bluuebunny/crossref_metadata_2025_split huggingface dataset:
Health-related quality of life and cancer related fatigue in patients with non-metastatic head and neck cancers
Authors: Sheida Fateme , Abbasi Rohollah , Ameri Ahmad , Tapak Leili , Mansouri Alireza , Jamalpour Mohammad Reza , Shariatpanahi Elnaz , Mirza-Aghaei Mehdi , Khajavi Mohamad , Amlashi Zahra Keshtpour 
Published: November 2024

Abstract

Objective: This study aimed to assess health-related quality of life (HRQoL) and cancer-related fatigue (CRF) in patients with non-metastatic head and neck cancers over a six-month period following treatment initiation.
Methods: Patients with non-metastatic head and neck cancers referring to two oncology clinics in Hamadan, Iran, from April 2022 to March 2023 were evaluated using the Brief Fatigue Inventory (BFI) and the European Organization for Research and Treatment of Cancer Quality of Life questionnaire (EORTC QLQ C-30) version 3. Scores were recorded at baseline and six-month post-treatment. All statistical analyses were performed using SPSS version 27, with a P-value of <0.05 considered statistically significant.
Results: Of the 47 patients in this study, 83% were male (n=39) and the mean age was 54.28±9.61 years. The oral cavity (n=25, 53.2%) and larynx (n=11, 23.4%) were the most frequent tumor sites. Significant increase in CRF were observed between baseline and six-month post-treatment (3.07 vs. 3.50, P <0.001). The result of EORTC QLQ C-30 showed that mean global QoL score was 73.91±8.89 at baseline and 63.40±9.20 six-month post-treatment (P<0.001). The majority of functional scales items reduced significantly (P<0.001), and symptom scale items increased over time.
Conclusion: In conclusion, this study demonstrates a significant decline in QoL and an increase in CRF among patients with non-metastatic head and neck cancers during the first six months of treatment. The findings emphasize the multifactorial impact of cancer treatments on physical, psychological, and financial aspects of patients’ life and vital need for supportive care.
WITHDRAWN: Designing of Optimized Offloading and Load Balancing Schemes for Cloud-Edge Computing in the Internet of Vehicles
Authors: Gourav Kumar , Gupta Kavita 
Published: November 2024

Abstract

Research aims to develop advanced optimization methods to improve cloud-edge computing efficiency, particularly focusing on two primary objectives. The first objective is to design an optimized offloading scheme that efficiently distributes computational tasks between cloud and edge servers, thereby optimizing energy consumption and reducing latency in edge computing environments. Leveraging hybrid optimization algorithms such as the Black Widow Whale Algorithm (BWWA), this study seeks to balance processing loads and resource utilization, leading to significant improvements in overall system performance. The second objective is to design a load-balancing scheme specially designed for Cloud-Edge Computing frameworks and the Internet of Vehicles (IoV). The suggested design is to achieve the optimal distribution of workloads among IoT devices, edge servers, and cloud resources, addressing the unique challenges posed by the dynamic and heterogeneous nature of vehicular networks. By incorporating real-time data analytics and adaptive algorithms, the load balancing mechanism will enhance system reliability, minimize latency, and improve energy efficiency. Both objectives are pursued through rigorous simulation and evaluation using MATLAB, assessing important performance indicators such as transmission rates, consumption of energy, processing time, and offloading utilization. The outcomes of this research are expected to provide substantial advancements in the field of cloud-edge computing, offering scalable and efficient solutions for complex, distributed computing environments.
The Clinico-Pathologic Profiles of Patients with Incidental Hyperuricemia Post Renal Transplant at the Kenyatta National Hospital
Authors: Adam Zoya , Kayima Joshua , Sokwala Ahmed 
Published: November 2024

Abstract

BACKGROUND:
Following a renal transplant hyperuricemia is a common complication, but its clinical and prognostic significance is controversial. Evidence is accumulating indicating a role for uric acid in causing graft dysfunction and hence decreasing survival of the graft. This study was conducted to determine the clinical characteristics, graft function and outcomes of hyperuricemic renal transplant recipients at the Kenyatta national hospital
METHODOLOGY:
The study population comprised all renal transplant recipients between January 2011 to June 2021 being seen at the Kenyatta National Hospital transplant clinic .Patients were above 18 years of age with at least 2 consecutive uric acid assays that were consistently high or normal. As many files as available, that met the inclusion criteria were analysed by consecutive sampling. The clinical characteristics, the graft function at the end of 1 year, 3 years and at the end of 5 years, as well as the outcomes were recorded
RESULTS:
All the patients were Black Africans and had received a kidney from a living allograft donor. 82 (65.6%) of the 125 patients were Male and 43 (34.4%) were Female. The mean age of the patients was 44.2 (SD 14.4) years. The prevalence of hyperuricemia was 30.4%.9 (23.7%) of the 38 patients with hyperuricemia had Diabetes as a comorbidity and all the 38(100%) patients had hypertension as a comorbidity. Most of the patients 25(65.8%) were of normal BMI.
The majority 23 (60.5%) had been transplanted 5 or more years ago. None of the patients with hyperuricemia had pre-transplant hyperuricemia. 29 (76.3%) were on Tacrolimus while 9 (23.7%) were on Cyclosporine. Only 1 (2.6%) of the patients with hyperuricemia was on a Thiazide diuretic.
Overall, there were no factors that were found to be independently associated with hyperuricemia, except the EGFR at 1, 3 and 5 years which were all significantly lower in the hyperuricemic group.
CONCLUSION:
Hyperuricemia was common in our transplant recipients and was seen more among males. All patients with hyperuricemia had hypertension but not diabetes and most had a normal BMI. No significant transplant associated risk factors among those with hyperuricemia, were found. However, among patients with hyperuricemia the EGFR was significantly lower at the end of 1, 3 and 5 years when compared to those with normal uric acid levels.
Development of a patient-centered communication skills training: A qualitative exploration of nurse managers‘ perspectives
Authors: Mielke Kendra , Frerichs Wiebke , Cöllen Katja , Lindig Anja , Härter Martin , Scholl Isabelle 
Published: November 2024

Abstract

Background: Communication skills trainings can enhance nursing professionals’ patient-centered communication skills, fostering positive health outcomes for both patients and nursing professionals. Understanding experiences and preferences of the target group is crucial when developing a specific communication skills training. Thus, it is essential to involve not only nurses but also nurse managers in the training development, as they offer a comprehensive overview of communication challenges experienced by nursing staff. Therefore, this study aimed to explore nurse managers’ perspectives on essential content for developing a patient-centered communication skills training.
Methods: We conducted semi-structured interviews with nurse managers working at an academic medical center in Germany. Transcripts of audio-recorded interviews were analyzed using Kuckatz’s qualitative content analysis.
Results: N = 18 nurse managers from various medical disciplines such as gynecology, psychiatry, pediatrics, and neurology participated in the study. The majority highlighted the importance of enhancing general communication skills and strategies such as showing empathy and applying tailored communication, as well as addressing communication challenges with patients. Fewer participants emphasized the need for training in professional self-awareness, communication with relatives, and culturally sensitive communication.
Conclusion: Nurse managers identified crucial aspects for a training to enhance nurses’ patient-centered communication, revealing similar communication challenges across all medical disciplines.
Effective Energy Aware Cluster based Routing to improve Lifetime of Mobile WSN: A Glowworm Swarm Optimization approach
Authors: Dey Ranadeep , Thakurta Parag Kumar Guha 
Published: November 2024

Abstract

The mobile wireless sensor networks (MWSNs) are comprised of small, low powered mobile sensor nodes to sense various types of data from the environment. A higher energy requirement of network with major power constraint makes it more challenging to develop an energy efficient routing of data towards sink node in this network. Hence in this paper, an energy efficient routing is proposed to improve the network lifetime under hierarchical arrangements of the nodes. In the proposed work, the mobile nodes are initially placed inside a sensing region randomly. Dendrogram technique is used to determine the optimal number of clusters for a set of nodes having their current locations. Then K-means algorithm is used to develop the clusters, which can provides centroid locations in each clusters. A cluster head in each cluster is selected by glowworm swarm optimization (GSO) algorithm, to route the collected data from other member nodes towards the sink node of the network. This cluster head is determined depending on three factors, such as distance from cluster centroid location, distance from sink node and the residual energy of a node from corresponding cluster. The simulation results indicate the less energy requirement by 4.9–24.2% in data transmission during each round of the proposed routing scheme over other existing methodologies. An improvement in the number of alive nodes in the MWSN is also determined by the proposed work, which eventually can prolong the average lifetime of the networks by 4.82–41.03% over other existing approaches.
Determining Irrigation Frequency and Water Amount for Irrigated Wheat Production in Amibara, Afar Region, Ethiopia
Authors: Hassen Jemal Mohammed , Borana Fikadu Robi 
Published: November 2024

Abstract

Irrigation water management is crucial for enhancing wheat production in Ethiopia, especially in lowland regions like Amibara, where salinity and improper irrigation practices affect crop yields. This study was conducted at Werer Agricultural Research Center to determine the optimal irrigation frequency and amount that enhances water use efficiency (WUE) and wheat yield. The experiment used a Randomized Complete Block Design (RCBD) with five treatments: 35mm every 6 days, 45mm every 8 days, 60mm every 10 days, 70mm every 12 days, and irrigation based on an Allowable Soil Moisture Depletion Level (ASMDL) of 50%. Bread wheat (Ga'ambo 2 variety) was grown, and agronomic data were collected over two years. Results indicated that the treatment of 35mm every 6 days achieved the highest grain yield (4104.74 kg/ha) and WUE (0.90 kg/m³), closely followed by the 45mm every 8 days treatment (3743.35 kg/ha and 0.78 kg/m³). Both treatments were statistically similar in terms of yield and WUE. The ASMDL and treatments with longer irrigation intervals (60 mm/10 days and 70 mm/12 days) resulted in significantly lower yields and WUE, demonstrating the critical role of more frequent irrigation in maintaining optimal soil moisture and mitigating salinity effects. The findings suggest that applying 45mm every 8 days provides a balanced approach, maximizing yield while offering practical labor and time savings compared to the more frequent 35 mm/6-day schedule. These results are essential for improving irrigation strategies in Ethiopia’s lowland wheat production areas, contributing to the country’s goal of wheat self-sufficiency. Further research is recommended to validate these findings under farmer-managed conditions and explore their long-term impacts on soil health and water resources.
A New Optimization-Based Framework for Enhanced Feature Selection with the Narwal Optimizer
Authors: Medjahed Seyyid Ahmed , Boukhatem Fatima 
Published: November 2024

Abstract

The selection of relevant features is a critical step in many machine learning and data analysis tasks, as it can significantly impact the performance and inter-pretability of the resulting models. In this work, we introduce a novel feature selection approach that draws inspiration from the unique movement patterns of the narwhal, a fascinating marine mammal, and the power of binary optimization. The proposed method, named BNO-FS is based on Narwhals Optimizer (NO) which is a new meta-heuristic recently developed recently and never been tested on feature selection problem. A binary version is proposed in this study. A new fitness function is proposed composed of two important terms: the classification accuracy rate obtained by three classifiers and the number of selected features. The algorithm aims to identify the optimal subset of features that maximizes the predictive performance of the model while minimizing the number of selected features. The effectiveness of the Binary Narwhals Optimizer approach is demonstrated through a series of experiments on benchmark datasets, where it is compared to other state-of-the-art feature selection techniques.
Prevalence of chronic fatigue syndrome and its related factors in covid- 19 patients discharged from at northwest of Iran hospitals in 2022
Authors: Alizadeh Arman , Dadkhah Behrouz , Mozaffari Nasser , Mohammadi Mohammad Ali 
Published: November 2024

Abstract

Introduction:
The COVID-19 pandemic has been a tragedy. It has devastated the health and financial well-being of many people around the world. Fatigue is one of the common symptoms and complications of covid-19, which can continue in patients for a long time after infection. The present study was also conducted with the aim of determining the prevalence of chronic fatigue syndrome and associated risk factors in covid-19 patients discharged from at northwest of Iran hospitals in 2022
Methods
This cross-sectional study was performed between May 2021 and January 2022 at northwest of Iran. 422 patients who had been discharged from hospital with COVID-19 were enrolled in our study. Chalder fatigue Assessment Scale (FAS) was used to measure post-COVID-19 chronic fatigue syndrome. Central and dispersion indicators were used to describe clinical and sociodemographic characteristics of patients. The chi-square test and person correlation were used to examine the statistical association between the CFS score and other clinical and sociodemographic factors.
Results
in overall, 72% of patients were female. The mean ± SD of the CFS score was 21.2 ± 9.0. The prevalence of post-COVID-19 CFS among our studied sample was %12.64. The findings showed that the prevalence of chronic fatigue and fatigue scores were higher in women (%13) than in men (%12.3) and this difference was statistically significant (P < 0.04). The findings showed that the prevalence of chronic fatigue with history of previous disease (P = 0.04), history of drug use (P = 0.01), history of smoking (P = 0.04), history of mechanical ventilation (P = 0.05) and age (P = 0.001) has a significant relationship.
Conclusion
Post-COVID-19 fatigue is a major issue following the initial acute illness with COVID-19, with a prevalence of %12.64. Therefore, the implementation of standardized measures for the screening of chronic fatigue after covid-19 and planning by the health staff in order to help these patients especially among female patients Seems necessary.
Groundwater level prediction in Datong Basin based on multivariate LSTM neural network
Authors: Wang Cang-Ning , Ning Ge , Su Dong-Yang , Zhang Ya-Ting , Zhang Fang 
Published: November 2024

Abstract

Considering that the groundwater level is influenced by a variety of complex factors, this paper constructs a groundwater level prediction model by introducing multiple variables to adapt to different geological and meteorological conditions. This model is established using a multivariate long short-term memory (M-LSTM) network in combination with an attention mechanism to provide an efficient artificial intelligence method for predicting groundwater levels. The model is trained using groundwater level data, normalized difference vegetation index (NDVI), rainfall, average air temperature, and relative humidity from 2018 to 2019 in the Datong Basin and validated with 2020 groundwater level data. Results indicate that the M-LSTM combined with the attention mechanism accurately predicts future groundwater level changes based on historical data, achieving root mean square errors (RMSE) of 0.2131, 0.2033, and 0.2844 in three experiments, demonstrating a high model fit. Meanwhile, an investigation of hydrogeology, meteorological data, and groundwater resources in the Datong Basin was conducted to perform a groundwater early warning analysis based on the extent of groundwater over-exploitation. The depth of groundwater in each area of the Datong Basin was predicted for the end of 2035 under high, medium, and low exploitation modes and varying precipitation conditions, with a quantitative analysis of the funnel center's location.
DiscoverNet: Unsupervised Video Object Discovery through Discrete Visual Tokenization and Language Model-Inspired Reconstruction
Authors: Joshi Amey , Joshi Gargi , Walambe Rahee , Kotecha Ketan 
Published: November 2024

Abstract

Object discovery, the task of separating objects from the background without human annotations, continues to be an unsolved challenge in the field of computer vision. Existing methods face difficulties due to object-background ambiguity and slot drift, as they rely on clustering based on only low-level features. In this work, we introduce DiscoverNet, a comprehensive framework that enhances the auto-encoder representation learning approach by incorporating a scalar quantized discrete latent space and reconstruction via a masked language model (LM). Our innovative visual tokenizer enables end-to-end object discovery by harnessing the synergy between discretization and image generation using a bidirectional transformer. The proposed method fosters the development of compatible object-centric features for language models, providing interpretability and memory efficiency. Experiments conducted on synthetic and real-world datasets show that DiscoverNet surpasses current state-of-the-art methods in object discovery and can be extended to various downstream computer vision tasks, such as image and video generation and action recognition. Our approach demonstrates significant improvements over existing techniques, as evidenced by the evaluation of the real-world KITTI benchmark
Delay in Receiving Lab Test Results Increases the Costs of Treating an Episode of Care for Urinary Tract Infections
Authors: Thorpe Kenneth E. , Woodruff Robert W. , Policy Health 
Published: November 2024

Abstract

Background
To provide the first study examining the impact on the total health care costs of treating urinary tract infections as a function of the time delay in receiving lab test results.
Methods
Retrospective analysis including those receiving the urine bacteria test and ultimately diagnosed with a urinary tract infection. We estimate episode of care-based treatment costs associated with each day of delay in receiving results Using regression analysis, we estimate the total costs of treating an episode of a urinary tract infection. We control for patient demographics (age, gender), region and number of comorbid conditions. The analysis focuses on commercially insured populations using the IBM MarketScan database The dependent variable is the per person costs of treating a urinary tract infection episode and our controls included the number of days between test and diagnosis and prescription filled.
Results
Delays in receiving test results that allowed for filling a prescription and a diagnosis increased health plan treatment costs. The analysis looked at patients with a urine bacteria culture test and an ultimate diagnosis of urinary tract infection. We examine the days between the test and two outcomes the diagnosis and the prescription filled. Each day of delay (up to 5) added $1000 to nearly $3000 in health plan spending to treat the condition. These additional costs are traced to increased hospitalizations for the condition for each day of delay.
Conclusions
Slow turn around times in receiving test results for bacterial infections increase health plan and employer spending for treating infections. The delays also result in poorer quality of health as hospitalization rates increased with the delayed results. We need new technologies that allow for rapid results for these tests.
Deep-Multiscale Stratified Aggregation
Authors: Wu Ziheng , Yang Song , Hu Fengxiang , Yao Jiaxiang , Zhou Jun , Wang Jingyuan , Li Yongtao 
Published: November 2024

Abstract

In deep learning based vision tasks, improving multiscale representation by combining shallow and deep features has consistently led to performance gains across a wide range of applications. However, significant discrepancies in both scale and semantic content often occur during the fusion of shallow and deep features. Most existing approaches rely on standard convolutional structures for representing multiscale features, which may not fully capture the complexity of the underlying data. To address this, we propose a novel deep-multiscale stratified aggregation (D-MSA) module, which could improve the extraction and fusion of multiscale features by efficiently aggregating features across multiple receptive fields. The novel D-MSA module was integrated into the YOLO architecture to enhance the capacity for processing complex multiscale features. Experiments on the PASCAL VOC 2012 dataset demonstrate that D-MSA could effectively handle complex multiscale features while improving computational efficiency, making it suitable for object detection in challenging environments.
Enhancing Maternal and Infant Health: Insights from Comprehensive PMTCT OF HIV Services in Rwanda
Authors: Rutayisire Gad , Ntale Roman Saba , Semwaga Emmanuel , Nsereko Vincent , Uwera Marie Grace , Rurangwa Mike , Musafiri Tumusiime , Ishami Innocent , Wekia Enock , Kiwanuka Noah , Bagaya Bernard Ssentalo 
Published: November 2024

Abstract

Background
Despite the remarkable achievements in preventing and controlling HIV-1 at global level, Mother-to-Child Transmission (MTCT) of HIV and the associated mortality continue to be unacceptably high. This remains a significant public health concern, with efforts focused on increasing the global coverage of Prevention of Mother-to-Child Transmission (PMTCT) of HIV programs.
Methods
This study expounds on the multifaceted dimensions of PMTCT interventions and their impact on maternal and child health outcomes in Rwanda, using a qualitative approach with pregnant mothers living with HIV navigating PMTCT clinics for 18 months.
Results
The results indicate that PMTCT services play a crucial role in enhancing the health and well-being of both pregnant mothers living with HIV and their infants. The primary reinforcements established include; nutritional support, continuous follow-up and postnatal care, education on HIV transmission, and access to government-provided medical insurance.
Conclusions
Based on the results, interventions for PMTCT services are compelling on economic and social grounds in many resource-limited settings in Rwanda and should remain at the forefront of global HIV prevention efforts.