Chinese herbal injections in combination with radiotherapy for advanced pancreatic cancer: A systematic review and network meta-analysis.

Background: Advanced pancreatic cancer (APC) is a fatal disease with limited treatment options. This study aims to evaluate the effectiveness and safety of different Chinese herbal injections (CHIs) as adjuvants for radiotherapy (RT) in APC and compare their treatment potentials using network meta-analysis. Methods: We systematically searched three English and four Chinese databases for randomized controlled trials (RCTs) from inception to July 25, 2023. The primary outcome was the objective response rate (ORR). Secondary outcomes included Karnofsky performance status (KPS) score, overall survival (OS), and adverse events (AEs). The treatment potentials of different CHIs were ranked using the surface under the cumulative ranking curve (SUCRA). The Cochrane RoB 2 tool and CINeMA were used for quality assessment and evidence grading. Results: Eighteen RCTs involving 1199 patients were included. Five CHIs were evaluated. Compound Kushen injection (CKI) combined with RT significantly improved ORR compared to RT alone (RR 1.49, 95 % CrI 1.21-1.86). Kanglaite (KLT) plus RT (RR 1.58, 95 % CrI 1.20-2.16) and CKI plus RT (RR 1.49, 95 % CrI 1.16-1.95) were associated with improved KPS score compared to radiation monotherapy, with KLT+RT being the highest rank (SUCRA 72.28 %). Regarding AEs, CKI plus RT was the most favorable in reducing the incidence of leukopenia (SUCRA 90.37 %) and nausea/vomiting (SUCRA 85.79 %). Conclusions: CKI may be the optimal choice of CHIs to combine with RT for APC as it may improve clinical response, quality of life, and reduce AEs. High-quality trials are necessary to establish a robust body of evidence. Protocol registration: PROSPERO, CRD42023396828.

Highly-accurate solvent identification using dynamic evaporation reflection spectra from an inverse opal sensor combined with a deep learning model.

Developing a low-cost, rapid, and highly accurate method for detecting solvents with similar structures and properties is highly demanded. In recent years, methods based on dynamic reflection spectroscopy have been developed to distinguish isomers and homologues. However, these methods heavily rely on responsive photonic crystals that can interact intricately with the solvent. In this work, we propose a deep learning approach for direct solvent identification from dynamic evaporative reflection spectra (DERS) obtained on a simple inverse opal (IO) sensor. The sensor was prepared using co-assembly and sacrificial template methods. Then, a dataset was constructed with 985 DERS obtained from 14 different solvents. Different classical machine learning and deep learning algorithms were employed for classifying these DERS. The results showed that ResNet18-CBAM, an improved convolutional neural network, outperformed all other algorithms, achieving 97.7 ± 0.9% on the 5-fold cross-validation set and 100% accuracy on the test set. This strategy presents not only a simple, efficient, and repeatable method for solvent detection but also, more importantly, by integrating the deep learning model, it allows an automatic, rapid, and accurate analysis of DERS data without the need for human intervention. It holds great application prospects in the field of solvent detection.

GC/MS-Based Analysis of Fatty Acids and Amino Acids in H460 Cells Treated with Short-Chain and Polyunsaturated Fatty Acids: A Highly Sensitive Approach.

The important metabolic characteristics of cancer cells include increased fat production and changes in amino acid metabolism. Based on the category of tumor, tumor cells are capable of synthesizing as much as 95% of saturated and monounsaturated fatty acids through de novo synthesis, even in the presence of sufficient dietary lipid intake. This fat transformation starts early when cell cancerization and further spread along with the tumor cells grow more malignant. In addition, local catabolism of tryptophan, a common feature, can weaken anti-tumor immunity in primary tumor lesions and TDLN. Arginine catabolism is likewise related with the inhibition of anti-tumor immunity. Due to the crucial role of amino acids in tumor growth, increasing tryptophan along with arginine catabolism will promote tumor growth. However, immune cells also require amino acids to expand and distinguish into effector cells that can kill tumor cells. Therefore, it is necessary to have a deeper understanding of the metabolism of amino acids and fatty acids within cells. In this study, we established a method for the simultaneous analysis of 64 metabolites consisting of fatty acids and amino acids, covering biosynthesis of unsaturated fatty acids, aminoacyl-tRNA biosynthesis, and fatty acid biosynthesis using the Agilent GC-MS system. We selected linoleic acid, linolenic acid, sodium acetate, and sodium butyrate to treat H460 cells to validate the current method. The differential metabolites observed in the four fatty acid groups in comparison with the control group indicate the metabolic effects of various fatty acids on H460 cells. These differential metabolites could potentially become biomarkers for the early diagnosis of lung cancer.

Meta-analysis of gemcitabine plus nab-paclitaxel combined with targeted agents in the treatment of metastatic pancreatic cancer.

BACKGROUND: Gemcitabine plus nab-paclitaxel (GA) is a commonly used first-line treatment regimen for metastatic pancreatic cancer, and many studies will add a novel targeted agent to this regimen for improving patient survival rate. However, the clinical effectiveness of GA is the most controversial issue. AIM: To compare the efficacy and safety of GA regimen with a targeted agent and GA regimen. METHODS: Up to 1 December 2021, the eligible randomized controlled trials (RCTs) relating to GA and GA with a targeted agent were searched on PubMed, EMBASE and Cochrane Library for eligible data. We screened out appropriate studies for overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and toxicity, which had been pooled and finally analyzed by using Stata version 15.1. In addition, we use Reference Citation Analysis (https://www.referencecitationanalysis.com/) to collect the latest related literature to improve the latest cutting-edge research results. RESULTS: Seven RCTs involving 1544 patients (848 men and 696 women) were included. There were no significant differences between GA with a targeted agent and GA in PFS [hazard ratio (HR): 1.18 95% confidence interval (CI): 0.91-1.53], OS (HR: 1.12 95%CI: 0.99-1.27), and ORR (HR: 0.96 95%CI: 0.71-1.29). There was no notable difference in the two groups in grade 3/4 toxicity (fatigue, anemia, vomiting and neutropenia), whereas the incidence of grade 3/4 diarrhea considerably increased in GA with a targeted drug. CONCLUSION: Adding a novel targeted agent to the GA regimen did not improve survival rate of patients with metastatic pancreatic cancer.

Network pharmacology and molecular docking analysis reveal insights into the molecular mechanism of shiliao decoction in the treatment of cancer-associated malnutrition.

Purpose: Shiliao Decoction (SLD) was developed for treatment and prevention of cancer-associated malnutrition (CAM) in China. In this study, we aim to discover SLD's active compounds and demonstrate the mechanisms of SLD that combat CAM through network pharmacology and molecular docking techniques. Methods: All components of SLD were retrieved from the pharmacology database of Traditional Chinese Medicine Systems Pharmacology (TCMSP). The GeneCards database and the Online Mendelian Inheritance in Man database (OMIM) were used to identify gene encoding target compounds, and Cytoscape was used to construct the drug compound-target network. The network of target protein-protein interactions (PPI) was constructed using the STRING database, while gene ontology (GO) functional terms and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways associated with potential targets were analyzed using a program in R language (version 4.2.0). Core genes linked with survival and the tumor microenvironment were analyzed using the Kaplan-Meier plotter and TIMER 2.0 databases, respectively. Protein expression and transcriptome expression levels of core gene were viewed using the Human Protein Atlas (HPA) and the Cancer Genome Atlas (TCGA). A component-target-pathway (C-T-P) network was created using Cytoscape, and Autodock Vina software was used to verify the molecular docking of SLD components and key targets. Results: The assembled compound-target network primarily contained 134 compounds and 147 targets of the SLD associated with JUN, TP53, MAPK3, MAPK1, MAPK14, STAT3, AKT1, HSP90AA1, FOS, and MYC, which were identified as core targets by the PPI network. KEGG pathway analysis revealed pathways involved in lipid and atherosclerosis, the PI3K/Akt signaling pathway, and immune-related pathways among others. JUN is expressed at different levels in normal and cancerous tissues, it is closely associated with the recruitment of different immune cells and has been shown to have a significant impact on prognosis. The C-T-P network suggests that the active component of SLD is capable of regulating target genes affecting these related pathways. Finally, the reliability of the core targets was evaluated using molecular docking technology. Conclusion: This study revealed insights into SLD's active components, potential targets, and possible molecular mechanisms, thereby demonstrating a potential method for examining the scientific basis and therapeutic mechanisms of TCM formulae.

Quantifying the Endogeneity in Online Donations.

Charitable crowdfunding provides a new channel for people and families suffering from unforeseen events, such as accidents, severe illness, and so on, to seek help from the public. Thus, finding the key determinants which drive the fundraising process of crowdfunding campaigns is of great importance, especially for those suffering. With a unique data set containing 210,907 crowdfunding projects covering a period from October 2015 to June 2020, from a famous charitable crowdfunding platform, specifically Qingsong Chou, we will reveal how many online donations are due to endogeneity, referring to the positive feedback process of attracting more people to donate through broadcasting campaigns in social networks by donors. For this aim, we calibrate three different Hawkes processes to the event data of online donations for each crowdfunding campaign on each day, which allows us to estimate the branching ratio, a measure of endogeneity. It is found that the online fundraising process works in a sub-critical state and nearly 70-90% of the online donations are endogenous. Furthermore, even though the fundraising amount, number of donations, and number of donors decrease rapidly after the crowdfunding project is created, the measure of endogeneity remains stable during the entire lifetime of crowdfunding projects. Our results not only deepen our understanding of online fundraising dynamics but also provide a quantitative framework to disentangle the endogenous and exogenous dynamics in complex systems.