Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort

Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease

Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.

Rat tracheal cartilage regeneration using mesenchymal stem cells derived from human iPS cells

Tracheal cartilage provides structural support to the airways to enable breathing. However, it can become damaged or impaired, sometimes requiring surgical resection and reconstruction. Previously, we clinically applied an artificial trachea composed of a polypropylene mesh and collagen sponge, with a favorable postoperative course. However, the artificial trachea presents a limitation, as the mesh is not biodegradable and cannot be used in pediatric patients. Compared to a polypropylene mesh, regenerated cartilage represents an ideal material for reconstruction of the damaged trachea. The use of mesenchymal stem cells (MSCs) as a source for cartilage regeneration has gained widespread acceptance, but challenges such as the invasiveness of harvesting and limited cell supply, persist. Therefore, we focused on the potential of human induced pluripotent stem cell (hiPSC)-derived mesenchymal stem cells (iMSCs) for tracheal cartilage regeneration. In this study, we aimed to regenerate tracheal cartilage on an artificial trachea as a preliminary step to replace the polypropylene mesh. iMSCs were induced from hiPSCs through neural crest cells and transplanted with a polypropylene mesh covered with a collagen sponge into the damaged tracheal cartilage in immunodeficient rats. Human nuclear antigen (HNA)-positive cells were observed in all six rats at 4 weeks and in six out of seven rats at 12 weeks after transplantation, indicating that transplanted iMSCs survived within the tracheal cartilage defects of rats. The HNA-positive cells co-expressed SOX9, and type II collagen was detected around HNA-positive cells in four of six rats at 4 weeks and in three of seven rats at 12 weeks after transplantation, reflecting cartilage-like tissue regeneration. These results indicate that the transplanted iMSCs could differentiate into chondrogenic cells and promote tracheal cartilage regeneration. iMSC transplantation thus represents a promising approach for human tracheal reconstruction.

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

OBJECTIVES

Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome.

METHODS

In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months.

RESULTS

Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age.

CONCLUSIONS

Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome.

CLINICAL RELEVANCE STATEMENT

Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population.

KEY POINTS

•Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

The role of cortical structural variance in deep learning-based prediction of fetal brain age

Background

Deep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age.

Methods

We examined the association between the predicted brain age difference (PAD: predicted brain age-chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis.

Results

Our results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age.

Conclusion

These results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

Evaluating the Therapeutic Potential of Durvalumab in Adults with Locally Advanced or Metastatic Biliary Tract Cancer: Evidence to Date

Advanced biliary tract cancers (BTCs) have historically been managed with chemotherapy but, in recent years, this treatment paradigm has begun to shift with the introduction of immune checkpoint inhibitors in addition to standard of care chemotherapy. The tumor microenvironment of BTC may be enriched with regulatory T lymphocytes and immune checkpoint expression in some patients. Durvalumab, an anti-programmed death ligand-1 (PD-L1) antibody, in combination with gemcitabine and cisplatin, has now received United States Food and Drug Administration approval for treatment of advanced BTC. Regulatory approval was based on the Phase III, randomized TOPAZ-1 trial that demonstrated survival benefit with addition of durvalumab to gemcitabine plus cisplatin compared to chemotherapy alone. The combination of chemotherapy and immunotherapy was well tolerated, and a subset of patients were able to achieve a durable response, with a 2-year overall survival rate of 23.6%. However, limitations remain in identifying which patients are most likely to benefit from immune checkpoint inhibition. Future study should aim to identify biomarkers predictive of substantial benefit, as well as the role of immune checkpoint inhibition in combination with targeted therapies and radiotherapy in the management of advanced BTC.

Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models

Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI), representing a straightforward diagnostic biomarker of brain aging and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results across studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and evaluation protocols used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models from recent literature. Four preprocessing pipelines, which differed in terms of registration transform, grayscale correction, and software implementation, were evaluated. The results showed that the choice of software or preprocessing steps could significantly affect the prediction error, with a maximum increase of 0.75 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, using affine rather than rigid registration to brain atlas statistically significantly improved MAE. Models trained on 3D images with isotropic 1mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Our findings indicate that extensive T1w preprocessing improves MAE, especially when predicting on a new dataset. This runs counter to prevailing research literature, which suggests that models trained on minimally preprocessed T1w scans are better suited for age predictions on MRIs from unseen scanners. We demonstrate that, irrespective of the model or T1w preprocessing used during training, applying some form of offset correction is essential to enable the model's performance to generalize effectively on datasets from unseen sites, regardless of whether they have undergone the same or different T1w preprocessing as the training set.

Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study

Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.

The interaction effects of age, APOE and common environmental risk factors on human brain structure

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.

Relative brain age is associated with socioeconomic status and anxiety/depression problems in youth

Brain age, a measure of biological aging in the brain, has been linked to psychiatric illness, principally in adult populations. Components of socioeconomic status (SES) associate with differences in brain structure and psychiatric risk across the lifespan. This study aimed to investigate the influence of SES on brain aging in childhood and adolescence, a period of rapid neurodevelopment and peak onset for many psychiatric disorders. We reanalyzed data from the Healthy Brain Network to examine the influence of SES components (occupational prestige, public assistance enrollment, parent education, and household income-to-needs ratio [INR]) on relative brain age (RBA). Analyses included 470 youth (5-17 years; 61.3% men), self-identifying as White (55%), African American (15%), Hispanic (9%), or multiracial (17.2%). Household income was 3.95 ± 2.33 (mean ± SD) times the federal poverty threshold. RBA quantified differences between chronological age and brain age using covariation patterns of morphological features and total volumes. We also examined associations between RBA and psychiatric symptoms (Child Behavior Checklist [CBCL]). Models covaried for sex, scan location, and parent psychiatric diagnoses. In a linear regression, lower RBA is associated with lower parent occupational prestige (p = .01), lower public assistance enrollment (p = .03), and more parent psychiatric diagnoses (p = .01), but not parent education or INR. Lower parent occupational prestige (p = .02) and lower RBA (p = .04) are associated with higher CBCL anxious/depressed scores. Our findings underscore the importance of including SES components in developmental brain research. Delayed brain aging may represent a potential biological pathway from SES to psychiatric risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Subchronic pulmonary toxicity of ambient particles containing cement production-related elements

Chronic respiratory disease is among the most common non-communicable diseases, and particulate materials (PM) are a major risk factor. Meanwhile, evidence of the relationship between the physicochemical characteristics of PM and pulmonary toxicity mechanism is still limited. Here, we collected particles (CPM) from the air of a port city adjacent to a cement factory, and we found that the CPM contained various elements, including heavy metals (such as arsenic, thallium, barium, and zirconium) which are predicted to have originated from a cement plant adjacent to the sampling site. We also delivered the CPM intratracheally to mice for 13 weeks to investigate the pulmonary toxicity of inhaled CPM. CPM-induced chronic inflammatory lesions with an increased total number of cells in the lung of mice. Meanwhile, among inflammatory mediators measured in this study, levels of IL-1β, TNF-α, CXCL-1, and IFN-γ were elevated in the treated group compared with the controls. Considering that the alveolar macrophage (known as dust cell) is a professional phagocyte that is responsible for the clearance of PM from the respiratory surfaces, we also investigated cellular responses following exposure to CPM in MH-S cells, a mouse alveolar macrophage cell line. CPM inhibited cell proliferation and formed autophagosome-like vacuoles. Intracellular calcium accumulation and oxidative stress, and altered expression of pyrimidine metabolism- and olfactory transduction-related genes were observed in CPM-treated cells. More interestingly, type I-LC3B and full-length PARP proteins were not replenished in CPM-treated cells, and cell cycle changes, apoptotic and necrotic cell death, and caspase-3 cleavage were not significantly detected in cells exposed to CPM. Taken together, we conclude that dysfunction of alveolar macrophages may contribute to CPM-induced pulmonary inflammation. In addition, given the possible transformation of heart tissue observed in CPM-treated mice, we suggest that further study is needed to clarify the systemic pathological changes and the molecular mechanisms following chronic exposure to CPM.

Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics

In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.

Biopatterning of 3D Cellular Model by Contactless Magnetic Manipulation for Cardiotoxicity Screening

Patterning cells to create 3D cell culture models by magnetic manipulation is a promising technique, which is rapid, simple, and cost-effective. This study introduces a new biopatterning approach based on magnetic manipulation of cells with a bio-ink that consists alginate, cells, and magnetic nanoparticles (MNPs). Plackett-Burman and Box-Behnken experimental design models were used to optimize bio-ink formulation where NIH-3T3 cells were utilized as a model cell line. The patterning capability was confirmed by light microscopy through 7 days culture time. Then, biopatterned 3D cardiac structures were formed using H9c2 cardiomyocyte cells. Cellular and extracellular components; F-actin and collagen Type I, and cardiac-specific biomarkers; Troponin T and MYH6, of biopatterned 3D cardiac structures were observed successfully. Moreover, DOX-induced cardiotoxicity was investigated for developed 3D model, and IC50 value was calculated as 8.1 µM for biopatterned 3D cardiac structures, which showed higher resistance against DOX-exposure compared to conventional 2D cell culture. Hereby, developed biopatterning methodology proved to be a simple and rapid approach to fabricate 3D cardiac models, especially for drug screening applications.

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age

The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.

Neonatal Fc Receptor Inhibition Enables Adeno-Associated Virus Gene Therapy Despite Pre-Existing Humoral Immunity

Advances in adeno-associated virus (AAV)-based gene therapy are transforming our ability to treat rare genetic disorders and address other unmet medical needs. However, the natural prevalence of anti-AAV neutralizing antibodies (NAbs) in humans currently limits the population who can benefit from AAV-based gene therapies. Neonatal Fc receptor (FcRn) plays an essential role in the long half-life of IgG, a key NAb. Researchers have developed several FcRn-inhibiting monoclonal antibodies to treat autoimmune diseases, as inhibiting the interaction between FcRn and IgG Fc can reduce circulating IgG levels to 20-30% of the baseline. We evaluated the utility of one such monoclonal antibody, M281, to reduce pre-existing NAb levels and to permit gene delivery to the liver and heart via systemic AAV gene therapy in mice and nonhuman primates. M281 successfully reduced NAb titers along with total IgG levels; it also enhanced gene delivery to the liver and other organs after intravenous administration of AAV in NAb-positive animals. These results indicate that mitigating pre-existing humoral immunity via disruption of the FcRn-IgG interaction may make AAV-based gene therapies effective in NAb-positive patients.

Neuroimaging-Derived Predicted Brain Age and Alcohol Use Among Community-Dwelling Older Adults

OBJECTIVES

Observational studies have suggested that moderate alcohol use is associated with reduced risk of dementia. However, the nature of this association is not understood. We investigated whether light to moderate alcohol use may be associated with slower brain aging, among a cohort of older community-dwelling adults using a biomarker of brain age based on structural neuroimaging measures.

DESIGN

Cross-sectional observational study.

PARTICIPANTS

Well-characterized members of a longitudinal cohort study who underwent neuroimaging. We categorized the 163 participants (mean age 76.7 ± 7.7, 60% women) into current nondrinkers, light drinkers (1-7 drinks/week) moderate drinkers (>7-14 drinks/week), or heavier drinkers (>14 drinks/week).

MEASUREMENTS

We calculated brain-predicted age using structural MRIs processed with the BrainAgeR program, and calculated the difference between brain-predicted age and chronological age (brain-predicted age difference, or brain-PAD). We used analysis of variance to determine if brain-PAD differed across alcohol groups, controlling for potential confounders.

RESULTS

Brain-PAD differed across alcohol groups (F[3, 150] = 4.02; p = 0.009) with heavier drinkers showing older brain-PAD than light drinkers (by about 6 years). Brain-PAD did not differ across light, moderate, and nondrinkers. Similar results were obtained after adjusting for potentially mediating health-related measures, and after excluding individuals with a history of heavier drinking.

DISCUSSION

Among this sample of healthy older adults, consumption of more than 14 drinks/week was associated with a biomarker of advanced brain aging. Light and moderate drinking was not associated with slower brain aging relative to non-drinking.

Three-dimensional irradiance and temperature distributions resulting from transdermal application of laser light to human knee-A numerical approach

The use of light for therapeutic applications requires light-absorption by cellular chromophores at the target tissues and the subsequent photobiomodulation (PBM) of cellular biochemical processes. For transdermal deep tissue light therapy (tDTLT) to be clinically effective, a sufficiently large number of photons must reach and be absorbed at the targeted deep tissue sites. Thus, delivering safe and effective tDTLT requires understanding the physics of light propagation in tissue. This study simulates laser light propagation in an anatomically accurate human knee model to assess the light transmittance and light absorption-driven thermal changes for eight commonly used laser therapy wavelengths (600-1200 nm) at multiple skin-applied irradiances (W cm-2 ) with continuous wave (CW) exposures. It shows that of the simulated parameters, 2.38 W cm-2 (30 W, 20 mm beam radius) of 1064 nm light generated the least tissue heating -4°C at skin surface, after 30 s of CW irradiation, and the highest overall transmission-approximately 3%, to the innermost muscle tissue.

Machine-learning-derived heart and brain age are independently associated with cognition

BACKGROUND AND PURPOSE

A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. Whether this biomarker is associated with cognitive function was investigated.

METHODS

Using 12-lead electrocardiograms, heart age was estimated for a population-based sample (N = 7779, age 40-85 years, 45.3% men). Associations between heart delta age (HDA) and cognitive test scores were studied adjusted for cardiovascular risk factors. In addition, the relationship between HDA, brain delta age (BDA) and cognitive test scores was investigated in mediation analysis.

RESULTS

Significant associations between HDA and the Word test, Digit Symbol Coding Test and tapping test scores were found. HDA was correlated with BDA (Pearson's r = 0.12, p = 0.0001). Moreover, 13% (95% confidence interval 3-36) of the HDA effect on the tapping test score was mediated through BDA.

DISCUSSION

Heart delta age, representing the cumulative effects of life-long exposures, was associated with brain age. HDA was associated with cognitive function that was minimally explained through BDA.

Environmental and genetic predictors of human cardiovascular ageing

Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we use machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.

Iron and iron-related proteins in COVID-19

COVID-19 can cause detrimental effects on health. Vaccines have helped in reducing disease severity and transmission but their long-term effects on health and effectiveness against future viral variants remain unknown. COVID-19 pathogenesis involves alteration in iron homeostasis. Thus, a contextual understanding of iron-related parameters would be very valuable for disease prognosis and therapeutics.Accordingly, we reviewed the status of iron and iron-related proteins in COVID-19. Iron-associated alterations in COVID-19 reported hitherto include anemia of inflammation, low levels of serum iron (hypoferremia), transferrin and transferrin saturation, and high levels of serum ferritin (hyperferritinemia), hepcidin, lipocalin-2, catalytic iron, and soluble transferrin receptor (in ICU patients). Hemoglobin levels can be low or normal, and compromised hemoglobin function has been proposed. Membrane-bound transferrin receptor may facilitate viral entry, so it acts as a potential target for antiviral therapy. Lactoferrin can provide natural defense by preventing viral entry and/or inhibiting viral replication. Serum iron and ferritin levels can predict COVID-19-related hospitalization, severity, and mortality. Serum hepcidin and ferritin/transferrin ratio can predict COVID-19 severity. Here, serum levels of these iron-related parameters are provided, caveats of iron chelation for therapy are discussed and the interplay of these iron-related parameters in COVID-19 is explained.This synopsis is crucial as it clearly presents the iron picture of COVID-19. The information may assist in disease prognosis and/or in formulating iron-related adjunctive strategies that can help reduce infection/inflammation and better manage COVID-19 caused by future variants. Indeed, the current picture will augment as more is revealed about these iron-related parameters in COVID-19.

Genetic architecture of brain age and its causal relations with brain and mental disorders

The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.

Ischemic Heart Disease and Vascular Risk Factors Are Associated With Accelerated Brain Aging

BACKGROUND

Ischemic heart disease (IHD) has been linked with poor brain outcomes. The brain magnetic resonance imaging-derived difference between predicted brain age and actual chronological age (brain-age delta in years, positive for accelerated brain aging) may serve as an effective means of communicating brain health to patients to promote healthier lifestyles.

OBJECTIVES

The authors investigated the impact of prevalent IHD on brain aging, potential underlying mechanisms, and its relationship with dementia risk, vascular risk factors, cardiovascular structure, and function.

METHODS

Brain age was estimated in subjects with prevalent IHD (n = 1,341) using a Bayesian ridge regression model with 25 structural (volumetric) brain magnetic resonance imaging features and built using UK Biobank participants with no prevalent IHD (n = 35,237).

RESULTS

Prevalent IHD was linked to significantly accelerated brain aging (P < 0.001) that was not fully mediated by microvascular injury. Brain aging (positive brain-age delta) was associated with increased risk of dementia (OR: 1.13 [95% CI: 1.04-1.22]; P = 0.002), vascular risk factors (such as diabetes), and high adiposity. In the absence of IHD, brain aging was also associated with cardiovascular structural and functional changes typically observed in aging hearts. However, such alterations were not linked with risk of dementia.

CONCLUSIONS

Prevalent IHD and coexisting vascular risk factors are associated with accelerated brain aging and risk of dementia. Positive brain-age delta representing accelerated brain aging may serve as an effective communication tool to show the impact of modifiable risk factors and disease supporting preventative strategies.

Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants

Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.

Evolutionary implications of host genetic control for engineering beneficial microbiomes

Engineering new functions in the microbiome requires understanding how host genetic control and microbe-microbe interactions shape the microbiome. One key genetic mechanism underlying host control is the immune system. The immune system can promote stability in the composition of the microbiome by reshaping the ecological dynamics of its members, but the degree of stability will depend on the interplay between ecological context, immune system development, and higher-order microbe-microbe interactions. The eco-evolutionary interplay affecting composition and stability should inform the strategies used to engineer new functions in the microbiome. We conclude with recent methodological developments that provide an important path forward for both engineering new functionality in the microbiome and broadly understanding how ecological interactions shape evolutionary processes in complex biological systems.

Conceptual Overview of Biological Age Estimation

Chronological age is an imperfect measure of the aging process, which is affected by a wide range of genetic and environmental exposures. Biological age estimates may be derived using mathematical modelling with biomarkers set as predictors and chronological age as the output. The difference between biological and chronological age is denoted the "age gap" and considered a complementary indicator of aging. The utility of the "age gap" metric is assessed through examination of its associations with exposures of interest and the demonstration of additional information provided by this metric over chronological age alone. This paper reviews the key concepts of biological age estimation, the age gap metric, and approaches to assessment of model performance in this context. We further discuss specific challenges for the field, in particular the limited generalisability of effect sizes across studies owing to dependency of the age gap metric on pre-processing and model building methods. The discussion will be centred on brain age estimation, but the concepts are transferable to all biological age estimation.

Evaluation of Health-Related Quality of Life in Patients Receiving Treatment for Penile Cancer: A Single-Center Cross- Sectional Study

INTRODUCTION

Penile cancer is one of the uncommon types of cancer in men. The treatment could significantly impact a patient's quality of life (QOL), leading to difficulties in fulfilling life functions.

METHODS

This descriptive observational study aimed to describe a situation using a cross-sectional design objectively. The population of this study was all patients with a diagnosis of penile cancer who underwent therapy at the Haji Adam Malik Hospital from September 2020 to September 2021. Quality of life was assessed using EORTC QLQ-C30.

RESULTS

The respondents' mean age and standard deviation were 54.44 and 8.647 years, respectively. The youngest was 38 years, while the oldest age was 64 years. Most respondents had no history of circumcision (55.6%). All respondents had a poor QOL based on the 28 components in the questionnaire. This study showed that erectile function, changes in sexual function, and overall sexual function were correlated with health-related quality of life (HRQoL) post-treatment. In general, lack of sexual activity is the primary factor responsible for decreasing HRQoL in penile cancer patients. It has been reported that 70% of patients experienced a negative impact on sexuality post-treatment.

CONCLUSION

The quality of life in patients receiving treatment for penile cancer at RSUP H. Adam Malik, Medan, was poor. It is associated with a lack of sexual activity.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

BACKGROUND AND AIMS

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.

DESIGN

Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.

SETTING

United Kingdom.

PARTICIPANTS

The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].

MEASUREMENTS

Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.

FINDINGS

The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results.

CONCLUSIONS

There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity

BACKGROUND

A growing body of evidence shows differences in the prevalence of cardiometabolic syndrome (CMS) and dementia based on gender and ethnicity. However, there is a paucity of information about ethnic- and gender-specific CMS effects on brain age. We investigated the different effects of CMS on brain age by gender in Korean and British cognitively unimpaired (CU) populations. We also determined whether the gender-specific difference in the effects of CMS on brain age changes depending on ethnicity.

METHODS

These analyses used de-identified, cross-sectional data on CU populations from Korea and United Kingdom (UK) that underwent brain MRI. After propensity score matching to balance the age and gender between the Korean and UK populations, 5759 Korean individuals (3042 males and 2717 females) and 9903 individuals from the UK (4736 males and 5167 females) were included in this study. Brain age index (BAI), calculated by the difference between the predicted brain age by the algorithm and the chronological age, was considered as main outcome and presence of CMS, including type 2 diabetes mellitus (T2DM), hypertension, obesity, and underweight was considered as a predictor. Gender (males and females) and ethnicity (Korean and UK) were considered as effect modifiers.

RESULTS

The presence of T2DM and hypertension was associated with a higher BAI regardless of gender and ethnicity (p < 0.001), except for hypertension in Korean males (p = 0.309). Among Koreans, there were interaction effects of gender and the presence of T2DM (p for T2DM*gender = 0.035) and hypertension (p for hypertension*gender = 0.046) on BAI in Koreans, suggesting that T2DM and hypertension are each associated with a higher BAI in females than in males. In contrast, among individuals from the UK, there were no differences in the effects of T2DM (p for T2DM*gender = 0.098) and hypertension (p for hypertension*gender = 0.203) on BAI between males and females.

CONCLUSIONS

Our results highlight gender and ethnic differences as important factors in mediating the effects of CMS on brain age. Furthermore, these results suggest that ethnic- and gender-specific prevention strategies may be needed to protect against accelerated brain aging.

Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.

Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications

BACKGROUND

There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual's risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the "true" age, which is an integrated result of an individual's level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan.

METHODS AND OBJECTIVE

Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months).

RESULTS

We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental-physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately.

CONCLUSIONS

Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.

Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES

While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.

METHODS

We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input.

RESULTS

We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.

DISCUSSION

T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.

Is an MRI-derived anatomical measure of dementia risk also a measure of brain aging?

Machine learning methods have been applied to estimate measures of brain aging from neuroimages. However, only rarely have these measures been examined in the context of biologic age. Here, we investigated associations of an MRI-based measure of dementia risk, the Alzheimer's disease pattern similarity (AD-PS) scores, with measures used to calculate biological age. Participants were those from visit 5 of the Atherosclerosis Risk in Communities Study with cognitive status adjudication, proteomic data, and AD-PS scores available. The AD-PS score estimation is based on previously reported machine learning methods. We evaluated associations of the AD-PS score with all-cause mortality. Sensitivity analyses using only cognitively normal (CN) individuals were performed treating CNS-related causes of death as competing risk. AD-PS score was examined in association with 32 proteins measured, using a Somalogic platform, previously reported to be associated with age. Finally, associations with a deficit accumulation index (DAI) based on a count of 38 health conditions were investigated. All analyses were adjusted for age, race, sex, education, smoking, hypertension, and diabetes. The AD-PS score was significantly associated with all-cause mortality and with levels of 9 of the 32 proteins. Growth/differentiation factor 15 (GDF-15) and pleiotrophin remained significant after accounting for multiple-testing and when restricting the analysis to CN participants. A linear regression model showed a significant association between DAI and AD-PS scores overall. While the AD-PS scores were created as a measure of dementia risk, our analyses suggest that they could also be capturing brain aging.

Surface functionalized silver-doped ZnO nanocatalyst: a sustainable cooperative catalytic, photocatalytic and antibacterial platform for waste treatment

The different dyes used and discharged in industrial settings and microbial pathogenic issues have raised serious concerns about the content of bodies of water and the impact that dyes and microbes have on the environment and human health. Efficient treatment of contaminated water is thus a major challenge that is of great interest to researchers around the world. In the present work, we have fabricated functionalized silver-doped ZnO nanoparticles (Ag-doped ZnO NPs) via a hydrothermal method for wastewater treatment. X-ray photoelectron spectroscopy analysis confirmed the doping of Ag with ZnO NPs, and X-ray diffractometry analysis showed a decreasing trend in the crystallite size of the synthesized ZnO NPs with increased Ag concentration. Field emission scanning electron microscopy study of pure ZnO NPs and Ag-doped ZnO NPs revealed nanocrystal aggregates with mixed morphologies, such as hexagonal and rod-shaped structures. Distribution of Ag on the ZnO lattice is confirmed by high-resolution transmission electron microscopy analysis. ZnO NPs with 4 wt% Ag doping showed a maximum degradation of ∼95% in 1.5 h of malachite green dye (80 mg L-1) under visible light and ∼85% in 4 h under dark conditions. Up to five successive treatment cycles using the 4 wt% Ag-doped ZnO NP nanocatalyst confirmed its reusability, as it was still capable of degrading ∼86% and 82% of the dye under visible light and dark conditions, respectively. This limits the risk of nanotoxicity and aids the cost-effectiveness of the overall treatment process. The synthesized NPs showed antibacterial activity in a dose-dependent manner. The zone of inhibition of the Ag-doped ZnO NPs was higher than that of the pure ZnO NPs for all doping content. The studied Ag-doped ZnO NPs thus offer a significant eco-friendly route for the effective treatment of water contaminated with synthetic dyes and fecal bacterial load.

Cholestasis: exploring the triangular relationship of gut microbiota-bile acid-cholestasis and the potential probiotic strategies

Cholestasis is a condition characterized by the abnormal production or excretion of bile, and it can be induced by a variety of causes, the factors of which are extremely complex. Although great progress has been made in understanding cholestasis pathogenesis, the specific mechanisms remain unclear. Therefore, it is important to understand and distinguish cholestasis from different etiologies, which will also provide indispensable theoretical support for the development of corresponding therapeutic drugs. At present, the treatment of cholestasis mainly involves several bile acids (BAs) and their derivatives, most of which are in the clinical stage of development. Multiple lines of evidence indicate that ecological disorders of the gut microbiota are strongly related to the occurrence of cholestasis, in which BAs also play a pivotal role. Recent studies indicate that probiotics seem to have certain effects on cholestasis, but further confirmation from clinical trials is required. This paper reviews the etiology of and therapeutic strategies for cholestasis; summarizes the similarities and differences in inducement, symptoms, and mechanisms of related diseases; and provides information about the latest pharmacological therapies currently available and those under research for cholestasis. We also reviewed the highly intertwined relationship between gut microbiota-BA-cholestasis, revealing the potential role and possible mechanism of probiotics in the treatment of cholestasis.

Psychometric evaluation of the Chinese version of the media Health Literacy Questionnaire: A validation study

Background

The media play an important role in health promotion and disease prevention, while at the same time, a variety of mixed health messages in the media are beginning to pose new challenges to them. However, there is a lack of media health literacy (MHL) assessment tools in China. Therefore, the purpose of this study was to translate the Media Health Literacy (MeHLit) questionnaire into Chinese and to assess its psychometric properties.

Methods

This cross-sectional study was conducted from October to December 2022, and a methodological study of the translation and validation of the MeHLit questionnaire was conducted.

Results

As a result of an extensive translation and cultural adaptation process, the final MeHLit questionnaire was developed, which includes five dimensions and 21 items. Cronbach's α value of the questionnaire was 0.859, and Cronbach's α value of the dimensions ranged from 0.776 to 0.911, which is fairly good. As a result, the test-retest reliability coefficient and the split-half reliability coefficient of the questionnaire are both equal to 0.907. Its content validity index was 0.946, suggesting a reasonable level of content validity. Through exploratory factor analysis, a five-factor structure was identified based on the eigenvalues, total variance explained, and scree plot. As a result of the validation factor analysis, all recommended fit indicators were appropriate.

Conclusion

The Chinese version of the MeHLit questionnaire has been successfully introduced in China. It has shown good psychometric properties among the Chinese public and can be used as a tool to evaluate MHL in health screening.

Advanced brain age correlates with greater rumination and less mindfulness in schizophrenia

BACKGROUND

Individual variation in brain aging trajectories is linked with several physical and mental health outcomes. Greater stress levels, worry, and rumination correspond with advanced brain age, while other individual characteristics, like mindfulness, may be protective of brain health. Multiple lines of evidence point to advanced brain aging in schizophrenia (i.e., neural age estimate > chronological age). Whether psychological dimensions such as mindfulness, rumination, and perceived stress contribute to brain aging in schizophrenia is unknown.

METHODS

We estimated brain age from high-resolution anatomical scans in 54 healthy controls (HC) and 52 individuals with schizophrenia (SZ) and computed the brain predicted age difference (BrainAGE-diff), i.e., the delta between estimated brain age and chronological age. Emotional well-being summary scores were empirically derived to reflect individual differences in trait mindfulness, rumination, and perceived stress. Core analyses evaluated relationships between BrainAGE-diff and emotional well-being, testing for slopes differences across groups.

RESULTS

HC showed higher emotional well-being (greater mindfulness and less rumination/stress), relative to SZ. We observed a significant group difference in the relationship between BrainAge-diff and emotional well-being, explained by BrainAGE-diff negatively correlating with emotional well-being scores in SZ, and not in HC. That is, SZ with younger appearing brains (predicted age < chronological age) had emotional summary scores that were more like HC, a relationship that endured after accounting for several demographic and clinical variables.

CONCLUSIONS

These data reveal clinically relevant aspects of brain age heterogeneity among SZ and point to case-control differences in the relationship between advanced brain aging and emotional well-being.

Mesial Prefrontal Cortex and Alcohol Misuse: Dissociating Cross-sectional and Longitudinal Relationships in UK Biobank

BACKGROUND

Alcohol misuse is a major global public health issue. The disorder is characterized by aberrant neural networks interacting with environment and genetics. Dissecting the neural substrates and functional networks that relate to longitudinal changes in alcohol use from those that relate to alcohol misuse cross-sectionally is important to elucidate therapeutic approaches.

METHODS

To assess how neuroimaging data, including T1, resting-state functional magnetic resonance imaging, and diffusion-weighted imaging, relate to alcohol misuse cross-sectionally and longitudinally in the UK Biobank, this study analyzed range of alcohol misuse in a population-based normative sample of 24,784 participants, ages 45 to 81 years old, in a cross-sectional analysis and a sample of 3070 participants in a longitudinal analysis 2 years later.

RESULTS

Cross-sectional analysis showed that alcohol use is associated with a reduction in dorsal anterior cingulate cortex and dorsomedial prefrontal cortex gray matter concentration and functional resting-state connectivity (nodal degree: t_24,422 = -12.99, p < 1 × 10^-17). Reduced dorsal anterior cingulate cortex/dorsomedial prefrontal cortex functional connections to the ventrolateral prefrontal cortex, amygdala, and striatum relate to greater alcohol use. In a longitudinal analysis, higher resting-state nodal degree (t₃₀₃₆ = -3.27, p = .0011) and T1 gray matter concentration in the ventromedial prefrontal cortex relate to reduced alcohol intake frequency 2 years later. Higher ventromedial prefrontal cortex and frontoparietal executive network functional connectivity is associated with lower subsequent drinking longitudinally.

CONCLUSIONS

Dorsal versus ventromedial prefrontal regions are differentially related to alcohol misuse cross-sectionally or longitudinally in a large UK Biobank normative dataset. Our study provides a comprehensive understanding of the neurobiological substrates of alcohol use as a state or prospectively, thereby providing potential targets for clinical treatment.

Diminished Structural Brain Integrity in Long-term Cannabis Users Reflects a History of Polysubstance Use

BACKGROUND

Cannabis legalization and use are outpacing our understanding of its long-term effects on brain and behavior, which is fundamental for effective policy and health practices. Existing studies are limited by small samples, cross-sectional measures, failure to separate long-term from recreational use, and inadequate control for other substance use. Here, we address these limitations by determining the structural brain integrity of long-term cannabis users in the Dunedin Study, a longitudinal investigation of a population-representative birth cohort followed to midlife.

METHODS

We leveraged prospective measures of cannabis, alcohol, tobacco, and other illicit drug use in addition to structural neuroimaging in 875 study members at age 45 to test for differences in both global and regional gray and white matter integrity between long-term cannabis users and lifelong nonusers. We additionally tested for dose-response associations between continuous measures of cannabis use and brain structure, including careful adjustments for use of other substances.

RESULTS

Long-term cannabis users had a thinner cortex, smaller subcortical gray matter volumes, and higher machine learning-predicted brain age than nonusers. However, these differences in structural brain integrity were explained by the propensity of long-term cannabis users to engage in polysubstance use, especially with alcohol and tobacco.

CONCLUSIONS

These findings suggest that diminished midlife structural brain integrity in long-term cannabis users reflects a broader pattern of polysubstance use, underlining the importance of understanding comorbid substance use in efforts to curb the negative effects of cannabis on brain and behavior as well as establish more effective policy and health practices.

Progress of delivery methods for CRISPR-Cas9

INTRODUCTION

Gene therapy is becoming increasingly common in clinical practice, giving hope for the correction of a wide range of human diseases and defects. The CRISPR/Cas9 system, consisting of the Cas9 nuclease and single-guide RNA (sgRNA), has revolutionized the field of gene editing. However, efficiently delivering the CRISPR-Cas9 to the target organ or cell remains a significant challenge. In recent years, with rapid advances in nanoscience, materials science, and medicine, researchers have developed various technologies that can deliver CRISPR-Cas9 in different forms for in vitro and in vivo gene editing. Here, we review the development of the CRISPR-Cas9 and describe the delivery forms and the vectors that have emerged in CRISPR-Cas9 delivery, summarizing the key barriers and the promising strategies that vectors currently face in delivering the CRISPR-Cas9.

AREAS COVERED

With the rapid development of CRISPR-Cas9, delivery methods are becoming increasingly important in the in vivo delivery of CRISPR-Cas9.

EXPERT OPINION

CRISPR-Cas9 is becoming increasingly common in clinical trials. However, the complex nuclease and protease environment is a tremendous challenge for in vivo clinical applications. Therefore, the development of delivery methods is highly likely to take the application of CRISPR-Cas9 technology to another level.

eDNA-based detection of the invasive crayfish Pacifastacus leniusculus in streams with a LAMP assay using dependent replicates to gain higher sensitivity

LAMP assays are becoming increasingly popular in the field of invasive species detection but are still underused in eDNA-based monitoring. Here, we propose a LAMP assay designed to detect the North American crayfish species Pacifastacus leniusculus in water samples from streams. The presence of P. leniusculus was detected through this new LAMP assay in all but one of the nine sites sampled. No correlation was found between ddPCR absolute concentration measurements and the number of LAMP-positive technical replicates. However, we showed that using dependent technical replicates could significantly enhance the detection sensitivity of the LAMP assay. Applied to other assays, it could improve sensitivity and thus allow for a more efficient use of eDNA-based LAMP assays for invasive species detection in aquatic ecosystems.

The Prognostic Role of CD73/A2AR Expression and Tumor Immune Response in Periampullary Carcinoma Subtypes

INTRODUCTION

Periampullary adenocarcinoma (PAAC) is a rare, lethal heterogeneous group of malignancy that differs in their molecular phenotypes. Ecto-5'-nucleotidase (CD73)/adenosine A2A Receptor (A2AR) pathway has shown an emerging role in cancer therapy through modulating the immune response. Therefore, this study aimed to explore the functional role of CD73 and A2AR in pancreatic ductal adenocarcinoma (PDAC) and ampullary carcinoma (AC).

MATERIAL AND METHODS

An immunohistochemical study for CD73 and A2AR carried on 48 PDAC cases, 21 AC cases and 34 adjacent non-tumor tissues that were taken from the farthest point of normal pancreatic tissue away from the tumor.

RESULTS

CD73 was overexpressed in the PDAC (p < 0.001), and AC (p = 0.004) groups compared to their non-tumor tissues. However, A2AR was overexpressed in the PDAC group (p = 0.003) but not in the AC group (p = 0.359) compared to non-tumor tissue. In the PDAC group, CD73 overexpression was significantly associated with longer overall survival (p = 0.018). In contrary, A2AR overexpression was significantly associated with high grade (p = 0.001) and late- stage (p = 0.01). Both markers had no prognostic impact on AC. In the meantime, tumor immune response showed a negative prognostic role in PDAC and AC. The prognostic role of tumor immune response in the PDAC group was strongly modulated by CD73 and A2AR expression.

CONCLUSIONS

PDAC and AC shared CD73 Overexpression while A2AR was overexpressed in PDAC only. In PDAC, CD73 and A2AR showed an opposed  prognostic effect but both had no prognostic impact on AC. In addition, tumor immune response showed a controversial impact on the prognosis of PDAC and AC.

Long-term associations of cigarette smoking in early mid-life with predicted brain aging from mid- to late life

BACKGROUND AND AIMS

Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later.

DESIGN

Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated.

SETTING

Population-based United States sample.

PARTICIPANTS/CASES

Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves.

MEASUREMENTS

Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19).

FINDINGS

In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD.

CONCLUSIONS

Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.

Limited evidence for a moderating effect of HIV status on brain age in heavy episodic drinkers

We set out to test the hypothesis that greater brain ageing will be observed in people with HIV (PWH) and those who engage in heavy episodic drinking (HED), with their combined effects being especially detrimental in cognitive control brain networks. We correlated measures of "brain age gap" (BAG) and neurocognitive impairment in participants with and without HIV and HED. Sixty-nine participants were recruited from a community health centre in Cape Town: HIV - /HED - (N = 17), HIV + /HED - (N = 14), HIV - /HED + (N = 21), and HIV + /HED + (N = 17). Brain age was modelled using structural MRI features from the whole brain or one of six brain regions. Linear regression models were employed to identify differences in BAG between patient groups and controls. Associations between BAG and clinical data were tested using bivariate statistical methods. Compared to controls, greater global BAG was observed in heavy drinkers, both with (Cohen's d = 1.52) and without (d = 1.61) HIV. Differences in BAG between HED participants and controls were observed for the cingulate and parietal cortex, as well as subcortically. A larger BAG was associated with higher total drinking scores but not nadir CD4 count or current HIV viral load. The association between heavy episodic drinking and BAG, independent of HIV status, points to the importance of screening for alcohol use disorders in primary care. The relatively large contribution of cognitive control brain regions to BAG highlights the utility of assessing the contribution of different brain regions to brain age.

Brain Age Difference at Baseline Predicts Clinical Dementia Rating Change in Approximately Two Years

BACKGROUND

The Clinical Dementia Rating (CDR) has been widely used to assess dementia severity, but it is limited in predicting dementia progression, thus unable to advise preventive measures to those who are at high risk.

OBJECTIVE

Predicted age difference (PAD) was proposed to predict CDR change.

METHODS

All diffusion magnetic resonance imaging and CDR scores were obtained from the OASIS-3 databank. A brain age model was trained by a machine learning algorithm using the imaging data of 258 cognitively healthy adults. Two diffusion indices, i.e., mean diffusivity and fractional anisotropy, over the whole brain white matter were extracted to serve as the features for model training. The validated brain age model was applied to a longitudinal cohort of 217 participants who had CDR = 0 (CDR0), 0.5 (CDR0.5), and 1 (CDR1) at baseline. Participants were grouped according to different baseline CDR and their subsequent CDR in approximately 2 years of follow-up. PAD was compared between different groups with multiple comparison correction.

RESULTS

PADs were significantly different among participants with different baseline CDRs. PAD in participants with relatively stable CDR0.5 was significantly smaller than PAD in participants who had CDR0.5 at baseline but converted to CDR1 in the follow-up. Similarly, participants with relatively stable CDR0 had significantly smaller PAD than those who were CDR0 at baseline but converted to CDR0.5 in the follow-up.

CONCLUSION

Our results imply that PAD might be a potential imaging biomarker for predicting CDR outcomes in patients with CDR0 or CDR0.5.

Associations between smoking and accelerated brain ageing

Smoking accelerates the ageing of multiple organs. However, few studies have quantified the association between smoking, especially smoking cessation, and brain ageing. Using structural magnetic resonance imaging data from the UK Biobank (n = 33,293), a brain age predictor was trained using a machine learning technique in the non-smoker group (n = 14,667) and then tested in the smoker group (n = 18,626) to determine the relationships between BrainAge Gap (predicted age - true age) and smoking parameters. Further, we examined whether smoking was associated with poorer cognition and whether this relationship was mediated by brain age. The predictor achieved an appreciable performance in training data (r = 0.712, mean-absolute-error [MAE] = 4.220) and test data (r = 0.725, MAE = 4.160). On average, smokers showed a larger BrainAge Gap (+0.304 years, Cohens'd = 0.083) than controls, more explicitly, the extents vary depending on their smoking characteristic that active regular smokers had the largest BrainAge Gap (+1.190 years, Cohens'd = 0.321), and light smokers had a moderate BrainAge Gap (+0.478, Cohens'd = 0.129). The increased smoking amount was associated with a larger BrainAge Gap (β = 0.035, p = 1.72 × 10^-20) while a longer duration of quitting smoking in ex-smokers was associated with a smaller BrainAge Gap (β = -0.015, p = 2.14 × 10^-05). Furthermore, smoking was associated with poorer cognition, and this relationship was partially mediated by BrainAge Gap. The study provides insight into the association between smoking, brain ageing, and cognition, which provide more publicly acceptable propaganda against smoking.

Dose-dependent relationship between social drinking and brain aging

Low-level alcohol consumption is commonly perceived as being inconsequential or even beneficial for overall health, with some reports suggesting that it may protect against dementia or cardiovascular risks. However, these potential benefits do not preclude the concurrent possibility of negative health outcomes related to alcohol consumption. To examine whether casual, non-heavy drinking is associated with premature brain aging, we utilized the Brain-Age Regression Analysis and Computational Utility Software package to predict brain age in a community sample of adults [n = 240, mean age 35.1 (±10.7) years, 48% male, 49% African American]. Accelerated brain aging was operationalized as the difference between predicted and chronological age ("brain age gap"). Multiple regression analysis revealed a significant association between previous 90-day alcohol consumption and brain age gap (β = 0.014, p = 0.023). We replicated these results in an independent cohort [n = 231 adults, mean age 34.3 (±11.1) years, 55% male, 28% African American: β = 0.014, p = 0.002]. Our results suggest that even low-level alcohol consumption is associated with premature brain aging. The clinical significance of these findings remains to be investigated.

Associations of Alcohol Consumption and Smoking With Disease Risk and Neurodegeneration in Individuals With Multiple Sclerosis in the United Kingdom

IMPORTANCE

Understanding the effects of modifiable risk factors on risk for multiple sclerosis (MS) and associated neurodegeneration is important to guide clinical counseling.

OBJECTIVE

To investigate associations of alcohol use, smoking, and obesity with odds of MS diagnosis and macular ganglion cell layer and inner plexiform layer (mGCIPL) thickness.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data from the community-based UK Biobank study on health behaviors and retinal thickness (measured by optical coherence tomography in both eyes) in individuals aged 40 to 69 years examined from December 1, 2009, to December 31, 2010. Risk factors were identified with multivariable logistic regression analyses. To adjust for intereye correlations, multivariable generalized estimating equations were used to explore associations of alcohol use and smoking with mGCIPL thickness. Finally, interaction models explored whether the correlations of alcohol and smoking with mGCIPL thickness differed for individuals with MS. Data were analyzed from February 1 to July 1, 2021.

EXPOSURES

Smoking status (never, previous, or current), alcohol intake (never or special occasions only [low], once per month to ≤4 times per week [moderate], or daily/almost daily [high]), and body mass index.

MAIN OUTCOMES AND MEASURES

Multiple sclerosis case status and mGCIPL thickness.

RESULTS

A total of 71 981 individuals (38 685 women [53.7%] and 33 296 men [46.3%]; mean [SD] age, 56.7 [8.0] years) were included in the analysis (20 065 healthy control individuals, 51 737 control individuals with comorbidities, and 179 individuals with MS). Modifiable risk factors significantly associated with MS case status were current smoking (odds ratio [OR], 3.05 [95% CI, 1.95-4.64]), moderate alcohol intake (OR, 0.62 [95% CI, 0.43-0.91]), and obesity (OR, 1.72 [95% CI, 1.15-2.56]) compared with healthy control individuals. Compared with the control individuals with comorbidities, only smoking was associated with case status (OR, 2.30 [95% CI, 1.48-3.51]). High alcohol intake was associated with a thinner mGCIPL in individuals with MS (adjusted β = -3.09 [95% CI, -5.70 to -0.48] μm; P = .02). In the alcohol interaction model, high alcohol intake was associated with thinner mGCIPL in control individuals (β = -0.93 [95% CI, -1.07 to -0.79] μm; P < .001), but there was no statistically significant association in individuals with MS (β = -2.27 [95% CI, -4.76 to 0.22] μm; P = .07). Smoking was not associated with mGCIPL thickness in MS. However, smoking was associated with greater mGCIPL thickness in control individuals (β = 0.89 [95% CI, 0.74-1.05 μm]; P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that high alcohol intake was associated with retinal features indicative of more severe neurodegeneration, whereas smoking was associated with higher odds of being diagnosed with MS.

Advanced brain age in deployment-related traumatic brain injury: A LIMBIC-CENC neuroimaging study

OBJECTIVE

To determine if history of mild traumatic brain injury (mTBI) is associated with advanced or accelerated brain aging among the United States (US) military Service Members and Veterans.

METHODS

Eight hundred and twenty-two participants (mean age = 40.4 years, 714 male/108 female) underwent MRI sessions at eight sites across the US. Two hundred and one participants completed a follow-up scan between five months and four years later. Predicted brain ages were calculated using T1-weighted MRIs and then compared with chronological ages to generate an Age Deviation Score for cross-sectional analyses and an Interval Deviation Score for longitudinal analyses. Participants also completed a neuropsychological battery, including measures of both cognitive functioning and psychological health.

RESULT

In cross-sectional analyses, males with a history of deployment-related mTBI showed advanced brain age compared to those without (t(884) = 2.1, p = .038), while this association was not significant in females. In follow-up analyses of the male participants, severity of posttraumatic stress disorder (PTSD), depression symptoms, and alcohol misuse were also associated with advanced brain age.

CONCLUSION

History of deployment-related mTBI, severity of PTSD and depression symptoms, and alcohol misuse are associated with advanced brain aging in male US military Service Members and Veterans.