bluuebunny/crossref_metadata_2025_split huggingface dataset:
IL-13 gene polymorphism at the -1055 locus and severe malaria – Is there a link?
Authors: Hassan Dalin A. , Elhussein Amar Babikir , Ali Mariam Siddig Balla , Fadlelseed Omar Elteyb , Qadri Marwa , Mawkili Wedad , Mohanan Anugeetha Thacheril , Ahmed Zeyad M. , Elmobark Mohamed Eltaib , Ali Eman Merghani , Dawod Ogail Yousif , Alfadol Soad M. A. , Ahmed Eman O. A. , Abu Ibrahim Ismail Mohammed , Rahma Nour Elhouda A. A. , Shalayel Mohammed Helmy Faris 
Published: November 2024

Abstract

Objective
This research examined how the IL-13-1055C/T promoter polymorphism affects the likelihood of Sudanese children developing severe malaria.
Method
The research was carried out in 2020 and included 115 children with malaria and 49 healthy youngsters from Al Gadaref, Sudan. To analyze the IL-13 1055C/T SNP, the DNA product was digested by the restriction enzyme BstFNI. The ELISA Sandwich method was used to investigate serum IL-13 and IgE.
Results
The IL-13 gene1055C/T SNP differed significantly between malaria patients and healthy individuals. The results revealed a highly significant difference between various alleles of the IL-13 gene 1055C/T position and malaria severity groups (P-value < 0.001). The IL-13 SNP was found to be closely associated with the allele frequency in the healthy control group. (C: T% = 70.4:29.6), with a p-value < 0.001. The findings show a significant positive relationship between IL-13 and IgE levels in individuals with malaria. (r = 811; P value < 0.001). IL-13 levels differ significantly between malaria patients and healthy individuals, with a P value = 0.000. Malaria patients exhibited considerably greater IgE levels than healthy children, P value < 0.001.
Conclusion
Our findings revealed substantial relationships between the IL-13 gene polymorphism at the − 1055 locus and severe malaria anemia, uncomplicated malaria, and cerebral malaria cases, implying that the IL-13 gene could have a significant impact on the development of severe malaria.
The associations between work stress load, Advance care planning practice preferences and self-efficacy in Intensive care unit nurses
Authors: Wang HaoJie , Zhang Weiqing , Wang Kun , Song Jielu , Cao Yabing , Wei ZhiHua 
Published: November 2024

Abstract

Objective: Given that Intensive care unit (ICU) patients are mostly in critical condition, unable to participate in decision-making communication, family members are prone to make medical care decisions against the patient's wishes, to safeguard the patient's right to autonomous decision-making. ICU nurses serve as vital conduits for Advance Care Planning (ACP) communication due to their professional expertise and understanding of patient conditions. However, they carry out the incidence of ACP is relatively low, the present study to investigate the communication self-efficacy level of advanced care planning (ACP) among ICU nurses in a tertiary care hospital and the factors affecting it, and to provide a reference for developing an intervention program.
Methods: From January 2024 to April 2024, 330 ICU nurses from six tertiary hospitals in *** Province were conveniently selected for the survey and analysis using the General Information Questionnaire, ACP Knowledge, Belief and Conduct Questionnaire, ACP Practice Preferences Scale, Work Stress Load Scale, and ACP Self-Efficacy Scale (Chinese version).
Results: ICU nurses' ACP self-efficacy score (44.42±9.14), Pearson's analysis of the correlation between variables, and multiple linear regression to analyze the main influencing factors of ICU nurses' ACP communication self-efficacy showed that marital status, participation in hospice training, ACP knowledge-attitude-behavioral willingness, ACP practice preference, and work stress load P <0.05,explaining 79.1% of the total variance.
Conclusion：ICU nurses' ACP communication self-efficacy is at a moderate level with many influencing factors, and it is recommended to implement interventions for the influencing factors to improve the level of ICU nurses' ACP communication self-efficacy.
SRHPENet: Super-Resolution Network for human pose estimation
Authors: Zou Xuelian , Bi Xiaojun 
Published: November 2024

Abstract

Human pose estimation is a basic task in the field of computer vision, so improving recognition accuracy is of great significance for advanced computer vision tasks. At present, there are many methods to solve the human pose estimation task of high-resolution images, and have achieved excellent results. However, current high-resolution human pose estimation methods still face two problems in solving low-resolution human pose estimation tasks. (1) Low-resolution images usually lose a lot of key information, such as texture information, joint point positions becoming blurred, etc., which will cause the recognition accuracy to decrease. (2) Existing super-resolution images generally have a fixed receptive field, and cannot extract features and fuse multi-scale information well for images with different magnifications. To fill the problem of super-resolution images, we proposed a method suitable for low-resolution images. Super-Resolution Human Pose Estimation Network (SRHPENet) for human pose estimation tasks. The network is composed of two parts. First, we designed SReNe super-resolution network. The network is composed of the MSE module we designed, which can effectively extract multi-scale information content and alleviate the problem of fixed receptive fields. Secondly, we input the high-resolution images obtained by the super-resolution network into the human pose estimation network. Additionally, during the training phase, we introduce real high-resolution images into the human pose estimation network in order to improve the accuracy of human pose estimation. Finally, through joint training of the two parts, we obtain low-resolution performance close to the performance of state-of the-art with high-resolution images as input.
Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
Authors: Nguyen Hoang-Quan , Nguyen Xuan Bac , Chen Samuel Yen-Chi , Churchill Hugh , Borys Nicholas , Khan Samee U. , Luu Khoa 
Published: November 2024

Abstract

Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading strategy to utilize near-term quantum advantages in multiple problems, including machine learning and combinatorial optimization. When applied to specific tasks, the parameters in the quantum circuits are trained to minimize the target function. Although there have been comprehensive studies to improve the performance of the PQCs on practical tasks, the errors caused by the quantum noise downgrade the performance when running on real quantum computers. In particular, when the quantum state is transformed through multiple quantum circuit layers, the effect of the quantum noise happens cumulatively and becomes closer to the maximally mixed state or complete noise. This paper studies the relationship between the quantum noise and the diffusion model. Then, we propose a novel diffusion-inspired learning approach to mitigate the quantum noise in the PQCs and reduce the error for specific tasks. Through our experiments, we illustrate the efficiency of the learning strategy and achieve state-of-the-art performance on classification tasks in the quantum noise scenarios.
Intracytoplasmic sperm injection hampers fertilization rate and pregnancy per initiated cycle in patients with extremely poor ovarian response
Authors: Chen Jinghua , Liu Lanlan , Liu Zhenfang , Pan Luxiang , Zhou Liying , Chen Kaijie , Yang Xiaolian , Chen Yurong , Jiang Xiaoming , Cai Jiali , Ren Jiangzhi 
Published: November 2024

Abstract

Purpose: To compare the clinical outcomes of extremely poor responders with one or two oocytes who receive in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI).
Methods: A retrospective study was carried out in 2572 patients with one or two oocytes retrieved from 2013 to 2022, of which 2159 patients were scheduled to receive IVF treatment and 413 patients were scheduled to receive ICSI treatment. The laboratory parameters and clinical outcomes were compared with adjusted multivariate regression and propensity score (PS) matching.
Results: In both matched and non-matched cohorts, the total fertilization failure (TFF) rate of the ICSI group was significantly higher than that of the IVF group (P < 0.05), but the multiple fertilization rate of the IVF group was significantly higher than the ICSI group (P < 0.05). After matching, the cumulative pregnancy rate per initiated cycle in the IVF group was significantly higher than in the ICSI group (28.7% vs 21.7, P=0.028). However, the difference in cumulative live births did not reach significant (21.2 % vs 17.2 %, P =0.179). The adjusted odds ratios for TFF, cumulative pregnancy, and cumulative live birth comparing ICSI versus IVF in multivariate models were 1.65(95%CI: 1.12, 2.43), 0.65(95%CI: 0.46, 0.91), and 0.76(95%CI:0.55, 1.04) respectively.
Conclusion: In poor responders with one or two oocytes retrieved, ICSI insemination cannot avoid TFF, and it may hamper the cumulative pregnancy rate.
MIND-HOPE: Multilingual Identification of Nuanced Dimensions of HOPE
Authors: Sidorov Grigori , Balouchzahi Fazlourrahman , Ramos Luis , Gómez-Adorno Helena , Gelbukh Alexander 
Published: November 2024

Abstract

Hope plays a crucial role in human psychology, yet research on its expression and detection across languages is limited. This study addresses this gap by developing the first multiclass hope speech detection datasets for Spanish and German. The research includes a comprehensive review of existing datasets and techniques and employs various learning approaches—traditional machine learning, deep learning, and transformer-based models—to evaluate their effectiveness. The new datasets significantly advance hope speech detection by incorporating diverse linguistic contexts. Comparative analyses reveal that transformer models, such as xlm-roberta-base for Spanish and uklfr/gottbert-base for German, achieve superior performance with F1 scores of 0.6801 and 0.6977, respectively, in multiclass detection tasks compared to the rest of the models. Multilingual transformers are also promising in cross-lingual applications but require further refinement. This study sets the groundwork for future advancements by enhancing dataset diversity and model performance, with future research focusing on refining multilingual models and expanding to various languages.
Plasma protein increase as a chronological aging factor in healthy toy poodles
Authors: Ozaki Satoru , Honme Yoshiko , Higashi Seiichiro , Hattori Kouya , Morifuji Masashi , Mizuno Eriko , Yoshida Minoru , Ito Takashi K. 
Published: November 2024

Abstract

Although extensive research has examined aging markers in larger dog breeds, little is known about small breeds. This study assesses the relevance of aging biomarkers examined in larger breeds and other biological species, focusing on toy poodles (N = 40) as a model of small breeds and retrievers (N = 17) serving as a large-sized reference. Healthy individuals with no significant health declines for up to a year post-data collection were studied for age-related changes in various parameters, excluding disease factors. Our cross-sectional analysis identified significant correlations between age and increases in plasma protein concentration and amylase levels across both breeds, with breed-specific age-related declines in vaccine responses to various viruses observed only in toy poodles. Longitudinal analysis over one year confirmed a significant temporal increase in plasma protein in toy poodles, with a similar, albeit non-significant, trend in retrievers. Unlike in other species, NAD⁺ levels and fecal microbiota showed no age-related changes. Additionally, the previously reported frailty index correlated with age in retrievers but not in toy poodles. Notably, including deceased individuals during the study strengthened correlations. These results suggest plasma protein increase as a chronological aging factor in toy poodles and enhance our understanding of aging in healthy small dog breeds.
Quantitative Analysis of Medical Image Data for Improved Diagnostic Accuracy
Authors: Umukoro Emmanuel Ediri , Agbajor Godwin Kparobo , Kemefa Christian Oyinkuro , Umukoro Oghenerukevwe Fiona 
Published: November 2024

Abstract

Background Quantitative analysis of medical image data has become essential for improving diagnostic accuracy by reducing subjectivity and enhancing clinical decision-making. With advancements in imaging technologies such as MRI, CT, and X-rays, large volumes of medical images are now available. However, traditional qualitative assessments are prone to errors and inconsistencies due to subjective interpretation. This study investigates how quantitative metrics like precision, recall, F1 score, and area under the curve (AUC) can enhance diagnostic performance by automating and standardizing medical image analysis.
Main text This research employed a diverse dataset of 1,000 medical images, including scans from MRI, CT, and X-ray modalities. These images were stratified to cover conditions such as tumors, fractures, and internal bleeding. Advanced image processing and machine learning techniques were used to extract quantitative features, allowing us to develop robust diagnostic models. Compared to traditional methods, the quantitative analysis demonstrated improved accuracy and consistency across all modalities. MRI scans, in particular, showed the highest accuracy at 85%, indicating their superiority in precise diagnostics.
Conclusion The findings highlight the potential of quantitative analysis in medical imaging to outperform existing diagnostic methods. With statistically significant improvements across all metrics—including precision, recall, and AUC—this method offers a reliable solution to reduce diagnostic errors. Adopting quantitative analysis tools in clinical practice could lead to better patient outcomes, especially in detecting complex medical conditions.
Hitchlearning: A general free-lunch paradigm for single-image enhancement by unifying inference and training
Authors: Liu Jiaxin , Guo Yuchen , Fang Lu , Dai Qionghai 
Published: November 2024

Abstract

Deep learning (DL) has ushered in a suite of promising tools for image processing, including denoising (DN), deblurring (DB), and super-resolution (SR). However, traditional DL methods assume independent and identically distributed (i.i.d.) data for model training and inference, which does not hold in practice due to factors such as sample variation, variability in imaging conditions, and temporal effects in living cells. This discrepancy prevents models, even when trained on extensive datasets, from reaching their full performance potential during inference. This practical issue, unfortunately, is still unexplored. To address this issue, drawing inspiration from the way biological intelligence adapts through past experiences to shape future learning, we introduce HitchLearning—a revolutionary paradigm that breaks away from the conventional separation of training and inference. In HitchLearning, we leverage a single inference image to unsupervisedly optimize the model by aligning the training images with it. Subsequently, we employ this optimized model for processing individual inference images. This approach allows the model to adapt to the specific characteristics of each inference image, leading to improved results in a manner reminiscent of a "free lunch." We conducted a thorough evaluation of our method across three distinct tasks within both supervised and unsupervised frameworks, utilizing four diverse datasets. Compared to conventional training methods, HitchLearning demonstrated average performance increases of 4.34 dB, 4.08 dB, and 0.54 dB for the DN, DN, and SR tasks, respectively. The experimental results unequivocally demonstrate that our algorithm offers a universally applicable and cost-free optimization solution for processing image and can be used in other fields as well.
Unveiling hidden risks in healthcare from flood-induced transportation disruption
Authors: Bryant Seth , Wassmer Jonas , Schimansky Paul , Keegan Lindsay , Pregnolato Maria , Kurths Jürgen , Marwan Norbert , Merz Bruno 
Published: November 2024

Abstract

Despite ongoing investments in disaster resilience, flooding continues to disrupt healthcare systems directly and indirectly through transportation network failures. Existing models used for mitigation planning from such disruptions often miss critical dynamics, particularly traffic rerouting, especially at the regional and national scales needed for robust resilience planning. To address this gap, we present a generalizable and scalable method to identify healthcare facilities that are prone to overcrowding, delays in emergency response, and subsequent declines in service induced by flood-related traffic disruptions. Our method joins a regional processes-based flood model chain with a gravity-based traffic model to simulate the flow of traffic before and after flooding on a road network constructed from open-source data. By using continuous simulation flood maps at 5-meter resolution, within a framework that captures post-disruption congestion caused by traffic rerouting, we provide simulations that are both plausible and computationally feasible at the regional scale. To quantify the implications of such flood-related traffic disruptions on the health sector, we compute the change in service population due to flooding for all healthcare facilities in Germany as a proxy for health-traffic system resilience. Our findings reveal hidden risks for healthcare facilities located far from flood zones, demonstrating how traffic rerouting caused by flood-related road disruptions can lead to a loss of healthcare service. In particular, we identified 75 (of 2,475) healthcare facilities at risk of patient surges beyond their regular capacity, driven solely by flood-related traffic disruptions. Of these, a third are more than 10 kilometers from the nearest inundation, suggesting these facilities may be unaware and thus under-prepared — risks that have, until now, remained hidden from assessments. To support improved resilience planning and facilitate communication with decision-makers, we provide interactive maps of all facilities found to be at risk of capacity exceedance, along with the corresponding flood simulations. This study underscores the necessity of integrating flood hazards and transportation dynamics during resilience planning to ensure healthcare services are not interrupted. Furthermore, the method developed here can be applied to other regions to identify similar vulnerabilities, ultimately enhancing the overall disaster preparedness and response capabilities of coupled healthcare-transportation systems.
The Effect of Social Support on Maternal Functional Status and Postpartum Depression Risk in COVID-19 Pandemic
Authors: Akkuş Miray , Yağcan Hande 
Published: November 2024

Abstract

Background
Coronavirus infection has also negatively affected women during pregnancy, labor, and postpartum periods. In the postpartum period, mothers need social support from their environment. Existing studies show that women did not receive sufficient social support in the postpartum period before the pandemic, and their expectations were not met.
Methods
A correlational and descriptive study was conducted by online interviews with 384 mothers who gave birth in a university between March 2021 and December 2021. In data analysis, independent t-test, one-way analysis of variance, Bonferroni, Pearson correlation, simple and multiple linear regression analysis were used.
Results
The mothers’ 92.3% needed help with baby care, and 22.1% received support from their husbands during the COVID-19 pandemic. Apart from their husbands, their mothers assisted 44.5% of women, and 39.6% could not find any assistance. It was found that 82.3% of the women had a high Edinburgh Postpartum Depression Scale score. The mean total score of the Multidimensional Perceived Social Support Scale was 30.92 ± 9.22 and the Barkin Maternal Functioning Scale was 62.22 ± 10.91.
Conclusions
During the COVID-19 pandemic, it was determined that mothers were in the risky group in terms of postpartum depression, received inadequate social support, and had good maternal functional status. However, their maternal functional status decreased compared to before the pandemic. The social support was found to be effective in reducing the risk of postpartum depression and increasing maternal functioning during the COVID-19 pandemic. It was determined that the risk of postpartum depression decreased as maternal functional status increased.
Expanding LAM’s use case: A new rapid urine assay for diagnosing tuberculosis with high sensitivity in HIV-positive and -negative South African and Ugandan outpatients, including in people with sputum-scarce tuberculosis
Authors: Hamasur Beston , Okunola Anna O. , Sserubiri James , Dube-Nwamba Welile V. , Abdulgader Shima M. , Ignatowich Lech , Rasool Omid , Nabatanzi Rose , Lorente Sara Puertas , Giraldos David , Joloba Moses , Warren Robin M. , Ssengooba Willy , Theron Grant 
Published: November 2024

Abstract

Introduction: Non-sputum tuberculosis (TB) tests are a global priority. Next-generation urine lipoarabinomannan (LAM) diagnostics with utility beyond only people with advanced immunosuppression are urgently needed.
Methods: We performed a two-centre diagnostic accuracy study to evaluate the performance of Biopromic TB LAM assay (BP-LAM assay) and the lateral flow Determine TB LAM Ag test (LF-LAM) on stored urine from 629 adults with presumptive pulmonary TB (315 with TB, 314 without) from primary care in South Africa and Uganda. Diagnostic accuracy was assessed against an extended sputum microbiological reference standard (eMRS) and sputum induction done if people could not expectorate.
Results: BP-LAM assay had higher sensitivity than LF-LAM [95% confidence interval (CI) 63% (58, 69) vs. 22% (18, 27); p<0.001] and similar specificities [93% (90, 96) vs. 89% (85, 92)], respectively. BP-LAM assay sensitivity did not differ in HIV-positive vs. -negative people [67% (58, 76) vs. 62% (55, 69)] nor its accuracy affected by CD4 counts. The design-locked version of BP-LAM assay had improved specificity compared to its prototype-version. BP-LAM assay diagnosed TB in people who could not expectorate sputum resulting in similar diagnostic yield amongst those tested (DYT) to Xpert MTB/RIF Ultra and culture (64% vs. 61-55%) in a scenario where sputum induction was unavailable. BP-LAM assay false-negatives had, compared to true-positives, less severe disease,and more previous TB. False-positive results were, vs. true-negatives, more likely to be non-tuberculous mycobacteria (NTM)-positive [19% (4/21) vs. 4% (12/293)].
Conclusions: BP-LAM assay has higher sensitivity than LF-LAM, including in HIV-negative people. BP-LAM assay holds promise and warrants further evaluation, especially in groups where non-sputum tests are a priority.
Funding: The Global Health Technology Fund (GHIT) programs RFP-TRP-2018-001 and RFP-PD-2020-002, SAMRC (MRC-RFA-IFSP-01-2013), EDCTP2 (RIA2018D-2509, PreFIT; RIA2018D-2493, SeroSelectTB; RIA2020I-3305, CAGE-TB; TMA2020CDF-3209, RADIANT).