The Preventive Effect of Gentamicin in the Irrigating Solution on Endophthalmitis Caused by Methicillin-Resistant Staphylococcus epidermidis After Phacoemulsification with Intraocular Lens Implantation in Rabbits.

OBJECTIVE: To analyze the effect of gentamicin in the irrigating solution on endophthalmitis caused by methicillin-resistant Staphylococcus epidermidis after phacoemulsification with intraocular lens implantation. METHODS: Fifteen rabbits were randomly assigned into three groups. During surgery, group A was irrigated with gentamicin-free solution and injected with 100 µL of normal saline postoperatively, group B was irrigated with 80 µg/mL gentamicin and injected with 100 µl of MRSE suspension, group C was irrigated with gentamicin-free solution and injected with 100 µl of MRSE suspension. At different times, corneal endothelial cell count (CEC), inflammation grading，B-scan ultrasonography and histological examination were analyzed. RESULTS: No endophthalmitis occurred in groups A and B. Group C developed severe endophthalmitis, with massive inflammatory exudation in the vitreous cavity. CONCLUSION: Irrigating solution containing gentamicin is favorable to reduce the incidence of MRSE endophthalmitis after phacoemulsification with IOL in rabbits.

Evaluation of the effect of gentamicin in surgical perfusion solution on cataract postoperative endophthalmitis.

OBJECTIVE: To evaluate the effect of gentamicin in surgical perfusion solution on endophthalmitis incidence after cataract surgery. METHODS: A retrospective analysis of endophthalmitis incidence was conducted in two groups of patients who underwent cataract surgery, with (Group B) or without gentamicin (Group A) in the surgical perfusion solution. Endophthalmitis incidence, the isolated pathogenic bacteria strains and their antibiotic sensitivity, and the drug-resistant genotype of the pathogens were examined. RESULTS: The incidence of endophthalmitis in patients of group A was 0.8‰. Thirteen pathogenic bacterial strains were isolated from the patient samples in group A, including 8 strains of Staphylococcus epidermidis, 1 Staphylococcus aureus, 1 Streptococcus pneumoniae, 1 Streptococcus bovis, 1 Enterococcus faecium and 1 Morganella sp. The incidence of endophthalmitis in group B patients was 0.2‰, which was significantly lower than that in group A (P<0.05). Five strains of pathogenic bacteria were successfully isolated, including 2 strains of Enterococcus faecium, 1 Enterococcus faecalis, 1 Staphylococcus epidermidis and 1 Staphylococcus aureus. There was no significant difference in the proportion of Staphylococcus strains in all isolated bacteria between the two groups (P > 0.05). However, the proportion of Enterococci isolated in group B samples was higher than that in group A (P < 0.05). There were more gentamicin-sensitive strains than levofloxacin-sensitive strains identified (P < 0.05). Interestingly, aminoglycoside-inactivating enzyme resistance gene was detected in Enterococcus strains. CONCLUSION: Our data suggest that gentamicin-containing perfusion solution can reduce the incidence of postoperative endophthalmitis in cataract patients. However, the selective pressure imposed by gentamicin may facilitate the development of aminoglycoside-resistant Enterococcos strains.

AI detection of mild COVID-19 pneumonia from chest CT scans.

OBJECTIVES: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. METHODS: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. RESULTS: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test. KEY POINTS: • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.

Highlighted Prospects of an IgM/IgG Antibodies Test in Identifying Individuals With Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection.

CONTEXT.­: Covert severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections could be seeding new outbreaks. How to identify asymptomatic SARS-CoV-2 infections early has become a global focus. OBJECTIVE.­: To explore the roles of immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies detection, nucleic acid tests, and computed tomography (CT) scanning to identify asymptomatic SARS-CoV-2 infection. DESIGN.­: The clinical data of 389 individuals with close contacts, including in general characteristics, SARS-CoV-2 etiology, serum-specific IgM and IgG antibody detection and CT imaging results, were systematically analyzed. RESULTS.­: The present study showed that only 89 of 389 individuals with close contacts were positive after the first nucleic acid test, while 300 individuals were still negative after 2 nucleic acid tests. Among the 300 individuals, 75 did not have pneumonia, and the other 225 individuals had pulmonary imaging changes. A total of 143 individuals were eventually diagnosed as having asymptomatic infection through IgM antibody and IgG antibody detection. The sensitivity, specificity, and false-negative rate of IgM and IgG antibody detection were approximately 97.1% (347 of 357), 95.3% (204 of 214), and 4.67% (10 of 214), respectively. It also indicated that during approximately 2 weeks, most individuals were both IgM positive and IgG positive, accounting for 68.57% (72 of 105). During approximately 3 weeks, the proportion of IgM-positive and IgG-positive individuals decreased to 8.57% (9 of 105), and the proportion of IgM-negative and IgG-positive individuals increased to 76.19% (80 of 105). CONCLUSIONS.­: There are highlighted prospects of IgM/IgG antibody detection as a preferred method in identifying the individuals with asymptomatic SARS-CoV-2 infection, especially combined with nucleic acid tests and pulmonary CT scanning.