Potential Bias Research Articles

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<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.

Ordering Effects versus Cognitive Burden: How Should We Structure Attributes in Conjoint Experiments?

Abstract Conjoint experiments offer a flexible way to elicit population preferences on complex decision tasks. We investigate whether we can improve respondents’ survey experience and, ultimately, choice quality by departing from the current recommendation of completely randomized conjoint attribute ordering. Such random ordering guarantees that potential bias from attribute order cancels out on average. However, in situations with many attributes, this may unnecessarily increase cognitive burden, as attributes belonging together conceptually are presented scattered across the choice table. Hence, we study experimentally whether purposeful ordering (“theoretically important” attributes first) or block randomized ordering (attributes belonging to the same theoretical concept displayed in randomized bundles) affects survey experience, response time, and choice itself, as compared to completely randomized ordering. Drawing on a complex preregistered choice design with nine attributes (N = 6,617), we find that ordering type affects neither self-reported survey experience, choice task timing, nor attribute weighting. Potentially, block randomization reduces cognitive burden for some subgroups. To our knowledge, we thereby provide the first systematic empirical evidence that ordering effects are likely of low relevance in conjoint choice experiments and that the trade-off between cognitive burden and ordering effects is minimal from the perspective of respondents, at least for our substantive application.

IFSO Bariatric Endoscopy Committee Evidence-Based Review and Position Statement on Endoscopic Sleeve Gastroplasty for Obesity Management.

Obesity is a significant global health issue. Metabolic and bariatric surgery (MBS) is the gold standard in the treatment of obesity due to its proven effectiveness and safety in the short and long term. However, MBS is not suitable for all patients. Some individuals are at high surgical risk or refuse surgical treatment, while others do not meet the criteria for MBS despite having obesity-related comorbidities. This gap has driven the development of endoscopic solutions like endoscopic sleeve gastroplasty (ESG), which offers a less invasive alternative that preserves organ function and reduces risks. A recent IFSO International Delphi consensus study highlighted that multidisciplinary experts agree on the utility of ESG for managing obesity in patients with class I and II obesity and for those with class III obesity who do not wish to pursue or qualify for MBS. This IFSO Bariatric Endoscopy Committee position statement aims to augment these consensus statements by providing a comprehensive systematic review of the evidence and delivering an evidence-based position on the value of ESG within the spectrum of obesity management. A comprehensive systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Cochrane guidelines. Systematic Review: The systematic review included 44 articles encompassing 15,714 patients receiving ESG. The studies varied from large case series to cohort studies and a randomized controlled trial (RCT). The mean baseline BMI was 37.56kg/m2. The review focused on weight loss outcomes and safety data. Time point Mean %EWL Mean %TBWL 6months 48.04 15.66 12months 53.09 17.56 18months 57.98 16.25 24months 46.57 15.2 36months 53.18 14.07 60months 45.3 15.9 These results demonstrate significant weight loss following ESG. The pooled serious adverse event (SAE) rate was 1.25%. This low rate of SAEs indicates that ESG is a relatively safe procedure. The quality of evidence from the included observational studies was assessed as very low, primarily due to the inherent limitations associated with observational study designs, such as potential biases and lack of randomization. In contrast, the quality of evidence from the single randomized controlled trial was rated as MODERATE, reflecting a more robust study design that provides a higher level of evidence despite some limitations. The IFSO Bariatric Endoscopy Committee, after conducting a comprehensive systematic review and meta-analysis, endorses endoscopic sleeve gastroplasty (ESG) as an effective and valuable treatment for obesity. ESG is particularly beneficial for patients with class I and II obesity, as well as for those with class III obesity who are not suitable candidates for metabolic bariatric surgery. ESG provides significant weight loss outcomes and demonstrates a favorable safety profile with a low rate of serious adverse events. Despite the limitations of the included observational studies, the randomized controlled trial included in the analysis reinforces the efficacy and safety of ESG and provides an evidence-based foundation for the position statement. Thus, the IFSO position statement supports and provides an evidence base for the role of ESG within the broader spectrum of obesity management.

Investigating the hemiretinal asymmetry in emotion processing as a function of spatial frequency.

The subcortical visual pathway to the amygdala has long been considered a rapid and crude stream for processing emotionally salient information that is reliant on low spatial frequency (LSF) information. Recently, research has called this LSF dependency into question. To resolve this debate, we take advantage of an anatomical hemiretinal asymmetry, whereby the nasal hemiretina sends a higher proportion of information through the subcortical pathway than the temporal hemiretina. We recorded brain activity using electroencephalography (EEG) in human participants (N = 40) while they completed a monocular viewing paradigm. Pairs of faces (one fearful and one neutral, or both neutral) were projected simultaneously to the nasal and temporal hemiretina in three contrast-equated blocks; faces filtered to display only (i) LSF, (ii) high spatial frequency (HSF), or (iii) unfiltered information (broadband spatial frequency; BSF). BSF fearful faces were found to produce a greater naso-temporal asymmetry, with greater N170 amplitudes evoked by BSF faces in the nasal field, compared to HSF faces. Conversely, the naso-temporal asymmetry for LSF fearful faces did not differ between BSF and HSF. Collectively, these findings provide crucial evidence that the subcortical pathway carries combined spatial frequency visual signals, with a potential bias against HSF content.

Metabolic pathways, pharmacokinetic, and brain neurochemicals effects of capsaicin: Comprehensively insights from in vivo studies

BackgroundCapsaicin (CAP), a prominent component of chili pepper known for its potent agonistic effects on TRPV1, has attracted significant attention for its diverse physiological effects. Nevertheless, there remains a paucity of data concerning its in vivo distribution, metabolism, pharmacodynamic properties, and influence on the metabolic profile of the brain. MethodsStable isotope tracing, in vitro enzyme incubation, microdialysis coupled with UHPLC-MS/MS techniques were employed to investigate the in vivo metabolic pathways, distribution, and pharmacokinetic properties of CAP, and the potential biases in metabolic pathways was elucidate through molecular docking. Furthermore, the effect of CAP on brain metabolic profiles was assessed using untargeted metabolomics, and spatial visualization analysis was conducted through mass spectrometry imaging. ResultsCAP was distributed predominantly in the kidneys, with lower content in the liver, heart, lungs, brain, and spleen following peripheral administration, and the absorption half-life in the body was about 20 minutes. CAP primarily underwent alkyl terminal dehydrogenation, hydroxylation, and macrocyclization metabolic pathways under the action of CYP2C9, CYP2C19 and CYP2D6, resulting in at least four metabolites. Among them, the hydroxylation products were main metabolites and the dehydrogenation product 16,17-dihydrocapsaicin could interact with the key binding sites Leu515 and Thr550 of TRPV1 like CAP. CAP quickly diffused to various brain regions and the metabolic characteristics in the striatum were relatively different from that in the blood. The distribution of CAP in the brain primarily triggered the release of neurotransmitters in areas associated with reward, cognition, and memory. Both acute and chronic exposure to CAP elevated amino acid levels in cortical regions, while producing contrasting effects on nucleotide metabolites. ConclusionThis study offers an initial in-depth analysis of the distribution patterns, metabolic pathways and pharmacodynamic properties of CAP in the body and brain. These findings established a basis for further studies on CAP's pharmacology properties and its influence on the central nervous system.

The challenges of diversity in nursing faculty recruitment: the case of Finland

Diversity considerations in hiring faculty in higher education have garnered significant attention globally in recent years. However, analyses of faculty recruitment dynamics outside the United States, particularly in schools and colleges of nursing, have been limited. This paper explores how concepts related to racism apply to recruitment in nursing education, focusing on the specific context of Finland. Despite the increased admission of foreign students to English-language nursing programs, potential biases against the recruitment of nurse educators from foreign backgrounds persist in Finland. Local data, anecdotes, and an analysis of the broader literature suggest that robust diversity, equity, and inclusion (DEI) policies and guidelines are necessary to enhance the experiences of both instructors and students. Additionally, training and support mechanisms may be needed to enable nursing faculty members from foreign backgrounds to thrive and fully participate in shared governance within academic institutions. By examining the challenges and proposing strategies for increasing diversity in Finnish nursing education, this paper aims to contribute to the broader discourse on diversity in nursing as a profession.

Revictimization of women victims of intimate partner violence in the French press: A social representations approach.

This study aims to analyze how the press portrays cases of revictimization experienced by women in France, through the lens of social representation theory. An exploratory, descriptive study was conducted using a corpus of 157 online press articles. A total of 187,773 words and 5,240 segments were analyzed using Iramuteq version 7, employing top-down hierarchical classification and lexical similarity analysis. The analysis identified four classes: 1. Violent incidents (25.5%); 2. Political actions against violence (26%); 3. Institutional revictimization (26.6%) and 4. Violence and breaking the silence (21.9%). The findings illustrate how media portrayals contribute to the construction of social representations surrounding revictimization. Class 1 reveals a focus on extreme cases of violence, such as femicides, and highlights a potential gender bias in media reporting through the omission of terms like 'femicide'. Class 2 demonstrates the influence of ideological perspectives on the portrayal of political measures against violence, with conservative outlets framing such actions within traditional values, while progressive newspapers advocate for systemic reform. Class 3 shows a gap in the conceptualization of institutional revictimization in the French press compared to other countries, revealing how conservative portrayals can minimize systemic issues and perpetuate harmful stereotypes. Class 4 shows the role of female journalists in addressing various forms of violence, despite the limited impact of the #MeToo movement in recent coverage. Collectively, these findings elucidate how media representations shape public attitudes and institutional responses to gender-based violence through the lens of social representations.

Application of artificial intelligence in career development of women in STEM field: Counseling, network construction and recruitment

With the rapid development of science and technology, the application of artificial intelligence (AI) in STEM (science, technology, engineering and mathematics) is increasingly extensive, which provides a new idea for solving the challenges of women's career development in STEM. This paper deeply discusses the application of AI in counseling, network construction and recruitment of women's STEM career development, aiming at promoting gender equality and women's career growth. In career counseling, AI technology provides customized career advice and development path for women through deep learning and big data analysis. According to personal education background, skill level, hobbies and career goals, the system intelligently recommends suitable career directions, and updates industry trends and job market data in real time to help women make more informed career choices. The AI tutoring system also enhances women's professional self-confidence through functions such as simulated interviews and career planning exercises. In terms of network construction, AI helps women to expand their professional networks more effectively through intelligent recommendation system. Based on user behavior data and preferences, AI accurately recommends industry seminars, technical exchange activities and online communities to promote women to establish contacts with like-minded professionals. AI also assists women in managing and maintaining network relationships, ensures the continuity of network interaction through intelligent reminding functions, and identifies key nodes and influential people to enhance network quality. In the recruitment process, AI technology reduces gender bias and improves the fairness and transparency of recruitment through automatic resume screening, standardized interview process and competency-based evaluation model. AI system can analyze a large amount of data, accurately match the skills and job requirements of female candidates, and improve the employment rate of women in STEM field. At the same time, through transparent algorithm decision-making process and fairness check, the potential gender bias problem in AI algorithm is overcome. The research in this paper provides theoretical support and practical guidance for using AI technology to promote women's career development in STEM field, and helps to build a more equal and inclusive scientific and technological working environment.

Autoimmune Diseases and Risk of Non-Hodgkin Lymphoma: A Mendelian Randomisation Study.

Non-Hodgkin lymphoma (NHL) is one of the most common haematologic malignancies in the world. Despite substantial efforts to identify causes and risk factors for NHL, its aetiology is largely unclear. Autoimmune diseases have long been considered potential risk factors for NHL. We carried out Mendelian randomisation (MR) analyses to examine whether genetically predicted susceptibility to ten autoimmune diseases (Behçet's disease, coeliac disease, dermatitis herpetiformis, lupus, psoriasis, rheumatoid arthritis, sarcoidosis, Sjögren's syndrome, systemic sclerosis, and type 1 diabetes) is associated with risk of NHL. Two-sample MR was performed using publicly available summary statistics from cohorts of European ancestry. For NHL and four NHL subtypes, we used data from UK Biobank, Kaiser Permanente cohorts, and FinnGen studies. Negative associations between type 1 diabetes and sarcoidosis and the risk of NHL were observed (odds ratio [OR] 0.95, 95% confidence interval [CI]: 0.92-0.98, p = 5 × 10-3, and OR 0.92, 95% CI: 0.85-0.99, p = 2.8 × 10-2, respectively). These findings were supported by the sensitivity analyses accounting for potential pleiotropy and weak instrument bias. No significant associations were found between the other eight autoimmune diseases and NHL risk. These findings suggest that genetically predicted susceptibility to type 1 diabetes, and to some extent sarcoidosis, might reduce the risk of NHL. However, future studies with different datasets, approaches, and populations are warranted to further examine the potential associations between these autoimmune diseases and the risk of NHL.

Development of a hybrid Karst Aquifer Vulnerability map by using geospatial and statistical tools. The case study of Ziria aquifer in Northern Peloponnese

Aquifer vulnerability assessment constitutes a crucial tool for the protection and sustainable management of groundwater resources, particularly in complex karst aquifers. This study conducted a detailed comparative analysis of two tailored and widely used vulnerability mapping methodologies, COP and PaPRIKa, to evaluate their performance within a Mediterranean karst groundwater system. The methods were employed for the development of a hybrid vulnerability map after geospatial and statistical analysis. Both COP and PaPRIKa were applied using a combination of geological, hydrological, and geomorphological aquifer characteristics that can potentially influence its vulnerability to contamination. While COP predominantly assigned Low and Very Low vulnerability values across the study area, PaPRIKa identified the Moderate class as dominant, suggesting a finer sensitivity to karst-specific features. To address potential biases in PaPRIKa, a single-parameter sensitivity analysis was performed, leading to adjusted weights and the development of a modified version, PaPRIKa-Mod. A quantitative comparison of all three methods highlighted the varying degrees of consensus and discord, with PaPRIKa and its modification showing the highest consistency, suggesting robust methodological integrity. Conversely, the comparison of COP with either version of PaPRIKa revealed a lesser yet notable concordance, underscoring their capacity to be integrated and their potential to complement each other in vulnerability assessments. Finally, a hybrid vulnerability map was developed from the integration of similar vulnerability classes, attributing the most vulnerable setting prevailing across all methods. The methodological approach that was followed is adaptable and can provide significant insights for vulnerability estimations across different regions and methodological concepts.

Exploring Two-Dimensional MOF-Derived Co/Ni Species for Efficient Electrocatalytic Hydrogen Evolution Reaction

Hydrogen evolution reaction (HER) has received a lot of attention due to its promising advantages in producing “green hydrogen” via electrocatalysis with low cost and high efficiency. Until now, developing transition metal-based electrocatalysts for HER has been a great challenge. In this regard, we reported a facile strategy to fabricate Co/Ni species encapsulated by carbon structure (CoNi@C) through annealing two-dimensional (2D) metal-organic framework (MOF) nanosheets. The CoNi@C that was prepared under 600 °C achieved the highest catalytic performance in 1.0 M KOH electrolyte with an overpotential of 330 mV to acquire 100 mA cm−2. In addition, the effect of KOH concentrations on the HER performance of CoNi@C-600 was explored. In 3.0 M KOH electrolyte, the current density of 100 mA cm−2 has been attained at a bias potential of 80 mV. The alkaline environment can improve the electrocatalytic performance and further enhance the stability of the as-prepared catalysts. This work would endow promising opportunities for manipulating MOF-based structures through pyrolysis to fabricate highly efficient electrocatalysts.

Contribution of 3D virtual modeling in locating hepatic metastases, particularly “vanishing tumors”: a pilot study

Aim: This article aims to explore how three dimensions (3D) virtual modeling enhances accuracy and efficiency in detecting hepatic metastases, with a specific focus on “vanishing tumors” that are difficult to detect using traditional imaging techniques. It also aims to demonstrate the potential impact of these advanced technologies on improving diagnostic and treatment strategies for patients with liver cancer. Methods: Eight patients with liver metastases from colorectal cancer were studied using magnetic resonance imaging (MRI) and 3D virtual modeling to enhance surgical planning by accurately locating lesions. The concordance between these imaging techniques and the Gold Standard was assessed using Gwet’s AC1 coefficient, with statistical analysis performed through resampling methods and non-parametric tests due to non-normal AC1 distribution. Liver segmentation was conducted semi-automatically using SurgiPrint software, which is crucial for detecting lesions undetectable post-chemotherapy. The study’s evaluation model involved questionnaires for medical professionals across four cohorts, aiming to determine the 3D model’s effectiveness in identifying lesion locations for surgery. Results: This study investigated the efficacy of 3D virtual modeling in identifying hepatic metastases, particularly comparing its accuracy with traditional MRI in locating lesions. The findings indicate that MRI generally provided better concordance with the Gold Standard for lesion localization, except for a few experienced users who had prior familiarity with the 3D models. Despite the mixed results in accuracy, the study suggests potential benefits of 3D modeling in enhancing surgical planning and execution, particularly in detecting “vanishing” liver metastases that are difficult to visualize with standard imaging techniques. The research aligns with broader evidence indicating the utility of 3D models in improving outcomes in hepatic surgery by enabling more precise resections and reducing postoperative complications. However, the study also notes the challenge of quantifying the added value of 3D modeling due to the unique nature of each surgical case and the potential bias in the user experience with the technology. Conclusion: The conclusion of this study underscores the significant potential of 3D virtual modeling in enhancing the precision of locating and resecting hepatic metastases, particularly those elusive “vanishing tumors”. This research underscores the value of integrating advanced 3D imaging techniques into surgical practice, suggesting a paradigm shift toward more accurate and less invasive liver surgeries. Future studies should aim to further quantify the benefits of 3D modeling in surgical outcomes, investigate its utility across different surgical experiences, and explore the integration of artificial intelligence (AI) to maximize its effectiveness in clinical settings.