OBJECTIVE To evaluate the germicidal efficacy of phthalaldehyde disinfectant.METHODS Suspension quantitative bactericidal test was used to observe its bactericidal efficacy and stability.RESULTS The temperature was 20-21℃.The results showed that the average killing rates of Escherichia coli,Staphylococcus aureus and Candida albicans exposed to the solution containing 5 664mg/L phthalaldehyde for 1 min were 99.99%,and the(average) killing rates of spores of Bacillus subtilis var.niger exposed to the solution containing 5 664mg/L(phthalaldehyde) for 60 min and 90 min was 99.94% and 100%,respectively.When its stock solution was stored at 56℃ under airtight condition for 2 weeks,the content of phthalaldehyde decreased by 3.27%.CONCLUSIONS The phthalaldehyde disinfectant is a good and stable bactericide.

Objectives:To investigate the effect of glutamine on the shape of residual intestine and colon in rats with short bowel syndrome.　Methods:23 male Sprague-Dawley rats,underwent a 80% small bowel resection,were randomly divided into three groups:food group(n=8) rats,fed rat chow and water libitum after operation;TPN group(n=8),infused with Gln-supplied TPN;and normal control group.On seventh day after operation,rats were weighted and remaining jejunum、remaining ileum and colon were harvested for histological observation(light microscopy and electron microscopy).　Results:There was significant difference in rat average weight between food group and Gln group after operation.Jejunal mucosal villus height(VH) and mucosal thickness(MT) and ileal mucosal VH in food group were significantly increased than those in control group.Jejunal mucosal VH and MT in control group were significantly higher than in TPN group.Ileal mucosal crypt depth(CD) and MT in control group were also significantly higher than in TPN group.Jejunal and ileal mucosal VH、CD and MT in Gln group were significantly higher than in TPN group.Colonic MT in food group was significantly higher than in control group.Colonic MT in Gln group was significantly bigger than in TPN group.　Conclusions:After 80% intestinal resection,the remaining intestine can develop the adaptation,but the adaptation is incomplete.TPN therapy can maintain body weight,but only TPN can not result in the adaption.Gln-supplied TPN can stop the remaining itestinal mucosal atrophy,and promote the remaining intestinal adaptation and colonic mucosal hypertrophy.

Objectives:To investigate the effects of intravenous LCT or MCT/LCT fat emulsions on the lipid mediators and pancreatic histological changes in ANP rats. Methods:Forty three male SD rats were randomized to groups as follow.Group A～C were without ANP, group A:normal controls, group B:normal rats having received lipid based TPN and group C:operation control(OC) group having received the glucose fluid.Group D～F were with ANP,glucose group,Intralipid group and Lipofundin group.The amylase and prostaglandins in serum were determined in group A.Pancreatic histological examinations were also performed.In group B～F,Amylases or prostaglandins in serum were determined at 4,48 and 72 h, and pancreatic histological examination and pathological scoring were also completed. Results:Intralipid had no effects on serum prostaglandins when it was infused to normal rats.In groups of ANP,intravenous fat emulsion increased the 6 keto PGF 1? ,and PGE 2 concentration in serum at 4 h.Pancreatic hemorrhagic and fat necrosis were significantly reduced in Lipofundin group. Conclusions:Intravenous fat emulsion does not worsen the damages to pancreas in ANP.MCT/LCT fat emulsion is more suitable for patients with ANP.

Objective Assessing the detection performance of testing mycoplasma pneumonia(MP) type-specific antibodies by Chemiluminescence immunoassay(CLIA), in order to evaluate the feasibility of screening MP infection by CLIA.Methods Total of 280 cases of respiratory disease patients,20 examples infected mycoplasma pneumonia and 20 cases health volunteers as the control group were enrolled in this study from August 2016 to October 2017 in the Nanfang Hospital,Southern Medical University,testing MP antibodies by CLIA,Enzyme linked immunosorbent assay(ELISA)and Passive agglutination method(PA) respectively.According to the performance evaluation scheme, we evaluate the performance indexs of detecting MP antibodies by CLIA, including lower limit of detection, intra-batch precision, inter-batch precision,linearity range,clinical coincidence rate and consistency compared with ELISA and PA,and the results were analyzed by EXCEL and SPSS version 22.0.Results MP-IgG CLIA reagent:Limit of blank, Limit of detection and Limit of quantitation were 1.9 AU/ml,4.5 AU/ml and 5.1 AU/ml respectively;Coefficient Variation(CV)of intra-batch precision in high and low concentration levels were 2.98% and 2.45%respectively; CV of inter-batch precision in high and low concentration levels were 6.44% and 6.83% respectively;both the Linear range and Clinical report range are from 2.0 AU/ml to 253.0 AU/ml;the linear regression equation R 2≥0.990 0,0.85≤b≤1.15.MP-IgM CLIA reagent: CV of intra-batch precision in high and low concentration levels were 2.55% and 2.86% respectively; CV of inter-batch precision in high and low concentration levels were 4.82% and 5.46% respectively.The total clinical coincidence rate of MP-IgG and MP-IgM detected by CLIA were 90.0%and 97.5%respectively.The kappa values of MP-IgG and MP-IgM detected by CLIA and ELISA were 0.763(P=0.000)and 0.804(P=0.023)respectively, with Consistent percentage of 88.9% and 91.4% respectively.The kappa value of CLIA and PA was 0.541(P=0.063)with a consistent percentage of 79.6%.Conclusions The results of study show that detecting MP type-specific antibodies by CLIA meet the prescribed performance indexes. Detecting MP type-specific antibodies by CLIA,which is precise, speedy and automated, could be applied to clinical and replace ELISA and PA, becoming the prior choice in clinical for MP infection screening.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

A 72-year-old female patient was admitted to the emergency department of Qintang District People′s Hospital of Guigang City in August 2023 due to chills and fever, abdominal distension and pain, diarrhea, cough and shortness of breath for 1 day. She had a history of chronic obstructive and pulmonary heart disease, stage Ⅲ hypertension, and ceftazidime allergy. Clinical diagnosis of acute bacterial infection of chronic obstructive pneumonia was made and levofloxacin combined with piperacillin/tazobactam were given as symptomatic treatment. The blood culture reported Campylobacter fetus after four days, and the patient was cured and discharged after seven days with negative blood culture. The morphology and mass spectrometry identification of the strain were consistent with the definition of Campylobacter fetus. Whole genome sequencing predicted the multi-site sequence type as Campylobacter fetus subsp. fetus( Cff) ST20, carrying the tetracycline resistance gene tet (O/M/O), 18 flagella genes (including rpoN gene from Campylobacter jejuni. these genes were not found in the other two Campylobacter fetus subspecies), and six virulence genes (including like-typhoidal toxin and typhoid toxin genes). The pathogen has the ecological characteristics of parasitic farmed animal colonization and the biological characteristics of high mobility and virulence. These attributes facilitated its entry into the bloodstream via the fecal-oral route, leading to invasive infections.

BACKGROUND: LY01005 (Goserelin acetate sustained-release microsphere injection) is a modified gonadotropin-releasing hormone (GnRH) agonist injected monthly. This phase III trial study aimed to evaluated the efficacy and safety of LY01005 in Chinese patients with prostate cancer. METHODS: We conducted a randomized controlled, open-label, non-inferiority trial across 49 sites in China. This study included 290 patients with prostate cancer who received either LY01005 or goserelin implants every 28 days for three injections. The primary efficacy endpoints were the percentage of patients with testosterone suppression ≤50 ng/dL at day 29 and the cumulative probability of testosterone ≤50 ng/dL from day 29 to 85. Non-inferiority was prespecified at a margin of -10%. Secondary endpoints included significant castration (≤20 ng/dL), testosterone surge within 72 h following repeated dosing, and changes in luteinizing hormone, follicle-stimulating hormone, and prostate specific antigen levels. RESULTS: On day 29, in the LY01005 and goserelin implant groups, testosterone concentrations fell below medical-castration levels in 99.3% (142/143) and 100% (140/140) of patients, respectively, with a difference of -0.7% (95% confidence interval [CI], -3.9% to 2.0%) between the two groups. The cumulative probabilities of maintaining castration from days 29 to 85 were 99.3% and 97.8%, respectively, with a between-group difference of 1.5% (95% CI, -1.3% to 4.4%). Both results met the criterion for non-inferiority. Secondary endpoints were similar between groups. Both treatments were well-tolerated. LY01005 was associated with fewer injection-site reactions than the goserelin implant (0% vs . 1.4% [2/145]). CONCLUSION: LY01005 is as effective as goserelin implants in reducing testosterone to castration levels, with a similar safety profile. TRIAL REGISTRATION ClinicalTrials.gov, NCT04563936.
Humans ; Male ; Antineoplastic Agents, Hormonal/therapeutic use* ; East Asian People ; Gonadotropin-Releasing Hormone/agonists* ; Goserelin/therapeutic use* ; Prostate-Specific Antigen ; Prostatic Neoplasms/drug therapy* ; Testosterone