bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations of the Body Roundness Index with Prediabetes and Diabetes in American Adults: Cross-Sectional Analysis
Authors: Song Shangyi , Yin Jiahui , Liu Yecun , Li Xuhao , Li Meng , Zhang Wenhui , Liu Yuanxiang , Yang Jiguo 
Published: September 2024

Abstract

Background
Prediabetes and diabetes are associated with obesity, and the body roundness index (BRI) is a new obesity index that more accurately reflects body fat and visceral fat levels. The relationships between BRI and prediabetes and diabetes are currently unknown, and we aimed to investigate the relationships between BRI and the prevalence of prediabetes and diabetes.
Methods
A cross-sectional study was conducted using data from the 2005–2020 NHANES, which included a total of 46,447 participants. We used restricted cubic spline (RCS) analysis, logistic regression analysis, and subgroup analysis to assess the associations of BRI with prediabetes and diabetes. We assessed the ability of the BRI and body mass index (BMI) to identify prediabetes and diabetes patients via receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) analysis and compared the results via the Delong test.
Results
Of the 46,447 participants aged 18 years and older included in the study, 15808 had prediabetes and diabetes. According to the fully adjusted models, a positive association was observed between BRI and the prevalence of prediabetes and diabetes (OR = 1.17, 95% CI: 1.14–1.20; P < 0.0001). Compared with those in the lowest quartile, individuals in the highest quartile of BRI had a 125% increased risk of prediabetes and diabetes (OR = 2.20, 95% CI: 1.88–2.57; P < 0.0001). The associations between BRI and prediabetes and diabetes persisted in the subgroup analyses. ROC analysis revealed that the BRI (AUC = 0.695) was a stronger predictor of prediabetes and diabetes than BMI was (AUC = 0.651).
Conclusions
An elevated BRI is associated with an increased prevalence of prediabetes and diabetes in the U.S. population, and the BRI is a stronger predictor of prediabetes and diabetes than BMI is. Maintaining an appropriate BRI is recommended to reduce the incidence of prediabetes and diabetes.
Evaluating Institutional Electives - Insights from stakeholders
Authors: Poorey Ketaki , Manzoor Abid , Mittal Sachendra , Tiwari Mukesh 
Published: September 2024

Abstract

Background:
Elective courses offer students the opportunity to pursue specialized interests beyond the standard curriculum. India adopted electives formally from MBBS 2019 batch, aligning with Competency-Based Medical Education (CBME). Challenges in organizing, teaching, and assessing electives persist, with disparities in execution and student perceptions. This project aims to assess the Electives program's effectiveness, gathering data on stakeholders' experiences and identifying challenges and recommendations to serve students' educational objectives and enhance their learning experience.
Aim
To Evaluate Institutional Electives in Undergraduate Medical Curriculum.
Objectives:
1. To analyze the students' & faculty perceptions for the elective program.
2. To identify the challenges faced by the students & faculty during electives.
3. To formulate recommendations based on the insights gathered from focus group discussions (FGDs) and survey responses.
Methodology:
Observational Qualitative Study, which will include volunteers from the MBBS 3rd year part 1, admission batch 2020 students and all the faculty and administrators involved in the Electives Program. Their perception on the effectiveness and overall impact of the Electives program would be collected in the form of questionnaires and Focused group discussions would be conducted to know in detail the challenges, best practices and recommendations from faculty and students to improve the Electives program.
Results:
The study found high satisfaction with the Electives program among both students and faculty. They emphasized that the time duration of direct engagement, teaching methodologies and assessment criteria had a significant impact on their choices and satisfaction. Participants provided suggestions for revising the electives to be more structured, clinical-oriented and the addition of sports/general electives to the pool.
Conclusions:
Gathering the stakeholder perceptions, challenges, and recommendations regarding the Electives program it is clear that Electives have immense potential to make an effective educational impact.
Proteomic mechanism of sugar and organic acid metabolism during Korla fragrant pear (Pyrus sinkiangensis Yü) fruit development
Authors: Wang Yi , Deng Yonghui , Jiang Wei , An Shijie , Ma Ling , Wang Zhengdong , Zheng Qiangqing , Yan Pan , Chen Qiling 
Published: September 2024

Abstract

Korla fragrant pear (Pyrus sinkiangensis Yü) fruit development involves complex physiological and biochemical processes; however, few data are available at the proteomic and metabolomic levels, which would be helpful for understanding the molecular mechanisms of fruit and quality development. In our study, Korla fragrant pear was investigated across three important stages, namely, early development (5 weeks after flower blooming, WAF), middle development (10WAF), and near ripening (15WAF), via tandem mass tag (TMT) labeling technology. Ultra-performance liquid chromatography‒tandem mass spectrometry (UPLC‒MS/MS) was subsequently performed to detect changes in the concentrations of sugars and organic acids in pear fruits at different developmental stages. Through proteomic and metabolomic analyses, we identified a total of 8487 proteins expressed during pear fruit development, and 3762 differentially expressed proteins (DEPs) were characterized in Korla fragrant pear at three fruit development stages. Moreover, 27 soluble sugars and 43 organic acids were found to accumulate differentially in the fruit at different developmental stages. The functional annotation of the DEPs revealed that many were involved in flavonoid biosynthesis, starch and sucrose metabolism, fructose and mannose metabolism, galactose metabolism, galactose metabolism and organic acid metabolism. The expression of proteins related to sugar metabolism and accumulation increased with increasing fruit development stage, which was consistent with the trend of soluble sugar content during fruit development. All 6 disaccharides, including cellobiose (Cel), lactose (Lac), maltose (Mal), trehalose (Tre), phenylglucoside (Phe) and sucrose (Suc), detected in this study were present at low levels in the early stages of fruit development but accumulated in large amounts from 15 WAF to 20 WAF, which may explain the high sweetness of ripe Korla fragrant pear. In addition, the expression levels of proteins related to organic acid metabolism were consistent with the trends of organic acid contents detected by UPLC‒ESI‒MS during fruit development. The contents of 34 organic acids are low in ripe Korla fragrant pear, and the ripe pear fruits used for consumption contain mainly nine organic acids, which are 2-hydroxyphenylacetic acid, homovanillic acid, pyruvic acid, cis-aconitic acid, 3-methyladipic acid, hydroxyphenyllactic acid, taurine, fumaric acid and tartaric acid. The low levels of organic acids detected during fruit development may explain the low organic acid content of Korla fragrant pear. Thus, our proteomic and metabolomic analyses reveal the molecular basis for the characteristics of high sweetness and low organic acidity in Korla fragrant pear.
Optimal Deep Barcodes for Fast Image Retrieval
Authors: Khosrowshahli Rasa , Kheiri Farnaz , Bidgoli Azam Asilian , Tizhoosh Hamid Reza , Makrehchi Masoud , Rahnamayan Shahryar 
Published: September 2024

Abstract

Binary encoding of features has proven to be a versatile tool for optimizing data processing and memory efficiency in various machine learning applications. This includes deep barcoding of images for retrieval of similar cases among millions of indexed images. Despite the recent advancement in barcode generation methods, converting hyperdimensional feature vectors (e.g., deep features) to compact and discriminative binary barcodes is still an urgent necessity and widely an unresolved problem. MinMax barcoding of features is one of the efficient binarization methods, where the accuracy of the generated barcodes is inherently dependent on the feature order due to its computation process. In this paper, we propose a combinatorial evolutionary framework for optimizing barcodes. The primary goal is to determine an optimal permutation of extracted deep features, leading to more accurate barcodes. The performance of the proposed model has been assessed in downstream image retrieval tasks using a variety of datasets. This evaluation includes medical images from The Cancer Genome Atlas (TCGA), a comprehensive publicly available dataset, as well as images from a COVID-19 dataset. In addition, the model's performance was tested on diverse non-medical image collections, such as CIFAR-10 and Fashion-MNIST. Our findings demonstrate that optimizing barcodes significantly enhances retrieval accuracy across a wide range of applications, highlighting the applicability and potential of barcodes in various domains.
Bartter Syndrome Type 2 in Adolescence: Clinical Features and Genetic Confirmation
Authors: Lamichhane Seshkanta , Wasti Kushal , Bajracharya Manil Ratna , Karki Buddha Bahadur , Achhami Eliz 
Published: September 2024

Abstract

Background
Bartter Syndrome (BS) is a rare salt-losing tubulopathy characterized by impaired sodium reabsorption and excessive salt excretion. Type II Bartter Syndrome, usually presenting in the neonatal period, is associated with mutations in the KCNJ1 gene. Reporting this case adds to the limited data on late-onset presentations of BS type 2, highlighting its clinical variability.
Case Presentation:
An 18-year-old female presented with polydipsia, frequent urination, muscle cramps, and fatigue. She was found to have hypokalemic metabolic alkalosis, hypercalciuria, and hypomagnesemia. Imaging revealed bilateral renal medullary nephrocalcinosis. Genetic testing identified heterozygous mutations in the KCNJ1 gene, confirming BS type 2. The patient's birth weight was 2.7 kg, with no history of polyhydramnios or post-natal complications. Management included potassium and magnesium supplementation, and NSAIDs to inhibit prostaglandin synthesis.
Conclusions
This case underscores the importance of considering rare genetic renal tubular disorders like BS type 2 in patients with persistent electrolyte abnormalities, polyuria, and nephrocalcinosis. Genetic testing is crucial for diagnosis and management, which can significantly improve the patient's quality of life and prevent disease progression.
Spatial Immunogenomic Patterns Associated with Lymph Node Metastasis in Lung Adenocarcinoma
Authors: Meng Fanjie , Li Hao , Jin Ruoyi , Yang Airong , Luo Hao , Li Xiao , Wang Peiyu , Zhao Yaxing , Chervova Olga , Tang Kaicheng , Li Qing , Cheng Sida , Hu Bin , Li Yun , Sheng Jianpeng , Yang Fan , Carbone David , Chen Kezhong , Wang Jun 
Published: September 2024

Abstract

Background
Lung adenocarcinoma (LUAD) with lymph node (LN) metastasis is associated with poor prognosis, yet the specific mechanisms involved remain unclear. The objective of this investigation is to elucidate the immunogenomic landscape associated with LUAD with LN metastasis.
Methods
We utilized broad-panel next-generation sequencing (NGS) on a cohort of 257 LUAD patients who underwent surgical treatment. This approach allowed us to understand the molecular landscape of tumors and identify targetable driver-gene alterations. We also employed multiplex immunohistochemistry (mIHC) on the propensity score matching cohort, which enables comprehensive profiling of the tumor immune microenvironment while preserving cellular metaclusters, interactions and neighborhood functional units. By integrating data from both NGS and mIHC, we not only discerned spatial immunogenomic patterns within this meticulously matched cohort but also developed and independently validated a predictive model for LN stage.
Results
Our analysis revealed distinct patterns of immunogenomic alterations correlated with LN metastasis stages. Specifically, increased mutation frequencies in genes such as PIK3CG, ATM, BRD4, and KMT2B were observed alongside LN metastasis. Additionally, an enrichment of macrophages and regulatory T cells was associated with the immunogenomic patterns. Furthermore, a novel predictive model for LN metastasis likelihood was developed, offering potential benefits for patients ineligible for surgery.
Conclusions
This study offers an in-depth analysis of the genetic and immune profiles in LUAD with LN metastasis, identifying key immunogenomic patterns linked to metastasis. The creation of a predictive model from these insights marks a critical advancement in personalized treatment, underscoring its promise for enhancing patient management.
Integrating 18 F-FDG PET/CT Radiomics and Body Composition for Enhanced Prognostic Assessment in Patients with Esophageal Cancer
Authors: Zhou Yeye , Zhou Jin , Cai Xiaowei , Ge Shushan , Sang Shibiao , yang Yi , Zhang Bin , Deng Shengming 
Published: September 2024

Abstract

Background This study aimed to develop a predictive model utilizing radiomics and body composition features derived from 18F-FDG PET/CT scans to forecast progression-free survival (PFS) and overall survival (OS) outcomes in patients with esophageal squamous cell carcinoma (ESCC).Methods We analyzed data from 91 patients who underwent baseline 18F-FDG PET/CT imaging. Radiomic features extracted from PET and CT images and subsequent radiomics scores (Rad-scores) were calculated. Body composition metrics were also quantified, including muscle and fat distribution at the L3 level from CT scans. Multiparametric survival models were constructed using Cox regression analysis, and their performance was assessed using the area under the time-dependent receiver operating characteristic (ROC) curve (AUC) and concordance index (C-index).Results Multivariate analysis identified Rad-scorePFS (P = 0.003), sarcopenia (P < 0.001), and visceral adipose tissue index (VATI) (P < 0.001) as independent predictors of PFS. For OS, Rad-scoreOS (P = 0.001), sarcopenia (P = 0.002), VATI (P = 0.037), stage (P = 0.042), and body mass index (BMI) (P = 0.008) were confirmed as independent prognostic factors. Integration of the Rad-score with clinical variables and body composition parameters enhanced predictive accuracy, yielding C-indices of 0.810 (95% CI: 0.737–0.884) for PFS and 0.806 (95% CI: 0.720–0.891) for OS.Conclusions This study underscored the potential of combining Rad-score with clinical and body composition data to refine prognostic assessment in ESCC patients.
Follicular lymphoma or diffuse large B-cell lymphoma: a population based analysis of epidemiological and health economic aspects in Germany
Authors: Berger Karin , Moertl Bernhard , von Bergwelt-Baildon Michael , Obermueller Dominik , Pawlowska-Phelan Dorota , Dreyling Martin 
Published: September 2024

Abstract

Epidemiological data and information on resource consumption, costs and clinical outcomes of the care of patients (pts) with follicular lymphoma (FL) or diffuse large b-cell lymphoma (DLBCL) in Germany are rare. Objective of this study was to generate information filling these evidence gaps. This retrospective cohort study (2015–2020) is based on anonymized, longitudinal health claims data. Subgroup analyses on pts with stem-cell transplant (SCT) were performed. About n = 950 annual prevalent FL-pts and n = 1.360 DLBCL-pts were analysed per year. Mean age of FL-pts was 67 years (SD ± 13), 50,7%-females. In the DLBCL-cohort mean age was 68,6 years (SD ± 13,6), 44,4%-females. The share of “agranulocytosis and neutropenia” as an example of the analyzed side effects was: FL 7,2% and DLBCL 16%. Of the FL-pts 64% had min. one hospital admission, with mean 2 admissions (SD ± 2,3) and a mean duration of 21 days (SD ± 44,7) per year. In the DLBCL-cohort 78% had a hospitalization with 2,9 admissions (SD ± 3,1) and 29 inpatient days (SD ± 47,5). Mean annual costs: FL €15.258 per-patient (SD ± 20.367) and DLBCL €23.455 (SD ± 32.892) per-patient. Mean 12-month costs after autologous-SCT were: FL €46.270 (SD ± 21.936) and DLBCL €56.558 (SD ± 45.926); for allogeneic-SCT (only DLBCL-cohort): €161.662 (SD ± 75.266). This study demonstrate a high burden associated with malignant lymphomas. A considerable number or side effects is documented, indicating a difference between the entities. Length of inpatient stay is stressful for patients and associated with significant costs. Total spending for r/r-pts who require intensive treatments like SCTs are significant. Future efforts including linkage to additional data sources with complementary clinical-information are needed.
Using Machine Learning Models to Predict the Impact of Template Mismatches on Polymerase Chain Reaction (PCR) Assay Performance
Authors: Knight Brittany , Otwell Taylor , Coryell Michael P. , Stone Jennifer , Davis Phillip , Necciai Bryan , Carlson Paul E. , Sozhamannan Shanmuga , Schubert Alyxandria M. , Yan Yi H. 
Published: September 2024

Abstract

Molecular assays are critical tools for the diagnosis of infectious diseases. These assays have been extremely valuable during the COVID pandemic, used to guide both patient management and infection control strategies. Sustained transmission and unhindered proliferation of the virus during the pandemic resulted in many variants with unique mutations. Some of these mutations could lead to signature erosion, where tests developed using the genetic sequence of an earlier version of the pathogen may produce false negative results when used to detect novel variants. In this study, we assessed the performance changes of 15 molecular assay designs when challenged with a variety of mutations that fall within the targeted region. Using data generated from this study, we trained and assessed the performance of seven different machine learning models to predict whether a specific set of mutations will result in significant change in the performance for a specific test design. The best performing model demonstrated acceptable performance with sensitivity of 82% and specificity of 87% when assessed using 10-fold cross validation. Our findings highlighted the potential of using machine learning models to predict the impact of emerging mutations on the performance of specific molecular test designs.
Study on energy-autonomous technology for energy consumption performance and climate adaptation in independent public toilets
Authors: Bi Dapeng , Zhao Zhonghua , Huang Qunwu , Wang Yiping 
Published: September 2024

Abstract

Public toilets are a vital safety feature for urban operations. Most independent public toilets currently necessitate the installation of energy, water, and sewage piping networks in order to function properly, and they frequently waste a significant amount of energy and water resources during the operation and disposal of excreta. The energy-autonomous technology (EAT) of freestanding public toilets can address the energy-balancing issue of toilet operation and excreta disposal. To assess the climate adaptability of this technology, the study employed DesignBuilder building energy simulation software to model and analyze energy use. According to the data, Nanning is the greatest choice in high-irradiance and high-temperature locations, while Xining provides the most advantage in high-irradiance and low-temperature areas. Battery management may be necessary in Lhasa, Xining, and Tianjin to balance supply and demand. Higher irradiance locations reduce emissions better, and Tianjin’s more balanced summer and winter energy usage makes it an excellent location to use EAT. The independent public toilets can be configured to meet usage demands without being limited by energy or water sources with EAT. This presents a novel idea for the promotion and use of toilets in water-scarce or underdeveloped areas where access to hygienic, sanitary toilets is limited.
Estimation of Daily Reference Evapotranspiration using Machine Learning and Deep Learning Techniques with Sparse Meteorological Data
Authors: Nayak Ajit Kumar , Sarangi A , Pradhan S , Panda R K , Jeepsa N M , Satpathy B S , Kumar Mithlesh 
Published: September 2024

Abstract

Accurate estimation of evapotranspiration is very crucial for enhancing the real time irrigation scheduling and decision making in water resources planning. Traditionally, empirical methods are used to calculate the reference evapotranspiration using available meteorological data. However, in many areas, such data is limited or unavailable for ETo estimation. Hence, this study aims to explore data-driven models like machine learning (ML) and deep learning (DL) for estimating ETo with minimal meteorological data. In this study, five ML models, including linear regression (LR), random forest (RF), support vector regression (SVR), XGBoost, KNN regression, and two deep learning methods such as feedforward neural networks and long-term short-term memory (LSTM), were used to estimate the reference evapotranspiration (ETo) over the Phulnahara canal command area using various combinations of meteorological parameters. The results of these models were compared with the Penman-Monteith-based ETo. The Penman-Monteith-based ETo exhibits a significant positive correlation with sunshine hour and maximum temperature, displaying correlation coefficients of 0.8 and 0.6, respectively, while RHmin and RHmax demonstrate a negative correlation. The findings revealed that when all climate data is available, the coefficient of determination (R2) rises to 0.98. However, when data is limited, it drops to 0.78. The SVR model outperformed other ML models with all input combinations. However, KNN emerged as the most reliable model for estimating ETo with input data of maximum and minimum temperature. Interestingly, we found that even using just three parameters (temperature, wind speed, and relative humidity) or two-parameter combinations (temperature and relative humidity or temperature and wind speed) can yield promising results in ETo estimation. The findings of this study offer valuable insights for estimating ETo in regions with limited climate data, which is crucial for effective agricultural water management.