Objective To investigate the clinical value of matrix-assisted laser desorption/ionization time of flight mass spectrometry(MALDI-TOF MS)in rapid detection of carbapenemase produced by Klebsiella pneumoniae and Pseudomonas aeruginosa.Methods A total of 60 strains of Klebsiella pneumoniae and 80 strains of Pseudomonas aeruginosa isolated from Pizhou People's Hospital affiliated to Xuzhou Medical University from January 2022 to October 2023 were collected,including 30 strains of carbapenem-resistant Klebsiella pneumoniae(CRKP),30 strains of carbapenem-sensitive Klebsiella pneumoniae(CSKP),50 strains of carbapenem-resistant Pseudo-monas aeruginosa(CRPA)and 30 strains of carbapenem-sensitive Pseudomonas aeruginosa(CSPA).Three detection methods were applied,i.e.,modified carbapenem inactivation method(mCIM),colloidal gold immunochromatography and Autof MS 1000 mass spectrometry identification system to evaluate the ability of Autof MS 1000 mass spectrometry identification system in detecting carbape-nase production of Klebsiella pneumoniae and Pseudomonas aeruginosa.Results The results of Autof MS 1000 mass spectrometry iden-tification system were consistent with those of both mCIM and colloidal gold immunochromatography.Carbapenemase was detected in 28 of the 30 CRKP strains,and it was negative in 2 CRKP strains.Carbapenamase was detected in 15 of the 50 CRPA strains and it was negative in 35 CRPA strains.Thirty strains of CSKP and 30 strains of CSPA were all Carbapenemase negative.The coincidence rate of the results of the three methods in the detection for carbapenase was 100％.Conclusion The result of Autof MS 1000 mass spectrome-try identification system has been consistent with those of mCIM and colloidal gold immunochromatography.It not only has the charac-teristics of cost-saving compare with of mCIM method,but also hold the advantages of fast speed and high accuracy of colloidal gold im-munochromatography method.Thus,Autof MS 1000 system can be used for the rapid identification of carbapenemase produced by Kleb-siella pneumoniae and Pseudomonas aeruginosa.

Objective:To screen high-risk population of chronic obstructive pulmonary disease (COPD) and to analyze the risk factors in Haicang District of Xiamen City.Methods:A questionnaire survey was conducted from February 2023 to May 2023 among residents who visited or underwent physical examinations at five community health service centers in Haicang District of Xiamen City selected by cluster sampling method. The self-designed general information questionnaire, COPD population screening questionnaire (COPD-PS) and COPD screening questionnaires (COPD-SQ) were applied in the survey. Individuals with COPD-PS scale>5 or COPD-SQ scale>16 were defined as COPD high-risk group. The association of COPD risk with gender, age, smoking, family history of COPD, history of tuberculosis, history of COVID-19 infection, and using coal/woodstove for cooking or heating was analyzed with chi-square test and binary logistic regression analysis.Results:A total of 4 260 questionnaires were distributed and 4 221 valid questionnaires were collected with a recovery rate of 99.6%. Among all respondents there were 1 904 males (45.11%) and 2 317 females (54.89%); and 217 individuals aged 40-<50 (5.14%), 434 aged 50-<60 (10.28%), 2 194 aged 60-<70 (51.98%), 1 302 aged 70-<80 (30.85%) and 74 aged≥80 (1.76%). The results showed that there were 269 respondents (6.4%) scored≥5 on the COPD-PS scale, 534 residents (12.7%) scored≥16 on the COPD-SQ scale, 646 (15.3%) scored≥5 on the COPD-PS scale or≥16 on the COPD-SQ scale. Male gender ( OR=2.592, 95% CI:2.135-3.146), second-hand smoke exposure ( OR=3.763, 95% CI:2.944-4.810), frequently catching cold before the age of 14 ( OR=3.804, 95% CI:2.927-4.944), history of tuberculosis ( OR=2.575, 95% CI:1.224-5.418), hypertension ( OR=1.547, 95% CI:1.277-1.875), and diabetes ( OR=1.791, 95% CI:1.027-3.121) were independently associated with the high-risk of COPD, while the history of COVID-19 ( OR=0.583, 95% CI:0.476-0.714) was a protective factor for COPD risk. Conclusion:Males, exposure to second-hand smoke, frequently catching cold before the age of 14, history of tuberculosis, hypertension, and diabetes will increase the risk of COPD, while the history of COVID-19 is a protective factor.

Objective:To develop the modified medication regimen complexity index of Chinese version (mMRCI-C)and test its reliability and validity.Method:The Chinese version of MRCI was developed by modification,translation and back translation. The MRCI was interculturally adapted by 2-rounds of expert consultation and pilot study to ensure the semantics, content and conceptual equivalence. The validation of the mMRCI-C scale was tested among 420 community-dwelling elderly patients with type 2 diabetes mellitus(T2DM) in Shanghai Changfeng Community Health Service Center from October to December 2020. SPSS 23.0 was used to analyze the reliability and validity of the scale.Results:The mMRCI-C scale included 3 dimensions, namely drug dosage form (14 entries), medication frequency (5 entries), and additional instructions (6 entries), with a total of 25 entries. Among 420 valid questionnaires collected,the respondents were 212 males (50.4%) and 208 females (49.6%) with a mean age of (71.4±8.1) years. The test-retest reliability was 0.999 and internal consistency reliability was 0.849. The content validity exceeded 0.80,the convergent validity was 0.932; and discriminant validity P<0.001. Conclusion:The preliminary testing results show that the reliability and validity of the mMRCI-C scale are satisfactory.

Objective:To investigate the predictive value of C-reactive protein (CRP), neutrophils-lymphocytes ratio (NLR) and leukocyte-erythrocyte ratio (LER) for aspiration pneumonia (AP) in patients with acute cerebral infarction (ACI).Methods:Retrospective analysis was performed on 989 consecutive hospitalized ACI patients in 2021 who were free of infection within 48 h after ACI onset. General information, past medical history, CRP and complete blood count within 24 h after admission were collected. NLR and LER were calculated based on neutrophil, lymphocyte, leukocyte and erythrocyte count. ACI patients were divided into two groups: non-AP group ( n = 883) and AP group ( n = 106) according to whether they had AP 48 h after admission. Spearman correlations of CRP, NLR and LER with AP were analyzed. The receiver operator characteristic (ROC) curves were plotted to evaluate the predictive values of CRP, NLR and LER for the occurrence of AP in ACI patients, and the sensitivity and specificity at the optimal cut-off value were also calculated. Logistic regression analysis was used for further verification. Results:Compared with the non-AP group, CRP, NLR and LER were significantly higher in the AP group ( P<0.05). Spearman correlation analysis showed that AP was positively correlated with CRP, NLR and LER ( r = 0.42, 0.36 and 0.35, P<0.01). ROC curve analysis showed that CRP, NLR and LER had certain predictive value for AP in ACI patients ( P<0.05), and the area under the curve (AUC) was 0.8917, 0.8349 and 0.8269, respectively. The optimal cutoff values of CRP, NLR and LER were 12.70 mg/L, 4.40 and 1.89 ×10 -3, respectively, with the sensitivity and specificity of 79.25% and 86.41%, 71.70% and 84.94%, and 75.47% and 79.95%, respectively. Multivariate Logistic regression analysis showed that CRP ( OR=6.65, 95% CI: 3.70-11.98, β=1.90, P<0.001), NLR ( OR=2.84,95% CI: 1.60-5.03, β=1.04, P<0.001) and LER ( OR=3.51, 95% CI: 2.00-6.16, β=1.26, P<0.001) were independent risk factors for AP in ACI patients. Conclusions:CRP, NLR and LER at baseline show certain predictive value for the occurrence of AP in ACI patients, and CRP has the strongest predictive power.

Objective:To propose and evaluate the cycle-constraint adversarial network (CycleGAN) for enhancing the low-quality fundus images such as the blurred, underexposed and overexposed etc.Methods:A dataset including 700 high-quality and 700 low-quality fundus images selected from the EyePACS dataset was used to train the image enhancement network in this study.The selected images were cropped and uniformly scaled to 512×512 pixels.Two generative models and two discriminative models were used to establish CycleGAN.The generative model generated matching high/low-quality images according to the input low/high-quality fundus images, and the discriminative model determined whether the image was original or generated.The algorithm proposed in this study was compared with three image enhancement algorithms of contrast limited adaptive histogram equalization (CLAHE), dynamic histogram equalization (DHE), and multi-scale retinex with color restoration (MSRCR) to perform qualitative visual assessment with clarity, BRISQUE, hue and saturation as quantitative indicators.The original and enhanced images were applied to the diabetic retinopathy (DR) diagnostic network to diagnose, and the accuracy and specificity were compared.Results:CycleGAN achieved the optimal results on enhancing the three types of low-quality fundus images including the blurred, underexposed and overexposed.The enhanced fundus images were of high contrast, rich colors, and with clear optic disc and blood vessel structures.The clarity of the images enhanced by CycleGAN was second only to the CLAHE algorithm.The BRISQUE quality score of the images enhanced by CycleGAN was 0.571, which was 10.2%, 7.3%, and 10.0% higher than that of CLAHE, DHE and MSRCR algorithms, respectively.CycleGAN achieved 103.03 in hue and 123.24 in saturation, both higher than those of the other three algorithms.CycleGAN took only 35 seconds to enhance 100 images, only slower than CLAHE.The images enhanced by CycleGAN achieved accuracy of 96.75% and specificity of 99.60% in DR diagnosis, which were higher than those of oringinal images.Conclusions:CycleGAN can effectively enhance low-quality blurry, underexposed and overexposed fundus images and improve the accuracy of computer-aided DR diagnostic network.The enhanced fundus image is helpful for doctors to carry out pathological analysis and may have great application value in clinical diagnosis of ophthalmology.

Objective:To evaluate the efficiency of ResNet50-OC model based on deep learning for multiple classification of color fundus photographs.Methods:The proprietary dataset (PD) collected in July 2018 in BenQ Hospital of Nanjing Medical University and EyePACS dataset were included.The included images were classified into five types of high quality, underexposure, overexposure, blurred edges and lens flare according to clinical ophthalmologists.There were 1 000 images (800 from EyePACS and 200 from PD) for each type in the training dataset and 500 images (400 from EyePACS and 100 from PD) for each type in the testing dataset.There were 5 000 images in the training dataset and 2 500 images in the testing dataset.All images were normalized and augmented.The transfer learning method was used to initialize the parameters of the network model, on the basis of which the current mainstream deep learning classification networks (VGG, Inception-resnet-v2, ResNet, DenseNet) were compared.The optimal network ResNet50 with best accuracy and Micro F1 value was selected as the main network of the classification model in this study.In the training process, the One-Cycle strategy was introduced to accelerate the model convergence speed to obtain the optimal model ResNet50-OC.ResNet50-OC was applied to multi-class classification of fundus image quality.The accuracy and Micro F1 value of multi-classification of color fundus photographs by ResNet50 and ResNet50-OC were evaluated.Results:The multi-classification accuracy and Micro F1 values of color fundus photographs of ResNet50 were significantly higher than those of VGG, Inception-resnet-v2, ResNet34 and DenseNet.The accuracy of multi-classification of fundus photographs in the ResNet50-OC model was 98.77% after 15 rounds of training, which was higher than 98.76% of the ResNet50 model after 50 rounds of training.The Micro F1 value of multi-classification of retinal images in ResNet50-OC model was 98.78% after 15 rounds of training, which was the same as that of ResNet50 model after 50 rounds of training.Conclusions:The proposed ResNet50-OC model can be accurate and effective in the multi-classification of color fundus photograph quality.One-Cycle strategy can reduce the frequency of training and improve the classification efficiency.

Dissociated optic nerve fiber layer (DONFL) appearance is consists of numerous arcuate dark striae within the posterior pole in the direction of the optic nerve fibers. And on the cross-sectional image, DONFL was correspond to inner retinal dimples. It mainly occurs after the internal limiting membrane (ILM) peeling due to the direct damage on the local retina and the broad tractional effect on the posterior retina during membrane peeling. DONFL appearance is considered to be a spontaneous and delayed morphologic change with a subclinical nature, and appears to have little effect on retinal function. In addition to avoiding the removal of ILM and reducing the area of ILM peeling, a novel technique for reposition of the fixed ILM flap with the assistance of perfluoro-n-octane and the temporal inverted ILM flap technique can also be used to prevent and reduce the occurrence of DONFL appearance. Further studies on its development, clinical features and clinical significance are still required.

Objective:To observe and analyze the accuracy of the optic disc positioning and segmentation method of fundus images based on deep learning.Methods:The model training strategies were training and evaluating deep learning-based optic disc positioning and segmentation methods on the ORIGA dataset. A deep convolutional neural network (CNN) was built on the Caffe framework of deep learning. A sliding window was used to cut the original image of the ORIGA data set into many small pieces of pictures, and the deep CNN was used to determine whether each small piece of picture contained the complete disc structure, so as to find the area of the disc. In order to avoid the influence of blood vessels on the segmentation of the optic disc, the blood vessels in the optic disc area were removed before segmentation of the optic disc boundary. A deep network of optic disc segmentation based on image pixel classification was used to realize the segmentation of the optic disc of fundus images. The accuracy of the optic disc positioning and segmentation method was calculated based on deep learning of fundus images. Positioning accuracy=T/N, T represented the number of fundus images with correct optic disc positioning, and N represented the total number of fundus images used for positioning. The overlap error was used to compare the difference between the segmentation result of the optic disc and the actual boundary of the optic disc.Results:On the dataset from ORIGA, the accuracy of the optic disc localization can reach 99.6%, the average overlap error of optic disc segmentation was 7.1%. The calculation errors of the average cup-to-disk ratio for glaucoma images and normal images were 0.066 and 0.049, respectively. Disc segmentation of each image took an average of 10 ms.Conclusion:The algorithm can locate the disc area quickly and accurately, and can also segment the disc boundary more accurately.

Chloroquine (CQ) phosphate has been suggested to be clinically effective in the treatment of coronavirus disease 2019 (COVID-19). To develop a physiologically-based pharmacokinetic (PBPK) model for predicting tissue distribution of CQ and apply it to optimize dosage regimens, a PBPK model, with parameterization of drug distribution extrapolated from animal data, was developed to predict human tissue distribution of CQ. The physiological characteristics of time-dependent accumulation was mimicked through an active transport mechanism. Several dosing regimens were proposed based on PBPK simulation combined with known clinical exposure-response relationships. The model was also validated by clinical data from Chinese patients with COVID-19. The novel PBPK model allows in-depth description of the pharmacokinetics of CQ in several key organs (lung, heart, liver, and kidney), and was applied to design dosing strategies in patients with acute COVID-19 (Day 1: 750 mg BID, Days 2-5: 500 mg BID, CQ phosphate), patients with moderate COVID-19 (Day 1: 750 mg and 500 mg, Days 2-3: 500 mg BID, Days 4-5: 250 mg BID, CQ phosphate), and other vulnerable populations (.., renal and hepatic impairment and elderly patients, Days 1-5: 250 mg BID, CQ phosphate). A PBPK model of CQ was successfully developed to optimize dosage regimens for patients with COVID-19.

Objective To observe the long-term clinical effect of pars plana vitrectomy combined with fovea-sparing internal limiting peeling in the treatment of macular foveoschisis in pathologic myopic. Methods A prospective case series study. Fifteen patients (15 eyes) with pathological myopic macular foveoschisis who received treatment in Eye Hospital of Wenzhou Medical University from December 2015 to December 2016 were enrolled. There were 4 males (4 eyes) and 11 females (11eyes), with an average age of 55.33±8.34 years. All patients underwent BCVA, diopter, spectral domain OCT and axial length measurement. The mean logMAR BCVA was 0.95±0.64. The mean central fovea thickness (CFT) was 576.00±185.32 μm. All patients underwent vitrectomy combined with fovea-sparing internal limiting peeling. After gas-liquid exchange, 12％ C3F8 was filled and followed up at 1, 3, 6 and 12 months after surgery. Follow-up time was more than 12 months. The structural changes of BCVA and macular area were observed.Results The foveal internal limiting membranes was successfully preserved in all eyes using the techinique. At the final follow-up, the CFT was 258.60±175.22 μm and the BCVA was 0.46±0.43, which were significantly improved compared with preoperative measurements (t=4.90, 5.20;P<0.001). Macular foveoschisis was resovled in 13 eyes. BCVA increased in 14 eyes. Internal limiting membranes proliferation and contraction occurred in 5 eyes and full-thickness macular hole occurred in 1 eye.Conclusions Pars plana vitrectomy with fovea-sparing internal limiting peeling is effective in the treatment of myopic macular retinoschisis. It can improve BCVA and CFT.