BACKGROUND:It remains controversial in term of efficacy for the lightweight mesh and heavyweight mesh in inguinal hernia repair.
OBJECTIVE:To compare the clinical therapeutic effects of lightweight mesh and heavyweight mesh in open methods of inguinal hernia repair with Meta-analysis.
METHODS:Comprehensive electronic search strategies were developed using the fol owing electronic databases:PubMed, Cochrane Library, EMBASE, Medline, Ovid, CNKI, VIP, Wanfang and FMJS. The Literature published before February 2013 was searched. The randomized control ed trials about comparing lightweight mesh and heavyweight mesh in open methods of inguinal hernia repair were included. A data-extraction sheet was developed based on the preset standards. The data from eligible studies were pooled using RevMan5.1 software through Meta-analysis.
RESULTS AND CONCLUSION:Eighteen trials with a total of 4 450 hernias met the inclusion criteria. The meta-analysis showed that there was a statistical difference between lightweight mesh group and the heavyweight mesh group on short-term pain [odd ratio (OR)=0.57, 95%confidence interval (CI) (0.43, 0.74), P<0.05] and a reduced risk of developing foreign body sensations [OR=0.49, 95%CI (0.35, 0.69), P<0.05]. No significant differences were found between the two groups in recurrence rate, testicular atrophy, seroma, hematoma, wound infection, urine retention (P>0.5). According to limited evidence, there are some findings as fol ows:the lightweight mesh is of feasibility, safety and effectiveness for inguinal hernia repair. Because of the limits of sample and quality, more large-sample and high-quality trials are required to make a definite clinical evidence to use lightweight mesh for inguinal hernia repair.

Objective To explore the postgraduate tutor assessment system in order to provide suggestions to improve the quality of postgraduate education. Methods Assessment indexes of post-graduate tutor were screened and assigned by methods of questionnaire investigation, interview, kruskal wallis H-test, taking a total of 109 students and professors in medical colleges in the south-western part as research objects. Results Different populations had different cognitions on the important degree of each index, P<0.05. Primary indexes of assessment system were determined based on the importance of indicators and the influence of different cognitive levels, including tutor personal accom-plishment, teaching ability, scientific research quality and guiding ability (P<0.05). Conclusions Assessment system and method fined by this study covers majority aspects of tutors and can provide feasible suggestions for tutor evaluation.

Objective:To analyze the medication rule of Duan Fujin in the treatment of liver cirrhosis based on data mining method.Methods:By searching for and collecting the medical records of patients with liver cirrhosis treated by Traditional Chinese Medicine master Duan Fujin, counting the frequently used Chinese medicines and analyzing their association rules to carry out the system cluster analysis of frequently showed medicines, and to gain new prescriptions.Results:A total of 162 prescriptions of Professor Duan Fujin were collected, including 115 Chinese medicines, most of which are bitter and pungent in terms of taste. The medicines which are frequently used are Bupleuri Radix, Curcumae Radix, Poria, Trionycis Carapax, Glycyrrhizae Radix; the medicine pairs which are frequently appeared are Bupleuri Radix- Curcumae Radix, Bupleuri Radix- Poria, Curcumae Radix- Poria, Curcumae Radix- Trionycis Carapax, Bupleuri Radix- Trionycis Carapax, Curcumae Radix- Poria- Bupleuri Radix, Curcumae Radix- Trionycis Carapax- Bupleuri Radix, Curcumae Radix- Glycyrrhizae Radix- Bupleuri Radix, Bupleuri Radix- Chuanxiong Rhizoma- Curcumae Radix, Curcumae Radix- Trionycis Carapax- Poria, Bupleuri Radix- Trionycis Carapax- Poria. Cluster analysis shows that there are two new prescriptions: the first prescription plays the role of invigorating spleen and removing dampness, regulating qi and resolving phlegm, which is composed of Pinelliae Rhizoma, Citri Reticulatae Pericarpium, Glycyrrhizae Radix, Amomi Fructus, Aurantii Fructus, Poria, Atractylodis Macrocephalae Rhizoma, Ginseng Radix, Magnoliae Officinalis Cortex, Arecae Pericarpium, Hordei Fructus Germinatus, Crataegi Fructus; the second prescription plays the role of invigorating Qi and activating blood circulation, Ruanjian Sanjie, which is composed of Angelicae Sinensis Radix, Astragali Radix, Trionycis Carapax, Ostreae Concha, Curcumae Radix, Corydalis Rhizoma, Persicae Semen, Toosendan Fructus, Paeoniae Radix Rubra, Salviae Miltiorrhizae Radix, Curcumae Rhizoma, Moutan Cortex, Hedyotis diffusa Willd. Conclusion:The treatment of liver cirrhosis by Duan Fujin's mainly focuse on soothing and promoting the liver qi, softening the hard and disperse the knot, promoting blood circulation and removing blood stasis, strengthening the spleen qi and removing dampness.

BACKGROUND:Root canal preparation is a key step in root canal therapy.In recent years,with the rapid development of new nickel-titanium instruments,the results of root canal shaping ability have been significantly improved.However,the shaping abilities of different nickel-titanium instruments are also different.At present,there are few reports about the influence of the shaping ability of R-phase heat treatment NIC X-FILE and DENCO Pre-Shaper in simulated curved canals. OBJECTIVE:To compare the shaping effect of NIC X-FILE and DENCO Pre-Shaper nickel-titanium instruments in preparation for simulated curved canals. METHODS:Fifty simulated resin-curved canals were selected and randomly divided into two groups(n=25).NIC X-FILE and DENCO Pre-Shaper nickel-titanium files were used for root canal preparation.The root canal preparation time was recorded.Images of pre-and post-root canal preparation were taken.Image processing and analysis software were used to measure the amount of resin removed from the inner and outer walls of the root canal at each observation point.Statistical software was used to compare the root canal preparation time and centering ability of two kinds of mechanical nickel-titanium files. RESULTS AND CONCLUSION:(1)The average root canal preparation time was(1.58±0.02)minutes in the DENCO Pre-Shaper group and(2.22±0.03)minutes in the NIC X-FILE group.There was a significant difference between the two groups(P<0.05).(2)The amount of resin removed from the inner walls of the root canal of the DENCO Pre-Shaper group at 4,5,7,8,9,and 10 mm from the apical foramen was higher than that of the NIC X-FILE group(P<0.05).The amount of resin removed from the outer walls of the root canal of the DENCO Pre-Shaper group at 3,5,6,7,and 8 mm from the apical foramen was lower than that of the NIC X-FILE group(P<0.05).(3)At 2,4,5,6,7,8,and 9 mm away from apical foramen,the centering ability of the NIC X-FILE group was better than that of the DENCO Pre-Shaper group(P<0.05),and the centering ability of the NIC X-FILE group was the best at 4 mm away from apical foramen.(4)The results show that DENCO Pre-Shaper has higher mechanical efficiency than NIC X-FILE nickel-titanium root canal files.However,the centering ability of DENCO Pre-Shaper is inferior to NIC X-FILE nickel-titanium root canal files.

Objective:To explore the value of interpretable machine learning model in predicting the prognoses of patients with acute ischemic stroke..Methods:A total of 296 patients with acute ischemic stroke who received intravenous thrombolysis in Zhanjiang Central Hospital, Guangdong Medical University from March 2020 to October 2023 were selected. Prognosis was assessed 3 months after follow-up using modified Rankin scale (scores of 0-2: good prognosis; scores of 3-6: poor prognosis). Clinical data were collected and analyzed retrospectively, and independent influencing factors for prognoses were analyzed by multivariate Logistic regression. These patients were randomly divided into training dataset ( n=178) and test dataset ( n=118) in a 3:2 ratio; independent influencing factors were used as characteristic variables to train these 10 machine learning models, including Logistic regression, random forest, support vector machine, naive Bayesian model, linear discriminant analysis, mixture discriminant analysis, flexible discriminant analysis, gradient boosting machine, extreme gradient boosting, and category boosting. Prediction performance of these 10 machine learning models were evaluated using calibration curve, precise-recall curve, precision-recall gain curve and receiver operating characteristic (ROC) curve. Interpretation and visualization were added via Shapley Additive exPlanation (SHAP) to the machine learning models (including global interpretation and local interpretation). Results:Of the 296 patients, 72 had a poor prognosis. Age ( OR=1.039, 95% CI: 1.008-1.072, P=0.015), National Institute of Health Stroke Scale score ( OR=1.213, 95% CI: 1.000-1.337, P<0.001), Glasgow Coma Scale score ( OR=0.470, 95% CI: 0.289-0.765, P=0.002), Stroke Prognostic Instrument-Ⅱ score ( OR=1.257, 95% CI: 1.043-1.516, P=0.016,), C-reactive protein ( OR=1.709, 95% CI: 1.398-2.087, P<0.001) and platelet count ( OR=0.988, 95% CI: 0.978-0.998, P=0.016) were independent influencing factors for prognoses. Among the 10 machine learning algorithms, calibration curve (C-inder: 0.896), precise-recall curve (area under the curve [AUC]: 0.791), precision-recall gain curve (AUC: 0.363), and ROC curve (AUC: 0.856) in both the training and test sets confirmed that the XGBoost model has the highest performance in predicting prognoses. SHAP visualisation diagram indicated that order of importance was C-reactive protein, National Institutes of Health Stroke Scale, platelet count, Glasgow Coma Scale, Stroke Prediction Tool-II, and age. SHAP scatter plot visualized the contribution direction of these 6 characteristic variables, with bimodal distribution. SHAP dependence plot indicated dependence between values of 6 characteristic variables and SHAP values, with C-reactive protein enjoying the most significant trend. SHAP plot provided local interpretation for individual sample, making the extreme gradient enhancement model more transparent and interpretable. Conclusion:XGBoost model incorporating age, National Institute of Health Stroke Scale, Glasgow Coma Scale, Stroke Prognostic Instrument-Ⅱ, C-reactive protein, and platelet count can differentiate poor prognosis from good prognosis in patients with acute ischemic stroke with high accuracy; on this basis, the model interpretation and visualization combined with SHAP are helpful to understand the contribution and direction of each characteristic variable to the prediction results.