Esophageal cancer （EC） is a highly prevalent malignant tumor in China. The phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway， as one of the key oncogenic pathways， can promote the cell cycle progression， proliferation， migration， and invasion， induce chemoresistance， and inhibit apoptosis and autophagy of EC cells. Traditional Chinese medicine （TCM）， with the advantages of targeting multiple points with multiple components to delay cancer progression， can target the PI3K/Akt signaling pathway for EC treatment. This article preliminarily discusses the molecular mechanism and role of the PI3K/Akt signaling pathway in EC and elaborates on the specific targets and efficacy of TCM in treating EC through intervention in the PI3K/Akt signaling pathway in the past five years. TCM materials and extracts inhibiting the PI3K/Akt signaling pathway in EC include Borneolum， spore powder of Ganoderma lucidum without spore coat， extract of Celastrus orbiculatus， root extract of Taraxacum， and Bruceae Fructus oil emulsion. TCM active ingredients exerting the effect include flavonoids， terpenoids， saponins， phenols， polysaccharides， alkaloids， and other compounds. TCM compound prescriptions with such effect include Qige San， Huqi San， Xuanfu Daizhetang， Tongyoutang and its decomposed prescriptions， Liujunzi Tang， and Xishenzhi Formula. In addition， TCM injections such as Compound Kushen Injection and Kang'ai injection also inhibit the PI3K/Akt signaling pathway in EC. This paper summarizes the role of the PI3K/Akt signaling pathway in EC and the TCM interventions， aiming to provide reference for the research and clinical application of new drugs for EC.

Objective: To analyze the serological phenotypes and molecular genetic characteristics of a proband with ABO forward and reverse typing discrepancy and their family members, and to investigate the molecular genetic mechanism underlying the Ael/AelB phenotype caused by the c. 575T>C variant on an ABO A1.02 background. Methods: Serological testing and adsorption-elution assays were performed for blood group identification of the proband and family members. PCR-SBT, cloning sequencing, and Nanopore third-generation sequencing were used to analyze ABO gene variants and haplotypes. Phyre2 and PyMOL were used to predict the effect of the p. Ile192Thr variant on the structure of glycosyltransferase A (GTA), and plasma GTA activity was evaluated using an in vitro transferase assay. Results: Among thirteen family members, four members showed a serological phenotype consistent with the Ael subtype and one with the AelB subtype. Cloning sequencing and third-generation sequencing confirmed that five family members carried the ABO A1.02 (c. 575T>C) variant, which co-segregated with the weak A phenotype in the family. Structural modeling suggested that p. Ile192Thr may alter the local spatial conformation and hydrogen-bond interactions of GTA. Plasma GTA activity testing showed markedly reduced GTA activity in the tested individuals. Conclusion: The ABO A1.02 (c. 575T>C) variant is associated with the Ael/AelB phenotype. The p. Ile192Thr substitution may weaken A antigen expression by altering GTA conformation and reducing enzymatic activity. This study provides additional evidence for the role of this variant in the Ael/AelB phenotype from the perspectives of familial inheritance, full-length haplotype analysis, and functional assessment, which is important for accurate ABO subtype identification and individualized transfusion strategies.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

The disturbance of the human microbiota influences the occurrence and progression of many diseases. Live therapeutic bacteria, with their genetic manipulability, anaerobic tendencies, and immunomodulatory properties, are emerging as promising therapeutic agents. However, their clinical applications face challenges in maintaining activity and achieving precise spatiotemporal release, particularly in the harsh gastrointestinal environment. This review highlights the innovative bacterial functionalized encapsulation strategies developed through advances in physicochemical and biological techniques. We comprehensively review how bacterial encapsulation strategies can be used to provide physical barriers and enhanced adhesion properties to live microorganisms, while introducing superior material properties to live bacteria. In addition, this review outlines how bacterial surface coating can facilitate targeted delivery and precise spatiotemporal release of live bacteria. Furthermore, it elucidates their potential applications for treating different diseases, along with critical perspectives on challenges in clinical translation. This review comprehensively analyzes the connection between functionalized bacterial encapsulation and innovative biomedical applications, providing a theoretical reference for the development of next-generation bacterial therapies.

Guided by molecular networking, nine novel curvularin derivatives (1-9) and 16 known analogs (10-25) were isolated from the hydrothermal vent sediment fungus Penicillium sp. HL-50. Notably, compounds 5-7 represented a hybrid of curvularin and purine. The structures and absolute configurations of compounds 1-9 were elucidated via nuclear magnetic resonance (NMR) spectroscopy, X-ray diffraction, electronic circular dichroism (ECD) calculations, ¹³C NMR calculation, modified Mosher's method, and chemical derivatization. Investigation of anti-inflammatory activities revealed that compounds 7-9, 11, 12, 14, 15, and 18 exhibited significant suppressive effects against lipopolysaccharide (LPS)-induced nitric oxide (NO) production in murine macrophage RAW264.7 cells, with IC₅₀ values ranging from 0.44 to 4.40 μmol·L^-1. Furthermore, these bioactive compounds were found to suppress the expression of inflammation-related proteins, including inducible NO synthase (iNOS), cyclooxygenase-2 (COX-2), NLR family pyrin domain-containing protein 3 (NLRP3), and nuclear factor kappa-B (NF-κB). Additional studies demonstrated that the novel compound 7 possessed potent anti-inflammatory activity by inhibiting the transcription of inflammation-related genes, downregulating the expression of inflammation-related proteins, and inhibiting the release of inflammatory cytokines, indicating its potential application in the treatment of inflammatory diseases.
Penicillium/chemistry* ; Mice ; Animals ; Anti-Inflammatory Agents/isolation & purification* ; RAW 264.7 Cells ; Nitric Oxide/metabolism* ; Hydrothermal Vents/microbiology* ; Macrophages/immunology* ; Molecular Structure ; Nitric Oxide Synthase Type II/immunology* ; Cyclooxygenase 2/immunology* ; Geologic Sediments/microbiology* ; NF-kappa B/immunology* ; NLR Family, Pyrin Domain-Containing 3 Protein/immunology*

BACKGROUND: Ferroptosis-related genes play a crucial role in regulating intracellular iron homeostasis and lipid peroxidation, and they are involved in the regulation of tumor growth and drug resistance. The expression of ferroptosis-related genes in tumor tissues can be used to predict patients' future survival times, aiding doctors and patients in anticipating disease progression. Based on the sequencing data of lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database, this study identified genes involved in the regulation of ferroptosis, constructed a prognostic model, and evaluated the predictive performance of the model. METHODS: A total of 1467 ferroptosis-related genes were obtained from the GeneCards database. Gene expression profiles and clinical data from 541 LUAD patients were collected from the TCGA database. The expression data of all ferroptosis-related genes were extracted, and differentially expressed genes were identified using R software. Survival analysis was performed on these genes to screen for those with prognostic value. Subsequently, a prognostic risk scoring model for ferroptosis-related genes was constructed using LASSO regression model. Each LUAD patient sample was scored, and the patients were divided into high-risk and low-risk groups based on the median score. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. Kaplan-Meier survival curves were generated to assess model performance, followed by validation in an external dataset. Finally, univariate and multivariate Cox regression analyses were conducted to evaluate the independent prognostic value and clinical relevance of the model. RESULTS: Through survival analysis, 121 ferroptosis-related genes associated with prognosis were initially identified. Based on this, a LUAD prognostic risk scoring model was constructed using 12 ferroptosis-related genes (ALG3, C1QTNF6, CCT6A, GLS2, KRT6A, LDHA, NUPR1, OGFRP1, PCSK9, TRIM6, IGF2BP1 and MIR31HG). The results indicated that patients in the high-risk group had significantly shorter survival time than those in the low-risk group (P<0.001), and the model demonstrated good predictive performance in both the training set (1-yr AUC=0.721) and the external validation set (1-yr AUC=0.768). Risk scores were significantly associated with the prognosis of LUAD patients in both univariate and multivariate Cox regression analyses (P<0.001), suggesting that this score is an important prognostic factor for LUAD patients. CONCLUSIONS This study successfully established a LUAD risk scoring model composed of 12 ferroptosis-related genes. In the future, this model is expected to be used in conjunction with the tumor-node-metastasis (TNM) staging system for prognostic predictions in LUAD patients.
Humans ; Ferroptosis/genetics* ; Prognosis ; Adenocarcinoma of Lung/pathology* ; Lung Neoplasms/pathology* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Middle Aged ; ROC Curve

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

A 28-year-old woman was found to have coagulation factor Ⅷ activity (FⅧ∶C) <1% and von Willebrand factor antigen (VWF∶Ag) <1% during routine prenatal examinations. No pathogenic variation was found in the exon region of the VWF gene using next-generation sequencing. The clinical presentation of this patient does not match the clinical characteristics of type Ⅲ hemophilia [von Willebrand disease (VWD) ]; therefore, third-generation sequencing technology was used to perform whole-genome sequencing on the patient and her family members. Multiple members of the patient’s paternal family carried a heterozygous variant of VPS33B, c.869G>C. The family members carrying this variant all had varying degrees of reduced VWF levels (39% -56% ). Moreover, the proband was detected with the heterozygous variant c.1474dupA in GP1BA. The ACMG and Clinvar databases determined that this variation was associated with platelet-type pseudo VWD. The decrease in VWF levels caused by heterozygous variations in VPS33B in families is the first international report, and no previous studies have reported cases of severe decrease in plasma VWF levels caused by double heterozygous variations in VPS33B and GP1BA.