bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the impact of large-scale photovoltaic development on regional economic growth-a case study of Qinghai Province
Authors: Zhun QU , Yang Su-Chang 
Published: November 2024

Abstract

Large-scale photovoltaic(PV) development has been widely promoted in northwest China and achieved significant economic and industrial benefits. However, previous studies have not fully explored the impact of large-scale PV development on regional economic growth. This paper selects 8 prefecture-level cities in Qinghai Province from 2018 to 2022, uses the static balanced panel data and empirical analysis of panel regression to test the impact of large-scale photovoltaic development on regional economic growth. The results show that large-scale photovoltaic development has a positive impact on regional economic growth, but there are regional differences in the impact. Through further regression analysis, it is found that the impact of photovoltaic power generation on regional economy follows a nonlinear relationship. When the photovoltaic power generation is lower than the critical value (47.71), its impact on regional economy is positive. In less developed regions, the increase of photovoltaic power generation in the initial stage will significantly promote economic growth, but when the critical value (1.505) is exceeded, the increase of photovoltaic power generation has a negative impact on the economy. There is no inverted U-shaped relationship between the impact of large-scale PV development on economic growth in developed regions.
Kidney Transplant in Low Resources Countries; 6 Years of Successes and Challenges at Benjamin Mkapa Hospital, Tanzania
Authors: Shija kessy charles , Makubi Abel Nkono , Chandika Alphonce B , Mwashambwa Masumbuko , Meremo Alfred , Kobayashi Shuzo , Hidaka Sumi , Tanabe Kazunari , Mohamed Awadh , Mputi Devotha , Mmbali Sabina Matulo , Misago Venance John , Sukunala Okoa , Kajerero Vitus , Rugakingira Remigius Andrew , Mkinga Reuben , Toure Ahmed , Ibrahim Hindu , Rainer Denis , Gyunda Anthony , Kagwe Nashon , Mwakilasa Christina Benny , Kilovele Andason 
Published: November 2024

Abstract

Background
Kidney Ttransplant (KT) is the best therapy for patients with Chronic Kidney Disease (CKD). It is advanced services involving multi discipline and highly skilled personnel, conducive infrastructure and sustainable availability of immunosuppressive drugs and consumables. Set-up of KT at Benjamin Mkapa Hospital (BMH) Tanzania in 2018 in collaboration with Shonan Kamakura General Hospital and Tokyo Women’s Medical University from Japan, was of reasonable success despite the challenges encountered. In this review, our experiences, success, challenges and project’s sustainability are discussed.
Methodology
This was a review study of the methods used to set-up a KT in Tanzania with incorporation of patient data from March, 2018 to July,2024 for 37 transplant recipients. Data was analysed using the SPSS version 27 to obtain the mean and proportions and for patient and gaft survival rate a Kaplan Meir survival curve analysis was applied.
Results
Succefully a KT was set by BMH in collaboration with Shonan Kamakura General Hospital and Tokyo Women’s Medical University from Japan and 11 medical personnel were trained. Challenges were lack of trained personnel, unreliable supply of medical supplies and immunosuppressive drugs in the local market, lack of fund and shortage of kidney donors.
A total of 37 out of 40 patients who have been transplanted were analyzed. The recipients were 73% male and 27% female and their mean age was 47.98 ± 10.81 years. Majority (84%) of kidney donors were blood related to the recipients and 16% spouses of recipients. Hypertension was the leading cause of ESRD with 45% followed by diabetes of 32.5%.
Graft survival rates at 1, 3, and 5 years were 90%, 85% and 83% respectively and patient's survival rates at 1, 3, and 5 years was 98.5%, 90% and 85.5% respectively.
Conclusion
Benjamin Mkapa Hospital has successful sustained KT program by local team after training and mentorship by Japanese experts. Outcomes at 1, 3 and 5 of recipients were good and similar to those in developed countries. The Hospital has demonstrated unique experience in establishing and sustaining KT services in the past 6 years signifying that low income countries can establish kidney transplant program.
Predicting the Molecular Subtypes of 2021 WHO Grade 4 Glioma by a Multiparametric MRI-Based Machine Learning Model
Authors: Xu Wenji , Li Yangyang , Zhang Jie , Zhang Zhiyi , Shen Pengxin , Wang Xiaochun , Yang Guoqiang , Du Jiangfeng , zhang hui , Tan Yan 
Published: November 2024

Abstract

Purpose: To develop and validate a machine learning (ML) model using multiparametric MRI for the preoperative differentiation of 2021 World Health Organization (WHO) grade 4 astrocytoma and glioblastoma (GBM) (Task 1), and to stratify grade 4 astrocytoma to distinguish isocitrate dehydrogenase-mutant (IDH-mut) from IDH-wild-type (IDH-wt) (Task 2). Additionally, to evaluate the model’s prognostic value.
Materials and methods: We retrospectively analyzed 320 glioma patients from three hospitals. Cases were randomly divided into training and validation sets with a 7:3 ratio. Features were extracted from tumor and edema on contrast-enhanced T1-weighted imaging (CE-T1WI) and T2 fluid-attenuated inversion recovery (T2-FLAIR). Extreme gradient boosting (XGBoost) was utilized for constructing ML, clinical, and combined models. Model performance was evaluated with receiver operating characteristic (ROC) curves, decision curves, and calibration curves. Stability was evaluated using six additional classifiers. Kaplan-Meier (KM) survival analysis and the log-rank test assessed the model’s prognostic value.
Results: In Task 1 (grade 4 vs GBM) and Task 2 (IDH-mut grade 4 vs IDH-wt grade 4), the combined model (AUC = 0.911 and 0.854, 0.902 and 0.909) and the optimal ML model (AUC = 0.902 and 0.855, 0.904 and 0.895) significantly outperformed the clinical model (AUC = 0.671 and 0.656, 0.619 and 0.605) in both the training and validation sets. Survival analysis showed the combined model performed similarly to molecular subtype in both tasks (P = 0.966 and P = 0.793).
Conclusion: The multiparametric MRI ML model effectively distinguished grade 4 astrocytoma from GBM and differentiated IDH-mut from IDH-wt grade 4 astrocytoma. Additionally, the model provides reliable survival stratification for glioma patients with various molecular subtypes.
Effect of Single-Parent versus Dual-Parent Households on Dietary Intake and Growth among Under-Five Children in Rwanda: An Analysis using Directed Acyclic Graph
Authors: Joyeuse Ukwishaka , Sekou Samadoulougou , Vincent Sezibera , Fati Kirakoya-Samadoulougou , Geneviève Lefebvre 
Published: November 2024

Abstract

Introduction
: Poor childhood diet and malnutrition are global issues, particularly in low- and middle-income countries. Adequate nutrition during the early years of life is crucial for optimal child growth. Household dynamics influence children’s well-being, with single-parent households facing unique challenges that can affect dietary intake and growth. In Rwanda, stunting and being underweight are still prevalent, and the role of household structure has not been studied. This study assessed the effects of single- versus dual-parent households on children’s diet and growth and explored the mediating role of wealth in these relationships.
Methodology
: This cross-sectional study used data from the 2019/2020 Rwanda Demographics and Health Survey (DHS). The analysis included households with single and dual parents who had children under five years of age at the time of the survey, with a specific focus on children aged 6–23 months for dietary information. We performed survey-weighted multivariate logistic regressions, adjusting for confounders identified by a directed acyclic graph (DAG). The mediation analysis explored the mediating effect of wealth on the relationships among household structure, the minimum acceptable diet (MAD), and child growth.
Results
Among the 3,531 participants, 26.9% lived in single-parent households.​​​​​​ Stunting affected 34% of the children under 5 years old, 8.3% were underweight, 1% were wasted, and only 21.1% of the children aged 6–23 months met MAD standards. Single-parent households were significantly associated with stunting (OR: 1.22; 95% CI: 1.01–1.46) and wasting (OR: 2.11; 95% CI: 1.11–3.99) but were not associated with underweight or MAD. Household structure indirectly influenced MAD (indirect effect (IE) = -0.007, p = 0.02), height-for-age (IE = -0.068, p < 0.001), and weight-for-age z scores (IE = -0.029, p = 0.001) through wealth, but no mediation effect was observed for height-for-weight z scores.
Conclusion
This study highlights the influence of family structure on children’s dietary intake and growth in Rwanda. To effectively combat malnutrition in Rwanda and similar settings, targeted interventions should address household structure, particularly that of single-parent households, and wealth disparities.
Folic acid supplementation and Neural Tube Defects: Knowledge and practice among pregnant mothers in Lira, northern Uganda
Authors: Nakaziba Rebecca , Muhindo Miriam 
Published: November 2024

Abstract

Background and Aim: Folic Acid requirements increase during pregnancy in order to meet the metabolic needs, DNA synthesis, and rapid cell division during fetal development. Low levels of folate before and during pregnancy augment the chances of poor pregnancy outcomes, like neural tube defects. In sub-Saharan Africa, very few pregnant women take folic acid pre- and post-conception. In Uganda, 1400 children are born with spinal bifida annually yet taking 400 micrograms daily of folic acid, before and during early pregnancy reduces the risk of neural tube defects. This study intended to explore the level of knowledge and practices of pregnant mothers regarding folic acid supplementation in preventing neural tube defects in Lira, northern Uganda.Study Design: This study employed a quantitative and descriptive approach of data collection and analysis. A consecutive sampling technique was employed to recruit and interview 199 respondents. SPSS V20 was used for data entry and statistical analysis for frequencies, and percentages. Correlation and chi-square tests were used to determine the association between knowledge and practices of folic acid supplementation for prevention of neural tube defects among pregnant mothers. A p-value of ≤ 0.05 was statistically significant.Results The knowledge level regarding Folic acid was adequate. 88% ever heard of it; 73.0% knew its benefits, but only 20.1% knew that it prevents neural tube defects; 83.9% were currently taking while only 18.0% took it before conception. In addition, the practice of Folic acid supplementation was significantly associated with cues to action (p = 0.000) and perceived risk (p = 0.000) while knowledge was significantly associated with practice (p = 0.000).Conclusion Very few mothers knew the value of Folic acid in preventing neural tube defects with very low pre-conception intake. Strategies should be designed to improve the knowledge regarding the role of folic acid in averting neural tube defects among women of childbearing age. These women should be encouraged to take Folic acid pre and post-conception, especially in the first trimester.
Stature Estimation from the Thumb and Foot length of Adult Port Harcourt Residents of Nigeria using linear regression equations
Authors: Orupabo Clinton David , Dinah Eldred Ayebatari , Baribor Maakai , Amadi Hebinuchi 
Published: November 2024

Abstract

Background
Stature estimation is as important as other biological data like age and sex in the identification of unknown persons in a forensic case investigation. Hence for its peculiarity there is a need to develop population-specific data for every population as stature could be subject to variations.
Methods
A sample size of 266 subjects comprising 133 males and 133 females of the ages 18 to 60 years, all of Nigerian origin and residing in Port Harcourt were recruited for the study after an informed consent was given to the participants. Anthropometric parameters measured include the right and left thumb length, right and left foot length and the height. Standardized protocols and techniques in anthropometry were adopted for the measurements using a digital sliding vernier caliper and stadiometer. The data obtained was analyzed with Microsoft Excel to obtain linear regression equations for stature estimation.
Results
The mean right thumb length (RTL) for males and females is 6.26 ± 4.82 and 5.41 ± 0.52 respectively. The mean right foot length (RFL) for males and females is 26.52 ± 1.23 and 25.48 ± 1.62 respectively. Student t test shows that sexual variation in all the measured parameters was statistically significant (p ≤ 0.05) at a confidence interval of 95% with males showing greater values in all parameters studied. There was statistically significant (p ≤ 0.05) bilateral symmetry for both right and left sides of the thumb and foot length. The females showed a strong positive correlation r of thumb length against height at 0.707.
Conclusion
The study shows that stature can be estimated using the thumb and foot length but that thumb length is more reliable and accurate in females. Also, both halves of the thumb or foot can accurately estimate stature in the study population as there were no bilateral asymmetry.
Injection of Mitomycin-C in combination with a bladder neck incision in patients with a bladder neck stenosis after prostatic surgery, a retrospective cohort study
Authors: Sandel Noah J. , Tijans Aline , Ronkes Brechje , De Bruin Martijn D. , Vis Andre N. , Pigot Garry L.S. 
Published: November 2024

Abstract

Purpose
Bladder neck stenosis (BNS) is a challenging complication following prostate cancer surgery or endoscopic procedures for benign prostatic hyperplasia. Bladder neck incision (BNI) is a common initial treatment, though recurrence occurs in up to 28% of patients. Mitomycin-C (MMC), an anti-fibrotic agent, has shown promise in reducing stenosis recurrence in other surgical contexts. This study retrospectively evaluates the efficacy and safety of MMC injection during BNI in reducing BNS recurrence.
Methods
We conducted a single-center, retrospective cohort study at the Amsterdam University Medical Center, including patients who underwent BNI with MMC injection. Patients were included if they had bothersome obstructive urinary symptoms and a history of at least one failed BNI. Recurrence-free rate was calculated, defined as a bladder neck not passable by a 16Fr cystoscope, requiring additional intervention. Postoperative complications and serious adverse events were recorded.
Results
Twelve patients were included in the study, with a median follow-up of 7 months (range 3–32 months) after BNI. Mean age was 69.1 (range 49–81 years). BNS etiology was robot-assisted laparoscopic prostatectomy in 41.7% of patients, while 58.3% of patients had an etiology of a transurethral resection of prostate (TURP). Recurrence-free rate was 83.3%. Two patients experienced recurrent BNS within 7 months post-procedure. No serious adverse events related to MMC were reported.
Conclusion
Our study demonstrates promising results for the use of MMC injection during BNI for recurrent BNS. Beside a relative high recurrence-free rate, the incidence of adverse events and of postprocedural complications was low.
Building and externally validating a prediction model for long COVID in severe and critical COVID-19 patients: A multicenter cohort study
Authors: Zhang Haojing , Kan Lin , Pan Dianzhu 
Published: November 2024

Abstract

Objective: To investigate the risk factors for corona virus disease 2019 (COVID-19) and construct a nomogram prediction model to evaluate the clinical treatment of long COVID.
Methods: Clinical data were collected from patients who were diagnosed with COVID-19 and hospitalized at the First Affiliated Hospital of Jinzhou Medical University from December 7, 2022, to February 1, 2023. The prediction model was constructed via a nomogram. External validation was carried out with clinical data from patients at Panjin Central Hospital.
Results: In the development cohort and the validation cohort of this study, 60.3% and 59.5% of the patients developed long COVID, respectively. After least absolute shrinkage and selection operator (Lasso) regression, the final variables included in the prediction model were the percentage of lymphocytes, the Charlson comorbidity index (CCI), computed tomography (CT) score, and oxygen requirement. The area under the receiver operating characteristic curve (AUROC) for external validation of the model was 0.794, and the p value of the calibration curve was 0.170. The decision curve analysis indicates that the model performs well.
Conclusion: The prediction model developed in this study is useful for assessing the likelihood of developing long COVID in hospitalized patients.
Comparison of Systemic Inflammation Indices With Post- Cardiac Surgery Mortality and Morbidity
Authors: Koyuncu Ahmet Ozan , YESILTAS Mehmet Ali , Ozsoy Sadiye Deniz , Mert Ali Murat 
Published: November 2024

Abstract

Background
The Neutrophil/Lymphocyte Ratio (NLR), Platelet/Lymphocyte Ratio (PLR), and Lymphocyte/Monocyte Ratio (LMR) are recognized markers linked to inflammation and have been associated with mortality and morbidity following cardiac surgery. This study aims to not only evaluate the relationships between these inflammatory indexes and specific clinical endpoints, such as in-hospital survival and one-year freedom from Major Adverse Cardiac and Cerebrovascular Events (MACCE), but also to directly compare the predictive accuracy of NLR, PLR, and LMR. By highlighting the comparative ability of these indexes, this study seeks to determine which marker offers superior predictive value for postoperative outcomes.
Method
A total of 1528 cardiac surgery patients were retrospectively analyzed, including a subset of 1205 patients who underwent coronary artery bypass grafting (CABG). The study assessed the associations between the NLR, PLR, LMR with in-hospital mortality/survival, as well as 1-month, 6-month, and 1-year mortality. In addition, postoperative complications, including MACCE, atrial fibrillation (AF), myocardial infarction (MI), low cardiac output syndrome (LCOS), acute kidney injury (AKI), cerebrovascular events (CVE), among others, were evaluated. The duration of intensive care unit (ICU) and hospital stays were also examined. The predictive accuracy of these indexes was specifically compared within the CABG subgroup. As the LMR ROC curve remained below 0.5, LMR was interpreted as "free of events." The cut-off values for the indexes were determined as NLR: 3.10, PLR: 143.9, and LMR: 3.52.
Results
NLR correlated with higher mortality and complications, whereas PLR showed no significant relation with in-hospital mortality or neurological complications. LMR was found to be related with free of events. Patients with higher NLR and PLR values experienced increased mortality and MACCE along with a higher incidence of complications such as postoperative revision, AF, LCOS, and renal issues. Conversely, higher LMR values corresponded with lower rates of such complications.
Conclusion
NLR emerges as a crucial indicator for predicting mortality and complications in cardiac surgery patients, more so than PLR and LMR. Especially NLR can be used to predict the risk of mortality and complications in cardiac surgery. This prediction can be support further with PLR and LMR.
Predicting Calorific Value through Proximate Analysis of Municipal Solid Waste Using Soft Computing System
Authors: Mondal Saptarshi , Rafizul Islam M. 
Published: November 2024

Abstract

This study investigated the accurate prediction of the calorific value of municipal solid waste (MSW) using soft computing systems, namely artificial neural networks (ANN), adaptive neural fuzzy inference system (ANFIS), support vector machine (SVM), and multi-layer perceptron (MLP). The research focused on proximate analysis parameters obtained from the laboratory and utilized the measured calorific value to develop predictive models. All the models demonstrated a very good correlation between input and output, with consistently strong values of the coefficient of determination (R2). ANFIS showed the best correlation with an exceptional predictive power. Statistical parameters were determined to compare model accuracy, with ANFIS exhibiting the top performance, followed by ANN, and then MLP, which had the lowest values of MSE, RMSE, MAD, and MAPE at 8.704E-07, 0.00019, 0.00016, and 1.295E-05 respectively. However, SVM has the least capability to predict calorific value accurately compared to other models. Soft computing models, specifically ANFIS, displayed exceptional precision in forecasting the calorific value.
Leveraging Machine Learning models to predict contraceptive nonuse among women of reproductive age in Rwanda: A machine learning approach
Authors: Nawe Norbert , Muhoza Dieudonné N. 
Published: November 2024

Abstract

Background: Despite efforts and commitments put in place by the Rwandan Government and collaborating great health organizations, contraceptive prevalence rate (CPR) remains low in Rwanda (64% in 2019 for married women using any method and 58.% for only modern methods. CPR has however been increasing from 45% in 2010 and 48% in 2015. Consequently, unmet need for family planning dropped from 34% to 14% between 2010 and 2020. This study aims to leverage Machine Learning to predict contraceptive nonuse among women of reproductive age in Rwanda.
Methods: A cross-sectional analysis of secondary data was conducted on 2020 Rwanda Demographic and Health Survey. We used six Machine Learning algorithms on the sample of 14,634 women of reproductive age, which were trained and evaluated using various metrics to know the best model. Moreover, multivariable binary logistic regression was used to determine key factors of contraceptive nonuse through Python software and to identify women at higher risk of not using contraceptives, providing valuable insights for targeted interventions and policy enhancements to improve access to reproductive health and family planning services for underserved populations in Rwanda.
Results: Findings revealed that woman age, residence region, education, wealth status, marital status, urban-rural residence, total children ever born, working status, partner's occupation, and the desire for more children are key determinants of not using contraceptives. Younger women, particularly those aged 15-24, urban residents, wealthier women, and those desiring more children are at a higher risk of not using contraceptives. Furthermore, Support Vector Machine model performed better than other five classifiers in predicting nonuse of contraceptives status with an accuracy of 75%, giving a ROC-AUC score of 83%, and making it the best model to predict contraceptive behavior in Rwanda.
Conclusion The results from this study suggest the use of Machine Learning to predict contraceptive outcomes accurately. Additionally, by tailoring focused interventions for identified women at higher risk of not using contraceptive could contribute to contraceptive use uptake in Rwanda.