No abstract

Progenitor cells at the basal layer of skin epidermis play an essential role in maintaining tissue homeostasis and enhancing wound repair in skin. The proliferation, differentiation, and cell death of epidermal progenitor cells have to be delicately regulated, as deregulation of this process can lead to many skin diseases, including skin cancers. However, the underlying molecular mechanisms involved in skin homeostasis remain poorly defined. In this study, with quantitative proteomics approach, we identified an important interaction between KDF1 (keratinocyte differentiation factor 1) and IKKa (IjB kinase a) in differentiating skin keratinocytes. Ablation of either KDF1 or IKKa in mice leads to similar but striking abnormalities in skin development, particularly in skin epidermal differentiation. With biochemical and mouse genetics approach, we further demonstrate that the interaction of IKKa and KDF1 is essential for epidermal differentiation. To probe deeper into the mechanisms, we find that KDF1 associates with a deubiquitinating protease USP7 (ubiquitinspecific peptidase 7), and KDF1 can regulate skin differentiation through deubiquitination and stabilization of IKKa. Taken together, our study unravels an important molecular mechanism underlying epidermal differentiation and skin tissue homeostasis.

Au nanoparticles (NPs) have important applications in bioimaging, clinical diagnosis and even therapy due to its water-solubility, easy modification and drug-loaded capability, however, easy aggregation of Au NPs in normal saline and serum greatly limits its applications. In this work, highly stabilized core-satellite Au nanoassemblies (CSAuNAs) were constructed by a hierarchical DNA-directed self-assembly strategy, in which satellite Au NPs number could be effectively tuned through varying the ratios of core-AuNPs-ssDNA and satellite-AuNPs-ssDNAc. It was especially interesting that PEG-functionalized CSAuNAs (PEG-CSAuNAs) could not only bear saline solution but also resist the enzymatic degradation in fetal calf serum. Moreover, cell targeting and imaging indicated that the PEG-CSAuNAs had promising biotargeting and bioimaging capability. Finally, fluorescence imaging in vivo revealed that PEG-CSAuNAs modified with N-acetylation chitosan (CSNA) could be selectively accumulate in the kidneys with satisfactory renal retention capability. Therefore, the highly stabilized PEG-CSAuNAs open a new avenue for its applications in vivo.

Background Bronchopulmonary dysplasia (BPD) is a common chronic lung disease in extremely preterm neonates. The outcome and clinical burden vary dramatically according to severity. Although some prediction tools for BPD exist, they seldom pay attention to disease severity and are based on populations in developed countries. This study aimed to develop machine learning prediction models for BPD severity based on selected clinical factors in a Chinese population. Methods In this retrospective, single-center study, we included patients with a gestational age < 32 weeks who were diagnosed with BPD in our neonatal intensive care unit from 2016 to 2020. We collected their clinical information during the maternal, birth and early postnatal periods. Risk factors were selected through univariable and ordinal logistic regression analyses. Prediction models based on logistic regression (LR), gradient boosting decision tree, XGBoost (XGB) and random forest (RF) models were implemented and assessed by the area under the receiver operating characteristic curve (AUC). Results We ultimately included 471 patients (279 mild, 147 moderate, and 45 severe cases). On ordinal logistic regression, gestational diabetes mellitus, initial fraction of inspiration O2 value, invasive ventilation, acidosis, hypochloremia, C-reactive protein level, patent ductus arteriosus and Gram-negative respiratory culture were independent risk factors for BPD severity. All the XGB, LR and RF models (AUC = 0.85, 0.86 and 0.84, respectively) all had good performance. Conclusions We found risk factors for BPD severity in our population and developed machine learning models based on them. The models have good performance and can be used to aid in predicting BPD severity in the Chinese population.