Sustainable land management enhances ecological and economic multifunctionality under ambient and future climate.

The currently dominant types of land management are threatening the multifunctionality of ecosystems, which is vital for human well-being. Here, we present a novel ecological-economic assessment of how multifunctionality of agroecosystems in Central Germany depends on land-use type and climate. Our analysis includes 14 ecosystem variables in a large-scale field experiment with five different land-use types under two different climate scenarios (ambient and future climate). We consider ecological multifunctionality measures using averaging approaches with different weights, reflecting preferences of four relevant stakeholders based on adapted survey data. Additionally, we propose an economic multifunctionality measure based on the aggregate economic value of ecosystem services. Results show that intensive management and future climate decrease ecological multifunctionality for most scenarios in both grassland and cropland. Only under a weighting based on farmers' preferences, intensively-managed grassland shows higher multifunctionality than sustainably-managed grassland. The economic multifunctionality measure is about ~1.7 to 1.9 times higher for sustainable, compared to intensive, management for both grassland and cropland. Soil biodiversity correlates positively with ecological multifunctionality and is expected to be one of its drivers. As the currently prevailing land management provides high multifunctionality for farmers, but not for society at large, we suggest to promote and economically incentivise sustainable land management that enhances both ecological and economic multifunctionality, also under future climatic conditions.

Pathogenic mechanisms of cardiovascular damage in COVID-19.

BACKGROUND: COVID-19 is a new infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). Since the outbreak in December 2019, it has caused an unprecedented world pandemic, leading to a global human health crisis. Although SARS CoV-2 mainly affects the lungs, causing interstitial pneumonia and severe acute respiratory distress syndrome, a number of patients often have extensive clinical manifestations, such as gastrointestinal symptoms, cardiovascular damage and renal dysfunction. PURPOSE: This review article discusses the pathogenic mechanisms of cardiovascular damage in COVID-19 patients and provides some useful suggestions for future clinical diagnosis, treatment and prevention. METHODS: An English-language literature search was conducted in PubMed and Web of Science databases up to 12th April, 2024 for the terms "COVID-19", "SARS CoV-2", "cardiovascular damage", "myocardial injury", "myocarditis", "hypertension", "arrhythmia", "heart failure" and "coronary heart disease", especially update articles in 2023 and 2024. Salient medical literatures regarding the cardiovascular damage of COVID-19 were selected, extracted and synthesized. RESULTS: The most common cardiovascular damage was myocarditis and pericarditis, hypertension, arrhythmia, myocardial injury and heart failure, coronary heart disease, stress cardiomyopathy, ischemic stroke, blood coagulation abnormalities, and dyslipidemia. Two important pathogenic mechanisms of the cardiovascular damage may be direct viral cytotoxicity as well as indirect hyperimmune responses of the body to SARS CoV-2 infection. CONCLUSIONS: Cardiovascular damage in COVID-19 patients is common and portends a worse prognosis. Although the underlying pathophysiological mechanisms of cardiovascular damage related to COVID-19 are not completely clear, two important pathogenic mechanisms of cardiovascular damage may be the direct damage of the SARSCoV-2 infection and the indirect hyperimmune responses.

Endothelial progenitor cell-derived extracellular vesicles: the world of potential prospects for the treatment of cardiovascular diseases.

Cardiovascular diseases (CVDs) have emerged as a predominant threat to human health, surpassing the incidence and mortality rates of neoplastic diseases. Extracellular vesicles (EVs) serve as vital mediators in intercellular communication and material exchange. Endothelial progenitor cells (EPCs), recognized as precursors of vascular endothelial cells (ECs), have garnered considerable attention in recent years due to the potential therapeutic value of their derived extracellular vesicles (EPC-EVs) in the context of CVDs. This comprehensive review systematically explores the origins, characteristics, and functions of EPCs, alongside the classification, properties, biogenesis, and extraction techniques of EVs, with particular emphasis on their protective roles in CVDs. Additionally, we delve into the essential bioactive components of EPC-EVs, including microRNAs, long non-coding RNAs, and proteins, analyzing their beneficial effects in promoting angiogenesis, anti-inflammatory and anti-oxidant activities, anti-fibrosis, anti-apoptosis, and myocardial regeneration. Furthermore, this review comprehensively investigates the therapeutic potential of EPC-EVs across various CVDs, encompassing acute myocardial infarction, myocardial ischemia-reperfusion injury, atherosclerosis, non-ischemic cardiomyopathies, and diabetic cardiovascular disease. Lastly, we summarize the potential challenges associated with the clinical application of EPC-EVs and outline future directions, aiming to offer a valuable resource for both theoretical insights and practical applications of EPC-EVs in managing CVDs.

Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens.

T cell receptors (TCRs) that recognize cancer neoantigens are important for anti-cancer immune responses and immunotherapy. Understanding the structural basis of TCR recognition of neoantigens provides insights into their exquisite specificity and can enable design of optimized TCRs. We determined crystal structures of a human TCR in complex with NRAS Q61K and Q61R neoantigen peptides and HLA-A1 MHC, revealing the molecular underpinnings for dual recognition and specificity versus wild-type NRAS peptide. We then used multiple versions of AlphaFold to model the corresponding complex structures, given the challenge of immune recognition for such methods. Interestingly, one implementation of AlphaFold2 (TCRmodel2) was able to generate accurate models of the complexes, while AlphaFold3 also showed strong performance, although success was lower for other complexes. This study provides insights into TCR recognition of a shared cancer neoantigen, as well as the utility and practical considerations for using AlphaFold to model TCR-peptide-MHC complexes.

Preoperative magnetic resonance imaging identify feasibility of breast-conserving surgery for breast cancer patients.

Background: Breast-conserving surgery (BCS) stands as the favored modality for treating early-stage breast cancer. Accurately forecasting the feasibility of BCS preoperatively can aid in surgical planning and reduce the rate of switching of surgical methods and reoperation. The objective of this study is to identify the radiomics features and preoperative breast magnetic resonance imaging (MRI) characteristics that are linked with positive margins following BCS in patients with breast cancer, with the ultimate aim of creating a predictive model for the feasibility of BCS. Methods: This study included a cohort of 221 pretreatment MRI images obtained from patients with breast cancer. A total of seven MRI semantic features and 1,561 radiomics features of lesions were extracted. The feature subset was determined by eliminating redundancy and correlation based on the features of the training set. The least absolute shrinkage and selection operator (LASSO) logistic regression was then trained with this subset to classify the final BCS positive and negative margins and subsequently validated using the test set. Results: Seven features were significant in the discrimination of cases achieving positive and negative margins. The radiomics signature achieved area under the curve (AUC), accuracy, sensitivity, and specificity of 0.760 [95% confidence interval (CI): 0.630, 0.891], 0.712 (95% CI: 0.569, 0.829), 0.882 (95% CI: 0.623, 0.979) and 0.629 (95% CI: 0.449, 0.780) in the test set, respectively. The combined model of radiomics signature and background parenchymal enhancement (BPE) demonstrated an AUC, accuracy, sensitivity, and specificity of 0.759 (95% CI: 0.628, 0.890), 0.654 (95% CI: 0.509, 0.780), 0.679 (95% CI: 0.476, 0.834) and 0.625 (95% CI: 0.408, 0.804). Conclusions: The combination of preoperative MRI radiomics features can well predict the success of breast conserving surgery.

Concanavalin A-assisted multiplex digital PCR assay for rapid capture and accurate quantification detection of foodborne pathogens.

Multiplex, sensitive, and rapid detection of pathogens is crucial for ensuring food safety and safeguarding human health, however, it remains a significant challenge. This study proposes a concanavalin A-assisted multiplex digital amplification (CAMDA) assay for simultaneous quantitative detection of multiple foodborne bacteria. The CAMDA assay enables the simultaneous detection of six foodborne pathogens within 1.1 h and the limit of detection is 101 CFU/mL. Furthermore, the CAMDA assay exhibits high specificity, with a rate of 97 % for Bacillus cereus and 100 % for other pathogens tested in this study. Moreover, practical application validation using eight milk powder samples demonstrates that the accuracy of the CAMDA assay reaches 100 % when compared to qPCR results. Therefore, our developed CAMDA assay holds great potential for accurate and rapid detection of multiple pathogens in complex food matrices while also promoting the utilization of microfluidic chips in food investigation.

Transformer-based Multi-label Deep Learning Model is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on MRI and Improving Clinicians' Diagnostic Accuracy for Rotational Chronic Ankle Instability.

PURPOSE: To develop a deep learning (DL) model that can simultaneously detect lateral and medial collateral ligament injuries of the ankle, aiding in the diagnosis of chronic ankle instability (CAI), and assess its impact on clinicians' diagnostic performance. METHODS: DL models were developed and external validated on retrospectively collected ankle MRIs between April 2016 and March 2022 respectively at three centers. Included patients were confirmed diagnoses of CAI through arthroscopy, as well as individuals who had undergone MRI and physical examinations that ruled out ligament injuries. DL models were constructed based on a multi-label paradigm. A transformer-based multi-label DL model (AnkleNet) was developed and compared with four convolution neural network (CNN) models. Subsequently, a reader study was conducted to evaluate the impact of model assistance on clinicians when diagnosing challenging cases: identifying rotational CAI (RCAI). Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS: Our transformer-based model achieved AUC of 0.910 and 0.892 for detecting lateral and medial collateral ligament injury, respectively, both of which was significantly higher than that of CNN-based models (all P < 0.001). In terms of further CAI diagnosis, it exhibited a macro-average AUC of 0.870 and a balanced accuracy of 0.805. The reader study indicated that incorporation with our model significantly enhanced the diagnostic accuracy of clinicians (P = 0.042), particularly junior clinicians, and led to a reduction in diagnostic variability. The code of the model can be accessed at https://github.com/ChiariRay/AnkleNet. CONCLUSION: Our transformer-based model was able to detect lateral and medial collateral ligament injuries based on MRI and outperformed CNN-based models, demonstrating a promising performance in diagnosing CAI, especially RCAI patients.

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Functional traits are indicators of the responses and adaptation of organisms to environmental changes and cascade to a series of ecosystem functions. The functional traits of soil animals are sensitive to environmental factors and may characterize and predict the changes of ecosystem functions. Multiple dimensions of biodiversity that combing species, phylogenetic, and functional diversity improves the understanding of distribution patterns, community assembly mechanisms and ecosystem functions of soil animals. In this review, we listed the categories of soil animal functional traits and their ecological significance, and summarized current researches on the responses of soil animal communities to environmental changes and the community assembly processes based on trait-based approaches. We proposed to strengthen the study on the impacts of eco-evolution processes of biotic interactions to soil animal functional traits, establish the database of soil animal functional traits, and apply trait-based approaches in the ecological restoration in the future, which would benefit soil biodiversity conservation and sustainability of soil ecosystems.

VarPPUD: Variant post prioritization developed for undiagnosed genetic disorders.

Rare and ultra-rare genetic conditions are estimated to impact nearly 1 in 17 people worldwide, yet accurately pinpointing the diagnostic variants underlying each of these conditions remains a formidable challenge. Because comprehensive, in vivo functional assessment of all possible genetic variants is infeasible, clinicians instead consider in silico variant pathogenicity predictions to distinguish plausibly disease-causing from benign variants across the genome. However, in the most difficult undiagnosed cases, such as those accepted to the Undiagnosed Diseases Network (UDN), existing pathogenicity predictions cannot reliably discern true etiological variant(s) from other deleterious candidate variants that were prioritized through N-of-1 efforts. Pinpointing the disease-causing variant from a pool of plausible candidates remains a largely manual effort requiring extensive clinical workups, functional and experimental assays, and eventual identification of genotype- and phenotype-matched individuals. Here, we introduce VarPPUD, a tool trained on prioritized variants from UDN cases, that leverages gene-, amino acid-, and nucleotide-level features to discern pathogenic variants from other deleterious variants that are unlikely to be confirmed as disease relevant. VarPPUD achieves a cross-validated accuracy of 79.3% and precision of 77.5% on a held-out subset of uniquely challenging UDN cases, respectively representing an average 18.6% and 23.4% improvement over nine traditional pathogenicity prediction approaches on this task. We validate VarPPUD's ability to discriminate likely from unlikely pathogenic variants on synthetic, GAN-generated candidate variants as well. Finally, we show how VarPPUD can be probed to evaluate each input feature's importance and contribution toward prediction-an essential step toward understanding the distinct characteristics of newly-uncovered disease-causing variants.

Preoperative CECT-Based Multitask Model Predicts Peritoneal Recurrence and Disease-Free Survival in Advanced Ovarian Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: Peritoneal recurrence is the predominant pattern of recurrence in advanced ovarian cancer (AOC) and portends a dismal prognosis. Accurate prediction of peritoneal recurrence and disease-free survival (DFS) is crucial to identify patients who might benefit from intensive treatment. We aimed to develop a predictive model for peritoneal recurrence and prognosis in AOC. METHODS: In this retrospective multi-institution study of 515 patients, an end-to-end multi-task convolutional neural network (MCNN) comprising a segmentation convolutional neural network (CNN) and a classification CNN was developed and tested using preoperative CT images, and MCNN-score was generated to indicate the peritoneal recurrence and DFS status in patients with AOC. We evaluated the accuracy of the model for automatic segmentation and predict prognosis. RESULTS: The MCNN achieved promising segmentation performances with a mean Dice coefficient of 84.3% (range: 78.8%-87.0%). The MCNN was able to predict peritoneal recurrence in the training (AUC 0.87; 95% CI 0.82-0.90), internal test (0.88; 0.85-0.92), and external test set (0.82; 0.78-0.86). Similarly, MCNN demonstrated consistently high accuracy in predicting recurrence, with an AUC of 0.85; 95% CI 0.82-0.88, 0.83; 95% CI 0.80-0.86, and 0.85; 95% CI 0.83-0.88. For patients with a high MCNN-score of recurrence, it was associated with poorer DFS with P < 0.0001 and hazard ratios of 0.1964 (95% CI: 0.1439-0.2680), 0.3249 (95% CI: 0.1896-0.5565), and 0.3458 (95% CI: 0.2582-0.4632). CONCLUSION: The MCNN approach demonstrated high performance in predicting peritoneal recurrence and DFS in patients with AOC.

Visualized Enzyme-Activated Fluorescence Probe for Accurately Detecting ß-Gal in Living Cells and BALB/c Nude Mice.

Colorectal cancer was one of the major malignant tumors threatening human health and ß-Gal was recognized as a principal biomarker for primary colorectal cancer. Thus, designing specific and efficient quantitative detection methods for measuring ß-Gal enzyme activity was of great clinical test significance. Herein, an ultrasensitive detection method based on Turn-on fluorescence probe (CS-ßGal) was reported for visualizing the detection of exogenous and endogenous ß-galactosidase enzyme activity. The test method possessed a series of excellent performances, such as a significant fluorescence enhancement (about 11.3-fold), high selectivity as well as superior sensitivity. Furthermore, under the optimal experimental conditions, a relatively low limit of detection down to 0.024 U/mL was achieved for fluorescence titration experiment. It was thanks to the better biocompatibility and low cytotoxicity, CS-ßGal had been triumphantly employed to visual detect endogenous and exogenous ß-Gal concentration variations in living cells with noteworthy anti-interference performance. More biologically significant was the fact that the application of CS-ßGal in BALB/c nude mice was also achieved successfully for monitoring endogenous ß-Gal enzyme activity.

High-Performance Solar-Blind Photodetector Based on (010)-Plane ß-Ga2O3 Thermally Oxidized from Nonpolar (110)-Plane GaN.

A high-performance planar structure metal-semiconductor-metal-type solar-blind photodetector (SBPD) was fabricated on the basis of (010)-plane ß-Ga2O3 thermally oxidized from nonpolar (110)-plane GaN. A full width at half maximum of 0.486° was achieved for the X-ray rocking curve associated with (020)-plane ß-Ga2O3, which is better than most reported results for the heteroepitaxially grown (-201)-plane ß-Ga2O3. As a result of the relatively high crystalline quality, a dark current as low as 6.30 × 10-12 A was achieved at 5 V, while the photocurrent reached 1.86 × 10-5 A under 254 nm illumination at 600 µW/cm2. As a result, the photo-to-dark current ratio, specific detectivity, responsivity, and external quantum efficiency were calculated to be 2.95 × 106, 2.39 × 1012 Jones, 3.72 A/W, and 1815%, respectively. Moreover, the SBPD showed excellent repeatability and stability in the time-dependent photoresponse characteristics with fast relaxation time constants for the rise and decay processes of only 0.238 and 0.062 s, respectively. This study provides a promising approach to fabricate the device-level (010)-plane ß-Ga2O3 film and a new way for the epitaxial growth of (010)-plane ß-Ga2O3 and (110)-plane GaN as mutual substrates.

An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer's Disease Using UK Biobank.

Alzheimer's disease (AD) manifests with varying progression rates across individuals, necessitating the understanding of their intricate patterns of cognition decline that could contribute to effective strategies for risk monitoring. In this study, we propose an innovative interpretable population graph network framework for identifying rapid progressors of AD by utilizing patient information from electronic health-related records in the UK Biobank. To achieve this, we first created a patient similarity graph, in which each AD patient is represented as a node; and an edge is established by patient clinical characteristics distance. We used graph neural networks (GNNs) to predict rapid progressors of AD and created a GNN Explainer with SHAP analysis for interpretability. The proposed model demonstrates superior predictive performance over the existing benchmark approaches. We also revealed several clinical features significantly associated with the prediction, which can be used to aid in effective interventions for the progression of AD patients.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer.

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.

Acetyl-CoA metabolic accumulation promotes hepatocellular carcinoma metastasis via enhancing CXCL1-dependent infiltration of tumor-associated neutrophils.

High levels of acetyl-CoA are considered a key metabolic feature of metastatic cancers. However, the impacts of acetyl-CoA metabolic accumulation on cancer microenvironment remodeling are poorly understood. In this study, using human hepatocellular carcinoma (HCC) tissues and orthotopic xenograft models, we found a close association between high acetyl-CoA levels in HCCs, increased infiltration of tumor-associated neutrophils (TANs) in the cancer microenvironment and HCC metastasis. Cytokine microarray and enzyme-linked immunosorbent assays (ELISA) revealed the crucial role of the chemokine (C-X-C motif) ligand 1(CXCL1). Mechanistically, acetyl-CoA accumulation induces H3 acetylation-dependent upregulation of CXCL1 gene expression. CXCL1 recruits TANs, leads to neutrophil extracellular traps (NETs) formation and promotes HCC metastasis. Collectively, our work linked the accumulation of acetyl-CoA in HCC cells and TANs infiltration, and revealed that the CXCL1-CXC receptor 2 (CXCR2)-TANs-NETs axis is a potential target for HCCs with high acetyl-CoA levels.

Low-level photodynamic therapy in chronic wounds.

BACKGROUND: Chronic wounds refer to those that can't reconstruct anatomical and physical functional integrity, and are usually associated with signs of microbial infection. Current therapies include debridement and dressing change, local or systemic application of antibiotics, and medical dressing care, which are not ideal for the healing of chronic wounds. OBJECTIVE: To explore the efficacy and safety of photodynamic therapy (ALA-PDT) for the treatment of chronic infectious wounds. MATERIALS AND METHODS: ALA-PDT was used in ten patients with persistent wound infections and systemic complications who did not respond to conventional treatment. 5 % ALA solution was applied to the wound surface after debridement, incubated for 3 h with light protection, and then irradiated with red light for 20 min. This procedure was repeated every two weeks, and any adverse reactions were recorded. After the end of three treatments, the patients were followed up for 3 months. RESULTS: Patients who exhibit resistance to traditional therapies demonstrate a favorable therapeutic outcome with ALA-PDT, although complications may impede wound healing. All participants successfully underwent ALA-PDT treatment and subsequent monitoring, with 90 % achieving complete healing. Common adverse reactions to ALA-PDT encompass treatment-related pain, temporary erythema, and swelling, all of which are well-tolerated by patients without enduring severe consequences. CONCLUSIONS: ALA-PDT proves to be an efficacious intervention for managing chronic wounds, irrespective of the presence of localized infections or systemic complications.

Valorization of Nannochloropsis oceanica for integrated co-production of violaxanthin cycle carotenoids.

The development of integrated co-production of multiple high-purity carotenoids from microalgal cells holds considerable significance for the valorization of microalgae. In this study, the economical microalga Nannochloropsis oceanica was identified as an accumulator of violaxanthin cycle carotenoids, including violaxanthin, antheraxanthin, and zeaxanthin. Notably, a novel and competent approach for the integrated co-production of violaxanthin cycle carotenoids was explored, encompassing four steps: microalgal cultivation, solvent extraction, octadecylsilyl open-column chromatography, and ethanol precipitation. Under optimal co-production conditions, the purities of the obtained violaxanthin, antheraxanthin, and zeaxanthin all exceeded 92%, with total recovery rates of approximately 51%, 40%, and 60%, respectively. Utilizing nuclear magnetic resonance techniques, the purified violaxanthin, antheraxanthin, and zeaxanthin were identified as all-trans-violaxanthin, all-trans-antheraxanthin, and all-trans-zeaxanthin, respectively. This method held significance for the multiproduct biorefinery of the microalga N. oceanica and carried potential future implications for the violaxanthin cycle carotenoids.

The PIEZO1/miR-155-5p/GDF6/SMAD2/3 signaling axis is involved in inducing the occurrence and progression of osteoarthritis under excessive mechanical stress.

OBJECTIVE: To elucidate the molecular mechanism of overloading-induced osteoarthritis (OA) and to find a novel therapeutic target. METHODS: We utilized human cartilage specimens, mouse chondrocytes, a destabilization of the medial meniscus (DMM) mouse model, and a mouse hindlimb weight-bearing model to validate the role of overloading on chondrocyte senescence and OA development. Then, we observed the effect of PIEZO1-miR-155-5p-GDF6-SMAD2/3 signaling axis on the preservation of joint metabolic homeostasis under overloading in vivo, in vitro and ex vivo by qPCR, Western blot, enzyme-linked immunosorbent assay (ELISA), immunohistochemistry, immunofluorescence, SA-ß-gal staining, CCK8 assay, et al. Finally, we verified the therapeutic effects of intra-articular injection of miR-155-5p inhibitor or recombinant GDF6 on the murine overloading-induced OA models. RESULTS: Chondrocytes sensesed the mechanical overloading through PIEZO1 and up-regulated miR-155-5p expression. MiR-155-5p mimics could copy the effects of overloading-induced chondrocyte senescence and OA. Additionally, miR-155-5p could suppress the mRNA expression of Gdf6-Smad2/3 in various tissues within the joint. Overloading could disrupt joint metabolic homeostasis by downregulating the expression of anabolism indicators and upregulating the expression of catabolism indicators in the chondrocytes and synoviocytes, while miR-155-5p inhibition or GDF6 supplementation could exert an antagonistic effect by preserving the joint homeostasis. Finally, in the in vivo overloading models, intra-articular injection of miR-155-5p inhibitor or recombinant GDF6 could significantly mitigate the severity of impending OA and lessened the progression of existing OA. CONCLUSION: GDF6 overexpression or miR-155-5p inhibition could attenuate overloading-induced chondrocyte senescence and OA through the PIEZO1-miR-155-5p-GDF6-SMAD2/3 signaling pathway. Our study provides a new therapeutic target for the treatment of overloading-induced OA.

EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN.

Analysis of brain signals is essential to the study of mental states and various neurological conditions. The two most prevalent noninvasive signals for measuring brain activities are electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG, characterized by its higher sampling frequency, captures more temporal features, while fNIRS, with a greater number of channels, provides richer spatial information. Although a few previous studies have explored the use of multimodal deep-learning models to analyze brain activity for both EEG and fNIRS, subject-independent training-testing split analysis remains underexplored. The results of the subject-independent setting directly show the model's ability on unseen subjects, which is crucial for real-world applications. In this paper, we introduce EF-Net, a new CNN-based multimodal deep-learning model. We evaluate EF-Net on an EEG-fNIRS word generation (WG) dataset on the mental state recognition task, primarily focusing on the subject-independent setting. For completeness, we report results in the subject-dependent and subject-semidependent settings as well. We compare our model with five baseline approaches, including three traditional machine learning methods and two deep learning methods. EF-Net demonstrates superior performance in both accuracy and F1 score, surpassing these baselines. Our model achieves F1 scores of 99.36%, 98.31%, and 65.05% in the subject-dependent, subject-semidependent, and subject-independent settings, respectively, surpassing the best baseline F1 scores by 1.83%, 4.34%, and 2.13% These results highlight EF-Net's capability to effectively learn and interpret mental states and brain activity across different and unseen subjects.

Salmonellosis outbreak archive in China: data collection and assembly.

Infectious disease outbreaks transcend the medical and public health realms, triggering widespread panic and impeding socio-economic development. Considering that self-limiting diarrhoea of sporadic cases is usually underreported, the Salmonella outbreak (SO) study offers a unique opportunity for source tracing, spatiotemporal correlation, and outbreak prediction. To summarize the pattern of SO and estimate observational epidemiological indicators, 1,134 qualitative reports screened from 1949 to 2023 were included in the systematic review dataset, which contained a 506-study meta-analysis dataset. In addition to the dataset comprising over 50 columns with a total of 46,494 entries eligible for inclusion in systematic reviews or input into prediction models, we also provide initial literature collection datasets and datasets containing socio-economic and climate information for relevant regions. This study has a broad impact on advancing knowledge regarding epidemic trends and prevention priorities in diverse salmonellosis outbreaks and guiding rational policy-making or predictive modeling to mitigate the infringement upon the right to life imposed by significant epidemics.