Machine Learning Models Reliably Predict Clinical Outcomes in Medial Patellofemoral Ligament Reconstruction.

PURPOSE: To develop the machine learning model to predict clinical outcomes following MPFLR and identify the important predictive indicators. METHODS: This study included patients who underwent MPFLR from January 2018 to December 2022. The exclusion criteria were as follows: 1) concurrent bony procedures, 2) history of other knee surgeries, and 3) follow-up period of less than 12 months. Forty-two predictive models were constructed for seven clinical outcomes (failure to achieve MCID of clinical scores, return to pre-injury sports, pivoting sports, and recurrent instability) using six machine learning algorithms (Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, implemented multilayer perceptron, and K-nearest neighbor). The performance of the model was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity. Additionally, Shapley Additive Explanation summary plot was employed to identify the important predictive factors of the best-performing model. RESULTS: A total of 218 patients met criteria. For the best-performing models in predicting failure to achieve the MCID for Lysholm, IKDC, Kujala, and Tegner scores, the AUCs and accuracies were 0.884 (good) and 87.3%, 0.859 (good) and 86.2%, 0.969 (excellent) and 97.0%, and 0.760 (fair) and 76.8%, respectively; 0.952 (excellent) and 95.2% for return to pre-injury sports; 0.756 (fair) and 75.4% for return to pivoting sports; and 0.943 (excellent) and 94.9% for recurrent instability. Low preoperative Tegner score, shorter time to surgery, and absence of severe trochlear dysplasia were significant predictors for return to pre-injury sports, while absence of severe trochlear dysplasia and patellar alta were significant predictors for return to pivoting sports. Older age, female sex, and low preoperative Lysholm score were highly predictive of recurrent instability. CONCLUSION: The predictive models developed using machine learning algorithms can reliably forecast the clinical outcomes of MPFLR, particularly demonstrating excellent performance in predicting recurrent instability. LEVEL OF EVIDENCE: Level III, case-control study.

Neighborhood Disadvantage and Prostate Tumor RNA Expression of Stress-Related Genes.

Importance: African American men experience greater prostate cancer incidence and mortality than White men. Growing literature supports associations of neighborhood disadvantage, which disproportionately affects African American men, with aggressive prostate cancer; chronic stress and downstream biological impacts (eg, increased inflammation) may contribute to these associations. Objective: To examine whether several neighborhood disadvantage metrics are associated with prostate tumor RNA expression of stress-related genes. Design, Setting, and Participants: This cross-sectional study leveraged prostate tumor transcriptomic data for African American and White men with prostate cancer who received radical prostatectomy at the University of Maryland Medical Center between August 1992 and January 2021. Data were analyzed from May 2023 to April 2024. Exposures: Using addresses at diagnosis, 2 neighborhood deprivation metrics (Area Deprivation Index [ADI] and validated bayesian Neighborhood Deprivation Index) as well as the Racial Isolation Index (RI) and historical redlining were applied to participants' addresses. Self-reported race was determined using electronic medical records. Main Outcomes and Measures: A total of 105 stress-related genes were evaluated with each neighborhood metric using linear regression, adjusting for race, age, and year of surgery. Genes in the Conserved Transcriptional Response to Adversity (CTRA) and stress-related signaling genes were included. Results: A total of 218 men (168 [77%] African American, 50 [23%] White) with a median (IQR) age of 58 (53-63) years were included. African American participants experienced greater neighborhood disadvantage than White participants (median [IQR] ADI, 115 [100-130] vs 92 [83-104]; median [IQR] RI, 0.68 [0.34-0.87] vs 0.11 [0.06-0.14]). ADI was positively associated with expression for 11 genes; HTR6 (serotonin pathway) remained significant after multiple-comparison adjustment (ß = 0.003; SE, 0.001; P < .001; Benjamini-Hochberg q value = .01). Several genes, including HTR6, were associated with multiple metrics. We observed higher expression of 5 proinflammatory genes in the CTRA with greater neighborhood disadvantage (eg, CXCL8 and ADI, ß = 0.008; SE, 0.003; P = .01; q value = .21). Conclusions and Relevance: In this cross-sectional study, the expression of several stress-related genes in prostate tumors was higher among men residing in disadvantaged neighborhoods. This study is one of the first to suggest associations of neighborhood disadvantage with prostate tumor RNA expression. Additional research is needed in larger studies to replicate findings and further investigate interrelationships of neighborhood factors, tumor biology, and aggressive prostate cancer to inform interventions to reduce disparities.

A Linear Relationship between the Number of Cancers among First-Degree Relatives and the Risk of Multiple Primary Cancers.

With advances in the early detection and treatment of cancer, the incidence of multiple primary cancers (MPC) or second primary cancers has increased over time. Characterization of etiologic risk factors, including family history of cancer, within the general population is critical for assessing MPC risk in patients. We examined the association between family history of cancer among first-degree relatives and MPC risk in a prospective study of 139,958 participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox proportional hazard models were used to calculate HRs and 95% confidence intervals (95% CI), adjusting for potential confounders. Over a median follow-up of 16 years (IQR: 11-19 years), 6,170 participants were diagnosed with MPC. Having a family history of cancer increased the risk of MPC by 18% (HR, 1.18; 95% CI, 1.12-1.24). A positive linear trend was observed between the reported number of cancers in the family history and MPC risk with HRs (95% CI) of 1.13 (1.07-1.20), 1.23 (1.14-1.33), 1.29 (1.15-1.45), and 1.42 (1.20-1.70) for one, two, three, and four or more cancers among first-degree relatives, respectively (Ptrend = 2.36 × 10-13). No significant differences were observed by cancer histology or specific cancer types reported in the family history. Our study demonstrates that the family history of cancer is an important risk factor for the development of MPCs and that a comprehensive assessment of the number of cancers reported among first-degree relatives may identify those at higher risk who may benefit from targeted cancer prevention and screening strategies. Prevention Relevance: Our study makes a substantial contribution to the understanding of risk factors for MPCs in the general population. It demonstrates that individuals with a strong family history of cancer are at higher risk for MPCs and may benefit from more targeted cancer prevention and screening interventions.

A Linear Relationship between the Number of Cancers among First-degree Relatives and the Risk of Multiple Primary Cancers.

With advances in the early detection and treatment of cancer, the incidence of multiple primary cancers (MPC), or second primary cancers, has risen over time. Characterization of etiologic risk factors, including family history of cancer, within the general population is critical for assessing MPC risk in patients. We examined the association between family history of cancer among first-degree relatives and MPC risk in a prospective study of 139,958 participants from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Cox proportional hazard models were used to calculate hazard ratios (HR) and 95% confidence intervals (95%CI), adjusting for potential confounders. Over a median follow-up of 16 years (interquartile range: 11-19 years), 6,170 participants were diagnosed with MPC. Having a family history of cancer increased the risk of MPC by 18% (HR=1.18, 95%CI: 1.12-1.24). A positive linear trend was observed between the reported number of cancers in the family history and MPC risk with HRs (95%CI) of 1.13 (1.07-1.20), 1.23 (1.14-1.33), 1.29 (1.15-1.45), and 1.42 (1.20-1.70) for 1, 2, 3, and 4+ cancers among first-degree relatives, respectively (Ptrend=2.36x10-13). No significant differences were observed by cancer histology or specific types of cancer reported in the family history. Our study demonstrates that family history of cancer is an important risk factor for the development of multiple primary cancers and that a comprehensive of assessment of the number of cancers reported among first-degree relatives may identify those at higher risk who may benefit from targeted cancer prevention and screening strategies.

Association between surrogate marker of insulin resistance and bone mineral density in US adults without diabetes.

This study examines the relationship between TyG-BMI, an indicator of insulin resistance, and bone mineral density in US adults without diabetes, revealing a positive association. The findings suggest that higher TyG-BMI levels may be linked to a lower risk of osteoporosis, providing a basis for future research in this area. OBJECTIVE: Patients with osteoporosis are often diagnosed with type 2 diabetes or prediabetes. Insulin resistance is a prediabetic state, and triglyceride glucose-body mass index (TyG-BMI) has been recognized as a potential predictor of it, valuable in assessing prediabetes, atherosclerosis, and other diseases. However, the validity of TyG-BMI in osteoporosis studies remains inadequate. PURPOSE: The purpose of this study was to evaluate the relationship between TyG-BMI and BMD as well as the effect of TyG-BMI on the odds of developing osteoporosis in US adults without diabetes. METHODS: National Health and Nutrition Examination Survey data were obtained. The relationship between TyG-BMI and BMD was evaluated via multivariate linear regression models. Smoothed curve fitting and threshold effect analysis explored potential non-linear relationships, and age, gender, and race subgroup analyses were performed. In addition, multivariate logistic regression models were employed to analyze its potential role in the development of osteoporosis. RESULTS: In a study of 6501 participants, we observed a significant positive correlation between the TyG-BMI index and BMD, even after adjusting for covariates and categorizing TyG-BMI. The study identified specific TyG-BMI folding points-112.476 for the total femur BMD, 100.66 for the femoral neck BMD, 107.291 for the intertrochanter BMD, and 116.58 for the trochanter BMD-indicating shifts in the relationship's strength at these thresholds. While the association's strength slightly decreased after the folding points, it remained significant. Subgroup analyses further confirmed the positive TyG-BMI and BMD correlation. Multivariate linear regression analyses indicated a lower osteoporosis risk in participants with higher TyG-BMI levels, particularly in menopausal women over 40 and men over 60. CONCLUSION: This study suggests a positive correlation between BMD and TyG-BMI in US adults without diabetes. Individuals with higher levels of TyG-BMI may have a lower risk of osteoporosis.

Learning Curve for No-Touch Vein Harvesting Technique in Off-Pump Coronary Artery Bypass Grafting.

INTRODUCTION: This study aims to evaluate the learning curve associated with the no-touch vein harvesting technique in off-pump coronary artery bypass grafting (CABG), highlighting its impact on surgical proficiency. METHODS: We employed logarithmic curve fitting to analyze the learning curves of 160 patients undergoing no-touch CABG, with a detailed retrospective examination of 89 patients who received three grafts using Cumulative Sum (CUSUM) analysis. Patients were categorized into two phases: the initial learning phase and the subsequent mastery phase, based on the chronological order of surgeries. We then compared perioperative outcomes between these phases. RESULTS: The learning curve for the no-touch vein harvesting technique was quantitatively established at 51 cases via CUSUM analysis, with supporting evidence from logarithmic curve fitting indicating a significant proficiency milestone. In the mastery phase, median operative times, aorta-saphenous vein graft (SVG) anastomosis, and SVG inspection durations were notably reduced (230 vs. 250 minutes, P = 0.002; 11.5 vs. 13.0 minutes, P = 0.025; 9.0 vs. 11.0 minutes, P = 0.002, respectively), alongside decreased initial 48-hour chest tube drainage, shorter postoperative hospital stays, and fewer incidences of delayed leg incision healing compared to the learning phase [312.6 (140.7) ml vs. 401.0 (233.5) ml, P = 0.029; 11.0 d vs. 12.0 d, P = 0.026; 15.7% vs. 2.6%, P = 0.043)]. CONCLUSION: Cardiac surgeons adopting the full-incision SVG harvesting method for no-touch CABG undergo a discernible learning curve before achieving early proficiency. It is crucial, especially during the initial learning phase, to focus on aorta-SVG anastomosis, the meticulous inspection for bleeding, and the management of wound complications to optimize patient outcomes.

Solving the Jigsaw puzzle of phytosterol diversity by a novel sterol methyltransferase from Zea mays.

Phytosterols are vital structural and regulatory components in plants. Zea mays produces a series of phytosterols that are specific to corn. However, the underline biosynthetic mechanism remains elusive. In this study, we identified a novel sterol methyltransferase from Z. mays (ZmSMT1-2) which showed a unique feature compared with documented plant SMTs. ZmSMT1-2 showed a substrate preference for cycloartenol. Using S-adenosyl-L-methionine (AdoMet) as a donor, ZmSMT1-2 converted cycloartenol into alkylated sterols with unique side-chain architectures, including Δ25(27) (i.e., cyclolaudenol and cycloneolitsol) and Δ24(25) (i.e., cyclobranol) sterols. Cycloneolitsol is identified as a product of SMTs for the first time. Our discovery provides a previously untapped mechanism for phytosterol biosynthesis and adds another layer of diversity of sterol biosynthesis.

Robust Artificial Intelligence Tool for Atrial Fibrillation Diagnosis: Novel Development Approach Incorporating Both Atrial Electrograms and Surface ECG and Evaluation by Head-to-Head Comparison With Hospital-Based Physician ECG Readers.

BACKGROUND: Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative patients, increases cost of care. Consequently, ECG screening for AF in high-risk patients is important but labor-intensive. Artificial intelligence (AI) may reduce AF detection workload, but AI development presents challenges. METHODS AND RESULTS: We used a novel approach to AI development for AF detection using both surface ECG recordings and atrial epicardial electrograms obtained in postoperative cardiac patients. Atrial electrograms were used only to facilitate establishing true AF for AI development; this permitted the establishment of an AI-based tool for subsequent AF detection using ECG records alone. A total of 5 million 30-second epochs from 329 patients were annotated as AF or non-AF by expert ECG readers for AI training and validation, while 5 million 30-second epochs from 330 different patients were used for AI testing. AI performance was assessed at the epoch level as well as AF burden at the patient level. AI achieved an area under the receiver operating characteristic curve of 0.932 on validation and 0.953 on testing. At the epoch level, testing results showed means of AF detection sensitivity, specificity, negative predictive value, positive predictive value, and F1 (harmonic mean of positive predictive value and sensitivity) as 0.970, 0.814, 0.976, 0.776, and 0.862, respectively, while the intraclass correlation coefficient for AF burden detection was 0.952. At the patient level, AF burden sensitivity and positive predictivity were 96.2% and 94.5%, respectively. CONCLUSIONS: Use of both atrial electrograms and surface ECG permitted development of a robust AI-based approach to postoperative AF recognition and AF burden assessment. This novel tool may enhance detection and management of AF, particularly in patients following operative cardiac surgery.

Does COVID-19 affect sperm quality in males? the answer may be yes, but only temporarily.

BACKGROUND: The Corona Virus Disease 2019 (COVID-19) pandemic has raised concerns regarding its potential impact on male reproductive health. However, the impact of COVID-19 on sperm quality remains uncertain. This retrospective study aimed to investigate the short-term and relatively long-term effects of COVID-19 infection on sperm quality. METHODS: A total of 85 males with fertility requirements, who underwent semen evaluation at Guilin People's Hospital between June 2022 and July 2023, were included in the study. Changes in semen parameters were analyzed across three specific timeframes: within 6 months before COVID-19 infection, within 3 months after COVID-19 infection, and 3-6 months after COVID-19 recovery. RESULTS: The results revealed that the sperm concentration and total sperm number were significantly lower after infection compared to before, while in the recovery period, the sperm concentration, total sperm count, progressive motility, and normal morphology significantly increased. Comparing the three periods, the most significant difference was observed in sperm concentration, which exhibited a significant decrease after infection but returned to normal levels after recovery from COVID-19. CONCLUSIONS: These findings suggest that COVID-19 may exert some impact on sperm quality, particularly evidenced by decreased sperm concentration post-infection. Fortunately, these effects on semen parameters appear to be temporary, with gradual restoration of semen parameters within 3-6 months after recovery. However, further research is needed to explore the underlying mechanisms and long-term implications of these observed changes in semen parameters.

Concomitant assessment of PD-1 and CD56 expression identifies subsets of resting cord blood Vδ2 T cells with disparate cytotoxic potential.

Vγ9Vδ2 T lymphocytes are programmed for broad antimicrobial responses with rapid production of Th1 cytokines even before birth, and thus thought to play key roles against pathogens in infants. The process regulating Vδ2 cell acquisition of cytotoxic potential shortly after birth remains understudied. We observed that perforin production in cord blood Vδ2 cells correlates with phenotypes defined by the concomitant assessment of PD-1 and CD56. Bulk RNA sequencing of sorted Vδ2 cell fractions indicated that transcripts related to cytotoxic activity and NK function are enriched in the subset with the highest proportion of perforin+ cells. Among differentially expressed transcripts, IRF8, previously linked to CD8 T cell effector differentiation and NK maturation, has the potential to mediate Vδ2 cell differentiation towards cytotoxic effectors. Our current and past results support the hypothesis that distinct mechanisms regulate Vδ2 cell cytotoxic function before and after birth, possibly linked to different levels of microbial exposure.