Pulmonary redox imbalance drives early fibroproliferative response in moderate/severe coronavirus disease-19 acute respiratory distress syndrome and impacts long-term lung abnormalities.

BACKGROUND: COVID-19-associated pulmonary fibrosis remains frequent. This study aimed to investigate pulmonary redox balance in COVID-19 ARDS patients and possible relationship with pulmonary fibrosis and long-term lung abnormalities. METHODS: Baseline data, chest CT fibrosis scores, N-terminal peptide of alveolar collagen III (NT-PCP-III), transforming growth factor (TGF)-ß1, superoxide dismutase (SOD), reduced glutathione (GSH), oxidized glutathione (GSSG) and malondialdehyde (MDA) in bronchoalveolar lavage fluid (BALF) were first collected and compared between SARS-CoV-2 RNA positive patients with moderate to severe ARDS (n = 65, COVID-19 ARDS) and SARS-CoV-2 RNA negative non-ARDS patients requiring mechanical ventilation (n = 63, non-ARDS). Then, correlations between fibroproliferative (NT-PCP-III and TGF-ß1) and redox markers were analyzed within COVID-19 ARDS group, and comparisons between survivor and non-survivor subgroups were performed. Finally, follow-up of COVID-19 ARDS survivors was performed to analyze the relationship between pulmonary abnormalities, fibroproliferative and redox markers 3 months after discharge. RESULTS: Compared with non-ARDS group, COVID-19 ARDS group had significantly elevated chest CT fibrosis scores (p < 0.001) and NT-PCP-III (p < 0.001), TGF-ß1 (p < 0.001), GSSG (p < 0.001), and MDA (p < 0.001) concentrations on admission, while decreased SOD (p < 0.001) and GSH (p < 0.001) levels were observed in BALF. Both NT-PCP-III and TGF-ß1 in BALF from COVID-19 ARDS group were directly correlated with GSSG (p < 0.001) and MDA (p < 0.001) and were inversely correlated with SOD (p < 0.001) and GSH (p < 0.001). Within COVID-19 ARDS group, non-survivors (n = 28) showed significant pulmonary fibroproliferation (p < 0.001) with more severe redox imbalance (p < 0.001) than survivors (n = 37). Furthermore, according to data from COVID-19 ARDS survivor follow-up (n = 37), radiographic residual pulmonary fibrosis and lung function impairment improved 3 months after discharge compared with discharge (p < 0.001) and were associated with early pulmonary fibroproliferation and redox imbalance (p < 0.01). CONCLUSIONS: Pulmonary redox imbalance occurring early in COVID-19 ARDS patients drives fibroproliferative response and increases the risk of death. Long-term lung abnormalities post-COVID-19 are associated with early pulmonary fibroproliferation and redox imbalance.

Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms.

Excitotoxicity represents the primary cause of neuronal death following spinal cord injury (SCI). While autophagy plays a critical and intricate role in SCI, the specific mechanism underlying the relationship between excitotoxicity and autophagy in SCI has been largely overlooked. In this study, we isolated primary spinal cord neurons from neonatal rats and induced excitotoxic neuronal injury by high concentrations of glutamic acid, mimicking an excitotoxic injury model. Subsequently, we performed transcriptome sequencing. Leveraging machine learning algorithms, including weighted correlation network analysis (WGCNA), random forest analysis (RF), and least absolute shrinkage and selection operator analysis (LASSO), we conducted a comprehensive investigation into key genes associated with spinal cord neuron injury. We also utilized protein-protein interaction network (PPI) analysis to identify pivotal proteins regulating key gene expression and analyzed key genes from public datasets (GSE2599, GSE20907, GSE45006, and GSE174549). Our findings revealed that six genes-Anxa2, S100a10, Ccng1, Timp1, Hspb1, and Lgals3-were significantly upregulated not only in vitro in neurons subjected to excitotoxic injury but also in rats with subacute SCI. Furthermore, Hspb1 and Lgals3 were closely linked to neuronal autophagy induced by excitotoxicity. Our findings contribute to a better understanding of excitotoxicity and autophagy, offering potential targets and a theoretical foundation for SCI diagnosis and treatment.

Error detection for radiotherapy planning validation based on deep learning networks.

BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. PURPOSE: The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. METHOD: We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. RESULTS: The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. CONCLUSION: When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Impact of PD-L1 gene polymorphisms and interactions with cooking with solid fuel exposure on tuberculosis.

Introduction Given that PD-L1 is a crucial immune checkpoint in regulating T cell responses, the aim of this study was to explore the impact of PD-L1 gene polymorphism and its interaction with cooking with solid fuel on susceptibility to tuberculosis (TB) in Chinese Han populations. Methods A total of 503 TB patients and 494 healthy controls were enrolled in this case-control study. Mass spectrometry (MS) technology was applied to genotype rs2297136 and rs4143815 of PD-L1 genes. The associations between SNPs and TB were assessed using unconditional logistic regression analysis. Marginal structural linear odds models were used to estimate the gene-environment interactions. Results Compared with genotype CC, genotypes GG and CG + GG at rs4143815 locus were significantly associated with susceptibility to TB (OR: 3.074 and 1.506, respectively, P<0.05). However, no statistical association was found between rs2297136 SNP and TB risk. Moreover, the relative excess risk of interaction (RERI) between rs4143815 of the PD-L1 gene and cooking with solid fuel was 2.365 (95% CI 1.922,2.809), suggesting positive interactions with TB susceptibility. Conclusion The rs4143815 polymorphism of the PD-L1 gene was associated with susceptibility to TB in Chinese Han populations. There were significantly positive interactions between rs4143815 and cooking with solid fuel.

Defect Engineering and Emission Tuning of Wide-Bandgap MAPbCl3 Perovskite.

Lead-chloride perovskites are promising candidates for optoelectronic applications, such as visible-blind UV photodetection. It remains unclear how the deep defects in this wide-bandgap material impact the carrier recombination dynamics. In this work, we study the defect properties of MAPbCl3 (MA = CH3NH3) based on photoluminescence (PL) measurements. Our investigations show that apart from the intrinsic emission, four sub-bandgap emissions emerge, which are very likely to originate from the radiative recombination of excitons bound to several intrinsic vacancy and interstitial defects. The intensity of various emission features can be tuned by adjusting the type and ratio of precursors used during synthesis. Our study not only provides important insights into the defect property and carrier recombination mechanism in this class of material but also demonstrates efficient strategies for defect passivation and engineering, paving the way for further development of lead-chloride perovskite-based optoelectronic devices.

An at-leg pellet and associated Penicillium sp. provide multiple protections to mealybugs.

Beneficial fungi are well known for their contribution to insects' adaptation to diverse habitats. However, where insect-associated fungi reside and the underlying mechanisms of insect-fungi interaction are not well understood. Here, we show a pellet-like structure on the legs of mealybugs, a group of economically important insect pests. This at-leg pellet, formed by mealybugs feeding on tomato but not by those on cotton, potato, or eggplant, originates jointly from host secretions and mealybug waxy filaments. A fungal strain, Penicillium citrinum, is present in the pellets and it colonizes honeydew. P. citrinum can inhibit mealybug fungal pathogens and is highly competitive in honeydew. Compounds within the pellets also have inhibitory activity against mealybug pathogens. Further bioassays suggest that at-leg pellets can improve the survival rate of Phenacoccus solenopsis under pathogen pressure, increase their sucking frequency, and decrease the defense response of host plants. Our study presents evidences on how a fungi-associated at-leg pellet provides multiple protections for mealybugs through suppressing pathogens and host defense, providing new insights into complex insect × fungi × plant interactions and their coevolution.

Ficolin-A induces macrophage polarization to a novel pro-inflammatory phenotype distinct from classical M1.

BACKGROUND: Macrophages are key inflammatory immune cells that orchestrate the initiation and progression of autoimmune diseases. The characters of macrophage in diseases are determined by its phenotype in response to the local microenvironment. Ficolins have been confirmed as crucial contributors to autoimmune diseases, with Ficolin-2 being particularly elevated in patients with autoimmune diseases. However, whether Ficolin-A stimulates macrophage polarization is still poorly understood. METHODS: We investigated the transcriptomic expression profile of murine bone marrow-derived macrophages (BMDMs) stimulated with Ficolin-A using RNA-sequencing. To further confirm a distinct phenotype activated by Ficolin-A, quantitative RT-PCR and Luminex assay were performed in this study. Additionally, we assessed the activation of underlying cell signaling pathways triggered by Ficolin-A. Finally, the impact of Ficolin-A on macrophages were investigated in vivo through building Collagen-induced arthritis (CIA) and Dextran Sulfate Sodium Salt (DSS)-induced colitis mouse models with Fcna-/- mice. RESULTS: Ficolin-A activated macrophages into a pro-inflammatory phenotype distinct to LPS-, IFN-γ- and IFN-γ + LPS-induced phenotypes. The transcriptomic profile induced by Ficolin-A was primarily characterized by upregulation of interleukins, chemokines, iNOS, and Arginase 1, along with downregulation of CD86 and CD206, setting it apart from the M1 and M2 phenotypes. The activation effect of Ficolin-A on macrophages deteriorated the symptoms of CIA and DSS mouse models, and the deletion of Fcna significantly alleviated the severity of diseases in mice. CONCLUSION: Our work used transcriptomic analysis by RNA-Seq to investigate the impact of Ficolin-A on macrophage polarization. Our findings demonstrate that Ficolin-A induces a novel pro-inflammatory phenotype distinct to the phenotypes activated by LPS, IFN-γ and IFN-γ + LPS on macrophages.

Triterpenoids with α-glucosidase inhibitory and cytotoxic activities from the fruits of Schisandra chinensis.

An intensive phytochemical investigation into the fruits of Schisandra chinensis afforded 28 triterpenoids incorporating diverse backbones with methyl-migration, ring-expansion and ring-opening features. Among them, ten compounds (1-10) including three likely extracting artefacts (8-10) were described for the first time. Their structures were fully characterized by comprehensive spectroscopic analyses, with the absolute configurations established via electronic circular dichroism and Mosher's NMR techniques. Preliminary biological evaluations revealed that nine isolates showed inhibitory activity against the hyperglycemic target α-glycosidase and 12 compounds exerted cytotoxicity toward three female tumor cell lines (Hela (cervical), MDA-MB231 and MCF-7 (breast)). Compound 6 exhibited the most promising potency on all the three tested cancer cells, and further assessment demonstrated that it could induce significant cell apoptosis and cycle arrest, as well as suppress cell migration, by regulating relevant proteins in MDA-MB231 cells.

Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database.

BACKGROUND: Mitral valve disorder (MVD) stands as the most prevalent valvular heart disease. Presently, a comprehensive clinical index to predict mortality in MVD remains elusive. The aim of our study is to construct and assess a nomogram for predicting the 28-day mortality risk of MVD patients. METHODS: Patients diagnosed with MVD were identified via ICD-9 code from the MIMIC-III database. Independent risk factors were identified utilizing the LASSO method and multivariate logistic regression to construct a nomogram model aimed at predicting the 28-day mortality risk. The nomogram's performance was assessed through various metrics including the area under the curve (AUC), calibration curves, Hosmer-Lemeshow test, integrated discriminant improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). RESULTS: The study encompassed a total of 2771 patients diagnosed with MVD. Logistic regression analysis identified several independent risk factors: age, anion gap, creatinine, glucose, blood urea nitrogen level (BUN), urine output, systolic blood pressure (SBP), respiratory rate, saturation of peripheral oxygen (SpO2), Glasgow Coma Scale score (GCS), and metastatic cancer. These factors were found to independently influence the 28-day mortality risk among patients with MVD. The calibration curve demonstrated adequate calibration of the nomogram. Furthermore, the nomogram exhibited favorable discrimination in both the training and validation cohorts. The calculations of IDI, NRI, and DCA analyses demonstrate that the nomogram model provides a greater net benefit compared to the Simplified Acute Physiology Score II (SAPSII), Acute Physiology Score III (APSIII), and Sequential Organ Failure Assessment (SOFA) scoring systems. CONCLUSION: This study successfully identified independent risk factors for 28-day mortality in patients with MVD. Additionally, a nomogram model was developed to predict mortality, offering potential assistance in enhancing the prognosis for MVD patients. It's helpful in persuading patients to receive early interventional catheterization treatment, for example, transcatheter mitral valve replacement (TMVR), transcatheter mitral valve implantation (TMVI).

NEDD1 overexpression increases cell proliferation, tumor immune escape, and drug resistance in LUAD.

Background: Neural Precursor Cell Expressed Developmentally Down-Regulated Protein 1 (NEDD1) serves as a crucial factor in promoting cellular mitosis by directly facilitating wheel assembly and daughter centriole biogenesis at the lateral site of parent centrioles, ultimately driving centrosome replication. The amplification of centrosomes and the abnormal expression of centrosome-associated proteins contribute to the invasion and metastasis of non-small cell lung cancer cells. However, the specific mechanism by which NEDD1 contributes to the progression of lung adenocarcinoma (LUAD) remains unexplored. Therefore, the objective of this study is to uncover the role played by NEDD1 in LUAD. Methods: To verify the expression of NEDD1 in pan-carcinoma. The feasibility of NEDD1 as a prognostic marker for LUAD in TCGA and GEO databases was verified. Subsequently, Cox proportional hazard regression analysis was used to screen the prognostic factors of LUAD, so as to analyze the correlation between prognostic factors and NEDD1 expression. For another, NEDD1-related genes were screened for pathway enrichment analysis to verify their possible functions. In addition, the expression of NEDD1 in LUAD was verified by qPCR and IHC, then siRNA was used to construct NEDD1-knocked lung cancer cells for subsequent cytobehavioral experiments. Finally, the distribution of NEDD1 in single-cell samples was revealed, and then the correlation between its overexpression and LUAD immune escape and drug resistance was analyzed. Results: LUAD exhibits upregulation of NEDD1, which in turn promotes the proliferation, migration, invasion, and epithelial-mesenchymal transition of lung cancer cells, thereby contributing to a poor prognosis. Furthermore, the overexpression of NEDD1 is closely associated with immune escape and drug resistance in LUAD. Conclusion: NEDD1 serves as a reliable prognostic marker for LUAD, and its upregulation is associated with increased immune escape and drug resistance. Given these findings, NEDD1 holds potential as a novel therapeutic target for the treatment of LUAD.

Preparation of CMC-poly(N-isopropylacrylamide) semi-interpenetrating hydrogel with temperature-sensitivity for water retention.

The CMC-PNIPAM hydrogel with semi-interpenetrating structure and temperature-sensitivity was prepared by in-situ polymerization of N-isopropylacrylamide (NIPAM) in sodium carboxymethylcellulose (CMC) solution at room temperature. The mass ratio of CMC to NIPAM was a key factor influencing the network structure and property of CMC-PNIPAM hydrogel. The low critical phase transition temperature (LCST) of CMC-PNIPAM hydrogels increased from 34.4 °C to 35.8 °C with the mass ratio of CMC to NIPAM rising from 0 to 1.2. The maximum compressive stress of CMC-PNIPAM hydrogel reached to 26.7 kPa and the relaxation elasticity was 52 % at strain of 60 %. The viscoelasticity of CMC-PNIPAM hydrogel was consistent with the generalized Maxwell model. The maximum swelling ratio in deionized water was 170.25 g·g-1 (dried hydrogel) with swelling rate of 2.57 g·g-1·min-1 at 25 °C. CMC-PNIPAM hydrogel hardly absorbed water above LCST, but the swollen hydrogel could release water at the rate of 0.36 g·g-1·min-1 once exceeding LCST. The test of water retention showed that soil mixed with 2 wt% dried CMC-PNIPAM hydrogel could retain 13.08 wt% water after 30 days at 25 °C that was 4.4 times than that of controlled soil without CMC-PNIPAM hydrogel. The semi-interpenetrating CMC-PNIPAM hydrogel showed a potential to conserve water responding to temperature.

Supporting equitable and responsible highway safety improvement funding allocation strategies - Why AI prediction biases matter.

The existing methodologies for allocating highway safety improvement funding closely rely on the utilization of crash prediction models. Specifically, these models produce predictions that estimate future crash hazard levels in different geographical areas, which subsequently support the future funding allocation strategies. In recent years, there is a burgeoning interest in applying artificial intelligence (AI)-based models to perform crash prediction tasks. Despite the remarkable accuracy of these AI-based crash prediction models, they have been observed to yield biased prediction outcomes across areas of different socioeconomic statuses. These biases are primarily attributed to the inherent measurement and representation biases of AI-based prediction models. More precisely, measurement bias arises from the selection of target variables to reflect crash hazard levels, while representation bias results from the issue of imbalanced number of samples representing areas with different socioeconomic statuses within the dataset. Consequently, these biased prediction outcomes have the potential to perpetuate an unfair allocation of funding resources, contributing to worsen social inequality over time. Drawing upon a real-world case study in North Carolina, this study designs an AI-based crash prediction model that utilizes previous sociodemographic and crash-related variables to predict future severe crash rate of each area to reflect the crash hazardous level. By incorporating a fair regression framework, this study endeavors to transform the crash prediction model to become both fair and accurate, aiming to support equitable and responsible safety improvement funding allocation strategies.

Precise Distance Control and Functionality Adjustment of Frustrated Lewis Pairs in Metal-Organic Frameworks.

We report the construction of frustrated Lewis pairs (FLPs) in a metal-organic framework (MOF), where both Lewis acid (LA) and Lewis base (LB) are fixed to the backbone. The anchoring of a tritopic organoboron linker as LA and a monotopic linker as LB to separate metal oxide clusters in a tetrahedron geometry allows for the precise control of distance between them. As the type of monotopic LB linker varies, pyridine, phenol, aniline, and benzyl alcohol, a series of 11 FLPs were constructed to give fixed distances of 7.1, 5.5, 5.4, and 4.8 Å, respectively, revealed by 11B-1H solid-state nuclear magnetic resonance spectroscopy. Keeping LA and LB apart by a fixed distance makes it possible to investigate the electrostatic effect by changing the functional groups in the monotopic LB linker, while the LA counterpart remains unaffected. This approach offers new chemical environments of the active site for FLP-induced catalysis.

Algae-leached DOM inhibits the Hg(II) reduction and uptake by lettuce in aquatic environments under light conditions.

The significant role of aquatic phytoplankton in global primary productivity, accounting for approximately 50 % on an annual basis, has been recognized as a crucial factor in the reduction of Hg(II). In this study, we compared the efficiency of Hg(II) photoreduction mediated by three types of algae leaching dissolved organic matter (DOM) and humic acid (DOM-HA). Especially, we investigated the potential effects of algae-leached DOM on the photoreduction of Hg(II) and its subsequent uptake by lettuce, which serves as an indicator of Hg bioavailability for aquatic plants. The results revealed that under light conditions, the conversion of Hg(II) to Hg(0) mediated by algae-leached DOM and DOM-HA was 6.4-39.9 % higher compared to dark condition. Furthermore, the free radical quenching experiment demonstrated that the reduction of Hg(II) mediated by DOM-HA was higher than algae-leached DOM, mainly due to its ability to generate superoxide anion (O2•-). Moreover, the photoreduction efficiences of Hg(II) mediated by algae-leached DOM were 29-18 % lower compared to DOM-HA. The FT-IR analysis revealed that the production of -SH from algae-leached DOM led to the formation of strong metal-complexes, which restricts the reduction process from Hg(II) to Hg(0). Finally, the hydroponics experiment demonstrated that algae-leached DOM inhibited the bioavailability of Hg(II) to plants more effectively than DOM-HA. Our research emphasizes the significant functional roles and potential mechanisms of algae in reducing Hg levels, thereby influencing the availability of Hg in aquatic ecosystems.

Efficient Charge Transport in Inverted Perovskite Solar Cells via 2D/3D Ferroelectric Heterojunction.

While the 2D/3D heterojunction is an effective method to improve the power conversion efficiency (PCE) of perovskite solar cells (PSCs), carriers are often confined in the quantum wells (QWs) due to the unique structure of 2D perovskite, which makes the charge transport along the out-of-plane direction difficult. Here, a 2D/3D ferroelectric heterojunction formed by 4,4-difluoropiperidine hydrochloride (2FPD) in inverted PSCs is reported. The enriched 2D perovskite (2FPD)2PbI4 layer with n = 1 on the perovskite surface exhibits ferroelectric response and has oriented dipoles along the out-of-plane direction. The ferroelectricity of the oriented dipole layer facilitates the enhancement of the built-in electric field (1.06 V) and the delay of the cooling process of hot carriers, reflected in the high carrier temperature (above 1400 K) and the prolonged photobleach recovery time (139.85 fs, measured at bandgap), improving the out-of-plane conductivity. In addition, the alignment of energy levels is optimized and exciton binding energy (32.8 meV) is reduced by changing the dielectric environment of the surface. Finally, the 2FPD-treated PSCs achieve a PCE of 24.82% (certified: 24.38%) with the synergistic effect of ferroelectricity and defect passivation, while maintaining over 90% of their initial efficiency after 1000 h of maximum power point tracking.

Laser-Induced Graphene-Based Sensors in Health Monitoring: Progress, Sensing Mechanisms, and Applications.

The rising global population and improved living standards have led to an alarming increase in non-communicable diseases, notably cardiovascular and chronic respiratory diseases, posing a severe threat to human health. Wearable sensing devices, utilizing micro-sensing technology for real-time monitoring, have emerged as promising tools for disease prevention. Among various sensing platforms, graphene-based sensors have shown exceptional performance in the field of micro-sensing. Laser-induced graphene (LIG) technology, a cost-effective and facile method for graphene preparation, has gained particular attention. By converting polymer films directly into patterned graphene materials at ambient temperature and pressure, LIG offers a convenient and environmentally friendly alternative to traditional methods, opening up innovative possibilities for electronic device fabrication. Integrating LIG-based sensors into health monitoring systems holds the potential to revolutionize health management. To commemorate the tenth anniversary of the discovery of LIG, this work provides a comprehensive overview of LIG's evolution and the progress of LIG-based sensors. Delving into the diverse sensing mechanisms of LIG-based sensors, recent research advances in the domain of health monitoring are explored. Furthermore, the opportunities and challenges associated with LIG-based sensors in health monitoring are briefly discussed.

Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment.

Background: Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI. Methods: A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages. Results: The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone. Conclusion: Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis.

Genome-wide methylation, transcriptome and characteristic metabolites reveal the balance between diosgenin and brassinosteroids in Dioscorea zingiberensis.

Diosgenin (DG) is a bioactive metabolite isolated from Dioscorea species, renowned for its medicinal properties. Brassinosteroids (BRs) are a class of crucial plant steroidal hormones. Cholesterol and campesterol are important intermediates of DG and BR biosynthesis, respectively. DG and BRs are structurally similar components; however, the regulatory network and metabolic interplays have not been fully elucidated. In an effort to decode these complex networks, we conducted a comprehensive study integrating genome-wide methylation, transcriptome and characteristic metabolite data from Dioscorea zingiberensis. Leveraging these data, we were able to construct a comprehensive regulatory network linking DG and BRs. Mass spectrometry results enabled us to clarify the alterations in cholesterol, campesterol, diosgenin, and castasterone (one of the major active BRs). The DG content decreased by 27.72% at 6 h after brassinolide treatment, whereas the content increased by 85.34% at 6 h after brassinazole treatment. Moreover, we pinpointed DG/BR-related genes, such as CASs, CYP90s, and B3-ARFs, implicated in the metabolic pathways of DG and BRs. Moreover, CASs and CYP90s exhibit hypomethylation, which is closely related to their high transcription. These findings provide robust evidence for the homeostasis between DG and BRs. In conclusion, our research revealed the existence of a balance between DG and BRs in D. zingiberensis. Furthermore, our work not only provides new insights into the relationship between the two pathways but also offers a fresh perspective on the functions of secondary metabolites.