Multivariable Approach Research Articles

Exploring the relationship between gut microbiota, immune characteristics, and female genital tract polyps using genetic evidence.

The study investigates the causal relationship between gut microbes and female genital tract polyps, exploring the potential mediating role of immune cells via Mendelian randomization (MR) analysis. Our MR study was designed following the STROBE-MR guidelines. We combined data from a large-scale GWAS meta-analysis, including 731 immune profiles and female genital tract polyps, with gut microbiology data sourced from the MiBioGen consortium. Univariate Mendelian randomization was employed to identify gut microbes and immune profiles significantly associated with female genital tract polyps causally. A 2-step MR analysis was utilized to investigate the potential mediating role of immune cells. Furthermore, we utilized the multivariable MR approach based on Bayesian model averaging (MR-BMA) to further assess the prioritization of gut microbiota vs immune characteristics in the development of female genital tract polyps. Through univariate MR analysis, we identified a significant causal link between 12 gut microbiota, 31 immune features, and female genital tract polyps. Four causal pathways involving gut microbiota, immune cells, and polyps were identified among them. MR-BMA analysis indicated marginal inclusion probability (MIP) values exceeding 0.1 for 5 gut microbiota groups: Victivallaceae (model-averaged causal estimate [MACE] = 0.060, MIP = 0.581, P = .0089), Ruminococcus gautreuii (MACE = 0.052, MIP = 0.346, P = .0640), Lachnoclostrium (MACE = 0.0380, MIP = 0.225, P = .1875), Alphaprobacter (MACE = 0.0186, MIP = 0.140, P = .3934), and Fusicatenibacter (MACE = 0.013, MIP = 0.110, P = .5818). Six immune features exhibit high priority, with MIP values exceeding 0.5, including HLA DR on CD33+ HLA DR+ CD14dim (MACE = -0.015, MIP = 0.753, P = .0853), HVEM on naive CD4+ T cell (MACE = 0.024, MIP = 0.737, P = .0053), CD80 on CD62L+ plastic cytoplasmic dendritic cell (MACE = 0.024, MIP = 0.721, P = .0228), CD28 on activated and secret CD4 regulatory T cell (MACE = 0.0054, MIP = 0.706, P = .3245), HLA DR on CD14+ CD16 monocyte (MACE = -0.0003, MIP = 0.520, P = .7927), HLA DR on CD14+ monocyte (MACE = -0.0029, MIP = 0.509, P = .5576). Our research indicates that gut microbiota exerts an independent causal influence on female genital tract polyps, potentially impacting them via various immune cells.

Exploring heterogeneity of perimenopause with application of multivariable analysis approaches

ObjectivesHeterogeneity of clinical appearance had made it a challenge to make individualized and comprehensive management of perimenopause. This study aimed to estimate the profiles over heterogenous appearances of perimenopause with application of latent variable analysis methods over an optimized multidimensional assessing framework.MethodsA two-phase clinical study was designed and advanced in the research center in Guangzhou, China. The assessing framework was developed over the initial item pool as integration of 4 scales including Insomnia severity index, Modified Kupperman index, Self-rating anxiety scale, and Self-rating depression scale. Validity and reliability of the instrument were evaluated and the psychometric properties of the items were estimated with multidimensional item response theory(MIRT). And then computer adaptive testing(CAT) was developed with the estimated model. We used latent profile analysis (LPA) to cluster patients into subgroups as patterns characterized by multidimensional latent trait scores. Finally, interpretability and efficiency were analyzed via comparison between the two assessing strategies.ResultThere were in total 336 patients diagnosed with perimenopause enrolled for the assessment. A conceptual framework was estimated consisting of 6 factors including sleep disturbance, mood swings, vasomotor symptoms, positive attitude towards life, multisystem abnormality, and fatigue. The construct validity was evaluated as optimized with CMIN/df = 1.814, GFI = 0.619, CFI = 0.721, TLI = 0.707 and RMSEA = 0.075. With scores in the simulated CAT, the 4 latent profiles model was estimated indicating the heterogeneity of perimenopause characterized by different severity of psychological and physical discomforts in the LPA.ConclusionThe quantitative paradigm raised in this study revealed the potential patterns presenting heterogeneity of perimenopause offering better interpretation for clinical assessment.

Increased population exposure to extreme droughts in Iberia due to 0.5ºC additional anthropogenic warming

Abstract This study investigates the effects of incremental global warming, specifically the transition from 1.5°C to 2.0°C, on drought conditions in the Iberian Peninsula (IP). Our findings confirm a substantial increase in the frequency and intensity of droughts in the IP due to anthropogenic climate change. We highlight the importance of temperature in drought representation and underscore the urgent need to limit global warming below 1.5°C, in line with international climate policies.
The analysis reveals that the exacerbation of drought conditions is more pronounced under higher emission scenarios, particularly RCP8.5, emphasizing the critical role of emission reduction in climate change mitigation. Furthermore, a substantial increase in affected land area and population exposure to drought is observed, especially under the high-emission scenario. Climate change emerges as the primary factor contributing to increased drought exposure, with emission reduction efforts offering potential mitigation. To overcome limitations associated with model uncertainties, a multi-model multi-variable ensemble approach was employed to enhance the regional specificity of the findings. This provides valuable insights for local climate adaptation and mitigation strategies.
Results suggest that mitigating anthropogenic warming by 0.5°C to achieve the 1.5°C warmer climate rather than 2.0°C may provide benefits for future drought risks and impacts in the IP and underscore the urgency of implementing stringent climate policies. By offering a comprehensive assessment of drought conditions and population exposure, this study informs decision-making and climate resilience strategies, emphasizing the need for immediate action to mitigate adverse impacts on ecosystems and human populations.

Important features of bench press performance in non-disabled and Para athletes: A scoping review.

Understanding important features for performance in non-disabled bench press and Paralympic powerlifting may inform talent identification and transfer models. The aim of this scoping review was to describe features associated with bench press performance. We conducted a systematic search of three electronic databases (PubMed, SportDiscus and EMBASE) to identify studies involving non-disabled and Para athlete populations that investigated features related to bench press one-repetition maximum (1RM), across six domains (anthropometric, body composition, demographic, technical, disability and neuromuscular). Search terms included "resistance training", "para powerlifting' and "one repetition max". No date restrictions were include in searches. Studies using adult participants, with at least six-months of bench press experience, who were able to bench press their body mass were included. Thirty-two studies met our inclusion criteria. Twenty-four studies involved non-disabled athletes (total n = 2,686; 21.9% female) and eight involved Para athletes (total n = 2,364; 39.4% female). Anthropometric (17 studies) and body composition (12 studies) features were most studied; half of the 32 studies investigated features from a single domain. Of anthropometric variables, upper arm circumference shared the strongest association with bench press 1RM in non-disabled (r = 0.87) and para-athletes (r = 0.65). Upper limb fat free mass (r = 0.91) and body mass index (r = 0.46) were the body composition variables sharing the strongest association with bench press 1RM in non-disabled and para-athletes. Few studies considering the uncertainty of their results. Practices of open and transparent science, such as pre-registration and data sharing, were absent. The development of bench press talent identification and sport transfer models will require future studies to investigate both non-training and training modifiable features, across multiple domains. Large longitudinal studies using information from athlete monitoring databases and multivariable model approaches are needed to understand the interacting features associated with bench press performance, and for the development of talent identification models.

Bidirectional causal relationship between glucose-lipid metabolism, obesity indicators, and myocardial infarction: a bidirectional Mendelian randomization analysis study

Objective: To explore the causal association of glucose-lipid metabolism and obesity indicators with myocardial infarction by a two-sample Mendelian randomization analysis. Methods: Single nucleotide polymorphisms (SNPs) related to phenotypes were obtained from genome-wide association study databases. The body mass index (BMI) and glycated hemoglobin dataset includes 99 998 samples and 8 126 035 SNPs; the waist-to-hip ratio dataset includes 224 459 samples and 2 562 516 SNPs; the waist circumference and hip circumference dataset includes 462 166 samples and 9 851 867 SNPs; the fasting glucose dataset includes approximately 12 million SNPs; the low-density lipoprotein cholesterol (LDL-C) dataset includes 201 678 samples and 12 321 875 SNPs; the high-density lipoprotein cholesterol (HDL-C), and triglycerides dataset includes 156 109 samples and 15 784 414 SNPs; and the body fat percentage, whole-body fat mass, trunk fat percentage, and trunk fat mass dataset includes 454 588 samples and 9 851 867 SNPs. This study primarily used inverse-variance weighted method to analyze the associations between various exposure factors and outcomes. Heterogeneity among SNPs was assessed using Cochran's Q test, and horizontal pleiotropy of SNPs was examined using the MR-Egger method. Additionally, a multivariable MR approach was used to adjust for BMI, further validating associations between exposure factors and the risk of myocardial infarction. Results: Higher BMI (OR=1.070, 95%CI: 1.041-1.100), waist-to-hip ratio (OR=1.366, 95%CI: 1.113-1.677), LDL-C (OR=1.638, 95%CI: 1.488-1.803), triglycerides (OR=1.445, 95%CI: 1.300-1.606), waist circumference (OR=1.841, 95%CI: 1.650-2.055), hip circumference (OR=1.247, 95%CI: 1.132-1.372), body fat percentage (OR=1.795, 95%CI: 1.568-2.055), whole-body fat mass (OR=1.519, 95%CI: 1.381-1.670), trunk fat percentage (OR=1.538, 95%CI: 1.374-1.723), and trunk fat mass (OR=1.421, 95%CI: 1.294-1.561), as well as lower HDL-C (OR=0.799, 95%CI: 0.729-0.875), have causal effects on myocardial infarction (all P<0.05). After adjusting for BMI, hip circumference, trunk fat percentage, and trunk fat mass were no longer associated with myocardial infarction. However, waist-to-hip ratio (OR=1.457, 95%CI: 1.132-1.877), fasting glucose (OR=1.191, 95%CI: 1.024-1.383), glycated hemoglobin (OR=1.129, 95%CI: 1.034-1.233), LDL-C (OR=1.592, 95%CI: 1.314-1.929), triglycerides (OR=1.410, 95%CI: 1.279-1.553), waist circumference (OR=1.922, 95%CI: 1.448-2.551), body fat percentage (OR=1.421, 95%CI: 1.072-1.884), and whole-body fat mass (OR=1.295, 95%CI: 1.031-1.626) remained positively associated with myocardial infarction, while HDL-C (OR=0.809, 95%CI: 0.729-0.897) remained negatively associated. Conclusions: Abdominal obesity and dysregulation of glucose-lipid metabolism are risk factors for myocardial infarction. Screening for glucose-lipid metabolism (fasting glucose, HDL-C, LDL-C, triglycerides) and obesity-related indicators (waist circumference, waist-to-hip ratio, body fat percentage, and whole-body fat mass) is of great importance for the primary prevention of myocardial infarction.

Walking pace is a protective factor for rheumatoid arthritis: a mendelian randomization study

Walking pace is a simple and functional form of exercise and a strong predictor of health, but little is known about its causal association with rheumatoid arthritis. This study aimed to investigate the causal effect of WP on the developing RA using Mendelian randomization analysis. The genetic variation associated with WP was selected as an instrumental variable from the latest genome-wide association studies. Summary-level data for the outcomes were obtained from the corresponding GWAS. The inverse-variance weighted method was used as the primary MR analysis. The results were further tested using a multivariable MR approach based on Bayesian model averaging. Confounders (BMI, SMK, HBP, TD) with close associations with RA were included in the analysis. An observational study with individual data from UK Biobank was performed to reinforce our findings. The MR results indicated the significant inverse associations of WP with the risk of RA (odds ratio (OR), 0.31; 95% confidence interval (CI), 0.15, 0.62; p = 1.05 × 10 −3). After adjusting for the risk factors, the associations for WP and RA did not change substantially. Observational study results demonstrated the same effect of WP on reducing the risk of RA. The Mendelian randomization analysis and observational study provide evidence suggesting that walking pace is a protective factor for rheumatoid arthritis. Given its simple measurement, walking pace may be a pragmatic target for interventions.

Determinants of failure to progress within 2 weeks of delivery: results of a multivariable analysis approach

ObjectiveThe incidence of cesarean section (CS) for failure to progress (FP) has progressively increased; thus, knowing the factors that increase this incidence has become of crucial importance. This study aimed to find the true determinants of CS for FP within 2 weeks of delivery, proposing strategies to reduce its incidence. Material and MethodsA group of 957 term and late preterm (≥34 weeks) singleton pregnancies with a complete gestational follow-up and an ultrasound examination within 2 weeks of delivery were included in a retrospective observational study. Epidemiological, sonographic, and perinatal data were recorded, and multivariable logistic regression analyses were applied to create models to predict the importance of different variables in the explanation of FP. ResultsInduction of labor was by far the most important modifiable factor, followed by smoking and maternal weight, while parity was the most important nonmodifiable factor, followed by maternal age and estimated fetal weight. The difference in days from the actual due date exerted no influence. ConclusionsTo reduce the incidence of CS for FP, inductions of labor should be performed only under evidence-based medicine indications and kept to a minimum. In addition, maternal overweight reduction and maternal smoking cessation should be promoted before the initiation of gestation.

Monitoring and preservation of stone cultural heritage using a fuzzy model for predicting salt crystallisation damage

In this study, a fuzzy model is presented for predicting the possibility of degradation due to salt crystallisation cycles. The formalization of the proposed model has been based on the multivariable approach which considers environmental data (such as temperature, solar radiation, wind speed, rain quantity, relative humidity), characteristic inflection points of specific salts and stone features derived from laboratory characterizations (including mechanical properties, porosity, and mineralogical composition). Modeling results have been compared with experimental data elaborations acquired by monitoring a semi-confined archaeological site situated in the city of Cagliari (Munatius Irenaus cubicle), revealing substantial alignment in the degradation kinetics trends. Moreover, the achieved outcomes show the remarkable capability to identify salt crystallisation phenomenon type (efflorescence or subflorescence).

Development of an ultrasound-based metric of muscle functional capacity for use in patients with neuromuscular disease.

Spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD) are progressive neuromuscular disorders characterized by severe muscle weakness and functional decline (Pillen et al., Muscle Nerve 2008; 37(6):679-693). With new therapeutics, objective methods with increased sensitivity are needed to assess muscle function. Ultrasound imaging is a promising approach for assessing muscle fat and fibrosis in neuromuscular disorders. This study builds on prior work by combining ultrasound-based measurements of muscle size, shape, and quality, relating these measures to muscle strength, and proposing a multivariable image-based estimate of muscle function. Maximum voluntary elbow flexion torque of 36 participants (SMA, DMD, and healthy controls) was measured by hand-held dynamometry and elbow flexor muscles were imaged using ultrasound. Muscle size (cross-sectional area, maximum Feret diameter or width, and thickness), quality (echogenicity, texture anisotropy index), and cross-sectional shape (diameter ratio) were measured. Multivariable regression was used to select ultrasound measurements that predict elbow flexion torque. Significant differences were observed in muscle size (decreased), shape (thinned), and quality (decreased) with increased disease severity and compared to healthy participants. CSA (brachioradialis R2 = 0.51), maximum Feret diameter (biceps R2 = 0.49, brachioradialis R2 = 0.58) and echogenicity (brachioradialis R2 = 0.61) were most correlated with torque production. Multivariable regression models identified that muscle size (CSA, maximum Feret diameter) and quality (echogenicity) were both essential to predict elbow flexion torque (R2 = 0.65). A multivariable approach combining muscle size and quality improves strength predictions over single variable approaches. These methods present a promising avenue for the development of sensitive and functionally relevant biomarkers of neuromuscular disease.

Investigating causal links between gallstones, cholecystectomy, and 33 site-specific cancers: a Mendelian randomization post-meta-analysis study

Background and aimThe association between gallstones/cholecystectomy and cancer remains inconclusive in the current literature. This study aimed to explore the causal connections between gallstones/cholecystectomy and cancer risk by utilizing a bidirectional two-sample multivariable Mendelian randomization approach with Genome-Wide Association Studies data.MethodsUtilizing Genome-Wide Association Studies data from the UK Biobank and FinnGen, this research employed multivariable Mendelian randomization analyses to explore the impact of gallstones and cholecystectomy on the risk of 33 distinct cancer types. Instrumental variables for gallstones and cholecystectomy were carefully selected to ensure robust analyses, and sensitivity and heterogeneity tests were conducted to verify the findings’ validity.ResultsMultivariable Mendelian randomization analysis, incorporating data from more than 450,000 individuals for gallstones and cholecystectomy, revealed nuanced associations with cancer risk. Cholecystectomy was associated with a significantly increased risk of nonmelanoma skin cancer (OR = 1.59, 95% CI: 1.21 to 2.10, P = 0.001), while gallstones were linked to a decreased risk of the same cancer type (OR = 0.63, 95% CI: 0.47 to 0.84, P = 0.002). Interestingly, the analysis also suggested that cholecystectomy may lower the risk of small intestine tumors (OR = 0.18, 95% CI: 0.043 to 0.71, P = 0.015), with gallstones showing an inverse relationship, indicating an increased risk (OR = 6.41, 95% CI: 1.48 to 27.80, P = 0.013).ConclusionsThe multivariable Mendelian randomization analysis highlights the differential impact of gallstones and cholecystectomy on cancer risk, specifically for nonmelanoma skin cancer and small intestine tumors. These results underscore the importance of nuanced clinical management strategies and further research to understand the underlying mechanisms and potential clinical implications of gallstone disease and cholecystectomy on cancer risk.

Heterogeneity of Treatment Effects in Clinical Trials: Overview of Multivariable Approaches and Practical Recommendations

Heterogeneity of Treatment Effects in Clinical Trials: Overview of Multivariable Approaches and Practical Recommendations

Predicting Primary Graft Dysfunction in Lung Transplantation: Machine Learning-Guided Biomarker Discovery.

There is an urgent need to better understand the pathophysiology of primary graft dysfunction (PGD) so that point-of-care methods can be developed to predict those at risk. Here we utilize a multiplex multivariable approach to define cytokine, chemokines, and growth factors in patient-matched biospecimens from multiple biological sites to identify factors predictive of PGD. Biospecimens were collected from patients undergoing bilateral LTx from three distinct sites: donor lung perfusate, post-transplant bronchoalveolar lavage (BAL) fluid (2h), and plasma (2h and 24h). A 71-multiplex panel was performed on each biospecimen. Cross-validated logistic regression (LR) and random forest (RF) machine learning models were used to determine whether analytes in each site or from combination of sites, with or without clinical data, could discriminate between PGD grade 0 (n = 9) and 3 (n = 8). Using optimal AUROC, BAL fluid at 2h was the most predictive of PGD (LR, 0.825; RF, 0.919), followed by multi-timepoint plasma (LR, 0.841; RF, 0.653), then perfusate (LR, 0.565; RF, 0.448). Combined clinical, BAL, and plasma data yielded strongest performance (LR, 1.000; RF, 1.000). Using a LASSO of the predictors obtained using LR, we selected IL-1RA, BCA-1, and Fractalkine, as most predictive of severe PGD. BAL samples collected 2h post-transplant were the strongest predictors of severe PGD. Our machine learning approach not only identified novel cytokines not previously associated with PGD, but identified analytes that could be used as a point-of-care cytokine panel aimed at identifying those at risk for developing severe PGD.

Spectrum of Ductal Carcinoma In Situ (DCIS) Lesions of the Breast: From Morphology to Molecular Characteristics.

Background Specific molecular characteristics of invasive breast cancer have been linked to an increased risk of early relapse. Similarly, ductal carcinoma in situ (DCIS) displays a comparable molecular profile, although their prevalence and implications are not yet fully understood. Aims and objectives The study design defined a multivariable statistical approach aimed at describing the interplay between the histopathological features of ductal carcinoma in situ (DCIS) and their molecular profile. The objective was to explore the correlations between the histopathological features of DCIS (tumor location, DCIS grade, DCIS type, and presence or absence of comedo necrosis) and various biomarkers like estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2/neu), androgen receptor (AR), and epidermal growth factor receptor (EGFR), in addition to the Ki-67 labeling index. Methods In this retrospective study, we selected and analyzed 100 diagnosed cases of ductal carcinoma in situ (DCIS) to represent various subtypes, grades, and morphological characteristics. A detailed histopathological review and immunohistopathological staining for ER, PR, HER2/neu, AR, EGFR, and Ki-67 were performed on formalin-fixed paraffin-embedded (FFPE) tumor tissue blocks. Molecular subtyping was done based on the biomarker analysis into luminal A, luminal B, luminal HER2/neu, HER2/neu enriched, and triple-negative subtypes. Statistical analysis was done to examine the correlation between tumor location, histopathological features of ductal carcinoma in situ (DCIS), and the expression of the immunohistochemical markers and the molecular subtypes. Results The majority of cases exhibited positivity for estrogen receptor (ER) and progesterone receptor (PR). A strong association was observed between histopathological features (DCIS grade, type, and comedo necrosis) of DCIS and ER/PR status. Additionally, a significant correlation was found between ductal carcinoma in situ (DCIS) grade and HER2/neu status. However, no association was identified between histopathological features and AR or EGFR status. Contrary to expectations, triple-negative DCIS did not show the most aggressive behavior, whereas HER2/neu-positive tumors, particularly high-grade ones, exhibited more aggressive features. No low-grade cases of luminal HER2/neu and HER2/neu-enriched tumors were found. A higher Ki-67 labeling index was found in cases with grade 3 and solid and comedo architectural types of DCIS, while low-grade tumors had a lower Ki-67 labeling index. Conclusion These findings suggest that hormone pathways play a crucial role in ductal carcinoma in situ (DCIS) progression, but the molecular interactions are complex and extend beyond simple binary associations. The complex relationship between the histopathological features of ductal carcinoma in situ (DCIS) and its hormone receptor status warrants further investigation with a larger sample size to fully understand the underlying mechanisms. The results challenge the expectation that triple-negative DCIS is the most aggressive subtype, highlighting the need for further research into the prognostic significance of different DCIS subtypes.

A multi-modal evaluation of experimental pain and psychological function in women with carpometacarpal osteoarthritis

ObjectiveThumb carpometacarpal osteoarthritis (CMC1 OA) is a prevalent and debilitating condition that lacks effective treatments. Understanding the multidimensional pain experience across CMC1 OA disease stages is crucial to improving treatment outcomes. This study examined how radiographic CMC1 OA severity is associated with physical, psychological, and somatosensory function. MethodThirty-one women with early-stage (Eaton-Littler 1–2) or end-stage (Eaton-Littler 3–4) radiographic CMC1 OA completed validated questionnaires to assess pain, disability, and psychological function. Additionally, experimental pain was measured in each participant using quantitative sensory testing (QST) (mechanical, pressure, vibratory, thermal) at seven body sites (thenar, hypothenar, brachioradialis bi-laterally; quadriceps on affected side). Cohort differences (early-vs. end-stage) across all variables were analyzed using a multivariable modeling approach that included fixed effects and interactions; notably, age was controlled as a confounder. ResultsEnd-stage CMC1 OA participants had higher scores in the pain (p ​= ​0.01) and function (p ​= ​0.02) portions of the AUSCAN assessment, self-reported disability of the DASH questionnaire (p ​= ​0.04), and painDETECT scores (p ​= ​0.03), indicating greater pain and disability compared to early-stage participants. Additionally, end-stage CMC1 OA participants demonstrated reduced vibratory detection and heat pain thresholds at multiple body sites (p's ​< ​0.05), with significant interactions observed across the mechanical and cold stimuli. ConclusionFindings revealed women with end-stage CMC1 OA exhibited increased neuropathic pain characteristics and somatosensory loss compared to those with early-stage CMC1 OA. These results underscore the importance of addressing both peripheral and centralized pain mechanisms and the need for multimodal approaches in the treatment of CMC1 OA.

Comparative Study of Risk Factors Associated with Normal Cognition and Cognitive Impairment in Rural West Elderly Texans.

Alzheimer's disease (AD) is related to one or more chronic illnesses, which may develop cognitive decline and dementia. Cognitive impairment is increasing, and public health officials must address risk factors for AD to improve the health of rural West Texas communities. The purpose of this study was to explore the sociodemographic and chronic disease risk factors related to cognitive impairment among elderly adults living in Cochran, Parmer, and Bailey counties in rural West Texas. Statistical methods such as Pearson's chi-squared, proportion tests, univariate binary logistic regression, and a multivariable logistic regression were utilized to analyze data. SPSS software was used to detect the significant relationship between cognitive impairment and risk factors. Summary statistics were obtained for sociodemographic and chronic diseases by using cross-tabulation analysis and comparing the county respondents with proportion tests. A univariate binary logistic regression method was utilized and found that age group 60-69, anxiety, depression, diabetes, hypertension, and cardiovascular disease were significantly associated with cognitive impairment. Using a multivariable logistic regression approach, it was found that Bailey County (age group 60-69) had a higher likelihood (p = 0.002) of cognitive impairment than Parmer (p = 0.067) and Cochran counties (p = 0.064). The risk of females (p = 0.033) in Parmer County was 78.3% lower compared to males in developing AD. Identifying significant risk factors for cognitive impairment are important in addressing issues of geographic variations and integrating such factors may guide relevant policy interventions to reduce cognitive impairment incidence in rural communities within West Texas.

Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum (CWmin) and CW Threshold (CWThreshold), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions.

Investigation of H∞$$ {\mathcal{H}}_{\infty } $$‐Tuned Individual Pitch Control for Wind Turbines

ABSTRACTLarge wind turbines experience amplified asymmetrical loads at particular harmonics of the rotational frequency. Individual pitch control (IPC) has emerged as a potential controls solution to this problem. control, which facilitates robust, multivariable controller synthesis in the frequency domain, is a candidate approach to IPC design for several reasons. First, the objectives of asymmetrical load attenuation can be readily described in the frequency domain. Second, the IPC signals and loads in orthogonal directions are known to be coupled, which necessitates a multivariable controller approach. Third, synthesis is a method that can explicitly impose constraints on the robustness of the closed‐loop system. A downside of IPC is the significant increase in blade‐pitch travel incurred, which introduces additional loading on the blade‐pitch bearings over time. We investigate strategies to constrain the blade‐pitch travel in the controller tuning procedure. A comprehensive study is thus presented for a range of ‐synthesized IPCs to attenuate asymmetrical loads on large rotors at harmonics of the rotational frequency while reducing blade‐pitch travel. All developed controllers are validated and compared using a 25‐MW fixed‐bottom offshore wind turbine model via (a) linear analysis of the response of the closed‐loop system to input and output disturbances, (b) nonlinear analysis in terms of structural load power spectra and damage‐equivalent loads (DELs), and (c) the actuator duty cycle (ADC) of the blade‐pitch actuator.

Exploring the causal relationships between cholelithiasis, cholecystitis, cholecystectomy, and gastroesophageal reflux disease: A bidirectional two-sample mendelian randomization study.

Biliary disorders and gastroesophageal reflux disease (GERD) frequently coexist. However, precise linkages between these conditions remain to be clarified. Univariable Mendelian randomization (MR), Bayesian weighted MR (BWMR) along with multivariable MR approaches were conducted using genetic instruments to evaluate the causality involving biliary disorders and GERD. Furthermore, an investigation was conducted on the potential mediating roles of biliary disorders (or GERD), on the linkage involving body mass index (BMI) and GERD (or biliary disorders). Univariable MR analyses revealed significant causal effects of genetically predicted cholelithiasis (odds ratio (OR)=1.04, P=0.0001), cholecystitis (OR=1.06, P=0.0004), and cholecystectomy (OR=2.56, P=1.05×10-6) on GERD. These findings were replicated in the FinnGen cohort and were also confirmed by BWMR and multivariable MR analyses. Additionally, mediation analyses demonstrated that cholelithiasis and cholecystitis acted as partial mediators, linking BMI causally to GERD. Conversely, GERD exhibited causal effect on cholelithiasis (OR=1.52, P=9.17×10-30) and cholecystitis (OR=1.90, P=3.32×10-28), which remained significant after BWMR and multivariable MR analyses. Mediation analyses further revealed significant mediating effect of GERD on how BMI influenced cholelithiasis/cholecystitis. Our study elucidates the bidirectional causal linkages involving cholelithiasis, cholecystitis, cholecystectomy, and GERD. These results highlight the significance of GERD risk assessment in individuals suffering from biliary diseases and vice versa.

Genetic predisposition to type 2 diabetes mellitus and aortic dissection: a Mendelian randomisation study.

This Mendelian randomization (MR) study aimed to explore the causal relationship between the genetic predisposition to type 2 diabetes mellitus (T2DM) and aortic dissection (AD), and to assess associations with genetically predicted glycemic traits. The study sought to verify the inverse relationship between T2DM and AD using a more robust and unbiased method, building on the observational studies previously established. The study employed a two-sample and multivariable MR approach to analyze genetic data from the DIAbetes Meta-ANalysis of Trans-Ethnic association studies (DIAMANTE) with 74,124 cases and 824,006 controls, and the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) involving up to 196,991 individuals. For AD data, FinnGen Release 10 was used, including 967 cases and 381,977 controls. The research focused on three foundational MR assumptions and controlled for confounders like hypertension. Genetic instruments were selected for their genome-wide significance, and multiple MR methods and sensitivity analyses were conducted. The study revealed no significant effect of genetic predisposition to T2DM on the risk of AD. Even after adjusting for potential confounders, the results were consistent, indicating no causal relationship. Additionally, glycemic traits such as fasting glucose, fasting insulin, and HbA1c levels did not show a significant impact on AD susceptibility. The findings remained stable across various MR models and sensitivity analyses. In contrast, genetic liability to T2DM and glycemic traits showed a significant association with coronary artery disease (CAD), aligning with the established understanding. Contrary to previous observational studies, this study concludes that genetic predisposition to T2DM does not confer protection against AD. These findings underscore the imperative for further research, particularly in exploring the preventative potential of T2DM treatments against AD and to facilitate the development of novel therapeutic interventions.

A load impedance emulation active interface for piezoelectric vibration energy harvesters

A single stage active AC/DC electronic interface able to emulate the optimal load impedance of a Resonant Piezoelectric Vibration Energy Harvester (RPVEH) is proposed. As theoretically shown, unlike an electronic interface that emulates an optimal load generator, an interface that emulates an optimal load impedance does not require adaptation to the acceleration of input vibrations. This allows the use of a very simple control, avoiding the implementation of Maximum Power Point Tracking algorithms that require lossy microcontrollers. Thus, the proposed interface is equipped with a simple analog controller allowing the RPVEH to work in its Maximum Power Point in both steady-state and variable conditions of vibrations, without recurring to multivariable perturbative approaches, as it happens for the most of single stage AC/DC interfaces proposed in the literature. The absence of perturbative techniques allows a significant improvement of both stationary and dynamic performances. Experimental tests of a prototype of the proposed interface confirm the theoretical findings and the predicted behavior.