OBJECTIVE: To study the clinical effects of pomalidomide-based regimen in the treatment of relapsed and refractory multiple myeloma (RRMM). METHODS: 60 patients with RRMM in hematology department of the First Affiliated Hospital of Xinxiang Medical University from November 2020 to January 2023 were selected. Among them, 15 cases were treated with PDD regimen (pomalidomide + daratumumab + dexamethasone), and 45 cases were treated with PCD regimen (pomalidomide + cyclophosphamide + dexamethasone). The clinical effects were evaluated. RESULTS: The median number of treatment cycles for the entire cohort was 5 (2-11), with an overall response rate (ORR) of 75.0%. The ORR of patients treated with PDD regimen was 73.3%, while the ORR of patients treated with PCD regimen was 75.6%. The ORR of 46 patients with non high-risk cytogenetic abnormalities (non-HRCA) was 86.9%, significantly higher than the 35.7% of 14 patients with HRCA (χ² =15.031, P < 0.05). The median PFS for all patients was 8.0(95%CI : 6.8-9.1) months and the median OS was 14.0 (95%CI : 11.3-16.7) months. Among patients treated with PDD regimen, the PFS and OS of patients with non-HRCA were significantly higher than those of patients with HRCA [PFS: 7.0(95%CI : 4.6-9.3) months vs 4.0(95%CI : 3.1-4.8) months, χ² =5.120, P < 0.05; OS: not reached vs 6.0(95%CI : 1.1-10.9) months, χ² =9.870, P < 0.05]. Among patients treated with PCD regimen, the PFS and OS of patients with non-HRCA were significantly higher than those of patients with HRCA [PFS: 9.0(95%CI : 6.2-11.8) months vs 6.0(95%CI : 5.4-6.6) months, χ²=14.396, P < 0.05; OS: not reached vs 11.0(95%CI : 6.4-15.6) months, χ² =7.471, P < 0.05]. CONCLUSION The pomalidomide-based regimen has a good clinical effect and safety in the treatment of RRMM.
Humans ; Multiple Myeloma/drug therapy* ; Thalidomide/administration & dosage* ; Dexamethasone/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Female ; Male ; Middle Aged ; Recurrence ; Aged ; Cyclophosphamide/therapeutic use* ; Treatment Outcome ; Antibodies, Monoclonal

OBJECTIVE: To explore the functional roles and underlying mechanisms of neferine in the context of angiotensin II (Ang II)-induced hypertension and vascular dysfunction. METHODS: Male mice were infused with Ang II to induce hypertension and randomly divided into treatment groups receiving neferine or a control vehicle based on baseline blood pressure using a random number table method. The hypertensive mouse model was constructed by infusing Ang II via a micro-osmotic pump (500 ng/kg per minute), and neferine (0.1, 1, or 10 mg/kg), valsartan (10 mg/kg), or double distilled water was administered intragastrically once daily for 6 weeks. A non-invasive blood pressure system, ultrasound, and hematoxylin and eosin staining were performed to assess blood pressure and vascular changes. RNA sequencing and network pharmacology were employed to identify differentially expressed transcripts (DETs) and pathways. Vascular ring tension assay was used to test vascular function. A7R5 cells were incubated with neferine for 24 h and then treated with Ang II to record the real-time Ca²⁺ concentration by confocal microscope. Immunohistochemistry (IHC) and Western blot were used to evaluate vasorelaxation, calcium, and the extracellular signal-regulated kinase (ERK)1/2 pathway. RESULTS: Neferine treatment effectively mitigated the elevation in blood pressure, pulse wave velocity, aortic thickening in the abdominal aorta of Ang II-infused mice (P<0.05). RNA sequencing and network pharmacology analysis identified 355 DETs that were significantly reversed by neferine treatment, along with 25 potential target genes, which were further enriched in multiple pathways and biological processes, such as ERK1 and ERK2 cascade regulation, calcium pathway, and vascular smooth muscle contraction. Further investigation revealed that neferine treatment enhanced vasorelaxation and reduced Ca²⁺-dependent contraction of abdominal aortic rings, independent of endothelium function (P<0.05). The underlying mechanisms were mediated, at least in part, via suppression of receptor-operated channels, store-operated channels, or voltage-operated calcium channels. Neferine pre-treatment demonstrated a reduction in intracellular Ca²⁺ release in Ang II stimulated A7R5 cells. IHC staining and Western blot confirmed that neferine treatment effectively attenuated the upregulation of p-ERK1/2 both in vivo and in vitro, which was similar with treatment of ERK1/2 inhibitor PD98059 (P<0.05). CONCLUSIONS Neferine remarkably alleviates Ang II-induced elevation of blood pressure, vascular dysfunction, and pathological changes in the abdominal aorta. This beneficial effect is mediated by the modulation of multiple pathways, including calcium and ERK1/2 pathways.
Animals ; Angiotensin II ; Male ; Benzylisoquinolines/therapeutic use* ; Network Pharmacology ; Blood Pressure/drug effects* ; Sequence Analysis, RNA ; Mice ; Hypertension/chemically induced* ; Mice, Inbred C57BL ; Calcium/metabolism*

This study investigated the role of the nuclear factor of activated T cells c3 (NFATc3) in vascular smooth muscle cells (VSMCs) during aortic aneurysm and dissection (AAD) progression and the underlying molecular mechanisms. Cytoplasmic and nuclear NFATc3 levels were elevated in human and mouse AAD. VSMC-NFATc3 deletion reduced thoracic AAD (TAAD) and abdominal aortic aneurysm (AAA) progression in mice, contrary to VSMC-NFATc3 overexpression. VSMC-NFATc3 deletion reduced extracellular matrix (ECM) degradation and maintained the VSMC contractile phenotype. Nuclear NFATc3 targeted and transcriptionally upregulated matrix metalloproteinase 9 (MMP9) and MMP2, promoting ECM degradation and AAD development. NFATc3 promoted VSMC phenotypic switching by binding to eukaryotic elongation factor 2 (eEF2) and inhibiting its phosphorylation in the VSMC cytoplasm. Restoring eEF2 reversed the beneficial effects in VSMC-specific NFATc3-knockout mice. Cabamiquine-targets eEF2 and inhibits protein synthesis-inhibited AAD development and progression in VSMC-NFATc3-overexpressing mice. VSMC-NFATc3 promoted VSMC switch and ECM degradation while exacerbating AAD development, making it a novel potential therapeutic target for preventing and treating AAD.

OBJECTIVES: To develop an early atrial fibrillation (AF) risk prediction model based on large-scale electrocardiogram (ECG) data from the Chinese population. METHODS: The data of multiple ECG records of 30 383 patients admitted in the Chinese PLA General Hospital between 2009 and 2023 were randomly divided into the training set and the internal testing set in a 7:3 ratio. The predictive factors were selected based on the training set using univariate analysis, LASSO regression, and the Boruta algorithm. Cox proportional hazards regression was used to establish the ECG model and the composite model incorporating age, gender, and ECG model score. The discrimination power, calibration, and clinical net benefits of the models were evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curves. RESULTS: The cohort included 51.1% male patients with a median age of the patients of 51 (36, 62) years and an AF incidence of 4.5% (1370/30 383). In the ECG model, the parameters related to the P wave and QRS complex were identified as significant predictors. In the testing set, the AUROC of the ECG model for predicting 5-year AF risk was 0.77 (95% CI: 0.74-0.80), which was increased to 0.81 (95% CI: 0.78-0.83) after incorporating age and gender, with a net reclassification improvement of 0.123 and an integrated discrimination improvement of 0.04 (P<0.05). The calibration curve of the model was close to the diagonal line. Decision curve analysis showed that the clinical net benefit of the composite model was higher than that of the ECG model across the majority of threshold probability. CONCLUSIONS The composite model incorporating quantitative ECG features during sinus rhythm, along with age and gender, can effectively predict AF risk in the Chinese population, thus providing a low-cost screening tool for early AF risk assessment and management.
Humans ; Atrial Fibrillation/epidemiology* ; Electrocardiography ; Middle Aged ; Male ; Female ; China/epidemiology* ; Proportional Hazards Models ; Adult ; Risk Factors ; Risk Assessment ; East Asian People

A new method was developed for simultaneous and efficient determination of linear perfluorooctanoic acid(n-PFOA)and linear perfluorooctane sulfonate(n-PFOS),and their typical branched isomers in seawater by online solid phase extraction-liquid chromatography-tandem mass spectrometry(Online SPE-LC-MS/MS).Only centrifugation of the seawater sample was required to remove the particulate matter,and then the seawater sample was directly injected and analyzed by online SPE-LC-MS/MS.An Eclipse Plus-C18 guard column was selected as SPE column for online enrichment of linear and branched isomers,and a F5 PFP column(150 mm×2.1 mm,2.7 μm)was used as the analytical column.Under the optimized experimental conditions,the separation and detection of all PFOA and PFOS linear and branched isomers could be completed within 20 min.The spiked recoveries of various target compounds ranged from 82.9%to 107.7%with detection limits and limits of quantification of 0.10-1.05 ng/L and 0.30-2.11 ng/L,respectively.The method was characterized by good precision(RSD≤9.10%)and linearity(R2≥0.990).Subsequently,linear and branched isomers of PFOA and PFOS in surface and bottom seawater samples collected from the Laizhou Bay of China were determined.The results showed that the detection rate of all the four branched PFOA isomers were 100%,with the highest average concentration of 25.85 ng/L found for 6m-PFOA,which accounted for 11.79%of the∑PFOA.For the five branched isomers of PFOS,the highest detection rate of 90.84%was found for 5m-PFOS.The highest average concentration of 0.64 ng/L was observed for 3m-PFOS,accounting for 19.88%of ∑PFOS.The proposed method provided an effective detection tool for qualitative and quantitative detection of PFOA and PFOS isomers in the marine aquatic environment.

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 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 investigate the effectiveness of the 5A (Ask, Assess, Advise, Assist, Arrange follow-up) management model based on personalized health education records in community patients with type 2 diabetes mellitus.Methods:A convenience sampling method was used to select patients with type 2 diabetes mellitus who were enrolled with family doctors in Yuxin Community, Haidian District, Beijing, from January 2022 to May 2023. Using a random number table method, the patients were divided into an observation group ( n=68) and a control group ( n=68). The control group received routine community management, while the observation group received management based on the 5A model and personalized health education records. Differences in metabolic indicators, including glycated hemoglobin (HbA1c), fasting blood glucose, 2-hour postprandial blood glucose, body mass index (BMI), and waist circumference, as well as self-management behaviors were compared between the two groups at 3, 6, and 12 months of intervention. Results:During the entire intervention period, four cases were lost in the observation group and eight cases were lost in the control group. Finally, data from 124 patients were included in the analysis, including 64 cases in the observation group and 60 cases in the control group. Repeated measures analysis of variance showed interaction effects between time points and groups for HbA1c, BMI, waist circumference, and self-management behavior scores among patients with diabetes, with statistically significant differences ( P<0.05) . Conclusions:The 5A management model based on personalized health education records has a positive impact on improving metabolic indicators and self-management behaviors in community patients with type 2 diabetes mellitus.

Objective:To investigate the effectiveness of the 5A (Ask, Assess, Advise, Assist, Arrange follow-up) management model based on personalized health education records in community patients with type 2 diabetes mellitus.Methods:A convenience sampling method was used to select patients with type 2 diabetes mellitus who were enrolled with family doctors in Yuxin Community, Haidian District, Beijing, from January 2022 to May 2023. Using a random number table method, the patients were divided into an observation group ( n=68) and a control group ( n=68). The control group received routine community management, while the observation group received management based on the 5A model and personalized health education records. Differences in metabolic indicators, including glycated hemoglobin (HbA1c), fasting blood glucose, 2-hour postprandial blood glucose, body mass index (BMI), and waist circumference, as well as self-management behaviors were compared between the two groups at 3, 6, and 12 months of intervention. Results:During the entire intervention period, four cases were lost in the observation group and eight cases were lost in the control group. Finally, data from 124 patients were included in the analysis, including 64 cases in the observation group and 60 cases in the control group. Repeated measures analysis of variance showed interaction effects between time points and groups for HbA1c, BMI, waist circumference, and self-management behavior scores among patients with diabetes, with statistically significant differences ( P<0.05) . Conclusions:The 5A management model based on personalized health education records has a positive impact on improving metabolic indicators and self-management behaviors in community patients with type 2 diabetes mellitus.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*