bluuebunny/crossref_metadata_2025_split huggingface dataset:
sFlT-1/PlGF ratio as a predictor of preeclampsia in COVID-19 pregnant patients
Authors: Pluta Kamil , Januszewski Marcin , Ziuzia-Januszewska Laura , Kudan Michał , Suchocka Maria , Kuśmierczuk Kinga , Issat Tadeusz , Jakimiuk Artur J. 
Published: January 2024

AbstractThe association between SARS-CoV-2 infection in pregnancy and preeclampsia is widely debated in numerous studies. The aim of our study was to investigate whether an increased sFlt-1/PlGF ratio is a good marker of preeclampsia in pregnant patients with COVID-19 infection. This single centre prospective study was conducted in the Department of Obstetrics and Gynaecology, at the Central Clinical Hospital of the Ministry of the Interior and Administration in Warsaw. The study group consisted of 68 COVID-19 pregnant patients and 57 SARS-CoV-2 negative pregnant controls. Serum sFlt-1/PlGF ratio was assessed. The two groups did not differ in terms of the frequency of IVF, nulliparity, history of hypertension, pre-gestational diabetes and chronic kidney disease. The primary outcome was the diagnosis of preeclampsia. Preeclampsia was diagnosed in 10 patients in both groups. The sFlt-1/PlGF ratio higher than 38, considered highly suggestive of developing preeclampsia, was found in 20 patients in the COVID-19 group and 15 patients in the control group. The odds of developing preeclampsia in patients with sFlt-1/PlGF ratio > 38 was approximately 4-fold higher in COVID-19 group and 11-fold higher in controls. Sflt-1/PlGF ratio does not differ significantly between the SARS-CoV-2-positive and SARS-COV-2-negative pregnant patients. The sFlt-1/PlGF ratio > 38 is associated with higher odds of the diagnosis of preeclampsia in both of these groups, and therefore may serve as its marker regardless of COVID-19 infection status.
Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification
Authors: Golestan Ali , Tahmasebi Ahmad , Maghsoodi Nafiseh , Faraji Seyed Nooreddin , Irajie Cambyz , Ramezani Amin 
Published: January 2024

Abstract
Background
Breast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy.

Materials and methods
Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers.

Results
The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples.

Conclusions
These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.
Subjective quality of life and schizophrenia: results from a large cohort study based in Chinese primary care
Authors: Migliorini Christine , Harvey Carol , Hou Cailan , Wang Shibin , Wang Fei , Huang Zhuo-Hui 
Published: January 2024

Abstract
Introduction
Much confusion exists between health-related QoL (HRQoL) scales and subjective QoL (SQoL) scales. One method to avoid confusion is use of a single question that asks What is your quality of life? or similar. This study explored the relationship between biopsychosocial factors and high SQoL, SQoL stability, and factors associated with improving SQoL.

Method
We conducted a large cohort study of community-dwelling Chinese adults with schizophrenia, with two data points (2015–2016 (N = 742), 2017–2018 (N = 491)). Demographic and clinically related items and a comprehensive suite of published measures were collected. Direct logistic regressions were used to explore links between biopsychosocial factors and high SQoL and Improvement in SQoL across time.

Results
Sample at Baseline: Male = 62.3%; Med age = 38.5 years; Med Age at illness onset = 24 years; SQoL Mode = neither poor nor good. Three independent variables predicted high SQoL at T1. Contemporary age and the presence of clinically relevant symptoms had a negative relationship with high SQoL; insight had a positive relationship with high SQoL. SQoL changed significantly across time with a modest effect size. Age at illness onset was the single independent variable linked to improving SQoL favoring being older at the time of illness onset.

Discussion/Conclusions
SQoL can be high and changeable. While symptomology and illness insight may affect SQoL self-appraisals at single points in time, only age of illness onset was connected with improving SQoL. Thus, public health measures to delay illness onset are important. In addition, care about the distinction between HRQoL and SQoL in study design and choice of measures is necessary and will depend on the purpose and context.
Men’s attitude towards wife-beating: understanding the pattern and trend in India
Authors: Pradhan Manas Ranjan , De Prasenjit 
Published: January 2024

Abstract
Background
Intimate partner violence (IPV) is a severe human rights violation and a global burden on public health. Wife-beating is a form of IPV and an extension of the patriarchal philosophy that legitimizes men’s control over their spouses. This study investigates (a) the trends and patterns of men’s attitudes towards justification of wife-beating and (b) the socio-demographic factors associated with changes in men’s attitudes towards wife-beating between 2005–06 and 2019–21 in India.

Methods
The present study utilized data from the last three rounds of the National Family Health Survey (NFHS): NFHS-3 (2005–06), NFHS-4 (2015–16), and NFHS-5 (2019–21) with a total sample of 2,76,672 men aged 15–54. The primary outcome variable was men’s attitudes toward wife-beating. Attitude towards the household and the sexual autonomy of the wife were the two key predictors, in addition to other structural factors. Descriptive, bivariate, and multivariate logistic regression analyses were performed on weighted data using Stata. Hosmer–Lemeshow test, Classification table, and ROC curve were carried out to enhance the robustness of the analysis and validity of the model.

Results
In 2005–06, 50% of men justified wife-beating in at least one of the seven contexts, which reduced to 42% in 2015–16 and then marginally increased to 44% in 2019–21. Men with an authoritarian attitude toward household autonomy (AOR: 2.34; CI: 2.30,2.38) and sexual autonomy of the wife (AOR: 1.68; CI: 1.65,1.71) were more likely to justify wife-beating than their egalitarian counterparts. Inadequate education, younger age, family history of IPV, alcohol consumption, poverty, and rural settings are associated with an elevated risk of abusive attitudes towards wife-beating.

Conclusion
A sizable percentage of men, more so those socio-economically marginalized, continue to justify wife-beating, albeit with considerable decline over the years. The findings suggest customized policies and programs enhancing gender egalitarian norms among young men, more opportunities to pursue higher education, alleviating poverty through employment opportunities, and raising awareness about domestic violence in rural settings would help develop more egalitarian gender norms and attitudes towards wife-beating.
Changes in the characteristics and outcomes of high-risk pregnant women who delivered prior to and after China’s universal two-child policy: a real-world retrospective study, 2010–2021
Authors: Zhu Caixia , Zhang Shaofeng , Shen Lixia , Ye Lisha , Zhan Minjin , Cai Shiqin , Huang Jingwan , Wang Zilian , Chen Haitian 
Published: January 2024

Abstract
Background
In 2016, the “universal two-child” policy, allowing each couple to have two children, was introduced in China. The characteristic change of the long-term period after the implementation of the universal two-child policy was unclear. We studied trends in the obstetric characteristics and their potential impact on the rates of cesarean section and preterm birth in the era of China’s universal two-child policy.

Methods
A tertiary center-based study (2010–2021) retrospectively focused single high-risk pregnancies who delivered from the one-child policy period (OCP, 2010–2015) to the universal two-child policy period (TCP, 2016–2021). A total of 39, 016 pregnancies were enrolled. Maternal demographics, complications, delivery mode and obstetric outcomes were analyzed. Furthermore, logistic regression analysis was used to explore the association between the cesarean section rate, preterm birth and implementation of the universal two-child policy, adjusting maternal age, parity, and fetal distress.

Results
Ultimately a total of 39,016 pregnant women met the criteria and were included in this analysis. The proportion of women with advanced maternal age (AMA) increased from 14.6% in the OCP to 31.6% in the TCP. The number of multiparous women increased 2-fold in the TCP. In addition, the overall rate of cesarean section significantly decreased over the policy change, regardless of maternal age, whereas the risk of preterm birth significantly increased in the TCP. Adjusting for maternal age, parity and fetal distress, the universal two-child policy showed a significantly favorable impact on the cesarean section rate (RR 0.745, 95%CI (0.714–0.777), P < 0.001). Compared to the OCP group, a higher increase in fetal distress and premature rupture of membranes (PROM) were observed in the TCP group. In pregnancies with AMA, there was no increase in the risk of postpartum hemorrhage, whereas more women who younger than 35 years old suffered from postpartum hemorrhage in TCP. The logistic regression model showed that the universal two-child policy was positively associated with the risk of postpartum hemorrhage (RR: 1.135, 95%CI: 1.025–1.257, P = 0.015).

Conclusions
After the implementation of the universal two-child policy in China, the rate of the cesarean section significantly decreased, especially for women under 35 years old. However, the overall risk of postpartum hemorrhage increased in women under 35 years old, while there was no change in women with AMA. Under the new population policy, the prevention of postpartum hemorrhage in the young women should not be neglected.
Percutaneous endoscopic lumbar discectomy for single and double segment lumbar disc herniation with sciatic scoliosis in adults: a retrospective study
Authors: Yang Jitao , Luan Haopeng , Ren Jiawei , Tao Jiyuan , Sheng Weibin , Guo Hailong , Deng Qiang 
Published: January 2024

Abstract
Objective
Sciatic scoliosis can be seen in patients with lumbar disc herniation. Percutaneous endoscopic lumbar discectomy (PELD) is a common surgical method for the treatment of lumbar disc herniation. The difference between single-segment lumbar disc herniation and double-segment lumbar disc herniation with Sciatic Scoliosis in adults after PELD needs further study. The aim of this study was to compare the imaging features of single-segment and double-segment lumbar disc herniation with Sciatic Scoliosis in adults and to further explore the clinical outcomes of functional improvement and scoliosis imaging parameters of the two groups after PELD.

Methods
Adult patients with lumbar disc herniation with sciatic scoliosis who received PELD from January 2019 to June 2022 were analyzed retrospectively. According to the number of operative segments, the patients were divided into a single-segment group and a double-segment group. Perioperative parameters were observed and compared between the two groups. The Visual Analogue Scale (VAS) score, Oswestry dysfunction index (ODI), Japanese Orthopaedic Association scores (JOA) and imaging parameters of the two groups were recorded and compared before the operation and during the follow-up.

Results
A total of 53 patients with single segments and 21 patients with double segments were included in this study. During the follow-up, the VAS score, ODI index and JOA score of the two groups were significantly improved as compared with those before the operation(P < 0. 05). Ninety-two point five percent of single-segment patients and 90.5% of double segment patients returned to normal scoliosis within 12 months after the operation. The operation time, number of intraoperative fluoroscopy times and the amount of intraoperative blood loss in single-segment patients were better than those in double-segment group(P < 0. 05). At the last follow-up, the AVT, CBD and SVA in the double-segment group were 5.2 ± 2.3, 5.1 ± 1.0 and 12.2 ± 3.0 mm, respectively, which were higher than those in the single-segment group (1.9 ± 0.4, 1.1 ± 1.6 and 3.9 ± 2.1 mm) (P < 0. 05).

Conclusion
PELD is an effective treatment for single-segment and double-segment lumbar disc herniation with Sciatic scoliosis. Double-segment patients can enjoy similar clinical efficacy to single-segment patients, avoiding complications caused by decompression, fusion, and internal fixation. Scoliosis was corrected spontaneously within 12 months after operation, and the sagittal curve was significantly improved in both groups. The improvement of coronal and sagittal balance in double -segment patients may take longer.
The oral microbiome of a family including Papillon-Lefèvre-syndrome patients and clinically healthy members
Authors: Vályi Péter , Wirth Roland , Minárovits János , Strang Orsolya , Maróti Gergely , Kovács Kornél L. 
Published: January 2024

Abstract
Aims
The oral microbiota composition of patients diagnosed with Papillon-Lefèvre-syndrome and treated for several years were compared to those existing in the oral cavity of the clinically healthy family members and a cohort of patients having various stages of chronic periodontitis.

Materials and methods
A family with two sisters affected with severe periodontitis and with the typical skin symptoms of Papillon-Lefèvre-syndrome, and symptomless parents and third sibling were investigated. The Patients received periodontal treatment for several years and their oral microbiome was analysed by amplicon sequencing. Data were evaluated by microbial cluster analysis.

Results
The microbiome of the patients with Papillon-Lefèvre-syndrome was predominated with Aggregatibacter actinomycetemcomitans and associated oral periodontopathogens. Although the clinically healthy family members showed no oral disorder, their microbiome resembled that of subjects having mild periodontitis.

Conclusions
Predominance of A. actinomycetemcomitans in the subgingival microbiome of patients with Papillon-Lefèvre-syndrome suggests that specific treatment strategies directed against this pathobiont may improve the oral health status of the affected individuals.

Trial registration
The study was conducted in accordance with the Declaration of Helsinki and the ethical permission has been issued by the Human Investigation Review Board of the University of Szeged, Albert Szent-Györgyi Clinical Centre (Permission No. 63/2017-SZTE). September 19, 2017. https://u-szeged.hu/klinikaikutatas/rkeb-altal-jovahagyott/rkeb-2017.
O2 supplementation disambiguation in clinical narratives to support retrospective COVID-19 studies
Authors: Abdulnazar Akhila , Kugic Amila , Schulz Stefan , Stadlbauer Vanessa , Kreuzthaler Markus 
Published: January 2024

Abstract
Background
Oxygen saturation, a key indicator of COVID-19 severity, poses challenges, especially in cases of silent hypoxemia. Electronic health records (EHRs) often contain supplemental oxygen information within clinical narratives. Streamlining patient identification based on oxygen levels is crucial for COVID-19 research, underscoring the need for automated classifiers in discharge summaries to ease the manual review burden on physicians.

Method
We analysed text lines extracted from anonymised COVID-19 patient discharge summaries in German to perform a binary classification task, differentiating patients who received oxygen supplementation and those who did not. Various machine learning (ML) algorithms, including classical ML to deep learning (DL) models, were compared. Classifier decisions were explained using Local Interpretable Model-agnostic Explanations (LIME), which visualize the model decisions.

Result
Classical ML to DL models achieved comparable performance in classification, with an F-measure varying between 0.942 and 0.955, whereas the classical ML approaches were faster. Visualisation of embedding representation of input data reveals notable variations in the encoding patterns between classic and DL encoders. Furthermore, LIME explanations provide insights into the most relevant features at token level that contribute to these observed differences.

Conclusion
Despite a general tendency towards deep learning, these use cases show that classical approaches yield comparable results at lower computational cost. Model prediction explanations using LIME in textual and visual layouts provided a qualitative explanation for the model performance.
An evaluation of GPT models for phenotype concept recognition
Authors: Groza Tudor , Caufield Harry , Gration Dylan , Baynam Gareth , Haendel Melissa A. , Robinson Peter N. , Mungall Christopher J. , Reese Justin T. 
Published: January 2024

Abstract
Objective
Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation.

Materials and methods
The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations.

Results
The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches.

Conclusion
Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.
Exploring patient safety risk in an emergency ward for substance use through a mixed-method analysis
Authors: Svensson Jakob 
Published: January 2024

Abstract
Background
There is limited knowledge of how psychiatric patient safety measures can detect and understand risk as an emergent property within a healthcare system. Overcrowding poses a risk for patient safety in psychiatric emergency wards and is associated with increased mortality and violence. This paper aims to explore patterns of risk emergence in a psychiatric ward and provide insights into the dynamics of workload pressure.

Method
A case study was conducted in a psychiatric emergency ward for patients with substance use disorders. The study employed a four-phased mixed-methods design. Phase one used clinical experts to identify patient safety pressure issues. Phase two used data on patient visits extracted from medical records between 2010 and 2020. In phase three, a quantitative analysis of patient visits and diagnosis was made. Phase four used a focus group of clinical experts for a semi-structured interview, analysing the result from phase three.

Result
Trend analysis demonstrated a steady growth of patient visits to the emergency ward over the studied ten-year period. The findings showed a decrease in patients being diagnosed with delirium when visiting the emergency ward and an increase in percentage of patients receiving a psychosis diagnosis. The focus group expressed concerns about delayed treatments, increased violence and underestimating patients’ needs.

Conclusion
This study indicated that increased workload pressure can be predicted at a system level by analysing patient visits and diagnostics trends over time. The study advocates for ongoing awareness of patient safety risks by monitoring factors identified by clinical front-end workers as potential sources of risk. Healthcare management could employ supportive tools to detect and address emerging risks, including expected workload, overcrowding, staffing issues or bed shortages.
Predicting the 10-year incidence of dyslipidemia based on novel anthropometric indices, using data mining
Authors: Takhttavous Alireza , Saberi-Karimian Maryam , Hafezi Somayeh Ghiasi , Esmaily Habibollah , Hosseini Marzieh , Ferns Gordon A. , Amirfakhrian Elham , Ghamsary Mark , Ghayour-Mobarhan Majid , Alinezhad-Namaghi Maryam 
Published: January 2024

Abstract
Background
The aim was to establish a 10-year dyslipidemia incidence model, investigating novel anthropometric indices using exploratory regression and data mining.

Methods
This data mining study was conducted on people who were diagnosed with dyslipidemia in phase 2 (n = 1097) of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, who were compared with healthy people in this phase (n = 679). The association of dyslipidemia with several novel anthropometric indices including Conicity Index (C-Index), Body Roundness Index (BRI), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), Abdominal Volume Index (AVI), Weight-Adjusted-Waist Index (WWI), A Body Shape Index (ABSI), Body Mass Index (BMI), Body Adiposity Index (BAI) and Body Surface Area (BSA) was evaluated. Logistic Regression (LR) and Decision Tree (DT) analysis were utilized to evaluate the association. The accuracy, sensitivity, and specificity of DT were assessed through the performance of a Receiver Operating Characteristic (ROC) curve using R software.

Results
A total of 1776 subjects without dyslipidemia during phase 1 were followed up in phase 2 and enrolled into the current study. The AUC of models A and B were 0.69 and 0.63 among subjects with dyslipidemia, respectively. VAI has been identified as a significant predictor of dyslipidemias (OR: 2.81, (95% CI: 2.07, 3.81)) in all models. Moreover, the DT showed that VAI followed by BMI and LAP were the most critical variables in predicting dyslipidemia incidence.

Conclusions
Based on the results, model A had an acceptable performance for predicting 10 years of dyslipidemia incidence. Furthermore, the VAI, BMI, and LAP were the principal anthropometric factors for predicting dyslipidemia incidence by LR and DT models.