bluuebunny/crossref_metadata_2025_split huggingface dataset:
The lived experience of adolescents with type 1 diabetes mellitus in Bahir Dar, Ethiopia: a phenomenological study
Authors: Seid Yassin Hussin , Workie Hailemariam Mekonnen , Amare Desalegne , Awle Desalegn 
Published: December 2024

Abstract

Background
Type 1 Diabetes Mellitus (DM) is one of the most common chronic diseases in adolescence posing a threat to health and potentially endangering life. It can have a significant impact on the physical, social and emotional development of adolescents. Understanding the lived experience of an adolescent with type 1 DM is crucial for improving their health outcomes and future aspirations. However, there is currently no study that highlights their life experiences in Ethiopia. Therefore, this study aimed to explore the lived experiences of adolescents living with type 1 DM.
Methods
A phenomenological study was conducted among adolescents living with type 1 DM who received medical follow-up in Bahir Dar from March, 2023 to May 2023. The participants were selected using a purposive sampling technique. Data were collected through face-to-face in-depth interviews using semi-structured guiding questions developed by the authors. Credibility, dependability, transferability, and conformability were employed to ensure the quality of the data. Verbatim transcription and conceptual translation were done. Thematic analysis with ATLAS.ti 8.4.24 software was used.
Results
In this study, the lived experiences of adolescents living with Type 1 DM were presented in four themes: psycho-emotional experiences, challenges related to diabetes management and well-being, supportive factors, and coping mechanisms.
Conclusion
This study revealed that adolescents with DM were experienced social, emotional, spiritual and psychological challenges. They also faced issues related to adherence, sleep and insulin availability, as well as a lack of coping strategies. They relied on support from family, friends, schoolteachers, healthcare providers, and the community. Coping mechanisms such as avoidance, adaptation, and acceptance were utilized to manage their emotional challenges.
Assessing potential game-changing interventions to reduce adolescent pregnancy in Africa and Latin America: A systematic review and meta-analysis
Authors: Wycliff Katwesige , Cordova-Pozo Kathya , Asori Moses , Ribeiro Julia D. , Irfan Muhammad , Kigongo Stephen , Evelyne Tibananuka 
Published: December 2024

Abstract

Introduction: Adolescent health remains a significant public health concern in Africa and Latin America, resulting in adverse consequences for young women’s health, education, and socioeconomic empowerment. This review aimed to identify potential interventions and their effects on reducing adolescent pregnancy.
Methods: The PRISMA 2020 guidelines were used to conduct the review. We searched electronic databases for studies published between 2014 and 2024. We included interventional studies evaluating adolescent pregnancy. Two authors extracted data and assessed study quality using the Cochrane risk of bias tool. The primaryoutcomes were adolescent pregnancy and meta-analysis sub grouped outcomes into economic, educational, health, and combined interventions employing a Mantel-Hazel method and inverse variance method using Stata software. A risk ratio (RR) was obtained viathe random effect module among the subgroups. PROSPERO registered the study under ID: Reg. CRD42022369044.
Results: Our search yielded 8,175 studies, 13 of which met theinclusion criteria. Meta-analysis revealed the following outcomes. Socioeconomic interventions RR 1.0153 (95% CI 0.9267; 1.1125, P value=0.7441), educational interventions RR 0.8476 (95% CI 0.5998; 1.1980, P value=0.3490), health interventions RR 1.0614 (95% CI 0.9908; 1.1369, P value=0.0895), and combined interventions RR 0.9880 (95% CI 0.8824; 1.1062, P value =0.8338). Subgroup analysis for African women revealed 18.2% odds of reducing adolescent pregnancy OR 0.8180 (95% CI 0.5696; 1.1746), and for Latin American women, 22.7% increased the risk of adolescent pregnancy from the interventions OR 1.2271 (0.7996; 1.8831). The subgroup analysis revealedno statistically significant findings, with a P value of 0.1563.
Conclusion: The findings confirm a limited reduction in the rates of adolescent pregnancies by combined approaches in Africa compared with those inLatin America,despite overall results indicating no statistically significant effects from all interventions. Therefore, policymakers must prioritize programming for rigorous game changing interventional research in Africa and Latin America.
Traffic-cognitive Slicing for Resource-efficient Offloading with Dual-distillation DRL in Multi-edge Systems
Authors: Xiaoyang Ting , Zhang Minfeng , Zhang Saimin Chen 
Published: December 2024

Abstract

In edge computing, emerging network slicing and computation offloading can support Edge Service Providers (ESPs) better handling diverse distributions of user requests, to improve Quality-of-Service (QoS) and resource efficiency. However, fluctuating traffic and heterogeneous resources seriously hinder their broader application in multi-edge systems. Existing solutions commonly rely on static configurations or prior knowledge, lacking adaptability to changeable multi-edge environments and thus causing unsatisfying QoS and improper resource provisioning. To address this important challenge, we propose SliceOff, a novel resource-efficient offloading framework with traffic-cognitive network slicing for dynamic multi-edge systems. First, we design a new traffic prediction model based on self-attention to capture traffic fluctuations among different edge regions. Next, an adaptive slicing strategy based on random rounding is devised to adjust the resource configuration according to the traffic and demands of edge regions. Finally, we develop an improved Deep Reinforcement Learning (DRL) method with a dual-distillation mechanism to address the complex offloading problem, where twin critics’ networks and dual policy distillation are integrated to improve the agent’s exploration and updating efficiency in huge decision spaces. Notably, we carry out rigorous theoretical analysis to prove the effectiveness of the proposed SliceOff. Using the real-world testbed and datasets of user traffic, extensive experiments are conducted to verify the superiority of the proposed SliceOff. The results show that the SliceOff improves resource inefficiency and ESP profits under dynamic multi-edge environments, which outperforms state-of-the-art methods on multiple metrics under various scenarios.
Introducing a Multi-Dimensional Cryptographic Behavioral Analysis Framework for Ransomware Detection
Authors: Hilaw Charles , Fotheringham Magnus , Pemberton Claudia , Sanderson Daphne 
Published: December 2024

Abstract

Effective detection mechanisms are crucial in addressing the growing sophistication and frequency of ransomware attacks. The proposed Multi-Dimensional Cryptographic Behavioral Analysis framework leverages advanced analytical techniques to identify and classify malicious activities through the examination of unique cryptographic and behavioral signatures. By focusing on encryption processes, file system interactions, and anomalous network behaviors, the framework achieves a comprehensive understanding of ransomware operations. A hybrid detection algorithm integrates statistical inference with machine learning, ensuring adaptability to emerging ransomware variants while maintaining low false positive rates. Experimental evaluation demonstrates the framework's superior accuracy and robustness compared to conventional detection approaches, particularly in distinguishing between legitimate and malicious operations. Cryptographic feature analysis highlights distinct behavioral patterns among ransomware families, enabling precise identification through a combination of mathematical modeling and ensemble learning techniques. Scalability and computational efficiency are achieved through an optimized multi-dimensional pipeline, addressing challenges inherent in real-time operational environments. Insights from empirical data suggest that the framework effectively mitigates limitations associated with static and heuristic methods, offering a robust foundation for addressing future threats. Variability in detection performance across different scenarios demonstrates the need for continued refinement and exploration of additional feature dimensions. Expanding the framework to incorporate temporal and contextual attributes further enhances its potential for broader applications. The proposed methodology contributes significantly to the domain of ransomware detection, advancing both theoretical understanding and practical implementation capabilities. Results affirm its applicability in safeguarding digital ecosystems from increasingly sophisticated ransomware threats.
The impact of the COVID-19 pandemic on maternal healthcare costs in a UK inner-city multi-ethnic population
Authors: McGreevy Alice , Soley-Bori Marina , Tydeman Florence , Dalrymple Kathryn , White Sara , Khalil Asma , Poston Lucilla , Duncan Emma , Dasgupta Tisha , Mistry Hiten , Fox-Rushby Julia , von Dadelszen Peter , Magee Laura 
Published: December 2024

Abstract

Background: During the COVID-19 pandemic, maternity care reconfigurations disrupted in-person care, which shifted towards virtual care and self-monitoring. We assessed the impact of these changes on healthcare costs.
Methods: Data from October 2018 to April 2023 were used from the population-based early-LIfe data cross-LInkage in Research-Born-in-South London (eLIXIR-BiSL) platform linking maternity, neonatal, and mental healthcare data from three National Health Service (NHS) hospitals in South London, United Kingdom. Pregnancy costs were generated from the NHS perspective, using national unit costs and individual-level use of maternity, mental health, and primary care services. Interrupted time series analysis estimated the pandemic impact on monthly mother-newborn costs over time. Cross-sectional pre-pregnancy cost models isolated the impact of virtual care, and gestational diabetes (GDM) self-monitoring using the GDm-Health app.
Findings: Among 36,895 pregnancies, the monthly cost trendline level dropped by 4% (£38, 95% confidence interval: [£10-65]), during the first pandemic lockdown, and by £72 [36-108], when lockdowns were lifted. However, the pre-pandemic upward trendline slope of costs was unchanged (£0.46 [-2.93 to 3.84]). Monthly costs increased with first lockdown for Black (£103 [26-181]) and Asian women (£128 [38-218]) and increased more slowly during post-lockdown (-£12 [-23 to -2]), for Asian women, remaining higher throughout the pandemic for Black and Asian women. A 1% increase in virtual care was associated with a £7 [3-10] increase in maternity costs. GDM self-monitoring via GDm-Health was cost-neutral (£140 [-68 to 348]).
Interpretation: The pandemic was associated with temporary reductions in maternity costs due to lower healthcare utilisation. Ongoing, rising maternity costs were unchanged. The pandemic had differential effects on Black and Asian women compared to White. Further research is needed into clinical outcomes of virtual care (associated with higher costs) and use of GDm-Health (cost-neutral).
Funding: National Institute for Health Research (NIHR134293), Medical Research Council (MR/P003060/1, MR/X009742/1).
Ransomware Detection through Probabilistic Code Anomaly Profiling
Authors: Leisner Richard , Kensington Samuel , Abernathy Laura , MacAllister Daniel 
Published: December 2024

Abstract

The Probabilistic Code Anomaly Profiling (PCAP) framework represents a significant advancement in the detection of ransomware through its innovative integration of probabilistic modeling and comprehensive behavioral analysis. By employing Gaussian Mixture Models (GMMs) to model benign and malicious behaviors, the framework effectively identifies deviations indicative of ransomware activity. The dual approach of static analysis, which examines code structures, and dynamic analysis, which monitors runtime behaviors, enables the extraction of distinctive features that enhance detection accuracy. This methodology allows for the identification of both known and emerging ransomware threats, addressing the limitations of traditional signature-based detection systems. The PCAP framework's adaptability is further demonstrated through its dynamic adjustment of probabilistic thresholds, balancing sensitivity and specificity to minimize false positives while maintaining high true positive rates. Experimental evaluations have shown that the framework achieves a mean detection accuracy of 94.0\% across various ransomware variants, including LockBit, BlackCat, Hive, and Conti. Additionally, the system maintains efficient resource utilization, with CPU usage averaging around 46\% and memory consumption within acceptable limits, ensuring its suitability for real-time deployment in diverse operational environments. Comparative analyses indicate that the PCAP framework outperforms existing detection solutions, achieving higher detection accuracy and lower false positive rates. Its scalability has been validated through consistent processing times across varying dataset sizes, confirming its capability to handle large volumes of data efficiently. The framework's ability to adapt to emerging ransomware variants, as evidenced by increasing detection rates for newly identified strains such as Rhysida and Sphynx, demonstrates its relevance in the evolving threat landscape. The integration of probabilistic profiling and behavioral analysis within the PCAP framework establishes a robust and adaptable approach to ransomware detection, offering significant potential for enhancing cybersecurity measures against sophisticated and evolving threats.
Anomaly-Based Detection of Ransomware Using Virtualized File System Entropy Analysis
Authors: Hivola Calvin , Evesham Julian , Wolverstone Grace , Baskerville Leo , Marchant Annabel 
Published: December 2024

Abstract

The rising complexity of cyber threats requires innovative detection frameworks capable of addressing sophisticated attack vectors. The Virtualized File System Entropy Analysis (VFSEA) framework represents a novel approach that leverages entropy metrics to identify anomalies indicative of ransomware encryption processes. Through a combination of real-time file system monitoring and controlled virtualized environments, the methodology achieves robust detection without reliance on signature databases or extensive feature engineering. The incorporation of dynamic thresholding mechanisms enhances the framework's adaptability, enabling it to respond effectively to previously unseen ransomware variants. Experimental evaluations demonstrated high detection accuracy, with consistently low false positive rates across diverse datasets encompassing both malicious and benign activities. The modular design of VFSEA facilitates seamless integration into existing cybersecurity infrastructures while maintaining computational efficiency suitable for real-time applications. By isolating file operations within a sandboxed environment, the framework mitigates risks associated with active ransomware activity, ensuring the protection of critical assets during detection processes. Entropy-based anomaly detection further provides a scalable foundation, adaptable to the evolving encryption methodologies employed in modern ransomware campaigns. Detailed performance analyses highlight the framework’s ability to handle high-volume workloads while maintaining precision and efficiency. Comparative assessments reveal significant advantages over traditional detection methods, particularly in identifying polymorphic and zero-day ransomware variants. The findings demonstrate the potential of entropy-driven techniques to transform automated detection strategies, offering a pathway to enhanced resilience against advanced cyber threats.
Evaluating the Impact of Heat Warning Systems on Mortality and Morbidity: A Systematic Review
Authors: Enaworu Oghenetanure Ryan 
Published: December 2024

Abstract

Background
Heat exposure poses a significant global health risk, leading to approximately 7 deaths per 100,000 individuals annually. Implementing heat warning systems(HWS) aims to mitigate the health impacts of extreme heat events. This systematic review evaluates the effectiveness of these systems in reducing heat-related morbidity and mortality.
Methodology
A systematic search of Embase and PubMed databases was conducted using terms related to heat warning systems and health outcomes. Studies were limited to English-language research articles from the United States and Canada, focusing on studies that assess the impact of HWS on morbidity and mortality outcomes.
Results
Four articles assessed the relationship between HWS and mortality. One study reported a significant reduction in heat-related deaths, with reductions between 17% and 51% of expected rates. Another study noted an average of 2.6 lives saved per day due to HWS implementation. However, two studies found no significant association between HWS and all-cause mortality, with relative risk values of 1.005 (95% CI: 0.997, 1.013) and a per cent change in mortality of -0.5% (95% CI: -2.8, 1.9). Regarding morbidity, three studies indicated mixed results. One study showed increased hospitalizations for fluid and electrolyte disorders (RR: 1.040 [95% CI: 1.015, 1.065]) and heat stroke (RR: 1.094 [95% CI: 1.038, 1.152]), while another reported a 17% to 51% reduction in paramedic emergency medical service runs. The third study found no significant differences in overall health services use.
Conclusion
This review reveals a complex relationship between heat warning systems (HWS) and heat-related mortality and morbidity. While some studies show significant reductions in deaths, others indicate that HWS alone may not sufficiently lower overall mortality rates. Localized strategies that consider socioeconomic factors and healthcare access are essential, along with continuous improvement of HWS. Future research should examine regional variations to develop tailored approaches for specific heat-related health issues.
Association of selected non-communicable diseases with HSV positivity in a subset of a group of oral fibroepithelial polyp patients in Sri Lanka: preliminary findings
Authors: Perera Manosha Lakmali , Perera Irosha 
Published: December 2024

Abstract

The relationship between noncommunicable diseases and the presence of HSV-DNA in oral epithelial cells remains largely unexplored. The research focused on a subset of male patients with oral fibroepithelial polyps (FEP) in Sri Lanka. We retrieved data from 25 out of 29 oral fibroepithelial polyps across nine oro-maxillofacial units in six provinces of Sri Lanka to determine any association between the presence of Herpes simplex virus (HSV) DNA and the selected non–communicable diseases, using the SPSS-21 statistical package for data analysis. We employed Fisher’s exact test for comparing groups with cell counts of less than five, specifically to examine the HPV-DNA status of T2DM. The results indicated a statistically significant association between T2DM and HSV positivity; however, no significant associations were found with hypertension or periodontal diseases. The findings from this retrospective study should be further investigated to validate the results and assess their implications with a larger sample size and controlling for confounders.
Xuebijing Exerts Protective Effects on Myocardial Cells by Upregulating TRIM16 and Inhibiting Oxidative Stress and Apoptosis
Authors: Meng Xiaoyan , Yan Xinming , Xue Peng , Xi Zhaoqing 
Published: December 2024

Objective:
This study utilized transcriptomic sequencing combined with cellular and
animal models to explore the potential mechanisms of Xuebijing in treating sepsis-induced myocardial
dysfunction, also known as sepsis-induced myocardial injury.

Methods:
We investigated potential targets and regulatory mechanisms of XBJ injection using
network pharmacology and RNA sequencing. The effects of XBJ on oxidative stress and apoptosis
levels in human cardiac myocytes (AC16) and C57BL/6 mice exposed to lipopolysaccharide
(LPS) were evaluated by Enzyme-Linked Immunosorbent Assay (ELISA), fluorescent probe,
Fluorescent Quantitative Polymerase Chain Reaction (qPCR), Western Blot, Transmission Electron
Microscopy, oxidative stress-related indicators detection kit, flow cytometry, and Immunohistochemistry
(IHC).

Results:
First, it was verified that XBJ can reduce the deformation of AC16 cardiomyocytes induced
by LPS and the production and secretion of ROS (P <0.01). The transcriptome sequencing
results showed that the TRIM16 gene was significantly increased after XBJ treatment, and the
data of KEGG and GO analyses demonstrated that XBJ could inhibit the pathway expression
of oxidative stress damage in AC16 cells, and PCR verified that XBJ could indeed increase
the expression level of TRIM16 gene in AC16 cells (P <0.01). Basic animal and cell experiments
showed that LPS could inhibit the expression of TRIM16 and NRF2 in cardiomyocytes
(P <0.05) and promote the expression of Keap1 (P <0.01), while XBJ could significantly upregulate
the expression levels of TRIM16 and NRF2 (P <0.01) and inhibit the expression of
Keap1 (P <0.01), thereby affecting the expression levels of downstream proinflammatory cytokines
and alleviating LPS-induced oxidative stress damage. In addition, XBJ also inhibited the
expression of the pro-apoptotic proteins Bax and c-caspase3 (P <0.01), promoted the expression
of the anti-apoptotic protein Bcl2 (P <0.01), and reduced LPS-induced apoptosis by upregulating
TRIM16.

Conclusion:
Our comprehensive data demonstrated that TRIM16 is a key gene in the therapeutic
action of Xuebijing in sepsis-induced myocardial dysfunction, protecting myocardial cells from
injury through antioxidative stress and anti-apoptotic mechanisms.
Autism Spectrum Disorder: Clinical and Genetic Aspects
Authors: Shukla Shalu 
Published: December 2024

Abstract:
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by
challenges in social communication and the presence of repetitive behaviors and restricted interests.
It is acknowledged as a complex, multifactorial disorder influenced by both genetic and nongenetic
factors. Recent advancements in neuroimaging studies have provided valuable insights into
the observable changes in the brains of individuals with ASD. It is estimated that worldwide,
about 1 in 100 children has autism. This estimate represents an average figure and reported prevalence
varies substantially across studies. This review aims to explore factors influencing prevalence
rates, highlighting the significant roles played by both genetic and environmental factors in
the development of ASD. Furthermore, a recent study delved into the cellular structure, size, and
shape of the brains of ASD individuals, revealing notable differences across various brain regions.
The disparities were particularly pronounced in certain areas. While educational and behavioral
treatments have traditionally been primary in managing ASD, there is growing recognition of the
potential benefits of pharmacological and interventional approaches. This review strives to elucidate
the diverse pathophysiological mechanisms contributing to the onset of Autism, as well as to
provide insights into therapeutic interventions for the condition, emphasizing a multifaceted approach
to treatment.