The dried mature peel of Citrus reticulata, a plant in the Rutaceae family and its cultivated varieties, is a commonly used Chinese medicinal material known as Chenpi(Citri Reticulatae Pericarpium). It is rich in nutritional components and medicinal value, with pharmacological effects including relieving cough and eliminating phlegm, strengthening the spleen and drying dampness, protecting the liver and benefiting the stomach, tonifying Qi, and calming the mind. Huanglongbing(HLB), also known as Citrus Huanglongbing, is a destructive disease in citrus production that seriously threatens the development of the citrus industry. HLB causes symptoms such as the inability of Rutaceae plants to produce mature fruit, gradual weakening of the tree, and eventual death, posing a significant threat to the yield and quality of Chenpi. Due to the uneven distribution of the HLB pathogen in infected plants, accurate detection of the pathogen requires the collection of a large number of plant samples. Current sample pretreatment methods, such as traditional extraction methods and commercial extraction kits, are time-consuming and involve multiple steps, which significantly increase the difficulty and workload of HLB diagnosis and have become a bottleneck in HLB detection. In this study, a rapid high-throughput detection method combining alkali lysis and TaqMan qPCR was developed. This method allows the pretreatment of multiple samples within 5 min, and the entire detection process can be completed within 45 min, with a detection limit of 6.67 fg·μL~(-1). The alkali lysis method and commercial kits were used for parallel detection of field-collected citrus samples, and the results showed no significant difference. The sample pretreatment method established in this study is characterized by low cost, simplicity, and high efficiency. Combined with TaqMan qPCR, it can provide technical support for early and on-site diagnosis of HLB. This method is of great significance for disease prevention and control in the citrus industry and is expected to help improve the yield and quality of citrus medicinal materials.
Citrus/microbiology* ; Plant Diseases/microbiology* ; Rhizobiaceae/physiology* ; High-Throughput Screening Assays/methods* ; Liberibacter/physiology*

Glucocorticoid-induced osteoporosis(GIOP) is a serious metabolic bone disease caused by long-term application of glucocorticoids(GCs). Traditional Chinese medicine(TCM) has unique advantages in improving bone microstructure and antagonizing hormone toxicity. This paper systematically reviews the theoretical research, clinical application, and basic research progress of TCM intervention in GIOP. In terms of theoretical research, the theory of "kidney governing bone and generating marrow" indicates that the kidney is closely related to bone development, revealing that core pathogenesis of GIOP is Yin-Yang disharmony, which can be discussed using the theories of "Yin fire", "ministerial fire", and "Yang pathogen damaging Yin". Thus, regulating Yin and Yang is the basic principle to treat GIOP. In terms of clinical application, effective empirical prescriptions(such as Bushen Zhuanggu Decoction, Bushen Jiangu Decoction, and Zibu Ganshen Formula) and Chinese patent medicines(Gushukang Capsules, Hugu Capsules, Xianling Gubao Capsules, etc.) can effectively increase bone mineral density(BMD) and improve calcium and phosphorus metabolism. The combination of traditional Chinese and western medicine can reduce the risk of fracture and play an anti-GIOP role. In terms of basic research, it has been clarified that active ingredients of TCM(such as fraxetin, ginsenoside Rg_1, and salidroside) reduce bone loss and promote bone formation by inhibiting oxidative stress, ferroptosis, and other pathways, effectively improving bone homeostasis. Additionally, classical prescriptions(Modified Yiguan Decoction, Modified Qing'e Pills, Zuogui Pills, etc.) and Chinese patent medicines(Gushukang Granules, Lurong Jiangu Dropping Pills, Gubao Capsules, etc.) can improve bone marrow microcirculation, promote osteoblast differentiation, and inhibit bone cell apoptosis through multiple pathways, multiple targets, and multiple mechanisms. Through the above three aspects, the TCM research status on GIOP is elucidated in the expectation of providing reference for its diagnosis and treatment using traditional Chinese and western medicine treatment programs.
Osteoporosis/physiopathology* ; Humans ; Glucocorticoids/adverse effects* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional ; Bone Density/drug effects*

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Objective To analyze the ultrasonographic features of secondary squamous cell carcinoma of the thyroid(SSCC-T)and evaluate the diagnostic value of ultrasound.Methods A retrospective analysis was conducted on clinical and ultrasonographic data from 12 patients with pathologically confirmed SSCC-T treated at Beijing Friendship Hospital,Capital Medical University between January 2016 and January 2025.Evaluated parameters included lesion size,echogenicity,edge,vascularity,calcification,and cervical lymph node metastasis.Descriptive statistical analysis was performed to analyze the ultrasonographic features of SSCC-T,and Fisher's exact test was conducted to analyze the correlation between different ultrasound classifications and thyroid dysfunction.Results The 12 patients showed the following ultrasound classifications:nodular type(50.0%,6/12),diffuse type(33.3%,4/12),and mixed type(16.7%,2/12).All diffuse-type patients exhibited a characteristic cord-like hypoechoic pattern.Cervical lymph node metastasis was observed in all the patients,with 75.0%(9/12)showing lymph nodes >2 cm in maximum diameter.Thyroid dysfunction occurred in 66.7%(8/12)of patients,including 2 patients with dynamic shifts from hyperthyroidism to hypothyroidism.Diffuse and mixed types were associated with hypothyroidism(P=0.038).Conclusions SSCC-T demonstrates specific ultrasonographic features,particularly the cord-like hypoechoic pattern in the diffuse type.For patients with squamous cell carcinoma,regular ultrasound examinations of the thyroid and cervical lymph nodes combined with changes in thyroid function are conducive to the timely detection of thyroid metastasis of squamous cell carcinoma.
Humans ; Retrospective Studies ; Ultrasonography ; Carcinoma, Squamous Cell/diagnostic imaging* ; Thyroid Neoplasms/diagnostic imaging* ; Lymphatic Metastasis ; Male ; Female ; Middle Aged ; Thyroid Gland/diagnostic imaging* ; Adult

Objective:To explore the therapeutic efficacy and prognostic factors of combined rituximab and intensive chemotherapy for sporadic adult Burkitt lymphoma (BL) .Methods:This retrospective study examined the clinical and survival data of 30 patients newly diagnosed with BL between July 2011 and February 2023 at the Blood Diseases Hospital. Kaplan-Meier method was used for survival analysis, and the log-rank test was used for univariate analysis of prognostic factors.Results:The median age of the 30 patients was 43 years (24 - 66 years), and the male to female ratio was 3: 2. Extranodal invasion was present in 80% of the patients, with involvement of the bone marrow in 53.3% and central nervous system in 10.0%. The Ann Arbor stage was Ⅲ and Ⅳ in 86.7%. According to the number of Burkitt Lymphoma International Prognostic Index (BL-IPI) risk factors, patients were classified as low risk (0) in 20.0%, intermediate risk (1) in 43.3%, and high risk (≥2) in 36.7%. All patients were treated with an induction regimen of rituximab combined with intensive chemotherapy, with objective and complete response rates of 80.0% and 76.7%, respectively. The median follow-up was 49 months (6-153 months), and the 5-year progression-free survival (PFS) and overall survival (OS) rates were both (76.7±7.7) %. All patients with limited stage ( n=4) achieved continuous complete remission (CCR). Patients who had high risk, advanced stage sensitive to induction therapy ( n=10) sequentially received first-line autologous hematopoietic stem cell transplantation (auto-HSCT) as consolidation therapy; 9 patients achieved CCR, whereas 1 patient with central nervous system invasion developed early disease progression and died. The BL-IPI low, intermediate, and high risk groups had respective 5-year PFS rates of (83.3±15.2) %, 100.0%, and (45.5±15.0) % ( P=0.0069) and OS rates of (83.3±15.2) %, 100.0%, and (45.5±15.0) % ( P=0.0075). The main adverse effects of induction therapy were myelosuppression and secondary infections, which were effectively managed by appropriate symptomatic treatment. Univariate analysis demonstrated that worse PFS was associated with BL-IPI score ≥2 ( HR=4.90, 95% CI 1.02-23.45, P=0.0329) ; extranodal invasion at ≥2 sites ( HR=12.62, 95% CI 2.59-61.62, P=0.0021) ; and failure to achieve first complete response (CR1) after induction therapy ( HR=31.86, 95% CI 4.19-242.20, P<0.0001) . Conclusions:Intensive immunochemotherapy regimens were effective and well-tolerated by adult patients with highly aggressive BL. Treatment efficacy was ideal in patients with limited-stage disease, whereas prognosis was unsatisfactory in patients with high-risk BL-IPI. Sequential first-line auto-HSCT consolidation therapy may further improve outcomes in patients with high-risk advanced-stage disease who are sensitive to induction therapy. BL-IPI score ≥2, extranodal invasion at ≥2 sites, and failure to achieve CR1 after induction therapy were adverse prognostic factors in adult patients with BL.

Objective:To explore the clinical application value of convolutional neural network（CNN）based on deep learning in the automatic measurement of dynamic pelvic floor ultrasound video parameters and the diagnosis and classification of cystocele.Methods:A retrospective analysis was conducted on dynamic pelvic floor ultrasound videos from 398 postpartum women who underwent examinations at the First People's Hospital of Foshan between June 2020 and June 2022. The lowest point of the posterior bladder wall（PWB），urethral rotation angle（URA），and retrovesical angle（RVA）were manually measured by a senior radiologist（R1）and a junior radiologist（R2），and cystocele was classified according to the Green standard. The CNN model was employed to automatically extract the above parameters and to diagnose and classify cystocele. Using R1 measurements as a reference，intraclass correlation coefficient（ICC）was used to evaluate the consistency between the CNN model and R1，as well as between R2 and R1. The Kappa value was used to assess the agreement between the CNN model，R2，and R1 in the diagnosis and classification of cystocele. Additionally，the time consumption of the three measurement methods was compared.Results:The CNN model showed good consistency with R1 in measuring PWB and URA（ICC = 0.983，0.894），while its consistency in measuring RVA was moderate（ICC = 0.614）. The ICC between R2 and R1 in measuring PWB，URA，and RVA was 0.979，0.815，and 0.627，respectively. In the measurement of PWB and URA，the consistency between the CNN model and R1 was superior to that between R2 and R1. For cystocele diagnosis，the Kappa value between the CNN model and R1 was 0.924，which was higher than that between R2 and R1（0.904）. In cystocele classification，the Kappa value between the CNN model and R1 was 0.503，also higher than that between R2 and R1（0.426）. The CNN model processed a single video in 2.5（0.6）s，significantly faster than R1［59.9（16.9）s］and R2［56.8（11.2）s］（all P < 0.001）. Conclusions:The CNN model demonstrates high accuracy and efficiency in the measurement，diagnosis，and classification of cystocele，outperforming a junior radiologist and showing potential for clinical application.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective:To explore the therapeutic efficacy and prognostic factors of combined rituximab and intensive chemotherapy for sporadic adult Burkitt lymphoma (BL) .Methods:This retrospective study examined the clinical and survival data of 30 patients newly diagnosed with BL between July 2011 and February 2023 at the Blood Diseases Hospital. Kaplan-Meier method was used for survival analysis, and the log-rank test was used for univariate analysis of prognostic factors.Results:The median age of the 30 patients was 43 years (24 - 66 years), and the male to female ratio was 3: 2. Extranodal invasion was present in 80% of the patients, with involvement of the bone marrow in 53.3% and central nervous system in 10.0%. The Ann Arbor stage was Ⅲ and Ⅳ in 86.7%. According to the number of Burkitt Lymphoma International Prognostic Index (BL-IPI) risk factors, patients were classified as low risk (0) in 20.0%, intermediate risk (1) in 43.3%, and high risk (≥2) in 36.7%. All patients were treated with an induction regimen of rituximab combined with intensive chemotherapy, with objective and complete response rates of 80.0% and 76.7%, respectively. The median follow-up was 49 months (6-153 months), and the 5-year progression-free survival (PFS) and overall survival (OS) rates were both (76.7±7.7) %. All patients with limited stage ( n=4) achieved continuous complete remission (CCR). Patients who had high risk, advanced stage sensitive to induction therapy ( n=10) sequentially received first-line autologous hematopoietic stem cell transplantation (auto-HSCT) as consolidation therapy; 9 patients achieved CCR, whereas 1 patient with central nervous system invasion developed early disease progression and died. The BL-IPI low, intermediate, and high risk groups had respective 5-year PFS rates of (83.3±15.2) %, 100.0%, and (45.5±15.0) % ( P=0.0069) and OS rates of (83.3±15.2) %, 100.0%, and (45.5±15.0) % ( P=0.0075). The main adverse effects of induction therapy were myelosuppression and secondary infections, which were effectively managed by appropriate symptomatic treatment. Univariate analysis demonstrated that worse PFS was associated with BL-IPI score ≥2 ( HR=4.90, 95% CI 1.02-23.45, P=0.0329) ; extranodal invasion at ≥2 sites ( HR=12.62, 95% CI 2.59-61.62, P=0.0021) ; and failure to achieve first complete response (CR1) after induction therapy ( HR=31.86, 95% CI 4.19-242.20, P<0.0001) . Conclusions:Intensive immunochemotherapy regimens were effective and well-tolerated by adult patients with highly aggressive BL. Treatment efficacy was ideal in patients with limited-stage disease, whereas prognosis was unsatisfactory in patients with high-risk BL-IPI. Sequential first-line auto-HSCT consolidation therapy may further improve outcomes in patients with high-risk advanced-stage disease who are sensitive to induction therapy. BL-IPI score ≥2, extranodal invasion at ≥2 sites, and failure to achieve CR1 after induction therapy were adverse prognostic factors in adult patients with BL.

Objective:To explore the clinical application value of convolutional neural network（CNN）based on deep learning in the automatic measurement of dynamic pelvic floor ultrasound video parameters and the diagnosis and classification of cystocele.Methods:A retrospective analysis was conducted on dynamic pelvic floor ultrasound videos from 398 postpartum women who underwent examinations at the First People's Hospital of Foshan between June 2020 and June 2022. The lowest point of the posterior bladder wall（PWB），urethral rotation angle（URA），and retrovesical angle（RVA）were manually measured by a senior radiologist（R1）and a junior radiologist（R2），and cystocele was classified according to the Green standard. The CNN model was employed to automatically extract the above parameters and to diagnose and classify cystocele. Using R1 measurements as a reference，intraclass correlation coefficient（ICC）was used to evaluate the consistency between the CNN model and R1，as well as between R2 and R1. The Kappa value was used to assess the agreement between the CNN model，R2，and R1 in the diagnosis and classification of cystocele. Additionally，the time consumption of the three measurement methods was compared.Results:The CNN model showed good consistency with R1 in measuring PWB and URA（ICC = 0.983，0.894），while its consistency in measuring RVA was moderate（ICC = 0.614）. The ICC between R2 and R1 in measuring PWB，URA，and RVA was 0.979，0.815，and 0.627，respectively. In the measurement of PWB and URA，the consistency between the CNN model and R1 was superior to that between R2 and R1. For cystocele diagnosis，the Kappa value between the CNN model and R1 was 0.924，which was higher than that between R2 and R1（0.904）. In cystocele classification，the Kappa value between the CNN model and R1 was 0.503，also higher than that between R2 and R1（0.426）. The CNN model processed a single video in 2.5（0.6）s，significantly faster than R1［59.9（16.9）s］and R2［56.8（11.2）s］（all P < 0.001）. Conclusions:The CNN model demonstrates high accuracy and efficiency in the measurement，diagnosis，and classification of cystocele，outperforming a junior radiologist and showing potential for clinical application.

Objective To explore the prognostic factor and its predictive value of patients with Wilson disease-related acute-on-chronic liver failure(WD-ACLF).Methods The clinical data of 70 patients diagnosed as WD-ACLF admitted to the Department of Encephalopathy of the First Affiliated Hospital of Anhui University of Chinese Medicine from January 1,2017 to January 1,2022 were retrospectively collected.According to the 12-week prognosis,patients were divided into survival group(n=36)and death group(n=34).The data of the two groups were analyzed by univariate and multivariate logistic analysis to screen the prognostic risk factors and evaluate their predictive value.The model coefficient is omnibus tested,and the model-fitting degree is evaluated by the Hosmer-Lemeshow test.ROC curve was used to analyze the prognostic value for WD-ACLF between the new model and chronic liver failure-sequential organ failure assessment(CLIF-SOFA)score,model for end-stage liver disease(MELD)score and Child-Turcotte-Pugh(CTP)score.Results A total of 70 WD-ACLF patients were enrolled in present study,including 36 cases in survival group[22 males and 14 females with median age of 30.0(17.3,40.0)]and 34 cases in death group[25 males and 9 females with median age of 34.0(28.8,41.0)].Univariate analysis showed that the course of disease,prothrombin time(PT),activated partial thromboplastin time(APTT)were shorter in survival group than that in death group,the white blood cells(WBC),international normalized ratio(INR),aspartate transaminase(AST),total bilirubin(TBIL),blood urea nitrogen(BUN),creatinine(Cre)and ceruloplasmin(CER)levels and the proportion of infection,ascites,and upper gastrointestinal bleeding were lower in survival group than those in death group,however,the proportion of infection,ascites and upper digestive bleeding in the survival group were lower than those in the death group.Meanwhile,the red blood cells(RBC),hemoglobin(Hb),Na+ and total cholesterol(TC)level in the survival group were higher than those in the death group(P<0.05 or P<0.01).The results of multivariate logistic regression analysis showed that disease course(OR=1.176,95%CI 1.043-1.325),INR(OR=7.635,95%CI 1.767-32.980),TBIL(OR=1.012,95%CI 1.003-1.021),and upper gastrointestinal bleeding(OR=11.654,95%CI 1.029-131.980)were independent risk factors affecting the prognosis of WD-ACLF(P<0.05).Based on the results of logistic regression analysis,a joint model for predicting the prognosis of WD-ACLF was established.The AUC of the model for evaluating the prognosis of WD-ACLF was 0.941,which was greater than the CLIF-SOFA score(AUC=0.802),MELD score(AUC=0.897),and CTP score(AUC=0.722).Conclusions The course of disease,TBIL,INR,and upper gastrointestinal bleeding are risk factors that affect the prognosis of WD-ACLF.The prognosis model established based on this can more accurately predict the prognosis of WD-ACLF patients,and its predictive value is superior to CLIF-SOFA score,MELD score,and CTP score.