Metal-organic framework (MOF) thin films are receiving increasing attention in a number of different application fields, such as optoelectronics, gas separation, catalysis electronic devices, and biomedicine. In particular, their tunable composition and structure, accessible metal sites and potential for post-synthetic modification for molecular recognition make MOF thin films promising candidates for biosensing applications. Compared with solution-based powdery probes, film-based probes have distinct advantages of good stability and portability, tunable shape and size, real-time detection, non-invasion, extensive suitability in gas/vapor sensing, and recycling. In this review, we summarize the recent advances in luminescent MOF thin films, including the fabrication methods and origins of luminescence. Specifically, luminescent MOF thin films as biosensors for temperature, ions, gases and biomolecules are highlighted.

Background: Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) are characterized by abnormal functional connectivity (FC) of default-mode network (DMN), salience network (SN), and central executive network (CEN). Static FC (sFC) and dynamic FC (dFC) combined with triple network model can better study the dynamic and static changes of brain networks, and improve its potential diagnostic value in the diagnosis of AD spectrum disorders.Methods: Differences in sFC values and dFC variability patterns among the three brain networks of the three groups (53 AD patients, 40 aMCI patients, and 40 NCs) were computed by ANOVA using Gaussian Random Field theory (GRF) correction. The correlation between FC values (sFC values and dFC variability) in the three networks and cognitive scores (MMSE and MoCA) in AD and aMCI groups was analyzed separately.Results: Within the DMN network, there were significant differences of sFC values in right/left medial superior frontal gyrus and dFC variability in left opercular part inferior frontal gyrus and right dorsolateral superior frontal gyrus among the three groups. Within the CEN network, there were significant differences of sFC values in left superior parietal gyrus. Within the SN network, there were significant differences of dFC variability in right Cerebelum_7b and left opercular part inferior frontal gyrus. In addition, there was a significant negative correlation between FC values (sFC values of CEN and dFC variability of SN) and MMSE and MoCA scores.Conclusion: It suggests that sFC, dFC combined with triple network model can be considered as potential biomarkers for AD and aMCI.

Purpose: There are some challenges to diagnosis in the context of similar diagnostic criteria for late-life depression (LLD) and adult depression due to cognitive impairment and other clinical manifestations. The association between gut microbiota and inflammation remains unclear in LLD. We analyzed gut microbiota characteristics and serum inflammatory cytokines in individuals with LLD to explore the combined role of these two factors in potential biomarkers of LLD. Methods: This was an observational cross-sectional study. Fecal samples and peripheral blood from 29 patients and 33 sex- and age-matched healthy controls (HCs) were collected to detect gut microbiota and 12 inflammatory factors. We analyzed differences in diversity and composition of gut microbiota and evaluated relations among gut microbiota, inflammatory factors, and neuropsychological scales. We extracted potential biomarkers using receiver-operating characteristic curve analysis to predict LLD utilizing the combination of the microbiota and inflammatory cytokines. Results: Elevated systemic inflammatory cytokine levels and gut microbiota dysbiosis were found in LLD patients. Relative abundance of Verrucomicrobia at the phylum level and Megamonas, Citrobacter, and Akkermansia at the genus level among LLD patients was lower than HCs. Abundance of Coprococcus, Lachnobacterium, Oscillospira, and Sutterella was higher in LLD patients. Notably, IL6, IFNγ, Verrucomicrobia, and Akkermansia levels were correlated with depression severity. Our study identified IL6, Akkermansia, and Sutterella as predictors of LLD, and their combination achieved an area under the curve of 0.962 in distinguishing LLD patients from HCs. Conclusion: This research offers evidence of changes within gut microbiota and systemic inflammation in LLD. These findings possibly help elucidate functions of gut microbiota and systemic inflammation in LLD development and offer fresh ideas on biomarkers for clinical practise in the context of LLD.

Background: In the spectrum of Alzheimer’s Disease (AD) and related disorders, the resting-state functional magnetic resonance imaging (rs-fMRI) signals within the cerebral cortex may exhibit distinct characteristics across various frequency ranges. Nevertheless, this hypothesis has not yet been substantiated within the broader context of whole-brain functional connectivity. This study aims to explore potential modifications in degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) among individuals with amnestic mild cognitive impairment (aMCI) and AD, while assessing whether these alterations differ across distinct frequency bands.Methods: This investigation encompassed a total of 53 AD patients, 40 aMCI patients, and 40 healthy controls (HCs). DC and VMHC values were computed within three distinct frequency bands: classical (0.01–0.08 Hz), slow-4 (0.027–0.073 Hz), and slow-5 (0.01–0.027 Hz) for the three respective groups. To discern differences among these groups, ANOVA and subsequent post hoc two-sample t-tests were employed. Cognitive function assessment utilized the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation analysis was applied to investigate the associations between MMSE and MoCA scores with DC and VMHC.Results: Significant variations in degree centrality (DC) were observed among different groups across diverse frequency bands. The most notable differences were identified in the bilateral caudate nucleus (CN), bilateral medial superior frontal gyrus (mSFG), bilateral Lobule VIII of the cerebellar hemisphere (Lobule VIII), left precuneus (PCu), right Lobule VI of the cerebellar hemisphere (Lobule VI), and right Lobule IV and V of the cerebellar hemisphere (Lobule IV, V). Likewise, disparities in voxel-mirrored homotopic connectivity (VMHC) among groups were predominantly localized to the posterior cingulate gyrus (PCG) and Crus II of the cerebellar hemisphere (Crus II). Across the three frequency bands, the brain regions exhibiting significant differences in various parameters were most abundant in the slow-5 frequency band.Conclusion: This study enhances our understanding of the pathological and physiological mechanisms associated with AD continuum. Moreover, it underscores the importance of researchers considering various frequency bands in their investigations of brain function.

Background: Extensive evidence demonstrates correlations among gut microbiota, lipid metabolism and cognitive function. However, there is still a lack of researches in the field of late-life depression (LLD). This research targeted at investigating the relationship among gut microbiota, lipid metabolism indexes, such as total free fatty acids (FFAs), and cognitive functions in LLD. Methods: Twenty-nine LLD patients from the Cognitive Outcome Cohort Study of Depression in Elderly were included. Cognitive functions were estimated through the Chinese version of Montreal Cognitive Assessment (MoCA). Blood samples were collected to evaluate serum lipid metabolism parameters. Fecal samples were evaluated for gut microbiota determination via 16S rRNA sequencing. Spearman correlation, linear regression and mediation analysis were utilized to explore relationship among gut microbiota, lipid metabolism and cognitive function in LLD patients. Results: Spearman correlation analysis revealed significant correlations among Akkermansia abundance, total Free Fatty Acids (FFAs) and MoCA scores (P < 0.05). Multiple regression indicated Akkermansia and total FFAs significantly predicted MoCA scores (P < 0.05). Mediation analysis demonstrated that the correlation between decreased Akkermansia relative abundance and cognitive decline in LLD patients was partially mediated by total FFAs (Bootstrap 95%CI: 0.023–0.557), accounting for 43.0% of the relative effect. Conclusion: These findings suggested a significant relationship between cognitive functions in LLD and Akkermansia, as well as total FFAs. Total FFAs partially mediated the relationship between Akkermansia and cognitive functions. These results contributed to understanding the gut microbial-host lipid metabolism axis in the cognitive function of LLD.

Background: Neuroimaging studies have demonstrated alterations in hippocampal volume and hippocampal subfields among individuals with amnestic mild cognitive impairment (aMCI). However, research on using hippocampal subfield volume modeling to differentiate aMCI from normal controls (NCs) is limited, and the relationship between hippocampal volume and overall cognitive scores remains unclear.Methods: We enrolled 50 subjects with aMCI and 44 NCs for this study. Initially, a univariate general linear model was employed to analyze differences in the volumes of hippocampal subfields. Subsequently, two sets of dimensionality reduction methods and four machine learning techniques were applied to distinguish aMCI from NCs based on hippocampal subfield volumes. Finally, we assessed the correlation between the relative volumes of hippocampal subfields and cognitive test variables (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA)).Results: Significant volume differences were observed in several hippocampal subfields, notably in the left hippocampus. Specifically, the volumes of the hippocampal tail, subiculum, CA1, presubiculum, molecular layer, GC-ML-DG, CA3, CA4, and fimbria differed significantly between the two groups. The highest area under the curve (AUC) values for left and right hippocampal machine learning classifiers were 0.678 and 0.701, respectively. Moreover, the volumes of the left subiculum, left molecular layer, right subiculum, right CA1, right molecular layer, right GC-ML-DG, and right CA4 exhibited the strongest and most consistent correlations with MoCA scores.Conclusion: Hippocampal subfield volume may serve as a predictive marker for aMCI. These findings underscore the sensitivity of hippocampal subfield volume to overall cognitive performance.