Search

Wind farm clusters can enhance their operational profitability while supporting grid frequency regulation by participating in frequency regulation markets through the leasing of energy storage. To achieve this， a game-theoretic optimization bidding model is proposed for leasing energy storage of wind farm clusters， considering frequency regulation performance indicator assessment. The model consists of two layers： the upper layer models the bidding process in the frequency regulation market with multiple participants， while the lower layer models a leader-follower game between energy storage lessors and wind farm clusters regarding leasing price and capacity. In this game， the energy storage operator acts as the leader， setting the leasing price in response to the leasing plan of wind farm clusters， and the wind farm clusters， as the follower， adjust the leasing plan according to the price. Additionally， an evolutionary threshold public goods game model is embedded within the wind farm clusters to address the cooperation dilemma caused by selfish behavior among individual members. By united solution of the model， the bidding strategy for leasing energy storage capacity and participating in the frequency regulation market is obtained. The case study results demonstrate that the proposed strategy effectively resolves the cooperation dilemma and enhances the overall profitability of the wind farm clusters.

Abstract Purpose To develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images. Methods This is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility. Results The T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram. Conclusion A nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.

Abstract Background Radiation-induced lung injury (RILI) is associated with alveolar epithelial cell death and secondary fibrosis in injured lung. Mesenchymal stem cell (MSC)-derived exosomes have regenerative effect against lung injury and the potential to intervene of RILI. However, their intervention efficacy is limited because they lack lung targeting characters and do not carry sufficient specific effectors. SARS-CoV-2 spike glycoprotein (SARS-CoV-2-S-RBD) binds angiotensin-converting enzyme 2 (ACE2) receptor and mediates interaction with host cells. MiR-486-5p is a multifunctional miRNA with angiogenic and antifibrotic potential and acts as an effector in MSC-derived exosomes. Ferroptosis is a form of cell death associated with radiation injury, its roles and mechanisms in RILI remain unclear. In this study, we developed an engineered MSC-derived exosomes with SARS-CoV-2-S-RBD- and miR-486-5p- modification and investigated their intervention effects on RIPF and action mechanisms via suppression of epithelial cell ferroptosis. Results Adenovirus-mediated gene modification led to miR-486-5p overexpression in human umbilical cord MSC exosomes (p

Abstract Background Castor is an important industrial raw material. Drought-induced oxidative stress leads to slow growth and decreased yields in castor. However, the mechanisms of drought-induced oxidative stress in castor remain unclear. Therefore, in this study, physiological, biochemical, and RNA-seq analyses were conducted on the roots of castor plants under PEG-6000 stress for 3 d and 7 d followed by 4 d of hydration. Results The photosynthetic rate of castor leaves was inhibited under PEG-6000 stress for 3 and 7 d. Biochemical analysis of castor roots stressed for 3 d and 7 d, and rehydrated for 4 d revealed that the activities of APX and CAT were highest after only 3 d of stress, whereas the activities of POD, GR, and SOD peaked after 7 d of stress. RNA-seq analysis revealed 2926, 1507, and 111 differentially expressed genes (DEGs) in the roots of castor plants under PEG-6000 stress for 3 d and 7 d and after 4 d of rehydration, respectively. GO analysis of the DEGs indicated significant enrichment in antioxidant activity. Furthermore, KEGG enrichment analysis of the DEGs revealed significantly enriched metabolic pathways, including glutathione metabolism, fatty acid metabolism, and plant hormone signal transduction. WGCNA identified the core genes PP2C39 and GA2ox4 in the navajowhite1 module, which was upregulated under PEG-6000 stress. On the basis of these results, we propose a model for the response to drought-induced oxidative stress in castor. Conclusions This study provides valuable antioxidant gene resources, deepening our understanding of antioxidant regulation and paving the way for further molecular breeding of castor plants.

Nitrile rubber latex was prepared by conventional emulsion polymerization combined with low-temperature batch polymerization process, the latex was hydrogenated with self-made ruthenium catalyst, and the influence of catalyst amount and hydrogen pressure on the hydrogenation effect of nitrile rubber latex was systematically studied. The results showed that the average particle size of the prepared nitrile rubber latex was 69.13 nm, and the particle size of the latex remained basically unchanged and the latex remained stable before and after hydrogenation. The higher the hydrogen pressure was, the better the catalytic hydrogenation effect was. When the mass fraction of catalyst was 0.017 5%, degree of hydrogenation of the product was more than 99% and no gel was obtained.

Abstract Objectives Post-myocardial infarction ventricular septal rupture (PIVSR) is one of the most severe types of mechanical complications after acute myocardial infarction (AMI) with high mortality and poor prognosis. The risk factors for short-term mortality of patients with PIVSR may be not widely recognized. We aimed to assess the prevalence and short-term mortality risk predictors of PIVSR. Methods A total of 62 patients with a diagnosis of PIVSR were admitted to three top general public hospitals in Chongqing, China. Clinical characteristics and short-term outcomes of patients with PIVSR were compared. Predictors of PIVSR were assessed using logistic regression analysis. Results Mean age was 70.7 ± 10.7 years (38.7% female). The overall in-hospital mortality of PIVSR remained high (71%). Most (47/62) of the patients were in Killip class III or IV at the time of rupture diagnosis. Logistic regression analysis revealed that white blood cell count (WBC, OR 1.619, 95% CI 1.172–2.237, P = 0.005), cardiogenic shock (OR 47.706, 95%CI 2.859-795.945, P = 0.007) and left ventricular ejection fraction (LVEF, OR 0.803, 95%CI 0.689–0.936, P = 0.009) were independent risk factors of in-hospital early mortality. The nomogram developed for predicting the risk of short-term mortality showed a robust discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.912-1.000). Conclusion The short-term mortality of PIVSR remained high. WBC, cardiogenic shock, and LVEF were the independent predictive factors of short-term mortality. Our nomogram might be used to predict early mortality of patients with PIVSR.

Abstract Background Maternal lipid metabolism has been implicated in elevating the risk of adverse pregnancy outcomes, which is a particularly significant concern in twin pregnancies. However, the precise relationship between early pregnancy dyslipidemia and the risk of preterm birth (PTB) in twin pregnancies remains unclear. Methods This retrospective cohort study included women with twin pregnancies between January 2018 and December 2023. Early pregnancy blood lipid profiles, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C), were examined. Dyslipidemia was diagnosed based on the diagnostic criteria outlined in the 2016 guidelines for the prevention and treatment of dyslipidemia in Chinese adults. PTB was defined as birth occurring before 37 weeks of pregnancy. Logistic regression models were used to evaluate the association of early pregnancy dyslipidemia with PTB in twin pregnancies. Results A total of 613 women with twin pregnancies were included, and 141 women were complicated with dyslipidemia. The incidence of PTB

Abstract Soil organic carbon (SOC) plays a vital role in maintaining or enhancing soil fertility and quality of paddy field, but there is still limited information about how SOC mineralization responds to different tillage managements under the double-cropping rice (Oryza sativa L.) system in southern of China. Therefore, this study was designed to explore the changes in SOC content, soil enzyme activities (invertase, cellulose and urease), SOC mineralization at 0–10 cm and 10–20 cm layers and its relationship with 7-years tillage management under the double-cropping rice system of southern China. The experiment included four tillage managements: rotary tillage with all residues removed as a control (RTO), conventional tillage with residue incorporation (CT), rotary tillage with residue incorporation (RT), and no-tillage with residue retention (NT). The results indicated that SOC and soil labile organic carbon contents at 0–10 cm and 10–20 cm layers in paddy field with CT and RT treatments were significantly higher than the RTO treatment. Compared to the RTO treatment, SOC mineralization and accumulation at 0–10 cm and 10–20 cm layers in paddy field with CT, RT and NT treatments were increased. SOC accumulation and potential mineralization at 0–10 cm layer with NT treatment were significantly higher than the CT, RT and RTO treatments. Soil mineralization constant at 10–20 cm layer with CT treatment was significantly higher than those of RT, NT and RTO treatments. This result indicated SOC mineralization rate and accumulation at 10–20 cm layer of CT, RT, NT and RTO treatments were lower than those of treatments at 0–10 cm layer. Compared to RTO treatment, soil invertase, cellulose and urease activities with CT and RT treatments were significantly increased. Compared to RTO treatment, soil invertase, cellulose and urease activities at 0–20 cm layer of CT treatment increased by 22.6%, 46.2% and 89.0%, respectively. There was significantly positive correlation between SOC accumulation and SOC content, soil invertase, cellulose, urease activities, but SOC accumulation was significantly negative correlated with soil pH, bulk density. Therefore, CT and RT treatments were beneficial managements to improve SOC content and SOC mineralization in the double-cropping rice field of southern China.

Abstract Background Non-suicidal self-injury (NSSI) is a significant public health concern among adolescents with major depressive disorders (MDD). Although previous research has linked child maltreatment (CM) to NSSI, the precise mechanisms remain unclear. This study aims to investigate the association between CM, cognitive reappraisal (CR), negative coping styles (NC) and NSSI in adolescents with MDD, from the perspectives of both Latent Variable Theory and the Network Theory of Mental Disorder. Methods A sample of 651 adolescents with MDD was recruited from January to December 2023. Data on CM, CR, NC, and NSSI were collected through paper-based self-reported questionnaires. Data analysis primarily involved structural equation modeling and network analysis. Results The reporting rate of NSSI among adolescents with MDD was 48.2%. CM showed a significant positive correlation with NSSI. NSSI was affected by CM through three paths: the mediating role of CR, the mediating role of NC, and the chain mediating role of both CR and NC. Emotional abuse (EA) was the central node, while NSSI, EA, and “The urge to cry quietly when faced with troubles“(NC10) were the key bridge nodes. Conclusions This study is the first to use both structural equation modeling and network analysis to explore the explore the relationship between CM, CR, NC, and NSSI in adolescents with MDD, providing a theoretical basis for future early prevention and targeted interventions for adolescents with MDD.

C17H12FN3O, orthorhombic, Pbca (no. 61), a = 14.7608(7) Å, b = 7.2802(4) Å, c = 24.4842(13) Å, V = 2,631.1(2) Å3, Z = 8, Rgt(F) = 0.0900, wRref(F2) = 0.1841, T = 170 K.

Abstract Background Collecting duct carcinoma (CDC) is a rare histological type of renal cell carcinoma that lacks a prognostic prediction model. In this study, we developed a nomogram to predict the prognosis of CDC patients. Methods Data for patients (n = 247) diagnosed with CDC from 2004 to 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and the patients were randomized into training (n = 165) and validation (n = 82) cohorts. Survival outcomes were evaluated by the Kaplan–Meier method. Significant variables determined by univariate and multivariate Cox regression analyses were used to construct the nomogram. C-indexes and calibration plots were applied to evaluate the performance of the nomogram. Results CDC patients had a median overall survival (OS) of 18.0 months (95% confidence interval: 13.7–22.3); 1-year, 3-year, and 5-year OS rates were 58.7%, 34.2%, and 29.4%, respectively. Independent prognostic factors, including age at diagnosis, tumor size, tumor grade, T stage, N stage, M stage, and surgery information, were identified by multivariate analysis. The nomogram was constructed based on significant factors in the training cohort. The C-indexes were 0.769 (training cohort) and 0.767 (validation cohort). The calibration curves for survival rates showed that the predicted and observed values were consistent. Conclusions This study constructed a nomogram to predict prognosis in patients with CDC. The nomogram performed well in predicting the 1-year, 3-year, and 5-year OS, which can help doctors actively monitor and follow up patients.

Abstract DNA nanostructures have long been developed for biomedical purposes, but their controlled delivery in vivo proposes a major challenge for disease theranostics. We previously reported that DNA nanostructures on the scales of tens and hundreds nanometers showed preferential renal excretion or kidney retention, allowing for sensitive evaluation and effective protection of kidney function, in response to events such as unilateral ureter obstruction or acute kidney injury. Encouraged by the positive results, we redirected our focus to the liver, specifically targeting organs noticeably lacking DNA materials, to explore the interaction between DNA nanostructures and the liver. Through PET imaging, we identified SDF and M13 as DNA nanostructures exhibiting significant accumulation in the liver among numerous candidates. Initially, we investigated and assessed their biodistribution, toxicity, and immunogenicity in healthy mice, establishing the structure-function relationship of DNA nanostructures in the normal murine. Subsequently, we employed a mouse model of liver ischemia-reperfusion injury (IRI) to validate the nano-bio interactions of SDF and M13 under more challenging pathological conditions. M13 not only exacerbated hepatic oxidative injury but also elevated local apoptosis levels. In contrast, SDF demonstrated remarkable ability to scavenge oxidative responses in the liver, thereby mitigating hepatocyte injury. These compelling results underscore the potential of SDF as a promising therapeutic agent for liver-related conditions. This aimed to elucidate their roles and mechanisms in liver injury, providing a new perspective for the biomedical applications of DNA nanostructures. Graphical Abstract Spherical DNA framework showed predominant liver retention to rescue hepatic 870 ischemia reperfusion injury

ObjectiveTo explore the association between violent behaviors and emotions in individuals with mental disorders, to evaluate the application value of facial expression analysis technology in violence risk assessment of individuals with mental disorders in supervised settings, and to provide a reference for violence risk assessment.MethodsThirty-nine male individuals with mental disorders in supervised settings were selected, the participant risk of violence, cognitive function, psychiatric symptoms and severity were assessed using the Modified Overt Aggression Scale （MOAS）, the Historical, Clinical, Risk Management-Chinese version（HCR-CV）, the Positive and Negative Syndrome Scale （PANSS） and the Brief Psychiatric Rating Scale （BPRS）. An emotional arousal was performed on the participants and the intensity of their emotions and facial expression action units was recorded before, during and after the arousal. One-way analysis of variance （ANOVA） was used to compare the differences in the intensity of emotions and facial expression action units before, during and after the arousal. Pearson correlation analysis was used to calculate the correlations between the intensity of the seven basic emotional facial expressions and the scores of the assessment scales.ResultsThe intensity difference of sadness, surprise and fear in different time periods was statistically significant （P

Objectives: This study addresses the critical issue of Cd contamination in agricultural soils, posing substantial risks to crop productivity and food safety. While prior pot experiment has undertook this issue on a small scale, this study aims to evaluate the efficacy of selected best soil amendments, at a large-scale field experiment. Methodology: Press mud and humic acid were applied at 0.5%, while gypsum and Fe2O3 were applied at 5 mg/kg alone and with foliar application of Fe nanoparticles at 5 mg/L. Analysis: Comparative analysis with control revealed the immobilization efficiency of all amendments in descending order of effectiveness as follows: 100, 102, 104, 104, 105, 102, 105, and 105% for PM, HA, GYP, Fe, PM + Fe Nps, HA + Fe Nps, GYP + Fe Nps, and Fe + Fe Nps. Additionally, reduced growth, photosynthetic activities, and elevated levels of malondialdehyde and hydrogen peroxide, indicative of oxidative damage in control plant. Findings: Application of these amendments with foliar spraying of Fe Nps effectively mitigates Cd toxicity in maize crops, leading to improved growth, biomass, photosynthetic pigments, and antioxidant enzyme activities. Novelty/Improvement: These findings highlight the significance of exploring innovative approach of combining different amendments with foliar application of nanoparticles to mitigate Cd contamination and enhance soil health, thereby contributing to global efforts in ensuring food safety and security.

Abstract Background Traditional fixed thresholds for oral glucose tolerance test (OGTT) results may inadequately prevent adverse pregnancy outcomes in twin pregnancies. This study explores latent OGTT patterns and their association with adverse outcomes. Methods This study retrospectively analyzed 2644 twin pregnancies using latent mixture models to identify glucose level patterns (high, HG; medium, MG; and low, LG) and their relationship with maternal/neonatal characteristics, gestational age at delivery, and adverse outcomes. Results Three distinct glucose patterns, HG, MG, and LG patterns were identified. Among the participants, 16.3% were categorized in the HG pattern. After adjustment, compared with the LG pattern, the HG pattern was associated with a 1.79‐fold, 1.66‐fold, and 1.32‐fold increased risk of stillbirth, neonatal respiratory distress, and neonatal hyperbilirubinemia, respectively. The risk of neonatal ICU admission for MG and HG patterns increased by 1.22 times and 1.32 times, respectively, compared with the LG pattern. As gestational weeks increase, although there is an overlap in the confidence intervals between the HG pattern and other patterns in the restricted cubic splines analysis, the trend suggests that pregnant women with the HG pattern are more likely to face risks of their newborns requiring neonatal intensive care unit admission, and adverse comprehensive outcomes, compared with other patterns. In addition, with age and body mass index increasing in HG mode, gestation weeks at delivery tend to be later than in other modes. Conclusion Distinct OGTT glucose patterns in twin pregnancies correlate with different risks of adverse perinatal outcomes. The HG pattern warrants closer glucose monitoring and targeted intervention.

Abstract Rock‐climbing robots have significant potential in fieldwork and planetary exploration. However, they currently face limitations such as a lack of stability and adaptability on extreme terrains, slow locomotion, and single functionality. This study introduces a novel multimodal and adaptive rock‐climbing robot (MARCBot), which addresses these limitations through spiny grippers that draw inspiration from morpho‐functionalities observed in beetles, arboreal birds, and hoofed animals. This hybrid bioinspired design enables high attachment strength, passive adaptability to different terrains, and quick attachment on rock surfaces. The multimodal functionality of the gripper allows for attachment during climbing and support during walking. A novel control strategy using dynamics and quadratic programming (QP) optimizes attachment wrench distribution, reducing cost‐of‐transport by 20.03% and 6.05% compared to closed‐loop inverse kinematic (CLIK) and virtual model control (VMC) methods, respectively. MARCBot achieved climbing speeds of 0.15 m min−1 on a vertical discrete rock surface under gravity and trotting speeds of up to 0.21 m s−1 on various complex terrains. It is the first robot capable of climbing on rock surfaces and trotting in complex terrains without the need for switching end‐effectors. This study highlights significant advancements in climbing and multimodal locomotion for robots in extreme environments.

BackgroundPatients with unruptured intracranial aneurysms (UIAs) often experience emotional changes and cognitive impairments. However, the specific mechanisms underlying these impairments are still not fully understood.MethodsIn the present study, voxel-based morphometry (VBM) and surface-based morphometry (SBM) were employed to investigate structural alterations in 49 patients diagnosed with UIAs compared with 50 healthy controls. Additionally, this study aimed to analyze the correlations among cortical morphological indices, cerebral blood perfusion values and neuropsychological test results.ResultsCompared with control group, UIA patients exhibited increased gray matter volume in the right anterior orbitofrontal cortex and decreased gray matter volume in the left thalamus pulvinar and hippocampus. Furthermore, the fractal dimension was lower in the right postcentral gyrus and entorhinal cortex. The cerebral perfusion values in the abnormal brain regions demonstrated a downward trend, which was associated with a reduction in gray matter volume in the left thalamus pulvinar and hippocampus, elevated anxiety levels and impaired executive function.ConclusionUIA patients are prone to cognitive impairment and emotional dysregulation, which are accompanied by subtle changes in local gray matter volume and decreases in fractal dimension and cerebral blood flow. These findings provide new insights into the potential mechanisms underlying the cognitive impairment observed in UIA patients.

Background: diabetices foot ulcer (DFU) are serious complications. It is crucial to detect and diagnose DFU early in order to provide timely treatment, improve patient quality of life, and avoid the social and economic consequences. Machine learning techniques can help identify risk factors associated with DFU development. Objective: The aim of this study was to establish correlations between clinical and biochemical risk factors of DFU through local foot examinations based on the construction of predictive models using automated machine learning techniques. Methods: The input dataset consisted of 566 diabetes cases and 50 DFU risk factors, including 9 local foot examinations. 340 patients with Class 0 labeling (low-risk DFU), 226 patients with Class 1 labeling (high-risk DFU). To divide the training group (consisting of 453 cases) and the validation group (consisting of 113 cases), as well as preprocess the data and develop a prediction model, a Monte Carlo cross-validation approach was employed. Furthermore, potential high-risk factors were analyzed using various algorithms, including Bayesian BYS, Multi-Gaussian Weighted Classifier (MGWC), Support Vector Machine (SVM), and Random Forest Classifier (RF). A three-layer machine learning training was constructed, and model performance was estimated using a Confusion Matrix. The top 30 ranking feature variables were ultimately determined. To reinforce the robustness and generalizability of the predictive model, an independent dataset comprising 248 cases was employed for external validation. This validation process evaluated the model's applicability and reliability across diverse populations and clinical settings. Importantly, the external dataset required no additional tuning or adjustment of parameters, enabling an unbiased assessment of the model's generalizability and its capacity to predict the risk of DFU. Results: The ensemble learning method outperformed individual classifiers in various performance evaluation metrics. Based on the ROC analysis, the AUC of the AutoML model for assessing diabetic foot risk was 88.48 % (74.44–97.83 %). Other results were found to be as follows: 87.23 % (63.33 %–100.00 %) for sensitivity, 87.43 % (70.00 %–100.00 %) for specificity, 87.33 % (76.66 %–95.00 %) for accuracy, 87.69 % (75.00 %–100.00 %) for positive predictive value, and 87.70 % (71.79 %–100.00 %) for negative predictive value. In addition to traditional DFU risk factors such as cardiovascular disorders, peripheral artery disease, and neurological damage, we identified new risk factors such as lower limb varicose veins, history of cerebral infarction, blood urea nitrogen, GFR (Glomerular Filtration Rate), and type of diabetes that may be related to the development of DFU. In the external validation set of 158 samples, originating from an initial 248 with exclusions due to missing labels or features, the model still exhibited strong predictive accuracy. The AUC score of 0.762 indicated a strong discriminatory capability of the model. Furthermore, the Sensitivity and Specificity values provided insights into the model's ability to correctly identify both DFU cases and non-cases, respectively. Conclusion: The predictive model, developed through AutoML and grounded in local foot examinations, has proven to be a robust and practical instrument for the screening, prediction, and diagnosis of DFU risk. This model not only aids medical practitioners in the identification of potential DFU cases but also plays a pivotal role in mitigating the progression towards adverse outcomes. And the recent successful external validation of our DFU risk prediction model marks a crucial advancement, indicating its readiness for clinical application. This validation reinforces the model's efficacy as an accessible and reliable tool for early DFU risk assessment, thereby facilitating prompt intervention strategies and enhancing overall patient outcomes.

Background Microangiogenesis and lymphangiogenesis are essential for tumor growth in the tumor microenvironment, contributing to tumor invasion and metastasis. Limited literature exists on these processes in esophageal squamous cell carcinoma (ESCC). Therefore, the purpose of this study is to explore the impacts of microangiogenesis and lymphangiogenesis on the occurrence, progression, and prognosis assessment of ESCC. Methods Surgical specimens and paraffin-embedded human tissues were procured from ESCC patients, encompassing 100 ESCC tissues and 100 cancer-adjacent normal (CAN) tissues. CD34 and D2-40 were utilized as markers for microvessel endothelial cells and lymphatic vessel endothelial cells, respectively. Microvascular density (MVD) and lymphatic vessel density (LVD) were evaluated through immunohistochemical quantification. Results We found that tumor tissues in ESCC patients had significantly higher MVD and LVD than cancer-adjacent normal (CAN) tissues. High MVD and LVD were associated with lymph node metastasis and advanced tumor clinical stages. Additionally, both high MVD and high LVD were strongly linked to poorer prognosis among cancer patients. Furthermore, a positive correlation was found between high MVD and high LVD (p < 0.05). The presence of these markers individually indicated a worse prognosis, with their combined assessment showcasing enhanced prognostic value. Conclusions Overall, the increased MVD and LVD indicates higher invasion and metastasis of ESCC, closely correlating with unfavorablefor poor prognosis of ESCC patients.

IntroductionIn this study, we report a novel therapeutic approach redirecting antigen-specific CD4+ T cells recognizing a hybrid insulin peptide (BDC2.5 T cell receptor (TCR) transgenic CD4+ T cells) to attract and suppress islet-specific CD8+ T cells T cells in the non-obese diabetic (NOD) mouse model, and prevent the development of autoimmune diabetes.MethodsPurified BDC2.5 CD4+ T cells were induced to differentiate into regulatory T cells (Tregs). The Tregs were then electroporated with mRNA encoding chimeric human β2 microglobulin (hβ2m) covalently linked to insulin B chain amino acids 15-23 (designated INS-eTreg) or islet-specific glucose-6-phosphatase related protein (IGRP) peptide 206-214 (designated IGRP-eTreg). Immunoregulatory functions of these engineered regulatory T cells (eTregs) were tested by in vitro assays and in vivo co-transfer experiments with β-cell-antigen-specific CD8+ T cells in NOD.Scid mice or by adoptive transfer into young, pre-diabetic NOD mice.ResultsThese eTregs were phenotyped by flow cytometry, and shown to have high expression of FoxP3, as well as other markers of Treg function, including IL-10. They suppressed polyclonal CD4+ T cells and antigen-specific CD8+ T cells (recognizing insulin or IGRP), decreasing proliferation and increasing exhaustion and regulatory markers in vitro. In vivo, eTregs reduced diabetes development in co-transfer experiments with pathogenic antigen-specific CD8+ T cells (INS-CD8+ or IGRP-CD8+ cells) into NOD.Scid mice. Finally, when the eTreg were injected into young NOD mice, they reduced insulitis and prevented spontaneous diabetes in the recipient mice.ConclusionOur results suggest a novel therapeutic strategy to protect NOD mice by targeting antigen-specific cytotoxic CD8+ T cells, using redirected antigen-specific CD4+ Treg cells, to suppress autoimmune diabetes. This may suggest an innovative therapy for protection of people at risk of development of type 1 diabetes.

We theoretically investigate Goos-Hänchen effect for electron in single-layered semiconductor microstructure (SLSM) modulated by Rashba spin–orbit coupling (SOC). Due to the SOC effect, GH displacement is obviously dependent on spins, which allows electron spins to be separated in space dimension and results in spin polarization of electrons in semiconductors. Spin polarization ratio is associated with incident energy, incident direction and in-plane wave vector, e.g., it reaches maximum at resonance, but no spin polarization effect appears at normal incidence. In particular, both magnitude and sign of spin polarization ratio are controlled by external electric field or semiconductor-layer thickness, therefore, a manipulable spatial electron-spin splitter is obtained for semiconductor spintronics device applications.

C21H20Cl2N2O2, orthorhombic, P21 (no. 4), a = 8.3667(14) Å, b = 15.444(3) Å, c = 15.105(2) Å, V = 1951.9(6) Å3, Z = 4, Rgt (F) = 0.0481, wRref (F 2) = 0.1574, T = 292 K.

C21H25N3O2, triclinic, P1̄ (no. 2), a = 7.6536(6) Å, b = 11.027(1) Å, c = 12.6899(11) Å, α = 66.863(2)°, β = 80.598(2)°, γ = 71.772(2)°, V = 934.38(14) Å3, Z = 2, R gt(F) = 0.0679, wR ref(F 2) = 0.1150, T = 296 K.

Abstract Toll-like receptor 9 (TLR9) recognizes bacterial, viral and self DNA and play an important role in immunity and inflammation. However, the role of TLR9 in obesity is less well-studied. Here, we generate B-cell-specific Tlr9-deficient (Tlr9 fl/fl /Cd19Cre +/- , KO) B6 mice and model obesity using a high-fat diet. Compared with control mice, B-cell-specific-Tlr9-deficient mice exhibited increased fat tissue inflammation, weight gain, and impaired glucose and insulin tolerance. Furthermore, the frequencies of IL-10-producing-B cells and marginal zone B cells were reduced, and those of follicular and germinal center B cells were increased. This was associated with increased frequencies of IFNγ-producing-T cells and increased follicular helper cells. In addition, gut microbiota from the KO mice induced a pro-inflammatory state leading to immunological and metabolic dysregulation when transferred to germ-free mice. Using 16 S rRNA gene sequencing, we identify altered gut microbial communities including reduced Lachnospiraceae, which may play a role in altered metabolism in KO mice. We identify an important network involving Tlr9, Irf4 and Il-10 interconnecting metabolic homeostasis, with the function of B and T cells, and gut microbiota in obesity.

Abstract Background Gestational diabetes mellitus (GDM) is a common complication of pregnancy, with significant short-term and long-term implications for both mothers and their offspring. Previous studies have indicated the potential benefits of vitamin D in reducing the risk of GDM, yet little is known about this association in twin pregnancies. This study aimed to investigate maternal vitamin D status in the second trimester and examine its association with the risk of GDM in twin pregnancies. Methods We conducted a prospective cohort study based on data from the Chongqing Longitudinal Twin Study (LoTiS). Peripheral blood serum was collected from the mothers in the second trimester to measure 25(OH)D concentrations. GDM was diagnosed at 23–26 weeks of gestation using a 75-g 2-h oral glucose tolerance test. We used multivariable logistic regression analyses to examine the correlations between vitamin D status and the risk of GDM. Results Of the total participants, 93 (29.9%) women were diagnosed with GDM. The mean serum 25(OH)D concentration in the second trimester was 31.1 ± 11.2 ng/mL, and the rate of vitamin D insufficiency and deficiency were 23.5% and 18.7%, respectively. Compared to women with a 25(OH)D concentration

Considering both Zeeman effect and spin–orbit coupling, we calculate dwell time for electron in single ferromagnetic-stripe device (SFSD), which can be constructed by patterning a nanosized ferromagnetic stripe on the surface of GaAs/AlxGa1-xAs heterostructure. Due to an intrinsic symmetry in the SFSD, dwell time is independent of electron spins, if only Zeeman effect is involved. However, the intrinsic symmetry is broken by spin–orbit coupling, which gives rise to spin-dependent dwell time for electron in the SFSD. As a result, electron spins can be separated in time dimension, which induces an obvious electron-spin polarization effect in the SFSD. Spin polarization ratio can be efficaciously modified by interfacial confining electric-field or strain engineering, which attributes to the dependence of effective potential felt by electron in the SFSD on spin–orbit couplings. Thus, the SFSD can act as a controllable temporal electron-spin splitter, a class of electron-spin polarized sources in semiconductor spintronics.

Scaly-sided Merganser is an endangered species confined to eastern Asia, primarily breeding in southeast Russia and northeast China. Its breeding range is quite large, with limited information on population dynamics. The Kievka River catchment in far east Russia is one of the most densely populated breeding grounds, providing the only extensive, long-term monitoring on breeding Scaly-sided Merganser. This study assessed the current status of Scaly-sided Merganser and investigated the environmental factors influencing the population. From 2014–2021, the population size at each river reach fluctuated with no directional trends, while the density of Scaly-sided Merganser increased. The result suggested a higher concentration of breeding Scaly-sided Merganser in Kievka River catchment in recent years, particularly preferring downstream area. The species also demonstrated an affinity to less riparian vegetation and higher flow rates. Notably, river flow rate emerges as a potential key issue for designing environmental flows of the breeding habitat for Scaly-sided Merganser, presenting a challenge for researchers and managers.

ABSTRACT Cholestasis is a common morbid state that may occur in different phases; however, a comprehensive evaluation of the long-term effect post-recovery is still lacking. In the hepatic cholestasis mouse model, which was induced by a temporary complete blockage of the bile duct, the stasis of bile acids and liver damage typically recovered within a short period. However, we found that the temporary hepatic cholestasis had a long-term effect on gut microbiota dysbiosis, including overgrowth of small intestinal bacteria, decreased diversity of the gut microbiota, and an overall imbalance in its composition accompanied by an elevated inflammation level. Additionally, we observed an increase in Escherichia-Shigella (represented by ASV136078), rich in virulence factors, in both small and large intestines following cholestasis. To confirm the causal role of dysregulated gut microbiota in promoting hepatic inflammation and injury, we conducted gut microbiota transplantation into germ-free mice. We found that recipient mice transplanted with feces from cholestasis mice exhibited liver inflammation, damage, and accumulation of hepatic bile acids. In conclusion, our study demonstrates that cholestasis disrupts the overall load and structural composition of the gut microbiota in mice, and these adverse effects persist after recovery from cholestatic liver injury. This finding suggests the importance of monitoring the structural composition of the gut microbiota in patients with cholestasis and during their recovery.IMPORTANCEOur pre-clinical study using a mouse model of cholestasis underscores that cholestasis not only disrupts the equilibrium and structural configuration of the gut microbiota but also emphasizes the persistence of these adverse effects even after bile stasis restoration. This suggests the need of monitoring and initiating interventions for gut microbiota structural restoration in patients with cholestasis during and after recovery. We believe that our study contributes to novel and better understanding of the intricate interplay among bile acid homeostasis, gut microbiota, and cholestasis-associated complications. Our pre-clinical findings may provide implications for the clinical management of patients with cholestasis.

BackgroundIt is vital to accurately and promptly distinguish unstable from stable intracranial aneurysms (IAs) to facilitate treatment optimization and avoid unnecessary treatment. The aim of this study is to develop a simple and effective predictive model for the clinical evaluation of the stability of IAs.MethodsIn total, 1,053 patients with 1,239 IAs were randomly divided the dataset into training (70%) and internal validation (30%) datasets. One hundred and ninety seven patients with 229 IAs from another hospital were evaluated as an external validation dataset. The prediction models were developed using machine learning based on clinical information, manual parameters, and radiomic features. In addition, a simple model for predicting the stability of IAs was developed, and a nomogram was drawn for clinical use.ResultsFourteen machine learning models exhibited excellent classification performance. Logistic regression Model E (clinical information, manual parameters, and radiomic shape features) had the highest AUC of 0.963 (95% CI 0.943–0.980). Compared to manual parameters, radiomic features did not significantly improve the identification of unstable IAs. In the external validation dataset, the simplified model demonstrated excellent performance (AUC = 0.950) using only five manual parameters.ConclusionMachine learning models have excellent potential in the classification of unstable IAs. The manual parameters from CTA images are sufficient for developing a simple and effective model for identifying unstable IAs.

IntroductionType 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of insulin-producing β cells. Toll-like receptor 9 (TLR9) plays a role in autoimmune diseases, and B cell-specific TLR9 deficiency delays T1D development. Gut microbiota are implicated in T1D, although the relationship is complex. However, the impact of B cell-specific deficiency of TLR9 on intestinal microbiota and the impact of altered intestinal microbiota on the development of T1D are unclear.ObjectivesThis study investigated how gut microbiota and the intestinal barrier contribute to T1D development in B cell-specific TLR9-deficient NOD mice. Additionally, this study explored the role of microbiota in immune regulation and T1D onset.MethodsThe study assessed gut permeability, gene expression related to gut barrier integrity, and gut microbiota composition. Antibiotics depleted gut microbiota, and fecal samples were transferred to germ-free mice. The study also examined IL-10 production, Breg cell differentiation, and their impact on T1D development.ResultsB cell-specific TLR9-deficient NOD mice exhibited increased gut permeability and downregulated gut barrier-related gene expression. Antibiotics restored gut permeability, suggesting microbiota influence. Altered microbiota were enriched in Lachnospiraceae, known for mucin degradation. Transferring this microbiota to germ-free mice increased gut permeability and promoted IL-10-expressing Breg cells. Rag-/- mice transplanted with fecal samples from Tlr9fl/flCd19-Cre+ mice showed delayed diabetes onset, indicating microbiota’s impact.ConclusionB cell-specific TLR9 deficiency alters gut microbiota, increasing gut permeability and promoting IL-10-expressing Breg cells, which delay T1D. This study uncovers a link between TLR9, gut microbiota, and immune regulation in T1D, with implications for microbiota-targeted T1D therapies.

Fixed wing UAV takes off and lands on the ground with high requirements，which limits the application and development of fixed wing aircraft. Therefore，a scheme of four-rocket boosting drone to realize short-range takeoff and landing process is proposed，and the takeoff and landing control algorithm of rocket boosting drone is studied. In the process of research，the control strategy of rocket boosting drone takeoff and landing process is designed；The mathematical models of UAV aerodynamic force，booster rocket force，engine thrust and control law are established；The dynamic equation of the takeoff and landing process of UAV is constructed；A control algorithm based on PID control is designed to control the target UAV to complete the take-off and landing process safely and stably under various interference environments. MATLAB software is used for programming and simulation calculation. The simulation results show that the control algorithm and control law designed meet the control requirements of the target UAV during takeoff and landing.

Abstract Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.

The field of biomedical radar has witnessed significant advancements in recent years, paving the way for innovative and transformative applications in clinical settings. Most medical instruments invented to measure human activities rely on contact electrodes, causing discomfort. Thanks to its non-invasive nature, biomedical radar is particularly valuable for clinical applications. A significant portion of the review discusses improvements in radar hardware, with a focus on miniaturization, increased resolution, and enhanced sensitivity. Then, this paper also delves into the signal processing and machine learning techniques tailored for radar data. This review will explore the recent breakthroughs and applications of biomedical radar technology, shedding light on its transformative potential in shaping the future of clinical diagnostics, patient and elderly care, and healthcare innovation.

As a non-destructive and rapid technique, optical scatterometry has gained widespread use in the measurement of film thickness and optical constants. The recent advances in deep learning have presented new and powerful approaches to the resolution of inverse scattering problems. However, the application of deep-neural-network-assisted optical scatterometry for nanostructures still faces significant challenges, including poor stability, limited functionalities, and high equipment requirements. In this paper, a novel characterization method is proposed, which employs deep-neural-network-assisted ellipsometry to address these challenges. The method processes ellipsometric angles, which are measured by basic ellipsometers, as functional signals. A comprehensive model is developed to profile nano-gratings fabricated by diverse techniques, by incorporating rounded corners, residual layers, and optical constants into an existing model. The stability of the model is enhanced by implementing several measures, including multiple sets of initial values and azimuth-resolved measurements. A simple compensation algorithm is also introduced to improve accuracy without compromising efficiency. Experimental results demonstrate that the proposed method can rapidly and accurately characterize nano-gratings fabricated by various methods, with relative errors of both geometric and optical parameters well controlled under 5 %. Thus, the method holds great promise to serve as an alternative to conventional characterization techniques for in-situ measurement.

The effects of different planting densities on the particle size distribution and pasting properties of starch granules in maize grains along the Huaihe River were analyzed. Taking 13 maize hybrids such as Yudan 132 and Jingke 968 as materials, three planting densities of 56 250 plants/hm2, 67 500 plants/hm2 and 77 250 plants/hm2 were set up. The effects of different planting densities on grain quality, starch particle size distribution and gelatinization characteristics of maize in the region along the Huaihe River were analyzed. The results indicated that the protein and fat content of maize grain decreased with the increase of planting density in the range of 56 250 ~ 77 250 plants/hm2. The starch content showed an increasing trend, which means, the starch/protein ratio increased. The volume and surface area percentage of large starch granules in maize grains increased significantly, while the small starch granules decreased significantly. At the same time, it was found that the peak viscosity, trough viscosity, final viscosity and breakdown value of corn starch increased significantly after rising planting density. Relevant analysis showed that the viscosity parameters such as peak viscosity of corn kernels were positively correlated with the volume percentage of small and large starch granules. There was a significant or extremely significant negative correlation with the volume percentage of medium starch granules. It shows that planting density affects the starch particle size distribution starch viscosity parameters and chemical components of corn kernels.

The fungal diversity of five sweet fermented oats samples in China was studied by Illumina sequencing. The results showed that the abundance of Mucoromycota and Ascomycota peaked in the oats Tianpei samples, and a bit Basidiomycota were also included. As to genus, Rhizopus had the highest abundance, followed by unclassified_p_Ascomycota, Saccharomyces and Issatchenkia. Principal component analysis (PCA) indicated that five kinds of oats Tianpei samples formed their own fungal clusters. The biomarker taxa of oats Tianpei were identified by Lefse, including Mucor and Pichia. The fungal composition of oats Tianpei was significantlly related to the chemical composition of oats Tianpei (lactic acid, free fatty acid, amino acid, reducing sugar, etc). The fermentation effects of Rhizopus oryzae on the chemical composition of oats Tianpei were studied also. The fermentation of Rhizopus oryzae significantly increased the chemical composition in oats Tianpei. The research provided a theoretical basis for the study of microbial flora, the discovery of related microbial resources, the fermented modification grains, and the improvement of oats Tianpei quality.

Abstract Aims/Introduction Simple renal cysts (SRC) are associated with age, hypertension and hyperuricemia, which are risk factors of renal impairment. This study aimed to investigate the prevalence of SRC and its association with measured glomerular filtration rate (mGFR) in patients with type 2 diabetes mellitus in China. Methods A total of 3,552 patients with type 2 diabetes mellitus admitted to Shanghai Sixth People's Hospital from January 2012 to December 2016 were investigated. All participants were diagnosed as SRC by color Doppler, and all participants were measured for GFR by using dynamic renal scintigraphy with technetium‐99m‐diethylene triamine peta‐acetic acid. Results The prevalence of SRC was 17.4% (619 cases). Participants with SRC had significantly lower mGFR values than those without SRC (86.16 ± 26.20 vs 94.88 ± 23.98 mL/min/1.73 m2, P

Abstract Background The aim of this study was to investigate the analgesic effect and safety of ultrasound‐guided thoracic paravertebral block (UG‐TPVB) in Chinese elderly patients undergoing video‐assisted thoracic lobectomy (VATL) and to study the influence of aging factors on these effects. Methods This study was a single‐center, single‐blind, prospective, randomized, controlled trial. A total of 300 patients scheduled for VATL were recruited and randomly divided into the UG‐TPVB group (T group) and conventional anesthesia group (C group) according to the recruitment order, and subgroups were set up according to whether the age was ≥65 years old or not. The postoperative 12, 24, and 48 h static/dynamic visual analog scale (VAS) scores, intraoperative fentanyl consumption, postoperative extubation time, post‐anesthesia care unit (PACU) stay time, hospitalization days, postoperative complications, and other indicators were compared between the two groups. Results The postoperative 12, 24, and 48 h static/dynamic VAS scores of the T group were significantly lower than those of the C group. The intraoperative fentanyl consumption, postoperative extubation time, PACU stay time, and postoperative hospitalization days were significantly lower than those of the C group. The incidence of postoperative 48 h urinary retention in the T group was significantly lower than that in the C group. These advantages showed no significant difference or slight difference between elderly patients and nonelderly patients, indicating that UG‐TPVB did not influence the analgesic effect and safety of VATL patients by age or age difference. Conclusion UG‐TPVB is an effective and safe perioperative analgesia method for elderly VATL patients. Its application improves the quality of life and prognosis of elderly VATL patients.

Cholestatic liver disease (CLD) is a range of conditions caused by the accumulation of bile acids (BAs) or disruptions in bile flow, which can harm the liver and bile ducts. To investigate its pathogenesis and treatment, it is essential to establish and assess experimental models of cholestasis, which have significant clinical value. However, owing to the complex pathogenesis of cholestasis, a single modelling method can merely reflect one or a few pathological mechanisms, and each method has its adaptability and limitations. We summarize the existing experimental models of cholestasis, including animal models, gene-knockout models, cell models, and organoid models. We also describe the main types of cholestatic disease simulated clinically. This review provides an overview of targeted therapy used for treating cholestasis based on the current research status of cholestasis models. In addition, we discuss the respective advantages and disadvantages of different models of cholestasis to help establish experimental models that resemble clinical disease conditions. In sum, this review not only outlines the current research with cholestasis models but also projects prospects for clinical treatment, thereby bridging basic research and practical therapeutic applications.

Summary: RNA molecules often play critical roles in assisting the formation of membraneless organelles in eukaryotic cells. Yet, little is known about the organization of RNAs within membraneless organelles. Here, using super-resolution imaging and nuclear speckles as a model system, we demonstrate that different sequence domains of RNA transcripts exhibit differential spatial distributions within speckles. Specifically, we image transcripts containing a region enriched in binding motifs of serine/arginine-rich (SR) proteins and another region enriched in binding motifs of heterogeneous nuclear ribonucleoproteins (hnRNPs). We show that these transcripts localize to the outer shell of speckles, with the SR motif-rich region localizing closer to the speckle center relative to the hnRNP motif-rich region. Further, we identify that this intra-speckle RNA organization is driven by the strength of RNA-protein interactions inside and outside speckles. Our results hint at novel functional roles of nuclear speckles and likely other membraneless organelles in organizing RNA substrates for biochemical reactions.

This study examines the quantitative conditions under which an energy metering pricing model is proposed to increase both gas merchant profits and gas customer consumer surplus compared to a volumetric pricing model. The quantitative condition is found to be related to factors such as the standard unit calorific value of natural gas prescribed by the National Development and Reform Commission (NDRC) and other relevant government departments under the energy metering pricing model. This paper establishes a mathematical model based on optimization theory to explore the operational decisions of city gas suppliers in the volumetric and energy metering and pricing modes, respectively, under the condition of relatively stable natural gas sales price. The results of the study show that DAC and other authorities can regulate the standard unit calorific value of natural gas under the energy metering and pricing model by regulating the standard unit calorific value of natural gas. This affects the incentives of gas dealers to produce and operate, guides the preference of gas users for natural gas energy metering and pricing, and results in the derivation of formulas for a reasonable range of standard unit calorific values for natural gas. The findings of this paper provide theoretical support to promote the reform of natural gas energy measurement and pricing, and contribute to the development of the natural gas industry.

Background: The occurrence and development of Hepatic fibrosis (HF) are closely related to the gut microbial composition and alterations in host metabolism. Qijia Rougan decoction (QJ) is a traditional Chinese medicine compound utilized clinically for the treatment of HF with remarkable clinical efficacy. However, its effect on the gut microbiota and metabolite alterations is unknown. Therefore, our objective was to examine the impact of QJ on the gut microbiota and metabolism in Carbon tetrachloride (CCl4)-induced HF.Methods: 40% CCl4 was used to induce HF, followed by QJ administration for 6 weeks. Serum biochemical analyses, histopathology, immunohistochemistry, RT-PCR, 16S rRNA gene sequencing, and non-targeted metabolomics techniques were employed in this study to investigate the interventional effects of QJ on a CCl4-induced HF model in rats.Results: This study demonstrated that QJ could effectively ameliorate CCl4-induced hepatic inflammation and fibrosis. Moreover, QJ upregulated the expression of intestinal tight junction proteins (TJPs) and notably altered the abundance of some gut microbes, for example, 10 genera closely associated with HF-related indicators and TJPs. In addition, metabolomics found 37 key metabolites responded to QJ treatment and strongly associated with HF-related indices and TJPs. Furthermore, a tight relation between 10 genera and 37 metabolites was found post correlation analysis. Among them, Turicibacter, Faecalibaculum, Prevotellaceae UCG 001, and unclassified Peptococcaceae may serve as the core gut microbes of QJ that inhibit HF.Conclusion: These results suggest that QJ ameliorates hepatic inflammation and fibrosis, which may be achieved by improving intestinal tight junctions and modulating gut microbiota composition as well as modulating host metabolism.

There has been a major increase in Type 2 diabetes and obesity in many countries, and this will lead to a global public health crisis, which not only impacts on the quality of life of individuals well but also places a substantial burden on healthcare systems and economies. Obesity is linked to not only to type 2 diabetes but also cardiovascular diseases, musculoskeletal disorders, and certain cancers, also resulting in increased medical costs and diminished quality of life. A number of studies have linked changes in gut in obesity development. Dysbiosis, a deleterious change in gut microbiota composition, leads to altered intestinal permeability, associated with obesity and Type 2 diabetes. Many factors affect the homeostasis of gut microbiota, including diet, genetics, circadian rhythms, medication, probiotics, and antibiotics. In addition, bariatric surgery induces changes in gut microbiota that contributes to the metabolic benefits observed post-surgery. Current obesity management strategies encompass dietary interventions, exercise, pharmacotherapy, and bariatric surgery, with emerging treatments including microbiota-altering approaches showing promising efficacy. While pharmacotherapy has demonstrated significant advancements in recent years, bariatric surgery remains one of the most effective treatments for sustainable weight loss. However, access to this is generally limited to those living with severe obesity. This underscores the need for non-surgical interventions, particularly for adolescents and mildly obese patients. In this comprehensive review, we assess longitudinal alterations in gut microbiota composition and functionality resulting from the two currently most effective anti-obesity treatments: pharmacotherapy and bariatric surgery. Additionally, we highlight the functions of gut microbiota, focusing on specific bacteria, their metabolites, and strategies for modulating gut microbiota to prevent and treat obesity. This review aims to provide insights into the evolving landscape of obesity management and the potential of microbiota-based approaches in addressing this pressing global health challenge.

Research on food-derived immunoregulatory peptides has attracted increasing attention of scientists worldwide. However, the structure–activity relationship of rice immunopeptides was not clearly. Herein, 114 rice immunopeptides were obtained by simulating the enzymatic hydrolysis of rice proteins and were further analyzed by NetMHCIipan-4.0. Subsequently, the molecular docking was used to simulate the binding of immunoreactive peptides to major histocompatibility complex class II (MHC-II) molecules. Results show that S, R, D, E, and T amino acid could easily form hydrogen bonds with MHC-II molecules, thus enhancing innate and adaptive immunity. Finally, glucose-modified rice immunopeptides were to investigate the binding of the peptides with MHC-II molecules after glycosylation modification; this provided a theoretical basis for the targeted modification of the generated immunopeptides. All in all, the present study provides a theoretical foundation to further utilize rice processing byproducts and other food products to enhance immunity.

Abstract The deep ocean, Earth’s untouched expanse, presents immense challenges for exploration due to its extreme pressure, temperature, and darkness. Unlike traditional marine robots that require specialized metallic vessels for protection, deep-sea species thrive without such cumbersome pressure-resistant designs. Their pressure-adaptive forms, unique propulsion methods, and advanced senses have inspired innovation in designing lightweight, compact soft machines. This perspective addresses challenges, recent strides, and design strategies for bioinspired deep-sea soft robots. Drawing from abyssal life, it explores the actuation, sensing, power, and pressure resilience of multifunctional deep-sea soft robots, offering game-changing solutions for profound exploration and operation in harsh conditions.

Abstract. Background:. Dual regimen dolutegravir (DTG) plus lamivudine (3TC) has demonstrated non-inferior efficacy compared to DTG-based three-drug regimens (3DRs), yet directly comparative data regarding the efficacy and safety of DTG + 3TC and bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF) for therapy-naïve people with human immunodeficiency virus (HIV)-1 (PWH) are still limited. We aimed to assess the antiviral potency and safety profiles of DTG + 3TC vs. B/F/TAF based on antiretroviral therapy (ART)-naïve PWH in China. Methods:. This retrospective multicenter study enrolled PWH initiating ART with DTG + 3TC or B/F/TAF from 2020 to 2022 in Guangdong and Guangxi. We analyzed response rates based on target not detected (TND) status using intention-to-treat (ITT) analysis. Subgroups were formed based on baseline viral load (VL) (

Objective: In order to solve the problem of manual weighting of dragon fruit, including time-consuming, laborious and expensive, an automated weight estimation method based on machine vision and machine learning was proposed in this research. Methods: Firstly, 106 dragon fruits were weighed, recorded and photographed, and images of dragon fruits were constructed. Secondly, binary images were obtained after denoising and segmentation. Moreover, the three features of pixel area, major axis pixel length and minor axis pixel length of dragon fruits were extracted on the basis of binary images. The three features of each image and their corresponding weights were combined into a set of data, which was divided into training set and test set according to the ratio of 7∶3. Finally, the training set was input into the Gradient Boosting, Random Forest, K-Neighbors and Artificial Neural Networks machine-learning models for training, and the test sets were used for model evaluation. Results: The evaluation index of the Artificial Neural network performed well compared with other models, with R2 of 0.986 and RMSE of 13.091. Conclusion: The experimental result demonstrates that the method proposed in this research can accomplish the weight estimation of dragon fruit effectively, and meet the weight estimation requirements of dragon fruit.

Objective: This study aimed to improve the separation efficiency of decolorizing agent and fish oil, and ensure high decolorization rate at the same time. Methods: The optimum absorption wavelength of fish oil was measured by spectrophotometer. Magnesium silicate was mixed with activated clay into compound decolorizers, and was used to evaluate the decolorization rate and filtration speed. The decolorization parameters of anchovy oil were optimized by single factor experiment and response surface experiment. The oil absorption rate and fish oil recovery rate of decolorizing agent were calculated. The decolorization and filter aid effect of decolorizing agent were verified through the plate and frame filter scale-up experiment. Fatty acid absolute content of crude oil, degummed oil, deacidified oil, and bleached oil was determined by gas chromatography. Results: The best absorption wavelength of anchovy oil was 671 nm. The compound results of mixed decolorizer was as follows: the ratio of activated clay∶magnesium silicate was 4∶1 and its filtration rate was (26.65±0.64) mL/min. Response surface optimization results showed that the decolorization rate was (95.74±1.35)%, at the dosage of 5.0%, 57 min, and 72 ℃. The oil absorption rate was (1.15±0.10)% and fish oil recovery was (91.72±1.60)%. In the plate and frame filtration experiment, the decolorization effect of the mixed decolorizer was not significantly different from the control group, but it had a faster filtration rate. The absolute content of SFA and MUFA in crude oil, degummed oil, deacidified oil, and bleached oil had no significant difference. Increasing from (117.42±0.23) mg/g and (65.20±2.31) mg/g to (128.97±0.86) mg/g and (68.62±1.59) mg/g, the absolute content of EPA and DHA in bleached oil increased significantly. Conclusion: Magnesium silicate is an effective filter aid and has adsorption effect. Its combination with activated clay can ensure the decolorization effect of fish oil. Additionally, it can solve the problem of blocking the filter cake of separating the decolorizing agent and fish oil by the plate and frame filter press. The mixed decolorizing agent has the advantages of low oil absorption, high decolorization efficiency, and fast filtration speed.