Developing photoactive nanosystems against microbial infection and its therapeutic application is compromised by the lack of suitable materials or molecular dyes activatable at biofriendly NIR light. In this direction, the upconverting nanoparticles based on core–shell lanthanide‐doped nanoclusters are developed synthetically to achieve a broad range of NIR‐active phototherapeutic antimicrobial agents. This review illustrates antimicrobial photodynamic therapy (aPDT) and multimodal therapy by NIR photoirradiation, generated by lanthanum doped upconverting nanoparticles (UCNPs). The objective herein is to discuss the insights in developing the UCNPs for designing efficient aPDTs and their efficacies against emerging antibiotic‐resistant bacterial colonies and their biofilms, drug‐resistant fungi, and viruses. The biosafety and biocompatibility of UCNPs at both in vitro and in vivo level are also presented in detail. Finally, our perspectives on the ways of future material engineering needed for the effective translation into their real‐world applications are also commented.

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Background: Pericardial adipose tissue (PAT) may represent a novel risk marker for cardiovascular disease. However, absence of rapid radiation-free PAT quantification methods has precluded its examination in large cohorts.Objectives: We developed a fully automated quality-controlled tool for cardiovascular magnetic resonance (CMR) PAT quantification in the UK Biobank (UKB).Methods: Image analysis comprised contouring an en-bloc PAT area on four-chamber cine images. We created a ground truth manual analysis dataset randomly split into training and test sets. We built a neural network for automated segmentation using a Multi-residual U-net architecture with incorporation of permanently active dropout layers to facilitate quality control of the model's output using Monte Carlo sampling. We developed an in-built quality control feature, which presents predicted Dice scores. We evaluated model performance against the test set (n = 87), the whole UKB Imaging cohort (n = 45,519), and an external dataset (n = 103). In an independent dataset, we compared automated CMR and cardiac computed tomography (CCT) PAT quantification. Finally, we tested association of CMR PAT with diabetes in the UKB (n = 42,928).Results: Agreement between automated and manual segmentations in the test set was almost identical to inter-observer variability (mean Dice score = 0.8). The quality control method predicted individual Dice scores with Pearson r = 0.75. Model performance remained high in the whole UKB Imaging cohort and in the external dataset, with medium–good quality segmentation in 94.3% (mean Dice score = 0.77) and 94.4% (mean Dice score = 0.78), respectively. There was high correlation between CMR and CCT PAT measures (Pearson r = 0.72, p-value 5.3 ×10−18). Larger CMR PAT area was associated with significantly greater odds of diabetes independent of age, sex, and body mass index.Conclusions: We present a novel fully automated method for CMR PAT quantification with good model performance on independent and external datasets, high correlation with reference standard CCT PAT measurement, and expected clinical associations with diabetes.

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