ObjectiveTo investigate the application value of a machine learning model based on preoperative clinical indicators in predicting the risk of hypoalbuminemia after partial hepatectomy. MethodsA retrospective analysis was performed for the clinical data of 700 patients who underwent partial hepatectomy in Nanfang Hospital， Southern Medical University， from January 2018 to January 2023， including demographic data， history of underlying diseases， tumor characteristics， preoperative laboratory markers， and perioperative indicators. The research data were divided into a training set and a test set at a ratio of 7∶3. The two-independent-samples t test was used for comparison of normally distributed continuous data between two groups； the two-independent-samples Wilcoxon rank-sum test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator （LASSO） regression analysis was used to identify characteristic variables， and 7 machine learning algorithms were used to construct predictive models， i.e.， logistic regression， decision tree， artificial neural network， K-nearest neighbors （KNN）， support vector machine， eXtreme gradient boosting， and light gradient boosting machine. The receiver operating characteristic （ROC） curve and the area under the ROC curve （AUC） were used to assess the discriminatory ability of models， and the DeLong test was used for comparison of AUC. The calibration curve and decision curve analysis were used to assess the calibration and clinical practicability of models， and the models were compared with albumin-bilirubin （ALBI） score and Model for End-Stage Liver Disease （MELD） score. SHapley Additive exPlanations （SHAP） were used to interpret the key influencing factors for the optimal model. ResultsA total of 700 patients were finally enrolled， 283 （40.42%） developed hypoalbuminemia after surgery. The LASSO regression analysis identified 8 predictive factors of age， hepatitis B， fatty liver， blockade time， preoperative albumin （Alb）， time of operation， intraoperative blood loss， and preoperative aspartate aminotransferase （AST）. Among the 7 machine learning models， the KNN model showed the best overall predictive performance， with an AUC of 0.835 （95% confidence interval： 0.781 — 0.889）， a sensitivity of 84.0%， and a specificity of 65.5% in the test set. ALBI and MELD scores had an AUC of 0.652 and 0.524， respectively， and the KNN model had a better predictive performance than these two scores （Z=5.309 and 8.945， both P <0.001）. The calibration curve showed good consistency between predicted probabilities and actual incidence rates， and the decision curve analysis showed that the KNN model had net clinical benefit across a wide threshold range. The SHAP analysis showed that preoperative Alb， hepatitis B， time of operation， and age were the most significant influencing factors， and a synergistic effect was observed between hepatitis B and age/time of operation. ConclusionThe KNN machine learning model constructed based on preoperative clinical indicators can effectively predict the risk of hypoalbuminemia after partial hepatectomy and has a better performance than traditional scoring models， which provides a reference for the early identification of high-risk patients in clinical practice.

OBJECTIVETo study the heat processing technics of Nanhanshuishi.

METHODTo find the best processing technic, the single factor experiments and orthogonal experiments were designed basing on the processing technics summarized by consulting documents, scriptures and investigating some Tibetan hospitals, meanwhile, the content of Ca, Fe, Mn, Zn, and Cu in the processed Nanhanshuishi in single factor experiments and orthogonal experiments were detected.

RESULTThe best processing technic of Nanhanshuishi was as follows: Nanhanshuishi was crashed to 10-20 mm in diameter, the ratio of the weight of Aconiti Kusnezoffii Radix and potassium nitrate was 1: 2, and the boiling time was 3 h.

CONCLUSIONThe work in this article provided a basic processing technic data for clarifying the mechanism of processing and establishing the perfect processing technics of Nanhanshuishi.

Medicine, Tibetan Traditional ; Technology, Pharmaceutical ; Trace Elements ; analysis

OBJECTIVETo develop an HPLC method for determination of gallic acid, hydroxysafflor yellow A, cinnamic aldehyde and piperine in Tibetan medicine Dangzuo, and to compare the content of four active components in Dangzuo of different Tibetan regions.

METHODThe separation was carried out on a Waters XTerra RP-C18 column ( 4.6 mm x 250 mm, 5 microm). The mobile phases were methanol and water, all contained 0.1% glacial acetic acid, for gradient elution. The gradient program was as follows: 0-22.5 min, methanol was changed from 5% to 50%; 22.5-40 min, changed to 80% 80:20. The flow rate was 1.0 mL x min(-1). The detection wavelength was 270 nm. The reference wavelength was 500 nm.

RESULTThe linear ranges of gallic acid, hydroxysafflor yellow A, cinnamic aldehyde and piperine were 0.040-0.640 microg (r = 0.999 8), 0.090-1.440 microg (r = 0.999 9), 0.031-0.500 microg (r = 0.999 9 ) and 0.092-41.477 microg (r = 0.998 9), respectively. The average recoveries (n = 6) were 97.42% (RSD 1.9%), 97.55% (RSD 2.9%), 98.69% (RSD 0.96%) and 96.72% (RSD 4.0%), respectively. The content ranges of gallic acid, hydroxysafflor yellow A, cinnamic aldehyde and piperine in Dangzuo samples of different Tibetan regions were 0.11341.69 mg x g(-1), 0.889-1.51 mg x g(-1), 0.000-40.606 mg x g(-1) and 1.96-2.73 mg x g(-1), respectively.

CONCLUSIONThe method is a simple and effective for quality control of Tibetan medicine Dangzuo.

Acrolein ; analogs & derivatives ; analysis ; isolation & purification ; Alkaloids ; analysis ; isolation & purification ; Benzodioxoles ; analysis ; isolation & purification ; Chalcone ; analogs & derivatives ; analysis ; isolation & purification ; Chromatography, High Pressure Liquid ; methods ; Drugs, Chinese Herbal ; chemistry ; Gallic Acid ; analysis ; isolation & purification ; Medicine, Tibetan Traditional ; Piperidines ; analysis ; isolation & purification ; Plant Components, Aerial ; chemistry ; Plant Extracts ; analysis ; isolation & purification ; Polyunsaturated Alkamides ; analysis ; isolation & purification ; Quality Control ; Quinones ; analysis ; isolation & purification ; Reference Standards ; Spectrophotometry, Ultraviolet ; methods