Decreased cobalamin sensitivity and biological aging acceleration in the general population.

BACKGROUND: The evidence on the association between cobalamin (Cbl) and aging or relevant outcomes is limited and controversial. We aimed to investigate the relationships between cobalamin intake- and function-related biomarkers and biological aging. METHODS: The study encompassed 22,812 participants aged 20 years and older from the National Health and Nutrition Examination Survey. A panel of biomarkers or algorithms was used to assess biological aging, including Klemera-Doubal Age Acceleration (KDMAccel), Phenotypic age acceleration (PhenoAgeAccel), telomere length, α-Klotho, and PhenoAge advancement. Weighted generalized linear regression analysis was used to assess the associations between cobalamin-intake biomarkers (serum cobalamin, cobalamin intake from food, cobalamin supplement use, serum methylmalonic acid [MMA], and homocysteine [Hcy]) and function-related biomarkers (functional cobalamin deficiency and cobalamin insensitivity index). RESULTS: Among the 22,812 individuals, the weighted mean (SE) age was 48.3 (0.2) years and 48.0% were males. Unexpectedly, serum and dietary cobalamin as well as serum MMA and Hcy levels were positively associated with most indicators of biological aging. Cobalamin sensitivity was assessed by the combination of binary Cbllow/high and MMAlow/high or Hcylow/high (cutoff values: 400 pg/mL for cobalamin, 250 nmol/L for MMA, and 12.1 µmol/l for Hcy) and a newly constructed cobalamin insensitivity index (based on the multiplicative term of serum cobalamin and serum MMA or Hcy). The multivariable-adjusted ß (95%CIs) of KDMAccel in the MMAlowCbllow, MMAlowCblhigh, MMAhighCbllow, and MMAhighCblhigh groups were reference, 0.27 (0.03 to 0.51), 0.85 (0.41 to 1.29), and 7.97 years (5.77 to 10.17) respectively, which were consistent for the combination of serum Hcy and cobalamin. Both cobalamin insensitivity indices were robustly associated with biological aging acceleration in a dose-response pattern (each p < 0.001). CONCLUSIONS: Decreased cobalamin sensitivity but not cobalamin insufficiency might be associated with biological aging acceleration. Further studies would improve understanding of the underlying mechanisms between decreased cobalamin sensitivity and biological aging acceleration.

Nystagmus patterns classification framework based on deep learning and optical flow.

Benign paroxysmal positional vertigo (BPPV) is the most common vestibular peripheral vertigo disease characterized by brief recurrent vertigo with positional nystagmus. Clinically, it is common to recognize the patterns of nystagmus by analyzing infrared nystagmus videos of patients. However, the existing approaches cannot effectively recognize different patterns of nystagmus, especially the torsional nystagmus. To improve the performance of recognizing different nystagmus patterns, this paper contributes an automatic recognizing method of BPPV nystagmus patterns based on deep learning and optical flow to assist doctors in analyzing the types of BPPV. Firstly, we present an adaptive method for eliminating invalid frames that caused by eyelid occlusion or blinking in nystagmus videos and an adaptive method for segmenting the iris and pupil area from video frames quickly and efficiently. Then, we use a deep learning-based optical flow method to extract nystagmus information. Finally, we propose a nystagmus video classification network (NVCN) to categorize the patterns of nystagmus. We use ConvNeXt to extract eye movement features and then use LSTM to extract temporal features. Experiments conducted on the clinically collected datasets of infrared nystagmus videos show that the NVCN model achieves an accuracy of 94.91% and an F1 score of 93.70% on nystagmus patterns classification task as well as an accuracy of 97.75% and an F1 score of 97.48% on torsional nystagmus recognition task. The experimental results prove that the framework we propose can effectively recognize different patterns of nystagmus.

Decreased striatal vesicular monoamine transporter 2 (VMAT2) expression in a type 1 diabetic rat model: A longitudinal study using micro-PET/CT.

AIMS: Diabetes mellitus is a risk factor for Parkinson's disease. These diseases share similar pathogenic pathways, such as mitochondrial dysfunction, inflammation, and altered metabolism. Despite these similarities, the pathogenic relationship between these two diseases is unclear. [18F]FP-(+)-DTBZ is a promising radiotracer targeting VMAT2, which has been used to measure ß-cell mass and to diagnose Parkinson's disease. The aim of this study was to examine the effect of type 1 diabetes on VMAT2 expression in the striatum using [18F]FP-(+)-DTBZ. MATERIALS AND METHODS: A longitudinal study of type 1 diabetic rats was established by intraperitoneally injecting male Wistar rats with streptozotocin. Rats injected with saline were used as the control group. Glucose level, body weight, and [18F]FP-(+)-DTBZ uptake in the striatum and pancreas were evaluated at 0.5, 1, 4, 6 and 12 months after STZ or saline injection. RESULTS: At one-half month post-STZ injection, the glucose levels in these rats increased and then returned to a normal level at 6 months. Along with increased glucose levels, body weight was also decreased significantly and returned slowly to a normal level. ß-Cell mass and striatal [18F]FP-(+)-DTBZ uptake were impaired significantly at 2 weeks post-STZ injection in type 1 diabetic rats and returned to a normal level at 6 and 4 months post-STZ injection. CONCLUSIONS: Due to increased glucose levels and decreased ß-cell mass, decreased [18F]FP-(+)-DTBZ uptake in the striatum was observed in type 1 diabetic rats. Decreased BCM and increased glucose levels were correlated with VMAT2 expression in the striatum which indicated DM is a risk factor for PD.

Study of amyloid-ß peptide functional brain networks in AD, MCI and HC.

One medical challenge in studying the amyloid-ß (Aß) peptide mechanism for Alzheimer's disease (AD) is exploring the law of beta toxic oligomers' diffusion in human brains in vivo. One beneficial means of solving this problem is brain network analysis based on graph theory. In this study, the characteristics of Aß functional brain networks of Healthy Control (HC), Mild Cognitive Impairment (MCI), and AD groups were compared by applying graph theoretical analyses to Carbon 11-labeled Pittsburgh compound B positron emission tomography (11C PiB-PET) data. 120 groups of PiB-PET images from the ADNI database were analyzed. The results showed that the small-world property of MCI and AD were lost as compared to HC. Furthermore, the local clustering of networks was higher in both MCI and AD as compared to HC, whereas the path length was similar among the three groups. The results also showed that there could be four potential Aß toxic oligomer seeds: Frontal_Sup_Medial_L, Parietal_Inf_L, Frontal_Med_Orb_R, and Parietal_Inf_R. These four seeds are corresponding to Regions of Interests referred by physicians to clinically diagnose AD.