Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Objective: To explore sex difference in the cardiovascular health (CVH) status of 6-8 year old children in Beijing, so as to inform the early intervention of CVH related lifestyles. Methods: Based on the Beijing Children s Growth and Health Cohort (PROC), baseline physical examination, sequential questionnaire survey, and laboratory tests were conducted among 1 914 grade 1 students. Children s CVH and its subscales (health behaviors and health factors) scores were calculated according to the Life s Essential 8 (LE 8) index and categorized into high, moderate, and low CVH. CVH scores were reported as medians and interquartile ranges; sex differences were compared using the Chi square test and Wilcoxon test. Results: Among the 1 914 participants, the percentages of high, moderate, and low CVH were 35.7%, 63.5%, and 0.8%, respectively, and the percentages of high, moderate, and low health behavior scores were 25.9%, 67.5%, and 6.6%, respectively, with no statistically significant differences between sex ( χ 2=2.30, 0.07, P >0.05). The rates of high, moderate, and low health factor scores for boys and girls were 61.1%, 36.0%, 2.9% and 71.1%, 28.4%, 0.5%, respectively, with a statistically significant sex difference ( χ 2=31.88, P < 0.01). The overall CVH score was 76.0(70.0, 83.0), 76.0(69.0, 82.0) for boys, and 77.0(71.0, 83.0) for girls. Among the health behavior metrics, sleep scores were the best and physical activity scores were the worst[100.0(90.0,100.0), 40.0(20.0, 80.0 )]; among the health factor metrics, blood glucose scores were the best and lipid scores were the worst[100.0(100.0,100.0), 60.0(40.0,100.0)]. In respect to health factors, there were significant gender differences in body mass index, blood lipids, blood sugar, and blood pressure scores ( Z =-6.92, 3.01, -6.60, -2.30, <0.05), but there were no significant gender differences in diet, physical activity, nicotine exposure, or sleep scores with regards to health behaviors ( Z =0.99, 0.88, -0.13, 0.36, P > 0.05 ). Compared to boys, girls in the low and moderate CVH groups had high health factor scores despite low health behavior scores. Conclusion Most 6 to 8-year-old children in Beijing were found to have relatively good CVH, and optimization of children s CVH status can be achieved by promoting healthier lifestyles and monitoring health factors, especially among boys.

Macroautophagy (referred to as autophagy hereafter) is a major intracellular lysosomal degradation pathway that is responsible for the degradation of misfolded/damaged proteins and organelles. Previous studies showed that autophagy protects against acetaminophen (APAP)-induced injury (AILI) via selective removal of damaged mitochondria and APAP protein adducts. The lysosome is a critical organelle sitting at the end stage of autophagy for autophagic degradation via fusion with autophagosomes. In the present study, we showed that transcription factor EB (TFEB), a master transcription factor for lysosomal biogenesis, was impaired by APAP resulting in decreased lysosomal biogenesis in mouse livers. Genetic loss-of and gain-of function of hepatic TFEB exacerbated or protected against AILI, respectively. Mechanistically, overexpression of TFEB increased clearance of APAP protein adducts and mitochondria biogenesis as well as SQSTM1/p62-dependent non-canonical nuclear factor erythroid 2-related factor 2 (NRF2) activation to protect against AILI. We also performed an unbiased cell-based imaging high-throughput chemical screening on TFEB and identified a group of TFEB agonists. Among these agonists, salinomycin, an anticoccidial and antibacterial agent, activated TFEB and protected against AILI in mice. In conclusion, genetic and pharmacological activating TFEB may be a promising approach for protecting against AILI.

Aim To explore the mechanism of hydroxy-a-sanshool in the treatment of diabetic cardiomyopathy ( DCM) based on label-free quantitative proteomics detection technique. Methods DCM model was established by high fat diet and intraperitoneal injection of streptozotocin ( STZ) . They were divided into control group ( CON group ) , diabetic cardiomyopathy group (DCM group) and hydroxy-a-sanshool treatment group ( DCM + SAN group) . The cardiac function of mice was evaluated by echocardiography, the myocardial morphology was observed by pathology staining, the protective mechanism of hydroxy-a-sanshool on diabetic cardiomyopathy was speculated by proteomic technique , and the expression level of cAMP/PKA signaling pathway and key proteins were verified by Western blotting. Results Cardiac ultrasound and pathology staining showed that hydroxy-a-sanshool had protective effect on the heart of DCM mice. Label-free quantitative proteomic analysis was carried out between DCM + SAN group and DCM group, and 160 differential pro-teins were identified by proteomics, in which 127 proteins were up-regulated and 33 proteins were down regulated ; GO secondary functional annotations showed the biological process, molecular function and cellular component; KEGG enrichment analysis showed that cAMP signaling pathway was the most abundant; protein interaction network showed that PKA as the central node interacted with many proteins in the cAMP signaling pathway. Western blot showed that the relative expression of с AMP, PKA protein in DCM group was significantly lower than that in CON group ( P < 0. 05 ) , while the relative expression of cAMP, PKA protein in DCM + SAN group was significantly higher than that in DCM group ( P < 0. 05 ) . Conclusions Hydroxy-a-sanshool has protective effect on heart function of mice with diabetes, which plays a role through cAMP signaling pathway.

Aim To explore the role of SIRT1/Nrf2 / HO-1 in alleviating the cognitive function impairment by sevoflurane treatment in a mouse model of postoperative cerebral reperfusion. Methods C57BL/6J mice were randomly divided into five groups: sham operation group, hemorrhagic shock reperfusion group, sevoflurane postconditioning group, sevoflurane postcondition-ing + SIRT1 inhibitor group and sevoflurane postconditioning + Nrf2 inhibitor group. Mice were subjected to Morris water maze test after cerebral ischemia reperfusion. The ATP, superoxide dismutase (SOD), ROS and MDA contents in tissue of mice were detected. SIRT1, Nrf2 and HO-1 proteins in tissue were detected by Western blot. Results After hemorrhagic shock, the learning and memory ability of mice was reduced.ATP and SOD concentration in hippocampus was reduced , MDA and ROS concentration increased, and the SIRT, Nrf2 and HO-1 concentration was reduced. Sevoflurane improved the cognitive dysfunction and oxi-dative damage in postoperative mice, and the neuro-protective effect of sevoflurane on hemorrhagic shock and resuscitation mice was weakened followed with SIRT1 and Nrf2 inhibitors. Conclusion Sevoflurane probably alleviates the oxidative reaction damage and cognitive impairment caused by cerebral reperfusion in mice through SIRT1/Nrf2/H0-1 pathway.

italic>Schisandra chinensis is a traditional Chinese medicine with the functions of reinforcing deficiency, strengthening, and inducing astringency, appliable to treat the chronic cough and deficiency in breath, palpitation, and insomnia, etc. A hybrid mass spectrometry scanning strategy (high-definition data-independent/data-dependent acquisition, HDDIDDA), enabling the ion mobility separation and alternating data-independent acquisition/data-dependent acquisition, was established, which, in combination with in-house library-driven automatic peak annotation workflows facilitated by the UNIFI software, was utilized to systematically characterize the multi-classes of chemical components from S. chinensis. The use of an HSS T3 column (100 mm × 2.1 mm, 1.8 μm), 0.1% formic acid in H₂O-acetonitrile as the mobile phase running at the flow rate of 0.3 mL·min^-1, and column temperature at 35 ℃, could enable good separation of the S. chinensis components within 42 min. HDDIDDA scan in both the positive and negative ion modes was employed for data acquisition. Based on the automatic peak annotation, reference standards comparison, MS² data interpretation, and literature analysis, we were able to identify or tentatively characterize 105 compounds in the S. chinensis decoction, involving 56 terpenoids, 42 lignans, five glycosides, one organic acid, and one flavonoid. HDDIDDA scanning can improve the coverage of data acquisition and improve the accuracy of identification, while CCS prediction analysis provides the possibility to distinguish isomers by the ion mobility technology. The results provide reference for the intelligent material basis research of TCM.