bluuebunny/crossref_metadata_2025_split huggingface dataset:
Serum- and glucocorticoid-induced kinase3, SGK3, enhances tamoxifen-sensitivity in ER positive breast cancer cell by inhibiting tamoxifen-induced autophagy
Authors: xu Liu , zeng Xin , xu jingjing 
Published: September 2023

Abstract
Tamoxifen, an estrogen antagonist that has been universally used for the treatment of estrogen receptor (ER) positive breast cancer, long-time treatment of tamoxifen may bring patients the drug-resistance ability. So, find the novel mechanisms behind the drug-resistance and discover drug that sensitized tamoxifen is of importance. Serum- and glucocorticoid-induced kinase3, SGK3, belongs to the SGK family of AGC kinase and functions in parallel to AKT downstream of PI3K. Previous studies have shown that SGK3 may be the major kinase responsible for the progression of breast cancer (BC) patients. Autophagy is associated with a variety of pathological conditions including cancer and neurodegenerative diseases. Autophagy has been discovered in tamoxifen resistance, showing important cell survival mechanism causing therapy resistance, combined with autophagy inhibitors increased rate of inhibition to tamoxifen, acquired tamoxifen-resistant breast cancer cell line TAM-R showed higher pro-survival autophagy compared to the parental cells. However, the resistance of tamoxifen whether associated with SGK3 expression level is unknown, and the mechanism of action is also unclear. Here, our study revealed that TAM-R cell line showed a low expression level of SGK3, along with high autophagy compared with parental cell line. Further, our study revealed that overexpression of SGK3 can increase the sensitivity of tamoxifen. Mechanistically, SGK3 increased the sensitivity of tamoxifen is associated with inhibition of autophagy. In total, targeting SGK3 opened a novel strategy to interrupt autophagy and tamoxifen resistance in breast cancer.
Effects of excess air and energy fraction on heat transfer for co-firing of eucalyptus bark and peanut shell blended fuel in a twin-cyclonic swirling fluidized-bed combustor
Authors: Sirisomboon Kasama , Sopamart Nattawat , Srisam Tananon 
Published: September 2023

Abstract
Biomass plays an important role in mitigating greenhouse gas (GHG) emissions. However, the major problem in using biomass is that inconsistent amounts are available throughout the year. The use of mixed fuels is one way to solve this problem. Based on the lumped system analysis method, this experimental study investigated the effects of excess air and energy fraction on the heat transfer coefficients of co-firing eucalyptus bark (EB) and peanut shell (PNS) in a twin-cyclonic swirling fluidized-bed combustor (TS-FBC). The blended fuel was fired at a fixed heat rate for various energy fractions of secondary fuel (EF2) and excess air (EA). The radial and axial heat transfer coefficients were observed: the average heat transfer coefficient of each operating condition showed significant effects for EA, while the effect for EF2 was not obvious. The heat transfer coefficient could be improved by up to 11% in the bed region and by as much as 22% in the freeboard area when EA was increased from 40–80%.
Compressive Mechanical Behaviours of Re-entrant and Anti-tetrachiral Auxetic Structures 3D Printed by Stereolithography Method
Authors: Dave Harshit K. , Saini Mukul , Rajpurohit Shilpesh R. , Bodaghi Mahdi 
Published: September 2023

Abstract

Auxetic cellular structures are a class of advanced structures that possess a negative Poisson’s ratio (NPR) by experiencing lateral expansion/shrinkage under tensile/compressive uniaxial loading. The development of additive manufacturing (AM) technology has made it relatively simple to create complicated shapes, which has gained a lot of attention in auxetic structures as established and novel structural shapes are constructed physically and evaluated to confirm theoretical predictions. In the present research work, an experimental investigation is carried out on re-entrant and anti-tetrachiral auxetic structures fabricated via stereolithography (SLA) based AM technique using Acrylonitrile Butadiene Styrene (ABS) resin. Geometric parameters of re-entrant and anti-tetrachiral auxetic structure are varied to study their influence on the compressive performance namely compression strength, compression modulus and specific energy absorption. Further, the deformation mechanism of auxetic structures is observed in order to comprehend the relationship between the geometric configuration, failure and deformation modes. The compressive behaviour of these auxetic structures is significantly influenced by the selection of geometric parameters. The auxetic structures can offer better mechanical strength and energy absorbing capability via tuning of the geometrical parameters. The findings of this study are anticipated to contribute to a better understanding of the mechanical behaviours of energy-absorbing re-entrant and anti-tetrachiral auxetic structures prepared by SLA because of the absence of such analysis in the specialist literature.
DFUCare: Deep learning platform for diabetic foot ulcer detection, analysis, and monitoring
Authors: Sendilraj Varun , Pilcher William , Choi Dahim , Bhasin Aarav , Bhadada Avika , Bhadada Sanjay , Bhasin Manoj 
Published: September 2023

Abstract
Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients and often result in amputation and even mortality. Early recognition of infection and ischemia is crucial for improved healing, but current methods are invasive, time-consuming, and expensive. To address this need, we have developed DFUCare, a platform that uses computer vision and deep learning (DL) algorithms to non-invasively localize, classify, and analyze DFUs. The platform uses a combination of CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization achieving an F1-score of 0.80 and an mAP of 0.861. Using DL algorithms to identify infection and ischemia, we achieved a binary accuracy of 79.76% for infection classification and 94.81% for ischemic classification on a validation set. DFUCare also measures wound size and performs tissue color and textural analysis to allow comparative analysis of macroscopic features of the wound. We tested DFUCare performance in a clinical setting to analyze the DFUs collected using a cell phone camera. DFUCare successfully segmented the skin from the background, localized the wound with less than 10% error, and predicted infection and ischemia with less than 10% error. This innovative approach has the potential to deliver a paradigm shift in diabetic foot care by providing a cost-effective, remote, and convenient healthcare solution.
Relationship between Body Mass Index and lower limb strength in children and adolescents
Authors: Gomez-Campos Rossana , Vidal-Espinoza Rubén , Lazari Evandro , Urra-Albornoz Camilo , de Campos Luis Felipe Castello Correia , Rivera-Portugal Margot , Luarte-Rocha Cristian , Cossio-Bolaños Marco 
Published: September 2023

Abstract
Background
Body mass index (BMI) is an anthropometric indicator used as a predictor of risk in cardiovascular disease and mortality.
Objective
to verify the linear and nonlinear (quadratic) relationships between BMI and lower limb strength in children and adolescents of both sexes in a region of Chile.
Methodology:
A descriptive (cross-sectional) study was carried out in children and adolescents of school age (6 to 17 years) of both sexes. The sample size was 863 schoolchildren (500 males and 363 females). Weight, height and the Horizontal jump test (HJ) were evaluated. Body Mass Index (BMI) and Z-BMI were calculated according to age and sex.
Results
In males, the explanatory power in the linear model (R = 0.15, R2 = 0.02, RMSE = 39.6) is lower than the non-linear quadratic model (R = 0.22, R2 = 0.05, RMSE = 39.0). In females, the explanatory power in the linear model (R = 0.12, R2 = 0.02, RMSE = 23.2) is lower than the quadratic nonlinear model (R = 0.19, R2 = 0.04, RMSE = 22.9). In the Z-IMC scale, men presented HJ values of: [Low BMI 145.4 ± 39.5cm, normal 164.2 ± 33.6cm, and high BMI 109.0 ± 23.2cm]. In females it was: [Low BMI 108.0 ± 23.0cm, normal 113.5 ± 36.3cm, and elevated BMI 91.5 ± 30.4cm].
Conclusion
The study verified a curvilinear relationship in the form of a parabola (quadratic) between BMI and the HJ test in children and adolescents of both sexes. Schoolchildren in the extreme BMI categories (low and high BMI) reflected low performance in the HJ in relation to schoolchildren with normal BMI.
Determinants of health-related quality of life of patients with type 2 diabetes and multimorbidity: a cross-sectional study
Authors: Kintzoglanakis Kyriakos , Pavlou-Skantzis Leonidas , Themeli Tatiana , Kyprianou Miltiades , Paschou Stavroula A. 
Published: September 2023

Abstract
Purpose
To examine the determinants of health-related quality of life (HRQoL) of patients with type 2 diabetes (T2D) and multimorbidity (MM) (at least one co-occurring condition besides T2D) among socio-demographic, disease-related and MM variables and the association of MM with therapeutic targets.
Methods
A total of 179 people with T2D attending primary care (PC) in Greece answered the 15 Dimension (15D) questionnaire between August 2019 and October 2020. Socio-demographic, disease-related and MM characteristics were recorded. MM was categorized as concordant or discordant whether or not was related to the pathophysiology of T2D. Independent predictors of the 15D score were examined in stepwise regression models among socio-demographic, disease-related and MM variables and the association of MM with glycated haemoglobin (A1C) and low-density lipoprotein cholesterol (LDL-C) was assessed.
Results
The mean 15D score was 0.85 ± 0.11 and the mean MM count was 4.3 ± 1.8. Significant predictors of higher 15D score were male gender, married state, higher monthly income and more physical activity. Significant predictors of lower 15D score were employment, depression, musculoskeletal disease, coronary artery disease, neuropathy and MM count but discordant MM had a more prominent effect. MM count was not significantly correlated with A1C and was correlated with lower LDL-C levels.
Conclusion
Socio-demographic rather than disease-related characteristics and discordant more than concordant co-occurring conditions affected HRQoL of multimorbid patients with T2D how did not have worse (A1C) or achieved better (LDL-C) therapeutic targets. A generalist approach in T2D could be supported in PC within the social determinants of health and MM.
Potentials of Rice bran and Oil palm fibre as substrate for Exo-β-Glucanase -Glucanase Production by Penicillium Chrysogenum
Authors: Onuoha C. C. , Doughari J. H 
Published: September 2023

Abstract
This study explores β-glucanase production from Penicillium chrysogenum using rice bran and oil palm fiber as substrates in a submerged fermentation system. The optimal conditions for exo-β-glucanase production were found to be 96 hours of incubation, a pH of 4.5 for rice bran and 5.5 for oil palm fiber, and temperatures of 30°C for rice bran and 35°C for oil palm fiber, with ammonium sulphate and oil palm fiber as the best nitrogen and carbon sources, respectively. The enzymes were purified with 80% ammonium sulphate and dialysis tubing, resulting in a 1.09 to 1.50-fold increase in purification. The addition of chloride salts, surfactants, and EDTA impacted the activity of the enzyme. Based on its physiochemical properties, the partially purified enzyme has potential industrial and biotechnological applications, such as in beer production and as an additive in laundry detergents and saccharification.
State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis
Authors: Islam Saiful , Khanra Pitambar , Nakuci Johan , Muldoon Sarah F. , Watanabe Takamitsu , Masuda Naoki 
Published: September 2023

Abstract
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
Comparison of Physical, Acid, and Hot Water Scarification on Combinational Dormancy in Eastern Redbud and Judas Tree Seeds
Authors: Geneve Robert L. , Caliskan Servet 
Published: September 2023

Abstract
Eastern redbud and Judas tree seeds show combinational dormancy requiring a scarification treatment to permit imbibition followed by a stratification treatment to relieve physiological dormancy. Hot water scarification was investigated as an alternative to physical and acid scarification in physically dormant eastern redbud and Judas tree seeds. Hot water scarification was an effective treatment to initiate seed imbibition in both species, but there was a relationship between treatment temperature and duration on dormancy release and embryo viability and vigor. The minimum temperature to initiate greater than 50% imbibition was 70 and 50oC in eastern redbud and Judas tree, respectively. Hot water scarification at 100ºC was very effective for physical dormancy release, but even brief exposure to 100ºC reduced embryo germination and growth potential compared to physical or acid stratification. The temperature and duration that provided the highest imbibition (~ 100%) and embryo viability (90–100%) in eastern redbud were 80ºC for 5 minutes and 70ºC for 15 minutes. For Judas tree, the best conditions for imbibition (~ 70–85%) and viability (> 80%) were 70ºC for 10 minutes and 60ºC for 10 or 15 minutes. It is suggested that these temperature and duration combinations be the initial treatments used to evaluate eastern redbud and Judas tree seed lots.
A systematic review of empirical literature on firearm rights restoration
Authors: Kafka Julie M , Rivara Frederick P , Ross Rachel , Rowhani-Rahbar Ali 
Published: September 2023

Abstract
Background: Firearms are commonly used in the United States to perpetrate serious interpersonal harm. Individuals with a criminal record are at elevated risk of perpetrating violence, particularly with a firearm. Accordingly, federal law prohibits the purchase and possession of firearms by certain high-risk groups including those with prior felony or domestic violence misdemeanor convictions. Evidence supports the effectiveness of these prohibitions, but little is known about how often prohibited persons later seek to have their firearm rights restored. We systematically reviewed empirical literature to describe who requests firearm rights restoration in the US, how often it is granted, and whether these individuals engage in subsequent violence or experience related harms. We searched six databases including peer-reviewed research manuscripts from across the disciplines of psychology, criminology, sociology, medicine, and public health, returning 3,990 unique records. Two coders independently reviewed all titles and abstracts.
Findings: After careful review, no articles met our inclusion criteria.
Conclusion: There is a dearth of empirical literature on firearm rights restoration and the risk for subsequent injuries or harms among prohibited persons or among former victims from prior prohibiting crimes. We call for attention to this gap in the research and emphasize a need to build an evidence base that can help inform state policy and courtroom practices regarding the eligibility, appropriateness, and risks related to firearm rights restoration among prohibited persons.
Enhancing Accuracy in Pediatric Inpatient Hospital Cost Estimation: A Machine Learning-Based Approach
Authors: Zafar Sarmad , Hajiani Mishaal Amin , Affan Muhammad , Zubairi Saleha , Hoodbhoy Zahra , Hasan Babar , Mahmood Tariq 
Published: September 2023

Abstract
Purpose: Accurately estimating inpatient billing costs during admission is important for financial planning in healthcare. Traditional methods have limitations in capturing true cost; hence, data-driven approaches are needed to improve hospital cost estimation in complex and dynamic environments. The main objective of this study is to predict a deviation between the initial hospital bill estimate and the actual bill charged at the time of discharge. This study is also focused on identifying the major factors contributing towards the cost of hospital stay.
Methods This study utilized dataset of approximately 22,000 pediatric patients (under 18 years of age). The main features of the dataset included medical conditions, hospital administration details, and socio-demographic information. The methodology utilizes named entity recognition techniques to extract structured data from unstructured textual data. Subsequently, a variety of machine learning classification models are trained and tested to predict deviations in hospital bill estimates.
Results: The boosting ensemble and artificial neural network classifier models performed best in predicting the deviations in the billing cost, with best accuracy, AUC and F1-scores of 80%, 77% and 77% respectively. The analysis of the important features revealed that age, length of stay, financial status of patients as main features to predict deviation in hospital bill estimates.
Conclusions: The results obtained from our study demonstrate that leveraging machine learning techniques provides a reliable and efficient means of improving the performance of hospital billing estimations. These findings have significant implications for healthcare practitioners, enabling them to make more informed decisions and allocate resources effectively.
How does family functioning contribute to adolescents’ positive or negative coping styles?The roles of core self-evaluations and strengths knowledge
Authors: Wen Lan , Huang Weiwei , Zhang Shuyue 
Published: September 2023

Abstract
Design
The current study focuses on the chain mediating roles of core self-evaluations (CSE) and strengths knowledge in the relationship between family functioning and positive coping styles (PCS) and the moderating role of strengths knowledge in the relationship between family functioning and negative coping styles (NCS).
Methods
A total of 2746 (560 boys, 2186 girls) Chinese adolescents participated in this research. These participants completed the Family Functioning Assessment Scale, CSE scale, Strengths knowledge Scale and Simple Coping Scale. Models 1 and 6 of Process were used in SPSS 25 to investigate moderation and mediation roles.
Results
According to the results of the study, on the positive dimension, family functioning positively predicted PCS, with CSE and strengths knowledge formed a chain mediation. CSE and strengths knowledge were partial mediators. On the negative dimension, the direct effect of family functioning on NCS was not significant, but it was indirectly influenced through chain mediation of CSE and strengths knowledge. So CSE and strengths knowledge were full mediators. Moreover, there was a moderating role of strengths knowledge between family functioning and NCS. Simply put: family functioning effectively reduced application of NCS as increase of strengths knowledge. In addition, a new model was constructed, excluding family functioning, and it was found that strengths knowledge had a "masking effect" in mediating between CSE and NCS.
Conclusions
Family functioning and CSE as well as strengths knowledge are protective factors for adolescents’ coping styles. Family is an important source of support for adolescents. Family members, especially parents, must be suitable strength coaches for children. Based on intrinsic aspirations, a good attachment relationship can promote individuals’ CSE and strengths knowledge. But we should pay attention to "Things will develop in the opposite direction when they become extreme." Strengths knowledge is not the higher the better, too much strengths knowledge can lead to too much NCS. By family members optimizing the ecological system and functioning of family, adolescents can optimize their coping styles.
Machine-learning model of eye metastasis in nasopharyngeal carcinoma based on the AdaBoost method
Authors: Rong Ru-Yi , Wen JiaYi , Hu Jin-Yu , Wu Shi-Nan , Xu San-Hua , Zou Jie , He Liangqi , Chen Cheng , Kang Min , Ying Ping , Wei Hong , Ling Qian , Ge Qian-Ming , Shao Yi 
Published: September 2023

Abstract
Background
Nasopharyngeal carcinoma (NPC) is a common cancer of the head and neck, and the eye is a common metastatic site of NPC. This study aimed to use machine learning (ML) to establish a clinical prediction model for ocular metastasis (OM) in NPC patients.
Methods
We retrospectively collected clinical data from 1,855 patients with NPC who were randomized to a training set and internal test set. Patients with NPC were divided into the OM group or the non-ocular metastasis (NOM) group. Independent risk factors for NPC-related hypertension risk were screened with multivariate logistic regression models. Six ML algorithms were used, including AdaBoost (AB), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), bagging (BAG), and XGBoost (XGB). The training set was used to tune the model parameters to determine the final prediction model, and the test set was used to evaluate the training model. We compared the accuracy, sensitivity, area under the ROC curve, F1 score, and specificity of the models to determine the best machine-learning algorithm for predicting the probability of OM in NPC patients. In addition, a web calculator was developed to facilitate its clinical application.
Results
Among these six models, the AB model had the best differential diagnostic ability (F1 score = 0.773, area under the curve = 0.995, accuracy = 0.983, sensitivity = 0.833, and specificity = 0.985). Based on this model, an online web calculator was constructed to calculate the probability of OM in NPC patients to help clinicians differentially diagnose the disease. Finally, the Shapley Supplementary Interpretation library was used to screen the five most important risk factors for OM in NPC patients: TG, Cyfra 21 1, CA199, Hb, TC, and Pathology type.
Conclusion
We developed a risk prediction model for OM in NPC patients using ML methods and demonstrated that the AB model performed best among six ML models. This prediction model can help to identify patients with OM from NPC, provide early and individualized diagnosis and treatment plans, protect patients from OM from NPC, and minimize the burden on society.