ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Definitive treatment of lung cancer with radiotherapy is challenging, as respiratory motion and anatomical changes can increase the risk of severe off-target effects during radiotherapy. Online adaptive radiotherapy (ART) is an evolving approach that enables timely modification of a treatment plan during the interfraction of radiotherapy, in response to physiologic or anatomic variations, aiming to improve the dose distribution for precise targeting and delivery in lung cancer patients. The effectiveness of online ART depends on the seamless integration of multiple components: sufficient quality of linear accelerator-integrated imaging guidance, deformable image registration, automatic recontouring, and efficient quality assurance and workflow. This review summarizes the present status of online ART for lung cancer, including key technologies, as well as the challenges and areas of active research in this field.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods*

OBJECTIVE: To elucidate how spinal manipulative therapy (SMT) exerts its analgesic effects through regulating brain function in lumbar disc herniation (LDH) patients by utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: From September 2021 to September 2023, we enrolled LDH patients (LDH group, n=31) and age- and sex-matched healthy controls (HCs, n=28). LDH group underwent rs-fMRI at 2 distinct time points (TPs): prior to the initiation of SMT (TP1) and subsequent to the completion of the SMT sessions (TP2). SMT was administered once every other day for 30 min per session, totally 14 treatment sessions over a span of 4 weeks. HCs did not receive SMT treatment and underwent only one fMRI scan. Additionally, participants in LDH group completed clinical questionnaires on pain using the Visual Analog Scale (VAS) and the Japanese Orthopedic Association (JOA) score, whereas HCs did not undergo clinical scale assessments. The effects on the brain were jointly characterized using the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). Correlation analyses were conducted between specific brain regions and clinical scales. RESULTS: Following SMT treatment, pain symptoms in LDH patients were notably alleviated and accompanied by evident activation of effects in the brain. In comparison to TP1, TP2 exhibited the most significant increase in ALFF values for Temporal_Sup_R and the most notable decrease in ALFF values for Paracentral_Lobule_L (voxelwise P<0.005; clusters >30; FDR correction). Additionally, the most substantial enhancement in ReHo values was observed for the Cuneus_R, while the most prominent reduction was noted for the Olfactory_R (voxelwise P<0.005; clusters >30; FDR correction). Moreover, a comparative analysis revealed that, in contrast to HCs, LDH patients at TP1 exhibited the most significant increase in ALFF values for Temporal_Pole_Sup_L and the most notable decrease in ALFF values for Frontal_Mid_L (voxelwise P<0.005; clusters >30; FDR correction). Furthermore, the most significant enhancement in ReHo values was observed for Postcentral_L, while the most prominent reduction was identified for ParaHippocampal_L (voxelwise P<0.005; clusters >30; FDR correction). Notably, correlation analysis with clinical scales revealed a robust positive correlation between the Cuneus_R score and the rate of change in the VAS score (r=0.9333, P<0.0001). CONCLUSIONS Long-term chronic lower back pain in patients with LDH manifests significant activation of the "AUN-DMN-S1-SAN" neural circuitry. The visual network, represented by the Cuneus_R, is highly likely to be a key brain network in which the analgesic efficacy of SMT becomes effective in treating LDH patients. (Trial registration No. NCT06277739).
Humans ; Magnetic Resonance Imaging ; Intervertebral Disc Displacement/diagnostic imaging* ; Male ; Female ; Brain/diagnostic imaging* ; Adult ; Manipulation, Spinal/methods* ; Middle Aged ; Lumbar Vertebrae/physiopathology* ; Pain Management ; Rest ; Case-Control Studies

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Rapid and ultrasensitive detection of pathogen-associated biomarkers is vital for the early diagnosis and therapy of bacterial infections. Herein, we developed a close-packed and ordered Au@AgPt array coupled with a cascade triggering strategy for surface-enhanced Raman scattering (SERS) and colorimetric identification of the Staphylococcus aureus biomarker micrococcal nuclease (MNase) in serum samples. The trimetallic Au@AgPt nanozymes can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) molecules to SERS-enhanced oxidized TMB (oxTMB), accompanied by the color change from colorless to blue. In the presence of S. aureus, the secreted MNase preferentially cut the nucleobase AT-rich regions of DNA sequences on magnetic beads (MBs) to release alkaline phosphatase (ALP), which subsequently mediated the oxTMB reduction for inducing the colorimetric/SERS signal fade away. Using this "on-to-off" triggering strategy, the target S. aureus can be recorded in a wide linear range with a limit of detection of 38 CFU/mL in the colorimetric mode and 6 CFU/mL in the SERS mode. Meanwhile, the MNase-mediated strategy characterized by high specificity and sensitivity successfully discriminated between patients with sepsis (n = 7) and healthy participants (n = 3), as well as monitored the prognostic progression of the disease (n = 2). Overall, benefiting from highly active and dense "hot spot" substrate, MNase-mediated cascade response strategy, and colorimetric/SERS dual-signal output, this methodology will offer a promising avenue for the early diagnosis of S. aureus infection.

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Inflammatory bowel disease (IBD) is characterized by chronic relapsing intestinal inflammation and encompasses ulcerative colitis (UC) and Crohn's disease (CD). IBD has emerged as a global healthcare problem. Clinically efficacious therapeutic agents are deficient. This study concentrates on models of ulcerative colitis with the objective of discovering novel therapeutic strategies. Previous investigations have established that schisandrin A demonstrates anti-inflammatory effects in vitro, concurrently enhancing the transcriptional activity of farnesoid X receptor (FXR). FXR inversely modulates the transcriptional activity of NF-κB, which has an important role in regulating inflammatory responses. Consequently, the current study was to explore the safeguarding influence of schisandrin A on ulcerative colitis and delineate the mechanism by which it regulates this effect through the FXR signaling pathway. The effect of schisandrin A on the mRNA levels of inflammatory factors was evaluated in RAW264.7 cells. The dual luciferase reporter gene assay was used to verify the relationship between schisandrin A and FXR targeting. The animal experiments were performed in accordance with the regulations of the Animal Ethics Committee of Shanghai University of Traditional Chinese Medicine (approval No. PZSHUTCM2304250005). Acute ulcerative colitis was induced in wild-type or FXR knockout C57BL/6 mice by drinking 3% dextran sodium sulfate (DSS) for 7 days, and schisandrin A was administered via gavage for a continuous treatment period of 7 days. The body weight and faecal were monitored daily. The mRNA levels of inflammatory factors in colon tissue and FXR target genes were measured by RT-qPCR. The findings revealed that schisandrin A, in vitro, impeded the lipopolysaccharide (LPS)-induced elevation in mRNA levels of inflammatory factors and schisandrin A could augment transcriptional activity of FXR. In wild-type mice, schisandrin A significantly improved weight loss, colon shortening, loose stools and blood in stools in mice with acute ulcerative colitis, and schisandrin A significantly reduced the expression of pro-inflammatory factors genes and significantly increased the expression of FXR target genes in colon tissues. In FXR knockout mice, the administration of schisandrin A failed to yield ameliorative effect on acute ulcerative colitis in mice. In conclusion, schisandrin A can reduce intestinal inflammation through the FXR signaling pathway to alleviate acute ulcerative colitis in mice. Implications arise that Schisandra lignans could serve as lead compounds for drug development aimed at inflammatory bowel disease.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.

Objective:To observe and evaluate the clinical efficacy and safety of albumin-bound paclitaxel as first-line treatment for patients with advanced pancreatic cancer in China, and to explore the prognosis-related molecules in pancreatic cancer based on next-generation sequencing (NGS) of tumor tissues.Methods:From December 2018 to December 2020, patients with locally advanced or metastatic pancreatic cancer were recruited to accept albumin-bound paclitaxel as first-line treatment in the oncology departments of 24 hospitals in East China. The primary endpoints were overall survival (OS) and treatment related adverse events, and the secondary endpoint was progression-free survival (PFS). Adverse effects were graded using Common Terminology Criteria for Adverse Events 5.0 (CTCAE 5.0). NGS sequencing on the primary or metastatic tissue samples of pancreatic cancer obtained through surgical resection or biopsy was performed.Results:This study recruited 229 patients, including 70 patients with locally advanced pancreatic cancer (LAPC) and 159 patients with metastatic pancreatic cancer (mPC). The disease control rate was 79.9% and the objective response rate is 36.3%.The common adverse effects during treatment were anaemia (159 cases), leucopenia (170 cases), neutropenia (169 cases), increased aminotransferases (110 cases), and thrombocytopenia (95 cases), and the incidence of grade 3-4 neutropenia is 12.2% (28/229). The median follow-up time was 21.2 months (95% CI: 18.5-23.1 months). The median PFS (mPFS) was 5.3 months (95% CI: 4.37-4.07 months) and the median OS (mOS) was 11.2 months (95% CI: 9.5-12.9 months). The mPFS of patients with LAPC was 7.4 months (95% CI: 6.6-11.2 months), and their mOS was 15.5 months (95% CI: 12.6-NA months). The mPFS of patients with mPC was 3.9 months (95% CI: 3.4-5.1 months), and their mOS was 9.3 months (95% CI: 8.0-10.8 months). Multivariate Cox regression analysis showed that clinical stage ( HR=1.47, 95% CI: 1.06-2.04), primary tumor site ( HR=0.64, 95% CI: 0.48-0.86), Eastern Cooperative Oncology Group Performance Status (ECOG PS) score ( HR=2.66, 95% CI: 1.53-4.65), and whether to combine radiotherapy ( HR=0.65, 95% CI: 0.42-1.00) were independent influencing factors for the PFS of these patients. The primary tumor site ( HR=0.68, 95% CI: 0.48-0.95), ECOG score ( HR=5.82, 95% CI: 3.14-10.82), and whether to combine radiotherapy ( HR=0.58, 95% CI: 0.35-0.96) were independent influencing factors of the OS of these patients. The most frequent gene mutations in these advanced stage pancreatic patients were KRAS (89.66%), TP53 (77.01%), CDKN2A (32.18%), and SMAD4 (21.84%) by NGS of tumor tissues from 87 pancreatic cancer patients with sufficient specimens. Further analysis revealed that mutations in CDKN2B, PTEN, FGF6, and RBBP8 genes were significantly associated with an increased risk of death ( P＜0.05). Conclusion:Albumin-bound paclitaxel as first-line treatment demonstrated feasible anti-tumor efficacy and manageable safety for patients with advanced pancreatic cancer in China.