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The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023
The 2023 update of the Chinese Society of Clinical Oncology (CSCO) Clinical Guidelines for Gastric Cancer focuses on standardizing cancer diagnosis and treatment in China, reflecting the latest advancements in evidence‐based medicine, healthcare resource availability, and precision medicine. These updates address the differences in epidemiological characteristics, clinicopathological features, tumor biology, treatment patterns, and drug selections between Eastern and Western gastric cancer patients. Key revisions include a structured template for imaging diagnosis reports, updated standards for molecular marker testing in pathological diagnosis, and an elevated recommendation for neoadjuvant chemotherapy in stage III gastric cancer. For advanced metastatic gastric cancer, the guidelines introduce new recommendations for immunotherapy, anti‐angiogenic therapy and targeted drugs, along with updated management strategies for human epidermal growth factor receptor 2 (HER2)‐positive and deficient DNA mismatch repair (dMMR)/microsatellite instability‐high (MSI‐H) patients. Additionally, the guidelines offer detailed screening recommendations for hereditary gastric cancer and an appendix listing drug treatment regimens for various stages of gastric cancer. The 2023 CSCO Clinical Guidelines for Gastric Cancer updates are based on both Chinese and international clinical research and expert consensus to enhance their applicability and relevance in clinical practice, particularly in the heterogeneous healthcare landscape of China, while maintaining a commitment to scientific rigor, impartiality, and timely revisions.
Journal Article
The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2021
There exist differences in the epidemiological characteristics, clinicopathological features, tumor biological characteristics, treatment patterns, and drug selections between gastric cancer patients from the Eastern and Western countries. The Chinese Society of Clinical Oncology (CSCO) has organized a panel of senior experts specializing in all sub‐specialties of gastric cancer to compile a clinical guideline for the diagnosis and treatment of gastric cancer since 2016 and renews it annually. Taking into account regional differences, giving full consideration to the accessibility of diagnosis and treatment resources, these experts have conducted expert consensus judgment on relevant evidence and made various grades of recommendations for the clinical diagnosis and treatment of gastric cancer to reflect the value of cancer treatment and meeting health economic indexes in China. The 2021 CSCO Clinical Practice Guidelines for Gastric Cancer covers the diagnosis, treatment, follow‐up, and screening of gastric cancer. Based on the 2020 version of the CSCO Chinese Gastric Cancer guidelines, this updated guideline integrates the results of major clinical studies from China and overseas for the past year, focused on the inclusion of research data from the Chinese population for more personalized and clinically relevant recommendations. For the comprehensive treatment of non‐metastatic gastric cancer, attentions were paid to neoadjuvant treatment. The value of perioperative chemotherapy is gradually becoming clearer and its recommendation level has been updated. For the comprehensive treatment of metastatic gastric cancer, recommendations for immunotherapy were included, and immune checkpoint inhibitors from third‐line to the first‐line of treatment for different patient groups with detailed notes are provided. The Chinese Society of Clinical Oncology (CSCO) organized a panel of senior experts specializing in all sub‐specialties of gastric cancer to compile the clinical guideline for gastric cancer in 2016 and then renewed it every year. The 2021 CSCO Clinical Practice Guidelines for gastric cancer covered the diagnosis, treatment, follow‐up and screening.
Journal Article
The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer
China is one of the countries with the highest incidence of gastric cancer. There are differences in epidemiological characteristics, clinicopathological features, tumor biological characteristics, treatment patterns, and drug selection between gastric cancer patients from the Eastern and Western countries. Non-Chinese guidelines cannot specifically reflect the diagnosis and treatment characteristics for the Chinese gastric cancer patients. The Chinese Society of Clinical Oncology (CSCO) arranged for a panel of senior experts specializing in all sub-specialties of gastric cancer to compile, discuss, and revise the guidelines on the diagnosis and treatment of gastric cancer based on the findings of evidence-based medicine in China and abroad. By referring to the opinions of industry experts, taking into account of regional differences, giving full consideration to the accessibility of diagnosis and treatment resources, these experts have conducted experts’ consensus judgement on relevant evidence and made various grades of recommendations for the clinical diagnosis and treatment of gastric cancer to reflect the value of cancer treatment and meeting health economic indexes. This guideline uses tables and is complemented by explanatory and descriptive notes covering the diagnosis, comprehensive treatment, and follow-up visits for gastric cancer.
Journal Article
Application of an Improved TF-IDF Method in Literary Text Classification
Literature is extremely important in the advancement of human civilization. Every day, many literary texts of various genres are produced, dating back to ancient times. An urgent concern for managers in the current literary activity is how to classify and save the expanding mass of literary text data for easy access by readers. In the realm of text classification, the TF-IDF algorithm is a widely used classification algorithm. However, there are significant issues with utilizing this approach, including a lack of distribution information inside categories, a lack of distribution information between categories, and an inability to adjust to skewed datasets. It is possible to improve classification accuracy by using the TF-IDF algorithm in this paper’s application situation by exploiting the association between feature words and the quantity of texts in which they appear, while ignoring the variation in feature word distribution across categories. With the purpose of classifying the literary texts in this study, this work proposes an improved IDF method for the problem of feature words appearing several times and having diverse meanings in different fields. The meanings of feature words in distinct domains are separated to increase the trust in the TF-IDF algorithm’s output. Using the improved TF-IDF method suggested in this research with the random forest (RF) classifier, the experimental results show that the classifier has a good classification impact, which can meet the actual work needs, based on comparative experiments on feature dimension selection, feature selection algorithm, feature weight algorithm, and classifier. It has a fair amount of historical significance.
Journal Article
Clustering of single-cell multi-omics data with a multimodal deep learning method
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is essential for the downstream complex biological functional studies. However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive simulation and real-data experiments reveal that scMDC outperforms existing single-cell single-modal and multimodal clustering methods on different single-cell multimodal datasets. The linear scalability of running time makes scMDC a promising method for analyzing large multimodal datasets.
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. Here the authors develops a multimodal deep clustering method for the analysis of single-cell multi-omics data that supports clustering different types of multi-omics data and multi-batch data, as well as downstream differential expression analysis.
Journal Article
Antibody responses to SARS-CoV-2 in patients with COVID-19
We report acute antibody responses to SARS-CoV-2 in 285 patients with COVID-19. Within 19 days after symptom onset, 100% of patients tested positive for antiviral immunoglobulin-G (IgG). Seroconversion for IgG and IgM occurred simultaneously or sequentially. Both IgG and IgM titers plateaued within 6 days after seroconversion. Serological testing may be helpful for the diagnosis of suspected patients with negative RT–PCR results and for the identification of asymptomatic infections.
A cross-sectional study of hospitalized patients with COVID-19 and a longitudinal follow-up study of patients with COVID-19 suggest that SARS-CoV2-specific IgG or IgM seroconversion occurs within 20 days post symptom onset.
Journal Article
The chromosome-level wintersweet (Chimonanthus praecox) genome provides insights into floral scent biosynthesis and flowering in winter
Background
Wintersweet (
Chimonanthus praecox
), an important ornamental plant, has evolved unique fragrant aroma and winter-flowering properties, which are critical for its successful sexual reproduction. However, the molecular mechanisms underlying these traits are largely unknown in this species. In addition, wintersweet is also a typical representative species of the magnoliids, where the phylogenetic position of which relative to eudicots and monocots has not been conclusively resolved.
Results
Here, we present a chromosome-level wintersweet genome assembly with a total size of 695.36 Mb and a draft genome assembly of
Calycanthus chinensis
. Phylogenetic analyses of 17 representative angiosperm genomes suggest that Magnoliids and eudicots are sister to monocots. Whole-genome duplication signatures reveal two major duplication events in the evolutionary history of the wintersweet genome, with an ancient one shared by Laurales, and a more recent one shared by the Calycantaceae. Whole-genome duplication and tandem duplication events have significant impacts on copy numbers of genes related to terpene and benzenoid/phenylpropanoid (the main floral scent volatiles) biosynthesis, which may contribute to the characteristic aroma formation. An integrative analysis combining cytology with genomic and transcriptomic data reveals biological characteristics of wintersweet, such as floral transition in spring, floral organ specification, low temperature-mediated floral bud break, early blooming in winter, and strong cold tolerance.
Conclusions
These findings provide insights into the evolutionary history of wintersweet and the relationships among the Magnoliids, monocots, and eudicots; the molecular basis underlying floral scent biosynthesis; and winter flowering, and highlight the utility of multi-omics data in deciphering important ornamental traits in wintersweet.
Journal Article
Natural Aporphine Alkaloids with Potential to Impact Metabolic Syndrome
The incidence and prevalence of metabolic syndrome has steadily increased worldwide. As a major risk factor for various diseases, metabolic syndrome has come into focus in recent years. Some natural aporphine alkaloids are very promising agents in the prevention and treatment of metabolic syndrome and its components because of their wide variety of biological activities. These natural aporphine alkaloids have protective effects on the different risk factors characterizing metabolic syndrome. In this review, we highlight the activities of bioactive aporphine alkaloids: thaliporphine, boldine, nuciferine, pronuciferine, roemerine, dicentrine, magnoflorine, anonaine, apomorphine, glaucine, predicentrine, isolaureline, xylopine, methylbulbocapnine, and crebanine. We particularly focused on their impact on metabolic syndrome and its components, including insulin resistance and type 2 diabetes mellitus, endothelial dysfunction, hypertension and cardiovascular disease, hyperlipidemia and obesity, non-alcoholic fatty liver disease, hyperuricemia and kidney damage, erectile dysfunction, central nervous system-related disorder, and intestinal microbiota dysbiosis. We also discussed the potential mechanisms of actions by aporphine alkaloids in metabolic syndrome.
Journal Article
Biomedical relation extraction method based on ensemble learning and attention mechanism
Background
Relation extraction (RE) plays a crucial role in biomedical research as it is essential for uncovering complex semantic relationships between entities in textual data. Given the significance of RE in biomedical informatics and the increasing volume of literature, there is an urgent need for advanced computational models capable of accurately and efficiently extracting these relationships on a large scale.
Results
This paper proposes a novel approach, SARE, combining ensemble learning Stacking and attention mechanisms to enhance the performance of biomedical relation extraction. By leveraging multiple pre-trained models, SARE demonstrates improved adaptability and robustness across diverse domains. The attention mechanisms enable the model to capture and utilize key information in the text more accurately. SARE achieved performance improvements of 4.8, 8.7, and 0.8 percentage points on the PPI, DDI, and ChemProt datasets, respectively, compared to the original BERT variant and the domain-specific PubMedBERT model.
Conclusions
SARE offers a promising solution for improving the accuracy and efficiency of relation extraction tasks in biomedical research, facilitating advancements in biomedical informatics. The results suggest that combining ensemble learning with attention mechanisms is effective for extracting complex relationships from biomedical texts. Our code and data are publicly available at:
https://github.com/GS233/Biomedical
.
Journal Article