Brain elastography in aging relates to fluid/solid trendlines.

The relatively new tools of brain elastography have established a general trendline for healthy, aging adult humans, whereby the brain's viscoelastic properties 'soften' over many decades. Earlier studies of the aging brain have demonstrated a wide spectrum of changes in morphology and composition towards the later decades of lifespan. This leads to a major question of causal mechanisms: of the many changes documented in structure and composition of the aging brain, which ones drive the long term trendline for viscoelastic properties of grey matter and white matter? The issue is important for illuminating which factors brain elastography is sensitive to, defining its unique role for study of the brain and clinical diagnoses of neurological disease and injury. We address these issues by examining trendlines in aging from our elastography data, also utilizing data from an earlier landmark study of brain composition, and from a biophysics model that captures the multiscale biphasic (fluid/solid) structure of the brain. Taken together, these imply that long term changes in extracellular water in the glymphatic system of the brain along with a decline in the extracellular matrix have a profound effect on the measured viscoelastic properties. Specifically, the trendlines indicate that water tends to replace solid fraction as a function of age, then grey matter stiffness decreases inversely as water fraction squared, whereas white matter stiffness declines inversely as water fraction to the 2/3 power, a behavior consistent with the cylindrical shape of the axons. These unique behaviors point to elastography of the brain as an important macroscopic measure of underlying microscopic structural change, with direct implications for clinical studies of aging, disease, and injury.

Brain white matter pathways of resilience to chronic back pain: a multisite validation.

Chronic back pain (CBP) is a global health concern with significant societal and economic burden. While various predictors of back pain chronicity have been proposed, including demographic and psychosocial factors, neuroimaging studies have shown that brain characteristics can serve as robust predictors of CBP. However, large-scale, multisite validation of these predictors is currently lacking. In two independent longitudinal studies, we examined white matter diffusion imaging data and pain characteristics in patients with subacute back pain (SBP) over six- and 12-month periods. Diffusion data from individuals with CBP and healthy controls (HC) were analyzed for comparison. Whole-brain tract-based spatial statistics analyses revealed that a cluster in the right superior longitudinal fasciculus (SLF) tract had larger fractional anisotropy (FA) values in patients who recovered (SBPr) compared to those with persistent pain (SBPp), and predicted changes in pain severity. The SLF FA values accurately classified patients at baseline and follow-up in a third publicly available dataset (Area under the Receiver Operating Curve ~ 0.70). Notably, patients who recovered had FA values larger than those of HC suggesting a potential role of SLF integrity in resilience to CBP. Structural connectivity-based models also classified SBPp and SBPr patients from the three data sets (validation accuracy 67%). Our results validate the right SLF as a robust predictor of CBP development, with potential for clinical translation. Cognitive and behavioral processes dependent on the right SLF, such as proprioception and visuospatial attention, should be analyzed in subacute stages as they could prove important for back pain chronicity.

Differences in resting state functional connectivity relative to multiple sclerosis and impaired information processing speed.

Background: Fifty-one percent of individuals with multiple sclerosis (MS) develop cognitive impairment (CI) in information processing speed (IPS). Although IPS scores are associated with health and well-being, neural changes that underlie IPS impairments in MS are not understood. Resting state fMRI can provide insight into brain function changes underlying impairment in persons with MS. Objectives: We aimed to assess functional connectivity (FC) differences in (i) persons with MS compared to healthy controls (HC), (ii) persons with both MS and CI (MS-CI) compared to HC, (iii) persons with MS that are cognitively preserved (MS-CP) compared to HC, (iv) MS-CI compared to MS-CP, and (v) in relation to cognition within the MS group. Methods: We included 107 participants with MS (age 49.5 ± 12.9, 82% women), and 94 controls (age 37.9 ± 15.4, 66% women). Each participant was administered the Symbol Digit Modalities Test (SDMT) and underwent a resting state fMRI scan. The MS-CI group was created by applying a z-score cut-off of ≤-1.5 to locally normalized SDMT scores. The MS-CP group was created by applying a z-score of ≥0. Control groups (HCMS-CI and HCMS-CP) were based on the nearest age-matched HC participants. A whole-brain ROI-to-ROI analysis was performed followed by specific contrasts and a regression analysis. Results: Individuals with MS showed FC differences compared to HC that involved the cerebellum, visual and language-associated brain regions, and the thalamus, hippocampus, and basal ganglia. The MS-CI showed FC differences compared to HCMS-CI that involved the cerebellum, visual and language-associated areas, thalamus, and caudate. SDMT scores were correlated with FC between the cerebellum and lateral occipital cortex in MS. No differences were observed between the MS-CP and HCMS-CP or MS-CI and MS-CP groups. Conclusion: Our findings emphasize FC changes of cerebellar, visual, and language-associated areas in persons with MS. These differences were apparent for (i) all MS participants compared to HC, (ii) MS-CI subgroup and their matched controls, and (iii) the association between FC and SDMT scores within the MS group. Our findings strongly suggest that future work that examines the associations between FC and IPS impairments in MS should focus on the involvement of these regions.

Enolase Inhibitors as Early Lead Therapeutics against Trypanosoma brucei.

Glucose metabolism is critical for the African trypanosome, Trypanosoma brucei, serving as the lone source of ATP production for the bloodstream form (BSF) parasite in the glucose-rich environment of the host blood. Recently, phosphonate inhibitors of human enolase (ENO), the enzyme responsible for the interconversion of 2-phosphoglycerate (2-PG) to phosphoenolpyruvate (PEP) in glycolysis or PEP to 2-PG in gluconeogenesis, have been developed for the treatment of glioblastoma multiforme (GBM). Here, we have tested these agents against T. brucei ENO (TbENO) and found the compounds to be potent enzyme inhibitors and trypanocides. For example, (1-hydroxy-2-oxopyrrolidin-3-yl) phosphonic acid (deoxy-SF2312) was a potent enzyme inhibitor (IC50 value of 0.60 ± 0.23 µM), while a six-membered ring-bearing phosphonate, (1-hydroxy-2-oxopiperidin-3-yl) phosphonic acid (HEX), was less potent (IC50 value of 2.1 ± 1.1 µM). An analog with a larger seven-membered ring, (1-hydroxy-2-oxoazepan-3-yl) phosphonic acid (HEPTA), was not active. Molecular docking simulations revealed that deoxy-SF2312 and HEX had binding affinities of -6.8 and -7.5 kcal/mol, respectively, while the larger HEPTA did not bind as well, with a binding of affinity of -4.8 kcal/mol. None of these compounds were toxic to BSF parasites; however, modification of enzyme-active phosphonates through the addition of pivaloyloxymethyl (POM) groups improved activity against T. brucei, with POM-modified (1,5-dihydroxy-2-oxopyrrolidin-3-yl) phosphonic acid (POMSF) and POMHEX having EC50 values of 0.45 ± 0.10 and 0.61 ± 0.08 µM, respectively. These findings suggest that HEX is a promising lead against T. brucei and that further development of prodrug HEX analogs is warranted.

Altered voxel-based and surface-based morphometry in inflammatory bowel disease.

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is characterized by inflammation of the gastrointestinal tract and is a disorder of the brain-gut axis. Neuroimaging studies of brain function and structure have helped better understand the relationships between the brain, gut, and comorbidity in IBD. Studies of brain structure have primarily employed voxel-based morphometry to measure grey matter volume and surface-based morphometry to measure cortical thickness. Far fewer studies have employed other surface-based morphometry metrics such as gyrification, cortical complexity, and sulcal depth. In this study, brain structure differences between 72 adults with IBD and 90 healthy controls were assessed using all five metrics. Significant differences were found for cortical thickness with the IBD group showing extensive left-lateralized thinning, and for cortical complexity with the IBD group showing greater complexity in the left fusiform and right posterior cingulate. No significant differences were found in grey matter volume, gyrification, or sulcal depth. Within the IBD group, a post hoc analysis identified that disease duration is associated with cortical complexity of the right supramarginal gyrus, albeit with a more lenient threshold applied.

Vascular Disease Is Associated With Differences in Brain Structure and Lower Cognitive Functioning in Inflammatory Bowel Disease: A Cross-Sectional Study.

BACKGROUND: Vascular disease and cognitive impairment have been increasingly documented in inflammatory bowel disease (IBD), and both have been individually correlated with changes in brain structure. This study aimed to determine if both macro- and microstructural brain changes are prevalent in IBD and whether alterations in brain structure mediate the relationship between vascular disease and cognitive functioning. METHODS: Eighty-four IBD participants underwent multimodal magnetic resonance imaging. Volumetric and mean diffusivity measures of the thalamus, hippocampus, normal-appearing white matter, and white matter lesions were converted to age- and sex-adjusted z scores. Vascular comorbidity was assessed using a modified Framingham Risk Score and cognition was assessed using a battery of neuropsychological tests. Test scores were standardized using local regression-based norms. We generated summary statistics for the magnetic resonance imaging metrics and cognitive tests, and these were examined using canonical correlation analysis and linear regression modeling. RESULTS: Greater vascular comorbidity was negatively correlated with thalamic, normal-appearing white matter, and white matter lesion volumes. Higher Framingham Risk Score were also correlated with lower processing speed, learning and memory, and verbal fluency. Increased vascular comorbidity was predictive of poorer cognitive functioning, and this effect was almost entirely mediated (94.76%) by differences in brain structure. CONCLUSIONS: Vascular comorbidity is associated with deleterious effects on brain structure and lower cognitive functioning in IBD. These findings suggest that proper identification and treatment of vascular disease is essential to the overall management of IBD, and that certain brain areas may serve as critical targets for predicting the response to therapeutic interventions.

Vascular disease is associated with decreased cognitive performance in persons with inflammatory bowel disease, and this is mainly driven by changes in the brain, including both gray matter and white matter regions.

Initiation of combined antiretroviral therapy confers suboptimal beneficial effects on neurovascular function in people with HIV.

Introduction: Due to advances in combined anti-retroviral treatment (cART), there is an increased burden of age-related cerebrovascular disease (CBVD), in people living with HIV (PWH). The underlying CNS injury can be assessed by measuring cerebral blood flow (CBF) and cerebrovascular reactivity (CVR). Methods: 35 treatment-naïve PWH and 53 HIV negative controls (HC) were enrolled in this study. Study participants underwent T1-weighted anatomical, pseudo-continuous arterial spin labeling, and resting-state functional MRI to obtain measures of CBF and CVR prior to starting cART treatment and at two-time points (12 weeks and 2 years) post-cART initiation. Controls were scanned at the baseline and 2-year visits. We also measured plasma levels of microparticles of endothelial and glial origin and well-known endothelial inflammation markers, ICAM-1 and VCAM-1, to assess HIV-associated endothelial inflammation and the interaction of these peripheral markers with brain neurovascular function. Results: HIV infection was found to be associated with reduced CVR and increased levels of endothelial and glial microparticles (MPs) prior to initiation of cART. Further, CVR correlated negatively with peripheral MP levels in PWH. Discussion: Our results suggest that while cART treatment has a beneficial effect on the neurovascular function after initiation, these benefits are suboptimal over time.

Gender Differences in a Mouse Model of Hepatocellular Carcinoma Revealed Using Multi-Modal Imaging.

The worldwide incidence of hepatocellular carcinoma (HCC) continues to rise, in part due to poor diet, limited exercise, and alcohol abuse. Numerous studies have suggested that the loss or mutation of PTEN plays a critical role in HCC tumorigenesis through the activation of the PI3K/Akt signaling axis. The homozygous knockout of PTEN in the livers of mice results in the accumulation of fat (steatosis), inflammation, fibrosis, and eventually progression to HCC. This phenotype bears a striking similarity to non-alcoholic steatohepatitis (NASH) which is thought to occupy an intermediate stage between non-alcoholic fatty liver disease (NAFLD), fibrosis, and HCC. The molecular and physiological phenotypes that manifest during the transition to HCC suggest that molecular imaging could provide a non-invasive screening platform to identify the hallmarks of HCC initiation prior to the presentation of clinical disease. We have carried out longitudinal imaging studies on the liver-specific PTEN knockout mouse model using CT, MRI, and multi-tracer PET to interrogate liver size, steatosis, inflammation, and apoptosis. In male PTEN knockout mice, significant steatosis was observed as early as 3 months using both magnetic resonance spectroscopy (MRS) and computed tomography (CT). Enhanced uptake of the apoptosis tracer 18F-TBD was also observed in the livers of male PTEN homozygous knockout mice between 3 and 4 months of age relative to heterozygous knockout controls. Liver uptake of the inflammation tracer [18F]4FN remained relatively low and constant over 7 months in male PTEN homozygous knockout mice, suggesting the suppression of high-energy ROS/RNS with PTEN deletion relative to heterozygous males where the [18F]4FN liver uptake was elevated at early and late time points. All male PTEN homozygous mice developed HCC lesions by month 10. In contrast to the male cohort, only 20% (2 out of 10) of female PTEN homozygous knockout mice developed HCC lesions by month 10. Steatosis was significantly less pronounced in the female PTEN homozygous knockout mice relative to males and could not accurately predict the eventual occurrence of HCC. As with the males, the [18F]4FN uptake in female PTEN homozygous knockout mice was low and constant throughout the time course. The liver uptake of 18F-TBD at 3 and 4.5 months was higher in the two female PTEN knockout mice that would eventually develop HCC and was the most predictive imaging biomarker for HCC in the female cohort. These studies demonstrate the diagnostic and prognostic role of multi-modal imaging in HCC mouse models and provide compelling evidence that disease progression in the PTEN knockout model is highly dependent on gender.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Periventricular and juxtacortical characterization of UManitoba-JHU functionally defined human white matter atlas networks.

Background: The open-access UManitoba-JHU functionally defined human white matter (WM) atlas contains specific WM pathways and general WM regions underlying 12 functional brain networks in ICBM152 template space. However, it is not known whether any of these WM networks are disproportionately co-localized with periventricular and/or juxtacortical WM (PVWM and JCWM), which could potentially impact their ability to infer network-specific effects in future studies-particularly in patient populations expected to have disproportionate PVWM and/or JCWM damage. Methods: The current study therefore identified intersecting regions of PVWM and JCWM (defined as WM within 5 mm of the ventricular and cortical boundaries) and: (1) the ICBM152 global WM mask, and (2) all 12 UManitoba-JHU WM networks. Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), and proportion of volume (POV) values between PVWM (and JCWM) and each functionally defined WM network were then compared to corresponding values between PVWM (and JCWM) and global WM. Results: Between the 12 WM networks and PVWM, 8 had lower DSC, JSC, and POV; 1 had lower DSC and JSC, but higher POV; and 3 had higher DSC, JSC, and POV compared to global WM. For JCWM, all 12 WM networks had lower DSC, JSC, and POV compared to global WM. Conclusion: The majority of UManitoba-JHU functionally defined WM networks exhibited lower than average spatial similarity with PVWM, and all exhibited lower than average spatial similarity with JCWM. This suggests that they can be used to explore network-specific WM changes, even in patient populations with known predispositions toward PVWM and/or JCWM damage.

Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview.

Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.

First report of bacterial panicle blight (BPB) in Rice caused by Burkholderia gladioli in Bangladesh.

Bacterial panicle blight (BPB) has become one of the most destructive diseases of rice worldwide and Burkholderia gladioli and B. glumae are two major pathogens causing BPB (1). This disease causes several types of damage, most importantly grain spotting, rot, and panicle blight, which can result in yield losses of 75% or more (1,3). In recent years, symptoms including sheath rot, grain spotting, grain rot, and panicle blight have been observed in both inbred and hybrid rice varieties. These symptoms resemble those of BPB and cause cultivar-dependent yield losses. (3) also reported the same symptoms for BPB. To confirm the cause of the disease, 21 rice panicles (Haridhan, a local variety) with typical BPB symptoms were collected from a farmer's field in the region of Mymensingh, Bangladesh during the rainy season in mid-October, 2021. Due to the severity of the outbreak, the panicles became dark brown and produced chaffy grains; nearly 100% of the rice panicles in that field were severely infected. To identify the causal pathogen(s), 1g of rice grains from 20 plants with typical BPB symptoms were surface-sterilized by immersing them in 70% ethanol for a few seconds followed by sodium hypochlorite solution (3%) for 1min. The grains were then rinsed with sterilized distilled water three times. Surface-sterilized grains were then ground with a mortar and pestle; 5mL of sterile distilled water was added during grinding. The extracted suspension (20µL) was then either streaked or spread onto the selective medium (S-PG) (2). Bacterial colonies showing purple color on the S-PG medium were selected and purified as candidate pathogens. For molecular characterization, species specific primers targeting gyrB gene were used to perform PCR and resulted in 479bp as reported by (4). To verify further, the PCR products of 16SF & 16SR were amplified and sequenced partially producing around 1400bp (1) and five 16SF partial sequences were deposited into NCBI GenBank (OP108276 to OP108280). 16S rDNA and gyrB revealed almost 99% homology with Burkholderia gladioli (KU851248.1, MZ425424.1) and B. gladioli (AB220893, CP033430) respectively using BLAST analysis. These purified bacterial isolates produced a diffusible light-yellow pigment on King's B medium indicating toxoflavin production (3). The candidate five bacterial isolates were then confirmed by inoculating 10ml suspension 108CFU/mL into the panicles and sheaths of BRRIdhan28 in net house condition as described previously (1). All of the bacterial isolates obtained from the spotted rice grains produced light brown lesions on the inoculated leaf sheath as well as spotting on the grain. To fulfill Koch's postulates, the bacteria were re-isolated from the symptomatic panicles and were confirmed as B. gladioli by analyzing the sequences of gyrB and 16s rDNA genes. Taken together, these results confirmed that B. gladioli is responsible for causing BPB in the rice grain samples that we collected. To our knowledge, this is the first report of BPB caused by B. gladioli in Bangladesh and further research is necessary to develop an effective disease management technique, or else the production of rice will be severely hampered.

Role of non-classical monocytes in HIV-associated vascular cognitive impairment.

Despite antiretroviral treatment (cART), people living with HIV (PLWH) are more susceptible to neurocognitive impairment (NCI), probably due to synergistic/additive contribution of traditional cerebrovascular risk factors. Specifically, altered blood brain barrier (BBB) and transmigration of inflammatory monocytes are risk factors for developing cerebral small vessel disease (CSVD). In order to investigate if inflammatory monocytes exacerbate CSVD and cognitive impairment, 110 PLWH on cART and 110 age-, sex- and Reynold’s cardiovascular risk score-matched uninfected individuals were enrolled. Neuropsychological testing, brain magnetic resonance imaging and whole blood analyses to measure platelet-monocyte interaction and monocyte, endothelial activation were performed. Results demonstrated that PLWH exhibited increased levels of platelet-monocyte complexes (PMCs) and higher expression of activation molecules on PMCs. PLWH with CSVD had the poorest cognitive performance and the highest circulating levels of non-classical monocytes which exhibited significant inverse correlation with each other. Furthermore, markers of monocyte and endothelium activation were significantly positively correlated indicating BBB impairment. Our results confirm that interaction with platelets activates and drives monocytes towards an inflammatory phenotype in PLWH. In particular, elevated levels of non-classical monocytes may represent a common pathway to neuroinflammation, CSVD and subsequent cognitive impairment, warranting further longitudinal studies to evaluate responsiveness of this potential biomarker.

Preliminary report on the comparative weed suppressibility of Bangladeshi Wheat varieties.

A key and ecologically sound strategy for integrated weed management is the use of varieties of weed-competitive crops. Utilizing wheat cultivars that are weed-competitive can lessen weed pressure and inordinate herbicide usage in wheat fields by a substantial amount. To assess the weed suppressibility of Bangladeshi wheat varieties, a field test was carried out in 2018 throughout the winter season at the Agronomy Field Laboratory, Bangladesh Agricultural University, Bangladesh. Tests on a total of 18 selected Bangladeshi wheat cultivars were conducted in both "weedy" and "weed-free" environments. Additionally, weed monoculture plots (without wheat) were kept. The experiment was replicated three times using a randomized complete block design (RCBD). The results demonstrated that wheat varieties' weed interference and production capabilities differed greatly. BARI Gom 22 permitted the most weed growth (35 m-2), whereas BARI Gom 23 allowed the least (15 m-2) at 60 DAS among the wheat types under study. Grain yield ranged between 4.42 t ha-1 (BARI Gom 20) and 5.45 t ha-1 (BARI Gom 26) in weed-free settings, whereas it fluctuated from 2.48 t ha-1 (BARI Gom 21) to 3.93 t ha-1(BARI Gom 33) in weedy condition. The extent of the relative yield loss brought on by weeds ranged from 24 to 53%, with BARI Gom 33 suffering the least and Binagom-1 suffering the most. The weed competitive index varied from 0.48 to 1.47 for the examined wheat types. Among the cultivars, Binagom-1 had the lowest WCI and BARI Gom 29 had the highest. Although BARI Gom 33 was the best yielder in weedy condition and had the lowest relative yield loss, its interference against weed was moderate. Relative to the other varieties under consideration, comparatively BARI Gom 33 was the best in terms of yield and weed interference, but it is also advocated that breeders should continually focus on developing a variety that has both excellent producing potential and robust weed suppression.

Vascular comorbidity is associated with decreased cognitive functioning in inflammatory bowel disease.

Reports of cognitive impairment in inflammatory bowel disease (IBD) have been mixed. IBD and cardiovascular disease are often co-morbid, yet it remains unknown whether vascular comorbidity confers a risk for decreased cognitive functioning, as observed in other populations. Participants with IBD were recruited from a longitudinal study of immune-mediated disease. Participants were administered a standardized neuropsychological test protocol, evaluating information processing speed, verbal learning and memory, visual learning and memory, and verbal fluency/executive function. Cognitive test scores were standardized using local regression-based norms, adjusting for age, sex, and education. Vascular risk was calculated using a modified Framingham Risk Score (FRS). We tested the association between FRS and cognitive test scores using a quantile regression model, adjusting for IBD type. Of 84 IBD participants, 54 had ulcerative colitis and 30 had Crohn's disease; mean (SD) age was 53.36 (13.95) years, and a high proportion were females (n = 58). As the risk score (FRS) increased, participants demonstrated lower performance in information processing speed (ß = - 0.12; 95% CI - 0.24, - 0.006) and verbal learning (ß = - 0.14; 95% CI - 0.28, - 0.01) at the 50th percentile. After adjusting for IBD type and disease activity, higher FRS remained associated with lower information processing speed (ß = - 0.14; 95% CI - 0.27, - 0.065). Vascular comorbidity is associated with lower cognitive functioning in persons with IBD, particularly in the area of information processing speed. These findings suggest that prevention, identification, and treatment of vascular comorbidity in IBD may play a critical role for improving functional outcomes in IBD.

Brain iron imaging markers in the presence of white matter hyperintensities.

PURPOSE: To investigate the relationship between pathological brain iron deposition and white matter hyperintensities (WMHs) in cerebral small vessel disease (CSVD), via Monte Carlo simulations of magnetic susceptibility imaging and the development of a novel imaging marker called the Expected Iron Coefficient (EIC). METHODS: A synthetic pathological model of a different number of impenetrable spheres at random locations was employed to represent pathological iron deposition. The diffusion process was simulated with a Monte Carlo method with adjustable parameters to manipulate sphere size, distribution, and extracellular properties. Quantitative susceptibility mapping (QSM) was performed in a clinical dataset to study CSVD to derive and evaluate QSM, R2*, the iron microenvironment coefficient (IMC), and the EIC in the presence of WMHs. RESULTS: The simulations show that QSM signals increase in the presence of increased tissue iron, confirming that the EIC increases with pathology. Clinical results demonstrate that while QSM, R2*, and the IMC do not show significant differences in brain iron, the EIC does in the context of CSVD. CONCLUSION: The EIC is more sensitive to subtle changes in brain iron deposition caused by pathology, even when QSM, R2*, and the IMC fail.

Longitudinal Effects of Combination Antiretroviral Therapy on Cognition and Neuroimaging Biomarkers in Treatment-Naive People With HIV.

BACKGROUND AND OBJECTIVES: While combination antiretroviral therapy (cART) has dramatically increased the life expectancy of people with HIV (PWH), nearly 50% develop HIV-associated neurocognitive disorders. This may be due to previously uncontrolled HIV viral replication, immune activation maintained by residual viral replication or activation from other sources, or cART-associated neurotoxicity. The aim of this study was to determine the effect of cART on cognition and neuroimaging biomarkers in PWH before and after initiation of cART compared with that in HIV-negative controls (HCs) and HIV elite controllers (ECs) who remain untreated. METHODS: We recruited 3 groups of participants from the University of Rochester, McGovern Medical School, and SUNY Upstate Medical University: (1) ART treatment-naive PWH; (2) age-matched HCs; and (3) ECs. Participants underwent brain MRI and clinical and neuropsychological assessments at baseline, 1 year, and 2 years. PWH were also assessed 12 weeks after initiating cART. Volumetric analysis and fractal dimensionality (FD) were calculated for cortical and subcortical regions. Mixed effect regressions examined the effect of group and imaging variables on cognition. RESULTS: We enrolled 47 PWH, 58 HCs, and 10 ECs. At baseline, PWH had worse cognition and lower cortical volumes than HCs. Cognition improved after initiation of cART and remained stable over time. Greater cortical thickness was associated with better cognition at baseline; greater FD of parietal, temporal, and occipital lobes was associated with better cognition at baseline and longitudinally. At baseline, ECs had worse cognition, lower cortical thickness, and lower FD in all 4 lobes and caudate than PWH and HCs. Greater cortical thickness, hippocampal volumes, and FD of frontal, temporal, and occipital lobes were associated with better cognition longitudinally. DISCUSSION: Initiation of cART in PWH is associated with improvement in brain structure and cognition. However, significant differences persist over time when compared with HCs. Similar trends in ECs suggest that results are due to HIV infection rather than treatment. Stronger associations between cognition and FD suggest this imaging metric may be a more sensitive marker of neuronal injury than cortical thickness and volumetric measures.

A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection.

Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model's efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.

Quantification of Phosphonate Drugs by 1H-31P HSQC Shows That Rats Are Better Models of Primate Drug Exposure than Mice.

The phosphonate group is a key pharmacophore in many antiviral, antimicrobial, and antineoplastic drugs. Due to its high polarity and short retention time, detecting and quantifying such phosphonate-containing drugs with LC/MS-based methods are challenging and require derivatization with hazardous reagents. Given the emerging importance of phosphonate-containing drugs, developing a practical, accessible, and safe method for their quantitation in pharmacokinetics (PK) studies is desirable. NMR-based methods are often employed in drug discovery but are seldom used for compound quantitation in PK studies. Here, we show that proton-phosphorous (1H-31P) heteronuclear single quantum correlation (HSQC) NMR allows for the quantitation of the phosphonate-containing enolase inhibitor HEX in plasma and tissues at micromolar concentrations. Although mice were shown to rapidly clear HEX from circulation (over 95% in <1 h), the plasma half-life of HEX was more than 1 h in rats and nonhuman primates. This slower clearance rate affords a significantly higher exposure of HEX in rat models compared to that in mouse models while maintaining a favorable safety profile. Similar results were observed for the phosphonate-containing antibiotic, fosfomycin. Our study demonstrates the applicability of the 1H-31P HSQC method to quantify phosphonate-containing drugs in complex biological samples and illustrates an important limitation of mice as preclinical model species for phosphonate-containing drugs.