Objective To study the anatomy of pelvic autonomic nerves, to avoid autonomic nerves damage during rectal cancer surgery. MethodsAnatomical dissections were carried out on 7 adult cadavers, and clinically autonomic nerve-preserving rectal cancer resection was performed. Results The superior hypogastric plexus lies just posterior to the inferior mesenteric vessels. There were no obvious autonomic nerve trunks in the loose areolar tissue plane between the parietal fascia and the visceral fascia right posterior to the rectum. There were no obvious autonomic nerve trunks between the rectum and the seminal vesicles or the prostate. The inferior hypogastric plexus was a rhomboid shaped plaque of nervous tissue. The main components of the lateral ligament were autonomic nerves passing from the pelvic plexus to the rectum within a condensation of connective tissue. WT5”HZConclusionsThe inferior mesenteric vessels can be used a landmark intraoperatively to identify the superior hypogastric plexus. In order to preserve the inferior hypogastric plexus while dissecting the lateral of rectum, we should dissect along the medial surface of the inferior hypogastric plexus, and along its curvature. JP2

OBJECTIVETo investigated the chemical structures and bioactivity of polysaccharides from Isatidis Radix.

METHODPolysaccharides were extracted and purified by column chromatograph and their chemical structures were identified by UV, IR, NMR, periodic acid oxadation and Smith degradation method and their stimulation effects to macrophage were evaluated by using MTT method.

RESULTFive polysaccharides, polysaccharide A , B, C, D and E were gotten and their molecular weights were 2 000, 1 757.1, 1 34 2.7, 955.6, 11.7 kDa, respectively. Polysaccharide A was composed of arabinose, polysaccharide E was composed of arabinose and galactose, polysaccharides B, C, D were composed of glucose and 1 --> 2, 1 --> 3, 1 --> 4, 1 --> 6 linkages existed in polysaccharides A-E, of A, B, C, D, E were alpha-configurations. Polysaccharides B, C and D showed better bioactivity than polysaccharides A and E with stimulation index (SI) of 5.31, 4.76, 5.17.

CONCLUSIONFive polysaccharides are seperated firstly from Isatidis Radix.

Animals ; Isatis ; chemistry ; Magnetic Resonance Spectroscopy ; Mice ; Polysaccharides ; chemistry ; pharmacology

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.