bluuebunny/crossref_metadata_2025_split huggingface dataset:
A promising anoikis-related prognostic signature predicts prognosis of cutaneous melanoma
Authors: Yue Zhanghui , Li Xuemei , Wang Dan 
Published: April 2023

Abstract
Skin cutaneous melanoma (SKCM) is a highly aggressive disease with a poor prognosis, especially for advanced tumors. Anoikis, which prevents detached cells from re-attaching to a new substrate in an incorrect location and inhibits their unwanted growth, plays a crucial role in organismal defense. However, little research has focused on the impact of anoikis on prognosis in SKCM. To address this, our study utilized the 27 anoikis-related genes (ARGs) identified from gene set enrichment analysis (GSEA) to divide SKCM patients in the TCGA-SKCM cohort into two clusters, and obtain differentially expressed genes (DEGs) for each cluster. These DEGs were then used in stepwise Cox regression analysis to develop a prediction model for SKCM patients consisting of nine ARGs, called the Anoikis-Related Signature (ARS). Subsequently, we used the risk scores calculated from the ARS to divide SKCM patients into two groups and explored differences in immune microenvironment, immune checkpoint reactivity, and drug sensitivity between the groups. Our results revealed that immunotherapy was ineffective in patients with high ARS. In summary, the ARS can be used to classify the risk and predict the survival outcome of SKCM patients, providing a basis for targeted therapy in SKCM. Keywords: cutaneous melanoma, anoikis, immune response, risk model, prognosis.
Establishment and evaluation of a novel practical tool for the diagnosis of Pre-Sarcopenia in young people with Diabetes Mellitus
Authors: Li Ruina , Lin Sixian , Tu Jiayuan , Chen Yongzhuang , Cheng Bin , Mo Xiaoqiao , Xie Tian 
Published: April 2023

Abstract
Objective: Sarcopenia has been recognized as a third category of complications in people with diabetes. However, few studies focus on the reduction of skeletal muscle mass in young people with diabetes. The aim of this study was to investigate risk factors of pre-sarcopenia in young patients with diabetes and establish a practical tool to diagnose pre-sarcopenia in those people.
Methods: Patients (n = 1246) enrolled from the National Health and Nutrition Examination Survey (NHANES) cycle year of 2011 to 2018 were randomly divided into the training set and validation set. The all-subsets regression analysis was used to select the risk factors of pre-sarcopenia. A nomogram model for the prediction of pre-sarcopenia in the diabetic population was established based on the risk factors. The model was evaluated by the area under the receiver operating characteristic curve for discrimination, calibration curves for calibration, and decision curve analysis curves for clinical utility.
Results: In this study, gender, height, and waist circumference were elected as predictive factors for pre-sarcopenia. The nomogram model presented excellent discrimination in training and validation sets with areas under the curve of 0.907 and 0.912, respectively. The calibration curve illustrated excellent calibration, and the decision curve analysis showed a wide range of good clinical utility.
Conclusions: This study develops a novel nomogram that integrates gender, height, and waist circumference and can be used to easily predict pre-sarcopenia in diabetics. The novel screen tool is accurate, specific, and low-cost, highlighting its potential value in clinical application.
BiodentineTM as a temporary filling in deep carious lesions in permanent teeth: a long-term follow-up
Authors: Martens Luc C. , Cauwels Rita G.E.C. , Acker Jakob W.G. , Joshi Kumbha Raj , Hanet Peter N. , Rahasekharan Sivaprakash 
Published: April 2023

Abstract
Objectives The study aimed to evaluate temporary fillings using Biodentine™ in asymptomatic deep carious lesions after 12, 24 and 36 months in school children from the remote village of Kerung, Nepal.
Material and Methods From November 2018 to November 2019, 96 temporary fillings were placed using Biodentine™ (a hydraulic calcium silicate cement) in permanent molars with deep carious lesions of schoolchildren in the remote district of Kerung, Nepal. These restorations were performed after selective caries removal in a non-dental setting with hand instruments and cotton roll isolation, as electric motors and saliva ejection systems were unavailable. In total, 81 single-surface and 15 multi-surface fillings were placed. Clinical and radiographic follow-up periods encompassed 12, 21 and 33 months, respectively.
Results After 12 months, all single-surface fillings (100%) survived, while all multi-surface fillings were partially or entirely lost. The survival rate of single-surface restorations after 21 and 33 months was 67,6% and 50%, respectively. Radiographically no pathology was observed.
Conclusion This study showed that Biodentine could be used in deep carious lesions as a temporary filling in single surface lesions for at least up to one year and in a substantial number of cases for up to 21 and 33 months.
Clinical relevance The present findings are very advantageous in paediatric patients, even in challenging conditions. Further, this study supports the clinical use of tricalcium silicates for selective caries removal in deep carious lesions.
A Data-driven Approach to Revamp the ACO Risk Adjustment Model
Authors: Park Yubin , Buchan Kevin , Piconne Jason , Sim Brandon 
Published: April 2023

Abstract
Accountable Care Organizations (ACOs) involve groups of healthcare providers who voluntarily come together to deliver coordinated, high-quality care to aligned beneficiaries. Many ACOs, such as the Medicare Shared Savings Program and the ACO REACH program, can participate in alternative payment models that differ from the prevalent Fee-for-Service model. In these alternative payment models, providers and payers share financial risk to align the ACOs' financial incentives with the dual aims of reducing the total cost of care and improving the quality of care. In other words, ACOs could profit by keeping their patients healthy and preventing unnecessary hospitalization. However, to make this financial structure work as intended, there needs to be a Risk Adjustment (RA) model to change reimbursement proportional to a beneficiary's risk; otherwise, ACOs may enroll only healthy patients, i.e., adverse selection. While most ACOs adopt RA models for this reason, the original RA methodology has mostly stayed the same over the last several decades. As a result, some ACO participants have found ways to ``game'' the system: to receive disproportional payments for the risk they bear. To mitigate the waste, the federal government has added various post-adjustment mechanisms, such as mixing the risk-adjusted benchmark with historical spending, adjusting by a coding intensity factor, capping risk score growth rate, and incorporating health equity incentives. Unfortunately, those mechanisms build on top of each other in nonlinear and discontinuous ways, causing their actual effects - and efficacy - to be difficult to disentangle and evaluate. In this paper, we will summarize our lessons from operating one of the most successful ACOs in the nation to help rebuild the RA model based on a data-driven approach. Next, we outline the characteristics of an ideal RA model. Then, we propose a new one that addresses such requirements, eliminating the need for a multi-step process involving nonlinear and discontinuous staging. Finally, we provide experimental results by applying this model to our ACO data and comparing them with the current RA implementation.
Untargeted metabolomic approach based on UHPL-ESI-HRMS to investigate metabolic profiles of different Coffea species and terroir
Authors: Artêncio Mateus Manfrin , Cassago Alvaro Luis Lamas , Silva Renata Kelly , Giraldi Janaina de Moura Engracia , Costa Fernando Batista Da 
Published: April 2023

Abstract
Coffee (Coffea spp) has evolved from an agricultural commodity to a specialty beverage, regarding the product’s trading, appreciation, philosophies, and purposes of consumption. Consequently, part of the coffee industry has focused on the sensory complexion and high-quality to meet engaged consumers. To evaluate the chemical profiles and distinctiveness of natural products from plants, metabolomics has emerged as a valuable tool. In this work, we carried out an untargeted metabolomic approach based on reversed-phase liquid chromatography coupled with mass spectrometry, followed by multivariate statistical analysis to obtain the metabolic fingerprints of 21 coffee samples belonging to two species and five botanical varieties, as follows: C. arabica (var. yellow catuai, yellow bourbon, and yellow obata) and C. canephora (var. conilon, and robusta). The samples were obtained in the 2022 Edition of the “Brazilian International Conference of Coffee Tasters”, state of Rondônia, Brazil. Principal Component Analysis and Orthogonal Projections Latent Structures Discriminant Analysis were performed using the metabolomic data, resulting in the discrimination of coffee samples based on their chemical profiles. Caffeine, DIMBOA-Gl, roemerine, and cajanin were determined as chemical markers for C. canephora samples, and toralactone, cnidilide, LysoPC(18:2(9Z,12Z)), Lysophosphatidylcholine(16:0/0:0), and 2,3-Dehydrosilybin for C. arabicasamples. In addition to the genetic variability, our results show the possible influence of a terroir factor in the production of secondary metabolites of coffee samples, mainly for individuals of C. canephora.
Novel sequence agnostic computational approach based on the proportion of amino acids of the spike protein for reliable discrimination of SARS-CoV2
Authors: C Benedict Paul , P Abusheikh S , R Harini , D Ramya Lakshmi , Fletcher G John 
Published: April 2023

Abstract
Protein sequencing is integral to discovery pipelines. We have developed a sequence agnostic computational approach based on the proportion of amino acids of the spike protein for the reliable discrimination of SARS-CoV2 and other coronaviruses. A total of 717 Complete Spike Protein sequences of Coronaviruses were retrieved from the NCBI Virus database. A Python based algorithm was developed to read the FASTA files and to extract the percentage of individual amino acid residues (https://github.com/ABUSHEIKHSP/aminoacidProfiler). A Go based computer algorithm was developed to analyze the sequences for discriminatory thresholds of signature amino acid proportions (https://github.com/drbenedictpaul/AminoacidSignatureGenerator). The algorithm developed was able to specifically discriminate SARS-CoV2 from other coronaviruses at the threshold of ≥ 7.4% with an amino acid signature comprising Leu, Ser, Thr, Val. In addition, it returned specific signatures for SARS-CoV and MERS as Leu, Ser, Thr and Leu, Ser respectively. The seasonal coronaviruses had exclusively asparagine along with their unique signatures. Study findings demonstrate that this novel pragmatic computational approach can be used assertively to discriminate SARS-CoV2 from other coronaviruses. Considering its versatile applications, the usefulness need not be restrictive but it can be leveraged for diverse applications in protein biology.
Effect of hyperhidrosis on quality of life and its correlation with anxiety
Authors: MONGA PRABHNOOR 
Published: April 2023

Abstract
Hyperhidrosis is a benign disorder (of no danger to health), is rare and is less known and understood however, it does affect a small percentage of people and carries serious implications for an individual’s quality of life and psychosocial well-being. The overhanging question that formed the basis of the study was how it affected various aspects of an individual’s life, their quality of life, and how it correlated to anxiety along with increasing its awareness amongst the general public.
The results were conclusive with the fact that the condition impaired daily activities and in doing so had a negative impact on quality of life. The results showed that associated negative emotions like embarrassment, frustration, and nervousness become second nature. People who experienced excessive sweating reported a lowered self-image, self-esteem, and self-confidence. Some people even reported that they avoided social situations altogether, and people reported that they avoided being in public view entirely pertaining of the fear of judgment and being ridiculed by others. The results were consistent with very few people approaching a doctor for their condition, those who did were suggested how they could greatly benefit from counselling or therapy in order to lessen the personal burden of dealing with excessive sweating during all aspects of their lives.
Analyzing scene image features with adaptive nonlinear diffusion equation filters for public safety project scheduling
Authors: Li Jinmei , Li Qiang , You Yayun , Li Jie 
Published: April 2023

Abstract

The current image feature detection methods fail to perform image edge filtering in all directions, leading to target deviation of the feature detection results, and narrower brightness level range of gray images. Hence, a novel approach based on adaptive nonlinear diffusion equation filtering is proposed for detecting security events in public safety project scheduling. An edge detection operator is developed that can work in any direction, and adaptive nonlinear diffusion image edge morphological composite filtering is implemented using lag threshold and NMS. The shape response of the image edge is calculated to select the adaptive threshold of feature extraction. A gaussian function pyramid is constructed to obtain the feature distribution sequence considering multi-scale factors. The wavelet analysis is used to analyze the gray scale and detail features of the scene images of public security events, and the gray projection method is utilized to detect the target features of dynamic images, completing the scene image feature detection of public security events. The experimental results demonstrate that the proposed feature detection method has high accuracy, shorter time, and ideal application performance.
Futuristic deep learning algorithms for long-term solar power prediction
Authors: Kumar V Suresh , Leelavathi M 
Published: April 2023

Abstract
Solar power(SP) prediction using a futuristic deep-learning(DL) algorithm is an important piece of research. The SP data is accumulated in real-time with a 1 kW capacity that is available in our laboratory. The collected data is pre-processed via, initialization, normalization, and validation for accurate prediction. Data sets are normalized in order to fill in missing values using the K-nearest neighbor(KNN) algorithm and the interpolation method. Then, the data is validated using a newly proposed deep long short-term memory(DLSTM) with a recurrent neural network(RNN) algorithm for solar power prediction(SPP). To express the superiority of the DLSTM-RNN model, it is contrasted to other exciting models. All the DL algorithms are trained and tested using three different activation functions: Sigmoid, ReLU, and tanh with varying values of an epoch. Finally, the precision is evaluated in terms of different performance error indexes, such as the basic error index(BEI) and promoting percentage error index(PPEI).
DNA methylation regulates Vip3Aa resistance in fall armyworm (Spodoptera frugiperda)
Authors: Zou Luming , Liu Zhenxing , Jin Minghui , Wang Peng , Shan Yinxue , Xiao Yutao 
Published: April 2023

Abstract
Vegetative insecticidal proteins (Vips) are widely used in pest management, but Vip resistance is a big threat. DNA methylation plays important roles in regulating the response of biological organisms to environmental stress. In this study, DNA methylation map was developed for fall armyworm (FAW, Spodoptera frugiperda), and its function in regulating FAW Vip3Aa resistance was explored. FAW was screened by Vip3Aa for 10 generations, and bioassays indicated that Vip3Aa resistance increased trans-generationally. Based on the comparison of DNA methylation maps between Vip3Aa-resistant and -susceptible strains showed that gene body methylation was positively correlated with its expression. Moreover, the study demonstrated that a reduction in the methylation density within the gene body of a 3'5'-cyclic nucleotide phosphodiesterase gene resulted in decreased expression and increased resistance of FAW to Vip3Aa, which was validated through RNAi experiments. The mechanism of Vip3Aa resistance will improve the understanding of DNA methylation and its function in lepidoptera and provide a new perspective for making strategies to pest management.
Uptake of modern contraceptive methods among women of reproductive age (18-45) at Chake District-Pemba Tanzania
Authors: Abeid Rehema Abdalla , Sumari Emmanuel I. , Qin Chunxiang 
Published: April 2023

Abstract
Background: The uptake of the modern contraceptive method provides chances for women and couples to reach optimal child spacing, achieve the desired family size and prevent unsafe abortions and maternal deaths. Despite the effort taken to improve the uptake of modern contraceptives, still, prevalence rate (CPR) in Zanzibar remains low (9.1%). In Pemba, few studies have been done on modern contraceptive uptake and little is known about factors that hinder the uptake of modern contraceptives among women of reproductive age. This study investigated the uptake of modern contraceptive methods among women of reproductive age (18-45) and its associated factors.
Methods: This was a quantitative cross-sectional study conducted in Chake District Hospital, Pemba Tanzania. A stratified random sampling technique was used to recruit 214 eligible participants for the study. After we informed the participants, data were collected using a structured English questionnaire. The collected data was analyzed using SPSS version 25, descriptive analysis was done to determine frequencies. A chi-square test was done to determine the association between the study variables and multivariate logistic regression to check the nature and strength of the association. The p<0.05 was considered statistically significant.
Results: This study included 214 women of with majority 79(36.9%) at the age group of 21-30 years, 100(46.7%) had secondary education and 187(87.4%) married. Most of the participants 212(99.1%) have heard about modern contraceptives, with health professionals 191(43.5) being the common source of information. More than half 120(56.1%) of the participants were not using any modern contraceptive method and injectable 38(17.8%) was the commonly reported method among users. Among the users of modern contraceptives reported that self-motivation 47(47.5%) is the midst drive while lack of power to decide 180(84%), fear of divorce 141(66%), and social perception of users as the cause of reduced workforce in future 160(75%) were common barriers. Participants provided suggestions to improve modern contraceptive uptake including male involvement 203(95%) and community awareness 182(85%). Further analysis revealed women with college/university education were 2 times more likely to use modern contraceptives method compare to those with primary or not attended school(p=0.023, OR=2.437, 95% CI: 1.129-5.259). Moreover employed women were 2 times more likely to uptake modern contraceptives uptake compared to unemployed/housewives (p=0.028, OR=1.844, CI=1.068-3.185).
Conclusion: Despite the campaigns and women having awareness of modern contraceptives, still its uptake remains low in Chake district Pemba. Lack of power to decide, fear of divorce, and social perception of users as the cause of reduced workforce in the future were common barriers; male involvement and community engagement in awareness were suggestions to increase modern contraceptive uptake.
Multiparametric MRI Radiomics in Prostate Cancer for Predicting Ki-67 Expression and Gleason Score: a Multicenter Retrospective Study
Authors: Zhou Chuan , Zhang Yun-Feng , Sheng Guo , Wang Dong , Lv Hao-Xuan , Qiao Xiao-Ni , Wang Rong , Chang De-Hui , Zhao Li-Ming , Zhou Fenghai 
Published: April 2023

Abstract
Purpose Prostate cancers (PCas) with high Ki-67 expression and high Gleason Scores (GS) tend to have aggressive clinicopathological characteristics and a dismal prognosis. In order to predict the Ki-67 expression status and the GS in PCa, we sought to construct and verify MRI-based radiomics signatures.Methods and materials: We collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 170 PCa patients at three institutions and extracted 321 original radiomic features from each image modality. We used support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression to select the most informative radiomic features and built predictive models using up sampling and feature selection techniques. Using receiver operating characteristic (ROC) analysis, the discriminating power of this feature was determined. Subsequent decision curve analysis (DCA) assessed the clinical utility of the radiomic features. The Kaplan-Meier (KM) test revealed that the radiomics-predicted Ki-67 expression status and GS were prognostic factors for PCa survival.Result The hypothesized radiomics signature, which included 15 and 9 selected radiomics features, respectively, was significantly correlated with pathological Ki-67 and GS outcomes in both the training and validation datasets. Areas under the curve (AUC) for the developed model were 0.813 (95% CI:0.681,0.930) and 0.793 (95% CI:0.621, 0.929) for the training and validation datasets, respectively, demonstrating discrimination and calibration performance. The model's clinical usefulness was verified by means of DCA. In both the training and validation sets, high Ki-67 expression and high GS predicted by radiomics using SVM models were substantially linked with poor overall survival (OS).Conclusions Both Ki-67 expression status and high GS correlate with PCa patient survival outcomes; therefore, the ability of the SVM classifier-based model to estimate Ki-67 expression status and the Lasso classifier-based model to assess high GS may enhance clinical decision making.
Dynamic migration of microservices for end-to-end latency control in 5G/6G networks
Authors: Kaur Kiranpreet , Guillemin Fabrice , Sailhan Francoise 
Published: April 2023

Abstract
With the adoption of the microservice paradigm by the telecom industry in the design of 5G/6G networks, complex network functions are decomposed into sets of chained sub-functions, which are further deployed using containerized technologies over geographically distributed cloud clusters. Latency-sensitive applications require to carefully orchestrate the allocation and re-arrangement of (micro)services to prevent from a largely segmented placement of microservices. To address this issue in the context of network functions, we introduce a novel placement and migration strategy that chooses those specific microservice(s) to migrate and the optimal destination (data center) while considering the impact of the migration on other microservices.We devise fast and effective heuristics that are extensively studied via simulation experiments, that show that our proposed approach significantly reduces the service latency.