bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation
Authors: Huang Yiran , Chen Fuhao , Sun Hongtao , Zhong Cheng 
Published: January 2024

Abstract
Background
Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes.

Results
Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden.

Conclusions
LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver.
An improved and extended dual-index multiplexed 16S rRNA sequencing for the Illumina HiSeq and MiSeq platform
Authors: Larin A.K. , Klimina K.M. , Veselovsky V.A. , Olekhnovich E.I. , Morozov M.D. , Boldyreva D.I. , Yunes R.A. , Manolov A.I. , Fedorov D.E. , Pavlenko A.V. , Galeeva Y.S. , Starikova E.V. , Ilina E.N. 
Published: January 2024

Abstract
Background
Recent advancements in next-generation sequencing (NGS) technology have ushered in significant improvements in sequencing speed and data throughput, thereby enabling the simultaneous analysis of a greater number of samples within a single sequencing run. This technology has proven particularly valuable in the context of microbial community profiling, offering a powerful tool for characterizing the microbial composition at the species level within a given sample. This profiling process typically involves the sequencing of 16S ribosomal RNA (rRNA) gene fragments. By scaling up the analysis to accommodate a substantial number of samples, sometimes as many as 2,000, it becomes possible to achieve cost-efficiency and minimize the introduction of potential batch effects. Our study was designed with the primary objective of devising an approach capable of facilitating the comprehensive analysis of 1,711 samples sourced from diverse origins, including oropharyngeal swabs, mouth cavity swabs, dental swabs, and human fecal samples. This analysis was based on data obtained from 16S rRNA metagenomic sequencing conducted on the Illumina MiSeq and HiSeq sequencing platforms.

Results
We have designed a custom set of 10-base pair indices specifically tailored for the preparation of libraries from amplicons derived from the V3-V4 region of the 16S rRNA gene. These indices are instrumental in the analysis of the microbial composition in clinical samples through sequencing on the Illumina MiSeq and HiSeq platforms. The utilization of our custom index set enables the consolidation of a significant number of libraries, enabling the efficient sequencing of these libraries in a single run.

Conclusions
The unique array of 10-base pair indices that we have developed, in conjunction with our sequencing methodology, will prove highly valuable to laboratories engaged in sequencing on Illumina platforms or utilizing Illumina-compatible kits.
A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks
Authors: Wang Jiahui , Liao Nanqing , Du Xiaofei , Chen Qingfeng , Wei Bizhong 
Published: January 2024

Abstract
Background and objectives
Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes.

Results
The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases.

Conclusion
MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.
Pseudogenomic insights into the evolution of Mycobacterium ulcerans
Authors: Kyei-Baffour Edwin Sakyi , Owusu-Boateng Kwabena , Isawumi Abiola , Mosi Lydia 
Published: January 2024

Abstract
Background
Buruli ulcer (BU) disease, caused by Mycobacterium ulcerans (MU), and characterized by necrotic ulcers is still a health problem in Africa and Australia. The genome of the bacterium has several pseudogenes due to recent evolutionary events and environmental pressures. Pseudogenes are genetic elements regarded as nonessential in bacteria, however, they are less studied due to limited available tools to provide understanding of their evolution and roles in MU pathogenicity.

Results
This study developed a bioinformatic pipeline to profile the pseudogenomes of sequenced MU clinical isolates from different countries. One hundred and seventy-two MU genomes analyzed revealed that pseudogenomes of African strains corresponded to the two African lineages 1 and 2. Pseudogenomes were lineage and location specific and African lineage 1 was further divided into A and B. Lineage 2 had less relaxation in positive selection than lineage 1 which may signify different evolutionary points. Based on the Gil-Latorre model, African MU strains may be in the latter stages of evolutionary adaption and are adapting to an environment rich in metabolic resources with a lower temperature and decreased UV radiation. The environment fosters oxidative metabolism and MU may be less reliant on some secondary metabolites. In-house pseudogenomes from Ghana and Cote d’Ivoire were different from other African strains, however, they were identified as African strains.

Conclusion
Our bioinformatic pipeline provides pseudogenomic insights to complement other whole genome analyses, providing a better view of the evolution of the genome of MU and suggest an adaptation model which is important in understanding transmission. MU pseudogene profiles vary based on lineage and country, and an apparent reduction in insertion sequences used for the detection of MU which may adversely affect the sensitivity of diagnosis.
Mitochondrial genome provides species-specific targets for the rapid detection of early invasive populations of Hylurgus ligniperda in China
Authors: Li Chengjin , Wang Buxin , Ji Yingchao , Huang Lan , Wang Xiaoyi , Zhao Wenxia , Wang Yanhong , Wang Hongyi , Yao Yanxia 
Published: January 2024

Abstract
Background
Hylurgus ligniperda, a major international forestry quarantine pest, was recently found to have invaded and posed a serious threat to the Pinus forests of the Jiaodong Peninsula in China. Continuous monitoring and vigilance of the early population is imperative, and rapid molecular detection technology is urgently needed. We focused on developing a single-gene-based species-specific PCR (SS-PCR) method.

Results
We sequenced and assembled the mitochondrial genome of H. ligniperda to identify suitable target genes. We identified three closely related species for detecting the specificity of SS-PCR through phylogenetic analysis based on 13 protein-coding genes (PCGs). Subsequently, we analyzed the evolution of 13 PCGs and selected four mitochondrial genes to represent slow-evolving gene (COI) and faster-evolving genes (e.g. ND2, ND4, and ND5), respectively. We developed four species-specific primers targeting COI, ND2, ND4, and ND5 to rapidly identify H. ligniperda. The results showed that the four species-specific primers exhibited excellent specificity and sensitivity in the PCR assays, with consistent performance across a broader range of species. This method demonstrates the ability to identify beetles promptly, even during their larval stage. The entire detection process can be completed within 2–3 h.

Conclusions
This method is suitable for large-scale species detection in laboratory settings. Moreover, the selection of target genes in the SS-PCR method is not affected by the evolutionary rate. SS-PCR can be widely implemented at port and forestry workstations, significantly enhancing early management strategies and quarantine measures against H. ligniperda. This approach will help prevent the spread of the pest and effectively preserve the resources of Chinese pine forests.
Disturbance of consciousness caused by dyclonine hydrochloride mucilage: a case report
Authors: Wang Li , Wen Qi-Hui , Wen Li-Juan , Qin Jia-Min , Ren Chun-Mei , Wen Li-Ming 
Published: January 2024

Abstract
Background
Dyclonine hydrochloride mucilage is a topical anaesthetic formulated for mucosal surfaces. It is employed frequently for topical anaesthesia of the pharynx prior to endoscopic examinations such as electronic gastroscopy, and few adverse reactions have been reported. This article describes a patient who experienced a transient but severe disturbance of consciousness following oral dyclonine hydrochloride mucilage administration.

Case presentation
A 75-year-old female presenting with gastrointestinal bleeding was examined by electronic gastroscopy. Six minutes after oral dyclonine hydrochloride mucilage administration, the patient entered a comatose-like state accompanied by loss of limb muscle tone and profuse perspiration. This response was not accompanied by changes in cardiac rhythm, blood pressure, or respiration rate, suggesting an effect on higher brain centres. After ten minutes, the patient’s symptoms were alleviated.

Conclusion
We suggest that sites of dyclonine hydrochloride mucilage use be equipped with appropriate rescue devices for these rare events.
Predicting lung cancer survival prognosis based on the conditional survival bayesian network
Authors: Zhong Lu , Yang Fan , Sun Shanshan , Wang Lijie , Yu Hong , Nie Xiushan , Liu Ailing , Xu Ning , Zhang Lanfang , Zhang Mingjuan , Qi Yue , Ji Huaijun , Liu Guiyuan , Zhao Huan , Jiang Yinan , Li Jingyi , Song Chengcun , Yu Xin , Yang Liu , Yu Jinchao , Feng Hu , Guo Xiaolei , Yang Fujun , Xue Fuzhong 
Published: January 2024

AbstractLung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.
A cautionary tale: an evaluation of the performance of treatment switching adjustment methods in a real world case study
Authors: Latimer Nicholas R , Dewdney Alice , Campioni Marco 
Published: January 2024

Abstract
Background
Treatment switching in randomised controlled trials (RCTs) is a problem for health technology assessment when substantial proportions of patients switch onto effective treatments that would not be available in standard clinical practice. Often statistical methods are used to adjust for switching: these can be applied in different ways, and performance has been assessed in simulation studies, but not in real-world case studies. We assessed the performance of adjustment methods described in National Institute for Health and Care Excellence Decision Support Unit Technical Support Document 16, applying them to an RCT comparing panitumumab to best supportive care (BSC) in colorectal cancer, in which 76% of patients randomised to BSC switched onto panitumumab. The RCT resulted in intention-to-treat hazard ratios (HR) for overall survival (OS) of 1.00 (95% confidence interval [CI] 0.82–1.22) for all patients, and 0.99 (95% CI 0.75–1.29) for patients with wild-type KRAS (Kirsten rat sarcoma virus).

Methods
We tested several applications of inverse probability of censoring weights (IPCW), rank preserving structural failure time models (RPSFTM) and simple and complex two-stage estimation (TSE) to estimate treatment effects that would have been observed if BSC patients had not switched onto panitumumab. To assess the performance of these analyses we ascertained the true effectiveness of panitumumab based on: (i) subsequent RCTs of panitumumab that disallowed treatment switching; (ii) studies of cetuximab that disallowed treatment switching, (iii) analyses demonstrating that only patients with wild-type KRAS benefit from panitumumab. These sources suggest the true OS HR for panitumumab is 0.76–0.77 (95% CI 0.60–0.98) for all patients, and 0.55–0.73 (95% CI 0.41–0.93) for patients with wild-type KRAS.

Results
Some applications of IPCW and TSE provided treatment effect estimates that closely matched the point-estimates and CIs of the expected truths. However, other applications produced estimates towards the boundaries of the expected truths, with some TSE applications producing estimates that lay outside the expected true confidence intervals. The RPSFTM performed relatively poorly, with all applications providing treatment effect estimates close to 1, often with extremely wide confidence intervals.

Conclusions
Adjustment analyses may provide unreliable results. How each method is applied must be scrutinised to assess reliability.
Latent transition analysis of instrumental activities of daily living in Chinese elderly: based on the 2014–2018 wave of the Chinese Longitudinal Healthy Longevity Survey
Authors: Wang Yaqi , Xu Xueying , Liu Jingwen , Lv Qingyun , Chang Hairong , He Yuan , Zhao Yue , Zhang Xiaonan , Zang Xiaoying 
Published: January 2024

Abstract
Background
The instrumental activities of daily living (IADL) among the elderly have been found to be heterogeneous, with different trajectories. However, the transition of the IADL over time remains unclear. We aimed to explore the transition probabilities and the predictors of IADL among the elderly.

Methods
Longitudinal data from the 2014 (T1) and 2018 (T2) waves of the Chinese Longitudinal Healthy Longevity Survey were extracted. A sample of 2,944 participants aged 65 years or older, with complete responses to the IADL scale, was included. Latent profile analysis (LPA) and latent transition analysis (LTA) were employed to identify latent profiles of IADL and investigate the transition probabilities between profiles from T1 to T2. The predictors of latent profiles and transition probabilities were examined using multinomial regression analysis.

Results
The results of LPA at both T1 and T2 supported a 4-profile model solution. They were labeled as the “Normal function profile,” “Mildly impaired profile,” “Moderately impaired profile,” and “Highly impaired profile”. The Normal function profile and Highly impaired profile were characterized by maintaining stability rather than transitioning over time, with transition probabilities of 0.71 and 0.68, respectively, for maintaining stability. The Mildly impaired profile and Moderately impaired profile were characterized by a stronger tendency towards transition rather than stability, with transition probabilities of 0.29 and 0.45, respectively, of transitioning to the Highly impaired profile. The transition probabilities from the three impaired function profiles to the Normal function profile ranged from 0.05 to 0.19. Age, gender, place of residence, and social participation were significant predictors of profile attribution at T1 and transition probabilities over time.

Conclusions
This study employed the LTA to examine the transition probability of IADL among the Chinese elderly. By recognizing the different profiles of IADL and understanding the factors associated with transitions among the elderly, interventions can be tailored to improve their functional independence and successful reintegration into families and society.
Does digital technology make people healthier: the impact of digital use on the lifestyle of Chinese older adults
Authors: Cui Kaichang , Zou Wei , Ji Xiang , Zhang Xinghui 
Published: January 2024

Abstract
Background
With the arrival of the era of large-scale production, sharing and application of data, digital use has gradually changed people’s daily entertainment, consumption, social interaction, learning and other behaviors in its efficient form. This paper mainly discusses whether this fast and convenient behavior leads Chinese older adults to adopt healthier lifestyles.

Methods
Using the most recent information from the Chinese Family Panel Studies (CFPS) in 2020, this paper conducted a descriptive statistical analysis on the basic situation of digital use and lifestyles among Chinese older adults and used a structural equation model to analyse the influence of frequency and types of digital use in a variety of different aspects of the real life of Chinese older adults.

Results
Research revealed that the quality of life of Chinese older adults improved significantly as a result of their use of digital technology. The frequency of digital use (FDU) significantly improved Chinese older adults’ diet, sleep, exercise, smoking and drinking, and relieved their depression. The types of digital use (TDU) had a significant positive correlation with the lifestyle of Chinese older adults, especially in the influence of digital entertainment (DE), digital consumption (DC) and digital social interaction (DI) on the lifestyle of Chinese older adults.

Conclusions
Digital use can improve the health of Chinese older adults by promoting a healthy lifestyle through various means. The findings of this study have a substantial positive impact on bridging the digital divide that Chinese older adults face, as well as fostering the integration of digital use into their healthy lifestyles.
Early postoperative neurocognitive complications in elderly patients: comparing those with and without preexisting mild cognitive impairment– a prospective study
Authors: Somnuke Pawit , Srishewachart Pensiri , Jiraphorncharas Chalita , Khempetch Asamaporn , Weeranithan Jirapa , Suraarunsumrit Patumporn , Srinonprasert Varalak , Siriussawakul Arunotai 
Published: January 2024

Abstract
Background
As societies age, increasing numbers of older adults undergo surgeries with anesthesia. Postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) frequently occur in older surgical patients. Most of these patients already have preoperative mild cognitive impairment (MCI). However, the correlation between MCI and POD remains unclear. This study aimed to determine the incidence of POD in elderly patients with and without preexisting MCI.

Methods
A prospective study enrolled patients aged 60 years and above scheduled for major surgeries between December 2017 and April 2022. Preoperative MCI was determined by a Montreal Cognitive Assessment (MoCA) score between 18 and 24. POD was diagnosed using criteria from the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). POCD was characterized by a MoCA score reduction of 2 or more points from the preoperative score. The primary outcome was the incidence of POD within the first 72 h postoperatively. Secondary outcomes encompassed other postoperative complications, including POCD.

Results
The study comprised 223 elderly patients with MCI and 56 without MCI. The incidence of POD was 16.6% in the MCI group and 14.3% in the non-MCI group (P = 0.839). POCD occurred in 24.3% of MCI patients and 50% of non-MCI patients (P = 0.001). There were no significant differences in other postoperative complications between the groups. Postoperatively, the MCI group notably declined in visuospatial, attention, and orientation domains, while the non-MCI group declined in all domains except delayed recall.

Conclusions
The incidence of POD was similar in the MCI and non-MCI groups. However, the non-MCI group demonstrated a higher incidence of POCD than the MCI group. This was identified by a reduction in postoperative MoCA scores for the visuospatial, naming, attention, language, abstraction, and orientation domains. These findings underscore the importance of postoperative cognitive assessments for both elderly patients with preexisting MCI and those with previously intact cognitive functions.

Trial registration
This trial was retrospectively registered in the Thai Clinical Trials Registry on 15/01/2019 (registration number: TCTR20190115001).