bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors Associated with Early Infant Survival During the first three months of life in Uganda: A Multilevel Weibull Model Approach
Authors: Prisca Nakawuki , Wandiembe Symon , Atuhaire Leonard , Nsimbe Dick , Tumusiime Sula 
Published: October 2024

Abstract

Background
The burden of early infant mortality is enormous. It directly contributes to under-five mortality and indirectly affects maternal and child health outcomes. Globally, over 8.2 million children under the age of 5 die each year, with more than 40% of these deaths occurring within the first 30 days of life. In sub-Saharan Africa alone, 1.2 million newborns die annually, with the highest risk of death occurring within the first three months of life. The aim of this paper is to investigate the factors associated with the survival of children within the first three months of life in Uganda.
Method
Self-reported data were extracted from the 2016 Uganda Demographic and Health Survey (UDHS), comprising a sub-sample of 10,263 children. The analysis employed a time-to-event approach, utilizing Kaplan-Meier survival curves and a multilevel proportional hazard.
Results
The multi-level Weibull distribution showed that the estimated frailty variance was 0.065, indicating heterogeneity of time to death of children within the first 3 months of life across the different enumeration areas in Uganda. The significant factors found to influence time to death of children within the first three months of life in Uganda included female gender of children (HR = 0.732, 95% CI = 0.57–0.94, p = 0.013), residing in eastern region (HR = 0.520, 95%CI = 0.35–0.79, p = 0.002), children whose mothers received the recommended dose of tetanus injection prior to birth (HR = 0.503, 95%CI = 0.34–0.74, p = 0.001), those whose mothers participated in postnatal check-ups (HR = 0.363, 95% CI = 0.24–0.55, p = 0.000) and survival rates and children with low birth size (HR = 2.216, 95%CI = 0.86–1.62, p = 0.000).
Conclusions
Survival within the first three months of life in Uganda is significantly influenced by child sex, geographical region, contraceptive use, tetanus vaccination, postnatal check-ups, and birth size. These findings highlight the need for targeted interventions that promote maternal health and improve infant outcomes. Strengthening family planning programs, increasing access to and awareness of postnatal care, and ensuring adequate maternal nutrition are essential strategies for improving infant survival. Additionally, health education campaigns encouraging timely medical care before, during, and after childbirth are critical for reducing early infant mortality rates.
Machine Learning Models to Develop Land Suitability Map for Coffee Cultivation by Integrating CHIRPS and SRTM DEM
Authors: Sinchana G. S. , Choodarathnakara A . L . , Arpitha G. A. 
Published: October 2024

Abstract

Kodagu region is a major coffee exporter, with production concentrated in three taluks, including the Somwarpet Taluk. Coffee yields have decreased due to unfavorable factors such as climate change, disease and insect outbreaks, landslides and inadequate land-use planning in turn affecting the family income. Thus, the goal of this research is to identify suitable land for cultivation of coffee based on Food and Agriculture Organization (FAO) land suitability assessment methodology for Somwarpet Taluk of Kodagu District. For this purpose, six soil chemical properties (potential of hydrogen, electrical conductivity, organic carbon, sulphur, iron, potassium and nitrogen), two topographic data (elevation and slope) and one climatic condition (rainfall) was considered to map land suitability for coffee crops. After determining land suitability classes for coffee cultivation, the study area was then mapped using machine learning (ML) methods such as random forest (RF), Naive Bayes (NB), K-Nearest Neigbhor (KNN), Extreme Gradient Boosting Tree (XgBoost) and Decision Tree (DT). The prediction of land suitability classes by ML model showed a significant variation. For example, in case of RF model, results showed the 94% of higher accuracy when compared to the XgBoost (93.5), DT (92%), NB (75%) and KNN (50%) models.
The area of S1 (highly suitable) classified through RF, XgBoost, DT, NB
and KNN was 8.66%, 8.75%, 8.57%, 19.17% and 28% respectively. Similarly, the S2 (moderately suitable) class area via RF, XgBoost, DT, NB and KNN was 84.17%, 82.18%, 81.33 %, 69.61% and 44%, respectively. Conversely, the area of S3 (marginally suitable) classified through RF, XgBoost, DT, NB and KNN was 6.64%, 7.64%, 8.5%, 10.52% and 27.8%. Correspondingly, the N (unsuitable) class area via RF outperformed the land suitability class for XgBoost, DT, NB and KNN by 0.53%, 1.43%, 1.6%, 0.7% and 0.2%. The
sulphur and pH were the major limiting factor affecting the land suitability to map coffee cultivation. Thus, the methodologies developed in this study area can be very useful tool to ensure food security and carry out an effective assessment of land suitability in coffee crop growth and production for Somwarpet Taluk of Kodagu District, Karnataka State.
The effect of repetitive transcranial magnetic stimulation of the cerebellum on cognitive control
Authors: Qu Liangcheng , So Bo , Shi Jingping , Yin Kuiying 
Published: October 2024

Abstract

Background Research on brain interventions utilizing transcranial magnetic stimulation (TMS) technology has advanced significantly, however, studies focusing on the transcranial magnetic stimulation of the cerebellum remain in their preliminary phase. Research in neuroscience has established the cerebellum as a critical component in cognitive control, decision-making, and response regulation.;Methods The present study focused on the Curs II subregion of the cerebellum and involved 125 participants, who were categorized into five distinct groups. Each group received repetitive transcranial magnetic stimulation (rTMS) at frequencies of 1 Hz, 5 Hz, 10 Hz, 20 Hz, as well as a sham stimulation. Participants were evaluated through a go/no-go task both prior to and following the stimulation, while their electroencephalogram (EEG) were concurrently recorded for subsequent analysis.;Results The results indicate that cerebellar transcranial magnetic stimulation has a significant impact on cognitive task performance throughout the brain. High-frequency stimulation at 5 Hz, 10 Hz, and 20 Hz appears to activate neurons in brain regions linked to cognitive functions, leading to an increase in event-related potential (ERP) peaks and enhancements in the global efficiency and clustering coefficient of the overall brain network. Among the high-frequency conditions, stimulation at 10 Hz produced the most pronounced improvement in cognitive task performance and significantly enhanced the global efficiency of brain networks In contrast, low-frequency stimulation at 1 Hz was found to suppress cognitive task performance..;Conclusions TMS of the cerebellum can either enhance or inhibit cognitive control functions, suggesting that the cerebellum plays a significant role in the participation and regulation of cognitive control.
Predicting stochastic distribution characteristics of high- speed train dynamic performance over a service cycle
Authors: Xue Bowen , Tao Zhecheng , Yang Zhenhuan , Huang Caihong , Gao Xu , Wang Yong , Ye Yunguang 
Published: October 2024

Abstract

The wheel-rail interface parameters and suspension parameters of high-speed trains exhibit stochastic distribution characteristics due to the influence of material properties, manufacturing processes, and operational environments, which have a significant impact on vehicle dynamic performance. However, existing simulation methods for dynamic performance prediction often fail to fully account for these stochastic characteristics, affecting both the reliability and practical value of the predictions. To address this limitation, this study first develops a polynomial regression model based on measured wheel wear data to correct conventional wear prediction results. Next, using Latin hypercube sampling (LHS), Monte Carlo (MC) simulations, and Kullback-Leibler (KL) divergence, a method is proposed to predict the stochastic distribution of dynamic performance over a wheel reprofiling cycle. Finally, by analyzing the stochastic combinations of wheel-rail interface geometry, wheel-rail friction coefficient, and suspension parameters, stochastic predictions of dynamic performance are obtained. The results show that after considering the stochastic characteristics of the wheel-rail interface and suspension system, the dynamic performance indicators follow a normal distribution. Moreover, compared to a model that only considers the stochastic nature of the wheel profile, the uncertainties in the lateral Sperling index and bogie lateral acceleration increase significantly. For the high-speed train analyzed in this study, the probability of the lateral Sperling index exceeding 2.5 rises from 0.4% at 50,000 km to 14% at 250,000 km, while the probability of lateral bogie acceleration exceeding 5 m/s² increases from 0–37.6%. Additionally, the probability of exceeding the instability threshold of 8 m/s² reaches 0.8% at 250,000 km, reflecting the increased risk of exceeding safety limits after extended service. This study offers a novel approach to assessing operational risks and improving the accuracy of dynamic performance predictions for high-speed trains under complex operating conditions.
Simultaneous evaluation of sensitivity and side effects of anti-cancer drugs using patient- derived cancer organoids and a microfluidic device
Authors: Kobayashi Yuki , Hashizume Honoka , Takiguchi Sotaro , Jiajue Ji , Kawano Ryuji , Koiwai Keiichiro , Yamamoto Haru , Elbadawy Mohamed , Omatsu Tsutomu , Abugomaa Amira , Kaneda Masahiro , Usui Tatsuya , Sasaki Kazuaki 
Published: October 2024

Abstract

Organoids are stem cell-derived three-dimensional tissue cultures composed of multiple cell types that recapitulate the morphology and functions of their in vivo counterparts. Organ-on-a-chip devices are tiny chips with interconnected wells and channels designed using a perfusion system and microfluidics to precisely mimic the in vivo physiology and mechanical forces experienced by cells in the body. These techniques have recently been used to reproduce the structure and function of organs in vitro and are expected to be promising alternatives for animal experiments in the future. In the present study, we designed and fabricated an organ-on-a-chip system for mounting organoids from mammary tumor-affected cats (FMT organoids) and normal intestinal organoids from mice (MI organoids) and perfused them with anti-cancer drugs. The effects of drug perfusion on FMT and MI organoids were examined by measuring cell viability and performing genetic analysis. After 48 h of perfusion with toceranib (10 µM) or doxorubicin (1 µM), cell viability of FMT organoids was decreased compared to the non-perfusion condition. The expression of apoptosis-related genes, such as p53 and Caspase-9 was significantly upregulated in FMT organoids with drug perfusion. The rate of cell death drastically differed before and after branching in the device, owing to differences in flow velocity and drug infiltration. Perfusion of MI organoids with toceranib also reduced viability, as observed in FMT organoids; however, this was due to the induction of necrosis rather than apoptosis. In conclusion, our established multi-organoid-on-chip system could be used to evaluate anti-cancer drug sensitivity and side effects in vitro, which might contribute to the development of personalized medicine for cancer patients.
A comparative study on the influencing factors and risk prediction models for stroke- associated pneumonia in patients with acute ischemic stroke and atrial fibrillation
Authors: Su Tai , Zhang Peng , Zhang Bingyin , Liu Zihao , Xie Zexing , Xin Tao , Ma Jixiang , Li xiaomei 
Published: October 2024

Abstract

Background
To analyze the factors influencing stroke-associated pneumonia (SAP) in patients with acute ischemic stroke (AIS) and atrial fibrillation (AF), and to explore an optimal model for risk prediction.
Methods
Data were sourced from the Shandong Provincial Center for Disease Control and Prevention, encompassing all patients diagnosed with AIS and AF from 2020 to 2023. First, univariate analysis and LASSO (Least absolute shrinkage and selection operator) regression analysis methods were used to screen predictors. Secondly, the patients with AIS and AF were randomly divided into a training set, validation set, and test set in a ratio of 7:2:1, which were utilized for model training, model parameter adjustment, and model performance evaluation, respectively. The training set was balanced by synthetic minority oversampling technique (SMOTE), logistic regression, random forest (RF), and support vector machine (SVM),extreme gradient boosting (XGboost) models were constructed. Finally, we compared the models based on accuracy, sensitivity, specificity, AUC (area under the curve), and Youden index. We clarified the optimal prediction model and influencing factors ,the nomogram for risk prediction was constructed for SAP in patients with AIS and AF.
Results
Among the 4496 patients with AIS and AF, SAP was identified in 10.16% of cases. In the test set, the AUC for logistic regression, RF, SVM, and XGboost models were 0.866, 0.817, 0.816, and 0.838, respectively. The most predictive factors included coronary heart disease [OR = 1.05 (1.03, 1.07), p < 0.001], hypertension [OR = 1.05 (1.04, 1.07), p < 0.001], consciousness disorder [OR = 1.19 (1.16–1.23), p < 0.001], cognitive impairment [OR = 1.10 (1.08–1.13), p < 0.001], limb movement disorder [OR = 1.07 (1.04–1.09), p < 0.001], dysphagia [OR = 1.13 (1.08–1.19), p < 0.001], nasal feeding [OR = 0.95 (0.92–0.98), p = 0.003], and oxygen intake [OR = 0.65 (0.62–0.67), p < 0.001]. the nomogram average absolute error of calibration curve was 0.014.
Conclusions
Coronary artery disease, hypertension, consciousness disorder, cognitive impairment, limb movement disorder, and dysphagia were identified as independent risk factors for SAP in patients with AIS and AF. In contrast, nasal feeding and oxygen intake served as independent protective factors. The logistic regression model demonstrated the best predictive performance for SAP in patients with AIS and AF compared to RF, SVM, and XGboost models. The risk prediction model established by nomogram can better predict the risk of SAP.
A comparison of the survival outcome of paclitaxel liposome-based chemoradiotherapy with or without rhEndostatin for unresectable esophageal squamous cell carcinoma: a retrospective study
Authors: Pan Xiaofen , Zhu Mengyuan , Wei Min , Huang Shan , Xu Junjie , Li Qun 
Published: October 2024

Abstract

Objectives
. This study aimed to compare the survival outcomes of paclitaxel liposome-based chemoradiotherapy, with or without rhEndostatin, in patients with unresectable locally advanced esophageal squamous cell carcinoma (ESCC).
Methods.
Patients with ESCC treated with paclitaxel liposome-based definitive chemoradiotherapy (dCRT), with or without rhEndostatin, between February 2015 and June 2020 were included. Patients received induction chemotherapy followed by concurrent radiochemotherapy, with or without rhEndostatin. The chemotherapy regimen consisted of paclitaxel liposome-based treatments. RhEndostatin was administered at a dose of 30 mg/d from day 1 to day 5 of each chemotherapy cycle. Total radiotherapy dose was 66–68 Gy, delivered in fractions of 2.0-2.2 Gy/d. Follow-up continued until December 2023. The primary endpoints were 3-year progression-free survival (PFS) and 3-year overall survival (OS). Secondary endpoints included objective response rate (ORR), disease control rate (DCR), and toxicity.
Results
A total of 80 patients were included, with 34 in the dCRT group and 46 in the E + dCRT group. The 3-year PFS was 26.47% (95% confidence interval [CI], 13.19–41.81) in the dCRT group and 56.29% (95% CI, 40.79–69.20) in the E + dCRT group (Hazard ratio (HR), 0.50; 95% CI, 0.28–0.89, P = 0.012). Patients in the E + dCRT group had a superior 3-year OS compared to those in the dCRT group (80.44% [95% CI, 65.77–89.30] vs. 47.06% [95% CI, 29.83–62.52]; HR, 0.40; 95% CI, 0.21–0.72; P = 0.003). The ORR was 91.18% in the dCRT group and 95.65% in the E + dCRT group. The most common grade 3–4 toxicities were leukopenia, neutropenia, and thrombocytopenia.
Conclusion
The addition of rhEndostatin to paclitaxel liposome-based dCRT may improve clinical outcomes for patients with unresectable ESCC while maintaining manageable toxicities. However, further prospective randomized controlled studies are necessary to confirm the survival benefits of this treatment strategy.
Enhancing brain image segmentation: A metaheuristic approach to multi-threshold optimization
Authors: Ramirez Fernando , Flores Emilio , Olivares Rodrigo 
Published: October 2024

Abstract

Brain segmentation is vital for the accurate diagnosis and treatment of neurological disorders, given the complexity and vulnerability of the brain to various pathologies such as strokes and tumors. The challenge lies in achieving precise delineation of anatomical and pathological structures within medical images, particularly under varying conditions of image quality and tissue irregularities. To address this, we applied eight metaheuristic optimization algorithms-Reptile Search Algorithm, Orca Predator Algorithm, Bald Eagle Search, Grey Wolf Optimizer, Honey Badger Algorithm, Crow Search Algorithm, Harris Hawk Optimization, and Tuna Swarm Optimization-to improve the accuracy of multi-threshold segmentation methods like Kapur’s entropy, Tsallis entropy, and the Otsu method. The results reveal that the Grey Wolf Optimizer and Tuna Swarm Optimization stand out, with the Grey Wolf Optimizer demonstrating superior performance across key metrics such as Peak Signal to Noise Ratio and Structural Similarity Index. These outcomes highlight the potential of the Grey Wolf Optimizer for advanced brain tissue segmentation, offering significant advantages in clinical and research environments where precision is essential for effective medical intervention.
The association between night eating syndrome and food preferences among Chinese college students: a large-scale cross-sectional study
Authors: Hao Zi xuan , Jing Qi , Zhao Bo yang , Ren Zhong yu 
Published: October 2024

Abstract

Background and aims:
Although the associations between NES and food preferences and the frequency of consumption of three meals a day have been examined among the European population, no studies have yet explored this association specifically among Chinese college students. Therefore, the purpose of this study was to investigate the associations between NES and food preferences and the frequency of consumption of three meals a day in Chinese college students.
Methods
This study used a cross-sectional design and included a sample of 11,856 Chinese university students, with an average age of 18.8 years (SD = 1.2 years). Food preferences were defined as five different food categories: fruits, vegetables, fast food (e.g. pizza, burgers, etc.), snacks (e.g. potato chips, crisps) and sugary beverages (e.g. soft drinks and sugary fruit drinks) and breakfast, lunch and dinner. The participants were invited to express their preferences for five food categories using a 5-point Likert scale, with the anchors "dislike very much" and "like very much". The frequency of consumption of three meals a day (including breakfast, lunch and dinner) was evaluated using a frequency scale ranging from 1 (once) to 7 (7 days per week). To evaluate the presence of Nocturnal Eating Syndrome (NES), we employed the Nocturnal Eating Questionnaire (NEQ) and the following criteria: no NES (total score < 25), mild NES (total score 25–30), and severe NES (total score > 30). Multivariate logistic regression analyses were used to examined the association between the level of NES and each food preference and the frequency of consumption of three meals a day.
Results
After controlling for potential confounding variables including gender, age, annual household income, place of residence, father's education and mother's education, multivariate logistic regression analyses observed a significant positive association between NES and the likelihood of exhibiting 1–6 food behaviors. We further found that, in addition to the fast food and dinner, there were significant positive associations between NES and remained food preferences (including, fruit, vegetable, snack and soft drinks) and two meals (including breakfast and lunch).
Conclusion
The results of this study suggest a strong positive association between NES and food preferences, and meal regularity. This implies that individuals with severe NES may exhibit poor food preferences and irregular meal habits. These findings emphasize the importance of intervening in NES, a condition that can assist individuals to modify their food preferences selection and adopt the habit of three meals a day. Future studies should employ longitudinal prospective or randomized controlled designs to confirm the above associations.
Label-free morphology-based phenotypic analysis of spinal and bulbar muscular atrophy muscle cell models
Authors: Sakakibara Kenji , Tanaka Kenjiro , Imai Yuta , Okada Mai , Iida Madoka , Sahashi Kentaro , Hirunagi Tomoki , Maeda Kentaro , Kato Ryuji , Katsuno Masahisa 
Published: October 2024

Abstract

Background
Spinal and bulbar muscular atrophy (SBMA) is a neuromuscular disorder caused by a CAG trinucleotide expansion in the androgen receptor (AR) gene. The pathogenesis of SBMA is not fully understood and there are less effective treatments for SBMA. To improve the quality of in vitro cell-based assays for the evaluation of potential drug candidates for SBMA, we aimed to develop a morphology-based phenotypic analysis for a muscle cell model of SBMA that involves multiparametric morphological profiling to quantitatively assess the therapeutic effects of drugs on muscle cell phenotypes.
Methods
We applied morphology-based phenotypic analysis to a muscle cell model of SBMA. The analysis was validated using dihydrotestosterone (DHT) and pioglitazone (PG), which have been shown to exacerbate and ameliorate the pathophysiology of SBMA, respectively. We then performed gene expression analysis to identify disease-associated pathways. We selected five compounds as potential candidates capable of modulating the disease state of SBMA and compared the morphological profiles using principal component analysis (PCA) and hierarchical clustering.
Results
The quantified morphological phenotypes of SBMA model cells treated with DHT or PG accurately reflected the exacerbation or amelioration of cell pathology. The results of gene expression analysis revealed that the JNK pathway was activated in the SBMA cells compared to the control cells. Phenotypic analysis revealed the effect of naratriptan (NRT), a JNK inhibitor, on the phenotypic changes of SBMA cells, and the results were confirmed by lactate dehydrogenase (LDH) assays. We then trained a predictive machine learning model to classify the drug responses, and it successfully discriminated between PG-type and NRT-type morphological profiles based on their morphological characteristics.
Conclusions
We developed a noninvasive and efficient in vitro drug screening technique using morphology-based phenotypic analysis and SBMA model C2C12 cells. Phenotypic analysis revealed that NRT rescued the morphological phenotype and exerted therapeutic effects via the JNK signaling pathway. Furthermore, the 'drug effect prediction model' was able to distinguish the different morphological changes caused by each drug. Our analysis will accelerate the development of the therapeutics for SBMA.