Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre-emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in-silico quantitative marker for hyperuricemia (ISHUA). The model demonstrates satisfactory performance in detecting HUA, exhibiting area under the curves (AUCs) of 0.859, 0.836, and 0.779 within the train, internal, and external test sets, respectively. ISHUA is significantly associated with gout and metabolism-related outcomes, effectively classifying individuals into low- and high-risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high-risk group shows increased susceptibility to metabolism-related outcomes, and participants with intermediate or favorable lifestyle profiles have hazard ratios of 0.75 and 0.53 for gout compared with those with unfavorable lifestyles. Similar trends are observed for other metabolism-related outcomes. The multimodal machine learning-based ISHUA marker enables personalized risk stratification for gout and metabolism-related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high-risk group, providing guidance for preventive interventions.

Inhibition of inflammation and apoptosis through the cyclic GMP-AMP synthase-stimulator of interferon genes pathway by stress granules after ALKBH5 demethylase activation during diabetic myocardial ischaemia-reperfusion injury.

AIM: Post-transcriptional modifications and their specific mechanisms are the focus of research on the regulation of myocardial damage. Stress granules (SGs) can inhibit the inflammatory response by inhibiting the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway. This study investigated whether alkylation repair homologue protein 5 (ALKBH5) could affect myocardial inflammation and apoptosis during diabetic myocardial ischaemia-reperfusion injury (IRI) through the cGAS-STING pathway via SGs. METHODS: A diabetes ischaemia-reperfusion rat model and a high glucose hypoxia/reoxygenation cell model were established. Adeno-associated virus (AAV) and lentivirus (LV) were used to overexpress ALKBH5, while the SG agonist arsenite (Ars) and the SG inhibitor anisomycin were used as interventions. Then, the levels of apoptosis and related indicators in the cell and rat models were measured. RESULTS: In the in vivo experiment, compared with the normal sham group, the degree of myocardial tissue damage, creatine kinase-MB and cardiac troponin I in serum, and myocardial apoptosis, the infarcted area of myocardium, and the level of B-cell lymphoma 2 associated X protein, cGAS-STING pathway and inflammatory factors in the diabetes ischaemia-reperfusion group were significantly increased. However, the expression of SGs and the levels of ALKBH5, rat sarcoma-GTPase-activating protein-binding protein 1, T-cell intracellular antigen-1 and Bcl2 were significantly decreased. After AAV-ALKBH5 intervention, the degree of myocardial tissue damage, degree of myocardial apoptosis, and extent of myocardial infarction in myocardial tissue were significantly decreased. In the in vitro experiment, compared with those in the normal control group, the levels of lactate dehydrogenase, inflammation and apoptosis were significantly greater, and cell viability and the levels of ALKBH5 and SGs were decreased in the high glucose and hypoxia/reoxygenation groups. In the high glucose hypoxia/reoxygenation cell model, the degree of cell damage, inflammation, and apoptosis was greater than those in the high glucose and hypoxia/reoxygenation models, and the levels of ALKBH5 and SGs were further decreased. LV-ALKBH5 and Ars alleviated the degree of cell damage and inhibited inflammation and cell apoptosis. The inhibition of SGs could partly reverse the protective effect of LV-ALKBH5. The cGAS agonist G140 antagonized the inhibitory effects of the SG agonist Ars on cardiomyocyte apoptosis, inflammation and the cGAS-STING pathway. CONCLUSION: Both ALKBH5 and SGs inhibited myocardial inflammation and apoptosis during diabetic myocardial ischaemia-reperfusion. Mechanistically, ALKBH5 might inhibit the apoptosis of cardiomyocytes by promoting the expression of SGs through the cGAS-STING pathway.

Effect of dapagliflozin on ferroptosis through the gut microbiota metabolite TMAO during myocardial ischemia-reperfusion injury in diabetes mellitus rats.

Dapagliflozin (DAPA) demonstrates promise in the management of diabetic mellitus (DM) and cardiomyopathy. Trimethylamine N-oxide (TMAO) is synthesized by the gut microbiota through the metabolic conversion of choline and phosphatidylcholine. Ferroptosis may offer novel therapeutic avenues for the management of diabetes and myocardial ischemia-reperfusion injury (IRI). However, the precise mechanism underlying ferroptosis in cardiomyocytes and the specific role of TMAO generated by gut microbiota in the therapeutic approach for DM and myocardial IRI utilizing DAPA need to be further explored. Nine male SD rats with specific pathogen-free (SPF) status were randomly divided equally into the normal group, the DM + IRI (DIR) group, and the DAPA group. The diversity of the gut microbiota was analyzed using 16S rRNA gene sequencing. Additionally, the Wekell technique was employed to measure the levels of TMAO in the three groups. Application of network pharmacology to search for intersection targets of DAPA, DIR, and ferroptosis, and RT-PCR experimental verification. Ultimately, the overlapping targets that were acquired were subjected to molecular docking analysis with TMAO. The changes of Bacteroidetes and Firmicutes in the gut microbiota of DIR rats were most significantly affected by DAPA. Escherichia-Shigella and Prevotella_9 within the phylum Bacteroidetes could be identified as the primary effects of DAPA on DIR. Compared with the normal group, the TMAO content in the DIR group was significantly increased, while the TMAO content in the DAPA group was decreased compared to the DIR group. For the network pharmacology analysis, DAPA and DIR generated 43 intersecting target genes, and then further intersected with ferroptosis-related genes, resulting in 11 overlapping target genes. The mRNA expression of ALB, HMOX1, PPARG, CBS, LCN2, and PPARA decreased in the DIR group through reverse transcription polymerase chain reaction (RT-PCR) validation, while the opposite trend was observed in the DAPA group. The docking score between TMAO and DPP4 was - 5.44, and the MM-GBSA result of - 22.02 kcal/mol. It epitomizes the finest docking performance among all the target genes with the lowest score. DAPA could reduce the levels of metabolite TMAO produced by gut microbiota, thereby regulating related target genes to decrease ferroptosis in DIR cardiomyocytes.

Crosstalk between Endoplasmic Reticulum Stress and Ferroptosis in Liver Diseases.

The endoplasmic reticulum (ER) played an important role in the folding, assembly and post-translational modification of proteins. ER homeostasis could be disrupted by the accumulation of misfolded proteins, elevated reactive oxygen species (ROS) levels, and abnormal Ca2+ signaling, which was referred to ER stress (ERS). Ferroptosis was a unique programmed cell death model mediated by iron-dependent phospholipid peroxidation and multiple signaling pathways. The changes of mitochondrial structure, the damage of glutathione peroxidase 4 (GPX4) and excess accumulation of iron were the main characteristics of ferroptosis. ROS produced by ferroptosis can interfere with the activity of protein-folding enzymes, leading to the accumulation of large amounts of unfolded proteins, thus causing ERS. On the contrary, the increase of ERS level could promote ferroptosis by the accumulation of iron ion and lipid peroxide, the up-regulation of ferroptosis related genes. At present, the studies on the relationship between ferroptosis and ERS were one-sided and lack of in-depth studies on the interaction mechanism. This review aimed to explore the molecular mechanism of cross-talk between ferroptosis and ERS, and provide new strategies and targets for the treatment of liver diseases.

Noninvasive Lung Cancer Subtype Classification Using Tumor-Derived Signatures and cfDNA Methylome.

Accurate diagnosis of lung cancer is important for treatment decision-making. Tumor biopsy and histologic examination are the standard for determining histologic lung cancer subtypes. Liquid biopsy, particularly cell-free DNA (cfDNA), has recently shown promising results in cancer detection and classification. In this study, we investigate the potential of cfDNA methylome for the noninvasive classification of lung cancer histologic subtypes. We focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcriptional activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in cross-validation and an AUC of 0.747 in the independent validation. Tumor copy-number alterations inferred from cfDNA methylome analysis revealed potential for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasive lung cancer subtyping, offering insights for cancer monitoring and early detection. SIGNIFICANCE: This study explores the use of cfDNA methylomes for the classification of lung cancer subtypes, vital for effective treatment. By identifying specific methylation markers in tumor tissues, we developed a robust classification model achieving high accuracy for noninvasive subtype detection. This cfDNA methylome approach offers promising avenues for early detection and monitoring.

A feature-aware multimodal framework with auto-fusion for Alzheimer's disease diagnosis.

Alzheimer's disease (AD), one of the most common dementias, has about 4.6 million new cases yearly worldwide. Due to the significant amount of suspected AD patients, early screening for the disease has become particularly important. There are diversified types of AD diagnosis data, such as cognitive tests, images, and risk factors, many prior investigations have primarily concentrated on integrating only high-dimensional features and simple fusion concatenation, resulting in less-than-optimal outcomes for AD diagnosis. Therefore, We propose an enhanced multimodal AD diagnostic framework comprising a feature-aware module and an automatic model fusion strategy (AMFS). To preserve the correlation and significance features within a low-dimensional space, the feature-aware module employs a low-dimensional SHapley Additive exPlanation (SHAP) boosting feature selection as the initial step, following this analysis, diverse tiers of low-dimensional features are extracted from patients' biological data. Besides, in the high-dimensional stage, the feature-aware module integrates cross-modal attention mechanisms to capture subtle relationships among different cognitive domains, neuroimaging modalities, and risk factors. Subsequently, we integrate the aforementioned feature-aware module with graph convolutional networks (GCN) to address heterogeneous data in multimodal AD, while also possessing the capability to perceive relationships between different modalities. Lastly, our proposed AMFS autonomously learns optimal parameters for aligning two sub-models. The validation tests using two ADNI datasets show the high accuracies of 95.9% and 91.9% respectively, in AD diagnosis. The methods efficiently select features from multimodal AD data, optimizing model fusion for potential clinical assistance in diagnostics.

Development and validation of a novel sleep health score in the sleep heart health study.

BACKGROUND: There is a lack of consensus in evaluating multidimensional sleep health, especially concerning its implication for mortality. A validated multidimensional sleep health score is the foundation of effective interventions. METHODS: We obtained data from 5706 participants in the Sleep Heart Health Study. First, random forest-recursive feature elimination algorithm was used to select potential predictive variables. Second, a sleep composite score was developed based on the regression coefficients from a Cox proportional hazards model evaluating the associations between selected sleep-related variables and mortality. Last, we validated the score by constructing Cox proportional hazards models to assess its association with mortality. RESULTS: The mean age of participants was 63.2 years old, and 47.6% (2715/5706) were male. Six sleep variables, including average oxygen saturation (%), spindle density (C3), sleep efficiency (%), spindle density (C4), percentage of fast spindles (%) and percentage of rapid eye movement (%) were selected to construct this multidimensional sleep health score. The average sleep composite score in participants was 6.8 of 22 (lower is better). Participants with a one-point increase in sleep composite score had an 10% higher risk of death (hazard ratio = 1.10, 95% confidence interval: 1.08-1.12). CONCLUSIONS: This study constructed and validated a novel multidimensional sleep health score to better predict death based on sleep, with significant associations between sleep composite score and all-cause mortality. Integrating questionnaire information and sleep microstructures, our sleep composite score is more appropriately applied for mortality risk stratification.

Modulation of glycolipid metabolism in T2DM rats by Rubus irritans Focke extract: Insights from metabolic profiling and ERK/IRS-1 signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: The extracellular regulated protein kinase (ERK) plays a crucial role in the mitogen-activated protein kinase (MAPK) family, influencing apoptosis, proliferation, and differentiation. It connection to the insulin (INS) signaling cascade and the development of type 2 diabetes mellitus (T2DM) has been established. Rubus irritans Focke, an indispensable herb in Chinese Tibetan medicine for diabetes mellitus treatment, lacks a comprehensive understanding of its effects and pharmacological mechanisms in T2DM. AIM OF THE STUDY: This study aimed to elucidate the effects of Rubus irritans Focke extract (Rife) on a T2DM rat model, exploring its impact on glycemic and lipid metabolism, histopathological changes, and its potential targeting of the extracellular regulated protein kinase/insulin receptor substrate-1 (ERK/IRS-1) signaling pathway. MATERIALS AND METHODS: A T2DM rat model was induced by streptozotocin (STZ) injection (40 mg/kg) in high-fat diet-fed (HFD) male Wistar rats. Rife and metformin (Met) were administered for 4 weeks, and glycemic, lipid metabolism indices, and histopathological changes were assessed. Protein expression of ERK, IRS-1 in rat liver tissues was examined to evaluate the impact on the ERK/IRS-1 pathway. RESULTS: Rife reducing hepatic ERK and IRS-1 protein expression in T2DM rats. Untargeted metabolomics identified 13 potential biomarkers and 4 differential metabolic pathways related to glycolipid metabolism disorders. CONCLUSIONS: Rife demonstrated improved glycolipid metabolism in T2DM rats by inhibiting the ERK/IRS-1 related signaling pathway and influencing multiple metabolic pathways. This study provides valuable insights into the potential therapeutic mechanisms of Rife in the context of T2DM.

Stimuli-Responsive Aptamer-Drug Conjugates for Targeted Drug Delivery and Controlled Drug Release.

Chemotherapy is widely used for cancer therapy but with unsatisfied efficacy, mainly due to the inefficient delivery of anticancer agents. Among the critical "five steps" drug delivery process, internalization into tumor cells and intracellular drug release are two important steps for the overall therapeutic efficiency. Strategy based on active targeting or TME-responsive is developed individually to improve therapeutic efficiency, but with limited improvement. However, the combination of these two strategies could potentially augment the drug delivery efficiency and therapeutic efficiency, consequently. Therefore, this work constructs a library of stimuli-responsive aptamer-drug conjugates (srApDCs), as "dual-targeted" strategy for cancer treatment that enables targeted drug delivery and controlled drug release. Specifically, this work uses different stimuli-responsive linkers to conjugate a tumor-targeting aptamer (i.e., AS1411) with drugs, forming the library of srApDCs for targeted cancer treatment. This design hypothesis is validated by the experimental data, which indicated that the aptamer could selectively enhance uptake of the srApDCs and the linkers could be cleaved by pathological cues in the TME to release the drug payload, leading to a significant enhancement of therapeutic efficacy. These results underscore the potential of the approach, providing a promising methodology for cancer therapy.

DJ-1 preserves ischemic postconditioning-induced cardioprotection in STZ-induced type 1 diabetic rats: role of PTEN and DJ-1 subcellular translocation.

BACKGROUND: Ischemic postconditioning (IPostC) has been reported as a promising method for protecting against myocardial ischemia-reperfusion (MI/R) injury. Our previous study found that the infarct-limiting effect of IPostC is abolished in the heart of diabetes whose cardiac expression of DJ-1 (also called PARK7, Parkinsonism associated deglycase) is reduced. However, the role and in particular the underlying mechanism of DJ-1 in the loss of sensitivity to IPostC-induced cardioprotection in diabetic hearts remains unclear. METHODS: Streptozotocin-induced type 1 diabetic rats were subjected to MI/R injury by occluding the left anterior descending artery (LAD) and followed by reperfusion. IPostC was induced by three cycles of 10s of reperfusion and ischemia at the onset of reperfusion. AAV9-CMV-DJ-1, AAV9-CMV-C106S-DJ-1 or AAV9-DJ-1 siRNA were injected via tail vein to either over-express or knock-down DJ-1 three weeks before inducing MI/R. RESULTS: Diabetic rats subjected to MI/R exhibited larger infarct area, more severe oxidative injury concomitant with significantly reduced cardiac DJ-1 expression and increased PTEN expression as compared to non-diabetic rats. AAV9-mediated cardiac DJ-1 overexpression, but not the cardiac overexpression of DJ-1 mutant C106S, restored IPostC-induced cardioprotection and this effect was accompanied by increased cytoplasmic DJ-1 translocation toward nuclear and mitochondrial, reduced PTEN expression, and increased Nrf-2/HO-1 transcription. Our further study showed that AAV9-mediated targeted DJ-1 gene knockdown aggravated MI/R injury in diabetic hearts, and this exacerbation of MI/R injury was partially reversed by IPostC in the presence of PTEN inhibition or Nrf-2 activation. CONCLUSIONS: These findings suggest that DJ-1 preserves the cardioprotective effect of IPostC against MI/R injury in diabetic rats through nuclear and mitochondrial DJ-1 translocation and that inhibition of cardiac PTEN and activation of Nrf-2/HO-1 may represent the major downstream mechanisms whereby DJ-1 preserves the cardioprotective effect of IPostC in diabetes.

Effects of Succinate on Growth Performance, Meat Quality and Lipid Synthesis in Bama Miniature Pigs.

Succinate, one of the intermediates of the tricarboxylic acid cycle, is now recognized to play a role in a broad range of physiological and pathophysiological settings, but its role in adipogenesis is unclear. Our study used Bama miniature pigs as a model to explore the effects of succinate on performance, meat quality, and fat formation. The results showed that adding 1% succinate significantly increased the average daily gain, feed/gain ratio, eye muscle area, and body fat content (p < 0.05), but had no effect on feed intake. Further meat quality analysis showed that succinate increased the marbling score and intramuscular fat content of longissimus dorsi muscle (LM), while decreasing the shear force and the cross-sectional area of LM (p < 0.05). Metabolomics analysis of LM revealed that succinate reshaped levels of fatty acids, triglycerides, glycerophospholipids, and sphingolipids in LM. Succinate promotes adipogenic differentiation in porcine primary preadipocytes. Finally, dietary succinate supplementation increased succinylation modification rather than acetylation modification in the adipose tissue pool. This study elucidated the effects of succinate on the growth and meat quality of pigs and its mechanism of action and provided a reference for the role of succinate in the nutrition and metabolism of pigs.

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS: The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION: We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION: Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.

Blood urea nitrogen to serum albumin ratio: a novel mortality indicator in intensive care unit patients with coronary heart disease.

The blood urea nitrogen to albumin ratio (BAR) has been demonstrated as a prognostic factor in sepsis and respiratory diseases, yet its role in severe coronary heart disease (CHD) remains unexplored. This retrospective study, utilizing data from the Medical Information Mart for Intensive Care-IV database, included 4254 CHD patients, predominantly male (63.54%), with a median age of 74 years (IQR 64-83). Primary outcomes included in-hospital, 28-day and 1-year all-cause mortality after ICU admission. The Kaplan-Meier curves, Cox regression analysis, multivariable restricted cubic spline regression were employed to assess association between BAR index and mortality. In-hospital, within 28-day and 1-year mortality rates were 16.93%, 20.76% and 38.11%, respectively. Multivariable Cox proportional hazards analysis revealed associations between the increased BAR index and higher in-hospital mortality (HR 1.11, 95% CI 1.02-1.21), 28-day mortality (HR 1.17, 95% CI 1.08-1.27) and 1-year mortality (HR 1.23, 95% CI 1.16-1.31). Non-linear relationships were observed for 28-day and 1-year mortality with increasing BAR index (both P for non-linearity < 0.05). Elevated BAR index was a predictor for mortality in ICU patients with CHD, offering potential value for early high-risk patient identification and proactive management by clinicians.

Evaluation of G3BP1 in the prognosis of acute and acute-on-chronic liver failure after the treatment of artificial liver support system.

BACKGROUND: The increased expression of G3BP1 was positively correlated with the prognosis of liver failure. AIM: To investigate the effect of G3BP1 on the prognosis of acute liver failure (ALF) and acute-on-chronic liver failure (ACLF) after the treatment of artificial liver support system (ALSS). METHODS: A total of 244 patients with ALF and ACLF were enrolled in this study. The levels of G3BP1 on admission and at discharge were detected. The validation set of 514 patients was collected to verify the predicted effect of G3BP1 and the viability of prognosis. RESULTS: This study was shown that lactate dehydrogenase (LDH), alpha-fetoprotein (AFP) and prothrombin time were closely related to the prognosis of patients. After the ALSS treatment, the patient' amount of decreased G3BP1 index in difference of G3BP1 between the value of discharge and admission (difG3BP1) < 0 group had a nearly 10-fold increased risk of progression compared with the amount of increased G3BP1 index. The subgroup analysis showed that the difG3BP1 < 0 group had a higher risk of progression, regardless of model for end-stage liver disease high-risk or low-risk group. At the same time, compared with the inflammatory marks [tumor necrosis factor-α, interleukin (IL)-1ß and IL-18], G3BP1 had higher discrimination and was more stable in the model analysis and validation set. When combined with AFP and LDH, concordance index was respectively 0.84 and 0.8 in training and validation cohorts. CONCLUSION: This study indicated that G3BP1 could predict the prognosis of ALF or ACLF patients treated with ALSS. The combination of G3BP1, AFP and LDH could accurately evaluate the disease condition and predict the clinical endpoint of patients.

Function and mechanism of exosomes derived from different cells as communication mediators in colorectal cancer metastasis.

Colorectal cancer (CRC) ranks as the second leading cause of cancer-related mortality, with metastasis being the primary determinant of poor prognosis in patients. Investigating the molecular mechanisms underlying CRC metastasis is currently a prominent and challenging area of research. Exosomes, as crucial intercellular communication mediators, facilitate the transfer of metabolic and genetic information from cells of origin to recipient cells. Their roles in mediating information exchange between CRC cells and immune cells, fibroblasts, and other cell types are pivotal in reshaping the tumor microenvironment, regulating key biological processes such as invasion, migration, and formation of pre-metastatic niche. This article comprehensively examines the communication function and mechanism of exosomes derived from different cells in cancer metastasis, while also presenting an outlook on current research advancements and future application prospects. The aim is to offer a distinctive perspective that contributes to accurate diagnosis and rational treatment strategies for CRC.

Effect of lipopolysaccharide on TAK1-mediated hepatocyte PANoptosis through Toll-like receptor 4 during acute liver failure.

BACKGROUND: Intestinal endotoxemia (IETM) is an important pathogenic mechanism of acute liver failure (ALF), and TAK1-mediated PANoptosis is a novel cell death mode. This study investigated whether IETM can induce hepatocyte PANoptosis during ALF. METHOD: PANoptosis cell and mouse models were generated, and lentiviruses (LVs), adeno-associated viral vectors (AVVs), and small interfering RNAs (siRNAs) were subsequently used to overexpress or knock down TLR and TAK1. Then, the levels of hepatocyte injury, TLR4, TAK1 and PANoptosis were detected via an enzyme-labeling instrument, tissue staining, RT-PCR, western blotting, immunofluorescence, and flow cytometry. RESULTS: The BioGRID database search revealed that TAK1 might interact with TLR4. According to the in vivo experiments, compared with those in ALF mice, liver tissue damage, hepatocyte mortality and PANoptosis in mice in the AAV-TAK1 group were significantly lower, and liver function was significantly improved. According to the in vitro experiments, after promoting the expression of TLR4 in the model group, the degree of cell damage, TLR4 expression and PANoptosis further increased, while the level of TAK1 further decreased. The opposite result was obtained when TLR4 expression was inhibited. The increase in TAK1 expression in the model group reduced the degree of cell damage and PANoptosis, but the level of TLR4 was not significantly changed. In the model group of cells that exhibited TAK1 expression, further promotion of TLR4 expression inhibited the protective effect of TAK1 on cells. In the model group of cells after TAK1 expression was promoted, if the expression of TLR4 was further promoted, the protective effect of TAK1 on cells was inhibited. CONCLUSION: IETM inhibited the expression of TAK1 by binding to TLR4 molecules and promoting hepatocyte PANoptosis during ALF. Promoting TAK1 expression effectively relieved lipopolysaccharide-induced hepatocyte PANoptosis.

Constructing a Nomogram Model to Estimate the Risk of Ventilator-Associated Pneumonia for Elderly Patients in the Intensive Care Unit.

BACKGROUND: Ventilator-associated pneumonia (VAP) causes heavy losses in terms of finances, hospitalization, and death for elderly patients in the intensive care unit (ICU); however, the risk is difficult to evaluate due to a lack of reliable assessment tools. We aimed to create and validate a nomogram to estimate VAP risk to provide early intervention for high-risk patients. METHODS: Between January 2016 and March 2021, 293 patients from a tertiary hospital in China were retrospectively reviewed as a training set. Another 84 patients were enrolled for model validation from April 2021 to February 2022. Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis were employed to select predictors, and a nomogram model was constructed. The calibration, discrimination, and clinical utility of the nomogram were verified. Finally, a web-based online scoring system was created to make the model more practical. RESULTS: The predictors were hypoproteinemia, long-term combined antibiotic use, intubation time, length of mechanical ventilation, and tracheotomy/intubation. The area under the curve (AUC) was 0.937 and 0.925 in the training and validation dataset, respectively, suggesting the model exhibited effective discrimination. The calibration curve demonstrated high consistency with the observed result and the estimated values. Decision curve analysis (DCA) demonstrated that the nomogram was clinically applicable. CONCLUSIONS: We have created a novel nomogram model that can be utilized to anticipate VAP risk in elderly ICU patients, which is helpful for healthcare professionals to detect patients at high risk early and adopt protective interventions.

Bone mineral density in patients with primary ovarian insufficiency: A systematic review and Meta-Analysis.

INTRODUCTION: Premature menopause is a major complication of primary ovarian insufficiency (POI), and this loss is closely relates to bone mineral density (BMD). Previous research has indicated potential associations between BMD and POI. This study set out to provide the first systematic literature review and meta-analysison account of BMD content among women with POI. METHODS: Studies including women with POI and controls were eligible from PubMed, Embase, Cochrane Library and Web of Science databases (from their inception to April 2022). Two reviewers independently evaluated study eligibility. The meta-analysis was performed using the DerSimonian and Laird random effects model. RESULTS: Ten studies featuring 578 women with POI and 480 controls were selected. BMD content of femur neck (SMD:-0.76; 95 % CI: -1.20 to -0.31; P = 0.0008), the BMD content of nondominating forearm (SMD:-0.67; 95 % CI: -1.15 to -0.18; P = 0.007) were significantly decreased in women with POI. However, no differences were seen in other regions (lumbar spine, total hip, hipneck). DISCUSSION: The results of this study indicate that BMD content altered in patients with primary ovarian insufficiency. An implication of this is the possibility that hormone replacement therapy to minimize the prevalence of fracture morbidity and mortality associated with osteopenia in patients with POI.

A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening.

Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency. Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results: A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and -3.26 points. Conclusions: Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy.