bluuebunny/crossref_metadata_2025_split huggingface dataset:
Addressing Musculoskeletal Curricular Inadequacies within Undergraduate Medical Education
Authors: Peeler Jason 
Published: June 2024

Abstract

Background: Musculoskeletal (MSK) injuries and diseases place a significant burden on the health care system. Despite this, research indicates that physician training in the area of MSK medicine has historically been inadequate, with a majority of medical students feeling that their training in MSK medicine is lacking. The goal of this investigation was to evaluate the efficacy of a new preclinical MSK curriculum that was implemented within a nationally accredited allopathic medical program.
Methods: Retrospective analysis was completed on five consecutive years of preclinical MSK curricular data, including mid and end-of-course examinations and end-of-course student satisfaction surveys. Both parametric and non-parametric methods of analysis were used to examine for within and between class differences (P < 0.05).
Results: The MSK curriculum covered 15 of 16 “core or must know” topics in MSK medicine, and academic performance was consistently high over the 5-year period (final course marks ranged from 76.6 ±7.1 to 81.4 ±8.1; failures/year: range from 0 to 4), being equal or above levels of student performance observed for other courses delivered during preclinical studies. Likert data from end-of-course surveys demonstrated that feedback was overwhelmingly positive (overall course satisfaction ranged from a low of 3.07/4.00 to a high of 3.56/4.00) and indicated that students felt that the new preclinical MSK curriculum did effectively support medical student learning and knowledge retention.
Conclusion:Results are expected to help advance the current body of knowledge that is dedicated to improving physician learning and knowledge retention in the area of MSK medicine and provide a curricular model that could be used by other nationally accredited medical programs to help enhance MSK learning and knowledge retention at the preclinical levels of physician training.
Comparison of the Effectiveness of Artificial Neural Networks and Elastic Net Regression in Surface Runoff Modeling
Authors: Buczyński Rafał 
Published: June 2024

Abstract

This study examines the comparative performance of Artificial Neural Networks and Elastic Net Regression in predicting surface runoff in urban stormwater catchments. Utilizing the Storm Water Management Model, a dataset of over 100,000 samples was prepared, incorporating diverse catchment parameters such as impervious surface percentage, overland flow path width, slope, Manning’s coefficients, and depression storage depths. Both models were developed and tuned to enhance runoff prediction accuracy. An assessment was carried out to gauge the predictive accuracy, computational efficiency, and ability to withstand data noise of the models. The Artificial Neural Networks demonstrated significant advantages in handling complex, non-linear relationships inherent in hydrological data, resulting in higher predictive accuracy and stability. Elastic Net Regression, while useful, exhibited limitations, particularly in lower runoff scenarios due to its regularization constraints. The outcomes highlight the relative advantages and drawbacks of each modeling technique, offering crucial perspectives for enhancing water resource management in urban settings.
Caring Through Uncertainty: A Qualitative Exploration of Older Colorectal Cancer Caregivers' Experiences During Palliative Chemotherapy
Authors: Li Mengxue , Su Liqing , Zhang Jie , Jia Lei , Zhang Yumeng , Zheng Ziyi , Chang Jian 
Published: June 2024

Abstract

Purpose: Despite the growing body of research on cancer patient caregivers, the unique experiences of spouses caring for elderly colorectal cancer patients remain underexplored. This study aimed to fill this gap by examining the caregiving experiences of spouses during palliative chemotherapy for elderly Chinese colorectal cancer patients.
Methods: Using a qualitative descriptive design, we recruited spousal caregivers of elderly colorectal cancer patients undergoing palliative chemotherapy. Semistructured interviews were conducted, and thematic analysis was employed to analyse the data.
Results: Sixteen caregivers aged 60 to 82 years participated in the interviews. The analysis revealed three main themes: ambivalence, multiple role adaptation to conflicts, and the coexistence of hope and pressure. These themes shed light on the behaviors of older caregivers and the challenges they face, including physical and psychosocial issues associated with aging.
Conclusion: This study highlights the significant stress and challenges experienced by older caregivers, characterized by intertwined emotions such as anxiety about their spouses’ health deterioration, exhaustion from long-term care responsibilities, and anticipation of treatment outcomes. These findings can inform future interventions and research aimed at understanding and supporting the unique needs of older spousal caregivers.
Simulation study of foreign exchange market interventions using an extended dealer model
Authors: Arita Rintaro , Takayasu Hideki , Takayasu Misako 
Published: June 2024

Abstract

This study analyzed yen-dollar exchange market price fluctuations and the shape of the order book observed in the foreign exchange (FX) market at the time of intervention by the Japanese government. Using the comprehensive data of the order book during the yen-sold (dollar-bought) intervention in 2011 and yen-bought (dollar-sold) intervention in 2022, we found that methods of intervention used by the Japanese government in 2011 and 2022 were quite different in the sense of strategy.Intervention in 2011 involved a strategy placing a large number of buy orders on a price absorbing a large number of market orders, while intervention in 2022 involved a strategy placing a large number of sell limit orders at a price lower than the best bid price. Next, simulations of market price movements during FX interventions were conducted by generalizing a model known as the spread dealer model.We introduced the concept of volume into this model and further modified and formulated the effects of stop-loss and take-profit, as well as modified the intervention effects, to simulate market price fluctuations during these interventions.We decomposed the time series into upward and downward trends, assuming that dealers have certain strategic parameters under each trend. Parameter search was performed by randomly selecting parameters within the search space.By searching for parameters that minimize the error function between the real time series and the simulated price time series, and optimizing dealer parameters under each trend, we obtained parameters for the dealer model that could replicate time series closer to real price fluctuation.The generalized dealer model developed in this study has the potential to be utilized in the future for estimating the number of interventions conducted and assessing the efficiency of interventions.
Non-Random Distribution of Plasmodium Species Infections and Associated Clinical Features in Children in The Lake Victoria Region, Kenya, 2012-2018
Authors: Omondi Protus , Musyoka Brian , Okai Takatsugu , Kongere James , Kagaya Wataru , Chan Chim , Ngara Mtakai , Kanoi Bernard , Kido Yasutoshi , Gitaka Jesse , Kaneko Akira 
Published: June 2024

Abstract

Background
While Plasmodium falciparum (Pf) stands out as the most lethal malaria parasite species in humans, the impact of other species should not be dismissed. Moreover, there is a notable lack of understanding of mixed-species infections and their clinical implications.
Methods
We conducted eight school-based cross-sectional malariometric surveys in the Lake Victoria region of western Kenya between 2012 and 2018. Plasmodium infection was determined by microscopy and nested polymerase chain reaction (PCR). The multiple-kind lottery (MKL) model calculated the expected distribution of Plasmodium infections in the population and compared it to observed values using a chi-squared test (χ2).
Results
The Plasmodium prevalence by PCR was 51.1% (4969/9724). Among all infections, Pf, P. malariae (Pm), and P. ovale (Po) mono-infections were 58.6%, 3.0%, and 1.7%, respectively. Pf/Pm, Pf/Po, Pm/Po, and Pf/Pm/Po co-infections were 23.5%, 4.3%, 0.1%, and 8.6%, respectively. MKL modelling revealed non-random distributions with frequencies of Pf/Pm and Pf/Pm/Po co-infections higher than expected (χ2 = 3385.60, p < 0.001). Pf co-infections with Pm and Po were associated with a decreased risk of fever (aOR 0.64, 95% CI 0.46–0.83; p = 0.01) and increased risks of splenomegaly (aOR 12.79, 95% CI 9.69–16.9; p < 0.001) and anaemia (aOR 2.57, 95% CI 2.09–3.15; p < 0.001), compared to single-species infections.
Conclusion
This study sheds light on the potential interaction between Pf and Pm and/or Po. Given the clinical significance of mixed-species infections, improved diagnostics, and case management of Pm and Po are urgently needed
Topological importance of CD8 + T-cell enrichment in the tumor microenvironment of classic Hodgkin lymphoma
Authors: Takahashi Hiromichi , Ito Shun , Nakanishi Yoko , Miura Katsuhiro , Nishimaki Haruna , Nakagawa Masaru , Otake Shimon , Hamada Takashi , Koike Takashi , Iizuka Kazuhide , Masuda Shinobu , Nakayama Tomohiro , Shimizu Tetsuo , Ishibashi Naoya , Kogure Hirofumi , Nakamura Hideki 
Published: June 2024

Abstract

Classic Hodgkin lymphoma (CHL) histologically consists of Hodgkin Reed-Sternberg (HRS) cells and the tumor microenvironment (TME), but the relationship between TME characteristics and clinical features of CHL remains unclear. We aimed to investigate the effects of the TME structure on the outcomes of patients with CHL. We performed a high-throughput analysis of HRS cells and their topological relationship with the reactive immune cells in the TME. After multiplexed immunofluorescence labeling against CD4, CD8, CD30, CD68, CD163, PD-1, and PD-L1, visual images were analyzed. Phenotypes were assigned to all reactive cells, such as CD4+ and CD8+ T-cells and macrophages. Since the densities of PD1+/CD4+ T-cells, CD8+ T-cells, and PD-L1+ macrophages were significantly higher in the area < 60 µm than in the area < 120 µm from each HRS cell in 45 tissue samples from 34 patients with CHL, we further analyzed the TME-component cells by focusing on the 60 µm radius in the initial samples. TMEs containing > 15 CD8+ T-cells were associated with a significantly better 3-year progression-free survival than those with ≤ 15 CD8+ T-cells (100% vs. 53%, p = 0.006). In comparison with TMEs containing ≤ 15 CD8+ T-cells, TMEs containing > 15 CD8+ T-cells had significantly more PD-L1− macrophages (mean 3 vs. 1 cell, p = 0.015) and fewer PD-1+/CD4+ T-cells (mean 16 vs. 28 cells, p = 0.036). In conclusion, the TME structure in patients with CHL can differ, enabling precision therapies.
Going beyond hardware limitations with advanced stray light calibration for the Metop-3MI space instrument
Authors: Clermont Lionel , Michel Céline , Chouffart Quentin , Zhao Yuchen 
Published: June 2024

Abstract

Space optical instruments play a pivotal role in enhancing our understanding of the universe and our planet, and are crucial in addressing the urgent challenges posed by climate change. In this context, stray light has emerged as a primary performance limitation. Originating from ghost reflections or scattering, it obscures essential details and introduces false information into images. With the demand for increasingly high-performing instruments, mitigation through hardware optimization is becoming insufficient. We are entering an era where future instruments require a stray light correction algorithm to meet user specifications, necessitating extensive on-ground calibration. This paper examines the Metop-3MI Earth observation instrument, which, with wide field of view, broad spectral range, and multi-polarization capabilities, epitomizes the challenges of stray light calibration and correction. A custom calibration apparatus was constructed to evaluate the complex stray light dependence on field-of-view, wavelength, and polarization. Data were processed, and stray light kernels database was derived, which then fed into a specially developed correction algorithm. Applied to the image of an extended scene, it effectively reduces stray light by a remarkable factor of 91. This achievement sets a new standard for low-stray-light instruments and provides a comprehensive case study for future missions.
Enhancement of SBR’s treatment under organic shock load
Authors: Ahmed Dalia , ELKaramany Hesham M. , Alged Ahmed 
Published: June 2024

Abstract

Organic shock loads often hinder biological wastewater treatment systems from meeting discharge standards, as a result, these systems frequently fail to achieve the required level of treatment so, the potential impact of these loads was investigated using laboratory-scale sequencing batch reactors (SBRs). Short-term (4.5 h) exposure to 721.25 - 1515.36 mg COD/L shock loads reduced the removal ratios of COD by 3.48 % – 31.73 % compared with the control. A mathematical model was developed for COD decomposition and the results obtained from the model were approximately close to the experimental data, and the maximum difference between the experimental and the theoretical removal rates was 3.36 % at a shock organic load of 1019.20 mg COD/L. In addition, the reactor performance was evaluated under successive organic shock loads so, a constant influent COD value of 1249.5 mg/L was applied for eight successive batch cycles and the results indicated that the removal ratio gradually improved with successive loading. As well, to enhance treatment efficiency under organic shock loads the performance of the reactor was evaluated for different MLSS concentrations of 2000, 2600, 3200, 3900, 4200, 4600, 4825, and 5250 mg/L. Three different organic shock loads with COD concentrations of 1043.04, 1205.36, and 1560 mg/L. COD removal ratios of 96.23 %, 86.18 %, and 75.81 % were achieved for the first, second, and third shock loads respectively at a MLSS concentration of 4825 mg/L however, it dropped when MLSS concentration increased to 5250 mg/L.
Effects of hyperbaric oxygen on inflammation and oxidative stress in PCPA-induced insomnia rat models
Authors: Wang Meng-meng , Yao Yang , Fu Yue , Huang tao , Chen Ying , Han Yi , Fang Yi-qun 
Published: June 2024

Abstract

To clarify the effect and mechanism of hyperbaric oxygen (HBO) on insomnia, SD rats were divided randomly into PCPA, HBO+PCPA and Control group (n=8). Rat insomnia model was established by intraperitoneal injection of para-chlorophenylalanine (PCPA), then 2.0ATA-60min HBO was given. After the intervention, general behavior, sleep status, brain tissue pathology, serum inflammatory and oxidative stress molecules were measured. Compared with the Control group, the rats in the PCPA group showed circadian rhythm reversal, daytime sleep time reduction, weight loss and temper irritability. Sleep latency was significantly prolonged and the sleep duration was significantly shortened in PCPA group. All above changes were significantly improved by HBO intervention. Positive Nissl staining in hippocampus, prefrontal cortex and hypothalamus of PCPA group was largely decreased compared with the controls, which was obviously reversed in HBO+PCPA group. Moreover, serum IL-1β, IL-6, IL-17A, TNF-Α and MDA were markedly increased, while IL-10, IL-12p70 and SOD were significantly decreased in PCPA group, compared to the Controls. When compared with PCPA group, HBO intervention can significantly decrease the levels of IL-1β, IL-6, IL-17A, TNF-Α and MDA, while significantly increased the levels of IL-10, IL-12p70 and SOD in serum. HBO can relieve PCPA-induced rat insomnia symptoms, reduce inflammation and oxidative stress injury in vivo, which is one of mechanisms of HBO treatment for insomnia.
Ventilator Treatment Policy Control based on BCQ off-line Deep Reinforcement Learning
Authors: MAO Jingkun , LI Fengxi , LIU Chunxin , ZHOU Pixuan 
Published: June 2024

Abstract

Ventilator plays a crucial role in treating cardiopulmonary disorders, and proper parameter settings are essential for the successful recovery of patients. Traditional ventilator control relies on the expertise of physicians, leading to delayed treatment responses. Although some machine learning methods have made improvements in this scenario, they are inadequate to adapt to dynamic changes of patient conditions. This paper proposes a dynamic ventilator control method based on the BCQ offline deep reinforcement learning algorithm, achieving real-time adjustment of ventilator treatment policies based on changes in the patient’s medical condition. In the experiments, the Double DQN and SAC algorithms are used as baseline algorithms. During the training phase, the algorithms’ optimal models under different hyperparameter combinations are determined using temporal difference error and average action values. In the testing phase, the model’s therapeutic efficacy is evaluated using the FQE method, while the safety of the treatment is assessed by statistically analyzing the predicted action distribution. Additionally, the algorithm’s generalization ability is further evaluated on an OOD test set. The experimental results demonstrate that the BCQ algorithm outperforms both in terms of treatment effectiveness, safety, and generalization ability, indicating its promising application prospects in medical scenarios.
Predicting single-cell drug sensitivity utilizing adaptive weighted features for deep transfer learning
Authors: Liu Hui , Duan Wei 
Published: June 2024

Abstract

The advancement of single-cell sequencing technology had promoted the generation of a large amount of single-cell transcriptional profiles, providing valuable opportunities to identify drug-resistant cell populations in a tumor. However, the drug sensitivity data at single-cell level is still scarce to date, posing an urgent but tough challenge for the computational prediction of drug sensitivity in individual cells. This paper proposed scAdaDrug, a deep transfer learning model with adaptive weighted features to predict single-cell drug sensitivity. We used an autoencoder to extract domain-invariant features related to drug sensitivity from multiple source domains by exploiting adversarial domain adaptation. Especially, we introduced an adaptive weight generator to produce importance-aware and mutually independent weights, which could adaptively modulate the embedding of each sample in dimension-level for both source and target domains. Extensive experimental results showed that our model achieved state-of-the-art performance in predicting drug sensitivity on multiple independent single-cell datasets derived from cell lines, patient-derived xenografts (PDX) models, and patient tumor tissues. Moreover, the ablation experiments demonstrated our model effectively captured the underlying patterns determining drug sensitivity from multiple source domains.
Physical Activity and Body Weight – Longitudinal Evidence from the Australian Working Age Population
Authors: Doan Nhan , Doan Tinh 
Published: June 2024

Abstract

Overweight and obesity is one of the leading causes of diseases in the developed world. In Australia, two thirds of Australians are overweight or obese causing high costs to the healthcare system. Physical inactivity is one of the key determinants of rising overweight and obesity. However, there is little evidence in this research area in Australia using non-experimental data to address biases due to individual unobserved heterogeneity that is linked to both body weight and physical activities (PA). We employ a fixed effect model, which can adjust for time invariant unobserved confounders that may affect both physical activity and body weight, to estimate the relationship between PA and body weight. We find that higher levels of physical activity are correlated with lower body weight or body mass index (BMI). The dose effect is found; the higher PA levels the lower BMI. Even low levels of physical activity are enough to generate a reduction in BMI (in comparison with sedentary activities). People need time and resources. It is therefore critical to have accessible and affordable sport facilities, and policies enable individuals to have more free time for PA if policies aim to tackle overweight and obesity in Australia.
Which Forms of Capital Are More Important for Economic Growth? Findings from Selected High-Income Countries
Authors: Bayraktar Astt. Prof. Dr. Murat 
Published: June 2024

Abstract

The level of social capital within an economy is one of the main factors influencing the likelihood of long-term sustainable economic development. This study examines empirical research on the relationships between social capital, natural capital, intellectual capital, and their impact on economic growth. It also presents evidence from data showing that social capital has a stronger impact on GDP per capita growth than other forms of capital.
Whereas the three forms of capital were found to have positive and significant effects, social capital increases the GDP per capita more than natural capital and intellectual capital. Econometric analysis reveals that a 1 percentage point increase in social capital increases GDP per capita by 1.15 percentage points, while a 1 percentage point increase in natural capital increases GDP per capita by 0.16 percentage points, and a 1 percentage point increase in intellectual capital increases GDP per capita by 0.87 percentage points. Therefore, to facilitate policymaking, this current research provides important policy recommendations to increase social capital levels among developing countries.
JEL Codes: B12, B13, B26, D24
The Human Pathology Atlas for deciphering the prognostic features of human cancers
Authors: Yuan Meng , Zhang Cheng , von Feilitzen Kalle ,   Martin Zwahlen1, Shi Mengnan , Li Xiangyu , Yang Hong , Song Xiya , Turkez Hasan , Uhlén Mathias , Mardinoglu Adil 
Published: June 2024

Abstract

Cancer is one of the leading causes of mortality worldwide, highlighting the urgent need for a deeper molecular understanding of the disease's heterogeneity and the development of personalized treatments. Since its establishment in 2017, the Human Pathology Atlas has been instrumental in linking gene expression profiling with patient survival outcomes, providing system-level insights and experimental validation across a wide range of cancer research. In this updated analysis, we analysed the expression profiles of 6,918 patients across 21 cancer types using the latest gene annotations. Our refined approach enabled us to offer an updated list of prognostic genes for human cancers, with a focus on hepatocellular, renal and colorectal cancers. To strengthen the reliability of our findings, we integrated data from 10 independent cancer cohorts, creating a cross-validated, reliable collection of prognostic genes. By applying a systems biology approach, we identified that patient survival outcomes in kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC) are strongly associated with gene expression profiles. We also developed a prognostic regulatory network specifically for KIRC and LIHC to enhance the utility of the Human Pathology Atlas for cancer research. The updated version of the Human Pathology Atlas lays the foundation for precision oncology and the development of personalized treatment strategies.