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Marburg Virus Disease in Rwanda — Centering Both Evidence and Equity
Lung Transplantation
Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT
Purpose This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver. Materials and methods The study retrospectively enrolled 840 individuals who underwent non-contrast abdominal CT and quantitative CT (QCT) examinations at the First Affiliated Hospital of Zhengzhou University from July 2022 to May 2023. Subsequently, these participants were divided into a training set (n = 539) and a testing set (n = 301) in a 9:5 ratio. The liver fat content measured by experienced radiologists using QCT technology served as the reference standard. The liver images from the non-contrast abdominal CT scans were then segmented as regions of interest (ROI) from which radiomics features were extracted. Two-dimensional (2D) and three-dimensional (3D) radiomics models, as well as 2D and 3D deep learning models, were developed, and machine learning models based on clinical data were constructed for the four-category diagnosis of fatty liver. The characteristic curves for each model were plotted, and area under the receiver operating characteristic curve (AUC) were calculated to assess their efficacy in the classification and diagnosis of fatty liver. Results A total of 840 participants were included (mean age 49.1 years ± 11.5 years [SD]; 581 males), of whom 610 (73%) had fatty liver. Among the patients with fatty liver, there were 302 with mild fatty liver (CT fat fraction of 5%–14%), 155 with moderate fatty liver (CT fat fraction of 14%–28%), and 153 with severe fatty liver (CT fat fraction >28%). Among all models used for diagnosing fatty liver, the 2D radiomics model based on the random forest algorithm achieved the highest AUC (0.973), while the 2D radiomics model based on the Bagging decision tree algorithm showed the highest sensitivity (0.873), specificity (0.939), accuracy (0.864), precision (0.880), and F1 score (0.876). Conclusion A systematic comparison was conducted on the performance of 2D and 3D radiomics models, as well as deep learning models, in the diagnosis of four-category fatty liver. This comprehensive model comparison provides a broader perspective for determining the optimal model for liver fat diagnosis. It was found that the 2D radiomics models based on the random forest and Bagging decision tree algorithms show high consistency with the QCT-based classification diagnosis of fatty liver used by experienced radiologists.
Pleural Paragonimiasis
Viral tolerance enabled by a bat-specific genomic tweak
Home cage-based insights into motor learning and strategy adaptation in a Huntington disease mouse model
Huntington disease (HD) is a genetic neurodegenerative disorder characterized by progressive motor dysfunction, cognitive decline, and neuropsychiatric symptoms. Assessing early motor skill deficits in HD mouse models is challenging with traditional behavioral tasks. This study uses a home cage-based lever-pulling task, PiPaw2.0, to evaluate motor learning in 6–7 months-old zQ175 knock-in HD mice in a more naturalistic environment. In this task, mice learn to pull a lever for a water reward, with the requirement to hold the lever within a specific goal range for a required hold time. As the mice improved, the required hold time increased, thereby gradually increasing the task demands. Both wild type (WT) and zQ175 mice initially showed similar task engagement, but zQ175 mice had significant deficits in adapting to increasing hold time. The WT mice refined their strategies over time, shifting from random to more precise lever pulls, while zQ175 mice failed to make this adjustment, maintaining erratic performance. Additionally, in group-housing WT mouse lever performance benefited from peer interactions, an effect absent in zQ175 mice. Post-task neural assessments revealed that WT mice developed experience-mediated synaptic plasticity in the left striatum (contralateral to lever-pulling paw), while zQ175 mice showed no significant changes, consistent with known corticostriatal plasticity impairments in HD mouse models. In conclusion, our findings demonstrate the effectiveness of group-housed, home cage-based assessments for evaluating motor learning and adaptation in HD mouse models. This study provides insights into the motor control and adaptive learning deficits in HD, emphasizing the value of automated home cage systems in advancing neurodegenerative disease research and highlighting the importance of peer influences on performance.
The Rise of Private Equity in Health Care — Not a Uniquely American Phenomenon
The composite governance mechanisms and sustainable economic performance of Pakistan’s textile industry
This study examines the influence of corporate governance mechanisms on the sustainable economic performance of the textile industry in Pakistan. Performance is measured by using a global reporting initiative (GRI 201–1), an economic performance approach. The composite corporate governance mechanism index (CGMI) construction consists of five sub-indices: board of directors, disclosure and transparency, audit committee, shareholder rights, and remuneration committee. The empirical analysis demonstrated the consistency of the fixed effect model through the Hausman model specification test for data from textile firms listed on the Pakistan Stock Exchange from 2008 to 2023. However, the investigation has invoked the Generalized Method of Moments (GMM) estimation model to check its robustness. The findings indicated the positive empirical relationship between the composite CGMI and sustainable economic performance, supported by modified theoretical logics: agency theory, stewardship theory, stakeholder theory, resource-based theory, and transaction-cost theory. The findings support policymakers, regulators, and managers in executing appropriate governance mechanisms with reduced agency cost and transaction costs and improve sustainable performance.
Effects of Food and Nutrition Insecurity on Global Health
A Nitrogen budget for Norway analysis of Nitrogen flows from societal and natural sources (1961–2020)
Nitrogen is a chemical element that is cycled through Earth’s lithosphere, hydrosphere, cryosphere and atmosphere, as well as a crucial component of the molecular machinery of life. Being an intrinsic part of the biosphere, the flow of nitrogen in a country can provide unique insights into the sustainability of a society. This study investigates how nitrogen is used in Norway between 1961 and 2020. Nitrogen inputs from atmospheric deposition, biological nitrogen fixation and from weathering, as well as from synthetic fertilizers are addressed. These sources of nitrogen are used by plants that form the basis of agriculture and forestry. Nitrogen, in the form of protein, is imported to sustain the Norwegian population and the production of animals in agriculture and fisheries. Agricultural livestock is used almost exclusively for domestic consumption, while fish captured and raised in aquaculture are the main sources of exported food. Even though petroleum and wood contain only a small proportion of nitrogen, due to the traded volume, a large amount of nitrogen contained in these goods is exported. Nitrogen is lost through sewage and manure leaching, as well as atmospheric emissions. These emissions are mostly in the form of nitrogen oxides and ammonia, which are released by burning fossil fuels and metabolizing animals, respectively.
Case 5-2025: A 30-Year-Old Woman with Headache and Dysesthesia
Effects of exogenous EBR on the physiology of cold resistance and the expression of the VcCBF3 gene in blueberries during low-temperature stress
The northern highbush blueberry variety ‘Duke’ was used as the test material, and different concentrations of 2,4-Epibrassinolide (EBR) (0, 0.2, 0.4, 0.6, and 0.8 mg·L-1) were applied during the bud expansion stage, with a second application administered at one-day intervals following the first. Samples were collected at the bud, flower, and fruit stages and subsequently treated with artificial low temperatures (2°C) after sampling. The effects of various concentrations of exogenous EBR on the physiological indices of cold resistance and the expression of the cold resistance gene VcCBF3 in blueberry buds, flowers, and young fruits were investigated through comprehensive evaluation and correlation analysis. The objective was to identify the optimal concentration of EBR to enhance the cold resistance of blueberries. The results indicate that: (1) Under low temperature stress, the contents of soluble sugar, soluble protein and proline increased, along with the activities of superoxide dismutase, peroxidase, and catalase. The expression of the VcCBF3 gene expression and the ascorbate-glutathione cycling system were up-regulated, and with the increase of EBR concentration, the expression of the VcCBF3 gene initially rose and then declined. The content of malondialdehyde and the production rate of superoxide anion radicals decreased, and with the increase of EBR concentration, the content of malondialdehyde first decreased and then increased. (2) Overall low temperature resistance, flowers > buds > young fruits. (3) Appropriate concentrations of exogenous EBR can effectively mitigate freezing damage in blueberries caused by low temperatures. A comprehensive evaluation and correlation analysis of each cold tolerance index and the expression of the VcCBF3 gene revealed that a treatment concentration of 0.4 mg·L-1 had the most significant mitigating effect among the sprayed EBR concentrations of 0, 0.2, 0.4, 0.6, and 0.8 mg·L-1.
CAR+ T-Cell Lymphoma after Cilta-cel Therapy for Relapsed or Refractory Myeloma
Learning based lower limb joint kinematic estimation using open source IMU data
Four-Electron-Transferred Pyrene-4,5,9,10-tetraone Derivatives Enabled High-Energy-Density Aqueous Organic Flow Batteries
Lower cardiorespiratory fitness is associated with an altered gut microbiome. The Study of Health in Pomerania (SHIP)
Abstract Sedentarism is characterized by low levels of physical activity, a risk factor for obesity and cardio-metabolic diseases. It can also adversely affect the composition and diversity of the gut microbiome which may result in harmful consequences for human health. While cardiorespiratory fitness (CRF) is inversely and independently associated with cardiovascular risk factors and diseases and all-cause mortality, the relationship between low CRF and the gut microbiome is not well known. A total of 3,616 individuals from two independent population-based cohorts of the Study of Health in Pomerania (SHIP-START and SHIP-TREND) performed standardized, symptom-limited cardiopulmonary exercise testing (CPET) and had faecal samples collected to determine gut microbiota profiles (16S rRNA gene sequencing). We analysed cross-sectional associations of CRF with the gut microbiome composition controlling for confounding factors. Lower CRF was associated with reduced microbial diversity, loss of beneficial short-chain fatty acid producing bacteria (i.e. Butyricoccus, Coprococcus, unclassified Ruminococcaceae or Lachnospiraceae) and an increase in opportunistic pathogens such as Escherichia/Shigella, or Citrobacter. Decreased cardiorespiratory performance was associated with a gut microbiota pattern that has been previously related to a proinflammatory state. These associations were independent of body weight or glycemic control.