A dynamic nomogram for predicting in-hospital major adverse cardiovascular and cerebrovascular events in patients with both coronary artery disease and atrial fibrillation: a multicenter retrospective study.

BACKGROUND AND OBJECTIVE: Patients with both coronary artery disease (CAD) and atrial fibrillation (AF) are at a high risk of major adverse cardiovascular and cerebrovascular events (MACCE) during hospitalization. Accurate prediction of MACCE can help identify high-risk patients and guide treatment decisions. This study was to elaborate and validate a dynamic nomogram for predicting the occurrence of MACCE during hospitalization in Patients with CAD combined with AF. METHODS: A total of 3550 patients with AF and CAD were collected. They were randomly assigned to a training group and a validation group in a ratio of 7 : 3. Univariate and multivariate analyses were utilized to identify risk factors (P < 0.05). To avoid multicollinearity and overfit of the model, the least absolute shrinkage and selection operator was conducted to further screen the risk factors. Calibration curves, receiver operating characteristic curves, and decision curve analyses are employed to assess the nomogram. For external validation, a cohort consisting of 249 patients was utilized from the Medical Information Mart for Intensive Care IV Clinical Database, version 2.2. RESULTS: Eight indicators with statistical differences were screened by univariate analysis, multivariate analysis, and the least absolute shrinkage and selection operator method (P < 0.05). The prediction model based on eight risk factors demonstrated good prediction performance in the training group, with an area under the curve (AUC) of 0.838. This performance was also maintained in the internal validation group (AUC = 0.835) and the external validation group (AUC = 0.806). Meanwhile, the calibration curve indicates that the nomogram was well-calibrated, and decision curve analysis revealed that the nomogram exhibited good clinical utility. CONCLUSION: The nomogram we constructed may aid in stratifying the risk and predicting the prognosis for patients with CAD and AF.

Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn's disease activity.

OBJECTIVES: The simplified endoscopic score of Crohn's disease (SES-CD) is the gold standard for quantitatively evaluating Crohn's disease (CD) activity but is invasive. This study aimed to develop and validate a machine learning (ML) model based on dual-energy CT enterography (DECTE) to noninvasively evaluate CD activity. METHODS: We evaluated the activity in 202 bowel segments of 46 CD patients according to the SES-CD score and divided the segments randomly into training set and testing set at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used for feature selection, and three models based on significant parameters were established based on logistic regression. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curves. RESULTS: There were 110 active and 92 inactive bowel segments. In univariate analysis, the slope of spectral curve in the venous phases (λHU-V) has the best diagnostic performance, with an area under the ROC curve (AUC) of 0.81 and an optimal threshold of 1.975. In the testing set, the AUC of the three models established by the 7 variables to differentiate CD activity was 0.81-0.87 (DeLong test p value was 0.071-0.766, p > 0.05), and the combined model had the highest AUC of 0.87 (95% confidence interval (CI): 0.779-0.959). CONCLUSIONS: The ML model based the DECTE can feasibly evaluate CD activity, and DECTE parameters provide a quantitative analysis basis for evaluating specific bowel activities in CD patients. CRITICAL RELEVANCE STATEMENT: The machine learning model based on dual-energy computed tomography enterography can be used for evaluating Crohn's disease activity noninvasively and quantitatively. KEY POINTS: Dual-energy CT parameters are related to Crohn's disease activity. Three machine learning models effectively evaluated Crohn's disease activity. Combined models based on conventional and dual-energy CT have the best performance.

Exertional heat stroke-induced changes in gut microbiota cause cognitive impairment in mice.

BACKGROUND: The incidence of exertional heat stroke (EHS) escalates during periods of elevated temperatures, potentially leading to persistent cognitive impairment postrecovery. Currently, effective prophylactic or therapeutic measures against EHS are nonexistent. METHODS: The selection of days 14 and 23 postinduction for detailed examination was guided by TEM of neuronal cells and HE staining of intestinal villi and the hippocampal regions. Fecal specimens from the ileum and cecum at these designated times were analyzed for changes in gut microbiota and metabolic products. Bioinformatic analyses facilitated the identification of pivotal microbial species and metabolites. The influence of supplementing these identified microorganisms on behavioral outcomes and the expression of functional proteins within the hippocampus was subsequently assessed. RESULTS: TEM analyses of neurons, coupled with HE staining of intestinal villi and the hippocampal region, indicated substantial recovery in intestinal morphology and neuronal injury on Day 14, indicating this time point for subsequent microbial and metabolomic analyses. Notably, a reduction in the Lactobacillaceae family, particularly Lactobacillus murinus, was observed. Functional annotation of 16S rDNA sequences suggested diminished lipid metabolism and glycan biosynthesis and metabolism in EHS models. Mice receiving this intervention (EHS + probiotics group) exhibited markedly reduced cognitive impairment and increased expression of BDNF/TrKB pathway molecules in the hippocampus during behavioral assessment on Day 28. CONCLUSION: Probiotic supplementation, specifically with Lactobacillus spp., appears to mitigate EHS-induced cognitive impairment, potentially through the modulation of the BDNF/TrKB signaling pathway within the hippocampus, illustrating the therapeutic potential of targeting the gut-brain axis.

Altered resting-state brain functional activities and networks in Crohn's disease: a systematic review.

Background: Crohn's disease (CD) is a non-specific chronic inflammatory disease of the gastrointestinal tract and is a phenotype of inflammatory bowel disease (IBD). The current study sought to compile the resting-state functional differences in the brain between CD patients and healthy controls. Methods: The online databases PubMed, Web of Science Core, and EMBASE were used to find the published neuroimage studies. The search period was from the beginning through December 15, 2023. The predetermined inclusion and exclusion criteria allowed for the identification of the studies. The studies were assembled by two impartial reviewers, who also assessed their quality and bias. Results: This review comprised 16 resting-state fMRI studies in total. The included studies generally had modest levels of bias. According to the research, emotional processing and pain processing were largely linked to increased or decreased brain activity in patients with CD. The DMN, CEN, and limbic systems may have abnormalities in patients with CD, according to research on brain networks. Several brain regions showed functional changes in the active CD group compared to the inactive CD group and the healthy control group, respectively. The abnormalities in brain areas were linked to changes in mood fluctuations (anxiety, melancholy) in patients with CD. Conclusion: Functional neuroimaging helps provide a better understanding of the underlying neuropathological processes in patients with CD. In this review, we summarize as follows: First, these findings indicate alterations in brain function in patients with CD, specifically affecting brain regions associated with pain, emotion, cognition, and visceral sensation; second, disease activity may have an impact on brain functions in patients with CD; and third, psychological factors may be associated with altered brain functions in patients with CD.

Body composition parameters correlate with the endoscopic severity in Crohn's disease patients treated with infliximab.

Background: The disease activity status and behavior of Crohn's disease (CD) can reflect the severity of the disease, and changes in body composition are common in CD patients. Aims: The aim of this study was to investigate the relationship between body composition parameters and disease severity in CD patients treated with infliximab (IFX). Methods: Patients with CD assessed with the simple endoscopic score (SES-CD) and were treated with IFX were retrospectively collected, and body composition parameters at the level of the 3rd lumbar vertebrae were calculated from computed tomography (CT) scans of the patients. The correlation of patients' body composition parameters with disease activity status and disease behavior was analyzed, and the diagnostic value of the relevant parameters was assessed using receiver operating characteristic (ROC) curves. Results: A total of 106 patients were included in this study. There were significant differences in the subcutaneous adiposity index (SAI) (p = 0.010), the visceral adiposity index (VAI) (p < 0.001), the skeletal muscle mass index (SMI) (p < 0.001), and decreased skeletal muscle mass (p < 0.001) among patients with different activity status. After Spearman and multivariate regression analysis, SAI (p = 0.006 and p = 0.001), VAI (p < 0.001 and p < 0.001), and SMI (p < 0.001and p = 0.007) were identified as independent correlates of disease activity status (both disease activity and moderate-to-severe activity), with disease activity status independently positively correlated with SAI and SMI and independently negatively correlated with VAI. In determining the disease activity and moderate-to-severe activity status, SMI performed best relative to SAI and VAI, with areas under the ROC curve of 0.865 and 0.801, respectively. SAI (p = 0.015), SMI (p = 0.011) and decreased skeletal muscle mass (p = 0.027) were significantly different between different disease behavior groups (inflammatory disease behavior group, complex disease behavior group) but were not independent correlates (p > 0.05). Conclusion: Body composition parameters of CD patients treated with IFX correlate with the endoscopic disease severity, and SMI can be used as a reliable indicator of disease activity status.

A web-based novel prediction model for predicting depression in elderly patients with coronary heart disease: A multicenter retrospective, propensity-score matched study.

Background: Depression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients with CHD and to construct a prediction model for early identification of depression in this patient population. Materials and methods: We used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set (n = 880) and an external validation set (n = 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively. Results: The predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722-0.803) and 0.679 (95% CI = 0.572-0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (https://cytjt007.shinyapps.io/dynnomapp_depression/). Conclusion: Reductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD.

Establishment and Validation of a Non-invasive Diagnostic Nomogram to Identify Heart Failure in Patients With Coronary Heart Disease.

Background: Heart failure (HF) is an end-stage manifestation of and cause of death in coronary heart disease (CHD). The objective of this study was to establish and validate a non-invasive diagnostic nomogram to identify HF in patients with CHD. Methods: We retrospectively analyzed the clinical data of 44,772 CHD patients from five tertiary hospitals. Univariate logistic regression analyses and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify independent factors. A nomogram based on the multivariate logistic regression model was constructed using these independent factors. The concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram. Results: The predictive factors in the multivariate model included hypertension, age, and the total bilirubin, uric acid, urea nitrogen, triglyceride, and total cholesterol levels. The area under the curve (AUC) values of the nomogram in the training set, internal validation set, external validation set1, and external validation set2 were 0.720 (95% CI: 0.712-0.727), 0.723 (95% CI: 0.712-0.735), 0.692 (95% CI: 0.674-0.710), and 0.655 (95% CI: 0.634-0.677), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. Conclusion: The developed predictive model combines the clinical and laboratory factors of patients with CHD and is useful in individualized prediction of HF probability for clinical decision-making during treatment and management.

In-patient Expenditure Between 2012 and 2020 Concerning Patients With Liver Cirrhosis in Chongqing: A Hospital-Based Multicenter Retrospective Study.

Background: Liver cirrhosis is a major global health and economic challenge, placing a heavy economic burden on patients, families, and society. This study aimed to investigate medical expenditure trends in patients with liver cirrhosis and assess the drivers for such medical expenditure among patients with liver cirrhosis. Methods: Medical expenditure data concerning patients with liver cirrhosis was collected in six tertiary hospitals in Chongqing, China, from 2012 to 2020. Trends in medical expenses over time and trends according to subgroups were described, and medical expenditure compositions were analyzed. A multiple linear regression model was constructed to evaluate the factors influencing medical expenditure. All expenditure data were reported in Chinese Yuan (CNY), based on the 2020 value, and adjusted using the year-specific health care consumer price index for Chongqing. Results: Medical expenditure for 7,095 patients was assessed. The average medical expenditure per patient was 16,177 CNY. An upward trend in medical expenditure was observed in almost all patient subgroups. Drug expenses were the largest contributor to medical expenditure in 2020. A multiple linear regression model showed that insurance type, sex, age at diagnosis, marital status, length of stay, smoking status, drinking status, number of complications, autoimmune liver disease, and the age-adjusted Charlson comorbidity index score were significantly related to medical expenditure. Conclusion: Conservative estimates suggest that the medical expenditure of patients with liver cirrhosis increased significantly from 2012 to 2020. Therefore, it is necessary to formulate targeted measures to reduce the personal burden on patients with liver cirrhosis.

Allantoin-functionalized silk fibroin/sodium alginate transparent scaffold for cutaneous wound healing.

In clinical application, it's highly desirable for developing bio-functionalized cutaneous scaffold with transparent features for convenient observation, excellent biocompatibility, and high efficiency for promoting wound repair. Herein, allantoin-functionalized composite hydrogel was developed by coupling silk fibroin (SF) and sodium alginate (SA) for treatment of cutaneous wounds. The prepared allantoin-functionalized SF-SA composite scaffolds (AFAS) exhibited excellent mechanical properties, especially featured by similar ultimate tensile strength (UTS) and elongation at breaking to human skin. Besides, the solvent-casting method guaranteed the AFAS to obtain highly transparent properties with sufficient moisture permeability and excellent adhesion in wet state. In vitro cellular experiments demonstrated excellent biocompatibility of the scaffold that attachment and proliferation of NIH-3T3 fibroblast cells was promoted in the presence of AFAS. Furthermore, the scaffolds exhibited efficient hemostatic property, based on rat hepatic hemorrhage model. In a cutaneous excisional mouse wound model, the AFAS significantly improved the wound closure rate, compared with pure SF-SA scaffolds and blank control. Moreover, the histomorphological assessments showed that AFAS facilitated the integrity of skin and wound healing process by enhancing collagen deposition, re-epithelialization and vascularization at wound site. The results demonstrate that the novel allantoin-functionalized SF/SA transparent hydrogel has great potential for clinical treatment of cutaneous wound.

Usnic-Acid-Functionalized Silk Fibroin Composite Scaffolds for Cutaneous Wounds Healing.

Despite the progress in chronic wound treatment, antibacterial cutaneous scaffold with high efficiency in wound healing is still the hot spot in the field. In present study, a functionalized silk fibroin (SF) cutaneous scaffold incorporated with natural medicine usnic acid (UA) is investigated, in which UA is used as an antibacterial and wound-healing reagent. Via electrospinning, UA-SF mixture is fabricated into UA-SF composite scaffold (USCS), which is composed of uniform nanofibers with average diameters of around 360 ± 10 nm. The interwoven nanofibers form mesh structure providing sufficient moisture permeability for scaffold. With methanol treatment, USCS presents improved mechanical properties and stability to protease XIV. In the presence of USCS, the growth rate of both Gram-positive and Gram-negative bacteria, including Staphylococcus aureus, Streptococci pyogenes, Escherichia coli, and Pseudomonas aeruginosa, is significantly inhibited in plate culture and suspension assays. In a cutaneous excisional mouse wound model, USCS presents a significant increase of wound closure rate, compared with pure SF scaffold and commercial dressing, Tegaderm Hydrocolloid 3M . The histological assessments further prove that USCS can enhance re-epithelialization, vascularization, and collagen deposition in wound site to promote the wound-healing process, which indicates the potential application of USCS in chronic wound treatment.

Preparation of DNA-functionalized surfaces for simultaneous homeotropic orientation of liquid crystals and optical recognition of analytes: application to the determination of progesterone.

The work describes a simplified method for the preparation of liquid crystal (LC) bioassay using DNA-based capture molecules and having lower detection limits. The capture DNA probes of the stem-loop structure were immobilized on the surface of a glass slide. A homeotropic orientation of LC molecules can be obtained with the proper surface coverage of capture DNA probes. In the presence of analytes (specifically shown here for the progesterone as a model analyte), the molecular binding between capture DNA probes and progesterone opens the loop of the capture DNA probes. The opened sequence is then amenable to hybridization with a reporter DNA probe that is immobilized on gold nanoparticles. This changes the surface microstructure, disrupts the orientation of LC molecules, and results in an enhanced optical response, expressed as the average grey value of the images. This new kind of surface treatment for simultaneous recognition of target molecules and homeotropic anchoring of LCs reduces the number of preparation steps and makes the process of LC bioassay easier. This method has a detection limit as low as 0.1 pmol·L-1 of progesterone. Graphical abstract Schematic presentation of the liquid crystal-based DNA assay. DMOAP: Dimethyloctadecyl[3-(trimethoxysilyl)propyl]ammonium chloride; TEA: Triethoxsilylbutyraldehyde; 5CB: 4-cyano-4'-pentylbiphenyl; P4: progesterone.