Alveolar soft part sarcoma (ASPS) is a kind of extremely rare malignant soft tissue sarcoma, which was first discovered and defined by American scholars in 1952. Its main manifestation is painless and slow growth mass, and distant metastasis often occurs in lung, bone, brain, liver with poor prognosis. In recent years, with the accumulation of the number of cases and the development of related technology, the diagnosis and treatment of ASPS have been improved greatly. However, the primary intracranial ASPS is still rare, this paper reviews the progress of primary intracranial ASPS, in order to provide help for its clinical diagnosis and treatment.

OBJECTIVETo prepare the submicron emulsion of fresh Zhongjiefeng volatile oil.

METHODThe Zhongjiefeng volatile oil submicron emulsion was obtained after passing the elementary emulsion through a high pressure homogenizer. The physical and chemical stability of the emulsion was evaluated with the stability parameter of centrifugation, appearance of emulsion and the pH. The formulation and processing factors were optimized by single factor reviewing and orthogonal experimental design.

RESULTBy controlling various processing factors and optimizing formulation, the stable submicron emulsion of Zhongjiefeng volatile oil was prepared. Its mean particle diameter was 164-169 nm with PDI 0.084-0.107 and Zeta electric potential was -40 mV.

CONCLUSIONThe formulation and preparation technique of the emulsion is reasonable.

Drug Stability ; Drugs, Chinese Herbal ; chemistry ; Emulsions ; chemistry ; Oils, Volatile ; chemistry

Objective To investigate the effect of adjusting tube current time product (mAs) according to head circumference index on head CT image quality and organ-specific-dose level based on Monte Carlo analysis platform.Methods A total of 92 patients including children and adolescents with different clinical symptoms undergoing head CT scan were prospectively selected between September 2017 and June 2018 in the First Hospital of Jilin University.Without limiting the size of the head circumference,there were 22 patients were selected as conventional group by random number table,whose head circumference was 48.1-59.2 cm.Low dose group was divided into following three subgroups according to different head circumferences:A group 54.1-57.0 cm (n=22);B group 51.1-54.0 cm (n=26) and C group 48.1-51.0 cm (n=22).Tube current time product was 250 mAs for conventional group,200 mAs for A group,150 mAs for B group and 100 mAs for C group,respectively.The organ-specific-radiation doses (brain,eye lens and salivary gland) were recorded by Monte Carlo analysis platform and the subjective and objective image quality score was evaluated.Analyses of the differences between four groups were compared with image quality score as well as organ-specific-radiation dose by single factor variance.Results Radiation dose to brain was conventional group (34.37±3.62),A group (25.91±0.99),B group (23.18±6.11) and C group (17.38 ± 3.23) mSv,respectively.The difference was of statistical significance in the four groups (F=54.51,P＜0.05).Dose to eye lens was conventional group (41.54± 1.04),A group (33.03±0.35),B group (26.18±2.72) and C group (20.88±4.45) mSv,with statistical significance in difference between the four groups (F=189.75,P＜0.05).Dose to salivary gland was conventional group (35.04 ± 4.94),A group (25.92 ± 0.99),B group (22.93 ± 6.54) and C (14.96±2.67) mSv,respectively,with statistical significance in difference between the four groups (F=65.74,P＜0.05).Image quality scores were respectively conventional group (4.97±0.13),A group (4.77 ± 0.49),B group (4.60 ± 0.49) and C group (3.98 ± 0.61),respectively,with statistical significance between them (F=3.89,P＜0.05),but without statistical significance in difference between the four groups (P ＞ 0.05).The signal-to-noise ratios of gray matter in A,B and C groups were conventional group (18.69 ± 3.55),A group (16.76 ± 2.87),B group (15.05 ± 2.80) and C group (13.65±2.53),respectively,without statistical significance in difference between the four groups (P＞ 0.05);The signal-to-noise ratios of white matter in conventional group (17.46±3.72),A group (15.54± 2.81),B group (13.71±2.43) and C group (11.77±2.18),respectively,without statistical significance in difference between the four groups (P＞0.05).Conclusions Adjusting the tube current time product (mAs) according to head circumference index of children and adolescents can make scanning program more personalized and reduce organ-specific-radiation doses to sensitive organs without compromise of image quality.

Objective To use feature selection and Likert grading method to quantify the data of lung cancer medical records,to construct a deep extreme learning machine model optimized by the sparrow search algorithm,to classify and predict the syndrome types of traditional Chinese medicine medical record data of lung cancer,and to provide scientific and effective research on syndrome type classification of traditional Chinese medicine.means.Methods The medical records of 497 cases diagnosed with lung cancer from January 2015 to December 2021 were collected from the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine,and 412 medical records were screened as the research objects.Syndromic factors of different syndromes were summarized by feature selection and feature importance ranking,and the syndrome factors were quantified by Likert grading method.Build a deep extreme learning machine optimized based on the sparrow search algorithm,and train and test the model.Finally,the model built in this paper is compared with other machine learning models according to three evaluation criteria.Results The average classification accuracy of the SSA-DELM model established in this paper was 88.44%,while the average accuracy of the support vector machine and Bayesian network was 83.39%and 84.53%,respectively.The recall rate and F1 value of the SSA-DELM model on the five syndrome types are mostly above 80%,which is also better than other traditional machine learning models.Conclusion The results of the study show that the use of feature selection combined with Likert grading method to quantify the lung cancer medical record data,compared with the 0-1 processing data,can show the characteristics of the data,improve the accuracy of the classification model,SSA-DELM new Compared with other traditional machine learning classification models,the model has better representation learning ability and learning speed.This model not only provides a scientific and technical means for the clinical treatment of lung cancer,but also provides a useful reference for the informatization and intelligent development of TCM syndrome differentiation and treatment.