Machine Learning-Based Technique for the Severity Classification of Sublingual Varices according to Traditional Chinese Medicine.

Tongue diagnosis, a noninvasive examination, is an essential step for syndrome differentiation and treatment in traditional Chinese medicine (TCM). Sublingual vein (SV) is examined to determine the presence of blood stasis and blood stasis syndrome. Many studies have shown that the degree of SV stasis positively correlates with disease severity. However, the diagnoses of SV examination are often subjective because they are influenced by factors such as physicians' experience and color perception, resulting in different interpretations. Therefore, objective and scientific diagnostic approaches are required to determine the severity of sublingual varices. This study aims at developing a computer-assisted system based on machine learning (ML) techniques for diagnosing the severity of sublingual varicose veins. We conducted a comparative study of the performance of several supervised ML models, including the support vendor machine, K-neighbor, decision tree, linear regression, and Ridge classifier and their variants. The main task was to differentiate sublingual varices into mild and severe by using images of patients' SVs. To improve diagnostic accuracy and to accelerate the training process, we proposed using two model reduction techniques, namely, the principal component analysis in conjunction with the slice inverse regression and the convolution neural network (CNN), to extract valuable features during the preprocessing of data. Our results showed that these two extraction methods can reduce the training time for the ML methods, and the Ridge-CNN method can achieve an accuracy rate as high as 87.5%, which is similar to that of experienced TCM physicians. This computer-aided tool can be used for reference clinical diagnosis. Furthermore, it can be employed by junior physicians to learn and to use in clinical settings.

Comparison of morphology, viability, and function between blood and milk neutrophils from peak lactating goats.

The morphological features of blood and milk neutrophils from peak lactating goats were compared using light microscopy, scanning electron microscopy and flow cytometry in order to investigate the cytological changes of neutrophils after migration into the mammary gland. The kinetics of reactive oxygen intermediates (ROI) generation and gelatinase release of blood and milk neutrophils, with or without stimulation of phorbol 12-myristate, 13-acetate ester (PMA), were used to characterize their responses to inflammatory stimuli. Neutrophils isolated from goat milk were highly segmented and contained multi-lobed nuclei. Ultrastructurally, milk neutrophils were more ruffled on the surface compared to blood neutrophils. Approximately 30% of milk neutrophils were undergoing cell death, either necrosis or apoptosis, in contrast to 8% of blood neutrophils. The ROI production of activated milk neutrophils peaked earlier than blood neutrophils, but the duration and the intensity were much less. Neutrophils from both sources augmented the release of gelatinase in response to PMA (1 ng/mL). However, the amount of gelatinase released from milk neutrophils was lower (P < 0.05) than that of blood neutrophils. In summary, more neutrophils become apoptotic and necrotic in the mammary gland, presumably due to spontaneous aging, the process of diapedesis, and the interaction with milk components. Milk neutrophils have impaired functionalities in comparison with blood neutrophils. The information is relevant when studying mammary gland immunity and related diseases, such as mastitis.