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 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.

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 β (Aβ) 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 Aβ 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.

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.

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.

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 β-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.

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.

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.

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-β (Aβ) positive SCD (SCD+) and Aβ 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.

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

Background

Plasma amyloid-β (Aβ) 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 Aβ and gut microbiota could identify preclinical AD, remains largely unknown.

Methods

This study recruited 34 Aβ-negative cognitively normal (CN−) participants, 32 Aβ-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 Aβ₄₀, Aβ₄₂, and Aβ₄₂/Aβ₄₀ 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 Aβ markers were separately evaluated. Furthermore, the discriminative power of the combination of gut microbiota and plasma Aβ markers for identifying CN+ individuals was investigated.

Results

Compared with the CN− group, the CN+ group showed significantly reduced plasma Aβ₄₂ (p = 0.011) and Aβ₄₂/Aβ₄₀ (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 Aβ burden was negatively associated with the plasma Aβ₄₂/Aβ₄₀ (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 Aβ 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 Aβ 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-00977-x.

Molecular Dynamics Investigation of the Thermo-Mechanical Properties of the Moisture Invaded and Cross-Linked Epoxy System

In spite of a high market share of plastic IC packaging, there are still reliability issues, especially for the effects of moisture. The mechanism between moisture and epoxy polymer is still obscure. A multi-step cross-linking approach was used to mimic the cross-linking process between the DGEBA resin and JEFFAMINE^®-D230 agent. Based on the molecular dynamics method, the thermo-mechanical properties and microstructure of epoxy polymer were analyzed. In this paper, the degree of cross-linking ranged from 0% to 85.4% and the moisture concentration ranged from 0 wt.% to 12 wt.%. The hydrogen bonds were investigated in the moisture invaded epoxy polymer. Although most of the hydrogen bonds were related to water molecules, the hydrogen bonds between the inside of epoxy polymer were reduced only a little as the concentration of moisture increased. The diffusion coefficient of the water molecules was found to increase with the increase of moisture concentration. When the moisture concentration was larger than 12 wt.% or smaller than 1.6 wt.%, the diffusion coefficient was less affected by the epoxy polymer. In addition, the free volume and the thermal conductivity of the epoxy polymer were considered. It was found that the moisture could increase the thermal conductivity from 0.24 to 0.31 W/m/K, identifying a coupling relationship between moisture and thermal properties. Finally, the mechanical properties of epoxy polymer were analyzed by uniaxial tensile simulation. The COMPASS and DREIDING force fields were used during the uniaxial tensile simulation. A better result was achieved from the DREIDING force field compared with the experiment. The degree of cross-linking was positively correlated with mechanical properties. For the system with the largest degree of cross-linking of 85.4%, the Young's modulus was 2.134 ± 0.522 GPa and the yield strength was 0.081 ± 0.01 GPa. There were both plasticizing and anti-plasticizing effects when the water molecules entered the epoxy polymer. Both the Young's moduli and yield strength varied in a large range from 1.38 to 2.344 GPa and from 0.062 to 0.128 GPa, respectively.

[Application of multi-stage competing risk model to survival data]

The traditional proportional hazard model is commonly used to investigate the association between main outcome and predictor variables. However, the endpoints in medical studies are often not unique. The analyses of labeling other competing outcomes other than the main outcome as censored data will theoretically lead to a biased estimate of the risk of main outcome. Although the traditional competitive risk model can adjust the influence of other outcomes on the risk of the main outcome, it can not directly compare the differences on the risks of different outcomes. The multi-state competing risk model provides a relatively suitable solution for this problem. In this study, based on a previously published follow-up data set for prostate cancer patients, we developed traditional proportional hazard model, traditional competitive risk model, and multi-state competing risk model, respectively. By comparing the advantages and disadvantages of the three models with the same survival data, we clarified the clinical application value of the multi-state competitive risk model in survival data with multiple outcomes.

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.

[Potential pleiotropism of cancer-related single nucleotide polymorphisms among Chinese population]

Objective: To investigate the potential pleiotropism of cancer-related single nucleotide polymorphisms (SNPs) among Chinese population. Methods: Based on the catalogue of GWAS jointly constructed by the National Human Genome Research Institute and the European Institute of Bioinformatics, according to population origin (Chinese population and non-Chinese population) and disease traits (cancer and non-cancer traits). All SNPs found by GWAS before August 2020 were divided into four categories: cancer in Chinese population, non-cancer in Chinese population, cancer in non-Chinese population and non-cancer in non-Chinese population. The number, correlation and linkage of the four categories of SNPs were described. Results: By August 2020, a total of 196 813 SNPs from 4 096 GWAS were included in the GWAS directory. The information that SNPs refer to unknown or were not related to the disease was excluded, and 117 441 independent SNPs were finally included. There were 619 SNPs related to cancer and 9 569 SNPs related to non-cancer disease in Chinese population, respectively. There were 4 624 SNPs related to cancer and 106 448 SNPs related to non-cancer disease (trait) in non-Chinese population, respectively. Three SNPs, rs2736100, rs6983267 and rs401681, were associated with two or more types of cancer in both Chinese and non-Chinese populations. Seven SNPs, rs7705526, rs2736100, rs10993994, rs2735839, rs4430796, rs174537 and rs9271588, were associated with cancer and non-cancer diseases in both Chinese and non-Chinese populations, respectively. Conclusion: There is a potential pleiotropism of cancer-related SNPs in Chinese population.

Combining Visual Rating Scales for Medial Temporal Lobe Atrophy and Posterior Atrophy to Identify Amnestic Mild Cognitive Impairment from Cognitively Normal Older Adults: Evidence Based on Two Cohorts

BACKGROUND

Visual rating scales for medial temporal lobe atrophy (MTA) and posterior atrophy (PA) have been reported to be useful for Alzheimer's disease diagnosis in routine clinical practice.

OBJECTIVE

To investigate the efficacy of combined MTA and PA visual rating scales to discriminate amnestic mild cognitive impairment (aMCI) patients from healthy controls.

METHODS

This study included T1-weighted MRI images from two different cohorts. In the first cohort, we recruited 73 patients with aMCI and 48 group-matched cognitively normal controls for training and validation. Visual assessments of MTA and PA were carried out for each participant. Global gray matter volume and density were estimated using voxel-based morphometry analysis as the objective reference. We investigated the discriminative power of a single visual rating scale and the combination of the MTA and PA rating scales for identifying aMCI. The second cohort, consisting of 33 aMCI patients and 45 controls, was used to verify the reliability of the visual assessments.

RESULTS

Compared with the single visual rating scale, the combination of the MTA and PA exhibited the best discriminative power, with an AUC of 0.818±0.041, which was similar to the diagnostic accuracy of the gray matter volumetric measures. The discriminative power of the combined MTA and PA was verified in the second cohort (AUC 0.824±0.058).

CONCLUSION

The combined MTA and PA rating scales demonstrated practical diagnostic value for distinguishing aMCI patients from controls, suggesting its potential to serve as a convenient and reproducible method to assess the degree of atrophy in clinical settings.

Comparing the bone mineral density among male patients with latent autoimmune diabetes and classical type 1 and type 2 diabetes, and exploring risk factors for osteoporosis

AIMS

This study aimed to compare the bone mineral densities (BMDs) among male patients with latent autoimmune diabetes in adults (LADA), classical type 1 diabetes (T1DM), and type 2 diabetes (T2DM), and to examine the risk factors for developing low BMD in these patients.

PATIENTS AND METHODS

Between January 2017 and October 2020, a total of 57, 67, and 223 male patients with LADA, classical T1DM, and T2DM, respectively, were recruited from the endocrinology department of Shanghai Tenth People's Hospital. Hormonal markers of bone metabolism, lipid profiles, uric acid, glycosylated hemoglobin A1c (HbA1c), and beta-cell function were measured using blood samples. BMD was measured at the lumbar spine, femoral neck, and right hip by dual-energy X-ray absorptiometry.

RESULTS

The mean BMD values from all three skeletal sites in male patients with LADA were comparable to those with classical T1DM but were much lower than those with T2DM. After adjusting for confounding factors, multiple linear regression analysis demonstrated that in all male patients with diabetes, body mass index (BMI), uric acid, and fasting C-peptide showed significant positive associations with BMD at all three skeletal sites; however, osteocalcin showed a negative association at all three sites.

CONCLUSIONS

Compared with male patients with T2DM, lower BMDs were observed in patients with LADA and T1DM. Low BMI, uric acid, C-peptide levels, and high osteocalcin levels are risk factors for developing low BMD in male patients with diabetes.

Altered Gut Microbiota in Adults with Subjective Cognitive Decline: The SILCODE Study

BACKGROUND

Subjective cognitive decline (SCD) is the earliest symptomatic manifestation of preclinical Alzheimer's disease (AD). Gut microbiota may serve as a susceptibility factor for AD. Altered gut microbiota has been reported in patients with mild cognitive impairment (MCI) and AD dementia. However, whether gut microbial compositions changed in SCD remains largely unknown.

OBJECTIVE

To characterize the gut microbiota in SCD.

METHODS

In this study, a total of 105 participants including 38 normal controls (NC), 53 individuals with SCD, and 14 patients with cognitive impairment (CI) were recruited. Gut microbiota of all participants isolated from fecal samples were investigated using 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. The gut microbial compositions were compared among the three groups, and the association between altered gut microbiota and cognitive performance was analyzed. To validate the alteration of gut microbiota in SCD, we conducted amyloid positron emission tomography (PET) in selected participants and further compared the gut microbiota among subgroups.

RESULTS

The abundance of phylum Firmicutes, class Clostridia, order Clostridiales, family Ruminococcaceae, and genus Faecalibacterium showed a trend toward a progressive decline from NC to SCD and CI. Specifically, the abundance of the anti-inflammatory genus Faecalibacterium was significantly decreased in SCD compared with NC. In addition, altered bacterial taxa among the three groups were associated with cognitive performance. The findings were validated in SCD participants with positive amyloid evidence.

CONCLUSION

The composition of gut microbiota is altered in individuals with SCD. This preliminary study will provide novel insights into the pathophysiological mechanism of AD.

Cognitive Reserve, Brain Reserve, APOEɛ4, and Cognition in Individuals with Subjective Cognitive Decline in the SILCODE Study

BACKGROUND

Cognitive reserve (CR) and brain reserve (BR) could offer protective effects on cognition in the early stage of Alzheimer's disease (AD). However, the effects of CR or BR on cognition in individuals with subjective cognitive decline (SCD) are not clear.

OBJECTIVE

To explore the effects of CR and BR on cognition in subjects with SCD.

METHODS

We included 149 subjects from the Sino Longitudinal Study on Cognitive Decline (SILCODE) study. Education was used as a proxy for CR, and head circumference was used as a proxy for BR. Multiple linear regression models were conducted to examine the effects of CR and BR on cognitive scores. Furthermore, we assessed differences in effects between APOEɛ4 carriers with SCD (n = 35) and APOEɛ4 non-carriers with SCD (n = 114) and linear trends among 4 reserve levels (low BR/CR, high BR/low CR, low BR/high CR, and high BR/high CR).

RESULTS

Both CR and BR had independent positive effects on multiple cognitive measures in SCD participants, and the effects of CR were greater than those of BR. CR has positive effects on cognitive measures in both APOEɛ4 carriers and non-carriers with SCD. However, the positive effects of BR on cognitive measures were observed in APOEɛ4 non-carriers with SCD but not in APOEɛ4 carriers with SCD. Furthermore, there was a linear trend toward better cognitive performance on all cognitive measures in the BR+/CR+ group, followed by the BR-/CR+, BR+/CR-, and BR-/CR-groups.

CONCLUSION

This study suggests that both CR and BR have the potential to delay or slow cognitive decline in individuals with SCD.

A Novel Detection Tool for Mild Cognitive Impairment Patients Based on Eye Movement and Electroencephalogram

BACKGROUND

Detecting subtle changes in visual attention from electroencephalography (EEG) and the perspective of eye movement in mild cognitive impairment (MCI) patients can be of great significance in screening early Alzheimer's disease (AD) in a large population at primary care.

OBJECTIVE

We proposed an automatic, non-invasive, and quick MCI detection approach based on multimodal physiological signals for clinical decision-marking.

METHODS

The proposed model recruited 152 patients with MCI and 184 healthy elderly controls (HC) who underwent EEG and eye movement signal recording under a visual stimuli task, as well as other neuropsychological assessments. Forty features were extracted from EEG and eye movement signals by linear and nonlinear analysis. The features related to MCI were selected by logistic regression analysis. To evaluate the efficacy of this MCI detection approach, we applied the same procedures to achieve the Clinical model, EEG model, Eye movement model, EEG+ Clinical model, Eye movement+ Clinical model, and Combined model, and compared the classification accuracy between the MCI and HC groups with the above six models.

RESULTS

After the penalization of logistic regression analysis, five features from EEG and eye movement features exhibited significant differences (p < 0.05). In the classification experiment, the combined model resulted in the best accuracy. The average accuracy for the Clinical/EEG/Eye movement/EEG+ Clinical/Eye movement+ Clinical/Combined model was 68.69%, 61.79%, 73.13%, 69.46%, 75.61%, and 81.51%, respectively.

CONCLUSION

These results suggest that the proposed MCI detection tool has the potential to screen MCI patients from HCs and may be a powerful tool for personalized precision MCI screening in the large-scale population under primary care condition.

Advances in Non-Pharmacological Interventions for Subjective Cognitive Decline: A Systematic Review and Meta-Analysis

Background: Subjective cognitive decline (SCD) is considered the earliest symptomatic manifestation of preclinical Alzheimer’s disease (AD). Currently, given the lack of effective and curable pharmacological treatments for AD, non-pharmacological interventions (NPIs) for individuals with SCD may provide a valuable opportunity for the secondary prevention of AD. Objective: This systematic review and meta-analysis, conducted in accordance with the PRISMA guidelines, aimed to investigate the benefits of current NPIs in the population with SCD. Methods: The online electronic databases, including MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, PsycInfo, and CINAHL, were searched to identify randomized controlled trials of NPIs for SCD. Intervention strategies were psychological and health-related education interventions, mind-body therapy, lifestyle modification, cognitive training, and multidomain interventions. Outcomes included subjective memory, objective memory, global cognitive function, psychological well-being, and mood. Study quality was determined using the criteria of the Cochrane collaboration’s tool. The Hedges’ g of change was analyzed. Results: Eighteen studies were included in this review and meta-analysis. Overall, psychological and health-related education interventions exhibited a medium effect on objective memory function (Hedges’ g = 0.53, p = 0.01). Cognitive training led to a small effect on objective memory, which was marginal statistically (Hedges’ g = 0.19, p = 0.05). In addition, cognitive training also significantly improved subjective memory performance (Hedges’ g = 0.49, p = 0.0003) and psychological well-being (Hedges’ g = 0.27, p = 0.03). Conclusion: Overall, the psychological intervention and cognitive training may be beneficial to cognitive function and psychological well-being. NPIs may be effectively implemented in older adults with SCD.

The relationship between urinary Alzheimer-associated neuronal thread protein and blood biochemical indicators in the general population

Urinary Alzheimer-associated neuronal thread protein (AD7c-NTP) is elevated in early Alzheimer's disease (AD) and mild cognitive impairment, and is considered a biomarker for the early diagnosis of AD. However, it has not yet been investigated whether urinary AD7c-NTP is elevated with increases in blood biochemical indicators related to AD risk factors. We recruited 2180 participants, aged 35-93 years, from communities of four districts in Beijing. Blood biochemical indicators, including blood glucose, blood lipids, renal function, and high-sensitivity C-reactive protein, were measured using routine methods. Urinary AD7c-NTP was detected using an enzyme-linked immunosorbent assay AD7c-NTP kit. In the general population, there were no significant differences in urinary AD7c-NTP levels in subjects with different Mini-Mental State Examination levels or C-reactive protein values. After adjusting for age and sex, there were significant differences in urinary AD7c-NTP levels between different education levels, marital statuses, blood glucose, blood lipids, and kidney function. There was a negative correlation between urinary AD7c-NTP levels and serum creatinine (r = -0.128). There was a positive correlation between urinary AD7c-NTP levels and HbA1c (r = 0.104), insulin (r = 0.101), and triglycerides (r = 0.093). Urinary AD7c-NTP might be useful as a potential indicator to predict AD risk.

Beneficial Effects of Brain Reserve on Cognition in Individuals with Subjective Cognitive Decline from the SILCODE Study

BACKGROUND

Clinical research has demonstrated that brain reserve (BR) could exert positive effects on cognition for patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the effects of BR on cognition in individuals with subjective cognitive decline (SCD) are not clear.

OBJECTIVE

To examine cross-sectional effects of BR on cognition in SCD populations.

METHODS

One hundred forty-nine subjects were studied from the Sino Longitudinal Study on Cognitive Decline (SILCODE) study. Head circumference was used as a proxy of BR. Cognition was assessed across four domains (memory, executive, language, and general cognitive functions). Multiple linear regression models were conducted to examine effects of BR on cognitive scores. Furthermore, we addressed the question that whether the degree of self-perception of cognitive decline modified the effect of BR on cognitive performance in SCD subjects.

RESULTS

We found a positive effect of BR on language cognition in subjects with SCD. Furthermore, the positive effect of BR on language cognition survived in SCD participants with a low degree of self-perception of cognitive decline while disappeared in SCD participants with a high degree of self-perception of cognitive decline.

CONCLUSION

This study suggests that BR has the potential to delay or slow down cognitive decline in SCD individuals, especially for mild SCD.

Extracellular vesicles as an emerging tool for the early detection of Alzheimer's disease

Alzheimer's disease (AD) is characterized by a series of interacting pathophysiological cascades, including the aggregation of β-amyloid plaques and the formation of neurofibrillary tangles derived from hyperphosphorylated tau proteins. AD is the cause of approximately 70 % of dementia, an irreversible and untreatable syndrome at its late stage. Hence, more efforts should be devoted to identifying at-risk or preclinical AD populations for early intervention and the improved design of drug trials. The exosome, a nanoscale subtype of extracellular vesicle that serves as a cell-to-cell communication messenger, is an emerging liquid biopsy tool for various diseases including AD. Recently, it has been discovered that brain-derived exosomes can flow through the blood-brain barrier to the peripheral blood, containing important protein and nucleic acid biomarkers that are associated with the pathogenesis and progression of AD. Other reports showed a strong involvement of exosomes in synaptic function, insulin resistance, and neuroinflammation, among others. Here, we summarize those studies and assess the value of exosomes as an emerging tool for the early detection of AD in conjunction with the current clinical diagnosis paradigm.

Dissection of prodromal Alzheimer's disease

Pathophysiological changes of Alzheimer's Disease (AD) begin decades before clinical symptoms become apparent, providing an important window for early diagnosis and intervention. Prodromal stage of AD, a great opportunity for effective treatment and postponing the disease onset, has drawn extensive attention. The application of different biomarkers including neuroimaging, biochemical substances and genes makes AD-related pathology detectable in vivo and exploring novel biomarkers with relatively non-invasive and low cost has intrigued a wide range of interests. To identify individuals with high risk of conversion to AD and apply the research concept of prodromal AD into clinical practice, the utility of various biomarkers for distinguishing prodromal AD is evaluated in this review. Additionally, clinical management focusing on the stage of prodromal AD is summarized in this review for dementia prevention.

Age-related differences in the topological efficiency of the brain structural connectome in amnestic mild cognitive impairment

Amnestic mild cognitive impairment (aMCI) is accompanied by the accelerated cognitive decline and rapid brain degeneration with aging. However, the age-related alterations of the topological organization of the brain connectome in aMCI patients remained largely unknown. In this study, we constructed the brain structural connectome in 51 aMCI patients and 51 healthy controls by diffusion magnetic resonance imaging and deterministic tractography. The different age-related alteration patterns of the global and regional network metrics between aMCI patients and healthy controls were assessed by a linear regression model. Compared with healthy controls, significantly decreased global and local network efficiency in aMCI patients were found. When correlating network efficiency with age, we observed a significant decline in network efficiency with aging in the aMCI patients, while not in the healthy controls. The age-related decreases of nodal efficiency in aMCI patients were mainly distributed in the key regions of the default-mode network, such as precuneus, anterior cingulate gyrus, and parahippocampal gyrus. In addition, age-related decreases in the connection strength of the edges between peripheral nodes were observed in aMCI patients. Moreover, the decreased regional efficiency of the parahippocampal gyrus was correlated with impaired memory performances in patients. The present study suggests an age-related disruption of the topological organization of the brain structural connectome in aMCI patients, which may provide evidence for different neural mechanisms underlying aging in aMCI and may serve as a potential imaging marker for the early diagnosis of Alzheimer's disease.

Abnormal global functional network connectivity and its relationship to medial temporal atrophy in patients with amnestic mild cognitive impairment

Background

Amnestic mild cognitive impairment (aMCI), which is recently considered as a high risk status for developing Alzheimer’s disease (AD), manifests with gray matter atrophy and increased focal functional activity in the medial temporal lobe (MTL). However, the abnormalities of whole-brain functional network connectivity in aMCI and its relationship to medial temporal atrophy (MTA) remain unknown.

Methods

In this study, thirty-six aMCI patients and thirty-five healthy controls (HCs) were recruited. Neuropsychological assessments and MTA visual rating scaling were carried out on all participants. Furthermore, whole brain functional network was constructed at voxel level, and functional connectivity strength (FCS) was computed as the sum of the connections for each node to capture its global integrity. General linear model was used to analyze the FCS values differences between aMCI and HCs. Then, the regions showing significant FCS differences were adopted as the imaging markers for discriminative analysis. Finally, the relationship between FCS values and clinical cognitive scores was correlated in patients with aMCI.

Results

Comparing to HCs, aMCI exhibited significant atrophy in the MTL, while higher FCS values within the bilateral MTL regions and orbitofrontal cortices. Notably, the right hippocampus had the highest classification power, with the area under receiver operating characteristics (ROC) curve (AUC) of 0.790 (confidence interval: 0.678, 0.901). Moreover, FCS values of the right hippocampus and the left temporal pole were positively correlated with the cognitive performance in aMCI.

Conclusion

This study demonstrated significantly structural atrophy and raised global functional integrity in the MTL, suggesting simultaneous disruption and compensation in prodromal AD. Increased intrinsic functional connectivity in the MTL may have the potential to discriminate subjects with tendency to develop AD.

Study on the clinical application of the MRS in the cognitive assessment after stroke

OBJECTIVE

To discuss the value of the application of the magnetic resonance spectroscopy (MRS) in the assessment of cognitive function impairment and the observation of therapeutic effect.

PATIENTS AND METHODS

30 patients with cognitive impairment after stroke (cognitive impairment group) and 30 patients with no apparent cognitive impairment (control group of the stroke) were selected through the screening of the mini-mental state examination (MMSE) scale, and another 30 healthy volunteers were selected (control group of the health). The general information of gender, age, education degree, disease duration and so on was matched among the 3 groups. MRS examined all the patients of the 3 groups at the time of enrollment, and the patients of both cognitive impairment and control groups received another assessment 2 months after treatment.

RESULTS

The NAA/Cr (N-acetyl aspartate/creatine) of the bilateral hippocampus of the cognitive impairment group was lower than those of both control group of stroke and health (p < 0.05), while the Cho/Cr was higher (p < 0.05). After treatment, both patients of the cognitive impairment group and the control group of the stroke experienced an increase of NAA/Cr and a decrease of Cho/Cr (p < 0.05).

CONCLUSIONS

MRS is applicative in the assessment of the cognitive impairment degree of the stroke patients and can also effectively identify the existence of the cognitive impairment, which makes it preferably valuable in the clinical application.

Identification of Amnestic Mild Cognitive Impairment Using Multi-Modal Brain Features: A Combined Structural MRI and Diffusion Tensor Imaging Study

Identifying amnestic mild cognitive impairment (aMCI) is of great clinical importance because aMCI is a putative prodromal stage of Alzheimer's disease. The present study aimed to explore the feasibility of accurately identifying aMCI with a magnetic resonance imaging (MRI) biomarker. We integrated measures of both gray matter (GM) abnormalities derived from structural MRI and white matter (WM) alterations acquired from diffusion tensor imaging at the voxel level across the entire brain. In particular, multi-modal brain features, including GM volume, WM fractional anisotropy, and mean diffusivity, were extracted from a relatively large sample of 64 Han Chinese aMCI patients and 64 matched controls. Then, support vector machine classifiers for GM volume, FA, and MD were fused to distinguish the aMCI patients from the controls. The fused classifier was evaluated with the leave-one-out and the 10-fold cross-validations, and the classifier had an accuracy of 83.59% and an area under the curve of 0.862. The most discriminative regions of GM were mainly located in the medial temporal lobe, temporal lobe, precuneus, cingulate gyrus, parietal lobe, and frontal lobe, whereas the most discriminative regions of WM were mainly located in the corpus callosum, cingulum, corona radiata, frontal lobe, and parietal lobe. Our findings suggest that aMCI is characterized by a distributed pattern of GM abnormalities and WM alterations that represent discriminative power and reflect relevant pathological changes in the brain, and these changes further highlight the advantage of multi-modal feature integration for identifying aMCI.

Refractive index sensor based on the leaky radiation of a microfiber

In this work we present a refractive index sensor based on the leaky radiation of a microfiber. The 5.3um diameter microfiber is fabricated by drawing a commercial optical fiber. When the microfiber is immersed into a liquid with larger refractive index than the effective index of fiber mode, the light will leak out through the leaky radiation process. The variation of refractive index of liquid can be monitored by measuring radiation angle of light. The refractive index sensitivity can be over 400 degree/RIU in theory. In the experiment, the variation value 0.001 of refractive index of liquid around this microfiber can be detected through this technique. This work provides a simple and sensitive method for refractive index sensing application.

Transformation bending device emulated by graded-index waveguide

We demonstrate that a transformation device can be emulated using a gradient-index waveguide. The effective index of the waveguide is spatially varied by tailoring a gradient thickness dielectric waveguide. Based on this technology, we demonstrate a transformation device guiding visible light around a sharp corner, with low scattering loss and reflection loss. The experimental results are in good agreement with the numerical results.

Simple way for achieving passive all-optical switching of continuous waves lasers using pure nematic liquid crystal

We have examined pure nematic liquid crystal (LC), 4'-pentyl-4-biphenylcarbonitrile (5-CB), with a 90° twisted alignment within a cell made of two cross-polarized absorptive plastic polarizers, and investigated the nonlinear transmission properties using cw (532 nm) lasers. We observed optically self-activated polarization switching with a factor of three lower switching power than a dye-doped LC cell with similar linear transmittance using glass substrates. We also studied the dynamics of the switching processes and observed millisecond switching time. These studies have demonstrated a simpler but more efficient way for fabricating broadband, low switching power, millisecond time scale switching, and optical limiting devices.

New lead structures in antifungal drug discovery

During the past two decades, the incidence of invasive fungal infections has been increasing dramatically. Clinical available antifungal agents have several drawbacks such as limited potency and spectrum, drug related toxicity, non-optimal pharmacokinetics, and severe resistance. There is an emergent need to develop new antifungal drugs with novel chemical structures and novel mechanism of action. This review will focus on the most significant achievements in the discovery of antifungal lead structures within last few years. In particular, we pay more attention on the structure-activity relationship of antifungal leads and provide perspectives for future antifungal drug discovery.