bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genome-wide association study exploring the genetic architecture of eggshell speckles in laying hens
Authors: Cheng Xue , Li Xinghua , Yang Mengyuan , Zheng Chuanwei , Li Haiying , Qu Lujiang , Ning Zhonghua 
Published: November 2023

Abstract
Background
Eggshell speckle phenotype is an important trait in poultry production because they affect eggshell quality. However, the genetic architecture of speckled eggshells remains unclear. In this study, we determined the heritability of eggshell speckles and conducted a genome-wide association study (GWAS) on purebred Rhode Island Red (RIR) hens at 28 weeks to detect potential genomic loci and candidate genes associated with eggshell speckles.

Results
The heritability of eggshell speckles was 0.35 at 28 weeks, and the speckle level is not related to other eggshell quality traits in terms of phenotypic correlation. We detected 311 SNPs (6 significantly, and 305 suggestively associated) and 39 candidate genes associated with eggshell speckles. Based on the pathway analysis, the 39 candidate genes were mainly involved in alpha-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, GnRH signaling pathway, vascular smooth muscle contraction, and MAPK signaling pathway. Ultimately, ten genes, LOC423226, SPTBN5, EHD4, LOC77155, TYRO3, ITPKA, DLL4, PLA2G4B, PLA2G4EL5, and PLA2G4EL6 were considered the most promising genes associated with eggshell speckles that were implicated in immunoregulation, calcium transport, and phospholipid metabolism, while its function in laying hens requires further studies.

Conclusions
This study provides new insights into understanding the genetic basis of eggshell speckles and has practical application value for the genetic improvement of eggshell quality.
Exploring the molecular makeup of support cells in insect camera eyes
Authors: Rathore Shubham , Stahl Aaron , Benoit Joshua B. , Buschbeck Elke K. 
Published: November 2023

AbstractAnimals typically have either compound eyes, or camera-type eyes, both of which have evolved repeatedly in the animal kingdom. Both eye types include two important kinds of cells: photoreceptor cells, which can be excited by light, and non-neuronal support cells (SupCs), which provide essential support to photoreceptors. At the molecular level deeply conserved genes that relate to the differentiation of photoreceptor cells have fueled a discussion on whether or not a shared evolutionary origin might be considered for this cell type. In contrast, only a handful of studies, primarily on the compound eyes of Drosophila melanogaster, have demonstrated molecular similarities in SupCs. D. melanogaster SupCs (Semper cells and primary pigment cells) are specialized eye glia that share several molecular similarities with certain vertebrate eye glia, including Müller glia. This led us to question if there could be conserved molecular signatures of SupCs, even in functionally different eyes such as the image-forming larval camera eyes of the sunburst diving beetle Thermonectus marmoratus. To investigate this possibility, we used an in-depth comparative whole-tissue transcriptomics approach. Specifically, we dissected the larval principal camera eyes into SupC- and retina-containing regions and generated the respective transcriptomes. Our analysis revealed several common features of SupCs including enrichment of genes that are important for glial function (e.g. gap junction proteins such as innexin 3), glycogen production (glycogenin), and energy metabolism (glutamine synthetase 1 and 2). To evaluate similarities, we compared our transcriptomes with those of fly (Semper cells) and vertebrate (Müller glia) eye glia as well as respective retinas. T. marmoratus SupCs were found to have distinct genetic overlap with both fly and vertebrate eye glia. These results suggest that T. marmoratus SupCs are a form of glia, and like photoreceptors, may be deeply conserved.
Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses
Authors: Mosharaf Md Parvez , Alam Khorshed , Gow Jeff , Mahumud Rashidul Alam 
Published: November 2023

Abstract
Background
Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment.

Methods
We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database.

Results
We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments.

Conclusion
This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.
Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI
Authors: Khanfari Hasan , Mehranfar Saeed , Cheki Mohsen , Mohammadi Sadr Mahmoud , Moniri Samir , Heydarheydari Sahel , Rezaeijo Seyed Masoud 
Published: November 2023

Abstract
Background
The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques to create 52 images or datasets for each patient. We evaluate the effectiveness of this approach in grading prostate cancer and compare it to traditional methods.

Methods
We used the PROSTATEx-2 dataset consisting of 111 patients’ images from T2W-transverse, T2W-sagittal, DWI, and ADC images. We used eight fusion techniques to merge T2W, DWI, and ADC images, namely Laplacian Pyramid, Ratio of the low-pass pyramid, Discrete Wavelet Transform, Dual-Tree Complex Wavelet Transform, Curvelet Transform, Wavelet Fusion, Weighted Fusion, and Principal Component Analysis. Prostate cancer images were manually segmented, and radiomics features were extracted using the Pyradiomics library in Python. We also used an Autoencoder for deep feature extraction. We used five different feature sets to train the classifiers: all radiomics features, all deep features, radiomics features linked with PCA, deep features linked with PCA, and a combination of radiomics and deep features. We processed the data, including balancing, standardization, PCA, correlation, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finally, we used nine classifiers to classify different Gleason grades.

Results
Our results show that the SVM classifier with deep features linked with PCA achieved the most promising results, with an AUC of 0.94 and a balanced accuracy of 0.79. Logistic regression performed best when using only the deep features, with an AUC of 0.93 and balanced accuracy of 0.76. Gaussian Naive Bayes had lower performance compared to other classifiers, while KNN achieved high performance using deep features linked with PCA. Random Forest performed well with the combination of deep features and radiomics features, achieving an AUC of 0.94 and balanced accuracy of 0.76. The Voting classifiers showed higher performance when using only the deep features, with Voting 2 achieving the highest performance, with an AUC of 0.95 and balanced accuracy of 0.78.

Conclusion
Our study concludes that the proposed multi-flavored feature extraction or tensor approach using radiomics and deep features can be an effective method for grading prostate cancer. Our findings suggest that deep features may be more effective than radiomics features alone in accurately classifying prostate cancer.
The impact of covid-19 pandemic on pregnancy outcome
Authors: Gholami Roya , Borumandnia Nasrin , Kalhori Elham , Taheri Mahshid , Khodakarami Nahid 
Published: November 2023

Abstract
Background
The acute respiratory disease caused by the coronavirus (COVID-19) has spread rapidly worldwide yet has not been eliminated. The infection is especially deadly in vulnerable populations. The current studies indicate that pregnant women are at greater risk of getting seriously ill. Even though fetuses protect against disease, the additional finding showed that the COVID-19 pandemic could increase fetal and maternal morbidities. In a situation where COVID-19 and new strains of the virus are still not controlled, scientists predicted that the world might experience another pandemic. Consequently, more research about the effects of COVID-19 infection on pregnancy outcomes is needed. This study aimed to compare the pregnancy outcomes of Iranian pregnant women in the first year of the pandemic with the previous year.

Methods
This prospective cross-sectional study was performed to compare the pregnancy outcome during the COVID-19 pandemic among Iranian pregnant women who gave birth during the pandemic and one year before the pandemic (2019–2020 and 2020–2021). The sample size was 2,371,332 births registered at hospitals and birth centers platforms. The studied variables include stillbirth, congenital anomaly, birth weight, preeclampsia, gestational diabetes, cesarean section, ICU admission, mean of the gestational age at birth, preterm births, NICU admission, neonatal mortality and the percentage of deliveries with at least one complication such as blood transfusion and postpartum ICU admission. Analyzing data was done by using SPSS version 25 software.

Results
We found statistical differences between pregnancy and birth outcomes during the COVID-19 pandemic compared to one year before. The risk of preeclampsia, gestational diabetes, cesarean section, preterm birth and NICU admission were clinically significant. Also, there was a significant decrease in mean gestational age.

Conclusion
The COVID-19 pandemic has affected the pregnancy outcome by increasing morbidities and complications during pregnancy, birth, and postpartum. In addition, extensive quarantine outbreaks disrupted the healthcare system and hindered access to prenatal services. It is necessary to develop preventive and therapeutic care protocols for similar pandemic conditions.
Application of CT and MRI images based on an artificial intelligence algorithm for predicting lymph node metastasis in breast cancer patients: a meta-analysis
Authors: Liu Cheng-Jie , Zhang Lei , Sun Yi , Geng Lei , Wang Rui , Shi Kai-Min , Wan Jin-Xin 
Published: November 2023

Abstract
Background
This study aimed to comprehensively evaluate the accuracy and effect of computed tomography (CT) and magnetic resonance imaging (MRI) based on artificial intelligence (AI) algorithms for predicting lymph node metastasis in breast cancer patients.

Methods
We systematically searched the PubMed, Embase and Cochrane Library databases for literature from inception to June 2023 using keywords that included ‘artificial intelligence’, ‘CT,’ ‘MRI’, ‘breast cancer’ and ‘lymph nodes’. Studies that met the inclusion criteria were screened and their data were extracted for analysis. The main outcome measures included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve (AUC).

Results
A total of 16 studies were included in the final meta-analysis, covering 4,764 breast cancer patients. Among them, 11 studies used the manual algorithm MRI to calculate breast cancer risk, which had a sensitivity of 0.85 (95% confidence interval [CI] 0.79–0.90; p < 0.001; I2 = 75.3%), specificity of 0.81 (95% CI 0.66–0.83; p < 0.001; I2 = 0%), a positive likelihood ratio of 4.6 (95% CI 4.0–4.8), a negative likelihood ratio of 0.18 (95% CI 0.13–0.26) and a diagnostic odds ratio of 25 (95% CI 17–38). Five studies used manual algorithm CT to calculate breast cancer risk, which had a sensitivity of 0.88 (95% CI 0.79–0.94; p < 0.001; I2 = 87.0%), specificity of 0.80 (95% CI 0.69–0.88; p < 0.001; I2 = 91.8%), a positive likelihood ratio of 4.4 (95% CI 2.7–7.0), a negative likelihood ratio of 0.15 (95% CI 0.08–0.27) and a diagnostic odds ratio of 30 (95% CI 12–72). For MRI and CT, the AUC after study pooling was 0.85 (95% CI 0.82–0.88) and 0.91 (95% CI 0.88–0.93), respectively.

Conclusion
Computed tomography and MRI images based on an AI algorithm have good diagnostic accuracy in predicting lymph node metastasis in breast cancer patients and have the potential for clinical application.
Efficacy and safety of first-line combination therapy versus monotherapy for vitreoretinal lymphoma: a systematic review and meta-analysis
Authors: Gao Jing , Peng Xiaoyan , Wang Liang 
Published: November 2023

Abstract
Background
Vitreoretinal lymphoma (VRL) is usually treated with a combination of intraocular methotrexate (ioMTX), high-dose intravenous methotrexate (HD-MTX), or local radiotherapy (RT) as the first options. The effectiveness and safety of monotherapy like bruton’s tyrosine kinase inhibitors (BTKi) for PVRL remain uncertain.

Methods
A systematic review and meta-analysis of clinical trial data and conference abstracts in VRL patients treated with first-line combination therapy or monotherapy were conducted through a search of PubMed, Embase, and Scopus databases until December 2022. A total of 24 studies comprising 517 patients were included, and survival data were extracted from 279 patients due to inconsistent units across studies.

Results
The combined treatment group used ioMTX + chemotherapy (in 4 studies), RT + chemotherapy (in 2 studies), ioMTX/HD-MTX based regimen (in 2 studies), ioMTX + RT + chemotherapy (in 2 studies), ioMTX + lenalidomide/BTKi (in 2 studies) and combination of multiple therapies (in 7 studies). The monotherapy group was mainly treated with oral monotherapies such as BTKi. The combination therapy had a higher overall response rate (ORR) and complete response rate (CRR) than monotherapy (ORR: 96% vs. 72%, CRR: 92% vs. 63%). Combination therapy also resulted in a longer median progression-free survival (28.8 months vs. 13 months, p = 0.012). However, the combination therapy group had more severe side effects (grade 3/4 toxicity) than the monotherapy group (45% vs. 8%).

Conclusion
The study showed combination therapy had better OR and CR rates, longer survival, and more toxicity than monotherapy. While BTK inhibitors were well-tolerated, long-term effectiveness needs confirmation from prospective studies. In addition, given the small number of studies of monotherapy for VRL, more studies are needed to validate its effects.

Trial registration
CRD42023400305.
Bedtime regularity predicts positive affect among veterans with posttraumatic stress disorder: an ecological momentary assessment study
Authors: Song Jiyoung , Fisher Aaron J. , Woodward Steven H. 
Published: November 2023

Abstract
Background
Regularizing bedtime and out-of-bed times is a core component of behavioral treatments for sleep disturbances common among patients with posttraumatic stress disorder (PTSD). Although improvements in subjective sleep complaints often accompany improvements in PTSD symptoms, the underlying mechanism for this relationship remains unclear. Given that night-to-night sleep variability is a predictor of physical and mental well-being, the present study sought to evaluate the effects of bedtime and out-of-bed time variability on daytime affect and explore the optimal window lengths of over which variability is calculated.

Methods
For about 30 days, male U.S. military veterans with PTSD (N = 64) in a residential treatment program provided ecological momentary assessment data on their affect and slept on beds equipped with mattress actigraphy. We computed bedtime and out-of-bed time variability indices with varying windows of days. We then constructed multilevel models to account for the nested structure of our data and evaluate the impact of bedtime and out-of-bed time variability on daytime affect.

Results
More regular bedtime across 6–9 days was associated with greater subsequent positive affect. No similar effects were observed between out-of-bed time variability and affect.

Conclusions
Multiple facets of sleep have been shown to differently predict daily affect, and bedtime regularity might represent one of such indices associated with positive, but not negative, affect. A better understanding of such differential effects of facets of sleep on affect will help further elucidate the complex and intertwined relationship between sleep and psychopathology.

Trial registration
The trial retrospectively was registered on the Defense Technical Information Center website: Award # W81XWH-15–2-0005.
Determine the factors that affected COVID-19 prevention behaviors based on constructs of social cognition theory
Authors: Soltani Raheleh , Shamsi Mohsen , Moradi Atefe 
Published: November 2023

Abstract
Background
COVID-19 is a universal challenge and novel disease is one of the core public health concerns. This study aimed to determine the factors that affected COVID-19 prevention behaviors (CPB) based on constructs of Social Cognition Theory (SCT).

Methods
This cross-sectional study was conducted on 426 participants referred to health centers of Arak from October 2021 to February 2022, and they were selected through a multi-stage random sampling method. Data were collected via a self-administered questionnaire which includes socio-demographic data (6 items), COVID-19 prevention behaviors (12 items), and SCT constructs related to COVID-19 (32 items). SPSS Ver-16 statistical software was used to analyze the data with one-way ANOVA, independent samples t-test, and multiple linear regressions. The significance level of α = 0.05 was considered for all tests.

Results
The mean age was 37.8 years (SD = 12.2) and ranged from 17 to 81. According to the results, 51.2% had higher education and 52.6% of the participants were female. The mean (SD) of COVID-19 preventive behaviors was 43 (SD = 7.8) out of 60. There was statistical association between CPB and three constructs of SCT. Multiple regression showed that the outcomes expectations (β = 0.11, p < 0.001), self-regulation (β = 0.41, p < 0.001), and self-efficacy (β = 0.30, p < 0.001), age, gender, and history of COVID 19infection were associated with CPB and those described 61% of the variance of CPB.

Conclusion
Rendering to the result of this study constructs of SCT is the key predictor of participants’ CPB. Hence, based on these predictors, effective interventions and healthy messages could be designed based on this predictor—outcomes expectations, self-regulation, social support, and self-efficacy which can be beneficial to improve healthy behavior.
The rate of ward to intensive care transfer and its predictors among hospitalized COPD patients, a retrospective study in a local tertiary center in Saudi Arabia
Authors: Naser Abdallah Y , Dairi Mohammad Saleh , Alwafi Hassan , Ashoor Deema Sami , Qadus Sami , Aldhahir Abdulelah M , Alqarni Abdullah A , Elrefaey Wael Aly , Qanash Sultan , Hafiz Waleed , Alqahtani Jaber S. , Ekram Rakan , Abuirmeileh Amjad , Jarab Anan S. , Badr Omaima Ibrahim 
Published: November 2023

Abstract
Objective
To investigate the prevalence of intensive care unit (ICU) admission and its predictors among hospitalized chronic obstructive pulmonary disease (COPD) patients.

Methods
An observational retrospective study was conducted. All patients with a confirmed diagnosis of COPD according to the GOLD guidelines between 28 and 2020 and 1 March 2023 at Al-Noor Specialist Hospital were included in this study. Patients were excluded if a preemptive diagnosis of COPD was made clinically without spirometry evidence of fixed airflow limitation. Descriptive results were presented as frequency (percentage) for categorical variables and mean (SD) for continuous variables and to estimate prevalence of ICU admission. Predictors of ICU admission among hospitalized COPD patients were determined using logistic regression analysis. A SPSS (Statistical Package for the Social Sciences) version 25 was used to perform all statistical analysis.

Results
A total of 705 patients with COPD were included in this study. The mean age was 65.4 (25.3) years. Around 12.4% of the hospitalized patients were admitted to the ICD. Logistic regression analysis identified that older age (OR; 1.92, (1.41–2.62)), smoking (OR; 1.60 (1.17–2.19)), and having specific comorbidities (Hypertension (OR; 1.98 (1.45–2.71)), Diabetes mellitus (OR; 1.42 (1.04–1.93)), GERD (OR; 2.81 (1.99–3.96)), Ischemic heart disease (OR; 3.22 (2.19–4.75)), Obstructive sleep apnea syndrome (OR; 2.14 (1.38–3.33)), stroke (OR; 4.51 (2.20–9.26))) were predictors of ICU admissions among patients with COPD.

Conclusions
Our study found that a step-up approach to inpatient COPD management requires admission to the ICU in 12.4%, for which age, smoking status, cardiovascular, and stroke were important predictors. Further clinical research is needed to provide a validated model that can be incorporated into clinical practice to monitor this patient population during their admission and identify at-risk individuals for early transfer to higher acuity settings and intensive care units.