Potential Bias Research Articles

Medical students as teachers: impact of peer-led teaching sessions on NBME exam performance

Introduction/BackgroundPeer-led teaching sessions (PLTS) supplement conventional medical education, allowing students to share knowledge effectively. PLTS have shown significant academic benefits for student instructors. However, the impact on attendees, especially concerning NBME-style examinations, remains underexplored.MethodsThis study analyzed de-identified data from 67 first-year medical students at Kirk Kerkorian School of Medicine, divided into PLTS attendees (study group) and non-attendees (control group) based on session participation. Data were collected before two NBME examinations, with attendees defined as those attending at least five Peer-led teaching sessions. Academic performance was measured using NBME Customized Assessment Services (CAS) exam scores. Statistical analyses, including p-tests, chi-square, and t-tests, assessed differences in performance and potential biases based on MCAT scores, first-generation status, and socioeconomic background.ResultsThe study group initially had 26 students, and the control group had 31 students. Post-first examination, the study group adjusted to 22 students, and the control group to 35 students. MCAT scores and demographics showed no significant differences between groups. The first examination revealed a significant higher score in PLTS attendees’ scores (4.28% increase, p = 0.036), indicating a positive impact of PLTS on basic science knowledge. The second examination on organ systems physiology showed no significant difference (p = 0.722).DiscussionPLTS effectively enhance foundational science understanding but may be less impactful for advanced topics. The diminishing effect may result from increased use of diverse study resources and adaptation to medical school. Future research should explore long-term benefits, optimal implementation strategies, and the impact of instructor quality and engagement. Therefore, the overall conclusion is that PLTS positively influence NBME-style exam performance in early medical education, but benefits decrease over time.

Comparing ACDF Outcomes by Cervical Spine Level: A Single Center Retrospective Cohort Study.

Previous research suggests a relationship between complications associated with anterior cervical discectomy and fusion and level involvement; however, there is limited research comparing postoperative outcomes of upper cervical fusions (UCFs) with middle-to-lower cervical fusions (MLCFs). This study aims to compare the outcomes of UCF with MLCF. A retrospective medical record review was conducted on 835 anterior cervical discectomy and fusion patients from 2012 to 2022. Patients were classified as UCF, defined as inclusion of C3 to C4 disc space, or MLCF, defined as lacking C3 to C4 disc space. Demographics were compared using χ 2 or Fisher exact tests. Clinical characteristics were compared in univariable analysis using χ 2 tests, linear-mixed effects models, or generalized linear-mixed models depending on distribution. Significant pre- and intraoperative characteristics were included in multivariable models to minimize confounding. Of the 835 patients included, 562 underwent MLCF and 281 underwent UCF. Median follow-up time was 211 days for UCF and 200 days for MLCF. UCF led to a 1.5-day longer length of stay in both univariable (1.5 vs 3.1, P < 0.0001) and multivariable analysis (2.3 days [95% CI: 1.8, 3.0] vs 3.3 days [2.6, 4.2], P < 0.0001). MLCF patients reported symptom improvement or resolution more often than UCF patients (0.43 [95% CI: 0.30, 0.62] and 0.46 [95% CI: 0.30, 0.70]). Additionally, a significantly higher rate of dysphagia was reported in the UCF group on both univariate and multivariable analysis, respectively (1.72 [95% CI: 1.18, 2.49] and 1.66 [95%CI: 1.08, 2.56]). To our knowledge, this is the first study to investigate the link between cervical fusion level and outcomes. UCF patients demonstrated greater rates of dysphagia, longer length of stay, and lower likelihood of improvement in neurological symptoms postoperatively both before and after controlling for differences in pre- and intraoperative characteristics. This study highlights that UCFs may be associated with worse postoperative outcomes when compared to MLCFs, which can inform surgical decision-making and patient counseling. The study represents Level 3 evidence due to its retrospective design and potential biases, indicating a need for future prospective randomized controlled trials to validate these findings.

Glucose-albumin ratio (GAR) as a novel biomarker for predicting postoperative pneumonia (POP) in older adults with hip fractures

Postoperative pneumonia (POP) is a common complication after hip fracture surgery and is associated with increased mortality and other complications in elderly patients. This study aims to evaluate biomarkers, especially the glucose-albumin ratio (GAR), for predicting POP in elderly hip fracture patients. A total of 1279 elderly patients admitted to our hospital with hip fractures were included. We assessed 29 biomarkers and focused on GAR to determine its prognostic and predictive value for POP. Multivariable logistic regression and propensity score-matched analyses were conducted to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for POP, adjusting for potential confounders. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off of GAR for predicting POP. Among the biomarkers and combinations assessed, GAR demonstrated superior predictive capability for POP in elderly hip fracture patients. ROC analyses showed moderate predictive accuracy of GAR for POP, with an area under the curve of 0.750. Using the optimal cut-off of 0.175, the high GAR group was significantly associated with increased odds of POP (adjusted OR 2.14, 95%, CI 1.50–3.05). These associations remained significant after propensity score matching and subgroup analyses. Dose–response relationships between GAR and POP were observed. In conclusion, GAR may be a promising biomarker to predict POP risk in elderly hip fracture patients. Further studies are warranted to validate its clinical utility. However, this study has certain limitations, including its retrospective design, potential for selection bias due to the exclusion criteria, and the single-center nature of the study, which should be addressed in future prospective, multicenter studies.

Beyond boundaries: exploring a generative artificial intelligence assignment in graduate, online science courses.

Generative artificial intelligence (GAI) offers increased accessibility and personalized learning, though the potential for inaccuracies, biases, and unethical use is concerning. We present a newly developed research paper assignment that required students to utilize GAI. The assignment was implemented within three online, asynchronous graduate courses for medical laboratory sciences. Student learning was assessed using a rubric, which rated students' effective integration and evaluation of GAI-generated content against peer-reviewed research articles, thus demonstrating their critical thinking and synthesis skills, among other metrics. Overall rubric scores were high, suggesting that learning outcomes were met. After field testing, we administered a 16-item survey about GAI utilization, contribution to learning, and ethical concerns. Data (n = 32) were analyzed, and free-response answers were thematically coded. While 93.8% of respondents found the GAI-generated content to be "very good" or "excellent," 28.1% found inaccuracies, and 68.8% "strongly agreed" or "agreed" that GAI should be allowed to be used as a tool to complete academic assignments. Interestingly, however, only 28.1% "strongly agreed" or "agreed" that GAI may be used for assignments if not explicitly authorized by the instructor. Though GAI allowed for more efficient completion of the project and better understanding of the topic, students noted concerns about academic integrity and the lack of citations in GAI responses. The assignment can easily be modified for different learning preferences and course environments. Raising awareness among students and faculty about the ethical use and limitations of GAI is crucial in today's evolving pedagogical landscape.

Prevalence and Prognostic Significance of Right Ventricular Dysfunction in Patients With Severe Low-Flow, Low-Gradient Aortic Stenosis Undergoing Transcatheter Aortic Valve Replacement.

Whether the presence of right ventricular (RV) dysfunction may influence the clinical outcome of patients with low-flow, low-gradient aortic stenosis (LFLG-AS) undergoing transcatheter aortic valve replacement (TAVR) has not yet been established. This study included consecutive patients with LFLG-AS undergoing TAVR at 2 high-volume Italian centers. RV dysfunction before TAVR procedure was defined as tricuspid annular plane systolic excursion assessed by transthoracic echocardiography lower than <17 mm. The primary outcome was all-cause death at 1 year. The propensity score weighting technique was implemented to account for potential selection bias between patients with and without RV dysfunction. A prespecified subgroup analysis was conducted to evaluate the consistency of the results in patients with classical and paradoxical LFLG-AS forms. This study included 392 patients; of them, 97 (24.7%) patients showed RV dysfunction before TAVR. At propensity score-weighted adjusted Cox regression analysis, RV dysfunction, according to dichotomous definition, was associated with an increased risk for the primary outcome (adjusted hazard ratio [HR], 3.11 [95% CI, 1.58-6.13]), cardiovascular death (adjusted HR, 3.26 [95% CI, 1.58-6.72]), and major adverse cardiovascular and cerebrovascular events (adjusted HR, 3.39 [95% CI, 1.76-6.53]). Conversely, no difference was detected for the risk of stroke and of permanent pacemaker implantation. No significant interaction of the classical and paradoxical LFLG-AS subgroups was detected for all the outcomes of interest. This study suggests that RV dysfunction echocardiographically assessed by tricuspid annular plane systolic excursion may improve the prognostic stratification of patients with LFLG-AS undergoing TAVR.

Extended Monitoring for Transition to Oral Corticosteroids in Acute Severe Ulcerative Colitis May Be Unnecessarily Prolonging Length of Stay.

There is no guideline regarding whether patients treated with intravenous corticosteroids for acute severe ulcerative colitis (ASUC) should be monitored in the hospital after transitioning to oral steroids. Our study aimed to: (1) compare rates of oral steroid transition failure and 30-day readmission between ASUC hospitalizations with extended inpatient monitoring compared to accelerated inpatient monitoring, and (2) identify predictors of oral steroid transition failure. A retrospective cohort study of ulcerative colitis (UC) related admissions at UCSF from 2014 to 2022 was conducted comparing rates of steroid transition failures in extended inpatient monitoring (≥ 24h on oral steroids prior to discharge) to accelerated inpatient monitoring (< 24h on oral steroids). Steroid transition failure was defined as worsening colitis activity with the need to return to IV steroids or undergo colectomy. Data analysis incorporated demographics, disease features, and treatment history. Transition failures from intravenous to oral corticosteroids occurred in 8% of all UC-related admissions. There was a significant difference in transition failure observed between the extended and accelerated monitoring groups, 13 vs 3% (p = 0.03), respectively, with 83.3% of total transition failures occurring within the extended monitoring group. The 30-day readmission rate was 6% in each group (p = 0.93). No significant predictors of transition failures were identified. Transition failures from IV to oral steroids are uncommon in ASUC hospitalizations. Transition failures were more likely to occur with extended monitoring, suggesting potential predictors and/or patient selection bias within this group. Further studies are needed to investigate the parameters driving clinician decision-making regarding oral steroid transitioning.

Preliminary Evidence for Neuronal Dysfunction Following Adverse Childhood Experiences: An Investigation of Salivary MicroRNA Within a High-Risk Youth Sample

Background/Objectives: Adverse childhood experiences (ACEs) are potent drivers of psychopathology and neurological disorders, especially within minoritized populations. Nonetheless, we lack a coherent understanding of the neuronal mechanisms through which ACEs impact gene expression and, thereby, the development of psychopathology. Methods: This observational pilot study used a novel marker of neuronal functioning (brain-derived micro ribonucleic acids, or miRNAs) collected via saliva to explore the connection between ACEs and neuronal gene expression in 45 adolescents with a collectively high ACE exposure (26 males and 19 females of diverse races/ethnicities, with six cumulative ACEs on average). We aimed to determine the feasibility of using salivary microRNA for probing neuronal gene expression with the goal of identifying cellular processes and genetic pathways perturbed by childhood adversity. Results: A total of 274 miRNAs exhibited reliable salivary expression (raw counts &gt; 10 in &gt; 10% of samples). Fourteen (5.1%) were associated with cumulative ACE exposure (p &lt; 0.05; r’s ≥ 0.31). ACE exposure correlated negatively with miR-92b-3p, 145a-5p, 31-5p, and 3065-5p, and positively with miR-15b-5p, 30b-5p, 30c-5p, 30e-3p, 199a-3p, 223-3p, 338-3p, 338-5p, 542-3p, and 582-5p. Most relations remained significant after controlling for multiple comparisons and potential retrospective bias in ACE reporting for miRNAs with particularly strong relations (p &lt; 0.03). We examined KEGG pathways targeted by miRNAs associated with total ACE scores. Results indicated putative miRNA targets over-represented 47 KEGG pathways (adjusted p &lt; 0.05) involved in neuronal signaling, brain development, and neuroinflammation. Conclusions: Although preliminary and with a small sample, the findings represent a novel contribution to the understanding of how childhood adversity impacts neuronal gene expression via miRNA signaling.

Moderating Role of Technology Self-Efficacy on Fintech Usage and Digital Financial Inclusion of Women in Rural Uganda

Digital financial inclusion (DFI) especially among rural women remains a significant challenge in Uganda. Despite the potential benefits of fintech’s, limited technology self-efficacy (TSE) among rural women hampers their effective usage. This study examined how TSE influences the relationship between fintech usage and DFI among these women. Data were gathered via a structured questionnaire from 384 women vendors in Mbale main market, employing an explanatory, quantitative, cross-sectional design. Using hierarchical regression and Hayes PROCESS Macro V 4.0, the study assessed both direct relationships and the moderating effect of TSE. Results indicate that both fintech usage and TSE positively impact DFI, with TSE significantly enhancing the association between fintech usage and DFI. Based on these findings, the study recommends that policymakers support fintech adoption through targeted education programs to improve TSE among rural women. Additionally, user-friendly interfaces and supportive systems are crucial in overcoming technological barriers and boosting confidence. However, limitations include the study’s cross-sectional approach, focus on women vendors in a specific district, and potential social desirability bias. Future research should consider longitudinal studies, cultural aspects of fintech adoption, and mixed-methods data collection to deepen understanding of the variables.

Coda-derived source properties estimated using local earthquakes in the Sea of Marmara, Türkiye

Abstract. Accurate estimates of the moment magnitude of earthquakes that physically measure the earthquake source energy are crucial for improving our understanding of seismic hazard in regions prone to tectonic activity. To address this demand, a method involving coda wave modelling was employed to estimate the moment magnitudes of earthquakes in the Sea of Marmara, northwestern Türkiye. This approach enabled us to model the source displacement spectrum of 303 local earthquakes efficiently recorded at 49 regional seismic stations between 2018 and 2020 in the region. The coda wave traces of individual events were inverted across 12 frequency ranges between 0.3 and 16 Hz. The resultant coda-derived moment magnitudes were found to be in good accordance with the conventional local magnitude estimates. However, the notable move-out between local magnitude and coda-derived moment magnitude estimates for smaller earthquakes less than a magnitude of 3.5 likely occurs due to potential biases arising from incorrect assumptions for anelastic attenuation and/or the finite sampling intervals of seismic recordings. Scaling relations between the total radiated energy and seismic moment imply a non-self-similar behaviour for the earthquakes in the Sea of Marmara. Our findings suggest that larger earthquakes in the study area exhibit distinct rupture dynamics compared to smaller ones, resulting in a more efficient release of seismic energy. Hence, we introduce an empirical relationship obtained from the scatter between local magnitude and coda-derived moment magnitude estimates.

A Critical Evaluation of Network Approaches for Studying Species Interactions

Ecological networks of species interactions are popular and provide powerful analytical tools for understanding variation in community structure and ecosystem functioning. However, network analyses and commonly used metrics such as nestedness and connectance have also attracted criticism. One major concern is that observed patterns are misinterpreted as niche properties such as specialization, whereas they may instead merely reflect variation in sampling, abundance, and/or diversity. As a result, studies potentially draw flawed conclusions about ecological function, stability, or coextinction risks. We highlight potential biases in analyzing and interpreting species-interaction networks and review the solutions available to overcome them, among which we particularly recommend the use of null models that account for species abundances. We show why considering variation across species and networks is important for understanding species interactions and their consequences. Network analyses can advance knowledge on the principles of species interactions but only when judiciously applied.

A simulation-based bias analysis to assess the impact of unmeasured confounding when designing nonrandomized database studies.

Unmeasured confounding is often raised as a source of potential bias during the design of nonrandomized studies, but quantifying such concerns is challenging. We developed a simulation-based approach to assess the potential impact of unmeasured confounding during the study design stage. The approach involved generation of hypothetical individual-level cohorts using realistic parameters, including a binary treatment (prevalence 25%), a time-to-event outcome (incidence 5%), 13 measured covariates, a binary unmeasured confounder (u1; 10%), and a binary measured "proxy" variable (p1) correlated with u1. Strengths of unmeasured confounding and correlations between u1 and p1 were varied in simulation scenarios. Treatment effects were estimated with (1) no adjustment, (2) adjustment for measured confounders (level 1), and (3) adjustment for measured confounders and their proxy (level 2). We computed absolute standardized mean differences in u1 and p1 and relative bias with each level of adjustment. Across all scenarios, level 2 adjustment led to improvement in the balance of u1, but this improvement was highly dependent on the correlation between u1 and p1. Level 2 adjustments also had lower relative bias than level 1 adjustments (in strong u1 scenarios: relative bias of 9.2%, 12.2%, and 13.5% at correlations of 0.7, 0.5, and 0.3, respectively, vs 16.4%, 15.8%, and 15.0% for level 1). An approach using simulated individual-level data is useful to explicitly convey the potential for bias due to unmeasured confounding while designing nonrandomized studies, and can be helpful in informing design choices. This article is part of a Special Collection on Pharmacoepidemiology.

Screening for Depression Using Natural Language Processing: Literature Review.

Depression is a prevalent global mental health disorder with substantial individual and societal impact. Natural language processing (NLP), a branch of artificial intelligence, offers the potential for improving depression screening by extracting meaningful information from textual data, but there are challenges and ethical considerations. This literature review aims to explore existing NLP methods for detecting depression, discuss successes and limitations, address ethical concerns, and highlight potential biases. A literature search was conducted using Semantic Scholar, PubMed, and Google Scholar to identify studies on depression screening using NLP. Keywords included "depression screening," "depression detection," and "natural language processing." Studies were included if they discussed the application of NLP techniques for depression screening or detection. Studies were screened and selected for relevance, with data extracted and synthesized to identify common themes and gaps in the literature. NLP techniques, including sentiment analysis, linguistic markers, and deep learning models, offer practical tools for depression screening. Supervised and unsupervised machine learning models and large language models like transformers have demonstrated high accuracy in a variety of application domains. However, ethical concerns related to privacy, bias, interpretability, and lack of regulations to protect individuals arise. Furthermore, cultural and multilingual perspectives highlight the need for culturally sensitive models. NLP presents opportunities to enhance depression detection, but considerable challenges persist. Ethical concerns must be addressed, governance guidance is needed to mitigate risks, and cross-cultural perspectives must be integrated. Future directions include improving interpretability, personalization, and increased collaboration with domain experts, such as data scientists and machine learning engineers. NLP's potential to enhance mental health care remains promising, depending on overcoming obstacles and continuing innovation.

Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score. Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report. Five teams participated, three of which included both informaticians and clinicians. Most (4/5) used Python for analyses, the remaining team used R. Common analysis themes included relationship of the GOSSIS-1 prediction score with demographics and care related variables; relationships between demographics and outcomes; calibration and factors related to the context of care; and the impact of missingness. Representativeness of the population, differences in calibration and model performance among groups, and differences in performance across hospital settings were identified as possible sources of bias. Datathons are a promising approach for challenging developers and users to explore questions relating to unrecognized biases in medical machine learning algorithms. Disadvantaged groups are at risk of being adversely impacted by biased medical machine learning models. To avoid these undesirable outcomes, developers and users must understand the challenges involved in identifying potential biases. We conducted a datathon aimed at challenging a diverse group of participants to explore an open-source patient severity model for potential biases. Five groups of clinicians and informaticians used tools of their choosing to evaluate possible sources of biases, applying a range of analytic techniques and exploring multiple features. By engaging diverse participants with hands-on data experience with meaningful data, datathons have the potential to raise awareness of potential biases and promote best practices in developing fair and equitable medical machine learning models.

Meta-analysis of nifedipine and enalapril combination therapy for hypertensive patients with coronary heart disease

Background: Numerous clinical studies have demonstrated that the therapeutic efficacy of combining nifedipine and enalapril in treating hypertension with coronary heart disease surpasses that of nifedipine as a stand-alone treatment. However, the current evidence is not yet sufficient due to limitations in the sample size, and further validation is needed. To analyze and assess the clinical impact of nifedipine combined with enalapril on hypertension complicated with coronary heart disease, and provide evidence for rational drug use in clinic. Methods: We employed a revised version of the Jadad scale to assess the quality of the research literature following a rigorous screening process. The statistical analysis was performed utilizing the software RevMan 5.4.1 for data analysis. Heterogeneity in the studies was evaluated based on the results of the Q test (P value), and the OR value of the combined effect was calculated using either the model with fixed effects or the one with random effects, with the results presented in a forest plot. Furthermore, a sensitivity analysis was conducted by excluding articles with the highest impact, and potential bias in publication was assessed through the utilization of a funnel plot. Results: A total of 183 articles were initially identified, and after a comprehensive review, 14 clinical randomized controlled trials were chosen for analysis. The meta-analysis findings revealed that the trial group displayed a significantly higher overall effectiveness rate compared to the control group (OR=3.47, 95%CI 2.40-5.03). Additionally, the trial group exhibited a more pronounced reduction in blood pressure and greater enhancement in cardiac function (OR=5.55, 95%CI). Conversely, the control group had a lower occurrence of ischemic events compared to the trial group (OR=0.35, 95%CI 0.24-0.50). Sensitivity analysis confirmed the stability and reliability of the combined effect size results (OR=3.91, 95%CI 2.51-6.09, P&lt;0.00001). However, based on an assessment using funnel plot results suggested potential bias in publication. Conclusion: The combined administration of nifedipine and enalapril exhibits enhanced effectiveness compared to the sole use of nifedipine in individuals diagnosed with hypertension and coronary heart disease, rendering it a valuable alternative for clinical application.

Evaluating the effectiveness of recovery-oriented interventions for youth who vape nicotine: a systematic review protocol.

The increase in nicotine vaping among youth has emerged as a critical public health concern. Vaping among youth is linked to adverse health outcomes, including nicotine addiction, cardiovascular and pulmonary diseases and mental health challenges. Recently, there has been a growing demand for research to expand on treatments to enhance the recovery process from vaping nicotine in youth. To this date, no comprehensive review identifies all interventions available for vaping recovery and compares their effectiveness in youth. This proposed systematic review seeks to identify all recovery-oriented interventions for youth aged 10-24 that facilitate vaping recovery and evaluate the measured effectiveness. This review will serve to support healthcare practitioners, nurses and policymakers to further understand recovery interventions and aid in improving vaping recovery among youth. This protocol, registered with PROSPERO, adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol (PRISMA-P) framework, and the final review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to ensure quality and replicability. The review will comprise studies including youth aged 10-24 engaging in nicotine vaping. A comprehensive search will be conducted across MEDLINE, Embase, PsycINFO, Scopus and CINAHL, with no restrictions on the date range due to the recency of the topic. Two independent reviewers will screen the identified research for eligibility at the title and abstract levels, with any disagreements resolved by a third reviewer. The review will include randomised control trials and quasi-experimental studies. Full-text review and data extraction will be standardised and completed by the primary researcher. Additionally, studies will be assessed for quality and potential biases. Given that this is a review of existing literature, no ethics approval is required. The focus on youth necessitates careful consideration of ethical standards. This review is committed to contributing responsibly to public health knowledge regarding youth vaping recovery. The results from this review will be disseminated through publication in an open-access, peer-reviewed journal and likely through posters and presentations at scientific conferences. CRD42024543994.

Effects of new hypoglycemic drugs on patients with heart failure: a systematic review and network meta-analysis.

Currently, there is no relevant study comparing sodium-dependent glucose transporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1RA), and dipeptidyl peptidase inhibitor (DPP4i) head to head to evaluate their comprehensive impact on heart failure patients. We conducted a comprehensive literature search across multiple databases. Utilizing the risk of bias tool from the Cochrane Collaboration, the methodological quality of included studies was critically assessed and potential publication bias was examined via funnel plots. All results are presented as mean difference; 95% confidence interval (MD; 95% CI). The network meta-analysis indicated that in regards to left ventricular function, there is a big difference in the left ventricular ejection fraction (LVEF) of Empagliflozin 25mg (13.64; 0.26, 27.01) compared to Canagliflozin 100mg; and significant differences in the left ventricular end-diastolic volume (LVEDV) and left ventricular end-systolic volume (LVESV) of Dapagliflozin 10mg (-0.76; -1.27, -0.25 and -0.95; -1.86, -0.05), Vildagliptin 50mg (-1.05; -1.47, -0.63 and -1.12; -2.19, -0.05), and Sitagliptin 100mg (-1.34; -2.31, -0.38 and -1.89; -3.50, -0.27) compared to Empagliflozin 10mg. In terms of the quality of life, there are significant differences in the N-terminal pro-B-type natriuretic peptide (NT-proBNP) and the Quality of life score of Sitagliptin 100mg (408.08; 213.59, 602.57 and 3.74; 1.57, 5.92) compared to Dapagliflozin 5mg. In terms of the cardiovascular outcome events, there is a significant difference in the heart failure rehospitalization rate of Dapagliflozin 10mg (0.45; 0.25, 0.82) and Empagliflozin 10mg (0.48; 0.28, 0.81) compared to Liraglutide 1.8mg. Further significant differences are found in the all-cause mortality of Dapagliflozin 10mg (0.81; 0.66, 0.98) compared to Vildagliptin 50mg; the cardiovascular death of Albiglutide 30mg (0.49; 0.28, 0.86) compared to Exenatide 2mg; and the arrhythmic events of Liraglutide 1.8mg (0.49; 0.26, 0.90) compared to Empagliflozin 10mg. The network meta-analysis of SGLT2i, GLP-1RA, and DPP4i as a class of drugs showed that GLP-1RA is superior to SGLT2i in improving LVEF and reducing myocardial infarction/acute coronary syndrome, whereas DPP4i is superior to SGLT2i in improving LVEDV and LVESV. GLP-1RA is superior to SGLT2i in improving LVEF and reducing myocardial infarction/acute coronary syndrome, whereas DPP4i is superior to SGLT2i in improving LVEDV and LVESV. Key message What is already known on this topic-It has been confirmed that three new hypoglycemic drugs have a protective effect on the cardiovascular system. Studies have shown that sodium-dependent glucose transporter 2 inhibitors (SGLT2i) can improve cardiovascular outcomes and enhance the quality of life of heart failure patients. Currently, SGLT2i is widely used in the clinical treatment of heart failure, and related studies have shown that glucagon-like peptide-1 receptor agonists (GLP-1RA) and dipeptidyl peptidase inhibitor (DPP4i) also play important roles in the treatment of heart failure. What this study adds-However, there is no relevant research on whether these drugs' clinical efficacy is dose-dependent. How this study might affect research, practice, or policy-This study included different doses of hypoglycemic drugs and used a network meta-analysis method to comprehensively evaluate the effects of three hypoglycemic drugs on heart function, quality of life, and prognosis in heart failure patients, providing a basis for clinical practice.

Skimmed milk intake reduces the risk of ER− breast cancer: a Mendelian randomization analysis

BackgroundIn prior observational investigations, it has been demonstrated that the consumption of milk is associated with the incidence of breast cancer (BC). Despite the existence of a two-sample Mendelian randomization (MR) that suggests a causal relationship between milk intake and breast cancer risk, the outcomes still lack a definitive conclusion. This ambiguity may be attributed to variables such as the variety of milk ingested, estrogen levels, the specific type of BC, and potential confounding factors. Therefore, our principal objective is to establish the causal association between the consumption of skimmed milk and full cream milk and the risk of different types of BC through the utilization of two-sample and two-step MR analyses.MethodsIn this study, we analyzed single nucleotide polymorphisms associated with skimmed and full-cream milk consumption in a cohort of 360,806 individuals from European populations through genome-wide association studies. We conducted a two-sample MR analysis using three different methods: inverse variance weighting (IVW) was used as the main analysis, and MR-Egger and Weighted median were used as supplementary analyses to IVW. We also performed sensitivity analysis, which included leave-one-out analysis, Cochran's Q test to detect heterogeneity, and MR-Egger intercept analysis to detect potential biases caused by pleiotropy. We used two-step MR analysis to evaluate potential mediators of associations.ResultsIn the two-sample MR analysis, IVW analysis suggests a potential inverse causal relationship between skimmed milk and BC [OR 0.34, 95% CI (0.12–1.00), P = 0.05]. Subgroup analysis revealed that skimmed milk reduces the risk of estrogen receptor-negative (ER−) breast cancer [OR 0.18, 95% CI (0.04–0.90), P = 0.04], but not estrogen receptor-positive (ER+) breast cancer [OR 0.42, 95% CI (0.15–1.22), P = 0.11]. MR Egger reached similar results, that is, skimmed milk reduces the risk of ER− breast cancer [OR 0.006, 95% CI (0.00–0.70), P = 0.04], but not BC [OR 0.16, 95% CI (0.01–4.66), P = 0.30] and ER+ breast cancer [OR 0.50, 95% CI (0.02–12.61), P = 0.65]. Additionally, we found no causal relationship between full cream milk and BC (P > 0.05). In two-step MR analysis, we found evidence for a mediating role of BMI in the relationship between skimmed milk intake and ER-breast cancer risk.ConclusionOur findings strengthen the evidence for a protective effect of skimmed milk consumption on ER-breast cancer risk. Further two-step MR analyses suggest that this protective effect may partly result from body mass index (BMI). There is no evidence that full cream milk consumption affects the risk of BC.

The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis.

The objective, unobtrusively collected GPS features (eg, homestay and distance) from everyday devices like smartphones may offer a promising augmentation to current assessment tools for depression. However, to date, there is no systematic and meta-analytical evidence on the associations between GPS features and depression. This study aimed to investigate the between-person and within-person correlations between GPS mobility and activity features and depressive symptoms, and to critically review the quality and potential publication bias in the field. We searched MEDLINE, PsycINFO, Embase, CENTRAL, ACM, IEEE Xplore, PubMed, and Web of Science to identify eligible articles focusing on the correlations between GPS features and depression from December 6, 2022, to March 24, 2023. Inclusion and exclusion criteria were applied in a 2-stage inclusion process conducted by 2 independent reviewers (YT and JK). To be eligible, studies needed to report correlations between wearable-based GPS variables (eg, total distance) and depression symptoms measured with a validated questionnaire. Studies with underage persons and other mental health disorders were excluded. Between- and within-person correlations were analyzed using random effects models. Study quality was determined by comparing studies against the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines. Publication bias was investigated using Egger test and funnel plots. A total of k=19 studies involving N=2930 participants were included in the analysis. The mean age was 38.42 (SD 18.96) years with 59.64% (SD 22.99%) of participants being female. Significant between-person correlations between GPS features and depression were identified: distance (r=-0.25, 95% CI -0.29 to -0.21), normalized entropy (r-0.17, 95% CI -0.29 to -0.04), location variance (r-0.17, 95% CI -0.26 to -0.04), entropy (r=-0.13, 95% CI -0.23 to -0.04), number of clusters (r=-0.11, 95% CI -0.18 to -0.03), and homestay (r=0.10, 95% CI 0.00 to 0.19). Studies reporting within-correlations (k=3) were too heterogeneous to conduct meta-analysis. A deficiency in study quality and research standards was identified: all studies followed exploratory observational designs, but no study referenced or fully adhered to the international guidelines for reporting observational studies (STROBE). A total of 79% (k=15) of the studies were underpowered to detect a small correlation (r=.20). Results showed evidence for potential publication bias. Our results provide meta-analytical evidence for between-person correlations of GPS mobility and activity features and depression. Hence, depression diagnostics may benefit from adding GPS mobility and activity features as an integral part of future assessment and expert tools. However, confirmatory studies for between-person correlations and further research on within-person correlations are needed. In addition, the methodological quality of the evidence needs to improve. OSF Registeries cwder; https://osf.io/cwder.

Effects of cognitive training on cognitive function in patients after cardiac surgery: A systematic review and meta-analysis of randomized controlled trials.

Postoperative cognitive deficits frequently occur in patients undergoing cardiac surgery, leaving them with reduced cognitive function. Cognitive training has been shown to improve cognitive function, however, the role in patients after cardiac surgery is unclear. In this study, we aimed to evaluate the effectiveness and safety of cognitive training in patients undergoing cardiac surgery. A systematic search of PubMed, Embase, Cochrane Library, CINAHL, Ovid Medline, Web of Science, CNKI, and Wanfang was conducted until March 2024. The risk of bias was assessed using the Cochrane Risk of Bias Tool. Data were meta-analyzed using RevMan 5.4 software. Potential bias and reliability of evidence were fairly assessed by using the Cochrane risk of bias method and the GRADE evidence grading method. A total of 16 studies involving 1335 cardiac surgery patients were included in this study. Compared with the control group, the cognitive training group had a significantly lower incidence of postoperative cognitive dysfunction (RR 0.35, 95% CI 0.18-0.65, P = .001), significantly improved cognitive function (MD 2.54, 95% CI 1.27-3.81, P < .001), and a significantly higher quality of life-mental component (MD 5.22, 95% CI 2.32-8.13, P < .001), anxiety (MD -6.05, 95% CI -10.96 to -1.15, P = .02) and depression (MD -3.97, 95% CI -7.15 to -0.80, P = .01) were significantly improved between groups. However, the differences were not statistically significant for postoperative delirium (RR 1, 95% CI 0.38-2.65, P = 1.00) and postoperative hospitalization (MD -0.95, 95% CI -2.90 to 1.00, P = .34). The present study, based on a low to moderate quality of evidence, suggests that cognitive training improves cognitive functioning, reduces the incidence of postoperative cognitive dysfunction, and has a positive impact on anxiety and depression in patients undergoing cardiac surgery. However, current evidence does not allow for the determination of effects on quality of life, postoperative delirium, and postoperative length of stay.

Screening and Analysis of Skin Cancer Treatment Using Biocomponents of Plants Using Backpropagation Neural Networks: A Comprehensive Review

: In recent years, the use of natural compounds derived from plants for the treatment of skin cancer has gained significant attention due to their potential therapeutic effects and minimal side effects. This review focuses on the innovative approach of utilizing biocomponents sourced from plants in combination with backpropagation neural networks (BPNN) for the screening and analysis of skin cancer treatments. The integration of plant-derived compounds and AI-driven algorithms holds promise for enhancing the precision and effectiveness of skin cancer therapies. The review begins by highlighting the escalating global burden of skin cancer and the limitations of conventional treatment approaches. With the rise in concerns about the adverse effects of synthetic drugs, researchers have turned their attention towards exploring the therapeutic potential of plant-derived biocomponents. These natural compounds are known for their rich bioactive constituents that exhibit anti-cancer properties, making them suitable candidates for skin cancer treatment. One of the key challenges in harnessing the potential of plant-derived compounds is the need for accurate screening and analysis of their effects. This is where backpropagation neural networks, a type of artificial neural network, comes into play. These networks can process complex data and recognize intricate patterns, enabling them to predict the efficacy of various biocomponents in combating skin cancer. The review delves into the functioning of BPNN and its applications in drug discovery and treatment evaluation. Furthermore, the review explores several case studies that demonstrate the successful integration of plant-derived compounds with BPNN in the context of skin cancer treatment. These studies provide evidence of how this synergistic approach can lead to improved treatment outcomes by minimizing adverse effects and maximizing therapeutic benefits. The methodology section discusses the steps involved in training the neural network using relevant datasets and optimizing its performance for accurate predictions. While the integration of plant-derived compounds and BPNN shows great promise, the review also addresses the existing challenges and limitations. These include the need for comprehensive and standardized datasets, potential biases in training data, and the complexity of neural network architectures. The regulatory considerations surrounding plant-based therapies are also discussed, highlighting the importance of rigorous testing and validation.