Human coronaviruses are RNA viruses that are sensitive to ultraviolet (UV) radiation. Sunlight contains UVA (320–400 nm), UVB (260–320 nm) and UVC (200–260 nm) action spectra. UVC can inactivate coronaviruses, including severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The incidence and mortality of coronavirus disease 2019 (COVID-19) are considered to be correlated with vitamin D levels. Vitamin D synthesis in human skin is closely related to exposure to UVB radiation. Therefore, the incidence and mortality of COVID-19 are also considered to be correlated with Vitamin D levels. In this study, Spearman and Kendall rank correlation analysis tests were used to analyze the correlation between the average percent positive of five human coronaviruses (SARS-CoV-2, CoVHKU1, CoVNL63, CoVOC43, and CoV229E) in the U.S. and the corresponding sunlight UV radiation dose The results indicated that the monthly average percent positive of four common coronaviruses was significantly negatively correlated with the sunlight UV radiation dose. The weekly percent positive of SARS-CoV-2 during April 17, 2020 to July 10, 2020 showed a significant negative correlation with the sunlight UV radiation dose in census regions 1 and 2 of the U.S. while no statistical significance in the other regions. Additionally, sunlight UV radiation also showed some negative effects with respect to the early SARS-CoV-2 transmission.

Selenoproteins form a group of proteins of which its members contain at least one selenocysteine, and most of them serve oxidoreductase functions. Selenoprotein F (SELENOF), one of the 25 currently identified selenoproteins, is located in the endoplasmic reticulum (ER) organelle and is abundantly expressed in many tissues. It is regulated according to its selenium status, as well as by cell stress conditions. SELENOF may be functionally linked to protein folding and the secretion process in the ER. Several studies have reported positive associations between SELENOF genetic variations and several types of cancer. Also, altered expression levels of SELENOF have been found in cancer cases and neurodegenerative diseases. In this review, we summarize the current understanding of the structure, expression, and potential function of SELENOF and discuss its possible relation with various pathological processes.

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Caries and dental erosion are common oral diseases. Traditional treatments involve the mechanical removal of decay and filling but these methods are not suitable for cases involving large-scale enamel erosion, such as hypoplasia. To develop a noninvasive treatment, promoting remineralisation in the early stage of caries is of considerable clinical significance. Therefore, biomimetic mineralisation is an ideal approach for restoring enamel. Biomimetic mineralisation forms a new mineral layer that is tightly attached to the surface of the enamel. This review details the state-of-art achievements on the application of amelogenin and non-amelogenin, amorphous calcium phosphate, ions flow and other techniques in the biomimetic mineralisation of enamel. The ultimate goal of this review was to shed light on the requirements for enamel biomineralisation. Hence, herein, we summarise two strategies of biological minimisation systems for in situ enamel restoration inspired by amelogenesis that have been developed in recent years and compare their advantages and disadvantages.

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Endophilin isoforms perform distinct characteristics in their interactions with N-type Ca2+ channels and dynamin. However, precise functional differences for the endophilin isoforms on synaptic vesicle (SV) endocytosis remain unknown. By coupling RNA interference and electrophysiological recording techniques in cultured rat hippocampal neurons, we investigated the functional differences of three isoforms of endophilin in SV endocytosis. The results showed that the amplitude of normalized evoked excitatory postsynaptic currents in endophilin1 knockdown neurons decreased significantly for both single train and multiple train stimulations. Similar results were found using endophilin2 knockdown neurons, whereas endophilin3 siRNA exhibited no change compared with control neurons. Endophilin1 and endophilin2 affected SV endocytosis, but the effect of endophilin1 and endophilin2 double knockdown was not different from that of either knockdown alone. This result suggested that endophilin1 and endophilin2 functioned together but not independently during SV endocytosis. Taken together, our results indicate that SV endocytosis is sustained by endophilin1 and endophilin2 isoforms, but not by endophilin3, in primary cultured hippocampal neurons.

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Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50–85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50–85 years) downloaded from multiple open‐source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia‐associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aβ40, Aβ42, T‐tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70–85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.