Asymptomatic Incidence of Monoclonal Gammopathy of Undetermined Significance and Preclinical Duration to Myeloma Diagnosis: A Modeling Study.

Background Monoclonal gammopathy of undetermined significance (MGUS) is the premalignant condition of multiple myeloma (MM). Given a lack of population-based screening for MGUS and its asymptomatic nature, the epidemiology of MGUS remains unknown. This study estimated age- and race/ethnicity-specific MGUS incidence and preclinical duration from MGUS to MM in the United States. Methods A previously published modeling approach was used to calculate national MGUS incidence using estimates of MGUS prevalence, MM incidence, MM-specific and all-cause mortality, and population age distribution from the National Health and Nutrition Examination Survey, 1999-2004, and Surveillance, Epidemiology, and End Results, 2000-2021. The estimated MGUS prevalence was divided by MGUS incidence to obtain preclinical duration of MM. Results MGUS incidence for non-Hispanic white (NHW) populations was 52, 86, 142, and 181 and for non-Hispanic black (NHB) population was 110, 212, 392, and 570 per 100,000 person-years at ages 50, 60, 70, and 80, respectively. The average preclinical duration was 20.5 (95% confidence interval, CI: 16.5, 26.1) years for the NHW population and 14.2 (95% CI: 11.5, 17.6) years for the NHB population. The cumulative risk of developing MGUS in age 50-85 was 2.8% (95% CI: 1.7%, 4.2%) for the NHW population and 6.1% (95% CI: 3.8%, 10.0%) for the NHB population. Conclusion NHB populations had a higher MGUS incidence rate at all ages and a shorter preclinical duration of MM compared to their NHW counterparts. Impact This study provides insights into the epidemiology of MGUS and enhances our understanding of the natural history of MM.

[Land Use Change Driving and Optimization of Carbon Budget in Hangzhou Metropolitan Area].

As climate change, such as global warming, has become a global environmental issue, clarifying the mechanism driving the carbon budget based on land use change has become an inevitable path to realize the "double carbon" goal. Based on the land use change characteristics in the Hangzhou Metropolitan Area from 1995 to 2020, this study employed the inventory accounting method, concentration index, and panel regression models to investigate the driving mechanisms of carbon budget dynamics influenced by land use changes. Moreover, the study utilized a "scenario-actor" policy analysis framework to propose low-carbon strategies through the integration of land use management within territorial spatial planning. The research findings were as follows： ① The carbon source capacity in the study area significantly surpassed its carbon sink capacity. The overall carbon budget concentration index had yet to exceed the 0.4 "alert threshold," with spatial concentration levels as follows： Hangzhou > Huangshan > Shaoxing > Quzhou > Jiaxing ≈ Huzhou. ② For croplands, larger areas and greater shape regularity contributed to a reduction in carbon budgets. Conversely, for constructed lands, expansive areas and increased fragmentation intensified the carbon budget levels, primarily driven by other urban land categories. ③ An increased proportion of croplands and higher land use heterogeneity promoted spatial equilibrium in carbon budgets, whereas the larger coverage and fragmentation of industrial and other urban lands led to an uneven spatial distribution of carbon budgets. ④ Low-carbon optimization of territorial space needs to adjust for the structure and form of carbon source functional land use as a key driver. At the policy implementation level, the central government and urban residents demonstrated strong support for low-carbon territorial control. However, cooperation from local governments, enterprises, and rural residents was suboptimal, necessitating complementary policies for effective guidance. This study holds practical significance for enhancing land use efficiency and promoting low-carbon urban development.

Transdermal characteristic study of bovine sialoglycoproteins with anti-skin aging and accelerating skin wound healing.

BACKGROUND: Sialoglycoproteins play important roles in various biological processes, including cell adhesion, immune response, and cell signaling. Our previous studies indicated that the bovine sialoglycoproteins could be developed as a reagent against skin aging and as a new candidate for accelerating skin wound healing as well as inhibiting scar formation. However, transdermal characteristic of the bovine sialoglycoproteins is still unknown. AIMS: This study investigated the transdermal permeation of the bovine sialoglycoproteins through porcine skin using the Franz diffusion cell method. RESULTS: Our study showed that the bovine sialoglycoproteins could penetrate through the porcine skin with a linear permeation pattern described by the regression equation N% = 11.49 t-3.858, with a high coefficient of determination (R2 = 0.9903). The histochemical results demonstrated the widespread distribution of the bovine sialoglycoproteins between the epidermal and dermal layers, which suggesting parts of the bovine sialoglycoproteins had ability to traverse the epidermal barrier. The results of the lectin microarrays indicated highly enriched glycopatterns on the bovine sialoglycoproteins, which also appeared in permeated porcine skin. The LC-MS/MS analysis further showed that the bovine sialoglycoproteins were composed of approximately 100 proteins with molecular weight ranging from 748.4 kDa to 10 kDa, and there were 23 specific bovine sialoglycoproteins with molecular weight ranging from 69.2 kDa to 10 kDa to be characterized in permeated porcine skin. CONCLUSIONS: Parts of the bovine sialoglycoproteins with molecular weight less than 69.2 kDa had ability to traverse the epidermal barrier. Understanding the permeation characteristics of the bovine sialoglycoproteins for developing innovative formulations with therapeutic benefits, contributing to advancements in cosmetic and dermatological fields.

Gut microbiota modulates neurotransmitter and gut-brain signaling.

Neurotransmitters, including 5-hydroxytryptamine (5-HT), dopamine (DA), gamma-aminobutyric acid (GABA), and glutamate, are essential transductors in the Gut-Brain Axis (GBA), playing critical roles both peripherally and centrally. Accumulating evidence suggests that the gut microbiota modulates intestinal neurotransmitter metabolism and gut-to-brain signaling, shedding light on the crucial role of the gut microbiota in brain function and the pathogenesis of various neuropsychiatric diseases, such as major depression disorder (MDD), anxiety, addiction and Parkinson's disease (PD). Despite the exciting findings, the mechanisms underlying the modulation of neurotransmitter metabolism and function by the gut microbiota are still being elucidated. In this review, we aim to provide a comprehensive overview of the existing knowledge about the role of the gut microbiota in neurotransmitter metabolism and function in animal and clinical experiments. Moreover, we will discuss the potential mechanisms through which gut microbiota-derived neurotransmitters contribute to the pathogenesis of neuropsychiatric diseases, thus highlighting a novel therapeutic target for these conditions.

Treatment Access among Younger Medicaid Beneficiaries with Multiple Myeloma.

PURPOSE: Continuous Medicaid coverage prior to a cancer diagnosis has been associated with earlier detection and better outcomes, for patients with solid tumors. In this study, we aimed to determine if this was observed among patients with multiple myeloma, a hematologic cancer where there are no routine screening tests and most are diagnosed through acute medical events. MATERIALS AND METHODS: This is an analysis of the Merative MarketScan Multistate Medicaid Database, a claims-based dataset. In total, 1105 patients < 65 years old were included in the analyses. Among them, 66% had continuous enrollment (at least 6 months enrollment prior to myeloma), and 34% had discontinuous enrollment (2-6 months enrollment prior to myeloma). Multivariable Cox regression was used to estimate the association between continuous enrollment status and receipt of myeloma treatment within 1 year of index date. RESULTS: Only 54% of all Medicaid enrollees received myeloma therapy and only 12% received stem cell transplant within the 1st year. Those with continuous enrollment were less likely to receive any treatment (adjusted hazard ratio [aHR] 0.59; 95% confidence interval [CI] 0.59-0.70; P < .001) and to receive stem cell transplant (aHR 0.51; 95% CI 0.32-0.81; P = .005). CONCLUSION: Patients with continuous Medicaid coverage prior to diagnosis were less likely to receive myeloma therapy. Future studies should examine whether myeloma patients with continuous Medicaid enrollment have more chronic financial instability and/or higher medical needs and, thus, have higher barriers to care.

A Human Feedback Strategy for Photoresponsive Molecules in Drug Delivery: Utilizing GPT-2 and Time-Dependent Density Functional Theory Calculations.

Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non-invasive, stable, and finely tunable nature of light-triggered methodologies. The generative pre-trained transformer (GPT) has been employed to generate molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV-GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through quantum chemical simulations. Based on the results of these calculations, we summarized some tips for chemical structures and integrated them into the alignment of large-scale language models within the reinforcement learning from human feedback (RLHF) framework. The synergy of these findings underscores the successful application of GPT technology in this critical domain.

Biological Roles of Lipids in Rice.

Lipids are organic nonpolar molecules with essential biological and economic importance. While the genetic pathways and regulatory networks of lipid biosynthesis and metabolism have been extensively studied and thoroughly reviewed in oil crops such as soybeans, less attention has been paid to the biological roles of lipids in rice, a staple food for the global population and a model species for plant molecular biology research, leaving a considerable knowledge gap in the biological roles of lipids. In this review, we endeavor to furnish a current overview of the advancements in understanding the genetic foundations and physiological functions of lipids, including triacylglycerol, fatty acids, and very-long-chain fatty acids. We aim to summarize the key genes in lipid biosynthesis, metabolism, and transcriptional regulation underpinning rice's developmental and growth processes, biotic stress responses, abiotic stress responses, fertility, seed longevity, and recent efforts in rice oil genetic improvement.

Engineering Bacillus licheniformis as industrial chassis for efficient bioproduction from starch.

Starch is an attractive feedstock in biorefinery processes, while the low natural conversion rate of most microorganisms limits its applications. Herein, starch metabolic pathway was systematically investigated using Bacillus licheniformis DW2 as the host organism. Initially, the effects of overexpressing amylolytic enzymes on starch hydrolysis were evaluated. Subsequently, the transmembrane transport system and intracellular degradation module were modified to accelerate the uptake of hydrolysates and their further conversion to glucose-6-phosphate. The DW2-derived strains exhibited robust growth in starch medium, and productivity of bacitracin and subtilisin were improved by 38.5% and 32.6%, with an 32.3% and 22.9% increase of starch conversion rate, respectively. Lastly, the employment of engineering strategies enabled another B. licheniformis WX-02 to produce poly-γ-glutamic acid from starch with a 2.1-fold increase of starch conversion rate. This study not only provided excellent B. licheniformis chassis for sustainable bioproduction from starch, but shed light on researches of substrate utilization.

Probing perfection: The relentless art of meddling for pulmonary airway segmentation from HRCT via a human-AI collaboration based active learning method.

In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors. Concurrently, most Deep Learning (DL) methodologies employed in this domain invariably grapple with other dual challenges: the inherent opacity of 'black box' models and the ongoing pursuit of performance enhancement. In response to these intertwined challenges, the core concept of our Human-Computer Interaction (HCI) based learning models (RS_UNet, LC_UNet, UUNet and WD_UNet) hinge on the versatile combination of diverse query strategies and an array of deep learning models. We train four HCI models based on the initial training dataset and sequentially repeat the following steps 1-4: (1) Query Strategy: Our proposed HCI models selects those samples which contribute the most additional representative information when labeled in each iteration of the query strategy (showing the names and sequence numbers of the samples to be annotated). Additionally, in this phase, the model selects the unlabeled samples with the greatest predictive disparity by calculating the Wasserstein Distance, Least Confidence, Entropy Sampling, and Random Sampling. (2) Central line correction: The selected samples in previous stage are then used for domain expert correction of the system-generated tracheal central lines in each training round. (3) Update training dataset: When domain experts are involved in each epoch of the DL model's training iterations, they update the training dataset with greater precision after each epoch, thereby enhancing the trustworthiness of the 'black box' DL model and improving the performance of models. (4) Model training: Proposed HCI model is trained using the updated training dataset and an enhanced version of existing UNet. Experimental results validate the effectiveness of this Human-Computer Interaction-based approaches, demonstrating that our proposed WD-UNet, LC-UNet, UUNet, RS-UNet achieve comparable or even superior performance than the state-of-the-art DL models, such as WD-UNet with only 15 %-35 % of the training data, leading to substantial reductions (65 %-85 % reduction of annotation effort) in physician annotation time.

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

A deep learning model integrating a wind direction-based dynamic graph network for ozone prediction.

Ozone pollution is an important environmental issue in many countries. Accurate forecasting of ozone concentration enables relevant authorities to enact timely policies to mitigate adverse impacts. This study develops a novel hybrid deep learning model, named wind direction-based dynamic spatio-temporal graph network (WDDSTG-Net), for hourly ozone concentration prediction. The model uses a dynamic directed graph structure based on hourly changing wind direction data to capture evolving spatial relationships between air quality monitoring stations. It applied the graph attention mechanism to compute dynamic weights between connected stations, thereby aggregating neighborhood information adaptively. For temporal modeling, it utilized a sequence-to-sequence model with attention mechanism to extract long-range temporal dependencies. Additionally, it integrated meteorological predictions to guide the ozone forecasting. The model achieves a mean absolute error of 6.69 µg/m3 and 18.63 µg/m3 for 1-h prediction and 24-h prediction, outperforming several classic models. The model's IAQI accuracy predictions at all stations are above 75 %, with a maximum of 81.74 %. It also exhibits strong capabilities in predicting severe ozone pollution events, with a 24-h true positive rate of 0.77. Compared to traditional static graph models, WDDSTG-Net demonstrates the importance of incorporating short-term wind fluctuations and transport dynamics for data-driven air quality modeling. In principle, it may serve as an effective data-driven approach for the concentration prediction of other airborne pollutants.

[Impacts of Urban Form on Carbon Emissions Under the Goal of Carbon Emission Peak and Carbon Neutrality: A Case Study of the Yangtze River Economic Belt].

Clarifying the mechanism of influence of urban form on carbon emissions is an important prerequisite for achieving urban carbon emission reduction. Taking the Yangtze River Economic Belt as an example, this study elaborated on the general mechanism of urban form on carbon emissions, used multi-source data to quantitatively evaluate the urban form, and explored the impacts of urban form indicators on carbon emissions from 2005 to 2020 at global and sub-regional scales with the help of spatial econometric models and geodetector, respectively. The results showed that:① The carbon emissions of the Yangtze River Economic Belt increased from 2 365.31 Mt to 4 230.67 Mt, but the growth rate gradually decreased. Its spatial distribution pattern was bipolar, with high-value areas mainly distributed in core cities such as Shanghai and Chongqing and low-value areas concentrated in the western regions of Sichuan and Yunnan. ② The area of construction land in the study area expanded over the past 15 years, but the population density of construction land had been decreasing. The degree of urban fragmentation was decreasing, and the difference between cities was also progressively narrowing. The average regularity of urban shape improved, and the compactness increased significantly. ③ All indicators of urban scale had significant positive effects on carbon emissions at the global scale, urban fragmentation had a significant negative effect in 2005, and the effective mesh size (MESH) indicator of urban compactness showed a significant negative correlation with carbon emissions in the study period. ④ Total class area, patch density, and effective mesh size had the most significant impacts on carbon emissions in upstream cities. Effective mesh size, mean perimeter-area ratio, and total class area had higher influences in midstream cities. Effective mesh size, percentage of like adjacencies, and largest patch index were the key factors to promote carbon reduction in downstream cities. Cities in different regions should comprehensively consider the impacts of various urban form indicators on carbon emissions and then optimize their urban form to promote sustainable development.

Astrocyte-secreted neurocan controls inhibitory synapse formation and function.

Astrocytes strongly promote the formation and maturation of synapses by secreted proteins. Several astrocyte-secreted synaptogenic proteins controlling excitatory synapse development were identified; however, those that induce inhibitory synaptogenesis remain elusive. Here, we identify neurocan as an astrocyte-secreted inhibitory synaptogenic protein. After secretion from astrocytes, neurocan is cleaved into N- and C-terminal fragments. We found that these fragments have distinct localizations in the extracellular matrix. The neurocan C-terminal fragment localizes to synapses and controls cortical inhibitory synapse formation and function. Neurocan knockout mice lacking the whole protein or only its C-terminal synaptogenic domain have reduced inhibitory synapse numbers and function. Through super-resolution microscopy, in vivo proximity labeling by secreted TurboID, and astrocyte-specific rescue approaches, we discovered that the synaptogenic domain of neurocan localizes to somatostatin-positive inhibitory synapses and strongly regulates their formation. Together, our results unveil a mechanism through which astrocytes control circuit-specific inhibitory synapse development in the mammalian brain.

Anaplasma bovis and Bartonella spp. in Libyan Jirds (Meriones libycus) from China.

Anaplasma bovis (1), Bartonella krasnovii (3), and Bartonella sp. (17) were detected in 80 Libyan jirds (Meriones libycus) from China. These findings extend the known host and geographic ranges of these pathogens, with neither A. bovis nor B. krasnovii previously confirmed in Libyan jirds.

ScRNA-seq reveals novel immune-suppressive T cells and investigates CMV-TCR-T cells cytotoxicity against GBM.

BACKGROUND: Glioblastoma (GBM) is a fatal primary brain malignancy in adults. Previous studies have shown that cytomegalovirus (CMV) is a risk factor for tumorigenesis and aggressiveness for glioblastoma. However, little is known about how CMV infection affects immune cells in the tumor microenvironment of GBM. Furthermore, there has been almost no engineered T-cell receptor (TCR)-T targeting CMV for GBM research to date. METHODS: We evaluated the CMV infection status of patients with GBM's tumor tissue by immune electron microscopy, immunofluorescence, and droplet digital PCR. We performed single-cell RNA sequencing for CMV-infected GBM to investigate the effects of CMV on the GBM immune microenvironment. CellChat was applied to analyze the interaction between cells in the GBM tumor microenvironment. Additionally, we conducted single-cell TCR/B cell receptor (BCR) sequencing and Grouping of Lymphocyte Interactions with Paratope Hotspots 2 algorithms to acquire specific CMV-TCR sequences. Genetic engineering was used to introduce CMV-TCR into primary T cells derived from patients with CMV-infected GBM. Flow cytometry was used to measure the proportion and cytotoxicity status of T cells in vitro. RESULTS: We identified two novel immune cell subpopulations in CMV-infected GBM, which were bipositive CD68+SOX2+ tumor-associated macrophages and FXYD6+ T cells. We highlighted that the interaction between bipositive TAMs or cancer cells and T cells was predominantly focused on FXYD6+ T cells rather than regulatory T cells (Tregs), whereas, FXYD6+ T cells were further identified as a group of novel immunosuppressive T cells. CMV-TCR-T cells showed significant therapeutic effects on the human-derived orthotopic GBM mice model. CONCLUSIONS: These findings provided an insight into the underlying mechanism of CMV infection promoting the GBM immunosuppression, and provided a novel potential immunotherapy strategy for patients with GBM.

Efficient removal of per/polyfluoroalkyl substances from water using recyclable chitosan-coated covalent organic frameworks: Experimental and theoretical methods.

Covalent organic frameworks (COFs) demonstrate remarkable potential for adsorbing per/polyfluoroalkyl substances (PFAS). Nevertheless, the challenge of recycling powdered COFs hampers their practical application in water treatment. In this research, a quaternary amine COF with inherent positive surface charge was synthesised to adsorb perfluorooctanoic acid (PFOA) via electrostatic interactions. The COF was then combined with chitosan (CS) through a simple dissolution-evaporation process, resulting in a composite gel material termed COF@CS. The findings indicated that the adsorption capacity of COF@CS significantly surpassed that of the original COF and CS. According to the Langmuir model, COF@CS achieved a maximum PFOA capacity of 2.8 mmol g-1 at pH 5. Furthermore, the adsorption rate increased significantly to 6.2 mmol g-1 h-1, compared to 5.9 mmol g-1 h-1 for COF and 3.4 mmol g-1 h-1 for CS. Notably, COF@CS exhibited excellent removal efficacy for ten other types of PFAS. Moreover, COF@CS could be successfully regenerated using a mixture of 70% ethanol and 1 wt% NaCl, and it exhibited stable reusability for up to five cycles. X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) characterisation, and theoretical calculations revealed that the quaternary amine functional group in COF served as the primary adsorption site in the composite gel material, while the protonated amino group on CS enhanced PFOA adsorption through electrostatic interaction. This study highlights the significant practical potential of COF@CS in the removal of PFAS from aqueous solution and environmental remediation.

Immunotherapy utilization patterns in patients with advanced cancer and autoimmune disease.

PURPOSE: Immunotherapy has been shown to improve cancer survival, but there are no consensus guidelines to inform use in patients with both cancer and autoimmune disease (AD). We sought to examine immunotherapy utilization patterns between cancer patients with and without AD. PATIENTS AND METHODS: This retrospective cohort study utilized data from a de-identified nationwide oncology database. Patients diagnosed with advanced melanoma, non-small cell lung cancer, and renal cell carcinoma were included. Outcomes of interest included first-line immunotherapy, overall immunotherapy, and number of immunotherapy cycles. We used logistic and Poisson regression models to examine associations between AD and immunotherapy utilization patterns. RESULTS: A total of 25,076 patients were included (796 with AD). Patients with AD were more likely to be female, White, receive care at academic centers, and have ECOG ≥ 3. Controlling for demographic and clinical variables, AD was associated with lower odds of receiving first-line (odds ratio [OR] = 0.68, 95% confidence interval [CI] 0.56-0.82) and overall (OR = 0.80, 95% CI 0.67-0.94) immunotherapy. Among patients who received at least one cycle of immunotherapy, there was no difference in mean number of cycles received between patients with and without AD (11.3 and 10.5 cycles respectively). The incident rate of immunotherapy cycles received for patients with AD was 1.03 times that of patients without AD (95% CI 1.01-1.06). DISCUSSION: Patients with AD were less likely to receive immunotherapy as first-line and overall therapy for treatment of their advanced cancer. However, among those who did receive at least one cycle of immunotherapy, patients with AD received a similar number of cycles compared to patients without AD. This not only indicates that AD is not an absolute contraindication for immunotherapy in clinical practice but may also demonstrate overall treatment tolerability and net benefit in patients with AD.

18Beta-Glycyrrhetinic Acid Attenuates H2O2-Induced Oxidative Damage and Apoptosis in Intestinal Epithelial Cells via Activating the PI3K/Akt Signaling Pathway.

Oxidative stress causes gut dysfunction and is a contributing factor in several intestinal disorders. Intestinal epithelial cell survival is essential for maintaining human and animal health under oxidative stress. 18beta-Glycyrrhetinic acid (GA) is known to have multiple beneficial effects, including antioxidant activity; however, the underlying molecular mechanisms have not been well established. Thus, the present study evaluated the therapeutic effects of GA on H2O2-induced oxidative stress in intestinal porcine epithelial cells. The results showed that pretreatment with GA (100 nM for 16 h) significantly increased the levels of several antioxidant enzymes and reduced corresponding intracellular levels of reactive oxidative species and malondialdehyde. GA inhibited cell apoptosis via activating the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) signaling pathway, as confirmed by RNA sequencing. Further analyses demonstrated that GA upregulated the phosphorylation levels of PI3K and Akt and the protein level of B cell lymphoma 2, whereas it downregulated Cytochrome c and tumor suppressor protein p53 levels. Moreover, molecular docking analysis predicted the binding of GA to Vasoactive intestinal peptide receptor 1, a primary membrane receptor, to activate the PI3K/Akt signaling pathway. Collectively, these results revealed that GA protected against H2O2-induced oxidative damage and cell apoptosis via activating the PI3K/Akt signaling pathway, suggesting the potential therapeutic use of GA to alleviate oxidative stress in humans/animals.

Molecular detection of Bartonella rochalimae and Hepatozoon canis in red foxes (Vulpes vulpes) from China.

Red foxes (Vulpes vulpes) have been recognized as natural reservoirs for multiple pathogens and a source of infection for domestic animals, wildlife and humans. To date, no reports are available on the Bartonella rochalimae and Hepatozoon canis infection in red foxes from China. In 2018-2022, a total of 16 red foxes were sampled in two counties and a city in Xinjiang Uygur Autonomous Region (XUAR) in northwest China. Subsequently analyzed by DNA extraction amplified by polymerase chain reaction (PCR). In the present study, based on nucleotide sequence and phylogenetic tree analyses, B. rochalimae and H. canis were molecularly identified in red foxes. Our findings provide the first molecular evidence of B. rochalimae and H. canis in red foxes from China.

Modifying temperature-related cardiovascular mortality through green-blue space exposure.

Green-blue spaces (GBS) are pivotal in mitigating thermal discomfort. However, their management lacks guidelines rooted in epidemiological evidence for specific planning and design. Here we show how various GBS types modify the link between non-optimal temperatures and cardiovascular mortality across different thermal extremes. We merged fine-scale population density and GBS data to create novel GBS exposure index. A case time series approach was employed to analyse temperature-cardiovascular mortality association and the effect modifications of type-specific GBSs across 1085 subdistricts in south-eastern China. Our findings indicate that both green and blue spaces may significantly reduce high-temperature-related cardiovascular mortality risks (e.g., for low (5%) vs. high (95%) level of overall green spaces at 99th vs. minimum mortality temperature (MMT), Ratio of relative risk (RRR) = 1.14 (95% CI: 1.07, 1.21); for overall blue spaces, RRR = 1.20 (95% CI: 1.12, 1.29)), while specific blue space types offer protection against cold temperatures (e.g., for the rivers at 1st vs MMT, RRR = 1.17 (95% CI: 1.07, 1.28)). Notably, forests, parks, nature reserves, street greenery, and lakes are linked with lower heat-related cardiovascular mortality, whereas rivers and coasts mitigate cold-related cardiovascular mortality. Blue spaces provide greater benefits than green spaces. The severity of temperature extremes further amplifies GBS's protective effects. This study enhances our understanding of how type-specific GBS influences health risks associated with non-optimal temperatures, offering valuable insights for integrating GBS into climate adaptation strategies for maximal health benefits.