bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Nomogram for Predicting the Risk of Coronary Atherosclerosis in Patients with Gastroesophageal Reflux Disease A Nomogram Predicting Coronary Atherosclerosis
Authors: Wu Ziyang , Wang Dong , Tang Chengchun 
Published: January 2024

Abstract
Aims
This study developed a novel nomogram to predict the incidence of coronary atherosclerosis (CA) in patients with gastroesophageal reflux disease (GERD) and evaluated the predictive value of the nomogram.
Methods
13658 patients of gastroesophageal reflux disease from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analysed. The patients were randomly divided into two groups in a seven-to-three ratio to form a training cohort (n = 9560) and a validation cohort (n = 4098). Least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify associated risk variables. A nomogram was established to predict the rate of coronary atherosclerosis in patients with gastroesophageal reflux disease. The new model was assessed in terms of the concordance index (C-index), the area under the curve (AUC) of receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA).
Results
Least absolute shrinkage and selection operator regression analysis identified nine potential predictors of coronary atherosclerosis. Multivariate logistic regression analysis was used to evaluate the effects of these predictors and create a final model. The concordance index values were 0.750. The areas under the curves for the training and validation sets were 0.7500 and 0.7297, respectively.
Conclusion
The age, white blood cells (WBC), hemoglobin, mean corpsular hemoglobin (MCH), mean corpuscular volume (MCV), sodium, bicarbonate, creatinine and chloride were identified as predictors. Our nomogram is a reliable convenient approach for predicting coronary atherosclerosis in patients with gastroesophageal reflux disease.
Reducing behavior problems in children born after an unintended pregnancy: A hypothetical intervention study in the population-based Generation R cohort
Authors: Enthoven Clair A. , Labrecque Jeremy A. , Koopman-Verhoeff M. Elisabeth , Berg Mijke P. Lambregtse-van den , Hillegers Manon H.J. , Marroun Hanan El , Jansen Pauline W. 
Published: January 2024

Abstract
Objectives:
To examine differences in behavior problems between children from intended versus unintended pregnancies, and to estimate how much the difference in problem behavior would be reduced if we had intervened on eliminating postnatal depression and increasing social support within 6 months after birth.
Methods:
Data from the Generation R Study were used, a population-based birth cohort in Rotterdam, the Netherlands (N = 9621). Differences in child internalizing and externalizing behavior at ages 1.5, 3, 6, 9 and 13 years between pregnancy intention groups were estimated using linear regression. Associations of postnatal depression and social support with internalizing and externalizing problems were also estimated using linear regression. Child behavior outcomes where compared by presence or absense of the hypothetical interventions of ‘no postnatal depression’ or ‘high social support’.
Results:
Most pregnancies (72.9%) were planned, 14.8% were unplanned and wanted, 10.8% were unplanned with initially ambivalent feelings and 1.5% with prolonged ambivalent feelings. Children from unplanned pregnancies had more internalizing and externalizing problems at all ages as compared to children from a planned pregnancy, especially when ambivalent feelings were present. Hypothetical interventions on postnatal depression reduced the differences in internalizing and externalizing problems by 0.02 to 0.16 standard deviation. Hypothetical interventions on social support did not significantly reduce the difference in internalizing and externalizing problems.
Conclusions:
Children from an unplanned pregnancy have more behavior problems, in particular when mothers had prolonged ambivalent feelings. Intervening on postnatal depression may reduce the inequality in child behavior related to pregnancy intention.
Rapid and Specific Differentiation of Salmonella enterica serotypes Typhi and Paratyphi by Multicolor Melting Curve Analysis
Authors: Jiang Yixiang , Jiang Min , Cai Rui , Shi Xiaolu , Hu Qinghua , Kan Biao 
Published: January 2024

Abstract

Rapid and accurate identification of Salmonella enterica serotypes Typhi and Paratyphi (A, B and C), the causal agents of enteric fever, is critical for timely treatment, case management and evaluation of health policies in low and middle-income countries where the disease still remains a serious public health problem. The present study describes the development of a multiplex assay (EFMAtyping) for simultaneous identification of pathogens causing typhoid and paratyphoid fever in a single reaction by the MeltArray approach, which could be finished within 2.5 h. Seven specific genes were chosen for differentiation of typhoidal and nontyphoidal Salmonella. All gene targets were able to be detected by the EFMAtyping assay, with expected Tm values and without cross-reactivity to other relevant Salmonella serovars. The limit of detection (LOD) for all gene targets was 50 copies per reaction. The LOD reached 102-103 CFU/ml for each pathogen in simulated clinical samples. The largest standard deviation value for mean Tm was below 0.5°C. This newly developed EFMAtyping assay was further evaluated by testing 551 clinical Salmonella isolates, corroborated in parallel by the traditional Salmonella identification workflow, and serotype prediction was enabled by whole-genome sequencing. Compared to the traditional method, our results exhibited 100% of specificity and greater than 96% of sensitivity with a kappa correlation ranging from 0.96 to 1.00. Thus, the EFMAtyping assay provides a rapid, high throughput, and promising tool for public health laboratories to monitor typhoid and paratyphoid fever.
Desire for pregnancy and fertility preservation in Japanese patients with breast cancer
Authors: Abe Tomomi , Kataoka Akemi , Uehiro Natsue , Yoshida Nao , Nishimura Meiko , Ozaki Yukinori , Kogawa Takahiro , Takano Toshimi , Ohno Shinji , Ueno Takayuki 
Published: January 2024

Abstract
Purpose Data on the desire for pregnancy and the status of fertility preservation (FP) in patients with breast cancer remains unclear. This study aimed to determine the status of patients with breast cancer under 43 years of age who desired pregnancy and FP implementation before systemic therapy.Methods This retrospective study surveyed the institutional clinical databases and electronic medical records of patients < 43 years with stage 0-III primary breast cancer at the time of surgery and treated between April 2020 and March 2021. All patients were enquired about their desire for pregnancy in a questionnaire by ‘present,’ ‘absent,’ and ‘unsure’ at their first visit. The correlation between the desire for pregnancy, FP, and clinicopathological factors was investigated.Results Among 1,005 patients who underwent surgery for primary breast cancer, 146 were included in the analysis. Thirty-four patients (23.3%) had a desire for pregnancy, and 45 patients (30.8%) chose ‘unsure’. Factors associated with the desire for pregnancy were younger age at the time of surgery (p < 0.0022), unmarried status (p < 0.001), nulliparity (p < 0.001), early-stage disease (p = 0.0016), and estrogen receptor positivity (p = 0.008). Among 115 patients who underwent systemic therapy, 13 patients (11.3%) implemented FP prior to systemic therapy. Patients who were nulliparous implemented FP more often (p = 0.0195). The FP rate was lower in patients who received neoadjuvant chemotherapy (p = 0.0863).Conclusions Our study suggests that unmarried, nulliparous, and younger patients were more interested in pregnancy, and nulliparous patients more often implemented FP.
Assessing the impact of drilling methods on formation damage - A case study of an Iranian oil field reservoir
Authors: Taheri Kioumars , Torab Farhad Mohammad , Nguyen Hai 
Published: January 2024

Abstract

Formation damage, resulting from the invasion of drilling and completion fluids, significantly affects reservoir productivity. This study investigates the relationship between drilling methods and formation damage using data from 54 vertical, horizontal, and directional wells. Core plug tests and well test data quantify formation damage. The findings reveal that drilling and completion operations contribute to 50% of observed damages in the reservoir. Parameters such as total permeability, radial permeability, productivity index (PI), average reservoir pressure, and skin index were calculated for each well. Results demonstrate that wells drilled under underbalanced drilling (UBD) exhibit at least 300 bbl/d/psi higher PI than overbalanced drilling. Vertical wells with low radial permeability display the highest PI and average production pressure compared to horizontal and directional wells. Conversely, horizontal and directional wells show higher damage rates and lower PI than vertical wells. Directional wells exhibit the lowest PI at 70 bbl/d/psi, followed by horizontal wells at 150 bbl/d/psi, while vertical wells exhibit the highest PI at 250 bbl/d/psi. These results suggest the use of oil-based mud and UBD methods to mitigate formation damage and optimize well productivity. The authors emphasize the importance of both proper fluid design and drilling method selection in preventing formation damage.
Genetic link between primary biliary cholangitis and extrahepatic autoimmune diseases: A bidirectional two-sample Mendelian randomization study
Authors: Chen Changan , Cai Yueqing , Jiang Danxian , Yang Tingmei , Zhou Yu , Ye Shicai , Yu Caiyuan , Weng Yijie 
Published: January 2024

Abstract

Background
Observational studies have shown that patients with primary biliary cholangitis (PBC) have a higher incidence of extrahepatic autoimmune diseases (EHAIDs) than healthy individuals. However, whether this correlation is causal remains unclear.
Methods
Genetic instrumental variables associated with PBC and 7 EHAIDs were derived from published genome-wide association studies (GWAS) of European ancestry. A bidirectional two-sample Mendelian randomization (MR) analysis was used to determine the causal relationship between PBC and EHAIDs.
Result
We found that PBC can increase the susceptibility to type 1 diabetes (TD1), autoimmune hyperthyroidism (AITD), systemic lupus erythematosus (SLE), ankylosing spondylitis (AS), Psoriasis and Sarcoidosis with IVW odds ratio (OR) of 1.35 (95% CI: 1.28 ~ 1.43, p = 1.76×10 − 28), 1.19 (95% CI: 1.09 ~ 1.30, p = 0.00014),1.48 (95% CI: 1.36 ~ 1.61, p = 2.43×10 − 19), 1.13 (95% CI: 1.04 ~ 1.22, p = 0.0030), 1.09 (95% CI: 1.05 ~ 1.14, p = 4.50×10 − 5) and 1.11(95% CI: 1.03 ~ 1.19, p = 0.0064) respectively. Using reverse MR analysis, we also found that TD1 can increase the susceptibility to PBC, with IVW OR of 1.29 (95% CI: 1.19 ~ 1.41, p = 1.77×10 − 9).
Conclusion
PBC and TD1 may be causally related to each other. PBC can increase the susceptibility to AITD, SLE, AS, Psoriasis and Sarcoidosis. It reminds us that we should pay attention to screening for these EHAIDs in clinical PBC patients to improve the patient's survival rate and quality of life.
BiLSTM-CNN Hybrid Intrusion Detection System for IoT Application
Authors: Sadhwani Sapna , Khan Mohammed Abdul Hafeez , Muthalagu Raja , Pawar Pranav Mothabhau 
Published: January 2024

Abstract
Intrusions in computer networks have increased significantly in recent times and network security mechanisms are not being developed at the same pace at which intrusion attacks are evolving. Therefore, a need has arisen to improve intrusion detection systems (IDS) to make network secure. This research focuses on anomaly-based IDS for security assaults. In this research, deep learning techniques such as Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Networks (CNN) are implemented and subsequently used to design a novel BiLSTM-CNN hybrid IDS for the Internet of Things (IoT). The hybrid intrusion detection system model is created by utilizing the advantages of both the BiLSTM and the CNN's ability to extract temporal and spatial features respectively. The research uses the UNSW-NB 15 dataset for proposed deep learning IDS for IoT networks. The dataset has been split into training and testing data for classifying traffic into normal or attack classes. The models are run on GPU and CPU to illustrate their efficacy and match real-world IoT network communication behavior. The BiLSTM, CNN, and hybrid BiLSTM-CNN models are assessed on various aspects like Precision, Sensitivity, F1-Score, Miscalculation Rate, False Positive Rate, False Negative Rate, and Matthews Correlation Coefficient to evaluate the model’s robustness. The findings revealed that the hybrid model surpassed the BiLSTM and CNN models in all aspects. Additionally, the proposed model is compared with the cutting-edge existing approaches in terms of different performance metrics and proved to be better than state-of-the-art models.
Abnormal cortical activity in postoperative delirium based on EEG microstate: a cross-sectional study
Authors: Wei Conghui , Rao Xiuqin , Wan Wei , Chen Siyi , Ji Xunhui , Tang Simin , Hua Fuzhou , Ying Jun , Luo Jun 
Published: January 2024

Abstract
Objective
This study aims to investigate the characteristics of resting-state electroencephalogram (EEG) microstate in elderly postoperative delirium patients and non-delirium patients, to increase understanding of the pathophysiology and phenomenology of postoperative delirium.
Methods
Resting-state EEG data and clinically relevant information were collected from 10 postoperative delirium patients and 18 postoperative non-delirium patients. The EEG microstate characteristic parameters of the two groups were compared, and Pearson analysis was used to analyze the correlation between the microstate characteristic parameters of the delirium group and the maximal concentration of intraoperative blood glucose.
Results
Intergroup comparisons of microstate characterization parameters found that microstate D duration was significantly higher in the delirium group than in the non-delirium group (P< 0.05), whereas frequency of occurrence and temporal coverage were significantly lower than in the non-delirium group (P< 0.05). Within-group comparisons of microstate feature parameters found that microstate D duration was prolonged and frequency of occurrence and temporal coverage decreased in the delirium group. A comparison of microstate transition characteristics found significant differences between the two groups for transitions from microstate B to microstate D, from microstate C to microstate B, and from microstate D to microstate B (P < 0.05). Correlation analysis found a significant positive correlation between intraoperative maximal blood glucose and the frequency of occurrence (P = 0.01) and temporal coverage (P = 0.006) of microstate C.
Conclusions
Our results suggest that postoperative delirium has an impact on the EEG microstates during the resting-state. Changes in these microstates may be associated with altered cognition and consciousness in individuals experiencing delirium. Therefore, EEG microstate analysis holds potential clinical value for predicting and aiding in the diagnosis of postoperative delirium.
Does a common genetic event exist for familial thyroid cancer? Results from a large family with FNMTC
Authors: Silvia Cantara , Margherita Baldassarri , Carlotta Marzocchi , Katia Capitani , Sagnella Alfonso , Laura Valerio , Antonia Salvemini , Ilaria Meloni , Alessandra Renieri , Capezzone Marco 
Published: January 2024

Abstract
Background
Despite several efforts, the genetic susceptibility of familial non medullary thyroid cancer (FNMTC), has remained still elusive.
Methods
We performed Whole Exome Sequencing (WES) in a large family with 9 available members, 6/9 (67%) affected by FNMTC.
Results
We found two missense variants, with CADD score > 20: the c.C1519A (p.Pro507Thr, rs773271544) in PRKCɛ gene and the c.G1019A (p.R340Q) in CCZ1B gene. These alterations were absent in healthy subjects (n = 40) and in 30 sporadic thyroid cancer patients. The p.P507T was possibly pathogenetic by SIFT and PRKCɛ is implicated with MAPK activation by STRING. When we searched for this mutation in other families, we failed to confirm this genetic event as causative of cancer in other 20 FNMTC patients belonging to 8 kindred.
Conclusions
We concluded that the PRKCɛ p.Pro507Thr possibly represents a private mutation even if other studies including large FNMTC family are needed to define the percentage of familial thyroid cancer cases due this alteration.
The (in)efficiency of USA Education Group stocks: before, during and after COVID-19
Authors: Fernandes Leonardo H S , Fernandes José P V , Silva Jose W L , Paiva Ranilson O A , Pinto Ibsen M B S , de Araujo Fernando Henrique Antunes 
Published: January 2024

Abstract
This paper represents a pioneering effort to investigate multifractal dynamics that exclusively encompass the return time series of USA Education Group Stocks concerning two non-overlapping periods (before, during, and after COVID-19). Given this, we employ the Multifractal Detrended Fluctuations Analysis (MF-DFA). In this sense, we investigate the generalized Hurst exponent ℎ(𝑞) and the Rényi exponent 𝜏 (𝑞) for each asset and quantify their statistical properties, which allowed us to observe separately the contributing small scale (primarily via the negative moments 𝑞) and the large scale (via the positive moments 𝑞). We perform a fourth-degree polynomial regression fit to estimate the complexity parameters that describe the degree of multifractality of the underlying process. Also, we shall apply the inefficiency multifractal index to assess the COVID-19 shock for both periods. Our findings show that for both periods, the majority of these assets are marked by multifractal dynamics associated with persistent behaviour (𝛼0 > 0.5), a higher degree of multifractality and the dominance of large fluctuations. At the same time, most of these assets show asymmetry parameter (𝑅 > 1) for both periods, indicating that large fluctuations contributed more to multifractality in the time series of returns.
Challenges of care coordination for complex patients among family medicine residents: A qualitative study
Authors: Tan Mong Heng Moses , Seng Gilbert Yeo Tian , Lee Eng Sing 
Published: January 2024

Abstract
Background
Care coordination has been identified as one of five focuses of HealthierSG. Family medicine residents are expected to collaborate with other healthcare professionals for complex patients by the end of residency. However, many residents felt that it was challenging to coordinate care effectively among healthcare stakeholders. However, to date, no qualitative studies have explored these challenges. Therefore, this study aimed to understand the challenges encountered by family medicine residents when coordinating care for complex patients.
Methods
This was a qualitative descriptive study in which semi structured in-depth interviews were conducted and guided by a topic guide. Total population sampling of 15 third-year family medicine residents in the National Healthcare Group Polyclinics was performed. The interviews were performed over Zoom and were transcribed. Thematic analysis was subsequently performed to analyse the transcripts. Coding was performed iteratively by two independent researchers. Disagreements were adjudicated by a third coder. A coding framework was agreed upon. Potential themes were then independently developed based on the coding framework.
Results
Six themes emerged from the data, namely, interprofessional communications, accessibility, personal knowledge, time constraints, patient factors and caregiver dissent.
Conclusion
Challenges faced by family medicine residents are multifaceted. While a few are systemic and pertain to the broader healthcare framework, others, such as issues of unfamiliarity with institutional workflows, community resources, and confidentiality, pertain to the microcosm of residency itself. These are reversible areas for improvement. These challenges can be addressed during planning of residency curricula to better equip family medicine residents with coordinating care for complex patients in the future.
Stochastic three-term conjugate gradient method with variance technique for non-convex learning
Authors: Ouyang Chen , Zhao Xiong , Huang Ruping , Yuan Gonglin , Jiang Yiyan 
Published: January 2024

Abstract
In the training process of machine learning, the minimization of the empirical risk loss function is often used to measure the difference between the model’s predicted value and the real value. Stochastic gradient descent is very popular for this type of optimization problem, but converges slowly in theoretical analysis. To solve this problem, there are already many algorithms with variance reduction techniques, such as SVRG, SAG, SAGA, etc. Some scholars apply the conjugate gradient method in traditional optimization to these algorithms, such as CGVR, SCGA, SCGN, etc., which can basically achieve linear convergence speed, but these conclusions often need to be established under some relatively strong assumptions. In traditional optimization, the conjugate gradient method often requires the use of line search techniques to achieve good experimental results. In a sense, line search embodies some properties of the conjugate methods. Taking inspiration from this, we apply the modified three-conjugate gradient method and line search technique to machine learning. In our theoretical analysis, we obtain the same convergence rate as SCGA under weaker conditional assumptions. We also test the convergence of our algorithm using two non-convex machine learning models.
Multi-task Deep Learning Pipeline for Irregular Macrophage Segmentation fusing Enhanced Microscopy Images and Auxiliary Boundary Maps
Authors: Zheng Lin , Lei Weimin , Zhao Xinzhuo , Bao Yanqing 
Published: January 2024

Abstract
Background:Accurate segmentation of macrophages from microscope images can be beneficial for understanding viral infection and immune development stages. There are two particularly challenging aspects in this task: (1) over-segmentation of macrophage with pseudopodia; (2)under-segmentation for clustering, overlapping macrophages and unclear boundary macrophages.
Method: This paper proposes a microscope image enhancement multi-task deep learning framework to achieve segmentation of macrophages with complex boundary conditions. The network initially utilizes PENet to enhance the quality of microscope image data, improving the overall image quality. Then, a multi-task U-Net architecture is employed to extract crucial feature information from masks, distance transforms, and heatmaps. By utilizing the cell segmentation achieved through masks, the distance transforms and heatmaps are used to further refine and capture the intricate boundary details of macrophages, including pseudopodia and other irregularities.
Results: Despite the challenges posed by partially or entirely obscured cells, the network demonstrates robust segmentation capabilities for surface-visible cells, achieving an accuracy of 61.24%, a precision of 78.79%, and a recall of 87.93%, outperforming some other segmentation networks, including SOTA Cellpose. Through experiments, it is possible to achieve precise segmentation of irregular boundaries and narrow pseudopodia of macrophages in low-quality microscope images.
Conclusions: Compared to current macrophage segmentation techniques, this network has two significant advantages: (1) supplementing rich boundary detail information to capture the microscopic features of macrophageswith elongated pseudopods; (2) enhancing underexposed cells due to limitations of microscopic imaging techniques and capturing their potential information.