Profiling of immune infiltration landscape of ruptured intracranial aneurysm.

BACKGROUND: Previous research has indicated that the rupture of intracranial aneurysm (IA) is a significant contributor to mortality from stroke. The objective of this present study was to examine the infiltration patterns in ruptured intracranial aneurysm (RIA), with the aim of generating insights that could inform the development of effective immunotherapeutic approaches. METHODS: To achieve this, we obtained Gene Expression Omnibus datasets pertaining to ruptured aneurysms, encompassing a total of 19 unruptured intracranial aneurysms (UIA) and 27 RIA. Subsequently, we conducted differential gene analysis and immune cell analysis specifically for the RIA. RESULTS: According to the conducted studies, the analysis has identified 10 hub genes within key modules. Through the utilization of Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology terms analyses, it has been established that genes exhibiting differential expression are associated with immune cell infiltration in the aneurysm wall. Furthermore, the implementation of the CIBERSORT algorithm has revealed that there are 22 distinct immune cells between RIA and tissues of UIA. IA samples contained a higher proportion of macrophages M1, mast cells resting, and CD4 naive T cells, while macrophages M0 and neutrophils were relatively lower in RIA compared with those in UIA. CONCLUSION: The current study initially identified highly conservative hub genes and immune cell infiltration patterns in IA. Data presented in the current study improved understanding of immune genes that drive IA which can be exploited in development of effective immunotherapies.

Integrated Microfluidic Chip Technology for Copper Ion Detection Using an All-Solid-State Ion-Selective Electrode.

This study involved the preparation of an all-solid-state ion-selective electrode (ASS-ISE) with copper and a poly(3,4-ethylenedioxythiophene) and polystyrene sulfonate (PEDOT/PSS) conversion layer through electrode deposition. The morphology of the PEDOT/PSS film was characterized, and the performance of the copper ion-selective film was optimized. Additionally, a microfluidic chip for the ASS-ISE with copper was designed and prepared. An integrated microfluidic chip test system with an ASS-ISE was developed using a self-constructed potential detection device. The accuracy of the system was validated through comparison testing with atomic absorption spectrophotometry (AAS). The experimental findings indicate that the relative standard deviation (RSD) of the integrated ASS-ISE with the copper microfluidic chip test system is 4.54%, as compared to the industry standard method. This value complies with the stipulated requirement of an RSD ≤ 5% in DL/T 955-2016.

Construction and evaluation of prognostic models of ECMO in elderly patients with cardiogenic shock based on BP neural network, random forest, and decision tree.

OBJECTIVE: To analyze the predictive effect of a back propagation (BP) neural network, random forest (RF) and decision tree model on the prognosis of elderly patients with cardiogenic shock after extracorporeal membrane oxygenation (ECMO). METHODS: This is a retrospective analysis of the clinical data of elderly patients with cardiogenic shock (258 cases) who underwent ECMO in People's Hospital of Guangxi Zhuang Autonomous Region from January 2016 to January 2022. All patients were followed up for 6 months after ECMO treatment. The prognosis was evaluated, and the prognostic factors were analyzed. BP neural network, RF and decision tree were used to establish predictive models, and the predictive performance of the models was evaluated. RESULTS: Among the 258 elderly patients with cardiogenic shock, 52 (20.16%) died 6 months after the ECMO treatment. Based on BP neural network, RF, and decision tree, predictive models for the prognosis and death of elderly patients with cardiogenic shock were constructed. A test set was used to predict the performance of the three models. The results showed that the predictive performances of the three models were all more than 80.00%. The accuracy, sensitivity, and specificity of the RF model were 0.987, 1.000, and 0.929 respectively, which were higher than those of the decision tree model. The area under the receiver operating characteristic curve (AUC) of the RF model was 1.000, which was higher than 0.916 for the decision tree model. DeLong test showed that there was a significant difference in the AUC of the RF model compared to the decision tree test set (D=-2.063, P=0.042 < 0.05). CONCLUSION: The predictive performance is good in all the three models, which have a high application value for prognosis of ECMO in elderly patients with cardiogenic shock. In clinical practice, predictive models should be selected according to the actual situation, so clinicians and patients can make decisions.

Three-dimensional laser combined with C-arm computed tomography-assisted puncture of intracerebral hemorrhage.

Background: Intracerebral hemorrhage (ICH) is the deadliest subtype of stroke, with a 30-day case fatality rate of approximately 40%. Timely and accurate treatment is essential to facilitate recovery. The introduction of stereotactic instruments and navigation systems has greatly improved the accuracy of surgical treatment. In this study, we explored the application and effects of a three-dimensional (3D) laser combined with C-arm computed tomography (CT) on ICH puncture. Materials and methods: According to the principle of randomness, 118 patients with ICH were divided into control and experimental groups. The control group was treated with CT-guided puncture, and the experimental group was treated with 3D laser combined with C-arm CT puncture. The hematoma clearance rates at 3, 5, and 7 days after surgery and the prognosis at 1, 3, and 6 months after surgery were compared between the two groups. Results: The hematoma clearance rates of the group using 3D laser combined with C-arm CT at 3, 5, and 7 days after surgery were significantly higher than those of the control group, and the difference was statistically significant (p < 0.05). One month postoperatively, the daily living ability (ADL) grading and recovery of the patients in the test group was significantly better than those of the control group (p < 0.05), but there was no statistically significant difference in ADL 3 and 6 months after surgery (p > 0.05). Conclusion: 3D laser combined with C-arm CT puncture has the advantages of real-time guidance, accurate positioning, and simple operation. It is an effective minimally invasive surgical method that is easy to master.

Laser Navigation Combined With XperCT Technology Assisted Puncture of Brainstem Hemorrhage.

Background: Brainstem hemorrhage has a rapid onset with high mortality and disability rates. In recent years, an increasing number of studies have reported on the surgical treatment of brainstem hemorrhage. The introduction of stereotaxic instruments and navigation systems has improved the accuracy of surgical treatment; however, the popularity of these devices in the primary hospitals is not high. In this study, we introduce laser navigation combined with the XperCT technology to assist in the puncture and drainage of brainstem hemorrhage, aiming to improve surgical accuracy and facilitate the drainage of brainstem hemorrhage in primary hospitals. Material and Methods: A total of five patients (four men and one woman), aged 34-70 years, who underwent hematoma puncture drainage with the assistance of laser navigation combined with XperCT technology at the Binzhou Medical University Hospital, China, between June 2020 and Aug 2021 were included in the study. The brainstem hemorrhages had volumes of 7-18 ml. Statistical analyses of the postoperative puncture deviation distance (distance between the actual puncture end and simulated puncture end) and postoperative improvement were also performed. Results: The operations were successfully completed in all five patients. The puncture deviation distance was <6 mm in all five patients and <2 mm in two patients. The postoperative hematoma clearance rate was about 70%-90%. Among four patients with respiratory failure, three had improved breathing and resumed spontaneous breathing. Out of three patients with high fever, one showed a substantial decrease in body temperature. There were no cases of postoperative infection. Of the five patients, two recovered consciousness, one died, and two voluntarily gave up further treatment and were discharged. Conclusions: Laser navigation combined with the XperCT technology could improve the accuracy of surgical puncture. The technique might be convenient for widespread clinical application because of its low trauma, high precision, short operation time, and low operation cost.

Association between vitamin D3 levels and insulin resistance: a large sample cross-sectional study.

Previous studies have shown that vitamin D3 may be a potential factor in insulin resistance, but the relationship between vitamin D3 and insulin resistance still remains controversial. At present, more research is needed to explore the relationship between vitamin D3 and insulin resistance. The samples from 2009 to 2018 in NHANES database were analyzed to Investigate the relationship and the potential mechanism. We performed a cross-sectional study of five periods in the NHANES database. Finally, 9298 participants were selected through strict inclusion and exclusion criteria, Multivariate logistic regression analysis and curve fitting were conducted to explore the relationship between vitamin D3 level and insulin resistance. Moreover, subgroup analysis was used to further prove the association. The results revealed that there was a strong association between vitamin D3 and insulin resistance (OR 0.82, 95% CI 0.72-0.93). However, subgroup analyses indicated that this correlation varied between individuals and races. There was a negative correlation between vitamin D3 level and insulin resistance, which provides a new proof for exploring the influencing factors of insulin resistance. More well-designed studies are still needed to further elaborate on these associations.

Long non-coding RNA HCG11 silencing protects against cerebral ischemia/reperfusion injury through microRNA miR-381-3p to regulate tumour protein p53.

INTRODUCTION: Long non-coding RNA (LncRNA) plays a critical role in cerebral ischemia-reperfusion (CI/R) injury. The purpose of the current research was to investigate the regulatory role of the LncRNA human leukocyte antigen complex group 11 (HCG11) in CI/R injury and explore its potential mechanism. MATERIAL AND METHODS: The rat middle cerebral artery occlusion (MCAO) model was established to simulate CI/R injury in vivo. mRNA levels of HCG11, microRNA (miR)-381-3p, tumour protein p53, and neuro-inflammatory factors were detected by quantitative reverse transcription PCR (RT-qPCR). Bederson score and Longa score were assessed for neurological deficits. Triphenyl tetrazolium chloride (TTC) staining was used to examine the cerebral infarct volume. What is more, oxidative stress was evaluated by the commercial kit. Finally, the relationship between HCG11, miR-381-3p, and p53 was verified by a dual-luciferase reporter assay. RESULTS: HCG11 was elevated in MCAO rats. And it competitively bound miR-381-3p and down-regulated the expression of p53. Inhibition of HCG11 inhibited cerebral infarct volume and neurological deficits in MCAO rats, and inhibited the secretion of neuro-inflammation and the over-activation of oxidative stress, exerting the protective effect of CI/R injury. However, inhibition of miR-381-3p in rats significantly weakened the protective effect of depression of HCG11 in MCAO rats, resulting in increased cerebral infarction volume and neurological deficits, elevated neuro-inflammatory secretion, and oxidative stress activation. CONCLUSIONS: The present research shows that LncRNA HCG11 silencing protects against cerebral ischemia/reperfusion injury through miR-381-3p to regulate p53.

Diagnostic and Prognostic Significance of miR-675-3p in Patients With Atherosclerosis.

In recent years, a rising number of studies have confirmed that microRNA (miRNA) plays a prominent role in the early diagnosis and prognostic value assessment of cardiovascular diseases. The current study was conducted to examine the expression of miR-675-3p in atherosclerosis (AS) patients and to evaluate its clinical diagnosis and prognostic value. 110 AS patients and 70 healthy controls were included in the study. Serum miR-675-3p levels were detected by quantitative real-time PCR (qRT-PCR). The clinical diagnostic significance of serum miR-675-3p in AS patients were investigated by the receiver operating characteristic (ROC) curve. The correlation between miRNA and carotid intima-media thickness (CIMT) was analyzed by the Spearman correlation coefficient. The prognostic significance of serum miR-675-3p was evaluated by the Kaplan-Meier method and Cox regression analysis. The patient's serum miR-675-3p was significantly increased than the healthy individuals (P < 0.05). An increase of carotid intima-media thickness (CIMT) was positively correlated with the promotion of serum miR-675-3p levels. The area under the ROC curve (AUC) was 0.918, with high sensitivity and specificity. miR-675-3p is a key independent predictor of cardiovascular adverse events in AS patients (HR = 5.375, 95%CI = 1.590-18.170, P = 0.007), and patients with elevated miR-675-3p were more likely to have cardiovascular adverse events (log-rank P = 0.030). Increased miR-675-3p can be used as a potential marker for the diagnosis of AS, and was associated with the poor prognosis of AS.

Paclitaxel-loaded nanoparticles decorated with bivalent fragment HAb18 F(ab')2 and cell penetrating peptide for improved therapeutic effect on hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) shows low response to most conventional treatment strategies. Therefore, there is an urgent need for new and effective chemotherapies. Nanotechnology gives a dramatic impact on medicine. In this work, paclitaxel loaded nanoparticles (NPs) decorated with bivalent fragment HAb18 F(ab')2 and/or cell penetrating peptide (CPP) were developed and evaluated. NPs were prepared by emulsification-solvent evaporation method and decorated by carbodiimide chemistry. The physicochemical characteristics of NPs (i.e. encapsulation efficiency, particle size distribution, morphology, release in vitro) were investigated. Cellular uptake and accumulation in tumor tissue of NPs were determined. To assess anti-tumor activity of NPs in vitro and in vivo, cell survival assay and tumor regression study were carried out using HCC cell lines (HepG2 and Huh7) and their xenografts. Average particle size of all NPs was between 100 and 200 nm. Drug-loaded NPs possessed spherical morphology and higher encapsulation efficiency. The accumulation of NPs decorated with HAb18 F(ab')2 and CPP depended on dual effects of passive and active targeting. Drug loaded nanoparticles showed cytotoxicity on the tumor cells in vitro and in vivo. NPs decorated with HAb18 F(ab')2 and CPP showed maximization of therapeutic action for targeting and effective endocytosis. These results suggest that the nano-drug delivery system could be a promising candidate with excellent therapeutic efficacy for HCC therapy.

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition.

Membrane proteins are vitally important for many biological processes and have become an attractive target for both basic research and drug design. Knowledge of membrane protein types often provides useful clues in deducing the functions of uncharacterized membrane proteins. With the unprecedented increasing of newly found protein sequences in the post-genomic era, it is highly demanded to develop an automated method for fast and accurately identifying the types of membrane proteins according to their amino acid sequences. Although quite a few identifiers have been developed in this regard through various approaches, such as covariant discriminant (CD), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN), classifier the way they operate the identification is basically individual. As is well known, wise persons usually take into account the opinions from several experts rather than rely on only one when they are making critical decisions. Likewise, a sophisticated identifier should be trained by several different modes. In view of this, based on the frame of pseudo-amino acid that can incorporate a considerable amount of sequence-order effects, a novel approach called "stacked generalization" or "stacking" has been introduced. Unlike the "bagging" and "boosting" approaches which only combine the classifiers of a same type, the stacking approach can combine several different types of classifiers through a meta-classifier to maximize the generalization accuracy. The results thus obtained were very encouraging. It is anticipated that the stacking approach may also hold a high potential to improve the identification quality for, among many other protein attributes, subcellular location, enzyme family class, protease type, and protein-protein interaction type. The stacked generalization classifier is available as a web-server named "SG-MPt_Pred" at: http://202.120.37.186/bioinf/wangsq/service.htm.