bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bridging regulation and practice: CJEU and Dutch case law on botanical health claims
Authors: Lenssen Karin G. M. , de Boer Alie 
Published: February 2025

IntroductionEven though botanicals are increasingly popular ingredients for food supplements, health claims related to their putative bene ts remain unclearly regulated.MethodsThrough an analysis of EU and national case law from the Netherlands, including self-regulatory decision-making, we have studied the implications of case law on botanical health claims.ResultsOur analysis reveals that there are multiple issues related to claims on botanical-containing products: whether it should be classi ed as food or medicine; what statements should be understood as health claims; what type of evidence should underlie health claims and, more specically, botanical health claims; and how to deal with online commercial communication. The case law analysis highlights rst that a gray area will continue to exist when classifying products as foods or medicinal products, particularly when it comes to products that contain botanical ingredients. Most importantly, our study also reveals that claims—even when they are on hold, like botanical claims—need a certain scienti c foundation before they can be used on products. Finally, the courts believe that even though on-hold claims will continue to give a certain level of uncertainty for food business operators, this is not a legal but rather a regulatory issue.DiscussionThe findings from our case law analysis highlight that even though case law is useful in further interpretation of legislation, it does not provide any policy advancement. In the case of botanicals, a political decision regarding their substantiation is highly desired.
Uncovering potential targets for antibody-drug conjugates in the treatment of gynecologic malignancies
Authors: Jiang Yuying , Xu Yuance , He Junqi , Sui Lei , Li Tian , Xia Nannan , Yao Qin 
Published: February 2025

BackgroundAntibody-drug conjugates (ADCs) play an important role in the targeted therapy of gynecological malignancies. The purpose of this study was to investigate the expression of targets in gynecologic malignancies in order to predict the selection of targets for the development of antibody-drug conjugates.MethodsIn this article, we identified existing ADCs and their targets through clinical trial databases and public genomic datasets, performed differential analysis of tumor antigen targets (TATs) expression between tumor and normal tissues, and evaluated the necessity of the targets for tumor cell lines.ResultsIn gynecologic malignancies, we have identified several highly expressed TATs, some of which have been targeted by FDA-approved ADCs, such as TROP2 and Nectin-4, although these drugs have not been approved for the treatment of gynecologic cancers. At the same time, we also observed that some targets of ADCs that have not yet been approved by the FDA also show high expression levels in gynecologic malignancies tissues, such as MSLN, ERBB3, NaPi2b, etc. Furthermore, we identified TATs with high expression levels in various pathological subtypes of ovarian, endometrial, and cervical cancer. Notably, some TATs are crucial to the survival of tumor cells, such as CD71, TOP1, and TDGF1, which are essential for the survival of ovarian, endometrial, cervical, and other tumor cells.ConclusionWe have innovatively predicted the potential targets of ADCs in treating gynecological malignancies and provided a new perspective on applying some FDA-approved ADCs in indications for gynecological cancers.
Dual inhibitor of MDM2 and NFAT1 for experimental therapy of breast cancer: in vitro and in vivo anticancer activities and newly discovered effects on cancer metabolic pathways
Authors: Wang Wei , Aguilar Marlene , Datta Sayantap , Alley Abigail , Tadesse Meheret , Wang Xinshi , Gao Xia , Zhang Ruiwen 
Published: February 2025

IntroductionThe oncogene MDM2 has garnered attention not only for its role in cancer as a negative regulator of the tumor suppressor p53 but also for its p53-independent oncogenic activities. MDM2 also involves metabolic reprogramming, such as serine metabolism, respiration, mitochondrial functions, the folate cycle, and redox balance. Traditional MDM2 inhibitors blocking the protein-protein binding between MDM2 and p53 have shown limited clinical success in various stages of clinical trials, most likely due to low efficacy, drug toxicity, and drug resistance, highlighting the need for a novel, p53-independent strategy to inhibit MDM2. The present study investigated the antitumor effects of MA242, a novel MDM2 and NFAT1 inhibitor, in breast cancer models.MethodsThe anticancer activity and underlying mechanisms of MA242 were evaluated in vitro using breast cancer cell lines with different p53 backgrounds and in vivo using orthotopic and patient-derived xenograft models.ResultsWe demonstrated that MA242 significantly inhibited cell viability and induced apoptosis in breast cancer cells, regardless of p53 status. Metabolic analysis revealed that MA242 notably disrupted nicotinamide metabolism, modified nucleotide metabolism, and elevated cellular oxidative stress by disturbing the redox balance. Furthermore, in animal models, MA242 reduced MDM2 expression and effectively inhibited tumor growth dependent on MDM2 expression without causing host toxicity.DiscussionThese findings highlight the potential of MA242 as a modulator of cancer metabolism and support its further development as a therapeutic option for aggressive breast cancers.
Analysis of cultivated land degradation in southern China: diagnostics, drivers, and restoration solutions
Authors: Liao Yanqing , Yu Zhihong , Kuang Lihua , Jiang Yefeng , Yu Chenxi , Li Weifeng , Liu Ming , Guo Xi , Ye Yingcong 
Published: February 2025

IntroductionCultivated land quality degradation is a critical challenge to food security, requiring effective nature-based restoration strategies based on comprehensive assessments of land quality. However, existing methods are often costly, limited in scope, and fail to capture the multidimensional complexity of the degradation processes.MethodsThis study integrated vegetation indices, topographic data, and soil physical and chemical properties to construct a model for identifying cultivated land degradation. Remote sensing indices were calculated using Google Earth Engine, enabling large-scale spatial analysis. Machine learning, combined with SHapley Additive exPlanations (SHAP), was employed to explore the driving factors of degradation.ResultsThe results indicate that 11.86% of cultivated land in Yugan County is degraded, primarily in the central plain and riparian zones, driven by both natural factors (precipitation, temperature) and anthropogenic factors (straw incorporation, fertilization management). Soil erosion was concentrated in southern hills and near rivers, fertility decline occurred in the central plain, and soil acidification was evenly distributed with generally low degradation levels.DiscussionBased on these findings, vegetation-based restoration solutions, including deep-rooted crops, crop rotation and intercropping, and straw incorporation, are proposed to address different types of cultivated land quality degradation and support sustainable land management.
Validation of the Slovenian versions of Child and Youth Resilience Measure-12 and Brief Resilience Scale among youth
Authors: Smrke Urška , Rehberger Ana , Močnik Sara , Špes Tanja , Mlakar Izidor , Plohl Nejc 
Published: February 2025

IntroductionResilience is the ability to adapt positively in the face of adversity, trauma, or significant stress and is a vital component of maintaining mental health and well-being. It is particularly shaped in young adulthood by navigating unique stressors, such as changes in living arrangements, relationships, and education. However, much of the existing research focuses on children or older adults, leaving a gap in our knowledge regarding resilience in young adulthood. Moreover, the existing resilience scales are seldom validated outside of English-speaking contexts. With this paper, we turn attention to validating two resilience measures, Child and Youth Resilience Measure-12 (CYRM-12) and Brief Resilience Scale (BRS), in Slovenian language, using a sample of young adults.MethodWe administered a survey among 330 young individuals (18–24 years) from Slovenia. Next to the central questionnaires, we also measured resilience with another scale, along with coping strategies, anxiety, depression, and quality of life.ResultsFor both resilience scales, one-factor structures fitted the data well and both scales demonstrated good internal consistency. CYRM-12 and BRS showed positive associations with another resilience scale and adaptive coping strategies, negative associations with anxiety, depression, and maladaptive coping strategies, and a unique contribution to predicting quality of life (with CYRM-12 demonstrating somewhat greater predictive value for quality of life than BRS), pointing to good convergent, discriminant, and incremental validity, respectively.DiscussionThe results of our study suggest that CYRM-12 and BRS are both sufficiently reliable and valid for use among Slovenian young adults, with slightly stronger evidence supporting the validity of CYRM-12 compared to BRS.
A randomized, sham-controlled clinical trial to evaluate the NET Device™ for reducing withdrawal symptom severity during opioid discontinuation
Authors: Greenwald Mark K. , Arfken Cynthia L. , Winston Joe R. 
Published: February 2025

BackgroundNeuromodulation is a promising approach for opioid discontinuation, as not all patients with opioid use disorder (OUD) seek pharmacotherapy. The NET Device is a non-invasive, battery-powered, portable, device that provides bilateral, transcranial, transcutaneous, alternating current stimulation (tACS) for patients experiencing opioid withdrawal. This clinical trial prospectively evaluated whether NET Device utilization is effective for persons with OUD undergoing opioid discontinuation without medications for OUD (MOUD).MethodsThis randomized, sham-controlled trial was conducted at a single residential treatment center. Persons with OUD undergoing opioid discontinuation were assigned to active or sham device treatment. Clinical Opiate Withdrawal Scale (COWS) total scores were measured prior to and during device use. We tested whether active stimulation would produce a clinically meaningful (≥15%) decrease in COWS score from baseline to 1-hr post-stimulation, compared to sham.Results108 participants (55 sham, 53 active; 59.3% male, 89.8% white; 71.3% fentanyl-positive) form the intent-to-treat dataset. Mean ( ± 1 SD) COWS score in the active device group decreased from baseline (18.1 + 4.4) to 1-hr (7.0 + 4.1); this 61.3% decrease (d=2.14) exceeded the pre-specified 15% criterion. COWS scores decreased more for active (-11.1 ± 5.2) than sham (-8.8 ± 6.3), p&lt;.05, d=-.41. A higher proportion of participants in active vs. sham showed ≥15% reduction in COWS (98.1% vs. 83.6%), p=.016. Device utilization was longer for active than sham, 43.9 + 46.2 vs. 30.0 + 39.2 hours, p=.008, and fewer participants requested MOUD (26% vs. 49%, p&lt;0.02).ConclusionThe NET Device is effective, safe and well-tolerated for reducing opioid withdrawal symptoms. This device received FDA market clearance in May 2024.Clinical trial registrationClinicalTrials.gov, identifier NCT04916600.
Global burden of viral skin diseases from 1990 to 2021: a systematic analysis for the global burden of disease study 2021
Authors: Li Deng , Chen Ming , Li Wei , Xu Xuewen , Li Qingfeng 
Published: February 2025

BackgroundThe scarcity of knowledge regarding the epidemiology and temporal patterns of viral skin diseases worldwide poses significant challenges to their control and management.MethodsWe analyzed the global incidence, prevalence, and age-standardized rates (ASR) of disability-adjusted life years (DALYs) for viral skin diseases in 2021. To examine temporal trends from 1990 to 2021, we employed the EPAC model, assessing changes by country, gender, age, Socio-demographic Index (SDI), and GBD regions. Additionally, we utilized the age-period-cohort (APC) model and the Bayesian age-period-cohort (BAPC) model to forecast the burden of viral skin diseases for the next 25 years.ResultsIn 2021, the global burden of viral skin diseases was estimated at 84.7 million incident cases, with a prevalence of over 130 million cases and 4.2 million DALYs. Males experienced a slightly higher ASR burden than females. The highest burden was observed among individuals aged 10 to 19, with significant geographical variations in cases and ASR, particularly in high SDI regions. Unexpected rises in incidence were noted in East Asia and Sub-Saharan Africa in the detected period. Despite modest declines in ASPR and ASDR, the global ASIR displayed a significant upward trend.ConclusionOur study provides detailed data on the global impact of viral skin diseases from 1990 to 2021, highlighting the need for continuous surveillance and tailored interventions to manage and reduce the effects of these diseases. Targeted public health measures are essential to address and mitigate the global health burden of viral skin diseases.
Personality traits and digital challenges in Honduran adults: exploring the Dark and Light Triads’ influence on internet gaming disorder and technology-related conflicts
Authors: Mejía-Suazo Claudio J. , Landa-Blanco Miguel , Mejía-Suazo Gliver Aarón , Martínez-Martínez Carlos A. 
Published: February 2025

IntroductionThe present study analyzed the effects of Light and Dark Triad traits scores on Internet Gaming Disorder, intrapersonal and interpersonal conflicts related to internet consumption, conflicts, and communicational emotional usage related to mobile phones. Light Triad traits include Faith in Humanity, Humanism, and Kantianism. Dark Triad traits include Machiavellianism, Narcissism, and Psychopathy.MethodsThe sample consisted of 450 adults of the Honduran population, of which 55.33% were women, and 44.67% were men, with an average mean age of 25.52 years (SD = 6.79).ResultsResults indicate that Machiavellianism and Psychopathy scores have significant effects over Internet Gaming Disorder scores. Intrapersonal conflicts related to internet consumption were negatively affected by Narcissism scores and positively affected by Machiavellianism. Psychopathy scores explained interpersonal conflicts related to internet consumption scores. On the other hand, conflicts related to mobile phone usage were negatively affected by Narcissism and Kantianism, while Machiavellianism had positive effects. Finally, communicational emotional usage related to mobile phones was significantly affected by Machiavellianism.DiscussionWhen comparing by respondent’s sex, Machiavellianism consistently emerges as a key predictor; however, its effects tend to be stronger in men. In contrast, Psychopathy, Kantianism, and Narcissism exhibit more sex-specific associations, influencing females and males differently across digital behaviors and conflicts.
Job motivation and associated factors among healthcare professionals serving in primary hospitals in the Central Gondar Zone, Northwest Ethiopia: a mixed-methods approach
Authors: Legesse Mandefro Tadesse , Abuhay Habtamu Wagnew , Worku Nigusu , Dellie Endalkachew 
Published: February 2025

BackgroundLow performance in health facilities is associated mostly with poor healthcare worker motivation in the workplace, and this burden is dominant in developing countries such as Ethiopia. Therefore, this study aimed to assess job motivation and associated factors among healthcare professionals in Central Gondar Zone primary hospitals in Northwest Ethiopia.MethodsAn institutionally based cross-sectional study with a qualitative component was conducted at Central Gondar Administrative Zone primary hospitals in the Amhara National Regional State, Ethiopia. A total of 396 healthcare professionals with more than six months of experience at primary hospitals in the Central Gondar Zone participated. Multiple logistic regression was fitted, and Odds ratio (OR) with 95% CIs was used to measure the strength of associations and variables with a p-value of &lt;0.05 were considered significantly associated with the outcome. Thematic analysis was performed for the qualitative part.ResultsA total of 41.81% of healthcare professionals with a 95% Cl [36.9, 46.6] had good job motivation. A degree of educational level (AOR = 1.84, 95% CI [1.178, 2.874]), being satisfied with training opportunities (AOR = 1.92, 95% CI [1.201, 3.083]), and having a written job description (AOR = 2.58, 95% CI [1.497, 4.460]) were significantly associated with job motivations.ConclusionHealthcare professionals’ job motivation status was relatively low. In addition, having a degree in educational level, being satisfied with training opportunities, and having a written job description were significant positive predictors of good job motivation. Additionally, the thematic analysis of qualitative data identified both short- and long-term training opportunities and the lack of clear, updated job descriptions, both of which significantly impacted job motivation.
Deductively coding psychosocial autopsy interview data using a few-shot learning large language model
Authors: Balt Elias , Salmi Salim , Bhulai Sandjai , Vrinzen Stefan , Eikelenboom Merijn , Gilissen Renske , Creemers Daan , Popma Arne , Mérelle Saskia 
Published: February 2025

BackgroundPsychosocial autopsy is a retrospective study of suicide, aimed to identify emerging themes and psychosocial risk factors. It typically relies heavily on qualitative data from interviews or medical documentation. However, qualitative research has often been scrutinized for being prone to bias and is notoriously time- and cost-intensive. Therefore, the current study aimed to investigate if a Large Language Model (LLM) can be feasibly integrated with qualitative research procedures, by evaluating the performance of the model in deductively coding and coherently summarizing interview data obtained in a psychosocial autopsy.MethodsData from 38 semi-structured interviews conducted with individuals bereaved by the suicide of a loved one was deductively coded by qualitative researchers and a server-installed LLAMA3 large language model. The model performance was evaluated in three tasks: (1) binary classification of coded segments, (2) independent classification using a sliding window approach, and (3) summarization of coded data. Intercoder agreement scores were calculated using Cohen’s Kappa, and the LLM’s summaries were qualitatively assessed using the Constant Comparative Method.ResultsThe results showed that the LLM achieved substantial agreement with the researchers for the binary classification (accuracy: 0.84) and the sliding window task (accuracy: 0.67). The performance had large variability across codes. LLM summaries were typically rich enough for subsequent analysis by the researcher, with around 80% of the summaries being rated independently by two researchers as ‘adequate’ or ‘good.’ Emerging themes in the qualitative assessment of the summaries included unsolicited elaboration and hallucination.ConclusionState-of-the-art LLMs show great potential to support researchers in deductively coding complex interview data, which would alleviate the investment of time and resources. Integrating models with qualitative research procedures can facilitate near real-time monitoring. Based on the findings, we recommend a collaborative model, whereby the LLM’s deductive coding is complemented by review, inductive coding and further interpretation by a researcher. Future research may aim to replicate the findings in different contexts and evaluate models with a larger context size.
Explore factors influencing residents' green lifestyle: evidence from the Chinese General Social Survey data
Authors: Li Yifei , Li Jiyue , Lu Chuntian 
Published: February 2025

IntroductionThis paper explores the multifaceted dynamics and various determinants impacting residents' green lifestyles in China, focusing on drop-off recycling, low-carbon purchasing, and civic advocating.MethodsUsing data from the 2021 Chinese General Social Survey and the Chinese Statistical Yearbook, we provide an integrated paradigm to position green lifestyles in a hierarchical framework, explore the individual-level mediating mechanisms nested within macro influences, and elucidate the relative strengths of determinants.Results and discussionFirstly, the random coefficient regression models show that environmental knowledge and environmental protection intention significantly positively affect green lifestyles. Adopting traditional media could enhance civic advocating, and using new media may bolster low-carbon purchasing tendencies. A higher per capita GDP, increased public expenditure on environmental protection, and a reduced proportion of secondary industry are significant factors that promote drop-off recycling and low-carbon purchasing. Secondly, the multilevel mediational effects identify that people's environmental protection intention is a salient mediating variable within the control of regional macro factors. Additionally, the sheaf coefficient clustering models underscore the prominence of ecological awareness and macro-level factors in shaping drop-off recycling and civic advocating.
Estimation of elbow flexion torque using equilibrium optimizer on feature selection of NMES MMG signals and hyperparameter tuning of random forest regression
Authors: Uwamahoro Raphael , Sundaraj Kenneth , Feroz Farah Shahnaz 
Published: February 2025

BackgroundThe assessment of limb joint torque is essential for understanding musculoskeletal system dynamics. Yet, the lack of direct muscle strength measurement techniques has prompted previous research to deploy joint torque estimation using machine learning models. These models often suffer from reduced estimation accuracies due to the presence of redundant and irrelevant information within the rapidly expanding complex biomedical datasets as well as suboptimal hyperparameters configurations.MethodsThis study utilized a random forest regression (RFR) model to estimate elbow flexion torque using mechanomyography (MMG) signals recorded during electrical stimulation of the biceps brachii (BB) muscle in 36 right-handed healthy subjects. Given the significance of both feature engineering and hyperparameter tuning in optimizing RFR performance, this study proposes a hybrid method leveraging the General Learning Equilibrium Optimizer (GLEO) to identify most informative MMG features and tune RFR hyperparameters. The performance of the GLEO-coupled with the RFR model was compared with the standard Equilibrium Optimizer (EO) and other state-of-the-art algorithms in physical and physiological function estimation using biological signals.ResultsExperimental results showed that selected features and tuned hyperparameters demonstrated a significant improvement in root mean square error (RMSE), coefficient of determination (R2) and slope with values improving from 0.1330 to 0.1174, 0.7228 to 0.7853 and 0.6946 to 0.7414, respectively for the test dataset. Convergence analysis further revealed that the GLEO algorithm exhibited a superior learning capability compared to EO.ConclusionThis study underscores the potential of the hybrid GLEO approach in selecting highly informative features and optimizing hyperparameters for machine learning models. These advancements are essential for evaluating muscle function and represent a significant advancement in musculoskeletal biomechanics research.