Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

ObjectiveTo explore the effect of different drying conditions on the appearance and intrinsic quality indicators of Pinelliae Rhizoma for screening suitable drying conditions， so as to provide reference for its standardized production and quality evaluation. MethodsDifferent dried samples of Pinelliae Rhizoma were prepared by lime-assisted sweating method and intermittent drying method. Visual analysis was employed to measure the color brightness values（L^*） of the surface， cross-section and powder of the samples， texture analyzer was used to determine the hardness of the samples under different drying conditions. The total starch content was calculated by measuring the contents of amylose and amylopectin in the samples with ultraviolet-visible spectroscopy. High performance liquid chromatography（HPLC） was used to determine the contents of seven nucleoside components（uracil， hypoxanthine， uridine， inosine， guanosine， β-thymidine and adenosine） in the samples. Pearson correlation analysis was conducted to explore the correlation between the external characteristics and intrinsic indicators of the different dried samples. Principal component analysis（PCA） was used to comprehensively rank the data of various indicators， and partial least squares-discriminant analysis（PLS-DA） was used to screen differential components with variable importance in the projection（VIP） value>1. Furthermore， the difference between the optimal drying condition for Pinelliae Rhizoma and the traditional sun-drying method was explored by independent samples t-test. ResultsWith the increase of temperature， the color of the intermittently dried samples gradually deepened， while their hardness gradually decreased. Concurrently， the contents of extract， total starch， uridine and adenosine exhibited an upward trend， whereas the contents of uracil， hypoxanthine and inosine displayed a downward trajectory. Compared with the intermittent drying group， the content of extract in the samples subjected to lime-assisted sweating increased. With the increase of lime dose， the hardness and the total content of nucleoside components in the samples showed a downward trend， while the total starch content showed an upward trend. Correlation analysis showed that the comprehensive score of L^* was negatively correlated with the contents of uracil， hypoxanthine and inosine， and positively correlated with the contents of uridine， guanosine and adenosine. Hardness was negatively correlated with adenosine content， and positively correlated with the contents of inosine， uracil and hypoxanthine. Through comprehensive consideration and comprehensive score of principal components， the method of 5% lime-mixed sweating for 6 days emerged as the top-ranking approach. Except for the extract， the results of independent samples t-test showed that there was no significant difference between the 5% lime-mixed sweating for 6 days and the traditional sun-drying in terms of other content indicators. ConclusionThe whiteness and firmness of Pinelliae Rhizoma exhibit significant correlations with its chemical composition， while uridine， uracil， guanosine， adenosine and inosine are the key constituents responsible for the quality difference of Pinelliae Rhizoma under different drying conditions. The lime-assisted sweating method optimized in this study can be proposed as a viable alternative to the traditional sun-drying method. This method not only ensures the quality of the medicinal material but also effectively reduces the drying time and prevents mold contamination， which provides a valuable reference for the standardization of drying conditions and the establishment of quality evaluation criteria for Pinelliae Rhizoma.

BACKGROUND: As an essential part of health services, rehabilitation is of great significance to improve the health and quality of life of the whole population. Accelerating aging calls for a significant expansion of rehabilitation services in China, but rehabilitation needs remain unclear. We conducted the study to explore the rehabilitation needs in China and project the trend of rehabilitation needs from 2020 to 2034. METHODS: The data of health conditions that might potentially benefit from rehabilitation were obtained from Global Burden of Disease (GBD) study. Estimated annual percentage changes (EAPCs) were calculated to quantify the trends of the age-standardized rates. Projections of rehabilitation needs were made until 2034 using Bayesian age-period-cohort analysis (BAPC). RESULTS: Approximately 460 million persons (33.3% of the total population) need rehabilitation in China, contributing to 63 million years lived with disabilities (YLDs) in 2019. The number of prevalent cases that need rehabilitation increased from around 268 (95% uncertainty interval [UI]: 257-282) million in 1990 to almost 460 (95% UI: 443-479) million in 2019, representing an increase of 71.3%. The highest contribution to the need for rehabilitation was musculoskeletal disorders with about 322 (95% UI: 302-343) million persons in seven aggregate disease and injury categories, and hearing loss with over 95 (95% UI: 84-107) million people among 25 health conditions. Based on the projection results, there will be almost 636 million people (45% of the total population) needing rehabilitation services in China by 2034, representing an increase of 38.3%. The rehabilitation needs of neoplasms, cardiovascular diseases, and neurological disorders are expected to increase significantly from 2019 to 2034, with increases of 102.3%, 88.8% and 73.2%, respectively. CONCLUSIONS The need for rehabilitation in China substantially increased over the last 30 years. It is predicted that over two in five people will require rehabilitation by 2034, thus suggesting the need to develop rehabilitation services that meet individuals' rehabilitation needs.
Humans ; China/epidemiology* ; Global Burden of Disease ; Female ; Male ; Musculoskeletal Diseases/epidemiology* ; Rehabilitation/trends* ; Quality of Life ; Middle Aged ; Aged ; Bayes Theorem

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.

Previous studies have found associations between color discrimination deficits and cognitive impairments besides aging. However, investigations into the microstructural pathology of brain white matter (WM) associated with these deficits remain limited. This study aimed to examine the microstructural characteristics of WM in the non-demented population with abnormal color discrimination, utilizing Neurite Orientation Dispersion and Density Imaging (NODDI), and to explore their correlations with cognitive functions and cognition-related plasma biomarkers. The tract-based spatial statistic analysis revealed significant differences in specific brain regions between the abnormal color discrimination group and the healthy controls, characterized by increased isotropic volume fraction and decreased neurite density index and orientation dispersion index. Further analysis of region-of-interest parameters revealed that the isotropic volume fraction in the bilateral anterior thalamic radiation, superior longitudinal fasciculus, cingulum, and forceps minor was significantly correlated with poorer performance on neuropsychological assessments and to varying degrees various cognition-related plasma biomarkers. These findings provide neuroimaging evidence that WM microstructural abnormalities in non-demented individuals with abnormal color discrimination are associated with cognitive dysfunction, potentially serving as early markers for cognitive decline.
Humans ; White Matter/pathology* ; Male ; Female ; Cognitive Dysfunction/physiopathology* ; Middle Aged ; Aged ; Color Perception/physiology* ; Brain/pathology* ; Neuropsychological Tests ; Diffusion Tensor Imaging

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Traditional Chinese medicine (TCM) exerts integrative effects on complex diseases owing to the characteristics of multiple components with multiple targets. However, the syndrome-based system of diagnosis and treatment in TCM can easily lead to bias because of varying medication preferences among physicians, which has been a major challenge in the global acceptance and application of TCM. Therefore, a standardized TCM prescription system needs to be explored to promote its clinical application. In this study, we first developed a gradient weighted disease-target-herbal ingredient-herb network to aid TCM formulation. We tested its efficacy against intracerebral hemorrhage (ICH). First, the top 100 ICH targets in the GeneCards database were screened according to their relevance scores. Then, SymMap and Traditional Chinese Medicine Systems Pharmacology (TCMSP) databases were applied to find out the target-related ingredients and ingredient-containing herbs, respectively. The relevance of the resulting ingredients and herbs to ICH was determined by adding the relevance scores of the corresponding targets. The top five ICH therapeutic herbs were combined to form a tailored TCM prescriptions. The absorbed components in the serum were detected. In a mouse model of ICH, the new prescription exerted multifaceted effects, including improved neurological function, as well as attenuated neuronal damage, cell apoptosis, vascular leakage, and neuroinflammation. These effects matched well with the core pathological changes in ICH. The multi-targets-directed gradient-weighting strategy presents a promising avenue for tailoring precise, multipronged, unbiased, and standardized TCM prescriptions for complex diseases. This study provides a paradigm for advanced achievements-driven modern innovation in TCM concepts.

Ferroptosis has been shown to mediate the development of fibrosis. Polyphyllin VII (PP7), a bioactive component of Paris polyphylla, exhibits potent anti-inflammatory activity and can significantly alleviate liver fibrosis. In this study, treatment with PP7 significantly inhibited the proliferation and activation of hepatic stellate cells (HSCs), which could be suppressed by a ferroptosis inhibitor. In addition, it promoted HSC ferroptosis by suppressing glutathione (GSH) peroxidase 4 (GPX4) and enhanced the expression of CX3C chemokine ligand 1 (CX3CL1). Depletion of CX3CL1 attenuated the effects of PP7 on the activation and ferroptosis of HSCs and the expression of GPX4. Notably, CX3CL1 directly interacted with GPX4, triggering HSC ferroptosis. The transcription factor hypermethylated in cancer 1 (Hic1), which binds to the Cx3cl1 promoter, increased the expression of CX3CL1. Its absence resulted in downregulation of CX3CL1, suppressing the GPX4-dependent ferroptosis of PP7-treated HSCs and promoting their activation. HIC1 was found to directly interact with PP7 at the GLY164 site. Co-culture experiments showed that PP7-induced HSC ferroptosis attenuated macrophage recruitment by regulating inflammation-related genes. HSC-specific inhibition of HIC1 counteracted PP7-induced collagen depletion and HSC ferroptosis in vivo. These findings suggest that PP7 induces HSC ferroptosis through the HIC1/CX3CL1/GPX4 axis.

OBJECTIVE: To explore the protective effects and underlying mechanisms of H ₂S against lipid peroxidation-mediated carbonyl stress in the uranium-treated NRK-52E cells. METHODS: Cell viability was evaluated using CCK-8 assay. Apoptosis was measured using flow cytometry. Reagent kits were used to detect carbonyl stress markers malondialdehyde, 4-hydroxynonenal, thiobarbituric acid reactive substances, and protein carbonylation. Aldehyde-protein adduct formation and alcohol dehydrogenase, aldehyde dehydrogenase 2, aldo-keto reductase, nuclear factor E2-related factor 2 (Nrf2), and cystathionine β-synthase (CBS) expression were determined using western blotting or real-time PCR. Sulforaphane (SFP) was used to activate Nrf2. RNA interference was used to inhibit CBS expression. RESULTS: GYY4137 (an H ₂S donor) pretreatment significantly reversed the uranium-induced increase in carbonyl stress markers and aldehyde-protein adducts. GYY4137 effectively restored the uranium-decreased Nrf2 expression, nuclear translocation, and ratio of nuclear to cytoplasmic Nrf2, accompanied by a reversal of the uranium-decreased expression of CBS and aldehyde-metabolizing enzymes. The application of CBS siRNA efficiently abrogated the SFP-enhanced effects on the expression of CBS, Nrf2 activation, nuclear translocation, and ratio of nuclear to cytoplasmic Nrf2 and concomitantly reversed the SFP-enhanced effects of the uranium-induced mRNA expression of aldehyde-metabolizing enzymes. Simultaneously, CBS siRNA reversed the SFP-mediated alleviation of the uranium-induced increase in reactive aldehyde levels, apoptosis rates, and uranium-induced cell viability. CONCLUSION H ₂S induces Nrf2 activation and nuclear translocation, which modulates the expression of aldehyde-metabolizing enzymes and the CBS/H ₂S axis. Simultaneously, the Nrf2-controlled CBS/H ₂S axis may at least partially promote Nrf2 activation and nuclear translocation. These events form a cycle-regulating mode through which H ₂S attenuates the carbonyl stress-mediated NRK-52E cytotoxicity triggered by uranium.
NF-E2-Related Factor 2/genetics* ; Animals ; Hydrogen Sulfide/pharmacology* ; Rats ; Signal Transduction/drug effects* ; Lipid Peroxidation/drug effects* ; Cell Line ; Uranium/toxicity* ; Antioxidant Response Elements ; Kidney/metabolism* ; Oxidative Stress/drug effects* ; Cell Survival/drug effects* ; Apoptosis/drug effects*

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