Treatment ideas of acupuncture and moxibustion for adenomyosis based on "etiology, location, nature and development of disease". / 基于"ç— å› ã€ç— ä½ã€ç— æ€§ã€ç— åŠ¿"æµ æžå­å®«è ºè‚Œç—‡çš„é’ˆç¸æ²»ç–—æ€è·¯.

Focusing on the syndrome/pattern differentiation to determine treatment, the approaches to the diagnosis and treatment of acupuncture and moxibustion for adenomyosis are explored by identifying the etiology, location, nature and development of disease. The syndromes/patterns of adenomyosis are differentiated in view of both zangfu and meridian theories. The treatment is delivered complying with the menstrual cycle and the basic rule of treatment, "treating the symptoms in the acute stage, while the root causes in the recovery stage". During menstrual period, stopping pain and eliminating stasis are dominant; while during the other days of menstrual cycle, regulating zangfu dysfunction (excess or deficiency) is emphasized. In general, the functions of the thoroughfare vessel and the conception vessel should be specially considered and adjusted, and the principles of treatment include strengthening the spleen, regulating the kidney and soothing the liver. Acupoints are selected mainly from the spleen meridian of foot-taiyin, the kidney meridian of foot-shaoyin and the conception vessel. Ciliao (BL 32), Shiqizhui (EX-B 8), Zigong (EX-CA 1), Diji (SP 8) and four-gate points (bilateral Hegu [LI 4] and Taichong [LR 3]) are used in menstrual period; Zusanli (ST 36), Sanyinjiao (SP 6) and Taixi (KI 3) in postmenstrual phase; Guanyuan (CV 4), Luanchao (Ovary, Extra) and Qihai (CV 6) in intermenstrual phase; while, Guanyuan (CV 4), Qihai (CV 6) and Shenque (CV 8), combined with Gongsun (SP 4), Neiguan (PC 6) and Jianshi (PC 5) in premenstrual phase. According to the dynamic development of patient's conditions, the reinforcing or reducing techniques of acupuncture and moxibustion are feasibly applied in treatment of adenomyosis.

Classifying and Scoring Major Depressive Disorders by Residual Neural Networks on Specific Frequencies and Brain Regions.

Major Depressive Disorder (MDD) - can be evaluated by advanced neurocomputing and traditional machine learning techniques. This study aims to develop an automatic system based on a Brain-Computer Interface (BCI) to classify and score depressive patients by specific frequency bands and electrodes. In this study, two Residual Neural Networks (ResNets) based on electroencephalogram (EEG) monitoring are presented for classifying depression (classifier) and for scoring depressive severity (regression). Significant frequency bands and specific brain regions are selected to improve the performance of the ResNets. The algorithm, which is estimated by 10-fold cross-validation, attained an average accuracy rate ranging from 0.371 to 0.571 and achieved average Root-Mean-Square Error (RMSE) from 7.25 to 8.41. After using the beta frequency band and 16 specific EEG channels, we obtained the best-classifying accuracy at 0.871 and the smallest RMSE at 2.80. It was discovered that signals extracted from the beta band are more distinctive in depression classification, and these selected channels tend to perform better on scoring depressive severity. Our study also uncovered the different brain architectural connections by relying on phase coherence analysis. Increased delta deactivation accompanied by strong beta activation is the main feature of depression when the depression symptom is becoming more severe. We can therefore conclude that the model developed here is acceptable for classifying depression and for scoring depressive severity. Our model can offer physicians a model that consists of topological dependency, quantified semantic depressive symptoms and clinical features by using EEG signals. These selected brain regions and significant beta frequency bands can improve the performance of the BCI system for detecting depression and scoring depressive severity.

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network.

Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient's secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.

CSGBBNet: An Explainable Deep Learning Framework for COVID-19 Detection.

The COVID-19 virus has swept the world and brought great impact to various fields, gaining wide attention from all walks of life since the end of 2019. At present, although the global epidemic situation is leveling off and vaccine doses have been administered in a large amount, confirmed cases are still emerging around the world. To make up for the missed diagnosis caused by the uncertainty of nucleic acid polymerase chain reaction (PCR) test, utilizing lung CT examination as a combined detection method to improve the diagnostic rate becomes a necessity. Our research considered the time-consuming and labor-intensive characteristics of the traditional CT analyzing process, and developed an efficient deep learning framework named CSGBBNet to solve the binary classification task of COVID-19 images based on a COVID-Seg model for image preprocessing and a GBBNet for classification. The five runs with random seed on the test set showed our novel framework can rapidly analyze CT scan images and give out effective results for assisting COVID-19 detection, with the mean accuracy of 98.49 ± 1.23%, the sensitivity of 99.00 ± 2.00%, the specificity of 97.95 ± 2.51%, the precision of 98.10 ± 2.61%, and the F1 score of 98.51 ± 1.22%. Moreover, our model CSGBBNet performs better when compared with seven previous state-of-the-art methods. In this research, the aim is to link together biomedical research and artificial intelligence and provide some insights into the field of COVID-19 detection.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation.

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.