bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 Impact Predicts Diabetes Distress Among Individuals With Type 2 Diabetes
Authors: Koonmen Leigh Anne , Lennie Terry A. , Hieronymus Laura B. , Rayens Mary Kay , Ickes Melinda , Miller Jennifer L. , Mudd-Martin Gia 
Published: September 2023

Purpose: The purpose of this study was to determine whether COVID-19 impact and Diabetes Self-Management Education and Support (DSMES) service attendance predicted diabetes distress among individuals with type 2 diabetes during the pandemic. Methods: Eighty-six adults with type 2 diabetes who either attended (n = 29) or did not previously attend (n = 57) DSMES services completed a cross-sectional survey. Participants’ mean age was 57 ± 12.3 years, 50% were female, and 71.3% were diagnosed with diabetes >5 years. The Coronavirus Impact Scale was used to measure impact of the pandemic on daily life. The Diabetes Distress Scale was used to measure distress overall and within 4 subscales (emotional burden, interpersonal distress, physician-related distress, regimen distress). Separate multiple linear regressions were conducted for each outcome, controlling for age, sex, marital status, financial status, and time since diabetes diagnosis. Results: Higher COVID-19 impact predicted higher diabetes-related distress for all subscales and overall. Only the subscale for interpersonal distress was predicted by DSMES attendance, which decreased with DSMES attendance. Conclusion: This study identifies a link between the effects of the COVID-19 pandemic and diabetes distress. The findings highlight the negative impact of the pandemic on diabetes distress and the importance of DSMES services for diabetes-related distress. Interventions are needed to reduce psychological distress among this population during public health crises.
Protective role of boron on hepatotoxicity and oxidative stress induced by trichloroacetic acid
Authors: Wang Chong , Shi Ying , Gu Wen , Wang Chao , Xu Yongjun , Li Li , Zhang Lixia , Zhang Shaoping , Zhi Hong , Ruan Hongjie , Kong Jian , Duan Lian , Tang Song 
Published: September 2023

AbstractWe conducted a comprehensive investigation into the protective roles of boron (B) against trichloroacetic acid (TCA)-induced hepatotoxicity by assessing TCA exposure in vivo and exploring the potential mechanisms by which B protects against TCA-induced hepatotoxicity in vitro. For the in vivo study, we evaluated TCA-induced hepatotoxicity in adult male B6C3F1 mice exposed to 25, 50, 125, and 500 mg/kg/day of TCA, respectively, for 21 days. We found that the mice’s liver weight was significantly increased, and that there were changes in hepatic histopathology, particularly in mice treated with the highest dosage (500 mg/kg/day). TCA also increased the hepatic oxidoreductase activity of medium-chain and long-chain acyl-coenzyme A (CoA), which are biomarkers of peroxisome proliferation, in a dose-dependent manner. Subsequently, we established a TCA-induced HepG2 cell model of oxidative damage to estimate the cytotoxicity and determine the positive effects of B administration in vitro. We found that B administration significantly reduced oxidative stress by attenuating the production of TCA-induced reactive oxygen species and malondialdehyde. B also significantly downregulated the concentrations of certain cytokines, including interleukin (IL)-6, IL-8, and transforming growth factor-beta, which are predominantly associated with the p38 mitogen-activated protein kinase (MAPK) signaling pathway. In addition, B significantly upregulated phospho-p38 levels and downregulated Bax and p21 levels in the cytoplasm and downregulated p38 and p21 levels in the nucleus. Taken together, our findings suggest that the protective role of B against TCA-induced hepatotoxicity primarily involves alleviation of oxidative damage and cell apoptosis caused by TCA and might be mediated via the p38 MAPK pathway.
Empirical evaluation of language modeling to ascertain cancer outcomes from clinical text reports
Authors: Elmarakeby Haitham A. , Trukhanov Pavel S. , Arroyo Vidal M. , Riaz Irbaz Bin , Schrag Deborah , Van Allen Eliezer M. , Kehl Kenneth L. 
Published: September 2023

Abstract
Background
Longitudinal data on key cancer outcomes for clinical research, such as response to treatment and disease progression, are not captured in standard cancer registry reporting. Manual extraction of such outcomes from unstructured electronic health records is a slow, resource-intensive process. Natural language processing (NLP) methods can accelerate outcome annotation, but they require substantial labeled data. Transfer learning based on language modeling, particularly using the Transformer architecture, has achieved improvements in NLP performance. However, there has been no systematic evaluation of NLP model training strategies on the extraction of cancer outcomes from unstructured text.

Results
We evaluated the performance of nine NLP models at the two tasks of identifying cancer response and cancer progression within imaging reports at a single academic center among patients with non-small cell lung cancer. We trained the classification models under different conditions, including training sample size, classification architecture, and language model pre-training. The training involved a labeled dataset of 14,218 imaging reports for 1112 patients with lung cancer. A subset of models was based on a pre-trained language model, DFCI-ImagingBERT, created by further pre-training a BERT-based model using an unlabeled dataset of 662,579 reports from 27,483 patients with cancer from our center. A classifier based on our DFCI-ImagingBERT, trained on more than 200 patients, achieved the best results in most experiments; however, these results were marginally better than simpler “bag of words” or convolutional neural network models.

Conclusion
When developing AI models to extract outcomes from imaging reports for clinical cancer research, if computational resources are plentiful but labeled training data are limited, large language models can be used for zero- or few-shot learning to achieve reasonable performance. When computational resources are more limited but labeled training data are readily available, even simple machine learning architectures can achieve good performance for such tasks.
AUD-DSS: a decision support system for early detection of patients with alcohol use disorder
Authors: Ebrahimi Ali , Wiil Uffe Kock , Baskaran Ruben , Peimankar Abdolrahman , Andersen Kjeld , Nielsen Anette Søgaard 
Published: September 2023

Abstract
Background
Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems. Many individuals suffering from AUD never undergo specialist treatment during their addiction due to obstacles such as taboo and the poor performance of current screening tools. Therefore, there is a lack of rapid intervention. This can be mitigated by the early detection of patients with AUD. A clinical decision support system (DSS) powered by machine learning (ML) methods can be used to diagnose patients’ AUD status earlier.

Methods
This study proposes an effective AUD prediction model (AUDPM), which can be used in a DSS. The proposed model consists of four distinct components: (1) imputation to address missing values using the k-nearest neighbours approach, (2) recursive feature elimination with cross validation to select the most relevant subset of features, (3) a hybrid synthetic minority oversampling technique-edited nearest neighbour approach to remove noise and balance the distribution of the training data, and (4) an ML model for the early detection of patients with AUD.
Two data sources, including a questionnaire and electronic health records of 2571 patients, were collected from Odense University Hospital in the Region of Southern Denmark for the AUD-Dataset. Then, the AUD-Dataset was used to build ML models. The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. Finally, a combination of all these models in an ensemble learning approach was selected for the AUDPM.

Results
The results revealed that the proposed ensemble AUDPM outperformed other single models and our previous study results, achieving 0.96, 0.94, 0.95, and 0.97 precision, recall, F1-score, and accuracy, respectively. In addition, we designed and developed an AUD-DSS prototype.

Conclusion
It was shown that our proposed AUDPM achieved high classification performance. In addition, we identified clinical factors related to the early detection of patients with AUD. The designed AUD-DSS is intended to be integrated into the existing Danish health care system to provide novel information to clinical staff if a patient shows signs of harmful alcohol use; in other words, it gives staff a good reason for having a conversation with patients for whom a conversation is relevant.
Acorn: an R package for de novo variant analysis
Authors: Turner Tychele N. 
Published: September 2023

Abstract
Background
The study of de novo variation is important for assessing biological characteristics of new variation and for studies related to human phenotypes. Software programs exist to call de novo variants and programs also exist to test the burden of these variants in genomic regions; however, I am unaware of a program that fits in between these two aspects of de novo variant assessment. This intermediate space is important for assessing the quality of de novo variants and to understand the characteristics of the callsets. For this reason, I developed an R package called acorn.

Results
Acorn is an R package that examines various features of de novo variants including subsetting the data by individual(s), variant type, or genomic region; calculating features including variant change counts, variant lengths, and presence/absence at CpG sites; and characteristics of parental age in relation to de novo variant counts.

Conclusions
Acorn is an R package that fills a critical gap in assessing de novo variants and will be of benefit to many investigators studying de novo variation.
Genetic variation for grain nutritional profile and yield potential in sorghum and the possibility of selection for drought tolerance under irrigated conditions
Authors: Kamal Nasrein Mohamed , Gorafi Yasir Serag Alnor , Tomemori Hisashi , Kim June-Sik , Elhadi Gamila Mohamed Idris , Tsujimoto Hisashi 
Published: September 2023

Abstract
Background
Increasing grain nutritional value in sorghum (Sorghum bicolor) is a paramount breeding objective, as is increasing drought resistance (DR), because sorghum is grown mainly in drought-prone areas. The genetic basis of grain nutritional traits remains largely unknown. Marker-assisted selection using significant loci identified through genome-wide association study (GWAS) shows potential for selecting desirable traits in crops. This study assessed natural variation available in sorghum accessions from around the globe to identify novel genes or genomic regions with potential for improving grain nutritional value, and to study associations between DR traits and grain weight and nutritional composition.

Results
We dissected the genetic architecture of grain nutritional composition, protein content, thousand-kernel weight (TKW), and plant height (PH) in sorghum through GWAS of 163 unique African and Asian accessions under irrigated and post-flowering drought conditions. Several QTLs were detected. Some were significantly associated with DR, TKW, PH, protein, and Zn, Mn, and Ca contents. Genomic regions on chromosomes 1, 2, 4, 8, 9, and 10 were associated with TKW, nutritional, and DR traits; colocalization patterns of these markers indicate potential for simultaneous improvement of these traits. In African accessions, markers associated with TKW were mapped to six regions also associated with protein, Zn, Ca, Mn, Na, and DR, suggesting the potential for simultaneous selection for higher grain nutrition and TKW. Our results indicate that it may be possible to select for increased DR on the basis of grain nutrition and weight potential.

Conclusions
This study provides a valuable resource for selecting landraces for use in plant breeding programs and for identifying loci that may contribute to grain nutrition and weight with the hope of producing cultivars that combine improved yield traits, nutrition, and DR.
Exploring multidrug-resistant Klebsiella pneumoniae antimicrobial resistance mechanisms through whole genome sequencing analysis
Authors: Yang Jing , Zhang Kai , Ding Chen , Wang Song , Wu Weiwei , Liu Xiangqun 
Published: September 2023

Abstract
Background
Antibiotic-resistant Klebsiella pneumoniae has emerged as a critical public health threat worldwide. Understanding the antimicrobial resistance mechanisms of multidrug-resistant K. pneumoniae (MDR-Kp) and its prevalence in time and space would provide clinical significance for managing pathogen infection.

Methods
Eighteen clinical MDR-Kp strains were analyzed by whole genome sequencing (WGS), and the antimicrobial resistance genes and associated resistance mechanisms were compared with results obtained from the conventional microbiological test (CMT). The sequence homology across strains in our study and those previously collected over time from a wide geographical region was assessed by phylogenetic analysis.

Results
MDR-Kp strains were collected from eighteen patients who had received empirical treatment before strain collection, with sputum (83.3%, 15/18) being the primary source of clinical samples. The commonly received treatments include β-lactamase inhibitors (55.6%, 10/18) and carbapenems (50%, 9/18). Using CMT, we found that all 18 strains were resistant to aztreonam and ciprofloxacin, while 14 (77.8%) showed resistance to carbapenem. Polymyxin B and tigecycline were the only antibiotics to which MDR-Kp strains were sensitive. A total of 42 antimicrobial resistance mechanisms were identified by WGS, surpassing the 40 detected by the conventional method, with 25 mechanisms shared between the two techniques. Despite a 100% accuracy rate of WGS in detecting penicillin-resistant strains, the accuracy in detecting cephalosporin-resistant strains was only at 60%. Among all resistance genes identified by WGS, Klebsiella pneumoniae carbapenemase-2 (KPC-2) was present in all 14 carbapenem-resistant strains. Phenotypic analysis indicated that sequence type (ST) 11 isolates were the primary cause of these MDR-Kp infections. Additionally, phylogenic clustering analysis, encompassing both the clinical and MDR-Kp strains previously reported in China, revealed four distinct subgroups. No significant difference was observed in the sequence homology between K. pneumoniae strains in our study and those previously collected in East China over time.

Conclusion
The application of WGS in identifying potential antimicrobial-resistant genes of MDR-Kp has demonstrated promising clinical significance. Comprehensive genomic information revealed by WGS holds the promise of guiding treatment decisions, enabling surveillance, and serving as a crucial asset in understanding antibiotic resistance.
Gut microbiota changes in horses with Chlamydia
Authors: Jin Youshun , Li Wei , Ba Xuli , Li Yunhui , Wang Yanyan , Zhang Huaiyu , Li Zhaocai , Zhou Jizhang 
Published: September 2023

Abstract
Background
Zoonotic diseases pose a significant threat to public health. Chlamydia, as an intracellular pathogen, can colonize the intestinal tract of humans and animals, changing the gut microbiota. However, only a few studies have evaluated alterations in the gut microbiota of horses infected with Chlamydia. Therefore, this study aimed to investigate gut microbiota and serum biochemical indicators in horses with Chlamydial infection (IG) and healthy horses (HG). Fecal and blood samples were collected from 16 horses (IG: 10; HG: 6) before morning feeding for the determination of gut microbiota and serum biochemical parameters.

Results
The results showed that total globulin (GLB), alanine aminotransferase (ALT), and creatine kinase (CK) levels were significantly increased in IG compared with HG. Notably, the gut microbial diversity increased in IG compared with HG. Furthermore, Moraxellaceae and Akkermanisa abundance decreased in IG, while Streptococcus, Treponema, Prevotella, and Paraprevotella abundances (13 genera of bacterial species) increased. Compared with HG, carbohydrate metabolism increased in IG while amino acid metabolism decreased. In addition, the abundance of 18 genera of bacteria was associated with the level of five serum biochemical indicators.

Conclusions
In summary, this study elucidated the influence of Chlamydia infection in horses on the gut microbiota, unraveling consequential alterations in its composition and metabolic profile. Therefore, this study improves the understanding of Chlamydia-induced intestinal infections.
GhCKX14 responding to drought stress by modulating antioxi-dative enzyme activity in Gossypium hirsutum compared to CKX family genes
Authors: Li Tengyu , Luo Kun , Wang Chenlei , Wu Lanxin , Pan Jingwen , Wang Mingyang , Liu Jinwei , Li Yan , Yao Jinbo , Chen Wei , Zhu Shouhong , Zhang Yongshan 
Published: September 2023

Abstract
Background
Cytokinin oxidase/dehydrogenase (CKX) plays a vital role in response to abiotic stress through modulating the antioxidant enzyme activities. Nevertheless, the biological function of the CKX gene family has yet to be reported in cotton.

Result
In this study, a total of 27 GhCKXs were identified by the genome-wide investigation and distributed across 18 chromosomes. Phylogenetic tree analysis revealed that CKX genes were clustered into four clades, and most gene expansions originated from segmental duplications. The CKXs gene structure and motif analysis displayed remarkably well conserved among the four groups. Moreover, the cis-acting elements related to the abiotic stress, hormones, and light response were identified within the promoter regions of GhCKXs. Transcriptome data and RT-qPCR showed that GhCKX genes demonstrated higher expression levels in various tissues and were involved in cotton’s abiotic stress and phytohormone response. The protein-protein interaction network indicates that the CKX family probably participated in redox regulation, including oxidoreduction or ATP levels, to mediate plant growth and development. Functionally identified via virus-induced gene silencing (VIGS) found that the GhCKX14 gene improved drought resistance by modulating the antioxidant-related activitie.

Conclusions
In this study, the CKX gene family members were analyzed by bioinformatics, and validates the response of GhCKX gene to various phytohormone treatment and abiotic stresses. Our findings established the foundation of GhCKXs in responding to abiotic stress and GhCKX14 in regulating drought resistance in cotton.
Study on the determinants of health professionals’ performance on diabetes management care in China
Authors: Jing Shanshan , Yu Yahang , Yuan Beibei 
Published: September 2023

Abstract
Background
As the direct providers of diabetes management care in primary health care facilities (PHFs) in China, health professionals’ performance on management care of diabetes determines the quality of services and patients’ outcomes. This study aims to analyze the key determinants of health professionals’ performance on diabetes management care in PHFs in China.

Methods
We conducted a cross-sectional study in 72 PHFs in 6 cities that piloted the contracted family doctor service (CFDS). Self-developed questionnaire was used to measure three kinds of factors (capacity, motivation and opportunity) potentially influencing the performance of health professionals. The performance of diabetes management care in the study was measured as whether health professionals delivered 7 service items required by the National Basic Public Health Service Guideline with a total of 7 points and was divided into three grades of good, medium and bad. The questionnaire is self-administered by all the health professionals involved in the study with the number of 434. The Chi-square tests were used to compare differences of performance on diabetes management care among health professionals with different characteristics. The ordinal logistic regression was used to analyze the determinants on the performance of diabetes management care.

Results
Health professionals who got higher score on diabetes knowledge test had odds of better performance on diabetes management care (OR = 1.529, P < 0.001). health professionals with higher degree of self-reported satisfaction on training (OR = 1.224, P < 0.05) and perception of decreasing workload (OR = 3.336, P < 0.01) had odds of better performance on diabetes management care. While health professionals with negative feeling on information system support had odds of worse performance on diabetes management care (OR = 0.664, P < 0.01).

Conclusions
Attention should be paid to the training of health professionals’ knowledge on diabetes management capacity. Furthermore, measures to improve training for health professionals could satisfying their needs for self-growth and improve the motivation of health professionals. The information system supporting management care should be improved continuously to improve the health professionals’ working opportunities and decrease the workload.
Seasonality of cholera in Kolkata and the influence of climate
Authors: Shackleton Debbie , Economou Theo , Memon Fayyaz Ali , Chen Albert , Dutta Shanta , Kanungo Suman , Deb Alok 
Published: September 2023

Abstract
Background
Cholera in Kolkata remains endemic and the Indian city is burdened with a high number of annual cases. Climate change is widely considered to exacerbate cholera, however the precise relationship between climate and cholera is highly heterogeneous in space and considerable variation can be observed even within the Indian subcontinent. To date, relatively few studies have been conducted regarding the influence of climate on cholera in Kolkata.

Methods
We considered 21 years of confirmed cholera cases from the Infectious Disease Hospital in Kolkata during the period of 1999–2019. We used Generalised Additive Modelling (GAM) to extract the non-linear relationship between cholera and different climatic factors; temperature, rainfall and sea surface temperature (SST). Peak associated lag times were identified using cross-correlation lag analysis.

Results
Our findings revealed a bi-annual pattern of cholera cases with two peaks coinciding with the increase in temperature in summer and the onset of monsoon rains. Variables selected as explanatory variables in the GAM model were temperature and rainfall. Temperature was the only significant factor associated with summer cholera (mean temperature of 30.3 °C associated with RR of 3.8) while rainfall was found to be the main driver of monsoon cholera (550 mm total monthly rainfall associated with RR of 3.38). Lag time analysis revealed that the association between temperature and cholera cases in the summer had a longer peak lag time compared to that between rainfall and cholera during the monsoon. We propose several mechanisms by which these relationships are mediated.

Conclusions
Kolkata exhibits a dual-peak phenomenon with independent mediating factors. We suggest that the summer peak is due to increased bacterial concentration in urban water bodies, while the monsoon peak is driven by contaminated flood waters. Our results underscore the potential utility of preventative strategies tailored to these seasonal and climatic patterns, including efforts to reduce direct contact with urban water bodies in summer and to protect residents from flood waters during monsoon.