bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prevalence of multidrug-resistant Escherichia coli isolates and virulence gene expression in poultry farms in Jos, Nigeria
Authors: Agusi Ebere Roseann , Kabantiyok Dennis , Mkpuma Nicodemus , Atai Rebecca Bitiyong , Okongwu-Ejike Chidinma , Bakare Ebun Lydia , Budaye James , Sule Kabiru Garba , Rindaps Rindah Joy , James Gyallak Kingsley , Audu Benshak John , Agada Godwin Ojonugwa , Adegboye Oyelola , Meseko Clement Adebajo 
Published: June 2024

IntroductionAntimicrobial resistance is increasingly becoming a global health concern. This study aimed to investigate and report MDR Escherichia coli (E. coli) prevalence, resistance, and virulence genes from poultry in Jos, Plateau State, Nigeria.MethodsThe samples were analyzed using microbiological standard methods and polymerase chain reactions (PCRs).ResultsA total of 179 cloacal swabs were collected from bothlocal and exotic poultry breeds, of which 99.4% (178/179) tested positive for E. coli. Among these culturally identified samples, 99.4% (177/178) were furtherconfirmed Escherichia coli with a molecular weight of 401 bp. Multidrugresistance of 45% (80/178) was observed from the confirmed isolates. PCR assays were conducted to detect genes associated with resistance to antibiotics, specifically, tetracycline (tetA gene), sulfonamide (sul1 gene), ampicillin (ampC gene), and quinolone (gyrA gene). Antimicrobial susceptibility test (AST) results revealed substantial antibiotic resistance, with 81.9% (145/177) of the isolates being resistant to tetracycline, 80.2% (142/177) to quinolone, 69.5% (123/177) to sulfonamide, and 66.1% (117/177) to ampicillin. Further analysis on 18 isolates that showed resistance to up to four different antibiotics was carried out using multiplex PCR to detect eae, hlyA, rfbE, fliC, and fstx virulence genes. The study found that 44.4% (15/18) of the isolates were positive for the eae gene, 27.7% (5/18) for stx, 22.2% (4/18) for rfbe gene, and 5.5% (1) for hlya gene, and none tested positive for fliC gene.ConclusionThese results showed high antibiotic resistance, virulent genes, and significant levels of MDR in E. coli from poultry. This study highlights the urgent need for antimicrobial stewardship practices within the poultry industry due to their profound implications for food safety and public health. This issue is particularly critical in Nigeria, where poultry farming constitutes a significant portion of smallholder farming practices.
Temporal heterogeneity of the root microbiome in Panax ginseng soils across ecological compartments under mild soil disturbance
Authors: Shi Zhenting , Yang Limin , Yang Meiling , Li Kexin , Yang Li , Han Mei 
Published: June 2024

IntroductionKnowledge on spatiotemporal heterogeneity of plant root microbiomes is lacking. The diversity of the root microbiome must be revealed for understanding plant–microbe interactions and the regulation of functionally crucial microbial taxa.MethodsWe here investigated the dynamics of microbial group characteristics within each soil ecological compartment [rhizoplane (B), rhizosphere (J), and bulk soil (T)] across different cultivation years (year 4: F4 and year 5: F5) by using high-throughput sequencing (16S and ITS).ResultsAccording to the species diversity, microbiome diversity and the ASV (amplified sequence variant) number in the rhizoplane ecotone increased significantly with an increase in the planting years. By contrast, the microbiome diversity of the rhizosphere soil remained relatively stable. PCoA and PERMANOVA analyses revealed that microbial taxa among different planting years and ecological compartments varied significantly. Planting years exerted the least effect on the rhizosphere microbiome, but their impact on fungi in the rhizoplane and bacteria in the bulk soil was the most significant.DiscussionPlanting years influenced the microbial community composition in various ecological compartments of ginseng root soil. Potentially harmful fungi such as Cryptococcus (2.83%), Neonectria (0.89%), llyonectria (0.56%), Gibberella (0.41%), Piloderma (4.44%), and Plectosphaerella (3.88%) were enriched in F5B with an increase in planting years, whereas the abundance of potentially beneficial Mortierella increased. Correlation analysis indicated associations between bacterial taxa and soil pH/S-CAT, and between fungal taxa and soil moisture content/total potassium. Our study highlights the significance of changes in rhizoplane fungi and the stability of the rhizosphere microbial community in comprehending plant ecological sustainability.
Enhancing pathogen description and antibiotic regimen selection in community-acquired pneumonia through RT-qPCR assays
Authors: Zhao Na , Ren Hongyu , Zhang Yingmiao , Jiang Yan , Deng Jianping , Jiang Luxi , Lu Zhongxin , Qin Tian 
Published: June 2024

BackgroundAdults with community-acquired pneumonia (CAP) in China suffer high morbidity. CAP is caused by a multitude of pathogens; however, pathogen-directed clinical symptoms are often lacking. Therefore, patients lacking an accurate microbiological diagnosis are administered with empirical antimicrobials.MethodsWe collected bronchoalveolar lavage fluid, as well as clinical and laboratory data from 650 adult patients with CAP admitted to three hospitals in Hubei, Sichuan, and Zhejiang provinces in China. Specimens were cultured and tested using real-time reverse transcription qPCR (RT-qPCR) assays for the presence of 42 respiratory bacteria and viruses. CAP was investigated with respect to regions, genders, and age and patterns of infections or co-infections. Employing clinical guidelines adapted for diagnosis, we assessed retrospectively the appropriate pathogen-directed therapy and compared it with the initial empirical therapies.ResultsOur study identified that 21.38% (139/650) of the patients were classified as having Severe CAP (S-CAP), with a higher prevalence among males, older adults, and during the warm season. Bacterial pathogens were detected in 35.53% (231/650) of cases. K. pneumoniae, H. influenzae, and S. aureus were the most prevalent bacteria across different demographics and regions. Viral pathogens were found in 48.76% (317/650) of patients Epstein-Barr, Human rhinovirus, and Cytomegalovirus were the most common viruses. Co-infections were present in 24.31% (158/650) of cases, with viral-bacterial co-infections being the most frequent. The RT-qPCR demonstrated significantly higher detection rates for key pathogens compared to standard culture methods. It showed potential in optimizing antimicrobial prescriptions by allowing for de-escalation in 18.30% (95/518) of patients, among which reducing the number of excessive antibiotics mainly comprised decreasing the use of 2nd or 3rd generation cephalosporins (5.79%, 30/518) and β-lactamase inhibitor combinations.ConclusionThe study highlights the significant burden of S-CAP, particularly among specific demographics and seasons. The prevalence of bacterial and viral pathogens, along with the high rate of co-infections, emphasizes the need for comprehensive diagnostic approaches. The RT-qPCR assays emerge as a superior diagnostic tool, offering enhanced pathogen detection capabilities and facilitating more precise antimicrobial therapy. This could lead to improved patient outcomes and contribute to the rational use of antimicrobials, addressing the growing concern of antibiotic resistance.
Less spatial exploration is associated with poorer spatial memory in midlife adults
Authors: Puthusseryppady Vaisakh , Cossio Daniela , Yu Shuying , Rezwana Farnaz , Hegarty Mary , Jacobs Emily G. , Chrastil Elizabeth R. 
Published: June 2024

IntroductionDespite its importance for navigation, very little is known about how the normal aging process affects spatial exploration behavior. We aimed to investigate: (1) how spatial exploration behavior may be altered early in the aging process, (2) the relationship between exploration behavior and subsequent spatial memory, and (3) whether exploration behavior can classify participants according to age.MethodsFifty healthy young (aged 18–28) and 87 healthy midlife adults (aged 43–61) freely explored a desktop virtual maze, learning the locations of nine target objects. Various exploration behaviors (object visits, distance traveled, turns made, etc.) were measured. In the test phase, participants navigated from one target object to another without feedback, and their wayfinding success (% correct trials) was measured.ResultsIn the exploration phase, midlife adults exhibited less exploration overall compared to young adults, and prioritized learning target object locations over maze layout. In the test phase, midlife adults exhibited less wayfinding success when compared to the young adults. Furthermore, following principal components analysis (PCA), regression analyses indicated that both exploration quantity and quality components were associated with wayfinding success in the midlife group, but not the young adults. Finally, we could classify participants according to age with similar accuracy using either their exploration behavior or wayfinding success scores.DiscussionOur results aid in the understanding of how aging impacts spatial exploration, and encourages future investigations into how pathological aging may affect spatial exploration behavior.
Investigating the effects of chiropractic care on resting-state EEG of MCI patients
Authors: Ziloochi Fahimeh , Niazi Imran Khan , Amjad Imran , Cade Alice , Duehr Jenna , Ghani Usman , Holt Kelly , Haavik Heidi , Shalchyan Vahid 
Published: June 2024

IntroductionMild cognitive impairment (MCI) is a stage between health and dementia, with various symptoms including memory, language, and visuospatial impairment. Chiropractic, a manual therapy that seeks to improve the function of the body and spine, has been shown to affect sensorimotor processing, multimodal sensory processing, and mental processing tasks.MethodsIn this paper, the effect of chiropractic intervention on Electroencephalogram (EEG) signals in patients with mild cognitive impairment was investigated. EEG signals from two groups of patients with mild cognitive impairment (n = 13 people in each group) were recorded pre- and post-control and chiropractic intervention. A comparison of relative power was done with the support vector machine (SVM) method and non-parametric cluster-based permutation test showing the two groups could be separately identified with high accuracy.ResultsThe highest accuracy was obtained in beta2 (25–35 Hz) and theta (4–8 Hz) bands. A comparison of different brain areas with the SVM method showed that the intervention had a greater effect on frontal areas. Also, interhemispheric coherence in all regions increased significantly after the intervention. The results of the Wilcoxon test showed that intrahemispheric coherence changes in frontal-occipital, frontal-temporal and right temporal-occipital regions were significantly different in two groups.DiscussionComparison of the results obtained from chiropractic intervention and previous studies shows that chiropractic intervention can have a positive effect on MCI disease and using this method may slow down the progression of mild cognitive impairment to Alzheimer’s disease.
Human cerebral organoids: cellular composition and subcellular morphological features
Authors: Mateos-Martínez Patricia , Coronel Raquel , Sachse Martin , González-Sastre Rosa , Maeso Laura , Rodriguez Maria Josefa , Terrón María C. , López-Alonso Victoria , Liste Isabel 
Published: June 2024

IntroductionHuman cerebral organoids (hCOs) derived from pluripotent stem cells are very promising for the study of neurodevelopment and the investigation of the healthy or diseased brain. To help establish hCOs as a powerful research model, it is essential to perform the morphological characterization of their cellular components in depth.MethodsIn this study, we analyzed the cell types consisting of hCOs after culturing for 45 days using immunofluorescence and reverse transcriptase qualitative polymerase chain reaction (RT-qPCR) assays. We also analyzed their subcellular morphological characteristics by transmission electron microscopy (TEM).ResultsOur results show the development of proliferative zones to be remarkably similar to those found in human brain development with cells having a polarized structure surrounding a central cavity with tight junctions and cilia. In addition, we describe the presence of immature and mature migrating neurons, astrocytes, oligodendrocyte precursor cells, and microglia-like cells.DiscussionThe ultrastructural characterization presented in this study provides valuable information on the structural development and morphology of the hCO, and this information is of general interest for future research on the mechanisms that alter the cell structure or function of hCOs.
Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke
Authors: Guo Kun , Zhu Bo , Li Rong , Xi Jing , Wang Qi , Chen KongBo , Shao Yuan , Liu Jiaqi , Cao Weili , Liu Zhiqin , Di Zhengli , Gu Naibing 
Published: June 2024

BackgroundAcute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis.ObjectiveTo develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS.MethodsThis retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation (n = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model’s predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS &gt; 2) outcomes.ResultsKey findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912–0.969) in the training set and 0.854 (95% CI: 0.781–0.926) in the validation set, underscoring its predictive reliability and clinical utility.ConclusionThe study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.
BrainCDNet: a concatenated deep neural network for the detection of brain tumors from MRI images
Authors: Reddy K. Rasool , Rajesh Kandala N. V. P. S. , Dhuli Ravindra , Kumar Vuddagiri Ravi 
Published: June 2024

IntroductionBrain cancer is a frequently occurring disease around the globe and mostly developed due to the presence of tumors in/around the brain. Generally, the prevalence and incidence of brain cancer are much lower than that of other cancer types (breast, skin, lung, etc.). However, brain cancers are associated with high mortality rates, especially in adults, due to the false identification of tumor types, and delay in the diagnosis. Therefore, the minimization of false detection of brain tumor types and early diagnosis plays a crucial role in the improvement of patient survival rate. To achieve this, many researchers have recently developed deep learning (DL)-based approaches since they showed a remarkable performance, particularly in the classification task.MethodsThis article proposes a novel DL architecture named BrainCDNet. This model was made by concatenating the pooling layers and dealing with the overfitting issues by initializing the weights into layers using ‘He Normal’ initialization along with the batch norm and global average pooling (GAP). Initially, we sharpen the input images using a Nimble filter, which results in maintaining the edges and fine details. After that, we employed the suggested BrainCDNet for the extraction of relevant features and classification. In this work, two different forms of magnetic resonance imaging (MRI) databases such as binary (healthy vs. pathological) and multiclass (glioma vs. meningioma vs. pituitary) are utilized to perform all these experiments.Results and discussionEmpirical evidence suggests that the presented model attained a significant accuracy on both datasets compared to the state-of-the-art approaches, with 99.45% (binary) and 96.78% (multiclass), respectively. Hence, the proposed model can be used as a decision-supportive tool for radiologists during the diagnosis of brain cancer patients.
Secular trends and determinants of ad libitum energy intake measured in a research setting from 1999-2020
Authors: Ahern Mary M. , Stinson Emma J. , Piaggi Paolo , Krakoff Jonathan , Votruba Susanne B. 
Published: June 2024

IntroductionHistorically, secular and seasonal trend analyses have been examined using self-report measures of intake. Rarely are objective measures and known determinants of dietary intake used in these analyses. Our objective was to quantify the seasonal and secular differences in an objective ad libitum intake paradigm while considering the contribution of determinants, such as fat-free mass (FFM) index and spontaneous physical activity (SPA) limited to the restricted space of a whole-room calorimeter.MethodsFor this study, recruitment of N = 292 healthy, diabetes free, adults occurred from 1999 to 2020. Assessment during their 10-day stay included body composition (by DXA), SPA (by an approximately 24-h stay in whole-room calorimetry), and ad libitum intake (by a vending machine for 3 days). This secondary analysis used general linear models (GLM) to investigate secular and seasonal differences while adjusting for sex, age, FFM index, FM (fat mass) index, SPA, and race/ethnicity.ResultsFFM index and SPA were positively associated with all intake measures (p &lt; 0.05). In all adjusted seasonal models, season did not affect intake. Adjusted secular trends models (kcals/year) demonstrated a decrease in total kcals (β = −55), intake as percent weight maintaining energy needs (β = −2), protein kcals (β = −10), fat kcals (β = −27), and carbohydrates kcals (β = −22) (all p &lt; 0.05). After further adjustment for SPA, significance remained in all intake measures (p &lt; 0.05). Secular trends in body composition revealed no changes in weight, BMI, and percent body fat (all p &gt; 0.20).DiscussionOur results indicate that over time, ad libitum intake decreased in this controlled research setting and remained significant even after accounting for positive determinants of intake. A significant ad libitum decrease, coupled with no change in body composition, may highlight a participant bias toward calorie restriction in a controlled setting over time and deserves further investigation.
A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients
Authors: Xinyang Song , Tianci Shen , Xiangyu Hu , Shuang Zhang , Yangyang Wang , Mengying Du , Tonghui Xu , Jingran Zhou , Feng Yang 
Published: June 2024

ObjectiveTo develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images.MethodsA retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor’s region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC).ResultsThe auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction’s internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort.ConclusionThe ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses.
Odontogenic cystic lesion segmentation on cone-beam CT using an auto-adapting multi-scaled UNet
Authors: Huang Zimo , Li Bo , Cheng Yong , Kim Jinman 
Published: June 2024

ObjectivesPrecise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.MethodsIn this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss. Anonymous CBCT images were collected retrospectively from one hospital. To assess the ability of our model to improve the diagnostic efficiency of maxillofacial surgeons, we conducted a diagnostic assessment where 7 clinicians were included to perform the diagnostic process with and without the assistance of auto-segmentation masks.ResultsWe collected 300 anonymous CBCT images which were manually annotated for segmentation masks. Extensive experiments demonstrate the effectiveness of our OCL-Net for CBCT OCLs segmentation, achieving an overall Dice score of 88.84%, an IoU score of 81.23%, and an AUC score of 92.37%. Through our diagnostic assessment, we found that when clinicians were assisted with segmentation labels from OCL-Net, their average diagnostic accuracy increased from 53.21% to 55.71%, while the average time spent significantly decreased from 101s to 47s (P&lt;0.05).ConclusionThe findings demonstrate the potential of our approach as a robust auto-segmentation system on OCLs in CBCT images, while the segmented masks can be used to further improve OCLs dental diagnostic efficiency.