Objective To analyze the cognitive function and sleep quality of the elderly in Shanghai community, and explore the related influencing factors. Methods A stratified cluster random sampling method was used to select 8 community health centers in Shanghai for a questionnaire survey, including 3 677 elderly individuals who completed the “Comprehensive Health Status Survey of Elderly Residents in Shanghai” from September 2023 to November 2023. Basic information of the elderly was collected, including age, gender, education level, smoking, drinking, mahjong playing behavior, and exercise habits. The Pittsburgh sleep quality index (PSQI) was used to assess the sleep quality of the elderly, subjective cognitive decline (SCD) self-assessment questionnaire and Mini-Mental State Examination (MMSE) were used to evaluate cognitive function, while the Hamilton Anxiety Scale (HAMA) and patient health questionnaire-9 (PHQ-9) were used to assess anxiety and depression levels, and the mini nutritional assessment (MNA) was used to evaluate nutritional status. According to the MMSE scores, the elderly were divided into three groups: no cognitive impairment (MMSE ≥ 27), mild cognitive impairment (MMSE 21-26), and moderate to severe cognitive impairment (MMSE ≤ 20). The general data, lifestyle habits, and scale scores of the three groups were compared. Ordered logistic regression was used to analyze the influencing factors of sleep quality. Results There were statistically significant differences in age, gender, waist circumference, body mass index (BMI), education level, pet ownership, smoking, drinking, mahjong playing behavior, exercise habits, and scale scores among the three groups (P＜0.05). Logistic regression analysis showed that age, waist circumference, gender, drinking habits, mahjong playing behavior, and chronic comorbidities are influencing factors for the PSQI grading in the elderly (P＜0.05). The MMSE score (OR＝1.037, P＝0.001), SCD score (OR＝1.123, P＜0.001), HAMA score (OR＝1.183, P＜0.001), PHQ-9 score (OR＝1.249, P＜0.001) are positive influencing factors for PSQI grading, while the MNA score is a negative influencing factor (OR＝0.960, P＝0.037). Conclusions Advanced age, female gender, low education level, no pet ownership, no mahjong playing behavior, no exercise habits, and poor sleep quality are risk factors for cognitive impairment in the elderly. Advanced age, female gender, no mahjong playing behavior and poor nutritional status are influencing factors for poor sleep quality in the elderly, and severe comorbidities, anxiety, depression, and subjective decline in cognitive function all affect sleep quality.

The Keap1-Nrf2 pathway has been shown to be an important defense mechanism against oxidative stress, which may be an effective therapeutic strategy for many diseases. The research progresses on Keap1-Nrf2 pathway in inflammatory diseases were mainly reviewed. The basic components and activation mechanism of Keap1-Nrf2 pathway were introduced. The relationship between Keap1-Nrf2 pathway and the crosstalk between NF-κB pathway and HO-1 pathway, the expression of inflammatory mediators and enzymes, and inflammatory bodies were expounded. Natural product-derived inhibitors, small molecule inhibitors targeting Keap1-Nrf2 pathway and their clinical progress were introduced, and the potential application value of Keap1-Nrf2 pathway in the treatment of inflammation was discussed.

ObjectiveBased on ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）， network pharmacology and molecular docking techniques， to explore the pharmacodynamic material basis and mechanism of Renshen Wuweizi Tang in treating spleen-lung Qi deficiency syndrome. MethodsThe chemical components in the decoction of Renshen Wuweizi Tang were systematically characterized and identified by UPLC-Q-TOF-MS/MS， and network pharmacology was used to screen potential active ingredients， collect component targets and gene sets related to spleen-lung Qi deficiency syndrome， and obtain protein interaction relationships through STRING. Cytoscape 3.9.1 was used to construct a "formula-syndrome" association network and calculate topological feature values. Gene ontology（GO） function and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis were performed on core genes to explore potential pharmacodynamic links， the average shortest path between the formula-drug target network and the pharmacodynamic link gene network was calculated to discover dominant pharmacodynamic links， and MCODE plugin was used to identify core gene clusters from the dominant pharmacodynamic links， which were validated using Gene Expression Omnibus（GEO）， and molecular docking was performed between key components and core targets. ResultsOne hundred and thirty-seven components were identified in the negative ion mode， and eighty components were identified in the positive ion mode. After deduplication， a total of 185 components were identified， mainly composed of triterpenoid saponins（49） and flavonoids（54）. Based on the "formula-syndrome" correlation network analysis， energy metabolism was determined to be the dominant pharmacodynamic link of Renshen Wuweizi Tang in the treatment of spleen-lung Qi deficiency syndrome. The results of molecular docking showed that 7 components（adenosine， atractylenolide Ⅱ， atractylenolide Ⅲ， ginsenoside Rg₁， glycyrrhizin B₂， glycyrrhizin E₂ and campesterol） from 4 medicinal materials（Ginseng Radix et Rhizoma， Atractylodis Macrocephalae Rhizoma， Glycyrrhizae Radix et Rhizoma and Poria） in this formula might regulate energy metabolism by acting on 6 targets， namely cyclic adenosine monophosphate-response element binding protein 1（CREB1）， glyceraldehyde-3-phosphate dehydrogenase（GAPDH）， interleukin（IL）-6， nuclear transcription factor（NF）-κB1， peroxisome proliferator-activated receptor α（PPARα）， and tumor necrosis factor（TNF）， thus improving the symptoms of diseases related to spleen-lung Qi deficiency syndrome. ConclusionThis study established a UPLC-Q-TOF-MS/MS for rapid characterization and identification of chemical components in the decoction of Renshen Wuweizi Tang， expanding the understanding of the material composition of this formula， and found that 7 components might act on the key advantageous pharmacodynamic link "energy metabolism" through 6 targets to improve the related symptoms of spleen-lung Qi deficiency syndrome. This can provide a reference for the subsequent exploration of the material benchmark and mechanism of the famous classical formula.

Objective To investigate and analyze the occupational stress levels and influencing factors among radiation workers in China, and provide a reference for alleviating occupational stress and promoting mental health. Methods Using the general situation questionnaire, Effort-Reward Imbalance questionnaire, and radiation protection knowledge questionnaire, a convenience sampling method was adopted to investigate the occupational stress of 243 radiation workers in Liaoning, Fujian, Guangdong, and Xinjiang provinces. The independent samples t-test, one-way analysis of variance, chi-square test, and binary logistic regression were used to analyze the influencing factors. Results The average score of Effort-Reward Imbalance was 0.97 ± 0.22, and 100 (41.15%) radiation workers had occupational stress. There were significant differences in the detection rate of occupational stress among radiation workers of different ages, working years in radiation positions, monthly incomes, daily sleep durations, and daily working hours (P < 0.05). Logistic regression analysis identified daily working hours as a factor contributing to occupational stress. Conclusion The occupational stress among radiation workers in China is relatively severe. It is recommended to pay attention to the associated risks and implement targeted intervention measures to reduce the impact of occupational stress.

Objective To apply automated enzyme immunoassay workstation instead of manual operation to facilitate detection of Streptococcus pneumoniae(S.pneumoniae) IgG antibodies, so as to improve the detection efficiency.Methods Procedures of the assay were set up on the automated workstation according to the World Health Organization(WHO) Pneumococcal Capsular Polysaccharide Types-pecific IgG Antibody Detection Manual Pn PS ELISA. The number of working station panels was maximized to preset all reagent consumables and more samples to be tested, thereby improving the detection flux and reducing the number of manual interventions. The serum PnG was repeatedly detected to verify the test precision. The WHO S.pneumoniae international calibration serum panel 12/278 was detected to verify the accuracy of the test. To compare the consistency of workstations and manual operation, 16 same serum samples were tested by manual operation and on the workstation meanwhile. Detections of four calibration serum samples were performed on the workstation by double-(WHO standard method) and single-column methods, and the consistency of the results was compared.Results With the layout of 21 plate positions, 24 serum samples could be examined in one program run, requiring only 2 manual interventions. The CV of 32 detection results of serum PnG was lower than 15%. The assay results of 75% of the calibration serum panel in 14 out of 23 serotypes exhibited a percent error of 40% or less compared to the assigned values, and the total accuracy was 80%.The correlation coefficient(Pc) of agreement between workstation and manual test results was 0. 996 7, and the correlation coefficient of agreement between workstation single-and double-column test results was 0. 999 8, both of which showed high agreement.Conclusion The automated enzyme immunoassay workstation has shown high precision, accuracy and large detection throughput, which is labor-saving, and can be applied to the detection of S.pneumoniae antibodies. The results of single-and double-column assay provide experimental basis for single-column detection in place of double-column detection.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Pyroptosis, an inflammatory caspase-dependent programmed cell death, plays a vital role in maintaining tissue homeostasis and activating inflammatory responses. Orthodontic tooth movement (OTM) is an aseptic force-induced inflammatory bone remodeling process mediated by the activation of periodontal ligament (PDL) progenitor cells. However, whether and how force induces PDL progenitor cell pyroptosis, thereby influencing OTM and alveolar bone remodeling remains unknown. In this study, we found that mechanical force induced the expression of pyroptosis-related markers in rat OTM and alveolar bone remodeling process. Blocking or enhancing pyroptosis level could suppress or promote OTM and alveolar bone remodeling respectively. Using Caspase-1^-/- mice, we further demonstrated that the functional role of the force-induced pyroptosis in PDL progenitor cells depended on Caspase-1. Moreover, mechanical force could also induce pyroptosis in human ex-vivo force-treated PDL progenitor cells and in compressive force-loaded PDL progenitor cells in vitro, which influenced osteoclastogenesis. Mechanistically, transient receptor potential subfamily V member 4 signaling was involved in force-induced Caspase-1-dependent pyroptosis in PDL progenitor cells. Overall, this study suggested a novel mechanism contributing to the modulation of osteoclastogenesis and alveolar bone remodeling under mechanical stimuli, indicating a promising approach to accelerate OTM by targeting Caspase-1.
Animals ; Humans ; Mice ; Rats ; Bone Remodeling/physiology* ; Caspase 1 ; Periodontal Ligament ; Pyroptosis ; Tooth Movement Techniques

Objective:Glioblastoma(GBM)and brain metastases(BMs)are the two most common malignant brain tumors in adults.Magnetic resonance imaging(MRI)is a commonly used method for screening and evaluating the prognosis of brain tumors,but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited.In recent years,deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system.This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases(SBMs),and to further explore the impact of multimodality data fusion on classification tasks. Methods:Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed.First,structural T1-weight,T1C-weight,and T2-weight were selected as 3 inputs to the entire model,regions of interest(ROIs)were manually delineated on the registered three modal MR images,and multimodality radiomics features were obtained,dimensions were reduced using a random forest(RF)-based feature selection method,and the importance of each feature was further analyzed.Secondly,we used the method of contrast disentangled to find the shared features and complementary features between different modal features.Finally,the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. Results:The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs.Furthermore,compared with single-modal data,the multimodal fusion models using machine learning algorithms such as support vector machine(SVM),Logistic regression,RF,adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT)achieved significant improvements,with area under the curve(AUC)values of 0.974,0.978,0.943,0.938,and 0.947,respectively;our comparative disentangled multi-modal MR fusion method performs well,and the results of AUC,accuracy(ACC),sensitivity(SEN)and specificity(SPE)in the test set were 0.985,0.984,0.900,and 0.990,respectively.Compared with other multi-modal fusion methods,AUC,ACC,and SEN in this study all achieved the best performance.In the ablation experiment to verify the effects of each module component in this study,AUC,ACC,and SEN increased by 1.6%,10.9%and 15.0%,respectively after 3 loss functions were used simultaneously. Conclusion:A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.