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Background Microcephalic osteodysplastic primordial dwarfism type II (MOPDII) is the most common form of primordial dwarfism, caused by bialleic mutations in the pericentrin gene ( PCNT ). Aside from its classic features, there are multiple associated medical complications, including a well-documented risk of neurovascular disease. Over the past several years, it has become apparent that additional vascular issues, as well as systemic hypertension and kidney disease may also be related to MOPDII. However, the frequency and extent of the vasculopathy was unclear. To help address this question, a vascular substudy was initiated within our Primordial Dwarfism Registry. Results Medical records from 47 individuals, living and deceased, ranging in age from 3 to 41 years of age were interrogated for this purpose. Of the total group, 64% were diagnosed with moyamoya, intracranial aneurysms, or both. In general, the age at diagnosis for moyamoya was younger than aneurysms, but the risk for neurovascular disease was throughout the shortened lifespan. In addition to neurovascular disease, renal, coronary and external carotid artery involvement are documented. 43% of the total group was diagnosed with hypertension, and 17% had myocardial infarctions. A total of 32% of the entire cohort had some form of chronic kidney disease, with 4% of the total group necessitating a kidney transplant. In addition, 38% had diabetes/insulin resistance. Ages of diagnoses, treatment modalities employed, and location of vasculopathies were notated as available and applicable, as well as frequencies of other comorbidities. Conclusions It is now clear that vascular disease in MOPDII is global and screening of the cardiac and renal vessels is warranted along with close monitoring of blood pressure. We recommend a blood pressure of 110/70 mmHg as a starting point for an upper limit, especially if the individual has a history of neurovascular disease, chronic kidney disease and/or diabetes. Additionally, providers need to be at high alert for the possibility of myocardial infarctions in young adults with MOPDII, so that appropriate treatment can be initiated promptly in an acute situation.

Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers. Here, we introduce Transition State identification via Dispersion and vAriational principle Regularized neural networks (TS-DAR), a deep learning framework inspired by out-of-distribution (OOD) detection in trustworthy artificial intelligence (AI). TS-DAR offers an end-to-end pipeline that can simultaneously detect all transition states between multiple free minima from MD simulations using the regularized hyperspherical embeddings in latent space. The key insight of TS-DAR lies in treating transition state structures as OOD data, recognizing that they are sparsely populated and exhibit a distributional shift from metastable states. We demonstrate the power of TS-DAR by applying it to a 2D potential, alanine dipeptide, and the translocation of a DNA motor protein on DNA, where it outperforms previous methods in identifying transition states. Understanding transition states (TS) of protein conformational changes is vital for biological discovery. Here, the authors present a deep learning method that simultaneously identifies all TS of conformational changes within a latent hypersphere.

BackgroundEarly vascular ageing (EVA) contributes to elevated risk of cardiovascular disease (CVD), which disproportionately affects African American women. Incarceration, an event disproportionately impacting African Americans, may be a stressor contributing to EVA in African American women. Further, the subjective perspective, commonly referred to as appraisal, of incarceration may also be important for health. We hypothesised that having family and/or friends incarcerated and appraising the incarceration as upsetting would be associated with indices of EVA.MethodsIn a community-based cohort of African American women aged 30–46 living in Atlanta, Georgia (n=391), participants were asked, at baseline, about family and/or friend incarceration and to appraise how upsetting the incarceration was. Multivariable linear regression examined associations between: (1) family and/or friend incarceration and indices of EVA (pulse wave velocity, augmentation index, central systolic blood pressure (SBP) and pulse pressure amplification) and (2) appraisal of incarceration and EVA indices.Results45% of participants (n=174) reported having a loved one incarcerated, and 59% (n=102) reported the incarceration as upsetting. Having a loved one incarcerated was associated with a higher central SBP (b=4.30; 95% CI 1.61, 6.99) and augmentation index (b=2.29; 95% CI 0.26, 4.33). Appraisal of incarceration was only associated with central SBP.ConclusionsFamily or friend incarceration was highly prevalent in this cohort of African American women and associated with indices of EVA. Mass incarceration of others may affect the physical health of African American women which may contribute to CVD disparities.

Background 90% of adults with Down syndrome (DS) will develop Alzheimer's dementia (AD) in their lifetime, the majority within 3 years of symptom onset. However, clinicians need to be confident that declines are not temporary or due to treatable causes before diagnosing prodromal AD. The present study investigates the medical, psychiatric, and life factors associated with atypical cognitive decline in DS, a population with genetic risk for AD. We aim to identify factors that contribute to temporary or non‐progressing cognitive decline over a three‐year period. Method Thirty‐eight adults with DS (ages 43 to 62) from the Alzheimer Biomarker Consortium in Down Syndrome (ABC‐DS) were classified as having prodromal AD (MCI‐DS) at one or more time points. AD clinical status was determined through a consensus process using only clinical and cognitive data. There were three trajectories based upon longitudinal data: (1) progressors (MCI‐DS to dementia), (2) reverters (MCI‐DS to cognitively stable), or (3) non‐progressors (MCI‐DS for 3 time points). Comorbidities and life stressors were collected through study partner interviews. Baseline amyloid PET scans and plasma AD biomarkers are used in the present study. Result Using chi‐square and Kruskal‐Wallis tests, non‐progressors (n = 6, Mage = 48.67) were younger (p = .032), had lower centiloid values (p = .002) and had higher prevalence of seizure disorder (p = < .001), hearing impairment (p = < .001), and a friend move away (p = .027) than progressors (n = 26, Mage = 52.88). Reverters (n = 6, Mage = 49.17) had a higher incidence of psychotic disorder (p = .028) compared to non‐progressors and progressors. Both atypical groups had lower incidence of COVID infection (p = .046), but still had high amyloid load (M=30.50CL and 48.02CL, respectively). Conclusion In our sample of adults with DS and MCI‐DS, 31.6% did not progress to dementia despite having high levels of amyloid PET. Those whose decline plateaued had more medical conditions and life stressors. Those with temporary decline had more psychiatric problems. Primary care physicians need to understand which reversible or treatable factors are most likely to lead to a misdiagnosis of prodromal AD and address them accordingly.

90% of adults with Down syndrome (DS) will develop Alzheimer's dementia (AD) in their lifetime, the majority within 3 years of symptom onset. However, clinicians need to be confident that declines are not temporary or due to treatable causes before diagnosing prodromal AD. The present study investigates the medical, psychiatric, and life factors associated with atypical cognitive decline in DS, a population with genetic risk for AD. We aim to identify factors that contribute to temporary or non-progressing cognitive decline over a three-year period. Thirty-eight adults with DS (ages 43 to 62) from the Alzheimer Biomarker Consortium in Down Syndrome (ABC-DS) were classified as having prodromal AD (MCI-DS) at one or more time points. AD clinical status was determined through a consensus process using only clinical and cognitive data. There were three trajectories based upon longitudinal data: (1) progressors (MCI-DS to dementia), (2) reverters (MCI-DS to cognitively stable), or (3) non-progressors (MCI-DS for 3 time points). Comorbidities and life stressors were collected through study partner interviews. Baseline amyloid PET scans and plasma AD biomarkers are used in the present study. Using chi-square and Kruskal-Wallis tests, non-progressors (n = 6, M  = 48.67) were younger (p = .032), had lower centiloid values (p = .002) and had higher prevalence of seizure disorder (p = < .001), hearing impairment (p = < .001), and a friend move away (p = .027) than progressors (n = 26, M  = 52.88). Reverters (n = 6, M  = 49.17) had a higher incidence of psychotic disorder (p = .028) compared to non-progressors and progressors. Both atypical groups had lower incidence of COVID infection (p = .046), but still had high amyloid load (M=30.50CL and 48.02CL, respectively). In our sample of adults with DS and MCI-DS, 31.6% did not progress to dementia despite having high levels of amyloid PET. Those whose decline plateaued had more medical conditions and life stressors. Those with temporary decline had more psychiatric problems. Primary care physicians need to understand which reversible or treatable factors are most likely to lead to a misdiagnosis of prodromal AD and address them accordingly.

Background Down syndrome (DS) represents a genetic form of Alzheimer's disease (AD) with an earlier expected symptom onset compared to late onset AD (LOAD). It is thought that the extra copy of the Amyloid Precursor Protein (APP) gene, located on chromosome 21 contributes to the earlier onset due to increased amyloid deposition in the brain. Hyperphosphorylation of tau protein is also thought to be elevated in the beginning stages of AD pathology within DS. Although APOEε4 has been associated with greater AD risk in LOAD, prior cross‐sectional investigations into the effects of APOEε4 in DS have suggested that there is no additional impact of APOEε4 on the accumulation of amyloid. We aimed to extend this work by examining the associations between longitudinal plasma pTau217 and amyloid PET as a function of APOEε4 status. Method Participants with DS were recruited from the Alzheimer's Biomarker Consortium‐Down Syndrome (ABC‐DS) study. Both p‐tau217 (N = 564 results from 223 individuals including 122 that had 3 results each, Lilly MSD) and Amyloid PET ([11C]‐PiB or [18F]‐AV45) (N = 366 scans with 253 unique participants including 113 that had 2 scans each) were acquired. We analyzed the influence ɛ4 allele carrier status had on changes in pTau217 and amyloid across age using linear mixed‐effects modeling, including the age, APOEε4 carrier status and their interaction as covariates. Age was also included as a covariate. Result Individuals that are carriers of the APOEε4 allele present with similar baseline amyloid and pTau217 values (p = .591 & p = .455 respectively). The rate of amyloid and pTau217 accumulation increased across age similarly for both groups (p = .772 & p = .657 respectively). Although not statistically significant, visual inspection suggests that, with a larger number of participants, individuals between the ages of 45 and 50 who are ɛ4 allele carriers may exhibit elevated pTau217 levels compared to non‐carriers. Conclusion We did not observe increased amyloidosis or tau phosphorylation in APOEε4 carriers with DS. Future studies targeting individuals aged 45‐50 are suggested to investigate the potential APOEε4 effect on tau phosphorylation observed in this narrow chronological window, which is close to the average expected age of symptom onset of 52.5 years for DS.

Background Potential anti‐amyloid therapy trials for Down syndrome (DS) require PET‐confirmed “amyloid positivity” for inclusion. Disease progression modeling predicts when individuals with low but meaningful amyloid levels will meet enrollment criteria. Predictions of time‐to‐biomarker‐threshold rely on published differential equation techniques. Time‐to‐amyloid‐positivity estimates have been published using longitudinal amyloid PET data. Although plasma pTau217 effectively determines amyloid plaque presence dichotomously, its ability to replace PET measures for forecasting future trial eligibility is unknown. Sensitivity at low amyloid levels is critical, as trials target early intervention. Method We included 329 people with DS enrolled in ABC‐DS, 167 completed one or more amyloid PET (PiB or AV45) and plasma pTau217 (Lilly assay) evaluations. The remaining participants completed either amyloid PET (N = 99) or venipuncture (N = 63). The optimal plasma pTau217 value for detecting amyloid positivity was evaluated at various thresholds using ROC analysis, maximizing Youden Index. Disease progression modeling estimated time‐to‐amyloid‐positivity using longitudinal amyloid PET and plasma pTau217 data. Temporal estimates for the two methods were compared using generalized additive models. We compared predictions of time‐to‐amyloid‐positivity using Mean Average Error (MAE) and Spearman correlations. Result A plasma pTau217 threshold of 0.4778 ug/mL was the optimal cutoff for amyloid positivity across 15 – 30 Centiloids (CL). Although 12 CL was the best‐performing cutoff (0.2271 µg/mL), its apparent high AUC is misleading, as the dataset imbalance (152 amyloid‐positive cases) results in 109 false positives out of 167 samples. Excluding 12 CL, plasma pTau217 accuracy improved with increasing cortical amyloid burden (Figure 1). At lower cortical amyloid levels (< 30 CL), plasma pTau217 was highly variable (MAE = 5.2 years, ρ = 0.226). Beyond 30 CL, plasma and PET estimates of time‐to‐positivity correlated well (MAE = 3.5 years; ρ = 0.629) (Figure 2). Conclusion Plasma pTau217 effectively predicts amyloid‐PET positivity in individuals with DS and generates continuous estimates of time‐to‐amyloid‐positivity in individuals with cortical amyloid burden > 30 CL. However, it is not as useful for estimating time‐to‐positivity at lower amyloid pathology levels (10 – 30 CL). This specific plasma pTau217 assay may lack sensitivity for detecting early amyloid positivity in people with DS.

Down syndrome (DS) represents a genetic form of Alzheimer's disease (AD) with an earlier expected symptom onset compared to late onset AD (LOAD). It is thought that the extra copy of the Amyloid Precursor Protein (APP) gene, located on chromosome 21 contributes to the earlier onset due to increased amyloid deposition in the brain. Hyperphosphorylation of tau protein is also thought to be elevated in the beginning stages of AD pathology within DS. Although APOEε4 has been associated with greater AD risk in LOAD, prior cross-sectional investigations into the effects of APOEε4 in DS have suggested that there is no additional impact of APOEε4 on the accumulation of amyloid. We aimed to extend this work by examining the associations between longitudinal plasma pTau217 and amyloid PET as a function of APOEε4 status. Participants with DS were recruited from the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study. Both p-tau217 (N = 564 results from 223 individuals including 122 that had 3 results each, Lilly MSD) and Amyloid PET ([11C]-PiB or [18F]-AV45) (N = 366 scans with 253 unique participants including 113 that had 2 scans each) were acquired. We analyzed the influence ɛ4 allele carrier status had on changes in pTau217 and amyloid across age using linear mixed-effects modeling, including the age, APOEε4 carrier status and their interaction as covariates. Age was also included as a covariate. Individuals that are carriers of the APOEε4 allele present with similar baseline amyloid and pTau217 values (p = .591 & p = .455 respectively). The rate of amyloid and pTau217 accumulation increased across age similarly for both groups (p = .772 & p = .657 respectively). Although not statistically significant, visual inspection suggests that, with a larger number of participants, individuals between the ages of 45 and 50 who are ɛ4 allele carriers may exhibit elevated pTau217 levels compared to non-carriers. We did not observe increased amyloidosis or tau phosphorylation in APOEε4 carriers with DS. Future studies targeting individuals aged 45-50 are suggested to investigate the potential APOEε4 effect on tau phosphorylation observed in this narrow chronological window, which is close to the average expected age of symptom onset of 52.5 years for DS.

Potential anti-amyloid therapy trials for Down syndrome (DS) require PET-confirmed \"amyloid positivity\" for inclusion. Disease progression modeling predicts when individuals with low but meaningful amyloid levels will meet enrollment criteria. Predictions of time-to-biomarker-threshold rely on published differential equation techniques. Time-to-amyloid-positivity estimates have been published using longitudinal amyloid PET data. Although plasma pTau217 effectively determines amyloid plaque presence dichotomously, its ability to replace PET measures for forecasting future trial eligibility is unknown. Sensitivity at low amyloid levels is critical, as trials target early intervention. We included 329 people with DS enrolled in ABC-DS, 167 completed one or more amyloid PET (PiB or AV45) and plasma pTau217 (Lilly assay) evaluations. The remaining participants completed either amyloid PET (N = 99) or venipuncture (N = 63). The optimal plasma pTau217 value for detecting amyloid positivity was evaluated at various thresholds using ROC analysis, maximizing Youden Index. Disease progression modeling estimated time-to-amyloid-positivity using longitudinal amyloid PET and plasma pTau217 data. Temporal estimates for the two methods were compared using generalized additive models. We compared predictions of time-to-amyloid-positivity using Mean Average Error (MAE) and Spearman correlations. A plasma pTau217 threshold of 0.4778 ug/mL was the optimal cutoff for amyloid positivity across 15 - 30 Centiloids (CL). Although 12 CL was the best-performing cutoff (0.2271 µg/mL), its apparent high AUC is misleading, as the dataset imbalance (152 amyloid-positive cases) results in 109 false positives out of 167 samples. Excluding 12 CL, plasma pTau217 accuracy improved with increasing cortical amyloid burden (Figure 1). At lower cortical amyloid levels (< 30 CL), plasma pTau217 was highly variable (MAE = 5.2 years, ρ = 0.226). Beyond 30 CL, plasma and PET estimates of time-to-positivity correlated well (MAE = 3.5 years; ρ = 0.629) (Figure 2). Plasma pTau217 effectively predicts amyloid-PET positivity in individuals with DS and generates continuous estimates of time-to-amyloid-positivity in individuals with cortical amyloid burden > 30 CL. However, it is not as useful for estimating time-to-positivity at lower amyloid pathology levels (10 - 30 CL). This specific plasma pTau217 assay may lack sensitivity for detecting early amyloid positivity in people with DS.