The incidence of papillary thyroid microcarcinoma (PTMC) is steadily rising. Although conventional thyroid surgery techniques are well-established, they present challenges such as functional impairment and overtreatment. Cellular-level fluorescence-guided imaging technology (EndoSCell^®) enables precise intraoperative tumor localization and real-time identification of neurovascular structures via high-resolution imaging and targeted labeling. This technology significantly improves surgical precision, reduces the incidence of postoperative complications including recurrent laryngeal nerve injury and hypoparathyroidism, and strengthens standards for functional preservation. This article aimed to systematically explore recent advances in the application of EndoSCell^® for PTMC management, analyze its influence on the innovation of surgical approaches and the enhancement of functional preservation criteria, and discuss future directions, thereby providing a theoretical basis for clinical practice.

BackgroundIn recent years， the incidence of depression among adolescents has been increasing steadily， posing a serious threat to their physical and mental health and even leading to severe consequences such as self-harm and suicide. At the same time， the detection rate of subclinical depression symptoms among adolescents is even higher. Although these symptoms do not meet the clinical diagnostic criteria， they have significantly affected their quality of life， and their persistence over time may further develop into depression. Therefore， in-depth exploration of adolescent depression symptoms and the predictive factors holds significant practical significance and research value. However， up to now， no large-scale investigation and research on depression symptoms among children and adolescents has been conducted in Inner Mongolia Autonomous Region. ObjectiveTo understand the prevalence of depressive symptoms among children and adolescents in Inner Mongolia Autonomous Region， in order to provide references for formulating scientific and effective prevention strategies and intervention measures. MethodsBy using the cluster stratified random sampling method， 6 281 students from the third grade of primary school to the second grade of high school in 12 leagues and cities of Inner Mongolia Autonomous Region were selected in March 2024. A self-designed questionnaire and the Self-rating Depression Scale （SDS） were used for on-site investigation. ResultsA total of 6 058 （96.45%） children and adolescents completed the valid questionnaire survey， and 2 728 cases （45.03%） were found to have depressive symptoms. There were statistically significant differences in the detection rates of depressive symptoms among children and adolescents of different genders， ages， whether they were only children， different family types， family monthly income， parents' educational levels， and whether the mother was employed （χ²=33.769， 40.618， 48.593， 29.972， 142.648， 195.999， 168.190， 5.445， P<0.05 or 0.01）.The results of the Logistic regression analysis showed that for children and adolescents， being female， aged between 12 and 16， over 16 years old， not being an only child， living in a reconstituted family， having a monthly family income of less than 5 000 yuan， and having parents with an education level of primary school or below were predictors of depressive symptoms （OR=1.241， 1.427， 1.273， 1.177， 1.549， 1.278， 1.462， 1.417， 1.514， 1.929， 1.660， 1.528， P<0.05 or 0.01）. ConclusionThe detection rate of depressive symptoms among children and adolescents in Inner Mongolia Autonomous Region is relatively high. Factors that may predict depressive symptoms in children and adolescents include female gender， ages between 12 and 16， ages over 16 years old， non-only children， families with a restructured structure， monthly family income of less than 5 000 yuan， and parents with an education level of primary school or below. ［Funded by Science and Technology Planning Project of the Inner Mongolia Autonomous Region （number， 2022YFSH0119）］

ObjectiveTo develop and validate a diagnostic prediction model for differentiating pulmonary tuberculosis from community-acquired pneumonia based on routine blood parameters. MethodsA total of 642 patients with pulmonary tuberculosis and 503 patients with community-acquired pneumonia were retrospectively enrolled from the Seventh Hospital of Tangshan. They were randomly divided into a training set and an internal validation set at a 7∶3 ratio. Additionally， 218 patients from the 981st Hospital were independently included as an external validation set. The Boruta algorithm and recursive feature elimination were employed to select predictors from 82 blood parameters， sex， and age. A multivariate Logistic regression model was established and evaluated using receiver operating characteristic （ROC） curves， calibration curves， and decision curve analysis. ResultsThe final model incorporated eight predictors， namely uric acid， urea nitrogen， alkaline phosphatase， monoamine oxidase， alanine aminotransferase， glutathione， neutrophil percentage， and age. The model achieved areas under the curve （AUCs） of 0.800 （95%CI： 0.769-0.830）， 0.787 （95%CI： 0.738-0.836）， and 0.736 （95%CI： 0.667-0.835） in the training， internal validation， and external validation sets， respectively. The model demonstrated good calibration， and decision curve analysis showed clinical net benefit within the threshold probability range of 10%-80%. ConclusionThe developed model exhibits good discriminative ability and clinical utility， serving as an effective early screening tool for primary healthcare institutions.

Objective To investigate the differences in radiation dose during chest CT examinations among children of different age groups and establish dose estimation regression models. Methods Chest CT data from 135 children aged 4 to 15 years were retrospectively collected from the First Affiliated Hospital of Soochow University between January 2022 and December 2023. The children were divided into three age groups: 4-5 years, 6-10 years and 11-15 years. CT scanning parameters (tube voltage, tube current, scan range) and dosimetry parameters including volume CT dose index (CTDI_vol) dose length product (DLP), and size-specific dose estimate (SSDE) were recorded. The Kruskal-Wallis test was used to compare intergroup differences. A Pearson correlation analysis was performed to assess the relationship between age and dose indicators. Both linear and nonlinear regression models were constructed. Results Age showed a weak positive correlation with CTDI_vol (r = 0.27), a moderate positive correlation with DLP (r = 0.60), and a moderate negative correlation with SSDE (r = −0.55). Linear regression analysis revealed that DLP increased with age (y = 117.85 + 9.81x, R² = 0.36), while SSDE decreased with age (y = 12.4 − 0.18x, R² = 0.32). Using orthogonal distance regression, the goodness-of-fit of the nonlinear models for DLP and SSDE significantly improved (R² = 0.99 and 0.94, respectively). Conclusion In pediatric chest CT dose assessment, CTDI_vol underestimates radiation dose compared to SSDE and fails to account for patient body size. The dose estimation models constructed with orthogonal distance regression outperform those established using the least squares method, demonstrating higher fitting accuracy, and can serve as a reference for personalized dose management in pediatric CT examinations.

A 36-year-old male developed unconsciousness and no response to voice stimuli after taking approximately 2 050 mg felodipine (the specific time was unknown). Two hours later, he was sent to the department of emergency by his family and admitted to the hospital. His vital signs showed body temperature 35.1 ℃, pulse 148 times/min, respiration 32 times/min, and blood pressure 65/34 mmHg. Acute drug poisoning, acute toxic cardiomyopathy, acute toxic shock, acute type Ⅱ respiratory failure, acute toxic encephalopathy, and acute renal failure were diagnosed based on the patient′s clinical manifestations combined with laboratory tests results, cardiac ultrasound, chest and abdominal CT scans. Endotracheal intubation connected to a ventilator for invasive assisted ventilation, pressure boosting, and fluid resuscitation were given. At the same time, repeated gastric lavage and enema were performed to remove toxins. Blood perfusion was intermittently and repeatedly administered, and continuous renal replacement therapy was used. The blood concentration of felodipine was 1 298 μg/L at 2 hours after admission, and cardiac arrest occurred at 4 hours. Venous-arterial extracorporeal membrane oxygenation (V-A ECMO) treatment was administered immediately. After 48 hours of ECMO operation, sedatives were discontinued and the patient′s consciousness was improved after 4 hours. On the 5th day of ECMO treatment, his heart rate was 72 beats per minute, and blood pressure was 127/65 mmHg. The blood concentration of felodipine decreased to 2 μg/L. The patient′s vital signs were significantly improved and ECMO supportive treatment was withdrawn. After 26 days of hospitalization, the patient recovered and was discharged.

Gastric cancer is a high-incidence malignancy that poses a serious threat to public health in China, ranking among the top three cancers in both incidence and mortality. The majority of patients are diagnosed at an advanced stage, resulting in limited treatment options and poor prognosis. To address key challenges in gastric cancer diagnosis and treatment, a research team led by Professor Jiafu Ji at Peking University Cancer Hospital has focused on the project "Development and Dissemination of Precision Medicine Approaches in Gastric Cancer Management". Through a series of high-quality multicenter clinical studies, the team established a set of new international standards in perioperative treatment, individua-lized drug selection, intelligent noninvasive diagnostics, and novel immunotherapy strategies. These advances have significantly improved treatment efficacy and reduced surgical trauma, achieving key technological breakthroughs in diagnosis, therapy, and mechanistic understanding, and systematically enhancing outcomes for gastric cancer patients. The project ' s findings had a broad international impact, including hosting China ' s first International Gastric Cancer Congress. Through nationwide dissemination, they have promoted the development of precision diagnosis and treatment of gastric cancer as a discipline, and led the formulation of the National Health Commission's guidelines for gastric cancer diagnosis and treatment. In recognition of its achievements, the project was awarded the First Prize of the 2024 Chinese Medical Science and Technology Award.
Stomach Neoplasms/genetics* ; Humans ; Precision Medicine/methods* ; China ; Immunotherapy/methods*

Objective:X-linked non-syndromic hearing loss is an extremely rare type of hearing impairment. This study conducted a phenotypic and genetic analysis of a family with X-linked dominant inheritance to explore the causes of hearing loss. Methods:Clinical data were collected from a patient with non-syndromic hearing loss who visited the Otorhinolaryngology Department of the First Affiliated Hospital of Zhengzhou University in June 2023. Phenotypic and genetic analyses were performed on family members, including audiometric tests, whole-exome sequencing, and PCR-Sanger sequencing verification. Audiological assessments comprised pure-tone audiometry, impedance audiometry, auditory brainstem response, and otoacoustic emission tests. Results:The affected individuals in this pedigree have X-linked dominant non-syndromic deafness caused by mutations in the SMPX gene. The proband, along with their mother and maternal grandmother, exhibit varying degrees of sensorineural hearing loss. Whole-exome sequencing revealed a novel pathogenic variant, NM_014332.3: c. 133-2A>C, in the SMPX gene in the proband. Sanger sequencing confirmed that the proband, proband's mother, and grandmother all carried this pathogenic variant. Conclusion:This study reports a novel pathogenic variant in the SMPX gene, providing additional medical evidence for the diagnosis and treatment of X-linked dominant inherited non-syndromic hearing loss. It enriches the mutation spectrum of the SMPX gene.
Humans ; Pedigree ; Mutation ; Phenotype ; Male ; Hearing Loss, Sensorineural/genetics* ; Exome Sequencing ; Female ; Adult ; Hearing Loss/genetics* ; Evoked Potentials, Auditory, Brain Stem ; Muscle Proteins

In current clinical practice, various dermal templates and skin substitutes are used to enhance wound healing. However, the role of wound commensal microbiome in regulating scaffold performance and the healing process remains unclear. In this study, we investigated the influence of both natural and synthetic scaffolds on the wound commensal microbiome and wound repair in three distinct models including diabetic wounds, burn injuries, and germ-free (GF) wounds. Remarkably, synthetic electrospun polycaprolactone (PCL) scaffolds were observed to positively promote microbiome diversity, leading to enhanced diabetic wound healing compared to the natural scaffolds Integra® (INT) and MatriDerm® (MAD). In contrast, both natural and synthetic scaffolds exhibited comparable effects on the diversity of the microbiome and the healing of burn injuries. In GF wounds with no detectable microorganisms, a reversed healing rate was noted showing natural scaffold (MAD) accelerated wound repair compared to the open or the synthetic scaffold (PCL) treatment. Furthermore, the response of the wound commensal microbiome to PCL scaffolds appears pivotal in promoting anti-inflammatory factors during diabetic wound healing. Our results emphasize that the wound commensal microbiome, mediated by different scaffolds plays an important role in the wound healing process.

Triple-negative breast cancer (TNBC) is a highly aggressive malignancy predominantly managed via chemotherapy. Our clinical sample analysis revealed a significant correlation between elevated CD24 expression in TNBC tumor cells and patient survival rates. We developed a novel antibody-drug conjugate (ADC), named HN03, consisting of an antibody with engineered cysteines for site-specific conjugation with a low toxic nitric oxide (NO) precursor as its payload through a novel Pt(IV)-mediated bioorthogonal self-cleavable linker. HN03 specifically targets tumor cells expressing high levels of CD24, concurrently generating cisplatin and releasing NO upon activation. HN03 also exhibited potent in vitro and in vivo antitumor activity. It significantly reduced tumor growth at various doses, prevented tumor metastasis, with markedly lower toxicity than traditional chemotherapy agents. We found that a key mechanism of its action involved inducing apoptosis and endoplasmic reticulum stress, substantially decreasing the number of M2-type macrophages. Overall, HN03 stands out as a promising therapeutic option for TNBC, offering a targeted treatment with reduced side effects and the potential for improved outcomes. Furthermore, using Pt(IV) in the linker and an NO precursor as the payload enhances the versatility of the Antibody-NO donor Conjugate (ANC), offering new avenues for the design of the next generation of ADCs.

OBJECTIVES: To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion. METHODS: The proposed model consists of the Input, Backbone Network and Classification Head modules. The Input module carries out data augmentation to improve the sample quality and generalization ability of the model. The Backbone Network was responsible for feature extraction based on Yolov8 combined with CBAM, ECA, PSA attention mechanism and AIFI feature interaction module. The Classification Head consists of a convolutional layer and a softmax function to output the final probability value of each class. The images of the key structures (the eyes, face, head, thalamus, and spine) were annotated with frames by physicians for model training to improve the classification accuracy of the anterior occipital, posterior occipital, and transverse occipital orientations. RESULTS: The experimental results showed that the proposed model had excellent performance in the tire orientation classification task with the classification accuracy reaching 0.984, an area under the PR curve (average accuracy) of 0.993, and area under the ROC curve of 0.984, and a kappa consistency test score of 0.974. The prediction results by the deep learning model were highly consistent with the actual classification results. CONCLUSIONS The multi-feature fusion model proposed in this study can efficiently and accurately classify fetal orientation in intrapartum ultrasound videos.
Humans ; Female ; Ultrasonography, Prenatal/methods* ; Pregnancy ; Fetus/diagnostic imaging* ; Neural Networks, Computer ; Video Recording