Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer.

BACKGROUND: The most important consideration in determining treatment strategies for undifferentiated early gastric cancer (UEGC) is the risk of lymph node metastasis (LNM). Therefore, identifying a potential biomarker that predicts LNM is quite useful in determining treatment. AIM: To develop a machine learning (ML)-based integral procedure to construct the LNM gray-level co-occurrence matrix (GLCM) prediction model. METHODS: We retrospectively selected 526 cases of UEGC confirmed through pathological examination after radical gastrectomy without endoscopic treatment in four tertiary hospitals between January 2015 to December 2021. We extracted GLCM-based features from grayscale images and applied ML to the classification of candidate predictive variables. The robustness and clinical utility of each model were evaluated based on the following factors: Receiver operating characteristic curve (ROC), decision curve analysis, and clinical impact curve. RESULTS: GLCM-based feature extraction significantly correlated with LNM. The top 7 GLCM-based factors included inertia value 0° (IV_0), inertia value 45° (IV_45), inverse gap 0° (IG_0), inverse gap 45° (IG_45), inverse gap full angle (IG_all), Haralick 30° (Haralick_30), Haralick full angle (Haralick_all), and Entropy. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine, eXtreme gradient boosting, artificial neural network, and decision tree ranged from 0.805 [95% confidence interval (CI): 0.258-1.352] to 0.925 (95%CI: 0.378-1.472) in the training set and from 0.794 (95%CI: 0.237-1.351) to 0.912 (95%CI: 0.355-1.469) in the testing set, respectively. The RFC (training set: AUC: 0.925, 95%CI: 0.378-1.472; testing set: AUC: 0.912, 95%CI: 0.355-1.469) model that incorporates Entropy, Haralick_all, Haralick_30, IG_all, IG_45, IG_0, and IV_45 had the highest predictive accuracy. CONCLUSION: The evaluation results indicate that the method of selecting radiological and textural features becomes more effective in the LNM discrimination against UEGC patients. Additionally, the ML-based prediction model developed using the RFC can be used to derive treatment options and identify LNM, which can hence improve clinical outcomes.

[Contributions of flavonoids from citri reticulatae pericarpium to gastric hormones, CD3~+ and TFF3 mRNA expression in rats with spleen deficiency intervened by Liujunzi Decoction].

The present study established the spectrum-effect relationship model of flavonoids in Citri Reticulatae Pericarpium(CRP) from 15 batches of Liujunzi Decoction and statistically analyzed the correlation between chemical peaks and efficacy to identify the main effective components. HPLC fingerprints of flavonoids in CRP from 15 batches of Liujunzi Decoction were established. HPLC analysis was carried out on the Venusil XBP C_(18)(L) column(4.6 mm×250 mm, 5 µm) at 30 ℃ with acetonitrile-water(containing 0.1% formic acid) as mobile phase for gradient elution, a flow rate of 1.0 mL·min~(-1), and detection wavelength of 300 nm to obtain chemical fingerprints. Additionally, the effects of flavonoids from CRP in 15 batches of Liujunzi Decoction on the content of GAS, MTL, and VIP, TFF3 mRNA expression, and percentage of CD3~+ T-cells of model rats with spleen deficiency were determined. The spectrum-effect relationship model was established by gray correlation analysis. The results showed that the main characteristic peaks with great contribution to the regulation of gastrointestinal tract were peak 16(vicenin-2), peak 63(sinensetin), peak 64(isosinensetin), peak 65(nobiletin), peak 67(3,5,6,7,8,3',4'-heptemthoxyflavone), peak 68(tangeretin), and peak 69(5-desmethylnobiletin). Therefore, there was a linear correlation between flavonoids from CRP in Liujunzi Decoction and the efficacy, and the medicinal effect was achieved by multi-component action. This study is expected to provide a new idea for exploring the material basis of the effect, i.e., regulating qi prior to replenishing qi, of CRP in Liujunzi Decoction.

Analysis of bioactive components and pharmacokinetics of Caulophyllum robustum in rat plasma after oral administration by UPLC-ESI-MS/MS.

A UPLC-MS/MS method was developed and validated the determination and pharmacokinetic study of magnoflorine, cauloside C, hederagenin, and oleanolic acid from Caulophyllum robustum. Digoxin was used as the internal standard. The pretreated plasma samples were carried out on a Waters ACQUITYUPLC HSS T3 column at 35 °C with a mobile phase of acetonitrile-water (90:10, v/v) at a flow rate of 0.2 mL/min. This article describes the most simple, sensitive, and validated UPLC-MS/MS method to date for the simultaneous successful determination of four compounds in rat plasma after oral administration of the extract of C. robustum and their pharmacokinetic studies.

Pharmacological Effect of Caulophyllum robustum on Collagen-Induced Arthritis and Regulation of Nitric Oxide, NF-κB, and Proinflammatory Cytokines In Vivo and In Vitro.

Caulophyllum robustum Maxim (C. robustum) has commonly been used as traditional Chinese medicine for the treatment of rheumatic pain and rheumatoid arthritis (RA) in China. This paper first investigated the anti-inflammation effect of C. robustum extraction (CRME) on RAW264.7 cells stimulated by lipopolysaccharide (LPS) and gene expression levels of inflammatory factors. Moreover, we first evaluated the anti-RA effects of CRME using collagen-induced arthritis (CIA) in DBA/1J mice, and the incidence, clinical score, and joint histopathology were evaluated. The levels of IL-1, IL-6, TNF-α, and PGE2 inflammatory factors in sera of mice were detected by enzyme-linked immunosorbent assay. The expression of NF-κB p65 in the joint was tested by immune histochemical technique. The results showed that, compared with the model group, CRME significantly improved symptoms of the arthritis index, limb swelling, and histological findings by decreasing synovial membrane damage, the extent of inflammatory cell infiltration, and the expansion of capillaries in CIA mice. The results also showed that CRME can reduce the levels of IL-1, IL-6, TNF-α, and PGE2 and inhibit the expression of NF-κB p65. All these results indicated the anti-inflammatory efficacy of CRME as a novel botanical extraction for the treatment of RA.

[Research progress on application of DNA barcoding technique in Culicidae taxonomy].

DNA barcoding technique is a fast and accurate method for species identification. The DNA barcoding based on cytochrome c oxidase subunit Ⅰ (COⅠ) has recently been successfully applied for species identification of Culicidae. In this paper, we introduce the technique and principle of DNA barcoding, application and limitation of the technique based on CO I gene for species identification, as well as research development on applications of other molecular makers such as COⅡ,16S RNA, and the first and the second internal transcribed spacers (ITS1 and ITS2) in species identification and to assist the COⅠ gene in identifying Culicidae species.

Apparent Diffusion Coefficient Predicts Pathology Complete Response of Rectal Cancer Treated with Neoadjuvant Chemoradiotherapy.

OBJECTIVE: To evaluate the predictive value of the apparent diffusion coefficient (ADC) for pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer. METHODS: A total of 265 patients with rectal adenocarcinoma, whole Diffusion-Weighted MRI (DWI-MRI) images, clinically stage II to III (cT3-4 and/or cN+) and treated with NCRT followed by TME were screened. Fifty patients with pCR and another 50 patients without pCR with similar clinical charcacters and treatment regimens were selected for statistical analysis. All the patients' pre-CRT and post-CRT average ADC values were calculated from the coefficient maps created by DWI-MRI and recorded independently. The difference in the ADC values between the pCR and non-pCR was analyzed by the Mann-Whitney U test. The cut-off ADC value of the receiver operating characteristic (ROC) curve with pCR was then established. RESULTS: The mean pre- and post-ADC values in all patients, and in pCR patients and non-pCR patients were 0.879±0.06 and 1.383±0.11, 0.859±0.04 and 1.440±0.10, 0.899±0.07 and 1.325±0.09 (×10(-3) mm(2)/s), respectively. The difference between the pre- and post-ADC values in all patients, pCR patients, and non-pCR patients were considered to be statistically significant. The pre-ADC value was significantly lower in the pCR patients than in the non-pCR patients (p = 0.003), whereas the post-ADC values were significantly higher in the pCR patients than in the non-pCR patients. The percentage increase of the ADC value (ΔADC%) in the pCR and non-pCR patients were 68% and 48% respectively (p<0.001). The ROC curves of the cut-off value of the pre-CRT patient ADC value was 0.866×10(-3) mm(2)/s. The AUC, sensitivity, specificity, PPV, NPV, and accuracy of diagnosing pCR were 0.670 (95% CI 0.563-0.777), 0.600, 0.640, 60%, 60%, and 60%, respectively. The cut-off value of ΔADC% was 58%. The corresponding AUC, sensitivity, specificity, PPV, NPV, and accuracy of diagnosing pCR were 0.856 (95% CI 0.783-0.930), 0.800, 0.760, 76.9%, 79.2%, and 78%, respectively. CONCLUSIONS: DWI-MRI technology can be efficient for predicting pCR for LARC after NCRT. Although the mean pre-CRT ADC value and the ΔADC% are moderate predictors for pCR, the latter would be more accurate.