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Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr).Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared.Results and Discussion: Gas chromatography–ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.

Purpose: To conduct a real-world evaluation of the efficacy and safety of combined Chinese and Western medicine in treating knee osteoarthritis (KOA).Methods: A multicenter, prospective cohort study design was employed, enrolling 450 KOA patients (Kellgren-Lawrence score of 3 or less). The patients were divided into a Western medicine treatment group (WM group) and a combined Western and traditional Chinese medicine treatment group (WM-CM group). A 6-week treatment plan was administered, and follow-up visits occurred at 2 weeks, 4 weeks, and 6 weeks after initiating treatment. The primary outcome indicator was the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score after 6 weeks of treatment. Secondary outcome indicators included WOMAC subscales for pain, stiffness, and joint function, visual analogue scale (VAS) score, physical component summary (PCS), mental component summary (MCS), and clinical effectiveness. The incidence of drug-related adverse events was used as a safety evaluation indicator.Results: A total of 419 patients were included in the final analysis: 98 in the WM group and 321 in the WM-CM group. The baseline characteristics of the two groups were comparable, except for the incidence of stiffness symptoms and stiffness scores. After 6 weeks of treatment, the WM-CM group exhibited superior results to the WM group in improving the total WOMAC score (24.71 ± 1.38 vs. 16.36 ± 0.62, p < 0.001). The WM-CM group also outperformed the WM group in WOMAC pain and joint function scores, VAS score, PCS score, MCS score, and clinical effectiveness (p < 0.05), which was consistent with the findings of the main evaluation index. Subgroup analysis indicated that the combined Chinese and Western medicine treatment showed more pronounced benefits in patients under 65 years of age and in those with a Kellgren-Lawrence (K-L) classification of 0-I. Throughout the study, no adverse effects were observed in either group.Conclusion: The combination of Chinese and Western medicine demonstrated superiority over Western medicine alone in relieving knee pain symptoms, improving knee function, and enhancing the quality of life for KOA patients with a K-L score of 3 or less. Moreover, the treatment exhibited a good safety profile.Clinical Trial Registration: (https://www.chictr.org.cn/), identifier (ChiCTR1900027175).

BackgroundThe impact of hospital volume on the long-term survival of esophageal squamous cell carcinoma (ESCC) has not been well assessed in China, especially for stage I–III stage ESCC. We performed a large sample size study to assess the relationships between hospital volume and the effectiveness of ESCC treatment and the hospital volume value at the lowest risk of all-cause mortality after esophagectomy in China.AimTo investigate the prognostic value of hospital volume for assessing postoperative long-term survival of ESCC patients in China.MethodsThe date of 158,618 patients with ESCC were collected from a database (1973–2020) established by the State Key Laboratory for Esophageal Cancer Prevention and Treatment, the database includes 500,000 patients with detailed clinical information of pathological diagnosis and staging, treatment approaches and survival follow-up for esophageal and gastric cardia cancers. Intergroup comparisons of patient and treatment characteristics were conducted with the X2 test and analysis of variance. The Kaplan-Meier method with the log-rank test was used to draw the survival curves for the variables tested. A Multivariate Cox proportional hazards regression model was used to analyze the independent prognostic factors for overall survival. The relationship between hospital volume and all-cause mortality was assessed using restricted cubic splines from Cox proportional hazards models. The primary outcome was all-cause mortality.ResultsIn both 1973-1996 and 1997-2020, patients with stage I-III stage ESCC who underwent surgery in high volume hospitals had better survival than those who underwent surgery in low volume hospitals (both P

BackgroundThis study was intended to construct a brand new prognostic nomogram after combine clinical and pathological characteristics to increases prognostic value in patients with esophageal squamous cell carcinoma.MethodsA total of 1,634 patients were included. Subsequently, the tumor tissues of all patients were prepared into tissue microarrays. AIPATHWELL software was employed to explore tissue microarrays and calculate the tumor-stroma ratio. X-tile was adopted to find the optimal cut-off value. Univariate and multivariate Cox analyses were used to screen out remarkable characteristics for constructing the nomogram in the total populations. A novel prognostic nomogram with clinical and pathological characteristics was constructed on the basis of the training cohort (n=1,144). What’s more performance was validated in the validation cohort (n=490). Clinical-pathological nomogram were assessed by concordance index, time-dependent receiver operating characteristic, calibration curve and decision curve analysis.ResultsThe patients can divide into two groups with cut-off value of 69.78 for the tumor-stroma ratio. It is noteworthy that the survival difference was noticeable (P

Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [hyodeoxycholic acid, (2S,3S)-3-methylphenylalanine, carnitine C9:1, and indole-3-carboxylic acid] were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed that perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.