Objective:To investigate the safety, feasibility and efficacy of trans-oral robotic surgery in the treatment of lingual thyroglossal duct cyst. Methods:The clinical data of 21 patients with lingual thyroglossal duct cyst underwent trans-oral robotic surgery from May 2017 to March 2025 were analyzed retrospectively. Results:The cysts in all 21 patients were successfully excised by trans-oral robotic surgery. The mean robotic set-up and exposure time, operation time, estimated intraoperative blood loss and recovery time for oral intake were （14.3±7.2） min （[range 5.0-32.0]min）, （17.0±8.4） min （range[6.0-36.0]min）, （8.4±5.9） mL （range[2.0-25.0]mL） and （2.1±2.2） days （range[0-7]days）, respectively. No patients required tracheostomy, and no severe postoperative complications occurred. The patients were followed up for 2 to 96 months, with median follow-up time of 47 months, and 1 recurrence was observed. Conclusion:Excision of lingual thyroglossal duct cyst by trans-oral robotic surgery is safe and feasible, with rapid recovery and low recurrence rate, which is worth popularizing in clinical practice.
Humans ; Thyroglossal Cyst/surgery* ; Robotic Surgical Procedures/methods* ; Retrospective Studies ; Female ; Male ; Adult ; Treatment Outcome ; Middle Aged ; Adolescent

Breast cancer， as one of the major cancers threatening women's health globally， is characterized by high aggressiveness， high malignancy， and poor prognosis. In 2022， according to the World Health Organization， breast cancer ranked second in the incidence of female cancers globally， accounting for 11.6% of all new cancer cases. Western medical doctors mainly use surgery， chemotherapy， radiotherapy， endocrine therapy， and molecular targeted therapy to treat breast cancer， which can effectively improve the recurrence rate and death rate of breast cancer patients and prolong the survival period of patients. However， its treatment process is often accompanied by a series of side effects， which bring challenges to patients' quality of life. However， traditional Chinese medicine （TCM） has demonstrated significant therapeutic effects in inhibiting the proliferation and metastasis of breast cancer cells， reducing toxic side effects produced by chemotherapy， and improving patients' survival rate and quality of life. It is therefore particularly necessary to investigate the pharmacological effects and mechanisms of TCM in breast cancer treatment. The authors combed the pharmacological effects and mechanisms of the etiology and pathogenesis of breast cancer， identification and treatment of breast cancer， TCM compound， TCM single medicine， TCM monomer， and external treatment of TCM to prevent and control breast cancer and found that TCM has a therapeutic effect on breast cancer. It can play a role in increasing the effectiveness， reducing the toxicity， and alleviating the adverse reactions. It can inhibit breast cancer cell proliferation， cell cycle， migration， invasion， immune escape， epithelial-mesenchymal transition （EMT）， aerobic glycolysis， mitochondrial biosynthesis， aminoacyl-tRNA biosynthesis， reduce drug resistance， promote apoptosis， ferroptosis， cell autophagy， and regulate the tumor immune microenvironment by regulating signaling pathways. This paper aims to provide new ideas and methods for experimental research and clinical treatment of breast cancer.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Lung cancer has the highest morbidity and mortality rate among all cancers. Because of the complex pathogenesis， there are limitations in the common Western medicine treatment methods. Clinical and experimental studies have proved that traditional Chinese medicine （TCM） can not only effectively treat lung cancer and alleviate the clinical symptoms of cancer patients but also reduce the adverse reactions and complications caused by surgery， chemotherapy， and radiotherapy to improve the quality of life of the patients. The biological behaviors of lung cancer cells， such as proliferation， invasion， and metastasis， are closely related to their metabolic reprogramming. Metabolic reprogramming in lung cancer involves a series of metabolic changes such as increased glucose uptake and consumption， enhanced glycolysis， increased amino acid uptake and catabolism， and enhanced lipid and protein synthesis. Studies have reported that TCM active components， extracts， and compound prescriptions can effectively inhibit the biological behaviors of lung cancer by regulating metabolic reprogramming. Therefore， this paper reviews the pharmacological mechanisms of TCM active components， extracts， and compound prescriptions in regulating metabolic reprogramming of lung cancer， with the aim of providing a new way of thinking for the treatment of lung cancer by TCM regulation of metabolic reprogramming of lung cancer cells. The available studies suggest that TCM mainly inhibits the extracellular signal-regulated protein kinase （ERK）/c-Myc， phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， and hypoxia-inducible factor-α （HIF-1α） pathways. Furthermore， the expression of monocarboxylate transporter 4 （MCT4）， glucose transporter 1 （GLUT1）， pyruvate dehydrogenase （PDH）， phosphofructokinase 1 （PFK1）， pyruvate dehydrogenase kinase 1 （PDK1）， pyruvate kinase M2 （PKM2）， hexokinase （HK）， lactate dehydrogenase （LDH）， and lactate dehydrogenase A （LDHA） are inhibited. In this way， TCM inhibits the glucose uptake by lung cancer cells and glycolysis in lung cancer cells to reduce the energy metabolism of tumor cells， ultimately achieving the therapeutic effect on lung cancer.

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Objective:To investigate the clinical distribution and diagnostic value of anti-total phospholipid-antibodies(aTPL) patients with in antiphospholipid syndrome(APS).Methods:We collected the clinical data and laboratory test results of patients diagnosed with APS, systemic lupus erythematosus, Sj?gren′s syndrome, osteoarthritis, rheumatoid arthritis, ankylosing spondylitis, mixed connective tissue disease, and adult Still′s disease in Peking University People′s Hospital from February 2009 to October 2017. A total of 335 cases were studied, of which 163 were APS patients, 122 were disease control(DC) and 50 were health control(HC). Enzyme-linked immunosorbent assay(ELISA) was used to measure aTPL, anti-cardiolipin antibody (aCL), and anti-beta-2-glycoprotein Ⅰ antibody (aβ 2GPⅠ). The Chi-square test was used to compare the differences between groups. Results:The prevalence of aTPL in APS, DC and HC were 39.9%, 3.3%and 2.0% respectively. The sensitivity and specificity were 39.9%, 97.1%. The proportion of thrombosis[75.4%(49/65) vs. 51.0%(50/98), χ2=9.73, P=0.002] and arterial thrombosis[49.2%(32/65) vs. 25.5%(25/98), χ2=9.67, P=0.002] was significantly higher in the aTPL positive group than that of the negative group. In aTPL positive group, the positive rate of aCL[84.6%(55/65) vs.29.6%(29/98), χ2=47.37, P<0.001], aβ 2GPⅠ[83.1%(54/65) vs.37.8%(37/98), χ2=32.55, P<0.001] and LA[61.5%(40/65) vs. 42.9%(42/98), χ2=5.46, P=0.020] was significantly higher than that of negative group.The area under ROC curve (95% CI) of aTPL [0.694(0.636, 0.751)] was slightly higher than that of aCL [0.668(0.610, 0.726)], but lower than that of aβ 2GPⅠ [0.746(0.694, 0.799)]. Conclusion:aTPL exhibits a strong correlation with thrombosis in patients with APS, particularly arterial thrombosis, and demonstrates high specificity, which can assist in the diagnosis of seronegative APS.

The study aims to elucidate the existence forms(original constituents and metabolites) of Notoginseng Radix et Rhizoma in rats and reveal its metabolic pathways. After Notoginseng Radix et Rhizoma was administered orally once a day for seven consecutive days to rats, all urine and feces samples were collected for seven days, while the blood samples were obtained 6 h after the last administration. Using the ultra high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS/MS) technique, this study identified 6, 73, and 156 existence forms of Notoginseng Radix et Rhizoma in the rat plasma, urine, and feces samples, respectively. Among them, 101 compounds were identified as new existence forms, and 13 original constituents were identified by comparing with reference compounds. The metabolic reactions of constituents from Notoginseng Radix et Rhizoma were mainly deglycosylation, dehydration, hydroxylation, hydrogenation, dehydrogenation, acetylation, and amino acid conjugation. Furthermore, the possible in vivo metabolic pathways of protopanaxatriol(PPT) in rats were proposed. Through comprehensive analysis of the liquid chromatography-mass spectrometry(LC-MS) data, isomeric compounds were discriminated, and the planar chemical structures of 32 metabolites were clearly identified. According to the literature, 48 original constituents possess antitumor and cardiovascular protective bioactivities. Additionally, 32 metabolites were predicted to have similar bioactivities by SuperPred. This research lays the foundation for further exploring the in vivo effective forms of Notoginseng Radix et Rhizoma.
Animals ; Rats ; Drugs, Chinese Herbal/pharmacokinetics* ; Rhizome/metabolism* ; Male ; Rats, Sprague-Dawley ; Chromatography, High Pressure Liquid ; Panax notoginseng/chemistry* ; Tandem Mass Spectrometry ; Feces/chemistry*

Following the core outcome set standards for development(COS-STAD), this study aims to construct core outcome set(COS) for clinical research on traditional Chinese medicine(TCM) treatment of simple obesity. Firstly, a comprehensive review was conducted on the randomized controlled trial(RCT) and systematic review(SR) about TCM treatment of simple obesity that were published in Chinese and English databases to collect reported outcomes. Additional outcomes were obtained through semi-structured interviews with patients and open-ended questionnaire surveys for clinicians. All the collected outcomes were then merged and organized as an initial outcome pool, and then a preliminary list of outcomes was formed after discussion by the working group. Subsequently, two rounds of Delphi surveys were conducted with clinicians, methodology experts, and patients to score the importance of outcomes in the list. Finally, a consensus meeting was held to establish the COS for clinical research on TCM treatment of simple obesity. A total of 221 RCTs and 12 SRs were included, and after integration of supplementary outcomes, an initial outcome pool of 141 outcomes were formed. Following discussions in the steering advisory group meeting, a preliminary list of 33 outcomes was finalized, encompassing 9 domains. Through two rounds of Delphi surveys and a consensus meeting, the final COS for clinical research on TCM treatment of simple obesity was determined to include 8 outcomes: TCM symptom scores, body mass index(BMI), waist-hip ratio, waist circumference, visceral fat index, body fat rate, quality of life, and safety, which were classified into 4 domains: TCM-related outcomes, anthropometric measurements, quality of life, and safety. This study has preliminarily established a COS for clinical research on TCM treatment of simple obesity. It helps reduce the heterogeneity in the selection and reporting of outcomes in similar clinical studies, thereby improving the comparability of research results and the feasibility of meta-analysis and providing higher-level evidence support for clinical practice.
Humans ; Obesity/therapy* ; Medicine, Chinese Traditional ; Randomized Controlled Trials as Topic ; Treatment Outcome ; Drugs, Chinese Herbal/therapeutic use*

The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can selectively enhance specific information. Building on this, the study introduces a dual-branch U-Net that integrates lateral inhibition and feedback-driven attention mechanisms. Noisy speech signals input into the first branch of the U-Net led to the selective feedback of time-frequency units with high confidence. The generated activation layer gradients, in conjunction with the lateral inhibition mechanism, were utilized to calculate attention maps. These maps were then concatenated to the second branch of the U-Net, directing the network's focus and achieving selective enhancement of auditory speech signals. The evaluation of the speech enhancement effect was conducted by utilising five metrics, including perceptual evaluation of speech quality. This method was compared horizontally with five other methods: Wiener, SEGAN, PHASEN, Demucs and GRN. The experimental results demonstrated that the proposed method improved speech signal enhancement capabilities in various noise scenarios by 18% to 21% compared to the baseline network across multiple performance metrics. This improvement was particularly notable in low signal-to-noise ratio conditions, where the proposed method exhibited a significant performance advantage over other methods. The speech enhancement technique based on lateral inhibition and feedback-driven attention mechanisms holds significant potential in auditory speech enhancement, making it suitable for clinical practices related to artificial cochleae and hearing aids.
Humans ; Attention/physiology* ; Speech Perception/physiology* ; Neural Networks, Computer ; Speech ; Noise ; Feedback