Search

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process. [ABSTRACT FROM AUTHOR]

Implantable sensors can directly interface with various organs for precise evaluation of health status. However, extracting signals from such sensors mainly requires transcutaneous wires, integrated circuit chips, or cumbersome readout equipment, which increases the risks of infection, reduces biocompatibility, or limits portability. Here, we develop a set of millimeter-scale, chip-less, and battery-less magnetic implants paired with a fully integrated wearable device for measuring biophysical and biochemical signals. The wearable device can induce a large amplitude damped vibration of the magnetic implants and capture their subsequent motions wirelessly. These motions reflect the biophysical conditions surrounding the implants and the concentration of a specific biochemical depending on the surface modification. Experiments in rat models demonstrate the capabilities of measuring cerebrospinal fluid (CSF) viscosity, intracranial pressure, and CSF glucose levels. This miniaturized system opens the possibility for continuous, wireless monitoring of a wide range of biophysical and biochemical conditions within the living organism. [ABSTRACT FROM AUTHOR]

Postoperative cognitive dysfunction contributes to memory impairment, which is involved in neurotoxicity, endoplasmic reticulum stress, calcium overload and hippocampal neuron apoptosis. Purpurogallin can mitigate ischemia-induced neuronal injury and microglial inflammation. However, whether purpurogallin affects cognitive dysfunction remains unclear. To investigate the efficacy of purpurogallin on cognitive dysfunction and explore the potential signal pathways, Sevoflurane (SEV)-induced model was established on mice, and water maze testing was performed to record escape latency, platform residence time, platform crossings and swimming speed analysis. The concentration of Interleukin (IL)-6, IL-1β and Tumor necrosis factor (TNF)- α in hippocampus tissue were evaluated using Enzyme-linked immunosorbent assay (ELISA). The hippocampal neurons were analyzed using Nissl staining. The Iba-1 was evaluated using immunofluorescent assay (IFA), while the cell apoptosis was assessed using terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. Western blots were performed to evaluate the expression of Ionized calcium binding adaptor molecule 1 (Iba1), integrin α-M (CD11b), Bax, integrin α-M (Bcl-2), p-65 and IΰBα, as well as the phosphorylation of p-65 and IΰBα. Purpurogallin at dose of 150 mg/kg significantly reduced escape latency in SEV-induced mice (p < 0.001), without affecting swimming speed. It increased time spent in the target quadrant and restored platform crossings reduced by SEV. Purpurogallin also mitigated the elevated IL-6, IL-1β and Transforming growth factor alpha (TGF-α) levels caused by SEV in the hippocampus, preserved Nissl bodies, and reduced Iba-I and CD11b expression (p < 0.01). It alleviated cell apoptosis by downregulating Bax and upregulating Bcl-2, while inhibiting p-65 phosphorylation and promoting IΰBα phosphorylation induced by SEV (p < 0.05). Purpurogallin effectively countered SEV-induced cognitive impairment, neuroinflammation, hippocampal injury, microglial activation and neuronal apoptosis through inhibiting nuclear factor kappa-light-chain-enhancer of activated B cells (NF-ΰB) pathway, which facilitate purpurogallin to become a drug candidate for postoperative cognitive dysfunction. [ABSTRACT FROM AUTHOR]