bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and Validation a Machine Learning Nomogram Model to Differentiation Mycoplasma Pneumoniae Coinfection with Other Pathogen in Children Patients
Authors: Xu Wenbei , Liu Xiaohan , Meng Lingjian , Sun Xiaonan , Dong Lina , Li Qiang , Kang Haiquan , Mao Yiping , Lin Huashan , Hu Chunfeng , Xu Kai , Meng Yankai 
Published: August 2024

Abstract

Objectives
The aim of this study was to develop and validate a machine learning model for distinguishing mycoplasma pneumoniae coinfection with other pathogens (Co-MPP) in children from mycoplasma pneumoniae pneumonia (MPP) in children.
Methods
Between June 2023 and March 2024, 191 consecutive pediatric patients were enrolled in this study. The latest laboratory test results before bronchoalveolar lavage (BAL) were included in the statistical analysis. After the least absolute shrinkage and selection operator (LASSO) feature screening, we input the final features into seven different machine learning classifiers (LR, SVM, KNN, Random Forest, Extra Trees, XGBoost, and LightGBM) and selected the optimal classifier for model construction. The nomogram model combined the radiomics (rad) signature and the clinical signature. The ROC curves were drawn to evaluate the diagnostic efficacy of different models. The calibration efficiency of the nomogram was evaluated by drawing calibration curves, and the Hosmer-Lemeshow test was used to evaluate the calibration ability of the models. Decision curve analysis (DCA) was utilized to evaluate the clinical utility of the models. Statistical significance was considered when the p-value was < 0.05. The statistical analysis in this study was conducted using R and SPSS 27.0 software.
Results
A total of 1834 handcrafted radiomics features were extracted, including 360 first-order features, 14 shape features, and texture features. The LR classifier achieved the best value of AUC, reaching 0.922 and 0.867 for distinguishing Co-MPP from MPP in the training and test cohorts, respectively. For building the clinical signature, LR was selected as the base model. The univariate analysis results of all clinical laboratory and CT imaging features showed that only reticulation and bronchial lumen occlusion were significantly different between MP and Co-MPP patients (p = 0.011, < 0.001, respectively). The performance showed that the clinical signature achieved AUC values of 0.729 and 0.706 in the training and test cohorts, respectively. The nomogram using the LR algorithm was performed to combine the clinical signature and rad signature. Delong test results showed the performance of the nomogram and rad signature were both higher than the clinical signature (p < 0.05), while the nomogram and rad signature showed no significant difference. Both rad signature and nomogram showed significant clinical benefit.
Conclusion
Our study demonstrated that machine learning can assist clinicians in distinguishing Co-MPP from MPP in children. Furthermore, the rad signature and nomogram model showed higher clinical benefit compared to the clinical signature.
Concept mapping to promote clinical reasoning in multimorbidity: a mixed methods study in undergraduate Family Medicine
Authors: Fonseca Marta , Broeiro-Gonçalves Paula , Barosa Mariana , Marvão Pedro , Carreira Marta , Azeredo Sofia , Pires Joana , Rendas António , Rosado-Pinto Patrícia , Heleno Bruno 
Published: August 2024

Abstract

Introduction
: Clinical reasoning significantly impacts physicians' performance and patient care quality. Research into learning transfer within clinical reasoning education, especially in managing multimorbidity in Family Medicine, is crucial. This study evaluates the impact of concept maps (CMs) on promoting clinical reasoning skills among undergraduate students, compared to traditional teaching methods (TM).
Methods
A mixed methods approach was used in a controlled, non-randomized study with fifth-year Family Medicine undergraduates allocated to sessions using either CMs or TM. Quantitative data included a feedback questionnaire and evaluation of an individual task. Qualitative data comprised responses to an open-ended question and analysis of problem representation in the individual task.
Results
Among 313 eligible students, 112 participated (CM: 60, TM: 52). Both groups reported high satisfaction with their teaching methods. The CM group valued the holistic view and organization for managing multimorbidity cases, showing higher odds of positive scores on individual tasks (differences not statistically significant). Additionally, the CM group had a more homogeneous code matrix for problem representation in two clinical vignettes.
Conclusions
While no definitive evidence supports the superiority of CMs over traditional methods, promising trends were noted. The CM group showed improved performance in individual tasks and better organization in managing multimorbidity cases. Further investigation is recommended to explore varying levels of CM usage and modifications to pre-class workloads.
Overuse of antibiotics in the management of acute diarrhea in children under five years in four provinces of Mozambique (2014-2019)
Authors: Salência-Ferrão Judite , Chissaque Assucênio , Manhique-Coutinho Lena , Kenga Andréa , Cassocera Marta , Deus Nilsa 
Published: August 2024

Abstract

Background
Even with great reduction in the last years, diarrhea continues as one of leading causes of hospital admission and mortality in children less than five years of age globally. The success of diarrheal disease management relies on the rapid recognition of the symptoms and adequate treatment, to which WHO guidelines, do not recommend antibiotic use for all cases of acute diarrhea in children under five years old, being reserved to specific conditions such as bloody diarrhea, co-infection, malnutrition and chronic associated conditions. Therefore, we aimed to evaluate the frequency of antibiotics use in diarrhea management in children aged 0–59 months admitted with acute diarrhea in four provinces of Mozambique, from 2014 to 2019.
Methods
A cross-sectional hospital-based surveillance was conducted from January 2015 to December 2019 in six sentinel sites located in four provinces. Socio-demographic, epidemiological and clinical data were obtained by interviewing the child’s caregivers and by accessing the child’s medical records and children’s vaccination card. Data collected through ViNaDia was double-entered in Epi InfoTM3.5.1. (Centers for Disease Control and Prevention, Atlanta 2008), followed by data comparison. IBM SPSS software was used to conduct the data analysis.
Results
During the study period 2382 children who presented at one of the study health facilities with diarrhea was enrolled through ViNaDia surveillance between 2015–2019 and a total of 85.1% (2028/2382) provided information about the use of ATB and were included in the present analysis. The majority was male. The most frequent age group was 0–11 months with 49.4%. Underweight was observed in 28.8% and wasting in 15.2%. The comorbidities observed was HIV (7.8%), malaria (7.0%), and pneumonia (2.1%). Bloody diarrhea and “rice-water” was reported in 1.5% (10/1664) and 2% (29/1664) respectively. Antibiotics use was 93.2% [95% CI: 92.0–94.2; 1890/2028]. Administration of multiple antibiotics was 49.1% [95% CI: 47.0–51.5; 930/1890]. The most prescribed antibiotics were Ampicillin (46.2%), Gentamicin (38.4%) and Cotrimoxazole (30.5%).
Conclusions
The results of our study indicate an overuse of antibiotics in the management of acute diarrhea in children attended in four provinces of Mozambique. The study suggests the drawing of a local guideline for the efficient management of acute diarrhea.
Development of Complete Atrioventricular Block Following the Administration of the COVID-19 vaccine in a Young Patient: a Case Report
Authors: AlHabil Yazan , Abuzahra Saad , Azamtta Murad , Daralamouri Yunis 
Published: August 2024

Abstract

Background
COVID-19 vaccines have been a crucial element in combating the COVID-19 pandemic. With a substantial portion of the world’s population receiving the vaccine, uncommon side effects, such as abnormalities in the cardiac conduction system, have become apparent.
Case Presentation
This case report details a 16-year-old female patient who presented with persistent generalized fatigue, excessive sleepiness, and dizziness after receiving her second dose of the Pfizer-BioNTech vaccine. Initially, the patient sought medical attention at an outpatient clinic, where she was diagnosed with pericarditis. The treatment plan included prednisolone, colchicine, and esomeprazole, but the patient did not experience any improvement. One month later, the patient sought medical attention at our hospital and underwent an extensive medical evaluation. Electrocardiogram (ECG) findings were significant for complete atrioventricular (AV) dissociation, confirming the diagnosis of a third-degree heart block. After a thorough discussion regarding all possible interventions with the patient took place, an informed decision was made to implant a permanent pacemaker. Complete resolution of symptoms was reported by the patient at the 3-month follow-up visit.
Conclusions
The association between COVID-19 vaccines and heart conduction issues has led to various theories, considering factors like hypoxia, electrolyte imbalance, and cytokine responses. While congenital heart block parallels have been explored, transient AV blocks challenge this notion. Cytokine-mediated immune responses are linked to vaccine-induced myocarditis. Recent literature reports myocardial injury following Pfizer-BioNTech and Sino-pharm vaccinations, notably in elderly patients. Management protocols for device implantation during COVID-19 infection remain unclear. In conclusion, despite the novel and vague nature of this case, medical professionals need to be cautious of this possible complication in young and healthy patients after receiving their COVID-19 vaccine shots.
Assessing the response lag and vulnerability of terrestrial vegetation to various compound climate events in mainland China under different vegetation types
Authors: Yao Tian , Wu Chuanhao , Yeh Pat J.-F. , HU Bill X. , Jiao Yufei 
Published: August 2024

Abstract

In the context of climate warming, the compound dry-hot (CDH), dry-cold (CDC), wet-hot (CWH), and wet-cold (CWC) events have become more frequent and widespread in recent decades, causing severe but disproportionate impacts on terrestrial vegetation. However, the understanding of how vegetation vulnerability responds to these compound climate events (CCEs) is still limited. Here, we developed a multivariate copula conditional probabilistic model integrating the Standardized Precipitation Index (SPI), Standardized Temperature Index (STI), and Normalized Difference Vegetation Index (NDVI) together to quantify the vegetation response to each of CDH, CDC, CWH and CWC events under diverse climates in mainland China. Results show that CDC events result in the largest probability of vegetation loss relative to other three CCEs, with the probability of NDVI below the 40% percentile being 4.8%-13.0% (0.5%-2.6%) larger than individual dry (cold) events. In contrast, CWH leads to the lowest vegetation loss probability among all CCEs, with the probability of NDVI below the 40% percentile being 5.6% ~ 6.9% (4.2% ~ 5%) less than individual wet (hot) events. The response of vegetation vulnerability to CCEs varies considerably with ecosystems and climate types. Vegetation in Loess Plateau and northwestern Xinjiang (Inner Mongolia) is highly susceptible to CDC (CDH) events, while that in northeastern and southern China (eastern coastal and southwestern regions) is more vulnerable to CWC (CWH) events. Shrubland, grassland and cropland exhibit higher vulnerability to CDC and CDH events, while deciduous (evergreen) forests are more vulnerable to CWC(CWH) events, which may be related to vegetation physiological characteristics, survival strategies, and climatic adaptations. This study enhances our understanding on the response of various vegetation types to CCEs, and provides theoretical support for the development of measures to mitigate climate hazards.
Medical image encryption algorithm based on DNA and spiral transformation
Authors: Zhang Zhao , Zhao Bing 
Published: August 2024

Abstract

Medical image encryption plays a key role in healthcare and privacy protection as an important application in the field of image encryption. In this paper, a new medical image encryption method is proposed based on spiral transform and DNA operations. The algorithm makes use of Chaotic sequences generated by a 5-dimensional hyperchaotic system to perform double helical transform, DNA encoding, arithmetic and decoding operations on medical images respectively. The emulation experiments show that the algorithm has good encryption performance and can resist various attacks, especially in the anti-clip attack with good results.
Impact of Multi-Parameter Images Obtained from Dual-Energy CT on Radiomicis to Predict Pathological Grading of Bladder Urothelial Carcinoma
Authors: Wei Wei , Wang Shigeng , Hu Mengting , Tong Xiaoyu , Fan Yong , Zhang Jingyi , Cheng Qiye , Dong Deshuo , Liu Lei 
Published: August 2024

Abstract

Objective: To investigate the effect of radiomics models obtained from dual-energy CT (DECT) material decomposition images and virtual monoenergetic images (VMIs) in predicting the pathological grading of bladder urothelial carcinoma (BUC).
Materials and Methods: Preoperative Energy-Spectrum CT images were retrospectively collected from 112 pathologically confirmed cases of BUC patients, including 76 cases of high-grade urothelial carcinoma and 36 cases of low-grade urothelial carcinoma. Enhanced CT venous phase images of all patients were reconstructed at 40 to 140 keV VMIs (interval 10 keV), Iodine maps, and Water maps, and a total of 13 sets of images were obtained, and imaging features were extracted in each of the 13 sets of images. The best features related to BUC were identified by recursive feature elimination (RFE), the Minimum Redundancy Maximum Relevance (mRMR), and the Least Absolute Shrinkage and Selection Operator (LASSO) in order. A five-fold cross-validation method was used to divide the samples into training and testing sets, and models for pathological prediction of BUC grading were constructed by a random forest (RF) classifier. ROC curves were plotted to evaluate the performance of 13 models obtained from reconstructed images.
Results: There were no statistically significant differences in the area under the curve (AUC) between the training set and the testing set for all 13 models, with the AUC ranging from 0.91 to 0.96 in the training set and 0.84 to 0.90 in the testing set for each group of reconstructed images. Although the features selected for the reconstructed images were very different among the groups, all the features selected from 40 to 100 keV VMIs had dependencevariance of the GLDM feature set.
Conclusion: The variation of spectral CT parameters did no effect on the radiomics-based prediction of the pathological grading of BUC and did not affect the accuracy of the model even if the relevant features differed between reconstructed images.
Euglycemic diabetic ketoacidosis associated metabolic encephalopathy caused by dapagliflozin：a rare case report
Authors: Chu Lulu , Xi Zhenhua , Ma Runzhi , Shi Weiliang , Yu Guoshen 
Published: August 2024

Abstract

Background
Sodium–glucose cotransporter-2(SGLT-2) inhibitors are a newer class of anti-diabetic drugs with the incresed risk of euglycemic diabetic ketoacidosis(EuDKA). Encephalopathy is a rare but life-threatening event of EuDKA. Due to paradoxically normal or slightly elevated serum glucose levels, it's easy to be mimicked by cerebral infarction, structural brain damage, thus leading to delayed diagnosis and causing seriously irreversible brain injury.
Case presentation: We report a severe EuDKA with metabolic encephalopathy secondary to dapagliflozin in a type 2 diabetes mellitus(T2DM) patient. A 72-year-old female presented with complaining of 70 minutes of coma. Laboratory evaluation revealed a severe metabolic acidosis with an elevated anion gap, and ketones were elevated in blood and positive in urine. The patient eventually was diagnosed with metabolic encephalopathy associated with EuDKA and managed accordingly. Unfortunately, owing to the irreversible brain damage, the patient's state of consciousness is not fully recovered.
Conclusions
Metabolic encephalopathy is a rare but life-threatening complication of EuDKA caused by SGLT-2 inhibitors, the imaging features are similar to those of other metabolic encephalopathy such as poisoning and hypoxia. The precise pathogenesis of encephalopathy in EuDKA remains poorly understood, potentially resulting from the toxic consequences of electrolyte disturbances, ketosis, and acidosis. For unconscious patients with taking SGLT-2 inhibitors, commonly testing the level of ketones and magnetic resonance imaging of the brain are essential.
Multigenerational exposure of Daphnia carinata to NSAIDs in individual concentrations and binary mixtures
Authors: de Souza Helena da Silva Viana , Kumar Anupama , Nugegoda Dayanthi 
Published: August 2024

Abstract

Pharmaceuticals, including non-steroidal anti-inflammatory drugs (NSAIDs) like ibuprofen and naproxen, are widely used for medical purposes but have also become prevalent environmental contaminants. However, there is limited understanding of their effects on aquatic organisms, especially regarding multigenerational and mixture exposures. This study aimed to evaluate the toxicological impacts of ibuprofen and naproxen, individually and in combination, on three generations of Daphnia carinata, a freshwater organism. Daphnids were exposed to environmentally relevant concentrations of ibuprofen and naproxen (0.1, 0.5, 2.5 µg/L and 0.1 + 0.1, 0.1 + 0.5, 2.5 + 2.5 µg/L) throughout multiple generations. Various endpoints were assessed, including reproduction, body size, reproduction recovery, and behaviour. The results revealed that both ibuprofen and naproxen had negative impacts on reproduction, leading to reduced reproductive output across generations.Additionally, daphnids exhibited changes in body size, with significant alterations observed in the F2 and F3 generations. Male individuals and ephippium were also present at all concentrations throughout all generations. Although reproduction recovery could not be observed in daphnids after one generation in clean water, the average number of neonates was higher in a few treatments in generation F4 compared to generation F3. In addition, binary mixtures of the drugs showed synergistic effects on daphnids' reproduction. The multigenerational approach provided valuable insights into the long-term effects of these NSAIDs on reproductive success and population dynamics. This study contributes to understanding the ecotoxicity of ibuprofen and naproxen in aquatic organisms, particularly in a multigenerational context and in the presence of mixture exposures.
Machine learning to improve the understanding of rabies epidemiology in low surveillance settings
Authors: Keshavamurthy Ravikiran , Boutelle Cassandra , Nakazawa Yoshinori , Joseph Haim , Joseph Dady W. , Dilius Pierre , Gibson Andrew D. , Wallace Ryan M. 
Published: August 2024

Abstract

In low and middle-income countries, a large proportion of animal rabies investigations end without a conclusive diagnosis leading to epidemiologic interpretations informed by clinical, rather than laboratory data. We compared Extreme Gradient Boosting (XGB) with Logistic Regression (LR) for their ability to estimate the probability of rabies in animals investigated as part of an Integrated Bite Case Management program (IBCM). To balance our training data, we used Random Oversampling (ROS) and Synthetic Minority Oversampling Technique. We developed a risk stratification framework based on predicted rabies probabilities. XGB performed better at predicting rabies cases than LR. Oversampling strategies enhanced the model sensitivity making them highly desirable to predict rare events like rabies in a biting animal. XGB-ROS classified most of the confirmed rabies cases and only a small proportion of non-cases as either high (confirmed cases = 85.2%, non-cases = 0.01%) or moderate (confirmed cases = 8.4%, non-cases = 4.0%) risk. Model-based risk stratification led to a 3.2-fold increase in epidemiologically useful data compared to a routine surveillance strategy using IBCM case definitions. Our study demonstrates the application of machine learning to strengthen zoonotic disease surveillance under resource-limited settings.
The role of sarcopenia in the development of early complications in patients with Advanced Epithelial Ovarian Carcinoma undergoing Cytoreductive Surgery
Authors: Coşkun Gökhan , Karabacak Ufuk , Türkan Halil , Mollaoğlu Murat Can , Bostancı Meriç Emre , Karadayı Kürşat 
Published: August 2024

Abstract

Purpose
Sarcopenia has been identified as a prognostic factor in various cancers. The aim of the study is to investigate the role of sarcopenia in the development of early complications following Primary Cytoreductive Surgery in advanced stage ovarian cancer patients.
Methods
A total of 57 patients who underwent Cytoreductive Surgery due to advanced stage ovarian cancer at the Surgical Oncology Clinic of ………….. University Hospital between January 2013 and June 2021 were retrospectively analyzed. Routine preoperative Computed Tomography(CT) images were used to measure the cross-sectional areas of the paraspinal muscles, abdominal wall muscles at the L3 level in cm2. The Skeletal Muscle Index (SMI) and Psoas Muscle Index (PMI) values were calculated. Postoperative early complications were classified as Clavien-Dindo grade 3 and above major complications.
Results
Based on SMI measurements, 23 patients with SMI < 38.5 were classified as sarcopenic, while 34 patients with SMI ≥ 38.5 were classified as nonsarcopenic; based on PMI measurements, 19 patients with PMI < 4.3 were classified as sarcopenic, while 38 patients with PMI ≥ 4.3 were classified as nonsarcopenic. Major complications occurring in the early postoperative period were significantly more common in the sarcopenic group. High Peritoneal Carcinomatosis Index (PCI) score and FIGO 3C stage were associated with a significantly higher incidence of early complications. High PCI score and SMI < 38.5 value were identified as risk factors for postoperative early complications.
Conclusions
The quantitative measurements of sarcopenia using routine CT imaging for preoperative staging are a useful and cost-effective method. Determining the sarcopenic status of patients before treatment offers an advantage in predicting treatment complications.