Objective Evaluation of Pulse Width Using an Array Pulse Diagram.

Background: Pulse width, which can reflect , excess, and deficiency, has been used for diagnosing diseases and determining the prognosis in traditional Chinese medicine (TCM). This study aimed to devise an objective method to measure the pulse width based on an array pulse diagram for objective diagnosis.

Methods: The channel 6, the region wherein the pulse wave signal is the strongest, is located in the middle of the pulse sensor array and at the position of during data collection.

Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study.

Objective: To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker.

Methods: Tongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, six classifiers, decision tree, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker.

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup.

Background: Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses.

A multi-step approach for tongue image classification in patients with diabetes.

Background: In China, diabetes is a common, high-incidence chronic disease. Diabetes has become a severe public health problem. However, the current diagnosis and treatment methods are difficult to control the progress of diabetes.

Relationships Between Diurnal Changes of Tongue Coating Microbiota and Intestinal Microbiota.

The oral cavity and the intestine are the main distribution locations of human digestive bacteria. Exploring the relationships between the tongue coating and gut microbiota, the influence of the diurnal variations of the tongue coating microbiota on the intestinal microbiota can provide a reference for the development of the disease diagnosis and monitoring, as well as the medication time. In this work, a total of 39 healthy college students were recruited.

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning.

Background: Fatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis.

Application of computer tongue image analysis technology in the diagnosis of NAFLD.

Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD).

Tongue image quality assessment based on a deep convolutional neural network.

Background: Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database.

Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.

Background: Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contribute to serious multisystem complications, which can be life threatening. Timely detection and intervention of prediabetes is very important to prevent diabetes, because it is inevitable in the development and progress of the disease.

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population.

Background: Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease.

A tongue features fusion approach to predicting prediabetes and diabetes with machine learning.

Background: Diabetics has become a serious public health burden in China. Multiple complications appear with the progression of diabetics pose a serious threat to the quality of human life and health. We can prevent the progression of prediabetics to diabetics and delay the progression to diabetics by early identification of diabetics and prediabetics and timely intervention, which have positive significance for improving public health.

Electrical coupling regulates layer 1 interneuron microcircuit formation in the neocortex.

The coexistence of electrical and chemical synapses among interneurons is essential for interneuron function in the neocortex. However, it remains largely unclear whether electrical coupling between interneurons influences chemical synapse formation and microcircuit assembly during development. Here, we show that electrical and GABAergic chemical connections robustly develop between interneurons in neocortical layer 1 over a similar time course.

Development of layer 1 neurons in the mouse neocortex.

Layer 1 of the neocortex harbors a unique group of neurons that play crucial roles in synaptic integration and information processing. Although extensive studies have characterized the properties of layer 1 neurons in the mature neocortex, it remains unclear how these neurons progressively acquire their distinct morphological, neurochemical, and physiological traits. In this study, we systematically examined the dynamic development of Cajal-Retzius cells and γ-aminobutyric acid (GABA)-ergic interneurons in layer 1 during the first 2 postnatal weeks.

Preferential electrical coupling regulates neocortical lineage-dependent microcircuit assembly.

Radial glial cells are the primary neural progenitor cells in the developing neocortex. Consecutive asymmetric divisions of individual radial glial progenitor cells produce a number of sister excitatory neurons that migrate along the elongated radial glial fibre, resulting in the formation of ontogenetic columns. Moreover, sister excitatory neurons in ontogenetic columns preferentially develop specific chemical synapses with each other rather than with nearby non-siblings.