Toxicity assessment of dioxins and their transformation by-products from inferred degradation pathways.

Due to the significant POPs characteristics, dioxins caused concern in public health and environmental protection. Evaluating the toxicity risk of dioxin degradation pathways is critical. OCDD, 1,2,3,4,6,7,8-HpCDD, and 1,2,3,4,6,7,8-HpCDF, which are highly abundant in the environment and have strong biodegradation capabilities, were selected as precursor molecules in this study. Firstly, their transformation pathways were deduced during the metabolism of biometabolism, microbial aerobic, microbial anaerobic, and photodegradation pathways, and density function theory (DFT) was used to calculate the Gibbs free energy to infer the possibility of the occurrence of the transformation pathway. Secondly, the carcinogenic potential of the precursors and their degradation products was evaluated using the TOPKAT modeling method. With the help of the positive indicator (0-1) normalization method and heat map analysis, a significant increase in the toxic effect of some of the transformation products was found, and it was inferred that it was related to the structure of the transformation products. Meanwhile, the strength of the endocrine disrupting effect of dioxin transformation products was quantitatively assessed using molecular docking and subjective assignment methods, and it was found that dioxin transformation products with a higher content of chlorine atoms and molecules similar to those of thyroid hormones exhibited a higher risk of endocrine disruption. Finally, the environmental health risks caused by each degradation pathway were comprehensively assessed with the help of the negative indicator (1-2) standardization method, which provides a theoretical basis for avoiding the toxicity risks caused by dioxin degradation transformation. In addition, the 3D-QSAR model was used to verify the necessity and rationality of this study. This paper provides theoretical support and reference significance for the toxicity assessment of dioxin degradation by-products from inferred degradation pathways.

Identification and Mechanistic Analysis of Toxic Degradation Products in the Advanced Oxidation Pathways of Fluoroquinolone Antibiotics.

The degradation of fluoroquinolones (FQs) via advanced oxidation processes (AOPs) is a promising avenue, yet the complete mineralization of certain FQ molecules remains elusive, raising concerns about the formation of toxic by-products. This study delineates five primary AOP degradation pathways for 16 commercially available FQ molecules, inferred from existing literature. Density functional theory (DFT) was employed to calculate the bond dissociation energies within these pathways to elucidate the correlation between bond strength and molecular architecture. Subsequently, Comparative Molecular Similarity Index Analysis (CoMSIA) models were constructed for various degradation reactions, including piperazine ring cleavage, defluorination, hydroxylation, and piperazine ring hydroxylation. Three-dimensional contour maps generated from these models provide a deeper understanding of the interplay between FQ molecular structure and bond dissociation energy. Furthermore, toxicity predictions for 16 FQ molecules and their advanced oxidation intermediates, conducted using VEGA 1.2.3 software, indicate that degradation products from pathways P2 and P5 pose a heightened health risk relative to their parent compounds. Furthermore, the application of the Multwfn program to compute the Fukui function for FQ molecules discerns the disparity in degradation propensities, highlighting that N atoms with higher f0 values can augment the likelihood of piperazine ring cleavage. HOMO-LUMO distribution diagrams further confirm that methoxy substitution at the 1-position leads to a dilution of HOMOs on the piperazine ring and an increased energy gap for free radical reactions, diminishing the reactivity with hydroxyl radicals. This study elucidates the pivotal role of structural characteristics in FQ antibiotics for their degradation efficiency within AOPs and unveils the underlying mechanisms of bond dissociation energy disparities. The toxicity parameter predictions for FQ molecules and their intermediates offer unique perspectives and theoretical underpinnings for mitigating the use of high-risk FQs and for devising targeted degradation strategies to circumvent the generation of toxic intermediates in AOPs through molecular structure optimization.

The genetic architecture of multimodal human brain age.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Accumulation characteristics of liquid crystal monomers in plants: A multidimensional analysis.

Liquid crystal monomers (LCMs), identified as emerging contaminations, have been detected in soils and plants, but their accumulation characteristics in plants haven't been studied. Therefore, this study systematically investigated the accumulation characteristics of LCMs in plants from four dimensions (i.e., plant fruit species, soil types, plant growth stages, and LCMs categories) for the first time. The LCMs concentrations (9.96 × 10-4 to 114.608 ng/g) in 22 plant fruits were predicted by the partition-limited model. Grains with the highest lipid content showed the highest LCMs accumulation propensity. Plants grown in paddy soil showed a strong LCMs accumulation capacity. Results showed that the LCMs accumulation capacity in plants from soils decreased when the soil organic matter content increased. A preferential accumulation of LCMs in plant root systems during growth was found by the molecular dynamics simulations. Compared to polychlorinated biphenyls (as the reference contaminants of LCMs), LCMs exhibit higher accumulation in plant roots and lower translocation to shoots. For the fourth dimension, lipophilicity was found to be the main reason of LCMs accumulation by intergraded stepwise linear regression with sensitivity analysis. This is the inaugural research concentrating on LCMs accumulation in plants, providing insights and theoretical guidance for future LCMs management strategies multidimensionally.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid ß (Aß), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aß positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

ZIF-derived sulfides with tremella-like core-shell structure for high performance supercapacitors.

Metal-organic frameworks (MOFs) have emerged as promising active electrode materials in supercapacitors for its controllable porous structure and excellent physio-chemical properties. However, the poor conductivities keep it from achieving its full capacitance potential, which greatly limits its practical application. Here, a facile pathway is reported to fabricate the GO/Ni2ZnS4@NiCo2S4 composite with large specific surface area and favorable electrical conductivity. Thanks to the novel tremella-like core-shell structure and high-efficient synergistic effects among multi-components, the designed GO/Ni2ZnS4@NiCo2S4 electrode shows a high specific capacitance of 2284 F/g at 1 A/g. Furthermore, the asymmetric supercapacitor fabricated by coupling GO/Ni2ZnS4@NiCo2S4 positive electrode with biological carbon negative electrode achieves a remarkable energy density of 120 Wh kg-1 at a power density of 750 W kg-1.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

The Genetic Architecture of Biological Age in Nine Human Organ Systems.

Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine.

Properties of Dietary Flavone Glycosides, Aglycones, and Metabolites on the Catalysis of Human Endoplasmic Reticulum Uridine Diphosphate Glucuronosyltransferase 2B7 (UGT2B7).

Flavone glycosides, their aglycones, and metabolites are the major phytochemicals in dietary intake. However, there are still many unknowns about the cellular utilization and active sites of these natural products. Uridine diphosphate glucuronosyltransferases (UGTs) in the endoplasmic reticulum have gene polymorphism distribution in the population and widely mediate the absorption and metabolism of endogenous and exogenous compounds by catalyzing the covalent addition of glucuronic acid and various lipophilic chemicals. Firstly, we found that rutin, a typical flavone O-glycoside, has a stronger UGT2B7 binding effect than its metabolites. After testing a larger number of flavonoids with different aglycones, their aglycones, and metabolites, we demonstrated that typical dietary flavone O-glycosides generally have high binding affinities towards UGT2B7 protein, but the flavone C-glycosides and the phenolic acid metabolites of flavones had no significant effect on this. With the disposition of 4-methylumbelliferone examined by HPLC assay, we determined that 10 µM rutin and nicotifiorin could significantly inhibit the activity of recombinant UGT2B7 protein, which is stronger than isovitexin, vitexin, 3-hydroxyphenylacetic acid and 3,4-dihydroxyphenylacetic acid. In addition, in vitro experiments showed that in normal and doxorubicin-induced lipid composition, both flavone O-glycosides rutin and flavone C-glycosides isovitexin at 10 µM had no significant effect on the expression of UGT1A1, UGT2B4, UGT2B7, and UGT2B15 genes for 24 h exposure. The obtained results enrich the regulatory properties of dietary flavone glycosides, aglycones, and metabolites towards the catalysis of UGTs and will contribute to the establishment of a precise nutritional intervention system based on lipid bilayers and theories of nutrients on endoplasmic reticulum and mitochondria communication.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability.

Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes - herein called dimensional neuroimaging endophenotypes (DNEs) - which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×1-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/.

Combined detection of vitamin D, CRP and TNF-α has high predictive value for osteoporosis in elderly men.

OBJECTIVE: To observe the predictive value of 25-hydroxyvitamin D3 (25(OH)D3), C-reactive protein (CRP), and tumor necrosis factor-α (TNF-α) levels for osteoporosis in elderly men. METHODS: A retrospective analysis was conducted in 122 elderly male patients that were tested in The Affiliated Hospital of Xinyang Vocational and Technical College between January 2020 and May 2022. The patients were divided into an osteoporosis group (OG, n = 77) and a control group (CG, n = 45) according to the results of bone mineral density. The formula N = Z^2*(P*(1-P))/E^2 was used to calculate the required sample size (N) for a given confidence interval (Z), total error (E), and proportion (P) of the target population. The proportion (P) is often assumed to be 0.5 and not randomly distributed across the population. The levels of cross-linked C-terminal telopeptide of type I collagen (CTX-I), procollagen type I N-terminal propeptide (PINP), intact parathyroid hormone (iPTH), osteocalcin (OC), and serum levels of 25(OH)D3, CRP, and TNF-α were measured and compared between the two groups. Pearson correlation was used to analyze the relationship between the parameters. The predictive value of 25(OH)D3, CRP and TNF-α for osteoporosis was also analyzed using receiver operating characteristic (ROC) curves. Logistic multivariate analysis was performed to analyze the risk factors for osteoporosis in elderly men. RESULTS: Compared with the CG, the OG exhibited evidently lower serum 25(OH)D3, but significantly higher CRP and TNF-α (P < 0.05). Pearson correlation demonstrated that the bone mineral density was negatively correlated with CTX-I, PINP, serum CRP and TNF-α, whereas it was positively correlated with OC and 25(OH)D3 in elderly men. The areas under the ROC curve (AUCs) of serum 25(OH)D3, CRP and TNF-α were identified as 0.931, 0.878 and 0.846, respectively, and the AUC of the combined detection of the three was 0.991. Furthermore, age, CTX-I, PINP, OC, 25(OH)D3, as well as serum CRP and TNF-α were identified as risk factors for osteoporosis among elderly men. CONCLUSION: Serum 25(OH)D3, CRP, and TNF-α are associated with osteoporosis in elderly men, and can serve as predictors for osteoporosis.

Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.

Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease.

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA.

INTRODUCTION: Little is known about the epidemiology of brain microbleeds in racially/ethnically diverse populations. METHODS: In the Multi-Ethnic Study of Atherosclerosis, brain microbleeds were identified from 3T magnetic resonance imaging susceptibility-weighted imaging sequences using deep learning models followed by radiologist review. RESULTS: Among 1016 participants without prior stroke (25% Black, 15% Chinese, 19% Hispanic, 41% White, mean age 72), microbleed prevalence was 20% at age 60 to 64.9 and 45% at ≥85 years. Deep microbleeds were associated with older age, hypertension, higher body mass index, and atrial fibrillation, and lobar microbleeds with male sex and atrial fibrillation. Overall, microbleeds were associated with greater white matter hyperintensity volume and lower total white matter fractional anisotropy. DISCUSSION: Results suggest differing associations for lobar versus deep locations. Sensitive microbleed quantification will facilitate future longitudinal studies of their potential role as an early indicator of vascular pathology.

The Genetic Architecture of Multimodal Human Brain Age.

The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.

Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes.

Importance: Little is known about structural brain changes in type 1 diabetes (T1D) and whether there are early manifestations of a neurodegenerative condition like Alzheimer disease (AD) or evidence of premature brain aging. Objective: To evaluate neuroimaging markers of brain age and AD-like atrophy in participants with T1D in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, identify which brain regions are associated with the greatest changes in patients with T1D, and assess the association between cognition and brain aging indices. Design, Setting, and Participants: This cohort study leveraged data collected during the combined DCCT (randomized clinical trial, 1983-1993) and EDIC (observational study, 1994 to present) studies at 27 clinical centers in the US and Canada. A total of 416 eligible EDIC participants and 99 demographically similar adults without diabetes were enrolled in the magnetic resonance imaging (MRI) ancillary study, which reports cross-sectional data collected in 2018 to 2019 and relates it to factors measured longitudinally in DCCT/EDIC. Data analyses were performed between July 2020 and April 2022. Exposure: T1D diagnosis. Main Outcomes and Measures: Psychomotor and mental efficiency were evaluated using verbal fluency, digit symbol substitution test, trail making part B, and the grooved pegboard. Immediate memory scores were derived from the logical memory subtest of the Wechsler memory scale and the Wechsler digit symbol substitution test. MRI and machine learning indices were calculated to predict brain age and quantify AD-like atrophy. Results: This study included 416 EDIC participants with a median (range) age of 60 (44-74) years (87 of 416 [21%] were older than 65 years) and a median (range) diabetes duration of 37 (30-51) years. EDIC participants had consistently higher brain age values compared with controls without diabetes, indicative of approximately 6 additional years of brain aging (EDIC participants: ß, 6.16; SE, 0.71; control participants: ß, 1.04; SE, 0.04; P < .001). In contrast, AD regional atrophy was comparable between the 2 groups. Regions with atrophy in EDIC participants vs controls were observed mainly in the bilateral thalamus and putamen. Greater brain age was associated with lower psychomotor and mental efficiency among EDIC participants (ß, -0.04; SE, 0.01; P < .001), but not among controls. Conclusions and Relevance: The findings of this study suggest an increase in brain aging among individuals with T1D without any early signs of AD-related neurodegeneration. These increases were associated with reduced cognitive performance, but overall, the abnormal patterns seen in this sample were modest, even after a mean of 38 years with T1D.

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

Sirt1 Negatively Regulates Cellular Antiviral Responses by Preventing the Cytoplasmic Translocation of Interferon-Inducible Protein 16 in Human Cells.

Interferon-inducible protein 16 (IFI16) plays a critical role in antiviral innate immune responses against DNA viruses. Although the acetylation of IFI16 is crucial to its cytoplasmic translocation and downstream signal transduction, the regulation of IFI16 acetylation remains unclear. In this study, we demonstrated that the NAD-dependent deacetylase silent information regulatory 1 (Sirtuin1, Sirt1) interacted with IFI16 and decreased the acetylation of IFI16, resulting in the inhibition of IFI16 cytoplasmic localization and antiviral responses against DNA virus and viral DNA in human cells. Meantime, Sirt1 could not inhibit RNA virus-triggered signal transduction. Interestingly, even p204, the murine ortholog of human IFI16, barely interacted with Sirt1. Thus, Sirt1 could not negatively regulate the acetylation of p204 and subsequent signal transduction upon herpes simplex virus 1 (HSV-1) infection in mouse cells. Taken together, our research work showed a new mechanism by which Sirt1 manipulated IFI16-mediated host defense. Our study also demonstrated a difference in the regulation of antiviral host defense between humans and mice, which might be considered in preclinical studies for antiviral treatment. IMPORTANCE DNA viruses, such as hepatitis B virus (HBV), human papillomavirus (HPV), human cytomegalovirus (HCMV), Epstein-Barr virus (EBV), and herpes simplex virus (HSV), can cause a wide range of diseases and are considered a global threat to human health. Interferon-inducible protein 16 (IFI16) binds virus DNA and triggers antiviral innate immune responses to restrict viral infection. In this study, we identified that silent information regulatory 1 (Sirtuin1, Sirt1) interacted with IFI16 and regulated IFI16-mediated innate host defense. Therefore, the activator or inhibitor of Sirt1 may have the potential to be used as a novel strategy to treat DNA virus-associated diseases. We also found that Sirt1 barely interacted with p204, the murine ortholog of human IFI16, and could not negatively regulate innate immune responses upon HSV-1 infection in mouse cells. This difference between humans and mice in the regulation of antiviral host defense might be considered in preclinical studies for antiviral treatment.

Ultraportable Flow Cytometer Based on an All-Glass Microfluidic Chip.

The flow cytometer has become a powerful and widely accepted measurement device in both biological studies and clinical diagnostics. The application of the flow cytometer in emerging point-of-care scenarios, such as instant detection in remote areas and emergency diagnosis, requires a significant reduction in physical dimension, cost, and power consumption. This requirement promotes studies to develop portable flow cytometers, mostly based on the utilization of polymer microfluidic chips. However, due to the relatively poor optical performance of polymer materials, existing microfluidic flow cytometers are incapable of accurate blood analysis, such as the four-part leukocyte differential count, which is necessary to monitor the immune system and to assess the risk of allergic inflammation or viral infection. To address this issue, an ultraportable flow cytometer based on an all-glass microfluidic chip (AG-UFCM) has been developed in this study. Compared with that of a typical commercial flow cytometer (BD FACSAria III), the volume of the AG-UFCM was reduced by 90 times (from 720 to 8 L). A two-step laser processing was employed to fabricate an all-glass microfluidic chip with a surface roughness of less than 1 nm, significantly improving the optical performance of on-chip micro-lens. The signal-to-noise ratio was enhanced by 3 dB, compared with that of polymer materials. For the first time, a four-part leukocyte differential count based on single fluorescence staining was realized using a miniaturized flow cytometer, laying a foundation for the point-of-care testing of miniaturized flow cytometers.