Several neurodegenerative diseases and brain injury involve reactive oxygen species and implicate oxidative stress in disease mechanisms. Hydrogen peroxide (H₂O₂) formation due to mitochondrial superoxide leakage perpetuates oxidative stress in neuronal injury. Catalase, an H₂O₂-degrading enzyme, thus remains an important antioxidant therapy target. However, catalase therapy is restricted by its labile nature and inadequate delivery. Here, a nanotechnology approach was evaluated using catalase-loaded, poly(lactic co-glycolic acid) nanoparticles (NPs) in human neuronal protection against oxidative damage. This study showed highly efficient catalase encapsulation capable of retaining∼99% enzymatic activity. NPs released catalase rapidly, and antioxidant activity was sustained for over a month. NP uptake in human neurons was rapid and nontoxic. Although human neurons were highly sensitive to H₂O₂, NP-mediated catalase delivery successfully protected cultured neurons from H₂O₂-induced oxidative stress. Catalase-loaded NPs significantly reduced H₂O₂-induced protein oxidation, DNA damage, mitochondrial membrane transition pore opening and loss of cell membrane integrity and restored neuronal morphology, neurite network and microtubule-associated protein-2 levels. Further, catalase-loaded NPs improved neuronal recovery from H₂O₂ pre-exposure better than free catalase, suggesting possible applications in ameliorating stroke-relevant oxidative stress. Brain targeting of catalase-loaded NPs may find wide therapeutic applications for oxidative stress-associated acute and chronic neurodegenerative disorders.

Cyclodextrins (CDs) are cyclic oligosaccharides; the most common CDs contain six, seven, or eight glucose units called α-CDs, β-CDs, and γ-CDs, respectively. The use of CDs in biomedical research is increasing due to their ability to interact with membrane lipids as well as a wide variety of poorly water-soluble molecules. We assessed the impact of CD cavity size, occupancy, and substitutions on cytotoxicity and cholesterol homeostasis. The potency of CD-mediated cytotoxicity was in the order of β-CDs, α-CDs, and γ-CDs. Substitutions with hydroxypropyl or carboxymethyl group attenuated cytotoxicity compared with the native CDs, whereas CDs substituted with methyl groups exhibited cytotoxicity that was similar to that of the native CDs. The lipid components in blood exerted remarkable hemolysis-alleviating effects in methyl-β-CD-induced hemolysis. Occupancy of the CD cavity with cholesterol or a structurally related lipid molecule abrogated the cytotoxic capacity of the CDs. Interestingly, hydroxypropyl-γ-CD (HPγCD) was able to reduce intracellular cholesterol accumulation in Niemann–Pick disease type C (NPC) patient-derived fibroblasts as efficiently as HPβCD. Proteomic study indicated that HPβCD and HPγCD treatments altered the expression pattern of cellular proteins, suggesting that some of the CD-induced cellular proteins may play an important function in modulating intracellular cholesterol homeostasis.

Background: Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. Objective: This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. Methods: We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. Results: The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. Conclusions: While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

Niemann–Pick type C (NPC) disease is a fatal hereditary neurodegenerative disorder characterized by a massive accumulation of cholesterol in lysosomes and late endosomes due to a defect in intracellular cholesterol trafficking. Dysfunction in intracellular cholesterol trafficking is responsible for about 50 rare inherited lysosomal storage disorders including NPC. The lysosomal proteins NPC1 and NPC2 play a crucial role in trafficking of cholesterol from late endosomes and lysosomes to other cellular compartments. However, the detailed mechanisms of cholesterol trafficking at the late endosomes/lysosomes (LE/LY) are poorly understood. Studies showed that 2-hydroxypropyl-β-cyclodextrin (HPβCD) alleviates the cholesterol accumulation defect in animal model and has been approved for a phase 2b/3 clinical trial for NPC. HPβCD is known to bind cholesterol; however, the mechanisms how HPβCD mediates the exit of cholesterol from the LE/LY compartments are still unknown. Further, another cyclodextrin (CD) derivative, 2-hydroxypropyl-γ-cyclodextrin (HPγCD), was shown to reduce intracellular cholesterol accumulation in NPC patient cells and NPC mice model. Herein, we identified a number of candidate proteins differentially expressed in NPC patient-derived cells compared to cells derived from a healthy donor using a proteomic approach. Interestingly, both HPβCD and HPγCD treatments modulated the expression of most of these NPC-specific proteins. Data showed that treatment with both CDs induces the expression of the lysosome-associated membrane protein 1 (LAMP-1) in NPC patient-derived cells. Remarkably, LAMP-1 overexpression in HeLa cells rescued U18666A-induced cholesterol accumulation suggesting a role of LAMP-1 in cholesterol trafficking. We propose that HPβCD and HPγCD facilitate cholesterol export from the LE/LY compartments via the LAMP-1 protein, which may play a crucial role in cholesterol trafficking at the LE/LY compartments when there is no functional NPC1 protein. Together, this study uncovers new cellular mechanisms for cholesterol trafficking, which will contribute to development of novel therapeutic approaches for lysosomal storage diseases.

Niemann-Pick type C (NPC) disease is a fatal neurodegenerative disorder caused by mutations in NPC1 and NPC2 genes that result in an accumulation of cholesterol in lysosomes. The majority of children with NPC die in adolescence. Currently, no FDA-approved therapies exist for NPC and the mechanisms of NPC disease are not fully understood. Our recent study and the reports from other laboratories showed that 2-hydroxypropyl-γ-cyclodextrin (HPγCD) alleviates cholesterol accumulation in NPC1-deficient cells in spite of its low binding affinity for cholesterol. In this study, we explored the cellular changes that are induced upon HPγCD treatment in NPC1 patient-derived fibroblasts. We show that HPγCD treatment increases lysosome-ER association and enhances autophagic activity. Our study indicates that HPγCD induces an activation of the transcription factor EB (TFEB), a master regulator of lysosomal functions and autophagy. Lysosome-ER association could potentially function as conduits for cholesterol transport from lysosomes to the ER. Accumulating evidence suggests a role for autophagy in rescuing the cholesterol accumulation in NPC and other degenerative diseases. Collectively, our findings suggest that HPγCD restores cellular homeostasis in NPC1-deficient cells via enhancing lysosomal dynamics and functions. Understanding the mechanisms of HPγCD-induced cellular pathways could contribute to effective NPC therapies.

Bortezomib augments lymphocyte stimulatory cytokine signaling in the tumor microenvironment to sustain CD8 + T cell antitumor function