An integrated wearable differential microneedle array for continuous glucose monitoring in interstitial fluids.

Monitoring biomarkers in human interstitial fluids (ISF) using microneedle sensors has been extensively studied. However, most of the previous studies were limited to simple in vitro demonstrations and lacked system integration and analytical performance. Here we report a miniaturized, high-precision, fully integrated wearable electrochemical microneedle sensing device that works with a customized smartphone application to wirelessly and in real-time monitor glucose in human ISF. A microneedle array fabrication method is proposed which enables multiple individually addressable, regionally separated sensing electrodes on a single microneedle system. As a demonstration, a glucose sensor and a differential sensor are integrated in a single sensing patch. The differential sensing electrodes can eliminate common-mode interference signals, thus significantly improving the detection accuracy. The basic mechanism of microneedle penetration into the skin was analyzed using the finite element method (FEM). By optimizing the structure of the microneedle, the puncture efficiency was improved while the puncture force was reduced. The electrochemical properties, biocompatibility, and system stability of the microneedle sensing device were characterized before human application. The test results were closely correlated with the gold standard (blood). The platform can be used not only for glucose detection, but also for various ISF biomarkers, and it expands the potential of microneedle technology in wearable sensing.

An integrated neuroimaging-omics approach for the gut-brain communication pathways in Alzheimer's disease.

A key role of the gut microbiota in the pathogenesis of neurodegenerative diseases, such as Alzheimer's disease (AD), has been identified over the past decades. Increasing clinical and preclinical evidence implicates that there is bidirectional communication between the gut microbiota and the central nervous system (CNS), which is also known as the microbiota-gut-brain axis. Nevertheless, current knowledge on the interplay between gut microbiota and the brain remains largely unclear. One of the primary mediating factors by which the gut microbiota interacts with the host is peripheral metabolites, including blood or gut-derived metabolites. However, mechanistic knowledge about the effect of the microbiome and metabolome signaling on the brain is limited. Neuroimaging techniques, such as multi-modal magnetic resonance imaging (MRI), and fluorodeoxyglucose-positron emission tomography (FDG-PET), have the potential to directly elucidate brain structural and functional changes corresponding with alterations of the gut microbiota and peripheral metabolites in vivo. Employing a combination of gut microbiota, metabolome, and advanced neuroimaging techniques provides a future perspective in illustrating the microbiota-gut-brain pathway and further unveiling potential therapeutic targets for AD treatments.

Alterations in Peripheral Metabolites as Key Actors in Alzheimer's Disease.

Growing evidence supports that Alzheimer's disease (AD) could be regarded as a metabolic disease, accompanying central and peripheral metabolic disturbance. Nowadays, exploring novel and potentially alternative hallmarks for AD is needed. Peripheral metabolites based on blood and gut may provide new biochemical insights about disease mechanisms. These metabolites can influence brain energy homeostasis, maintain gut mucosal integrity, and regulate the host immune system, which may further play a key role in modulating the cognitive function and behavior of AD. Recently, metabolomics has been used to identify key AD-related metabolic changes and define metabolic changes during AD disease trajectory. This review aims to summarize the key blood- and microbial-derived metabolites that are altered in AD and identify the potential metabolic biomarkers of AD, which will provide future targets for precision therapeutic modulation.

Alterations of gut microbiota are associated with brain structural changes in the spectrum of Alzheimer's disease: the SILCODE study in Hainan cohort.

Background: The correlation between gut microbiota and Alzheimer's disease (AD) is increasingly being recognized by clinicians. However, knowledge about the gut-brain-cognition interaction remains largely unknown. Methods: One hundred and twenty-seven participants, including 35 normal controls (NCs), 62 with subjective cognitive decline (SCD), and 30 with cognitive impairment (CI), were included in this study. The participants underwent neuropsychological assessments and fecal microbiota analysis through 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. Structural MRI data were analyzed for cortical anatomical features, including thickness, sulcus depth, fractal dimension, and Toro's gyrification index using the SBM method. The association of altered gut microbiota among the three groups with structural MRI metrics and cognitive function was evaluated. Furthermore, co-expression network analysis was conducted to investigate the gut-brain-cognition interactions. Results: The abundance of Lachnospiraceae, Lachnospiracea_incertae_sedis, Fusicatenibacter, and Anaerobutyricum decreased with cognitive ability. Rikenellaceae, Odoribacteraceae, and Alistipes were specifically enriched in the CI group. Mediterraneibacter abundance was correlated with changes in brain gray matter and cerebrospinal fluid volume (p = 0.0214, p = 0.0162) and significantly with changes in cortical structures in brain regions, such as the internal olfactory area and the parahippocampal gyrus. The three colonies enriched in the CI group were positively correlated with cognitive function and significantly associated with changes in cortical structure related to cognitive function, such as the precuneus and syrinx gyrus. Conclusion: This study provided evidence that there was an inner relationship among the altered gut microbiota, brain atrophy, and cognitive decline. Targeting the gut microbiota may be a novel therapeutic strategy for early AD.

Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment.

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 ± 0.003), peak signal-to-noise ratio (31.04 ± 0.09), and mean squared error (0.0014 ± 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t-test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837-0.897) and 0.752 (95%: 0.71-0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection.

Identification of subjective cognitive decline due to Alzheimer's disease using multimodal MRI combining with machine learning.

Subjective cognitive decline (SCD) is a preclinical asymptomatic stage of Alzheimer's disease (AD). Accurate diagnosis of SCD represents the greatest challenge for current clinical practice. The multimodal magnetic resonance imaging (MRI) features of 7 brain networks and 90 regions of interests from Chinese and ANDI cohorts were calculated. Machine learning (ML) methods based on support vector machine (SVM) were used to classify SCD plus and normal control. To assure the robustness of ML model, above analyses were repeated in amyloid ß (Aß) and apolipoprotein E (APOE) ɛ4 subgroups. We found that the accuracy of the proposed multimodal SVM method achieved 79.49% and 83.13%, respectively, in Chinese and ANDI cohorts for the diagnosis of the SCD plus individuals. Furthermore, adding Aß pathology and ApoE ɛ4 genotype information can further improve the accuracy to 85.36% and 82.52%. More importantly, the classification model exhibited the robustness in the crossracial cohorts and different subgroups, which outperforms any single and 2 modalities. The study indicates that multimodal MRI imaging combining with ML classification method yields excellent and powerful performances at categorizing SCD due to AD, suggesting potential for clinical utility.

Differences in treatment for Alzheimer's disease between urban and rural areas in China.

Introduction: In China, the increasing number of people with Alzheimer's disease (AD) poses a great challenge to families and the country. Economic and cultural differences cause a urban-rural gap in medical resources. This multicenter survey aimed to investigate the real-world practice of disease treatment among people with AD. Methods: People with AD and their caregivers from 30 provincial regions in mainland China were enrolled from October 2020 to December 2020 to be surveyed for their treatment experience. Logistic regression was used to explore the factors that influence medication adherence in all areas, urban areas, and rural areas. Results: In this survey, 1,427 participants came from urban areas, and 539 participants came from rural areas. Patients in urban areas were older (mean age 74 vs. 70, p = 0.001), less frequently had mild AD (36.0 vs. 52.1%, p < 0.001), and more often were cared for at professional institutions (8.8 vs. 3.2%, p < 0.001). In terms of pharmacotherapy, 77.8% of people accepted taking lifelong medication, whereas 61.3% of patients insisted on taking medications. Although 72.0% of rural people believed in taking lifelong medication, only 30.0% adhered to drug use. The major factors that influenced medication adherence for all patients with AD were regional distribution (p < 0.001, OR = 6.18, 95% CI: 4.93-7.74) and family earnings (p = 0.003, OR = 1.22, 95% CI: 1.07-1.38). In rural areas, family earnings (p = 0.008, OR = 1.44, 95% CI: 1.10-1.89) and severity of AD (p = 0.033, OR = 1.31, 95% CI: 1.02-1.68) were the main factors. Family earnings (p = 0.038, OR = 1.16, 95% CI: 1.01-1.34) was the only factor among urban areas. Among all non-pharmaceutical activities except for cognitive intervention, the participation rates of rural patients were significantly higher than those of urban patients (p < 0.05). Conclusion: Although national progress has been made in the public awareness of disease treatment, adequate diagnosis and medication adherence need to be prompted, especially in rural areas. Furthermore, lifelong treatment should be improved based on regional characteristics through the joint efforts of the government, health workers, and social volunteers.

Longitudinal cognitive change and duration of Alzheimer's disease stages in relation to cognitive reserve.

The relationship of cognitive reserve and measures of reserve with longitudinal cognitive change and the duration of preclinical, prodromal, and mild Alzheimer disease (AD) dementia remains to be fully characterized. In our study, 660 ß-amyloid-positive participants staged with preclinical AD, prodromal AD, and dementia due to AD from the Alzheimer's Disease Neuroimaging Initiative were selected. Cognitive reserve and brain reserve were defined by conventional proxies or the residual method at baseline. We evaluated the utility of these reserves in predicting longitudinal cognitive change by mixed effects models and used a multi-state model to estimate stage duration stratified by reserve groups. Corrected for age, sex, and APOE-ε4 status, reserve was associated with cognitive decline, and the effects changed depending on the specific measures of reserve and the stage of AD. Reserves defined by the residual method were stronger predictors of cognitive change than those defined by conventional proxies. The estimated time from preclinical to mild AD dementia varied from 15-24 years based on the different reserve groups, and we observed a linear trend for the longest duration in individuals with high cognitive reserve/high brain reserve, followed by those with high cognitive reserve/low brain reserve, low cognitive reserve/high brain reserve, and low cognitive reserve/low brain reserve. This study showed a reduced risk of cognitive decline for individuals with higher level of reserve regardless of methods for measuring reserve. Interindividual differences in reserve may be important for clinical practice and trial design.

A review of brain imaging biomarker genomics in Alzheimer's disease: implementation and perspectives.

Alzheimer's disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.

Electronic structure modulation of nickel hydroxide porous nanowire arrays via manganese doping for urea-assisted energy-efficient hydrogen generation.

Replacement of the sluggish anodic reaction in water electrocatalysis by a thermodynamically favorable urea oxidation reaction (UOR) offers the prospect of energy-saving H2 generation, additionally mitigating urea-rich wastewater pollution, whereas the lack of highly efficient and earth-abundant UOR catalysts severely restricts widespread use of this catalytic system. Herein, Mn-doped nickel hydroxide porous nanowire arrays (denoted Mn-Ni(OH)2 PNAs) are rationally developed and evaluated as efficient catalysts for the UOR in an alkaline solution via the in situ electrochemical conversion of NiMn-based metal-organic frameworks. Mn doping can modulate the electronic structural configuration of Ni(OH)2 to significantly increase the electron density and optimize the energy barriers of the CO*/NH2* intermediates of the UOR. Meanwhile, porous nanowire arrays will afford abundant spaces/channels to facilitate active site exposure and electron/mass transfer. As a result, the Mn-Ni(OH)2 PNAs delivered superior UOR performance with a small potential of 1.37 V vs. RHE at 50 mA cm-2, a Tafel slope of 31 mV dec-1, and robust stability. Notably, the overall urea electrolysis system coupled with a commercial Pt/C cathode demonstrated excellent activity (1.40 V at 20 mA cm-2) and durability operation (only 1.40% decay after 48 h).

[SHENG Can-ruo's experience in treatment of goiter with integrated acupuncture and herbal medication].

The paper introduces professor SHENG Can-ruo's experience in treatment of goiter with the combination of acupuncture and herbal medication. Professor SHENG believes that this disease is mostly related with emotional injury, improper diet and geographical and climatic factors, as well as body constitution. Qi stagnation, phlegm retention, blood stagnation and interaction of phlegm and stasis are the essential pathogenesis of goiter. Either acupuncture or herbal medication should focus on "phlegm and stasis" in treatment. Besides, the theory of western medicine should also be considered. In western medicine, thyroid enlargement is classified into Ⅰ, Ⅱ and Ⅲ degrees of struma, thus, the pathogenesis and treatment with Chinese medicine should be adjusted accordingly. The created "four throat points", combined with acupuncture at distal points, relieve the local masses. The basic herbal formula is prepared and the couplet medicines are modified based on syndrome differentiation. The integrated acupuncture and herbal medication regulates emotions and provides a comprehensive treatment for goiter.

Construction of NiS/Ni3S4 heteronanorod arrays in graphitized carbonized wood frameworks as versatile catalysts for efficient urea-assisted water splitting.

Exploring highly efficient, robust, and stable catalysts for urea electrolysis is intensively desirable for hydrogen production but remains a challenging task. In this work, novel, well-aligned, self-supported NiS/Ni3S4 heteronanorod arrays deposited on graphitized carbonized wood (GCW) are designed (denoted as NiS/Ni3S4/GCW) and synthesized by a facile hydrothermal sulfidation strategy. Benefitting from the optimized surface hydrophilicity/aerophobicity, enhanced electrical conductivity, and 3D hierarchical directional porous architectures, the NiS/Ni3S4/GCW show excellent activity toward the urea oxidation reaction and hydrogen evolution reaction in alkaline electrolytes. The arrays achieved a low potential of 1.33 V (vs. RHE) and 91 mV (overpotential) at 10 mA cm-2 as well as robust stability for 100 h. Significantly, when employed as anode and cathode simultaneously, the urea electrolyzer constructed by NiS/Ni3S4/GCW catalysts merely requires a low voltage of 1.44 V to drive 10 mA cm-2 with superior stability for 50 h. This work not only demonstrates the application of heterogeneous sulfide for urea-assisted hydrogen production but also provides an effective guideline for using renewable wood for designing efficient catalysts in an economical way.

Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment.

Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 ~ 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment.

CoP Nanoparticle Confined in P, N Co-Doped Porous Carbon Anchored on P-Doped Carbonized Wood Fibers with Tailored Electronic Structure for Efficient Urea Electro-Oxidation.

Electronic structure optimization and architecture modulation are widely regarded as rational strategies to enhance the electrocatalysts catalytic performance. Herein, a hybridization of ZIF-67-derived CoP nanoparticles embedded in P, N co-doped carbon matrix (PNC) and anchored on P-doped carbonized wood fibers (PCWF) is constructed using a simple simultaneous phosphorization and carbonization strategy. Benefiting from the optimized surface/interface electronic structures, abundant exposed active sites, and outstanding conductivity, the CoP@PNC/PCWF can drive the urea oxidation reaction (UOR) with greater activity and better stability than most recently reported electrocatalysts, in which a potential as low as 1.32 V (vs reversible hydrogen electrode, RHE) is needed to reach 50 mA cm-2 and shows excellent durability. Furthermore, for overall urea splitting, using the CoP@PNC/PCWF electrocatalyst as the anode and commercial Pt/C supported on nickel foam as the cathode, an ultralow cell voltage of 1.50 V (vs RHE) is expected to achieve the 50 mA cm-2 and operate continuously for more than 50 h at 20 mA cm-2 . The reported strategy may shed light on the use of renewable resources to design and synthesize high-performance non-Ni-based phosphides UOR electrocatalysts for energy-saving H2 production.

Cross-Cultural Longitudinal Study on Cognitive Decline (CLoCODE) for Subjective Cognitive Decline in China and Germany: A Protocol for Study Design.

BACKGROUND: Subjective cognitive decline (SCD) is considered as the first symptomatic manifestation of Alzheimer's disease (AD), which is also affected by different cultural backgrounds. Establishing cross-cultural prediction models of SCD is challenging. OBJECTIVE: To establish prediction models of SCD available for both the Chinese and European populations. METHODS: In this project, 330 SCD from China and 380 SCD from Germany are intended to be recruited. For all participants, standardized assessments, including clinical, neuropsychological, apolipoprotein E (APOE) genotype, blood, and multi-parameter magnetic resonance imaging (MRI) at baseline will be conducted. Participants will voluntarily undergo amyloid positron emission tomography (PET) and are classified into amyloid-ß (Aß) positive SCD (SCD+) and Aß negative SCD (SCD-). First, baseline data of all SCD individuals between the two cohorts will be compared. Then, key features associated with brain amyloidosis will be extracted in SCD+ individuals, and the diagnosis model will be established using the radiomics method. Finally, the follow-up visits will be conducted every 12 months and the primary outcome is the conversion to mild cognitive impairment or dementia. After a 4-year follow-up, we will extract factors associated with the conversion risk of SCD using Cox regression analysis. RESULTS: At present, 141 SCD from China and 338 SCD from Germany have been recruited. Initial analysis showed significant differences in demographic information, neuropsychological tests, and regional brain atrophy in SCD compared with controls in both cohorts. CONCLUSION: This project may be of great value for future implications of SCD studies in different cultural backgrounds. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04696315. Registered 3 January 2021.

Mechanical Behavior and Constitutive Model Characterization of Optically Clear Adhesive in Flexible Devices.

Optically clear adhesive (OCA) has been widely used in flexible devices, where wavy stripes that cause troublesome long-term reliability problems often occur. The complex mechanical behavior of OCA should be studied, as it is related to the aforementioned problems. Therefore, it is necessary to establish reasonable mechanical constitutive models for deformation and stress control. In this work, hyperelastic and viscoelastic mechanical tests were carried out systematically and relative constitutive models of OCA material were established. We found that temperature has a great influence on OCA's mechanical properties. The stress and modulus both decreased rapidly as the temperature increased. In the static viscoelasticity test, the initial stress at 85 °C was only 12.6 kPa, 57.4% lower than the initial stress at 30 °C. However, in the dynamic test, the storage modulus monotonically decreased from 1666.3 MPa to 0.6628 MPa as the temperature rose, and the decline rate reached the maximum near the glass transition temperature (Tg = 0 °C). The test data and constitutive models can be used as design references in the manufacturing process, as well as for product reliability evaluation.

Combination of gut microbiota and plasma amyloid-ß as a potential index for identifying preclinical Alzheimer's disease: a cross-sectional analysis from the SILCODE study.

BACKGROUND: Plasma amyloid-ß (Aß) may facilitate identification of individuals with brain amyloidosis. Gut microbial dysbiosis in Alzheimer's disease (AD) is increasingly being recognized. However, knowledge about alterations of gut microbiota in preclinical AD, as well as whether the combination of plasma Aß and gut microbiota could identify preclinical AD, remains largely unknown. METHODS: This study recruited 34 Aß-negative cognitively normal (CN-) participants, 32 Aß-positive cognitively normal (CN+) participants, and 22 patients with cognitive impairment (CI), including 11 patients with mild cognitive impairment (MCI) and 11 AD dementia patients. All participants underwent neuropsychological assessments and fecal microbiota analysis through 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. Meso Scale Discovery (MSD) kits were used to quantify the plasma Aß40, Aß42, and Aß42/Aß40 in CN- and CN+ participants. Using Spearman's correlation analysis, the associations of global standard uptake value rate (SUVR) with altered gut microbiota and plasma Aß markers were separately evaluated. Furthermore, the discriminative power of the combination of gut microbiota and plasma Aß markers for identifying CN+ individuals was investigated. RESULTS: Compared with the CN- group, the CN+ group showed significantly reduced plasma Aß42 (p = 0.011) and Aß42/Aß40 (p = 0.003). The relative abundance of phylum Bacteroidetes was significantly enriched, whereas phylum Firmicutes and class Deltaproteobacteria were significantly decreased in CN+ individuals in comparison with that in CN- individuals. Particularly, the relative abundance of phylum Firmicutes and its corresponding SCFA-producing bacteria exhibited a progressive decline tendency from CN- to CN+ and CI. Besides, the global brain Aß burden was negatively associated with the plasma Aß42/Aß40 (r = -0.298, p = 0.015), family Desulfovibrionaceae (r = -0.331, p = 0.007), genus Bilophila (r = -0.247, p = 0.046), and genus Faecalibacterium (r = -0.291, p = 0.018) for all CN participants. Finally, the combination of plasma Aß markers, altered gut microbiota, and cognitive performance reached a relatively good discriminative power in identifying individuals with CN+ from CN- (AUC = 0.869, 95% CI 0.782 ~ 0.955). CONCLUSIONS: This study provided the evidence that the gut microbial composition was altered in preclinical AD. The combination of plasma Aß and gut microbiota may serve as a non-invasive, cost-effective diagnostic tool for early AD screening. Targeting the gut microbiota may be a novel therapeutic strategy for AD. TRIAL REGISTRATION: This study has been registered in ClinicalTrials.gov (NCT03370744, https://www.clinicaltrials.gov ) in November 15, 2017.

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE.

BACKGROUND: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. OBJECTIVE: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. METHODS: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. RESULTS: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p < 0.001). The above results appeared in both the whole brain and DMN. CONCLUSION: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

The Impact of Study Setting on Clinical Characteristics in Older Chinese Adults with Subjective Cognitive Decline: Baseline Investigation of Convenience and Population-Based Samples.

BACKGROUND: Subjective cognitive decline (SCD) is the earliest symptom stage of Alzheimer's disease (AD). Previous studies have shown that the study setting is an important influence factor of SCD. However, the effect of this factor among a Chinese population with SCD is not clear. Here, we aim to compare the clinical characteristics of SCD between a convenience and a population-based sample in China. METHODS: We included a convenience sample of 212 SCD subjects and a population-based sample of 110 SCD subjects. We performed univariate analysis to evaluate the between-group differences in sociodemographic characteristics, neuropsychological performance, psychiatric conditions, different cognitive domains, and the SCD-plus criteria. Multiple linear regression model was established, adjusted for sex, age, and education, and compared the neuropsychological performance between the groups. RESULTS: The convenience sample had more years of education, a higher family history of dementia, and higher neuropsychological and anxiety depression score than the population-based sample. Using sex, age, education, group as the independent variables, and neuropsychological score as the dependent variable, multiple linear regression model was established; a statistically significant neuropsychological score difference (MoCA-B, AVLT-H-N4, AVLT-H-N5, AVLT-H-N7, AFT, and STT-B) was found between the two samples. In the SCD cognitive domains, the population-based sample had more complaints about declines in their language and planning domains. For SCD-plus criteria in memory domain, the convenience sample had more complaints, worry, and cognitive decline within the last 5 years, along with medical help-seeking. CONCLUSION: There were some different characteristics among SCD individuals between convenience samples and population-based samples in China.

[Professor SHENG Can-ruo's experience in treatment of peripheral facial paralysis with combined therapy of acupuncture-moxibustion and herbal medicine].

This paper introduces the specific clinical experience of professor SHENG Can-ruo in treatment of peripheral facial paralysis with the combination of acupuncture-moxibustion and herbal medicine. Professor SHENG Can-ruo advocates "acupuncture-moxibustion for the external and herbal decoction for the internal", inventes "three facial points" and "jianei point" and experienced in "pair points" and "pair herbs". Professor SHENG rationally applies penetrating needling technique, mutually uses acupuncture-moxibustion and herbal medicine and determines the treatment in stages so that the remarkably clinical effect has been achieved on peripheral facial paralysis.