LAG3 is the most promising immune checkpoint next to PD-1 and CTLA-4. High LAG3 and FGL1 expression boosts tumor growth by inhibiting the immune microenvironment. This review comprises four sections presenting the structure/expression, interaction, biological effects, and clinical application of LAG3/FGL1. D1 and D2 of LAG3 and FD of FGL1 are the LAG3-FGL1 interaction domains. LAG3 accumulates on the surface of lymphocytes in various tumors, but is also found in the cytoplasm in non-small cell lung cancer (NSCLC) cells. FGL1 is found in the cytoplasm in NSCLC cells and on the surface of breast cancer cells.
The LAG3-FGL1 interaction mechanism remains unclear, and the intracellular signals require elucidation. LAG3/FGL1 activity is associated with immune cell infiltration, proliferation, and secretion. Cytokine production is enhanced when LAG3/FGL1 are co-expressed with PD-1. IMP321 and relatlimab are promising monoclonal antibodies targeting LAG3 in melanoma. The clinical use of anti-FGL1 antibodies has not been reported. Finally, high FGL1 and LAG3 expression induces EGFR-TKI and gefitinib resistance, and anti-PD-1 therapy resistance, respectively. We present a comprehensive overview of the role of LAG3/FGL1 in cancer, suggesting novel anti-tumor therapy strategies.
Futility in Transcatheter Aortic Valve Implantation: A Search for Clarity
Although transcatheter aortic valve implantation (TAVI) has revolutionised the landscape of treatment for aortic stenosis, there exists a cohort of patients where TAVI is deemed futile. Among the pivotal high-risk trials, one-third to half of patients either died or received no symptomatic benefit from the procedure at 1 year. Futility of TAVI results in the unnecessary exposure of risk for patients and inefficient resource utilisation for healthcare services. Several cardiac and extra-cardiac conditions and frailty increase the risk of mortality despite TAVI. Among the survivors, these comorbidities can inhibit improvements in symptoms and quality of life.
However, certain conditions are reversible with TAVI (e.g. functional mitral regurgitation), attenuating the risk and improving outcomes. Quantification of disease severity, identification of reversible factors and a systematic evaluation of frailty can substantially improve risk stratification and outcomes. This review examines the contribution of pre-existing comorbidities towards futility in TAVI and suggests a systematic approach to guide patient evaluation.
Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy
Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous.
With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas.
In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.
Computational Methods for Single-Cell Imaging and Omics Data Integration
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels.
Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive.
This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
Procedure-Related Access Site Pain Multimodal Management following Percutaneous Cardiac Intervention: A Randomized Control Trial
Methods: 137 patients who underwent PCI procedure via radial artery were randomly assigned (1 : 1) to the control (CG, n = 68) and intervention (IG, n = 65) groups. IG received MPM (paracetamol, ibuprofen, and the arm physiotherapy), CG received pain medication “as needed.” Outcomes were assessed immediately after, 2, 12, 24, and 48 h, 1 week, and 1 and 3 months after PCI. The primary outcome was A-S pain prevalence and pain intensity numeric rating scale (NRS) 0-10.
Results: Results showed that A-S pain prevalence during the 3-month follow-up period was decreasing. Statistically significant difference between the groups (CG versus IG) was after 24 h (41.2% versus 18.5, p=0.005), 48 h (30.9% versus 1.5%, p ≤ 0.001), 1 week (25% versus 10.8%, p=0.042), 1 month (23.5% versus 7.7%, p=0.017) after the procedure. The mean of the highest pain intensity was after 2 h (IG-2.17 ± 2.07; CG-3.53 ± 2.69) and the lowest 3 months (IG-0.02 ± 0.12; CG-0.09 ± 0.45) after the procedure. A-S pain intensity mean scores were statistically significantly higher in CG during the follow-up period (Wilks’ λ = 0.84 F (7,125) = 3.37, p=0.002).