This work was supported by the National Nature Foundation of China (grant no.82004504), the National Key Research and Development Program of the Ministry of Science and Technology of China (grant no.2018YFC1707606), Chinese Medicine Administration of Sichuan Province (grant no.2021MS199) and National Nature Foundation of China (grant no.82174236).

This study has been approved by the National Natural Science Foundation of China project, Constructing Dynamic Change rules of TCM Facial image Based on Association Analysis. The ethics approval number is 2021KL-027, and the ethics committee has approved the clinical study to be carried out in accordance with the approved documents which include clinical research protocol (2021.04.12, V2.0), informed consent (2021.04.12, V2.0), subject recruitment materials (2021.04.12, V2.0), study cases and/or case reports, subject diary cards and other questionnaires (2021.04.12, V2.0), a list of participants in the clinical trial, research project approval, etc. Informed consent from the patients participating in the study was obtained. The main experimental approach of this study is to use real tongue images to validate and compare the model segmentation effects. Figure 1 presents the components of tongue diagnosis objectification.
1. Image acquisition
<ol>
	<li>Use the self-developed hand-held lingual face diagnostic instrument to collect lingual face images of patients.</li>
	<li>Fill in the patient&#39;s name, gender, age, and disease on the computer page. Images included here are from patients who came to the clinic and agreed to be photographed after being informed of the purpose and content of the study. Confirm that the patient is sitting upright, place the whole face in the image acquisition instrument, and instruct the patient to extend their tongue out of their mouth to the maximum extent.</li>
	<li>Hold the image acquisition device connected to a computer and verify through the images on the computer screen that the patient is in the correct position and that the tongue and face are fully exposed.</li>
	<li>Press the Shoot button on the computer screen three times to take three pictures. 
	NOTE: The image acquisition instrument is currently only at the patent application stage and is not for commercial use, so it is not for sale.</li>
	<li>Manually select and filter the collected tongue and face images. Filter and exclude images that have incomplete tongue and face exposure, as well as images that are too dark due to insufficient light. Figure 2 shows the image acquisition page of the software.</li>
	<li>In the experimental design, collect three images from each patient at a time as alternatives and select a relatively standard, fully exposed, well-illuminated, and clear image as the sample for subsequent algorithm training and testing.</li>
	<li>Collect data after the shooting, export the data for manual screening, and delete the non-standard images visible to the naked eye. Use the following filtering and exclusion criteria: incomplete tongue and face exposure, and images that are too dark as a result of insufficient light. An example of an under-lit, an incomplete, and a standard image is shown in Figure 3. 
	&#8203;NOTE: Insufficient light is generally caused by failure of the patient to place the face entirely into the instrument. Complete exposure is usually only obtained by correctly photographing the patient.</li>
</ol>
2. Tongue segmentation
<ol>
	<li>Perform tongue image segmentation using an online annotation tool, as described below.
	<ol>
		<li>Install Labelme, click on the Open button in the upper left corner of the label interface, select the folder where the image is located, and open the photos.</li>
		<li>Click on create polygon to start tracking points, track the tongue and lingual shapes, name them according to the selected areas (e.g., tongue and lingual surface), and save them.</li>
		<li>When all the marks are complete, click Save to save the image to the data folder. See Figure 4 for a detailed flow chart. 
		NOTE: As the images may have pixel differences, the images cannot be directly used for algorithm training and testing.</li>
	</ol>
	</li>
	<li>Unify the images to the same size by edge-filling the images, with the long side of the image as the target fill length and performing white edge-filling to fill the images to a square, with the long side of the image as the edge length. The image size captured by the device is 1080 x 1920 pixels, and the size of the filled image is 1920 x 1920 pixels. See Figure 5.</li>
	<li>Apply image enhancement if needed. No enhancement was applied in this study, as the images used were taken in a fixed scene and were less affected by the environment, lighting, and other factors.</li>
	<li>Because three images were collected for each patient during the shooting process to account for uncontrollable factors, such as subject blinking and lens blocking, manually screen the images from each patient to retain one image per patient.</li>
	<li>For the purpose of training the model, collect data from 200 people, or 600 images. After the screening, retain about 200 usable images.</li>
	<li>According to the image number, randomly divide all the tongue images, placing 70% of them into the training set and 30% into the test set in a spreadsheet.</li>
</ol>
3. Tongue classification
<ol>
	<li>Go to the official websites and download and install Anaconda, Python, and Labelme. Activate the environment and complete the installation and adjustment of the overall environment. See Figure 6 for a flow chart describing the installing and setting up of the software.</li>
	<li>Build the deep learning algorithm model in the installed environment, tune the parameters, and complete the model training using the training set. Perform model selection and tuning as described in the following steps.
	<ol>
		<li>Model selection: Choose the appropriate model based on the purpose of the research. After reviewing research on tongue image processing in the last 5 years, four algorithms, U-Net, Seg-Net, DeeplabV3, and PSPNet, were selected for validation in this study (see Supplementary Coding File 1, Supplementary Coding File 2, Supplementary Coding File 3, and Supplementary Coding File 4 for model codes).</li>
		<li>Data set construction: After completing the model selection, construct the required data set in conjunction with the research content, mainly using Labelme annotation and the uniform image size methods, as described above.</li>
	</ol>
	</li>
	<li>Perform model training as described below. Figure 7 shows details of the algorithm training operation.
	<ol>
		<li>Input the data into the neural network for forward propagation, with each neuron first inputting a weighted accumulation of values and then inputting an activation function as the output value of that neuron to obtain the result.</li>
		<li>Input the result into the error function and compare it with the expected value to get the error and judge the degree of recognition by mistake. The smaller the loss function is, the better the model will be.</li>
		<li>Reduce the error by back propagation and determine the gradient vector. Adjust the weights by the gradient vector to the trend toward results so that the error tends to zero or shrinks.</li>
		<li>Repeat this training process until the set is completed or the error value no longer declines, at which point the model training is complete. See Figure 8 for a flow chart of the algorithm model in training and testing.</li>
	</ol>
	</li>
	<li>Test the four models using the same test data for segmentation and judge the model performance according to the segmentation effect. The four metrics of precision, recall, mean pixel accuracy (MPA), and MIoU provide a more comprehensive model performance evaluation.</li>
	<li>After the results of the four models are generated, compare their values horizontally; the higher the value is, the higher the segmentation accuracy and the better the model&#39;s performance. See Figure 9, Figure 10, and Figure 11.</li>
</ol>

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The authors have no conflict of interest to declare.

Based on the comparison results presented above, it is evident that the characteristics of the four algorithms under consideration are varied, and their distinct advantages and disadvantages are described below. The U-Net structure, based on the modification and expansion of a full convolution network, can obtain contextual information and precise positioning through a contracting path and a symmetrical expanding path. By classifying each pixel point, this algorithm achieves a higher segmentation accuracy and segments th...

Tongue observation is a widely utilized technique in traditional Chinese ethnic medicine (TCM). The color and shape of the tongue can reflect the physical condition and various disease properties, severities, and prognoses. For instance, in traditional Hmong medicine, the tongue&#39;s color is used to identify body temperature e.g., a red or purple tongue indicates pathological factors related to heat. In Tibetan medicine, a condition is judged by observing the tongue of a patient, paying attention to the color, shape, and moisture of the mucus. For instance, the tongues of patients with Heyi disease become red and rough or black and dry1; patients with Xieri disease2 have yellow and dry tongues; meanwhile, patients with Badakan disease3 have a white, humid, and soft tongue4. These observations reveal the close relationship between tongue features and physiology and pathology. Overall, the state of the tongue plays a vital role in diagnosis, disease identification, and evaluation of the treatment effect.
Simultaneously, owing to diverse living conditions and dietary practices among different ethnic groups, variations in tongue images are evident. The Lab model, established on the basis of an international standard for the determination of color, was formulated by the Commission International Eclairage (CIE) in 1931. In 1976, a color pattern was modified and named. The Lab color model is composed of three elements: L corresponds to brightness, while a and b are two color channels. a includes colors from dark green (low brightness value) to gray (medium brightness value) to bright pink (high brightness value); b goes from bright blue (low brightness value) to gray (medium brightness value) to yellow (high brightness value). By comparing the L x a x b values of the tongue color of five ethnic groups, Yang et al.5 found that the characteristics of tongue images of the Hmong, Hui, Zhuang, Han, and Mongolian groups were significantly distinct from each other. For example, the Mongolians have dark tongues with a yellow tongue coating, while the Hmong have light tongues with a white tongue coating, suggesting that tongue features can be used as a diagnostic indicator for assessing the health status of a population. Moreover, tongue images can function as an evaluation index for evidence-based medicine in clinical research of ethnic medicine. He et al.6 employed tongue images as a foundation for TCM diagnosis and systematically evaluated the safety and efficacy of Chou-Ling-Dan pellets (CLD granules-used to treat inflammatory and febrile diseases, including seasonal influenza in TCM) combined with Chinese and Western medicine. The results established the scientific validity of tongue images as an evaluation index for clinical studies. Nevertheless, traditional medical practitioners generally rely on subjectivity to observe tongue characteristics and assess patients&#39; physiological and pathological conditions, requiring more precise indicators.
The emergence of the internet and artificial intelligence technology has paved the way for digitizing and objectifying tongue diagnosis. This process involves using mathematical models to provide a qualitative and objective description of tongue images7, reflecting the content of the tongue image. The process includes several steps: image acquisition, optical compensation, color correction, and geometric transformation. The pre-processed images are then fed into an algorithmic model for image positioning and segmentation, feature extraction, pattern recognition, etc. The output of this process is a highly efficient and precise diagnosis of tongue image data, thereby achieving the goal of objectification, quantification, and informatization of tongue diagnosis8. Thus, the purpose of high efficiency and high precision processing of tongue diagnosis data is achieved. Based on tongue diagnosis knowledge and deep learning technology, this study automatically separated the tongue body and tongue coating from tongue images using a computer algorithm, in order to extract the quantitative features of tongues for doctors, improve the reliability and consistency of diagnosis, and provide methods for subsequent tongue diagnosis objectification research9.

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			<td>CPU</td>
			<td>Intel(R) Core(TM) i7-9700K</td>
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			<td>GPU</td>
			<td>&#160;NVIDIA GeForce RTX 3070 Ti (8192MB)</td>
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			<td>Operating systems</td>
			<td>Microsoft Windows 10 Professional Edition (64-bit)</td>
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objectification of tongue diagnosis in traditional medicine, data analysis, and study application

Tongue diagnosis is an essential technique of traditional Chinese medicine (TCM) diagnosis, and the need for objectifying tongue images through image processing technology is growing. The present study provides an overview of the progress made in tongue objectification over the past decade and compares segmentation models. Various deep learning models are constructed to verify and compare algorithms using real tongue image sets. The strengths and weaknesses of each model are analyzed. The findings indicate that the U-Net algorithm outperforms other models regarding precision accuracy (PA), recall, and mean intersection over union (MIoU) metrics. However, despite the significant progress in tongue image acquisition and processing, a uniform standard for objectifying tongue diagnosis has yet to be established. To facilitate the widespread application of tongue images captured using mobile devices in tongue diagnosis objectification, further research could address the challenges posed by tongue images captured in complex environments.

For the comparison results, see Figure 12, Figure 13, and Table 1, where the environment constructed by this study uses the same samples to train and test the algorithm model. MIoU indicator: U-Net &#62; Seg-Net &#62; PSPNet &#62; DeeplabV3; MPA indicator: U-Net &#62; Seg-Net &#62; PSPNet &#62; DeeplabV3; precision indicator: U-Net &#62; Seg-Net &#62; DeeplabV3 &#62; PSPNet; recall: U-Net &#62; Seg-Net &#62; PSPNet &#62; DeeplabV3. The larger th...

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

The present study employed U-Net and other deep learning algorithms to segment a tongue image and compared the segmentation results to investigate the objectification of tongue diagnosis.

Tongue diagnosis is an essential technique of traditional Chinese medicine (TCM) diagnosis, and the need for objectifying tongue images through image ...

objectification-tongue-diagnosis-traditional-medicine-data-analysis

Research

JoVE Journal

Medicine

2.2K Views.  Chengdu University of Traditional Chinese Medicine. The present study employed U-Net and other deep learning algorithms to segment a tongue image and compared the segmentation results to investigate the objectification of tongue diagnosis.

Video: Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Technique to Collect Fungiform (Taste) Papillae from Human Tongue

The sense of taste is critical for human life. It informs the body about the quality of food that will be potentially ingested and stimulates metabolic processes that prepare the alimentary canal for digestion. Steady progress is being made towards understanding the early biochemical and molecular events underlying taste transduction (for a review, Breslin and Spector, 20081). However, progress to date has largely resulted from animal models. Yet, since marked differences in receptor specificity and receptor density vary among species, human taste transduction will only be understood by using human taste tissue. Here we describe a biopsy technique to collect human fungiform papillae, visible as rounded pink anterior structures, about 0.5 mm in diameter that contain taste buds. These biopsied papillae are used for several purposes including the isolation of viable taste bud cells, in situ hybridization, immunohistochemistry and, through techniques of molecular biology, the identification of taste-specific novel proteins.

Technique to Collect Fungiform Taste Papillae from Human Tongue

Knowledge of molecular mechanisms underlying gustatory transduction has recently enjoyed significant advances, largely due to using animal models. However, the wide diversity in taste sensitivity and specificity among mammals warrants studies in human tissue. We describe a biopsy technique to collect living taste cells from the papillae on human tongue.

The sense of taste is critical for human life. It informs the body about the quality of food that will be potentially ingested and stimulates metabolic ...

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, particularly in low dosage values1&#8203;&#8203;. &#8216;How to use PSA&#39; remains a current issue, either for diagnosis as a gray zone corresponding to a concentration in serum of 2.5-10 ng/ml which does not allow a clear differentiation to be made between cancer and noncancer2 or for patient follow-up as analysis of post-operative PSA kinetic parameters can pose considerable challenges for their practical application3,4. Alternatively, noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease, e.g. PCA3 in prostate cancer5,6 and to reveal uncharacterized aspects of tumor biology. Moreover, data from the ENCODE project published in 2012 showed that different RNA types cover about 62% of the genome. It also appears that the amount of transcriptional regulatory motifs is at least 4.5x higher than the one corresponding to protein-coding exons. Thus, long terminal repeats (LTRs) of human endogenous retroviruses (HERVs) constitute a wide range of putative/candidate transcriptional regulatory sequences, as it is their primary function in infectious retroviruses. HERVs, which are spread throughout the human genome, originate from ancestral and independent infections within the germ line, followed by copy-paste propagation processes and leading to multicopy families occupying 8% of the human genome (note that exons span 2% of our genome). Some HERV loci still express proteins that have been associated with several pathologies including cancer7-10. We have designed a high-density microarray, in Affymetrix format, aiming to optimally characterize individual HERV loci expression, in order to better understand whether they can be active, if they drive ncRNA transcription or modulate coding gene expression. This tool has been applied in the prostate cancer field (Figure 1).

Human endogenous retroviruses (HERV), which occupy 8% of the human genome, retain scarce coding capacities but a hundred thousand long terminal repeats (LTRs). A custom Affymetrix microarray was designed to identify individual HERV locus expression and was used on prostate cancer tissues as a proof of concept for future clinical studies.

The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, ...

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we present, in a step-by-step guide, the use of ElectroMap for analysis, quantification, and mapping of high-resolution voltage and calcium datasets acquired by optical mapping. ElectroMap analysis options cover a wide variety of key electrophysiological parameters, and the graphical user interface allows straightforward modification of pre-processing and parameter definitions, making ElectroMap applicable to a wide range of experimental models. We show how built-in pacing frequency detection and signal segmentation allows high-throughput analysis of entire experimental recordings, acute responses, and single beat-to-beat variability. Additionally, ElectroMap incorporates automated multi-beat averaging to improve signal quality of noisy datasets, and here we demonstrate how this feature can help elucidate electrophysiological changes that might otherwise go undetected when using single beat analysis. Custom modules are included within the software for detailed investigation of conduction, single file analysis, and alternans, as demonstrated here. This software platform can be used to enable and accelerate the processing, analysis, and mapping of complex cardiac electrophysiology.

This protocol describes the setup and use of ElectroMap, a MATLAB-based open-source software platform for analysis of cardiac optical mapping data. ElectroMap provides a versatile high-throughput tool for analysis of optical mapping voltage and calcium datasets across a wide range of cardiac experimental models.

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we ...

Preparation of Naringenin Solution for In Vivo Application

The preparation of a compound (phytochemical) solution is an overlooked but critical step prior to its application in studies such as drug screening. The complete solubilization of the compound is necessary for its safe use and relatively stable results. Here, a protocol for preparing naringenin solution and its intraperitoneal administration in a high-fat diet and streptozotocin (STZ)-induced diabetic model is demonstrated as an example. A small amount of naringenin (3.52-6.69 mg) was used to test its solubilization in solvents, including ethanol, dimethylsulfoxide (DMSO), and DMSO plus Tween 80 reconstituted in physiological saline (PS), respectively. Complete solubilization of the compound is determined by observing the color of the solution, the presence of precipitates after centrifugation (2000 x g for 30 s), or allowing the solution to stand for 2 h at room temperature (RT). After obtaining a stable compound/phytochemical solution, the final concentration/amount of the compound required for in vivo studies can be prepared in a solvent-only (no PS) stock solution, and then diluted/mixed with PS as desired. The antidiabetic osteoporotic effects of naringenin in mice (intraperitoneal administration at 20 mg/kg b.w., 2 mg/mL) were assessed by measuring blood glucose, bone mass (micro-CT), and bone resorption rate (TRAP staining and ELISA). Researchers looking for detailed organic/phytochemical solution preparations will benefit from this technique.

Preparation of Naringenin Solution for In Vivo Application

Here, the protocol presents the preparation of naringenin solution for in vivo intraperitoneal administration. Naringenin is fully dissolved in a mixture of dimethylsulfoxide, Tween 80, and saline. The antidiabetic osteoporotic effects of naringenin were assessed by blood glucose testing, tartrate-resistant acid phosphatase staining, and enzyme-linked immunosorbent assay.

The preparation of a compound (phytochemical) solution is an overlooked but critical step prior to its application in studies such as drug screening. The ...

An In Vitro Dissolution Determination of Multi-Index Components in Tibetan Medicine Rhodiola Granules

The composition of the Tibetan medicine Rhodiola granules (RG) is complex, and the overall quality of RG is difficult to determine. Therefore, establishing a method to determine the multi-component in vitro dissolution of RG is of great significance for quality control. This study uses the second paddle method of the fourth general rule 0931 from the Chinese Pharmacopoeia (2020 edition), compliant with apparatus 2 of the United States Pharmacopeia (USP). The dissolution apparatus was set to a rotation speed of 100 rpm with ultrapure water as the dissolution medium. A sample volume of 1 mL was collected at each timepoint. Furthermore, the cumulative dissolution of gallic acid, salidroside, and ethyl gallic acid in RG at different time points was determined by high-performance liquid chromatography (HPLC). Finally, the dissolution curves were drawn, and the curves were fitted to the GompertzMod, the Gompertz, the Logistic, and the Weibull equations. The results showed that the cumulative dissolution of gallic acid in RG was over 80% at 1 min, the cumulative dissolution of salidroside and ethyl gallic acid was over 65% at 5 min, and the cumulative dissolution of each index component decreased after 30 min. The curve fitting demonstrated that the GompertzMod equation was the best-fitting model for each index component of RG. In conclusion, the dissolution test method described in this protocol is simple, accurate, and reliable. It can characterize the dissolution behavior of the index components in RG in vitro, which provides a methodological reference for quality control of RG and quality evaluation of other ethnic compounds.

An In Vitro Dissolution Determination of Multi-Index Components in Tibetan Medicine Rhodiola Granules

Here, we test the dissolution of Rhodiola granules (RG) in vitro, draw dissolution curves of salidroside, gallic acid, and ethyl gallate in ultrapure water, and fit the curves to different mathematical models. This protocol provides information and guidance for in vivo bioequivalence and in vivo-in vitro correlation studies of RG.

The composition of the Tibetan medicine Rhodiola granules (RG) is complex, and the overall quality of RG is difficult to determine. Therefore, ...

Visualization of Metabolites Identified in the Spatial Metabolome of Traditional Chinese Medicine Using DESI-MSI

The medicinal use of traditional Chinese medicine is mainly due to its secondary metabolites. Visualization of the distribution of these metabolites has become a crucial topic in plant science. Mass spectrometry imaging can extract huge volumes of data and provide spatial distribution information about these by analyzing tissue slices. With the advantage of high throughput and higher accuracy, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is often used in biological research and in the study of traditional Chinese medicine. However, the procedures used in this research are complicated and not affordable. In this study, we optimized sectioning and DESI imaging procedures and developed a more cost-effective method to identify the distribution of metabolites and categorize these compounds in plant tissues, with a special focus on traditional Chinese medicines. The study will promote the utilization of DESI in metabolite analysis and standardization of traditional Chinese medicine/ethnic medicine for research-related technologies.

In this study, a series of methods are presented to prepare DESI-MSI samples from plants, and a procedure of DESI assembly installation, MSI data acquisition, and processing is described in detail. This protocol can be applied in several conditions for acquiring spatial metabolome information in plants.

The medicinal use of traditional Chinese medicine is mainly due to its secondary metabolites. Visualization of the distribution of these metabolites has ...

Enema of Traditional Chinese Medicine for Patients with Severe Acute Pancreatitis

Severe acute pancreatitis (SAP) is characterized by high mortality rates, numerous complications, and extreme difficulties in treatment. A traditional Chinese medicine (TCM) enema has been widely utilized in clinical and experimental studies of SAP. It has been demonstrated to have beneficial effects in protecting pancreatic function and delaying disease progression. The procedure of TCM enema is to perfuse the Chinese medicinal liquid of raw rhubarb (Rheum officinale Baill. DaHuang) from the anus into the rectum and colon. Basic steps of the procedure include boiling the herbal solution in advance, using a disposable enema kit to assist patients in taking a left lying position, raising the patient's hip 10 cm, and carefully inserting the enema 30-35 cm into the anus, with a drip speed of 60-80 drops/min. The medicine liquid temperature should be equivalent to the patient's body temperature, typically between 37-39 &#176;C. The end of the enema operation should be slow, and Tui na (massage therapy) should be administered before extubation. The medication liquid should be kept in the rectum for more than 1 h. After issuing an enema, it is essential to observe the patient's feces and abdomen, and to evaluate the remission of abdominal signs and symptoms.

An enema is a type of traditional Chinese medicine (TCM) therapy used to treat severe acute pancreatitis (SAP). The therapy involves instilling a TCM liquid into the rectum and colon through the anus to keep the liquid in the intestine, absorbing through the intestinal mucosa. This study aims to standardize the enema procedure for SAP patients.

Severe acute pancreatitis (SAP) is characterized by high mortality rates, numerous complications, and extreme difficulties in treatment. A traditional ...

A Traditional Chinese Medicine Characteristic Therapy for Bronchial Asthma: Moxibustion

Bronchial asthma is a chronic inflammatory disease of the airway which can lead to symptoms such as recurrent wheezing, shortness of breath, chest tightness, or cough, as a result of increased airway reactivity. With high diurnal variation, these symptoms often occur or worsen at night or in the morning. By burning and roasting the Chinese medica above human acupoints, moxibustion is a type of treatment that stimulates the activity of the human meridians through drugs and heat stimulation to prevent and treat diseases. According to the principle of syndrome differentiation and treatment of traditional Chinese medicine, acupoints are selected on the corresponding parts, which has a definite effect. It is regarded as a characteristic traditional Chinese medicine therapy for bronchial asthma. This protocol elaborates the methods and steps of patient management, material preparation, selection of acupoints, operation, and postoperative nursing to ensure safe and effective moxibustion treatment to significantly improve the clinical symptoms and quality of life of patients with bronchial asthma.

This protocol presents a curative procedure of moxibustion in treating patients with bronchial asthma.

Bronchial asthma is a chronic inflammatory disease of the airway which can lead to symptoms such as recurrent wheezing, shortness of breath, chest ...

Standardized Tuina Manipulation for Knee Osteoarthritis in Rats

Tuina Manipulation to Reduce Inflammation and Cartilage Loss in Knee Osteoarthritis Rats

Clinical trials suggest that Tuina manipulation is effective in treating knee osteoarthritis (KOA), while further studies are required to discover its mechanism. Therefore, the manipulation of animal models of knee osteoarthritis is critical. This protocol provides a standard process for Tuina manipulation on KOA rats and a preliminary exploration of the mechanism of Tuina for KOA. The press and kneading manipulation method (a kind of Tuina manipulation that refers to pressing and kneading the specific area of the body surface) is applied on 5 acupoints around the knee joint of rats. The force and frequency of the manipulation were standardized by finger pressure recordings, and the position of the rat during manipulation is described in detail in the protocol. The effect of manipulation can be measured by pain behavior tests and microscopic findings in synovial and cartilage. KOA rats showed significant improvement in pain behavior. The synovial tissue inflammatory infiltration was reduced in the Tuina group, and the expression of tumor necrosis factor (TNF)-&#945; was significantly lower. Compared to the control group, chondrocyte apoptosis was less in the Tuina group. This study provides a standardized protocol for Tuina manipulation on KOA rats and preliminary proof that the therapeutic effects of Tuina may be related to reducing synovial inflammation and delayed chondrocyte apoptosis.

Author Spotlight: Understanding the Mechanism of Tuina Manipulation in Rats 

We present a protocol for Tuina manipulation performed on plaster-immobilized-induced knee osteoarthritis rats. Based on the preliminary results, it suggested that the efficacy of the method relied on the reduction of inflammation and cartilage loss.

Clinical trials suggest that Tuina manipulation is effective in treating knee osteoarthritis (KOA), while further studies are required to discover its ...

Establishment of the KOA Model in Rabbits Using the Modified Videman Method

Application of Acupotomy in a Knee Osteoarthritis Model in Rabbit

Knee osteoarthritis (KOA) is one of the most frequently encountered diseases in the orthopedic department, which seriously reduces the quality of life of people with KOA. Among several pathogenic factors, the biomechanical imbalance of the knee joint is one of the main causes of KOA. Acupotomology believes that restoring the mechanical balance of the knee joint is the key to treating KOA. Clinical studies have shown that acupotomy can effectively reduce pain and improve knee mobility by reducing adhesion, contracture of soft tissues, and stress concentration points in muscles and tendons around the knee joint. 
In this protocol, we used the modified Videman method to establish a KOA model by immobilizing the left hindlimb in a straight position. We have outlined the method of operation and the precautions related to acupotomy in detail and evaluated the efficacy of acupotomy in conjunction with the theory of "Modulating Muscles and Tendons to Treat Bone Disorders" through the detection of the mechanical properties of quadriceps femoris and tendon, as well as cartilage mechanics and morphology. The results show that acupotomy has a protective effect on cartilage by adjusting the mechanical properties of the soft tissues around the knee joint, improving the cartilage stress environment, and delaying cartilage degeneration.

Author Spotlight: Investigating the Mechanism of Action of Acupotomy in Treating Knee Osteoarthritis

In this protocol, a knee osteoarthritis model was prepared using the modified Videman method, and the operation procedures and precautions of acupotomy are detailed. The effectiveness of acupotomy has been demonstrated by testing the mechanical properties of quadriceps femoris and tendon and the mechanical and morphological properties of cartilage.

Knee osteoarthritis (KOA) is one of the most frequently encountered diseases in the orthopedic department, which seriously reduces the quality of life of ...