This study investigated the effects of Rehmanniae Radix Praeparata on striatal neuronal apoptosis in rats with attention deficit hyperactivity disorder(ADHD) based on the B-cell lymphoma-2(Bcl-2)/Bcl-2-associated X protein(Bax)/caspase-3 signaling pathway. Twenty-four 3-week-old male spontaneously hypertensive rats(SHR) were randomly divided into a model group, a methylphenidate group(2 mg·kg~(-1)·d~(-1)), and a Rehmanniae Radix Praeparata group(2.4 mg·kg~(-1)·d~(-1)). Age-matched male Wistar Kyoto(WKY) rats were used as the normal control group, with 8 rats in each group. The rats were administered by gavage for 28 days. Body weight and food intake were recorded for each group. The open field test and elevated plus maze test were used to assess hyperactivity and impulsive behaviors. Nissl staining was used to detect changes in striatal neurons and Nissl bodies. Terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL) fluorescence staining was used to detect striatal cell apoptosis. Western blot was employed to detect the expression levels of Bcl-2, Bax, and caspase-3 proteins in the striatum. The results showed that compared with the model group, Rehmanniae Radix Praeparata significantly reduced the total movement distance, average movement speed, and central area residence time in the open field test, and significantly reduced the ratio of open arm entries, open arm stay time, and head dipping in the elevated plus maze test. Furthermore, it increased the number of Nissl bodies in striatal neurons, significantly downregulated the apoptosis index, significantly increased Bcl-2 protein expression and the Bcl-2/Bax ratio, and reduced Bax and caspase-3 protein expression. In conclusion, Rehmanniae Radix Praeparata can reduce hyperactivity and impulsive behaviors in ADHD rats. Its mechanism may be related to the regulation of the Bcl-2/Bax/caspase-3 signaling pathway in the striatum, enhancing the anti-apoptotic capacity of striatal neurons.
Animals ; Male ; Apoptosis/drug effects* ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Caspase 3/genetics* ; Proto-Oncogene Proteins c-bcl-2/genetics* ; bcl-2-Associated X Protein/genetics* ; Rehmannia/chemistry* ; Attention Deficit Disorder with Hyperactivity/physiopathology* ; Signal Transduction/drug effects* ; Neurons/cytology* ; Rats, Inbred SHR ; Rats, Inbred WKY ; Humans ; Corpus Striatum/cytology* ; Plant Extracts

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*

OBJECTIVE: Investigating the clinical efficacy of treating dorsally displaced distal radial double-column Die-punch fractures using a dorsal approach external fixator combined with Kirschner wires. METHODS: Retrospectively analyzed the clinical data of 15 patients with distal radial double-column Die-punch fractures treated with an external fixator combined with Kirschner wire between July 2020 and January 2023. There were 10 males and 5 females;6 cases on the left side and 9 on the right;age ranged from 22 to 76 years old. Recorded the preoperative and the final follow-up Cooney wrist function scores for the patients. The fracture healing time, and occurrence of complications were recorded. RESULTS: All 15 patients were followed up ranged from 12 to 16 months post-operation. All fractures achieved bony union, healing time ranging form 8 to 16 weeks. Not a single patient exhibited complications such as surgical site infection, fracture redislocation, or tendon injury. All individuals had their Kirschner wires and external fixation devices removed six weeks post-operatively and commenced rehabilitative therapy for wrist articulation. The Cooney wrist function scores at preoperative and ranged from 5 to 45 scores, at the latest follow-up ranged from 65 to 100 scores. At the final follow-up, the results were assessed as excellent in 10 patients, good in 4 patients, and fair in 1 patient. CONCLUSION The clinical efficacy of treating distal radial double-column Die-punch fractures using a dorsal approach external fixator combined with Kirschner wires is satisfactory.
Humans ; Male ; Female ; Middle Aged ; Adult ; External Fixators ; Bone Wires ; Aged ; Retrospective Studies ; Radius Fractures/physiopathology* ; Young Adult ; Fracture Fixation/methods*

OBJECTIVES: To summarize the clinical and genetic characteristics of epilepsy with intellectual disability caused by SETD1B gene variants in children. METHODS: A retrospective analysis was conducted on the clinical data of three children with SETD1B gene variants diagnosed and treated at the Department of Pediatric Neurology of Xiangya Hospital of Central South University. Relevant literature was reviewed to summarize the clinical characteristics of this condition. RESULTS: All three children presented with symptoms during infancy or early childhood, including mild intellectual disability and myoclonic seizures, with two cases exhibiting eyelid myoclonia. After treatment with three or more antiepileptic drugs, two cases achieved seizure control or partial control, while one case remained refractory. Each of the three children was found to have a heterozygous variant in the SETD1B gene (one deletion, one frameshift, and one missense variant). To date, 54 cases with SETD1B gene variants have been reported, involving a total of 56 variants, predominantly missense variants (64%, 36/56). The main clinical manifestations included varying degrees of developmental delay (96%, 52/54) and seizures (81%, 44/54). Among the 44 patients with seizures, myoclonic (20%, 9/44) and absence seizures (34%, 15/44) were common, with eyelid myoclonia reported in six cases. Approximately one-fifth of these patients had poorly controlled seizures. CONCLUSIONS The primary phenotypes associated with SETD1B gene variants are intellectual disability and seizures, and seizures exhibit distinct characteristics. Eyelid myoclonia is not uncommon.
Humans ; Intellectual Disability/complications* ; Epilepsy/complications* ; Male ; Female ; Histone-Lysine N-Methyltransferase/genetics* ; Child, Preschool ; Child ; Retrospective Studies

Objective To investigate the clinical features of chronic hepatitis C(CHC)patients with hepatitis C virus genotype 3(HCV GT3)infection and the risk factors for disease progression.Methods A multicenter retrospective cohort study was conducted among 1 002 CHC patients from 11 clinical centers in Northwest China from December 2017 to November 2023,and according to their genotype,they were divided into GT1,GT2,GT3,and GT6 groups.Clinical features were compared between the patients with different genotypes.The one-way analysis of variance was used for comparison of normally distributed continuous data between groups,and the Scheffe test was used for further comparison between two groups.The Kruskal-Wallis H test was used for comparison of data with skewed distribution between groups;the chi-square test or Fisher test was used for comparison of categorical data between groups.The multivariate logistic regression analysis was used to explore the influencing factors for the progression of CHC to liver cirrhosis.Results In terms of the genotype,there were 427 patients with GT1 infection,242 with GT2 infection,299 with GT3 infection(210 patients with GT3a infection,87 with GT3b infection,and 2 with unclassified genotype),and 34 with GT6 infection.The patients with GT3 infection had a significantly younger age than those with GT1 infection(51.3±0.5 years vs 53.2±0.6 years,P<0.05)or GT2 infection(51.3±0.5 years vs 53.7±0.8 years,P<0.05),and for the patients with liver cirrhosis,the patients with GT3 infection had a significantly younger age than those with GT1 infection(52.1±0.5 years vs 59.4±0.9 years,P<0.001)or GT2 infection(52.1±0.5 years vs 58.1±1.1 years,P<0.001).Among the patients with GT3 infection,male patients accounted for 77.9%and the patients with liver cirrhosis accounted for 46.2%,which were significantly higher than those among the patients with GT1,GT2 or GT6 infection(all P<0.001).At baseline,the patients with GT3 infection had significantly higher levels of alanine aminotransferase(ALT)and aspartate aminotransferase(AST)than those with GT1 or GT2 infection,significantly higher aspartate aminotransferase-to-platelet ratio index(APRI)and fibrosis-4(FIB4)than those with GT1,GT2 or GT6 infection,a significantly lower platelet count(PLT)than those with GT2 or GT6 infection,a significantly higher level of alpha-fetoprotein than those with GT2 or GT6 infection,and a significantly lower level of albumin(Alb)than those with GT6 infection(all P<0.05).There were no significant differences between the patients with GT3a infection and those with GT3b infection in age,sex,the proportion of patients with liver cirrhosis,comorbidities,HCV RNA quantification,PLT,ALT,AST,alkaline phosphatase,Alb,APRI,and FIB-4(all P>0.05).The multivariate logistic regression analysis showed that PLT≤150×109/L(odds ratio[OR]=10.72,95%confidence interval[CI]:5.76-35.86,P<0.001)and Alb≤35 g/L(OR=3.74,95%CI:1.22-11.45,P=0.021)were risk factors for liver cirrhosis.Conclusion Most CHC patients with GT3 infection are male in Northwest China,and compared with the patients with other genotypes,such patients tend to have a younger age of onset and higher degrees of liver inflammation activity and fibrosis.Low PLT and a low level of Alb are risk factors for progression to liver cirrhosis in CHC patients with GT3 infection.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

ObjectiveTo investigate the possible mechanism of Shufeng Jiedu capsules （SFJD） in alleviating influenza A （H1N1） virus pneumonia and focus on its effect on Toll-like receptor （TLR） signaling pathway in respiratory epithelial cells. MethodsA mouse model of viral pneumonia was established via the A/PR/8/34 （PR8） strain of influenza A virus. Mice were randomly divided into a normal group， a PR8 infection （PR8） group， and an SFJD group （8.4 g·kg^-1）， with 10 mice in each group. The day of infection was designated as day 1. The SFJD group was administered intragastrically at a volume of 20 mL·kg^-1 daily， while the normal and PR8 groups were given an equal volume of deionized water. Micro-computed tomography （Micro-CT） was performed on day 5， and the mice were dissected to collect their lungs， after which the lung index was calculated to verify the therapeutic effect of SFJD. Single-cell sequencing was used to analyze the differentially expressed genes in respiratory epithelial cells. Multiplex fluorescence immunohistochemistry was employed to detect the expression of TLR， tumor necrosis factor receptor-associated factor 6 （TRAF6）， and myeloid differentiation factor 88 （MyD88） proteins in epithelial cell adhesion molecule （EpCAM）-positive cells， and the proportion of respiratory epithelial cells expressing TLR pathway proteins was calculated. Respiratory epithelial cells were then sorted by flow cytometry， and Western blot was used to detect the expression of TLR， MyD88， TRAF6， Toll-interleukin receptor domain-containing adaptor inducing interferon-β （TRIF）， inhibitor of κB kinase α （IKKα）， and nuclear factor-κB （NF-κB） in the sorted epithelial cells. Enzyme-linked immunosorbent assay （ELISA） was used to measure the levels of interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α） in lung tissue. ResultsAt the transcriptional level， SFJD reversed the expression of TLR signaling pathway genes in respiratory epithelial cells， downregulating multiple TLR signaling pathway-related genes （P<0.01）. At the protein level， SFJD significantly reduced the proportion of respiratory epithelial cells expressing TLR3 （P<0.05）， the expression levels of TLR2， TLR3， TLR4， TRIF， TRAF6， IKKα， and NF-κB in epithelial cells（P<0.05， P<0.01）， as well as the levels of pro-inflammatory cytokines IL-1β and TNF-α in lung tissue （P<0.01）. ConclusionSFJD may alleviate viral pneumonia by suppressing the expression of TLR in respiratory epithelial cells and their subsequent signaling cascades.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.