bluuebunny/crossref_metadata_2025_split huggingface dataset:
Determinants of mortality risk among Indonesian patients with COVID-19
Authors: Efendi Ferry , Haryanto Joni , Has Eka Mishbahatul Mar'ah , Makhfudli Makhfudli , Indarwati Retno , Kuswanto Heri , Wahyuhadi Joni , Farabi Makhyan Jibril Al , Ho Ken Hok Man 
Published: April 2023

Background: The mortality risk of the novel coronavirus disease 19 (COVID-19) remains a global concern. Particularly in Indonesia, patients aged 18–59 years have a high risk of death due to COVID-19. In this study, we analysed the determinants of mortality risk among patients with confirmed COVID-19. Methods: A secondary analysis of data from the Indonesian COVID-19 dashboard, tracking COVID-19 cases from April 2020 to May 2021 was performed. Data of 22,314 patients with COVID-19 aged 18–59 years were analysed using descriptive analysis, chi-square test, and binary logistic regression to obtain adjusted odds ratios (AORs) with 95% confidence intervals (CIs). Results: Factors associated with confirmed mortality risk among patients aged 18–59 years with COVID-19 included increasing age (AOR = 1.08; 95% CI = 1.07–1.09), hospitalised and on ventilator support (AOR = 130.75; 95% CI = 69.03–247.63), having severe disease (AOR = 15.24; 95% CI = 11.51–20.17), and travelling history (AOR = 1.36; 95% CI = 1.13–1.63). Conclusions: These findings confirmed that the increasing risk of death due to COVID-19 was associated with increasing age, ventilator support during hospitalisation, developing severe disease, and having a travelling history. This suggests that curative strategies should be strengthened with a focus on improving clinical governance by prioritising patients with the above variables.
Carotenoids from female Grapsus albolineatus as potential anti-ageing compounds
Authors: Paransa Darus Saadah Johanis , Kemer Kurniati , Mantiri Desy Maria Helena , Kepel Rene Charles , Suleman Dininurilmi Putri , Soemantri Ara Deani , Kurnia Dikdik 
Published: April 2023

Introduction: Grapsus albolineatus is thought to have a high concentration of carotenoid pigments. Although studies on male G. albolineatus have been conducted, no studies on pigment extraction from female G. albolineatus have been carried out. Carotenoids have a high ability to fight free radicals. Previous research has shown that carotenoids can fight free radicals that cause premature skin-aging. Aim: The purpose of this research was to find out what kinds of carotenoids are found in G. albolineatus and whether they can act as natural inhibitors of proteins that cause premature aging, such as glucogenase, elastase, and hyaluronidase enzymes. Methods: Carotenoids were extracted from G. albolineatus using column chromatography and high-performance liquid chromatography (HPLC); molecular docking and visualization were done with Autodock 4.2 and Discovery Studio/Biovia, respectively. Results: According to HPLC data, there are carotenoid pigments such as didehydroastaxanthin, tetrahydroastaxanthin, dihydroastaxanthin, diatoxanthin, astaxanthin, and adonixanthin. According to molecular docking experiments, pigment carotenoids from G. albolineatus are efficient inhibitors of protein elastase and hyaluronidase with binding energy range -7.58 kcal/mol - -9.03 kcal/mol and -6.16 kcal/mol - -7.71 kcal/mol, respectively. Conclusions: G. albolineatus carotenoids have the potential to be anti-aging since they are more effective as protein elastase and hyaluronidase inhibitors than their native inhibitors.
Cell-type classification of cancer single-cell RNA-seq data using the Subsemble ensemble-based machine learning classifier
Authors: Chen David , Shooshtari Parisa 
Published: April 2023

Background The advent of single-cell RNA sequencing (scRNA-seq) has provided a high-resolution overview of the cellular heterogeneity of different tissue types. Manual cell type annotation of gene expression datasets remains a useful but time-intensive task. Ensemble machine learning methods leverage the predictive power of multiple classifiers and can be applied to classify high-dimensional gene expression data. Here, we present a novel application of the Subsemble supervised ensemble machine learning classifier used to classify novel cells with known cell type labels using gene expression data. Methods First, we tested the classification performance of different pre-processing steps used to normalize and upsample the training dataset for the Subsemble using a colorectal cancer dataset. Second, we conducted a cross-validated performance benchmark of the Subsemble classifier compared to nine other cell type classification methods across five metrics tested, using an acute myeloid leukemia dataset. Third, we conducted a comparative performance benchmark of the Subsemble classifier using a patient-based leave-one-out cross-validation scheme. Rank normalized scores were calculated for each classifier to aggregate performance across multiple metrics. Results The Subsemble classifier performed best when trained on a dataset that was log-transformed then upsampled to generate balanced class distributions. The Subsemble classifier was consistently the top-ranked classifier across five classification performance metrics compared to the nine other baseline classifiers and showed an improvement in performance as the training dataset increased. When tested using the patient-based leave-one-out cross-validation scheme, the Subsemble was the top-ranked classifier based on rank normalized scores. Conclusions Our proof-of-concept study showed that the Subsemble classifier can be used to accurately predict known cell type labels from single-cell gene expression data. The top-ranked classification performance of the Subsemble across two validation datasets, two cross-validation schemes, and five performance metrics motivates future development of accurate ensemble classifiers of scRNA-seq datasets.
Evaluation of clinical efficacy of Ginkgo biloba extract in the treatment of knee osteoarthritis: a randomized clinical trial
Authors: Al-Haddad Maryam , Bairam Ahsan , Hussain Haider , Majeed Suhad , Al-Jalehawi Ahmad 
Published: April 2023

Background: Osteoarthritis (OA) is a degenerative joint disease characterized by deteriorating articular cartilage. Most patients may not get enough control of symptoms in spite of the availability of various treatment options. Because of the synergistic impact of flavonoids and ginkgolides, Ginkgo biloba (GB) has been shown to possess antioxidant and anti-inflammatory properties besides its generally safe profile. This study aims to assess the efficacy and safety of the Ginkgo biloba extract (GBE) in patient with Knee OA . Methods: This is a randomized double blinded clinical trial conducted in a private orthopaedic clinic in Al-Najaf Government-Iraq between 1st November 2021 to 1st June 2022. Patients were randomized into two groups; Group A administered the standard treatment (diclofenac capsule 100 milligram sustained released with paracetamol 1 gram twice daily) with GBE capsule 120 milligram twice daily, while Group B administered the standard treatment with placebo (starch) capsule only. The Knee injury and Osteoarthritis Outcome Score (KOOS) was performed to assess the efficacy of the GBE in patient with Knee OA for eight weeks.Results: 60 patients (Group A: mean age 54.2±8.6, male 24.24%, female 75.76%; Group B: mean age 58.0±8.4, male 22.22%, female 77.78%) successfully completed the eight weeks follow up. Pain, symptoms, sport, activity of daily living (ADL) scores showed significant improvement in Group A from the first two weeks after treatment with GB in comparison with group B. however, scores of quality of life (QOL) were not significantly improved in two group comparison at each assessment week. Only 10 patients (group A:8, group B:2) reported gastrointestinal adverse effects during the study which were all mild. Conclusions: GB may provide additional beneficial health effects to patients with knee OA based on KOOS scores when add to the standard treatment regime. Registration: ClincialTrials.gov (NCT05398874, https://clinicaltrials.gov/ct2/show/NCT05398874).
Introducing technology of planting methods to increase rice production for sustainable farmers’ income
Authors: Witjaksono Julian , Indrasti Rita , Bungati Bungati , B Markus Rawung Jefny , Sehat Tan Siti , Gaffar Abdul , Sunandar Nandang , Sirnawati Enti , N Manoppo Conny , Assagaf Muhammad , Sulistiono Wawan , Sihombing Donald , Handayati Wahyu , Purba Resmayati , Asmin Asmin 
Published: April 2023

Background: Rice self-sufficiency is still a great concern in Indonesia; in order to feed the population of 270 million people, farmers must implement new technology to boost rice production. This study aimed to provide information on the differences between direct seed planting and its conventional counterpart in terms of productivity and farmer income. Secondly, we aimed to characterize the productivity and farmer income for two legowo planting systems (legowo 4:1 and legowo 2:1). Finally, farmers’ perceptions of the legowo implementation were evaluated. Methods: This study was carried out in Konawe District at the farm level in Karandu Village, Anggotoa Sub District of Southeast Sulawesi Province, Indonesia. The research design was implemented to allow comparison between legowo 4:1 and legowo 2:1. 60 farmers who implemented legowo 2:1 and legowo 4:1 respectively, were selected. Moreover, 30 farmers who did not implement both 4:1 and 2:1 were selected as the conventional planting method. This research design was implemented in order to compare between legowo and non legowo planting systems, as mentioned above, 60 respondents will compare with 30 farmers in terms of yield and economic advantage. Lastly, in order to determine technological perceptions, a total of 150 farmers were interviewed. Results: The study case results showed that direct seed planting is much more profitable than its conventional counterpart. The introduction of legowo 4:1 was highly significant in boosting rice production compared to legowo 2:1. In terms of perception, we found that the low cost and the availability of legowo are the most important parameters for the adoption of this technology. Conclusions: In summary, we recommend the implementation of the legowo 4:1 planting system to boost rice productivity. By reducing labor costs, the direct seeding method increased farmers’ income.
Use of non-intrusive laser exfoliation to improve substance uptake into citrus leaves
Authors: Ponce Cabrera Luis , Etxeberria Ed , Gonzalez Pedro , Flores Reyes Teresa 
Published: April 2023

Background: Despite the presence of stomata in leaves, foliar application of agrochemicals can be extremely inefficient due to the low permeability of leaf cuticular surfaces to polar compounds. Methods: This study introduced a laser-based “wax exfoliation” method to facilitate the penetration of substances into the leaf and, together with enhancing their uptake into the phloem and subsequent transport across tissue. This investigation demonstrated the effectiveness and non-invasive properties of laser exfoliation to improve the penetration of foliar-applied substances into citrus leaves. Results: This work presents the use of laser energy to exfoliate the cuticle of a leaf, with the highest energy density of 0.76 J/ cm2 resulting in 85-90% exfoliation across the entire laser-spot area. The infrared wavelength of the erbium laser is specifically chosen to target the wax cuticle without causing damage to the underlying epidermal cells. This selective ablation allows for increased penetration of therapeutic compounds into the leaf and transportation throughout the plant's vasculature. This is demonstrated using a fluorescent glucose analog applied to the laser treated leaves, showing increased penetration and transport throughout the leaf. Conclusions: Our findings demonstrate that the use of laser technology for the foliar application of agrochemicals provides significant advantages, including improved foliage uptake of therapeutic compounds. The method of cuticle exfoliation presented in this study is highly effective and non-intrusive, limiting its effects to the cuticle only. Future work should focus on the development of prototypes for in-field applications, including testing at longer distances as the Er:YAG laser does not require a lens for this application.
Anticancer activities of tocotrienols: A Systematic Scoping Review
Authors: Mohamedahmed Shaza M , Kamarudin Muhamad Noor Alfarizal , Ramdas Premdass , Khalid Ali Qusay , Sundralingam Usha , Radhakrishnan Ammu Kutty 
Published: April 2023

Background: The increasing number of cancer cases requires developing newer approaches to treat this disease. One approach uses natural compounds with known anticancer effects, such as tocotrienols. Many cell-based and animal-model studies found that tocotrienols possess potent anticancer activities. However, the exact molecular regulatory mechanism through which tocotrienols exert anticancer actions remains unclear. Methods: This scoping review analysed data from original research articles reporting on the anticancer effects of tocotrienols on human cancer cell lines published in the last seven years (January 2015 and September 2021) using a systematic scoping review approach. From the initial 619 research papers [ProQuest (n= 61), PubMed (n= 84), Embase (n = 148), Ovid Medline (n =53), Scopus (n = 137), Web of Science (n =136)] identified using pre-defined keywords, only 37 articles met the inclusion and exclusion criteria for this review. Human cancers commonly studied in the 37 research articles include breast, lung, prostate and colorectal cancer cell lines. Results: The analysis showed that exposing human cancer cell lines to tocotrienols triggered common anticancer mechanisms such as activation of apoptosis and inhibition of proliferation, angiogenesis and cell migration through regulation of key regulatory genes and proteins involved in these pathways. Conclusions: The findings show that tocotrienols regulate a number of biomarkers that induce cell death and regulate cell cycle in various types of human cancer cells. Further targeted studies are required to map the definite pathways by which T3 exerts their action and to better understand the cellular actions and the regulatory pathways.
The role of salivary orosomucoid 1 as an early diagnostic and prognostic biomarker of hepatocellular carcinoma related to Hepatitis B: A systematic review
Authors: Limijadi Edward Kurnia Setiawan , Ar Ardiyana , Rahmawati Nurul Azizah Dian , Sudiasa I Nyoman Sebastian , Tjandra Kevin Christian 
Published: April 2023

Background: Salivary orosomucoid 1 (ORM1) is highly increased in hepatocellular carcinoma related to hepatitis B. Thus, this study aims to investigate the role of salivary ORM1 as an early diagnostic and prognostic biomarker of HCC related to hepatitis B. Methods: The sources included were original articles published from 2013 until 2023 (last date searched, January 2023) from ProQuest, Google Scholar, Springer, and ScienceDirect. The inclusion criteria were original research articles (observational cohort or diagnostic studies). Other article reviews, meta-analyses, non-comparative research, and in silico, in vitro and in vivo studies, technical reports, editor responses, conference abstracts, non-English, non-full-text, and irrelevant articles that were not related to either salivary ORM1, or hepatocellular carcinoma, hepatitis B, or kidney failure were excluded. Then, the ROBINS-I took was used to assess bias . The result was constructed with PICOS criteria within the table created in the google spreadsheet. This systematic review followed the PRISMA guidelines. Results: We included five diagnostic studies with 533 samples conducted in China and Japan. Even though limited original studies with homogenous PICO was a limitation, the evidence output of this study can still be well presented. Salivary ORM1 may be useful to detect early cancer diagnosis as rapidly increased levels of ORM1 can be observed in the early stages of HCC (four times higher than usual) and the biomarker has a sensitivity of 81.67% and a specificity of 77.5%. This biomarker is also able to detect the prognosis of individuals with the disease with or without chemotherapy because the higher the level of ORM1, the more liver damage occurs that leads to a poorer prognosis. Conclusions: Salivary ORM1 is a potential early diagnostic biomarker of HCC related to hepatitis B and a biomarker of the disease prognosis. Registration: Open Science Framework (OSF) (March 16, 2023).
Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models
Authors: Xiao Jin , Tian Yuhang , Jia Yanlin , Jiang Xiaoyi , Yu Lean , Wang Shouyang 
Published: April 2023

The security of credit card fraud detection (CCFD) models based on machine learning is important but rarely considered in the existing research. To this end, we propose a black-box attack-based security evaluation framework for CCFD models. Under this framework, the semisupervised learning technique and transfer-based black-box attack are combined to construct two versions of a semisupervised transfer black-box attack algorithm. Moreover, we introduce a new nonlinear optimization model to generate the adversarial examples against CCFD models and a security evaluation index to quantitatively evaluate the security of them. Computing experiments on two real data sets demonstrate that, facing the adversarial examples generated by the proposed attack algorithms, all six supervised models considered largely lose their ability to identify the fraudulent transactions, whereas the two unsupervised models are less affected. This indicates that the CCFD models based on supervised machine learning may possess substantial security risks. In addition, the evaluation results for the security of the models generate important managerial implications that help banks reasonably evaluate and enhance the model security. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 72171160 and 71988101], Key Program of National Natural Science Foundation of China and Quebec Research Foundation (NSFC-FRQ) Joint Project [Grant 7191101304], Key Program of NSFC-FRQSC Joint Project [Grant 72061127002], Excellent Youth Foundation of Sichuan Province [Grant 2020JDJQ0021], and National Leading Talent Cultivation Project of Sichuan University [Grant SKSYL2021-03]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1297 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0076 ) at ( http://dx.doi.org/10.5281/zenodo.7631457 ).
A combination of three nerve entrapment syndromes, which was difficult to differentiate from a vertebral compression fracture
Authors: Harada Yukinori , Kakura Kentaro , Shimizu Taro 
Published: April 2023

Introduction: This report describes the educational case of a patient who developed three different cutaneous nerve entrapment syndromes at the same level of the thoracic nerve, which was difficult to differentiate from a vertebral compression fracture. Case Description: A 74-year-old woman presented with right lower abdominal pain, followed by back and flank pain. During later assessment, anterior, posterior and lateral cutaneous nerve entrapment syndromes at the Th11 level were diagnosed. Conclusion: A combination of three different cutaneous nerve entrapment syndromes can occur in the same patient.
Development and validation of the nomogram to predict the risk of hospital drug shortages: A prediction model
Authors: Dong Jie , Gao Yang , Liu Yi , Yang Xiuling 
Published: April 2023

IntroductionReasons for drug shortages are multi-factorial, and patients are greatly injured. So we needed to reduce the frequency and risk of drug shortages in hospitals. At present, the risk of drug shortages in medical institutions rarely used prediction models. To this end, we attempted to proactively predict the risk of drug shortages in hospital drug procurement to make further decisions or implement interventions.ObjectivesThe aim of this study is to establish a nomogram to show the risk of drug shortages.MethodsWe collated data obtained using the centralized procurement platform of Hebei Province and defined independent and dependent variables to be included in the model. The data were divided into a training set and a validation set according to 7:3. Univariate and multivariate logistic regression were used to determine independent risk factors, and discrimination (using the receiver operating characteristic curve), calibration (Hosmer-Lemeshow test), and decision curve analysis were validated.ResultsAs a result, volume-based procurement, therapeutic class, dosage form, distribution firm, take orders, order date, and unit price were regarded as independent risk factors for drug shortages. In the training (AUC = 0.707) and validation (AUC = 0.688) sets, the nomogram exhibited a sufficient level of discrimination.ConclusionsThe model can predict the risk of drug shortages in the hospital drug purchase process. The application of this model will help optimize the management of drug shortages in hospitals.
Application of machine learning tools for feature selection in the identification of prognostic markers in COVID-19
Authors: Johm Jaime Sprockel Diaz , Restrepo Guerrero Hector Fabio , Fernandez Juan Jose Diaztagle 
Published: April 2023

Abstract

Objective
To identify prognostic markers by applying machine learning strategies to the feature selection.

Methods
An observational, retrospective, multi-center study that included hospitalized patients with a confirmed diagnosis of COVID-19 in three hospitals in Colombia. Eight strategies were applied to select prognostic-related characteristics. Eight logistic regression models were built from each set of variables and the predictive ability of the outcome was evaluated. The primary endpoint was transfer to intensive care or in-hospital death.

Results
The database consisted of 969 patients of which 486 had complete data. The main outcome occurred in 169 cases. The development database included 220 patients, 137 (62.3%) were men with a median age of 58.2, 39 (17.7%) were diabetic, 62 (28.2%) had high blood pressure, and 32 (14.5%) had chronic lung disease. Thirty-three variables were identified as prognostic markers, and those selected most frequently were: LDH, PaO2/FIO2 ratio, CRP, age, neutrophil and lymphocyte counts, respiratory rate, oxygen saturation, ferritin, and HCO3. The eight logistic regressions developed were validated on 266 patients in whom similar results were reached (accuracy: 65.8–72.9%).

Conclusions
The combined use of strategies for selecting characteristics through machine learning techniques makes it possible to identify a broad set of prognostic markers in patients hospitalized for COVID-19 for death or hospitalization in intensive care.