bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of differential performance between advanced placement exam scores and class grades using machine learning
Authors: Suzuki Honoka , Hong Maxwell , Ober Teresa , Cheng Ying 
Published: December 2022

IntroductionPast studies have found students to perform differently between class grades and standardized test scores – two essential and complementary measures of student achievement. This study examines predictors of the relative performance between these two measures in the context of the advanced placement (AP) program, namely, we compared students’ AP exam scores to the class grade they received in the corresponding AP course. For example, if a student received a high AP class grade but a low AP exam score, what characteristics about the student or their learning context might explain such discrepancy?MethodsWe used machine learning, specifically random forests, and model interpretation methods on data collected from 381 high school students enrolled in an AP Statistics course in the 2017–2018 academic year, and additionally replicated our analyses on a separate cohort of 422 AP Statistics students from the 2018–2019 academic year.ResultsBoth analyses highlighted students’ school and behavioral engagement as predictors of differential performance between AP class grades and AP exam scores.DiscussionAssociations between behavioral engagement and differential performance suggest that the ways in which a student interacts with AP course material to obtain high class grades can differ from study habits that lead to optimal performance on the AP exam. Additionally, school-level differences in relative performance pose equity concerns towards the use of AP exam scores in high-stakes decisions, such as college admissions. Implications are discussed from a pedagogical and policy perspective.
Ungulate use of an at-grade fauna passage and roadside animal detection system: A pilot study from Southern Sweden
Authors: Bhardwaj Manisha , Erixon Filippa , Holmberg Isak , Seiler Andreas , Håkansson Emma , Elfström Marcus , Olsson Mattias 
Published: December 2022

Wildlife-vehicle collisions (WVC) cost millions of euros each year in loss of life and damages. Wildlife crossing structures and fencing are commonly implemented mitigation strategies to reduce WVC and increase landscape connectivity for wildlife. Typically, crossing structures are over- or under-pass structures that allow animals to safely cross the road, while separating them from the road and traffic. An alternative strategy could be an at-grade fauna passage coupled with a Roadside Animal Detection System (RADS). At-grade fauna passages are designated locations where a gap in fencing allows animals to cross over the road, while RADS alert drivers of animals at the upcoming passage, so that they can adjust their driving behaviour accordingly and avoid collisions. In this pilot study, we investigated the use of one at-grade fauna passage by roe deer (Capreolus capreolus), red deer (Cervus elaphus) and wild boar (Sus scrofa) in southern Sweden, and compared changes in the number of WVCs before, during and after the construction of the passage. We collected a total of 326 wildlife crossings from 722 individuals over 1 year of monitoring (24 January 2020–24 January 2021). We found that crossing events tended to be dominated by the time animals spent in the roadside verge from which they approached the at-grade fauna passage, particularly for roe deer that spent a lot of time browsing in the roadside verge during dusk. We also found that animals spent longer in the passage if vehicles were present. In our 1 year of surveys, we only recorded three accidents, and when comparing the annual collision statistics before, during, and after construction of the at-grade fauna passage, we demonstrated an overall reduction in collisions by 66%. While our pilot only evaluates a single site, it does provide promising preliminary results that suggest that at-grade fauna passages can help in efforts to reduce collisions, while maintaining connectivity over medium-sized roads for large ungulates.
Estimating age specific prevalence and force of infection in Zimbabwe using combined cross-sectional surveys from 2005 to 2015
Authors: Birri Makota Rutendo , Musenge Eustasius 
Published: December 2022

ObjectiveAge structured sexual mixing patterns have been noted to be associated with HIV prevalence and force of infection. Therefore, this study aimed to estimate the age dependent HIV force of infection using survey cross-sectional data from Zimbabwe.MethodsWe fit generalized additive models namely; linear, semi-parametric, non-parametric and non-proportional hazards models. Using the 2005–06, 2010–11 and 2015 Zimbabwe Demographic Health Surveys data. The Akaike Information Criteria was used to select the best model. The best model was then used to estimate the age dependent HIV prevalence and force-of-infection.ResultsBased on birth year cohort-specific prevalence, the female HIV prevalence reaches the highest peak at around 29 years of age, then declines thereafter. Males have a lower cohort specific prevalence between 15 and 30 years than females. Male cohort-specific prevalence slightly decreases between the ages of 33 and 39, then peaks around the age of 40. The cohort-specific FOI is greater in females than in males throughout all age categories. In addition, the cohort-specific HIV FOI peaked at ages 22 and 40 for females and males, respectively. The observed 18-year age difference between the HIV FOI peaks of males and females.ConclusionOur model was appealing because we did not assume that the FOI is stationary over time; however, we used serological survey data to distinguish the FOI's age-and-time effect. The cohort-specific FOI peaked 18 years earlier in females than males, indicative of age-mixing patterns. We recommend interventions that target younger females so as to reduce HIV transmission rates.
Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer
Authors: Jiang Shaoqin , Li Zhihao , Dou Ruiling , Lin Zequn , Zhang Jili , Zhang Wenhui , Chen Zeyu , Shen Xianqi , Ji Jin , Qu Min , Wang Yan , Li Mengqiang , Gao Xu 
Published: December 2022

Background: Prostate cancer (PCa) is one of the most common tumors of the urinary system. Cuproptosis is a novel mode of controlled cell death that is related to the development of various tumor types. However, the functions of cuproptosis-related long noncoding RNAs (CRLs) in PCa have not yet been well studied.Methods: In this study, data of PCa patients were obtained from The Cancer Genome Atlas (TCGA) and from the Changhai Hospital. Univariate and multivariate Cox regression analyses and LASSO regression analysis were conducted to screen CRLs linked to the prognosis of PCa patients. A risk score model was constructed on the basis of CRLs to predict prognosis. PCa patients were categorized into high- and low-risk cohorts. The predictive value of the risk score was evaluated by Kaplan–Meier survival analysis, receiver operating characteristic curves, and nomograms. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore possible pathways involving CRLs in PCa. Immune function analysis confirmed the correlation between CRLs and immunity in PCa. Finally, we explored the tumor mutational burden and drug response in the high- and low-risk cohorts.Results: First, we identified seven CRLs (C1orf229, C9orf139, LIPE-AS1, MCPH1-AS1, PRR26, SGMS1-AS1, and SNHG1) that were closely related to prognosis in PCa. The risk score model based on the selected CRLs could accurately predict the prognosis of PCa patients. The high-risk cohort was associated with worse disease-free survival (DFS) time in PCa patients (p &lt; 0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 1 year: 0.703). Nomograms were constructed based on the risk score and clinicopathological features and also exhibited great predictive efficiency for PCa. GO and KEGG analyses showed that the CRLs were mainly enriched in metabolism-related biological pathways in PCa. In addition, immune function analysis showed that patients in the high-risk cohort had higher TMB and were more sensitive to conventional chemotherapy and targeted drugs including doxorubicin, epothilone B, etoposide, and mitomycin C.Conclusion: We constructed a novel CRL-related risk score model to accurately predict the prognosis of PCa patients. Our results indicate that CRLs are potential targets for drug therapy in PCa and provide a possible new direction for personalized treatment of PCa patients.
Staphylococcus aureus endocarditis: Identifying prognostic factors using a method derived from morbidity and mortality conferences
Authors: Lefèvre Benjamin , Legoff Antoine , Boutrou Mathilde , Goehringer François , Ngueyon-Sime Willy , Chirouze Catherine , Revest Matthieu , Vernet Garnier Véronique , Duval Xavier , Delahaye François , Le Moing Vincent , Selton-Suty Christine , Filippetti Laura , Hoen Bruno , Agrinier Nelly 
Published: December 2022

ObjectivesLethality of Staphylococcus aureus (Sa) infective endocarditis (IE) is high and might be due to yet unidentified prognostic factors. The aim of this study was to search for new potential prognostic factors and assess their prognostic value in SaIE.Materials and methodsWe used a two-step exploratory approach. First, using a qualitative approach derived from mortality and morbidity conferences, we conducted a review of the medical records of 30 patients with SaIE (15 deceased and 15 survivors), randomly extracted from an IE cohort database (NCT03295045), to detect new factors of possible prognostic interest. Second, we collected quantitative data for these factors in the entire set of SaIE patients and used multivariate Cox models to estimate their prognostic value.ResultsA total of 134 patients with modified Duke definite SaIE were included, 64 of whom died during follow-up. Of the 56 candidate prognostic factors identified at the first step, 3 had a significant prognostic value in multivariate analysis: the prior use of non-steroidal anti-inflammatory drugs [aHR 3.60, 95% CI (1.59–8.15), p = 0.002]; the non-performance of valve surgery when indicated [aHR 1.85, 95% CI (1.01–3.39), p = 0.046]; and the decrease of vegetation size on antibiotic treatment [aHR 0.34, 95% CI (0.12–0.97), p = 0.044].ConclusionWe identified three potential SaIE prognostic factors. These results, if externally validated, might eventually help improve the management of patients with SaIE.
Corroboration of cross-reactivity between Mycobacterium leprae and hosts’ salivary and cutaneous proteins: A hope for prognostic biomarkers for the pathogenesis of reactions in leprosy
Authors: Pathak Vinay Kumar , Singh Itu , Singh Shoor Vir , Sengupta Utpal 
Published: December 2022

IntroductionImmunological reactions are frequent complications that may occur either before, during, or after treatment and affect 30–50% of leprosy patients. The presence of autoantibodies like rheumatoid factor, antinuclear factor, and antibodies to host collagen, keratin, actin, myosin, endothelial cells, and myelin basic protein (MBP) has been earlier reported in leprosy patients. The purpose of this study was to identify cross-reactive proteins in clinical samples such as saliva and slit skin scrapings (SSS) of leprosy patients which could be utilised as prognostic biomarkers for Type 1 Reaction (T1R) in leprosy.MethodA total of 10 leprosy patients in T1R and 5 healthy volunteers were recruited. The protein was extracted from their SSS and saliva samples, thereafter, isoelectric focusing (IEF) and two-dimensional PAGE were performed to analyse the proteins. Furthermore, the cross-reactivity was identified by western blotting host proteins in gel against purified IgG from Mycobacterium leprae soluble antigen (MLSA)- hyperimmunized rabbit sera, thereafter, cross-reactive proteins were identified by MS/MS. The cross-reactive host proteins were analysed for homologous bacterial proteins and B cell epitopes (BCEs) were predicted by using bioinformatic tools.ResultsA total of five spots of salivary proteins namely S100-A9, 35.3 kDa, and 41.5 kDa proteins, Serpin peptidase inhibitor (clade A), Cystatin SA-III, and four spots of SSS namely 41.4 kDa protein, Alpha-1 antitrypsin, vimentin, and keratin 1, were identified as cross-reactive. Further, a total of 22 BCEs of cross-reactive host proteins were predicted and visualised.DiscussionThis data provides strong evidence of cross-reactivity/molecular mimicry between host and pathogen in leprosy patients with reaction. These BCEs of cross-reactive proteins could be further studied to predict reactions and may be utilised as an early diagnostic biomarker for T1R in leprosy.
Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset
Authors: Khayretdinova Mariam , Shovkun Alexey , Degtyarev Vladislav , Kiryasov Andrey , Pshonkovskaya Polina , Zakharov Ilya 
Published: December 2022

IntroductionBrain age prediction has been shown to be clinically relevant, with errors in its prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether the same results can be achieved with electroencephalography is unclear.MethodsThe current study aimed to create a new deep learning solution for brain age prediction using raw resting-state scalp EEG. To this end, we utilized the TD-BRAIN dataset, including 1,274 subjects (both healthy controls and individuals with various psychiatric disorders, with a total of 1,335 recording sessions). To achieve the best age prediction, we used data augmentation techniques to increase the diversity of the training set and developed a deep convolutional neural network model.ResultsThe model’s training was done with 10-fold cross-subject cross-validation, with the EEG recordings of the subjects used for training not considered to test the model. In training, using the relative rather than the absolute loss function led to a better mean absolute error of 5.96 years in cross-validation. We found that the best performance could be achieved when both eyes-open and eyes-closed states are used simultaneously. The frontocentral electrodes played the most important role in age prediction.DiscussionThe architecture and training method of the proposed deep convolutional neural networks (DCNN) improve state-of-the-art metrics in the age prediction task using raw resting-state EEG data by 13%. Given that brain age prediction might be a potential biomarker of numerous brain diseases, inexpensive and precise EEG-based estimation of brain age will be in demand for clinical practice.
Hippocampal gene expression patterns in Sevoflurane anesthesia associated neurocognitive disorders: A bioinformatic analysis
Authors: Li Weiwei , Yi Qijun , Shi Huijian 
Published: December 2022

BackgroundSeveral studies indicate general anesthetics can produce lasting effects on cognitive function. The commonly utilized anesthetic agent Sevoflurane has been implicated in neurodegenerative processes. The present study aimed to identify molecular underpinnings of Sevoflurane anesthesia linked neurocognitive changes by leveraging publically available datasets for bioinformatics analysis.MethodsA Sevoflurane anesthesia related gene expression dataset was obtained. Sevoflurane related genes were obtained from the CTD database. Neurocognitive disorders (NCD) related genes were downloaded from DisGeNET and CTD. Intersecting differentially expressed genes between Sevoflurane and NCD were identified as cross-talk genes. A protein-protein interaction (PPI) network was constructed. Hub genes were selected using LASSO regression. Single sample gene set enrichment analysis; functional network analysis, pathway correlations, composite network analysis and drug sensitivity analysis were performed.ResultsFourteen intersecting cross-talk genes potentially were identified. These were mainly involved in biological processes including peptidyl-serine phosphorylation, cellular response to starvation, and response to gamma radiation, regulation of p53 signaling pathway, AGE-RAGE signaling pathway and FoxO signaling. Egr1 showed a central role in the PPI network. Cdkn1a, Egr1, Gadd45a, Slc2a1, and Slc3a2 were identified as important or hub cross-talk genes. Among the interacting pathways, Interleukin-10 signaling and NF-kappa B signaling enriched among Sevoflurane-related DEGs were highly correlated with HIF-1 signaling enriched in NCD-related genes. Composite network analysis showed Egr1 interacted with AGE-RAGE signaling and Apelin signaling pathways, Cdkn1a, and Gadd45a. Cdkn1a was implicated in in FoxO signaling, PI3K-Akt signaling, ErbB signaling, and Oxytocin signaling pathways, and Gadd45a. Gadd45a was involved in NF-kappa B signaling and FoxO signaling pathways. Drug sensitivity analysis showed Egr1 was highly sensitive to GENIPIN.ConclusionA suite of bioinformatics analysis revealed several key candidate hippocampal genes and associated functional signaling pathways that could underlie Sevoflurane associated neurodegenerative processes.
Causal effects of fatty acids on depression: Mendelian randomization study
Authors: Zeng Lingsi , Lv Honggang , Wang Xubo , Xue Ranran , Zhou Cong , Liu Xia , Yu Hao 
Published: December 2022

ObjectivesFatty acids (FA) are widely believed to play a role in the pathophysiology of depression. However, the causal relationships between FA and depression remain elusive and warrant further research. We aimed to investigate the potential causal relationship between FA [saturated fatty acids (SFA), mono-unsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA)] and the risk of depression using Mendelian randomization (MR) analysis.MethodsWe conducted a two-sample MR analysis using large-scale European-based genome-wide association studies (GWASs) summary data related to depression (n = 500,199 individuals) and FA [saturated fatty acids (SFA), mono-unsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA)] levels. MR analysis was performed using the Wald ratio and inverse variance-weighted (IVW) methods, and sensitivity analysis was conducted by the simple mode, weighted mode, weighted median method, and MR-Egger method.ResultsWe found the causal effects for the levels of oleic acid (OA; OR = 1.07, p = 5.72 × 10–4), adrenic acid (OR = 0.74, p = 1.01 × 10–3), α-linolenic acid (ALA; OR = 2.52, p = 1.01 × 10–3), eicosapentaenoic acid (EPA; OR = 0.84, p = 3.11 × 10–3) on depression risk, after Bonferroni correction. The sensitivity analyses indicated similar trends. No causal effect between the levels of SFA and depression risk was observed.ConclusionOur study suggests that adrenic acid and EPA are protective against the risk of depression, while OA and ALA are potential risk factors for depression. Nonetheless, the underlying mechanisms that mediate the association between these FAs and depression risk should be investigated in further experiments.
Urine trace element disorder along with renal function injury in vitamin D deficient diabetic rats and intervention effect of 1α,25-dihydroxyvitamin D3
Authors: Wang Dongxia , Wang Ning , Zhou Juan , Luo Gang , Li Yanyan , Yu Wei , Tan Hongxing , Liu Gang , Wang Jun , Hao Liping 
Published: December 2022

IntroductionTrace element metabolism disorders are often secondary to disorders of glucose metabolism in diabetes. Although 1α,25-dihydroxyvitamin D3 [1,25(OH)2D3] could ameliorate abnormal glucose metabolism in the development of diabetes, the effect on trace element metabolism is unclear. The objective of this study was to evaluate the influence of 1,25(OH)2D3 on urinary excretions of trace elements in Zucker diabetic fatty (ZDF) rats.MethodsAt 6 weeks of age, male ZDF (n = 40) rats were subdivided into four groups: diabetic model (ZDF), low-dose (ZDF + VL, 2 μg/kg⋅bw), middle-dose (ZDF + VM, 8 μg/kg⋅bw) and high-dose (ZDF + VH, 16 μg/kg⋅bw) 1,25(OH)2D3 groups. Another 10 Zucker lean (ZL) rats served as a control group. All rats were given vitamin D deficient Purina #5008 chow and the intervention groups were given the corresponding dose of 1,25(OH)2D3 by gavage on alternate days for 7 weeks. Microalbuminuria (MALB) and urinary creatinine concentration were detected by a biochemical autoanalyzer. Urine trace element concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS) and were corrected by urinary creatinine.ResultsThroughout the intervention phase, MALB, UACR and urinary creatinine levels in the ZDF group were significantly higher than those in the ZL group, and showed a gradual increase with the prolongation of the intervention time. These changes were reversed in a dose-dependent manner after 1,25(OH)2D3 intervention (P &lt; 0.05). Correspondingly, most of the urinary trace element excretions in the ZDF rats were significantly increased compared with the ZL group, and 1,25(OH)2D3 intervention significantly reduced the urinary copper (Cu), zinc (Zn), selenium (Se) and molybdenum (Mo) levels in the ZDF rats (P &lt; 0.05), especially in the medium and high dose groups.Conclusion1,25(OH)2D3 had improvement effects on urinary Cu, Zn, Se, and Mo excretions in ZDF rats, suggesting that it may be related to the reduction of diabetic renal impairment and renal oxidative damage.
Association between the geriatric nutritional risk index and the risk of stroke in elderly patients with hypertension: A longitudinal and cohort study
Authors: Cai Xintian , Hu Junli , Wen Wen , Wang Mengru , Zhu Qing , Liu Shasha , Yang Wenbo , Dang Yujie , Hong Jing , Li Nanfang 
Published: December 2022

ObjectiveWe aimed to investigate the association between the GNRI and the risk of stroke in elderly patients with hypertension.MethodsA total of 5312 elderly hypertensive patients free of history of stroke were included. Multivariate Cox models were used to calculate hazard ratios (HRs) and their 95% confidence intervals (CIs) for stroke and its subtypes.ResultsThe average time of follow-up was 3.8 years, and the median time was 3.2 years. We identified 640 individuals with stroke, of whom 526 had an ischemic stroke (IS) and 114 had a hemorrhagic stroke (HS). After adjusting for confounding variables, compared with participants in the lowest quartile of the GNRI, those in the third and fourth quartiles were associated with a decreased risk of stroke (adjusted HR 0.72, 95% CI 0.58–0.90, and adjusted HR 0.58, 95% CI 0.46–0.74, respectively, P for trend &lt; 0.001). Similar results were found for IS and HS. Moreover, there were L-shaped associations of GNRI with new-onset HS (P for non-linearity = 0.034). Multiple sensitivity analyses and stratified analyses did not materially change the results.ConclusionsIn summary, we found that a lower GNRI was associated with a higher risk of incident stroke in elderly hypertensive patients. Additional prospective data collection is required to confirm our findings.