A novel type of cellular-uptake-shielding multifunctional envelope-type mesoporous silica nanoparticle (MEMSN) was designed for tumor-triggered targeting drug delivery to cancerous cells. β-Cyclodextrin (β-CD) was anchored on the surface of mesoporous silica nanoparticles via disulfide linking for glutathione-induced intracellular drug release. Then a peptide sequence containing Arg-Gly-Asp (RGD) motif and matrix metalloproteinase (MMP) substrate peptide Pro-Leu-Gly-Val-Arg (PLGVR) was introduced onto the surface of the nanoparticles via host-guest interaction. To protect the targeting ligand and prevent the nanoparticles from being uptaken by normal cells, the nanoparticles were further decorated with poly(aspartic acid) (PASP) to obtain MEMSN. In vitro study demonstrated that MEMSN was shielded against normal cells. After reaching the tumor cells, the targeting property could be switched on by removing the PASP protection layer via hydrolyzation of PLGVR at the MMP-rich tumor cells, which enabled the easy uptake of drug-loaded nanoparticles by tumor cells and subsequent glutathione-induced drug release intracellularly.

Damage induced in the DNA after exposure of cells to ionizing radiation activates checkpoint pathways that inhibit progression of cells through the G1 and G2 phases and induce a transient delay in the progression through S phase. Checkpoints together with repair and apoptosis are integrated in a circuitry that determines the ultimate response of a cell to DNA damage. Checkpoint activation typically requires sensors and mediators of DNA damage, signal transducers and effectors. Here, we review the current state of knowledge regarding mechanisms of checkpoint activation and proteins involved in the different steps of the process. Emphasis is placed on the role of ATM and ATR, as well on CHK1 and CHK2 kinases in checkpoint response. The roles of downstream effectors, such as P53 and the CDC25 family of proteins, are also described, and connections between repair and checkpoint activation are attempted. The role of checkpoints in genomic stability and the potential of improving the treatment of cancer by DNA damage inducing agents through checkpoint abrogation are also briefly outlined.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

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