Background: Hepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses. Methods: In the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein–protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC. Results: First, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival. Conclusions: The present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

Background: Hypoxic–ischemic encephalopathy (HIE) is a severe anoxic brain injury that leads to premature mortality or long-term disabilities in infants. Neuroinflammation is a vital contributor to the pathogenic cascade post-HIE and a mediator to secondary neuronal death. As a plasma membrane G-protein-coupled receptor, GPR39, exhibits anti-inflammatory activity in several diseases. This study aimed to explore the neuroprotective function of GPR39 through inhibition of inflammation post-hypoxic–ischemic (HI) injury and to elaborate the contribution of sirtuin 1(SIRT1)/peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α)/nuclear factor, erythroid 2 like 2(Nrf2) in G-protein-coupled receptor 39 (GPR39)-mediated protection. Methods: A total of 206 10-day-old Sprague Dawley rat pups were subjected to HIE or sham surgery. TC-G 1008 was administered intranasally at 1 h, 25 h, 49 h, and 73 h post-HIE induction. SIRT1 inhibitor EX527, GPR39 CRISPR, and PGC-1α CRISPR were administered to elucidate the underlying mechanisms. Brain infarct area, short-term and long-term neurobehavioral tests, Nissl staining, western blot, and immunofluorescence staining were performed post-HIE. Results: The expression of GPR39 and pathway-related proteins, SIRT1, PGC-1α and Nrf2 were increased in a time-dependent manner, peaking at 24 h or 48-h post-HIE. Intranasal administration of TC-G 1008 reduced the percent infarcted area and improved short-term and long-term neurological deficits. Moreover, TC-G 1008 treatment significantly increased the expression of SIRT1, PGC-1α and Nrf2, but downregulated the expressions of IL-6, IL-1β, and TNF-α. GPR39 CRISPR EX527 and PGC-1α CRISPR abolished GPR39’s neuroprotective effects post-HIE. Conclusions: TC-G 1008 attenuated neuroinflammation in part via the SIRT1/PGC-1α/Nrf2 pathway in a neonatal rat model of HIE. TC-G 1008 may be a novel therapeutic target for treatment post-neonatal HIE injury.

Sepsis-associated encephalopathy (SAE) represents a prevalent and serious complication in patients with sepsis, contributing to substantial cognitive dysfunction and persistent neurological deficits. The cerebral clearance mechanism, consisting of the glymphatic system and meningeal lymphatic vessels (MLVs), serves a pivotal function in cerebrospinal fluid (CSF) drainage and the elimination of metabolic byproducts. The study demonstrated that intraperitoneal administration of LPS (3 mg/kg) resulted in a reduction in Aβ clearance 28 h post-injection. Moreover, acute exposure to a lower dose of LPS (1 mg/kg) led to a decreased distribution of CSF around the middle cerebral artery as early as 3 h. These concurrent observations imply that compromised functionality of this clearance mechanism may contribute to cognitive impairments induced by sepsis. This review comprehensively investigates the involvement of the cerebral clearance mechanism in the pathophysiological processes of SAE, with the objective of uncovering innovative therapeutic approaches.

Background: Hyperthermic intraperitoneal chemotherapy (HIPEC) produces unwanted side-effects that are mainly caused by chemotherapeutic drugs in the treatment of gastrointestinal (GI) cancers, and these effects have not been systematically summarized. The aim of this article was to provide a comprehensive overview of the side-effects of HIPEC for GI cancers and propose practical strategies for adverse event management. Methodology: PubMed, Web of Science, and the Cochrane Library were systematically searched for side-effects of HIPEC in GI cancers prior to October 20, 2022. A total of 79 articles were included in this review. Results: Adverse events, such as enterocutaneous digestive fistulas, GI tract perforation, neutropenia, postoperative bleeding, ventricular tachycardia, hyperglycemia, hypocalcemia, renal impairment, encapsulating peritoneal sclerosis, scrotal ulceration, and sarcopenia were described, and their clinical management was discussed. These side-effects involve the digestive, hematopoietic, circulatory, metabolic, and urinary systems. Effective methods for adverse event management included an expert multidisciplinary team, replacing chemotherapy drugs, using Chinese medicine, and careful preoperative assessments. Conclusion: The side-effects of HIPEC are frequent and can be minimized by several effective methods. This study proposes practical strategies for adverse event management of HIPEC to assist physicians in choosing the optimal treatment method.

Background: Hepatitis B virus (HBV) is the dominant pathogenic factor of hepatocellular carcinoma (HCC) in Asia and Africa. Early identification and clinical diagnosis are crucial for HBV-related HCC. Random forest (RF) and artificial neural network (ANN) were an innovative and highly effective supervised machine learning (ML) algorithm for the early diagnosis and screening of HBV-related HCC. This study aims to identify significant biomarkers and develop a novel genetic model for the efficient diagnosis of HBV-related HCC. Methods: Gene Expression Omnibus (GEO) Series (GSE)19665, GSE55092, and GSE121248 were used to identify significant differentially expressed genes (DEGs). The enrichment analysis was performed on Metascape online tool. The RF algorithm and ANN were used to select the potential predictive gene panels and construct an HBV-related HCC diagnostic model. Subsequently, GSE17548, GSE104310, GSE44074, and GSE136247 were used to test the accuracy of the ANN model. Finally, the CIBERSORT algorithm was used to assess the abundance of immune infiltrates in all samples. Results: First, 116 genes were identified as DEGs, and the DEGs were particularly enriched in cellular hormone metabolic process, monocarboxylic acid metabolic process, NABA extracellular matrix (ECM) AFFILIATED steroid metabolic process and metabolism of bile acid and bile salt. DNA topoisomerase II alpha (TOP2A), C-type lectin domain family 1 member B (CLEC1B), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), ficolin 2 (FCN2), C-X-C motif chemokine ligand 14 (CXCL14), cyclase associated actin cytoskeleton regulatory protein 2 (CAP2), ficolin 3 (FCN3), kynurenine 3-monooxygenase (KMO) and cadherin related family member 2 (CDHR2) were available to develop an HBV-related HCC diagnostic model. After validation, the diagnostic model showed high sensitivity (88.5%, 90%, 88.5%, 76.5%) and specificity (100%, 81.8%, 89.5%, 72.2%), and the areas under the receiver operating characteristic (ROC) curves showed excellent efficiency (1, 0.927, 0.921, 0.833). Finally, the percentage of infiltrating immune cell types [B cells naïve, B cells memory, plasma cells, T cells CD8, T cells CD4 memory resting, T cells regulatory (Tregs), T cells gamma delta, natural killer (NK) cells resting, NK cells activated, Macrophages M0, Dendritic cells activated, Mast cells activated] for hepatitis B-related HCC were significantly different from that of non-cancerous liver tissue with HBV. Conclusions: A novel early diagnostic model of HBV-related HCC was established, and the model showed better efficiency in distinguishing HBV-related HCC from other non-cancerous with HBV individuals.