The influence of knee varus and valgus on quadriceps muscle activity changes induced by stretching and kneeling.

This study aimed to determine the influence of knee varus (VARUS) and valgus (VALGUS) on the differences in individual quadriceps muscle (QM) activity during knee extension maximum voluntary isometric contractions (MVICs) and sit/stand transitions and on the changes in individual QM activity during sit/stand transitions after QM stretching and kneeling. Ten young healthy males each with VARUS and VALGUS were included. The electromyography signals of the vastus medialis (VM), vastus lateralis, and rectus femoris were recorded during sit/stand transitions before and after rest, stretching, and kneeling and during knee extension MVICs after rest. The individual muscle-to-total muscle activity ratio was assessed. The VARUS group exhibited a significantly higher VM muscle activity ratio in the sit-to-stand and stand-to-sit tasks than in knee extension MVICs (p = 0.004 and p = 0.044, respectively) and a tendency that the VM muscle activity ratio increased in the sit-to-stand task after stretching (p = 0.051), whereas the VALGUS group exhibited no significance. Individuals with VARUS required high VM muscle activity ratios during sit/stand transitions. Future studies should be conducted to determine whether habitual sit-to-stand exercises after QM stretching are effective in preventing knee medial osteoarthritis development in individuals with VARUS.

Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone.

BACKGROUND/AIMS: To validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone. METHODS: A training dataset consisting of 1364 colour fundus photographs with glaucomatous indications and 1768 colour fundus photographs without glaucomatous features was obtained using an ordinary fundus camera. The testing dataset consisted of 73 eyes of 73 patients with glaucoma and 89 eyes of 89 normative subjects. In the testing dataset, fundus photographs were acquired using an ordinary fundus camera and a smartphone. A deep learning algorithm was developed to diagnose glaucoma using a training dataset. The trained neural network was evaluated by prediction result of the diagnostic of glaucoma or normal over the test datasets, using images from both an ordinary fundus camera and a smartphone. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC). RESULTS: The AROC with a fundus camera was 98.9% and 84.2% with a smartphone. When validated only in eyes with advanced glaucoma (mean deviation value < -12 dB, N=26), the AROC with a fundus camera was 99.3% and 90.0% with a smartphone. There were significant differences between these AROC values using different cameras. CONCLUSION: The usefulness of a deep learning algorithm to automatically screen for glaucoma from smartphone-based fundus photographs was validated. The algorithm had a considerable high diagnostic ability, particularly in eyes with advanced glaucoma.

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

Purpose: To compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models. Methods: Two 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. Results: The 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. Conclusions: Deep 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 Relevance: High 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.

A Compact Mid-Infrared Spectroscopy System for Healthcare Applications Based on a Wavelength-Swept, Pulsed Quantum Cascade Laser.

A mid-infrared spectroscopic system using a high-speed wavelength-swept and pulsed quantum cascade laser (QCL) for healthcare applications such as blood glucose measurement is proposed. We developed an attenuated total reflection measurement system comprising the QCL with a micro-electromechanical system (MEMS)-scanning grating, hollow optical fibers, and InAsSb detector and tested its feasibility for healthcare applications. A continuous spectrum was obtained by integrating comb-shaped spectra, the timing of which was slightly shifted. As this method does not require complex calculations, absorption spectra are obtained in real-time. We found that the signal-to-noise ratio of the obtained spectrum had been improved by increasing the number of spectra that were integrated into the spectrum calculation. Accordingly, we succeeded in measuring the absorption spectrum of a 0.1% aqueous glucose solution. Furthermore, the absorption spectra of human lips were measured, and it was shown that estimation of blood glucose levels were possible using a model equation derived using a partial least squares regression analysis of the measured absorption spectra. The spectroscopic system based on the QCL with MEMS-scanning grating has the advantages of compactness and low cost over conventional Fourier transform infrared-based systems and common spectroscopic systems with a tunable QCL that has a relatively large, movable grating.

Validation of a Deep Learning Model to Screen for Glaucoma Using Images from Different Fundus Cameras and Data Augmentation.

PURPOSE: To validate a deep residual learning algorithm to diagnose glaucoma from fundus photography using different fundus cameras at different institutes. DESIGN: Cross-sectional study. PARTICIPANTS: A training dataset consisted of 1364 color fundus photographs with glaucomatous indications and 1768 color fundus photographs without glaucomatous features. Two testing datasets consisted of (1) 95 images of 95 glaucomatous eyes and 110 images of 110 normative eyes, and (2) 93 images of 93 glaucomatous eyes and 78 images of 78 normative eyes. METHODS: A deep learning algorithm known as Residual Network (ResNet) was used to diagnose glaucoma using a training dataset. The 2 testing datasets were obtained using different fundus cameras (different manufacturers) across multiple institutes. The size of the training data was artificially increased by adding minor alterations to the original data, known as "image augmentation." Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC). MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve. RESULTS: When image augmentation was not used, the AROC was 94.8% (90.3-96.8) in the first testing dataset and 99.7% (99.4-100.0) in the second dataset. These AROC values were significantly (P < 0.05) smaller without augmentation (87.7% [82.8-92.6] in the first testing dataset and 94.5% [91.3-97.6] in the second testing dataset). CONCLUSIONS: The previously developed deep residual learning algorithm achieved high diagnostic performance with different fundus cameras across multiple institutes, in particular when image augmentation was used.

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundus photographs with glaucomatous indications and 1,768 color fundus photographs without glaucomatous features. A testing dataset consisted of 60 eyes of 60 glaucoma patients and 50 eyes of 50 normal subjects. Using the training dataset, a deep learning algorithm known as Deep Residual Learning for Image Recognition (ResNet) was developed to discriminate glaucoma, and its diagnostic accuracy was validated in the testing dataset, using the area under the receiver operating characteristic curve (AROC). The Deep Residual Learning for Image Recognition was constructed using the training dataset and validated using the testing dataset. The presence of glaucoma in the testing dataset was also confirmed by three Residents in Ophthalmology. The deep learning algorithm achieved significantly higher diagnostic performance compared to Residents in Ophthalmology; with ResNet, the AROC from all testing data was 96.5 (95% confidence interval [CI]: 93.5 to 99.6)% while the AROCs obtained by the three Residents were between 72.6% and 91.2%.

Theoretical study of magnesium fluoride in aqueous solution.

A series of magnesium fluorides (MgF(n)(2-n)), multiply charged anions, in the gas phase and in aqueous solution were theoretically studied with a hybrid approach of quantum chemistry and statistical mechanics, called RISM-SCF-SEDD theory. In the gas phase, MgF(3)(-) is the most stable species among the complexes (n = 1-6). In contrast, due to compensation between the intramolecular energy and solvation free energy, the stabilities of a number of complexes with different n are comparable in aqueous solution. Based on accurate evaluation of free energy change, the mole fraction of MgF(4)(2-) is the highest in the range from pF = 2.0 to 3.0 of aqueous solution. This is consistent with the available PDB data of the enzymes that catalyze the phosphoryl transfer reactions. The hydration structures of magnesium fluorides obtained by RISM-SCF-SEDD theory provide insight into their structural changes from the gas phase to aqueous solution.

Rhodium-catalyzed oxidative 1:1, 1:2, and 1:4 coupling reactions of phenylazoles with internal alkynes through the regioselective cleavages of multiple C-H bonds.

The direct oxidative coupling of phenylazoles with internal alkynes proceeds efficiently in the presence of a rhodium catalyst and a copper oxidant accompanied by double or quadruple C-H bond cleavages. Thus, as a representative example, 4,5-diphenylpyrazolo[1,5-a]quinoline, 1-(1,2,3,4-tetraphenylnaphthalen-5-yl)pyrazole, and 1-(1,2,3,4,5,6,7,8-octaphenylanthracen-9-yl)pyrazole can be obtained selectively through the coupling of 1-phenylpyrazole and diphenylacetylene in 1:1, 1:2, and 1:4 manners, respectively. The reactions preferentially take place at the electron-deficient sites on the aromatic substrates. A comparison of reactivities of variously substituted and deuterated substrates sheds light on the mechanism of C-H bond cleavage steps. The reaction pathway is highly dependent on reaction conditions employed, especially on the nature of solvent. The influence of solvation of a key rhodacycle intermediate has been investigated computationally. In addition, some of the condensed aromatic products have been found to exhibit intense fluorescence in the solid state.