Integrating artificial intelligence in osteosarcoma prognosis: the prognostic significance of SERPINE2 and CPT1B biomarkers.

The principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate goal to enhance prognostic prediction. Expression profiles from 88 OS cases and 396 normal samples were procured from accessible public databases. Prognostic models were established using univariate COX regression analysis and an array of AI methodologies including the XGB method, RF method, GLM method, SVM method, and LASSO regression analysis. Multivariate COX regression analysis was also employed. Immune cell variations in OS were examined using the CIBERSORT software, and a differential analysis was conducted. Routine blood data from 20,679 normal samples and 437 OS cases were analyzed to validate lymphocyte disparity. Histological assessments of the study's postulates were performed through immunohistochemistry and hematoxylin and eosin (HE) staining. AI facilitated the identification of differentially expressed genes, which were utilized to construct a prognostic model. This model discerned that the survival rate in the high-risk category was significantly inferior compared to the low-risk cohort (p < 0.05). SERPINE2 was found to be positively associated with memory B cells, while CPT1B correlated positively with CD8 T cells. Immunohistochemical assessments indicated that SERPINE2 was more prominently expressed in OS tissues relative to adjacent non-tumorous tissues. Conversely, CPT1B expression was elevated in the adjacent non-tumorous tissues compared to OS tissues. Lymphocyte counts from routine blood evaluations exhibited marked differences between normal and OS groups (p < 0.001). The study highlights SERPINE2 and CPT1B as crucial biomarkers for OS prognosis and suggests that dysregulation of lymphocytes plays a significant role in OS pathogenesis. Both SERPINE2 and CPT1B have potential utility as prognostic biomarkers for OS.

Bioinformatics-based and multiscale convolutional neural network screening of herbal medicines for improving the prognosis of liver cancer: a novel approach.

Background: Liver cancer is one of the major diseases threatening human life and health, and this study aims to explore new methods for treating liver cancer. Methods: A deep learning model for the efficacy of clinical herbal medicines for liver cancer was constructed based on NDCNN, combined with the natural evolutionary rules of a genetic algorithm to obtain the herbal compound for liver cancer treatment. We obtained differential genes between liver cancer tissues and normal tissues from the analysis of TCGA database, screened the active ingredients and corresponding targets of the herbal compound using the TCMSP database, mapped the intersection to obtain the potential targets of the herbal compound for liver cancer treatment in the Venny platform, constructed a PPI network, and conducted GO analysis and KEGG analysis on the targets of the herbal compound for liver cancer treatment. Finally, the key active ingredients and important targets were molecularly docked. Results: The accuracy of the NDCNN training set was 0.92, and the accuracy of the test set was 0.84. After combining with the genetic algorithm for 1,000 iterations, a set of Chinese herbal compound prescriptions was finally the output. A total of 86 targets of the herbal compound for liver cancer were obtained, mainly five core targets of IL-6, ESR1, JUN, IL1ß, and MMP9. Among them, quercetin, kaempferol, and stigmasterol may be the key active ingredients in hepatocellular carcinoma, and the herbal compound may be participating in an inflammatory response and the immune regulation process by mediating the IL-17 signaling pathway, the TNF signaling pathway, and so on. The anticancer effects of the herbal compound may be mediated by the IL-17 signaling pathway, the TNF signaling pathway, and other signaling pathways involved in inflammatory response and immune regulation. Molecular docking showed that the three core target proteins produced stable binding to the two main active ingredients. Conclusion: The screening of effective herbal compounds for the clinical treatment of liver cancer based on NDCNN and genetic algorithms is a feasible approach and will provide ideas for the development of herbal medicines for the treatment of liver cancer and other cancers.

Nanoparticle Delivery of Active Traditional Chinese Medicine Ingredients: A New Strategy for the Treatment of Liver Cancer.

The precise treatment of liver cancer is receiving much research attention. Surgery, chemoradiotherapy, and other methods remain the mainstream of this treatment, but many chemotherapeutic drugs used to treat advanced liver cancer cause adverse reactions and have unstable efficiencies. Active ingredients used in traditional Chinese medicine (TCM) have been examined widely in anti-cancer research due to their advantages of multi-pathway targeting and rich pharmacological effects. However, these active components have poor water solubility, bioavailability, and targeting efficiency. Nanomedicine has been applied to improve the efficacy of TCM ingredients in the treatment of liver cancer. Nanoparticles (NPs) show great potential in this context due to their excellent bioavailability, high controlled agent release efficiency, and clear targeting. This paper reviews the application of NPs loaded with active TCM ingredients in the treatment of liver cancer, with the aim of facilitating new vector development and improving the precision treatment of liver cancer.

Polysaccharide-modified liposomes and their application in cancer research.

Nanodrug delivery systems have been widely used in cancer treatment. Among these, liposomal drug carriers have gained considerable attention due to their biocompatibility, biodegradability, and low toxicity. However, conventional liposomes have several shortcomings, such as poor stability, rapid clearance, aggregation, fusion, degradation, hydrolysis, and oxidation of phospholipids. Polysaccharides are natural polymers of biological origin that exhibit structural stability, excellent biocompatibility and biodegradability, flexibility, non-immunogenicity, low toxicity, and targetability. Therefore, they represent a promising class of polymers for the modification of the surface properties of liposomes to overcome their shortcomings. In addition, polysaccharides can be readily combined with other materials to develop new composite materials. Hence, they represent the optimal choice for liposomal modification to improve pharmacokinetics and clinical utility. Polysaccharide-coated liposomes exhibit better stability, drug release kinetics, and cellular uptake than conventional liposomes. The oncologic application of polysaccharide-coated liposomes has become a research hotspot. We summarize the preparation, physicochemical properties, and antineoplastic effects of polysaccharide-coated liposomes to facilitate antitumor drug development.

Global burden of thermal burns, 1990-2017: Unbalanced distributions and temporal trends assessed from the Global Burden of Disease Study 2017.

BACKGROUND: Many studies demonstrate that being burned has both physical and psychological sequelae that affect quality of life. Further, these effects may be more prevalent in some regions and populations. We sought to access the unbalanced distributions and temporal trends concerning the health burden of thermal burns. METHODS: Data were collected from the Global Burden of Disease Study 2017, and the disability-adjusted life year (DALY)1 was used as a measure of health burden. Linear regression was used to evaluate the relationship between the age-standardized DALY rate and socio-demographic index.2 Joinpoint regression analysis and comparison line charts were all applied to assess the temporal trends of burns. RESULTS: The age-standardized DALY rate of global thermal burns decreased by 43.7%, from 197 (95% CI: 152-228) per 100,000 in 1990 to 111 (95% CI: 93-129) per 100,000 in 2017. The burden was borne mainly by children 1-4 years of age and people over 80 years. Socio-demographic index was negatively correlated with the age-standardized DALY rate. In low-middle and low socio-demographic index regions, the decreasing trends were slower than other regions with an average annual percentage change of -2.1% (95% CI: -2.2 to -2.0) and -2.1% (95% CI: -2.1 to -2.0), respectively. Among six geographical regions, Africa presented the highest age-standardized DALY rates of 352 (95% CI: 275-410) per 100,000 in 1990 and 208 (95% CI: 175-236) per 100,000 in 2017, and also the slowest average decreasing trend, with an average annual percentage change of -1.9% (95% CI: -2 to -1.8). CONCLUSIONS: The global burden of thermal burns shows a downward trend from 1990 to 2017, and regions with lower socio-demographic index and Africa show greater burdens and smaller downward trends.