Elucidating Bacterial Spore Dynamics through Lanthanide-Enhanced Live Imaging.

Identifying and distinguishing dormant and active bacterial spores are vital for biosecurity, food safety, and space exploration. Yet, there is a lack of simple, quick, and nondestructive methods to achieve this. The common Schaeffer-Fulton method is both sample-destructive and requires significant operator involvement. In this study, we employed lanthanide-beta-diketonate complexes to directly observe both dormant and germinated single spores. Staining is instantaneous and requires minimal sample processing. The complex stains areas outside the core of dormant spores, leaving the core hollow and nonfluorescent. However, upon germination, the complex enters the core, making it brightly fluorescent. This difference was noted in five bacterial species including Bacillus, Clostridium, and Clostridioides. Various lanthanides and beta-diketonates can be mixed to form a range of spore-visualizing complexes. Due to their low toxicity, these complexes allow for live imaging of single germinating spores. We demonstrate low-cost imaging using a USB microscope as well as imaging of spores in milk matrices. This method provides a valuable tool for studying bacterial spores.

A design of experiments screen reveals that Clostridium novyi-NT spore germinant sensing is stereoflexible for valine and its analogs.

Although Clostridium novyi-NT is an anti-cancer bacterial therapeutic which germinates within hypoxic tumors to kill cancer cells, the actual germination triggers for C. novyi-NT are still unknown. In this study, we screen candidate germinants using combinatorial experimental designs and discover by serendipity that D-valine is a potent germinant, inducing 50% spore germination at 4.2 mM concentration. Further investigation revealed that five D-valine analogs are also germinants and four of these analogs are enantiomeric pairs. This stereoflexible effect of L- and D-amino acids shows that spore germination is a complex process where enantiomeric interactions can be confounders. This study also identifies L-cysteine as a germinant, and hypoxanthine and inosine as co-germinants. Several other amino acids promote (L-valine, L-histidine, L-threonine and L-alanine) or inhibit (L-arginine, L-glycine, L-lysine, L-tryptophan) germination in an interaction-dependent manner. D-alanine inhibits all germination, even in complex growth media. This work lays the foundation for improving the germination efficacy of C. novyi-NT spores in tumors.

Deep Learning Fuzzy Inference: An Interpretable Model for Detecting Indirect Immunofluorescence Patterns Associated with Nasopharyngeal Cancer.

The detection of serum Epstein-Barr virus antibodies by immunofluorescence assay (IFA) is considered the gold standard screening test for nasopharyngeal cancer (NPC) in high-risk populations. Given the high survival rate after early detection in asymptomatic patients, compared to the poor prognosis in patients with late-stage NPC, screening using IFA has tremendous potential for saving lives in the general population. However, IFA requires visual interpretation of cellular staining patterns by trained pathology staff, making it labor intensive and hence nonscalable. In this study, an automated fuzzy inference (FI) system achieved high agreement with a human IFA expert in identifying cellular patterns associated with NPC (κ = 0.82). The integration of a deep learning module into FI further improved the performance of FI (κ = 0.90) and reduced the number of uncertain cases that required manual evaluation. The performance of the resulting hybrid model, termed deep learning FI (DeLFI), was then evaluated with a separate testing set of clinical samples. In this clinical validation, DeLFI outperformed human evaluation on the area under the curve (0.926 versus 0.821) and closely matched human performance on Youden J index (0.81 versus 0.80). Data from this study indicate that the combination of deep learning with FI in DeLFI has the potential to improve the scalability and accuracy of NPC detection.

Lipid-polymer hybrid nanoparticles as a new generation therapeutic delivery platform: a review.

Lipid-polymer hybrid nanoparticles (LPNs) are core-shell nanoparticle structures comprising polymer cores and lipid/lipid-PEG shells, which exhibit complementary characteristics of both polymeric nanoparticles and liposomes, particularly in terms of their physical stability and biocompatibility. Significantly, the LPNs have recently been demonstrated to exhibit superior in vivo cellular delivery efficacy compared to that obtained from polymeric nanoparticles and liposomes. Since their inception, the LPNs have advanced significantly in terms of their preparation strategy and scope of applications. Their preparation strategy has undergone a shift from the conceptually simple two-step method, involving preformed polymeric nanoparticles and lipid vesicles, to the more principally complex, yet easier to perform, one-step method, relying on simultaneous self-assembly of the lipid and polymer, which has resulted in better products and higher production throughput. The scope of LPNs' applications has also been extended beyond single drug delivery for anticancer therapy, to include combinatorial and active targeted drug deliveries, and deliveries of genetic materials, vaccines, and diagnostic imaging agents. This review details the current state of development for the LPNs preparation and applications from which we identify future research works needed to bring the LPNs closer to its clinical realization.