bluuebunny/crossref_metadata_2025_split huggingface dataset:
Longitudinal trajectories of treatment burden: A prospective survey study of adults living with multiple chronic conditions in the midwestern United States
Authors: Eton David T , Anderson Roger T , St Sauver Jennifer L , Rogers Elizabeth A , Linzer Mark , Lee Minji K 
Published: February 2022

Objectives Determine whether there are different longitudinal patterns of treatment burden in people living with multiple chronic conditions (MCC) and, if so, explore predictors that might reveal potential routes of intervention. Methods We analyzed data from a prospective mailed survey study of 396 adults living with MCC in southeastern Minnesota, USA. Participants completed a measure of treatment burden, the Patient Experience with Treatment and Self-management (PETS), and valid measures of health-related and psycho-social concepts at baseline, 6, 12, and 24 months. Latent class growth mixture modeling (LCGM) determined trajectories of treatment burden in two summary index scores of the PETS: Workload and Impact. Multivariable logistic regressions were used to identify independent predictors of the trajectories. Results LCGM supported a 2-class model for PETS Workload, including a group of consistently high workload ( N = 69) and a group of consistently low workload ( N = 311) over time. A 3-class model was supported for PETS Impact, including groups of consistently high impact ( N = 62), consistently low impact ( N = 278), and increasing impact ( N = 51) over time. Logistic regression analyses showed that the following factors were associated with patterns of consistently high or increasing treatment burden over time: lower health literacy, lower self-efficacy, more interpersonal challenges with others, and worse subjective reports of physical and mental health (all p < .05). Conclusions Different longitudinal patterns of treatment burden exist among people with MCC. Raising health literacy, enhancing self-efficacy, and lessening the effects of negative social interactions might help reduce treatment burden.
Impact of former smoking exposure on airway eosinophilic activation and autoimmunity in patients with severe asthma
Authors: Klein Ditte K. , Silberbrandt Alexander , Frøssing Laurits , Hvidtfeldt Morten , von Bülow Anna , Nair Parameswaran , Mukherjee Manali , Porsbjerg Celeste 
Published: March 2022

IntroductionSevere eosinophilic asthma is characterised by frequent exacerbations and a relative insensitivity to steroids. Experimentally, smoking may induce eosinophilic airway inflammation, but the impact in patients with severe asthma is not clear.ObjectiveTo investigate the association between smoking exposure in patients with severe asthma, and eosinophilic inflammation and activation, as well as airway autoimmunity and steroid responsiveness.MethodsPatients with severe asthma according to European Respiratory Society/American Thoracic Society criteria were assessed with sputum samples, analysed by cell differential count, and for the presence of free eosinophil granules (FEGs), autoantibodies against eosinophil peroxidase (EPX) and macrophage receptor with collagenous structure (MARCO). A subgroup of patients with eosinophilic airway inflammation was re-assessed after a 2-week course of prednisolone.Results132 severe asthmatics were included in the study. 39 (29.5%) patients had ≥10 pack-years of smoking history: 36 (27.3%) were former smokers and three (2.3%) current smokers; and 93 (70.5%) had <10 pack-years exposure. Eosinophilic airway inflammation was more prevalent among patients with ≥10 pack-years (66.7%), compared to patients with <10 pack-years (38.7%, p=0.03), as was the level of FEGs (p=0.001) and both anti-EPX and anti-MARCO (p<0.05 and p<0.0001, respectively). Omitting current smokers did not affect these associations. Furthermore, prednisolone reduced, but did not normalise, sputum eosinophils in patients with a ≥10 pack-year smoking history.ConclusionIn patients with severe asthma, a former smoking history is associated with eosinophilic airway inflammation and activation and relative insensitivity to steroids, as well as airway autoimmunity.
Integrative analysis of transcriptomics and metabolomics to reveal the melanogenesis pathway of muscle and related meat characters in Wuliangshan black-boned chickens
Authors: Dou Tengfei , Yan Shixiong , Liu Lixian , Wang Kun , Jian Zonghui , Xu Zhiqiang , Zhao Jingying , Wang Qiuting , Sun Shuai , Talpur Mir Zulqarnain , Duan Xiaohua , Gu Dahai , He Yang , Du Yanli , Abdulwahid Alsoufi Mohammed , Li Qihua , Rong Hua , Cao Weina , Su Zhengchang , Zhao Guiping , Liu Ranran , Zhao Sumei , Huang Ying , te Pas Marinus F. W. , Ge Changrong , Jia Junjing 
Published: March 2022

Abstract
Background
Melanin is an important antioxidant in food and has been used in medicine and cosmetology. Chicken meat with high melanin content from black-boned chickens have been considered a high nutritious food with potential medicinal properties. The molecular mechanism of melanogenesis of skeletal muscle in black-boned chickens remain poorly understood. This study investigated the biological gene-metabolite associations regulating the muscle melanogenesis pathways in Wuliangshan black-boned chickens with two normal boned chicken breeds as control.

Results
We identified 25 differentially expressed genes and 11 transcription factors in the melanogenesis pathways. High levels of the meat flavor compounds inosine monophosphate, hypoxanthine, lysophospholipid, hydroxyoctadecadienoic acid, and nicotinamide mononucleotide were found in Wuliangshan black-boned chickens.

Conclusion
Integrative analysis of transcriptomics and metabolomics revealed the dual physiological functions of the PDZK1 gene, involved in pigmentation and/or melanogenesis and regulating the phospholipid signaling processes in muscle of black boned chickens.
Network meta-analysis of transcriptome expression changes in different manifestations of dengue virus infection
Authors: Winter Christine , Camarão António A. R. , Steffen Imke , Jung Klaus 
Published: February 2022

AbstractBackgroundSeveral studies have been performed to study transcriptome profiles after dengue virus infections with partly different results. Due to slightly different settings of the individual studies, different genes and enriched gene sets are reported in these studies. The main aim of this network meta-analysis was to aggregate a selection of these studies to identify genes and gene sets that are more generally associated with dengue virus infection, i.e. with less dependence on the individual study settings.MethodsWe performed network meta-analysis by different approaches using publicly available gene expression data of five selected studies from the Gene Expression Omnibus database. The study network includes dengue fever (DF), hemorrhagic fever (DHF), shock syndrome (DSS) patients as well as convalescent and healthy control individuals. After data merging and missing value imputation, study-specific batch effects were removed. Pairwise differential expression analysis and subsequent gene-set enrichment analysis were performed between the five study groups. Furthermore, mutual information networks were derived from the top genes of each group comparison, and the separability between the three patient groups was studied by machine learning models.ResultsFrom the 10 possible pairwise group comparisons in the study network, six genes (IFI27, TPX2, CDT1, DTL, KCTD14 and CDCA3) occur with a noticeable frequency among the top listed genes of each comparison. Thus, there is an increased evidence that these genes play a general role in dengue virus infections. IFI27 and TPX2 have also been highlighted in the context of dengue virus infection by other studies. A few of the identified gene sets from the network meta-analysis overlap with findings from the original studies. Mutual information networks yield additional genes for which the observed pairwise correlation is different between the patient groups. Machine learning analysis shows a moderate separability of samples from the DF, DHF and DSS groups (accuracy about 80%).ConclusionsDue to an increased sample size, the network meta-analysis could reveal additional genes which are called differentially expressed between the studied groups and that may help to better understand the molecular basis of this disease.
Interchangeability and optimization of heart rate methods for estimating oxygen uptake in ergometer cycling, level treadmill walking and running
Authors: Olsson Karin Sofia Elisabeth , Rosdahl Hans , Schantz Peter 
Published: February 2022

AbstractBackgroundThe heart rate (HR) method enables estimating oxygen uptake (V̇O2) in physical activities. However, there is a paucity in knowledge about the interchangeability of this method when applied to cycling, walking and running. Furthermore, with the aim of optimization, there is a need to compare different models for establishing HR-V̇O2relationships.MethodsTwenty-four physically active individuals (12 males and 12 females) participated. For each participant, two models of HR-V̇O2relationships were individually established in ergometer cycling, level treadmill walking and running. Model 1 consisted of five submaximal workloads, whereas model 2 included also a maximal workload. Linear regression equations were used to estimate V̇O2at seven intensity levels ranging between 25 and 85% of heart rate reserve (HRR). The estimated V̇O2levels were compared between the exercise modalities and models, as well as with data from a previous study.ResultsA high level of resemblance in estimated V̇O2was noted between running and cycling as well as between running and walking, with both model 1 and model 2. When comparing walking and cycling, the V̇O2levels for given intensities of %HRR were frequently slightly higher in walking with both models (range of significant differences: 5–12%). The variations of the estimated individual V̇O2values were reduced when using model 2 compared to model 1, both between and within the exercise modalities.ConclusionThe HR method is optimized by more workloads and wider ranges. This leads to overall high levels of interchangeability when HR methods are applied in ergometer cycling, level treadmill walking and running.
Comparative analysis of explainable machine learning prediction models for hospital mortality
Authors: Stenwig Eline , Salvi Giampiero , Rossi Pierluigi Salvo , Skjærvold Nils Kristian 
Published: February 2022

Abstract
Background
Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acceptance of this technology in healthcare. This mistrust is often credited to the shortage of model explainability and interpretability, where the relationship between the input and output of the models is unclear. Improving trust requires the development of more transparent ML methods.

Methods
In this paper, we use the publicly available eICU database to construct a number of ML models before examining their internal behaviour with SHapley Additive exPlanations (SHAP) values. Our four models predicted hospital mortality in ICU patients using a selection of the same features used to calculate the APACHE IV score and were based on random forest, logistic regression, naive Bayes, and adaptive boosting algorithms.

Results
The results showed the models had similar discriminative abilities and mostly agreed on feature importance while calibration and impact of individual features differed considerably and did in multiple cases not correspond to common medical theory.

Conclusions
We already know that ML models treat data differently depending on the underlying algorithm. Our comparative analysis visualises implications of these differences and their importance in a healthcare setting. SHAP value analysis is a promising method for incorporating explainability in model development and usage and might yield better and more trustworthy ML models in the future.
Total polyp number may be more important than size and histology of polyps for prediction of metachronous high-risk colorectal neoplasms
Authors: Yoon Hyuk , Shin Cheol Min , Park Young Soo , Kim Nayoung , Lee Dong Ho 
Published: March 2022

Abstract
Background
To evaluate whether the risk of metachronous high-risk colorectal neoplasm (HR-CRN) differs according to the indication for surveillance colonoscopy.

Methods
Patients who underwent polypectomy or endoscopic resection of colorectal neoplasms were enrolled and classified into three groups according to the indication for surveillance colonoscopy: advanced colorectal neoplasm (ACRN: adenoma ≥ 10 mm, adenoma with high-degree dysplasia and/or villous component), advanced serrated polyps (ASP: hyperplastic polyp or sessile serrated lesion ≥ 10 mm, traditional serrated polyp), and high-risk polyps (HRP: 3 or more adenomas or serrated polyps). The primary outcome was the development of metachronous HR-CRN, defined as ACRN, ASP, or HRP at the first follow-up colonoscopy.

Results
In total, 367 patients were enrolled (ACRN group: N = 264; ASP group: N = 33; HRP group: N = 70). Among the 160 patients who underwent follow-up colonoscopy, 28 (18%) had HR-CRN. In univariable analysis, indication for surveillance colonoscopy was not found to be associated with the development of metachronous HR-CRN. Instead, the total polyp number at index colonoscopy showed a positive association with the risk of metachronous HR-CRN in trend analysis (p = 0.001). In multivariable analysis, the presence of 5 or more polyps at index colonoscopy was found to be associated with the risk of metachronous HR-CRN (OR, 2.575, p = 0.049) after adjusting for risk factors, such as obesity, diabetes, and smoking.

Conclusions
The risk of metachronous HR-CRN did not differ according to the main indications for surveillance colonoscopy. The presence of 5 or more polyps at index colonoscopy was the only risk factor for metachronous HR-CRN.
Efficacy of sigmoidoscopy for evaluating disease activity in patients with ulcerative colitis
Authors: Park Su Bum , Kim Seong-Jung , Lee Jun , Lee Yoo Jin , Baek Dong Hoon , Seo Geom Seog , Kim Eun Soo , Kim Sang-Wook , Kim So Yeong 
Published: February 2022

Abstract
Background
Endoscopic assessment of disease activity is a key parameter in the management of ulcerative colitis. Whether sigmoidoscopy alone is sufficient to evaluate the disease activity in ulcerative colitis lacks studies.

Methods
We retrospectively analyzed the medical records and endoscopic results of patients with ulcerative colitis followed by colonoscopy in seven tertiary hospitals between January 2012 and December 2018. Endoscopic disease activity was scored using the Mayo endoscopic subscore (MES) and Ulcerative Colitis Endoscopic Index of Severity (UCEIS) for each segment from the colonoscopy images. Concordance was evaluated by comparing the highest MES and UCEIS in the rectosigmoid and proximal regions to confirm the usefulness of sigmoidoscopy.

Results
A total of 500 colonoscopic examinations from 333 patients were enrolled. Only in 7.6% [k(kappa): 0.893, r(Spearman): 0.906, p < 0.001] and 8.6% [k(kappa): 0.890, r(Spearman): 0.914; p < 0.001] of cases, MES and UCEIS scored more severely in the proximal colon. Comparison of active disease (MES ≥ 2) in the rectosigmoid area and the entire colon showed a high concordance rate [k(kappa): 0.899, r(Spearman): 0.904, p < 0.001]. Endoscopic healing (MES = 0) also showed a high concordance rate [k(kappa): 0.882, r(Spearman): 0.887, p < 0.001]. In 38 cases (7.6%) of patients with a higher MES in the proximal area, it was significantly higher in patients with previous extensive colitis.

Conclusions
Sigmoidoscopy and colonoscopy showed a high concordance rate. Therefore, sigmoidoscopy is considered a sufficient substitute for colonoscopy. However, colonoscopy should be considered in patients with previous extensive colitis.
Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data
Authors: Noh Byeonggwan , Park Young Mok , Kwon Yujin , Choi Chang In , Choi Byung Kwan , Seo Kwang il , Park Yo-Han , Yang Kwangho , Lee Sunju , Ha Taeyoung , Hyon YunKyong , Yoon Myunghee 
Published: February 2022

Abstract
Aim
To predict survival time of Korean hepatocellular carcinoma (HCC) patients using multi-center data as a foundation for the development of a predictive artificial intelligence model according to treatment methods based on machine learning.

Methods
Data of patients who underwent treatment for HCC from 2008 to 2015 was provided by Korean Liver Cancer Study Group and Korea Central Cancer Registry. A total of 10,742 patients with HCC were divided into two groups, with Group I (2920 patients) confirmed on biopsy and Group II (5562 patients) diagnosed as HCC according to HCC diagnostic criteria as outlined in Korean Liver Cancer Association guidelines. The data were modeled according to features of patient clinical characteristics. Features effective in predicting survival rate were analyzed retrospectively. Various machine learning methods were used.

Results
Target was overall survival time, which divided into approximately 60 months (= /< 60 m, > 60 m). Target distribution in Group I (total 514 samples) was 28.8%: (148 samples) less than 60 months, 71.2% (366 samples) greater than 60 months, and in Group II (total 757 samples) was 66.6% (504 samples) less than 60 months, 33.4% (253 samples) greater than 60 months. Using NG Boost method, its accuracy was 83%, precision 84%, sensitivity 95%, and F1 score 89% for more than 60 months survival time in Group I with surgical resection. Moreover, its accuracy was 79%, precision 82%, sensitivity 87%, and F1 score 84% for less than 60 months survival time in Group II with TACE. The feature importance with gain criterion indicated that pathology, portal vein invasion, surgery, metastasis, and needle biopsy features could be explained as important factors for prediction in case of biopsy (Group I).

Conclusion
By developing a predictive model using machine learning algorithms to predict prognosis of HCC patients, it is possible to project optimized treatment by case according to liver function and tumor status.
Tropheryma whipplei in the stool samples of children with acute diarrhea: a study from Tehran, Iran
Authors: Sayyahfar Shirin , Latifian Mina , Esmaeili Parisa , Baseri Neda , Bagheri Amiri Fahimeh , Bakhshi Bita , Esteghamati Abdoulreza , Esmaeili Saber 
Published: February 2022

Abstract
Background
Recently, Tropheryma whipplei has been suggested as one of the causative agents of diarrhea among children worldwide. Limited data is available on the prevalence of T. whipplei among children with diarrhea in most countries such as Iran. This study was conducted to evaluate the prevalence of T. whipplei in children with acute diarrhea in Iran.

Methods
In this study, the stool samples were collected from 130 children under 10 years old with acute diarrhea from children's hospitals in Tehran city. Genomic DNA was extracted from stool samples and was tested for the presence of DNA of T. whipplei using the SYBR Green Real-time PCR method. Positive T. whipplei samples were finally confirmed by PCR Product sequencing.

Results
The mean age of participants was 32.5 months, and 54.6% of children were female. Using the SYBR Green Real-time PCR, 9.23% (12/130) of samples were positive for T. whipplei, which were confirmed by sequencing. 66.67% of positive cases were males. The duration of diarrhea in infected children with T. whipplei (83.3%) was significantly longer (OR: 5.93, 95% CI 1.24–28.22) compared to children with negative results (45.8%). Other demographic factors and clinical signs had not a statistically significant relationship with T. whipplei infection.

Conclusions
In this study, T. whipplei was detected in stool samples of children with acute diarrhea. The results indicated that T. whipplei could be associated with childhood diarrhea in Iran. The health care system and physicians should be aware of the presence of T. whipplei infection in Iran, especially in childhood diarrhea.
Grayscale medical image segmentation method based on 2D&3D object detection with deep learning
Authors: Ge Yunfei , Zhang Qing , Sun Yuantao , Shen Yidong , Wang Xijiong 
Published: February 2022

AbstractBackgroundGrayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data.MethodsCombining thresholding method and deep learning, this paper presents a novel method by using 2D&3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes’ bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object.ResultsThree groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance.ConclusionsThe prominent segmentation results demonstrate that the built method based on 2D&3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images.