Objective: To investigate the Alzheimer's disease (AD) influencing factors among the elderly, so as to provide a basis for early prevention and intervention. Methods: From March to June 2024, participants aged 60 years and above from a sub-district in Haishu District, Ningbo City, Zhejiang Province were selected using a convenience sampling method. Data on demographics, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were evaluated using the short-form Geriatric Depression Scale. The Chinese Mini-Mental State Examination (MMSE) was used for the initial screening of AD, and individuals who screened positive were further diagnosed by psychiatrists. Factors affecting AD among the elderly were analyzed using a multivariable logistic regression model. Results: A total of 3 644 individuals were surveyed, comprising 1 526 males (41.88%) and 2 118 females (58.12%). The mean age was (71.85±7.44) years. AD was detected in 200 cases, with a detection rate of 5.49%. Multivariable logistic regression analysis showed that individuals aged ≥65 years (65-<70 years, OR=3.012, 95%CI: 1.007-9.012; 70-<75 years, OR=3.131, 95%CI: 1.059-9.260; 75-<80 years, OR=5.779, 95%CI: 1.989-16.784; ≥80 years, OR=16.810, 95%CI: 5.926-47.685), those who were unmarried, divorced, or widowed (OR=1.973, 95%CI: 1.383-2.815), those with hearing loss (OR=1.573, 95%CI: 1.128-2.193), those with diabetes mellitus (OR=1.958, 95%CI: 1.362-2.814), and those with depressive symptoms (OR=4.143, 95%CI: 2.997-5.728) had a higher risk of AD. Conversely, individuals with an educational level of primary school or above (primary school, OR=0.579, 95%CI: 0.401-0.835; junior high school or above, OR=0.438, 95%CI: 0.259-0.741), and those who engaged in regular physical exercise (OR=0.414, 95%CI: 0.264-0.649) had a lower risk of AD. Conclusions The detection rate of AD was relatively high among the elderly in Haishu District. AD among the elderly was related to age, educational level, marital status, physical exercise, hearing loss, diabetes mellitus, and depressive symptoms.

OBJECTIVE: To screen anti-tumor drugs that improve antigen processing and presentation in acute myeloid leukemia (AML) cells. METHODS: A TCR-like or TCR mimic antibody that can specifically recognize HLA-A*0201:WT1_126-134 ( RMFPNAPYL) complex (hereafter referred to as HLA-A2:WT1) was synthesized to evaluate the function of antigen processing and presentation machinery (APM) in AML cells. AML cell line THP1 was incubated with increasing concentrations of IFN-γ, hypomethylating agents (HMA), immunomodulatory drugs (IMiD), proteasome inhibitors (PI) and γ-secretase inhibitors (GSI), followed by measuring of HLA-ABC, HLA-A2 and HLA-A2:WT1 levels by flow cytometry at consecutive time points. RESULTS: The TCR-like antibody we generated only binds to HLA-A*0201⁺WT1⁺ cells, indicating the specificity of the antibody. HLA-A2:WT1 level of THP-1 cells detected with the TCR-like antibody was increased significantly after co-incubation with IFN-γ, showing that the HLA-A2:WT1 TCR like antibody could evaluate the function of APM. Among the anti-tumor agents screened in this study, GSI (LY-411575) and HMA (decitabine and azacitidine) could significantly increase the HLA-A2:WT1 level. The IMiD lenalidomide and pomalidomide could aslo upregulate the expression of HLA-A2:WT1 complex under certain concentrations of the drugs and incubation time. As proteasome inhibitors, carfilzomib could significantly decreased the expression of HLA-A2:WT1, while bortezomib had no significant effect on HLA-A2:WT1 expression. CONCLUSION HLA-A2:WT1 TCR-like antibody can effectively reflect the APM function. Some of the anti-tumor drugs can affect the APM function and immunogenicity of tumor cells.
Humans ; Leukemia, Myeloid, Acute/immunology* ; Antineoplastic Agents/pharmacology* ; Antigen Presentation/drug effects* ; HLA-A2 Antigen/immunology* ; Receptors, Antigen, T-Cell/immunology* ; Cell Line, Tumor ; Interferon-gamma

Hepatic stellate cells (HSCs) are the primary fibrogenic cells in the liver, and their activation plays a crucial role in the development and progression of hepatic fibrosis. Here, we report that retinoid X receptor-alpha (RXRα), a unique member of the nuclear receptor superfamily, is a key modulator of HSC activation and liver fibrosis. RXRα exerts its effects by modulating calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ)-mediated activation of AMP-activated protein kinase-alpha (AMPKα). In addition, we demonstrate that K-80003, which binds RXRα by a unique mechanism, effectively suppresses HSC activation, proliferation, and migration, thereby inhibiting liver fibrosis in the CCl₄ and amylin liver NASH (AMLN) diet animal models. The effect is mediated by AMPKα activation, promoting mitophagy in HSCs. Mechanistically, K-80003 activates AMPKα by inducing RXRα to form condensates with CaMKKβ and AMPKα via a two-phase process. The formation of RXRα condensates is driven by its N-terminal intrinsic disorder region and requires phosphorylation by CaMKKβ. Our results reveal a crucial role of RXRα in liver fibrosis regulation through modulating mitochondrial activities in HSCs. Furthermore, they suggest that K-80003 and related RXRα modulators hold promise as therapeutic agents for fibrosis-related diseases.

Objective To systematically evaluate the effects of total intravenous anesthesia and inhalational anesthesia on postoperative recovery in patients undergoing transsphenoidal pituitary tumor resection.Methods A comprehensive search was conducted in international biomedical databases including Ovid Medline,Embase,CINAHL(EBSCO),Cochrane Library,and Web of Science,from inception to July 4,2023.Additionally,ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform were searched for ongoing and completed trials.The randomized controlled trials(RCT)comparing total intravenous anesthesia and inhalational anesthesia in patients undergoing transsphenoidal surgery for pituitary tumors were included.The methodological quality of the included studies was evaluated by the Cochrane Collaboration tool.Relevant data were extracted and synthesized for analysis.Results A total of 327 records were identified,of which eight RCTs met the inclusion criteria.Four studies showed that the patients receiving desflurane or sevoflurane anesthesia experienced faster emergence from anesthesia than those receiving propofol.Two studies indicated that patients in the propofol group had lower levels of emergence agitation and a lower incidence of early postoperative nausea and vomiting.The results on postoperative cognitive function were inconsistent across studies.No differences were found between the groups in terms of postoperative complications or overall recovery quality during hospitalization.Conclusions Inhalational anesthesia appears to provide an advantage in promoting faster emergence following transsphenoidal pituitary surgery,whereas total intravenous anesthesia may contribute to smoother and more stable recovery.Further high-quality studies are needed to clarify the effects of different anesthetic techniques on both short- and long-term postoperative recovery.
Humans ; Anesthesia, Intravenous ; Pituitary Neoplasms/surgery* ; Anesthesia, Inhalation ; Randomized Controlled Trials as Topic ; Anesthesia Recovery Period ; Pituitary Gland/surgery* ; Postoperative Period

Combined seawater Hf and Nd isotope compositions have been applied as reliable tracers of water mass mixing and weathering input changes.The establishment of standardized synchronous extraction methods for Fe-Mn oxyhydroxide in sediments,which serve as effective carriers for isotopic signals of ancient seawater,is a prerequisite and key for paleoceanography research.The research material of this study was the surface sediments of the Western Arctic Ocean obtained from China's 7th Arctic Scientific Expedition,and a reliable simultaneous Nd-Hf isotope extraction method in Fe-Mn oxyhydroxide fraction was established through systematic optimization of pretreatment procedures and key extraction parameters.This research focused on analyzing the effects of pretreatment(Milli-Q water pre-wash)and key extraction parameters(leaching time,solution/solid ratios,concentration of EDTA-2Na,and concentration of mixed solution of hydroxyamine hydrochloride(HH)and acetic acid(HAc))on element extraction efficiency and isotopic composition.The experimental results indicated that the Milli-Q water pre-wash was not necessary,Fe-Mn oxyhydroxide extraction could be directly performed.Nd extraction amount in Fe-Mn oxyhydroxide fraction was not sensitive to changes in extraction conditions,while the Hf extraction amount was significantly affected by leaching time,solution/solid ratios and concentration of EDTA-2Na.The optimal conditions for synchronously extracting Nd-Hf isotopes were as follows:adding 20 mL of a mixed solution of 0.005 mol/L HH+1.5%HAc+0.03 mol/L EDTA-2Na per gram of sample,and reaction at room temperature for 3 h under shaking.Through systematic verification of 87Sr/86Sr ratio,εNd value,εHf value,Al/Nd ratio and Al/Hf ratio,it was confirmed that this method could effectively avoid interferences from residual components and accurately obtain Nd-Hf isotope signals of ancient seawater preserved in sediments.The standardized method established in this work provided reliable technical support for reconstructing water mass mixing between the Arctic,North Atlantic,and North Pacific Oceans and riverine input histories.It also provided important reference for the extraction methods of Nd and Hf isotopes from marine sources in sediments from other sea areas around the world.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Aim To investigate the intervention effect of Rehmannia radix water extract on bleomycin(BLM)-induced pulmonary fibrosis in mice combined with metabolomics and to reveal the potential mechanism,in order to provide new ideas for clinical treatment of pul-monary fibrosis.Methods Male C57BL/6N mice were randomly divided into the control group,model group,pirfenidone group(positive control,PFD,270 mg·kg-1),and low dose(DH-L,4.55 g·kg-1)group,medium dose(DH-M,9.1 g·kg-1)group and high dose(DH-H,18.2 g·kg-1)group of Rehman-nia.Except for the control group,BLM(5 mg·kg-1)was instilled into the trachea to establish the model of pulmonary fibrosis in the other groups.The survival rate,lung index and blood oxygen saturation of mice in each group were evaluated.HE and Masson staining were used to observe the pathological changes of lung tissue.WBP was used to detect lung function.Flow cytometry was used to detect the apoptosis of primary lung cells,ROS and immune cells.ELISA was used to detect the levels of fibrosis markers and inflammatory factors(α-SMA,collagen Ⅰ,collagen Ⅲ,TGF-β1,TNF-α,IL-1 β,and IL-6).Biochemical method was employed to detect the contents of GSH-Px,T-SOD and MDA.Liquid chromatograph mass spectrometer(LC-MS)metabolomics was used to analyze the changes of serum metabolic profile.Results Water extract of Re-hmannia significantly increased the survival rate,oxy-gen saturation and lung function of mice with pulmona-ry fibrosis,reduced the lung coefficient,ameliorated pathological damage and collagen deposition in lung tissue,reduced the levels of apoptosis and oxidative stress,and down-regulated the levels of inflammatory factors in lung tissue.It regulated the levels of metabo-lites such as bile acid metabolism,sphingolipid metabo-lism,and unsaturated fatty acid metabolism.Conclu-sions Water extract of Rehmannia inhibits lung injury and collagen deposition in mice with pulmonary fibrosis by inhibiting inflammatory response,which may be a-chieved by regulating the levels of inflammatory factors through the metabolic pathways of bile acid and sphin-golipid.

Aim To investigate the intervention effect of Rehmannia radix water extract on bleomycin(BLM)-induced pulmonary fibrosis in mice combined with metabolomics and to reveal the potential mechanism,in order to provide new ideas for clinical treatment of pul-monary fibrosis.Methods Male C57BL/6N mice were randomly divided into the control group,model group,pirfenidone group(positive control,PFD,270 mg·kg-1),and low dose(DH-L,4.55 g·kg-1)group,medium dose(DH-M,9.1 g·kg-1)group and high dose(DH-H,18.2 g·kg-1)group of Rehman-nia.Except for the control group,BLM(5 mg·kg-1)was instilled into the trachea to establish the model of pulmonary fibrosis in the other groups.The survival rate,lung index and blood oxygen saturation of mice in each group were evaluated.HE and Masson staining were used to observe the pathological changes of lung tissue.WBP was used to detect lung function.Flow cytometry was used to detect the apoptosis of primary lung cells,ROS and immune cells.ELISA was used to detect the levels of fibrosis markers and inflammatory factors(α-SMA,collagen Ⅰ,collagen Ⅲ,TGF-β1,TNF-α,IL-1 β,and IL-6).Biochemical method was employed to detect the contents of GSH-Px,T-SOD and MDA.Liquid chromatograph mass spectrometer(LC-MS)metabolomics was used to analyze the changes of serum metabolic profile.Results Water extract of Re-hmannia significantly increased the survival rate,oxy-gen saturation and lung function of mice with pulmona-ry fibrosis,reduced the lung coefficient,ameliorated pathological damage and collagen deposition in lung tissue,reduced the levels of apoptosis and oxidative stress,and down-regulated the levels of inflammatory factors in lung tissue.It regulated the levels of metabo-lites such as bile acid metabolism,sphingolipid metabo-lism,and unsaturated fatty acid metabolism.Conclu-sions Water extract of Rehmannia inhibits lung injury and collagen deposition in mice with pulmonary fibrosis by inhibiting inflammatory response,which may be a-chieved by regulating the levels of inflammatory factors through the metabolic pathways of bile acid and sphin-golipid.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To analyze the factors affecting delayed chemotherapy in children with Burkitt lymphoma (BL) and their influence on prognosis.Methods:Retrospective cohort study. Clinical data of 591 children aged ≤18 years with BL from May 2017 to December 2022 in China Net Childhood Lymphoma (CNCL) was collected. The patients were treated according to the protocol CNCL-BL-2017. According to the clinical characteristics, therapeutic regimen was divided into group A, group B and group C .Based on whether the total chemotherapy time was delayed, patients were divided into two groups: the delayed chemotherapy group and the non-delayed chemotherapy group. Based on the total delayed time of chemotherapy, patients in group C were divided into non-delayed chemotherapy group, 1-7 days delayed group and more than 7 days delayed group. Relationships between delayed chemotherapy and gender, age, tumor lysis syndrome before chemotherapy, bone marrow involvement, disease group (B/C group), serum lactate dehydrogenase (LDH) > 4 times than normal, grade Ⅲ-Ⅳ myelosuppression after chemotherapy, minimal residual disease in the interim assessment, and severe infection (including severe pneumonia, sepsis, meningitis, chickenpox, etc.) were analyzed. Logistic analysis was used to identify the relevant factors. Kaplan-Meier method was used to analyze the patients' survival information. Log-Rank was used for comparison between groups.Results:Among 591 patients, 504 were males and 87 were females, the follow-up time was 34.8 (18.6,50.1) months. The 3-year overall survival (OS) rate was (92.5±1.1)%,and the 3-year event-free survival (EFS) rate was (90.5±1.2)%. Seventy-three (12.4%) patients were in delayed chemotherapy group and 518 (87.6%) patients were in non-delayed chemotherapy group. The reasons for chemotherapy delay included 72 cases (98.6%) of severe infection, 65 cases (89.0%) of bone marrow suppression, 35 cases (47.9%) of organ dysfunction, 22 cases (30.1%) of tumor lysis syndrome,etc. There were 7 cases of chemotherapy delay in group B, which were seen in COPADM (vincristine+cyclophosphamide+prednisone+daunorubicin+methotrexate+intrathecal injection,4 cases) and CYM (methotrexate+cytarabine+intrathecal injection,3 cases) stages. There were 66 cases of chemotherapy delay in group C, which were common in COPADM (28 cases) and CYVE 1 (low dose cytarabine+high dose cytarabine+etoposide+methotrexate, 12 cases) stages. Multinomial Logistic regression analysis showed that the age over 10 years old ( OR=0.54,95% CI 0.30-0.93), tumor lysis syndrome before chemotherapy ( OR=0.48,95% CI 0.27-0.84) and grade Ⅲ-Ⅳ myelosuppression after chemotherapy ( OR=0.55,95% CI 0.33-0.91)were independent risk factors for chemotherapy delay.The 3-year OS rate and the 3-year EFS rate of children with Burkitt lymphoma in the delayed chemotherapy group were lower than those in the non-delayed chemotherapy group ((79.4±4.9)% vs. (94.2±1.1)%, (80.2±4.8)% vs. (92.0±1.2)%,both P<0.05). The 3-year OS rate of the group C with chemotherapy delay >7 days (42 cases) was lower than that of the group with chemotherapy delay of 1-7 days (22 cases) and the non-delay group (399 cases) ((76.7±6.9)% vs. (81.8±8.2)% vs. (92.7±1.3)%, P=0.002).The 3-year OS rate of the chemotherapy delay group (9 cases) in the COP (vincristine+cyclophosphamide+prednisone) phase was lower than that of the non-chemotherapy delay group (454 cases) ((66.7±15.7)% vs. (91.3±1.4)%, P=0.005). Similarly, the 3-year OS rate of the chemotherapy delay group (11 cases) in the COPADM1 phase was lower than that of the non-chemotherapy delay group (452 cases) ((63.6±14.5)% vs. (91.5±1.3)%, P=0.001). Conclusions:The delayed chemotherapy was related to the age over 10 years old, tumor lysis syndrome before chemotherapy and grade Ⅲ-Ⅳ myelosuppression after chemotherapy in pediatric BL. There is a significant relationship between delayed chemotherapy and prognosis of BL in children.