bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the inherent resilience of health districts in a context of chronic armed conflict: a case study in Eastern DRCongo
Authors: Makali Samuel Lwamushi , Louis Patricia St , Karemere Hermès , Wautié Alice , Pavignani Enrico , Eboma Christian Molima , Bigirinama Rosine , Lembebu Corneille , Porignon Denis , Balaluka Ghislain Bisimwa , Donnen Philippe , Paul Elisabeth 
Published: June 2024

Abstract

Background
In South Kivu (Eastern Democratic Republic of the Congo (DRC), health districts (HDs) affected by chronic armed conflicts are devising coping mechanisms to continue offering healthcare services to the population. Nonetheless, this alone does not suffice to make them fully resilient to such conflicts. This study aims to explore the characteristics of these HDs resilience.
Methods
This study uses mixed methods and an extreme case study design to compare a HD with no history of armed conflict (Idjwi, case 1) and another one experiencing armed conflict (Fizi, case 2) in South Kivu. The Kruk et al. (2017) index was employed as a theoretical framework for exploring resilience characteristics. Qualitative data were collected through a literature review and semi-structured individual interviews with purposively sampled key stakeholders. They were subjected to deductive-inductive thematic analysis. Quantitative data were collated using a questionnaire (Likert scale) administered to stakeholders in the selected HDs using non- probabilistic methods. These data were subsequently analysed for comparative descriptive purposes.
Results
Recurring short-term shocks as well as chronic stress factors whether or not linked to chronic armed conflicts were identified. According to most respondents (> 98%), essential components of a resilient HDs were prior knowledge of the strengths and weaknesses as well as the public health risk posed by the crisis (Awareness); together with community and non-health actors’ involvement during the crisis (Integration). Common resilience mechanisms (e.g., epidemic management plan, crisis management committee) were identified, and Case 2 implemented additional mechanisms, including DRC armed forces involvement, community healthcare sites and mobile clinics, specific warning system, regular socio-demographic monitoring of displaced persons alongside promotion of activities for social cohesion strengthening.
Conclusions
For meaningful resilience to emerge, HDs need to be better resourced in 'normal' times. With this condition fulfilled, quality of care could improve and spare capacity could be created to withstand disturbances.
Deep Transfer Learning-based 3D super-resolution Radiomics modeling of CT to discriminate Brucella Spondylitis from Tuberculous Spondylitis: A multicenter, retrospective study
Authors: Wang Kaifeng , Qi Lixia , Zhang Tingxin , Li Jing , Zhang Meilan , Du Hai 
Published: June 2024

Abstract

To improve the accuracy in distinguishing between Tuberculosis Spondylitis (TBS) and Brucella spondylitis (BS) through the creation of radiomics deep Learning models. These models incorporate Deep Learning techniques and leverage CT image enhanced by Generative Adversarial Network-based Super-Resolution (GAN-SR).94 patients from Ordos Central and Second People's Hospitals diagnosed with BS or TBS. These patients were divided into a training set (n = 65) and a validation set (n = 29). We utilized conventional CT images to generate SR-CT with twice SR based on GANs-SR. The ROIs underwent manual segmentation, and extraction of Radiomics features, and ResNet18 and ResNet34 were used as Deep Learning models for training and DL feature extraction. Feature fusion and selected were performed, and four models were built based on MLP: clinical; radiomics (Rad); Deep Learning (DL) and combined models. The model efficacy was evaluated using ROC and DCA, etc. In terms of efficacy, the integrated model outperformed others, showing the highest AUC, with the DL model following closely behind. Notably, ResNet34 demonstrated superior performance over ResNet18, SR enhanced model performance across metrics for SR-CT compared to CT. The combination of Rad + ResNet34_SR achieved the best results, with AUC of 0.952, Sensitivity of 0.909, and Specificity of 0.941. DCA curve confirmed the model's clinical decision-making potential.In conclusion, our study creates a 3D Super-Resolution model based on SR-CT images using Deep Learning Radiomics to differentiate between TBS and BS, which is significantly enhanced compared to CT images.
Convolutional neural network-based detection of early-stage Parkinson’s disease using the six-minute walk test
Authors: Choi Hyejin , Youm Changhong , Park Hwayoung , Kim Bohyun , Hwang Juseon , Cheon Sang-Myung , Shin Sungtae 
Published: June 2024

Abstract

The heterogeneity of Parkinson’s disease (PD) generates significant challenges for accurate diagnosis, especially in early-stage disease, when symptoms may be very subtle. This study aimed to determine the accuracy of a convolutional neural network (CNN) technique based on a 6-min walk test (6MWT) using wearable sensors for distinguishing patients with early-stage PD (n = 78) from healthy controls (n = 50). Wearing six sensors, the participants performed the 6MWT, and the time-series data were converted into new images. The main results showed that the gyroscopic vertical component of the lumbar spine had the highest classification accuracy of 83.5%, followed by the thoracic spine (83.1%) and right thigh (79.5%) segment. These results suggest that the 6MWT and CNN models may pave the way for clinicians to diagnose and track PD symptoms earlier and thus provide timely treatment during the golden transition from geriatric to pathologic gait patterns.
Scoping Review: Emerging (Bio)markers for the Early Detection of Breast Cancer Recurrence
Authors: Sallauka Rigon , Horvat Matej , Ravnik Maja , Rashwan Hatem , Arioz Umut , Mlakar Izidor 
Published: June 2024

Abstract

Background The aim of this study is to gain a comprehensive understanding of the latest advancements in breast cancer recurrence markers, with the aim of identifying minimally invasive or minimally intrusive markers as necessary approach for screening for breast cancer recurrence.
Methods We followed PRISMA guidelines, systematically searching Web of Science, Scopus, and PubMed from 2010 to December 2023 for secondary papers on breast cancer markers of recurrence. Keywords used to search the databases include but are not limited to: “breast cancer recurrence”, “markers”, “radiology”, “pathology”, “clinical features”. The search was limited to English language. Selected papers underwent screening process according to inclusion/exclusion criteria, and data extraction included publication details, markers, marker modality, among others.
Results The number of papers considered for this review was 1,138. After two phases of screening process, a total number of 28 reviews were included in this scoping review. We have categorized markers into radiological, clinical, and histopathological types. Among the most relevant clinical markers correlated with breast cancer (BC) recurrence are clinical stage, carcinoembryogenic antigen (CEA), and cancer antigen 15.3 (CA 15.3). We have also identified that the following radiological markers are the most mentioned markers associated with recurrence: mammographic density (MD), tumor heterogeneity, most enhancing tumor volume (METV), radiomic features, and more. Furthermore, we identified nuclear grade, microenvironment heterogeneity, estrogen receptor (ER), androgen receptor (AR), human epidermal growth factor receptor 2 (HER2), Ki-67 antigen, as the most significant histopathological markers of breast cancer recurrence.
Conclusion This review identified promising markers for breast cancer recurrence in three categories: clinical, radiological and histopathological. General practitioners can leverage these insights for enhanced pre-screening, aiding in earlier detection and intervention, thus improving patient outcomes. Unclear cut-off values and disagreement on their use remain obstacles.
Transcriptomic analysis of SEL1L and HRD1 knockout cell lines reveals multifaceted roles of SEL1L beyond the ER quality control
Authors: Badawi Sally , Gariballa Nesrin , Kizhakkedath Praseetha , Ali Bassam R. 
Published: June 2024

Abstract

The endoplasmic reticulum (ER) orchestrates major cellular processes, including protein synthesis, folding, assembly and degradation, to maintain cellular proteostasis. Central to these processes are highly stringent quality control machineries like the ER-associated protein degradation (ERAD). Key players in ERAD include HRD1 and SEL1L, which target misfolded proteins for ubiquitination and facilitate their retro-translocation to the cytosol. Bi-allelic loss-of-function of HRD1 and SEL1L is considered lethal, with hypomorphic variants linked to human diseases, including neurodevelopmental disorders. Despite their well-known roles, a comprehensive transcriptomic characterization of their bi-allelic loss has been lacking. In this study, we employed CRISPR/Cas9 to generate bi-allelic HRD1-KO and SEL1L-KO HEK293 cell models. Through differential gene expression analysis and co-expression network construction, we identified hub genes and novel regulatory networks. HRD1-KO cells displayed enrichment solely in ER-related processes, suggesting its specific role in ER protein quality control. Conversely, SEL1L-KO cells exhibited a broader impact, affecting mitochondrial function, ERAD-ribosomal quality control interactions, ER-Golgi transport, and Wnt signaling pathway. These results highlight the distinct roles of HRD1 and SEL1L in ERAD. By unraveling their whole transcriptome impact, our study sheds light on their potential involvement in diverse cellular processes, potentially enhancing our understanding of their cellular processes and disease mechanisms.
N-acetylcysteine Supplementation Improves Endocrine-Metabolism Profiles and Ovulation Induction Efficacy in Polycystic Ovary Syndrome
Authors: Fang Yu-Qing , Ding Hui , Li Tao , Zhao Xiao-Jie , Luo Dan , Liu Yi , Li Yan-Hui 
Published: June 2024

Abstract

Background
Polycystic ovary syndrome (PCOS) affects 6–20% of women worldwide, with insulin resistance and hyperinsulinemia occurring in 50%-70% of patients. Hyperinsulinemia exacerbates oxidative stress, contributing to PCOS pathogenesis. N-acetylcysteine (NAC) is an antioxidant and insulin sensitizer that shows promise as a therapeutic for PCOS. Our current study aimed to investigate the effects of NAC supplementation on endocrine-metabolic parameters in PCOS mice and its effect on ovulation induction (OI) efficacy in women with PCOS.
Methods:
Female C57BL/6 mice were orally administered letrozole (LE) to induce PCOS and then randomly divided into groups receiving daily oral administration of 160 mg/kg NAC (PCOS + NAC group), 200 mg/kg metformin (PCOS + Met group), or 0.5% carboxymethyl cellulose (drug solvent) (pure PCOS group) for 12 days. Healthy female mice served as pure controls. Estrous cycles were monitored during the intervention. Metabolic and hormone levels, ovarian phenotypes, antioxidant activity in ovarian tissues, and oxidative stress levels in oocytes were assessed postintervention. This pragmatic, randomized, controlled clinical study included 230 women with PCOS who were randomly assigned to the NAC group (1.8 g/day oral NAC, n = 115) or the control group (n = 115). Patients in both groups underwent ≤ 3 cycles of OI with sequential LE and urinary follicle-stimulating hormone (uFSH). Cycle characteristics and pregnancy outcomes were compared between groups.
Results
Similar to metformin, NAC supplementation significantly improved the estrous cycles and ovarian phenotypes of PCOS mice; reduced the LH concentration, LH/FSH ratio, and T level; and increased glucose clearance and insulin sensitivity. Notably, NAC significantly reduced oocyte ROS levels and increased the mitochondrial membrane potential in PCOS mice. Additionally, NAC significantly enhanced enzymatic and nonenzymatic antioxidant activities in PCOS mouse ovaries, whereas metformin had no such effect. In the clinical trial, compared to women in the control group, women receiving NAC had significantly lower average uFSH dosage and duration (p < 0.005) and significantly greater clinical pregnancy rates per OI cycle and cumulative clinical pregnancy rates per patient (p < 0.005).
Conclusion
NAC supplementation improved endocrine-metabolic parameters in PCOS mice and significantly enhanced OI efficacy with sequential LE and uFSH in women with PCOS. Therefore, NAC could be a valuable adjuvant in OI for women with PCOS.
Oral Nonabsorbable Antibiotics for Prevention of Recurrent Cholangitis; a Brief Report Study
Authors: Fortun Jesus , Rodríguez-Gandía Miguel Angel , Pintado Vicente , MartÍn-Dávila Pilar , García-González Miguel , Graus Javier , Martín-Mateos Rosa , Fuente Javier Saez-de la , Muriel Alfonso , Moreno Santiago 
Published: June 2024

Abstract

Background
Patients with recurrent cholangitis are at risk of developing life-threatening sepsis. Selective decontamination of the digestive tract (SDD) involving oral nonabsorbable antibiotics has been primarily applied to children undergoing Kasai portoenterostomy surgery.
Methods
In this study, SDD containing colistin, tobramycin, and nystatin was administered to eight patients with recurrent cholangitis, and the incidence density before and after SDD administration was analyzed.
Results
The overall incidence density of cholangitis requiring hospital admission was 0.37 per 100 patient days during the SDD period and was significantly lower than observed before SDD administration (1.05 per 100 patient days) [RR: 0.35 (95% CI: 0.21–0.59); p: <0.001, two-sided]. This was not associated with an increased risk of resistance during SDD administration.
Conclusion
In this study SDD reduced by 65% the frequency and severity of recurrent cholangitis. In addition, this procedure is patient-friendly and microbiologically safe.
Study on Monitoring Spinal Cord Blood Flow During Spinal Osteotomy Using a Laser Speckle Imaging System
Authors: Ren Zheng , Wang Jing , Guo Le , Ye Xiaolong , Xu Chang , Ma Yuan 
Published: June 2024

Abstract

Objective: This study aims to utilize Laser Speckle Imaging (LSI) technology to thoroughly monitor and analyze the changes in spinal cord hemodynamics in rabbits undergoing Posterior Thoracolumbar Spinal Osteotomy (PTSO) surgery.
Methods: Initially, we prepared the model for posterior thoracolumbar spine osteotomy.Subsequently, rabbits underwent monitoring using a laser speckle imaging system to document the different stages of spinal shortening,encompassing pre-osteotomy, post-osteotomy, post-shortening, and dural removal.
Results: After statistical analysis, the blood perfusion volume of posterior spinal artery before and after osteotomy was significantly different (t=7.413, p=0.001), 95%CI (147.8-280.22). The blood perfusion volume of posterior spinal artery was significantly different before and after osteotomy (t=4.111, p=0.001), 95%CI (55.39 ~ 164.74). There were significant differences in posterior spinal artery perfusion before osteotomy and after dural removal (t=10.472, p=0.001), 95%CI (226.6-336.39). The blood perfusion volume of posterior spinal artery after osteotomy was significantly different from that after osteotomy shortening (t=-6.949, p=0.001), 95%CI (-66.34-6.949). The blood perfusion volume of posterior spinal artery after bone amputation was significantly different from that after dural removal (t=8.167, p=0.001), 95%CI (58.10 ~ 96.85). There were significant differences in posterior spinal artery perfusion between the patients with bone truncation and the patients with dural removal (t=13.227, p=0.001), 95%CI (144.96-197.90).
Conclusion: Spinal cord blood flow can be utilized as a monitoring indicator for early prediction of spinal cord injury, while maintaining stable spinal cord blood flow may be a crucial factor in reducing spinal cord damage.
Mortality trends for colorectal cancer during the COVID-19 pandemic in the US
Authors: Mattiuzzi Camilla , Lippi Giuseppe 
Published: June 2024

Abstract

It can be assumed that a reduction in patient access to colorectal cancer screening and early management during the coronavirus disease 2019 (COVID-19) pandemic could have generated a negative impact on subsequent diagnoses and survival probabilities. We report here the recent statistics on colorectal cancer-related deaths in the US. We searched the CDC online database WONDER database, which reports mortality data for the entire country, using specific ICD-10 codes for colorectal cancer (from C18 to C20). The age-adjusted death rate ×100,000 for colorectal cancer displayed a gradual decrease from 2018 (13.06) to 2019 (12.76) and 2020 (12.55), but then reversed in 2021, when the age-adjusted death rate increased to 12.94, decreasing to 12.58 in 2022 and increasing again to 12.75 in 2023. The results of our analysis suggest that the COVID-19 pandemic has likely had a negative impact on early mortality from colorectal cancer, as shown by the significant increase in the age-adjusted death rate recorded in 2021.
A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Based on Hassanat Distance Metric
Authors: Hassanat Ahmad , Alqaralleh Mohammad Khaled , Tarawneh Ahmad S. , Almohammadi Khalid , Alamri Maha , Alzahrani Abdulkareem , Altarawneh Ghada A. , Alhalaseh Rania 
Published: June 2024

Abstract

Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression model accuracy measure based on Hassanat distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0-100\%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models' accuracy.
Compressive strength prediction of basalt fiber reinforced concrete based on Interpretive Machine Learning
Authors: wang xuewei , Ke Zhijie , liu wenjun , Zhang peiqiang , cui Sheng’ai , zhao Ning 
Published: June 2024

Abstract

The compressive strength prediction of Basalt Fiber Reinforced Concrete (BFRC) is a complex task influenced by numerous factors. For traditional empirical formula methods, difficulties such as the limited number of experiments and the time-intensive nature of the process may hinder the accurate prediction of the compressive strength. The compressive strength of BFRC was predicted using a machine learning approach, considering various combinations of characteristics, and the predictive ability of the model was verified using experimental data. A database of 309 sets of BFRC compressive strength data was assembled, and eight sets of BFRC compressive strength experimental data were experimentally obtained. After the hyperparameter optimization four machine learning models were employed to investigate their performances. Combined with the SHAP algorithm, an interpretive analysis of the input parameters of the XGBoost model was specifically conducted, and the predictive ability and interpretability of the model were verified using experimental data. The research results indicated that the XGBoost model surpasses the other three machine learning models in terms of prediction accuracy, achieving an R2 value of 0.9431, RMSE of 3.2325, and MAE of 2.3355. The SHAP analysis revealed that among the basalt fiber (BF) parameters, the volume content of BF had the most significant impact on the XGBoost model output. Furthermore, the optimal range of volume content is 0.1%, the optimal range of diameter is 15–20 µm, and the optimal range of length is 8–15 mm.
Statistical Theory on Variation of Carbon Dioxide Concentration in Global Warming
Authors: Luo Liaofu , Lv Jun 
Published: June 2024

Abstract

To comprehend the relationship between global warming and CO2 emissions more comprehensively, we simulated historical data. This endeavor aimed to discern the underlying patterns governing the fluctuation of CO2 concentration and annual CO2 emissions. Our analysis revealed a consistent pattern: both variables followed time-dependent exponential functions characterized by positive feedback mechanisms. Expanding our investigation, we integrated these findings with the framework of five Shared Socio-economic Pathways (SSPs). Extending our simulations from 2015 to 2500, we uncovered a shift in CO2 concentration dynamics. While exponential growth persisted in earlier periods, a transition to negative feedback mechanisms became evident in later stages. We devised a modified exponential function to model CO2 concentration variation to capture this transition, facilitating the transition from positive to negative feedback. Subsequently, we explored the correlation between temperature anomalies and CO2 concentrations. Our analysis revealed a linear relationship between these variables and demonstrated that their physical correlation manifests only over the long term. Leveraging this relationship, we formulated predictions for global temperature anomalies up to 2500. Furthermore, we examined the impact of land-use changes as a strategy to mitigate CO2 emissions partially. By introducing an illustrative example, we elucidated the potential effectiveness of such measures. Finally, we delved into the short-term evolution of the climate system, focusing on peak series in CO2 concentration and temperature anomalies. This analysis provided valuable insights into the immediate trajectories of these critical variables.