bluuebunny/crossref_metadata_2025_split huggingface dataset:
A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data
Authors: Salehi Amir Reza , Khedmati Majid 
Published: March 2024

AbstractIn this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a combination of over-sampling, under-sampling, and different ensemble algorithms, including Extreme Gradient Boosting (XGBoost), random forest, and bagging, is employed in order to achieve a balanced dataset and address the issues including redundancy of data after over-sampling, information loss in under-sampling, and random sample selection for sampling and sample generation. The performance of the proposed algorithm is evaluated and compared to different state-of-the-art competing algorithms based on 20 benchmark imbalanced datasets in terms of the harmonic mean of precision and recall (F1) and area under the receiver operating characteristics curve (AUC) measures. Based on the results, the proposed CSBBoost algorithm performs significantly better than the competing algorithms. In addition, a real-world dataset is used to demonstrate the applicability of the proposed algorithm.
Analysis of immunoinfiltration and EndoMT based on TGF-β signaling pathway-related genes in acute myocardial infarction
Authors: Shen Jun , Liang Junqing , Rejiepu Manzeremu , Ma Zhiqin , Zhao Jixian , Li Jia , Zhang Ling , Yuan Ping , Wang Jianing , Tang Baopeng 
Published: March 2024

AbstractAcute myocardial infarction (AMI), a critical manifestation of coronary heart disease, presents a complex and not entirely understood etiology. This study investigates the potential role of immune infiltration and endothelial-mesenchymal transition (EndoMT) in AMI pathogenesis. We conducted an analysis of the GSE24519 and MSigDB datasets to identify differentially expressed genes associated with the TGF-β signaling pathway (DE-TSRGs) and carried out a functional enrichment analysis. Additionally, we evaluated immune infiltration in AMI and its possible link to myocardial fibrosis. Key genes were identified using machine learning and LASSO logistic regression. The expression of MEOX1 in the ventricular muscles and endothelial cells of Sprague–Dawley rats was assessed through RT-qPCR, immunohistochemical and immunofluorescence assays, and the effect of MEOX1 overexpression on EndoMT was investigated. Our study identified five DE-TSRGs, among which MEOX1, SMURF1, and SPTBN1 exhibited the most significant associations with AMI. Notably, we detected substantial immune infiltration in AMI specimens, with a marked increase in neutrophils and macrophages. MEOX1 demonstrated consistent expression patterns in rat ventricular muscle tissue and endothelial cells, and its overexpression induced EndoMT. Our findings suggest that the TGF-β signaling pathway may contribute to AMI progression by activating the immune response. MEOX1, linked to the TGF-β signaling pathway, appears to facilitate myocardial fibrosis via EndoMT following AMI. These novel insights into the mechanisms of AMI pathogenesis could offer promising therapeutic targets for intervention.
Knowledge, attitude, and practice toward photoaging in the Chinese population: a cross-sectional study
Authors: Li Yaoying , Hu Tianxing , Xia Xiaoqin , Ge Lan 
Published: March 2024

AbstractTo investigate the knowledge, attitude, and practice (KAP) of photoaging in the Chinese population. This web-based cross-sectional study was conducted between January 2023 and March 2023 among the Chinese population aged 18–80 years old. Participants’ knowledge, attitude, and practice toward photoaging were collected through a self-administered questionnaire. A total of 830 questionnaires were collected, with 826 valid questionnaires and an efficiency rate of 99.52%. There were 274 (33.17%) males and 532 (64.41%) aged 31–51 years old. The average knowledge, attitude, and practice scores were 7 (4, 9) (possible range 0–12), 31.5 (28, 34) (possible range 8–40), and 33 (24, 42) (possible range 11–55), respectively, indicating poor knowledge, good attitude, and moderate practice. Spearman correlation analysis showed that knowledge was negatively correlated with attitude (r = − 0.111, P < 0.05) and practice (r = − 0.113, P < 0.05), and attitude was positively correlated with practice (r = 0.992, P < 0.05). The multivariable linear regression model showed that for each point increase in attitude score, the practice score increased by 2.96 points (β = 2.96, 95% CI 2.91–3.01, P < 0.001). The Chinese population has poor knowledge, good attitude, and moderate practice toward photoaging. A good attitude toward photoaging would lead to good practice, and more outreach and education for the Chinese population might be needed.
Impact of COVID-19 versus other pneumonia on in-hospital mortality and functional decline among Japanese dialysis patients: a retrospective cohort study
Authors: Ikenouchi Ken , Takahashi Daiei , Mandai Shintaro , Watada Mizuki , Koyama Sayumi , Hoshino Motoki , Takahashi Naohiro , Shoda Wakana , Kuyama Tamaki , Mori Yutaro , Ando Fumiaki , Susa Koichiro , Mori Takayasu , Iimori Soichiro , Naito Shotaro , Sohara Eisei , Fushimi Kiyohide , Uchida Shinichi 
Published: March 2024

AbstractCoronavirus disease 2019 (COVID-19) affects both life and health. However, the differentiation from other types of pneumonia and effect of kidney disease remains uncertain. This retrospective observational study investigated the risk of in-hospital death and functional decline in ≥ 20% of Barthel Index scores after COVID-19 compared to other forms of pneumonia among Japanese adults, both with and without end-stage kidney disease (ESKD). The study enrolled 123,378 patients aged 18 years and older from a national inpatient administrative claims database in Japan that covers the first three waves of the COVID-19 pandemic in 2020. After a 1:1:1:1 propensity score matching into non-COVID-19/non-dialysis, COVID-19/non-dialysis, non-COVID-19/dialysis, and COVID-19/dialysis groups, 2136 adults were included in the analyses. The multivariable logistic regression analyses revealed greater odds ratios (ORs) of death [5.92 (95% CI 3.62–9.96)] and functional decline [1.93 (95% CI 1.26–2.99)] only in the COVID-19/dialysis group versus the non-COVID-19/non-dialysis group. The COVID-19/dialysis group had a higher risk of death directly due to pneumonia (OR 6.02, 95% CI 3.50–10.8) or death due to other diseases (OR 3.00, 95% CI 1.11–8.48; versus the non-COVID-19/non-dialysis group). COVID-19 displayed a greater impact on physical function than other types of pneumonia particularly in ESKD.
Psychophysiological strategies for enhancing performance through imagery–skin conductance level analysis in guided vs. self-produced imagery
Authors: Budnik-Przybylska Dagmara , Syty Paweł , Kaźmierczak Maria , Przybylski Jacek , Doliński Łukasz , Łabuda Marta , Jasik Patryk , Kastrau Adrian , di Fronso Selenia , Bertollo Maurizio 
Published: March 2024

AbstractAthletes need to achieve their optimal level of arousal for peak performance. Visualization or mental rehearsal (i.e., Imagery) often helps to obtain an appropriate level of activation, which can be detected by monitoring Skin Conductance Level (SCL). However, different types of imagery could elicit different amount of physiological arousal. Therefore, this study aims: (1) to investigate differences in SCL associated with two instructional modalities of imagery (guided vs. self-produced) and six different scripts; (2) to check if SCL could differentiate respondents according to their sport expertise. Thirty participants, aged between 14 and 42 years (M = 22.93; SD = 5.24), with different sport levels took part in the study. Participants listened to each previously recorded script and then were asked to imagine the scene for a minute. During the task, SCL was monitored. We analysed the mean value, variance, slope and number of fluctuations per minute of the electrodermal signal. Unsupervised machine learning models were used for measuring the resemblance of the signal. The Wilcoxon signed-rank test was used for distinguishing guided and self-produced imagery, and The Mann–Whitney U test was used for distinguishing results of different level athletes. We discovered that among others, self-produced imagery generates lower SCL, higher variance, and a higher number of fluctuations compared to guided imagery. Moreover, we found similarities of the SCL signal among the groups of athletes (i.e. expertise level). From a practical point of view, our findings suggest that different imagery instructional modalities can be implemented for specific purposes of mental preparation.
Exploring the impact of immersive virtual reality on depression knowledge and stigma reduction: a cross-over RCT fMRI study
Authors: Lem Wey Guan , Kawata Kelssy Hitomi dos Santos , Oyama Hiroshi 
Published: March 2024

AbstractThe stigma of mental illness is a form of negative judgmental knowledge and is a barrier to individual seeking treatment. Contact-based educational interventions with first-person perspective (1PP) combined with immersive virtual reality (IVR) is promising as an anti-stigma intervention. This study aims to investigate the effectiveness of 1PP anti-stigma IVR intervention compared to video in enhancing depression knowledge and reducing stigma, as well as to examine the corresponding depression knowledge brain activity change using functional magnetic resonance imaging (fMRI). Participants engaged in a 1PP anti-stigma intervention using both IVR and conventional video, focusing on the daily life and recovery of a patient with mild depression. The change in depression knowledge, stigma-related behavioral, and brain activity using fMRI were measured at pre- and post-interventions. Depression knowledge improved for both interventions; however, only the IVR intervention reduced stigma. In the IVR intervention, depression knowledge score was positively associated with neural response in the right superior frontal gyrus activation, indicative of empathic concern. Conversely, the video intervention correlated with increased activity in the right anterior insula, suggesting a distress-related response. The findings demonstrate that the immersive nature of IVR can reduce stigma more effectively than video intervention. This effectiveness is underpinned by the change in depression knowledge on neural activity, with IVR fostering empathy-related behavioral responses. The results highlight the potential of IVR in enhancing empathic understanding and reducing stigma towards mental illness, emphasizing the need for further exploration into immersive technologies for mental health education.
Migraine headache (MH) classification using machine learning methods with data augmentation
Authors: Khan Lal , Shahreen Moudasra , Qazi Atika , Jamil Ahmed Shah Syed , Hussain Sabir , Chang Hsien-Tsung 
Published: March 2024

AbstractMigraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing techniques that use subjective pain intensity measures are insufficiently accurate to make a reliable diagnosis. Even though headaches are a common condition with poor diagnostic specificity, they have a significant negative influence on the brain, body, and general human function. In this era of deeply intertwined health and technology, machine learning (ML) has emerged as a crucial force in transforming every aspect of healthcare, utilizing advanced facilities ML has shown groundbreaking achievements related to developing classification and automatic predictors. With this, deep learning models, in particular, have proven effective in solving complex problems spanning computer vision and data analytics. Consequently, the integration of ML in healthcare has become vital, especially in developing countries where limited medical resources and lack of awareness prevail, the urgent need to forecast and categorize migraines using artificial intelligence (AI) becomes even more crucial. By training these models on a publicly available dataset, with and without data augmentation. This study focuses on leveraging state-of-the-art ML algorithms, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DST), and deep neural networks (DNN), to predict and classify various types of migraines. The proposed models with data augmentations were trained to classify seven various types of migraine. The proposed models with data augmentations were trained to classify seven various types of migraine. The revealed results show that DNN, SVM, KNN, DST, and RF achieved an accuracy of 99.66%, 94.60%, 97.10%, 88.20%, and 98.50% respectively with data augmentation highlighting the transformative potential of AI in enhancing migraine diagnosis.
eHealth tools use and mental health: a cross-sectional network analysis in a representative sample
Authors: Ochnik Dominika , Cholewa-Wiktor Marta , Jakubiak Monika , Pataj Magdalena 
Published: March 2024

AbstracteHealth tools usage is vital for health care systems and increased significantly after the COVID-19 pandemic, which aggravated mental health issues. This cross-sectional study explored whether sociodemographic characteristics and mental health indices (stress and symptoms of anxiety and depression) were linked to the behavioral intention to use eHealth tools and eHealth tools usage in a representative sample from Poland using a network approach. Measurements were conducted in March 2023 among 1000 participants with a mean age of 42.98 (18–87) years, with 51.50% women. The measures included the behavioral intention to use eHealth tools (BI) based on the UTUAT2; eHealth tool use frequency (use behavior) including ePrescription, eSick leave, eReferral, electronic medical documentation (EMD), Internet Patient Account (IKP), telephone consultation, video consultation, mobile health applications, and private and public health care use; and the PSS-4, GAD-2, and PHQ-2. Furthermore, sociodemographic factors (sex, age, children, relationship status, education, and employment) were included in the research model. Network analysis revealed that mental health indices were weakly related to eHealth tools use. Higher stress was positively linked with mobile health application use but negatively linked to video consultation use. Use of various eHealth tools was intercorrelated. Sociodemographic factors were differentially related to the use of the eight specific eHealth tools. Although mental health indices did not have strong associations in the eHealth tools use network, attention should be given to anxiety levels as the factor with the high expected influence.
Associations of concentrations of eight urinary phthalate metabolites with the frequency of use of common adult consumer and personal-care products
Authors: Jang Eun A , Kim Kyu Nam , Bae Sang Hyuk 
Published: March 2024

AbstractThis study analyzed the relationship between urine concentrations of phthalate metabolites (UCOM) and personal care products (PCPs) used in adults and examined the change in UCOM according to the usage frequency of PCPs based on raw data from the 3rd Korean National Environmental Health Survey conducted between 2015 and 2017. The relationship between PCP use frequency and UCOM was analyzed using multiple regression analysis, adjusting for baseline factors. The regression model consisted of a Crude Model with log-transformed UCOM before and after adjustment for urine creatinine concentrations. Model 1 was additionally adjusted for age, sex, and obesity, while Model 2 was additionally adjusted for smoking, alcohol consumption, pregnancy history, average monthly income of the household, and PCP exposure within the past 2 days. PCP usage frequency was significantly associated with the UCOM without adjustment for urine creatinine and correlated with demographic characteristics, urine creatinine concentration, and PCP exposure within the past 2 days. This study on exposure to urinary phthalates will play a crucial role in Korean public health by aligning with the fundamentals of research priorities and providing representative data on phthalate exposure for conducting population-level studies.
Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms
Authors: Zhang Yufeng , Li Gang 
Published: March 2024

AbstractAbdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.
Rational design of an artificial tethered enzyme for non-templated post-transcriptional mRNA polyadenylation by the second generation of the C3P3 system
Authors: Le Boulch Marine , Jacquet Eric , Nhiri Naïma , Shmulevitz Maya , Jaïs Philippe H. 
Published: March 2024

AbstractWe have previously introduced the first generation of C3P3, an artificial system that allows the autonomous in-vivo production of mRNA with m7GpppN-cap. While C3P3-G1 synthesized much larger amounts of capped mRNA in human cells than conventional nuclear expression systems, it produced a proportionately much smaller amount of the corresponding proteins, indicating a clear defect of mRNA translatability. A possible mechanism for this poor translatability could be the rudimentary polyadenylation of the mRNA produced by the C3P3-G1 system. We therefore sought to develop the C3P3-G2 system using an artificial enzyme to post-transcriptionally lengthen the poly(A) tail. This system is based on the mutant mouse poly(A) polymerase alpha fused at its N terminus with an N peptide from the λ virus, which binds to BoxBr sequences placed in the 3′UTR region of the mRNA of interest. The resulting system selectively brings mPAPαm7 to the target mRNA to elongate its poly(A)-tail to a length of few hundred adenosine. Such elongation of the poly(A) tail leads to an increase in protein expression levels of about 2.5–3 times in cultured human cells compared to the C3P3-G1 system. Finally, the coding sequence of the tethered mutant poly(A) polymerase can be efficiently fused to that of the C3P3-G1 enzyme via an F2A sequence, thus constituting the single-ORF C3P3-G2 enzyme. These technical developments constitute an important milestone in improving the performance of the C3P3 system, paving the way for its applications in bioproduction and non-viral human gene therapy.
Comparative analysis of hemoglobin, potassium, sodium, and glucose in arterial blood gas and venous blood of patients with COPD
Authors: Tang Sitian , Mei Zhu , Huang Dongmei , Liu Ling , Yang Lunyu , Yin Dan , Hu Liyi 
Published: March 2024

AbstractThe study aims to assess the accuracy of the arterial blood gas (ABG) analysis in measuring hemoglobin, potassium, sodium, and glucose concentrations in comparison to standard venous blood analysis among patients diagnosed with chronic obstructive pulmonary disease (COPD). From January to March 2023, results of ABG analysis and simultaneous venous blood sampling among patients with COPD were retrospectively compared, without any intervention being applied between the two methods. The differences in hemoglobin, potassium, sodium, and glucose concentrations were assessed using a statistical software program (R software). There were significant differences in the mean concentrations of hemoglobin (p < 0.001), potassium (p < 0.001), and sodium (p = 0.001) between the results from ABG and standard venous blood analysis. However, the magnitude of the difference was within the total error allowance (TEa) of the United States of Clinical Laboratory Improvement Amendments (US-CLIA). As for the innovatively studied glucose concentrations, a statistically significant difference between the results obtained from ABG (7.8 ± 3.00) mmol·L−1 and venous blood (6.72 ± 2.44) mmol·L−1 was noted (p < 0.001), with the difference exceeding the TEa of US-CLIA. A linear relationship between venous blood glucose and ABG was obtained: venous blood glucose (mmol·L−1) =  − 0.487 + 0.923 × ABG glucose (mmol·L−1), with R2 of 0.882. The hemoglobin, potassium, and sodium concentrations in ABG were reliable for guiding treatment in managing COPD emergencies. However, the ABG analysis of glucose was significantly higher as compared to venous blood glucose, and there was a positive correlation between the two methods. Thus, a linear regression equation in this study combined with ABG analysis could be helpful in quickly estimating venous blood glucose during COPD emergency treatment before the standard venous blood glucose was available from the medical laboratory.
Sorbitol production from mixtures of molasses and sugarcane bagasse hydrolysate using the thermally adapted Zymomonas mobilis ZM AD41
Authors: Phannarangsee Yupaporn , Jiawkhangphlu Bunyapon , Thanonkeo Sudarat , Klanrit Preekamol , Yamada Mamoru , Thanonkeo Pornthap 
Published: March 2024

AbstractByproducts from the sugarcane manufacturing process, specifically sugarcane molasses (SM) and sugarcane bagasse (SB), can be used as alternative raw materials for sorbitol production via the biological fermentation process. This study investigated the production of sorbitol from SM and sugarcane bagasse hydrolysate (SBH) using a thermally adapted Zymomonas mobilis ZM AD41. Various combinations of SM and SBH on sorbitol production using batch fermentation process were tested. The results revealed that SM alone (FM1) or a mixture of SM and SBH at a ratio of 3:1 (FM2) based on the sugar mass in the raw material proved to be the best condition for sorbitol production by ZM AD41 at 37 °C. Further optimization conditions for sorbitol production revealed that a sugar concentration of 200 g/L and a CaCl2 concentration of 5.0 g/L yielded the highest sorbitol content. The maximum sorbitol concentrations produced by ZM AD41 in the fermentation medium containing SM (FM1) or a mixture of SM and SBH (FM2) were 31.23 and 30.45 g/L, respectively, comparable to those reported in the literature using sucrose or a mixture of sucrose and maltose as feedstock. These results suggested that SBH could be used as an alternative feedstock to supplement or blend with SM for sustainable sorbitol production. In addition, the fermentation conditions established in this study could also be applied to large-scale sorbitol production. Moreover, the thermally adapted Z. mobilis ZM AD41 is also a promising sorbitol-producing bacterium for large-scale production at a relatively high fermentation temperature using agricultural byproducts, specifically SM and SB, as feedstock, which could reduce the operating cost due to minimizing the energy required for the cooling system.