The surgical procedure in skin-tumor therapy usually results in cutaneous defects, and multidrug-resistant bacterial infection could cause chronic wounds. Here, for the first time, an injectable self-healing antibacterial bioactive polypeptide-based hybrid nanosystem is developed for treating multidrug resistant infection, skin-tumor therapy, and wound healing. The multifunctional hydrogel is successfully prepared through incorporating monodispersed polydopamine functionalized bioactive glass nanoparticles (BGN@ PDA) into an antibacterial F127-ε-Poly-L-lysine hydrogel. The nanocomposites hydrogel displays excellent self-healing and injectable ability, as well as robust antibacterial activity, especially against multidrug-resistant bacteria in vitro and in vivo. The nanocomposites hydrogel also demonstrates outstanding photothermal performance with (near-infrared laser irradiation) NIR irradiation, which could effectively kill the tumor cell (>90%) and inhibit tumor growth (inhibition rate up to 94%) in a subcutaneous skin-tumor model. In addition, the nanocomposites hydrogel effectively accelerates wound healing in vivo. These results suggest that the BGN-based nanocomposite hydrogel is a promising candidate for skin-tumor therapy, wound healing, and antiinfection. This work may offer a facile strategy to prepare multifunctional bioactive hydrogels for simultaneous tumor therapy, tissue regeneration, and anti-infection.

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The surgical procedure in skin-tumor therapy usually results in cutaneous defects, and multidrug-resistant bacterial infection could cause chronic wounds. Here, for the first time, an injectable self-healing antibacterial bioactive polypeptide-based hybrid nanosystem is developed for treating multidrug resistant infection, skin-tumor therapy, and wound healing. The multifunctional hydrogel is successfully prepared through incorporating monodispersed polydopamine functionalized bioactive glass nanoparticles (BGN@ PDA) into an antibacterial F127-ε-Poly-L-lysine hydrogel. The nanocomposites hydrogel displays excellent self-healing and injectable ability, as well as robust antibacterial activity, especially against multidrug-resistant bacteria in vitro and in vivo. The nanocomposites hydrogel also demonstrates outstanding photothermal performance with (near-infrared laser irradiation) NIR irradiation, which could effectively kill the tumor cell (>90%) and inhibit tumor growth (inhibition rate up to 94%) in a subcutaneous skin-tumor model. In addition, the nanocomposites hydrogel effectively accelerates wound healing in vivo. These results suggest that the BGN-based nanocomposite hydrogel is a promising candidate for skin-tumor therapy, wound healing, and antiinfection. This work may offer a facile strategy to prepare multifunctional bioactive hydrogels for simultaneous tumor therapy, tissue regeneration, and anti-infection.

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Background Age estimation from panoramic radiographs is a fundamental task in forensic sciences. Previous age assessment studies mainly focused on juvenile rather than elderly populations (> 25 years old). Most proposed studies were statistical or scoring-based, requiring wet-lab experiments and professional skills, and suffering from low reliability. Result Based on Soft Stagewise Regression Network (SSR-Net), we developed DENSEN to estimate the chronological age for both juvenile and older adults, based on their orthopantomograms (OPTs, also known as orthopantomographs, pantomograms, or panoramic radiographs). We collected 1903 clinical panoramic radiographs of individuals between 3 and 85 years old to train and validate the model. We evaluated the model by the mean absolute error (MAE) between the estimated age and ground truth. For different age groups, 3–11 (children), 12–18 (teens), 19–25 (young adults), and 25+ (adults), DENSEN produced MAEs as 0.6885, 0.7615, 1.3502, and 2.8770, respectively. Our results imply that the model works in situations where genders are unknown. Moreover, DENSEN has lower errors for the adult group (> 25 years) than other methods. The proposed model is memory compact, consuming about 1.0 MB of memory overhead. Conclusions We introduced a novel deep learning approach DENSEN to estimate a subject’s age from a panoramic radiograph for the first time. Our approach required less laboratory work compared with existing methods. The package we developed is an open-source tool and applies to all different age groups.

Following publication of the original article [1]. the authors flagged an error in the affiliations information of their article: affiliation '5' , which is only applicable for the fourth author, Yin Chen, had been assigned to all authors.The article has now been corrected and the corrected affiliation information may be seen in this erratum. The authors thank you for reading and apologize for any inconvenience caused.

Aimed at the "growing information flood" caused by the power energy management system, this article presents a solution with three functions: the multi-dimensional analysis when the power grid at normal state, the visualization display at abnormal state, and the intelligent fault analysis at fault state. According to these three functions, correspondingly presents three key technologies: the CIM+GPS+GML+SVG modeling method for power equipment and spatial information, the visualization method for power grid abnormal information, and the rapid fault recognition method based on the power flow translation factor.

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