bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis
Authors: Li He , Yang Lei , Wang Yuanyuan , Wang Lingchan , Chen Gang , Zhang Li , Wang Dongchang 
Published: April 2023

Abstract
Background
The TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD.

Methods
Genomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein–protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD.

Results
TP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R2 < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates.

Conclusions
TP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration.
Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis
Authors: Hosseini-Gerami Layla , Higgins Ixavier Alonzo , Collier David A. , Laing Emma , Evans David , Broughton Howard , Bender Andreas 
Published: April 2023

AbstractBackgroundElucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks.ResultsAccording to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number ofdirect targets. With respect to the recovery ofsignalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 ‘landmark’ genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets.ConclusionsOverall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.
PyAGH: a python package to fast construct kinship matrices based on different levels of omic data
Authors: Zhao Wei , Qadri Qamar Raza , Zhang Zhenyang , Wang Zhen , Pan Yuchun , Wang Qishan , Zhang Zhe 
Published: April 2023

Abstract
Background
Construction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are becoming diverse and different methods have their specific appropriate scenes. However, software that can comprehensively calculate kinship matrices for a variety of scenarios is still in an urgent demand.

Results
In this study, we developed an efficient and user-friendly python module, PyAGH, that can accomplish (1) conventional additive kinship matrces construction based on pedigree, genotypes, abundance data from transcriptome or microbiome; (2) genomic kinship matrices construction in combined population; (3) dominant and epistatic effects kinship matrices construction; (4) pedigree selection, tracing, detection and visualization; (5) visualization of cluster, heatmap and PCA analysis based on kinship matrices. The output from PyAGH can be easily integrated in other mainstream software based on users’ purposes. Compared with other softwares, PyAGH integrates multiple methods for calculating the kinship matrix and has advantages in terms of speed and data size compared to other software. PyAGH is developed in python and C +  + and can be easily installed by pip tool. Installation instructions and a manual document can be freely available from https://github.com/zhaow-01/PyAGH.

Conclusion
PyAGH is a fast and user-friendly Python package for calculating kinship matrices using pedigree, genotype, microbiome and transcriptome data as well as processing, analyzing and visualizing data and results. This package makes it easier to perform predictions and association studies processes based on different levels of omic data.
Identification and characterisation of de novo germline structural variants in two commercial pig lines using trio-based whole genome sequencing
Authors: Steensma Marije J. , Lee Y. L. , Bouwman A. C. , Pita Barros C. , Derks M. F.L. , Bink M. C.A.M. , Harlizius B. , Huisman A. E. , Crooijmans R. P.M.A. , Groenen M. A.M. , Mulder H. A. , Rochus C. M. 
Published: April 2023

Abstract
Background
De novo mutations arising in the germline are a source of genetic variation and their discovery broadens our understanding of genetic disorders and evolutionary patterns. Although the number of de novo single nucleotide variants (dnSNVs) has been studied in a number of species, relatively little is known about the occurrence of de novo structural variants (dnSVs). In this study, we investigated 37 deeply sequenced pig trios from two commercial lines to identify dnSVs present in the offspring. The identified dnSVs were characterised by identifying their parent of origin, their functional annotations and characterizing sequence homology at the breakpoints.

Results
We identified four swine germline dnSVs, all located in intronic regions of protein-coding genes. Our conservative, first estimate of the swine germline dnSV rate is 0.108 (95% CI 0.038–0.255) per generation (one dnSV per nine offspring), detected using short-read sequencing. Two detected dnSVs are clusters of mutations. Mutation cluster 1 contains a de novo duplication, a dnSNV and a de novo deletion. Mutation cluster 2 contains a de novo deletion and three de novo duplications, of which one is inverted. Mutation cluster 2 is 25 kb in size, whereas mutation cluster 1 (197 bp) and the other two individual dnSVs (64 and 573 bp) are smaller. Only mutation cluster 2 could be phased and is located on the paternal haplotype. Mutation cluster 2 originates from both micro-homology as well as non-homology mutation mechanisms, where mutation cluster 1 and the other two dnSVs are caused by mutation mechanisms lacking sequence homology. The 64 bp deletion and mutation cluster 1 were validated through PCR. Lastly, the 64 bp deletion and the 573 bp duplication were validated in sequenced offspring of probands with three generations of sequence data.

Conclusions
Our estimate of 0.108 dnSVs per generation in the swine germline is conservative, due to our small sample size and restricted possibilities of dnSV detection from short-read sequencing. The current study highlights the complexity of dnSVs and shows the potential of breeding programs for pigs and livestock species in general, to provide a suitable population structure for identification and characterisation of dnSVs.
Definition of the transcriptional units of inherited retinal disease genes by meta-analysis of human retinal transcriptome data
Authors: Ruiz-Ceja Karla Alejandra , Capasso Dalila , Pinelli Michele , Del Prete Eugenio , Carrella Diego , di Bernardo Diego , Banfi Sandro 
Published: April 2023

Abstract
Background
Inherited retinal diseases (IRD) are genetically heterogeneous disorders that cause the dysfunction or loss of photoreceptor cells and ultimately lead to blindness. To date, next-generation sequencing procedures fail to detect pathogenic sequence variants in coding regions of known IRD disease genes in about 30–40% of patients. One of the possible explanations for this missing heritability is the presence of yet unidentified transcripts of known IRD genes. Here, we aimed to define the transcript composition of IRD genes in the human retina by a meta-analysis of publicly available RNA-seq datasets using an ad-hoc designed pipeline.

Results
We analysed 218 IRD genes and identified 5,054 transcripts, 3,367 of which were not previously reported. We assessed their putative expression levels and focused our attention on 435 transcripts predicted to account for at least 5% of the expression of the corresponding gene. We looked at the possible impact of the newly identified transcripts at the protein level and experimentally validated a subset of them.

Conclusions
This study provides an unprecedented, detailed overview of the complexity of the human retinal transcriptome that can be instrumental in contributing to the resolution of some cases of missing heritability in IRD patients.
Development and usability of a hospital standardized ADL ratio (HSAR) for elderly patients with cerebral infarction: a retrospective observational study using administrative claim data from 2012 to 2019 in Japan
Authors: Onishi Ryo , Hatakeyama Yosuke , Hirata Koki , Matsumoto Kunichika , Seto Kanako , Wu Yinghui , Kitazawa Takefumi , Hasegawa Tomonori 
Published: April 2023

Abstract
Background
Maintenance of activities of daily living (ADL) during acute hospitalization is an important treatment goal, especially for elderly inpatients with diseases that often leave disabilities, such as cerebral infarction. However, studies assessing risk-adjusted ADL changes are limited. In this study, we developed and calculated a hospital standardized ADL ratio (HSAR) using Japanese administrative claims data to measure the quality of hospitalization care for patients with cerebral infarction.

Methods
This study was designed as a retrospective observational study using the Japanese administrative claim data from 2012 to 2019. The data of all hospital admissions with a primary diagnosis of cerebral infarction (ICD-10, I63) were used. The HSAR was defined as the ratio of the observed number of ADL maintenance patients to the expected number of ADL maintenance patients multiplied by 100, and ratio of ADL maintenance patients was risk-adjusted using multivariable logistic regression analyses. The c-statistic was used to evaluate the predictive accuracy of the logistic models. Changes in HSARs in each consecutive period were assessed using Spearman’s correlation coefficient.

Results
A total of 36,401 patients from 22 hospitals were included in this study. All variables used in the analyses were associated with ADL maintenance, and evaluations using the HSAR model showed predictive ability with c-statistics (area under the curve, 0.89; 95% confidence interval, 0.88–0.89).

Conclusions
The findings indicated a need to support hospitals with a low HSAR because hospitals with high/low HSAR were likely to produce the same results in the subsequent periods. HSAR can be used as a new quality indicator of in-hospital care and may contribute to the assessment and improvement of the quality of care.
Strategies to increase HIV testing among men who have sex with men and transgender women: an integrative review
Authors: Rocha Gustavo Machado , Cândido Raissa Carolina Fonseca , de Carvalho Nathália Pacífico , Carvalho Emilly Gabrielly Araujo , Costa Alícia Amanda Moreira , Machado Ives Vieira , da Cruz Pimenta Marcos Paulo , de Paula Júnior José Anastácio , Guimarães Mark Drew Crosland , de Pádua Cristiane Aparecida Menezes 
Published: April 2023

Abstract
Background
Men who have sex with men (MSM) and transgender women (TGW) are disproportionately affected by HIV, with much higher incidence and prevalence rates than in the general population in different countries. There are several barriers to testing among MSM and TGW, such as low risk perception, anticipation of HIV-related stigma, discrimination of sexual orientation, in addition to difficulties related to care and access to health services. Therefore, analyzing the available evidence of the effectiveness of strategies for scaling up HIV testing among key populations is essential to point out potential knowledge gaps which may need to be addressed and develop public health policies to promote testing and early diagnosis of HIV infection.

Methods
An integrative review was carried out to evaluate strategies for scaling up HIV testing in these populations. Search strategy was performed on eight electronic databases, without language restriction. We included clinical trials, quasi-experimental studies, and non-randomized studies. Study selection and data extraction were both performed independently by pairs and disagreements were solved by a third revisor. The screening of the studies was carried out through the selection of titles/abstracts and the reading of the full texts of the pre-selected studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Data extraction was performed using a structured form.

Results
Thirty-seven publications referring to 35 studies were included, mostly being carried out in the United States of America and Australia. No studies were found evaluating disaggregated data on TGW. The studies were grouped into four types of intervention strategies: self-test distribution system (n = 10), organization of health services (n = 9), peer education (n = 6), and social marketing campaign (n = 10). Strategies that focused on the first three groups, combined or not, were more effective in increasing HIV testing among MSM.

Conclusions
Considering the diversity of interventions and the methodological heterogeneity of the included studies, strategies especially involving self-test distribution systems, associated with new information and communication technologies, should be evaluated in different communities and social contexts. Research evaluating specific studies on TGW population is still needed.
Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models
Authors: Danielsen Anders Skyrud , Franconeri Léa , Page Samantha , Myhre Anders Eivind , Tornes Ragnhild Agathe , Kacelnik Oliver , Bjørnholt Jørgen Vildershøj 
Published: April 2023

Abstract
Background
Infections are major causes of disease in cancer patients and pose a major obstacle to the success of cancer care. The global rise of antimicrobial resistance threatens to make these obstacles even greater and hinder continuing progress in cancer care. To prevent and handle such infections, better models of clinical outcomes building on current knowledge are needed. This internally funded systematic review (PROSPERO registration: CRD42021282769) aimed to review multivariable models of resistant infections/colonisations and corresponding mortality, what risk factors have been investigated, and with what methodological approaches.

Methods
We employed two broad searches of antimicrobial resistance in cancer patients, using terms associated with antimicrobial resistance, in MEDLINE and Embase through Ovid, in addition to Cinahl through EBSCOhost and Web of Science Core Collection. Primary, observational studies in English from January 2015 to November 2021 on human cancer patients that explicitly modelled infection/colonisation or mortality associated with antimicrobial resistance in a multivariable model were included. We extracted data on the study populations and their malignancies, risk factors, microbial aetiology, and methods for variable selection, and assessed the risk of bias using the NHLBI Study Quality Assessment Tools.

Results
Two searches yielded a total of 27,151 unique records, of which 144 studies were included after screening and reading. Of the outcomes studied, mortality was the most common (68/144, 47%). Forty-five per cent (65/144) of the studies focused on haemato-oncological patients, and 27% (39/144) studied several bacteria or fungi. Studies included a median of 200 patients and 46 events. One-hundred-and-three (72%) studies used a p-value-based variable selection. Studies included a median of seven variables in the final (and largest) model, which yielded a median of 7 events per variable. An in-depth example of vancomycin-resistant enterococci was reported.

Conclusions
We found the current research to be heterogeneous in the approaches to studying this topic. Methodological choices resulting in very diverse models made it difficult or even impossible to draw statistical inferences and summarise what risk factors were of clinical relevance. The development and adherence to more standardised protocols that build on existing literature are urgent.
Network analysis of polymicrobial chronic wound infections in Masanga, Sierra Leone
Authors: Sandmann Sarah , Nunes Jonathan Vas , Grobusch Martin P. , Sesay Maxwell , Kriegel Martin A. , Varghese Julian , Schaumburg Frieder 
Published: April 2023

Abstract
Background
Chronic wounds are frequently colonized or infected with multiple bacterial or fungal species, which can both promote or inhibit each other. Network analyses are helpful to understand the interplay of these species in polymicrobial infections. Our aim was to analyse the network of bacterial and fungal species in chronic wounds.

Methods
Swabs (n = 163) from chronic wound infections (Masanga, Sierra Leone, 2019–2020) were screened for bacterial and fungal species using non-selective agars. Some of these wounds were suspected but not confirmed Buruli ulcer. Species identification was done with MALDI-TOF mass spectrometry. Network analysis was performed to investigate co-occurrence of different species within one patient. All species with n ≥ 10 isolates were taken into account.

Results
Of the 163 patients, 156 had a positive wound culture (median of three different species per patient; range 1–7). Pseudomonas aeruginosa (n = 75) was the dominating species with frequent co-detections of Klebsiella pneumoniae (21 cases; OR = 1.36, 95%CI: 0.63–2.96, p = 0.47), Staphylococcus aureus (14 cases; OR = 1.06, 95%CI: 0.44–2.55, p = 1) and Proteus mirabilis (13 cases; OR = 0.84, 95%CI: 0.35–1.99, p = 0.69).

Conclusion
The culturome of chronic wounds in Sierra Leonean patients is highly diverse and characterized by the co-occurrence of P. aeruginosa, K. pneumoniae and S. aureus.
Fungal infection profile in critically ill COVID-19 patients: a prospective study at a large teaching hospital in a middle-income country
Authors: Negm Essamedin M. , Mohamed Mohamed Sorour , Rabie Rehab A. , Fouad Walaa S. , Beniamen Ahmed , Mosallem Ahmed , Tawfik Ahmed E. , Salama Hussein M. 
Published: April 2023

Abstract
Background
Critically ill COVID-19 patients are highly susceptible to opportunistic fungal infection due to many factors, including virus-induced immune dysregulation, host-related comorbidities, overuse and misuse of antibiotics or corticosteroids, immune modulator drugs, and the emergencies caused by the pandemic. This study aimed to assess the incidence, identify the potential risk factors, and examine the impact of fungal coinfection on the outcomes of COVID-19 patients admitted to the intensive care unit (ICU).

Methods
A prospective cohort study including 253 critically ill COVID-19 patients aged 18 years or older admitted to the isolation ICU of Zagazig University Hospitals over a 4-month period from May 2021 to August 2021 was conducted. The detection of a fungal infection was carried out.

Results
Eighty-three (83) patients (32.8%) were diagnosed with a fungal coinfection. Candida was the most frequently isolated fungus in 61 (24.1%) of 253 critically ill COVID-19 patients, followed by molds, which included Aspergillus 11 (4.3%) and mucormycosis in five patients (1.97%), and six patients (2.4%) diagnosed with other rare fungi. Poor diabetic control, prolonged or high-dose steroids, and multiple comorbidities were all possible risk factors for fungal coinfection [OR (95% CI) = 10.21 (3.43–30.39), 14.1 (5.67–35.10), 14.57 (5.83–33.78), and 4.57 (1.83–14.88), respectively].

Conclusion
Fungal coinfection is a common complication of critically ill COVID-19 patients admitted to the ICU. Candidiasis, aspergillosis, and mucormycosis are the most common COVID-19-associated fungal infections and have a great impact on mortality rates.