Aerospace Center Hospital

Germline pathogenic alleles predispose carriers to malignancies; failure to recognize the underlying cancer susceptibility and subsequent delayed diagnosis may lead to severe health impairment. It remains largely undetermined what the somatic mutation characteristics are in colorectal cancer (CRC) patients with pathogenic/likely pathogenic (P/LP) germline mutations and whether, and how, these germline mutations typically located in non-mismatch repair genes, are associated with CRC tumorigenesis. From 8,676 Chinese patients with CRC, we initially screened those who had undergone both germline and somatic mutation testing. We analyzed the relationship between germline mutations and the somatic mutational landscape in this cohort. Germline alterations were examined with a 556- or 105- gene next-generation sequencing panel, and somatic alterations were examined with a 556-gene panel. Overall, 80 CRC patients were identified as carriers of P/LP germline mutations. After excluding 6 patients lacking somatic mutation testing data, 74 patients with P/LP germline mutations and 100 patients without germline mutations were finally enrolled in this study. Twenty-one germline genes were detected in the 74 patients. Compared with patients without P/LP mutations (non-P group), those in the P/LP mutations (P group) had significantly lower age and a higher ratio of microsatellite instability. Patients harboring P/LP germline mutations in MMR genes (P-MMR) were significantly younger and more frequently exhibited high tumor mutational burden. The most frequent somatic variations were KRAS and TP53. The somatic mutational landscape revealed a significantly higher mutational frequency in the P group than in the non-P group, and in the P-MMR group than in the P-non-MMR group. The MAPK pathway was significantly enriched in the P and P-MMR groups. Our data showed lower mutation rate of MSH2 among all MMR genes than in the Western population. Population-based risk analysis indicated that the odds ratio of BRCA2 and ATM mutations exceeded 10. Our findings offer a comprehensive overview of genetic susceptibility in Chinese CRC, which may help to shape a more comprehensive understanding of genetic structure of CRC and generate accurate individualized risk management strategies for mutation carriers.

• Systematic analysis of liver fibrosis mechanisms and biomaterial design. • Advanced strategies: exosomes, nanoparticles, nanozymes with clinical cases. • Multifunctional biomaterials for microenvironment reprogramming. • Clinical translation challenges and future development directions. • Next-generation smart materials and interdisciplinary integration. Liver fibrosis (LF) is a central pathological process in the progression of chronic liver disease toward cirrhosis and hepatocellular carcinoma. Currently, there is a lack of effective pharmacological agents capable of reversing liver fibrosis in clinical settings. Although liver transplantation remains the definitive treatment for end-stage disease, it is constrained by several limitations, including donor scarcity, surgical complexity, and immune rejection. In this context, bioengineered materials have emerged as a promising strategy for reversing liver fibrosis, owing to their superior capabilities in targeted delivery, microenvironment regulation, and tissue regeneration. Critically, these materials exert multi-faceted effects rather than targeting a single pathological pathway; they simultaneously intervene in key processes including hepatic stellate cell (HSC) activation, excessive extracellular matrix (ECM) deposition, inflammatory signaling activation, and pathological angiogenesis, thereby enabling precise reconstruction of the liver microenvironment and restoration of function. This review first systematically elaborates the multifaceted pathological mechanisms and key therapeutic targets of liver fibrosis, clarifying the interconnections between different pathological events. It then provides a comprehensive analysis of the categories and design strategies of biomaterials used for liver fibrosis treatment, with an in-depth discussion on the rationale for matching material properties with specific pathological targets. The review highlights the therapeutic mechanisms and application advantages of cutting-edge strategies such as engineered exosomes, biomimetic nanoparticles, and inorganic nanozymes, supplemented with clinical-stage material cases. Finally, it addresses the challenges in clinical translation and potential directions for breakthroughs, aiming to provide a theoretical foundation and technical insights for the development of next-generation anti-fibrotic biomaterials and to facilitate their transition from basic research to clinical application, while also offering valuable references for the treatment of other fibrotic diseases.

To develop a pre-percutaneous microwave ablation (PMWA) model combining conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) for predicting non-perfused volume ratio (NPVR) ≥ 80% in patients with uterine fibroids. This multicentre, retrospective study enrolled 276 patients with pathologically confirmed uterine fibroids (training/validation = 140/136). CUS features (maximum diameter, pre-operative volume, location, echo), CEUS parameters (maximum intensity: IMAX), and ablation parameters (energy, time, energy per unit volume [EPV], and time per unit volume [TPV]) were analysed. Multivariate logistic regression identified predictors, and model performance was assessed using the area under the receiver operating characteristic curve (AUC) and clinical decision curve analysis. Multivariate analysis identified four independent predictors: maximum diameter (odds ratio [OR] = 0.47, p < 0.001), IMAX (OR = 0.34, p = 0.013), energy (OR = 1.01, p = 0.002), and TPV (OR = 0.69, p < 0.001).The prediction model performed well in both the training (AUC = 0.79, 95% confidence interval [CI] = 0.71-0.87) and validation (AUC = 0.70, 95%CI = 0.61-0.79) datasets. P values for the Hosmer-Lemeshow test were 0.263 and 0.980, respectively, and the Brier Score was 0.184. This study established a prediction model based on CUS features, CEUS perfusion parameters, and ablation parameters to evaluate the efficacy of PMWA for uterine fibroids; NPVR ≥ 80% can be predicted using the nomogram. This model enables accurate prediction of the efficacy of PMWA for uterine fibroids, providing profound guidance for pre-operative assessment and intraoperative management in clinical practice and facilitating personalised, precision treatment.

BackgroundCervicogenic headache (CEH) is a chronic secondary headache syndrome originating from the upper cervical spine. Although conventional treatments such as pharmacological management, nerve blocks, and radiofrequency ablation are effective for many patients, a subset remains refractory to standard therapies. Soft tissue adhesions around the atlas (C1) are a potential but often overlooked pain generator. This report describes the successful management of intractable CEH through use of ultrasound-guided needle-knife release targeting the transverse process of the atlas.Case DescriptionA 32-year-old male presented with a 15-year history of persistent, left-sided pulsatile headache following cervical trauma. The patient had previously undergone extensive treatments, including oral analgesics, C2 nerve blocks, pulsed radiofrequency, and “blind” (nonvisualized) needle-knife therapy, all of which failed to provide sustained relief. Physical examination revealed distinct tenderness at the posterior arch of the atlas. Under high-frequency ultrasound guidance, we identified the transverse process of the atlas and performed precise needle-knife release on the adherent soft tissues attached to the bone surface. This visualized approach allowed for the safe avoidance of critical neurovascular structures such as the vertebral artery. Following a course of six weekly sessions, the patient reported a 90% reduction in pain intensity. At the 6-month follow-up, the therapeutic effect was sustained without recurrence, and the patient’s quality of life and daily functioning were significantly improved.ConclusionsThis case highlights that soft tissue pathology around the C1 transverse process can be a critical etiology in refractory CEH. Conventional “blind” needle-knife therapy may fail due to safety concerns limiting the depth of release. Ultrasound-guided needle-knife therapy offers a visualized, safe, and precise minimally invasive alternative for patients unresponsive to traditional C2-targeted therapies, allowing for the effective release of deep soft tissue adhesions.

The "Healthy China 2030" initiative, which aims to achieve a 30% reduction in premature mortality by 2030, recognizes leukemia as the primary cause of cancer-related deaths. Given the limited previous research, providing comprehensive national and subnational estimates and temporal trends of the premature leukemia mortality burden from 2005 to 2020 is crucial. Using data from various sources acquired through the national surveillance mortality system, estimates were derived and compared across 31 provincial-level administrative regions in the Chinese mainland, revealing the magnitude of mortality rates and years of life lost (YLL) due to leukemia in the Chinese population between 2005 and 2020. In 2020, the estimated number of leukemia-related deaths decreased from 61.93 thousand in 2005 to 54.86 thousand, accompanied by a decline in age-standardized mortality rate (ASMR) from 4.97 per 100,000 population in 2005 to 3.49 per 100,000 population. The burden of premature mortality caused by leukemia exhibited a significant reduction, as indicated by a nationwide decline of -39.37% (from 239.46 to 145.18 per 100,000 population) in the age-standardized YLL rate. Despite a notable decrease (-11.41%) in leukemia mortality, China has the highest burden of leukemia-related deaths due to its large population, primarily driven by population aging (12.76%), which has contributed to an upsurge in leukemia fatalities, followed by population growth (7.67%). Leukemia mortality exhibited distinct variations among different provinces and demonstrated a positive correlation with economic activity, particularly in coastal provinces characterized by higher levels of economic development. Xizang is currently the only province experiencing a significant increase in mortality rate, necessitating urgent government support. Significant disparities in premature mortality attributed to leukemia were observed across diverse regions of China, underscoring the need for targeted interventions aimed at promoting a health-protective lifestyles and expanding timely access to basic medical insurance for diagnosis and cost-effective therapy, particularly among high-risk populations and regions with limited healthcare resources.

BackgroundQuantitative magnetic resonance imaging (MRI) is an advanced technique that can map the physical properties (T1, T2, and proton density [PD]) of different tissues, offering crucial insights for disease diagnosis. Nonetheless, the practical application of this technology is indeed constrained by several factors, with the most notable being the protracted scanning duration.ObjectiveThis study aimed to explore whether deep learning (DL)–based superresolution reconstruction of ultrafast whole brain synthetic MRI can obtain quantitative T1/T2/PD maps that are closely approximated to those from routine clinical scans, while substantially shortening scan time and preserving diagnostic image quality.MethodsA total of 151 healthy adults and 7 individuals with different pathologies were prospectively enrolled. Each individual was examined twice on a 3.0T scanner using routine and fast synthetic MRI protocols. The routine scans (acquisition matrix: 320×256) were interpolated to 512 by 512 for clinical display and served as reference images. The fast scans (acquisition matrix: 192×128) were preprocessed to 256 by 256 and used as inputs to a superresolution generative adversarial network (SRGAN), which reconstructed them to the same 512 by 512 interpolated resolution as the reference. For each quantitative chart, 120 (75.95%) healthy individuals’ images were used for training, and 38 (24.05%) individuals’ images (healthy individuals: n=31, 19.62%; patients: n=7, 4.43%) were used for testing. Agreement was assessed with a paired t test, two 1-sided tests, Bland-Altman analysis, and coefficients of variation.ResultsDL reconstructed and reference T1/T2/PD values were strongly correlated (T1: R²=0.98; T2: R²=0.97; and PD: R²=0.99). The slopes of the linear regression were near 1.0 both for T1 (0.9418) and PD (0.9946), whereas T2 values were moderate, as the slope of the linear regression was 0.8057. Additionally, the average biases of T1, T2, and PD values were small (0.93%, −0.85%, and 0.31%, respectively). The intra- and intergroup coefficient of variation for most of the brain regions stayed below 5%, especially for PD values, and after DL reconstruction, it still has quantitative accuracy for lesions. Quantitative and qualitative analyses of image quality also indicate that SRGAN markedly suppressed noise and artifacts in fast acquisitions, restoring structural fidelity (structural similarity image measure) and signal fidelity (peak signal-to-noise ratio) close to the level of routine scans while substantially improving perceptual naturalness over fast scans (as measured by the naturalness image quality evaluator), although not yet matching that of routine imaging.ConclusionsSRGAN superresolution applied to ultrafast synthetic MRI yields whole brain T1, T2, and PD maps that show strong correlation with routine synthetic MRI while halving acquisition time and maintaining diagnostic image quality. Although T1 and PD values exhibit near-ideal agreement, and T2 values demonstrate a moderate systematic underestimation, this approach represents a promising step toward accelerating clinical deployment of quantitative brain imaging.

The purpose of this study is to develop a deep learning model that can identify the maxillary sinus, posterior superior alveolar artery(PSAA), and alveolar ridge, and evaluate its diagnostic performance. Based on this, relevant parameters for preoperative design of maxillary sinus elevation can be measured to achieve intelligent preoperative design for maxillary posterior tooth implantation surgery. A total of 2400 CBCT slices from patients with maxillary posterior tooth loss was selected as the initial dataset. Anatomical structure annotation and enhanced YOLOv11 architecture were used for model training to achieve segmentation of maxillary sinus, PSAA, and alveolar ridge. Intersection over union (IoU), average precision (AP), average recall (AR) and the Euclidean distance were used to evaluate the accuracy of structure segmentation. On the basis of the segmentation of the three important anatomical structures mentioned above, five anatomical parameters (A1-A5) related to maxillary posterior tooth implantation were set, and their errors were statistically analyzed. The median IoU for maxillary sinus segmentation was 0.945 (IQR: 0.934-0.951, 95%CI: 0.935-0.941), while the median IoU for PSAA segmentation was 0.991 (IQR: 0.982-1.000, 95%CI: 0.948-0.974). The model achieved an average precision of 0.902 ± 0.023 and a recall of 0.937 ± 0.024 for PSAA segmentation. For alveolar crest localization, the mean Euclidean distance errors between predicted and ground-truth landmarks were 0.50 ± 0.31mm and 0.38 ± 0.24mm for the two key points, respectively. 95% of AI prediction errors for A1-A4 were within 1mm, while 95% of AI prediction errors for A5 were within 10 mm2. The enhanced YOLOv11 framework reliably and autonomously identifies critical anatomical structures for maxillary sinus elevation including the maxillary sinus, PSAA, and maxillary alveolar crest in CBCT images. This model enables the acquisition of reliable clinical parameters, demonstrating its potential for future intelligent assisted preoperative evaluation and design of maxillary posterior dental implant surgery.