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 β (Aβ), 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 Aβ 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.

[Analysis of the progress in the field of oral microbiology and regenerative medicine from 2014 to 2023]

Objective: To analyze the trends in literature related to oral microbiology and regenerative medicine from 2014 to 2023. By identifying key research countries, institutions, and their collaboration networks, as well as exploring research hotspots and development directions, the study seeks to provide references for researchers and decision-makers in the field of oral microbiology and regenerative medicine, thereby guiding the direction of future research. Methods: Relevant literature was retrieved using the Web of Science Core Collection database, with data processing and analysis conducted using CiteSpace 6.2.R6 software. Time slicing, node type selection, and the application of the g-index (g-index) were used for filtering, analyzing countries, institutions, authors, journals, and keywords. Results: The volume of literature in the field of oral microbiology and regenerative medicine has steadily increased from 2014 to 2023, with the number of publications first exceeding one hundred in 2020 and reaching 134 in 2022, accompanied by a citation frequency of 3 363 times. China and the United States have been at the forefront in terms of the volume of publications, while the United States and Germany lead in terms of intermediary centrality. The research primarily spans disciplines such as oral medicine, interdisciplinary studies, materials science, and immunology. High-frequency keywords include stem cells, scaffold materials, and gut microbiota, while cluster analysis indicates that inflammation, drug delivery, and antimicrobial activity remain consistent research themes. In recent years, the research heat in "tissue regeneration""gut microbiota " and "maxillofacial surgery" has risen, suggesting these may become focal points of future research. Conclusions: Over the past decade, the volume of literature published in the fields of oral microbiology and regenerative medicine, along with their citation frequencies, has increased annually. The research focus has shifted over time. Understanding the interactions between oral and gut microbiomes is crucial for developing innovative regenerative treatment strategies.

Adaptive robust structure exploration for complex systems based on model configuration and fusion

Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures.

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.

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 .

Inflammatory factors mediated the effect of air pollution on ischemic stroke: a two-step, mediation Mendelian randomization study

OBJECTIVE

Numerous investigations have indicated a correlation between air pollution (AP) and an elevated ischemic stroke (IS) likelihood. The existing literature does not provide a consensus about the possible link between AP and IS. A two-sample Mendelian randomization (MR) analysis was utilized to systematically measure the causal link between AP and ischemic stroke. Furthermore, the mediating impact of inflammatory factors was also performed by a two-step MR.

MATERIALS AND METHODS

A two-sample MR analysis was utilized to examine the AP impact on the incidence of IS. Additionally, a two-step MR approach was carried out to account for possible mediating variables. The indirect impact was determined by employing the product approach, which included multiplying the AP impact on inflammatory factors by the inflammatory factors' impacts on IS. The MR effect was identified through inverse variance-weighted (IVW) meta-analysis of each Wald Ratio. Additionally, complementary studies were conducted using the weighted median and MR-egger approaches.

RESULTS

The IVW method with random effects showed that the per unit increase in genetically predicted PM2.5 was linked to the 0.362-fold elevated ischemic stroke risk (OR: 1.362, 95% CI: 1.032-1.796, p=0.029). Furthermore, the IVM technique, incorporating random effects, demonstrated that the per unit increase in genetically predicted PM2.5 was related to an elevated Interleukin (IL)-1β risk (OR: 1.529, 95% CI: 1.191-1.963, p=0.001), IL-6 (OR: 1.498, 95% CI: 1.094-2.052, p=0.012) and IL-17 (OR: 1.478, 95% CI: 1.021-2.139, p=0.038). IL-1β, IL-6, and IL-17 modulated the PM2.5 impact on ischemic stroke, while the proportion mediated by them was 59.5%.

CONCLUSIONS

A positive correlation between genetically predicted PM2.5 levels and elevated ischemic stroke risk is mediated by IL-1β, IL-6, and IL-17.

[The association between body mass index and in-hospital major adverse cardiovascular and cerebral events in patients with acute coronary syndrome]

Objective: To assess the association between body mass index (BMI) and major adverse cardiovascular and cerebrovascular events (MACCE) among patients with acute coronary syndrome (ACS). Methods: This was a multicenter prospective cohort study, which was based on the Improving Care for Cardiovascular Disease in China (CCC) project. The hospitalized patients with ACS aged between 18 and 80 years, registered in CCC project from November 1, 2014 to December 31, 2019 were included. The included patients were categorized into four groups based on their BMI at the time of admission: underweight (BMI<18.5 kg/m2), normal weight (BMI between 18.5 and 24.9 kg/m2), overweight (BMI between 25.0 and 29.9 kg/m2), and obese (BMI≥30.0 kg/m2). Multivariate logistic regression models was used to analyze the relationship between BMI and the risk of in-hospital MACCE. Results: A total of 71 681 ACS inpatients were included in the study. The age was (63.4±14.7) years, and 26.5% (18 979/71 681) were female. And the incidence of MACCE for the underweight, normal weight, overweight, and obese groups were 14.9% (322/2 154), 9.5% (3 997/41 960), 7.9% (1 908/24 140) and 7.0% (240/3 427), respectively (P<0.001). Multivariate logistic regression analysis showed a higher incidence of MACCE in the underweight group compared to the normal weight group (OR=1.30, 95%CI 1.13-1.49, P<0.001), while the overweight and obese groups exhibited no statistically significant difference in the incidence of MACCE compared to the normal weight group (both P>0.05). Conclusion: ACS patients with BMI below normal have a higher risk of in-hospital MACCE, suggesting that BMI may be an indicator for evaluating short-term prognosis in ACS patients.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.

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.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.

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.

Chromothripsis orchestrates leukemic transformation in blast phase MPN through targetable amplification of DYRK1A

Chromothripsis, the process of catastrophic shattering and haphazard repair of chromosomes, is a common event in cancer. Whether chromothripsis might constitute an actionable molecular event amenable to therapeutic targeting remains an open question. We describe recurrent chromothripsis of chromosome 21 in a subset of patients in blast phase of a myeloproliferative neoplasm (BP-MPN), which alongside other structural variants leads to amplification of a region of chromosome 21 in ∼25% of patients ('chr21amp'). We report that chr21amp BP-MPN has a particularly aggressive and treatment-resistant phenotype. The chr21amp event is highly clonal and present throughout the hematopoietic hierarchy. DYRK1A , a serine threonine kinase and transcription factor, is the only gene in the 2.7Mb minimally amplified region which showed both increased expression and chromatin accessibility compared to non-chr21amp BP-MPN controls. We demonstrate that DYRK1A is a central node at the nexus of multiple cellular functions critical for BP-MPN development, including DNA repair, STAT signalling and BCL2 overexpression. DYRK1A is essential for BP-MPN cell proliferation in vitro and in vivo , and DYRK1A inhibition synergises with BCL2 targeting to induce BP-MPN cell apoptosis. Collectively, these findings define the chr21amp event as a prognostic biomarker in BP-MPN and link chromothripsis to a druggable target.

Genome-wide association study identifies variants associated with semen volume in white-feathered broilers

Semen is a measure of the reproductive efficiency of roosters, which affects the economic benefits of white-feathered broilers. Over the years, research in this field has mainly focused on hens, while there have been fewer studies on the reproductive traits of roosters. To identify the genes related to the semen traits of roosters, we used a chicken 55 K SNP chip to genetically type the white-feathered population (220) and performed imputation with resequencing data from 97 roosters. In total, 1 048 576 SNPs were obtained and used for genome-wide association analysis of semen volume, from which 197 genome-wide significant markers were identified, all within the interval of 13.82-16.12 Mb on chromosome 7. By combining our results with the biological functions of genes in the interval, four candidate genes were identified that potentially relate to semen volume: FAPP1, OSBPL6, SESTD1 and SSFA2. Our findings may provide a basis for further research on the genetic mechanism and marker-assisted selection of semen volume in white-feathered broilers.

LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification

Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.

[Application effects of 3D visualization reconstruction technique in pheochromocytoma/ paraganglioma surgery]

To investigate the value of 3D visualization reconstruction technology in pheochromocytoma/paraganglioma surgery.The clinical data of 87 patients with pheochromocytoma/paraganglioma admitted to the Department of Urology of Peking Union Medical College Hospital between January 2019 and December 2022 were retrospectively analyzed, and 3D visualization model reconstruction was performed preoperatively in 47 patients [Group A:males was 24 cases,the age M(Q1, Q3)42.00(30.00, 54.00)]. while the remaining 40 patients [Group B: males was 23 cases,the age M(Q1, Q3) 44.00(30.25, 53.75)] was not. The maximum tumor diameter, operation time, intraoperative bleeding, drain retention time and postoperative hospital stay were compared between the two groups. Surgery was successfully completed in both groups. 37 (78.7%) patients in group A underwent laparoscopic surgery, 7 (14.9%) patients underwent open surgery, and 3 (6.4%) patients underwent laparoscopic-to-open surgery. Thirty-one (77.5%) patients in group B underwent laparoscopic surgery, 5 (12.5%) patients underwent open surgery, and 4 (10.0%) patients underwent laparoscopic to open surgery. There was a difference in the maximum diameter of the tumor between the two groups [(6.09±3.02) cm vs (5.32±1.76) cm, P<0.05], the retention time of the drainage tube was significantly shorter in group A compared with group B [(3.20±1.38) d vs (4.02±1.98) d, P<0.05], and the length of the hospital stay after surgery was significantly shorter [(5.75±2.12) d vs (6.49±3.37) d, P<0.05]. Comparison of operation time and intraoperative bleeding between the two groups showed no statistically significant difference (P>0.05).Two cases of postoperative anemia and one case of pulmonary atelectasis in group B patients improved before discharge. Conclusion when the tumor diameter is>6 cm or has a close relationship with the surrounding organs and blood vessels, the use of 3D visual reconstruction technology can formulate and implement a more accurate and safe surgical plan, shorten the retention time of the drainage tube and postoperative hospitalization time, which is conducive to the patient's postoperative recovery and reduce postoperative complications.

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.

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.

Preliminary results of the 105 GHz collective Thomson scattering system on HL-2A

A 105 GHz collective Thomson scattering (CTS) diagnostic has been successfully developed for fast-ion measurements on the HL-2A tokamak, and it has been deployed during an experimental campaign. Enhanced signals exhibiting synchronous modulation characteristics have been observed across all CTS channels upon the launch of a modulated probe wave. Results show that the intensity of the CTS signal increases with Neutral Beam Injection (NBI) power and is proportional to neutron count, indicating that the scattering signal contains a contribution from fast ions. Compared with the signal without NBI, the enhanced scattering spectrum due to NBI is slightly wider than the predicted fast ion range. Such broadening might be attributed to the heating effects of the gyrotron.

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/.

Plasma position measurements by O-mode and X-mode reflectometry systems in tokamak plasmas

Plasma Position Reflectometry (PPR) is planned to provide plasma position and shape information for plasma operation in future fusion reactors. Its primary function is to calibrate the drift of the magnetic signals due to the integral nature of magnetic measurement. Here, we attempt to measure plasma position using ordinary mode (O-mode) and extraordinary mode (X-mode) reflectometry systems on two tokamaks. A new physical model based on the phase shift is proposed to deduce the relative movement of the cut-off layer without density inversion. We demonstrate the plasma position measurements by absolute measurement from density profile inversion and relative measurement from phase shift. The combination of X-mode and O-mode reflectometers can minimize the limitations of single polarization reflectometry and further increase the accuracy of plasma position measurement. These results could provide an important technical basis for the further development of a real-time control system based on PPR.

Highly activated oxygen redox enabling large-capacity Li-rich layered manganese-based oxide cathodes

Li-rich Mn-based layered materials are considered the most promising next-generation high-energy-density cathode materials due to their high capacity, but their large irreversible capacity loss and severe voltage attenuation hinder their practical application. The limited operating voltage also makes it difficult to satisfy the increasing demand of high energy density in future applications. Inspired by the high voltage platform of Ni-rich LiNi_0.8Co_0.1Mn_0.1O₂, we design and prepare a Li_1.2Ni_0.32Co_0.04Mn_0.44O₂ (LLMO811) cathode material with increased Ni content via the acrylic acid polymerization method and regulate the amounts of excess lithium of LLMO. It is found that LLMO-L3 with 3% excess lithium has the highest initial discharge capacity of 250 mA h g^-1 with a coulombic efficiency of 83.8%. Taking advantage of a high operating voltage of about 3.75 V, the material achieves an impressive high energy density of 947 W h kg^-1. Moreover, the capacity at 1C reaches 193.2 mA h g^-1, which is higher than that of ordinary LLMO811. This large capacity is attributed to the highly reversible O redox reaction, and the strategy used to achieve this would throw some light on the exploration of high-energy-density cathodes.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

COVID-19 pandemic and neonatal birth weight: a systematic review and meta-analysis

OBJECTIVES

Lockdown was implemented in many countries during the pandemic, which led to myriad changes in pregnant women's lives. However, the potential impacts of the COVID-19 pandemic on neonatal outcomes remain unclear. We aimed to evaluate the association between the pandemic and neonatal birth weight.

STUDY DESIGN

This was a systematic review and meta-analysis of the previous literature.

METHODS

We searched the MEDLINE and Embase databases up to May 2022 and extracted 36 eligible studies that compared neonatal birth weight between the pandemic and the prepandemic period. The following outcomes were included: mean birth weight, low birth weight (LBW), very low birth weight (VLBW), macrosomia, small for gestational age (SGA), very small for gestational age (VSGA), and large for gestational age (LGA). Statistical heterogeneity among studies was assessed to determine whether a random effects model or fixed effects model was conducted.

RESULTS

Of the 4514 studies identified, 36 articles were eligible for inclusion. A total of 1,883,936 neonates during the pandemic and 4,667,133 neonates during the prepandemic were reported. We identified a significant increase in mean birth weight (pooled mean difference [95% confidence interval (CI)] = 15.06 [10.36, 19.76], I² = 0.0%, 12 studies) and a reduction in VLBW (pooled OR [95% CI] = 0.86 [0.77, 0.97], I² = 55.4%, 12 studies). No overall effect was identified for other outcomes: LBW, macrosomia, SGA, VSGA, and LGA. There was publication bias for mean birth weight with a borderline significance (Egger's P = 0.050).

CONCLUSION

Pooled results showed the pandemic was significantly associated with an increase in mean birth weight and a reduction in VLBW, but not for other outcomes. This review provided clues about the indirect effects of the pandemic on neonatal birth weight and more healthcare measures needed to improve neonatal long-term health.

Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population

Importance

Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment.

Objective

To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations.

Design, Setting, and Participants

This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022.

Main Outcomes and Measures

The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations.

Results

Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10-6).

Conclusions and Relevance

This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

Modified transcrestal sinus floor elevation with concomitant implant placement in edentulous posterior maxillae with residual bone height of 5 mm or less: a non-controlled prospective study

The aim of this study was to describe a modified transcrestal sinus floor elevation (mTSFE) technique and to evaluate its clinical effectiveness and reliability when residual bone height is severely reduced. Forty-three maxillary edentulous patients who met the inclusion criteria were enrolled. All patients underwent the mTSFE technique; 66 dental implants were inserted simultaneously. Patient-reported outcomes were assessed 2 weeks after surgery. Prosthetic crowns were placed 6 months after surgery. Radiographic analyses and clinical analyses were conducted to assess the clinical effectiveness and feasibility of mTSFE during a follow-up period of 2-8 years. The mean vertical bone increase after surgery was 8.09 mm, and it decreased to 6.56 mm at 6 months after surgery. Two cases of membrane perforation occurred during surgery and one implant was lost in the third year after surgery; the survival rate at the implant level was 98.48%. No severe postoperative complication was reported and the subjective feeling of patients was acceptable. This mTSFE technique could simplify the operative procedure and might be helpful to reduce intraoperative trauma, as well as to alleviate postoperative discomfort.

Applications of generative adversarial networks in neuroimaging and clinical neuroscience

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

MHD instability dynamics and turbulence enhancement towards the plasma disruption at the HL-2A tokamak

The evolutions of MHD instability behaviors and enhancement of both electrostatic and electromagnetic turbulence towards the plasma disruption have been clearly observed in the HL-2A plasmas. Two types of plasma disruptive discharges have been investigated for similar equilibrium parameters: one with a distinct stage of a small central temperature collapse ([Formula: see text] 5-10%) around 1 millisecond before the thermal quench (TQ), while the other without. For both types, the TQ phase is preceded by a rotating 2/1 tearing mode, and it is the development of the cold bubble from the inner region of the 2/1 island O-point along with its inward convection that causes the massive energy loss. In addition, the micro-scale turbulence, including magnetic fluctuations and density fluctuations, increases before the small collapse, and more significantly towards the TQ. Also, temperature fluctuations measured by electron cyclotron emission imaging enhances dramatically at the reconnection site and expand into the island when approaching the small collapse and TQ, and the expansion is more significant close to the TQ. The observed turbulence enhancement near the X-point cannot be fully interpreted by the linear stability analysis by GENE. Evidences suggest that nonlinear effects, such as the reduction of local [Formula: see text] shear and turbulence spreading, may play an important role in governing turbulence enhancement and expansion. These results imply that the turbulence and its interaction with the island facilitate the stochasticity of the magnetic flux and formation of the cold bubble, and hence, the plasma disruption.

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.

Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits 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 genetic-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 correlation 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.

Impacts of COVID-19 pandemic on preterm birth: a systematic review and meta-analysis

OBJECTIVES

The COVID-19 pandemic has significantly affected healthcare systems and daily well-being. However, the reports of the indirect impacts of the pandemic on preterm birth remain conflicting. We performed a meta-analysis to examine whether the pandemic altered the risk of preterm birth.

STUDY DESIGN

This was a systematic review and meta-analysis of the previous literature.

METHODS

We searched MEDLINE and Embase databases until March 2022 using appropriate keywords and extracted 63 eligible studies that compared preterm between the COVID-19 pandemic period and the prepandemic period. A random effects model was used to obtain the pooled odds of each outcome. The study protocol was registered with PROSPERO (No. CRD42022326717).

RESULTS

The search identified 3827 studies, of which 63 reports were included. A total of 3,220,370 pregnancies during the COVID-19 pandemic period and 6,122,615 pregnancies during the prepandemic period were studied. Compared with the prepandemic period, we identified a significant decreased odds of preterm birth (PTB; <37 weeks' gestation; pooled odds ratio [OR; 95% confidence interval (CI)] = 0.96 [0.94, 0.98]; I² = 78.7%; 62 studies) and extremely PTB (<28 weeks' gestation; pooled OR [95% CI] = 0.92 [0.87, 0.97]; I² = 26.4%; 25 studies) during the pandemic, whereas there was only a borderline significant reduction in the odds of very PTB (<32 weeks' gestation; pooled OR [95% CI] = 0.93 [0.86, 1.01]; I² = 90.1%; 33 studies) between the two periods. There was significant publication bias for PTB.

CONCLUSION

Pooled results suggested the COVID-19 pandemic was associated with preterm birth, although there was only a borderline significant reduction for very PTB during the pandemic compared with the prepandemic period. Large studies showed conflicting results, and further research on whether the change is related to pandemic mitigation measures was warranted.

(Cd,Mg)Te crystals for picosecond-response optical-to-x-ray radiation detectors

We demonstrate a photodetector sensitive to both optical and x-ray picosecond pulses based on our in-house grown cadmium magnesium telluride (Cd,Mg)Te single crystal. Specifically, we developed In-doped Cd_0.96Mg_0.04Te material and discuss its femtosecond optical photoresponse, as well as the detector performance, such as <100-pA dark current and up to 0.22-mA/W responsivity for 780-nm wavelength optical radiation. The detector exposed to Ti fluorescence (K alpha) x-ray pulses at 4.5 keV, generated by a free-electron laser beam with the central energy of 9.8 keV and <100 fs pulse width, exhibited readout-electronics-limited 200-ps full-width-at-half-maximum photoresponse, demonstrating that it is suitable for coarse timing in free-electron laser x-ray/optical femtosecond pump-probe spectroscopy applications.

Neurobiologically Based Stratification of Recent-Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes

BACKGROUND

Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures.

METHODS

HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470).

RESULTS

The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures.

CONCLUSIONS

We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population

OBJECTIVE

The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia-signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume-could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk.

METHODS

This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16-57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16-23 years) and adults in the UK Biobank study (N=836; ages 44-50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures.

RESULTS

Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2.

CONCLUSIONS

The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

[Analysis and significance of HBV DNA below the lower detection limit of HBV RNA levels after long-term NAs antiviral therapy in patients with hepatitis B virus cirrhosis]

Objective: To analyze the significance of HBV DNA below the lower detection limit of HBV RNA levels after long-term nucleos(t)ide analogues (NAs) antiviral therapy in patients with hepatitis B virus cirrhosis. Methods: 97 cases with hepatitis B virus cirrhosis treated with NAs antiviral therapy for at least 3 years between May 2018 to July 2019 were selected. High-sensitivity HBV DNA (<20 IU/ml), alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltransferase (GGT), HBsAg, HBeAg and HBV RNA at least twice every 6 months were detected. According to Child-Pugh classification, HBeAg, HBsAg level, and HBV RNA level intergroup comparison was performed. Rank sum test, χ² test and linear regression analysis were performed on the data. Results: Compared with the HBV RNA level of child-Pugh class A patients, the HBV RNA level of Child-Pugh class B+C patients were significantly higher [4.1 (0,4.9) log₁₀ copies/ml and 2.0 (0,3.5) log₁₀ copies/ml], and the difference was statistically significant (Z=2.370, P<0.05). According to different HBeAg levels, they were divided into HBeAg positive and negative group, and the quantitative comparison of HBV RNA levels between the two groups were 2.0 (0, 4.5) log₁₀ copies/ml and 1.0 (1.0, 2.0) log₁₀ copies/ml, respectively, and the difference was statistically significant (Z=3.233, P<0.05). According to different HBsAg levels, they were divided into three groups: HBsAg≤100 IU/ml, 100<HBsAg<1 000 IU/ml, and HBsAg≥1 000 IU/ml, and the quantitative comparison of HBV RNA levels among the three groups were 0 (0, 2.0) log₁₀, 2.0 (0,4.6) log_10, and 2.2 (2.0, 4.7) log₁₀ copies/ml, respectively, and the difference was statistically significant (H=11.265, P<0.05). Gender, age, ALT, AST, GGT, HBsAg, and HBeAg were included for linear regression analysis, and the HBsAg and AST levels were correlated with HBV RNA quantification (P<0.05). Adverse events occurrence during 1-year follow-up were recorded. 19 (31.7%) out of 60 cases had adverse events with detectable HBV RNA, and 3 (8.1%) out of 37 cases had adverse events with undetectable HBV RNA, and the difference was statistically significant (χ²=7.24, P<0.05). Conclusion: HBV RNA can still be detected after HBV DNA falls below the detection limit in patients with hepatitis B virus cirrhosis treated with long-term NAs antiviral therapy. HBV RNA quantification level is higher in patients with Child Pugh class B and C. Patients with detectable HBV RNA has higher proportion of adverse events, and AST and HBsAg levels may be correlated with serum HBV RNA.

[Correlation analysis between blood routine-derived inflammatory markers and respiratory function in pneumoconiosis patients]

Objective: To analyze the correlation between blood routine-derived inflammation indicators and respiratory function in patients with pneumoconiosis. Methods: In January 2021, 492 male pneumoconiosis patients hospitalized in Hefei Institute of Occupational Disease Control and Prevention from 2012 to 2020 were randomly selected as the case group, 492 dust exposed non pneumoconiosis workers who underwent occupational health examination at the same time were taken as the control group. The occupational history and clinical examination data of the two groups of subjects were collected, the correlation between blood routine-derived inflammatory indexes and pulmonary function and blood gas analysis was analyzed retrospectively. Results: Compared with the control group, the lymphocyte monocyte ratio (LMR) in the case group was decreased, and the neutrophil lymphocyte ratio (NLR) was increased, and the difference was statistically significant (P<0.05) . There were significant differences in forced vital capacity as a percentage of the predicted value (FVC) , forced expiratory volume in the first second as a percentage of the predicted value (FEV(1)%) , one second rate (FEV(1)/FVC) , partial pressure of oxygen (PaO(2)) , partial pressure of carbon dioxide (PaCO(2)) , and pH among pneumoconiosis patients at different stages (P<0.05) . FVC%, FEV(1)%, FEV(1)/FVC, and PaO(2) decreased with the increase of the stage, the trend test was statistically significant (tau-b=-0.24, -0.34, -0.37, -0.17, P<0.05) , PaCO(2) and pH increased with the increase of the stage, and the trend test was statistically significant (tau-b=0.10, 0.08, P<0.05) . There were statistically significant differences in LYM, LMR, NLR, platelet lymphocyte ratio (PLR) in patients with pneumoconiosis at different stages (P<0.05) , and LYM and LMR decreased with the increase of stage, trend test showed that there was statistically significant (tau-b=-0.11, -0.13, P<0.05) . There were significant differences in FVC%, FEV(1)%, FEV(1)/FVC, PaO(2), pH, LMR, NLR, PLR among patients with different types of pneumoconiosis (P<0.05) . LMR in pneumoconiosis patients was significantly positively correlated with FVC%, FEV(1)%, FEV(1)/FVC and PaO(2) (r(s)=0.342, 0.324, 0.203, 0.207, P<0.05) , NLR was significantly negatively correlated with FVC%, FEV(1)%, FEV(1)/FVC and PaO(2) (r(s)=-0.193, -0.202, -0.164, -0.177, P<0.05) , PLR was significantly negatively correlated with FVC%, FEV(1)%, FEV(1)/FVC and PaO(2) (r(s)=-0.194, -0.193, -0.106, -0.113, P<0.05) . Multiple linear regression analysis showed that LMR in pneumoconiosis patients was positively related with FVC%, FEV(1)% and PaO(2) (P<0.05) . Conclusion: LMR in patients with pneumoconiosis has a certain correlation with lung function and blood gas analysis, LMR is expected to become a sensitive indicator for evaluating pneumoconiosis.

[Inhibition of Sonic Hedgehog signaling inhibits fibrous scar formation and adversely affects functional outcome after ischemic brain injury in rats]

OBJECTIVE

To investigate the effects of inhibiting Sonic Hedgehog (Shh) signaling on fibrous scar formation and functional outcome after ischemic brain injury.

METHODS

Adult SD rats were randomized into sham-operated group, middle cerebral artery occlusion (MCAO) and reperfusion (I/R) group, I/R with intraventricular empty adenoviral vector (rAd-NC) injection group, and I/R with adenovirus-mediated Shh knockdown (rAd-ShShh) group. After the treatments, the neurological deficits of the rats were assessed, and the protein and mRNA expressions of fibronectin (Fn), α-SMA, and Shh in the ischemic hemisphere were detected with immunofluorescence assay and qPCR; TUNEL staining was used for detecting neural cell apoptosis. In the cell experiment, primary meningeal fibroblasts isolated from neonatal SD rats were pretreated for 24 h with TGF-β1 or TGF-β1 plus cyclopamine (CYC) before oxygen-glucose deprivation for 150 min followed by reoxygenation for 72 h (OGD/R). CCK-8 assay and scratch test were performed to examine the changes in cell proliferation and migration, and immunofluorescence assay, qPCR and Western blotting were used for detecting cell transformation and the expressions of Shh, α-SMA, and Fn.

RESULTS

Cerebral I/R injury significantly increased the protein and mRNA expressions of Shh, α-SMA, and Fn in the ischemic hemisphere of the rats, but their expression levels were significantly lowered by intraventricular injection of rAd-Shshh (P < 0.05), which obviously increased cell apoptosis in the ischemic hemisphere (P < 0.05) and improved modified mNSS and modified Bederson scores of the rats (P < 0.05). In the cell experiment, pretreatment with TGF-β1 and TGF-β1+CYC both increased the viability of the primary meningeal fibroblasts after OGD/R. TGF-β1 significantly enhanced the migration ability and induced obvious transformation of the exposed cells (P < 0.05), but these effects were significantly attenuated by co-treatment with CYC (P < 0.05). The expressions of Shh, α-SMA and Fn in the TGF-β1 group were all significantly higher in TGF-β1-treated cells (P < 0.05) and were obviously lowered by co-treatment with CYC (P < 0.05).

CONCLUSION

Inhibition of Shh signaling may inhibit fibrous scar formation and functional recovery in rats after ischemic brain injury.

POS0409 INTESTINAL HIF1α EXPRESSION PROTECTS AGAINST EPITHELIAL CELL DEATH IN ARTHRITIS

Background

While a so-called gut-joint axis is supported by many clinical observations, the current knowledge on such axis is mostly confined to descriptive and correlative data, e.g. showing the microbiota changes are associated with arthritis. In contrast, mechanistic data on how molecular changes in the intestinal epithelium influence the development of arthritis are scarce.

Objectives

To investigate, whether the mucosal barrier in the intestine dependent of the epithelial cell survival maintenance, influences the development of arthritis.

Methods

Intestinal hypoxia inducible factor (HIF)-1α expression was assessed before, at onset and during experimental arthritis and human rheumatoid arthritis (RA). Intestinal epithelial cell-specific HIF1α conditional knock-out mice were generated (HIF1αΔIEC) and subjected to collagen-induced arthritis (CIA). Clinical and histological courses of arthritis were recorded, and T and B cell subsets were analyzed in the gut and secondary lymphatic organs, and intestinal epithelial cells were subjected to molecular mRNA sequencing in HIF1αΔIEC and littermate control mice. Furthermore, pharmacologic HIF1α stabilization by PHD inhibitor was used for the treatment of arthritis.

Results

Intestinal HIF1α expression peaked at onset and remained high in experimental arthritis and RA. Conditionally deletion of HIF1α in gut epithelial cells strongly exacerbate arthritis and was associated with increased gut epithelial cell death, intestinal and lymphatic Th1 and Th17 activation. Mechanistically, HIF1α inhibits the transcription of necroptotic and apoptotic markers, which leads to a defect in the intestinal barrier integrity. Furthermore, treatment with HIF1α stabilization reinforced the gut epithelial cell survival and inhibited arthritis.

Conclusion

These findings show that the HIF1α regulating epithelial cells survival is critical for the breakdown of the intestinal barrier function in arthritis highlighting the functional link between intestinal homeostasis and arthritis.

Disclosure of Interests

None declared.

Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression

Importance

Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine.

Objective

To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles.

Design, Setting, and Participants

The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021.

Main Outcomes and Measures

Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics.

Results

A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets).

Conclusions and Relevance

This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.

Deep Learning Approach for Automatic Microaneurysms Detection

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely "E-Ophtha" and "DIARETDB1", and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.