Erythropoietin-producing hepatocellular carcinoma A2 (EphA2) is overexpressed in more than 90% of non-small cell lung cancer (NSCLC) but not significantly in normal lung tissue. It is therefore an important tumor antigen target for chimeric antigen receptors (CAR)-T-based therapy in NSCLC. Here, we developed a specific CAR targeted to EphA2, and the anti-tumor effects of this CAR were investigated. A second generation CAR with co-stimulatory receptor 4-1BB targeted to EphA2 was developed. The functionality of EphA2-specific T cells in vitro was tested with flow cytometry and real-time cell electronic sensing system assays. The effect in vivo was evaluated in xenograft SCID Beige mouse model of EphA2 positive NSCLC. These EphA2-specifc T cells can cause tumor cell lysis by producing the cytokines IFN-γ when cocultured with EphA2-positive targets, and the cytotoxicity effects was specific in vitro. In vivo, the tumor signals of mice treated with EphA2-specifc T cells presented the tendency of decrease, and was much lower than the mice treated with non-transduced T cells. The anti-tumor effects of this CAR-T technology in vivo and vitro had been confirmed. Thus, EphA2-specific T-cell immunotherapy may be a promising approach for the treatment of EphA2-positive NSCLC.

Lymph node metastasis is a frequent occurrence in a variety of tumour forms and poses an enormous challenge to cancer treatment. This process is critical to the development of the disease and is frequently linked to a poor prognosis. Over 90% of cancerous cells move through lymph nodes, making them important entry routes for the spread of cancer cells. The prognosis of cancer patients is significantly impacted by lymph node metastases, which also affects treatment choices. Targeting lymph node metastases presents numerous difficulties for conventional medication delivery techniques. It is still very difficult to selectively target cancer cells in lymph nodes without risking injury to healthy organs and unforeseen consequences. Additionally, systemic delivery of drugs is hampered by the slow flow rate of lymphatic vessels. Chemotherapeutic medicines’ poor solubility and stability further reduce their effectiveness when taken orally. Additionally, the extracellular matrix that surrounds lymph node tumours is extensive, which makes it difficult for conventional pharmaceutical delivery systems to reach cancer cells. The development of nanocarriers for precise drug delivery to LNs has attracted a lot of interest to overcome these obstacles. Most solid tumours first spread through the lymphatic system, hence effective drug administration to these tissues is essential for better therapeutic results. Nanocarriers have several benefits, including the capacity to pass through barriers like blood-brain barriers and membranes to reach the lymphatic system. High medication dosages can be enclosed thanks to the physicochemical characteristics of nanocarriers, such as their higher surface-to-volume ratio. Additionally, ligands, antibodies, polymers, or biological molecules can be attached to nanocarrier surfaces to change their properties, allowing for the targeted delivery of lymph node epithelial cells. This use of nanocarriers for drug delivery maximizes on-target effects and related adverse effects while improving the effectiveness of medication delivery to target locations. More research and development in this field is needed to optimize nanocarrier design, increase targeting capabilities, and expand clinical applications for better cancer care.

Colorectal cancer (CRC) is one of the most prevalent malignancies in the world and developed drug resistance has represented one of the most challenging tasks for management. The current therapeutic regimens may select and enrich cancer stem-like cells (CSCs) resulting in the increased resistance against treatment, metastatic potential and mortality. Regorafenib is a multi-kinase inhibitor, an FDA-approved last-of-line treatment for patients with chemo-refractory metastatic CRC. However, regorafenib’s potential effects on CSCs have not been fully elucidated. Here, we developed two 5-FU resistant CRC cell lines, HCT-116R and DLD-1R and showed the increased CSCs characteristics such as increased side-population cells, tumor sphere formation and expression of stemness markers. These cell lines and CSCs properties were used for evaluating the potential of regorafenib in suppressing CSCs. We showed that regorafenib treatment decreased the stemness phenotypes including tumor sphere formation, and side-population, of both HCT-116R and DLD-1R cells. Additionally, regorafenib suppressed the cell viability in both cell lines synergistically with 5-FU. In vivo, the combination of regorafenib and 5-FU significantly suppressed the tumorigenesis and stemness markers of 5-FU resistant DLD-1R. Mechanistically, regorafenib-mediated effects were associated with the induction of tumor suppressor miR-34a and suppression of WNT/β-catenin signaling. Our findings demonstrated that regorafenib treatment was associated with the increased level of miR-34a, resulting in reversing drug resistance and cancer-initiating cell phenotypes by degrading WNT/β-catenin in CRC. Regorafenib might be a potential drug for colon cancer stem-like cells and it should be investigated in future clinical trials.

Background: Early identification of Alzheimer’s disease or mild cognitive impairment can help guide direct prevention and supportive treatments, improve outcomes, and reduce medical costs. Existing advanced diagnostic tools are mostly based on neuroimaging and suffer from certain problems in cost, reliability, repeatability, accessibility, ease of use, and clinical integration. To address these problems, we developed, evaluated, and implemented an early diagnostic tool using machine learning and non-imaging factors.Methods and results: A total of 654 participants aged 65 or older from the Nursing Home in Hangzhou, China were identified. Information collected from these patients includes dementia status and 70 demographic, cognitive, socioeconomic, and clinical features. Logistic regression, support vector machine (SVM), neural network, random forest, extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and best subset models were trained, tuned, and internally validated using a novel double cross validation algorithm and multiple evaluation metrics. The trained models were also compared and externally validated using a separate dataset with 1,100 participants from four communities in Zhejiang Province, China. The model with the best performance was then identified and implemented online with a friendly user interface. For the nursing dataset, the top three models are the neural network (AUROC = 0.9435), XGBoost (AUROC = 0.9398), and SVM with the polynomial kernel (AUROC = 0.9213). With the community dataset, the best three models are the random forest (AUROC = 0.9259), SVM with linear kernel (AUROC = 0.9282), and SVM with polynomial kernel (AUROC = 0.9213). The F1 scores and area under the precision-recall curve showed that the SVMs, neural network, and random forest were robust on the unbalanced community dataset. Overall the SVM with the polynomial kernel was found to be the best model. The LASSO and best subset models identified 17 features most relevant to dementia prediction, mostly from cognitive test results and socioeconomic characteristics.Conclusion: Our non-imaging-based diagnostic tool can effectively predict dementia outcomes. The tool can be conveniently incorporated into clinical practice. Its online implementation allows zero barriers to its use, which enhances the disease’s diagnosis, improves the quality of care, and reduces costs.

Refractory MRSA infections seriously threaten human health, which compromises antibiotic efficacy. Although phototherapy has exhibited great feasibility in the fight against drug-resistant pathogens, a single-modal therapeutic agent without targeted performance has resulted in low drug utilization and obvious side effects. Herein, near-infrared (NIR) light-triggered theranostic nanoparticles (NDIA@PEG-Ce6/B NPs) were prepared by employing naphthalene diimide derivative NDIA as a photothermal agent, PEGylated chlorin e6 (PEG-Ce6) as a photosensitizer and PEGylated phenylboronic acid (PEG-B) as a bacteria-targeting agent. Upon laser irradiation, the singlet oxygen produced by Ce6 can efficiently kill bacteria through photodynamic therapy. Moreover, the fluorescence emission of Ce6 can be absorbed by the photothermal agent NDIA with a fluorescence resonance energy transfer (FRET) efficiency of 78% to reinforce the photothermal effect of NDIA, and the photothermal conversion efficiency of NDIA@PEG-Ce6/B NPs was up to 49.7%. In vitro and in vivo antibacterial experiments indicated that NDIA@PEG-Ce6/B NPs could targeted assembly and effectively destroy multidrug-resistant bacteria through synergistic photothermal and photodynamic therapy with 99.999% antibacterial efficacy under NIR light illumination. The study provides a nanosystem for the antibacterial treatment of synergistic photodynamic and photothermal therapy for subcutaneous abscesses, as well as a novel FRET strategy for further design of light-triggered antibacterial nanoplatforms.