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: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

Background: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

Background: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

ObjectiveBased on functional near-infrared spectroscopy (fNIRS), we investigated the brain activity characteristics of gross motor tasks in children with autism spectrum disorder (ASD) and motor dysfunctions (MDs) to provide a theoretical basis for further understanding the mechanism of MDs in children with ASD and designing targeted intervention programs from a central perspective. MethodsAccording to the inclusion and exclusion criteria, 48 children with ASD accompanied by MDs were recruited into the ASD group and 40 children with typically developing (TD) into the TD group. The fNIRS device was used to collect the information of blood oxygen changes in the cortical motor-related brain regions during single-handed bag throwing and tiptoe walking, and the differences in brain activation and functional connectivity between the two groups of children were analyzed from the perspective of brain activation and functional connectivity. ResultsCompared to the TD group, in the object manipulative motor task (one-handed bag throwing), the ASD group showed significantly reduced activation in both left sensorimotor cortex (SMC) and right secondary visual cortex (V2) (P<0.05), whereas the right pre-motor and supplementary motor cortex (PMC&SMA) had significantly higher activation (P<0.01) and showed bilateral brain region activity; in terms of brain functional integration, there was a significant decrease in the strength of brain functional connectivity (P<0.05) and was mainly associated with dorsolateral prefrontal cortex (DLPFC) and V2. In the body stability motor task (tiptoe walking), the ASD group had significantly higher activation in motor-related brain regions such as the DLPFC, SMC, and PMC&SMA (P<0.05) and showed bilateral brain region activity; in terms of brain functional integration, the ASD group had lower strength of brain functional connectivity (P<0.05) and was mainly associated with PMC&SMA and V2. ConclusionChildren with ASD exhibit abnormal brain functional activity characteristics specific to different gross motor tasks in object manipulative and body stability, reflecting insufficient or excessive compensatory activation of local brain regions and impaired cross-regions integration, which may be a potential reason for the poorer gross motor performance of children with ASD, and meanwhile provides data support for further unraveling the mechanisms underlying the occurrence of MDs in the context of ASD and designing targeted intervention programs from a central perspective.

ObjectiveDisruption of epithelial layer may instantaneously induce the generation of endogenous electric fields, which was proved to play an important role in guiding the cell migration and promoting wound healing. PIEZO1 is a kind of mechanic sensitive channel, may be regulated by voltage, is proved to involve in chemotactic migration of cells and play an important role in the process of wound healing. In this paper, the role of PIEZO1 and its downstream proteins FAK and integrin β1 in the electric field guided cell migration were investigated by HaCaT cells (human immortalized keratinocyte). MethodsCell migration was tracked by Living Cell Imaging System in directed current (DC) electric field (EF). Inhibitors and RNAi techniques were applied to study the function of PIEZO1 and other related proteins in electric fields. Western blot was used to detect the expression and phosphorylation levels of integrin β1 and FAK in electric field guided migration under EF stimulation. ResultsPiezo1 RNAi as well as Ruthenium red and GsMTx4 treatment all significantly inhibited the electrotaxis of HaCaT cells. Electric field stimulation with GsMTx4 treatment alone increased FAK phosphorylation level and the expression of integrin β1. Electric field promoted the expression level of integrin β1 and the phosphorylation level of FAK. Inhibiting the expression of PIEZO1 by RNAi significantly attenuated the phosphorylation level of FAK under EF stimulation. Inhibition of integrin β1 and FAK by inhibitor significantly decrease the electric field guided cell migration. ConclusionPIEZO1 as well as integrin β1 and FAK are involved in the electric field guided cell migration of HaCaT cells. Electric field signals regulate the expression of integrin β1 and the activation of FAK through PIEZO1-mediated signal pathway to orchestrate cell migration.

Objective:To explore if miR-133a is involved in the occurrence and development of hepatocellular carcinoma(HCC)via regulating G6PD.Methods:Bioinformatics analysis predicted the binding sites of miR-133a and G6PD;RT-PCR or western blot was used to assess the expres-sion of miR-133a and G6PD in HCC tissues and the adjacent normal tissues;CCK-8 and flow cy-tometry assays were performed to evaluate the effects of miR-133a/G6PD on cell proliferation,apop-tosis;Fluorescent reporter gene and western blot assays were used to assess the effect of miR-133a on G6PD expression.Results:miR-133a expression was decreased in HCC tissues while G6PD was increased(P0.01);Up-regulation of miR-133a significantly reduced G6PD expression(P＜0.01);up-reg-ulation of miR-133a inhibited cell growth and promoted cell apoptosis(P＜0.05),whereas these effects induced by miR-133a over-expression were all abolished when G6PD was up-regulated(P＜0.01).Conclusion:miR-133a represses the occurrence and development of HCC via targeting G6PD.

AIM To analyze the component composition of Xeriga-4 Powder,and to determine the contents of phellodendrine,chlorogenic acid,gardenoside,berberine,rutin and curcumin.METHODS The high performance liquid chromatography-Q-exactive orbitrap mass spectrometry(HPLC-Q-Exactive-MS)qualitative analysis was performed on a 35℃thermostatic Agilent ZORBAX SB-Aq column(4.6 mm×150 mm,5 μm),with the mobile phase comprising of methanol-0.1%formic acid flowing at 0.35 mL/min in a gradient elution manner,and electron spray ionization source was adopted in positive and negative ion scanning.High performance liquid chromatography tandem mass spectrometry(HPLC-MS/MS)quantitative analysis was performed on a 35℃thermostatic Shim-pack GIST-HP C18 column(2.1 mm×100 mm,3 μm),with the mobile phase comprising of methanol-0.1%formic acid flowing at 0.25 mL/min in a gradient elution manner,and electron spray ionization source was adopted in positive and negative ion scanning with multiple reaction monitoring mode.RESULTS Total 65 constituents were identified,containing 19 alkaloids,13 organic acids,13 flavonoids,7 curcumins,6 iridoids,4 fatty acids,2 aldehydes,and 1 amino acid.Six constituents showed good linear relationships within their own ranges(r≥0.999 1),whose average recoveries were 96.44%-102.37%with the RSDs of 2.05%-3.74%.CONCLUSION This study can provide a reference for the quality control for Xieriga-4 Powder.

Objective To detect the levels of serum collagen type Ⅰ alpha 1 chain(COL1A1)and collagen type Ⅰ alpha 2 chain(COL1A2)in patients with intracranial aneurysm(IA),and explore their correlations with aneurysm rupture.Methods A total of 110 IA patients admitted to our hospital were regarded as the IA group and another 100 volunteers who underwent physical examination in our hospital were regarded as the control group.The expression levels of serum COL1A1 and COL1A2 were detected by ELISA.The IA patients were divided into the ruptured group(n=66)and unruptured group(n=44)according to the presence or absence of aneurysm rupture,and the clinical data and expression levels of serum COL1A1 and COL1A2 were compared between the two groups.The expression levels of serum COL1A1 and COL1A2 in patients with different Hunt-Hess grades were compared.The risk factors of aneurysm rupture in patients with IA were analyzed by multivariate Logistic regression analysis.The predictive value of serum COL1A1 and COL1A2 for aneurysm rupture in patients with IA were evaluated by receiver operating characteristic(ROC)curve.The correlation of serum COL1A1 and COL1A2 with Hunt-Hess grade for patients in rupture group was analyzed by Spearman correlation analysis.Results The expression levels of serum COL1A1 and COL1A2 for patients in the IA group were significantly higher than those in the control group(P<0.05).The number of patients with hypertension,diabetes mellitus,hyperlipidemia,aneurysm diameter>10 mm,and the expression levels of serum COL1A1 and COL1A2 in the rupture group were significantly more/higher than those in the unruptured group(P<0.05).The expression levels of serum COL1A1 and COL1A2 in patients with Hunt-Hess grades from Ⅲ to Ⅳ were significantly higher than those in patients with grades from Ⅰ to Ⅱ(P<0.05).The expression levels of serum COL1A1 and COL1A2 for patients in the rupture group were positively correlated with Hunt-Hess grade(r=0.562,0.414,P<0.05).Multivariate Logistic regression analysis showed that hypertension,diabetes mellitus,aneurysm diameter>10 mm,and increased expression levels of COL1A1 and COL1A2 were risk factors for aneurysm rupture in IA patients(P<0.05).The area under the curve(AUC)of aneurysm rupture predicted by serum COL1A1 and COL1A2 together was significantly higher than that predicted by COL1A1 alone(Z=1.905,P=0.028)and COL1A2 alone(Z=1.754,P=0.040).Conclusion The increased expression levels of serum COL1A1 and COL1A2 are risk factors for aneurysm rupture in patients with IA,and their combined prediction of aneurysm rupture in IA patients has certain clinical value.

Objective To compare the predictive effects of logistic regression and machine learning models on occurrence of low peripheral oxygen saturation(SpO2)during one-lung ventilation(OLV)in pa-tients undergoing thoracoscopic partial pulmonary resection(TPPR),and to explore risk factors of low SpO2.Methods A total of 127 patients undergoing unilateral TPPR from August 1,2022 to April 30,2023 were enrolled,61 males and 66 females,aged 18-80 years,ASA physical status Ⅰ-Ⅲ.Based on whether intraoperative SpO2 during OLV was less than 90％,the patients were divided into two groups:low SpO2 group(n=21)and normal SpO2 group(n=106).Perioperative data were collected and a predic-tive model was constructed using logistic regression.This model was compared with predictive models con-structed using five machine learning models,including random forest(RF),extreme gradient boosting(XGBoost),decision tree(DT),logistic regression(LogR),and support vector machine(SVM).The re-ceiver operating characteristic(ROC)curve was plotted,and the performance of the predictive models were evaluated by the area under the curve(AUC).The best output model was interpreted using Shapley additive explanations(SHAP)to identify the risk factors of low SpO2 during OLV in patients undergoing TPPR.Results Multivariate logistic regression analysis showed that increased age(OR=1.087,95％CI 1.006-1.175,P=0.036),increased BMI(OR=1.299,95％CI 1.050-1.608,P=0.016),increased pre-operative blood glucose(OR=2.028,95％CI 1.378-2.983,P＜0.001),and decreased RV/TLC％Pred(OR=0.936,95％CI 0.892-0.983,P=0.008)were independent risk factors of low SpO2 during OLV.The predictive model was Logit(p)=-10.098+0.08 × age+0.231 × BMI+0.633 × blood glu-cose-0.059 × RV/TLC％Pred,with an AUC of 0.873(95％CI 0.803-0.943,P＜0.001).After optimi-zing parameters of machine learning models using grid search combined with five-fold cross-validation,the model training results were satisfactory.ROC curve analysis showed that the AUC for RF was 0.921(95％CI 0.840-0.979),XGBoost was 0.940(95％CI 0.812-0.981),DT was 0.919(95％CI 0.828-0.982),LogR was 0.892(95％CI 0.831-0.980),and SVM was 0.922(95％CI 0.832-0.982).XG-Boost had the highest AUC,surpassing the logistic regression model.SHAP analysis indicated that the most important risk factors in the XGBoost output model were increased age,BMI,and preoperative blood glucose concentration.Conclusion Increased age,BMI,and preoperative blood glucose concentration are signifi-cant risk factors for low SpO2 during OLV in patients undergoing TPPR.The XGBoost machine learning model outperformed traditional logistic regression in predicting the occurrence of low SpO2 during OLV.XG-Boost can analyze more complex relationships between variables and outcomes and provide more accurate in-dividualized predictions of the risk of low SpO2 during OLV.