In recent years， repetitive transcranial magnetic stimulation （rTMS） has garnered significant attention as a therapeutic approach for sleep disorders in the elderly. However， the prevailing rTMS protocols are predominantly developed based on normative neurophysiological data derived from young adults and fail to incorporate individualized parameters tailored to the brain characteristics of the elderly. To address this gap， the consensus development group synthesized the latest evidence from 2010 to 2025 and established a standardized rTMS protocol specifically for elderly patients with sleep disorders. Adhering to the Appraisal of Guidelines for Research and Evaluation II （AGREE II） framework， systematically screened randomized controlled trials （RCTs） and systematic reviews regarding rTMS in the treatment of sleep disorders across various conditions. Meanwhile， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system was employed to rigorously grade the quality of evidence and the strength of recommendations. This consensus guideline delineates precise rTMS protocols for the management of sleep disorders in the elderly， highlights the adjustment of stimulation intensity according to scalp-cortex distance recommends either MRI‑guided neuronavigation or the Beam F3/F4 heuristic approach for accurate target localization， thereby providing precise rTMS intervention protocol for sleep disorders in the elderly， aiming to enhance clinical efficacy while ensuring treatment safety. ［Funded by National Key Research and Development Program （number， 2023YFC3603200）； General Program of Shenzhen Science and Technology Innovation Commission （number， JCYJ20240813112859008， JCYJ20240813112900002）； Youth Program of Shenzhen Kangning Hospital （number， KN2023A004）； www.guidelines-registry.cn number， PREPARE-2026CN530］

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

The mental health management of laboratory animals is a critical factor in ensuring the reliability of scientific research data. However, due to the subtle nature of mental state alterations and the limitations of current assessment methods, the mental health of laboratory animals is often overlooked by researchers. Therefore, there is an urgent need to explore a new, objective, simple, and practical method for evaluating mental health. Stress, as a primary factor inducing alterations in the mental state of animals, can influence experimental results across multiple research fields through the neuroendocrine-immune network. This paper first elucidates the necessity of mental health management in laboratory animals from three perspectives: factors contributing to stress, neural mechanisms of stress, and research areas affected by stress. It highlights that excluding animals with abnormal mental states before experiments can enhance the efficiency and reproducibility of biomedical studies. Second, this paper briefly summarizes existing methods for assessing the health of laboratory animals, pointing out that approaches such as behavioral tests and metabolomics have many limitations in evaluating stress responses. Existing methods struggle to meet the demand for simple, objective, and non-invasive assessment methods in animal management. Therefore, this paper focuses on hair, a biological sample with the advantages of cumulative information and non-invasive collection, and systematically describes the theoretical basis and technological advances in using hair characteristics as indicators of stress responses from three perspectives: hair traits, the levels of substances in hair, and artificial intelligence (AI). It proposes and supports an innovative technical pathway that combines hair traits with AI-based image analysis, with the aim of offering an improved solution for non-invasive and objective stress assessment, while providing theoretical support for improving the health management system of laboratory animals.

Purpose: Diagnosing sarcopenia necessitates the measurement of skeletal muscle mass. However, guidelines lack a standardized imaging modality with thresholds validated among Asians. This systematic review compared ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and bioelectrical impedance analysis (BIA)/body composition monitoring in the detection of sarcopenia within Asian populations. Methods: PubMed and Embase were systematically searched for studies analyzing ultrasonography, CT, MRI, and BIA in diagnosing sarcopenia among Asians. Study quality was assessed using the Newcastle-Ottawa scale. Results: Pooled findings from 21,598 patients across 25 studies were examined. In receiver operating characteristic analysis, ultrasound displayed a pooled mean area under the curve (AUC) of 0.767 (95% confidence interval [CI], 0.709–0.806), with mean sensitivity of 81.1% (95% CI, 0.744–0.846) and specificity of 73.1% (95% CI, 0.648–0.774), for detecting sarcopenia in Asian populations. CT exhibited an AUC of 0.720 (sensitivity, 54.0%; specificity, 92.0%). MRI demonstrated an AUC of 0.839 (sensitivity, 67.0%; specificity, 66.0%). BIA displayed an AUC of 0.905 (95% CI, 0.842–0.968), 80.7% sensitivity (95% CI, 0.129–0.679), and 82.4% specificity (95% CI, 0.191–0.633). Conclusion Various modalities aid in diagnosing sarcopenia, and selection should be individualized. Although only BIA and dual-energy x-ray absorptiometry are recommended by the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People, ultrasound imaging may hold diagnostic value for sarcopenia in the Asian population. In certain groups, diagnostic use of CT and MRI is warranted. Future research can standardize and validate modality-specific thresholds and protocols within Asian populations.

Purpose: Diagnosing sarcopenia necessitates the measurement of skeletal muscle mass. However, guidelines lack a standardized imaging modality with thresholds validated among Asians. This systematic review compared ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and bioelectrical impedance analysis (BIA)/body composition monitoring in the detection of sarcopenia within Asian populations. Methods: PubMed and Embase were systematically searched for studies analyzing ultrasonography, CT, MRI, and BIA in diagnosing sarcopenia among Asians. Study quality was assessed using the Newcastle-Ottawa scale. Results: Pooled findings from 21,598 patients across 25 studies were examined. In receiver operating characteristic analysis, ultrasound displayed a pooled mean area under the curve (AUC) of 0.767 (95% confidence interval [CI], 0.709–0.806), with mean sensitivity of 81.1% (95% CI, 0.744–0.846) and specificity of 73.1% (95% CI, 0.648–0.774), for detecting sarcopenia in Asian populations. CT exhibited an AUC of 0.720 (sensitivity, 54.0%; specificity, 92.0%). MRI demonstrated an AUC of 0.839 (sensitivity, 67.0%; specificity, 66.0%). BIA displayed an AUC of 0.905 (95% CI, 0.842–0.968), 80.7% sensitivity (95% CI, 0.129–0.679), and 82.4% specificity (95% CI, 0.191–0.633). Conclusion Various modalities aid in diagnosing sarcopenia, and selection should be individualized. Although only BIA and dual-energy x-ray absorptiometry are recommended by the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People, ultrasound imaging may hold diagnostic value for sarcopenia in the Asian population. In certain groups, diagnostic use of CT and MRI is warranted. Future research can standardize and validate modality-specific thresholds and protocols within Asian populations.

Purpose: Diagnosing sarcopenia necessitates the measurement of skeletal muscle mass. However, guidelines lack a standardized imaging modality with thresholds validated among Asians. This systematic review compared ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and bioelectrical impedance analysis (BIA)/body composition monitoring in the detection of sarcopenia within Asian populations. Methods: PubMed and Embase were systematically searched for studies analyzing ultrasonography, CT, MRI, and BIA in diagnosing sarcopenia among Asians. Study quality was assessed using the Newcastle-Ottawa scale. Results: Pooled findings from 21,598 patients across 25 studies were examined. In receiver operating characteristic analysis, ultrasound displayed a pooled mean area under the curve (AUC) of 0.767 (95% confidence interval [CI], 0.709–0.806), with mean sensitivity of 81.1% (95% CI, 0.744–0.846) and specificity of 73.1% (95% CI, 0.648–0.774), for detecting sarcopenia in Asian populations. CT exhibited an AUC of 0.720 (sensitivity, 54.0%; specificity, 92.0%). MRI demonstrated an AUC of 0.839 (sensitivity, 67.0%; specificity, 66.0%). BIA displayed an AUC of 0.905 (95% CI, 0.842–0.968), 80.7% sensitivity (95% CI, 0.129–0.679), and 82.4% specificity (95% CI, 0.191–0.633). Conclusion Various modalities aid in diagnosing sarcopenia, and selection should be individualized. Although only BIA and dual-energy x-ray absorptiometry are recommended by the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People, ultrasound imaging may hold diagnostic value for sarcopenia in the Asian population. In certain groups, diagnostic use of CT and MRI is warranted. Future research can standardize and validate modality-specific thresholds and protocols within Asian populations.

Purpose: Diagnosing sarcopenia necessitates the measurement of skeletal muscle mass. However, guidelines lack a standardized imaging modality with thresholds validated among Asians. This systematic review compared ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and bioelectrical impedance analysis (BIA)/body composition monitoring in the detection of sarcopenia within Asian populations. Methods: PubMed and Embase were systematically searched for studies analyzing ultrasonography, CT, MRI, and BIA in diagnosing sarcopenia among Asians. Study quality was assessed using the Newcastle-Ottawa scale. Results: Pooled findings from 21,598 patients across 25 studies were examined. In receiver operating characteristic analysis, ultrasound displayed a pooled mean area under the curve (AUC) of 0.767 (95% confidence interval [CI], 0.709–0.806), with mean sensitivity of 81.1% (95% CI, 0.744–0.846) and specificity of 73.1% (95% CI, 0.648–0.774), for detecting sarcopenia in Asian populations. CT exhibited an AUC of 0.720 (sensitivity, 54.0%; specificity, 92.0%). MRI demonstrated an AUC of 0.839 (sensitivity, 67.0%; specificity, 66.0%). BIA displayed an AUC of 0.905 (95% CI, 0.842–0.968), 80.7% sensitivity (95% CI, 0.129–0.679), and 82.4% specificity (95% CI, 0.191–0.633). Conclusion Various modalities aid in diagnosing sarcopenia, and selection should be individualized. Although only BIA and dual-energy x-ray absorptiometry are recommended by the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People, ultrasound imaging may hold diagnostic value for sarcopenia in the Asian population. In certain groups, diagnostic use of CT and MRI is warranted. Future research can standardize and validate modality-specific thresholds and protocols within Asian populations.

Purpose: Diagnosing sarcopenia necessitates the measurement of skeletal muscle mass. However, guidelines lack a standardized imaging modality with thresholds validated among Asians. This systematic review compared ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and bioelectrical impedance analysis (BIA)/body composition monitoring in the detection of sarcopenia within Asian populations. Methods: PubMed and Embase were systematically searched for studies analyzing ultrasonography, CT, MRI, and BIA in diagnosing sarcopenia among Asians. Study quality was assessed using the Newcastle-Ottawa scale. Results: Pooled findings from 21,598 patients across 25 studies were examined. In receiver operating characteristic analysis, ultrasound displayed a pooled mean area under the curve (AUC) of 0.767 (95% confidence interval [CI], 0.709–0.806), with mean sensitivity of 81.1% (95% CI, 0.744–0.846) and specificity of 73.1% (95% CI, 0.648–0.774), for detecting sarcopenia in Asian populations. CT exhibited an AUC of 0.720 (sensitivity, 54.0%; specificity, 92.0%). MRI demonstrated an AUC of 0.839 (sensitivity, 67.0%; specificity, 66.0%). BIA displayed an AUC of 0.905 (95% CI, 0.842–0.968), 80.7% sensitivity (95% CI, 0.129–0.679), and 82.4% specificity (95% CI, 0.191–0.633). Conclusion Various modalities aid in diagnosing sarcopenia, and selection should be individualized. Although only BIA and dual-energy x-ray absorptiometry are recommended by the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People, ultrasound imaging may hold diagnostic value for sarcopenia in the Asian population. In certain groups, diagnostic use of CT and MRI is warranted. Future research can standardize and validate modality-specific thresholds and protocols within Asian populations.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

The prevalence of Class III malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore, early orthodontic treatment for Class III malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class III malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class III malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class III malocclusion through early orthodontic treatment.
Humans ; Malocclusion, Angle Class III/classification* ; Orthodontics, Corrective/methods* ; Consensus ; Child