bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influences on Mathematics Achievement in Rural Schools: A Nested Study of Underprivileged Learners
Authors: Mafugu Tafirenyika 
Published: November 2024

AbstractUsing a nested design and guided by the Social Control Theory, this study examined the factors that influence learners’ achievement in mathematics in South Africa. The researchers collected data from 32 participants through an online questionnaire with open-ended and closed-ended questions. Quantitative data was analysed using SPSS to produce descriptive statistics and correlations, while qualitative data were subjected to thematic analysis. The findings indicated several important correlations. The engagement of learners with mathematics was positively correlated with their overall performance (r(30) = 0.468, p = 0.007). Similarly, concentration during mathematics tests was moderately positively correlated with performance (r(30) = 0.362, p < 0.042). In particular, the enjoyment of mathematical problem-solving by the learners showed a highly positive correlation with their overall performance (r(30) = 0.599, p < 0.001). Additionally, the belief of the students in their ability to achieve academic goals through mathematics was strongly correlated with their performance (r(30) = 0.571, p < 0.001). Qualitative data highlighted the lack of regular practice as a significant factor affecting maths performance. This lack of practice was attributed to factors such as limited time due to household responsibilities, ineffective time management, and a lack of motivation. The study underscores the importance of creating a supportive learning environment that fosters students’ enthusiasm for mathematics, ultimately improving academic performance and fostering a positive emotional connection to the subject. It also encourages students to overcome procrastination, laziness, and distractions, enhancing their concentration on their studies.
Invasive Fungal Diseases of Combat Wounds: Burden, Epidemiology, and Mycology
Authors: Roberds Ashleigh , Bobrov Alexander G. , Rautemaa-Richardson Riina , Walsh Thomas J. 
Published: November 2024

AbstractDuring the last two decades, wound invasive fungal diseases (WIFDs) have reemerged as important causes of mortality and morbidity in military personnel and civilian casualties in war areas. Historically, mycotic infections acquired in combat operations during Vietnam War and were associated with burn wounds. Modern combat related WIFDs are almost exclusively associated with severe traumatic events which encompass blast exposure as the primary mechanism of injury and subsequent extremity amputation and extensive blood loss. Such infections often lead to deep tissue necrosis, long hospitalizations, extensive surgeries, and more severe amputation. Studies of combat related WIFDs among U.S. military personnel in Operation Enduring Freedom (Afghanistan) demonstrated incidence rates of approximately 7% and crude mortality of 8.5%. WIFDs were also seen in U.K. military personnel returning from Afghanistan and are common in the current Ukraine and Gaza conflicts. Mucorales, Aspergillus and Fusarium species are the predominant causes of WIFDs. These molds are opportunistic pathogens which thrive in patients with immune system imbalances following traumatic injury. They are ubiquitous environmental fungi found in a variety of soils but there are significant regional differences depending on the local soil type, vegetation, and climate. The management of WIFDs is complicated by the limited efficacy of current antifungals on many of these environmental species and by emerging antifungal resistance globally. This review provides an overview of the global burden, epidemiology, and clinical features of combat-related fungal infections with the aim to provide a better understanding of the threat posed for wounded Service Members and civilians.
Development of a robust predictive model for neutropenia after esophageal cancer chemotherapy using GLMMLasso
Authors: Sugaya Shuhei , Uchida Masashi , Suzuki Takaaki , Kawakami Eiryo , Ishii Itsuko 
Published: November 2024

Abstract
Background
Neutropenia can easily progress to febrile neutropenia and is a risk factor for life-threatening infections. Predicting and preventing severe neutropenia can help avoid such infections.

Aim
This study aimed to develop an optimal model using advanced statistical methods to predict neutropenia after 5-fluorouracil/cisplatin chemotherapy for esophageal cancer and to create a nomogram for clinical application.

Method
Patients who received 5-fluorouracil/cisplatin chemotherapy at Chiba University Hospital, Japan, between January 2011 and March 2021 were included. Clinical parameters were measured before the first, second, and third chemotherapy cycles and were randomly divided by patient into a training cohort (60%) and test cohort (40%). The predictive performance of Logistic, Stepwise, Lasso, and GLMMLasso models was evaluated by the area under the receiver-operating characteristic curve (AUC). A nomogram based on GLMMLasso was developed, and the accuracy of probabilistic predictions was evaluated by the Brier score.

Results
The AUC for the first cycle of chemotherapy was 0.781 for GLMMLasso, 0.751 for Lasso, 0.697 for Stepwise, and 0.669 for Logistic. The respective AUCs for GLMMLasso in the second and third cycles were 0.704 and 0.900. The variables selected by GLMMLasso were cisplatin dose, 5-fluorouracil dose, use of leucovorin, sex, cholinesterase, and platelets. A nomogram predicting neutropenia was created based on each regression coefficient. The Brier score for the nomogram was 0.139.

Conclusion
We have developed a predictive model with high performance using GLMMLasso. Our nomogram can represent risk visually and may facilitate the assessment of the probability of chemotherapy-induced severe neutropenia in clinical practice.
An overview of quantum software engineering in Latin America
Authors: Aparicio-Morales Álvaro M. , Moguel Enrique , Bibbo Luis Mariano , Fernandez Alejandro , Garcia-Alonso Jose , Murillo Juan M. 
Published: November 2024

AbstractQuantum computing represents a revolutionary computational paradigm with the potential to address challenges beyond classical computers’ capabilities. The development of robust quantum software is indispensable to unlock the full potential of quantum computing. Like classical software, quantum software is expected to be complex and extensive, needing the establishment of a specialized field known as Quantum Software Engineering. Recognizing the regional focus on Latin America within this special issue, we have boarded on an in-depth inquiry encompassing a systematic mapping study of existing literature and a comprehensive survey of experts in the field. This rigorous research effort aims to illuminate the current landscape of Quantum Software Engineering initiatives undertaken by universities, research institutes, and companies across Latin America. This exhaustive study aims to provide information on the progress, challenges, and opportunities in Quantum Software Engineering in the Latin American context. By promoting a more in-depth understanding of cutting-edge developments in this burgeoning field, our research aims to serve as a potential stimulus to initiate pioneering initiatives and encourage collaborative efforts among Latin American researchers.
Trends in Coverage and Content of Maternal and Neonatal Care in Bamako, Mali
Authors: Traoré Mariam , Coulibaly Djeneba , Diawara Fatou , Terera Ibrahim , Dembelé Haoua , Maiga Ababacar I. , Iknane Akory Ag , Maïga Abdoulaye , Amouzou Agbessi 
Published: November 2024

AbstractCoverage levels of maternal and neonatal health services in Mali’s major cities vary due to the combined effect of several factors, including poverty and migration to urban centers. This worsened from 2012 due to the security crisis. We conducted an analysis of the trends and differences in several indicators of maternal and neonatal care coverage in Bamako using secondary data from Mali’s Demographic and Health Surveys from 2001 to 2018. Our results highlighted differential access to antenatal and childbirth care for non-poor and non-migrant women compared to their counterparts categorized as poor and migrant. The gaps were much larger depending on migration status (i.e., number of years since resettling in Bamako) and even tended to increase over time. This was particularly the case regarding the number of antenatal visits (ANC 4+), with differences according to poverty level at 7 percentage points in 2001 and 8.3 percentage points in 2018. Migration status showed even larger gaps to the disadvantage of migrant women of 13.4 percentage points (2006) and 24.4 percentage points (2018). There is a higher proportion of cesarean section among non-poor women. The results suggested an opposite pattern for postnatal care of newborns, with a difference of 6.8 percentage points of coverage in favor of the poor in 2018. The high coverage of maternal and newborn health interventions in Bamako city conceals intra-urban disparities to the detriment of poor migrant women and those who recently migrated to the city, partly due to the conflicts and security issues. A redefinition of health programs to include such targets would be desirable from an equity perspective.
Application of machine learning in drug side effect prediction: databases, methods, and challenges
Authors: Zhao Haochen , Zhong Jian , Liang Xiao , Xie Chenliang , Wang Shaokai 
Published: November 2024

AbstractDrug side effects have become paramount concerns in drug safety research, ranking as the fourth leading cause of mortality following cardiovascular diseases, cancer, and infectious diseases. Simultaneously, the widespread use of multiple prescription and over-the-counter medications by many patients in their daily lives has heightened the occurrence of side effects resulting from Drug-Drug Interactions (DDIs). Traditionally, assessments of drug side effects relied on resource-intensive and time-consuming laboratory experiments. However, recent advancements in bioinformatics and the rapid evolution of artificial intelligence technology have led to the accumulation of extensive biomedical data. Based on this foundation, researchers have developed diverse machine learning methods for discovering and detecting drug side effects. This paper provides a comprehensive overview of recent advancements in predicting drug side effects, encompassing the entire spectrum from biological data acquisition to the development of sophisticated machine learning models. The review commences by elucidating widely recognized datasets and Web servers relevant to the field of drug side effect prediction. Subsequently, The study delves into machine learning methods customized for binary, multi-class, and multi-label classification tasks associated with drug side effects. These methods are applied to a variety of representative computational models designed for identifying side effects induced by single drugs and DDIs. Finally, the review outlines the challenges encountered in predicting drug side effects using machine learning approaches and concludes by illuminating important future research directions in this dynamic field.
Thermodynamic-based ecological scaling theory in urban metabolic framework: a review
Authors: Liu Gengyuan , Wu Mingwan 
Published: November 2024

AbstractPrior research has consistently demonstrated that urban economic and social systems adhere to the empirical scaling law. Furthermore, a plethora of evidence, including the scale-free networks of energy metabolism, the allometric growth patterns of species and populations, and the scaling law relationship between exergy and transformity in biosphere systems across various levels, indicates that urban ecosystems exhibit multi-level scaling law characteristics in energy metabolism under self-organization, alongside significant human activity imprints. This study synthesizes these findings to hypothesize that urban ecological components are also aligned with system-level scaling theory within the urban metabolism framework. This encompasses: 1) the existence of multistable coexistence and mutual transformation phenomena, mirroring the dynamic nature of scaling laws; and 2) a nuanced balance between the ecosystem and the socio-economic system, particularly in the realms of spatial competition and output efficiency. The ecosystem scaling theory hypotheses of urban metabolic processes offer a theoretical foundation for identifying ecological security tipping points, which are pivotal in the strategic decision-making for ecological planning and management in the future.
Dual roles of extracellular vesicles in acute lymphoblastic leukemia: implications for disease progression and theranostic strategies
Authors: Lajevardi Mahya Sadat , Ashrafpour Mahshad , Mubarak Shaden M. H. , Rafieyan Behnoosh , Kiani Arash , Noori Effat , Roayaei Ardakani Marzieh , Montazeri Maryam , Kouhi Esfahani Niloofar , Asadimanesh Naghmeh , Khalili Saeed , Payandeh Zahra 
Published: November 2024

Abstract
Acute Lymphoblastic Leukemia (ALL) is a heterogeneous blood cancer characterized by the uncontrolled growth of immature lymphoid cells due to dysregulated signaling pathways. It is the most common pediatric cancer, with high cure rates in children, but significantly lower survival rates in adults. Current theranostic strategies, including chemotherapy, immunotherapy, and nanomedicine, aim to improve detection and treatment precision but are limited by side effects, drug resistance, high costs, and stability issues. Notably, extracellular vesicles (EVs) offer a promising alternative, addressing these limitations through their natural biocompatibility and targeted delivery capabilities. EVs play a dual role in ALL: they contribute to leukemia progression by promoting tumor growth, immune suppression, and drug resistance via the transfer of oncogenic molecules, while also serving as valuable non-invasive biomarkers due to their specific miRNA and protein content. Their ability to deliver therapeutic agents directly to leukemic cells, combined with their stability and low immunogenicity, makes EVs a compelling tool for improving ALL treatments. Indeed, by targeting the molecular pathways influenced by EVs or leveraging them for drug delivery, innovative therapeutic strategies can be developed to enhance treatment outcomes and reduce side effects. Thus, EVs represent a promising frontier for advancing theranostic strategies in ALL, offering new opportunities to improve diagnosis and treatment while overcoming the limitations of traditional therapies. This review will explore the dual roles of EVs in ALL, addressing their contributions to disease progression and their potential as therapeutic agents and biomarkers for early diagnosis and targeted therapies.
A phase 2 clinical trial of luspatercept in non-transfusion-dependent patients with myelodysplastic syndromes
Authors: Kosugi Hiroshi , Fujisaki Tomoaki , Iwasaki Hiromi , Shinagawa Atsushi , Iida Hiroatsu , Jo Tatsuro , Kubonishi Shiro , Morita Yasuyoshi , Nakashima Yasuhiro , Onodera Koichi , Suzuki Kenshi , Suzuki Takahiro , Tamai Yotaro , Usuki Kensuke , Yokota Akira , Yonaga Hideyuki , Hayakawa Jin , Midorikawa Shuichi , Nishio Mitsufumi , Suda Makoto , Matsue Kosei 
Published: November 2024

Abstract
Luspatercept has shown durable clinical efficacy for the treatment of anemia in transfusion-dependent patients with lower-risk myelodysplastic syndromes (LR-MDS). We report the results of a prespecified primary analysis of a phase 2 trial of luspatercept in non-transfusion-dependent (NTD) Japanese patients with anemia due to LR-MDS. Luspatercept (starting dose 1.0 mg/kg) was administered subcutaneously once every 3 weeks. The primary endpoint was the proportion of patients who achieved hematological improvement-erythroid (HI-E) response (≥ 1.5 g/dL increase in hemoglobin level for 8 weeks) without transfusions within the first 24 weeks of treatment. At the primary analysis data cutoff, 21 patients had been enrolled/treated; 17 and 10 patients had completed 24 and 48 weeks of treatment, respectively. HI-E response occurred within 24 weeks in 10 patients (47.6%; 95% confidence interval, 25.7–70.2; P < 0.0001), which was significantly higher than the predefined threshold (10%). By week 48, HI-E response occurred in 12 patients (57.1%) and 17 patients (81.0%) remained NTD. Luspatercept was well tolerated. Three patients (14.3%) had grade 3–4 treatment-related treatment-emergent adverse events. Luspatercept resulted in statistically and clinically significant improvements in hemoglobin levels, and may help delay the need for transfusions in NTD patients with LR-MDS.
Unraveling (K)RAS in pancreatic ductal adenocarcinoma
Authors: Doleschal Bernhard 
Published: November 2024

SummaryPancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a poor 5‑year survival rate. The majority of PDAC cases harbor KRAS mutations, predominantly at codon 12, with G12D being the most common. While selective inhibitors like sotorasib have shown promise in KRASG12C-mutated PDAC, these mutations are rare, and resistance develops rapidly. Efforts to target more prevalent mutations like KRASG12D are ongoing, with compounds such as MRTX1133 showing preclinical efficacy. Resistance mechanisms include secondary mutations and pathway reactivation, prompting the development of pan-(K)RAS inhibitors (e.g., RMC-6236) and combination strategies targeting upstream effectors. Novel approaches, such as KRAS-targeted vaccines and T‑cell receptor (TCR) therapies, offer additional potential. Continued clinical trials are crucial to optimizing KRAS-targeted therapies in PDAC.
Naturstoffe aus der Vergangenheit
Authors: Klapper Martin , Ramm Michael , Stallforth Pierre 
Published: November 2024

AbstractLow molecular weight compounds biosynthesized by microorganisms, commonly known as natural products, are indispensable in modern medicine. They serve as the basis for many antibiotics, immunomodulators, and anti cancer agents. The new research field of paleobiotechnology allows access to natural products of the past. Based on ancient DNA from dental calculus, it was possible to generate bacterial natural products dating back over 100,000 years.
The impact of urban pollution on the metal contamination of the Zandvlei estuary, Cape Town, South Africa
Authors: Sidondi L. , Odendaal J. P. , Walker D. R. , Paulse A. N. 
Published: November 2024

AbstractMetal contamination of aquatic systems is a global concern, negatively impacting ecosystems and human health. However, limited information is available on metal concentration levels in South African estuaries, including the Zandvlei estuary in Cape Town, a highly urbanised system with an extensive stormwater drainage network. The objective of this study was to determine spatial and temporal variations of metal concentrations (aluminium, zinc, lead, and copper) in water, sediment, and macroalgae (i.e. Enteromorpha spp.) in the Zandvlei estuary. Samples were collected from five sites over four seasons in 2017 and analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS), with metal levels were compared to water and sediment quality guidelines. Results revealed significant spatial and temporal variations, influenced by the proximity to pollution sources, physicochemical parameters, river inputs, sediment characteristics, weather conditions, and anthropogenic activities (i.e. industrial activities and urban development). In water samples, metals were most concentrated in the order Al > Zn > Cu > Pb; in sediment and macroalgae, the order was Al > Zn > Pb > Cu. Notably, Enteromorpha spp. accumulated higher metal concentrations than water and sediments, and some metal levels exceeded guideline thresholds, indicating contamination risks. These findings highlight the importance of Enteromorpha spp. as a biomonitoring tool for metal pollution in urban estuarine environments. This study addresses significant knowledge gaps in South African estuaries, supporting enhanced environmental monitoring and conservation strategies.
A virtual data generator system for shape recognition in haptic robotics
Authors: Gutiérrez Anna , Garrofé Guillem , Nonell Pau , Serrano Claudia , Parés-Morlans Carlota , van den Heijkant Tomás , Vera Mireia , Ruiz Conrado , Vidal Laia , González Alejandro , de Jesús Òscar , Ros Raquel , Miralles David 
Published: November 2024

AbstractIn robotics, the current state of object recognition in haptic sensory mode falls significantly short of the results obtained in visual mode. One of the main reasons for this is the lack of haptic data sets for training recognition models. A major impediment is the time-consuming and difficult task for a real robot to capture large amounts of haptic information. This paper introduces a virtual haptic dataset generator system that captures haptic features based on the curvatures of an object. The main goal is to show that this capture system is a feasible approach that can eventually be implemented not only in virtual settings but in actual robots. The virtual haptic capture system described speeds up the learning process, where a real robot would learn through virtual simulation. The paper shows three important points that make the system feasible. The capture is independent of the angle of inclination of the end-effector as it approaches the explored object. The system recognition is performed on everyday objects. Since a real system is exposed to noise during data acquisition, the data of the virtual system must also contain noise. High performance is still achieved within the noise ranges of current sensor systems.
Chemical composition and consumer acceptability of oyster mushroom and sorghum-pearl millet based composite flours
Authors: Ndunge Charles Alice , Mburu Monica , Njoroge Daniel , Zettel Viktoria 
Published: November 2024

AbstractDue to over-reliance on starchy staple foods in Kenya Micronutrient deficiency (MD) and Protein Energy Malnutrition (PEM) are the major nutritional concerns. Despite these foods being characterized by low nutrient densities, they are still used as main food ingredient especially in making porridges. Therefore, this study intended to develop composite flour of sorghum-pearl millet blends fortified with oyster mushroom and further evaluate its chemical composition and consumer acceptability. The sorghum and pearl millet were soaked in tap water then solar dried followed by milling into flour. Oyster mushrooms were also solar dried and milled. Subsequently, oyster mushroom was substituted to sorghum-pearl millet blends at 10–50 % to achieve the composite flours. Proximate composition of the samples was determined using standard methods, Inductively Coupled Plasma Optical Emission Spectroscopy and Ultra Performance Liquid Chromatography Ultraviolet Spectroscopy/Mass Spectrometry were used for mineral and vitamin analyses, respectively. In addition, consumer acceptability test was done on thin porridges was also carried out using untrained panelists. Protein content of the composite flours increased from 11.15–19.74 %, and there were significant increases in fiber (2.68–10.66 %) and ash (1.38 % to 5–79 %). Mineral content increased with Ca, P, Na, K, Mg, Zn and Fe as high as 286.25 mg/100 g, 2088.05 mg/100 g, 167.55 mg/100 g, 1833.05 mg/100 g, 136.75 mg/100 g, 4.22 mg/100 g and 9.46 mg/100 g respectively. An improvement of B1, B2, B3, B6 and B9 vitamin contents was also observed. Sensory analysis showed relatively moderate overall acceptance of thin porridges from the composite flours, although the acceptability decreased with the increase in oyster mushroom. Specifically, composite flour with 50% oyster mushroom was rich in most nutrients but it had the lowest scores for the sensory attributes. In conclusion, addition of oyster mushroom increased the overall nutritional composition of local cereal based staple foods an indication of potential solution to PEM and MD.