Features Of Tongue Research Articles

Study of the Correlation between the Extent and Clinical Severity, and the Histopathological Characteristics of Geographic Tongue.

Geographic tongue is an oral lesion with an unknown etiology. Recently, the Geographic Tongue Area and Severity Index (GTASI) has been proposed to assess the area and severity of geographic tongue, aiming to measure the clinical severity of the condition. However, this index does not account for the histopathology, which vary based on the clinical stage of the lesion and the biopsy area. The present study aimed to evaluate the correlation between GTASI score and its histopathological features. This cross-sectional observational study included 40 participants diagnosed with GT confirmed both clinically and histopathologically. Considering GT severity, a vast majority 60% of cases were classified as mild, with females' predominance in both mild and severe categories. The average age of participants was 56 years for mild and severe cases and 47 years for moderate ones. The prevalent histopathological features of geographic tongue included parakeratosis, acanthosis, spongiosis, basal layer hyperplasia, mono- and polymorphonuclear exocytosis, suprapapillary hypotrophy, claviform epithelial ridges, fusion of epithelial ridges, conjunctival papillary edema, and chronic subepithelial infiltration, with no significant differences taking into consideration clinical severity level. Papillary vascular ectasia, Munro microabscesses, Kogoj pustules, and dense connective tissue were more prevalent in with more severe cases of GT. Mild inflammatory infiltrate intensity was predominant in persons with mild GT, while moderate infiltrate intensity was found predominantly in moderate cases of GT. The clinical severity level of GT closely corresponds with its histopathological characteristics.

Influence of Qigong Wuqinxi on Pain, Sleep, and Tongue Features in Older Adults.

Chronic pain and sleep disorders are commonly seen symptoms in community-dwelling older adults at traditional Chinese medicine (TCM) clinics. TCM modalities such as herbal medication and acupuncture have side effects and usage limitations. Therefore, nonpharmacological and noninvasive interventions may be applied to relieve the symptoms of these conditions. This study was designed to test the effect of Qigong Wuqinxi exercise on chronic pain, sleep quality, TCM constitutions, and tongue features in community-dwelling older adults. This was a cluster randomized controlled trial. Older adults in two community care centers who met the inclusion criteria were assigned by drawing lots to either the experimental group (n = 32) or the control group (n = 31). Experimental group members practiced the Qigong Wuqinxi exercise for 50 minutes three times each week for 12 weeks, whereas those in the control group maintained their normal daily routine. Before and after the 12-week study, data on chronic pain, sleep quality, TCM constitutions, and tongue features were collected from all participants to assess the effectiveness of the intervention. Between-group pretest and posttest comparisons were analyzed using independent samples t tests, and within-group differences were analyzed using paired t tests. The experimental group reported significantly greater average posttest improvements in chronic pain, yang deficiency, phlegm-stasis, thick fur, and red dots than the control group (all ps < .05). The maximum change in pretest to posttest pain scores favored the experimental group (-0.56 ± 0.76 vs. 0.39 ± 1.91, p = .011). Also, the experimental group had a significantly better change in the pain interference index (-3.31 ± 3.30 vs. -0.58 ± 1.91, p < .001). In addition, the experimental group exhibited greater improvements in yang-deficiency tendency (-2.38 ± 4.89 vs. 0.35 ± 4.67, p = .027), phlegm and stasis tendency (-2.19 ± 4.52 vs. 1.77 ± 2.47, p < .001), thick fur (1.44 ± 11.28 vs. 6.03 ± 6.04, p = .049), and red dots (-5.09 ± 21.45 vs. 2.81 ± 4.03, p = .048). However, no significant between-group difference in posttest sleep quality (p = .357) was observed. The Qigong Wuqinxi exercise was found to positively improve chronic pain and TCM constitutions in community-dwelling older adults and may be promoted in community care centers to improve the health status of older adults.

Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.

This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in diabetes progression. Participants included 30 Pre-DM patients, 37 individuals with T2DM, and 28 healthy controls. Tongue images and oral/fecal samples were analyzed using image processing and 16S rRNA sequencing. Machine learning techniques, including support vector machine (SVM), random forest, gradient boosting, adaptive boosting, and K-nearest neighbors, were applied to integrate tongue image data with microbiota profiles to construct predictive models for Pre-DM and T2DM classification. Significant shifts in tongue characteristics were identified during the progression from Pre-DM to T2DM. Elevated Firmicutes levels along the oral-gut axis were associated with white greasy fur, indicative of underlying metabolic changes. An SVM-based predictive model demonstrated an accuracy of 78.9%, with an AUC of 86.9%. Notably, tongue image parameters (TB-a, perALL) and specific microbiota (Escherichia, Porphyromonas-A) emerged as prominent diagnostic markers for Pre-DM and T2DM. The integration of tongue diagnosis with microbiome analysis reveals distinct tongue features and microbial markers. This approach significantly improves the diagnostic capability for Pre-DM and T2DM.

The Relationship Between Computerized Face and Tongue Image Segmentation and Metabolic Parameters in Patients with Type 2 Diabetes Based on Machine Learning.

We aim to examine and reestablish the correlational and linear regression relationships, as well as the predictive value, between the significant facial and tongue features and the metabolic parameters in type 2 diabetes mellitus (T2DM). From March to May 2024, we studied 269 patients with T2DM in the endocrinology department of Shanghai Pudong Hospital. The patients' facial and tongue characteristics were sampling by a tongue imaging device equipped with artificial intelligence (AI) (XiMaLife, Sinology, China) of automated and advanced machine learning algorithms. Then, the imaging features were examined in relation to the blood examination. Multiple facial and tongue features, as well as dimensional facial and tongue color parameters, were significantly correlated with glycated hemoglobin A1c (HbA1c) (r < 0.3, p < 0.05), glycated albumin (GA) (-0.20 < 0.30, p < 0.05), C-peptide (-0.20.20, p < 0.05), plasma insulin (r < 0.30, p < 0.05), fasting plasma glucose (FPG) (r < 0.3, p < 0.05), significant hepatic and renal function indicators (-0.30 < r < 0.20, p<0.05), cardiac injury markers (-0.30 < r < 0.30, p < 0.05), tumor markers (-0.5 < r < 0.5, p < 0.05), thyroid function (-0.15 < r < 0.55, p < 0.05), and blood cell count, including white blood cells (r < 0.2, p < 0.05), and hemoglobin (Hb) (-0.30 < r < 0.3, 0.0001. The correlational results demonstrated that the tongue's characteristics and signsmay be linked with the dynamic of the metabolic status of T2DM. In order to examine the causal relationships, we performed linear regression analyses, which revealed that various facial and tongue imaging parameters partially determined the metabolic indicators. The predictive value of imaging features was evaluated by receiver operating characteristic curve (ROC) to assess metabolic status in T2DM. This study demonstrated that metabolic status, renal and hepatic, cardiac, and thyroid function, the proportion of blood cells, and Hb in T2DM were intimately associated with facial and tongue features. The precise analysis of facial and tongue features through AI and advanced machine learning could be used to predict T2DM's conditions and progression.

Patterns of identity: Lip prints, tongue prints and mandibular canine width as markers for gender determination—A cross-sectional study

ABSTRACT Introduction: Forensic odontology plays a pivotal role in identifying individuals in legal contexts. This study analyses the efficacy of lip prints, tongue prints and mandibular canine width in determining gender, aiming to contribute to the evolving field of forensic dentistry. Methods: A cross-sectional study was conducted involving 100 participants, analysing lip prints based on Tsuchihashi’s classification, recording tongue shapes and features through the use of alginate impressions and measuring mandibular canine width using a vernier calliper. The findings were reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology checklist. Results: In lip prints, Tsuchihashi’s Type I lip print was predominantly found in females, whereas Tsuchihashi’s Type III prevailed in males. Tongue shapes revealed a predominance of pentagonal shapes in males and ovoid shapes in females. The lingual apex was blunt in males and sharp in females. Mandibular canine width was higher in males than females on both the left and right sides. Conclusion: The findings of this study support the use of lip prints, tongue prints and canine width in determining gender as ancillary and supporting methods.

Microbial and metabolic characteristics of tongue coating in halitosis

Microbial and metabolic characteristics of tongue coating in halitosis

A new method for identification of traditional Chinese medicine constitution based on tongue features with machine learning.

The theory of Chinese medicine (TCM) constitution contributes to the optimisation of individualised healthcare programmes. However, at present, TCM constitution identification mainly relies on inefficient questionnaires with subjective bias. Efficient and accurate TCM constitution identification can play an important role in individualised medicine and healthcare. Building an efficient model for identifying traditional Chinese medicine constitutions using objective tongue features and machine learning techniques. The DS01-A device was applied to collect tongue images and extract features. We trained and evaluated five machine learning models: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), LightGBM (LGBM), and CatBoost (CB). Among these, we selected the model with the best performance as the base classifier for constructing our heterogeneous ensemble learning model. Using various performance metrics, including classification accuracy, precision, recall, F1 score, and area under curve (AUC), to comprehensively evaluate model performance. A total of 1149 tongue images were obtained and 45 features were extracted, forming dataset 1. RF, LGBM, and CB were selected as the base learners for the RLC-Stacking. On dataset 1, RLC-Stacking1 achieved an accuracy of 0.8122, outperforming individual classifiers. After feature selection, the classification accuracy of RLC-Stacking2 improved to 0.8287, an improvement of 0.00165 compared to RLC-Stacking1. RLC-Stacking2 achieved an accuracy exceeding 0.85 for identifying each TCM constitution type, indicating excellent identification performance. The study provides a reliable method for the accurate and rapid identification of TCM constitutions and can assist clinicians in tailoring individualized medical treatments based on personal constitution types and guide daily health care. The information extracted from tongue images serves as an effective marker for objective TCM constitution identification.

Intelligent tongue diagnosis model for gastrointestinal diseases based on tongue images

BackgroundCurrently, the diagnosis of gastrointestinal diseases relies heavily on electronic endoscopy, which is not suitable for all individuals and insufficient for dynamic patient monitoring. Therefore, there is an urgent need for a convenient and noninvasive examination method to enhance the diagnosis and monitoring of gastrointestinal diseases. MethodsSince the tongue is a significant reflection of the digestive system and closely associated with gastrointestinal diseases, our study aims to develop a diagnostic framework based on tongue features. We have collected a dataset of 2,167 tongue coating images from 949 consecutive patients, including 905 images from healthy individuals and 1,262 images from patients with various gastrointestinal conditions. Notably, each patient sample may represent distinct gastrointestinal diseases. This study introduces a novel approach to information fusion detection, integrating hand-crafted and auto-encoded features, alongside disease detection employing Squeeze and Excitation (SE) and Slot attention mechanisms. These two branches respectively analyze image feature and spatial position information when utilizing tongue images for diagnosing gastrointestinal diseases. The amalgamation of predictions from both aspects yields the final detection outcome. ResultsFor both healthy and diseased cohorts, the optimal classification metrics were achieved: AUC = 0.886, ACC = 0.849, and TPR = 0.965, indicating satisfactory performance. Notably, our framework demonstrated promising results in detecting five common gastrointestinal diseases: Helicobacter pylori infection, bile reflux, reflux esophagitis, gastric erosion, and duodenal erosion. Ablation experiments underscored the efficacy of mixed features in multi-color space, showcasing an increase in AUC and ACC by 13.48 % and 12.05 % respectively. Furthermore, the attention mechanism, by focusing on the middle region of tongue images, contributed to a 6.12 % increase in AUC and a 6.35 % increase in ACC. ConclusionsIn this study, we successfully develop and validate a diagnosis framework for gastrointestinal diseases based on tongue image features. By utilizing tongue images, we achieve intelligent diagnosis of gastrointestinal diseases, establishing tongue diagnosis as a convenient, cost-effective, and noninvasive method for diagnosing and screening gastrointestinal diseases.

Tongue Disease Prediction Based on Machine Learning Algorithms

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.

Circadian Rhythms in Tongue Features.

(1) Background: The aim of this study was to investigate the circadian rhythms of tongue features according to the effects of physiological phases over a 24 h period. (2) Methods: Fifteen healthy participants aged 20 to 69 years were recruited. The participants did not have current chronic diseases or past diseases and had to meet the inclusion and exclusion criteria. The participants stayed at the Gil Hospital for a duration of 2 nights and 3 days. On the first day, at 18:00, they consumed their allocated portions of food and water and then completed a questionnaire. At approximately 21:00, their tongue images were acquired using a computerized tongue image acquisition system, following which they slept for 8 h, commencing at 23:00. Measurements were taken from 07:00 through 21:00 on the second day, and the final acquisition was taken at 07:00 on the following morning, resulting in a total of eight images. The circadian rhythm was authenticated and quantified utilizing the single cosinor analysis, a technique for periodic regression analysis for fitting a 24 h cosine curve. (3) Results: Cosinor analysis revealed that all tongue features were significantly related to circadian rhythm. (4) Conclusions: The results of this study may be important for considering the time of day at which the tongue is observed and tongue status is evaluated.

Tongue coating metabolic profiles of intra‐oral halitosis patients

AbstractObjectiveThis study focused on the metabolic characteristics of tongue coating in patients with intra‐oral halitosis (IOH) to investigate potential diagnostic biomarkers for IOH.MethodsOral healthy participants were enrolled in this study. Halitosis was evaluated with an organoleptic assessment, a Halimeter®, and an OralChroma™. Tongue coating samples were collected from 18 halitosis patients and 18 healthy controls. Liquid chromatography–mass spectrometry was conducted to reveal the IOH‐related metabolic variations in tongue coating.ResultsA total of 2214 metabolites were obtained. Most metabolites were shared between the two groups. A total of 274 upregulated metabolites, such as paramethasone acetate and indole‐3‐acetic acid, and 43 downregulated metabolites, including deoxyadenosine and valyl‐arginine, were detected in the halitosis group. Functional analysis indicated that several metabolic pathways, including arginine biosynthesis, arginine and proline metabolism, histidine metabolism, and lysine degradation were significantly enriched in the IOH group. The least absolute shrinkage and selection operator logistic regression analysis revealed that paramethasone acetate, {1‐[2‐(4‐carbamimidoyl‐benzoylamino)‐propionyl]‐piperidin‐4‐yloxy}‐acetic acid, indole‐3‐acetic acid, and valyl‐arginine were remarkably associated with IOH.ConclusionsThis study revealed the metabolites present in tongue coating and identified effective biomarkers, providing essential insights into the prediction, pathogenesis, and diagnosis of IOH.

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

Tongue diagnosis is a crucial traditional Chinese medicine (TCM) inspection method for TCM syndrome differentiation and treatment. 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. 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. 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. 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.

Tongue feature recognition to monitor rehabilitation: deep neural network with visual attention mechanism.

We endeavor to develop a novel deep learning architecture tailored specifically for the analysis and classification of tongue features, including color, shape, and coating. Unlike conventional methods based on architectures like VGG or ResNet, our proposed method aims to address the challenges arising from their extensive size, thereby mitigating the overfitting problem. Through this research, we aim to contribute to the advancement of techniques in tongue feature recognition, ultimately leading to more precise diagnoses and better patient rehabilitation in Traditional Chinese Medicine (TCM). In this study, we introduce TGANet (Tongue Feature Attention Network) to enhance model performance. TGANet utilizes the initial five convolutional blocks of pre-trained VGG16 as the backbone and integrates an attention mechanism into this backbone. The integration of the attention mechanism aims to mimic human cognitive attention, emphasizing model weights on pivotal regions of the image. During the learning process, the allocation of attention weights facilitates the interpretation of causal relationships in the model's decision-making. Experimental results demonstrate that TGANet outperforms baseline models, including VGG16, ResNet18, and TSC-WNet, in terms of accuracy, precision, F1 score, and AUC metrics. Additionally, TGANet provides a more intuitive and meaningful understanding of tongue feature classification models through the visualization of attention weights. In conclusion, TGANet presents an effective approach to tongue feature classification, addressing challenges associated with model size and overfitting. By leveraging the attention mechanism and pre-trained VGG16 backbone, TGANet achieves superior performance metrics and enhances the interpretability of the model's decision-making process. The visualization of attention weights contributes to a more intuitive understanding of the classification process, making TGANet a promising tool in tongue diagnosis and rehabilitation.

BiLSTM-CNN with fixed weight approach for tracking speech articulatory features

Speech production is a complex sequential process which involve the coordination of various articulatory features. Among them tongue being a highly versatile active articulator responsible for shaping airflow to produce targeted speech sounds that are intellectual, clear, and distinct. This paper presents a novel approach for predicting tongue and lip articulatory features involved in a given speech acoustics using a stacked Bidirectional Long Short-Term Memory (BiLSTM) architecture, combined with a one-dimensional Convolutional Neural Network (CNN) for post-processing with fixed weights initialization. The proposed network is trained with two datasets consisting of simultaneously recorded speech and Electromagnetic Articulography (EMA) datasets, each introducing variations in terms of geographical origin, linguistic characteristics, phonetic diversity, and recording equipment. The performance of the model is assessed in Speaker Dependent (SD), Speaker Independent (SI), corpus dependent (CD) and cross corpus (CC) modes. Experimental results indicate that the proposed model with fixed weights approach outperformed the adaptive weights initialization with in relatively minimal number of training epochs. These findings contribute to the development of robust and efficient models for articulatory feature prediction, paving the way for advancements in speech production research and applications.

Thermal radiation of tongue surface as a human computer interaction diagnostics technique based on image classification with software interface

Backgroundand Purpose: Thermal radiation plays a pivotal role in addressing recognition limitations encountered by traditional Chinese medicine's tongue diagnosis. Modern thermal radiation diagnostic instruments offer enhanced objectivity, and in this study, we strive to further improve the diagnostic process through the integration of Human-Computer Interaction (HCI) principles. Our objective is to introduce the thermal radiation DenseNet ensemble model, synergizing the strengths of Convolutional Neural Networks (CNNs) and HCI principles to advance tongue image recognition in healthcare. This approach aims to enhance classification accuracy, even when faced with limited training data. MethodologyTransfer learning, utilizing fine-tuned CNNs and DenseNet, is employed for precise classification, with a specific focus on distinguishing tooth-marked tongues. A portable hand-held thermal radiation diagnostic instrument, seamlessly integrated with HCI, is developed for both clinical use and research. Experimental resultsMeticulously curated datasets, formed through the annotation of thermal radiation images, confirm the superior performance of the DenseNet architecture in tooth mark and tongue feature recognition. The proposed tooth mark tongue recognition model exhibits a significant accuracy improvement of approximately 25% compared to similar tasks in existing studies. Additionally, an AI health detector tailored for Traditional Chinese Medicine (TCM) thermal radiation image recognition is introduced, showcasing the seamless integration of HCI principles into healthcare applications. This innovative tool not only assesses users' health but also provides personalized health recommendations, illustrating the transformative potential of combining technology and healthcare. Discussion and conclusionIn addressing the limitations of traditional tongue diagnosis, thermal radiation emerges as a key factor enhancing objectivity and accuracy. The thermal radiation DenseNet ensemble model, along with HCI integration through the portable hand-held device, proves effective for improved TCM thermal radiation image identification in healthcare. The fusion of technology and HCI principles underscores the potential to elevate health assessments and offer valuable guidance to users.

Tongue feature dataset construction and real-time detection.

Tongue diagnosis in traditional Chinese medicine (TCM) provides clinically important, objective evidence from direct observation of specific features that assist with diagnosis. However, the current interpretation of tongue features requires a significant amount of manpower and time. TCM physicians may have different interpretations of features displayed by the same tongue. An automated interpretation system that interprets tongue features would expedite the interpretation process and yield more consistent results. This study applied deep learning visualization to tongue diagnosis. After collecting tongue images and corresponding interpretation reports by TCM physicians in a single teaching hospital, various tongue features such as fissures, tooth marks, and different types of coatings were annotated manually with rectangles. These annotated data and images were used to train a deep learning object detection model. Upon completion of training, the position of each tongue feature was dynamically marked. A large high-quality manually annotated tongue feature dataset was constructed and analyzed. A detection model was trained with average precision (AP) 47.67%, 58.94%, 71.25% and 59.78% for fissures, tooth marks, thick and yellow coatings, respectively. At over 40 frames per second on a NVIDIA GeForce GTX 1060, the model was capable of detecting tongue features from any viewpoint in real time. This study constructed a tongue feature dataset and trained a deep learning object detection model to locate tongue features in real time. The model provided interpretability and intuitiveness that are often lacking in general neural network models and implies good feasibility for clinical application.

Retrospective cross-sectional survey of clinical characteristics and syndrome elements distribution at different stages of coronary heart disease

The clinical data of coronary heart disease(CHD) patients treated in the First Affiliated Hospital of Guangzhou University of Chinese Medicine and Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine from January 2022 to March 2023 were retrospectively collected. This study involved the descriptive analysis of demographic characteristics, clinical symptoms, and tongue and pulse features. The χ~2 test was conducted to analyze the distribution of syndrome elements and their combinations at diffe-rent stages of CHD, so as to reveal the clinical characteristics and syndrome patterns at various pathological stages of CHD. This study extracted 28 symptom entries, 10 tongue manifestation entries, and 7 pulse manifestation entries, summarized the 5 main disease locations of the heart, lung, liver, spleen, and kidney, and the 8 main disease natures of blood stasis, phlegm turbidity, Qi stagnation, heat(fire), fluid retention, Qi deficiency, Yin deficiency, and Yang deficiency and 8 combinations of disease natures. The χ~2 test showed significant differences in the distribution of syndrome elements including the lung, liver, spleen, kidney, blood stasis, heat(fire), Qi stagnation, heat syndrome, water retention, Qi deficiency, Yin deficiency, and Yang deficiency between different disease stages. Specifically, the liver, blood stasis, heat(fire), and Qi stagnation accounted for the highest proportion during unstable stage, and the lung, spleen, kidney, water retention, Qi deficiency, Yin deficiency, and Yang deficiency accounted for the highest proportion at the end stage. The distribution of Qi deficiency varied in the different time periods after percutaneous coronary intervention(PCI). As shown by the χ~2 test of the syndrome elements combination, the distribution of single disease location, multiple disease locations, single disease nature, double disease natures, multiple natures, excess syndrome, and mixture of deficiency and excess varied significantly at different stages of CHD. Specifically, single disease location, single disease nature, and excess syndrome accounted for the highest proportion during the stable stage, and double disease natures accounted for the highest proportion during the unstable stage. Multiple disease locations, multiple disease natures, and mixture of deficiency and excess accounted for the highest proportion during the end stage. In conclusion, phlegm turbidity and blood stasis were equally serious during the stable stage, and a pathological mechanism caused by blood stasis and toxin existed during the unstable stage. The overall Qi deficiency worsened after PCI, and the end stage was accompanied by the Yin and Yang damage and the aggravation of water retention. There were significant differences in the distribution of clinical characteristics and syndrome elements at different stages of CHD. The pathological process of CHD witnessed the growth and decline of deficiency and excess and the combination of phlegm turbidity and blood stasis, which constituted the basic pathogenesis.

TIM‐Net: A multi‐label classification network for TCM tongue images fusing global‐local features

AbstractCombining the extracted tongue features with other medical indicators can effectively judge the diseases of patients. The previous work usually only analyzes a certain feature of the tongue body and is unable to extract multiple features simultaneously. In this study, a multi‐label classification network named TIM‐Net is proposed, which integrates global and local features to achieve multi‐label intelligent diagnosis of Chinese medicine tongue images. First, a feature extraction network based on ResNet is proposed to capture the features of tongue images more sufficiently. Then, a multi‐label classification algorithm fusing global and local features is proposed, and targeted screening operations are carried out on the class‐related feature maps based on global confidence. In addition, a logical masking algorithm is proposed to ensure that the local features can only correct the feature labels they represent, and do not interfere with other feature labels. The classification accuracy is further improved by using local feature confidence and correcting the global classification results. Finally, the experimental results indicate that the classification accuracy of the tongue images is gradually improved through optimizing the feature extraction network and fusing local features, and it exceeds other state‐of‐the‐art multi‐label classification networks.

Mechanisms of model bias impacting responses of the Atlantic cold tongue to greenhouse warming

The Atlantic cold tongue, which typically peaks in boreal summer, exerts a pronounced regional and global impact on the climate and socio-economy. Projected future changes in the Atlantic cold tongue are full of uncertainty, mainly arising from a model bias in simulating its mean state, with less biased models projecting a stronger weakening in amplitude. However, the underlying mechanisms remain unknown. Here, we find that model bias exerts its influence through modulating atmospheric thermal damping and upwelling of subsurface anomalous warming induced by the weakened Atlantic meridional overturning circulation (AMOC). In less biased models, the Atlantic cold tongue, compared to the western equatorial Atlantic, features a cooler mean climate sea surface temperature (SST), and is subjected to smaller thermal damping induced by mean climate evaporation and consequently, faster SST warming. Moreover, equatorial subsurface warming associated with a reduced AMOC is advected to the surface via mean climate upwelling, enhancing faster SST warming in the east, a feedback stronger in less biased models that produce greater climatological upwelling. The above asymmetric SST warming would be amplified by the Bjerknes feedback, leading to a weakened Atlantic cold tongue. These findings may help to predict future changes in the Atlantic cold tongue and its influences.

Comparative histological and ultrastructural features of the tongue of the mallard domestic duck, Anas platyrhynchos f. domestica, Anatidae (Linnaeus, 1758) in different two age stages (post-hatching [P2] and adult female) captured from Egypt.

The domestic duck is classified as a specialist filter-feeder bird living in the water. These birds also use grazing and pecking as terrestrial feeding methods. The tongues of domestic ducks, similar to those of other Anseriformes, exhibit numerous types and shapes of mechanical papillae that serve a number of purposes when collecting food. The current study attempts to describe the morphological characteristics of the tongue as well as the mechanical papillae's development. In addition, the study aims to determine whether the papillae observed post-hatching (P2) exhibit similar morphology to those found in adult female avian species, as well as to investigate the readiness of the tongue to fulfill its feeding function following hatching. The comprehensive examination of lingual mucosa is examined about the structural modifications necessary for this variety of feeding activities. In this study, the tongues of nine young (P2) and adult female were used. The tongue had three distinct parts: the apex, which had a lingual nail on its ventral surface; the body, which exhibits numerous small and large conical papillae on its lateral sides and a lingual prominence in the caudal region; and the root, which is covered with numerous conical papillae of varying sizes. Conical, filiform, and hair-like mechanical papillae, the three types of food filtration apparatus, are present in both stages. The intraoral transfer involves several structures, including the median groove, lingual combs, and the rostral border of the lingual prominence. The rostral border of the lingual prominence is characterized by distinct rows of conical papillae. The histological analysis demonstrated the presence of both keratinized and nonkeratinized epithelium on different tongue regions. The lingual salivary glands in the rostral and caudal lingual salivary glands exhibit a pronounced periodic acid-Schiff-positive reaction. Additionally, the yellow adipose tissue and sensory receptors, namely the Grandry and Herbst corpuscles, which collectively form the bill-tongue organ that monitors the movement of food. These results conclude the presence of microstructural species-specific alterations in specific tongue areas of domestic ducks' lingual mucosa. These modifications are formed by the filtering mechanism and terrestrial feeding mechanisms, such as grazing or pecking. Following hatching, the tongue of the domestic duck undergoes significant development, primarily in preparation for grazing activities. The anatomical and histological structure of the young (P2) tongue exhibited similarities to that of the adult female domestic duck while also displaying certain variations that could potentially be attributed to the bird's habitat and mode of feeding. RESEARCH HIGHLIGHTS: The results of this study concluded that the domestic duck exhibit a complex tongue structure characterized by the arrangement and morphology of its mechanical papillae, the presence of the lingual prominence with distinctive shape and the lingual comb. These features are believed to be adaptations that enable the duck to actively and efficiently filter food particles from water, serving as its primary feeding mechanism. Additionally, the tongue of domestic ducks is specifically adapted to facilitate various terrestrial activities, such as grazing and pecking. This adaptation is achieved through the presence of conical papillae and a lingual nail. These investigations facilitate our comprehension of both the anatomical and histological characteristics of the domestic duck tongue, as well as enhance our understanding of bird adaptations to various feeding mechanisms.