bluuebunny/crossref_metadata_2025_split huggingface dataset:
Causal Relationships Between 4 Exposure Factors and Rotator Cuff Syndrome Using Mendelian Randomization Analysis
Authors: Zhang Zeyang , Han Shun , Sun Xiaowei , Guo Zelin , Wang Zhiqiang , Sha Peng , Liu Yuchen , Zhang Bing , Liu Yupeng 
Published: January 2025

Background: Although previous studies have investigated the risk factors for rotator cuff syndrome (RCS), there remains controversy due to uncontrolled and uncertain confounding factors in their analyses. Purpose: To perform Mendelian randomization (MR) analysis using single-nucleotide polymorphisms to investigate the causal relationship between RCS and 4 risk factors: type 2 diabetes mellitus (T2DM), high blood pressure (HBP), body mass index (BMI), and low high-density lipoprotein cholesterol (HDL-C). Study Design: Descriptive epidemiology study. Methods: Genome-wide association study (GWAS) data for T2DM (ebi-a-GCST006867), BMI (ieu-b-40), HBP (finn-b-I9_HYPTENS), HDL-C (ieu-b-109), and RCS (ukb-b-50) were obtained from the IEU Open GWAS Project. The dataset of each risk factor was combined with the dataset of RCS, generating 4 datasets. Potential confounders and single-nucleotide polymorphisms related to RCS were excluded from these datasets. The causal relationships between the exposure factors and RCS were analyzed using 5 regression models: MR-Egger, weighted median estimate (WME), inverse-variance weighting (IVW), simple mode, and weighted mode. Heterogeneity in the causal effects was assessed using MR-Egger regression and IVW analyses. Sensitivity analyses were performed to determine the stability of the results. Results: The MR-Egger regression intercepts for T2DM, BMI, HBP, and HDL-C showed no horizontal pleiotropic effects. The results of the Cochran Q test showed P values of .075 and .080 for BMI in the MR-Egger regression and IVW models, respectively, indicating the absence of heterogeneity between BMI and RCS. The results of the weighted median estimate and IVW regression analyses showed a significant causal association between BMI and RCS, with odds ratios of 1.002 (95% CI, 1-1.004; P = .038) and 1.003 (95% CI, 1.001-1.005; P = .0003), respectively. No significant associations were found for T2DM, HDL-C, or HBP. Conclusion: In the present study, BMI was positively associated with the risk of developing RCS, while T2DM, HBP, and low HDL-C were not associated with RCS development.
Working toward Personalized Intervention Advice: A Survey Study on Preference Heterogeneity in Patients with Breast Cancer–Related Fatigue
Authors: Beenhakker Lian , Wijlens Kim A. E. , Bode Christina , Vollenbroek-Hutten Miriam M. R. , Siesling Sabine , van Til Janine A. , Witteveen Annemieke 
Published: January 2025

Introduction. Many breast cancer survivors experience cancer-related fatigue (CRF), and several interventions to treat CRF are available. One way to tailor intervention advice is based on patient preferences. In this study, we explore preference heterogeneity regarding between-attribute and within-attribute preferences. In addition, we propose simple decision rules to match preferences to interventions. Methods. Nine attributes were included with dichotomized levels. Participants selected their preferred level per attribute and ranked the attributes using best-worst scaling. Between-attribute and within-attribute preferences were determined, together with their heterogeneity. Using decision rules, matching scores were calculated for a hypothetical intervention. Results. Sixty-seven breast cancer survivors completed the survey. They were on average 52 y old, 4.5 y after diagnosis, experienced CRF (6.5–7.2/10) on 3 dimensions (physical, mental, and emotional), and 43% already followed an intervention for CRF. Overall, participants ranked costs highest. Next to costs, proven effectiveness and type of intervention were also frequently ranked first. Only 13 participants (19%) shared the most common preference pattern of shorter interventions, daily sessions, shorter session time, a psychosocial intervention, no anonymity, and contact with a therapist and peers. Matching scores for a hypothetical intervention with attributes corresponding with the overall within-attribute preferences varied from 44% to 100%. Conclusion. A large heterogeneity in preferences of breast cancer survivors for CRF intervention attributes was demonstrated. Using simple decision rules, the effect of this heterogeneity on linking preferences to interventions with matching scores was demonstrated. Implications. Personalization of intervention advice is necessary due to preference heterogeneity. Tailored advice can result in higher involvement of patients in decision making, intervention adherence and satisfaction, and subsequently a potential higher quality of life after breast cancer. Highlights Many breast cancer survivors experience cancer-related fatigue for which many interventions exist. Our results show large preference heterogeneity in breast cancer patients’ preferences for attributes of eHealth interventions. Based on this preference heterogeneity, intervention advice for cancer-related fatigue after breast cancer can be personalized, ultimately improving quality of life after breast cancer.
Self-Assessment of Medical Ethics and Professionalism: Comparison Between Preclinical and Clinical Medical Students
Authors: Naz Shaikh Saima , Abbas Uzair , Shah Tazeen , Uqaili Arsalan Ahmad , Allah Buksh Hudebia , Fatima Mehreen 
Published: January 2025

BACKGROUND Ethics and professionalism are fundamental elements in the practice of medicine consisting of a set of values, behaviors, and relationships that reinforce the trust and respect for the physician in a society. It consists of several key elements that are learnt during their formative years of study. This study aims to compare knowledge and self-reported practice of ethics and professionalism between preclinical and clinical undergraduate medical students. METHODS We included 427 medical students including males and females of age 18 and above. They were further divided into preclinical and clinical groups. We assessed knowledge and practice of medical professionalism through a validated questionnaire containing 11 core elements (components) of medical professionalism and ethics. The assessment was carried out by comparing median scores assigned to the components of professionalism against each question. Data was analyzed through SPSS v.22 and GraphPad Prism v.8. RESULTS Clinical medical students had significantly higher knowledge of medical professionalism as compared to nonclinical students ( P = .0016). Moreover, among the components of professionalism, there was a significantly higher score for confidentiality ( P = .006), communication skills ( P = .001), self-directed learning ( P = .011), peer and self-assessment ( P = .0011) along with ethics in research and patient care ( P = .0042) in clinical students as compared to preclinical medical students. However, no significant difference was found between students of preclinical and clinical years for honesty, accountability, respect, responsibility, compassion, and maturity ( P > .05). The findings were comparable across students of different ethnicities in our study cohort ( P > .05). CONCLUSION Our findings suggest that knowledge and practice of certain elements of ethics and professionalism are enhanced through clinical exposure, however, few remain consistent across different stages of study. Moreover, lack of significant differences in practice of professionalism based on ethnic differences underscores the potential universal standards in medical education.
Criticality is Associated with Future Psychotherapy Response in Patients with Post-Traumatic Stress Disorder—A Pilot Study
Authors: van Lutterveld Remko , Sterk Myrthe , Spitoni Cristian , Kennis Mitzy , van Rooij Sanne J. H. , Geuze Elbert 
Published: January 2025

Background Trauma-focused psychotherapy is treatment of choice for post-traumatic stress disorder (PTSD). However, about half of patients do not respond. Recently, there is increased interest in brain criticality, which assesses the phase transition between order and disorder in brain activity. Operating close to this borderline is theorized to facilitate optimal information processing. We studied if brain criticality is related to future response to treatment, hypothesizing that treatment responders’ brains function closer to criticality. Methods Functional magnetic resonance imaging resting-state scans were acquired from 46 male veterans with PTSD around the start of treatment. Psychotherapy consisted of trauma-focused cognitive behavioral therapy, eye movement desensitization and reprocessing, or a combination thereof. Treatment response was assessed using the Clinician-Administered PTSD Scale, and criticality was assessed using an Ising temperature approach for seven canonical brain networks (ie, the visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal and default mode networks) to measure distance to criticality. Results The brains of prospective treatment responders were closer to criticality than nonresponders (P = 0.017), while no significant interaction effect between group and brain network was observed (P = 0.486). In addition, average criticality across networks correlated with future treatment response (P = 0.028). Conclusion These results show that the brains of prospective PTSD psychotherapy treatment responders operate closer to criticality than nonresponders, and this occurs across the entire brain instead of in separate canonical brain networks. These results suggest that effective psychotherapy is mediated by brains operating closer to criticality.
Sleepiness, sleep time, and depression of caregivers are linked with sleep and behaviors of their paired partners with dementia
Authors: Manning Carol A , Youngkin Anna , Quigg Mark 
Published: January 2025

Background Sleep difficulties in people with Alzheimer's disease (AD) and their caregivers (CGs) have been documented. Additionally, sleep disturbances are a risk for AD indicating that poor sleep in CGs may place them at risk for AD. Little is known about the relationship between sleep in people with dementia (PWD) and their CGs. Objective This pilot study examines sleep in PWD and CGs dyads, and the relationship between PWD sleep and CG sleep, cognition, and burden. We explore whether disordered sleep, degree of dementia and PWD behaviors are related to CG sleep difficulties and burden. Methods We examined sleep using overnight polysomnography (PSG) and day/night activity using 14-day actigraphy in PWD/CG dyads form the Virginia Alzheimer's Disease Center Clinical Cohort. Dyad members received the Montreal Cognitive Assessment (MoCA), behavioral and mood assessments. CGs completed CG burden and preparedness assessments. Results Mean activity from actigraphy did not differ within dyad members. PSG measurement of total sleep time (TST), sleep onset latency (SOL), sleep efficiency (SEff), and wake after sleep onset (WASO) revealed that CGs had significantly decreased TST compared to their PWD and experienced greater SOL. Lower PWD MoCA scores were unrelated to CG sleep. However, PWD neuropsychiatric symptoms and CG burden correlated with worse CG SOL. Conclusions Our findings suggest that chronic rest and activity are linked within dyad members and that when separated, CGs experience shorter TST, lower SEff, and longer SOL than their partners. Additionally neuropsychiatric symptoms and CG burden were associated with worse CG sleep.
Neuropsychological and clinical indicators of Lewy body and Alzheimer's pathology
Authors: Simpson Austin J , Wyman-Chick Kathryn A , Daniel Michael S 
Published: January 2025

Background Clinical distinction between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) poses significant challenges due to pathological comorbidity. Similar ages of onset and overlapping cognitive and psychiatric symptoms can lead to diagnostic inaccuracy and inappropriate treatment recommendations. Objective Identify the best combination of clinical and neuropsychological predictors of AD, DLB, and mixed DLB/AD neuropathology in dementia patients. Methods Using the National Alzheimer's Coordinating Center dataset, we selected either pure AD ( n = 189), DLB ( n = 21), or mixed DLB/AD ( n = 42) patients on autopsy. Neuropsychological and clinical predictors, including core clinical features of DLB, were entered into multivariable logistic regressions. Results Gait disturbances (odds ratio (OR) = 19.32; p = 0.01), visual-spatial complaints (OR = 6.06; p = 0.03), and visual hallucinations (OR = 31.06; p = 0.002) predicted DLB compared to AD, along with better memory (OR = 3.42; p = 0.003), naming (OR = 3.35; p = 0.002), and worse processing speed (OR = 0.51; p = 0.01). When comparing DLB to DLB/AD, gait disturbances (OR = 6.33; p = 0.01), increased depressive symptoms (OR = 1.44; p = 0.03), and better memory (OR = 3.01; p = 0.004) predicted DLB. Finally, rapid eye movement sleep behavior disorder (RBD) (OR = 6.44; p = 0.004), parkinsonism severity (OR = 1.07; p = 0.02), and lower depressive symptoms (OR = 0.70; p = 0.006) and memory impairment (OR = 0.57; p = 0.02) distinguished DLB/AD from AD. Conclusions Our study converges with prior research suggesting specific neuropsychological and clinical features can help distinguish DLB from AD. Neuropsychological differentiation becomes more challenging among mixed pathologies and in advanced cognitive impairment, although the presence of RBD and parkinsonism distinguished DLB. Earlier clinical assessment and incorporation of in vivo and postmortem biomarkers should enhance diagnostic accuracy and understanding of disease characteristics, offering significant relevance for disease-modifying treatments.
Temporal modeling and AT profiles in the early phase of Alzheimer's disease
Authors: Platero Carlos 
Published: January 2025

Background Identifying markers that have predictive value for disease progression and clinical manifestations, such as mild cognitive impairment (MCI), is important to detect Alzheimer's disease (AD) early. Objective In this study, we combined biomarkers from amyloid and tau pathologies (AT profiles) with the prediction of diagnosis and the temporal evolution of clinical symptoms. Methods Multiple disease progression models were developed through supervised and unsupervised techniques via a two-stage data-driven approach. Models whose natural histories most closely resembled the diagnostic predictions were selected. Finally, various hypotheses regarding the progression of cognitive decline were tested using longitudinal patient data and AT profiles. Results At baseline, 22.5% of the cognitively unimpaired (CU) individuals and 46.2% of the CU converters (who progressed to MCI during the first four years) in the studied population had amyloid pathology (A+). Only a small subset of neuropsychological measures was used to predict cognitive decline and its timeline. The study revealed that amyloid pathology occurred approximately 5 years after cognitive decline in the population under investigation. Additionally, patients who were A+ at baseline experienced a more accelerated progression toward cognitive decline than those who were not (A−). Conclusions Based on the proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicate that only a single cerebrospinal fluid sample is necessary during the early phase of AD. The progression from CU to MCI and its timeline can be predicted exclusively through neuropsychological measures.
Alzheimer's disease classification by supervised and intelligent techniques
Authors: Mohamed Amine Jabli , Mourad Moussa ,   for the Alzheimer's Disease Neuroimaging Initiative (ADNI)
Published: January 2025

Background Significant advancements in neuroimaging have emerged over the past decade, notably through positron emission tomography (PET) and magnetic resonance imaging (MRI) for diagnosing Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI). Combining imaging modalities with machine learning (ML) techniques enhances diagnostic accuracy. Objective To develop predictive models using pre-treatment brain imaging data to distinguish between normal controls (NC), MCI, and AD stages, improving diagnostic precision. Methods We utilized the Alzheimer's Disease Neuroimaging Initiative database, processing 3D MRI, PET Florbetaben, and PET Flortaucipir images. Techniques included convolutional neural networks (CNN), fuzzy logic, and multi-layer perceptron (MLP). Feature extraction involved amyloid-β volume, tau protein levels, and empty space volumes. Results The fuzzy logic approach achieved a classification accuracy of 99.1%, outperforming CNN (90.67%) and MLP (94%). Integration of multimodal data significantly enhanced performance compared to single-modality approaches. Conclusions Our study demonstrates that integrating advanced ML techniques with multimodal neuroimaging can effectively classify AD stages. These findings address critical gaps in early detection and provide a foundation for future clinical applications.
The causal role of lipids in dementia: A Mendelian randomization study
Authors: Su Boyang , He Zhengqing , Mao Li , Huang Xusheng 
Published: January 2025

Background Increasing evidence suggests that abnormal lipid metabolism is one of the pathogeneses of dementia. It is necessary to reveal the relationship between lipids and dementia. Objective This study used bidirectional two-sample Mendelian randomization to explore the causal relationship between 179 lipid species and the risk of dementia. Methods We assessed the causal effects of 179 lipid species and four subtypes of dementia including Alzheimer's disease (AD), vascular dementia (VaD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). Inverse variance weighting, MR-Egger method, weighted median, simple mode, and weighted mode were used to analyze the relationship between lipids and dementia. Cochran's Q, MR-Egger intercept test, and MR-PRESSO test were used to test the heterogeneity and pleiotropy of the results. In addition, we performed an inverse MR analysis testing the causal effects of dementia on lipids. Results Our study revealed causal effects of glycerophospholipid, glycerolipid, and sterol on the risk of dementia. Phosphatidylcholine, phosphatidylinositol, and triglycerides play significant roles in AD. Notably, phosphatidylcholine played a protective role in both FTD and DLB. However, this study did not observe a significant effect of phosphatidylinositol on FTD. In the case of VaD, not only glycerophospholipid, but also glycerolipid, exerted an influence, but sterol was also a risk factor. Conclusions Our study provided new evidence supporting the causal role of genetically predicted lipid species in dementia. Future clinical trials are necessary to evaluate the potential role of lipid levels in dementia prevention.
Optimal REIT Portfolio Selection Using Machine Learning
Authors: Zhang Wendi , Li Bin , Singh Tarlok , Liew Alan Wee-Chung , Roca Eduardo 
Published: January 2025

Purpose This study examines whether machine learning approaches can effectively solve the portfolio selection and optimization problems related to Real Estate Investment Trusts (REITs)-mixed portfolios. It also investigates the impact of different proportions of Equity-REIT (EREIT) and Mortgage-REIT (MREIT) on portfolio returns. Study design This research collects daily data from 157 EREITs and MREITs, totaling 1,283,800 observations, and construct 126 portfolios in the U.S. market, covering the period from January 1, 2000, to July 31, 2022. Using a genetic algorithm (GA), we optimize REITs combined with stocks, bonds, and gold, and compare GA’s performance with the classical mean-variance (MV) method. To assess robustness, authors also perform sub-period testing to examine portfolio performance under adverse economic conditions. Findings The results show that the GA outperforms the MV method in optimizing REIT-mixed portfolios, especially during periods such as the pre-Global Financial Crisis (GFC) and COVID-19. Portfolios with higher allocations to REITs exhibit superior returns, with EREIT outperforming MREIT in enhancing returns and controlling risk. However, MV shows better results in some low-volatility periods, such as during the GFC. Contributions This study advances existing literature by applying machine learning techniques—specifically genetic algorithms—to the optimization of REIT-mixed portfolios. The contributions include demonstrating the effectiveness of GA in achieving better risk-adjusted returns compared to traditional methods, providing practical insights for investors on optimizing their portfolios in volatile markets. Additionally, it highlights opportunities for researchers to further explore the integration of heuristic methods in portfolio management, expanding the understanding of portfolio optimization in the context of REITs. Implications Our findings provide practical implications for investors and portfolio managers, demonstrating the benefits of GA-based optimization in the REIT sector, particularly in turbulent market conditions.
What About Nights, Weekends, and Wait Times? Adding an On-Demand Facilitated Telemedicine Buprenorphine Component to a Community-Based Peer Support Outreach Program
Authors: Winograd Rachel P. , Brown Katherine C. , Newman Schyler T. , Banks Devin E. , Carpenter Ryan W. , Budesa Zach , Lunceford Jennifer , Siddiqui Saad T. , Jasdanwala Sarfaraz , Wing Bradley , Orson Wendy 
Published: January 2025

Background: In 2020, loosened federal regulations allowed for buprenorphine for opioid use disorder to be initiated via telemedicine. In response to these regulatory changes and growing racial inequities in overdose in St. Louis, MO, a local, peer-led outreach program incorporated a new rapid access (RA) to buprenorphine program. RA facilitated same-day buprenorphine access by conducting peer-facilitated telemedicine visits in community settings. Methods: This study used logistic regression to compare 1- and 3-month treatment and buprenorphine retention between clients in the RA and Traditional (ie, linkage to brick-and-mortar prescribers within 1-3 days) programmatic arms of a peer-led outreach program between December 2020 and June 2022. Propensity score matching allowed participants across groups to be matched on key factors (eg, sex, race, homelessness). Racial differences in program receipt/enrollment were also assessed through Chi-squared tests. Results: Total program participation (N = 4139) included 1% (n = 52) in the RA program. Those in RA were more likely to be in treatment at 1 and 3 months and more likely to be on buprenorphine at 3 months compared to those in the Traditional group. The RA program included more White and fewer Black clients than expected. Four of the 8 reasons for non-enrollment differed by race, with Black individuals more likely to already have treatment secured but also more likely to decline the program or be unable to be contacted. Conclusions: This study is the first to demonstrate sustained benefits in treatment and medication engagement following same-day access to buprenorphine via community-based, peer-facilitated telemedicine. Lower-than-expected enrollment of Black individuals indicates a need for more culturally responsive implementation strategies for peer- and telemedicine-facilitated programs.