Community general practitioners are the main force of primary health care, however, the lack of effective incentive mechanisms would affect their work enthusiasm. Therefore, to enhance incentive mechanisms,particularly from the perspective of the career development,will improve the work enthusiasm and post competeny of general practitioners in primary health care services. This article summarizes five major influencing factors of the incentives for community general practitioners, namely salary and benefits, performance evaluation, promotion and development, working conditions and interpersonal relationships, and proposes the operational strategies of incentive improvement, to provide reference for improving working conditions and strengthening incentive measures for community general practioners in primary community hospitals.

Invasive fungal infections (IFIs) have become prominent global health threats, escalating the burden on public health systems. The increasing occurrence of invasive fungal infections is due primarily to the extensive application of chemotherapy, immunosuppressive therapies, and broad-spectrum antifungal agents. At present, therapeutic practices utilize multiple categories of antifungal agents, such as azoles, polyenes, echinocandins, and pyrimidine analogs. Nevertheless, the clinical effectiveness of these treatments is progressively weakened by the emergence of drug resistance, thereby substantially restricting their therapeutic utility. Consequently, there is an imperative need to expedite the discovery of novel antifungal agents. This review seeks to present an exhaustive synthesis of novel antifungal drugs and candidate agents that are either under current clinical investigation or anticipated to progress into clinical evaluation. These emerging compounds exhibit unique benefits concerning their modes of action, antimicrobial spectra, and pharmacokinetic characteristics, potentially leading to improved therapeutic outcomes relative to conventional antifungal regimens. It is anticipated that these novel therapeutic agents will furnish innovative treatment modalities and enhance clinical outcomes in managing invasive fungal infections.

Objective:To investigate the value of machine learning models based on CT radiomics for predicting the outcome of neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC).Methods:A retrospective case series study was conducted. A total of 279 LAGC patients receiving NAC before surgery in Shanxi Province Cancer Hospital from January 2018 to November 2020 were included. According to a ratio of 7∶3, all patients were randomly divided into the training set (196 cases) and the validation set (83 cases). According to the tumor regression grade (TRG), the pathological grade was divided into the good response of NAC (GR) group (TRG 0-1, 55 cases) and the poor response of NAC (PR) group (TRG 2-3, 224 cases). The clinicopathological data of patients were collected, such as age, gender, differentiation degree, clinical T and N staging, carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) level. Radiomics features were extracted from the enhanced CT images in the vein phase, and the features were screened by 3-step dimensionality reduction. And then 5 machine learning algorithms including logistic regression (LR), naive bayes (NB), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) were applied to build prediction models based on the CT radiomics. The receiver operating characteristic (ROC) curve and the decision analysis (DCA) curve were drawn to evaluate the predictive performance and clinical benefit of each model on the outcome of NAC in patients with LAGC.Results:Among 196 patients in the training set, there were 39 cases in GR group and 157 cases in PR group; among 83 patients in the validation set, there were 16 cases in GR group and 67 cases in PR group. There were no statistically significant differences in clinicopathological data of patients between the training and validation sets, or between GR and PR groups in the training and validation sets (all P > 0.05). A total of 102 radiomics features were extracted from region of interest of CT images in the vein phase, and 6 key features were finally selected including original_firstorder_10Percentile, original_firstorder_RoubustMeanAbsoluteDeviation, original_glcm_Idmn, original_glcm_MCC, original_ngtdm_Busyness, original_ngtdm_Contrast; and there were statistically significant differences in 6 features between the GR and PR groups (all P < 0.05). LR, NB, RF, SVM and XGB machine learning algorithms were used to construct 5 prediction models based on the CT radiomics. The area under ROC curve for NAC prediction in the training set was 0.553, 0.709, 0.668, 0.772 and 0.790, respectively; in the validation set was 0.662, 0.622, 0.683, 0.752 and 0.784, respectively. The model constructed by XGB showed the best comprehensive performance, and its accuracy, sensitivity and specificity was 0.771, 0.562 and 0.821, respectively. In the DCA of 5 machine learning models in the training set, XGB-based model provided a higher net benefit. Conclusions:Machine learning models based on enhanced CT radiomics in the vein phase have a high predictive efficacy in the outcome of NAC in LAGC patients before surgery and it helps make clinical personalized treatment decisions.

Community general practitioners are the main force of primary health care, however, the lack of effective incentive mechanisms would affect their work enthusiasm. Therefore, to enhance incentive mechanisms,particularly from the perspective of the career development,will improve the work enthusiasm and post competeny of general practitioners in primary health care services. This article summarizes five major influencing factors of the incentives for community general practitioners, namely salary and benefits, performance evaluation, promotion and development, working conditions and interpersonal relationships, and proposes the operational strategies of incentive improvement, to provide reference for improving working conditions and strengthening incentive measures for community general practioners in primary community hospitals.

Objective:To investigate the value of machine learning models based on CT radiomics for predicting the outcome of neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC).Methods:A retrospective case series study was conducted. A total of 279 LAGC patients receiving NAC before surgery in Shanxi Province Cancer Hospital from January 2018 to November 2020 were included. According to a ratio of 7∶3, all patients were randomly divided into the training set (196 cases) and the validation set (83 cases). According to the tumor regression grade (TRG), the pathological grade was divided into the good response of NAC (GR) group (TRG 0-1, 55 cases) and the poor response of NAC (PR) group (TRG 2-3, 224 cases). The clinicopathological data of patients were collected, such as age, gender, differentiation degree, clinical T and N staging, carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) level. Radiomics features were extracted from the enhanced CT images in the vein phase, and the features were screened by 3-step dimensionality reduction. And then 5 machine learning algorithms including logistic regression (LR), naive bayes (NB), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) were applied to build prediction models based on the CT radiomics. The receiver operating characteristic (ROC) curve and the decision analysis (DCA) curve were drawn to evaluate the predictive performance and clinical benefit of each model on the outcome of NAC in patients with LAGC.Results:Among 196 patients in the training set, there were 39 cases in GR group and 157 cases in PR group; among 83 patients in the validation set, there were 16 cases in GR group and 67 cases in PR group. There were no statistically significant differences in clinicopathological data of patients between the training and validation sets, or between GR and PR groups in the training and validation sets (all P > 0.05). A total of 102 radiomics features were extracted from region of interest of CT images in the vein phase, and 6 key features were finally selected including original_firstorder_10Percentile, original_firstorder_RoubustMeanAbsoluteDeviation, original_glcm_Idmn, original_glcm_MCC, original_ngtdm_Busyness, original_ngtdm_Contrast; and there were statistically significant differences in 6 features between the GR and PR groups (all P < 0.05). LR, NB, RF, SVM and XGB machine learning algorithms were used to construct 5 prediction models based on the CT radiomics. The area under ROC curve for NAC prediction in the training set was 0.553, 0.709, 0.668, 0.772 and 0.790, respectively; in the validation set was 0.662, 0.622, 0.683, 0.752 and 0.784, respectively. The model constructed by XGB showed the best comprehensive performance, and its accuracy, sensitivity and specificity was 0.771, 0.562 and 0.821, respectively. In the DCA of 5 machine learning models in the training set, XGB-based model provided a higher net benefit. Conclusions:Machine learning models based on enhanced CT radiomics in the vein phase have a high predictive efficacy in the outcome of NAC in LAGC patients before surgery and it helps make clinical personalized treatment decisions.

Objective To evaluate clinical efficacy and safety of Bizheng Granules combined with basic therapy in the treatment of liver and kidney insufficiency and phlegm-dampness cross-blocking syndrome of nerve root type cervical spondylosis radiculopathy.Methods Totally 108 patients with nerve root type cervical spondylosis radiculopathy were selected and divided into Bizheng Granules group,Jingfukang Granules group,and Bizheng Granules placebo group according to random number table method,with 36 cases in each group.The three groups were treated with the basic treatment(oral methylcobalamin tablets of 0.5 mg,three times a day,and cervical spine intermittent traction once a day,each time for 15 min),and Bizheng Granules were given in the Bizheng Granules group,and Jingfukang Granules in the Jingfukang Granules group,and Bizheng Granules placebo in the Bizheng Granules placebo group,twice a day after meals.All three groups were treated continuously for 2 weeks and followed up at 4 and 12 weeks after treatment.Visual analog scale(VAS),cervical dysfunction index(NDI),and 36-items short form survey(SF-36)score were recorded in the 3 groups before and after treatment and 4 and 12 weeks after treatment,respectively.Adverse reactions during treatment and follow-up were recorded in the 3 groups.Results 3 cases lost in the Bizheng Granules group,3 cases in the Jingfukang Granules group,and 1 case in the Bizheng Granules placebo group.Compared with the pre-treatment period,the differences in VAS score,NDI score,and SF-36 score among the three groups of patients at all time points of treatment and follow-up were statistically significant(P<0.05).At 12 weeks after treatment,the VAS score and NDI score of the Bizheng Granules group and the Jingfukang Granules group were lower than those of the Bizheng Granules placebo group,but the differences in VAS score and NDI score between Bizheng Granules group and Jingfukang Granules group were not statistically significant(P>0.05).12 weeks after treatment,the SF-36 score of Bizheng Granules group were higher than those of Bizheng Granules placebo group,but the difference in SF-36 score between Bizheng Granules group and Jingfukang Granules group was not statistically significant(P>0.05).The total effective rate of Bizheng Granules group was better than that of the Jingfukang Granules group and Bizheng Granules placebo group(P<0.05).The incidence of adverse reactions in the three groups was not statistically significant(P>0.05).There were no serious adverse events in the three groups.Conclusion Bizheng Granules combined with basic therapy has a definite therapeutic effect in improving pain,restoring cervical dysfunction,and improving quality of life in the treatment of liver and kidney insufficiency and phlegm-dampness cross-blocking syndrome of nerve root type cervical spondylosis radiculopathy,with good safety.

Objective To study the effects of excitation source, intraocular pressure and material parameters on frequency response of human eye under acoustic excitation. Methods Based on the three-dimensional (3D) finite element model of the whole eye, as well as the deformation and stress distribution of human eye, the effects of various parameters on frequency response of human eye were quantitatively analyzed. Results When other parameters were fixed and only the position and size of the excitation source changed, the amplitude at resonance of human eye (the acoustic excitation source was placed directly above the cornea, at an offset of 45°, and at larger excitation) at 134 Hz was 35, 48 and 133 μm, respectively. When only the intraocular pressure changed, the first-order resonance frequency was almost unaffected by the intraocular pressure, and the resonance frequencies of other orders would shift slightly to the left as the intraocular pressure increased. When only the scleral elastic parameters changed, the resonance frequency of eye tissues increased with the increase of the scleral elasticity. Conclusions The position and size of the excitation source have no effects on resonance frequency of human eye, but they have a greater effect on the resonance amplitude. The material parameters of human eye tissues have a greater effect on the frequency response, and there is a linear relationship between the intraocular pressure and resonance frequency. The results provide the theoretical basis for clinical development of high-performance intraocular pressure monitoring technology.

Heparin is a very important anticoagulant drug. Currently, heparin is mainly extracted from porcine mucosa. However, animal-derived heparin shows low anticoagulant activity due to the low proportion of the anticoagulant active unit, the GlcNS6S-GlcA-GlcNS6S3S-Ido2S-GlcNS6S pentasaccharide. In this study we proposed an enzymatic strategy to sulfate the animal-sourced heparin to increase the proportion of anticoagulant pentasaccharide and the anticoagulant activity. First, three sulfotransferases HS2ST, HS6ST, and HS3ST were expressed tentatively in Escherichia coli and Pichia pastoris. After measuring the sulfotransferase activity, we confirmed P. pastoris GS115 is the better host for sulfotransferases production. Then, the maltose binding protein (MBP) and thioredoxin (TrxA) were fused separately to the N-terminal of sulfotransferases to increase enzyme solubility. As a result, the yields of HS2ST and HS6ST were increased to (839±14) U/L and (792±23) U/L, respectively. Subsequent sulfation of the animal-sourced heparin with the recombinant HS2ST, HS6ST and HS3ST increased the anticoagulant activity from (76±2) IU/mg to (189±17) IU/mg.
Animals ; Escherichia coli ; Heparin ; chemistry ; Oligosaccharides ; chemistry ; Pichia ; Sulfotransferases ; biosynthesis ; Swine