bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gene representation in scRNA-seq is correlated with common motifs at the 3′ end of transcripts
Authors: Li Xinling , Gibson Greg , Qiu Peng 
Published: May 2023

One important characteristic of single-cell RNA sequencing (scRNA-seq) data is its high sparsity, where the gene-cell count data matrix contains high proportion of zeros. The sparsity has motivated widespread discussions on dropouts and missing data, as well as imputation algorithms of scRNA-seq analysis. Here, we aim to investigate whether there exist genes that are more prone to be under-detected in scRNA-seq, and if yes, what commonalities those genes may share. From public data sources, we gathered paired bulk RNA-seq and scRNA-seq data from 53 human samples, which were generated in diverse biological contexts. We derived pseudo-bulk gene expression by averaging the scRNA-seq data across cells. Comparisons of the paired bulk and pseudo-bulk gene expression profiles revealed that there indeed exists a collection of genes that are frequently under-detected in scRNA-seq compared to bulk RNA-seq. This result was robust to randomization when unpaired bulk and pseudo-bulk gene expression profiles were compared. We performed motif search to the last 350 bp of the identified genes, and observed an enrichment of poly(T) motif. The poly(T) motif toward the tails of those genes may be able to form hairpin structures with the poly(A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during scRNA-seq library preparation, which is a mechanistic conjecture of why certain genes may be more prone to be under-detected in scRNA-seq.
Pre-epithelialized cryopreserved tracheal allograft for neo-trachea flap engineering
Authors: Zeng Ning , Chen Youbai , Wu Yewen , Zang Mengqing , Largo Rene D. , Chang Edward I. , Schaverien Mark V. , Yu Peirong , Zhang Qixu 
Published: May 2023

Graphical AbstractScheme of the experimental design. 1, CTA derived from donor Brown Norway rats was de-epithelialized to create DeCTA. 2, RECs were isolated from syngeneic recipient Lewis rats. 3, DeCTA was pre-epithelialized to create ReCTA. 4, In the heterotopic implantation model, ReCTA was subcutaneously implanted into the groin area, and an adipose tissue flap pedicled by superficial epigastric blood vessels was used for neovascularization. 5, In the orthotopic implantation model, DeCTA was implanted into the neck to reconstruct a 4-ring tracheal defect.
A bibliometric analysis of artificial intelligence applications in macular edema: exploring research hotspots and Frontiers
Authors: Feng Haiwen , Chen Jiaqi , Zhang Zhichang , Lou Yan , Zhang Shaochong , Yang Weihua 
Published: May 2023

Background: Artificial intelligence (AI) is used in ophthalmological disease screening and diagnostics, medical image diagnostics, and predicting late-disease progression rates. We reviewed all AI publications associated with macular edema (ME) research Between 2011 and 2022 and performed modeling, quantitative, and qualitative investigations.Methods: On 1st February 2023, we screened the Web of Science Core Collection for AI applications related to ME, from which 297 studies were identified and analyzed (2011–2022). We collected information on: publications, institutions, country/region, keywords, journal name, references, and research hotspots. Literature clustering networks and Frontier knowledge bases were investigated using bibliometrix-BiblioShiny, VOSviewer, and CiteSpace bibliometric platforms. We used the R “bibliometrix” package to synopsize our observations, enumerate keywords, visualize collaboration networks between countries/regions, and generate a topic trends plot. VOSviewer was used to examine cooperation between institutions and identify citation relationships between journals. We used CiteSpace to identify clustering keywords over the timeline and identify keywords with the strongest citation bursts.Results: In total, 47 countries published AI studies related to ME; the United States had the highest H-index, thus the greatest influence. China and the United States cooperated most closely between all countries. Also, 613 institutions generated publications - the Medical University of Vienna had the highest number of studies. This publication record and H-index meant the university was the most influential in the ME field. Reference clusters were also categorized into 10 headings: retinal Optical Coherence Tomography (OCT) fluid detection, convolutional network models, deep learning (DL)-based single-shot predictions, retinal vascular disease, diabetic retinopathy (DR), convolutional neural networks (CNNs), automated macular pathology diagnosis, dry age-related macular degeneration (DARMD), class weight, and advanced DL architecture systems. Frontier keywords were represented by diabetic macular edema (DME) (2021–2022).Conclusion: Our review of the AI-related ME literature was comprehensive, systematic, and objective, and identified future trends and current hotspots. With increased DL outputs, the ME research focus has gradually shifted from manual ME examinations to automatic ME detection and associated symptoms. In this review, we present a comprehensive and dynamic overview of AI in ME and identify future research areas.
Hospital and community wastewater as a source of multidrug-resistant ESBL-producing Escherichia coli
Authors: Davidova-Gerzova Lenka , Lausova Jarmila , Sukkar Iva , Nesporova Kristina , Nechutna Lucie , Vlkova Katerina , Chudejova Katerina , Krutova Marcela , Palkovicova Jana , Kaspar Jakub , Dolejska Monika 
Published: May 2023

IntroductionHospitals and wastewater are recognized hot spots for the selection and dissemination of antibiotic-resistant bacteria to the environment, but the total participation of hospitals in the spread of nosocomial pathogens to municipal wastewater treatment plants (WWTPs) and adjacent rivers had not previously been revealed.MethodsWe used a combination of culturing and whole-genome sequencing to explore the transmission routes of Escherichia coli from hospitalized patients suffering from urinary tract infections (UTI) via wastewater to the environment. Samples were collected in two periods in three locations (A, B, and C) and cultured on selective antibiotic-enhanced plates.ResultsIn total, 408 E. coli isolates were obtained from patients with UTI (n=81), raw hospital sewage (n=73), WWTPs inflow (n=96)/outflow (n=106), and river upstream (n=21)/downstream (n=31) of WWTPs. The majority of the isolates produced extended-spectrum beta-lactamase (ESBL), mainly CTX-M-15, and showed multidrug resistance (MDR) profiles. Seven carbapenemase-producing isolates with GES-5 or OXA-244 were obtained in two locations from wastewater and river samples. Isolates were assigned to 74 different sequence types (ST), with the predominance of ST131 (n=80) found in all sources including rivers. Extraintestinal pathogenic lineages frequently found in hospital sewage (ST10, ST38, and ST69) were also found in river water. Despite generally high genetic diversity, phylogenetic analysis of ST10, ST295, and ST744 showed highly related isolates (SNP 0-18) from different sources, providing the evidence for the transmission of resistant strains through WWTPs to surface waters.DiscussionResults of this study suggest that 1) UTI share a minor participation in hospitals wastewaters; 2) a high diversity of STs and phylogenetic groups in municipal wastewaters derive from the urban influence rather than hospitals; and 3) pathogenic lineages and bacteria with emerging resistance genotypes associated with hospitals spread into surface waters. Our study highlights the contribution of hospital and municipal wastewater to the transmission of ESBL- and carbapenemase-producing E. coli with MDR profiles to the environment.
Effectiveness of regdanvimab treatment for SARS-CoV-2 delta variant, which exhibited decreased in vitro activity: a nationwide real-world multicenter cohort study
Authors: Kim Haein , Jang Young Rock , Lee Ji Yeon , Ko Jae-Hoon , Lee Jee Young , Cho Seongcheol , Lee Yong Dae , Song Junghoon , Hyun Miri , Kim Hyun Ah , Hwang Soyoon , Ryou Sangmi , Na Yoo Jin , Lee Joo-Yeon , Lee Changhee , Lee Nan Young , Shin Seunghwan , Kwon Ki Tae , Kim Jin Yong , Peck Kyong Ran 
Published: May 2023

BackgroundImmune-evading severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are emerging continuously. The clinical effectiveness of monoclonal antibody agents that exhibit decreased in vitro activity against SARS-CoV-2 variants needs to be elucidated.MethodsA nationwide, multicenter, retrospective cohort study was designed to evaluate the effectiveness of regdanvimab, an anti-SARS-CoV-2 monoclonal antibody agent. Regdanvimab was prescribed in South Korea before and after the emergence of the delta variant, against which the in vitro activity of regdanvimab was decreased but present. Mild to moderate coronavirus 2019 (COVID-19) patients with risk factors for disease progression who were admitted within seven days of symptom onset were screened in four designated hospitals between December 2020 and September 2021. The primary outcomes, O2 requirements and progression to severe disease within 21 days of admission, were compared between the regdanvimab and supportive care groups, with a subgroup analysis of delta variant–confirmed patients.ResultsA total of 2,214 mild to moderate COVID-19 patients were included, of whom 1,095 (49.5%) received regdanvimab treatment. In the analysis of the total cohort, significantly fewer patients in the regdanvimab group than the supportive care group required O2 support (18.4% vs. 27.1%, P &lt; 0.001) and progressed to severe disease (4.0% vs. 8.0%, P &lt; 0.001). In the multivariable analysis, regdanvimab was significantly associated with a decreased risk for O2 support (HR 0.677, 95% CI 0.561–0.816) and progression to severe disease (HR 0.489, 95% CI 0.337–0.709). Among the 939 delta-confirmed patients, O2 support (21.5% vs. 23.5%, P = 0.526) and progression to severe disease (4.2% vs. 7.3%, P = 0.055) did not differ significantly between the regdanvimab and supportive care groups. In the multivariable analyses, regdanvimab treatment was not significantly associated with a decreased risk for O2 support (HR 0.963, 95% CI 0.697–1.329) or progression to severe disease (HR 0.665, 95% CI 0.349–1.268) in delta-confirmed group.ConclusionsRegdanvimab treatment effectively reduced progression to severe disease in the overall study population, but did not show significant effectiveness in the delta-confirmed patients. The effectiveness of dose increment of monoclonal antibody agents should be evaluated for variant strains exhibiting reduced susceptibility.
Association between MetS-IR and prediabetes risk and sex differences: a cohort study based on the Chinese population
Authors: Xie Qiyang , Kuang Maobin , Lu Song , Huang Xin , Wang Chao , Zhang Shuhua , Sheng Guotai , Zou Yang 
Published: May 2023

ObjectiveThe metabolic score for insulin resistance (MetS-IR) is an emerging surrogate marker for insulin resistance (IR). This study aimed to investigate the association and sex differences between MetS-IR and prediabetes risk in a Chinese population.MethodsThis cohort study included 100,309 adults with normoglycemia at baseline and had followed longitudinally for 5 years, and with prediabetes, defined according to the 2018 American Diabetes Association (ADA) recommended diagnostic criteria, as the outcome of interest. Multivariate Cox proportional hazards regression and restricted cubic spline (RCS) regression models were used to assess the association between MetS-IR and prediabetes risk.ResultsDuring an observation period of 312,843 person-years, 7,735 (14.84%) men and 4,617 (9.57%) women with pre-diabetes onset were recorded. After fully adjusting for confounders, we found an independent and positive correlation between MetS-IR and the risk of prediabetes in the Chinese population, and the degree of correlation was stronger in women than in men (HR: 1.24 vs 1.16, P-interaction&lt;0.05). Furthermore, using RCS nested in the Cox regression model, we found that there was a nonlinear correlation between MetS-IR and prediabetes risk in both sexes with an obvious saturation effect point, and when the MetS-IR was greater than the value of the saturation effect point, the risk of prediabetes was gradually leveling off. We further calculated the saturation effect points of MetS-IR used to evaluate the risk of prediabetes which in men was 42.82, and in women was 41.78.ConclusionIn this large cohort study, our results supported that MetS-IR was independently and positively associated with the risk of prediabetes in the Chinese population, with the association being stronger in women than in men.
Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information
Authors: Ryan C. A. , Berry D. P. , O’Brien A. , Pabiou T. , Purfield D. C. 
Published: May 2023

Introduction: The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition.Methods: All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle.Results: When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels &lt;2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction.Discussion: Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software.
Serum immune mediators as novel predictors of response to anti-PD-1/PD-L1 therapy in non-small cell lung cancer patients with high tissue-PD-L1 expression
Authors: Raza Afsheen , Mohsen Reyad , Kanbour Aladdin , Zar Gul Abdul Rehman , Philip Anite , Vijayakumar Suma , Hydrose Shereena , Prabhu Kirti S. , Al-Suwaidi Aisha Khamis , Inchakalody Varghese Philipose , Merhi Maysaloun , Abo El-Ella Dina M. , Tauro Melissa Annrose , Akbar Shayista , Al-Bozom Issam , Abualainin Wafa , Al-Abdulla Rajaa , Sirriya Shaza Abu , Hassnad Suparna , Uddin Shahab , Mohamed Ibrahim Mohamed Izham , Al Homsi Ussama , Demime Said 
Published: May 2023

BackgroundNon-small cell lung cancer (NSCLC) is the leading cause of cancer-related morbidity and mortality worldwide. Immune checkpoint inhibitors (ICIs) including anti-PD-1 and anti-PD-L1 antibodies, have significantly changed the treatment outcomes with better overall survival, but only 15-40% of the patients respond to ICIs therapy. The search for predictive biomarkers of responses is warranted for better clinical outcomes. We aim here to identify pre-treatment soluble immune molecules as surrogate biomarkers for tissue PD-L1 (TPD-L1) status and as predictors of response to anti-PD-1/PD-L1 therapy in NSCLC patients. Sera from 31 metastatic NSCLC patients, eligible for anti-PD-1/PD-L1 or combined chemoimmunotherapy, were collected prior to treatment. Analysis of soluble biomarkers with TPD-L1 status showed significant up/down regulation of the immune inhibitory checkpoint markers (sSiglec7, sSiglec9, sULBP4 and sPD-L2) in patients with higher TPD-L1 (TPD-L1 &gt;50%) expression. Moreover, correlation analysis showed significant positive linear correlation of soluble PD-L1 (sPD-L1) with higher TPD-L1 expression. Interestingly, only responders in the TPD-L1 &gt;50% group showed significant down regulation of the immune inhibitory markers (sPD-L2, sTIMD4, sNectin2 and CEA). When responders vs. non-responders were compared, significant down regulation of other immune inhibitory biomarkers (sCD80, sTIMD4 and CEA) was recorded only in responding patients. In this, the optimal cut-off values of CD80 &lt;91.7 pg/ml and CEA &lt;1614 pg/ml were found to be significantly associated with better progression free survival (PFS). Indeed, multivariate analysis identified the cutoff-value of CEA &lt;1614 pg/ml as an independent predictor of response in our patients. We identified here novel immune inhibitory/stimulatory soluble mediators as potential surrogate/predictive biomarkers for TPD-L1 status, treatment response and PFS in NSCLC patients treated with anti-PD-1/PD-L1 therapy.
Prediction of risk and overall survival of pancreatic cancer from blood soluble immune checkpoint-related proteins
Authors: Pan Sai , Zhao Wenting , Li Yizhan , Ying Zhijun , Luo Yihong , Wang Qinchuan , Li Xiawei , Lu Wenjie , Dong Xin , Wu Yulian , Wu Xifeng 
Published: May 2023

BackgroundImmune checkpoint inhibition holds promise as a novel treatment for pancreatic ductal adenocarcinoma (PDAC). The clinical significance of soluble immune checkpoint (ICK) related proteins have not yet fully explored in PDAC.MethodsWe comprehensively profiled 14 soluble ICK-related proteins in plasma in 70 PDAC patients and 70 matched healthy controls. Epidemiological data of all subjects were obtained through structured interviews, and patients’ clinical data were retrieved from electronical health records. We evaluated the associations between the biomarkers with the risk of PDAC using unconditional multivariate logistic regression. Consensus clustering (k-means algorithm) with significant biomarkers was performed to identify immune subtypes in PDAC patients. Prediction models for overall survival (OS) in PDAC patients were developed using multivariate Cox proportional hazards regression. Harrell’s concordance index (C-index), time-dependent receiver operating characteristic (ROC) curve and calibration curve were utilized to evaluate performance of prediction models. Gene expressions of the identified ICK-related proteins in tumors from TCGA were analyzed to provide insight into underlying mechanisms.ResultsSoluble BTLA, CD28, CD137, GITR and LAG-3 were significantly upregulated in PDAC patients (all q &lt; 0.05), and elevation of each of them was correlated with PDAC increased risk (all p &lt; 0.05). PDAC patients were classified into soluble immune-high and soluble immune-low subtypes, using these 5 biomarkers. Patients in soluble immune-high subtype had significantly poorer OS than those in soluble immune-low subtype (log-rank p = 9.7E-03). The model with clinical variables and soluble immune subtypes had excellent predictive power (C-index = 0.809) for the OS of PDAC patients. Furthermore, the immune subtypes identified with corresponding genes’ expression in PDAC tumor samples in TCGA showed an opposite correlation with OS to that of immune subtypes based on blood soluble ICK-related proteins (log-rank p =0.02). The immune-high subtype tumors displayed higher cytolytic activity (CYT) score than immune-low subtype tumors (p &lt; 2E-16).ConclusionFive soluble ICK-related proteins were identified to be significantly associated with the risk and prognosis of PDAC. Patients who were classified as soluble immune-low subtype based on these biomarkers had better overall survival than those of the soluble immune-high subtype.
Automated detection of hippocampal sclerosis using real-world clinical MRI images
Authors: Jiang Jingwen , Qiu Jiajun , Yin Jin , Wang Junren , Jiang Xinyue , Yi Zuo , Chen Yang , Zhou Xiaobo , Sima Xiutian 
Published: May 2023

BackgroundHippocampal sclerosis (HS) is the most common pathological type of temporal lobe epilepsy (TLE) and one of the important surgical markers. Currently, HS is mainly diagnosed manually by radiologists based on visual inspection of MRI, which greatly relies on MRI quality and physician experience. In clinical practice, non-thin MRI scans are often used due to the time and efficiency needed for the acquisition. However, these scans can be difficult for junior physicians to interpret accurately. Thus, the rapid and accurate diagnosis of HS using real-world MRI images in clinical settings is a challenging task.ObjectiveOur aim was to explore the feasibility of using computer vision methods to diagnose HS on real-world clinical MRI images and to provide a reference for future clinical applications of artificial intelligence methods to aid in detecting HS.MethodsWe proposed a deep learning algorithm called “HS-Net” to discriminate HS using real-world clinical MRI images. First, we delineated and segmented a region of interest (ROI) around the hippocampus. Then, we utilized the fractional differential (FD) method to enhance the textures of the ROIs. Finally, we used a small-sample image classification method based on transfer learning to fine-tune the feature extraction part of a pretrained model and added two fully connected layers and an output layer. In the study, 96 TLE patients with HS confirmed by postoperative pathology and 89 healthy controls were retrospectively enrolled. All subjects were cross-validated, and models were evaluated for performance, robustness, and clinical utility.ResultsThe HS-Net model achieved an area under the curve (AUC) of 0.894, an accuracy of 82.88%, an F1-score of 84.08% in the test cohort based on real, routine, clinical T2-weighted fluid attenuated inversion recovery (FLAIR) sequence MRI images. Additionally, the AUC, accuracy and F1 scores of our model all increased by around 3 percentage points when the inputs were augmented with the ROIs of the textures enhanced using the FD method.ConclusionsOur computational model has the potential to be used for the diagnosis of HS in real clinical MRI images, which could assist physicians, particularly junior physicians, in improving the accuracy of discrimination.