Purification and Properties of Hyaluronidase (EC 4. 2. 2. 1) from an Oral Strain of Propionibacterium acnes

From a culture supernatant of P. acnes isolated from a lesion of periodontal disease, hyaluronidase was purified to homogeneity by the sequential procedures including ammonium sulfate precipitation, carboxy methy-cellulose column chromatography, and Sephadex G-100 gel filtration. Specific activity increased 1,027 fold and the recovery of the enzymatic activity was 12.4%. Molecular weight was determined to be 67,000 and isoelectric point was 7.2. Optimal pH for the activity was found at 5.5. The enzyme was inactivated by heating at 60℃ for 10 min. The purified hyaluronidase degraded hyaluronic acid, chondroitin, chondroitin sulfate A and chondroitin sulfate C. From the degradation products of these substrates, unsaturated disaccharides were detected by paper chromatography. When the rate of reaction of this enzyme against hyaluronic acid is supposed to be 100%, the corresponding values against chondroitin, chondroitin sulfate A, and C were 47%, 8%, and 8%, respectively. No degradation by this enzyme of heparin and heparan sulfate was demonstrated

Purification and Partial Characterization of Leucine Aminopeptidase from Actinomyces viscosus

Leucine aminopeptidase was purified from cell-free extracts of Actinomyces viscosus ATCC 19246 and some properties were investigated. The enzyme had a molecular weight of 65,000 and its isoelectric point was 4.0. Optimum pH was found at 7.0. The enzyme was quite labile over 40℃. It was sensitive to inhibition by diisopropylfluorophosphate, phenylmethane sulfonylfluoride, or tosyl-L-lysine chloromethyl ketone. Inhibition by urea was also obvious and this inhibition was found to be irreversible. Ca^, Mg^, Mn^ or Co^ had no no effect on the activity. Leucine-P-nitroanilide was the most suitable substrate among the tested synthetic substrates

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

PurposeTo compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models.MethodsTwo fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study and Matsue Red Cross Hospital were used to independently develop deep learning algorithms for detection of glaucoma at the University of California, San Diego, and the University of Tokyo. We compared three versions of the University of California, San Diego, and University of Tokyo models: original (no retraining), sequential (retraining only on new data), and combined (training on combined data). Independent datasets were used to test the algorithms.ResultsThe original University of California, San Diego and University of Tokyo models performed similarly (area under the receiver operating characteristic curve = 0.96 and 0.97, respectively) for detection of glaucoma in the Matsue Red Cross Hospital dataset, but not the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study data (0.79 and 0.92; P < .001), respectively. Model performance was higher when classifying moderate-to-severe compared with mild disease (area under the receiver operating characteristic curve = 0.98 and 0.91; P < .001), respectively. Models trained with the combined strategy generally had better performance across all datasets than the original strategy.ConclusionsDeep learning glaucoma detection can achieve high accuracy across diverse datasets with appropriate training strategies. Because model performance was influenced by the severity of disease, labeling, training strategies, and population characteristics, reporting accuracy stratified by relevant covariates is important for cross study comparisons.Translational relevanceHigh sensitivity and specificity of deep learning algorithms for moderate-to-severe glaucoma across diverse populations suggest a role for artificial intelligence in the detection of glaucoma in primary care

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

PurposeTo compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models.MethodsTwo fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study and Matsue Red Cross Hospital were used to independently develop deep learning algorithms for detection of glaucoma at the University of California, San Diego, and the University of Tokyo. We compared three versions of the University of California, San Diego, and University of Tokyo models: original (no retraining), sequential (retraining only on new data), and combined (training on combined data). Independent datasets were used to test the algorithms.ResultsThe original University of California, San Diego and University of Tokyo models performed similarly (area under the receiver operating characteristic curve = 0.96 and 0.97, respectively) for detection of glaucoma in the Matsue Red Cross Hospital dataset, but not the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study data (0.79 and 0.92; P < .001), respectively. Model performance was higher when classifying moderate-to-severe compared with mild disease (area under the receiver operating characteristic curve = 0.98 and 0.91; P < .001), respectively. Models trained with the combined strategy generally had better performance across all datasets than the original strategy.ConclusionsDeep learning glaucoma detection can achieve high accuracy across diverse datasets with appropriate training strategies. Because model performance was influenced by the severity of disease, labeling, training strategies, and population characteristics, reporting accuracy stratified by relevant covariates is important for cross study comparisons.Translational relevanceHigh sensitivity and specificity of deep learning algorithms for moderate-to-severe glaucoma across diverse populations suggest a role for artificial intelligence in the detection of glaucoma in primary care

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