Xiaoyuan Li

This study uses the modified artificial neural networks (ANN) model to predict the impact of PSMB9 indicator changes on the prognosis of patients in the death group. Building and training a binary classification ANN based on survival and death groups of patients with cutaneous melanoma (CM), the 10 therapeutic decision biomarkers as input data which were selected by previously our study. Using the death group as the prediction dataset, the expression level of PSMB9 is modified to observe how many samples in the prediction dataset are classified into the survival group or the death group by the ANN model. Four hundred sixty-seven CM patients with different prognoses were included in the TCGA-SKCM dataset, with 10 decision-making treatment biomarkers selected as input data. A binary classification ANN model was built based on the prognosis of CM patients, achieving 100% accuracy. After adjusting the expression of PSMB9 twice, based on the original values, the model predicted that 5 to 35 patients in the prediction dataset would be classified as the survival group. Based on the cutoff value increased to the maximum, the model predicted that 21 to 187 patients in the prediction dataset would be classified as the survival group. Statistical analysis of PSMB9 expression at various stages indicated that its expression decreases with the progression of the T stage, with no differences in the M- and N-stages. Immune infiltration analysis suggested that PSMB9 is also involved in regulating multiple immune cell abundances. The expression of PSMB9 at various stages and the screening of related immune cells were explored. The developed ANN model can predict changes in prognosis based on changes in some indicators in the medical field.

Introduction: Widespread opportunistic pathogens pose a serious threat to global health, particularly in susceptible hospital populations. The escalating crisis of antibiotic resistance highlights the urgent need for novel antibacterial agents and alternative treatment approaches. Traditional Chinese Medicine (TCM) and its compounds have deep roots in the treatment of infectious diseases. It has a variety of active ingredients and multi-target properties, opening up new avenues for the discovery and development of antimicrobial drugs.Methods: This study focuses on assessing the efficacy of the Shensheng-Piwen changed medicinal powder (SPC) extracts against opportunistic pathogen infections by broth microdilution and agar disc diffusion methods. Additionally, biofilm inhibition and eradication assays were performed to evaluate the antibiofilm effects of SPC extracts.Results: Metabolite profiles were analyzed by LC-MS. Furthermore, the potential synergistic effect between SPC and Metal-Organic Framework (MOF) was investigated by bacterial growth curve analysis. The results indicated that the SPC extracts exhibited antibacterial activity against S. aureus, with a minimum inhibitory concentration (MIC) of 7.8 mg/mL (crude drug concentration). Notably, at 1/2 MIC, the SPC extracts significantly inhibited biofilm formation, with over 80% inhibition, which was critical in tackling chronic and hospital-acquired infections. Metabolomic analysis of S. aureus revealed that SPC extracts induced a notable reduction in the levels of various metabolites, including L-proline, L-asparagine. This suggested that the SPC extracts could interfere with the metabolism of S. aureus. Meanwhile, the growth curve experiment proved that SPC extracts and MOFs had a synergistic antibacterial effect.Discussion: In conclusion, the present study highlights the potential of SPC extracts as a novel antibacterial agent against S. aureus infections, with promising biofilm inhibition properties. The observed synergistic effect between SPC extracts and MOFs further supports the exploration of this combination as an alternative treatment approach.

Background: The traditional Chinese medicine prescription Ping-Wei-San decoction (PWS) has been used historically in East Asian countries. This study aimed to investigate the ingredients of the decoction and explore its potential role in the adjuvant treatment of pulmonary malignancies. Objectives: The primary objective was to analyze the ingredients of PWS and understand its potential in the treatment of pulmonary malignancy. Methods: Medical records of 143 394 patients using PWS over a decade were collected from the Affiliated Hospital of Qingdao University. UPLC-QE-Orbitrap-MS detected 13 chemical components, including 12 known components. Lung adenocarcinoma data were obtained from the TCGA-LUAD dataset, and differential genes were screened using R studio. The EVENN website and the STRING database were used to identify drug action and disease targets. Setting: The study was conducted at the Affiliated Hospital of Qingdao University. Participants: The study included 3147 patients with “pulmonary malignancies.” Intervention: PWS was used as an adjuvant treatment. Results: Network analysis revealed 10 core targets (CCNA2, CHEK1, TOP2A, CDK1, CCNB1, CCNB2, AURKA, AURKB, KIF11, and MELK) for the treatment of lung adenocarcinoma and associated pathways. Conclusion: PWS may offer multifunctional efficacy in the treatment of pulmonary malignancies based on constituent analysis. Further research and clinical trials are needed to explore its potential.

Introduction: Parkinson’s disease is a neurodegenerative disorder involving loss of dopaminergic neurons. Multiple studies implicate the microbiota-gut-brain axis in Parkinson’s disease pathophysiology. Ping-wei-san plus Herbal Decoction, a traditional Chinese medicine composition with beneficial effects in Parkinson’s disease, may have a complex array of actions. Here we sought to determine whether gut microbiota and metabolic pathways are involved in Ping-wei-san plus herbal therapy for Parkinson’s disease and to identify functional pathways to guide research.Methods and results: The model of Parkinson’s disease were induced with the rotenone. The Ping-wei-san plus group received the PWP herbal decoction for 90 days, after which all groups were analyzed experimentally. PWP herbal treatment improved motor behavior and emotional performance, balanced gut microbiota, and benefited dietary metabolism. Tandem Mass Tags mass spectrometry identified many differentially expressed proteins (DEPs) in the substantia nigra and duodenum in the PWP group, and these DEPs were enriched in pathways such as those involving cAMP signaling, glutamatergic synapses, dopaminergic synapses, and ribosome-rich functions in the gut. The PWP group showed increases in recombinant tissue inhibitors of metalloproteinase 3, and nucleotide-binding oligomerization domain, leucine rich repeat, and pyrin domain containing proteins 6 in the substantia nigra and decreased parkin, gasdermin D, recombinant tissue inhibitors of metalloproteinase 3, and nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing proteins 6 in the duodenum.Discussion: In conclusion, this study combined gut microbiota, metabolomics, and proteomics to evaluate the mechanism of action of Ping-wei-san plus on Parkinson’s disease and revealed that PWP herbal treatment modulated gut microbiota, altered metabolite biological pathways, and affected functional pathway protein expression in Parkinson’s disease mice, resulting in therapeutic effects.

Introduction. The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with different prognoses and to explore the application value of deep learning in the prognosis of cutaneous melanoma patients. Methods. In deep learning, VGG‐19 is selected as the network architecture and learning model for learning and classification. In machine learning, deep features are extracted through the VGG‐19 network architecture, and the support vector machine (SVM) model is selected for learning and classification. Compare and explore the application value of deep learning and machine learning in predicting the prognosis of patients with cutaneous melanoma. Result. According to receiver operating characteristic (ROC) curves and area under the curve (AUC), the average accuracy of deep learning is higher than that of machine learning, and even the lowest accuracy is better than that of machine learning. Conclusion. As the number of learning increases, the accuracy of machine learning and deep learning will increase, but in the same number of cutaneous melanoma patient pathology maps, the accuracy of deep learning will be higher. This study provides new ideas and theories for computational pathology in predicting the prognosis of patients with cutaneous melanoma.

Objective. To explore the potential mechanism of Huanglian Jiedu Decoction (HJD) treatment and prevention of metastatic Cutaneous Melanoma (CM) occurrence and metastasis based on network pharmacological methods and immune infiltration analysis. Methods. The GEO database was used to obtain metastatic CM disease targets, the TCMSP database and the HERB database were used to obtain HJD action targets, core genes were screened by protein interaction network, and the potential mechanism of HJD in the treatment of metastatic CM was explored by enrichment analysis, prognostic analysis and immune infiltration analysis. Results. HJD treatment of metastatic CM involved 60 targets, enrichment analysis showed that HJD treatment of metastatic CM involved Chemokine signaling pathway, NF-kappa B signaling pathway, and Fluid shear stress and atherosclerosis, etc. Prognostic analysis revealed that HJD had a certain ability to improve the prognosis of metastatic CM patients. Immune infiltration analysis showed that HJD could inhibit the immune cell infiltration of metastatic CM patients by acting on related targets. Conclusions. Our study identified the potential mechanism of HJD in the treatment of metastatic CM through network pharmacology, and revealed the mechanism of HJD in the prevention of Skin Cutaneous Melanoma metastasis through immune infiltration analysis and prognostic analysis.