Objective To investigate the value of serum cholinesterase (S-ChE) levels in judgment of severity and prognosis in patients with severe pneumonia. Methods The clinical data of patients with severe pneumonia, who were admitted to the Department of Internal Medicine in the First Affiliated Hospital of Sun Yat-sen University, or the Department of Neurology in the Third People's Hospital of Foshan from May 2011 to May 2015, whose hospital time was longer than 24 hours, were retrospectively analyzed. They were divided into survival group and death group according to the final outcome. Lab data, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score, multiple organ dysfunction syndrome (MODS) score, the improved pneumonia score of British Thoracic Society (confusion, uremia, respiratory, blood pressure, age 65 years, CURB-65), and S-ChE levels of all patients were collected after they were hospitalized into the intensive care unit (ICU) within 24 hours. Independent risk factors for prognosis were analyzed by binary logistic regression analysis, and receiver operating characteristic curve (ROC) was plotted. Best truncation point analysis was used to compare their estimated value for prognosis of patients with severe pneumonia. Results Eighty-six patients with severe pneumonia were studied. Among them 46 patients survived, and 40 patients died. By the single factor analysis, the following lab data in the death group were found significantly lower than those in the survival group: S-ChE levels (kU/L: 2.748±0.826 vs. 4.489±1.360, t' = 7.274, P = 0.000), arterial partial pressure of oxygen [PaO2 (mmHg, 1 mmHg = 0.133 kPa): 52.55±18.29 vs. 60.83±16.65, t = 2.196, P = 0.031], oxygenation index (mmHg: 114.20±48.01 vs. 167.10±69.68, t' = 4.229, P = 0.000), and carbon dioxide combining power [CO2-CP (mmol/L): 22.85±5.44 vs. 26.00±7.63, t' = 2.225, P = 0.029]. The following clinical data were significantly higher in the death group than those in the survival group, namely body temperature (℃: 38.67±1.18 vs. 37.74±1.18, t = -3.627, P = 0.000), pulse (bpm: 130.65±15.72 vs. 107.26±19.61, t' = -6.133, P = 0.000), the ratio of concomitant chronic lung disease [45.0% (18/40) vs. 13.0% (6/46), χ2 = 10.860, P = 0.001], fraction of inspired oxygen [FiO2: 0.495 (0.410, 0.600) vs. 0.380 (0.290, 0.500), Z = -3.265, P = 0.001], APACHE Ⅱ score (25.80±5.07 vs. 16.39±5.12, t =-8.540, P = 0.000), CURB-65 score [3 (3, 4) vs. 2 (1, 2), Z = -5.562, P = 0.000], MODS score (8.15±2.49 vs. 4.35±2.01, t = -7.832, P = 0.000), international normalized ratio [INR: 1.22 (1.08, 1.31) vs. 1.07 (1.00, 1.10), Z = -4.231, P = 0.000], and activated partial thromboplastin time [APTT (s): 33.80 (32.13, 38.75) vs. 28.50 (25.70, 36.00), Z = -3.482, P = 0.000]. Binary logistic regression analysis showed that, S-ChE levels, APACHE Ⅱ score and MODS score were found to be the independent risk factors for prognosis in the patients with severe pneumonia, respectively [S-ChE: odds ratio (OR) = 0.084, 95% confidence interval (95%CI) = 0.017-0.424, P = 0.003; APACHE Ⅱ score: OR = 1.675, 95%CI = 1.098-2.556, P = 0.017; MODS score: OR = 2.189, 95%CI = 1.262-3.800, P = 0.005]. The area under ROC (AUC) for S-ChE levels, APACHE Ⅱ score and MODS score were 0.874±0.036, 0.889±0.033 and 0.884±0.035, respectively (all P > 0.05 as compared between any two means). At the best truncation points of S-ChE levels, APACHE Ⅱ score and MODS score were 3.372 kU/L, 19.5 score, and 6.5 score respectively. The sensitivity, specificity, positive predictive value and negative predictive value in predicting death risk in patients with severe pneumonia were (80.0%, 78.0%, 76.19% and 81.82%), (95.0%, 70.0%, 73.08% and 94.12%) and (70.0%, 91.0%, 87.50%, 77.78%), respectively. If S-ChE levels was combined with APACHE Ⅱ score or combined with MODS score, the sensitivity, specificity, positive predictive value and negative predictive value [S-ChE levels combined APACHE Ⅱ score: 100%, 92.0%, 93.75% and 100%; S-ChE levels combined MODS score: all 100%] were higher than single power of S-ChE levels, APACHE Ⅱ score or MODS score. Conclusions S-ChE levels can be considered as an effective and practical index to estimate the severity and prognosis in patients with severe pneumonia. The combined application of S-ChE levels and APACHE Ⅱ score or MODS score can obviously improve the prognostic power in patients with severe pneumonia.

Background and purpose:Risk of invasive cervical carcinoma (ICC) and its precancerous lesions following high risk human papillomavirus (hrHPV) infection may vary according to HPV types and geographic regions. Analyzing HPV-type distribution in cervical samples from local women aged 30 years and older, this study aimed to identify HPV types with higher risk of developing CIN2+, and to compare diagnostic performance for CIN2+using these types and type combinations. Methods:Cervical samples with histology follow-up from patients of a tertiary cancer center in Shanghai were collected for HPV genotyping by PCR-RDB. The risk associations of HPV types with CIN2+were estimated by logistic regression analysis, and ROC curves were plotted for diagnostic performance evaluation. Results:A total number of 413 specimens were obtained, including 38 CIN1, 184 CIN2/3, 126 ICC patients and 65 negative control people. The 4 most common HPV types in CIN2+were HPV16, 58, 33 and 18, in descending order. And only HPV16 (P<0.000 1), 58 (P=0.002), 33 (P=0.015) were signiifcantly associated with CIN2+lesions. Besides, the area under the ROC curve of the HPV16/18/33/58 test scored statistically higher than the HPV16/18 test did (P=0.006 6). Conclusion:A combined test of HPV16/18/33/58 may offer better performance for detecting CIN2+lesions in our geographic region.

OBJECTIVETake human oral squamous cell carcinoma Tca8113 as experimental model, and study the anti oral squamous cell carcinoma activity of high mobility group chromosomal protein N2 (HMGN2) molecule.

METHODSTrain a large number of recombinant human HMGN2 expression vector Escherichia coli BL21. HMGN2 was expressed under isopropyl-1-thio-beta-galactopyranoside (IPTG) induction and purified by B-PER GST Fusion Protein Purification Kit. A variety of concentrations HMGN2 were added to cell culture medium, cells were tested by MTT, Hoechst 33342 fluorescence staining, flow cytometry assay and Western-blot.

RESULTSMTT results proved that HMGN2 could significantly inhibit human oral squamous cell carcinoma Tca8113 growth. Hoechst 33342 fluorescence staining, flow cytometry assay test and Western-blot proved HMGN2 could make Tca8113 cells morphological change, make Tca8113 cells block in S period of cell cycle and strongly promote Tca8113 cells to apoptosis.

CONCLUSIONHMGN2 can promote apoptosis of oral squamous cell carcinoma cells.

Apoptosis ; Carcinoma, Squamous Cell ; Cell Proliferation ; High Mobility Group Proteins ; Humans ; In Vitro Techniques ; Mouth Neoplasms ; Recombinant Proteins

Objective:To evaluate the diagnostic performance of the prostate imaging reporting and data system version 2.1 (PI-RADS v2.1) based on multiparametric MRI (mpMRI) in the detection of clinically significant prostate cancer (csPCa).Methods:A total of 561 patients who underwent prostate mpMRI in the First Affiliated Hospital of Guangxi Medical University from June 2015 to December 2020 due to elevated prostate specific antigen were collected ambispectively. The patients were divided into csPCa group (276 cases) and non-csPCa group (285 cases) according to pathological findings. Prostate were scored according to the PI-RADS v2.1 scoring standard by a junior and a senior radiologist. The prostate volume was measured and the prostate specific antigen density (PSAD) was calculated. The diffusion-weighted imaging and dynamic contrast-enhanced MRI images were processed to measure the quantitative parameters of the index lesion, including apparent diffusion coefficient (ADC), volume transfer constant (K trans) and rate constant (K ep) values. The Mann-Whitney U test was used to compare the difference in parameters between the two groups. The predictors of csPCa were screened by logistic regression analysis. Predictive model of multi-parameter was established. The receiver operator characteristic curves were used to evaluate the efficacy of PI-RADS v2.1 and the model in diagnosing csPCa, and the comparisons of area under the curve (AUC) were conducted by DeLong test. Results:Compared with non-csPCa group, the patients in csPCa group had higher PI-RADS score of senior physician, PSAD, K trans and K ep value, lower ADC value ( Z=-16.69, -12.49, -3.43, -4.67, 13.91, all P<0.001). The PI-RADS scores of senior physician (OR=3.064, 95%CI 2.428-3.866, P<0.001), PSAD (OR=1.554, 95%CI 1.170-2.064, P=0.002) and ADC value (OR=0.095, 95%CI 0.032-0.288, P<0.001) were the predictors of csPCa. The AUC of junior, senior physician PI-RADS and combined prediction model were 0.861 (95%CI 0.830-0.892), 0.895 (95%CI 0.868-0.922) and 0.923 (95%CI 0.898-0.944). The pairwise difference was statistically significant (the PI-RADS score between the junior and senior physicians Z=3.24, P=0.001, the difference between the PI-RADS score of junior physician and prediction model Z=5.54, P<0.001, the difference between the PI-RADS score of senior physician and prediction model Z=4.20, P<0.001). Conclusion:Based on mpMRI, the application of PI-RADS v2.1 by junior and senior radiologists has the high diagnostic efficacy for csPCa, and the multi-parameter model has the best diagnostic efficacy for csPCa.

We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4-3 Hz), θ wave (4-7 Hz), α wave (8-13 Hz) and β wave (14-30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.