Objective:To explore the correlative factors for insomnia severity in chronic insomnia patients using MemTrax memory test.Methods:Two hundred and twenty-two chronic insomnia patients (insomnia≥3 days per week with a duration≥3 months) recruited from Center for Preventive Treatment of Diseases, Guangdong Provincial Hospital of Chinese Medicine or through in-hospital advertisements from April 2024 to September 2024 were chosen. Pittsburgh Sleep Quality Index (PSQI) was used to evaluate the sleep quality over the last month; according to PSQI score, these patients were divided into mild insomnia group (scores of 7-10), moderate insomnia group (scores of 11-15) and severe insomnia group (scores of 16-21). MemTrax memory test was used to record the picture recognition accuracy and picture recognition reaction time, and MemTrax comprehensive index (MTx-Cp) was calculated; Patients' Health Questionnaire (PHQ-9), and Generalized Anxiety Disorder-7 scale(GAD-7) were used to evaluate the depression and anxiety status of these patients in recent 2 weeks. The clinical data, MemTrax test results, PHQ-9 and GAD-7 scores of patients with different degrees of chronic insomnia were compared. Spearman rank correlation analysis was used to investigate the correlation between insomnia severity and clinical data such as cognitive memory function in chronic insomnia patients.Results:Among the 220 chronic insomnia patients, 54 had mild insomnia, 111 had moderate insomnia, and 55 had severe insomnia. Severe insomnia patients had significantly higher percentages of those>50 years old and those using hypnotics compared with mild insomnia patients and moderate insomnia patients ( P<0.05). Compared with the mild insomnia patients and moderate insomnia patients, the severe insomnia patients exhibited significantly lower picture recognition accuracy (90%[86%, 94%], 88%[82%, 94%], 84%[78%, 92%]), significantly lower MTx-Cp (88.55±18.67, 84.41±20.93, 76.69±17.43), and significantly higher PHQ-9 score (9[6, 11], 9[6, 15], 12[8, 16], P<0.05). Moreover, severe insomnia patients had significantly longer picture recognition reaction time and higher GAD-7 score than mild insomnia patients (1.11[1.03, 1.24] s vs. 1.04[0.90, 1.15] s; 7[5, 13] vs. 6[3, 9], P<0.05). Spearman rank correlation analysis showed that insomnia severity in chronic insomnia patients was positively correlated with age, PHQ-9 score, GAD-7 score, and picture recognition reaction time, and negatively correlated with picture recognition accuracy and MTx-Cp ( P<0.05). Conclusion:Insomnia severity in patients with chronic insomnia is correlated with age, cognitive memory function, depression and anxiety.

Compared to traditional machine learning algorithms,which often suffer from low feature extraction efficiency,insufficient nonlinear pattern recognition capabilities and slow training speeds,the broad learning system(BLS)enhances the learning ability and efficiency by horizontally expanding the network structure.BLS offers advantages such as a simple structure,fast training speed,and strong generalization capabilities.While BLS has demonstrated potential in various fields,but its application in identification of complex samples has not been fully explored.This research investigated the feasibility of using BLS algorithm for identification of complex samples based on physicochemical indicators.Using the iris,wine,and breast cancer datasets,the length and width of petals and sepals of iris flowers,the physicochemical properties of wine,and the nuclear characteristics of breast cancer cells were used as input variables to establish BLS models for identifying iris species,wine varieties,and benign versus malignant nucleus.The model performance was evaluated by confusion matrices,accuracy,and runtime.Compared with partial least squares-discriminant analysis(PLS-DA),soft independent modeling of class analogies(SIMCA),and artificial neural networks(ANN),the results indicated that BLS demonstrated significant advantages in computational efficiency and recognition accuracy.Thus,BLS was an efficient and reliable method for identification of complex samples.

Objective:To explore the correlative factors for insomnia severity in chronic insomnia patients using MemTrax memory test.Methods:Two hundred and twenty-two chronic insomnia patients (insomnia≥3 days per week with a duration≥3 months) recruited from Center for Preventive Treatment of Diseases, Guangdong Provincial Hospital of Chinese Medicine or through in-hospital advertisements from April 2024 to September 2024 were chosen. Pittsburgh Sleep Quality Index (PSQI) was used to evaluate the sleep quality over the last month; according to PSQI score, these patients were divided into mild insomnia group (scores of 7-10), moderate insomnia group (scores of 11-15) and severe insomnia group (scores of 16-21). MemTrax memory test was used to record the picture recognition accuracy and picture recognition reaction time, and MemTrax comprehensive index (MTx-Cp) was calculated; Patients' Health Questionnaire (PHQ-9), and Generalized Anxiety Disorder-7 scale(GAD-7) were used to evaluate the depression and anxiety status of these patients in recent 2 weeks. The clinical data, MemTrax test results, PHQ-9 and GAD-7 scores of patients with different degrees of chronic insomnia were compared. Spearman rank correlation analysis was used to investigate the correlation between insomnia severity and clinical data such as cognitive memory function in chronic insomnia patients.Results:Among the 220 chronic insomnia patients, 54 had mild insomnia, 111 had moderate insomnia, and 55 had severe insomnia. Severe insomnia patients had significantly higher percentages of those>50 years old and those using hypnotics compared with mild insomnia patients and moderate insomnia patients ( P<0.05). Compared with the mild insomnia patients and moderate insomnia patients, the severe insomnia patients exhibited significantly lower picture recognition accuracy (90%[86%, 94%], 88%[82%, 94%], 84%[78%, 92%]), significantly lower MTx-Cp (88.55±18.67, 84.41±20.93, 76.69±17.43), and significantly higher PHQ-9 score (9[6, 11], 9[6, 15], 12[8, 16], P<0.05). Moreover, severe insomnia patients had significantly longer picture recognition reaction time and higher GAD-7 score than mild insomnia patients (1.11[1.03, 1.24] s vs. 1.04[0.90, 1.15] s; 7[5, 13] vs. 6[3, 9], P<0.05). Spearman rank correlation analysis showed that insomnia severity in chronic insomnia patients was positively correlated with age, PHQ-9 score, GAD-7 score, and picture recognition reaction time, and negatively correlated with picture recognition accuracy and MTx-Cp ( P<0.05). Conclusion:Insomnia severity in patients with chronic insomnia is correlated with age, cognitive memory function, depression and anxiety.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective:To investigate the impact of ionizing radiation(IR)on the structure and function of the testis and pro-vide some strategies for the prevention and treatment of IR-induced damage(IRD).Methods:Using radiation dose simulation,se-men analysis,hormone testing,electron microscopy and single-cell transcriptome sequencing,we assessed and analyzed a case of IRD.We established a mouse model of IRD to validate the results of single-cell sequencing,and investigated the specific biological mecha-nisms of IRD and potential strategies for its intervention.Results:IR at 1-2 Gy significantly reduced sperm concentration and mo-tility,which gradually recovered after 12 months but the percentage of morphologically normal sperm remained low.It also caused im-balanced levels of various steroid hormones,decreased testosterone and dehydroepiandrosterone sulfate,increased progesterone,prolac-tin,luteinizing hormone,and follicle-stimulating hormone.Electron microscopy revealed damages to the testis structure,including loss of germ cells,atrophy of the seminiferous tubules,nuclear membrane depression of the spermatocytes,mitochondrial atrophy and de-formation,and reduction of mitochondrial cristae.Single-cell sequencing indicated significant changes in the function of the Leydig cells and macrophages and disrupted lipid-related metabolic pathways after IRD.Administration of L-carnitine to the mouse model im-proved lipid metabolism disorders and partially alleviated IRD to the germ cells.Conclusion:Ionizing radiation can cause disorders of testicular spermatogenesis and sexual hormones and inhibit lipid metabolism pathways in Leydig cells and macrophages.Improving lipid metabolism can alleviate IRD to germ cells.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

Objective: The aging demographic landscape worldwide portends a heightened prevalence of neurodegenerative disorders. Foremost among these is Alzheimer’s disease (AD), the foremost cause of dementia in older adults. The shortage of efficacious therapies and early diagnostic indicators underscores the imperative to identify non-invasive biomarkers for early detection and disease monitoring. Recently, blood metabolites have emerged as promising candidates for AD biomarkers. Methods: Leveraging nuclear magnetic resonance (NMR) spectroscopy on plasma specimens, we conducted a cross-sectional study encompassing 35 AD patients and 35 age-matched healthy controls. Cognitive function was evaluated using the mini-mental state examination in all participants, followed by peripheral blood sample collection. We utilized univariate and multivariate analyses to perform targeted lipidomic profiling via NMR spectroscopy. Results: Our study revealed significant differences in the expression profiles of low-density lipoprotein-associated subfractions in females and high-density lipoprotein-associated subfractions in males between AD patients and healthy controls (all p<0.05). However, there was no significant metabolite overlap between males and females. Furthermore, receiver operating characteristic curve analysis demonstrated that the combination of lipid metabolites had good diagnostic values (all area under the curve>0.70; p<0.05). Conclusion Our findings suggest that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, with significant clinical implications for advancing AD diagnostic methodologies.

OBJECTIVE: To screen the prognostic biomarkers of metabolic genes in patients with multiple myeloma (MM), and construct a prognostic model of metabolic genes. METHODS: The histological database related to MM patients was searched. Data from MM patients and healthy controls with complete clinical information were selected for analysis.The second generation sequencing data and clinical information of bone marrow tissue of MM patients and healthy controls were collected from human protein atlas (HPA) and multiple myeloma research foundation (MMRF) databases. The gene set of metabolism-related pathways was extracted from Molecular Signatures Database (MSigDB) by Perl language. The biomarkers related to MM metabolism were screened by difference analysis, univariate Cox risk regression analysis and LASSO regression analysis, and the risk prognostic model and Nomogram were constructed. Risk curve and survival curve were used to verify the grouping effect of the model. Gene set enrichment analysis (GSEA) was used to study the difference of biological pathway enrichment between high risk group and low risk group. Multivariate Cox risk regression analysis was used to verify the independent prognostic ability of risk score. RESULTS: A total of 8 mRNAs which were significantly related to the survival and prognosis of MM patients were obtained (P<0.01). As molecular markers, MM patients could be divided into high-risk group and low-risk group. Survival curve and risk curve showed that the overall survival time of patients in the low-risk group was significantly better than that in the high risk group (P<0.001). GSEA results showed that signal pathways related to basic metabolism, cell differentiation and cell cycle were significantly enriched in the high-risk group, while ribosome and N polysaccharide biosynthesis signaling pathway were more enriched in the low-risk group. Multivariate Cox regression analysis showed that the risk score composed of the eight metabolism-related genes could be used as an independent risk factor for the prognosis of MM patients, and receiver operating characteristic curve (ROC) showed that the molecular signatures of metabolism-related genes had the best predictive effect. CONCLUSION Metabolism-related pathways play an important role in the pathogenesis and prognosis of patients with MM. The clinical significance of the risk assessment model for patients with MM constructed based on eight metabolism-related core genes needs to be confirmed by further clinical studies.
Humans ; Cell Cycle ; Multiple Myeloma/genetics* ; Prognosis ; Risk Factors