bluuebunny/crossref_metadata_2025_split huggingface dataset:
Community-based health education led by women’s groups significantly improved maternal health service utilization in southern Ethiopia: A cluster randomized controlled trial
Authors: Yoseph Amanuel , Teklesilasie Wondwosen , Guillen-Grima Francisco , Astatkie Ayalew 
Published: January 2024

Abstract
Introduction: Maternal health service utilization (MHSU) is cost-effective to reduce maternal mortality. One of the methods to increase its utilization is via health education intervention (HEI). Yet, the impact of HEI on MHSU had not been comprehensively investigated, and previous studies reported controversial findings. Thus, this study aimed to evaluate the effect of HEI on MHSU in southern Ethiopia.
Methods: From January 10 to August 1, 2023, a community-based, two-arm, parallel-group cRCT was conducted among pregnant mothers in the Northern Zone of Sidama National Regional State, Ethiopia. Pregnant mothers < 12 weeks of gestation were eligible for this study. The pregnant women in treatment clusters (kebeles) received standard and pre-prepared audio-based HEI led by women development team leaders, whereas comparator clusters received routine HEI for six months. Six months later, MHSU was assessed in both groups by data collectors who were masked from treatment allocation. The results of the two groups were compared using the intention-to-treat analysis. We utilized multilevel mixed-effects modified Poisson regression with robust variance to control for the effects of clustering and potential confounders. The level of significance was adjusted for multiple comparisons.
Results: The overall utilization of at least one antenatal care (ANC) visit was 90.2% in the treatment group and 59.5% in the comparator group (c2 = 89.22, df =1, p < 0.001). Health facility delivery (HFD) utilization was considerably different between the treatment group (74.3%) and the comparator group (50.8%) (c2 = 70.50, df =1, p < 0.001). HEI significantly increased ANC utilization (adjusted risk ratio [ARR]: 1.32; 99% CI: 1.12-1.56) and HFD utilization (ARR: 1.24; 99% CI: 1.06-1.46). The utilization of at least one postnatal care (PNC) was 65.4% in the treatment group and 52.1% in the comparator group (c2 = 19.51, df =1, p = 0.01). However, after controlling for the effects of confounders and clustering, the impact of HEI on PNC utilization was insignificant between the two groups (ARR: 1.15; 99% CI: 0.89-1.48).
Conclusion: A community-based HEI significantly increased ANC and HFD utilization but did not increase PNC utilization. Expanding the HEI with certain modifications will have a superior effect on improving MHSU.
Trial registration number: NCT05865873.
A New Nomogram for Predicting Extrahepatic Metastases in Patients With Hepatocellular Carcinoma： A population-based study of the SEER database and a Chinese single-institutional cohort
Authors: Xu Li , Li Zhi-Lei , Zhang Na , Sun Quan-Quan , Liu Peng 
Published: January 2024

Abstract
Purpose
This study aimed to identify risk factors associated with the occurrence of extrahepatic metastases (EHM) in patients with hepatocellular carcinoma (HCC) and to establish an effective predictive nomogram.
Methods
We extracted eligible data of HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database. This study also included 196 HCC patients from the Zhejiang Cancer Hospital in China. A nomogram for predicting extrahepatic metastases in patients with hepatocellular carcinoma was developed according to the independent variables that were found by univariate and multivariate logistic analysis analyses. The effective performance of the nomogram was evaluated using the areas under the curves (AUC), receiver operating characteristic curve (ROC), and calibration curves. The clinical practicability was evaluated using decision curve analysis (DCA).
Results
Sex, N stage, histological grade, tumor size, AFP, vascular Invasion (VI), and surgery were all included as independent predictors in a nomogram to predict HCC patients for extrahepatic metastases. In the training cohort, internal validation cohort, and external validation cohort, the AUC of the prediction model were 0.830, 0.834, and 0.831, respectively, while the AUC of the AJCC Stage were 0.692, 0.693, and 0.650. Among patients with extrahepatic metastases, the most common metastasis site was lung (37.38%), followed by bone (36.0%), and lymph nodes (30.6%).
Conclusion
Based on the SEER database and the Chinese single-institutional cohort, we have developed and validated a nomogram to forecast EHM in HCC patients. The AUC indicated that the nomogram showed adequate accuracy in discriminating EHM. Additionally, the nomogram fared well in the validation cohort and could support clinical decision-making.
Clinicopathological analysis of primary central nervous system lymphoma in patients with or without AIDS
Authors: Li Man , Chen Jia-min , Feng En-shan , Chen Xiang-mei , Gao Hai-li , Ma Zhi-yuan , Wang Peng , Zhou Xin-gang 
Published: January 2024

Abstract
Background AIDS-related primary central nervous system lymphoma (AR-PCNSL) differs from immunocompetent-primary central nervous system lymphoma (IC-PCNSL) in certain features. The main objective of this study was to investigate the differences in clinicopathological features between AR-PCNSL and IC-PCNSL.
Methods Thirty-seven AR-PCNSL patients and thirty IC-PCNSL patients were included. hematoxylin & eosin staining; immunohistochemical detection using CD20, Bcl-2, Bcl-6, p53, C-MYC, Ki67, and METTL3 antibodies; and Epstein–Barr encoding region (EBER) in situ hybridization were performed.
Results All of the observed patients were classified as the DLBCL histological type. AR-PCNSL were younger (37.7 vs. 60.5 years) and had a higher likelihood of being male (86.5% vs. 63.3%) than non-AIDS patients were. Elevated LDH and low sugar content in cerebrospinal fluid (CSF) were more common among AR-PCNSL. The expression levels of METTL3, Bcl-2 and p53 expressions were significantly higher in AR-PCNSL patients than in PCNSL patients without AIDS. In contrast, AR-PCNSL patients exhibited lower levels of Bcl-6 expression. AR-PCNSL patients were more likely to be positive for EBER, accounting for 81.1% of these patients. Furthermore, we also found that the expression of METTL3 was lower in GCB-like DLBCL (n=7) than in ABC-like DLBCL (n=32) in AR-PCNSL (p=0.041); however, in IC-PCNSL patients, the expression of METTL3 was not significantly different between GCB-like DLBCL and ABC-like DLBCL (p=0.710).
Conclusions Our study of Chinese AR-PCNSL and IC-PCNSL patients revealed new findings, indicating that METTL3, Bcl-2 and p53 were increased in AR-PCNSL patients compared to IC-PCNSL patients and that METTL3 was higher in ABC-like DLBCL patients than in GCB-like DLBCL in AR-PCNSL patients, suggesting a notable distinction in the pathological characteristics between PCNSL patients with or without AIDS.
Plasma Metabolomics and Olink Proteomics Profiling Predict Outcome of Fufang Duzhong Jiangu Granules in Kashin-beck Disease
Authors: Deng Xingxing , Niu Hui , Zhang Qian , Wen Jinfeng , Zhao Yijun , Naren Gaowa , Liu Huan , Guo Xiong , Zhang Feng , Wu Cuiyan 
Published: January 2024

Abstract
Objectives
To investigate predictive biomarkers that could be used to identify patients’ response to treatment, plasma metabolomics and proteomics analyses were performed in Kashin-beck disease (KBD) patients treated with Fufang Duzhong Jiangu Granules (FDJG).
Methods
Plasma were collected from twelve KBD patients before treatment and one month after FDJG treatment. LC-MS was employed for plasma metabolomics profiling, while the olink inflammatory protein panel was used for obtaining inflammatory protein profiles. Patients were classified into responders and non-responders based on drug efficacy after one-month treatment. Enrichment analyses of differential metabolites and proteins of the responders at baseline and after treatment were conducted to study the mechanism of drug action. Differential metabolites and proteins between the two groups were screened as biomarkers to predict drug efficacy. ROC curve was used to evaluate the prediction accuracy of biomarkers.
Results
The changes of metabolites and inflammatory proteins in responders after treatment reflected the mechanism of FDJG treatment for KBD, which may act on glycerophospholipid metabolism, D-glutamine and D-glutamate metabolism, nitrogen metabolism, and NF-kappa B signaling pathway. We identified three metabolites as potential predictors: N-undecanoylglycine, beta-aminopropionitrile, and PC (18:3(6Z,9Z,12Z)/20:4(8Z,11Z,14Z,17Z)). For inflammatory protein, IL-8 was identified as predictive biomarkers to detect responders. Additionally, IL-8 persisted significant in comparison responders to non-responders after one month of therapy. Combined use of these four biomarkers had high predictive ability (AUC = 0.972).
Conclusion
Metabolomics and olink proteomics provide new insights for precision therapy. In our study, IL-8 may be a novel stable potential biomarker for predicting the efficacy of FDJG in KBD treatment.
Analysis of Immune Cell Infiltration Characteristics in Severe Acute Pancreatitis through Integrated Bioinformatics
Authors: Chen Rui , Xiao Shuai , Xiao Han , Bai Shuhui 
Published: January 2024

Abstract
Objective: The etiopathogenesisof severe acute pancreatitis(SAP) remains poorly understood.We aim to investigate the role of immune cells Infiltration Characteristics during SAP progression.
Methods/Design:Gene expression profiles of the GSE194331 dataset were retrieved from the GEO. Lasso regression and random forest algorithms were employed to select feature genes from genes related to SAP progression and immune responses. CIBERSORT was utilized to estimate differences in immune cell types and proportions and the relationship between immune cells and gene expression. We performed pathway enrichment analysis using GSEA to examine disparities in KEGG signaling pathways when comparing the two groups. Additionally, CMap analysis was executed to identify prospective small molecular compounds.
Results: The three hub genes (CBLB,JADE2,RNF144A) were identified that can predict SAP progression. Analysis of CIBERSORT and TISIDB databases has shown that there are significant differences in immune cell expression levels between the normal and SAP groups, and three hub genes (CBLB,JADE2,RNF144A) were highly correlated with multiple immune cells, regulating the characteristics of immune cell infiltration in the microenvironment.Finally,drug prediction through the Connectivity Map database suggested that compounds such as Entecavir, KU-0063794, Y-27632, and Antipyrine have certain effects as potential targeted drugs for the treatment of SAP.
Conclusion: CBLB, JADE2, and RNF144A are hub genes in SAP, potentially playing important roles in SAP progression. This finding further broadens the understanding of the etiopathogenesis of SAP and provides a feasible basis for future research on diagnostic and immunotherapeutic targets for SAP.
Strengths and limitations of this study:
To find new hub genes related to severe pancreatitis and to suggest the key role of immunoinfiltrating cells in the occurrence and development of severe acute pancreatitis.
There is a lack of relevant basic experiments to further verify the pathogenesis of severe acute pancreatitis
Single-cell Data Unveil the Multifaceted Role of LRP1 in Alzheimer’s Disease Pathogenesis
Authors: Chen Kang , Wei ZiLin , Wei AiLi , Qin YingKai , Liu Chen , Xin Haolin , Chen Shisheng , Ge Yi , Li Bin , Wang Kun , Wang TianHui 
Published: January 2024

Abstract
Background
The role of low-density lipoprotein receptor-related protein 1 (LRP1) in Alzheimer’s disease (AD) has been a focal point of research, highlighting its crucial function in amyloid-beta (Aβ) metabolism in the central nervous system. Despite these significant findings, the detailed mechanisms of LRP1 in neuronal Aβ handling and its systemic effects in AD have not been fully elucidated. Here, we employ innovative single-cell data analysis and temporal analysis complemented by multi-tissue data integration to shed light on the specific roles and systemic impact of LRP1 in AD.
Methods
We conducted in-depth single-cell data mining and analysis using publicly available datasets from the GSE181279, GSE174367, and GSE173429. These findings were augmented with complementary in vivo and in vitro experiments aimed at substantiating the function of LRP1 in the phagocytosis of Aβ by hepatic macrophages.
Results
Detailed data mining and analysis of public datasets have revealed the nuanced role of LRP1 in AD, notably impacting the brain, blood, and liver. Our findings demonstrated pronounced upregulation of LRP1 in brain astrocyte, microglia, and oligodendrocyte progenitor cells (OPCs), establishing a direct correlation with the neurological manifestations of AD. In peripheral blood mononuclear cells (PBMC), LRP1 exhibited unique expression patterns, underscoring its systemic influence on AD pathology. Critically, our data reveal that physical exercise modulates LRP1 expression in Kupffer cells, identifying lifestyle factors as key modulators of the molecular processes involved in AD. Experimental evidence further confirms that reduced LRP1 expression is associated with impaired amyloid-beta clearance, highlighting its pivotal role in the progression of AD. Collectively, these insights position LRP1 as a central molecular player in AD, suggesting novel avenues for therapeutic intervention.
Conclusions
This study, which involved leveraging multi-tissue single-cell analyses of human and mice samples, investigated the multifaceted functionality and molecular dynamics of LRP1 in AD progression. Additionally, this study unveiled the modulatory effects of physical exercise on LRP1 expression, paving the way for novel therapeutic approaches for AD management.
The exploration of the pathogenesis and causative relationship of hypertrophic cardiomyopathy mediated by STAT3 through bioinformatics and Mendelian randomization
Authors: LI yaling , TANG LIANG , FAN Ouyang 
Published: January 2024

Abstract
Purpose
Hypertrophic cardiomyopathy (HCM) is a prevalent condition posing a severe threat to human health. This study aims to investigate the expression of STAT3 in HCM and its potential mechanisms.
Methods
Two sets of data from hypertrophic cardiomyopathy patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO) database. After batch effect removal and merging, differential analysis of STAT3 between healthy individuals and HCM such as limma and Weighted correlation network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed for gene function enrichment. In vitro experiments involved constructing angiotensin II (Ang II)-induced H9c2 cardiomyocytes to validate STAT3 expression and explore the impact of hydrogen sulfide(H2S) intervention on improving drug targets for H9c2 cardiomyocyte hypertrophy. Lastly, MR was utilized to explore the causal relationship between STAT3 and HCM.
Results
STAT3 exhibited high expression in HCM patients. GO analysis indicated enrichment in immune responses, cell proliferation, and transcription. KEGG analysis suggested associations between HCM and pathways like JAK/STAT and NF-kβ. In vitro experiments demonstrated no significant change in STAT3 within Ang II-induced H9c2 cardiomyocytes, with an upregulation of P-STAT3 and hypertrophy-related proteins ANP and BNP. However, these changes were attenuated following H2S intervention. MR showed no causal relationship between STAT3 and HCM. In conclusion, while STAT3 may be associated with HCM occurrence, its expression doesn't exhibit a causal relationship with HCM. The mechanism for STAT3-induced HCM might be linked to increased P-STAT3 levels, and H2S might ameliorate HCM by inhibiting STAT3 phosphorylation.
Echocardiographic Strain imaging to assess immediate and short term effects of successful percutaneous balloon mitral valvotomy on Left Atrial and Right Ventricular Mechanics
Authors: Ranjan Rout Smarak , Sudeep Kumar , Aditya Kapoor , Ankit Sahu , Roopali Khanna , Naveen Garg , Satyendra Tewari 
Published: January 2024

Abstract
Background
Strain imaging for Left atrium and Right Ventricle has been used as a novel assessment tool to evaluate LA and RV functional status. However, their implication is not fully established regarding the effect of percutaneous balloon mitral valvotomy (BMV) in patients with rheumatic severe mitral stenosis (MS). In the present study, we sought to assess the Global LA Strain and RV Strain in this patient population.
Methods
It is an observational study and was conducted in a tertiary health care centre of North India from the Year 2018 to 2020. 66 consecutive patients with suitable mitral valve anatomy for BMV procedure were taken, out of which 38 patients could be followed up till 3 months. Global LA strain and RV Strain were being calculated using special software (GE Healthcare, ECHO PAC Version 203) at three temporal sequences.
Results
The mean age of the study population was 31.63 ± 10.15 years. Both Peak Left atrial longitudinal strain (PALS) and Global RV Longitudinal strain (GLSRV) were impaired in patients with severe MS and both significantly improved both at 24 hrs and 3 months following BMV. There was a significant positive correlation of LA strain and RV strain both at 24 hours and 3 months of follow up. (Pearson r = 0.9142).
Conclusions
Peak LA strain (PALS) and Global RV strain (GLS RV ) can be considered as an indicator of respectively left atrial and Right ventricular function, and its improvement may be taken as a good indicator of successful BMV.
PDIA6 promotes immune escape of head and neck squamous cell carcinoma by regulating TIGIT/CD155 axis
Authors: Jiang Weiwei , Wang Ji , Song Kai , Gong Zhengpeng , Yu Ming 
Published: January 2024

Abstract
BACKGROUND
Protein disulfide isomerases A6 (PDIA6) is a family member of the PDI enzymes. Recently, it was found that PDIA6 is closely associated with a variety of cancers. Here, we studied the role of PDIA6 in pan-cancer and further verified the results in head and neck squamous cell carcinoma (HNSCC) through in vitro experiments.
METHODS
The TCGA database and GEPIA2 were used to assess PDIA6's potential as a diagnostic and prognostic biomarker in pan-cancer. The cBioPortal database was used to examine the PDIA6 mutation character. PDIA6 function analysis was carried out using the LinkedOmics and CancerSEA databases. The TIMER and TISIDB websites were used to investigate the role of PDIA6 in immunological infiltration.PDIA6 mRNA and protein expression were identified in HNSCC tissues. PDIA6 expression was detected in primary HNSCC tissues and cell lines using RT-qPCR, Western blotting, and immunohistochemistry. Furthermore, PDIA6 deletion and overexpression cell lines were developed to investigate the interaction of PDIA6 and TIGIT/CD155, as well as the influence of PDIA6 on cell proliferation and metastasis in vitro.
RESULTS
PDIA6 was elevated in the majority of malignancies. In numerous malignancies, the overexpression and mutation of PDIA6 were associated with a poorer prognosis. In the majority of malignancies, the results revealed a strong positive association between PDIA6 and myeloid-derived suppressor cell (MDSC) infiltration and a significant negative correlation between PDIA6 and T-cell natural killer (NK) infiltration. Experiments in vitro revealed that PDIA6 levels were elevated in HNSCC tissues and cell lines. The connection between PDIA6 expression and CD8 + T-cell infiltration was negative. In addition, PDIA6 was found to be co-expressed with immune-related genes and immune-checkpoints. The effect of PDIA6 on immune evasion of head and neck cancers may be mediated by the TIGIT/CD155 signal pathway.
CONCLUSION
PDIA6 could be a biomarker and an oncogene in HNSCC and other types of cancer. Also, PDIA6 was linked to immune cell infiltration in pan-cancer, which could be a target for immune therapy to treat tumors.
Chaperonin Containing TCP-1 subunit 6A (CCT6A) is involved in development of colon cancer
Authors: Jiang Ze-Kun , Wu Yi-Xiang , Chen Gao-Min , Yang Xiao , Huang Jiong-Qiang , Chen Jing-Song 
Published: January 2024

Abstract
Purpose
Worldwide, colon cancer (CC) is one of the most commonly occurring malignancies. However, the molecular basis of the pathogenesis of CRC needs to be further explored. Studies have demonstrated that the chaperonin-containing TCP1 (CCT) complex contributes to the development and progression of various tumors. However, the functional significance of CCT in CC is unclear.
Methods
This study explored the potential functions of CCT family genes in CC by bioinformatics analysis. In addition, we established a risk score model based on the CCT family genes, which was validated to effectively predict the prognosis of CC patients.
Results
We found that CCTA6, one of the CCT family genes, was significantly more highly expressed in CC tissues than in normal tissues, and that increased expression of CCT6A was associated with a lower survival rate in CC patients. These findings were validated by real-world data.
Conclusion
Through the preliminary exploration of the role of CCT family genes in CC in this study, we found that CCT6A may contribute significantly in CC, and thus this gene may be an attractive therapeutic target for CC patients.
Applying Protein Language Models Using Limited Dataset. Sequence-Based Hot Spot Prediction in Protein Interactions Using AutoGluon
Authors: Sargsyan Karen , Lim Carmay 
Published: January 2024

Abstract
Background
Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained significant attention for their promising applications across various areas, including the sequence-based prediction of secondary and tertiary protein structure, the discovery of new functional protein sequences/folds, and the assessment of mutational impact on protein fitness. However, their utility in learning to predict protein residue properties based on scant datasets, such as protein-protein interaction (PPI)-hotspots whose mutations significantly impair PPIs, remained unclear. Here, we explore the feasibility of using protein language-learned representations as features for machine learning to predict PPI hotspots using a dataset containing 414 experimentally confirmed PPI-hot spots and 504 PPI-nonhot spots.
Results
Our findings showcase the capacity of unsupervised learning with protein language models in capturing critical functional attributes of protein residues derived from the evolutionary information encoded within amino acid sequences. We show that methods relying on protein language models can compete with methods employing sequence and structure-based features to predict PPI hotspots from the free protein structure. We observed an optimal number of features for model precision, suggesting a balance between information and overfitting.
Conclusions
This study underscores the potential of transformer-based protein language models to extract critical knowledge from sparse datasets, exemplified here by the challenging realm of predicting PPI hotspots. These models offer a cost-effective and time-efficient alternative to traditional experimental methods for predicting certain residue properties. However, the challenge of explaining the importance of specific features in determining residue properties remains.