Chronic inflammation within the tumor microenvironment (TME) is a critical driver of immune evasion, resistance to therapy, and progression of cancer, whil

Objective. The aim of this study was to systematically summarize and form an expert consensus on the theoretical experience of tongue and facial features for the identification of nine types of traditional Chinese medicine (TCM) constitution. Additionally, we sought to explore the feasibility of TCM constitution identification through objective tongue and facial features. Methods. We used Delphi method to investigate the opinions of experts on facial and tongue feature items for identifying TCM constitution. We developed and validated a diagnostic nomogram for blood stasis constitution (BSC) based on objective facial and tongue features to demonstrate the reliability of expert consultation. Results. Eleven experts participated in two rounds of expert consultation. The recovery rates of the two rounds of expert consultation were 100.0% and 90.9%. After the first round, 39 items were screened out from 147 initial items, and 2 items were supplemented by experts. In the second round, 7 items were eliminated, leaving 34 items for 8 types of TCM constitution. The coefficient of variation in the first round was 0.11–0.49 for the 147 items and 0.11–0.29 for the included items. The coefficient of variation in the second round was 0.10–0.27 for the 41 items and 0.10–0.20 for the included items. The W value was 0.548 ( ) in the first round and 0.240 ( ) in the second round. Based on expert consultation, we selected BSC as an example and developed and validated a diagnostic nomogram consisting of six indicators: sex, hair volume, lip color-dark purple, susceptibility-facial pigmentation/chloasma/ecchymosis, zygomatic texture-red blood streaks, and sublingual vein-varicose and dark purple. The nomogram showed good discrimination (AUC: 0.917 [95% confidence interval [CI], 0.877–0.956] for the primary dataset, 0.902 [95% CI, 0.828–0.976] for the validation dataset) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion. This is the first study to systematically summarize the existing knowledge and clinical experience to form an expert consensus on the tongue and facial features of nine types of TCM constitution. Our results will provide important prior knowledge and expert experience for future constitution identification research. Based on expert consultation, this study presents a nomogram for BSC that incorporates objective facial and tongue features, which can be conveniently used to facilitate the individualized identification of BSC.

Background: Human health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer's, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types. Methods: Participants, aged 18–45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models—1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)—were implemented using Python to classify health status. Saliency maps were generated to provide model explainability. Results: The study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy. Conclusions: The deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.

Eosinophil-associated diseases are a group of inflammatory disorders characterized by abnormal eosinophil infiltration, which significantly impacts patients’ quality of life. Interleukin-5 (IL-5), a critical cytokine that regulates eosinophil development, activation, chemotaxis, and survival, plays a central role in the pathogenesis of these diseases. This review systematically examines the molecular structure and signaling pathways of IL-5, its mechanisms of action in asthma and chronic obstructive pulmonary disease (COPD), and the development and clinical applications of monoclonal antibodies (e.g., mepolizumab, benralizumab, reslizumab) and other biologics targeting IL-5. Although IL-5-targeted therapies have yielded significant results, single-target interventions still exhibit limitations, including insufficient responses in certain patients. To address this, we explore the strategy of multi-target combination therapies, such as the synergistic inhibition of IL-5 with the IL-4/IL-13 and IL-33/ST2 pathways. We also discuss the potential of novel therapeutic approaches, including bispecific antibodies and small-molecule inhibitors. Ultimately, multi-targeted precision therapies, tailored to individual inflammatory phenotypes, are anticipated to represent a new frontier in the management of eosinophil-associated diseases.

Introduction: Sub-health is an intermediate state between health and disease. Early diagnosis in clinical practice is difficult, which delays timely intervention. Traditional Chinese Medicine (TCM) constitution theory divides sub-health into eight categories; however, existing diagnostic tools mainly focus on psychological assessment and lack objective phenotypic indicators. Multimodal technology provides a new direction for the objective diagnosis of sub-health by integrating objective human phenotypic images with expert experience. Methods: Tongue images, facial images, and infrared thermal images were collected from 1389 participants. In the pre-experiment, Model-1 consisted of a ResNet-18 network, an extended binary classification strategy, and a cross-attention fusion mechanism. Model-2 was developed based on Model-1 by integrating expert experience via Qwen2.5 using structural prompting. Accuracy, recall, precision, specificity, and AUC were used to evaluate model performance. Results: The overall performance of Model-1 was good, with most core indicators of body constitution exceeding 0.7. However, PDC (accuracy 0.722, AUC 0.682) and QDC (accuracy 0.732, AUC 0.705) showed relatively poor performance, characterized by high recall but low specificity. The core indicators of Model-2 improved further. Notably, PDC (accuracy 0.772, AUC 0.732) and QDC (accuracy 0.778, AUC 0.756) were significantly improved, and the imbalance between recall and specificity was also alleviated. Discussion: Multimodal fusion is critical for improving classification performance in TCM constitution recognition. The reasonable integration of clinical expert experience effectively optimizes feature fusion efficacy, highlighting the indispensable value of empirical expertise. Rooted in the empirical characteristics of TCM, embedding expert knowledge into the data-driven framework further enhances the robustness and credibility of the screening model. Conclusion: This study established an expert experience-enhanced multimodal model for subhealth and TCM constitution identification. The model achieves objective quantification and accurate classification of sub-health states, providing a valuable technical reference for population health management and early sub-health risk screening.