Application of intelligent tongue image analysis in Conjunction with microbiomes in the diagnosis of MAFLD.

Background: Metabolic associated fatty liver disease (MAFLD) is a widespread liver disease that can lead to liver fibrosis and cirrhosis. Therefore, it is essential to develop early diagnosic and screening methods. Methods: We performed a cross-sectional observational study. In this study, based on data from 92 patients with MAFLD and 74 healthy individuals, we observed the characteristics of tongue images, tongue coating and intestinal flora. A generative adversarial network was used to extract tongue image features, and 16S rRNA sequencing was performed using the tongue coating and intestinal flora. We then applied tongue image analysis technology combined with microbiome technology to obtain an MAFLD early screening model with higher accuracy. In addition, we compared different modelling methods, including Extreme Gradient Boosting (XGBoost), random forest, neural networks(MLP), stochastic gradient descent(SGD), and support vector machine(SVM). Results: The results show that tongue-coating Streptococcus and Rothia, intestinal Blautia, and Streptococcus are potential biomarkers for MAFLD. The diagnostic model jointly incorporating tongue image features, basic information (gender, age, BMI), and tongue coating marker flora (Streptococcus, Rothia), can have an accuracy of 96.39%, higher than the accuracy value except for bacteria. Conclusion: Combining computer-intelligent tongue diagnosis with microbiome technology enhances MAFLD diagnostic accuracy and provides a convenient early screening reference.

Evaluation of reducing the south and reinforcing the north method on postmenopausal atherosclerosis disease based on 4 diagnostic objectifications: A prospective observational study.

Reducing the south and reinforcing the north method (RSRN) has a positive effect on atherosclerosis disease. However, there is a lack of objective standards based on the quantification of 4 diagnostic methods in evaluating the improvement or effectiveness of the treatment. This study aimed to explore the quantitative evaluation of the therapeutic effect of RSRN on postmenopausal atherosclerosis based on the 4 diagnostic methods. The observational prospective cohort study was conducted at Longhua hospital Shanghai University of traditional Chinese medicine. According to the inclusion criteria, 96 patients (disease group) and 38 healthy cases (control group) were selected, the pulse parameters were compared between the 2 groups to demonstrate the reliability and success of the disease model. Then 4 diagnostic information before and after RSRN treatment were collected and statistical analyzed by 1-way analysis of variance (ANOVA) (with Bonferroni correction). Furthermore, social network analysis was used to analyze the changes of symptoms, tongue, pulse, and complexion characteristics before and after treatment. There was a significant difference in pulse parameters between the disease group and the control group. The pulse parameters t1, h3, h3/h1, h4/h1, S, As, and w values in disease group were higher than those in control group, while the h5, h5/h1, and Ad values were lower than those in control group (P < .05). After the treatment of RSRN, the clinical symptoms of patients were greatly improved. The facial color indexes L, a, b values of the disease group at week 6 were different from those at week 0 (P < .05). The overall brightness and chroma of the patient's facial color were significantly improved. The patients had virtual string pulse at week 0, and mainly string I and string II at week 7. The pulse parameters t1, t5, w, w/t, h1, h5, h3/h1, and h5/h1 values at week 7 were different from those at weeks 0, 1, 2 (P < .05); the tongue image was mainly red and crimson, peeling or greasy fur at week 0, while at weeks 6, 7, mainly light red, or thin white tongue. The RSRN method can regulate the complexion, tongue and pulse condition, clinical symptoms of postmenopausal atherosclerosis.

Objective Evaluation of Pulse Width Using an Array Pulse Diagram.

BACKGROUND: Pulse width, which can reflect qi, blood 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 guan position of cunkou during data collection. Therefore, the main wave (h1) time of the pulse wave was collected from the channel 6 through calculation. The left h1 time was collected from the remaining 11 channels. The amplitudes at these time points were extracted as the h1 amplitudes for each channel. However, the pulse width could not be calculated accurately at 12 points. Consequently, a bioharmonic spline interpolation algorithm was used to interpolate the h1 amplitude data obtained from the horizontal and vertical points, yielding 651 (31 × 21) h1 amplitude data. The 651 data points were converted into a heat map to intuitively calculate the pulse width. The pulse width was calculated by multiplying the number of grids on the vertical axis with the unit length of the grid. The pulse width was determined by TCM doctors to verify the pulse width measurement accuracy. Meanwhile, a color Doppler ultrasound examination of the volunteers' radial arteries was performed and the intravascular meridian widths of the radial artery compared with the calculated pulse widths to determine the reliability. RESULTS: The pulse width determined using the maximal h1 amplitude method was comparable with the radial artery intravascular meridian widths measured using color Doppler ultrasound. The h1 amplitude was higher in the high blood pressure group and the pulse width was greater. CONCLUSIONS: The pulse width determined using the maximal h1 amplitude was objective and accurate. Comparison between the pulse widths of the normal and high blood pressure groups verified the reliability of the method.

A new method for vascular age estimation based on relative risk difference in vascular aging.

OBJECTIVE: The current models of estimating vascular age (VA) primarily rely on the regression label expressed with chronological age (CA), which does not account individual differences in vascular aging (IDVA) that are difficult to describe by CA. This may lead to inaccuracies in assessing the risk of cardiovascular disease based on VA. To address this limitation, this work aims to develop a new method for estimating VA by considering IDVA. This method will provide a more accurate assessment of cardiovascular disease risk. METHODS: Relative risk difference in vascular aging (RRDVA) is proposed to replace IDVA, which is represented as the numerical difference between individual predicted age (PA) and the corresponding mean PA of healthy population. RRDVA and CA are regard as the influence factors to acquire VA. In order to acquire PA of all samples, this work takes CA as the dependent variable, and mines the two most representative indicators from arteriosclerosis data as the independent variables, to establish a regression model for obtaining PA. RESULTS: The proposed VA based on RRDVA is significantly correlated with 27 indirect indicators for vascular aging evaluation. Moreover, VA is better than CA by comparing the correlation coefficients between VA, CA and 27 indirect indicators, and RRDVA greater than zero presents a higher risk of disease. CONCLUSION: The proposed VA overcomes the limitation of CA in characterizing IDVA, which may help young groups with high disease risk to promote healthy behaviors.

Effects of the Prolong Life With Nine Turn-Method Qigong on Fatigue, Insomnia, Anxiety, and Gastrointestinal Disorders in Patients With Chronic Fatigue Syndrome: Protocol for a Randomized Controlled Trial.

BACKGROUND: Chronic fatigue syndrome (CFS) is a debilitating multisystem disorder that can lead to various pathophysiological abnormalities and symptoms, including insomnia, gastrointestinal disorders, and anxiety. Due to the side effects of currently available drugs, there is a growing need for safe and effective nondrug therapies. The Prolong Life With Nine Turn (PLWNT) Qigong method is a system of mind-body exercise with restorative benefits that can alleviate the clinical symptoms of CFS and impart a significant inhibitory effect. Various studies have proven the treatment efficacy of PLWNT; however, the impact on insomnia, gastrointestinal disorders, and anxiety in patients with CFS has not yet been investigated. OBJECTIVE: This study aims to evaluate the efficacy and safety of the PLWNT method in terms of its effects on fatigue, insomnia, anxiety, and gastrointestinal symptoms in patients with CFS. METHODS: We will conduct a randomized, analyst-blinded, parallel-controlled trial with a 12-week intervention and 8-week follow-up. A total of 208 patients of age 20-60 years will be recruited. The patients will be randomly divided into a PLWNT Qigong exercise group (PLWNT Group) and a control group treated with cognitive behavioral therapy at a ratio of 1:1. Participants from the treatment groups will be taught by a highly qualified professor at the Shanghai University of Traditional Chinese Medicine once a week and will be supervised via web during the remaining 6 days at home, over 12 consecutive weeks. The primary outcome will be the Multidimensional Fatigue Inventory 20, while the secondary outcomes include the Pittsburgh Sleep Quality Index, Gastrointestinal Symptom Rating Scale, Hospital Anxiety and Depression Scale, functional magnetic resonance imaging, gut microbiota, and peripheral blood. RESULTS: The study was approved by the ethics committee of Shanghai Municipal Hospital of Traditional Chinese Medicine in March 2022 (Ethics Approval Number 2022SHL-KY-05). Recruitment started in July 2022. The intervention is scheduled to be completed in December 2024, and data collection will be completed by the end of January 2025. Over the 3-year recruitment period, 208 participants will be recruited. Data management is still in progress; therefore, data analysis has yet to be performed. CONCLUSIONS: This randomized trial will evaluate the effectiveness of the PLWNT method in relieving fatigue, insomnia, anxiety, and gastrointestinal symptoms in patients with CFS. If proven effective, it will provide a promising alternative intervention for patients with CFS. TRIAL REGISTRATION: China Clinical Trials Registry ChiCTR2200061229; https://www.chictr.org.cn/showproj.html?proj=162803. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/53347.

Study on the difference and regularity of tongue images in 309 patients with different pathological stages of non-small cell lung cancer.

BACKGROUND: Tongue diagnosis is a crucial traditional Chinese medicine (TCM) inspection method for TCM syndrome differentiation and treatment. OBJECTIVE: The primary research focus was on tongue image characteristic parameters of patients with non-small cell lung cancer (NSCLC). Analysis of the tongue image parameters of various pathological stages of NSCLC provides technical support for establishing an integrated Chinese and Western auxiliary diagnosis and efficacy evaluation medicine system for lung cancer that integrates tongue image features. METHODS: Tongue image characteristics of 309 patients with NSCLC and 206 controls were collected and analyzed clinically. The T-test or rank sum test and logistic regression analysis were applied to analyze the characteristics of tongue image indicators of different pathological stages of NSCLC. RESULTS: There were differences in tongue image characteristics in the NSCLC group compared to the control group. The tongue quality and brightness of the tongue coating in the NSCLC group increased, the red component was reduced, the tongue coating thickened, and the yellow component increased compared to the healthy control group. A comparison of tongue image indexes of NSCLC in different pathological stages showed that stage IV had lower TB-b and higher TB-a than stage I. In addition, stage IV had lower TB-b than stage II + III, showing an increase in the blue and red components of the tongue in stage IV and the appearance of cyanotic tongue features. CONCLUSION: The tongue image characteristics of NSCLC patients differed from those of the control group. Tongue imaging indicators can reflect the characteristics of tongue images of patients with NSCLC. The tongue image characteristics of patients with stage IV lung cancer are bluish and purple compared with those with stage I, II, and III. It is suggested that the tongue's image characteristics can be used as a reference for the pathological classification of NSCLC and judgment of the disease process.

Core characteristics of sublingual veins analysis and its relationship with hypertension.

BACKGROUND: The sublingual vein (SV) is a specialized diagnostic method used in Traditional Chinese Medicine (TCM). Despite its ability to objectively reflect blood flow, SV is often overlooked in clinical practice. OBJECTIVE: This study aims to analyze the core characteristics of SV and investigate the in-depth relationship between its digital characteristics and hypertension. The goal is to find a link between SV and hypertension and break out of the current situation. METHODS: Modern digital analysis techniques were applied to the traditional SV diagnostic theory. In a controlled study with 204 participants, the digital characteristics of SV were documented using TFDA-1, and its color value was analyzed using TDAS. Morphological characteristics of SV, such as trunklength, width, and tortuosity, were examined by combining computer vision with expert interpretation. This involved the application of automatic ranging methods and a rectangular approximation algorithm, which are novel approaches in the field of TCM. The t-test and Mann-Whitney U test were used to analyze the digital characteristics of SV in hypertension. Binary logistic regression and neural network models were established using machine learning to explore the deep relationship between SV characteristics and hypertension. RESULTS: There was a significant difference of the tortuosity of SV between the two groups (Z=-2.629, p= 0.009). The results revealed thick width of SV (OR = 2.64, 95% CI: 1.02-6.79) was the risk factor for hypertension. Addition of SV characteristics improved overall percent correct for hypertension prediction to 80%. CONCLUSION: TCM method of diagnosis of SV has been greatly expanded in terms of technical means, and the close relationship between SV and hypertension has been found in clinical data.

Assessment of aortic to peripheral vascular stiffness and gradient by segmented upper limb PWV in healthy and hypertensive individuals.

Theoretically pulse wave velocity (PWV) is obtained by calculating the distance between two waveform probes divided by the time difference, and PWV ratio is used to assess the arterial stiffness gradient (SG) from proximal to distal. The aim was to investigate segmental upper-limb PWV (ulPWV) differences and the effects of hypertension and or aging on each ulPWV and SG. The study collected multi-waveform signals and conduction distances from 167 healthy individuals and 92 hypertensive patients. The results showed significant differences between ulPWVs (P < 0.001), with increased and then decreased vascular stiffness along the proximal transmission to the distal peripheral artery and then to the finger. Adjusted for age and sex, ulPWVs in hypertension exceeded that of healthy individuals, with significant differences between groups aged ≥ 50 years (P < 0.05). The hrPWV/rfPWV (heart-radial/radial-finger) was reduced in hypertension and differed significantly between the aged ≥ 50 years (P = 0.015); the ratio of baPWV (brachial-ankle) to ulPWV differed significantly between groups (P < 0.05). Hypertension affected the consistency of rfPWV with hfPWV (heart-finger). The findings suggest that segmented ulPWV is instrumental in providing stiffness corresponding to the physiological structure of the vessel. The superimposition of hypertension and or aging exacerbates peripheral arterial stiffness, as well as alteration in stiffness gradient.

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. RESULTS: There were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. In the advanced NSCLC group, the number of indexes with statistically significant correlations between tongue feature and tumor marker was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. Support Vector Machine (SVM), decision tree, and logistic regression among the machine learning methods performed poorly in models with different stages of NSCLC. Neural network, random forest and naive bayes had better classification efficiency for the data set of tongue feature and tumor marker and baseline. The models' classification accuracies were 0.767 ± 0.081, 0.718 ± 0.062, and 0.688 ± 0.070, respectively, and the AUCs were 0.793 ± 0.086, 0.779 ± 0.075, and 0.771 ± 0.072, respectively. CONCLUSIONS: There were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. Due to the limited information, single data sources including baseline, tongue feature, and tumor marker cannot be used to identify the different stages of NSCLC in this pilot study. In addition to the logistic regression method, other machine learning methods, based on tumor marker and baseline data sets, can effectively improve the differential diagnosis efficiency of different stages of NSCLC by adding tongue image data, which requires further verification based on large sample studies in the future.

Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM approach.

Subhealth is a transitional state between health and disease, and it can be detected through routine physical check-ups. However, the complexity and diversity of physical examination items and the difficulty of quantifying subhealth manifestations are the main problems that hinder its treatment. The aim of this study was to systematically investigate the physical examination performance of the subhealthy population and further explore the deeper relationships between indicators. Indicators were obtained for 878 subjects, including basic information, Western medicine indicators, inquiries of traditional Chinese medicine and sublingual vein (SV) characteristics. Statistical differences were analysed using R software. To explore the distribution of symptoms and symptom clusters in subhealth, partial least squares-structural equation modelling (PLS-SEM) was applied to the subhealth physical examination index, and a structural model was developed to verify whether the relationship chain between the latent variables was reasonable. Finally, the reliability and validity of the PLS-SE model were assessed. The most common subclinical clinical symptoms were limb soreness (37.6%), fatigue (31.6%), shoulder and neck pain (30.5%) and dry eyes (29.2%). The redness of the SV in the subhealthy group was paler than that in the healthy group (p < 0.001). This study validates the establishment of the directed acyclic relationship chain in the subhealthy group: the path from routine blood tests to lipid metabolism (t = 7.878, p < 0.001), the path from lipid metabolism to obesity (t = 8.410, p < 0.001), the path from obesity to SV characteristics (t = 2.237, p = 0.025), and the path from liver function to SV characteristics (t = 2.215, p = 0.027). The innovative application of PLS-SEM to the study of subhealth has revealed the existence of a chain of relationships between physical examination indicators, which will provide a basis for further exploration of subhealth mechanisms and causal inference. This study has identified the typical symptoms of subhealth, and their early management will help to advance the treatment of diseases.

A lung cancer risk warning model based on tongue images.

Objective: To investigate the tongue image features of patients with lung cancer and benign pulmonary nodules and to construct a lung cancer risk warning model using machine learning methods. Methods: From July 2020 to March 2022, we collected 862 participants including 263 patients with lung cancer, 292 patients with benign pulmonary nodules, and 307 healthy subjects. The TFDA-1 digital tongue diagnosis instrument was used to capture tongue images, using feature extraction technology to obtain the index of the tongue images. The statistical characteristics and correlations of the tongue index were analyzed, and six machine learning algorithms were used to build prediction models of lung cancer based on different data sets. Results: Patients with benign pulmonary nodules had different statistical characteristics and correlations of tongue image data than patients with lung cancer. Among the models based on tongue image data, the random forest prediction model performed the best, with a model accuracy of 0.679 ± 0.048 and an AUC of 0.752 ± 0.051. The accuracy for the logistic regression, decision tree, SVM, random forest, neural network, and naïve bayes models based on both the baseline and tongue image data were 0.760 ± 0.021, 0.764 ± 0.043, 0.774 ± 0.029, 0.770 ± 0.050, 0.762 ± 0.059, and 0.709 ± 0.052, respectively, while the corresponding AUCs were 0.808 ± 0.031, 0.764 ± 0.033, 0.755 ± 0.027, 0.804 ± 0.029, 0.777 ± 0.044, and 0.795 ± 0.039, respectively. Conclusion: The tongue diagnosis data under the guidance of traditional Chinese medicine diagnostic theory was useful. The performance of models built on tongue image and baseline data was superior to that of the models built using only the tongue image data or the baseline data. Adding objective tongue image data to baseline data can significantly improve the efficacy of lung cancer prediction models.

A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse.

Background and objective: Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. Methods: A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. Results: TB-L, TB-a, TB-b, TC-L, TC-a, h3, and h4/h1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. Conclusion: The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely.

Distribution characteristics of oral microbiota and its relationship with intestinal microbiota in patients with type 2 diabetes mellitus.

Introduction: Type 2 diabetes mellitus (T2DM) has a high incidence rate globally, increasing the burden of death, disability, and the economy worldwide. Previous studies have found that the compositions of oral and intestinal microbiota changed respectively in T2DM; whether the changes were associated or interacted between the two sites and whether there were some associations between T2DM and the ectopic colonization of oral microbiota in the gut still need to be identified. Research design and methods: We performed a cross-sectional observational study; 183 diabetes and 74 controls were enrolled. We used high-throughput sequencing technology to detect the V3-V4 region of 16S rRNA in oral and stool samples. The Source Tracker method was used to identify the proportion of the intestinal microbiota that ectopic colonized from the oral cavity. Results: The oral marker bacteria of T2DM were found, such as Actinobacteria, Streptococcus, Rothia, and the intestinal marker bacteria were Bifidobacterium, Streptococcus, and Blautia at the genus level. Among them, Actinobacteria and Blautia played a vital role in different symbiotic relationships of oral and intestinal microbiota. The commonly distributed bacteria, such as Firmicutes, Bacteroidetes, and Actinobacteria, were found in both oral and intestine. Moreover, the relative abundance and composition of bacteria were different between the two sites. The glycine betaine degradation I pathway was the significantly up-regulated pathway in the oral and intestinal flora of T2DM. The main serum indexes related to oral and intestinal flora were inflammatory. The relative abundance of Proteobacteria in the intestine and the Spirochete in oral was positively correlated, and the correlation coefficient was the highest, was 0.240 (P<0.01). The proportion of ectopic colonization of oral flora in the gut of T2DM was 2.36%. Conclusion: The dysbacteriosis exited in the oral and intestine simultaneously, and there were differences and connections in the flora composition at the two sites in T2DM. Ectopic colonization of oral flora in the intestine might relate to T2DM. Further, clarifying the oral-gut-transmitting bacteria can provide an essential reference for diagnosing and treating T2DM in the future.

The sodium new houttuyfonate suppresses NSCLC via activating pyroptosis through TCONS-14036/miR-1228-5p/PRKCDBP pathway.

Several studies have suggested the potential value of Houttuynia cordata as a therapeutic agent in lung cancer, but direct evidence is still lacking. The study aimed to determine the regulatory impact of a major H. cordata constituent derivative (sodium new houttuyfonate [SNH]) on lncRNA networks in non-small cell lung cancer (NSCLC) to identify new potential therapeutic targets. After exposing NSCLC cells to SNH, we analysed the following: cell death (via flow cytometry, TUNEL and ASC speck formation assays), immune factors (via ELISA), gene transcription (via RT-qPCR), subcellular localisation (via FISH), gene-gene and gene-protein interactions (via dual-luciferase reporter and RNA immunoprecipitation assays, respectively) and protein expression and distribution (via western blotting and immunocytochemistry or immunohistochemistry). In addition, statistical analysis (via one-way ANOVA or unpaired t-tests) was performed. Exposure to SNH promoted NSCLC cell pyroptosis, concomitant with significant up-regulation of TCONS-14036, a novel lncRNA. Mechanistic research demonstrated that TCONS-14036 functions as a competing endogenous (ce)RNA by sequestering microRNA (miR)-1228-5p, thereby up-regulating PRKCDBP-encoding transcript levels. Indeed, PRKCDBP promoted pyroptosis by activating the NLRP3 inflammasome, resulting in CASP1, IL-1ß and GSDMD cleavage. Our findings elucidate the potential molecular mechanisms underlying the ability of SNH to suppress NSCLC growth through activation of pyroptosis via the TCONS-14036/miR-1228-5p/PRKCDBP pathway. Thus, we identify a new potential therapeutic targets for NSCLC.

Parameter study on characteristic pulse diagram of polycystic ovary syndrome based on logistic regression analysis.

This study aimed to explore the parameters of the independent predictive characteristic pulse diagram of polycystic ovary syndrome (PCOS) by analysing the pulse characteristics between healthy women and the PCOS group. A total of 278 women were recruited for this study. Pulse wave parameters were collected by the pulse spectrum analyser. The single-factor analysis of the pulse diagram parameters was used to identify significant indicators, and the logistic regression analysis was carried out on the above indicators with statistical differences to obtain independent predictors. According to the single-factor and multi-factor analyses, h1, h5, h3/h1, t, t1 and t5 were independent predictors of PCOS diagnosis. The results showed that PCOS patients had a faster heart rate, decreased left ventricular systolic function and decreased aortic compliance compared to healthy individuals. These findings suggested that the characteristic pulse parameters screened out are valuable for the diagnosis of PCOS.IMPACT STATEMENTWhat is already known on this subject? Polycystic ovary syndrome (PCOS) is a common gynecological reproductive endocrine and metabolic disease, which is significant for screening and early intervention in the disease. However, due to the lack of pulse's diagnostic evidence of PCOS, there is still an unknown area in the research on the correlation between PCOS and pulse diagram parameters.What do the results of this study add? This study fills the gap between the research on PCOS and pulse wave. The study also shows that the pulse characteristic parameters h1, h5, h3/h1, t, t1, and t5 are independent predictors of PCOS, suggesting that the patients have a higher heart rate, lower ventricular systolic function, and aortic compliance than healthy individuals.What are the implications of these findings for clinical practice and/or further research? Prominent risk factors for pulse parameters associated with the occurrence of PCOS facilitate early screening and diagnosis of the disease. The objectification of pulse diagnosis helps to establish a health management model, which can be used for the accurate assessment and treatment of PCOS by traditional Chinese medicine (TCM). It provides a clinical reference for the study of pulse diagnosis objectification.

Association of CYP24A1 with survival and drug resistance in clinical cancer patients: a meta-analysis.

BACKGROUND: Acquired chemo-drug resistance constantly led to the failure of chemotherapy for malignant cancers, consequently causing cancer relapse. Hence, identifying the biomarker of drug resistance is vital to improve the treatment efficacy in cancer. The clinical prognostic value of CYP24A1 remains inconclusive, hence we aim to evaluate the association between CYP24A1 and the drug resistance in cancer patients through a meta-analysis approach. METHOD: Relevant studies detecting the expression or SNP of CYP24A1 in cancer patients up till May 2022 were systematically searched in four common scientific databases including PubMed, EMBASE, Cochrane library and ISI Web of Science. The pooled hazard ratios (HRs) indicating the ratio of hazard rate of survival time between CYP24A1high population vs CYP24A1low population were calculated. The pooled HRs and odds ratios (ORs) with 95% confidence intervals (CIs) were used to explore the association between CYP24A1's expression or SNP with survival, metastasis, recurrence, and drug resistance in cancer patients. RESULT: Fifteen studies were included in the meta-analysis after an initial screening according to the inclusion and exclusion criteria. There was a total of 3784 patients pooled from all the included studies. Results indicated that higher expression or SNP of CYP24A1 was significantly correlated with shorter survival time with pooled HRs (95% CI) of 1.21 (1.12, 1.31), metastasis with pooled ORs (95% CI) of 1.81 (1.11, 2.96), recurrence with pooled ORs (95% CI) of 2.14 (1.45, 3.18) and drug resistance with pooled HRs (95% CI) of 1.42 (1.17, 1.68). In the subgroup analysis, cancer type, treatment, ethnicity, and detection approach for CYP24A1 did not affect the significance of the association between CYP24A1 expression and poor prognosis. CONCLUSION: Findings from our meta-analysis demonstrated that CYP24A1's expression or SNP was correlated with cancer progression and drug resistance. Therefore, CYP24A1 could be a potential molecular marker for cancer resistance.

Tongue features of patients with granulomatous lobular mastitis.

Traditional Chinese tongue diagnosis plays an irreplaceable role in disease diagnosis. This study aimed to describe the tongue characteristics of patients with granulomatous lobular mastitis (GLM). Forty GLM patients and 40 non-GLM controls were evaluated using the Traditional Chinese Medicine subjective clinical interpretation and a TDA-1 Tongue Diagnostic and Analysis system. The associations between the image features of the tongue body and coating and the profiling of immune-inflammatory parameters were analyzed. GLM patients were prone to a reddish tongue bodies with thick, white, and greasy coatings. Thick and greasy tongue coating features are risk factors for GLM. GLM patients had higher levels of white blood cells (WBC), platelets, C-reactive protein, interleukin-2, and transforming growth factor-ß (TGF-ß) than non-GLM controls (P < .05). Also, tongue coating contrast and entropy values were significantly correlated with WBC or TGF-ß levels in GLM patients (r < -0.310 and P < .05). We demonstrated that the hot evil and phlegm-dampness constitutions are the main characteristics of GLM. This might provide a reference for GLM diagnosis.

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. Methods: A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results: The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion: The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

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. Traditional Chinese Medicine (TCM) has become an option for the treatment of diabetes due to its low cost, good curative effect, and good accessibility. OBJECTIVE: Based on the tongue images data to realize the fine classification of the diabetic population, provide a diagnostic basis for the formulation of individualized treatment plans for diabetes, ensure the accuracy and consistency of the TCM diagnosis, and promote the objective and standardized development of TCM diagnosis. METHODS: We use the TFDA-1 tongue examination instrument to collect the tongue images of the subjects. Tongue Diagnosis Analysis System (TDAS) is used to extract the TDAS features of the tongue images. Vector Quantized Variational Autoencoder (VQ-VAE) extracts VQ-VAE features from tongue images. Based on VQ-VAE features, K-means clustering tongue images. TDAS features are used to describe the differences between clusters. Vision Transformer (ViT) combined with Grad-weighted Class Activation Mapping (Grad-CAM) is used to verify the clustering results and calculate positioning diagnostic information. RESULTS: Based on VQ-VAE features, K-means divides the diabetic population into 4 clusters with clear boundaries. The silhouette, calinski harabasz, and davies bouldin scores are 0.391, 673.256, and 0.809, respectively. Cluster 1 had the highest Tongue Body L (TB-L) and Tongue Coating L (TC-L) and the lowest Tongue Coating Angular second moment (TC-ASM), with a pale red tongue and white coating. Cluster 2 had the highest TC-b with a yellow tongue coating. Cluster 3 had the highest TB-a with a red tongue. Group 4 had the lowest TB-L, TC-L, and TB-b and the highest Per-all with a purple tongue and the largest tongue coating area. ViT verifies the clustering results of K-means, the highest Top-1 Classification Accuracy (CA) is 87.8%, and the average CA is 84.4%. CONCLUSIONS: The study organically combined unsupervised learning, self-supervised learning, and supervised learning and designed a complete diabetic tongue image classification method. This method does not rely on human intervention, makes decisions based entirely on tongue image data, and achieves state-of-the-art results. Our research will help TCM deeply participate in the individualized treatment of diabetes and provide new ideas for promoting the standardization of TCM diagnosis.

The Qigong of Prolong Life With Nine Turn Method Relieve Fatigue, Sleep, Anxiety and Depression in Patients With Chronic Fatigue Syndrome: A Randomized Controlled Clinical Study.

Background: Chronic fatigue syndrome (CFS) is a complex disease of unknown etiology and mechanism. The purpose of this study was to investigate the effect of Prolong Life with Nine Turn Method (PLWNT) Qigong exercise on CFS focusing on fatigue, sleep quality, depression, and anxiety. Methods: A total of 90 participants diagnosed with CFS were randomly assigned into two parallel groups: PLWNT and cognitive behavioral therapy (CBT). The participants in the PLWNT or CBT group participated in qigong exercise or cognitive behavior education program, respectively, once a week in-person and were supervised online during the remaining 6 days at home, over 12 consecutive weeks. The primary outcome was fatigue (Multi-dimensional Fatigue Inventory 20 [MFI-20]), and secondary outcomes were sleep quality (Pittsburgh Sleep Quality Index [PSQI]), anxiety, depression (Hospital Anxiety and Depression Scale [HADS]), and changes in the Neuropeptide Y (NPY) of peripheral blood. Results: The within-group comparisons of the PLWNT and CBT groups revealed significant improvement in both groups in MFI-20, PSQI, and HADS scores (P < 0.05). No significant difference were found between the PLWNT and CBT groups, even though the effective rate of the PLWNT group was 62.22%, which is slightly than 50.00% of the CBT group. The fatigue scores in the PLWNT group were positively correlated with sleep degree (r = 0.315) and anxiety degree (r = 0.333), only anxiety degree (r = 0.332) was found to be positively correlated with fatigue in the CBT group. The analysis of peripheral blood showed that NPY decreased after PLWNT intervention but increased significantly in the CBT. Conclusion: The PLWNT qigong exercise has potential to be an effective rehabilitation method for CFS symptoms including fatigue, sleep disturbance, anxiety, and depression. Future studies should expand study sample size for in-depth investigation to determine the optimal frequency and intensity of PLWNT qigong intervention in CFS patients. The study was registered in the ClinicalTrials.gov database on April 12, 2018, with registration number NCT03496961.