The role of 6-phosphogluconate dehydrogenase in vascular smooth muscle cell phenotypic switching and angioplasty-induced intimal hyperplasia.

Background: Restenosis poses a significant challenge for individuals afflicted with peripheral artery diseases, often leading to considerable morbidity and necessitating repeated interventions. The primary culprit behind the pathogenesis of restenosis is intimal hyperplasia (IH), in which the hyperproliferative and migratory vascular smooth muscle cell (VSMC) accumulate excessively in the tunica intima. 6-Phosphogluconate dehydrogenase (6PGD), sometimes referred to as PGD, is one of the critical enzymes in pentose phosphate pathway (PPP). In this study, we sought to probe whether 6PGD is aberrantly regulated in IH and contributes to VSMC phenotypic switching. Methods: We used clinical specimens of diseased human coronary arteries with IH lesions and observed robust upregulation of 6PGD at protein level in both the medial and intimal layers in comparison with healthy arterial segments. Results: 6PGD activity and protein expression were profoundly stimulated upon platelet-derived growth factor-induced VSMC phenotypic switching. Using gain-of-function (dCas9-mediated transcriptional activation) and loss-of-function (small interfering RNA-mediated) silencing, we were able to demonstrate the pathogenic role of 6PGD in driving VSMC hyperproliferation, migration, dedifferentiation, and inflammation. Finally, we conducted a rat model of balloon angioplasty in the common carotid artery, with Pluronic hydrogel-assisted perivascular delivery of Physcion, a selective 6PGD inhibitor with poor systemic bioavailability, and observed effective mitigation of IH. Conclusions: We contend that aberrant 6PGD expression and activity-indicative of a metabolic shift toward pentose phosphate pathway-could serve as a new disease-driving mechanism and, hence, an actionable target for the development of effective new therapies for IH and restenosis after endovascular interventions.

Development of a Specifically Labeled 89Zr Antibody for the Noninvasive Imaging of Tumors Overexpressing B7-H3.

B7-H3 has emerged as a promising target and potential biomarker for diagnosing tumors, evaluating treatment efficacy, and determining patient prognosis. Hu4G4 is a recombinant humanized antibody that selectively targets the extracellular domain of human B7-H3. In this study, we describe the radiolabeling of hu4G4 with the positron emission tomography (PET) emitter radionuclide zirconium 89 (89Zr) and evaluate its potency as an immuno-PET tracer for B7-H3-targeted imaging by comparing it in vitro and in vivo to [89Zr]Zr-DFO-DS-5573a using various models. The radiolabeled compound, [89Zr]Zr-desferrioxamine-hu4G4 ([89Zr]Zr-DFO-hu4G4), demonstrated a high radiochemical purity (RCP) of greater than 99% and a specific activity of 74 MBq/mg following purification. Additionally, it maintained stability in human serum albumin (HSA) and acetate buffer, preserving over 90% of its RCP after 7 days. Three cell lines targeting human B7-H3(U87/CT26-CD276/GL261-CD276) were used. Flow cytometry analysis indicated that the B7-H3-positive cells (U87/CT26-CD276/GL261-CD276) had a higher B7-H3 protein level with no expression in the B7-H3-negative cells (CT26-wt/GL261-wt) (P < 0.001). Moreover, the cellular uptake was 45.71 ± 3.78% for [89Zr]Zr-DFO-hu4G4 in CT26-CD276 cells versus only 0.93 ± 0.47% in CT26-wt cells and 30.26 ± 0.70% when [89Zr]Zr-DFO-hu4G4 in CT26-CD276 cells were blocked with 100× 8H9. The cellular uptake of [89Zr]Zr-DFO-hu4G4 was akin to that observed with [89Zr]Zr-DFO-DS-5573a with no significant differences (45.71 ± 3.78 % vs 47.07 ± 0.86 %) in CT26-CD276 cells. Similarly, the CT26-CD276 mouse model demonstrated markedly low organ uptake and elevated tumor uptake 48 h after [89Zr]Zr-DFO-hu4G4 injection. PET/CT analysis showed that the tumor-to-muscle (T/M) ratios were substantially higher compared to other imaging groups: 27.65 ± 3.17 in CT26-CD276 mice versus 11.68 ± 4.19 in CT26-wt mice (P < 0.001) and 16.40 ± 0.78 when 100× 8H9 was used to block [89Zr]Zr-DFO-hu4G4 in CT26-CD276 mice (P < 0.01) at 48 h post-injection. Additionally, the tracer showed markedly high accumulation in the tumor region (22.57 ± 3.03% ID/g), comparable to the uptake of [89Zr]Zr-DFO-DS-5573a (24.76 ± 5.36% ID/g). A dosimetry estimation study revealed that the effective dose for [89Zr]Zr-DFO-hu4G4 was 2.96 × 10-01 mSv/MBq, which falls within the acceptable range for further research in nuclear medicine. Collectively, these results indicated that [89Zr]Zr-DFO-hu4G4 was successfully fabricated and applied in B7-H3-targeted tumor PET/CT imaging, which showed excellent imaging quality and tumor detection efficacy in tumor-bearing mice. It is a promising imaging agent for identifying tumors that overexpress B7-H3 for future clinical applications.

Sonothrombolysis Using Microfluidically Produced Microbubbles in a Murine Model of Deep Vein Thrombosis.

The need for safe and effective methods to manage deep vein thrombosis (DVT), given the risks associated with anticoagulants and thrombolytic agents, motivated research into innovative approaches to resolve blood clots. In response to this challenge, sonothrombolysis is being explored as a technique that combines microbubbles, ultrasound, and thrombolytic agents to facilitate the aggressive dissolution of thrombi. Prior studies have indicated that relatively large microbubbles accelerate the dissolution process, either in an in vitro or an arterial model. However, sonothrombolysis using large microbubbles must be evaluated in venous thromboembolism diseases, where blood flow velocity is not comparable. In this study, the efficacy of sonothrombolysis was validated in a murine model of pre-existing DVT. During therapy, microfluidically produced microbubbles of 18 µm diameter and recombinant tissue plasminogen activator (rt-PA) were administered through a tail vein catheter for 30 min, while ultrasound was applied to the abdominal region of the mice. Three-dimensional ultrasound scans were performed before and after therapy for quantification. The residual volume of the thrombi was 20% in animals post sonothrombolysis versus 52% without therapy ( p = 0.012 < 0.05 ), indicating a significant reduction in DVT volume. Histological analysis of tissue sections confirmed a reduction in DVT volume post-therapy. Therefore, large microbubbles generated from a microfluidic device show promise in ultrasound-assisted therapy to address concerns related to venous thromboembolism.

An interpretable machine learning method for risk stratification of patients with acute coronary syndrome.

Backgrounds: Risk stratification for major adverse cardiovascular events (MACE) within one year in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) remains a challenge. Although several predictive models based on machine learning have emerged, they are difficult to understand. This study aimed to develop a machine learning prediction model that is easy to understand and trustworthy by lay people to assess the risk of MACE in ACS patients undergoing PCI within one year of the procedure. Methods: This retrospective cohort study used medical data from 1105 patients to construct a machine-learning model. To ensure thoroughness and multidimensionality of model parsing, Shapley Additive explanations (SHAP) analysis and Local interpretable model-agnostic explanations (LIME) interpretation techniques were used to systematically and deeply interpret the constructed models from a global to a detailed level. Results: The study assessed 12 machine learning methods' prediction models and found that the Random Forest model was the most effective in predicting the risk of MACE in ACS patients after undergoing PCI. The model achieved an AUC value of 0.807 in the validation set, with an accuracy of 0.82, and a stable F1 score of 0.51. SHAP analysis ranked eight key feature variables, such as LVEF, in global importance. The weights of each feature range in the prediction model were revealed using LIME analysis. Conclusion: The machine learning prediction model we developed is capable of accurately predicting the likelihood of patients with ACS experiencing a MACE within one year of surgery.

Sex differences in large-scale brain network connectivity for mental rotation performance.

Mental rotation has emerged as an important predictor for success in science, technology, engineering, and math fields. Previous studies have shown that males and females perform mental rotation tasks differently. However, how the brain functions to support this difference remains poorly understood. Recent advancements in neuroimaging techniques have enabled the identification of sex differences in large-scale brain network connectivity. Using a classic mental rotation task with functional magnetic resonance imaging, the present study investigated whether there are any sex differences in large-scale brain network connectivity for mental rotation performance. Our results revealed that, relative to females, males exhibited less cross-network interaction (i.e. lower inter-network connectivity and participation coefficient) of the visual network but more intra-network integration (i.e. higher intra-network connectivity and local efficiency) and cross-network interaction (i.e. higher inter-network connectivity and participation coefficient) of the salience network. Across all participants, mental rotation performance was negatively correlated with cross-network interaction (i.e. participation coefficient) of the visual network, was positively correlated with cross-network interaction (i.e. inter-network connectivity) of the salience network, and was positively correlated with intra-network integration (i.e. local efficiency) of the somato-motor network. Interestingly, the cross-network integration indexes of both the visual and salience networks significantly mediated sex difference in mental rotation performance. The present findings suggest that large-scale brain network connectivity may constitute an essential neural basis for sex difference in mental rotation, and highlight the importance of considering sex as a research variable in investigating the complex network underpinnings of spatial cognition.

Comparison of the Value of Four Objective Nutritional Indices in Assessing the Long-Term Prognosis of Elderly Patients with Heart Failure with Preserved Ejection Fraction.

Background: The long-term prognosis of heart failure with preserved ejection fraction (HFpEF) is influenced by malnutrition. Currently, there's a deficit in objective and comprehensive nutritional assessment methods to evaluate the nutritional status and predicting the long-term outcomes of HFpEF patients. Methods: Our retrospective study included two hundred and eighteen elderly HFpEF patients admitted to the cardiovascular ward at the Air Force Medical Centre from January 2016 to December 2021. Based on follow-up outcomes, patients were categorized into all-cause death (99 cases) and Survival (119 cases) groups. We compared general data, laboratory results, and nutritional indexes between groups. Differences in subgroups based on Triglyceride-Total Cholesterol-Body Weight Index (TCBI), Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), and Controlled Nutrition Score (CONUT) were analyzed using Kaplan-Meier survival curves and log-rank test. COX regression was used to identify all-cause mortality risk factors, and the predictive accuracy of the four nutritional indices was assessed using receiver operating characteristic (ROC) curves and Delong test analysis. Results: A total of 101 (45.41%) HFpEF patients experienced all-cause mortality during 59.02 ± 1.79 months of follow-up. The all-cause mortality group exhibited lower GNRI and PNI levels, and higher CONUT levels than the Survival group (p < 0.05). Kaplan-Meier analysis revealed lower cumulative survival in the low GNRI ( ≤ 96.50) and low PNI ( ≤ 43.75) groups, but higher in the low CONUT ( ≤ 2) group, compared to their respective medium and high-value groups. Multifactorial COX regression identified low PNI ( ≤ 43.75) as an independent all-cause mortality risk factor in elderly HFpEF patients. ROC and Delong's test indicated PNI (area under the curve [AUC] = 0.698, 95% confidence interval [CI] 0.629-0.768) as a more effective predictor of all-cause mortality than TCBI (AUC = 0.533, 95% CI 0.456-0.610) and CONUT (AUC = 0.621, 95% CI 0.547-0.695; p < 0.05). However, there was no significant difference compared to GNRI (AUC = 0.663, 95% CI 0.590-0.735; p > 0.05). Conclusions: In elderly HFpEF patients a PNI ≤ 43.75 was identified as an independent risk factor for all-cause mortality. Moreover, PNI demonstrates superior prognostic performance in predicting all-cause mortality in elderly patients with HFpEF when compared to TCBI, GNRI, and COUNT.

Effects of different sodium-glucose cotransporter 2 inhibitors in heart failure with reduced or preserved ejection fraction: a network meta-analysis.

Background: This systematic review and meta-analysis aimed to explore the effects of different sodium-glucose cotransporter-2 inhibitors (SGLT2i) on prognosis and cardiac structural remodeling in patients with heart failure (HF). Methods: Relevant studies published up to 20 March 2024 were retrieved from PubMed, EMBASE, Web of Science, and Cochrane Library CNKI, China Biomedical Literature Service, VIP, and WanFang databases. We included randomized controlled trials of different SGLT2i and pooled the prognosis data of patients with HF. We compared the efficacy of different SGLT2i in patients with HF and conducted a sub-analysis based on left ventricular ejection fraction (LVEF). Results: We identified 77 randomized controlled trials involving 43,561 patients. The results showed that SGLT2i significantly enhanced outcomes in HF, including a composite of hospitalizations for HF and cardiovascular death, individual hospitalizations for HF, Kansas City Cardiomyopathy Questionnaire (KCCQ) scores, left atrial volume index (LAVi), and LVEF among all HF patients (P < 0.05) compared to a placebo. Sotagliflozin was superior to empagliflozin [RR = 0.88, CI (0.79-0.97)] and dapagliflozin [RR = 0.86, CI (0.77-0.96)] in reducing hospitalizations for HF and CV death. Dapagliflozin significantly reduced hospitalizations [RR = 0.51, CI (0.33-0.80)], CV death [RR = 0.73, CI (0.54-0.97)], and all-cause mortality [RR = 0.69, CI (0.48-0.99)] in patients with HF with reduced ejection fraction (HFrEF). SGLT2i also plays a significant role in improving cardiac remodeling and quality of life (LVMi, LVEDV, KCQQ) (P < 0.05). Among patients with HF with preserved ejection fraction (HFpEF), SGLT2i significantly improved cardiac function in HFpEF patients (P < 0.05). In addition, canagliflozin [RR = 0.09, CI (0.01-0.86)] demonstrated greater safety compared to sotagliflozin in a composite of urinary and reproductive infections of HFpEF patients. Conclusion: Our systematic review showed that SGLT2i generally enhances the prognosis of patients with HF. Sotagliflozin demonstrated superiority over empagliflozin and dapagliflozin in a composite of hospitalization for HF and CV death in the overall HF patients. Canagliflozin exhibited greater safety compared to sotagliflozin in a composite of urinary and reproductive infections of HFpEF. Overall, the efficacy of SGLT2i was greater in HFrEF patients than in HFpEF patients.

Adaptively identify and refine ill-posed regions for accurate stereo matching.

Stereo matching cost constrains the consistency between pixel pairs. However, the consistency constraint becomes unreliable in ill-posed regions such as occluded or ambiguous regions of the images, making it difficult to explore hidden correspondences. To address this challenge, we introduce an Error-area Feature Refinement Mechanism (EFR) that supplies context features for ill-posed regions. In EFR, we innovatively obtain the suspected error region according to aggregation perturbations, then a simple Transformer module is designed to synthesize global context and correspondence relation with the identified error mask. To better overcome existing texture overfitting, we put forward a Dual-constraint Cost Volume (DCV) that integrates supplementary constraints. This effectively improves the robustness and diversity of disparity clues, resulting in enhanced details and structural accuracy. Finally, we propose a highly accurate stereo matching network called Error-rectify Feature Guided Stereo Matching Network (ERCNet), which is based on DCV and EFR. We evaluate our model on several benchmark datasets, achieving state-of-the-art performance and demonstrating excellent generalization across datasets. The code is available at https://github.com/dean7liu/ERCNet_2023.

Prognostic value of Geriatric Nutritional Risk Index and systemic immune-inflammatory index in elderly patients with acute coronary syndromes.

The objective of this study was to evaluate the predictive value of the Geriatric Nutritional Risk Index (GNRI) combined with the Systemic Immunoinflammatory Index (SII) for the risk of major adverse cardiovascular events (MACE) following percutaneous coronary intervention in elderly patients with acute coronary syndrome (ACS). We retrospectively reviewed the medical records of 1202 elderly patients with acute coronary syndromes divided into MACE and non-MACE groups according to whether they had a MACE. The sensitivity analysis utilized advanced machine learning algorithms to preliminarily identify the critical role of GNRI versus SII in predicting MACE risk. We conducted a detailed analysis using a restricted cubic spline approach to investigate the nonlinear relationship between GNRI, SII, and MACE risk further. We constructed a clinical prediction model based on three key factors: GNRI, SII, and Age. To validate the accuracy and usefulness of this model, we compared it to the widely used GRACE score using subject work and recall curves. Additionally, we compared the predictive value of models and GRACE scores in assessing the risk of MACE using the Integrated Discriminant Improvement Index (IDI) and the Net Reclassification Index (NRI). This study included 827 patients. The GNRI scores were lower in the MACE group than in the non-MACE group, while the SII scores were higher in the MACE group (P < 0.001). The multifactorial analysis revealed a low GNRI (OR = 2.863, 95% CI: 2.026-4.047, P = 0.001), High SII (OR = 3.102, 95% CI: 2.213-4.348, P = 0.001). The area under the curve (AUC) for the predictive model was 0.778 (95% CI: 0.744-0.813, P = 0.001), while the AUC for the GRACE score was 0.744 (95% CI: 0.708-0.779, P = 0.001). NRI was calculated to be 0.5569, with NRI + at 0.1860 and NRI- at 0.3708. The IDI was found to be 0.0571, with a P-value of less than 0.001. These results suggest that the newly developed prediction model is more suitable for use with the population in this study than the GRACE score. The model constructed using GNRI and SII demonstrated good standardization and clinical impact, as evidenced by the standard, DCA, and clinical impact curves. The study shows that combining GNRI and SII can be a simple, cost-effective, and valuable way to predict the risk of MACE within one year in elderly acute coronary syndromes.

A Quantitative Method for the Evaluation of Deep Vein Thrombosis in a Murine Model Using Three-Dimensional Ultrasound Imaging.

Deep vein thrombosis (DVT) is a life-threatening condition that can lead to its sequelae pulmonary embolism (PE) or post-thrombotic syndrome (PTS). Murine models of DVT are frequently used in early-stage disease research and to assess potential therapies. This creates the need for the reliable and easy quantification of blood clots. In this paper, we present a novel high-frequency 3D ultrasound approach for the quantitative evaluation of the volume of DVT in an in vitro model and an in vivo murine model. The proposed method involves the use of a high-resolution ultrasound acquisition system and semiautomatic segmentation of the clot. The measured 3D volume of blood clots was validated to be correlated with in vitro blood clot weights with an R2 of 0.89. Additionally, the method was confirmed with an R2 of 0.91 in the in vivo mouse model with a cylindrical volume from macroscopic measurement. We anticipate that the proposed method will be useful in pharmacological or therapeutic studies in murine models of DVT.

Comparative analysis of four nutritional scores predicting the incidence of MACE in older adults with acute coronary syndromes after PCI.

To determine the most appropriate nutritional assessment tool for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI from four nutritional assessment tools including PNI, GNRI, CONUT, and BMI. Consecutive cases diagnosed with acute coronary syndrome (ACS) and underwent percutaneous coronary intervention (PCI) in the Department of Cardiovascular Medicine of the Air force characteristic medical center from 1 January 2020 to 1 April 2022 were retrospectively collected. The basic clinical characteristics and relevant test and examination indexes were collected uniformly, and the cases were divided into the MACE group (174 cases) and the non-MACE group (372 cases) according to whether a major adverse cardiovascular event (MACE) had occurred within 1 year. Predictive models were constructed to assess the nutritional status of patients with the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling nutritional status (CONUT) scores, and Body Mass Index (BMI), respectively, and to analyze their relationship with prognosis. The incremental value of the four nutritional assessment tools in predicting risk was compared using the Integrated Discriminant Improvement (IDI) and the net reclassification improvement (NRI). The predictive effect of each model on the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI was assessed using area under the ROC curve (AUC), calibration curves, decision analysis curves, and clinical impact curves; comparative analyses were performed. Among the four nutritional assessment tools, the area under the curve (AUC) was significantly higher for the PNI (AUC: 0.798, 95%CI 0.755-0.840 P < 0.001) and GNRI (AUC: 0.760, 95%CI 0.715-0.804 P < 0.001) than for the CONUT (AUC: 0.719,95%CI 0.673-0.765 P < 0.001) and BMI (AUC: 0.576, 95%CI 0.522-0.630 P < 0.001). The positive predictive value (PPV) of PNI: 67.67% was better than GNRI, CONUT, and BMI, and the negative predictive value (NPV): of 83.90% was better than CONUT and BMI and similar to the NPV of GNRI. The PNI, GNRI, and CONUT were compared with BMI, respectively. The PNI had the most significant improvement in the Integrated Discriminant Improvement Index (IDI) (IDI: 0.1732, P < 0.001); the PNI also had the most significant improvement in the Net Reclassification Index (NRI) (NRI: 0.8185, P < 0.001). In addition, of the four nutritional assessment tools used in this study, the PNI was more appropriate for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI.

Comprehensive bioinformatics analysis revealed potential key genes and pathways underlying abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is a permanent, asymptomatic segmental dilatation of the abdominal aorta, with a high mortality risk upon rupture. Identification of potential key genes and pathways may help to develop curative drugs for AAA. We conducted RNA-seq on abdominal aortic tissues from both AAA patients and normal individuals as a control group. Integrated bioinformatic analysis was subsequently performed to comprehensively reveal potential key genes and pathways. A total of 1148 differential expressed genes (DEGs) (631 up-regulated and 517 down-regulated) were identified in our study. Gene Ontology (GO) analysis revealed enrichment in terms related to extracellular matrix organization, while KEGG analysis indicated enrichment in hematopoietic cell lineage and ECM-receptor interaction. Protein-protein interaction (PPI) network analysis revealed several candidate key genes, and differential expression of 6 key genes (CXCL8, CCL2, PTGS2, SELL, CCR7, and CXCL1) was validated by Gene Expression Omnibus (GEO) datasets. Receiver operating characteristic curve (ROC) analysis demonstrated these genes' high discriminatory ability between AAA and normal tissues. Immunohistochemistry indicated that several key genes were highly expressed in AAA tissues. Single-cell RNA sequencing revealed differential distribution patterns of these identified key genes among various cell types. 26 potential drugs linked to our key genes were found through DGIdb. Overall, our study provides a comprehensive evaluation of potential key genes and pathways in AAA, which could pave the way for the development of curative pharmacological therapies.

Radioimmunotherapy Targeting B7-H3 in situ glioma models enhanced antitumor efficacy by Reconstructing the tumor microenvironment.

Radionuclide drug conjugates (RDCs) with antibodies serve as a novel approach for the treatment of malignant tumors including glioblastoma. However, RDCs require optimal antibodies to work efficiently. Hu4G4, a novel B7-H3-targeting humanized monoclonal IgG1 antibody, is highly specific for the human B7-H3 protein (a marker of tumor cells, including glioblastoma cells). Herein, we established 131I-labeled hu4G4 (131I-hu4G4) and showed that it specifically bound to B7-H3 with high affinity (Kd = 0.99 ± 0.07 nM) and inhibited the growth of U87 cells in vitro. 131I-hu4G4 displayed potent in situ antitumor activity in a mouse model of glioma based on GL261 Red-Fluc-B7-H3 cells. More importantly, 131I-hu4G4 remodeled the tumor microenvironment and promoted the transformation of glioma from "cold" to "hot" tumors by promoting CD4+ and CD8+ T cell infiltration and the polarization of M2 to M1. Therefore, the antitumor activity observed with 131I-hu4G4, together with its ability to enhance antitumor immune responses, makes it a novel candidate for radioimmunotherapy of glioblastoma.

Impact of green digital finance on green economic recovery and green agricultural development: implications for green environment.

This study explores the potential of green digital finance and green agricultural growth to contribute to the green economic recovery of the BRICS economies. We examine the relationship between these variables using empirical data and various statistical techniques, including vector error correction, co-integration, unit root tests and long-run analysis. Our results suggest that well-functioning financial institutions play a crucial role in facilitating the structural transformation of green digital finance and promoting green agricultural growth to achieve green economic recovery in the BRICS region. Our findings underscore the need for pro-financial and green economic development policies and institutions to support and enhance economic recovery. Our results are robust and supported by our study. We also suggest future research directions for stakeholders interested in promoting sustainable economic growth in the BRICS countries.

N-acyl homoserine lactone mediating initial adhesion of microalgal biofilm formation.

While pioneering methods have demonstrated that bacterial N-acyl homoserine lactone (AHL) signaling molecules can influence the growth and self-aggregation of suspended microalgae, whether AHLs can affect the initial adhesion to a carrier has remained an open question. Here we revealed that the microalgae exhibited different adhesion potential under AHL mediation, where the performance was affiliated to both AHL types and concentrations. The result can be well explained by the interaction energy theory, where the energy barrier between the carriers and the cells varied due to AHL mediation. Depth analyses revealed that AHL acted through modifying the properties of the surface electron donor of the cells, which were dependent upon three major components, i.e., extracellular protein (PN) secretion, the PN secondary structure, and the PN amino acid composition. These findings expand the known diversity of AHLs mediation on microalgal initial adhesion and metabolisms, which may interface with other major cycles and become helpful to theoretically guide the application of AHLs in microalgal culture and harvesting.

The relation between college students' neuroticism and loneliness: The chain mediating roles of self-efficacy, social avoidance and distress.

Background: Recently, identifying the psychological mechanism of college students' loneliness has attracted wide attention because the maladjustment caused by college students' loneliness is increasingly common. This study explored the relationship and potential mechanism between college students' neuroticism and loneliness in a large sample. Methods: A total of 4,600 college students completed the Big Five Personality Scale, Loneliness Scale, Self-efficacy Scale and Social Avoidance and Distress Scale. Results: By examining the chain mediating roles of self-efficacy, social avoidance and distress (SAD) in the relation between neuroticism and loneliness, the present study found that college students' neuroticism was positively associated with loneliness via self-efficacy and SAD, respectively, and sequentially. Conclusions: The results suggest a significant positive association between neuroticism and loneliness, which is influenced by the mediating effects of both self-efficacy and social avoidance and distress (SAD), as well as the chained mediating effects of self-efficacy and SAD.

Smoking and coronary artery disease risk in patients with diabetes: A Mendelian randomization study.

Background: Previous observational studies have shown an association between smoking and coronary artery disease (CAD) in patients with diabetes. Whether this association reflects causality remains unestablished. This study aimed to explore the causal effect of smoking on CAD in patients with diabetes. Methods: Genetic signatures for smoking were extracted from a large genome-wide association study (GWAS), consisted of up to 1.2 million participants. Four smoking phenotypes were included: smoking initiation, cigarettes per day, age at initiation of regular smoking, and smoking cessation. Genetic associations with CAD in patients with diabetes were extracted from another GWAS, which included 15,666 participants (3,968 CAD cases and 11,696 controls). The analyses were performed using the univariable and multivariable Mendelian randomization (MR) method. Results: MR analysis revealed that smoking initiation was positively related to CAD risk in patients with diabetes (OR = 1.322, 95% CI = 1.114 - 1.568, P = 0.001), but this association was attenuated when adjusted for cardiovascular risk factors (OR = 1.212, 95% CI = 1.008 - 1.457, P = 0.041). Age at initiation of regular smoking was negatively related to CAD in patients with diabetes (OR = 0.214, 95% CI = 0.070 - 0.656, P = 0.007), but this association became insignificant when adjusted for cardiovascular risk factors. Conclusions: This study supported the effect of smoking initiation on the risk of CAD in patients with diabetes.

Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis.

Background: Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature. Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type. Results: After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables. Conclusions: ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.

Blue Light Signaling Regulates Escherichia coli W1688 Biofilm Formation and l-Threonine Production.

Escherichia coli biofilm may form naturally on biotic and abiotic surfaces; this represents a promising approach for efficient biochemical production in industrial fermentation. Recently, industrial exploitation of the advantages of optogenetics, such as simple operation, high spatiotemporal control, and programmability, for regulation of biofilm formation has garnered considerable attention. In this study, we used the blue light signaling-induced optogenetic system Magnet in an E. coli biofilm-based immobilized fermentation system to produce l-threonine in sufficient quantity. Blue light signaling significantly affected the phenotype of E. coli W1688. A series of biofilm-related experiments confirmed the inhibitory effect of blue light signaling on E. coli W1688 biofilm. Subsequently, a strain lacking a blue light-sensing protein (YcgF) was constructed via genetic engineering, which substantially reduced the inhibitory effect of blue light signaling on biofilm. A high-efficiency biofilm-forming system, Magnet, was constructed, which enhanced bacterial aggregation and biofilm formation. Furthermore, l-threonine production was increased from 10.12 to 16.57 g/L during immobilized fermentation, and the fermentation period was shortened by 6 h. IMPORTANCE We confirmed the mechanism underlying the inhibitory effects of blue light signaling on E. coli biofilm formation and constructed a strain lacking a blue light-sensing protein; this mitigated the aforementioned effects of blue light signaling and ensured normal fermentation performance. Furthermore, this study elucidated that the blue light signaling-induced optogenetic system Magnet effectively regulates E. coli biofilm formation and contributes to l-threonine production. This study not only enriches the mechanism of blue light signaling to regulate E. coli biofilm formation but also provides a theoretical basis and feasibility reference for the application of optogenetics technology in biofilm-based immobilized fermentation systems.

Intraspecific morphological variation in myxosporeans: high pleomorphic myxospores in the same plasmodium of Myxobolus drjagini (Akhmerov, 1954).

The taxonomy of myxosporeans was traditionally dependent solely upon the spore morphological and morphometric data. Intensive reports of intraspecific morphological variation, however, are increasingly challenging the taxonomic approaches for myxosporeans. In the present work, the morphological pleomorphism of myxospores of Myxobolus drjagini (Akhmerov, 1954) was observed. More interestingly, all of these pleomorphic myxospores occurred in the same plasmodium of M. drjagini, which refutes the previous hypothesis that morphological variation of M. drjagini was derived from its responses to differences in nutrition and immunological responses associated with different host tissues. Bearing the intraspecific morphometric and morphotype variation in mind, the combination of morphological, ecological and molecular data should be applied to the species identification and delimitation for myxosporeans. This is the first reported myxobolid species with high pleomorphic myxospores which are present in the same plasmodium.