bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differentiation of lacrimal gland tumors using the multi-model MRI: classification and regression tree (CART)-based analysis
Authors: Li Xiaofeng , Wu Xue , Qian Jiang , Yuan Yifei , Wang Shenjiang , Ye Xinpei , Sha Yan , Zhang Rui , Ren Hui 
Published: June 2021

Background Little is known about the value of dynamic contrast-enhanced (DCE) in combination with diffusion-weighted imaging (DWI) for the differentiation of lacrimal gland tumors. Purpose To evaluate the ability of DCE and DWI in differentiating lacrimal gland tumors. Material and Methods DCE and DWI were performed in 72 patients with lacrimal gland tumors. Time-intensity curve (TIC) patterns were categorized as type A, type B, type C, and type D. Apparent diffusion coefficient (ADC) was measured on DWI. Then, the diagnostic effectiveness of TIC in conjunction with ADC was assessed using classification and regression tree (CART) analysis. Results Type A tumors were all epithelial; they could be further separated into pleomorphic adenoma sand carcinomas. Type B tumors were all non-epithelial tumors, which could be further separated into benign inflammatory infiltrates (BIIs) and lymphomas. Type C tumors contained both carcinomas and non-epithelial tumors, which could be diagnosed into carcinomas, BIIs and lymphomas. Type D tumors were all PAs. The mean ADC of epithelial tumors was significantly higher than that of non-epithelial tumors, and the mean ADC values were significantly different between PAs and carcinomas. Besides, the mean ADC value of BIIs was higher than that of lymphomas. Therefore, the CART decision tree made by ADC and TIC had a predictive accuracy of 86.1%, differentiating lacrimal gland tumors effectively. Conclusion Combined DCE and DWI-MRI can efficiently differentiate lacrimal gland tumors which can be of help to ophthalmologists in the diagnosis and treatment of these tumors.
Knockdown of long non-coding RNA CCAT2 suppresses growth and metastasis of esophageal squamous cell carcinoma by inhibiting the β-catenin/WISP1 signaling pathway
Authors: Yang Canlin , Li Fei , Zhou Wenbiao , Huang Junxing 
Published: June 2021

Objective Long non-coding RNA (lncRNA) colon cancer-associated transcript 2 (CCAT2) plays oncogenic roles in several cancers, including esophageal squamous cell carcinoma (ESCC). However, the specific mechanism of how CCAT2 influences ESCC tumorigenesis is still unknown. Methods Using RT-qPCR, the mRNA expression levels of CCAT2 in 33 paired ESCC and adjacent non-cancer tissues and cell lines were measured. Lentiviral vector sh-CCAT2 was designed and transfected into TE10 cells. CCK-8 and transwell assays were employed to detect the effects of CCAT2 knockdown on cell proliferation and invasion, respectively. RT-qPCR and western blots were used to detect the effects of CCAT2 knockdown. Results CCAT2 was overexpressed in ESCC tissues compared with corresponding adjacent tissues. CCAT2 knockdown could suppress cell proliferation and invasion in vitro. Furthermore, knockdown of CCAT2 could suppress the mRNA and protein levels of β-catenin and Wnt-induced-secreted-protein-1 (WISP1), as well as the mRNA levels of their downstream targets VEGF-A, MMP2, and ICAM-1. High expression of CCAT2 and WISP1 were associated with poor prognosis of ESCC patients. Conclusions In conclusion, a novel CCAT2/β-catenin/WISP1 axis was revealed in ESCC progression and may provide a promising therapeutic target against ESCC. CCAT2 and WISP1 are potential molecular biomarkers for predicting prognosis of ESCC.
Clinical factors affecting cerebral oxygenation in patients undergoing peritoneal dialysis
Authors: Kitano Taisuke , Ito Kiyonori , Ookawara Susumu , Hoshino Taro , Hayasaka Hideyuki , Kofuji Masaya , Uchida Takayuki , Morino Junki , Minato Saori , Kaneko Shohei , Yanai Katsunori , Mutsuyoshi Yuko , Ishii Hiroki , Matsuyama-Hirata Momoko , Shindo Mitsutoshi , Miyazawa Haruhisa , Ueda Yuichiro , Hirai Keiji , Morishita Yoshiyuki 
Published: June 2021

Background: Although cerebral regional oxygen saturation (rSO2) is significantly lower in hemodialysis (HD) patients than that in healthy controls, investigations on cerebral oxygenation in peritoneal dialysis (PD) patients are limited. We aimed to confirm the cerebral oxygenation status and identify the factors affecting cerebral rSO2 in PD patients. Methods: Thirty-six PD patients (21 men and 15 women; mean age, 62.8 ± 12.7 years) were recruited. In addition, 27 healthy volunteers (17 men and 10 women; mean age, 43.5 ± 18.8 years) were recruited as a control group. Cerebral rSO2 was monitored at the forehead using an INVOS 5100c oxygen saturation monitor. Results: Cerebral rSO2 was significantly lower in PD patients than that in healthy controls (57.0 ± 7.3% vs 68.9 ± 8.6%, p < 0.001); moreover, cerebral rSO2 was significantly correlated with natural logarithm (Ln)-PD duration ( r = −0.389, p = 0.019) and serum albumin concentration ( r = 0.370, p = 0.026) in a simple linear regression analysis. Multivariable linear regression analysis was performed using variables that showed a significant correlation and p < 0.20 (serum creatinine, serum sodium, Ln-C-reactive protein, and dosage of erythropoiesis-stimulating agent) with the cerebral rSO2. Cerebral rSO2 was independently associated with Ln-PD duration (standardized coefficient: −0.339) and serum albumin concentration (standardized coefficient: 0.316). Conclusions: Cerebral rSO2 was significantly affected by the PD duration and serum albumin concentration. Further prospective studies are needed to clarify whether preventing a decrease in serum albumin concentration leads to the maintenance of cerebral oxygenation in patients undergoing PD.
Communicating to Non-Speaking Critically Ill Patients: Augmentative and Alternative Communication Technique as an Essential Strategy
Authors: Al-Yahyai, RN, BSN Asaad Nasser Salim , Arulappan, RN, RM, BSC (N), MSC (N), PhD (N), DNSc Judie , Matua Gerald Amandu , Al-Ghafri, RN, BSN Sultan Marhoon , Al-Sarakhi, RN, BSN Sami Hamood , Al-Rahbi, RN, BSN Khalid Khalfan Said , Jayapal Sathish Kumar 
Published: June 2021

Introduction Communication with hospitalized patients is crucial to improve the quality and safety of health care. Methods The study assessed the communication methods used by nurses while communicating to non-speaking critically ill patients. The participants included staff nurses working in ICU, CICU, HD units of neuro- surgical, orthopedic, medical and oncology wards. Purposive sampling technique was used to recruit a total number of 194 nurses. The communication methods used were assessed by a questionnaire comprised of a list of 21 strategies used to communicate with non-speaking patients. Results The most commonly used strategies were reading the patient’s mouthing words, encouraging the patient by telling them that they are doing well and nurses helping them to get better, assessing the patients for their communication ability, “thumps up” to indicate “yes”, “shake head” indicating “no”, use OK, or point to body parts, speaking slowly and waiting for the patient’s response, spending time to listen patiently to what the patient say and touching the non-speaking critically ill patient when the nurse speaks with the patient. Conclusion The study reported that the nurses used variety of communication strategies while communicating to non- speaking critically ill patients. However very few nurses used Augmentative and alternative communication strategies to communicate to non-speaking critically ill patients. The study recommends the importance of establishing Augmentative and Alternative Communication strategies in the hospitals.
Regulation of RNA polymerase II activity is essential for terminal erythroid maturation
Authors: Murphy Zachary C. , Murphy Kristin , Myers Jacquelyn , Getman Michael , Couch Tyler , Schulz Vincent P. , Lezon-Geyda Kimberly , Palumbo Cal , Yan Hongxia , Mohandas Narla , Gallagher Patrick G. , Steiner Laurie A. 
Published: June 2021

Abstract
The terminal maturation of human erythroblasts requires significant changes in gene expression in the context of dramatic nuclear condensation. Defects in this process are associated with inherited anemias and myelodysplastic syndromes. The progressively dense appearance of the condensing nucleus in maturing erythroblasts led to the assumption that heterochromatin accumulation underlies this process, but despite extensive study, the precise mechanisms underlying this essential biologic process remain elusive. To delineate the epigenetic changes associated with the terminal maturation of human erythroblasts, we performed mass spectrometry of histone posttranslational modifications combined with chromatin immunoprecipitation coupled with high-throughput sequencing, Assay for Transposase Accessible Chromatin, and RNA sequencing. Our studies revealed that the terminal maturation of human erythroblasts is associated with a dramatic decline in histone marks associated with active transcription elongation, without accumulation of heterochromatin. Chromatin structure and gene expression were instead correlated with dynamic changes in occupancy of elongation competent RNA polymerase II, suggesting that terminal erythroid maturation is controlled largely at the level of transcription. We further demonstrate that RNA polymerase II “pausing” is highly correlated with transcriptional repression, with elongation competent RNA polymerase II becoming a scare resource in late-stage erythroblasts, allocated to erythroid-specific genes. Functional studies confirmed an essential role for maturation stage-specific regulation of RNA polymerase II activity during erythroid maturation and demonstrate a critical role for HEXIM1 in the regulation of gene expression and RNA polymerase II activity in maturing erythroblasts. Taken together, our findings reveal important insights into the mechanisms that regulate terminal erythroid maturation and provide a novel paradigm for understanding normal and perturbed erythropoiesis.
Exploring the pathways to chronic lymphocytic leukemia
Authors: Stevenson Freda K. , Forconi Francesco , Kipps Thomas J. 
Published: June 2021

AbstractIn chronic lymphocytic leukemia (CLL), increasing knowledge of the biology of the tumor cells has led to transformative improvements in our capacity to assess and treat patients. The dependence of tumor cells on surface immunoglobulin receptor signaling, survival pathways, and accessory cells within the microenvironment has led to a successful double-barreled attack with designer drugs. Studies have revealed that CLL should be classified based on the mutational status of the expressed IGHV sequences into 2 diseases, either unmutated (U) or mutated (M) CLL, each with a distinctive cellular origin, biology, epigenetics/genetics, and clinical behavior. The origin of U-CLL lies among the natural antibody repertoire, and dominance of IGHV1-69 reveals a superantigenic driver. In both U-CLL and M-CLL, a calibrated stimulation of tumor cells by self-antigens apparently generates a dynamic reiterative cycle as cells, protected from apoptosis, transit between blood and tissue sites. But there are differences in outcome, with the balance between proliferation and anergy favoring anergy in M-CLL. Responses are modulated by an array of microenvironmental interactions. Availability of T-cell help is a likely determinant of cell fate, the dependency on which varies between U-CLL and M-CLL, reflecting the different cells of origin, and affecting clinical behavior. Despite such advances, cell-escape strategies, Richter transformation, and immunosuppression remain as challenges, which only may be met by continued research into the biology of CLL.
Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia
Authors: Awada Hassan , Durmaz Arda , Gurnari Carmelo , Kishtagari Ashwin , Meggendorfer Manja , Kerr Cassandra M. , Kuzmanovic Teodora , Durrani Jibran , Shreve Jacob , Nagata Yasunobu , Radivoyevitch Tomas , Advani Anjali S. , Ravandi Farhad , Carraway Hetty E. , Nazha Aziz , Haferlach Claudia , Saunthararajah Yogen , Scott Jacob , Visconte Valeria , Kantarjian Hagop , Kadia Tapan , Sekeres Mikkael A. , Haferlach Torsten , Maciejewski Jaroslaw P. 
Published: June 2021

Abstract
Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and prototypic genetic founder lesions define only a small proportion of AML patients. The historical subdivision of primary/de novo AML and secondary AML has shown to variably correlate with genetic patterns. The combinatorial complexity and heterogeneity of AML genomic architecture may have thus far precluded genomic-based subclassification to identify distinct molecularly defined subtypes more reflective of shared pathogenesis. We integrated cytogenetic and gene sequencing data from a multicenter cohort of 6788 AML patients that were analyzed using standard and machine learning methods to generate a novel AML molecular subclassification with biologic correlates corresponding to underlying pathogenesis. Standard supervised analyses resulted in modest cross-validation accuracy when attempting to use molecular patterns to predict traditional pathomorphologic AML classifications. We performed unsupervised analysis by applying the Bayesian latent class method that identified 4 unique genomic clusters of distinct prognoses. Invariant genomic features driving each cluster were extracted and resulted in 97% cross-validation accuracy when used for genomic subclassification. Subclasses of AML defined by molecular signatures overlapped current pathomorphologic and clinically defined AML subtypes. We internally and externally validated our results and share an open-access molecular classification scheme for AML patients. Although the heterogeneity inherent in the genomic changes across nearly 7000 AML patients was too vast for traditional prediction methods, machine learning methods allowed for the definition of novel genomic AML subclasses, indicating that traditional pathomorphologic definitions may be less reflective of overlapping pathogenesis.
Hepatitis B and C virus infection among healthcare workers in Africa: a systematic review and meta-analysis
Authors: Atlaw Daniel , Sahiledengle Biniyam , Tariku Zerihun 
Published: June 2021

Abstract
Background
Healthcare workers are at risk of acquiring hepatitis B and C virus infections through patients’ blood and bodily fluids exposure. So far, there is no pooled data that shows the prevalence of HBV and HCV among health care workers in Africa. This study aimed to determine the pooled prevalence of hepatitis B and C infections among health care workers in Africa.

Methods
Studies reporting the prevalence of HBV and HCV were identified from major databases and gray literature. PubMed, CINAHL, POPLINE, ScienceDirect, African Journals Online (AJOL), and Google Scholar were systematically searched to identify relevant studies. A random-effect model was used to estimate the pooled prevalence of hepatitis B and C among health care workers in Africa. The heterogeneity of studies was assessed using Cochran Q statistics and I2 tests. Publication bias was assessed using Begg’s tests.

Result
In total, 1885 articles were retrieved, and 44 studies met the inclusion criteria and included in the final analysis. A total of 17,510 healthcare workers were included. The pooled prevalence of hepatitis B virus infection among health care workers in Africa is estimated to be 6.81% (95% CI 5.67–7.95) with a significant level of heterogeneity (I2 = 91.6%; p < 0.001). While the pooled prevalence of hepatitis C virus infection using the random-effects model was 5.58% (95% CI 3.55–7.61) with a significant level of heterogeneity (I2 = 95.1%; p < 0.001).

Conclusion
Overall, one in fifteen and more than one in twenty healthcare workers were infected by HBV and HCV, respectively. The high burden of HBV and HCV infections remains a significant problem among healthcare workers in Africa.
On the origin and diversification of Podolian cattle breeds: testing scenarios of European colonization using genome-wide SNP data
Authors: Senczuk Gabriele , Mastrangelo Salvatore , Ajmone-Marsan Paolo , Becskei Zsolt , Colangelo Paolo , Colli Licia , Ferretti Luca , Karsli Taki , Lancioni Hovirag , Lasagna Emiliano , Marletta Donata , Persichilli Christian , Portolano Baldassare , Sarti Francesca M. , Ciani Elena , Pilla Fabio 
Published: June 2021

AbstractBackgroundDuring the Neolithic expansion, cattle accompanied humans and spread from their domestication centres to colonize the ancient world. In addition, European cattle occasionally intermingled with both indicine cattle and local aurochs resulting in an exclusive pattern of genetic diversity. Among the most ancient European cattle are breeds that belong to the so-called Podolian trunk, the history of which is still not well established. Here, we used genome-wide single nucleotide polymorphism (SNP) data on 806 individuals belonging to 36 breeds to reconstruct the origin and diversification of Podolian cattle and to provide a reliable scenario of the European colonization, through an approximate Bayesian computation random forest (ABC-RF) approach.ResultsOur results indicate that European Podolian cattle display higher values of genetic diversity indices than both African taurine and Asian indicine breeds. Clustering analyses show that Podolian breeds share close genomic relationships, which suggests a likely common genetic ancestry. Among the simulated and tested scenarios of the colonization of Europe from taurine cattle, the greatest support was obtained for the model assuming at least two waves of diffusion. Time estimates are in line with an early migration from the domestication centre of non-Podolian taurine breeds followed by a secondary migration of Podolian breeds. The best fitting model also suggests that the Italian Podolian breeds are the result of admixture between different genomic pools.ConclusionsThis comprehensive dataset that includes most of the autochthonous cattle breeds belonging to the so-called Podolian trunk allowed us not only to shed light onto the origin and diversification of this group of cattle, but also to gain new insights into the diffusion of European cattle. The most well-supported scenario of colonization points to two main waves of migrations: with one that occurred alongside with the Neolithic human expansion and gave rise to the non-Podolian taurine breeds, and a more recent one that favoured the diffusion of European Podolian. In this process, we highlight the importance of both the Mediterranean and Danube routes in promoting European cattle colonization. Moreover, we identified admixture as a driver of diversification in Italy, which could represent a melting pot for Podolian cattle.
A semi-supervised deep learning approach for predicting the functional effects of genomic non-coding variations
Authors: Jia Hao , Park Sung-Joon , Nakai Kenta 
Published: June 2021

Abstract
Background
Understanding the functional effects of non-coding variants is important as they are often associated with gene-expression alteration and disease development. Over the past few years, many computational tools have been developed to predict their functional impact. However, the intrinsic difficulty in dealing with the scarcity of data leads to the necessity to further improve the algorithms. In this work, we propose a novel method, employing a semi-supervised deep-learning model with pseudo labels, which takes advantage of learning from both experimentally annotated and unannotated data.

Results
We prepared known functional non-coding variants with histone marks, DNA accessibility, and sequence context in GM12878, HepG2, and K562 cell lines. Applying our method to the dataset demonstrated its outstanding performance, compared with that of existing tools. Our results also indicated that the semi-supervised model with pseudo labels achieves higher predictive performance than the supervised model without pseudo labels. Interestingly, a model trained with the data in a certain cell line is unlikely to succeed in other cell lines, which implies the cell-type-specific nature of the non-coding variants. Remarkably, we found that DNA accessibility significantly contributes to the functional consequence of variants, which suggests the importance of open chromatin conformation prior to establishing the interaction of non-coding variants with gene regulation.

Conclusions
The semi-supervised deep learning model coupled with pseudo labeling has advantages in studying with limited datasets, which is not unusual in biology. Our study provides an effective approach in finding non-coding mutations potentially associated with various biological phenomena, including human diseases.
A multi-task CNN learning model for taxonomic assignment of human viruses
Authors: Ma Haoran , Tan Tin Wee , Ban Kenneth Hon Kim 
Published: June 2021

Abstract
Background
Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample.

Results
In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data.

Conclusions
We have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus.