The role of Ca2+ signalling and InsP3R in the pathogenesis of intrahepatic cholestasis of pregnancy.

BACKGROUND: In order to contribute new insights for future prevention and treatment of intrahepatic cholestasis of pregnancy (ICP), and to promote positive pregnancy outcomes, we evaluated serum Ca2+ levels and inositol 1,4,5-trisphosphate receptor (InsP3R) expression in the liver tissue of a rat ICP model. METHODS: After establishing the model by injection of oestradiol benzoate and progesterone into pregnant rats, animals were divided into normal control (n = 5) and ICP model groups (n = 5). The expression of InsP3R protein in the liver, and serum levels of Ca2+, glycocholic acid and bile acid were detected. RESULTS: InsP3R mRNA and protein were significantly lower in the ICP model group compared to the normal group, as determined by qPCR and immunohistochemistry, respectively. Serum enzyme-linked immunosorbent assay results revealed significantly higher levels of glycocholic acid and bile acid in the ICP model group compared to the normal group, while Ca2+ levels were significantly lower. The levers of Ca2+ were significantly and negatively correlated with the levels of glycocholic acid. The observed decrease in Ca2+ was associated with an increase in total bile acids, but there was no significant correlation. CONCLUSIONS: Our results revealed that the expression of InsP3R and serum Ca2+ levels was significantly decreased in the liver tissue of ICP model rats. Additionally, Ca2+ levels were found to be negatively correlated with the level of glycocholic acid.

This study investigated the relationship between serum Ca²⁺ levels, inositol 1,4,5-trisphosphate receptor (InsP3R) expression and intrahepatic cholestasis of pregnancy (ICP) in a rat model. The results indicated a significant decrease in InsP3R expression and Ca²⁺ in the disease group compared to the control group, alongside elevated levels of glycocholic acid and bile acid. The levels of Ca²⁺ exhibited a negative correlation with the levels of glycocholic acid. These findings indicated that the decrease of InsP3R expression and Ca²⁺ levels may be related to the pathogenesis of ICP. The study provides further insight into the treatment of this disease.

A novel mitochondria-related core gene signature to predict the prognosis and evaluate tumour microenvironment in CESC single-cell validation.

Mitochondria and their related genes (MTRGs) are pivotal in the tumour microenvironment (TME) of cervical cancer, influencing prognosis and treatment response. This study developed a prognostic model using MTRGs to predict overall survival (OS) in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), aiming for personalized therapy. Analysing 14 MTRGs like ISCU and NDUFA11 through techniques such as univariate Cox regression, we found that a low mitochondrial (MT) score is associated with better survival, while a high MT score predicts poorer outcomes. The TME score, particularly influenced by CD8 T cells, also correlates with prognosis, with a high score indicating favourable outcomes. The interplay between MT and TME subtypes revealed that the best prognosis is seen in patients with a low MT and high TME score. Our findings highlight the role of MTRGs as potential biomarkers and therapeutic targets in cervical cancer, offering a novel approach to improving patient outcomes through a more nuanced understanding of mitochondrial function and immune interactions within the TME. This model presents a promising avenue for enhancing the precision of prognostic assessments in CESC.

Magnetic solid-phase extraction based on GO/Fe3O4 coupled with UPLC-MS/MS for determining nitroimidazoles and their metabolites in honey.

A magnetic graphene oxide (GO/Fe3O4) nanocomposite was synthesized in one step by a chemical coprecipitation method, which was further used for magnetic solid-phase extraction (MSPE). This study aimed to combine GO/Fe3O4 with ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to detect the nitroimidazoles (NDZs) and their three major metabolites in honey samples. GO/Fe3O4 was characterized by transmission electron microscopy (TEM), Fourier transform-infrared (FT-IR) spectroscopy, and magnetic property measurement system (MPMS), and the influencing parameters such as adsorbent amount, pH of the dissolved sample solution, sample volume, type and volume of the eluent, shaking speed, and adsorption and desorption time were optimized. Under the optimized conditions, the limits of detection (LOD) and quantitation (LOQ) of the method were 0.003-0.08 µg kg-1 and 0.009-0.3 µg kg-1, respectively, with good linearity reported in the range of 0.5-20 µg kg-1 (R2 ≥ 0.9991). The average recoveries of 10 analytes were in the range of 66.0%-90.8% with relative standard deviations (RSD) lower than 6.9% (n = 6). The preparation of GO/Fe3O4 and the extraction process were convenient and rapid, and consumed small amounts of organic solvents. The optimized method was successfully applied for extracting NDZs and their three major metabolites from honey samples with good accuracy.

Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method.

Subspace methods are widely used in FMCW-MIMO radars for target parameter estimations. However, the performances of the existing algorithms degrade rapidly in non-ideal situations. For example, a small number of snapshots may result in the distortion of the covariance matrix estimation and a low signal-to-noise ratio (SNR) can lead to subspace leakage problems, which affects the parameter estimation accuracy. In this paper, a joint DOA-range estimation algorithm is proposed to solve the above issues. Firstly, the improved unitary root-MUSIC algorithm is applied to reduce the influence of non-ideal terms in building the covariance matrix. Subsequently, the least squares method is employed to process the data and obtain paired range estimation. However, in a small number of snapshots and low SNR scenarios, even if the impact of non-ideal terms is reduced, there will still be cases where the estimators sometimes deviate from the true target. The estimators that deviate greatly from targets are regarded as outliers. Therefore, threshold detection is applied to determine whether outliers exist. After that, a pseudo-noise resampling (PR) technology is proposed to form a new data observation matrix, which further alleviates the error of the estimators. The proposed method overcomes performance degradation in a small number of snapshots or low SNRs simultaneously. Theoretical analyses and simulation results demonstrate the effectiveness and superiority.

Convolution neural network with batch normalization and inception-residual modules for Android malware classification.

Deep learning technology is changing the landscape of cybersecurity research, especially the study of large amounts of data. With the rapid growth in the number of malware, developing of an efficient and reliable method for classifying malware has become one of the research priorities. In this paper, a new method, BIR-CNN, is proposed to classify of Android malware. It combines convolution neural network (CNN) with batch normalization and inception-residual (BIR) network modules by using 347-dim network traffic features. CNN combines inception-residual modules with a convolution layer that can enhance the learning ability of the model. Batch Normalization can speed up the training process and avoid over-fitting of the model. Finally, experiments are conducted on the publicly available network traffic dataset CICAndMal2017 and compared with three traditional machine learning algorithms and CNN. The accuracy of BIR-CNN is 99.73% in binary classification (2-classifier). Moreover, the BIR-CNN can classify malware by its category (4-classifier) and malicious family (35-classifier), with a classification accuracy of 99.53% and 94.38%, respectively. The experimental results show that the proposed model is an effective method for Android malware classification, especially in malware category and family classifier.

[Comparison of combined immunization schemes influence on anti-HBs of babies born to mothers with high-load hepatitis B virus infection].

OBJECTIVE: To compare the various combined immunization schemes available for treatment of babies born to mothers with high-load hepatitis B virus (HBV) infection. METHODS: A total of 118 mothers with HBV infection status of hepatitis B surface antigen-positive (HBsAg+), hepatitis B e antigen-positive (HBeAg+) and HBV DNA load of more than 1.0 * 61og10 IU/mL were included in the study. All of the participants' babies received the main-passive immunization therapy according to the wishes of their families. For analysis,the infants were grouped according to the various dosages of the vaccine program (group A: hepatitis B immunoglobulin (HBIG) 200 IU and HBVac 20 mug intramuscular;group B:HBIG 200 IU and HBVac 10 mug intramuscular; group C HBIG 100 IU and HBVac 20 mug intramuscular injection) and times, and followed-up to 7 months of age.All results were statistically analyzed using SPSS software. RESULTS: All of the infants produced anti-HBs after vaccination.After the HBIG injection schedule was completed in January, the mean concentrations of anti-HBs in groups A, B, and C were 263.56 ± 50.98,231.06 ± 74.07, and 99.23 ± 29.82 mIU/mL respectively;the concentrations were significantly different between groups A and C, and between groups B and C (P < 0.001). In July, the titers of anti-HBs in groups A, B, and C were 788.10 ± 281.96,428.39 ± 347.48, and 708.44 ± 315.69 mIU/mL respectively; the concentrations were significantly different between groups A and B, and between groups B and C (P < 0.05). CONCLUSION: AdminisWation of the hepatitis B vaccine combined with HBIG at birth can achieve immune protection for babies born to highly viremic mothers. In January, the HBIG dosage of 200 IU was more reliable than 100 IU. The hepatitis B 20 tg dose vaccine was safe and effective.