Effect of Early Aqueous Suppression After Valved Tube Shunt Surgery for Uveitic Glaucoma.

PURPOSE: To compare outcomes of early aqueous suppression (EAS) and standard therapy (ST) after Ahmed Glaucoma Valve (AGV) implantation for uveitic glaucoma. DESIGN: Retrospective comparative cohort study. PARTICIPANTS: All patients with uveitic glaucoma underwent AGV implantation from January 2010 to October 2020 at Northwestern Medicine. METHODS: Excluding the first postoperative day 1 (POD1), only eyes with IOP 10-15 mmHg at their first visit with IOP ≥ 10 mmHg were included in the main analysis. Early aqueous suppression (EAS) was defined as initiation of ocular hypotensive therapy when IOP was first 10-15 mmHg. Standard therapy was initiation of therapy at any later time. Failure was defined as IOP > 21 mmHg, < 5 mmHg, or < 20% reduction in IOP from baseline after 3 months, for 2 consecutive study visits. Hypotony was defined as IOP ≤ 5 mmHg for ≥ 2 visits. Hypertensive phase was defined as IOP > 21 mmHg for 2 consecutive visits in the first 3 months. MAIN OUTCOME MEASURES: Proportion achieving overall success; incidence of hypotony and hypertensive phase. RESULTS: Twenty-eight eyes of 26 patients were in the EAS group and 20 eyes of 19 patients were in the ST group, with a mean follow-up of 17.7 and 28.2 months, respectively. Baseline IOP was similar in the EAS (31.2 ± 10.1 mmHg) and ST (34.6 ± 12.2 mmHg) groups; P = 0.18. Final IOP was lower in the EAS group (12.9 ± 4.6 mmHg) than the ST group (16.4 ± 5.7 mmHg; P = 0.02) on 2.6 ± 0.9 medications in the EAS group and 1.8 ± 1.5 in the ST group (P = 0.07). Overall success was achieved in 87% of EAS eyes and 74% of ST eyes (P = 0.43). There were no statistically significant differences in the occurrence of additional glaucoma surgery (4% for EAS, 20% for ST; P = 0.11), hypotony (7% for EAS, 0% for ST; P = 0.50), or hypertensive phase (4% for EAS, 21% for ST; P = 0.09). CONCLUSIONS: EAS was associated with a lower final IOP after AGV in uveitic glaucoma eyes; however, more medications were in use at the final visit. No statistically significant differences in overall success or the incidence of adverse events were observed. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Integrity of Neuronal Size in the Entorhinal Cortex Is a Biological Substrate of Exceptional Cognitive Aging.

Average aging is associated with a gradual decline of memory capacity. SuperAgers are humans ≥80 years of age who show exceptional episodic memory at least as good as individuals 20-30 years their junior. This study investigated whether neuronal integrity in the entorhinal cortex (ERC), an area critical for memory and selectively vulnerable to neurofibrillary degeneration, differentiated SuperAgers from cognitively healthy younger individuals, cognitively average peers ("Normal Elderly"), and individuals with amnestic mild cognitive impairment. Postmortem sections of the ERC were stained with cresyl violet to visualize neurons and immunostained with mouse monoclonal antibody PHF-1 to visualize neurofibrillary tangles. The cross-sectional area (i.e., size) of layer II and layer III/V ERC neurons were quantified. Two-thirds of total participants were female. Unbiased stereology was used to quantitate tangles in a subgroup of SuperAgers and Normal Elderly. Linear mixed-effect models were used to determine differences across groups. Quantitative measurements found that the soma size of layer II ERC neurons in postmortem brain specimens were significantly larger in SuperAgers compared with all groups (p < 0.05)-including younger individuals 20-30 years their junior (p < 0.005). SuperAgers had significantly fewer stereologically quantified Alzheimer's disease-related neurofibrillary tangles in layer II ERC than Normal Elderly (p < 0.05). This difference in tangle burden in layer II between SuperAgers and Normal Elderly suggests that tangle-bearing neurons may be prone to shrinkage during aging. The finding that SuperAgers show ERC layer II neurons that are substantially larger even compared with individuals 20-30 years younger is remarkable, suggesting that layer II ERC integrity is a biological substrate of exceptional memory in old age.SIGNIFICANCE STATEMENT Average aging is associated with a gradual decline of memory. Previous research shows that an area critical for memory, the entorhinal cortex (ERC), is susceptible to the early formation of Alzheimer's disease neuropathology, even during average (or typical) trajectories of aging. The Northwestern University SuperAging Research Program studies unique individuals known as SuperAgers, individuals ≥80 years old who show exceptional memory that is at least as good as individuals 20-30 years their junior. In this study, we show that SuperAgers harbor larger, healthier neurons in the ERC compared with their cognitively average same-aged peers, those with amnestic mild cognitive impairment, and - remarkably - even compared with individuals 20-30 years younger. We conclude that larger ERC neurons are a biological signature of the SuperAging trajectory.

Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging.

Background: Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods: MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings: Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion: Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.

Path-level interpretation of Gaussian graphical models using the pair-path subscore.

BACKGROUND : Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall 'network-level' correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. RESULTS: We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https://github.com/nathan-gill/pps . CONCLUSIONS: The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.