Predicting the Pro-Inflammatory Effects of Oxidized Methyl Oleate Based on the Volatile Compounds.

The negative impact of lipid peroxidation on health is intimately tied to its oxidation products. In this study, methyl oleate was oxidized at 180℃ for 0, 2, 4, 8 and 12 h respectively. The free radicals and volatile components generated during the oxidation process were determined using electron spin resonance and headspace solid-phase microextraction (HS-SPME)-GC-MS. The pro-inflammatory effects of oxidized methyl oleate were evaluated in RAW264.7 cells. Then partial least-squares regression (PLSR) models were established for predicting the 3 pro-inflammatory genes expression based on the volatile components. The results revealed that the alkoxy radical content increased rapidly during oxidation from 4 h to 8 h, and the rate of oxidation of methyl oleate dropped after 8 h. A total of 27 volatile oxidation compounds were detected by HS-SPME-GC-MS. The content of most compounds, including aldehydes, esters, and acids, exhibited a pattern of initial increase and then decrease as the oxidation time increased. Similarly, the proinflammatory effects of oxidized methyl oleate peaked after 8 h of oxidation. The PLSR quantitative prediction models showed that the coefficient of determination (R2P) between the predicted and measured values of the 3 inflammatory gene expressions were 0.915, 0.946 and 0.951 respectively. The established PLSR model predicts the pro-inflammatory effects of oxidized methyl oleate well and provides a theoretical foundation for quick evaluation of the pro-inflammatory effects of oxidized lipids.

Neutrophil extracellular traps and neutrophil extracellular traps-related genes are involved in new-onset atrial fibrillation in LPS-induced sepsis.

BACKGROUND: Sepsis is considered a high risk factor for new-onset atrial fibrillation (NOAF), with neutrophil extracellular traps (NETs) being implicated in the pathogenesis of numerous diseases. However, the precise role of NETs and NETs-related genes (NRGs) in the occurrence of NOAF in sepsis remains inadequately elucidated. The objective of this study was to identify hub NRGs connecting sepsis and AF, and to investigate the potential association between NETs and NOAF in sepsis. METHODS: The AF and sepsis microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database for analysis of shared pathophysiological mechanisms and NRGs implicated in both sepsis and AF using bioinformatics techniques. The CIBERSORT algorithm was employed to assess immune cell infiltration and identify common immune characteristics in these diseases. Additionally, a rat model of lipopolysaccharide (LPS)-induced sepsis was utilized to investigate the association between NETs, NRGs, and sepsis-induced AF. Western blotting, enzyme-linked immunosorbent assay, hematoxylin-eosin staining, immunohistochemistry, and immunofluorescence were employed to assess the expression of NRGs, the formation of NETs, and the infiltration of neutrophils. Electrophysiological analysis and multi-electrode array techniques were utilized to examine the vulnerability and conduction heterogeneity of AF in septic rats. Furthermore, intervention was conducted in LPS-induced sepsis rats using DNase I, a pharmacological agent that specifically targets NETs, in order to assess its impact on neutrophil infiltration, NETs formation, hub NRGs protein expression, and AF vulnerability. RESULTS: A total of 61 commonly differentially expressed genes (DEGs) and four hub DE-NRGs were identified in the context of sepsis and AF. Functional enrichment analysis revealed that these DEGs were predominantly associated with processes related to inflammation and immunity. Immune infiltration analysis further demonstrated the presence of immune infiltrating cells, specifically neutrophil infiltration, in both sepsis and AF. Additionally, a positive correlation was observed between the relative expression of the four hub DE-NRGs and neutrophil infiltration. In rats with LPS-induced sepsis, we observed a notable upregulation in the expression of four DE-NRGs, the formation of NETs, and infiltration of neutrophils in atrial tissue. Through electrophysiological assessments, we identified heightened vulnerability to AF, reduced atrial surface conduction velocity, and increased conduction heterogeneity in LPS-induced sepsis rats. Notably, these detrimental effects can be partially ameliorated by treatment with DNase I. CONCLUSIONS: Through bioinformatics analysis and experimental validation, we identified four hub NRGs in sepsis and AF. Subsequent experiments indicated that the formation of NETs in the atria may contribute to the pathogenesis of NOAF in sepsis. These discoveries offer potential novel targets and insights for the prevention and treatment of NOAF in sepsis.

Relationship Between Temporomandibular Joint Effusion, Pain, and Jaw Function Limitation: A 2D and 3D Comparative Study.

Purpose: This study aimed to investigate the relationship between temporomandibular joint (TMJ) effusion and TMJ pain, as well as jaw function limitation in patients via two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) evaluation. Patients and Methods: 121 patients diagnosed with temporomandibular disorder (TMD) were included. TMJ effusion was assessed qualitatively using MRI and quantified with 3D Slicer software, then graded accordingly. In addition, a visual analogue scale (VAS) was employed for pain reporting and an 8-item Jaw Functional Limitations Scale (JFLS-8) was utilized to evaluate jaw function limitation. Statistical analyses were performed appropriately for group comparisons and association determination. A probability of p<0.05 was considered statistically significant. Results: 2D qualitative and 3D quantitative strategies were in high agreement for TMJ effusion grades (κ = 0.766). No significant associations were found between joint effusion and TMJ pain, nor with disc displacement and JLFS-8 scores. Moreover, the binary logistic regression analysis showed significant association between sex and the presence of TMJ effusion, exhibiting an Odds Ratio of 5.168 for females (p = 0.008). Conclusion: 2D qualitative evaluation was as effective as 3D quantitative assessment for TMJ effusion diagnosis. No significant associations were found between TMJ effusion and TMJ pain, disc displacement or jaw function limitation. However, it was suggested that female patients suffering from TMD may be at a risk for TMJ effusion. Further prospective research is needed for validation.

Biological scaffold as potential platforms for stem cells: Current development and applications in wound healing.

Wound repair is a complex challenge for both clinical practitioners and researchers. Conventional approaches for wound repair have several limitations. Stem cell-based therapy has emerged as a novel strategy to address this issue, exhibiting significant potential for enhancing wound healing rates, improving wound quality, and promoting skin regeneration. However, the use of stem cells in skin regeneration presents several challenges. Recently, stem cells and biomaterials have been identified as crucial components of the wound-healing process. Combination therapy involving the development of biocompatible scaffolds, accompanying cells, multiple biological factors, and structures resembling the natural extracellular matrix (ECM) has gained considerable attention. Biological scaffolds encompass a range of biomaterials that serve as platforms for seeding stem cells, providing them with an environment conducive to growth, similar to that of the ECM. These scaffolds facilitate the delivery and application of stem cells for tissue regeneration and wound healing. This article provides a comprehensive review of the current developments and applications of biological scaffolds for stem cells in wound healing, emphasizing their capacity to facilitate stem cell adhesion, proliferation, differentiation, and paracrine functions. Additionally, we identify the pivotal characteristics of the scaffolds that contribute to enhanced cellular activity.

Red cell distribution width to total serum calcium ratio and in-hospital mortality risk in patients with acute ischemic stroke: A MIMIC-IV retrospective analysis.

We investigated the relationship among red cell distribution width (RDW), to total serum calcium (TSC) ratio (RCR), and in-hospital mortality in patients with acute ischemic stroke (AIS). This study was a retrospective analysis. The data of 2700 AIS patients was retrospectively analyzed from the Medical Information Mart for Intensive Care database (version IV). The main outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether RCR was independently associated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves for RCR. Subgroup analyses were performed to measure the mortality across various subgroups. The area under curve (AUC) of receiver operating characteristic curve (ROC) was calculated to ascertain the quality of RCR as a predictor of in-hospital mortality in patients with AIS. In the multivariate analysis, statistically significant differences were identified in age, ethnicity, length of ICU stay, mechanical ventilation, sequential organ failure assessment (SOFA) score, RDW, hemoglobin, RCR, whether taking anticoagulants, hyperlipidemia, and atrial fibrillation (P < .05). A threshold inflection point value of 1.83 was obtained through a two-piecewise regression model. There was a non-linear relationship between RCR and hospital mortality in patients with AIS. The hazard ratio (HR) and the 95% confidence intervals (CI) on the right and left of the inflection point were 0.93 (0.57-1.51; P = .7660) and 2.96 (1.37-6.42; P = .0060), respectively. The Kaplan-Meier curve indicated that survival rates were higher when RCR was ≤ 1.83 and lower when RDW was > 1.83 after adjustment for age, gender, BMI, ethnicity. The area under curve (AUC) of RCR was 0.715. A higher RCR was associated with an increased risk of in-hospital mortality in patients with AIS.

Modular multimodal hospital-home chain physical activity rehabilitation programme (3M2H-PARP) in liver cancer: a protocol for a randomised controlled trial.

INTRODUCTION: Patients with liver cancer are susceptible to experiencing a decline in muscle mass and function, which can lead to physical frailty and have a negative impact on prognosis. However, there is currently a lack of physical activity interventions specifically tailored for these patients. Therefore, we have developed a modular multimodal hospital-home chain physical activity rehabilitation programme (3M2H-PARP) designed specifically for patients with liver cancer undergoing transarterial chemoembolisation (TACE). We aim to validate the effectiveness and feasibility of this programme through a randomised controlled trial (RCT). METHODS AND ANALYSIS: 3M2H-PARP RCT will compare a 12-week, modular, multimodal physical activity rehabilitation programme that includes supervised exercise in a hospital setting and self-management exercise at home. The programmes consist of aerobic, resistance, flexibility and balance exercise modules, and standard survivorship care in a cohort of liver cancer survivors who have undergone TACE. The control group will receive standard care. A total of 152 participants will be randomly assigned to either the 3M2H-PARP group or the control group. Assessments will be conducted at three time points: baseline, after completing the intervention and a 24-week follow-up visit. The following variables will be evaluated: liver frailty index, Functional Assessment of Cancer Therapy-Hepatobiliary subscale, Cancer Fatigue Scale, Pittsburgh Sleep Quality Index, Hospital Anxiety and Depression Scale and physical activity level. After the completion of the training programme, semi-structured interviews will be conducted with participants from the 3M2H-PARP group to investigate the programme's impact on their overall well-being. SPSS V.26.0 software will be used for statistical analyses. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Jiangnan University School of Medicine Research Ethics Committee. The findings will be disseminated through publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: ChiCTR2300076800.

Tricuspid valve flow measurement using a deep learning framework for automated valve-tracking 2D phase contrast.

PURPOSE: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve. METHODS: Nine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC scans acquired in a static slice localized at the end-systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels. RESULTS: The mean tricuspid valve systolic excursion was 17.8 ± 2.5 mm. The 2D valve-tracking PC net diastolic flow showed excellent correlation with SV by right-ventricle planimetry (bias ± 1.96 SD = -0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (-1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right-ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin-systole. In one patient, valve-tracking PC displayed a high-velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography. CONCLUSION: Automated valve-tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge.

Capsaicin combined with stem cells improved mitochondrial dysfunction in PIG3V cells, an immortalized human vitiligo melanocyte cell line, by inhibiting the HSP70/TLR4/mTOR/FAK signaling axis.

BACKGROUND: Vitiligo is a common autoimmune skin disease. Capsaicin has been found to exert a positive effect on vitiligo treatment, and mesenchymal stem cells (MSCs) are also confirmed to be an ideal cell type. This study aimed to explore the influence of capsaicin combined with stem cells on the treatment of vitiligo and to confirm the molecular mechanism of capsaicin combined with stem cells in treating vitiligo. METHODS AND RESULTS: PIG3V cell proliferation and apoptosis were detected using CCK-8 and TUNEL assays, MitoSOX Red fluorescence staining was used to measure the mitochondrial ROS level, and JC-1 staining was used to detect the mitochondrial membrane potential. The expression of related genes and proteins was detected using RT‒qPCR and Western blotting. Coimmunoprecipitation was used to analyze the protein interactions between HSP70 and TLR4 or between TLR4 and mTOR. The results showed higher expression of HSP70 in PIG3V cells than in PIG1 cells. The overexpression of HSP70 reduced the proliferation of PIG3V cells, promoted apoptosis, and aggravated mitochondrial dysfunction and autophagy abnormalities. The expression of HSP70 could be inhibited by capsaicin combined with MSCs, which increased the levels of Tyr, Tyrp1 and DCT, promoted the proliferation of PIG3V cells, inhibited apoptosis, activated autophagy, and improved mitochondrial dysfunction. In addition, capsaicin combined with MSCs regulated the expression of TLR4 through HSP70 and subsequently affected the mTOR/FAK signaling pathway CONCLUSIONS: Capsaicin combined with MSCs inhibits TLR4 through HSP70, and the mTOR/FAK signaling pathway is inhibited to alleviate mitochondrial dysfunction and autophagy abnormalities in PIG3V cells.

Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability.

The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.

Multi-hierarchy Network Configuration Can Predict Brain States and Performance.

The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we proposed an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules, and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.

Multi-Stimulus Least-Squares Transformation With Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-Based BCIs.

Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort for SSVEP-based BCI. However, the transformation matrices constructed by current LST methods are not precise enough, resulting in large differences between the transformed data and the real data of the target subject. This ultimately leads to the constructed spatial filters and reference templates not being effective enough. To address these issues, this paper proposes multi-stimulus LST with online adaptation scheme (ms-LST-OA). METHODS: The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial. RESULTS: ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01±10.10 bits/min, 172.31±7.26 bits/min, and 139.04±14.90 bits/min for all three datasets, respectively. CONCLUSION: Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.

The relationship between red cell distribution width, serum calcium ratio, and in-hospital mortality among patients with acute respiratory failure: A retrospective cohort study of the MIMIC-IV database.

To investigate the impact of RDW/CA (the ratio of red cell distribution width to calcium) on in-hospital mortality in patients with acute respiratory failure (ARF). This retrospective cohort study analyzed the data of 6981 ARF patients from the Medical Information Mart for Intensive Care (MIMIC-IV) database 2.0. Critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston. The primary outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether the RDW/CA ratio independently correlated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves of the RDW/CA. Subgroup analyses were performed to measure the mortality across various subgroups. After adjusting for potential covariates, we found that a higher RDW/CA was associated with an increased risk of in-hospital mortality (HR = 1.17, 95% CI: 1.01-1.35, P = .0365) in ARF patients. A nonlinear relationship was observed between RDW/CA and in-hospital mortality, with an inflection point of 1.97. When RDW/CA ≥ 1.97 was positively correlated with in-hospital mortality in patients with ARF (HR = 1.554, 95% CI: 1.183-2.042, P = .0015). The Kaplan-Meier curve indicated the higher survival rates for RDW/CA < 1.97 and the lower for RDW/CA ≥ 1.97 after adjustment for age, gender, body mass index, and ethnicity. RDW/CA is an independent predictor of in-hospital mortality in patients with ARF. Furthermore, a nonlinear relationship was observed between RDW/CA and in-hospital mortality in patients with ARF.

Isoliquiritigenin limits inflammasome activation of macrophage via docking into Syk to alleviate murine non-alcoholic fatty liver disease.

Isoliquiritigenin (ISL) is a chalcone-type flavonoid derived from the root of licorice with antioxidant, anti-inflammatory, anti-tumour and neuroprotective properties. ISL has been proven to downregulate the productions of IL-1ß, TNF-α and IL-6 by macrophages. However, detailed molecular mechanisms of this modulation remain elusive. Here, ISL suppressed Syk phosphorylation and CD80, CD86, IL-1ß, TNF-α and IL-6 expressions in lipopolysaccharide-stimulated macrophages ex vivo. ApoC3-transgenic (ApoC3TG) mice had more activated macrophages. ISL was also able to downregulate the inflammatory activities of macrophages from ApoC3TG mice. Administration of ISL inhibited Syk activation and inflammatory activities of macrophages in ApoC3TG mice in vivo. The treatment of ISL further alleviated MCD-induced non-alcoholic fatty liver disease (NAFLD) in wild-type and ApoC3TG mice, accompanied by less recruitment and activation of liver macrophages. Due to the inhibition of Syk phosphorylation, ISL-treated macrophages displayed less production of cytoplasmic ROS, NLRP3, cleaved-GSDMD and cleaved-IL-1ß, suggesting less inflammasome activation. Finally, the molecular docking study demonstrated that ISL bound to Syk directly with the Kd of 1.273 × 10-8 M. When the Syk expression was knocked down by its shRNA, the inhibitory effects of ISL on activated macrophages disappeared, indicating that Syk was at least one of key docking-molecules of ISL. Collectively, ISL could alleviate MCD-induced NAFLD in mice involved with the inhibition of macrophage inflammatory activity by the blockade of Syk-induced inflammasome activation.

Functional overlap between the mammalian Sar1a and Sar1b paralogs in vivo.

Proteins carrying a signal peptide and/or a transmembrane domain enter the intracellular secretory pathway at the endoplasmic reticulum (ER) and are transported to the Golgi apparatus via COPII vesicles or tubules. SAR1 initiates COPII coat assembly by recruiting other coat proteins to the ER membrane. Mammalian genomes encode two SAR1 paralogs, SAR1A and SAR1B. While these paralogs exhibit ~90% amino acid sequence identity, it is unknown whether they perform distinct or overlapping functions in vivo. We now report that genetic inactivation of Sar1a in mice results in lethality during midembryogenesis. We also confirm previous reports that complete deficiency of murine Sar1b results in perinatal lethality. In contrast, we demonstrate that deletion of Sar1b restricted to hepatocytes is compatible with survival, though resulting in hypocholesterolemia that can be rescued by adenovirus-mediated overexpression of either SAR1A or SAR1B. To further examine the in vivo function of these two paralogs, we genetically engineered mice with the Sar1a coding sequence replacing that of Sar1b at the endogenous Sar1b locus. Mice homozygous for this allele survive to adulthood and are phenotypically normal, demonstrating complete or near-complete overlap in function between the two SAR1 protein paralogs in mice. These data also suggest upregulation of SAR1A gene expression as a potential approach for the treatment of SAR1B deficiency (chylomicron retention disease) in humans.

Beta-informativeness-diffusion multilayer graph embedding for brain network analysis.

Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.

Heterometallic MIL-125(Ti-Al) frameworks for electrochemical determination of ascorbic acid, dopamine and uric acid.

The detection of ascorbic acid (AA), dopamine (DA), and uric acid (UA) is not only of great significance in the areas of biomedicine and neurochemistry but also helpful in disease diagnosis and pathology research. Due to their diverse structures, designability, and large specific surface areas, metal-organic frameworks (MOFs) have recently caught considerable attention in the electrochemical field. Herein, a family of heterometallic MOFs with amino modification, MIL-125(Ti-Al)-xNH2 (x = 0%, 25%, 50%, 75%, and 100%), were synthesized and employed as electrochemical sensors for the detection of AA, DA, and UA. Among them, MIL-125(Ti-Al)-75%NH2 exhibited the most promising electrochemical behavior with 40% doping of carbon black in 0.1 M PBS (pH = 7.10), which displayed individual detection performance with wide linear detection ranges (1.0-6.5 mM for AA, 5-100 µM for DA and 5-120 µM for UA) and low limits of detection (0.215 mM for AA, 0.086 µM for DA, and 0.876 µM for UA, S/N = 3). Furthermore, the as-prepared MIL-125(Ti-Al)-75%NH2/GCE provided a promising platform for future application in real sample analysis, owing to its excellent anti-interference performance and good stability.

Visualization Analysis of Artificial Intelligence Literature in Forensic Research.

OBJECTIVES: To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database, to explore research hotspots and developmental trends. METHODS: A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the literature measuring tool CiteSpace. The authors, institution, country (region), title, journal, keywords, cited references and other information of relevant literatures were analyzed. RESULTS: A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries (regions) were identified, with the number of articles published showing an increasing trend year by year. Among them, the United States had the highest number of publications and China ranked the second. Academy of Forensic Science had the highest number of publications among the institutions. Forensic Science International, Journal of Forensic Sciences, International Journal of Legal Medicine ranked high in publication and citation frequency. Through the analysis of keywords, it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technology for sex and age estimation, cause of death analysis, postmortem interval estimation, individual identification and so on. CONCLUSIONS: It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research. Exploring the combination of advanced artificial intelligence technologies with forensic research will be a hotspot and direction for future research.

Exploring the effect of disc displacement on the risk and severity of condylar erosion in adult temporomandibular disorder patients: A CBCT and MRI study.

OBJECTIVE: The objective of the study was to investigate the relationship between types of disc displacement (DD) diagnosed by magnetic resonance imaging (MRI), and the risk (presence or absence) and severity of condylar erosion (CE) graded using cone-beam computed tomography (CBCT) in adult Temporomandibular disorders (TMD) patients. METHODS: A total of 353 TMD patients (283 females, 70 males) underwent MRI scans to categorise DD as normal (NA), anterior displacement with reduction (ADDR), or anterior displacement without reduction (ADDNR). CE severity was graded on a scale of 0-3 (absence, mild, moderate or severe) using CBCT. To establish the plausibility and cut-off points for CE diagnosis, the severity of CE was then further divided into three classifications: Grade 0 versus 1 + 2 + 3; Grades 0 + 1 versus 2 + 3; Grades 0 + 1 + 2 versus 3. Logistic regression analysis was performed, adjusting for age, gender and joint correlation. RESULTS: ADDNR significantly increased the risk of CE compared with NA (OR = 10.04, 95% CI: [6.41, 15.73]) and showed a significant increase in CE severity across all classifications (ORs = 10.04-18.95). The effects of ADDNR were significant in both genders (p < .001) and had a greater impact in females. ADDR was predominantly associated with mild CE. CONCLUSIONS: ADDNR significantly increased the risk and severity of CE independent of gender when compared to NA, whereas ADDR was mainly associated with mild CE. Slight cortical discontinuity may represent a subclinical diagnosis requiring further investigation.

Repressing miR-23a promotes the transdifferentiation of pancreatic α cells to ß cells via negatively regulating the expression of SDF-1α.

Pancreatic ß-cell failure is a pathological feature in type 1 diabetes. One promising approach involves inducing transdifferentiation of related pancreatic cell types, specifically α cells that produce glucagon. The chemokine stromal cell-derived factor-1 alpha (SDF-1α) is implicated in pancreatic α-to-ß like cell transition. Here, the serum level of SDF-1α was lower in T1D with C-peptide loss, the miR-23a was negatively correlated with SDF-1α. We discovered that exosomal miR-23a, secreted from ß cells, functionally downregulates the expression of SDF-1α, leading to increased Pax4 expression and decreased Arx expression in vivo. Adenovirus-vectored miR-23a sponge and mimic were constructed to further explored the miR-23a on pancreatic α-to-ß like cell transition in vitro, which yielded results consistent with our cell-based assays. Suppression of miR-23a upregulated insulin level and downregulated glucagon level in STZ-induced diabetes mice models, effectively promoting α-to-ß like cell transition. Our findings highlight miR-23a as a new therapeutic target for regenerating pancreatic ß cells from α cells.

Functional overlap between the mammalian Sar1a and Sar1b paralogs in vivo.

Proteins carrying a signal peptide and/or a transmembrane domain enter the intracellular secretory pathway at the endoplasmic reticulum (ER) and are transported to the Golgi apparatus via COPII vesicles or tubules. SAR1 initiates COPII coat assembly by recruiting other coat proteins to the ER membrane. Mammalian genomes encode two SAR1 paralogs, SAR1A and SAR1B. While these paralogs exhibit ~90% amino acid sequence identity, it is unknown whether they perform distinct or overlapping functions in vivo. We now report that genetic inactivation of Sar1a in mice results in lethality during mid-embryogenesis. We also confirm previous reports that complete deficiency of murine Sar1b results in perinatal lethality. In contrast, we demonstrate that deletion of Sar1b restricted to hepatocytes is compatible with survival, though resulting in hypocholesterolemia that can be rescued by adenovirus-mediated overexpression of either SAR1A or SAR1B. To further examine the in vivo function of these 2 paralogs, we genetically engineered mice with the Sar1a coding sequence replacing that of Sar1b at the endogenous Sar1b locus. Mice homozygous for this allele survive to adulthood and are phenotypically normal, demonstrating complete or near-complete overlap in function between the two SAR1 protein paralogs in mice. These data also suggest upregulation of SAR1A gene expression as a potential approach for the treatment of SAR1B deficiency (chylomicron retention disease) in humans.