bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictive Factors of Methotrexate Monotherapy Success in Patients with Rheumatoid Arthritis in a National Referral Center: A Cohort Study
Authors: Hidayat Rudy , Fauzia Fara , Parlindungan Faisal , Wibowo Suryo Anggoro Kusumo , Ariane Anna , Damanik Johanda , Araminta Abirianty Priandani 
Published: July 2024

Abstract

Background
Methotrexate (MTX) remains the recommended first-line treatment for rheumatoid arthritis (RA); however, its response varies and is influenced by various factors. This study aimed to identify predictors of MTX monotherapy treatment success in an Indonesian cohort.
Methods
This retrospective cohort study included newly diagnosed RA patients receiving MTX monotherapy. Treatment success was defined as achieving remission or low disease activity according to Disease Activity Score-28 with erythrocyte sedimentation rate (DAS28-ESR) after 12 months of MTX therapy. The association between demographic, clinical, and laboratory factors and achieving therapy targets was evaluated using multivariate logistic regression analysis.
Results
Among 254 subjects, 59.4% achieved treatment success with MTX monotherapy, with remission attained in 33% and low disease activity in 26.4%. Most subjects were female (95.7%) with a mean age of 48 ± 11 years. Multivariate analysis revealed that lower disease activity (OR 1.97; 95% CI [1.04–3.72]), normal ESR (OR 2.58; 95% CI [1.05–6.34]), normoweight (OR 2.55, 95% CI [1.45–4.49]), and tender joint count ≤ 5 (OR 2.45, 95% CI [1.31–4.58]) were significant predictors of treatment success.
Conclusion
The rate of MTX monotherapy success in our study was 59.4%. Lower disease activity, normal ESR, normoweight, and fewer tender joints at baseline were significant predictors of treatment success.
Satellite Remote Sensing Analysis Using Effective Feature Extraction &amp; Classification Using Deep Learning Technique
Authors: K Thilagavathi , Kumar Rajiv , Ramadass Sudhir , Narayanan Sreekumar 
Published: July 2024

Abstract

In recent years, deep learning (DL) algorithms have earned more attention and popularity in image processing, especially in satellite remote sensing analysis, as they can learn the hierarchical and discriminative feature representations within the data. This research aims to enhance the efficiency of the satellite remote sensing image classification by applying deep learning algorithms. The satellite images from the National Agriculture Imagery Program (NAIP) database are initially collected and fed into the system. Consequently, the collected images are pre-processed to enhance the image quality, further improving the developed system's performance. The image pre-processing module utilizes the Patching/Slicing of HS image and the Image Normalization algorithm for performing tasks such as data cleaning, data interpolation, and data discretization, which aids in minimizing the overfitting challenge of the DL algorithm. Further, feature engineering was done to extract the most important features using the pre-trained Autoencoder model, which reduces the data dimensionality. Finally, train the dense Convolutional Neural Network (CNN) with the extracted features to classify the satellite RS images. The experimental results demonstrate that the developed DL strategy obtained an improved accuracy of 93%, which is greater than the existing cutting-edge models. Also, the proposed algorithm attained 96% specificity, 96% sensitivity, 87% precision, and 90% detection rate. This superior performance of the designed methodology highlights its efficiency in analyzing satellite images.
Prevalence and the associated factors of burnout among the critical healthcare professionals during the post-pandemic era: a multi-institutional survey in Taiwan with a systematic review of the Asian literatures
Authors: Lee Yueh-Lin , Dai Jhih-Wei , Li Xiu-Wei , Chiang Min-Ying , Chen Po-Ting , Lin Yu-Chen , Wang Chien-Ho 
Published: July 2024

Abstract

Background & Aims:
Burnout is a global concern, and critical healthcare professionals have been identified as a high-risk population of burnout. Early identification is crucial, but the prevalence of burnout and its risk factors demonstrate significant geographical variations. This study aims to investigate the prevalence of burnout among critical healthcare professionals and explore potential risk factors during the post-pandemic era in Taiwan.
Methods:
A web-based questionnaire survey was conducted from December 1, 2023, to January 31, 2024, targeting critical healthcare professionals employed in selected medical institutions affiliated with the Chang Gung Memorial Hospital Foundation, one of Taiwan's largest healthcare organizations. Demographic information, the Subjective Happiness Scale (SHS), current work stressors and self-reported general health data were collected. The study utilized the MBI-Human Services Survey for Medical Personnel (MBI-MP). Univariate and multivariate logistic regression were employed to investigate the association between risk factors and each burnout subscales. A systematic review of Asian literature concerning burnout among critical care practitioners was also conducted in accordance with PRISMA guideline.
Results:
In our study, 254 participants were enrolled, with an overall burnout rate of 35.4%. The prevalence of high emotional exhaustion (EE) was 70.9%, high depersonalization (DP) was 56.3%, and low personal accomplishment (PA) was 60.6%. Young, unmarried populations, individuals with limited work experience, longer working hours, and night shifts are potential vulnerable groups susceptible to burnout. The top three stressors identified were excessive workload, the burden of administrative tasks, and a shortage of vacation time. Our systematic review included 20 Asian studies on the same issue, with variable burnout prevalence ranging from 16.3–82.1%.
Conclusion:
The prevalence of burnout was high among critical healthcare professionals in post-pandemic Taiwan, particularly affecting younger, unmarried populations and individuals with limited work experience, longer hours, and more night shifts. The influence of pandemic-related factors has decreased. Regional variations in burnout have been observed across Asia, highlighting the need for further research to identify local risk factors and protect the well-being of professionals and healthcare quality.
Postoperative blood pressure variability as a risk factor for postoperative delirium in the patients receiving cardiac surgery
Authors: Shen# Xiao , Tao# Hong , Chen Wenxiu , Sun Jiakui , Jin Renhua , Zhang Wenhao , Hong Liang , Zhang Cui 
Published: July 2024

Abstract

Background: Delirium is one of the most common neurological complications after cardiac surgery. The purpose of our study was to assess the relationship between perioperative blood pressure variability (BPV) and postoperative delirium (POD) in the patients after cardiac surgery.
Methods: Adult patients received cardiac surgery and stayed in Cardiovascular Intensive Care Unit (ICU) for more than 24h after surgery during the study period between June 2019 and December 2022 were included in this study. Baseline characteristics, perioperative hemodynamic variables and postoperative laboratory results of the cardiac patients were collected and analyzed. Perioperative BPV was quantified by calculating the standard deviation (SD) and average real variability (ARV) of blood pressure. Assessment of delirium was based on the mental status of the patients and CAM-positive. The relationship between perioperative BPV and POD was analyzed by LASSO and logistic regression using R (R package, 4.3.2).
Results: The incidence of POD was 15.0% (324/2164) in the patients receiving cardiac surgery, and the average day for POD occurred at day 3 after surgery. Patients with delirium had markedly lower levels of intraoperative mean blood pressure (BP_mean, P=0.015) and BP variability (BP_arv, P<0.001) as well as postoperative mean blood pressure within 24h (PM_IBPm_24h_mean, P=0.003) when compared to those patients without delirium. Whereas, postoperative ARV for systolic blood pressure (PM_IBPs_24h_arv, 8.64 [7.32, 10.2] vs. 7.91 [6.57, 9.43] mmHg, P<0.001), diastolic blood pressure (PM_IBPd_24h_arv, 4.00 [3.17, 4.83] vs. 3.77 [3.11, 4.60] mmHg, P=0.014) and mean blood pressure (PM_IBPm_24h_arv, 5.23 [4.46, 6.19] vs. 4.94 [4.11, 5.94] mmHg, P=0.001) at 24h was significantly higher in the patients with POD than those without. LASSO regression and further logistic regression revealed that intraoperative BP_arv (OR:0.92, 95%CI: 0.89-0.96, P<0.001), PM_CVPm_24h_mean (mean central venous pressure at 24h postoperatively, OR:1.05, 95%CI: 1.00-1.10, P=0.048) and PM_IBPs_24h_arv (OR:1.17, 95%CI: 1.06-1.30, P=0.002) were independent risk factors for POD.
Conclusions: Postoperatively high BPV exposure rather than hypotension contributed to the occurrence of POD in the patients after cardiac surgery. Maintaining a relatively stable blood pressure after surgery might be beneficial in reducing the incidence of POD in the patients receiving cardiac surgery.
iTRAQ proteomic analysis of exosomes derived from synovial fluid reveals disease patterns and potential biomarkers of Osteoarthritis
Authors: Wu Xiaomin , Li Huaiming , Zou Huichun , Li Jianchi , Meng Fengzhen , Li Zhongjie , Pan Yu , Lui Tun Hing , Pan Xiaohua 
Published: July 2024

Abstract

The exosomes derived from synovial fluid (SF-exo) are indicative of the condition of the originating cell. The proteomics profiles of SF-exo are of great interest for the diagnosis of Osteoarthritis (OA). To delineate the proteomic differences between SF-exo from OA patients and those from healthy individuals, an iTRAQ quantitative proteomics was performed. A total of 439 proteins were identified, with 20 proteins exhibiting increased expression and 5 showing decreased expression in OA group. Bioinformatic analysis showed these differentially expressed proteins (DEPs) were involved in various immunity-related process terms, including complement activation and antigen binding. For further screening, we download a publicly dataset of synovial fluid and compared with our dataset. This comparison identified 5 DEPs were overlapping in two datasets, Protein-protein interaction showed C3, C4B and APOM were part of a tight interaction network. Receiver operating characteristic curve analysis and enzyme-linked immunosorbent assay implied that the 5 DEPs (C3, C4B, APOM, MMP3, DPYSL2) derived from SF-exo were potential diagnostic biomarkers for OA. In conclusion, our study first described the proteomic landscape of SF-exo in OA and identified several potential biomarkers. These findings may provide valuable insights for diagnosis and treatment of OA.
Facile preparation of hydrophobic coatings using modified ZnO/Silica composite nanoparticles
Authors: Kianpour Amir Hossein , Ahmadi-hamedani Mahmood , Alamdari Sanaz 
Published: July 2024

Abstract

Collecting urine samples from cats, which instinctively bury their waste, is challenging for veterinarians. Superhydrophobic cat litter offers a solution by preventing urine absorption, making it easier to collect uncontaminated samples. This innovation simplifies diagnostics for veterinarians and reduces stress for both pets and their owners. In the present study, the hydrophobic coating was synthesized by zinc oxide/silica composite nanoparticles (ZnO/SiO2 CNPs) modified with perfluorodecyltrichlorosilane (FDTS) compounds for soil/stone/grain sand and glass surfaces. Tetraethoxysilane (TEOS) and zinc acetate dihydrate were used as precursors and FDTS was used to modify the surface of a synthesized composite nanoparticles. Prepared modified ZnO/SiO2 composite sol was sprayed on various coatings (usual/cat soil, sand, stone, glass) and the water contact angles were measured. The all surfaces became hydrophobic after applying the modified composite coating; For the cat litter, the average contact angle of 110.3° was achieved that confirm the hydrophobicity properties. XRD, FTIR, EDX and FESEM analyzes were performed to investigate the structural and morphological features of the synthesized ZnO/SiO2 CNPs. The coated stone's chemical resistance was tested by subjecting it to acidic and neutral environments for 20 hours and it was found that the coating was more hydrophobic in the neutral environment than in the acidic one. Obtained results showed that facile synthesized composite has the potential to improve agricultural practices in dry areas, gather pet urine for use in veterinary medicine, and have a number of environmental benefits.
Standardizing Corneal Transplantation Records Using openEHR: Case Study
Authors: Ferreira Diana , Neto Cristiana , Hak Francini , Abelha António , Santos Manuel , Machado José 
Published: July 2024

Background
Corneal transplantation, also known as keratoplasty, is a widely performed surgical procedure that aims to restore vision in patients with corneal damage. The success of corneal transplantation relies on the accurate and timely management of patient information, which can be enhanced using electronic health records (EHRs). However, conventional EHRs are often fragmented and lack standardization, leading to difficulties in information access and sharing, increased medical errors, and decreased patient safety. In the wake of these problems, there is a growing demand for standardized EHRs that can ensure the accuracy and consistency of patient data across health care organizations.

Objective
This paper proposes the use of openEHR structures for standardizing corneal transplantation records. The main objective of this research was to improve the quality and interoperability of EHRs in corneal transplantation, making it easier for health care providers to capture, share, and analyze clinical information.

Methods
A series of sequential steps were carried out in this study to implement standardized clinical records using openEHR specifications. These specifications furnish a methodical approach that ascertains the development of high-quality clinical records. In broad terms, the methodology followed encompasses the conduction of meetings with health care professionals and the modeling of archetypes, templates, forms, decision rules, and work plans.

Results
This research resulted in a tailored solution that streamlines health care delivery and meets the needs of medical professionals involved in the corneal transplantation process while seamlessly aligning with contemporary clinical practices. The proposed solution culminated in the successful integration within a Portuguese hospital of 3 key components of openEHR specifications: forms, Decision Logic Modules, and Work Plans. A statistical analysis of data collected from May 1, 2022, to March 31, 2023, allowed for the perception of the use of the new technologies within the corneal transplantation workflow. Despite the completion rate being only 63.9% (530/830), which can be explained by external factors such as patient health and availability of donor organs, there was an overall improvement in terms of task control and follow-up of the patients’ clinical process.

Conclusions
This study shows that the adoption of openEHR structures represents a significant step forward in the standardization and optimization of corneal transplantation records. It offers a detailed demonstration of how to implement openEHR specifications and highlights the different advantages of standardizing EHRs in the field of corneal transplantation. Furthermore, it serves as a valuable reference for researchers and practitioners who are interested in advancing and improving the exploitation of EHRs in health care.
True Mitotic Count Prediction in Gastrointestinal Stromal Tumors: Bayesian Network Model and PROMETheus (Preoperative Mitosis Estimator Tool) Application Development
Authors: Renne Salvatore Lorenzo , Cammelli Manuela , Santori Ilaria , Tassan-Mangina Marta , Samà Laura , Ruspi Laura , Sicoli Federico , Colombo Piergiuseppe , Terracciano Luigi Maria , Quagliuolo Vittorio , Cananzi Ferdinando Carlo Maria 
Published: July 2024

Background
Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy.

Objective
The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs.

Methods
Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed.

Results
Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model’s accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model.

Conclusions
The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.
Targeted Development and Validation of Clinical Prediction Models in Secondary Care Settings: Opportunities and Challenges for Electronic Health Record Data
Authors: van Maurik I S , Doodeman H J , Veeger-Nuijens B W , Möhringer R P M , Sudiono D R , Jongbloed W , van Soelen E 
Published: July 2024

Abstract
Before deploying a clinical prediction model (CPM) in clinical practice, its performance needs to be demonstrated in the population of intended use. This is also called “targeted validation.” Many CPMs developed in tertiary settings may be most useful in secondary care, where the patient case mix is broad and practitioners need to triage patients efficiently. However, since structured or rich datasets of sufficient quality from secondary to assess the performance of a CPM are scarce, a validation gap exists that hampers the implementation of CPMs in secondary care settings. In this viewpoint, we highlight the importance of targeted validation and the use of CPMs in secondary care settings and discuss the potential and challenges of using electronic health record (EHR) data to overcome the existing validation gap. The introduction of software applications for text mining of EHRs allows the generation of structured “big” datasets, but the imperfection of EHRs as a research database requires careful validation of data quality. When using EHR data for the development and validation of CPMs, in addition to widely accepted checklists, we propose considering three additional practical steps: (1) involve a local EHR expert (clinician or nurse) in the data extraction process, (2) perform validity checks on the generated datasets, and (3) provide metadata on how variables were constructed from EHRs. These steps help to generate EHR datasets that are statistically powerful, of sufficient quality and replicable, and enable targeted development and validation of CPMs in secondary care settings. This approach can fill a major gap in prediction modeling research and appropriately advance CPMs into clinical practice.
Enhancing Surgery Scheduling in Health Care Settings With Metaheuristic Optimization Models: Algorithm Validation Study
Authors: Lopes João , Guimarães Tiago , Duarte Júlio , Santos Manuel 
Published: July 2024

Abstract

Background
Health care is facing many challenges. The recent pandemic has caused a global reflection on how clinical and organizational processes should be organized, which requires the optimization of decision-making among managers and health care professionals to deliver care that is increasingly patient-centered. The efficiency of surgical scheduling is particularly critical, as it affects waiting list management and is susceptible to suboptimal decisions due to its complexity and constraints.

Objective
In this study, in collaboration with one of the leading hospitals in Portugal, Centro Hospitalar e Universitário de Santo António (CHUdSA), a heuristic approach is proposed to optimize the management of the surgical center.

Methods
CHUdSA’s surgical scheduling process was analyzed over a specific period. By testing an optimization approach, the research team was able to prove the potential of artificial intelligence (AI)–based heuristic models in minimizing scheduling penalties—the financial costs incurred by procedures that were not scheduled on time.

Results
The application of this approach demonstrated potential for significant improvements in scheduling efficiency. Notably, the implementation of the hill climbing (HC) and simulated annealing (SA) algorithms stood out in this implementation and minimized the scheduling penalty, scheduling 96.7% (415/429) and 84.4% (362/429) of surgeries, respectively. For the HC algorithm, the penalty score was 0 in the urology, obesity, and pediatric plastic surgery medical specialties. For the SA algorithm, the penalty score was 5100 in urology, 1240 in obesity, and 30 in pediatric plastic surgery. Together, this highlighted the ability of AI-heuristics to optimize the efficiency of this process and allowed for the scheduling of surgeries at closer dates compared to the manual method used by hospital professionals.

Conclusions
Integrating these solutions into the surgical scheduling process increases efficiency and reduces costs. The practical implications are significant. By implementing these AI-driven strategies, hospitals can minimize patient wait times, maximize resource use, and enhance surgical outcomes through improved planning. This development highlights how AI algorithms can effectively adapt to changing health care environments, having a transformative impact.