Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study.

BACKGROUND: Gastric cancer is one of the most common malignant tumors in the digestive system, ranking sixth in incidence and fourth in mortality worldwide. Since 42.5% of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type, the application of imaging diagnosis is restricted. AIM: To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) algorithms and to evaluate their predictive performance in clinical practice. METHODS: Data of a total of 369 patients who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Seven ML models, including decision tree, random forest, support vector machine (SVM), gradient boosting machine, naive Bayes, neural network, and logistic regression, were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML models were established following ten cross-validation iterations using the training dataset, and subsequently, each model was assessed using the test dataset. The models' performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. RESULTS: Among the seven ML models, except for SVM, the other ones exhibited higher accuracy and reliability, and the influences of various risk factors on the models are intuitive. CONCLUSION: The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer, which can aid in personalized clinical diagnosis and treatment.

Comparison of Machine Learning and Logic Regression Algorithms for Predicting Lymph Node Metastasis in Patients with Gastric Cancer: A two-Center Study.

OBJECTIVES: This two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice. METHODS: Data of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. RESULTS: Compared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive. CONCLUSION: For the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.

Rationale and design of a comparison of angiography-derived fractional flow reserve-guided and intravascular ultrasound-guided intervention strategy for clinical outcomes in patients with coronary artery disease: a randomised controlled trial (FLAVOUR II).

INTRODUCTION: Percutaneous coronary intervention (PCI) guided by coronary angiography-derived fractional flow reserve (FFR) or intravascular ultrasound (IVUS) has shown improved clinical outcomes compared with angiography-only-guided PCI. In patients with intermediate stenoses, FFR resulted in fewer coronary interventions and was non-inferior to IVUS with respect to clinical outcomes. However, whether this finding can be applied to angiography-derived FFR in significant coronary artery disease (CAD) remains unclear. METHOD AND ANALYSIS: The comparison of angiography-derived FFR-guided and IVUS-guided intervention strategies for clinical outcomes in patients with coronary artery disease (FLAVOUR II) trial is a multicentre, prospective, randomised controlled trial. A total of 1872 patients with angiographically significant CAD (stenoses of at least 50% as estimated visually through angiography) in a major epicardial coronary artery will be randomised 1:1 to receive either angiography-derived FFR-guided or IVUS-guided PCI. Patients will be treated with second-generation drug-eluting stent according to the predefined criteria for revascularisation: angiography-derived FFR≤0.8 and minimal lumen area (MLA)≤3 mm2 or 3 mm270%. The primary endpoint is a composite of all-cause death, myocardial infarction and revascularisation at 12 months after randomisation. We will test the non-inferiority of the angiography-derived FFR-guided strategy compared with the IVUS-guided decision for PCI and the stent optimisation strategy.The FLAVOUR II trial will provide new insights into optimal evaluation and treatment strategies for patients with CAD. ETHICS AND DISSEMINATION: FLAVOUR II was approved by the institutional review board at each participating site (The Second Affiliated Hospital of Zhejiang University School of Medicine Approval No: 2020LSYD410) and will be conducted in line with the Declaration of Helsinki. Informed consent would be obtained from each patient before their participation. The study results will be submitted to a scientific journal. TRIAL REGISTRATION NUMBER: NCT04397211.

Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study.

Objective: Gastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer patients who underwent radical gastrectomy using machine learning, employing advanced computational techniques to investigate postoperative mortality risk factors in such patients. Methods: Data of 295 patients with gastric cancer who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between March 2016 and November 2019 were retrospectively analyzed as the training group. Additionally, 109 patients who underwent radical gastrectomy at the Department of General Surgery Affiliated to Jining First People's Hospital (Jining, China) were included for external validation. Four machine learning models, including logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), were utilized. Model performance was assessed by comparing the area under the curve (AUC) for each model. An LR-based nomogram model was constructed to assess patients' clinical prognosis. Results: Lasso regression identified eight associated factors: age, sex, maximum tumor diameter, nerve or vascular invasion, TNM stage, gastrectomy type, lymphocyte count, and carcinoembryonic antigen (CEA) level. The performance of these models was evaluated using the AUC. In the training group, the AUC values were 0.795, 0.759, 0.873, and 0.853 for LR, DT, RF, and GBM, respectively. In the validation group, the AUC values were 0.734, 0.708, 0.746, and 0.707 for LR, DT, RF, and GBM, respectively. The nomogram model, constructed based on LR, demonstrated excellent clinical prognostic evaluation capabilities. Conclusion: Machine learning algorithms are robust performance assessment tools for evaluating the prognosis of gastric cancer patients who have undergone radical gastrectomy. The LR-based nomogram model can aid clinicians in making more reliable clinical decisions.

[Reduction of Soil Cadmium Activity and Rice Cadmium Content by 4-year-consecutive Application of Organic Fertilizer].

Because commercial organic fertilizers may contain cadmium (Cd) and may cause the dual effect of "inhibition" and "activation" on Cd availability in paddy soil with organic fertilizer input, the reduction of rice Cd following organic fertilizer application is still uncertain. Herewith, typical purple mud paddy fields were selected in the eastern Hunan Province. The effect of commercial organic fertilizer input on Cd reduction of double-rice paddy ecosystem was monitored for four consecutive years. The relationships between brown rice Cd content, soil available Cd, and soil factors (pH, soil labile organic carbon fractions, and iron oxide) at different growth stages in double-rice paddy fields were investigated. Results showed that the input of organic fertilizer reduced the Cd content in brown rice by 28%-56%. Meanwhile, the decrease of Cd content in brown rice of late rice (43%-56%) was higher than that of early rice (28%-45%), and the inter-annual fluctuation of the decrease was relatively small. On the one hand, soil available Cd content decreased by 6%-7% during several growth stages of double-rice (from tillering peak stage to full heading stage) with organic fertilizer input. Additionally, the content of soil exchangeable Cd was decreased by 11%, whereas the content of organic bound Cd was increased by 14%. This directly reflects the decrease of soil Cd availability. On the other hand, the soil pH value was steadily increased by 0.1-0.3 units following organic fertilizer input, which promoted the development of soil from acidity to slight acidity. Besides, the content of soil active organic carbon (light fraction organic carbon, coarse particulate organic carbon, and fine particulate organic carbon) was increased significantly (53%, 77%, and 107%, respectively). This indirectly reflects the decrease in soil Cd availability. This study implies that the decrease of soil Cd availability may be the primary driving force for the reduction of rice Cd content with consecutive organic fertilizer input in purple mud paddy fields.

Euxanthone represses the proliferation, migration, and invasion of glioblastoma cells by modulating STAT3/SHP-1 signaling.

Glioblastoma is one of the most prevalent primary malignant brain tumors. Glioblastoma often develops resistance to conventional chemoradiotherapy, and thus, new ways to treat glioblastoma are urgently required. The aim of this study was to investigate the effect of euxanthone on the anticancer activities of glioblastoma and its potential mechanism. The U87 and U251 glioblastoma cell lines were cultured in media containing different concentrations of euxanthone. CCK-8 and colony formation assay were used to evaluate the cell proliferation. Cell migration and invasion were evaluated by wound healing and Transwell assays. Flow cytometry was used to assess the cell cycle and apoptosis rate. TUNEL assay was also employed to evaluate the apoptosis rate. Gene and protein expressions were determined by RT-qPCR and western blotting, respectively. A xenograft model was established to evaluate the efficacy of euxanthone in vivo. Euxanthone significantly repressed cell viability, migration, invasion, and epithelial-to-mesenchymal transition of U87 and U251 cells; and increased the rate of apoptosis. Western blotting results revealed that the levels of p21, p27, cleaved caspase-3, Bax, TIMP-3, and E-cadherin were upregulated while, the levels of CDK4, CDK6, pro-caspase-3, Bcl-2, MMP-2, MMP-9, N-cadherin, and Vimentin were downregulated by euxanthone. In addition, the expression of p-STAT3 was decreased, while the expression of SHP-1 was upregulated by euxanthone. We proposed that euxanthone could repress the malignant behavior of glioblastoma cells through suppression of STAT3 phosphorylation and activation of SHP-1. Further, in vivo data demonstrated that euxanthone repressed tumor growth and promoted apoptosis.

RETRACTED: Physcion 8-O-ß-glucopyranosideregulates cell cycle, apoptosis, and invasion in glioblastoma cells through modulating Skp2.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief, following the initial request of the corresponding author. The journal has further requested the author to provide explanations for the figure similarities with papers previously published by different authors. However, the author was not able to fulfil the request. The panels U251/PG+Skp2 cDNA from Figure 4E and U251/Vehicle from Figure 6D appear similar to the panels SOX2 shRNA from Figure 3D and CoCl2 from Figure 6B of the article previously published by Yan-tao Han, Xue-hong Chen, Hui Gao, Jun-li Ye and Chun-bo Wang in Acta Pharmacologica Sinica 37(2) (2016) 264­275 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753366/. The panel U251/PG from Figure 6B appears similar to the panel KYSE30/miR-370 mimic + PIM1 vector from Figure 5D of the article previously published by Yantao Han, Xiuwei Yang, Ning Zhao, Jianjun Peng, Hui Gao and Xia Qiu in the American Journal of Cancer Research 6(12) (2016) 2755­2771 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5199752/.

DNA recombination activity in soybean mitochondria.

Mitochondrial genomes in higher plants are much larger and more complex as compared to animal mitochondrial genomes. There is growing evidence that plant mitochondrial genomes exist predominantly as a collection of linear and highly branched DNA molecules and replicate by a recombination-dependent mechanism. However, biochemical evidence of mitochondrial DNA (mtDNA) recombination activity in plants has previously been lacking. We provide the first report of strand-invasion activity in plant mitochondria. Similar to bacterial RecA, this activity from soybean is dependent on the presence of ATP and Mg(2+). Western blot analysis using an antibody against the Arabidopsis mitochondrial RecA protein shows cross-reaction with a soybean protein of about 44 kDa, indicating conservation of this protein in at least these two plant species. mtDNA structure was analyzed by electron microscopy of total soybean mtDNA and molecules recovered after field-inversion gel electrophoresis (FIGE). While most molecules were found to be linear, some molecules contained highly branched DNA structures and a small but reproducible proportion consisted of circular molecules (many with tails) similar to recombination intermediates. The presence of recombination intermediates in plant mitochondria preparations is further supported by analysis of mtDNA molecules by 2-D agarose gel electrophoresis, which indicated the presence of complex recombination structures along with a considerable amount of single-stranded DNA. These data collectively provide convincing evidence for the occurrence of homologous DNA recombination in plant mitochondria.

Characterization of a mitochondrially targeted single-stranded DNA-binding protein in Arabidopsis thaliana.

A gene encoding a predicted mitochondrially targeted single-stranded DNA binding protein (mtSSB) was identified in the Arabidopsis thaliana genome sequence. This gene (At4g11060) codes for a protein of 201 amino acids, including a 28-residue putative mitochondrial targeting transit peptide. Protein sequence alignment shows high similarity between the mtSSB protein and single-stranded DNA binding proteins (SSB) from bacteria, including residues conserved for SSB function. Phylogenetic analysis indicates a close relationship between this protein and other mitochondrially targeted SSB proteins. The predicted targeting sequence was fused with the GFP coding region, and the organellar localization of the expressed fusion protein was determined. Specific targeting to mitochondria was observed in in-vitro import experiments and by transient expression of a GFP fusion construct in Arabidopsis leaves after microprojectile bombardment. The mature mtSSB coding region was overexpressed in Escherichia coli and the protein was purified for biochemical characterization. The purified protein binds single-stranded, but not double-stranded, DNA. MtSSB stimulates the homologous strand-exchange activity of E. coli RecA. These results indicate that mtSSB is a functional homologue of the E. coli SSB, and that it may play a role in mitochondrial DNA recombination.

[The misdiagnosis of testicular torsion (a report of 113 cases)].

OBJECTIVE: To improve the diagnosis and treatment of testicular torsion (or spermatic cord torsion), and reduce its misdiagnosis and mistreatment. METHODS: One hundred and thirteen misdiagnosed clinical cases of testicular torsion from 1994 to 2004 were reviewed and analysed. RESULTS: The error rate of initial diagnosis was 84.3%, among which 81 cases (71.7%) were misdiagnosed as acute epididymitis or testis, 10 (8.8%) as hydrocele of the tunica vaginalis, and 7 (6.2%) as acute enteritis. The lengths of time between the income and diagnosis of the disease varied from 2 hours to 2 months, averaging 6.3 days. Hand replacement succeeded in 3 cases, surgical examination was carried out in 92, resection of the testis or epididymis was performed in 64, testis atrophy occurred in 26, and the total testis impairment rate was 79.6%. CONCLUSION: The key to the reduction of misdiagnosis is to improve the diagnostic methods, which can be achieved by the combined use of case history, physical signs and color ultrasonography. Surgical examination of the scrotum is the best option for both the diagnosis and the treatment of testicular torsion.