Using acupuncture to treat cerebral hypoperfusion is a hot topic. However, there is a lack of effective tools to clarify the therapeutic effect of acupuncture on cerebral hypoperfusion. Here, we show in a mouse model of cerebral hypoperfusion that photoacoustic tomography (PAT) can noninvasively image cerebral vasculature and track total hemoglobin (HbT) concentration changes in cerebral hypoperfusion with acupuncture stimulation on the YangLingQuan (GB34) point. We measured the changes of HbT concentration and found that the HbT concentration in hypoperfusion regions was clearly lower than that in the control regions when the acupuncture was absent; however, it was significantly increased when the acupuncture was implemented on the GB34 point. We also observed the increase of vessel size and the generation of new vessels in cerebral hypoperfusion during acupuncture. Laser speckle imaging (LSI) was employed to validate some of the PAT findings.
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The results of our neuroimaging study suggest that the combination of acupoints could more widely activate areas of the brain compared to a single acupoint. Additionally, the combination of acupoints can activate some new brain areas and generate new curative effects.
MRI is suitable to evaluate tumor extent with high accuracy, and it can offer more objective information for the diagnosis and staging of UCC. Compared with clinical examinations based on FIGO, MRI illustrated relatively high accuracy in evaluating UCC staging, and is worthwhile to be recommended in future clinical practice.
In this paper, we propose a novel technique termed as optimized swarm search-based feature selection (OS-FS), which is a swarm-type of searching function that selects an ideal subset of features for enhanced classification accuracy. In terms of gaining insights from unstructured medical based texts, sentiment prediction is becoming an increasingly crucial machine learning technique. In fact, due to its robustness and accuracy, it recently gained popularity in the medical industries. Medical text mining is well known as a fundamental data analytic for sentiment prediction. To form a high-dimensional sparse matrix, a popular preprocessing step in text mining is employed to transform medical text strings to word vectors. However, such a sparse matrix poses problems to the induction of accurate sentiment prediction model. The swarm search in our proposed OS-FS can be optimized by a new feature evaluation technique called clustering-by-coefficient-of-variation. In order to find a subset of features from all the original features from the sparse matrix, this type of feature selection has been a commonly utilized dimensionality reduction technique, and has the capability to improve accuracy of the prediction model. We implement this method based on a case scenario where 279 medical articles related to 'meaningful use functionalities on health care quality, safety, and efficiency' from a systematic review of previous medical IT literature. For this medical text mining, a multi-class of sentiments, positive, mixed-positive, neutral and negative is recognized from the document contents. Our experimental results demonstrate the superiority of OS-FS over traditional feature selection methods in literature.
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