bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neonatal Sepsis Among Neonates Admitted to Neonatal Intensive Care Unit in Hawassa University Comprehensive Specialized Hospital, Southern Ethiopia, 2022
Authors: Geta Temesgen , Kassa Ethiopia. Tsiyon , Israel Eskinder 
Published: September 2022

Abstract
BackgroundNeonatal sepsis is a leading cause of neonatal death across the globe, which accounted 15% and 30% of neonatal mortality in low and middle income and developing countries including Ethiopia, respectively. Despite various efforts have been made by Ethiopian government to prevent neonatal mortality and morbidity, sepsis related to neonatal mortality is still high. So the objective of this study was to assess the magnitude and affecting factors of neonatal sepsis among neonate admitted to neonate intensive care units in Hawassa University Comprehensive Specialized Hospital, Southern Ethiopia.Methods and materialsFacility based cross-sectional study was conducted from January to February, 2022 at Hawassa university comprehensive specialized hospital, Southern Ethiopia. The total of 216 neonate-mother pairs who were admitted to intensive care unit was included in the study and a systematic random sampling technique was used to select study participant. Structured questionnaire was used to collect data. The data was entered in to EPI Data 3.1 version transported to SPSS version 25 for data analysis. Multi regression analysis was done for predictor variables associated at p-value < 0.05 with the dependent variable.ResultA total of 74 (34.3%) neonates were admitted with neonatal sepsis. A surgical procedure is done before sepsis (AOR = 2.9, 95% of CI(1.5 to5.6), last pregnancy wanted (AOR = 2.8, 95% of CI(1.0 to 7.3), more frequent per-vaginal examination (AOR = 2.6, 95% of CI (1.3 to 5.3), educational level unable to read and write(AOR = 5.4, 95% of CI(1.0 to 27.7), low birth weight (AOR = 5.4, 95% of CI(3.0 to 9.8), and getting care from unskilled health workers(AOR = 2.7, 95% of CI(1.5 to 4.9) were significantly associated with neonatal sepsis at multivariable with less than 0.05 p values.ConclusionThis study indicated that the prevalence of neonatal sepsis was still high. Thus, immediate action should be taken before it leads to neonatal mortality and further complications. The study used only physician diagnosis to confirm neonatal sepsis, which may affect its prevalence and needs further investigation.
Provider perspectives on clinical decision support to improve HIV prevention in pediatric primary care: A mixed methods study
Authors: Pickel Julia , Fiks Alexander G. , Karavite Dean , Maleki Pegah , Beidas Rinad S. , Dowshen Nadia , Petsis Danielle , Gross Robert , Wood Sarah 
Published: September 2022

Abstract
Background: Clinical decision support (CDS) is a promising implementation strategy for improving uptake of HIV testing and pre-exposure prophylaxis (PrEP). However, little is known regarding provider perspectives on acceptability, appropriateness, and feasibility of CDS for HIV prevention in pediatric primary care, a key implementation setting. Methods: Cross-sectional mixed methods study utilizing surveys and in-depth interviews with pediatricians to 1) assess acceptability, appropriateness, and feasibility of CDS for HIV prevention, 2) assess optimal timing and contextual barriers and facilitators to CDS implementation, and 3) develop an Implementation Research Logic Model to conceptualize implementation determinants, mechanisms, and outcomes of potential CDS use. Interview scripts were grounded in the Consolidated Framework of Implementation Research. Qualitative analysis utilized a mixed inductive and deductive approach, and quantitative and qualitative data were merged to develop the logic model. Results: Participants (n=26) were primarily white (92%), female (88%), and physicians (73%). Using CDS to improve HIV testing and PrEP delivery was perceived as highly acceptable (median score 5), IQR [4-5]), appropriate (5, IQR [4-5]), and feasible (4, IQR [3.75-4.75]) using a five-point Likert scale. Providers identified confidentiality and time constraints as two key barriers to HIV prevention care spanning every workflow step. With respect to desired CDS features, providers sought interventions that were integrated into the primary care workflow; standardized to promote universal testing yet adaptable to the level of patient’s HIV risk; and addressed providers knowledge gaps and bolstered self-efficacy in providing HIV prevention services. Conclusions: This mixed methods study indicates that clinical decision support in the pediatric primary care setting may be an acceptable, feasible, and appropriate strategy for improving the reach and equitable delivery of HIV screening and PrEP. Design considerations for CDS in this setting should include deploying interventions early in the visit workflow, and prioritizing standardized but flexible designs.
External validation of a prognostic model for 1-year survival after fragility hip fracture: A retrospective cohort study
Authors: Fu Hairui , Li Feixiong , Wei Fangfang , Qu Weihan , Wu Dou , Liang Bin , Zhang Xuan , Liu Qiang 
Published: September 2022

Abstract
Background
To scientifically manage fragility hip fracture patients, we have developed a survival prediction model for predicting 1-year survival after fragility hip fracture (SFHF). This study aims to validate the model.
Methods
This was a retrospective cohort study conducted in the orthopaedics department at a regional medical centre in central China. Fragility hip fracture patients who were consecutively admitted to the orthopaedics department from January 2016 to October 31, 2021. We evaluated the performance of an SFHF model (including the full model and the simplified model) from the following three aspects: (1) Discrimination. We presented the concordance (c) index of the model, including Harrell's c-index and Uno's c-value. Overall performance was measured using Nagelkerke R2 values. (2) Calibration. The calibration plot method was used to evaluate the calibration of the model. (3) Decision curve analysis (DCA) was used to judge whether the model had clinical value in the validation population.
Results
A total of 877 (≥ 50 years) (out of 1132) eligible fragility hip fracture patients were included in this study. Among them, 47 patients were lost to follow-up. Among the patients who were successfully followed up, 87 died within 1 year of the fracture. After applying simple imputation to deal with missing values, the final effective sample size was 93 cases. The 1-year mortality rate after fracture was 10.6%. The Harrell’s c-index values of the full and simple SFHF models were 0.764 (standard error, 0.024) and 0.763 (0.024), respectively. Uno’s c-values were 0.765 (0.024) and 0.763 (0.024), respectively. The Nagelkerke R2 values were 0.144 and 0.144, respectively. The calibration plot showed that the model had good calibration between predicted and actual values. DCA showed that the model was clinically useful within a threshold range of risk of death of 0.03–0.38.
Conclusion
Our study preliminarily confirmed that the SFHF model has good accuracy and generalizability in predicting the one-year survival rate of fragility hip fracture patients, and that it has good clinical value. This predictive model may be considered for local clinical practice.
Global Meta-Analysis of Urine Microbiome: Colonization of PAH-degrading bacteria among bladder cancer patients
Authors: Bukavina Laura , Isali Ilaha , Ginwala Rashida , Sindhani Mohit , Calaway Adam , Magee Diana , Miron Benjamin , Correa Andres , Kutikov Alexander , Zibelman Matthew , Ghannoum Mahmoud , Retuerto Mauricio , Ponsky Lee , Markt Sarah , Uzzo Robert , Abbosh Philip 
Published: September 2022

Abstract
Background
The application of next generation sequencing techniques has enabled characterization of urinary tract microbiome. Although many studies have demonstrated associations between the human microbiome and bladder cancer, they have not always reported consistent results, thereby necessitating cross-study comparisons. Thus, the fundamental questions remain how we can utilize this knowledge. The aim of our study was to examine for disease-associated changes in urine microbiome communities globally utilizing machine learning algorithm. The results were further validated using our own prospectively collected urine of bladder cancer patients.
Results
Our study included 129 bladder cancer urine samples, and 60 healthy controls across four different countries. At a meta-analysis false discovery rate (FDR) of 0.01, we identified a total of 97/548 genera to be differentially abundant in the BCa microbiome compared to healthy patients. Overall, while the differences in diversity metrics were clustered around the country of origin (Kruskal Wallis, P < 0.001), collection methodology was a driver of microbiome composition. When assessing dataset from the China, Hungary and Croatia, ML data demonstrated no discrimination capacity to distinguish between BCa and healthy (AUC 0.577). However, inclusion of samples with catheterized urine only improved the diagnostic accuracy of prediction for BCa to AUC 0.995, with precision recall AUC = 0.994. Through elimination of contaminants associated with collection methodology among all cohorts, our study identified increased abundance of polycyclic aromatic hydrocarbon (PAHs) degrading bacteria Sphingomonas, Acinetobacter, Micrococcus, and Ralstonia consistently present in BCa patients.
Conclusions
The microbiota of the bladder cancer population may be a reflection of PAH exposure from smoking, environmental pollutants and ingestion. Presence of PAHs in urine of bladder cancer patients may allow for a unique metabolic niche, and provide necessary metabolic resources where other bacteria are not able to flourish. Furthermore, we found that while compositional differences associated with geography more than disease, many are driven by collection methodology.
Focus Upper, Process Larger: Looking at the Upper or Lower Facial Half can Change Holistic Face Processing Range
Authors: Sun Yu-Hao P. , Zhang Xiaohui , Ni Hao , Zhou Xin , Yang Xiteng , Zheng Ziyi , Wang Zhe , Jin Haiyang 
Published: September 2022

Abstract
Previous studies suggested that upper and lower facial halves might be involved in the human holistic face processing differently. In this study, we replicated and extended the finding above. In Experiment 1, we used the standard composite-face task to measure holistic face processing when participants made judgements on the upper and lower facial halves separately. Results showed that the composite-face effect was stronger for the upper facial half compared to the lower half. In Experiment 2, we investigated how facial information was integrated when participants focused on different features, using the perceptual field paradigm. Results showed that: (1) more “peripheral faces” were chosen when participants fixated at the eyes than when they fixated at the mouth; (2) less “peripheral faces” were chosen for inverted faces regardless of the fixated features. Findings from both experiments together indicate that more peripheral facial information were integrated when participants focused on the upper facial half, highlighting the significance of focusing on the upper facial half in face processing.
Endodontic versus coronal treatment quality on the periapical status
Authors: MA Gasqui , Pérard M , Decup F , Villat C , Chevalier C , Nony P , Gueyffier F , Iwaz J , Roche L , Aslanian S , Grosgogeat B , Zanini M 
Published: September 2022

Abstract
INTRODUCTION: Coronal sealing, quality of root canal treatment, and periapical status have been widely investigated. However, which of two practitioner-dependent determinants (endodontic treatment or coronal restoration) is the most important to the periapical status remains scientifically debated. OBJECTIVES: This study investigated the relative importance of the technical qualities of root canal filling and marginal sealing of the coronal restoration on the periapical status and screened previously published data. METHODS: A retrospective study collected and analyzed clinical and radiological data on 246 endodontically-treated teeth. Six paired comparisons were carried out between groups combining good or poor endodontic treatment (G/P E) with good or poor coronal restoration G/P R). Results are displayed through forest plots of odds ratios for each comparison.RESULTS: In the retrospective study, the endodontic determinant seems to be more important than the restorative one (37.5% of diseased periapical status with GE versus 41.2% with PE). In the systematic review, odds ratio analyses showed that group GE+GR had 1.7 less periapical lesions than group PE+GR and 2.7 less lesions that group PE+PR. Belonging to group PE+PR was found significantly associated with ‘diseased’ periapical status. CONCLUSION: The retrospective study and the systematic review agreed that endodontic treatment (precisely, root canal filling) was found more important than coronal restoration (precisely, marginal sealing) for healthy periapical tissue.
The effect of depression on compliance with physical distancing during the COVID-19 pandemic
Authors: Kim Jinhyun , Yun Il , Kim Hyunkyu , Park Eun-Cheol , Jang Sung-In 
Published: September 2022

Abstract
Background:
This study aimed to investigate the possible factors associated with compliance with physical distancing during the early stage of the COVID-19 pandemic, focusing on depression.
Methods:
Korea Community Health Survey data of 2020 were investigated. The group with depression was subdivided according to whether or not they were concerned about COVID-19. The association between depression and compliance was analyzed using multivariate Poisson regression analysis with robust variance estimates.
Results:
The number of participants was 132,595, and the proportion of compliance with physical distancing was 86.1% in men and 89.1% in women. The prevalence of compliance was 0.939 times lower in men (95% confidence interval [CI], 0.915–0.963) and 0.953 times lower in women (95% CI, 0.938–0.969) living with depression than in participants who were not depressed. Despite its positive association with compliance, participants who were depressed and had concerns about COVID-19 were not statistically significantly different from those who were not depressed and had no concerns.
Conclusions:
Depression was associated with poor compliance, and the positive effect of concern about COVID-19 was canceled out by depression. Preparing interventions for depression is necessary in the early stages of a pandemic.
Association between Phthalate Metabolites in Cord Blood and Preterm Birth, a Nested Case-Control Study in Tianjin, China
Authors: Liu Peng , Chang Ying , Cai Yan , Li Changping , Tang Naijun , Gao Na , Sun Zengrong 
Published: September 2022

Abstract
The influences of maternal exposure to endocrine disrupting chemicals on birth outcomes are not well understood. To assess phthalates exposure levels in utero and investigate their associations with preterm birth (PTB)，we performed a case-control study of 30 PTB newborns and 60 matched control newborns from a prospective mother-baby cohort. Control subjects were matched to case subjects by maternal age (+/- 3 years) and birth date. We quantified concentrations of MBP, MBzP, MMP, MEP and MEHP in cord blood. Compared with control infants, the concentrations of MMP, MEP and MEHP in cord blood were significantly higher in preterm newborns. With or without adjusting for potential confounders, MMP, MEHP and high molecular weight phthalate metabolites concentrations above the median levels were observed to be associated with an increased risk of PTB. In our study, prenatal phthalates exposure was related to preterm birth and may be a risk factor.
HMRFLR: A Hybrid Model for Sentiment Analysis of Social Media Surveillance on Airlines
Authors: Singh Neelam , Upreti Manisha 
Published: September 2022

Abstract
Social Media is a major part of human life in the current era. People posts their regular activities, self-indulgent feelings, and real-life experiences on various platforms such as Twitter, Instagram, Facebook, YouTube etc. For social media surveillance, Twitter is considered to be the most widely used platform (about 64%). Twitter data is a valuable method to gather tweets and analyses of user perspectives. Along with the other industries the airline industry also wants to be up to date and keep its sectors alive having the current scenario. Airlines use traditional types of customer feedback that are very common and require a great deal of time. To minimize the problems, the analysis of feelings is considered to be a mandatory approach. After the pandemic when traveling is again resuming and flights are finally taking off, the airline industry is also giving its best to keep in touch with their customers more than ever. The dynamic evolution of the airline industry is commendable over the last decade. Millions of people share their experiences related to different airline companies every day where happy customers are posting their pictures with clouds and staff, some angry customers are complaining about bad services and difficulties they faced like missing baggage, delayed flights, changes in boarding schedules, an IT system failure, etc. This kind of real-time feedback not only helps the passengers to decide which flight they have to choose but also helps the management team and staff of airlines to analyze the situation and take immediate action regarding it to improve their services for passengers’ better experience. In the research paper, a hybrid model composed of Machine Learning algorithms including the classifiers of Random Forest and Logistic Regression named as HMRFLR is proposed to analyze the tweets of Airlines in the US for categorization of the posts according to positivity, neutrality, and negativity of the posts. For revealing the current level of customer satisfaction towards the airlines, sentiment analysis is undertaken. This hybrid model achieves a better accuracy score of 88.16%, however, the individual accuracy score of Logistic Regression is 79.1% and Random Forest is 76.87% respectively.
Comprehensive analysis on subchondral bone marrow lesions of human osteoarthritis by integrating bulk and single-cell transcriptomes
Authors: Zeng Muhui , Wang Xiaoshuai , Chen Tianyu , Ruan Guangfeng , Li Jia , Xue Song , Zhao Yang , Hu Zhiyang , Xie Ye , Fan Tianxiang , Chen Shibo , Li Yang , Wang Qianyi , Zhang Yue , Zhang Rongkai , Lin Lijun , Ding Changhai , Zhu Zhaohua 
Published: September 2022

Abstract
Objective
This study aims to demonstrate the cellular composition and underlying mechanisms in subchondral bone marrow lesions (BMLs) of knee osteoarthritis (OA).
Methods
BMLs were accessed by MRI Osteoarthritis Knee Score (MOAKS) ≥ 2. Bulk RNA-sequencing (bulk-seq) and BML-specific differentially expressed genes (DEGs) analysis were performed among subchondral bone samples (including OA-BML = 3, paired OA-NBML = 3; non-OA = 3). The hub genes of BMLs were identified by verifying in independent datasets and multiple bioinformatic analyses. To further estimate cell-type composition of subchondral bone, we utilized two newly developed deconvolution algorithms (MuSiC, MCP-counter) in transcriptomic datasets, based on signatures from open-accessed single-cell RNA sequencing (scRNA-seq). Finally, competing endogenous RNA (ceRNA) and transcription factor (TF) networks were constructed through multiple predictive databases, and validated by public non-coding RNA profiles.
Results
A total of 86 BML-specific DEGs (up 79, down 7) were identified. IL11 and VCAN were identified as core hub genes. The “has-miR-424-5p/lncRNA PVT1” was determined as crucial network, targeting IL11 and VCAN, respectively. More importantly, two deconvolution algorithms produced approximate estimations of cell-type composition, and the cluster of heterotopic-chondrocyte was discovered abundant in BMLs, and positively correlated with the expression of hub genes.
Conclusion
IL11 and VCAN were identified as the core hub genes of BMLs, and their molecular networks were determined as well. We profiled the characteristics of subchondral bone at single-cell level and determined that the heterotopic-chondrocyte was abundant in BMLs and was closely linked to IL11 and VCAN. Our study may provide new insights into the microenvironment and pathological molecular mechanism of BMLs, and could lead to novel therapeutic strategies.
Diffusion tensor imaging metrics associated with future disability in multiple sclerosis
Authors: Lopez-Soley Elisabet , Martinez-Heras Eloy , Solana Elisabeth , Solanes Aleix , Radua Joaquim , Vivo Francesc , Prados Ferran , Sepulveda Maria , Cabrera-Maqueda Jose Maria , Fonseca Elianet , Blanco Yolanda , Alba-Arbalat Salut , Martinez-Lapiscina Elena H. , Villoslada Pablo , Saiz Albert , Llufriu Sara 
Published: September 2022

Abstract
The relationship between brain diffusion microstructural changes and disability in multiple sclerosis (MS) remains poorly understood. We aimed to explore the predictive value of microstructural properties in white (WM) and grey matter (GM), and identify areas associated with mid-term disability in MS patients. We studied 185 patients with the Expanded Disability Status Scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), and Symbol Digit Modalities Test (SDMT) at two time-points. We used machine learning to analyse the predictive value of baseline WM fractional anisotropy and GM mean diffusivity, and to identify areas related to each outcome at 4.1 years follow-up. Motor performance was associated with WM (T25FW: RMSE = 0.524, R2 = 0.304; 9HPT dominant hand: RMSE = 0.662, R2 = 0.062; 9HPT non-dominant hand: RMSE = 0.649, R2 = 0.139), and SDMT with GM diffusion metrics (RMSE = 0.772, R2 = 0.186). Cingulum, longitudinal fasciculus, optic radiation, forceps minor and frontal aslant were the WM tracts most closely linked to motor dysfunction, and temporal and frontal cortex were relevant for cognition. Regional specificity related to clinical outcomes provide valuable information that can be used to develop more accurate predictive models that could improve therapeutic strategies.
Analysis and Detection of Tomatoes Quality using Machine Learning Algorithm and Image Processing
Authors: Zuo Haichun 
Published: September 2022

Abstract
Grading of agricultural products Methods based on artificial intelligence is more important. Because these methods have the ability to learn and thus increase the flexibility of the system. In this paper, image processing systems and detection analysis methods, and artificial intelligence are used to grade tomatoes, and the success rate of grading these methods is compared with each other. However, the purpose of this study is to obtain a solution to detect appearance defects and grade and sort the tomato crop and provide an efficient system in this field. A visual dataset is created, to investigate the approach of image processing and machine learning based on a tomato image. Tomato models are placed individually under the camera and samples are classified in a lighting box away from the effects of ambient light. Data sets have been used in three types of first, second, and third quality categories. It should be noted that quality category one has the best quality and quality category two has the medium quality and category three has the worst quality, Also, each data class contains 80 samples. Using tomato appearance such as size, texture, color, shape, etc. Image processing is performed for extract features. Tomato images are pre-processed for optimization. Then, to prepare for classification, the dimensions of the images are reduced by principal component analysis (PCA). Three categories of an artificial neural network, a support vector machine, and a decision tree are compared to show the most efficient support machine. The analysis is examined in two classes and three classes. The support vector machine has the best accuracy compared to other methods so this rate is 99.9% for two classes and 99.79% for three classes.