bluuebunny/crossref_metadata_2025_split huggingface dataset:
Persistent advanced HIV disease in rural KwaZulu-Natal, South Africa: Trends, characteristics, and the urgent need for targeted interventions
Authors: Kitenge Marcel K. , Fatti Geoffrey , Eshun-Wilson Ingrid , Nyasulu Peter S. 
Published: February 2025

Background
Advanced HIV disease (AHD) remains a persistent public health challenge. Data regarding the burden, characteristics and predictors of AHD is scarce specifically for rural settings of sub-Saharan Africa. This study aimed to describe trends in annual CD4 count distribution and to characterise adult persons living with HIV (PLWH) on ART who have AHD in rural KwaZulu-Natal, South Africa.

Methods
A retrospective cohort design of annual CD4 count distribution was conducted among antiretroviral therapy (ART) patients aged 18 years and older. We used routinely collected data from adults receiving ART in Eshowe and Mbongolwane areas in KwaZulu-Natal, South Africa, between January 1, 2008, and June 30, 2021. Fine-Greys competing risks regression with proportional sub-distribution hazard models was used to determine factors associated with time to CD4 recovery.

Results
A total of 34,729 patients were included of which 68.5% were females. The median age of the study sample was 33.5 years (interquartile range [IQR] 27–41 years), and the median CD4 count was 277 cells/μL (IQR, 149–452 cells/μL). The proportion of patients entering care with AHD declined over time from 62% in 2008 to 20% in 2021. Across all periods, those entering care with AHD were more likely to be men when compared to women (Relative risk [RR] 1.49; 95% 1.33–1.67). In addition, the proportion of patients with AHD who were ART-experienced increased from 4% in 2008 to 63% in 2021. Among ART-experienced, men were more likely to present with AHD compared to women (RR 1.79; 95% CI 1.52–2.11). Among those with AHD, the cumulative incidence of CD4 recovery to > 350 cells/μL was 3.21 (95% CI 3.13–3.29) per 100 adult-years follow-up time.

Conclusion
Over time fewer patients with AHD are entering care in KwaZulu-Natal, South Africa. However, the proportion of PLWH entering care with AHD remains consistently high, affecting 1 in 4 PLWH accessing HIV services. In addition, there is an increasing number of ART-experienced patients with AHD. Implementation of male-friendly services, combined with intensified adherence support and re-engagement initiatives should be considered to reduce mortality risk for PLWH in rural regions in South Africa.
Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit
Authors: Liu Zheng , Shu Wenqi , Liu Hongyan , Zhang Xuan , Chong Wei 
Published: February 2025

Objectives
Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the intensive care unit (ICU).

Measures
The study analyzed 189,167 emergency patients from the Medical Information Mart for Intensive Care IV database, with the outcome being ICU admission. Three models were compared: Model 1 based on Emergency Severity Index (ESI), Model 2 on vital signs, and Model 3 on vital signs, demographic characteristics, medical history, and chief complaints. Nine ML algorithms were employed. The area under the receiver operating characteristic curve (AUC), F1 Score, Positive Predictive Value, Negative Predictive Value, Brier score, calibration curves, and decision curves analysis were used to evaluate the performance of the models. SHapley Additive exPlanations was used for explaining ML models.

Results
The AUC of Model 3 was superior to that of Model 1 and Model 2. In Model 3, the top four algorithms with the highest AUC were Gradient Boosting (0.81), Logistic Regression (0.81), naive Bayes (0.80), and Random Forest (0.80). Upon further comparison of the four algorithms, Gradient Boosting was slightly superior to Random Forest and Logistic Regression, while naive Bayes performed the worst.

Conclusions
This study developed an interpretable ML triage model using vital signs, demographics, medical history, and chief complaints, proving more effective than traditional models in predicting ICU admission. Interpretable ML aids clinical decisions during triage.
Identification of biomarkers in Alzheimer’s disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms
Authors: Li Juntu , Tao Linfeng , Zhou Yanyou , Zhu Yue , Li Chao , Pan Yiyuan , Yao Ping , Qian Xuefeng , Liu Jun 
Published: February 2025

Background
Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer’s disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. Therefore, we conducted this study to identify key genes in COVID-19 associated with AD, and evaluate their correlation with immune cells characteristics and metabolic pathways.

Methods
Transcriptome analyses were used to identify common biomolecular markers of AD and COVID-19. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on gene chip datasets (GSE213313, GSE5281, and GSE63060) from AD and COVID-19 patients to identify genes associated with both conditions. Gene ontology (GO) enrichment analysis identified common molecular mechanisms. The core genes were identified using machine learning. Subsequently, we evaluated the relationship between these core genes and immune cells and metabolic pathways. Finally, our findings were validated through single-cell analysis.

Results
The study identified 484 common differentially expressed genes (DEGs) by taking the intersection of genes between AD and COVID-19. The black module, containing 132 genes, showed the highest association between the two diseases according to WGCNA. GO enrichment analysis revealed that these genes mainly affect inflammation, cytokines, immune-related functions, and signaling pathways related to metal ions. Additionally, a machine learning approach identified eight core genes. We identified links between these genes and immune cells and also found a association between EIF3H and oxidative phosphorylation.

Conclusion
This study identifies shared genes, pathways, immune alterations, and metabolic changes potentially contributing to the pathogenesis of both COVID-19 and AD.
A community based, bottom-up, multi-pronged, technology integrated approach to enhance tuberculosis related awareness and treatment adherence in Uganda: The ACTS model
Authors: Turyahabwe Stavia , Ramachandra Srikrishna Sulgodu , Quraishi Subhi , Fasih Ilmana , Quraishi Hilmi , Peddapalegani Palavardhan , Ahmad Akram 
Published: February 2025

Background
Tuberculosis (TB) is still a major public health challenge globally and Uganda is one among the top 30 high TB burden countries. One of the key factors determining TB treatment success rates and thereby Cure Rates is the adherence to TB treatment, which is still a major challenge globally. WHO DOTS (Directly Observed Treatment Short course) strategy has several limitations and WHO End TB Strategy 2017 suggests a suite of new interventions to improve adherence.

Objectives of the study

a. To present the development and design of ZMQ’s Active Care and Treatment Strategy (ACTS) Model.
b. To present the results of a pilot study done using the ACTS Model.
c. To compare Treatment Adherence Rates between DOTS and Video Observed Treatment (VOT) in the four districts of Uganda.

Materials and methods
Includes presenting the ACTS Model, a pilot study to assess the AGB and ACF by way of a pre-post (Quasi-experimental) study (n = 1000) to assess the impact of AGB exercises, Focus Group Discussions (FGD) to get insights into factors contributing to treatment non-adherence and a comparison between VOT (n = 800) vs. DOTS for treatment adherence.

Results
There was a significant improvement (p<0.01) in knowledge and awareness levels of community members post the AGB exercises which included creating awareness using digital storytelling, house visits and more. TB treatment adherence rates among TB patients using VOT was significantly better (p<0.01) than those using DOTS.

Conclusion
AGB and ACF play a significant role in creating more awareness amongst the community members and identifying more number of cases. It helps in better treatment seeking behaviour, improved treatment rates and treatment adherence rates and in turn better cure rates. VOT is far more superior to DOTS, as a strategy for TB treatment adherence and VOT decreases the resources required in terms of human resource, time and money and is also a more sustainable mode of treatment adherence.
Integration of pharmacodynamics, network pharmacology and metabolomics to elucidate the effect and mechanism of Jingfang Granule in the treatment of Paraquat induced Pulmonary fibrosis
Authors: Yue Rujing , Yang Tianye , Niu Dejun , Zeng Zhen , Wang Xishuang , Pan Lihong , Yao Jingchun 
Published: February 2025

Objective
One of the main risk factors of COVID-19 is Pulmonary fibrosis (PF). The protective effect of Jingfang Granule (JF) to bleomycin-induced PF has been confirmed in our previous studies. This work was designed to reveal the effect and mechanism of JF on PF which induced by Paraquat (PQ).

Methods
In this study, the PF mice model was induced by PQ with the administration of 1, 0.5, and 0.25 g/kg JF or Nintedanib (NTNB) 45 mg/kg by oral administration. The ameliorating effects of JF were reflected by the survival curve and lung coefficient. And the pathological alterations of lung were observed by H&E, Masson and Sirius red staining. Then, the expression of fibrosis-associated protein α-SMA and TGFβ1/Smad2,3 signaling pathway was detected by immunohistochemistry and western blot. An integrated approach combined metabolomics with network pharmacology was applied to recognize the mechanism of JF on ameliorated the PQ-induced PF, and the result of integrated was verified by western blot.

Results
The experiment results showed that JF could inhibit the progression of PQ-induced PF and delay the death of mice after PQ poisoning, and the inhibit effect was similar to NTNB. JF also reduced fibroblasts in lung tissue of the PF mice model by significantly down- regulated the expression of α-SMA and TGFβ1/Smad2,3 signaling pathway. In addition, JF intervened 16 serum metabolites compared with PQ-induced PF mice, and the differential metabolites were linked 241 corresponding targeted proteins obtained by database, which have 79 common targets to JF related targets. The integrated results of metabolomics, network pharmacology and western blot showed that apoptosis was a crucial way for JF to relieve the PQ-induced PF, and JF regulated the signals of Bcl-2, Bax, Caspase-3 protein and PI3k/Akt pathway to inhibit the apoptosis.

Conclusion
These findings demonstrate that JF down-regulated the TGFβ1/Smad2,3 signaling pathway to reduce the fibroblasts, regulate the expression of Bcl-2, Bax, Caspase-3 and PI3k/Akt pathway to inhibit the apoptosis, and display a favorable effect on inhibiting the development of pulmonary fibrosis and delaying the death of PQ-induced PF mice.
Inverse association between prognostic nutritional index and kidney stone prevalence: A population-based study
Authors: Wang Lei , Yu Yi , Jiang Ziwen , Lin Fuxiang , Zhong Yuxiang , Wang Chao , Huang Sidan , Xu Zhanping 
Published: February 2025

Background
Kidney stones frequently occur due to metabolic disorders, dietary habits, and lifestyle influences. The Prognostic Nutritional Index, which reflects an individual’s nutritional condition, might be associated with kidney stone prevalence. This study examines the association between PNI and kidney stone prevalence in US adults.

Methods
The study used data from the National Health and Nutrition Examination Survey database from 2009–2018 and excluded pregnant women, and individuals who lacked data on kidney stones, or had incomplete Prognostic Nutritional Index data. Independent associations between Prognostic Nutritional Index and kidney stones were investigated by multivariate logistic regression and subgroup analyses, in addition to exploring nonlinear associations using smoothed curves and threshold effects.

Results
A total of 13,835 participants aged ≥ 20 years were included, with a kidney stone prevalence of 8.48%. An inverse association was observed between the Prognostic Nutritional Index and kidney stone prevalence (OR =  0.97, 95% CI =  0.96–0.98, P <  0.001). This relationship was not significantly modified by race, education, marital status, or comorbidities such as hypertension, diabetes, and hyperlipidemia. However, sex and total cholesterol levels influenced the association. Stratified analysis showed a significant negative association in men (OR =  0.98, 95% CI =  0.96–0.99, P =  0.031), but not in women. A nonlinear relationship was identified in individuals with total cholesterol ≥  5.2 mmol/L, with a significant negative association below the inflection point of 57 (OR =  0.96, P =  0.012) and a positive association above it (OR =  1.11, P =  0.03). These findings suggest that the Prognostic Nutritional Index is inversely associated with kidney stones, particularly in men and those with high cholesterol levels.

Conclusion
The Prognostic Nutritional Index was negatively associated with the risk of kidney stones, particularly in men and individuals with high cholesterol levels below the identified inflection point, suggesting that tailored nutritional management may be crucial for these subgroups.
Powering In-Database Dynamic Model Slicing for Structured Data Analytics
Authors: Zeng Lingze , Xing Naili , Cai Shaofeng , Chen Gang , Ooi Beng Chin , Pei Jian , Wu Yuncheng 
Published: February 2025

Relational database management systems (RDBMS) are widely used for the storage of structured data. To derive insights beyond statistical aggregation, we typically have to extract specific subdatasets from the database using conventional database operations, and then apply deep neural networks (DNN) training and inference on these subdatasets in a separate analytics system. The process can be prohibitively expensive, especially when there are various subdatasets extracted for different analytical purposes. This calls for efficient in-database support of advanced analytical methods.
In this paper, we introduce LEADS, a novel SQL-aware dynamic model slicing technique to customize models for specified SQL queries. LEADS improves the predictive modeling of structured data via the mixture of experts (MoE) and maintains efficiency by a SQL-aware gating network. At the core of LEADS is the construction of a general model with multiple expert sub-models trained over the database. The MoE scales up the modeling capacity, enhances effectiveness, and preserves efficiency by activating necessary experts via the SQL-aware gating network during inference. To support in-database analytics, we build an inference extension that integrates LEADS onto PostgreSQL. Our extensive experiments on real-world datasets demonstrate that LEADS consistently outperforms the baseline models, and the in-database inference extension delivers a considerable reduction in inference latency compared to traditional solutions.
Evaluation of current indirect methods for measuring LDL-cholesterol
Authors: Drobnik Sophia , Scharnagl Hubert , Samani Nilesh J. , Braund Peter S. , Nelson Christopher P. , Hollstein Tim , Kassner Ursula , Dressel Alexander , Drobnik Wolfgang , März Winfried 
Published: February 2025

Abstract

Objectives
Accurately quantifying low-density lipoprotein cholesterol (LDL-C) is crucial for precise cardiovascular disease risk assessment and treatment decisions. The commonly used Friedewald equation (LDL-CFW) has faced criticism for its tendency to underestimate LDL-C, particularly at high triglycerides (TG) or low LDL-C, potentially leading to undertreatment. Newer equations, such as those by Martin and Hopkins (LDL-CMH) or Sampson (LDL-CSN), have been proposed as alternatives. Our study aimed to assess the validity of LDL-CFW, LDL-CMH, and LDL-CSN compared to ß-quantification (LDL-CUC), the reference method recommended by the Lipid Research Clinics.

Methods
Using data from three studies comprising 5,738 datasets, LDL-C was determined with the four methods in samples with TG up to 5.65 mmol/L. We calculated median and mean differences, correlations, and used the Passing and Bablok regression for comparisons. Concordance/discordance analyses were conducted.

Results
All equations provided generally accurate LDL-C estimations with slight differences among them. At TG<1.69 mmol/L, no clinically significant divergences were observed. As TG values increased, LDL-CFW offered the most accurate estimation, followed by LDL-CSN, while LDL-CMH exhibited increasingly strong positive bias. LDL-CFW was not inferior to LDL-CSN and LDL-CMH in terms of concordance/discordance.

Conclusions
LDL-CFW generally provided reliable estimates of LDL-C in most samples, showing non-inferiority to LDL-CSN or LDL-CMH, thereby confirming its legitimacy for routine use. Since current treatment recommendations are based on studies employing LDL-CFW, its replacement by alternatives is not justified.
Differential levels of thyroid hormones, cortisol, and apolipoprotein M in fatty liver disease
Authors: Zargar Amir Mohammad , Tayebinia Heidar , Hasanzarrini Maryam , Bahiraei Mohamad , Mohagheghi Sina 
Published: February 2025

Abstract

Objectives
Non-alcoholic fatty liver disease (NAFLD), recently reclassified as metabolic dysfunction-associated fatty liver disease (MAFLD), can also manifest in patients classified as non-MAFLD who do not meet MAFLD criteria. The involvement of cortisol and thyroid hormones may play a role in the pathogenesis of FLD by modifying the metabolism of specific lipoproteins, particularly apolipoprotein M (Apo M). This study investigated cortisol and thyroid hormones levels and Apo M gene expression in white blood cells (WBCs) of individuals with MAFLD, non-MAFLD, and healthy controls.

Methods
The serum and WBCs of the study subjects were collected from patients with FLD (n=99) including 58 MAFLD and 41 non-MAFLD and healthy individuals (n=23). To investigate the gene expression of Apo M and thyroid and cortisol hormones, qRT-PCR and ELISA methods were used, respectively.

Results
The Apo M gene expression was significantly lower in FLD patients, both non-MAFLD, and MAFLD patients compared to the control group (p<0.05). Total T4 and TSH hormone levels in the MAFLD patients were significantly decreased and increased compared to the control group, respectively (p<0.05). The cortisol level was significantly elevated in the FLD and MAFLD patients compared to the control group (p<0.01).

Conclusions
Alterations in Apo M gene expression also cortisol and thyroid hormones levels in non-MAFLD patients were milder than MAFLD patients when compared to the control. Also, likely Apo M may be involved in FLD pathogenesis.
Effects of heat treatment on microstructure and properties of CrVNiAlCu high-entropy alloy
Authors: Bai Shijie , Xiao Yifeng , Wu Liang , Zhang Qiankun 
Published: February 2025

Abstract
This study employs X-ray diffraction, scanning electron microscopy, electrochemical workstations, and hardness testers to evaluate the impact of annealing temperatures (600, 700, and 800°C) on the microstructure and corrosion resistance of the CrVNiAlCu high-entropy alloy. Findings demonstrate that the as-cast alloy is composed of copper-rich FCC and chromium-rich BCC phases. With increasing annealing temperatures, the alloy’s crystals transform into dendritic structures, accompanied by the precipitation of new phases. The as-cast alloy displays a peak hardness of 674 HV, which significantly reduces after annealing. Moreover, annealing treatment reduces lattice distortion and element segregation in the alloy, thereby improving the corrosion resistance of the alloy. Importantly, the alloy attains its highest corrosion resistance after annealing at 800°C.
Mechanism research on inhibition of gastric cancer in vitro by the extract of Pinellia ternata based on network pharmacology and cellular metabolomics
Authors: Feng Fan , Hu Ping , Chen Jun , Peng Lei , Wang Luyao , Tao Xingkui , Lian Chaoqun 
Published: February 2025

Abstract

Background and purpose
Gastric cancer is a kind of malignant tumor with high incidence and high mortality, which has strong tumor heterogeneity. A classic Chinese medicine, Pinellia ternata (PT), was shown to exert therapeutic effects on gastric cancer cells. However, its chemical and pharmacological profiles remain to be elucidated. In the current study, we aimed to reveal the mechanism of PT in treating gastric cancer cells through metabolomic analysis and network pharmacology.

Methods
Metabolomic analysis of two strains of gastric cancer cells treated with the Pinellia ternata extract (PTE) was used to identify differential metabolites, and the metabolic pathways were enriched by MetaboAnalyst. Then, network pharmacology was applied to dig out the potential targets against gastric cancer cells induced by PTE. The integrated network of metabolomics and network pharmacology was constructed based on Cytoscape.

Results
The PTE was confirmed to significantly inhibit cell proliferation, migration, and invasion of HGC-27 and BGC-823 cells. The results of cellular metabolomics showed that 61 metabolites were differently expressed in gastric cancer cells of the experimental and control groups. Through pathway enrichment analysis, 16 metabolites were found to be involved in the glycerophospholipid metabolism, purine metabolism, sphingolipid metabolism, and tryptophan metabolism. Combined with network pharmacology, seven bioactive compounds were found in PT, and the networks of bioactive compound–target gene–metabolic enzyme–metabolite interactions were constructed.

Conclusions
In conclusion, this study revealed the complicated mechanisms of PT against gastric cancer. Our work provides a novel paradigm to identify the potential mechanisms of pharmacological effects derived from a natural compound.
Knowledge, awareness, and … skills? Instructional design choices in multicultural counseling
Authors: Redmond-Matz Trina , Mitchell Renae C. , James Kimberly M. , Flinn Jennifer A. , Jocoy Kathleen A. 
Published: February 2025

Abstract
The development of multiculturally competent counselors is an objective of graduate training programs affiliated with national counseling and psychology associations in the United States. While this area of scholarship and training is rapidly developing; it is not clear the degree to which “multicultural competency” is achieved, especially, the development of multicultural counseling skills. To attain information about multicultural counseling training, two surveys were conducted that queried graduate multicultural counseling instructors about curriculum and experiences teaching. These individuals were solicited by e-mail to take part in online surveys, in 2009 and 2020. Educators indicated that they developed diverse curricula using simulations, advocacy instruction, field experiences, and cultural exposure. This diversity however, also indicates lack of uniformity and consensus regarding multicultural skills training, both in curricular content and assessment. Additionally, training appears to be culturally constrained due to the homogeneity in instructor and student identities, inattention to communication/language differences and exclusion of indigenous/traditional healing practices and practitioners. The multicultural counseling field may be enhanced by increasing diversity in instructors and trainees, explicit attention to diversity in language and communication, and incorporating the contributions of international, traditional/indigenous investigators and practitioners.