Microglia,which comprise approximately 10%of total cells in the brain,are the resident immune cells in the central nervous system and contribute to maintaining the brain homeostasis through monitoring their microenvir...
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Microglia,which comprise approximately 10%of total cells in the brain,are the resident immune cells in the central nervous system and contribute to maintaining the brain homeostasis through monitoring their microenvironment(Kettenmann et al.,2011).Recent studies have reported that microglia also regulate neural circuit formation after *** functional transition in microglia has been considered to correlate with their morphological changes over time.
Second harmonic generation imaging is a powerful tool for visualizing molecular structures in living organisms without the need for exogenous dyes. However, SHG signal lacks molecular specificity in identifying the so...
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ISBN:
(纸本)9781510669703;9781510669697
Second harmonic generation imaging is a powerful tool for visualizing molecular structures in living organisms without the need for exogenous dyes. However, SHG signal lacks molecular specificity in identifying the source. This study aimed at molecular identification of SHG sources in the mouse brain using a multimodal imaging technique combining SHG and multiplex coherent anti Stokes Raman scattering ( CARS) spectroscopy. We performed multimodal imaging in two different regions, the surface and dentate gyrus of the brain tissue. For the brain surface, the SHG signal was recognized through CARS spectrum analysis, indicating its origin in collagen. In the dentate gyrus, CARS images did not unveil corresponding molecular origins;however, morphologically, the SHG signal likely originated from Rootletin within neurons. Overall, Multimodal imaging approach to molecular identification of SHG has the potential to contribute to a comprehensive understanding of the molecular and structural features of the mouse brain. These findings advance label-free imaging techniques and have implications for brain tissue analysis and functional mapping research.
Background: Few studies have explored the relationship between macronutrient intake and sleep outcomesusing daily data from mobile apps. Objective: This cross-sectional study aimed to examine the associations between ...
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Background: Few studies have explored the relationship between macronutrient intake and sleep outcomesusing daily data from mobile apps. Objective: This cross-sectional study aimed to examine the associations between macronutrients, dietary components, and sleep parameters, considering their interdependencies. Methods: We analyzed data from 4825 users of the Pok & eacute;mon Sleep and Asken smartphone apps, each used for at least 7 days to record objective sleep parameters and dietary components, respectively. Multivariable regression explored the associations between quartiles of macronutrients (protein;carbohydrate;and total fat, including saturated, monounsaturated, and polyunsaturated fats), dietary components (sodium, potassium, dietary fiber, and sodium-to-potassium ratio), and sleep variables (total sleep time [TST],sleep latency [SL], and percentageofwakefulness after sleep onset[%WASO]). Thelowestintakegroupwasthereference. Compositional data analysis accounted for macronutrient interdependencies. Models were adjusted for age, sex, and BMI. Results: Greater protein intake was associated with longer TST in the third (+0.17, 95% CI 0.09-0.26 h) and fourth (+0.18, 95% CI 0.09-0.27 h) quartiles. In contrast, greater fat intake was linked to shorter TST in the third (-0.11, 95% CI -0.20 to -0.27 h) and fourth (-0.16, 95% CI -0.25 to -0.07 h) quartiles. Greater carbohydrate intake was associated with shorter %WASO in thethird (-0.82%, 95% CI -1.37% to -0.26%) and fourth (-0.57%, 95% CI -1.13% to -0.01%) quartiles, whilegreaterfat intake was linked to longer %WASO in the fourth quartile (+0.62%, 95% CI 0.06%-1.18%). Dietary fiber intake correlated with longer TST and shorter SL. A greater sodium-to-potassium ratio was associated with shorter TST in the third (-0.11, 95% CI -0.20 to -0.02 h) and fourth (-0.19, 95%CI -0.28 to -0.10 h) quartiles;longer SL in thesecond (+1.03, 95%CI 0.08-1.98 min) and fourth (+1.50, 95% CI 0.53-2.47min) quartiles;and longer %WASO inth
Manual sleep and arousal scoring is a labor-intensive task that demands significant time and effort. To speed up this process, several automatic scoring models based on deep learning have been proposed. These models p...
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ISBN:
(数字)9798350383737
ISBN:
(纸本)9798350383744
Manual sleep and arousal scoring is a labor-intensive task that demands significant time and effort. To speed up this process, several automatic scoring models based on deep learning have been proposed. These models primarily focus on scoring PSG (Polysomnogram) signals by separately classifying sleep stages and arousal events. This study introduces a novel methodology for concurrent sleep stage classification and arousal scoring, employing multitask learning for the analysis of in-home EEG (Electroencephalogram) signals. Our approach led to improvements in overall precision and sensitivity of arousal scoring, with values increasing by 0.3% to 4%. Notably, this approach did not yield improvements in sleep scoring. We validated our methodology on two private datasets collected from in-home loT (internet of Things) EEG devices and achieved consistent outcomes. Collectively, our research underscores the benefits of multitask learning for arousal scoring in in-home EEG signals.
Myofibers are broadly characterized as fatigue-resistant slow-twitch (type I) fibers and rapidly fatiguing fast -twitch (type IIa/IIx/IIb) fibers. However, the molecular regulation of myofiber type is not entirely und...
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Myofibers are broadly characterized as fatigue-resistant slow-twitch (type I) fibers and rapidly fatiguing fast -twitch (type IIa/IIx/IIb) fibers. However, the molecular regulation of myofiber type is not entirely understood;particularly, information on regulators of fast-twitch muscle is scarce. Here, we demonstrate that the large Maf transcription factor family dictates fast type IIb myofiber specification in mice. Remarkably, the ablation of three large Mafs leads to the drastic loss of type IIb myofibers, resulting in enhanced endurance capacity and the reduction of muscle force. Conversely, the overexpression of each large Maf in the type I soleus mus-cle induces type IIb myofibers. Mechanistically, a large Maf directly binds to the Maf recognition element on the promoter of myosin heavy chain 4, which encodes the type IIb myosin heavy chain, driving its expression. This work identifies the large Maf transcription factor family as a major regulator for fast type IIb muscle determination.
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