bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantifying the effect of isolation and negative certification on COVID-19 transmission
Authors: Takeshita Jun-ichi , Murakami Michio , Kamo Masashi , Naito Wataru , Yasutaka Tetsuo , Imoto Seiya 
Published: March 2024

Abstract
Background: Isolation of close contact people and negative test certification are used to manage the spread of new coronavirus infections worldwide. These effectively prevent the spread of infection in advance, but they can lead to a decline in socioeconomic activity. Thus, the present study quantified the extent to which isolation and negative test certification respectively reduce the risk of infection.
Methods: A discrete-time SEIR model was used as the infectious disease model, and equations for calculating the conditional probability of non-infection status given negative test results on two different days were derived.
Results: The respective non-infection probabilities with two negative PCR test results, and with one negative PCR test result and one antigen test result, were quantified. By substituting initial parameters of the SEIR model into these probabilities, the present study revealed the following: (1) isolating close contact individuals can reduce by 80% the risk of infection during the first five days, but five more days are needed to reduce the risk 10% more, and seven more days to reduce the risk 20% more; and (2) if an individual with a negative PCR test result has a negative antigen test result the next day, then his or her infection probability is between 0.6% and 0.7%.
Conclusions: Five-day isolation has a proportionally greater effect on risk reduction, compared to longer isolation; and thus, if an isolation period of longer than five days is contemplated, both the risk reduction and the negative effects from such increased isolation should be considered. Regarding negative test certification, our results provide those in managerial positions, who must decide whether to accept the risk and hold mass-gathering events, with quantitative information that may be useful in their decision-making.
Trial registrations: Not applicable
The impact of missing data on the validity and applicability of the SOFA score to predict mortality – a secondary analysis of a systematic review
Authors: Molinnus Denise , Beulertz Michael , Bickenbach Johannes , Marx Gernot , Benstöm Carina 
Published: March 2024

Abstract
The Sequential Organ Failure Assessment, also known as SOFA score, was introduced to assess organ dysfunction of critical ill patients. However, understanding the impact of missing SOFA scores in randomized controlled trials and how this affect the validity and applicability of the SOFA score as a surrogate endpoint for predicting mortality has been a matter of interest. To address this, a secondary analysis of a systematic review was conducted to quantify the relationship between SOFA scores and the prediction of mortality in critically ill adults in randomized controlled trials (RCTs). The systematic review included 87 RCTs with a total of 12,064 critically ill patients. This analysis focused on missing SOFA score data in relation to the length of stay on the intensive care unit (ICU) and the methods used to handle missing data. SOFA score measurements from the included studies were categorized into three time frames: Early (t ≤ 4 days), Intermediate (t = 5–10 days) and Late (t > 10 days) measurement. Only one study reported a complete data set for calculating the SOFA score for an Early measurement. When considering all methods used to address missing data, 32% of studies still had missing data for Early measurements, and this percentage increased to 64% for Late measurements. These findings suggested that, over time, the number of studies with incomplete data sets has been increasing. The longer a patient is treated on the ICU, the higher the number of missing data which can impact the validity of SOFA score analyses. There was no clear trend towards a specific method for compensating missing data. An exemplary calculation demonstrated that ignoring missing data may lead to an underestimated variability of the treatment effect. This, in turn, could bias the interpretation of study results by policy- and clinical decision-makers. Overall, there are several limitations that need to be considered when using SOFA score as a surrogate endpoint for mortality. When employed as an outcome, the SOFA score is frequently missing and most studies do not adequately describe the amount or nature of missing data, or the methods used to handle missing data in the analysis.
Comparison of ChatGPT 3.5 Turbo and Human Performance in taking the European Board of Ophthalmology Diploma (EBOD) Exam
Authors: Maino Anna , Klikowski Jakub , Strong Brendan , Ghaffari Wahid , Woźniak Michał , BOURCIER Tristan , Grzybowski Andrzej 
Published: March 2024

Abstract

Background/Objectives:
This paper aims to assess ChatGPT’s performance in answering European Board of Ophthalmology Diploma (EBOD) examination papers and to compare these results to pass benchmarks and candidate results.
Methods
This cross-sectional study used a sample of previous past exam papers from 2012, 2013, 2020–2023 EBOD examinations. This study analysed ChatGPT’s responses to 392 Multiple Choice Questions (MCQ), each containing 5 true/false statements (1432 statements in total) and 48 Single Best Answer (SBA) questions.
Results
ChatGPT’s performance for MCQ questions scored on average 64.39%. ChatGPT’s strongest metric performance for MCQ was precision (68.76%). ChatGPT performed best at answering Pathology questions (Grubbs test p < .05). Optics and refraction had the lowest-scoring MCQ performance across all metrics. ChatGPT’s SBA performance averaged 28.43%, with the highest score and strongest performance in precision (29.36%). Pathology SBA questions were consistently the lowest-scoring topic across most metrics. ChatGPT chose option 1 more than other options (p = 0.19). When answering SBAs, human candidates scored higher than ChatGPT in all metric areas measured.
Conclusion
ChatGPT performed stronger for true/false questions, scoring a pass mark in most instances. Performance was poorer for SBA questions, especially as ChatGPT was more likely to choose the first answer out of four. Our results suggest that ChatGPT’s ability in information retrieval is better than knowledge integration.
Source identification, Accumulation and Dispersion of Heavy Metals pollution into the Surface Sediments of Urban Runoff (Case Study, Channels of Tehran City
Authors: Moghaddam Amin Hasani , Hashemi Seyed Hossein 
Published: March 2024

Abstract
Heavy metals contamination is vitally crucial issue for human health and natural ecosystems worldwide. The sediments are extremely responsible for accumulation, transfer, and the fate of them into the receiving ecosystems. In this research, the heavy metals pollution status of surface sediments, possible pollution source as well as the correlation between heavy metals with total organic carbons (TOC%) and particle size distribution were evaluated. In this sense, 30 surface sediment samples of three urban channels from three sub-catchments across Tehran megacity was collected, followed by the concentration of key elements strontium (Sr), lead (Pb), cadmium (Cd), nickel (Ni), and copper (Cu) was quantified by applying Inductively coupled plasma-optical emission spectrometry(ICP-OES). According to factor analysis(FA), the possible accumulation reason of (Pb) and (Sr) is as different as Copper, Nickle, and Cadmium elements. The Pearson test and multiple linear regression assigned that the TOC% is the leading reason of Strontium accumulation in surface sediments (r = 0.901). Ultimately, the vehicle transportation and land use type around the urban channels were recognized as the feasible principal origin of heavy metals contamination.
Dynamic culture system advances the applications of breast cancer organoids for precision medicine
Authors: Yang Jun , Qu Junyuan , Zhang Mei , Li Xiang , Jiang Qian , Kang Jinxiu , Jing Na , Wang Xianling 
Published: March 2024

Abstract
Tumor organoid-based drug sensitivity prediction is a new approach for precision medicine, which has wide applications in cancer treatment and attracts increasing attention. In the field of breast cancer, conventional organoid culture methods often require more than three weeks of culture period. The culture time greatly limits the further extension of the application scenarios of breast cancer organoids. We developed a fluid system that builds on the conventional organoid “dome” culture method, which continuously and stably supplies the nutrients for the growth of breast cancer organoids. We demonstrated that this is an effective optimization method, which can shorten the culture period of breast cancer organoids without significant changes in histological characteristics and drug sensitivity features.
Assessment of Noise Levels and Perceptions of Its Health Impact at Kejetia Market in Ghana
Authors: Sackey Lyndon Nii Adjiri , Agyemang Lawrencia S.Y. , Acheampong Patrick E. , Owusu Michael Afriyie , Amoah Jennifer 
Published: March 2024

Abstract

Noise pollution in developing countries like Nigeria and Ghana is causing adverse effects on citizens, including hearing impairment, sleep disturbances, adverse social behavior, and cardiovascular diseases. This study assessed noise levels at the Kejetia market in Ghana and the perceptions of health impact. The noise levels were measured at various of the amrket using sound level meters (JD-801A). The Results showed noise exposure levels were not within Ghana Environmental Protection Agency standards 2008, with predominantly commercial areas of 75 dB in the night and 60 dB during the day. The sources of noise pollution were from loud music, advertisements, human congestion, and vehicles. Respondents perceived noise pollution sources as an annoyance, mental stress, sleep disturbances, lack of concentration, hearing, and cardiovascular effects. The study suggests stakeholders and authorities should educate the public on the health effects of noise pollution.
Sustainability agency in Unsustainable structures: Rhetoric of a capable transformative individual
Authors: Salovaara Janne J. , Hagolani-Albov Sophia E. 
Published: March 2024

Abstract
While sustainability has been implemented in education for a long time, the conceptualisation of an impactful sustainability actor has not been as thoroughly formulated. The theory of structuration is relevant as a lens for critical reflection on the transformative potential of individuals educated in sustainability as a practice, who are often described as change-makers and mandated to enact sustainability. This paper examines sustainability agency using materials produced for a set of online sustainability courses. This text corpus is utilised as a representation of climate and sustainability education. The materials repeat systemic framings present in sustainability rhetoric, such as the contexts and targets in which sustainability transformation occurs. Additionally, through the analysis of the textual corpus descriptions emerged as attributes of the agents to enact sustainability. This uncovers the juxtaposition between the often-repeated perception that sustainability must be achieved by capable agents and the capacities they possess. Further, our content analysis suggests a rhetorical process of othering within sustainability, for example by positioning “people”—the general population—as the cause of the sustainability crisis, and “us”—the educated—as the solution. Through our findings, the article concludes that when utilising the concept of sustainability agency—be it in theoretical, educational, or practical work—one ought to pay significant attention to the structures wherein the activities take place. The full comprehension of the transformative potential of individual agents ought to include an acknowledgement of the difference between educated capability and capacity—the latter of which necessitates dominance over the structural resources.
Bridging the Computational Gap: Sliding Window Technique Meets GCNN for Enhanced Molecular Charge Predictions
Authors: Domínguez-Arca Vicente 
Published: March 2024

Abstract
In the quest for advancing computational tools capable of accurately calculating, estimating, or predicting partial atomic charges in organic molecules, this work introduces a pioneering Machine Learning-based tool designed to transcend the limitations of traditional methods like DFT, Mulliken, and semi-empirical approaches such as MOPAC and Gaussian. Recognizing the crucial role of partial atomic charges in molecular dynamics simulations for studying solvation, protein interactions, substrate interactions, and membrane permeability, we aim to introduce a tool that not only offers enhanced computational efficiency but also extends the predictive capabilities to molecules larger than those in the QM9 dataset, traditionally analyzed using Mulliken charges. Employing a novel neural network architecture adept at learning graph properties and, by extension, the characteristics of organic molecules, this study presents a "sliding window" technique. This method segments larger molecules into smaller, manageable substructures for charge prediction, significantly reducing computational demands and processing times. Our results highlight the model's predictive accuracy for unseen molecules from the QM9 database and its successful application to the resveratrol molecule, providing insights into the hydrogen-donating capabilities of CH groups in aromatic rings—a feature not predicted by existing tools like CGenFF or ATB but supported by literature. This breakthrough not only presents a novel alternative for determining partial atomic charges in computational chemistry but also underscores the potential of convolutional neural networks to discern molecular features based on stoichiometry and geometric configuration. Such advancements hint at the future possibility of designing molecules with desired charge sequences, promising a transformative impact on drug discovery.
Identification of PANoptosis genes in w based on bioinformatics analysis and machine learning
Authors: Jing Huan , Cheng Jiurong , Zhang Xiangsheng , Chen Yanna , Chen Hongtao , Fan Youling , Zhou Jun 
Published: March 2024

Abstract
Background
Diabetic nephropathy (DN) is a prominent etiological factor that contributes to the development of end-stage renal disease (ESRD). PANoptosis is an inflammatory programmed cell death pathway, and its involvement in the pathogenesis of DN has been demonstrated. The objective of this research was to examine the potential role of key PANoptosis-related genes in the occurrence of DN and to assess the clinical utility of these genes in predicting DN.
Methods
This study employed bioinformatics analysis to acquire a dataset of gene expression data for patients with DN from the Gene Expression Omnibus (GEO) database. Furthermore, we identified and functionally annotated differentially expressed genes (DEGs) and performed immune cell infiltration analysis. Consensus clustering was employed to identify molecular subtypes associated with PANoptosis. The least absolute shrinkage and selection operator (LASSO) technique was utilized to screen crucial PANoptosis genes, leading to the development of a prediction model for DN. Additionally, a clinical nomogram prediction model was constructed to validate the correlation between the core genes and DN. Finally, Mendelian randomization (MR) analysis was conducted using genome-wide association studies to ascertain the causal impact of ITM2C on DN.
Results
A total of eight genes (PROM1, MAFF, CLEC2B, CX3CR1, CXCL6, EVI2B, ITM2C, and VIM) associated with the incidence of DN were identified.
Conclusions
We successfully constructed a nomogram utilizing PANoptosis-related genes for the purpose of predicting the incidence of DN. This novel model holds potential as a valuable instrument for evaluating the imperative need for timely medical intervention to mitigate the onset of DN.
Pt nanoshell with ultra-high NIR-Ⅱ photothermal conversion efficiency mediates multifunctional neuromodulation for cardiac protection
Authors: Fu Lei , Wang Chenlu , Zhou Liping , Liu Chengzhe , Qiao Jiaming , Han Xinrui , Wang Luyang , Liu Yaxi , Xu Bi , Qiu Qinfang , Zhang Zizhuo , Wang Jiale , Zhou Xiaoya , Zeng Mengqi , Yu Lilei 
Published: March 2024

Abstract

The autonomic nervous system plays a pivotal role in the pathophysiology of cardiovascular diseases. Regulating it is essential for preventing and treating acute ventricular arrhythmias (VAs). Photothermal neuromodulation is a nonimplanted technique, but the response temperature ranges of transient receptor potential vanilloid 1 (TRPV1) and TWIK-elated K+ Channel 1 (TREK1) exhibit differences while being closely aligned, and the acute nature of VAs require that it must be rapid and precise. However, the low photothermal conversion efficiency (PCE) still poses limitations on achieving rapid and precise treatment. Here, we achieved nearly perfect blackbody absorption and one of the highest PCE in the second near infrared (NIR-II) window (73.7% at 1064 nm) via a Pt nanoparticle shell (PtNP-shell). By precisely manipulating the photothermal effect, we successfully achieved rapid and precise multifunctional neuromodulation encompassing neural activation (41.0–42.9 oC) and inhibition (45.0–46.9 oC). The NIR-II photothermal modulation additionally achieved bi-directional reversible autonomic modulation and conferred protection against acute VAs associated with myocardial ischemia and reperfusion injury in interventional therapy.
Organomineral fertilizer based on swine manure and Trichoderma harzianum improves the performance of Eucalyptus grandis
Authors: Turchetto Felipe , Santos Breno Magno Silva , Griebeler Adriana Maria , Ros Clovis Orlando , Araujo Maristela Machado , Waimer Gabriel Coelho , Jachi Janaine Giombelli , Andriguetto Kellin Vanessa , Silva Tauã Osaida , Silva Rodrigo Ferreira , Montó Pedro Mucin , Biesdorf Nilson 
Published: March 2024

Abstract
The productivity of forest plantations of the Eucalyptus genus in Brazil depends on applying large amounts of fertilizers. Controlled-release or organomineral fertilizers represent sustainable alternatives to traditional mineral fertilizers, allowing reduced production costs, higher growth rates, and environmental benefits. Additionally, using biological inputs such as fungi of the Trichoderma genus can increase forest productivity and improve plant quality and vigor. Therefore, this study aimed to characterize the influence of different types of fertilizers associated with Trichoderma harzianum on the morpho-physiological attributes of young Eucalyptus grandis plants. This study was conducted in a greenhouse using a completely randomized design, with six replicates in a 5 × 2 factorial scheme, considering four types of mineral or organomineral fertilizers and a control treatment, with or without T. harzianum inoculation. At 180 d after planting, the morphology of the aerial and roots and the aspects of the photosynthetic apparatus were evaluated using the Falker chlorophyll index and chlorophyll a fluorescence. For basic fertilization, E. grandis plants cultivated using a controlled-release fertilizer and an organomineral fertilizer based on swine manure showed the best results for the morphophysiological features evaluated. T. harzianum inoculation also improved the E. grandis development, especially when associated with controlled-release or organomineral fertilizers. Therefore, using swine manure to formulate organomineral fertilizers combined with inoculating T. harzianum in seedlings is a sustainable alternative fertilizer for young E. grandis plants.
Machine Learning-Based Prediction of Pulmonary Embolism to Reduce Unnecessary Computed Tomography Scans in Gastrointestinal Cancer Patients: A Retrospective Multicenter Study
Authors: Kim Joo Seong , Kwon Doyun , Kim Kyungdo , Lee Sang Hyub , Lee Seung-Bo , Kim Kwangsoo , Kim Dongmin , Lee Min Woo , Park Namyoung , Choi Jin Ho , Jang Eun Sun , Cho In Rae , Paik Woo Hyun , Lee Jun Kyu , Ryu Ji Kon , Kim Yong-Tae 
Published: March 2024

Abstract

Background
Pulmonary embolism (PE) is one of the most important complications in cancer patients. Gastrointestinal cancers entail an increased risk of PE. However, there were few researches on predicting pulmonary embolism using machine learning (ML) in cancer patients. The purpose of this study was to develop an ML based prediction model for PE in gastrointestinal cancer patients.
Methods
We conducted a retrospective, multicenter study in which ML model was developed and subsequently internally and externally validated. We reviewed gastrointestinal cancer patients who had undergone computed tomographic pulmonary angiography (CTPA) from 2010 to 2020. Demographic and predictor variables including the Wells score and D-dimer were investigated. The ML model was based on the random forest model. The area under receiver operating curve (AUROC) was used to evaluate the performance of ML model.
Results
446 patients in hospital A and 139 patients in hospital B were analyzed in this study. The training set comprised 356 patients in hospital A. The ML model was validated both internally (90 patients) and externally (139 patients). AUROC was 0.736 in hospital A and 0.669 in hospital B. The number of patients classified as requiring CTPA was significantly reduced according to the prediction with ML (hospital A; 100.0% vs 91.1%, P < 0.001, hospital B; 100.0% vs. 93.5%, P = 0.003).
Conclusion
Prediction model based on ML might have advantages in reducing the number of CTPA compared to the conventional diagnostic strategy for PE in patients with gastrointestinal cancer.
Comparative Genomics of Giardia duodenalis Sub-assemblage A1 Beaver (Be-2) and Human (WB-C6) Strains Show Remarkable Homozygosity, Sequence Similarity, and Conservation of VSP Genes
Authors: Baptista Rodrigo de Paula , Tucker Matthew S. , Valente Matthew J. , Srivas Subodh K. , Chehab Nadya , Li Alison , Shaik Jahangheer S. , Ramirez Juan David , Rosenthal Benjamin M. , Khan Asis 
Published: March 2024

Abstract
Giardia duodenalis, a major cause of waterborne infection, infects a wide range of mammalian hosts and is subdivided into eight genetically well-defined assemblages named A through H. However, fragmented genomes and a lack of comparative analysis within and between the assemblages render unclear the molecular mechanisms controlling host specificity and differential disease outcomes. To address this, we generated a near-complete de novo genome of AI assemblage using the Oxford Nanopore platform by sequencing the Be-2 genome. We generated 148,144 long-reads with quality scores of >7. The final genome assembly consists of only nine contigs with an N50 of 3,045,186 bp. This assembly agrees closely with the assembly of another strain in the AI assemblage (WB-C6). However, a critical difference is that a region previously placed in the five-prime region of Chr5 belongs to Chr4 of Be-2. We find a high degree of conservation in the ploidy, homozygosity, and the presence of cysteine-rich variant-specific surface proteins (VSPs) within the AI assemblage. Our assembly provides a nearly complete genome of a member of the AI assemblage of G. duodenalis, aiding population genomic studies capable of elucidating Giardia transmission, host range, and pathogenicity.