Dilated residual FPN-based segmentation for mouse retinal images.

Background and objective: Diabetes can induce diabetic retinopathy (DR), and the blindness caused by this disease is irreversible. The early analysis of mouse retinal images, including the layer and cell segmentation properties of these images, can help to effectively diagnose this disease. Method: In the study, we design a dilated residual method based on a feature pyramid network (FPN), in which the FPN is adopted as the base network for solving the multiscale segmentation problem concerning mouse retinal images. In the bottom-up encoding pathway, we construct our backbone feature extraction network via the combination of dilated convolution and a residual block, further increasing the range of the receptive field to obtain more context information. At the same time, we integrate a squeeze-and-excitation (SE) attention module into the backbone network to obtain more small object details. In the top-down decoding pathway, we replace the traditional nearest-neighbor upsampling method with the transposed convolution method and add a segmentation head to obtain semantic segmentation results. Results: The effectiveness of our network model is verified in two segmentation tasks: ganglion cell segmentation and mouse retinal cell and layer segmentation. The outcomes demonstrate that, compared to other supervised segmentation methods based on deep learning, our model attains the utmost precision in both binary segmentation and multiclass semantic segmentation tasks. Conclusion: The dilated residual FPN is a robust method for mouse retinal image segmentation and it can effectively assist DR diagnosis.

Predicting blood pressure from face videos using face diagnosis theory and deep neural networks technique.

Hypertension is a major cause of cardiovascular diseases. Accurate and convenient measurement of blood pressure are necessary for the detection, treatment, and control of hypertension. In recent years, face video based non-contact blood pressure prediction is a promising research topic. Interestingly, face diagnosis has been an important part of traditional Chinese medicine (TCM) for thousands of years. TCM practitioners observe some typical regions of the face to determine the health status of the Zang Fu organs (i.e., heart). However, the effectiveness of face diagnosis theory in conjunction with computer vision analysis techniques to predict blood pressure is unclear. We proposed an artificial intelligence framework for predicting blood pressure using deep convolutional neural networks in this study. First, we extracted pulse wave signals through 652 facial videos. Then, we trained and compared nine artificial neural networks and chose the best performed prediction model, with an overall true predict rate of 90%. We also investigated the impact of face reflex regions selection on blood pressure prediction model, and the five face regions outperformed. Our high effectiveness and stability framework may provide an objective and convenient computer-aided blood pressure prediction method for hypertension screening and disease prevention.

Effects of acupoint application therapy combined with chinese herbal medicine on perimenopausal syndrome: A systematic review and meta-analysis of randomized controlled trails.

BACKGROUND: An increasing number of women suffer from perimenopausal syndrome (PMS) and the global burden of this disease has been steadily rising. Acupoint application therapy and Chinese herbal medicine (CHM) are widely used as effective methods for treating PMS, but the efficacy was inconsistent and the evidence should be summarized by quantitively analysis. OBJECTIVE: The purpose of this systematic review and meta-analysis was to evaluate the clinical efficacy and safety of the acupoint application combined with the CHM for the treatment of PMS. METHODS: We searched eight databases from their inception to August 2022 to identify relevant studies. Only randomized controlled trials (RCTs) focusing on acupoint application combined with CHM for the treatment of PMS were included in this study. To assess the clinical efficacy and safety, meta-analysis was used to quantitively synthesize the effect estimates. Subgroup analysis, publication bias assessment and sensitivity analysis were also performed. We further assessed whether the included studies had reported on the purity and potency of the CHM used in their trials. RESULTS: A total of 8 RCTs with 560 participants were included in the systematic review and meta-analysis, of which none of them included a description of an independent testing of purity or potency of the CHM product used. There were significant differences between the acupoint application combined with CHM and CHM alone in terms of Kupperman Menopausal Index (KMI) score (MD = -2.91, 95%CI: -3.91 to -1.91), total effective rate (RR = 1.22, 95% CI: 1.11-1.34), Pittsburgh Sleep Quality Interview (PSQI) score (MD = -2.86, 95% CI: -3.61 to -2.10) and reduction in the serum level of luteinizing hormone (LH) (MD = -2.52, 95% CI: -4.70 to -0.34), whereas there were no differences between the two groups regarding lowering serum level of follicle-stimulating hormone (FSH) (MD = -1.66, 95% CI: -3.98-0.67) and elevating serum level of oestradiol (E2) (MD = 2.41, 95% CI: -0.70-5.52). For the comparation between the acupoint application combined with CHM and western medicine (WM), the KMI score (MD = -6.80, 95%CI: -7.95 to -5.65) was substantially different, while the PSQI score (MD = -0.60, 95% CI: -1.88-0.68) was not substantially different. The total effective rate in the combined group (91.7%) was higher than the western medicine group (83.49%). CONCLUSION: Acupoint application combined with CHM may enhance the efficacy and safety of patients with PMS. However, due to the lack of description of an independent testing of purity or potency of the CHM product used in the trials, as well as blinding of participants and investigators, these results should be interpreted with caution.

Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation.

ETHNOPHARMACOLOGICAL RELEVANCE: Tongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms based on TCM theory. AIM OF THE STUDY: The present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19. MATERIALS AND METHODS: Herein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doctors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19. RESULTS: The overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and accuracy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors' examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet. CONCLUSIONS: Our framework may provide an important research paradigm for differentiating tongue characteristics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.

Deciphering the Pharmacological Mechanisms of Ma Xing Shi Gan Decoction against COVID-19 through Integrating Network Pharmacology and Experimental Exploration.

The outbreak of new infectious pneumonia caused by SARS-CoV-2 has posed a significant threat to public health, but specific medicines and vaccines are still being developed. Traditional Chinese medicine (TCM) has thousands of years of experience in facing the epidemic disease, such as influenza and viral pneumonia. In this study, we revealed the efficacy and pharmacological mechanism of Ma Xing Shi Gan (MXSG) Decoction against COVID-19. First, we used liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) to analyze the chemical components in MXSG and identified a total of 97 components from MXSG. Then, the intervention pathway of MXSG based on these components was analyzed with network pharmacology, and it was found that the pathways related to the virus infection process were enriched in some of MXSG component targets. Simultaneously, through literature research, it was preliminarily determined that MXSG, which is an essential prescription for treating COVID-19, shared the feature of antiviral, improving clinical symptoms, regulating immune inflammation, and inhibiting lung injury. The regulatory mechanisms associated with its treatment of COVID-19 were proposed. That MXSG might directly inhibit the adsorption and replication of SARS-CoV-2 at the viral entry step. Besides, MXSG might play a critical role in inflammation and immune regulatory, that is, to prevent cytokine storm and relieve lung injury through toll-like receptors signaling pathway. Next, in this study, the regulatory effect of MXSG on inflammatory lung injury was validated through transcriptome results. In summary, MXSG is a relatively active and safe treatment for influenza and viral pneumonia, and its therapeutic effect may be attributed to its antiviral and anti-inflammatory effects.