Strategies for Small Extracellular Vesicle-Based Cancer Immunotherapy.

Extracellular vesicles (EVs) are lipid bilayer-enclosed vesicles released by cells. EVs encapsulate proteins and nucleic acids of their parental cell and efficiently deliver the cargo to recipient cells. These vesicles act as mediators of intercellular communication and thus play a crucial role in various physiological and pathological processes. Moreover, EVs hold promise for clinical use. They have been explored as drug delivery vehicles, therapeutic agents, and targets for disease diagnosis. In the landscape of cancer research, while strides have been made in EV-focused cancer physiopathology, liquid biopsy, and drug delivery, the exploration of EVs as immunotherapeutic agents may not have seen substantial progress to date. Despite promising findings reported in cell and animal studies, the clinical translation of EV-based cancer immunotherapeutics encounters challenges. Here, we review the existing strategies used in EV-based cancer immunotherapy, aiming to propel the development of this emerging yet crucial field.

Comparative effectiveness of eHealth interventions on the exercise endurance and quality of life of patients with COPD: A systematic review and network meta-analysis.

AIMS: To compare the effectiveness of different types of eHealth interventions in improving exercise endurance and quality of life in chronic obstructive pulmonary disease (COPD) patients. BACKGROUND: COPD is a chronic airway disease characterized by persistent respiratory symptoms and airflow limitation. eHealth interventions have been accepted and recognized by healthcare professionals and COPD patients as an effective alternative to pulmonary rehabilitation. However, it is not clear which eHealth interventions are effective and preferred for exercise endurance and quality of life in COPD patients. DESIGN: A systematic review and network meta-analysis based on PRISMA-NMA. METHODS: We searched nine electronic databases to identify randomized controlled trials addressing the effect of eHealth interventions on the exercise endurance and quality of life of COPD patients from their inception to 30 October 2022. First, a random-effects model was chosen to conduct a traditional meta-analysis to directly investigate the efficacy of different eHealth interventions. Next, a network meta-analysis was performed to evaluate the relative efficacy of the eHealth interventions for COPD. The quality of the data was assessed using the Cochrane Risk of Bias tool. RESULTS: Fifty-one studies containing six eHealth interventions (telemonitoring, application [APP], web-based interventions, phone calls, virtual reality and combined interventions [≥two types]) were included in the final analysis. Network meta-analysis showed that telemonitoring, APP, web-based interventions and combined interventions improved exercise endurance in COPD patients, with telemonitoring being the most effective. Web-based interventions and apps are effective in improving the quality of life, and web-based interventions are the most effective. CONCLUSIONS: This study confirms that eHealth interventions can improve exercise endurance and quality of life in COPD patients. In the future, healthcare professionals can promote the use of telemedicine in COPD patients to enhance their exercise endurance and quality of life according to their individual needs. RELEVANCE TO CLINICAL PRACTICE: This evidence suggests that eHealth interventions can improve exercise endurance and quality of life in COPD patients. Therefore, in the future, eHealth interventions could be used to maximize their effectiveness in improving exercise endurance and quality of life in COPD patients.

An Anthropomorphic Diagnosis System of Pulmonary Nodules using Weak Annotation-Based Deep Learning.

Purpose: To develop an anthropomorphic diagnosis system of pulmonary nodules (PN) based on Deep learning (DL) that is trained by weak annotation data and has comparable performance to full-annotation based diagnosis systems. Methods: The proposed system uses deep learning (DL) models to classify PNs (benign vs. malignant) with weak annotations, which eliminates the need for time-consuming and labor-intensive manual annotations of PNs. Moreover, the PN classification networks, augmented with handcrafted shape features acquired through the ball-scale transform technique, demonstrate capability to differentiate PNs with diverse labels, including pure ground-glass opacities, part-solid nodules, and solid nodules. Results: The experiments were conducted on two lung CT datasets: (1) public LIDC-IDRI dataset with 1,018 subjects, (2) In-house dataset with 2740 subjects. Through 5-fold cross-validation on two datasets, the system achieved the following results: (1) an Area Under Curve (AUC) of 0.938 for PN localization and an AUC of 0.912 for PN differential diagnosis on the LIDC-IDRI dataset of 814 testing cases, (2) an AUC of 0.943 for PN localization and an AUC of 0.815 for PN differential diagnosis on the in-house dataset of 822 testing cases. These results demonstrate comparable performance to full annotation-based diagnosis systems. Conclusions: Our system can efficiently localize and differentially diagnose PNs even in resource-limited environments with good robustness across different grade and morphology sub-groups in the presence of deviations due to the size, shape, and texture of the nodule, indicating its potential for future clinical translation. Summary: An anthropomorphic diagnosis system of pulmonary nodules (PN) based on deep learning and weak annotation was found to achieve comparable performance to full-annotation dataset-based diagnosis systems, significantly reducing the time and the cost associated with the annotation. Key Points: A fully automatic system for the diagnosis of PN in CT scans using a suitable deep learning model and weak annotations was developed to achieve comparable performance (AUC = 0.938 for PN localization, AUC = 0.912 for PN differential diagnosis) with the full-annotation based deep learning models, reducing around 30%∼80% of annotation time for the experts.The integration of the hand-crafted feature acquired from human experts (natural intelligence) into the deep learning networks and the fusion of the classification results of multi-scale networks can efficiently improve the PN classification performance across different diameters and sub-groups of the nodule.

Rapid in situ mutation detection in extracellular vesicle-DNA.

A PCR- and sequencing-free mutation detection assay facilitates cancer diagnosis and reduces over-reliance on specialized equipment. This benefit was highlighted during the pandemic when high demand for viral nucleic acid testing often sidelined mutation analysis. This shift led to substantial challenges for patients on targeted therapy in tracking mutations. Here, we report a 30-minute DNA mutation detection technique using Cas12a-loaded liposomes in a microplate reader, a fundamental laboratory tool. CRISPR-Cas12a complex and fluorescence-quenching (FQ) probes are introduced into tumor-derived extracellular vesicles (EV) through membrane fusion. When CRISPR-RNA hybridizes with the DNA target, activated Cas12a can trans-cleave FQ probes, resulting in fluorescence signals for the quantification of DNA mutation. Future advancements in multiplex and high-throughput mutation detection using this assay will streamline self-diagnosis and treatment monitoring at home.

Comparative efficacy of non-pharmacological interventions on sleep quality in old adults: A systematic review and network meta-analysis.

AIMS AND OBJECTIVES: To compare the effectiveness of non-pharmacological interventions in enhancing sleep quality in older people. BACKGROUND: Sleep problems in older adults have become increasingly prominent. Sleep problems not only affect the health and quality of life of older people, but also the range of chronic diseases caused by sleep problems also impose a huge burden on social services and health care. Non-pharmacological interventions are an effective alternative to pharmacological therapies, but it is unclear which non-pharmacological therapies are most effective in enhancing sleep quality in older adults. DESIGN: A systematic review and network meta-analysis based on PRISMA-NMA. METHODS: A total of seven databases were searched from the establishment of the database to March 2023. After literature screening and data extraction, the Cochrane Bias assessment tool 2.0 version of randomised controlled trials (RCTs) was used to evaluate literature quality. A network meta-analysis was performed to evaluate the relative efficacy of the non-pharmacological interventions on sleep quality. RESULTS: A total of 71 RCTs involving nine non-pharmacological interventions were included. The results of the network meta-analysis showed that the joint intervention may be the most effective non-pharmacological intervention to enhance sleep quality in older adults. CONCLUSION: This study confirms that non-pharmacological interventions can improve sleep quality in older adults. The use of non-pharmacological interventions can be promoted by healthcare professionals in the future to improve the quality of sleep and thus the physical and mental health of older people. RELEVANCE TO CLINICAL PRACTICE: This evidence suggests that joint interventions may be most effective. Therefore, in the future, a combination of non-pharmacological interventions could be used to maximise their effectiveness in improving sleep quality in older people and promoting healthy aging. NO PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution is not applicable to this study.

Chimeric nanobody-decorated liposomes by self-assembly.

Liposomes as drug vehicles have advantages, such as payload protection, tunable carrying capacity and improved biodistribution. However, due to the dysfunction of targeting moieties and payload loss during preparation, immunoliposomes have yet to be favoured in commercial manufacturing. Here we report a chemical modification-free biophysical approach for producing immunoliposomes in one step through the self-assembly of a chimeric nanobody (cNB) into liposome bilayers. cNB consists of a nanobody against human epidermal growth factor receptor 2 (HER2), a flexible peptide linker and a hydrophobic single transmembrane domain. We determined that 64% of therapeutic compounds can be encapsulated into 100-nm liposomes, and up to 2,500 cNBs can be anchored on liposomal membranes without steric hindrance under facile conditions. Subsequently, we demonstrate that drug-loaded immunoliposomes increase cytotoxicity on HER2-overexpressing cancer cell lines by 10- to 20-fold, inhibit the growth of xenograft tumours by 3.4-fold and improve survival by more than twofold.