Suvorexant Acutely Decreases Tau Phosphorylation and Aß in the Human CNS.

OBJECTIVE: In Alzheimer's disease, hyperphosphorylated tau is associated with formation of insoluble paired helical filaments that aggregate as neurofibrillary tau tangles and are associated with neuronal loss and cognitive symptoms. Dual orexin receptor antagonists decrease soluble amyloid-ß levels and amyloid plaques in mouse models overexpressing amyloid-ß, but have not been reported to affect tau phosphorylation. In this randomized controlled trial, we tested the acute effect of suvorexant, a dual orexin receptor antagonist, on amyloid-ß, tau, and phospho-tau. METHODS: Thirty-eight cognitively unimpaired participants aged 45 to 65 years were randomized to placebo (N = 13), suvorexant 10 mg (N = 13), and suvorexant 20 mg (N = 12). Six milliliters of cerebrospinal fluid were collected via an indwelling lumbar catheter every 2 hours for 36 hours starting at 20:00. Participants received placebo or suvorexant at 21:00. All samples were processed and measured for multiple forms of amyloid-ß, tau, and phospho-tau via immunoprecipitation and liquid chromatography-mass spectrometry. RESULTS: The ratio of phosphorylated-tau-threonine-181 to unphosphorylated-tau-threonine-181, a measure of phosphorylation at this tau phosphosite, decreased ~10% to 15% in participants treated with suvorexant 20 mg compared to placebo. However, phosphorylation at tau-serine-202 and tau-threonine-217 were not decreased by suvorexant. Suvorexant decreased amyloid-ß ~10% to 20% compared to placebo starting 5 hours after drug administration. INTERPRETATION: In this study, suvorexant acutely decreased tau phosphorylation and amyloid-ß concentrations in the central nervous system. Suvorexant is approved by the US Food and Drug Administration to treatment insomnia and may have potential as a repurposed drug for the prevention of Alzheimer's disease, however, future studies with chronic treatment are needed. ANN NEUROL 2023;94:27-40.

Dual Blockade of EP2 and EP4 Signaling is Required for Optimal Immune Activation and Antitumor Activity Against Prostaglandin-Expressing Tumors.

While the role of prostaglandin E2 (PGE2) in promoting malignant progression is well established, how to optimally block the activity of PGE2 signaling remains to be demonstrated. Clinical trials with prostaglandin pathway targeted agents have shown activity but without sufficient significance or dose-limiting toxicities that have prevented approval. PGE2 signals through four receptors (EP1-4) to modulate tumor progression. EP2 and EP4 signaling exacerbates tumor pathology and is immunosuppressive through potentiating cAMP production. EP1 and EP3 signaling has the opposite effect through increasing IP3 and decreasing cAMP. Using available small-molecule antagonists of single EP receptors, the cyclooxygenase-2 (COX-2) inhibitor celecoxib, or a novel dual EP2/EP4 antagonist generated in this investigation, we tested which approach to block PGE2 signaling optimally restored immunologic activity in mouse and human immune cells and antitumor activity in syngeneic, spontaneous, and xenograft tumor models. We found that dual antagonism of EP2 and EP4 together significantly enhanced the activation of PGE2-suppressed mouse and human monocytes and CD8+ T cells in vitro as compared with single EP antagonists. CD8+ T-cell activation was dampened by single EP1 and EP3 antagonists. Dual EP2/EP4 PGE2 receptor antagonists increased tumor microenvironment lymphocyte infiltration and significantly reduced disease burden in multiple tumor models, including in the adenomatous polyposis coli (APC)min+/- spontaneous colorectal tumor model, compared with celecoxib. These results support a hypothesis that redundancy of EP2 and EP4 receptor signaling necessitates a therapeutic strategy of dual blockade of EP2 and EP4. Here we describe TPST-1495, a first-in-class orally available small-molecule dual EP2/EP4 antagonist. Significance: Prostaglandin (PGE2) drives tumor progression but the pathway has not been effectively drugged. We demonstrate significantly enhanced immunologic potency and antitumor activity through blockade of EP2 and EP4 PGE2 receptor signaling together with a single molecule.

Adverse driving behaviors are associated with sleep apnea severity and age in cognitively normal older adults at risk for Alzheimer's disease.

Alzheimer's disease (AD) pathology accumulates for decades before the onset of cognitive decline. Cognitively normal individuals with biomarker evidence of AD brain pathology (i.e. biomarker + or preclinical AD) can be differentiated from individuals without AD brain pathology based on naturalistic driving data, such as hard acceleration or braking and speeding, measured using in-vehicle dataloggers. Older adults are at increased risk of injury and death from motor vehicle crashes and driving cessation is also linked to negative health outcomes. Identifying potentially modifiable risk factors that increase driving risk may prolong safe driving in old age. Sleep apnea is associated with adverse driving behaviors across the age span. In this study, we hypothesized that high-risk driving behaviors would be associated with increased sleep apnea severity and AD pathology. We found that higher sleep apnea severity measured by a home sleep apnea test was associated with a higher incidence of adverse driving behaviors even after controlling for multiple confounders (ß = 0.24 ±â€…0.09, p < 0.01). This association was independent of AD biomarker positivity (i.e. increased t-tau/Aß 42 ratio). Increasing age was associated with a higher likelihood of high-risk driving behaviors in individuals with AD brain pathology (ß = 0.12 ±â€…0.04, p < 0.01), but a lower likelihood in individuals without AD brain pathology (ß = -0.06 ±â€…0.03, p < 0.05). These findings suggest that adverse driving behaviors linked to a higher rate of traffic crashes in older adults are associated with sleep apnea severity and AD pathology even in cognitively unimpaired individuals. Further studies are needed to determine if treatment of sleep apnea decreases high-risk driving behaviors and therefore motor vehicle crashes.

Comparison of single-channel EEG, actigraphy, and sleep diary in cognitively normal and mildly impaired older adults.

STUDY OBJECTIVES: Multiple methods for monitoring sleep-wake activity have identified sleep disturbances as risk factors for Alzheimer disease (AD). In order to identify the level of agreement between different methods, we compared sleep parameters derived from single-channel EEG (scEEG), actigraphy, and sleep diaries in cognitively normal and mildly impaired older adults. METHODS: Two hundred ninety-three participants were monitored at home for up to six nights with scEEG, actigraphy, and sleep diaries. Total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO) were calculated using each of these methods. In 109 of the 293 participants, the ratio of cerebrospinal fluid concentrations of phosphorylated tau (p-tau) and amyloid-ß-42 (Aß42) was used as a biomarker for AD pathology. RESULTS: Agreement was highest for TST across instruments, especially in cognitively normal older adults. Overall, scEEG and actigraphy appeared to have greater agreement for multiple sleep parameters than for scEEG and diary or actigraphy and diary. Levels of agreement between scEEG and actigraphy overall decreased in mildly impaired participants and those with biomarker evidence of AD pathology, especially for measurements of TST. CONCLUSIONS: Caution should be exercised when comparing scEEG and actigraphy in individuals with mild cognitive impairment or with AD pathology. Sleep diaries may capture different aspects of sleep compared to scEEG and actigraphy. Additional studies comparing different methods of measuring sleep-wake activity in older adults are necessary to allow for comparison between studies using different methods.

Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration.

Importance: Although deep learning (DL) can identify the intermediate or advanced stages of age-related macular degeneration (AMD) as a binary yes or no, stratified gradings using the more granular Age-Related Eye Disease Study (AREDS) 9-step detailed severity scale for AMD provide more precise estimation of 5-year progression to advanced stages. The AREDS 9-step detailed scale's complexity and implementation solely with highly trained fundus photograph graders potentially hampered its clinical use, warranting development and use of an alternate AREDS simple scale, which although valuable, has less predictive ability. Objective: To describe DL techniques for the AREDS 9-step detailed severity scale for AMD to estimate 5-year risk probability with reasonable accuracy. Design, Setting, and Participants: This study used data collected from November 13, 1992, to November 30, 2005, from 4613 study participants of the AREDS data set to develop deep convolutional neural networks that were trained to provide detailed automated AMD grading on several AMD severity classification scales, using a multiclass classification setting. Two AMD severity classification problems using criteria based on 4-step (AMD-1, AMD-2, AMD-3, and AMD-4 from classifications developed for AREDS eligibility criteria) and 9-step (from AREDS detailed severity scale) AMD severity scales were investigated. The performance of these algorithms was compared with a contemporary human grader and against a criterion standard (fundus photograph reading center graders) used at the time of AREDS enrollment and follow-up. Three methods for estimating 5-year risk were developed, including one based on DL regression. Data were analyzed from December 1, 2017, through April 15, 2018. Main Outcomes and Measures: Weighted κ scores and mean unsigned errors for estimating 5-year risk probability of progression to advanced AMD. Results: This study used 67 401 color fundus images from the 4613 study participants. The weighted κ scores were 0.77 for the 4-step and 0.74 for the 9-step AMD severity scales. The overall mean estimation error for the 5-year risk ranged from 3.5% to 5.3%. Conclusions and Relevance: These findings suggest that DL AMD grading has, for the 4-step classification evaluation, performance comparable with that of humans and achieves promising results for providing AMD detailed severity grading (9-step classification), which normally requires highly trained graders, and for estimating 5-year risk of progression to advanced AMD. Use of DL has the potential to assist physicians in longitudinal care for individualized, detailed risk assessment as well as clinical studies of disease progression during treatment or as public screening or monitoring worldwide.

Links between blood parasites, blood chemistry, and the survival of nestling American crows.

Many studies have used the avian hemosporidians (Leucocytozoon, Plasmodium, and Hemoproteus) to test hypotheses of host-parasite co-evolution, yet documented health and survival consequences of these blood parasites vary among studies and generalizations about their pathogenicity are debatable. In general, the negative effects of the hemosporidians are likely to be greatest during acute infections of young birds, yet most previous studies in wild passerines have examined chronic effects in adults. Here, we evaluated responses of nestling American crows (Corvus brachyrhynchos) to acute infection (prevalence and burden), as well as its short- and long-term survival consequences. We used panel of nine hematological and biochemical parameters that are regularly used to evaluate the health of domestic animals, including leukocyte profiles, hematocrit, and plasma proteins. We assessed the effects of infection on survival in a mark-recapture framework. Overall, 56% of crows (n = 321 samples) were infected by at least one of the three genera. Infections by all genera were associated with elevated plasma proteins and globulins, which could indicate an adaptive immune response. However, only Plasmodium infections were associated with low hematocrit (anemia) and lower fledging success, possibly mediated by the negative effect of low hematocrit values on body condition. Moreover, early Plasmodium infection (<40 days of age) had long-term survival implications: it was associated with lower apparent survival probability within 3 years after fledging. These results suggest that young crows mounted an adaptive immune response to all three genera. Short- and long-term pathological effects, however, were only apparent with Plasmodium infections.

Light-scattering and ultrastructure of healed penetrating corneal wounds.

PURPOSE: To investigate quantitatively for the first time the relationship between light-scattering and ultrastructure of semitransparent scars resulting from penetrating wounds in rabbit cornea. METHODS: Penetrating wounds, 2 mm in diameter, were made in the central cornea and allowed to heal for 3.6 to 4.5 years at which time the rabbits were killed. The scar and cornea thickness outside the scar were measured using ultrasonic pachymetry. Corneas were excised immediately and their transmissivity was measured from 400 to 700 nm. The tissue was then prepared for transmission electron microscopy. Transmission electron micrographs (TEMs) were analyzed to determine fibril positions and radii. Scattering was calculated using the direct summation of fields (DSF) METHOD: RESULTS: Scar thickness averaged 0.26 +/- 0.04 mm, and the scars were flat. Thickness outside the scars averaged 0.40 +/- 0.04 mm. Three scars were moderately transparent, five were less transparent, and one was much less transparent. The wavelength dependence of the measured total scattering cross- section was indicative of the presence of voids (lakes) in the collagen fibril distribution, and lakes were evident in the TEMs. The images showed enlarged fibrils and some showed bimodal distributions of fibril diameters. Calculated scattering was characteristic of that expected from regions containing lakes-a finding consistent with the scattering measurements. CONCLUSIONS: Despite the long healing time, these scars remained highly scattering. A combination of lakes, disordered fibril distributions, and a significant population of enlarged fibrils can explain the scattering. A possible cellular contribution cannot be ruled out.

Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.

BACKGROUND: When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing to the advanced stage where the often treatable choroidal neovascular form of AMD can occur. Careful monitoring to detect the onset and prompt treatment of the neovascular form as well as dietary supplementation can reduce the risk of vision loss from AMD, therefore, preferred practice patterns recommend identifying individuals with the intermediate stage in a timely manner. METHODS: Past automated retinal image analysis (ARIA) methods applied on fundus imagery have relied on engineered and hand-designed visual features. We instead detail the novel application of a machine learning approach using deep learning for the problem of ARIA and AMD analysis. We use transfer learning and universal features derived from deep convolutional neural networks (DCNN). We address clinically relevant 4-class, 3-class, and 2-class AMD severity classification problems. RESULTS: Using 5664 color fundus images from the NIH AREDS dataset and DCNN universal features, we obtain values for accuracy for the (4-, 3-, 2-) class classification problem of (79.4%, 81.5%, 93.4%) for machine vs. (75.8%, 85.0%, 95.2%) for physician grading. DISCUSSION: This study demonstrates the efficacy of machine grading based on deep universal features/transfer learning when applied to ARIA and is a promising step in providing a pre-screener to identify individuals with intermediate AMD and also as a tool that can facilitate identifying such individuals for clinical studies aimed at developing improved therapies. It also demonstrates comparable performance between computer and physician grading.

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.

Importance: Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. Objective: To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Design, Setting, and Participants: Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Main Outcomes and Measures: Accuracy, receiver operating characteristics and area under the curve, and kappa score. Results: The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-related Eye Disease Study data set. Conclusions and Relevance: Applying a deep learning-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.

Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images.

BACKGROUND: Age-related macular degeneration (AMD), left untreated, is the leading cause of vision loss in people older than 55. Severe central vision loss occurs in the advanced stage of the disease, characterized by either the in growth of choroidal neovascularization (CNV), termed the "wet" form, or by geographic atrophy (GA) of the retinal pigment epithelium (RPE) involving the center of the macula, termed the "dry" form. Tracking the change in GA area over time is important since it allows for the characterization of the effectiveness of GA treatments. Tracking GA evolution can be achieved by physicians performing manual delineation of GA area on retinal fundus images. However, manual GA delineation is time-consuming and subject to inter-and intra-observer variability. METHODS: We have developed a fully automated GA segmentation algorithm in color fundus images that uses a supervised machine learning approach employing a random forest classifier. This algorithm is developed and tested using a dataset of images from the NIH-sponsored Age Related Eye Disease Study (AREDS). GA segmentation output was compared against a manual delineation by a retina specialist. RESULTS: Using 143 color fundus images from 55 different patient eyes, our algorithm achieved PPV of 0.82±0.19, and NPV of 0:95±0.07. DISCUSSION: This is the first study, to our knowledge, applying machine learning methods to GA segmentation on color fundus images and using AREDS imagery for testing. These preliminary results show promising evidence that machine learning methods may have utility in automated characterization of GA from color fundus images.

Automated classification of severity of age-related macular degeneration from fundus photographs.

PURPOSE: To evaluate an automated analysis of retinal fundus photographs to detect and classify severity of age-related macular degeneration compared with grading by the Age-Related Eye Disease Study (AREDS) protocol. METHODS: Following approval by the Johns Hopkins University School of Medicine's Institution Review Board, digitized images (downloaded AT http://www.ncbi.nlm.nih.gov/gap/) of field 2 (macular) fundus photographs from AREDS obtained over a 12-year longitudinal study were classified automatically using a visual words method to compare with severity by expert graders. RESULTS: Sensitivities and specificities, respectively, of automated imaging, when compared with expert fundus grading of 468 patients and 2145 fundus images are: 98.6% and 96.3% when classifying categories 1 and 2 versus categories 3 and 4; 96.1% and 96.1% when classifying categories 1 and 2 versus category 3; 98.6% and 95.7% when classifying category 1 versus category 3; and 96.0% and 94.7% when classifying category 1 versus categories 3 and 4; CONCLUSIONS: Development of an automated analysis for classification of age-related macular degeneration from digitized fundus photographs has high sensitivity and specificity when compared with expert graders and may have a role in screening or monitoring.

Structure and properties of collagen vitrigel membranes for ocular repair and regeneration applications.

The frequency of ocular injuries on the battlefield has been steadily increasing during recent conflicts. Combat-related eye injuries are difficult to treat and solutions requiring donor tissue are not ideal and are often not readily available. Collagen vitrigels have previously been developed for corneal reconstruction, but increased transparency and mechanical strength are desired for improved vision and ease of handling. In this study, by systematically varying vitrification temperature, relative humidity and time, the collagen vitrigel synthesis conditions were optimized to yield the best combination of high transparency and high mechanical strength. Optical, mechanical, and thermal properties were characterized for each set of conditions to evaluate the effects of the vitrification parameters on material properties. Changes in denaturing temperature and collagen fibril morphology were evaluated to correlate properties with structure. Collagen vitrigels with transmittance up to 90%, tensile strength up to 12 MPa, and denaturing temperatures that significantly exceed the eye/body temperature have been synthesized at 40 °C and 40% relative humidity for one week. This optimal set of conditions enabled improvements of 100% in tensile strength and 11% in transmittance, compared to the previously developed collagen vitrigels.