Search

Objective: Rapid-onset obesity with hypoventilation, hypothalamic dysfunction, and autonomic dysregulation (ROHHAD) is rare, and manifestations of autonomic dysregulation are diverse and may be overlooked. We aimed to evaluate the incidence of these manifestations. Methods: Patients with ROHHAD syndrome reported before and after 2019 were divided into groups 1 and 2. Patients who were diagnosed at three regional hospitals in China were included in group 3. We collected the age of each specific term of the ROHHAD (neurogenic tumor, NET) acronym and the detailed manifestations of each term, and compared them among the three groups. Results: A total of 16 patients were diagnosed within the 2-year period. Two had neurogenic tumors and cognitive and behavioral abnormalities before developing rapid obesity. At least 93.8% of the patients had ≥ 4 symptoms of autonomic dysregulation. When comparing autonomic dysregulation among groups 1–3, the rates of cardiovascular manifestations were NA vs 12.8% vs 81.2%; gastrointestinal disturbances were 11.4% vs 8.5% vs 62.5%; strabismus was 25.7% vs 12.8% vs 62.5%; sleep disturbance was NA vs 6.4% vs 50.0%; and abnormal pain threshold was NA vs 10.6% vs 25.0% (all P < 0.05). The rates of cognitive and behavioral abnormalities were NA vs 29.8% and 87.5% (P < 0.01). Conclusion: Rapid-onset obesity is not always the first sign of ROHHAD syndrome. Higher rates of autonomic dysregulation and cognitive and behavioral abnormalities with multiple manifestations of autonomic dysregulation coexisted in our cohort, indicating that evaluations of autonomic function and the limbic system should be strengthened when assessing this condition.

Aroma is a critical quality of fruit and vegetable juices, drawing considerable attention. High-pressure processing (HPP) is demonstrated to possess more advantageous than thermal sterilization in preserving the aroma quality of fruit and vegetable juices and reducing processing off-flavors. In this paper, the changes in aroma quality of HPP fruit and vegetable juices during processing and storage are reviewed. The HPP treatment process has little effect on the overall aroma quality and sensory attributes of fruit and vegetable juices. The deterioration of aroma quality mainly occurs during the subsequent storage period. This paper elucidates the mechanism of aroma quality change of HPP fruit and vegetable juices from aspects such as the activation or inactivation of enzymes by HPP and the interaction between matrix molecules and aroma. It summarizes the currently common aroma quality control measures for HPP fruit and vegetable juices. The enzyme inactivation effects could be improved by combining temperature and pressure, optimizing the pressurization mode, and using technologies such as HPP in combination with CO2 and ultrafiltration. Product quality could also be improved by improving storage conditions. This paper reviews the research progress on aroma quality of ultra-high pressure fruit and vegetable juices, in order to provide ideas and references for subsequent product research and development and the study of aroma change mechanisms.

Abstract Background Observational studies have found associations between sex hormones and metabolic syndrome(Mets), but the causal relationships remains unclear. This study utilizes univariable and multivariable Mendelian randomization (MR) to elucidate the associations between sex hormones (including sex hormone-binding globulin(SHBG), estradiol(E2), testosterone(T)) and Mets and its subtypes (including waist circumference(WC), fasting blood glucose(FBG), high blood pressure(HBP), high-density lipoprotein(HDL-C), triglycerides(TG)). Methods We utilized summary data from large-scale genome-wide association studies. Univariable Mendelian randomization (UMVMR) analysis was primarily conducted using the inverse variance weighted method (IVW), with secondary analyses employing the weighted median, MR-Egger regression, simple mode method, and weighted mode method. Subsequently, multivariable Mendelian randomization (MVMR) was employed to assess the causal relationships between SHBG, T, E2, and MetS and its components: WC, FPG, HBP, HDL-C, and TG. Sensitivity analyses were conducted to assess result reliability. Results Genetically predicted SHBG was significantly negatively associated with MetS (UMVMR: β=-0.72; 95% CI = 0.41 to 0.57; P = 1.28e-17; MVMR: β=-0.60; 95% CI=-0.83 to -0.38; P

Abstract This case report presents the clinical findings of a female patient diagnosed with Wernicke’s encephalopathy, characterized by pinpoint pupils. While pupillary changes can occur in Wernicke’s encephalopathy, the presence of pinpoint pupils is exceedingly rare. In this report, we aim to document and discuss this unusual presentation, as well as speculate on the potential mechanisms underlying this atypical manifestation of the disease.

Abstract Background Sexual minority student-athletes (SMSAs) face discrimination and identity conflicts in intercollegiate sport, impacting their participation and mental health. This study explores the perceptions of Chinese SMSAs regarding their sexual minority identities, aiming to fill the current gap in research related to non-Western countries. Methods A qualitative methodology was adopted, utilising the Interpretive Phenomenological Analysis (IPA) approach with self-categorization theory as the theoretical framework. Participants were recruited through purposive and snowball sampling, and data were collected via semi-structured interviews, documents, and field notes. Sixteen former and current Chinese SMSAs participated in this study. Results The study reveals four themes: hidden truths, prioritisation of athlete identity, self-stereotyping, and attempt. The results revealed that while SMSAs were common in intercollegiate sport, their identities were often concealed and not openly discussed. The predominant focus on athlete identity in sport overshadowed their sexual minority identities. Additionally, SMSAs developed self-stereotypes that influenced their thoughts and behaviours. The non-heterosexual team atmosphere in women’s teams led to the development of intimate relationships among teammates. Conclusions The findings from this study could be incorporated into existing sport policies to ensure the safe participation of SMSAs in Chinese intercollegiate sports. This research offers valuable insights for the development and implementation of inclusive policies. Future research in China could investigate the attitudes of coaches and heterosexual student-athletes toward sexual minority identities to inform targeted interventions.

Abstract Background Nursing students are faced with multiple challenges and have a higher probability of suffering from anxiety. The current study aims to explore the relation between empathy and anxiety, examining the mediation and moderation effects of insomnia and self-compassion, respectively. Methods This study employed a convenient sampling method, recruiting 1,161 nursing students (female = 923, male = 238, Meanage = 18.37, SDage = 2.38) from three universities in China. These students completed the questionnaires online, including General Anxiety Disorder -7 (GAD-7), Jefferson Scale of Physician Empathy-Nursing student (JSPE-NS), Youth Self-rating Insomnia Scale -8 (YSIS-8), and Self-Compassion Scale (SCS). The study employed latent variable structural equation models to analyze the relation and mechanisms between empathy and anxiety. Then, the mediated role of insomnia and the moderated role of self-compassion were examined. Results The prevalence rates of anxiety and insomnia in the current sample are 18.24% and 26.76%, respectively. The results showed that empathy could negatively predict anxiety, with a significant mediating effect of insomnia between them (B = -0.081, p

Background Insulin resistance (IR) is the key pathological basis of many metabolic disorders. Lack of asialoglycoprotein receptor 1 (ASGR1) decreased the serum lipid levels and reduced the risk of coronary artery disease. However, whether ASGR1 also participates in the regulatory network of insulin sensitivity and glucose metabolism remains unknown. Methods The constructed ASGR1 knockout mice and ASGR1-/- HepG2 cell lines were used to establish the animal model of metabolic syndrome and the IR cell model by high-fat diet (HFD) or drug induction, respectively. Then we evaluated the glucose metabolism and insulin signaling in vivo and in vitro. Results ASGR1 deficiency ameliorated systemic IR in mice fed with HFD, evidenced by improved insulin intolerance, serum insulin, and homeostasis model assessment of IR index, mainly contributed from increased insulin signaling in the liver, but not in muscle or adipose tissues. Meanwhile, the insulin signal transduction was significantly enhanced in ASGR1-/- HepG2 cells. By transcriptome analyses and comparison, those differentially expressed genes between ASGR1 null and wild type were enriched in the insulin signal pathway, particularly in phosphoinositide 3-kinase-AKT signaling. Notably, ASGR1 deficiency significantly reduced hepatic gluconeogenesis and glycogenolysis. Conclusion The ASGR1 deficiency was consequentially linked with improved hepatic insulin sensitivity under metabolic stress, hepatic IR was the core factor of systemic IR, and overcoming hepatic IR significantly relieved the systemic IR. It suggests that ASGR1 is a potential intervention target for improving systemic IR in metabolic disorders.

Objective: Mitochondrial fatty acid oxidation is a metabolic pathway whose dysregulation is recognized as a critical factor in various cancers, because it sustains cancer cell survival, proliferation, and metastasis. The acyl-CoA synthetase long-chain (ACSL) family is known to activate long-chain fatty acids, yet the specific role of ACSL3 in breast cancer has not been determined. Methods: We assessed the prognostic value of ACSL3 in breast cancer by using data from tumor samples. Gain-of-function and loss-of-function assays were also conducted to determine the roles and downstream regulatory mechanisms of ACSL3 in vitro and in vivo . Results: ACSL3 expression was notably downregulated in breast cancer tissues compared with normal tissues, and this phenotype correlated with improved survival outcomes. Functional experiments revealed that ACSL3 knockdown in breast cancer cells promoted cell proliferation, migration, and epithelial–mesenchymal transition. Mechanistically, ACSL3 was found to inhibit β-oxidation and the formation of associated byproducts, thereby suppressing malignant behavior in breast cancer. Importantly, ACSL3 was found to interact with YES proto-oncogene 1, a member of the Src family of tyrosine kinases, and to suppress its activation through phosphorylation at Tyr419. The decrease in activated YES1 consequently inhibited YAP1 nuclear colocalization and transcriptional complex formation, and the expression of its downstream genes in breast cancer cell nuclei. Conclusions: ACSL3 suppresses breast cancer progression by impeding lipid metabolism reprogramming, and inhibiting malignant behaviors through phospho-YES1 mediated inhibition of YAP1 and its downstream pathways. These findings suggest that ACSL3 may serve as a potential biomarker and target for comprehensive therapeutic strategies for breast cancer.

Abstract Osteoporosis is the most common bone metabolic disease that affects the health of middle-aged and elderly people, which is hallmarked by imbalanced bone remodeling and a deteriorating immune microenvironment. Magnesium and calcium are pivotal matrix components that participate in the bone formation process, especially in the immune microenvironment regulation and bone remodeling stages. Nevertheless, how to potently deliver magnesium and calcium to bone tissue remains a challenge. Here, we have constructed a multifunctional nanoplatform composed of calcium-based upconversion nanoparticles and magnesium organic frameworks (CM-NH2-PAA-Ald, denoted as CMPA), which features bone-targeting and pH-responsive properties, effectively regulating the inflammatory microenvironment and promoting the coordination of osteogenic functions for treating osteoporosis. The nanoplatform can efficaciously target bone tissue and gradually degrade in response to the acidic microenvironment of osteoporosis to release magnesium and calcium ions. This study validates that CMPA possessing favorable biocompatibility can suppress inflammation and facilitate osteogenesis to treat osteoporosis. Importantly, high-throughput sequencing results demonstrate that the nanoplatform exerts a good inflammatory regulation effect through inhibition of the nuclear factor kappa-B signaling pathway, thereby normalizing the osteoporotic microenvironment. This collaborative therapeutic strategy that focuses on improving bone microenvironment and promoting osteogenesis provides new insight for the treatment of metabolic diseases such as osteoporosis.

Abstract Background The accuracy of artificial intelligence (AI) and experts in diagnosing early esophageal cancer (EC) and its infiltration depth was summarized and analyzed, thus identifying the advantages of AI over traditional manual diagnosis, with a view to more accurately assisting doctors in evaluating the patients' conditions and improving their cure and survival rates. Methods The PubMed, EMBASE, Cochrane, Google, and CNKI databases were searched for relevant literature related to AI diagnosis of early EC and its invasion depth published before August 2023. Summary analysis of pooled sensitivity, specificity, summary receiver operating characteristics (SROC) and area under the curve (AUC) of AI in diagnosing early EC were performed, and Review Manager and Stata were adopted for data analysis. Results A total of 19 studies were enrolled with a low to moderate total risk of bias. The pooled sensitivity of AI for diagnosing early EC was markedly higher than that of novices and comparable to that of endoscopists. Moreover, AI predicted early EC with markedly higher AUCs than novices and experts (0.93 vs. 0.74 vs. 0.89). In addition, pooled sensitivity and specificity in the diagnosis of invasion depth in early EC were higher than that of experts, with AUCs of 0.97 and 0.92, respectively. Conclusion AI‐assistance can diagnose early EC and its infiltration depth more accurately, which can help in its early intervention and the customization of personalized treatment plans. Therefore, AI systems have great potential in the early diagnosis of EC.

BackgroundRadiation-induced lymphopenia (RIL) is closely related to the prognosis of cervical cancer patients and may affect the efficacy of immune checkpoint inhibitors (ICIs). However, the factors influencing RIL are not very clear. In addition to bone marrow (BM) dose-volume, animal studies indicate radiation-induced bowel injury may be a more crucial factor. Further clarification of the correlation between RIL and bowel dose-volume is important for cervical cancer treatment.MethodsCervical cancer patients treated with postoperative radiotherapy or radical radiotherapy were eligible for this retrospective study. Clinical characteristics, dose parameters of bowel and BM, planning target volume (PTV) size, overall survival (OS) and progression-free survival (PFS) were recorded. The absolute lymphocyte count

IntroductionReconciling economic development and emission reduction of polluting gases requires balancing long-term and short-term goals across various markets. As a new production cost, the price of carbon allowances is assumed to affect the supply and demand of carbon-intensive sectors. Therefore, this study examines the dynamic price spillover effects between China as the largest carbon emission market and the carbon-intensive building materials industry in a unified time-frequency framework.MethodsThis study measures the dynamic overall and directional spillover effects of carbon and building materials markets in three frequency bands, considering eight carbon pilots and the national carbon market in China and four important building materials.ResultsThe empirical results show that the bi-directional spillover between carbon and the building materials market shows pronounced characteristics in the time-frequency domain, especially in the short-term frequency band of one day to one week, with strong connectivity. After the launch of the national carbon market, the information spillover from the building materials market to the carbon pilots become stronger. Both the carbon pilots and the national carbon market have significant short- and long-term impacts on the building materials market. In addition, there are differences in the impact of carbon markets on various types of carbon intensive building materials.DiscussionCompliance cycles in carbon markets are likely to induce sharp fluctuations in spillovers between the two markets. Therefore, balancing industrial development and stabilizing carbon prices requires a refined policy design that considers the diversified impacts of carbon markets on different industries at across frequencies.

Depression is a common psychological complication in osteoarthritis (OA) patients, and its incidence gets more and more attention year by year worldwide. This study investigates the association between OA and depression through a bibliometric analysis of published studies. It aims to identify leading authors, institutions, and countries to highlight research hotspots and suggest potential future directions. We collected publications on OA and depression from 1994 to 2024 using the Web of Science Core Collection (WOSCC) database. Bibliographic information, including authorship, country of origin, citation frequency, and visualizations, was generated using VOSviewer, R software, and CiteSpace. A total of 2,342 articles were identified. The United States led in publications with 906 articles, Boston University was the most prolific institution with 56 publications, BMC Musculoskeletal Disorders was the top journal with 71 publications, and Stefania Maggi was the most productive author with 19 publications. The primary research hotspots identified were: “The relationship between depression and OA,” “Disability and prevalence,” and “Characteristics of older people suffering depression after OA.” Predicted future research frontiers include: “Treating depression in OA patients with multimorbidity,” “Psychometric properties of instruments for assessing depression and anxiety in OA patients,” “Depression or anxiety in patients with surgical intervention,” and “Other mental diseases in OA patients.” This bibliometric analysis underscores the importance of understanding the link between OA and depressive disorders, potentially guiding new research directions.

ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by increasing prevalence, diverse impairments, and unclear origins and mechanisms. To gain a better grasp of the origins of ASD, it is essential to identify the most distinctive structural brain abnormalities in individuals with ASD.MethodsA Multi-Stage Progressive Feature Refinement Approach was employed to identify the most pivotal structural magnetic resonance imaging (MRI) features that distinguish individuals with ASD from typically developing (TD) individuals. The study included 175 individuals with ASD and 69 TD individuals, all aged between 7 and 18 years, matched in terms of age and gender. Both cortical and subcortical features were integrated, with a particular focus on hippocampal subfields.ResultsOut of 317 features, 9 had the most significant impact on distinguishing ASD from TD individuals. These structural features, which include a specific hippocampal subfield, are closely related to the brain areas associated with the reward system.ConclusionStructural irregularities in the reward system may play a crucial role in the pathophysiology of ASD, and specific hippocampal subfields may also contribute uniquely, warranting further investigation.

Abstract Background Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis. Results Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples. Conclusion A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

Abstract Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

Abstract Nuclear magnetic resonance (NMR)-based plasma fatty acids are objective biomarkers of many diseases. Herein, we aim to explore the associations of NMR-based plasma fatty acids with the risk of hepatocellular carcinoma (HCC) and chronic liver disease (CLD) mortality in 252,398 UK Biobank participants. Here we show plasma levels of n-3 poly-unsaturated fatty acids (PUFA) and n-6 PUFA are negatively associated with the risk of incident HCC [HRQ4vsQ1: 0.48 (95% CI: 0.33–0.69) and 0.48 (95% CI: 0.28–0.81), respectively] and CLD mortality [HRQ4vsQ1: 0.21 (95% CI: 0.13–0.33) and 0.15 (95% CI: 0.08–0.30), respectively], whereas plasma levels of saturated fatty acids are positively associated with these outcomes [HRQ4vsQ1: 3.55 (95% CI: 2.25–5.61) for HCC and 6.34 (95% CI: 3.68–10.92) for CLD mortality]. Furthermore, fibrosis stage significantly modifies the associations between PUFA and CLD mortality. This study contributes to the limited prospective evidence on the associations between plasma-specific fatty acids and end-stage liver outcomes.

Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses.

Abstract Liver fibrosis is a chronic pathological condition lacking specific clinical treatments. Stem cells, with notable potential in regenerative medicine, offer promise in treating liver fibrosis. However, stem cell therapy is hindered by potential immunological rejection, carcinogenesis risk, efficacy variation, and high cost. Stem cell secretome‐based cell‐free therapy offers potential solutions to address these challenges, but it is limited by low delivery efficiency and rapid clearance. Herein, an innovative approach for in situ implantation of smart microneedle (MN) arrays enabling precisely controlled delivery of multiple therapeutic agents directly into fibrotic liver tissues is developed. By integrating cell‐free and platinum‐based nanocatalytic combination therapy, the MN arrays can deactivate hepatic stellate cells. Moreover, they promote excessive extracellular matrix degradation by more than 75%, approaching normal levels. Additionally, the smart MN arrays can provide hepatocyte protection while reducing inflammation levels by ≈70–90%. They can also exhibit remarkable capability in scavenging almost 100% of reactive oxygen species and alleviating hypoxia. Ultimately, this treatment strategy can effectively restrain fibrosis progression. The comprehensive in vitro and in vivo experiments, supplemented by proteome and transcriptome analyses, substantiate the effectiveness of the approach in treating liver fibrosis, holding immense promise for clinical applications.

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

Study region: Flood formation involves complex nonlinear processes and numerous variables, with data-driven models becoming a key non-engineering approach to flood prevention and mitigation. Yet, single machine learning models are insufficient to fully capture the complex dynamics of the flood process. Study focus: We propose an interpretable flood forecasting hybrid model based on Transformer, LSTM, and Adaptive Random Search Algorithm (AGRS), termed as AGRS-LSTM-Transformer. Investigating the predictive performance of the hybrid model, this study compares it against AGRS-LSTM, AGRS-Transformer, AGRS-BP, and AGRS-MLP models, utilizing flood data from 1971 to 2013 years in the Jingle watershed. New hydrological insights for the region: The AGRS-LSTM-Transformer model demonstrates superior performance over benchmark models, achieving accurate runoff forecasts with a lead time of 1–6 hours. It achieves a Nash-Sutcliffe Efficiency (NSE) greater than 0.905, and root mean squared error (RMSE), mean absolute error (MAE), Bias, and relative error (RE) values for the runoff process below 34.891 m³/s, 25.125 m³/s, 9.537 %, and 8.025 %, respectively. The coupling between the LSTM layers and the Transformer input components plays a crucial role in the architecture of the AGRS-LSTM-Transformer model. Rainfall from stations situated near the main river channel and downstream flow sections exerted a positive influence. Runoff from the preceding moment significantly impacts the predicted flow, with the contribution of runoff inputs exceeding that of rainfall. Inputs nearer to the forecast moment do not invariably improve forecasting accuracy, and historical rainfall and runoff volumes with extended lag times may detrimentally impact the model prediction. The study highlights the potential of hybrid data-driven models in enhancing the accuracy of flood forecasting, offering insights for reducing uncertainty in flood prediction and interpreting machine learning flood forecasting models. This provides a scientific basis for flood early warning systems and water resource management.

Objective: The prognosis of malignant tumors with peritoneal metastases and cancerous ascites has generally been poor, with limited treatment options. The PRaG regimen, which comprised of hypofractionated radiotherapy, programmed cell death-1 (PD-1) inhibitor, and granulocyte-macrophage colony-stimulating factor (GM-CSF), showed a survival advantage in patients with advanced solid tumors who failed at least the first line of standard systemic treatment. Intraperitoneal infusion of PD-1 inhibitors may be a novel therapeutic strategy for managing malignant ascites. Integrating the PRaG regimen with intraperitoneal perfusion of a PD-1 inhibitor might control malignant ascites and provide further survival benefits in these patients. This proposed study aims to investigate the safety and efficacy of intraperitoneal infusion of serplulimab in combination with the PRaG regimen in patients with simultaneous advanced solid tumors and cancerous ascites who fail at least the first-line treatment. Methods: This proposed study is a prospective, single-arm, open-label, multicenter clinical trial. All eligible patients will receive 2 cycles of intensive treatment, a combination of PRaG regimen with an intraperitoneal infusion of PD-1 inhibitor. The patients who are beneficially treated with intensive treatment will receive consolidation treatment every 2 weeks until ascites disappear, disease progression occurs, intolerable toxicity occurs, or for up to 1 year. Phase I of this study will be conducted using a modified 3 + 3 design. The dose of intraperitoneal infusion of PD-1 inhibitor for phase II will be determined according to dose-limiting toxicity evaluation in the phase I study. Conclusion: This prospective, open-label, multicenter study will potentially lead to intraperitoneal perfusion of a PD-1 inhibitor being a new strategy for malignant ascites patients and provide a meaningful efficacy and safety of the combination of PRaG regimen with an intraperitoneal infusion of PD-1 inhibitor for these patients.

Diabetic nephropathy (DN) is a major complication of diabetes. Interleukin-1 receptor-associated kinase 2 (IRAK2) has been implicated in various diseases. This study aimed to investigate the role of IRAK2 in DN progression and its association with inflammation and the nuclear factor-kappa B (NF-κB) signaling pathway. DN model mice were generated by intraperitoneal injection of streptozotocin. IRAK2 expression was upregulated in the DN model mice. IRAK2 knockdown increased weight and reduced blood glucose levels in DN model mice. In addition, IRAK2 downregulation improved glomerular morphology in DN mice. IRAK2 knockdown reduced the levels of kidney damage biomarkers (24-h urinary protein, urine albumin-creatinine ratio, and plasma creatinine) and inflammatory cytokines (IL-6, tumor necrosis factor [TNF]-α, TNF-1R, and TNF-2R). Moreover, IRAK2 activated the NF-κB signaling pathway in DN model mice. Overexpression of NF-κB exacerbated DN progression, and IRAK2 knockdown reversed these effects. IRAK2 promoted DN progression and inflammation by activating the NF-κB signaling pathway. These findings suggest that IRAK2 is a potential therapeutic target for DN treatment.

Abstract Background Owing to poor organ function reserve, older adults have a high risk of postoperative complications. However, there is no well-established system for assessing the risk of complications after hepatectomy in older adults. Methods This study aimed to design a risk assessment tool to predict the risk of complications after hepatectomy in adults older than 75 years. A total of 326 patients were identified. A logistic regression equation was used to create the Risk Assessment System of Hepatectomy in Adults (RASHA) for the prediction of complications (Clavien‒Dindo classification ≥ II). Results Multivariate correlation analysis revealed that comorbidity (≥ 5 kinds of disease or

The development of biodegradable Zn alloys with comprehensive performance for biomedical applications is still challenging. Hence, the nanocrystalline Zn–Cu–Sr–Li alloys were prepared by alloying design and multi-pass high-speed hot rolling. The Zn–3Cu-0.2Sr-0.4Li alloy achieved nanoscale microstructures, resulting in high strength and an ideal corrosion rate that is more suitable for tissue repair cycles. Scanning Kelvin probe force microscopy analysis revealed the potential from high to low was ranked as: ε phase > SrZn13 phase > Zn grains > β phase. The nanoscale Zn grains and β particles formed numerous microcells, enhancing the microgalvanic corrosion effect. The Sr ions could be released more efficiently during the degradation in Li-containing alloys, which endowed the alloys with outstanding biocompatibility. Besides, the synergistic release of Zn ions and Cu ions during alloy degradation endowed the alloy with excellent antimicrobial ability against Staphylococcus aureus (inhibition zone diameter ≈ 7.5 mm). The antimicrobial ability of Zn–3Cu–0.2Sr–0.4Li alloy was superior among the reported biodegradable Zn alloys. These results demonstrated nanocrystalline Zn–Cu–Sr–Li alloys have comprehensive performance and broad application prospects in biodegradable bone implants, providing the experimental and theoretical basis for future research in the biomedical field.

Due to the huge volume of gearless ball mill direct drive permanent magnet synchronous machine (PMSM), an unwinding modular technology is convenient for its redundant power control, manufacture and maintenance. Several module units are wrapped around the ball mill roller for direct drive. The motor frame adopts a modular structure to achieve complete decoupling between modules. And the cooling problem of the motor is very important for its stable operation. The traditional waterway structure design and cooling analysis methods are difficult to meet the engineering requirements of large modular permanent magnet synchronous machine (MPMSM). A 3D equivalent thermal network method is proposed to study the cooling problem of MPMSM. The design program of modular frame cooling structure is written based on a 3D equivalent thermal network method. According to the temperature distribution of each node, the cyclic check is carried out to quickly design the modular waterway. The cooling effect of the designed waterway structure is analyzed by fluid-solid coupled temperature field simulation. An 80 kW prototype is developed and the temperature rise experiment is carried out. The simulation and experiment results prove the rationality of a 3D equivalent thermal network method for the MPMSM cooling structure design.

Background:The glucagon-like peptide-1 (GLP-1) receptor agonist liraglutide (LIRA) is a potential hypoglycemic drug with anti-atherosclerosis (AS) effects. Autophagy in the vascular smooth muscle cells (VSMCs) facilitates AS. However, the role of autophagy in the anti-AS mechanism of LIRA remains unclear.Aims:To examine the role and mechanisms of autophagy in LIRA’s improvement of the biological characteristics of VSMCs in high glucose conditions.Study Design:Experimental animal study.Methods:VSMCs isolated from the thoracic aorta of male SD rats were subjected to a high glucose (HG) condition (25 mM) in Dulbecco’s Modified Eagle’s Medium with or without LIRA, the GLP-1 receptor antagonist exendin9-39 (Exe9-39), a phosphatidylinositol 3-kinase (PI3K) inhibitor (LY294002), and autophagy inhibitors (3-methyladenine [3-MA] and bafilomycin A1 [Baf A1]). Acridine orange staining, western blotting, transmission electron microscopy, and mCherry-GFP-LC3 transfection were performed to evaluate the autophagy flux. Additionally, VSMC migration, calcification, proliferation, and apoptosis in HG conditions were observed.Results:Addition of LIRA alone or in combination with autophagy inhibitors significantly downregulated Beclin, increased the LC3-II/LC3-I ratio, and upregulated p62 in VSMCs in HG conditions. Furthermore, autophagolysosome formation was markedly curbed after treatment with LIRA and/or autophagy inhibitors. Inhibition of autophagy by LIRA and/or the autophagy inhibitors attenuated VSMC phenotype conversion, proliferation, migration, and calcification and promoted VSMC apoptosis in HG conditions. This protective role of LIRA was augmented by LY294002, but inhibited by Exe9-39.Conclusion:LIRA plays a significant role in the improvement of the biological features of VSMCs in HG conditions.

As the manufacturing industry is the foundation of the national economy, many countries have made concerted efforts to promote the transition upgrading of manufacturing enterprises from political, industrial, and academic circles. This study employed content analysis to explore the transition upgrading factors for manufacturing enterprises from an energy source perspective. With the help of FsQCA (Fuzzy-sets qualitative comparative analysis), which can mine multivariate combination relationships using a small number of samples, this study quantitatively calculated the transition paths and energy source combinations of manufacturing enterprises. Three transition upgrading paths were proposed, including an innovation-oriented path, a business-oriented path, and an efficiency-oriented path. The results indicate that manufacturing enterprises can realize transition upgrading based on three key methods, including innovation resources and innovation activities, strategic layout, and production operation + network relationship (peripheral). Such a finding validated the rationality of the theoretical path design and improved the research depth and precision. Multiple case analyses of Huawei, Haier, and Lenovo reveal that manufacturing enterprises used three ways to promote transition upgrading: the joint action of innovation activities and innovation resources; the uni-directional causal effect of strategic layout adjustment to increase market value; and the coordination and matching role of network relations dominated by production and operation. We conclude that this study provides an effective decision-making basis and management reference for manufacturing enterprises, so that enterprises can choose an appropriate energy accumulation method and scientifically realize the transition upgrading.

Background: In the contemporary academic milieu, an array of erudite investigations has meticulously delved into the ramifications of social media paradigms on the nuances of modern societal interactions. Predominantly, these scholarly endeavors have harnessed quantitative methodologies to discern both the advantageous and detrimental implications of social media. Notwithstanding these extensive analyses, there appears to be a conspicuous lacuna in the literature about the subjective repercussions of social media on the well-being and contentment of tertiary education students. In light of this gap, the present qualitative exploration seeks to elucidate the perceptions of Chinese collegiate individuals vis-à-vis the influence of social media platforms on their experiential happiness. Method: Guided by interpretative phenomenological analysis (Jonathan et al., 2022) [1], we conducted 3 semi-structured interviews with 5 university students. Result: Using an interpretive phenomenology analysis approach four themes. They are (1) The Paradox of Temperance and Indulgence, (2) Identity Construction and Presentation, (3) Social Support and Connection, and (4) Social Comparison and Self-Evaluation. Discussion: Findings suggest that the individual well-being of college students may be affected by ambivalence between moderate and indulgent use of social media, social media interactions to maintain and enhance personal identity, and comparisons between individuals and certain online groups. Therefore, the government, higher education institutions, and college students should work together to build a safe and happy university life.

Summary: Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

Abstract Background The FRAIL scale for evaluating frailty consists of five items: fatigue, resistance, aerobic, illness, and loss of weight. However, it is difficult to obtain a specific weight loss value. Since the Timed Up and Go Test (TUGT) is simple, accurate, and easy to perform, we replaced weight loss with the TUGT in the FRAIL scale, with the remaining four items unchanged, and named it the FRAIT scale. The aim of this study was to determine the value of the FRAIT scale in predicting the peri-operative outcome of hepatectomy. Methods This model development study was conducted between January 2017 and December 2021. The reliability, validity and area under the curve (AUC) of the FRAIL/FRAIT scales were calculated. The frailty status of patients aged ≥ 75 years who underwent hepatectomy was measured using the FRAIL/FRAIT scales. Logistic regression was used to compare the relationship between FRAIL/FRAIT scores/grades and perioperative outcomes. Results The AUCs for predicting operation duration, intraoperative bleeding, complications, and death based on the FRAIL score were 0.692, 0.740, 0.709, and 0.733, respectively, and those based on the FRAIT score were 0.700, 0.745, 0.708, and 0.724, respectively. The AUCs for predicting operation duration, intraoperative bleeding, complications, and death based on the FRAIL grade were 0.693, 0.735, 0.695, and 0.755, respectively, and those based on the FRAIT grades were 0.700, 0.758, 0.699, and 0.750, respectively. The FRAIL score has three effective predictors (intraoperative bleeding, complications, and death), while the FRAIT score has four effective predictors (operation duration, intraoperative bleeding, complications, and death). The FRAIL grade has two effective predictors (intraoperative bleeding and death), while the FRAIT grade has three effective predictors (operation duration, intraoperative bleeding, and death). Conclusions This study describes a new and more effective tool for the assessment of preoperative frailty in older adults undergoing hepatectomy. The items of the FRAIT scale are easier to obtain than those of the FRAIL scale, and the predictive effect of the FRAIT scale is stronger than that of the FRAIL scale.

Caries is considered to be the most prevalent non-communicable disease in humans, mainly deriving from acidogenic bacterial biofilm and resulting in the demineralization and decomposition of hard dental tissue. Herein, a composite responsive foam brace loaded with magnesium organic framework (MPC) is designed for caries prevention and tooth remineralization. MPC can intelligently release organic antibacterial molecules (gallic acid) and mineralized ions (Mg2+, Ca2+ and PO43−) under acidic conditions (pH

True morels (Morchella spp.) are renowned for their aroma and taste, and hot air drying is widely used to extend the shelf life of harvested morels. However, the effects of different drying temperatures on volatile flavor compounds and non-volatile metabolites in the morel are poorly understood. Here, fresh morels (Morchella sextelata) were air-dried at low (45 °C, LT), medium (55 °C, MT), and high temperatures (65 °C, HT). The volatile flavor compounds and non-volatile metabolites were analyzed using GC-IMS and LC-MS/MS, respectively. The GC-IMS revealed that aldehydes, hydrocarbons, and pyrazines increased at greater temperatures, while acids, alcohols, and esters decreased. Random forest machine learning indicated that 1-hexanol and ethyl 3-methylbutanoate were indicative flavor compounds at LTs, while those at MTs and HTs were hexanal and valeraldehyde, respectively. Greater temperatures reduced acetic acid, an unpleasant sour flavor. The LC-MS/MS showed that the relative abundance of amino acids and nucleotides increased with the temperature, with the same trend in 5′-nucleotides and flavor amino acids. Sorbitol 6-phosphate was indicative of the non-volatile metabolites at LTs, while several amino acids were indicative at MTs and HTs. This study revealed the flavor and taste characteristics of morels dried at different temperatures, providing a theoretical reference for establishing a standardized postharvest morel drying process and maintaining morel quality.

From the perspective of optimization, most of the current mainstream remote sensing data fusion methods are based on traditional mathematical optimization or single objective optimization. The former requires manual parameter tuning and easily falls into local optimum. Although the latter can overcome the shortcomings of traditional methods, the single optimization objective makes it unable to combine the advantages of multiple models, which may lead to distortion of the fused image. To address the problems of missing multi-model combination and parameters needing to be set manually in the existing methods, a pansharpening method based on multi-model collaboration and multi-objective optimization is proposed, called MMCMOO. In the proposed new method, the multi-spectral image fusion problem is transformed into a multi-objective optimization problem. Different evolutionary strategies are used to design a variety of population generation mechanisms, and a non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the two proposed target models, so as to obtain the best pansharpening quality. The experimental results show that the proposed method is superior to the traditional methods and single objective methods in terms of visual comparison and quantitative analysis on our datasets.

The power sector is one of the major CO2-emitting industries in China. It is also the first key emissions control industry included in China’s national carbon trading market established in 2021. Therefore, based on the data of 30 provinces and cities in China from 2015 to 2022, this study analyzes the impact of the national carbon market policy on the carbon emissions of the power industry based on the generalized Difference-in-Difference (DID) model. Based on the method of text analysis, this paper constructs the carbon market policy intensity index of each region and incorporates it into the DID model. Empirical analysis finds that the national carbon market policy can significantly inhibit the carbon emissions of the power industry. However, the heterogeneity analysis of 30 provinces and cities shows that the national carbon market policy has a significant impact on the regions that already have a carbon pilot, but not on the non-pilot regions. Therefore, this paper demonstrates the effectiveness of the national carbon market policy in the power industry emissions control, and also exposes the heterogeneity between regions, providing an important empirical basis for the inclusion of other energy-intensive industries.

Abstract It is of engineering interest to explore recovered shale gas composition and its effects on total gas production trend over a long-term extraction period. However, there are previous experimental studies mostly focused on short term development for small scaled cores, which is less convincing to mimic reservoir-scaled shale production process. In addition, the previous production models mostly failed to account for comprehensive gas nonlinear effects. As a result, in this paper, to illustrate the full-life-cycle production decline phenomenon for shale gas reservoir, dynamic physical simulation was performed for more than 3433 days to simulate shale gas transport out of the formations over a relatively long production period. Moreover, a five-region seepage mathematical model was then developed and was subsequently validated by the experimental results and shale well production data. Our findings show that for physical simulation, both the pressure and production declined steadily at an annual rate of less than 5%, and 67% of the total gas in the core was recovered. These test data supported earlier finding that shale gas is of low flow ability and slow pressure decline in the shale matrices. The production model indicated that free gas accounts for the majority of recovered shale gas at the initial stage. Based on a shale gas well example, free gas extraction makes up 90% of produced total gas. The adsorbed gas constitutes a primary gas source during the later stage. Adsorbed gas contributes more than 50% of the gas produced in the seventh year. The 20-year-cumulative adsorbed gas makes up 21% of the EUR for a single shale gas well. The results of this study can provide a reference for optimizing production systems and adjusting development techniques for shale gas wells throughout the combinations of mathematical modeling and experimental approaches.

Study region: Typical basin in humid areas in the Huaihe River Study focus: Accurate flood forecasting is essential for making timely decisions regarding flood control and disaster reduction. The theory of watershed runoff generation and convergence serves as a crucial foundation for flood forecasting, while the calculation of runoff is necessary to simulate flood discharge. Identifying watershed runoff generation mechanisms has been a challenging task, particularly under complex underlying surface conditions. To improve the accuracy of flood simulation, this study examines the underlying surface information in the watershed, such as particle composition and content, soil bulk density, geological slope, land use, and other spatial attributes, aiming to analyze the mechanisms of runoff generation. In the study of sub-watersheds, various combinations of runoff generation mechanisms are identified to determine the patterns of runoff. Subsequently, a semi-distributed hydrological model is developed, which incorporates both saturation-excess and infiltration-excess runoff, utilizing the information obtained from the underlying surface. The model is validated using rainfall-runoff data from 14 events at the Xiagushan watershed. New hydrological insights for the region: The analysis of the fundamental physical conditions of the underlying surface of the watershed revealed that 69.70% of the area is prone to saturation-excess runoff, with an additional 30.30% of the area being susceptible to infiltration-excess runoff. The model considers the spatial distribution of runoff patterns by incorporating complex underlying surface information and demonstrates high accuracy in simulating flood events (NSE= 0.87, Epeak = 12.08%, Wpeak = 13.16%, Tpeak = 0.14 h, R2 = 0.90). The model is straightforward, practical, and exhibits promising potential in terms of timeliness and applicability, thus lending itself well to further application in other watersheds, contributing to the scientific foundation of flood warning and forecasting efforts.

BackgroundObservational studies found associations between ankylosing spondylitis (AS) and iridocyclitis (IC), but the causality remained unconfirmed.MethodsWe employed two-sample Mendelian randomization (MR) to investigate the bidirectional causal relationships between AS and IC. Single-nucleotide polymorphisms (SNPs) were chosen from the FinnGen database’s genome-wide association studies (GWAS) following a rigorous evaluation of the studies’ quality. Sensitivity analysis was performed to assess the potential influence of pleiotropy and heterogeneity on the MR findings.ResultsElevated genetic risk for AS showed positive causal effects on IC and its subtypes (IC, OR = 1.094, 95% CI = 1.035-1.157, P = 0.00156; Acute/Subacute IC, OR = 1.327, 95% CI = 1.266-1.392, P = 8.73×10-32; Chronic IC, OR = 1.454, 95% CI = 1.308-1.618, P = 5.19×10-12). Significant causal association was specifically observed between Acute/Subacute IC and AS (OR = 1.944, 95% CI = 1.316-2.873, P = 8.38×10-4). Sensitivity analysis suggested that horizontal pleiotropy was unlikely to influence the causality, and the leave-one-out analysis confirmed that a single SNP did not drive the observed associations.ConclusionOur findings provide new proof of a positive causal relationship between AS and IC in the European population. Notably, it is Acute/Subacute IC, rather than IC as a whole or Chronic IC, that is associated with an elevated risk of AS. These results emphasize the significance of considering AS characteristics in the diagnosis of Acute/Subacute IC.

With the development of new energy industries, the demand for the driving range and power quality of electric vehicle (EV) drive systems is growing rapidly. The drive motor is faced with the challenge of continuously improving power density and performance. This paper proposes a multiobjective optimization method for an interior permanent magnet synchronous motor for a traction drive (IPMSMTD). Based on the flat wire winding technology, the multiobjective optimization design of the IPMSMTD is carried out to improve the motor power density and high-efficiency range, reduce the torque ripple, and suppress the electromagnetic vibration and noise. The structure and size equation of the IPMSMTD are described. The mathematical model considering iron losses is established, and the optimization objectives are determined. Based on the genetic algorithm, a multiobjective optimization mechanism of the magnetic pole structure is established. The operation performance of the motor is analyzed by the finite element simulation and efficiency map. In order to ensure the comprehensive operation index of the IPMSMTD, the vibration noise and modal analysis are carried out, which verifies the rationality of the designed motor and the optimization method.

AbstractInhalable messenger RNA (mRNA) has demonstrated great potential in therapy and vaccine development to confront various lung diseases. However, few gene vectors could overcome the airway mucus and intracellular barriers for successful pulmonary mRNA delivery. Apart from the low pulmonary gene delivery efficiency, nonnegligible toxicity is another common problem that impedes the clinical application of many non-viral vectors. PEGylated cationic peptide-based mRNA delivery vector is a prospective approach to enhance the pulmonary delivery efficacy and safety of aerosolized mRNA by oral inhalation administration. In this study, different lengths of hydrophilic PEG chains were covalently linked to an amphiphilic, water-soluble pH-responsive peptide, and the peptide/mRNA nano self-assemblies were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The in vitro mRNA binding and release, cellular uptake, transfection, and cytotoxicity were studied, and finally, a proper PEGylated peptide with enhanced pulmonary mRNA delivery efficiency and improved safety in mice was identified. These results showed that a proper N-terminus PEGylation strategy using 12-monomer linear monodisperse PEG could significantly improve the mRNA transfection efficiency and biocompatibility of the non-PEGylated cationic peptide carrier, while a longer PEG chain modification adversely decreased the cellular uptake and transfection on A549 and HepG2 cells, emphasizing the importance of a proper PEG chain length selection. Moreover, the optimized PEGylated peptide showed a significantly enhanced mRNA pulmonary delivery efficiency and ameliorated safety profiles over the non-PEGylated peptide and LipofectamineTM 2000 in mice. Our results reveal that the PEGylated peptide could be a promising mRNA delivery vector candidate for inhaled mRNA vaccines and therapeutic applications for the prevention and treatment of different respiratory diseases in the future.

Abstract Background Current studies on the role of ARHGAP39 mainly focused on its effect on neurodevelopment. However, there are few studies on the comprehensive analysis of ARHGAP39 in breast cancer. Methods ARHGAP39 expression level was analyzed based on the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression Project (GTEx), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database and validated by qPCR in various cell lines and tumor tissues. The prognostic value was analyzed using Kaplan–Meier curve analysis. CCK-8 and transwell assays were conducted to identify the biological function of ARHGAP39 in tumorigenesis. Signaling pathways related to ARHGAP39 expression were identified by the GO and KEGG enrichment analysis and gene set enrichment analysis (GSEA). The correlations between ARHGAP39 and cancer immune infiltrates were investigated via TIMER, CIBERSORT, ESTIMATE and tumor-immune system interactions database (TISIDB). Results ARHGAP39 was overexpressed in breast cancer and associated with poor survival outcomes. In vitro experiments revealed that ARHGAP39 could facilitate the proliferation, migration, and invasion capability of breast cancer cells. GSEA analysis showed that the main enrichment pathways of ARHGAP39 was immunity-related pathways. Considering the immune infiltration level, ARHGAP39 was negatively associated with infiltrating levels of CD8 + T cell and macrophage, and positively associated with CD4 + T cell. Furthermore, ARHGAP39 was significantly negatively correlated with immune score, stromal score, and ESTIMATE score. Conclusions Our findings suggested that ARHGAP39 can be used as a potential therapeutic target and prognostic biomarker in breast cancer. ARHGAP39 was indeed a determinant factor of immune infiltration.

Abstract The use of gaseous and air-captured CO2 for technical biosynthesis is highly desired, but elusive so far due to several obstacles including high energy (ATP, NADPH) demand, low thermodynamic driving force and limited biosynthesis rate. Here, we present an ATP and NAD(P)H-free chemoenzymatic system for amino acid and pyruvate biosynthesis by coupling methanol with CO2. It relies on a re-engineered glycine cleavage system with the NAD(P)H-dependent L protein replaced by biocompatible chemical reduction of protein H with dithiothreitol. The latter provides a higher thermodynamic driving force, determines the reaction direction, and avoids protein polymerization of the rate-limiting enzyme carboxylase. Engineering of H protein to effectively release the lipoamide arm from a protected state further enhanced the system performance, achieving the synthesis of glycine, serine and pyruvate at g/L level from methanol and air-captured CO2. This work opens up the door for biosynthesis of amino acids and derived products from air.

Abstract Background Electronic nicotine delivery systems (ENDS), such as the JUUL system, are nicotine products for adults who currently smoke cigarettes but are looking for an alternative to combustible cigarettes. Sales of ENDS products were legislatively acknowledged and authorized federally in Canada with the Royal Assent of the Tobacco and Vaping Products Act in 2018. Methods With the unique dataset from a major chain retailer in Canada, we evaluated the impacts of JUUL market entry on cigarette sales across Canada from January 2017 to August 2019 using two-way fixed effects panel regression models by leveraging on the entry time variation at the city level. We conducted various robustness checks and a permutation test to validate our results. Results Our estimates suggested that JUUL market entry was, on average, significantly correlated with a 1.65% per-month decrease in cigarette sales during the initial months, and with a potentially larger impact on urban areas. Our results were robust across various specifications and tests. These findings implied that JUUL and combustible cigarettes act as economic substitutes during the study time period in Canada. Conclusions These results suggested that local availability of ENDS products, such as JUUL, has the potential to reduce local cigarette consumption.

Obesity is a well-known modifiable risk factor for breast cancer and is considered a poor prognostic factor in pre- and post-menopausal women. While the systemic effects of obesity have been extensively studied, less is known about the mechanisms underlying obesity-associated cancer risk and the local consequences of obesity. Thus, obesity-induced inflammation has become the focus of research interest. Biologically, the development of cancer involves a complex interaction with numerous components. As the tumor immune microenvironment changes due to obesity-triggered inflammation, an increase in infiltration occurs for proinflammatory cytokines and adipokines, as well as adipocytes, immune cells, and tumor cells in the expanded adipose tissue. Complicated cellular-molecular crosstalk networks change critical pathways, mediate metabolic and immune function reprogramming, and have a significant role in tumor metastasis, proliferation, resistance, angiogenesis, and tumorigenesis. This review summarizes recent research findings on how inflammatory mediators in the in situ tumor microenvironment regulate the occurrence and development of breast cancer in the context of obesity. We analyzed the heterogeneity and potential mechanisms of the breast cancer immune microenvironment from the perspective of inflammation to provide a reference for the clinical transformation of precision targeted cancer therapy.

Abstract Background Innate immune responses play essential roles in skeletal muscle recovery after injury. Programmed cell death protein 1 (PD-1) contributes to skeletal muscle regeneration by promoting macrophage proinflammatory to anti-inflammatory phenotype transition. Interferon (IFN)-γ induces proinflammatory macrophages that appear to hinder myogenesis in vitro. Therefore, we tested the hypothesis that blocking IFN-γ in PD-1 knockout mice may dampen inflammation and promote skeletal muscle regeneration via regulating the macrophage phenotype and neutrophils. Methods Anti-IFN-γ antibody was administered in PD-1 knockout mice, and cardiotoxin (CTX) injection was performed to induce acute skeletal muscle injury. Hematoxylin and eosin (HE) staining was used to view morphological changes of injured and regenerated skeletal muscle. Masson’s trichrome staining was used to assess the degree of fibrosis. Gene expressions of proinflammatory and anti-inflammatory factors, fibrosis-related factors, and myogenic regulator factors were determined by real-time polymerase chain reaction (PCR). Changes in macrophage phenotype were examined by western blot and real-time PCR. Immunofluorescence was used to detect the accumulation of proinflammatory macrophages, anti-inflammatory macrophages, and neutrophils. Results IFN-γ blockade in PD-1 knockout mice did not alleviate skeletal muscle damage or improve regeneration following acute cardiotoxin-induced injury. Instead, it exacerbated skeletal muscle inflammation and fibrosis, and impaired regeneration via inhibiting macrophage accumulation, blocking macrophage proinflammatory to anti-inflammatory transition, and enhancing infiltration of neutrophils. Conclusion IFN-γ is crucial for efficient skeletal muscle regeneration in the absence of PD-1.

Objective: Neoadjuvant therapy (NAT) has been widely implemented as an essential treatment to improve therapeutic efficacy in patients with locally-advanced cancer to reduce tumor burden and prolong survival, particularly for human epidermal growth receptor 2-positive and triple-negative breast cancer. The role of peripheral immune components in predicting therapeutic responses has received limited attention. Herein we determined the relationship between dynamic changes in peripheral immune indices and therapeutic responses during NAT administration. Methods: Peripheral immune index data were collected from 134 patients before and after NAT. Logistic regression and machine learning algorithms were applied to the feature selection and model construction processes, respectively. Results: Peripheral immune status with a greater number of CD3+ T cells before and after NAT, and a greater number of CD8+ T cells, fewer CD4+ T cells, and fewer NK cells after NAT was significantly related to a pathological complete response (P < 0.05). The post-NAT NK cell-to-pre-NAT NK cell ratio was negatively correlated with the response to NAT (HR = 0.13, P = 0.008). Based on the results of logistic regression, 14 reliable features (P < 0.05) were selected to construct the machine learning model. The random forest model exhibited the best power to predict efficacy of NAT among 10 machine learning model approaches (AUC = 0.733). Conclusions: Statistically significant relationships between several specific immune indices and the efficacy of NAT were revealed. A random forest model based on dynamic changes in peripheral immune indices showed robust performance in predicting NAT efficacy.

In this paper, we ponder a kind of discrete-time fractional-order complex-valued fuzzy BAM neural network. Firstly, in order to guarantee the quasi-projective synchronization of the considered networks, an original quantitative control strategy is designed. Next, by virtue of the relevant definitions and properties of the Mittag-Leffler function, we propose a novel discrete-time fractional-order Halanay inequality, which is more efficient for disposing of the discrete-time fractional-order models with time delays. Then, based on the new lemma, fractional-order h-difference theory, and comparison principle, we obtain some easy-to-verify synchronization criteria in terms of algebraic inequalities. Finally, numerical simulations are provided to check the accuracy of the proposed theoretical results.