Objective To establish an effective method for determination of synthetic cannabinoid JWH-122 by the high-performance liquid chromatography (HPLC), which is controled by China’s Regulations on non-clinical narcotic and psychoactive drug. Methods Methanol-deionized water (50%-50%) was used as mobile phase for gradient elution. In addition to the initial concentration in organic phase, gradient steepness, column temperature, flow rate and other chromatographic conditions, the determine wavelengths were tested so as to ifnd out optimal experimental conditions. Linearity range and speciifcity were tested under optimal conditions, and actual samples were used to verify the method established. Results Under the condition of ultraviolet spectrum detection wavelength at 221nm, initial concentration of 70%, organic phase gradient steepness of 0.5%/min, lfow rate at 1.2 ml/min and column temperature of 30℃,excellent linearity of JWH-122 was observed at 0.002mg/mL-0.1mg/mL and the detection limit (S/N≥3) was 0.1μg/mL. The test of actual samples suggested that JWH-122 was able to be well separated from the sample under the optimal conditions. Conclusion Our method has advantages of rapidity, sensitivity, accuracy and excellent separation efifciency, and is capable of the detection of synthetic cannabinoid JWH-122 of the novel “spice” drugs.

Objective To study the epidemic situation and characteristic of subtypes of HIV-1 strains prevalent in Hebei Province. Methods Viral RNA was extracted from plasma samples, HIV-1 genes (env and gag ) were amplified by RT and nested-PCR using specific primer pairs and sequenced directly.The acquired sequences were compared with international subtypes references and their phylogenetic-trees were analyzed to determine the subtype. Results Among 154 HIV-1 antibody positive cases , 148 HIV-1 gene fragments were amplified and analyzed. There were 6 kinds of HIV-1 subtypes and recombinants, moreover unidentified 2 cases in 148 samples, of which 61 (41.2%) cases of B', 59(39.9% ) cases of CRF01_AE, 16( 10.8% ) cases of CRF07_BC, 6(4.1% ) cases of CRF08_BC, and 2( 1.4% ) cases of C and B01 each. HIV-1 B01 was detected firstly in Hebei Province. Conclusion Six HIV-1 subtypes were identified in Hebei Province. B' and CRF01_AE are the primary subtypes and recombinants of HIV-1 existed in Hebei Province. The surveillance of HIV-1 gene variation should be paid more attention to.

Objective:To investigate the feasibility of deep learning radiomics model in the prediction of neoadjuvant chemotherapy (NAC) response in breast cancer based on ultrasound images at an early stage.Methods:Between January 2018 and June 2021, 218 patients with breast cancer who underwent NAC were enrolled in the retrospective study. All patients received a full cycle of NAC before surgery and underwent standard ultrasound examination before NAC and after the second cycles of NAC. Of all the patients, 166 patients came from institution 1 (the First Affiliated Hospital of Nanjing Medical University) were allocated into a primary cohort.Based on the architecture of Resnet 50 convolutional neural, a deep learning prediction model was built.Further validation was performed in an external testing cohort ( n=52) from institution 2 (General Hospital of Eastern Theater Command, PLA). The clinical model was constructed using independent clinical variables. To evaluate the predictive performance, areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. Results:The Resnet 50 model predicted the response of NAC with accuracy. The deep learning model, achieving an AUC of 0.923 (95% CI=0.884-0.962) in the primary cohort and an AUC of 0.896 (95% CI=0.807-0.980) in the test cohort, outperformed the clinical model and also performed better than two radiologists′ prediction (all P<0.05). Furthermore, the two radiologists achieved a better predictive efficacy (AUC 0.832 and 0.808 for radiologists 1 and 2, respectively) when assisted by the DL model (all P<0.01). Conclusions:The deep learning radiomics model is able to predict therapy response in the early-stage of NAC for breast cancer patients, which could guide clinicians and provide benefit for timely treatment strategy adjustment.