NIR-responsive copper nanoliposome composites for cascaded ferrotherapy via ferroptosis actived ICD and IFN-γ released.

Metallic biomaterials activate tumor ferroptosis by increasing oxidative stress, but their efficacy is severely limited in tumor microenvironment. Although interferon gamma (IFN-γ) can promote tumor ferroptosis sensitivity by inhibiting the antioxidant system and promoting lipid accumulation, this effect limited by the lack of IFN-γ accumulation in tumors. Herein, we report a near-infrared (NIR)-responsive HCuS nanocomposite (HCuS-PE@TSL-tlyp-1) that can stimulate immunogenic cell death (ICD)-mediated IFN-γ secretion through exogenous oxidative stress, thereby achieving cascaded ferrotherapy by mutually reinforcing ferroptosis and systemic immunity. Upon laser irradiation, the dissolution of the thermal coating, and the introduction of Cu ions and piperazine-erastin (PE) simultaneously induce oxidative stress by reactive oxygen species (ROS)/lipid peroxide (LPO) accumulation and deplete cystine-glutamate transporter (xCT)/GSH. The onset of oxidative stress-mediated ferroptosis is thus achieved, and ICD is triggered, significantly promoting cytotoxic T-cell (CTL) infiltration for IFN-γ secretion. Furthermore, IFN-γ induces immunogenic tumor ferroptosis by inhibiting xCT-antioxidant pathways and enhancing the ACSL4-fatty acid recruitment pathway, which further promotes sensitivity to ferroptosis in cells. These HCuS nanocomposites combined with aPD-L1 effectively in inhibiting tumor metastasis and recurrence. Importantly, these cascade ferrotherapy results broadens the application of HCuS biomaterials.

A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening.

Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency. Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results: A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and -3.26 points. Conclusions: Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy.