1986) to Y30 (2015-2016), and participant blood DNA methylation information was obtained at Y15 (2000-2001) and Y20 (2005-2006). Individuals from Y15 and Y20 with available DNA methylation data and complete variables for ACEs and covariates were included. Data were analyzed from September 2021 to August 2022.
EXPOSURES: Participant ACEs (general negligence, emotional negligence, physical violence, physical negligence, household substance abuse, verbal and emotional abuse, and household dysfunction) were obtained at Y15.
MAIN OUTCOMES AND MEASURES: The primary outcome consisted of results from 5 DNA methylation-based EAA measurements known to be associated with biological aging and long-term health: intrinsic EAA (IEAA), extrinsic EAA (EEAA), PhenoAge acceleration (PhenoAA), GrimAge acceleration (GrimAA), and Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), measured at Y15 and Y20. Linear regression and generalized estimating equations were used to assess associations of the burden of ACEs (≥4 vs <4 ACEs) with EAA adjusting for demographics, health-related behaviors, and early life and adult socioeconomic status.
RESULTS: A total of 895 participants for Y15 (mean [SD] age, 40.4 [3.5] years; 450 males [50.3%] and 445 females [49.7%]; 319 Black [35.6%] and 576 White [64.4%]) and 867 participants for Y20 (mean [SD] age, 45.4 [3.5] years; 432 males [49.8%] and 435 females [50.2%]; 306 Black [35.3%] and 561 White [64.7%]) were included after excluding participants with missing data. There were 185 participants with (20.7%) vs 710 participants without (79.3%) 4 or more ACEs at Y15 and 179 participants with (20.6%) vs 688 participants without (79.4%) 4 or more ACEs at Y20. Having 4 or more ACEs was positively associated with EAA in years at Y15 (EEAA: β = 0.60 years; 95% CI, 0.18-1.02 years; PhenoAA: β = 0.62 years; 95% CI = 0.13-1.11 years; GrimAA: β = 0.71 years; 95% CI, 0.42-1.00 years; DunedinPACE: β = 0.01; 95% CI, 0.01-0.02) and Y20 (IEAA: β = 0.41 years; 95% CI, 0.05-0.77 years; EEAA: β = 1.05 years; 95% CI, 0.66-1.44 years; PhenoAA: β = 0.57 years; 95% CI, 0.08-1.05 years; GrimAA: β = 0.57 years; 95% CI, 0.28-0.87 years; DunedinPACE: β = 0.01; 95% CI, 0.01-0.02) after adjusting for demographics, health-related behaviors, and socioeconomic status.
CONCLUSIONS AND RELEVANCE: In this cohort study, ACEs were associated with EAA among middle-aged adults after controlling for demographics, behavior, and socioeconomic status. These findings of the associations between early life experience and the biological aging process in midlife may contribute to health promotion in a life course perspective.
STUDY DESIGN: A systematic search was performed using the MEDLINE, EMBASE, PsycINFO, CINAHL, and Web of Science databases to identify English-language articles published through June 2018. Articles were included if they were longitudinal studies in community-based populations, the primary exposure occurred during childhood, and the primary outcome was either a measure of subclinical CVD or a clinical CVD event occurring in adulthood. Two independent reviewers screened determined whether eligibility criteria were met.
RESULTS: There were 210 articles that met the predefined criteria. The greatest number of publications examined associations of clinical risk factors, including childhood adiposity, blood pressure, and cholesterol, with the development of adult CVD. Few studies examined childhood lifestyle factors including diet quality, physical activity, and tobacco exposure. Domains of risk beyond "traditional" cardiovascular risk factors, such as childhood psychosocial adversity, seemed to have strong published associations with the development of CVD.
CONCLUSIONS: Although the evidence was fairly consistent in direction and magnitude for exposures such as childhood adiposity, hypertension, and hyperlipidemia, significant gaps remain in the understanding of how childhood health and behaviors translate to the risk of adulthood CVD, particularly in lesser studied exposures like glycemic indicators, physical activity, diet quality, very early life course exposure, and population subgroups.
METHODS: We used data spanning 2010-2018 from children aged 2-12 years within the Chicago Area Patient-Centered Outcomes Research Network-an electronic health record network. Four clinical systems comprised the derivation sample and a fifth the validation sample. Body mass index, blood pressure, cholesterol, and blood glucose were categorized as ideal, intermediate, and poor using clinical measurements, laboratory readings, and International Classification of Diseases diagnosis codes and summed for an overall CVH score. Group-based trajectory modeling was used to create CVH score trajectories which were assessed for classification accuracy in the validation sample.
RESULTS: Using data from 122,363 children (47% female, 47% non-Hispanic White) three trajectories were identified: 59.5% maintained high levels of clinical CVH, 23.4% had high levels of CVH that declined, and 17.1% had intermediate levels of CVH that further declined with age. A similar classification emerged when the trajectories were fitted in the validation sample.
CONCLUSIONS: Stratification of CVH was present by age 2, implicating the need for early life and preconception prevention strategies.
METHODS: Data were used from children and adolescents aged 8-19 years in six pooled childhood cohorts (19,261 participants, collected between 1972 and 2010) to create reference standards for fasting glucose and total cholesterol. Using the models for glucose and cholesterol as well as previously published reference standards for body mass index and blood pressure, clinical cardiovascular health charts were developed. All models were estimated using sex-specific random-effects linear regression, and modeling was performed during 2020-2022.
RESULTS: Models were created to generate charts with smoothed means, percentiles, and standard deviations of clinical cardiovascular health for each year of childhood. For example, a 10-year-old girl with a body mass index of 16 kg/m2 (30th percentile), blood pressure of 100/60 mm Hg (46th/50th), glucose of 80 mg/dL (31st), and total cholesterol of 160 mg/dL (46th) (lower implies better) would have a clinical cardiovascular health percentile of 62 (higher implies better).
CONCLUSIONS: Clinical cardiovascular health charts based on pediatric data offer a standardized approach to express clinical cardiovascular health as an age- and sex-standardized percentile for clinicians to assess cardiovascular health in childhood to consider preventive approaches at early ages and proactively optimize lifetime trajectories of cardiovascular health.