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A group from Department of Gastroenterological surgery, Gifu University School of Medicine, Gifu, Japan, has reported about differences in glycocalyx morphology and composition in frozen and formalin-fixed liver tumor sections.
https://www.sciencedirect.com/science/article/pii/S0344033824005715?via%3Dihub

It was found that surprisingly, there were many differences in lectin staining findings between frozen and formali-fixed tissue preparations, suggesting that FFPE processing affects lectin receptors, rendering frozen sections more reliable for accurate lectin staining.

From the lectin staining in frozen sections, the followings were found:
Normal hepatocytes showed strongly positive staining for PNA, RCA I, SBA, UEA I, GSL I, succinylated WGA, ECL, GSL II, STL, and VVL.
In contrast, hepatocellular carcinoma samples were strongly positive for DSL and GSL II.
Normal hepatocytes were positive for multiple GalNAc-related lectins (PNA, SBA, GSL I, and VVL), but these were not detected in hepatocellular carcinoma samples.

It was also found that DBA and UEA I staining were strongly positive in liver metastases from colorectal cancer and in melanoma liver metastases, ConA, WGA, succinylated WGA, and GSL II were strongly expressed.

It is well-known that the 2024 Nobel Prize in Physics was awarded to two scientists who contributed to neural networks.
When I was working at Fujitsu, I read papers written by Prof. Shun-ichi Amari, a pioneer in neural networks, and I remembered that I was passionately discussing the possibilities of future computers with my colleagues, and I was surprised that Prof. Amari was not included as one of the recipients of the award. I felt very uncomfortable with it.
By the way, with the power of computers at that time, it was impossible to realize a neural network as software, so I and my colleagues were thinking about building it as hardware.

Prof. Amari’s pioneering researches:
1. A Theory of Adaptive Pattern Classifiers、1967
2. Characteristics of randomly connected threshold-element networks and network systems、1971
3. Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements、1972
4. Characteristics of Random Nets of Analog Neuron-Like Elements、1972

A group from UMR INSERM 1308 CAPTuR, Faculty of Medicine, University of Limoges, Limoges, France has reported about a new approach which enables of accurately detecting Canser Stem Cells (CSCs).
https://www.nature.com/articles/s41416-024-02839-9

A combination of plant lectins (MIX: UEA-1 and GSL-I) was used as a new approach to detect CSCs from a heterogeneous non-small cell lung cancer (NSCLC) population.

It was demonstrated that the combination of those lectins recognizing glycosylated pattern exposed on CSCs were more efficient for detecting and sorting CSCs than CD133.
CD133 is know as a CSCs marker.

Department of Biological Physical Chemistry, Instituto de Química Física Blas Cabrera, Consejo Superior de Investigaciones Científicas, Madrid, Spain has reported about the recognition of hypermucoviscous Klebsiella pneumoniae clinical isolates by innate immune lectins of the Siglec and galectin families.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324429/

Klebsiella pneumoniae is an opportunistic bacterium that frequently colonizes the nasopharynx and gastrointestinal tract and can also cause severe infections when invading other tissues, particularly in immunocompromised individuals.
There were striking differences in the recognition of those bacteria by sialic acid-, galactose- and mannose-specific lectins as shown below.

A group from Department of Biotechnology, Delft University of Technology, The Netherlands has reported about extracellular polymeric substances (EPS) produced by bacteria used in biological wastewater treatment.
https://pubs.acs.org/doi/epdf/10.1021/acsestwater.4c00247

Unlike DNA replication or protein translation, glycan biosynthesis is not directed by a preexisting templatemolecule. The production of glycans is decided by a few factors: the biosynthetic machinery, the available nucleotidesugars, and signals from the intracellular and extracellular environment.
Thus, the presence of glycans is dynamic and is influenced by both geneticand environmental factors. Therefore, under such conditions, it is tremendously challenging to study the glycan composition in EPS.

Typically, approaches for studying glycoproteins in environ-mental samples involve identifying individual glycan structuresand further characterizing the proteins with mass spectrometry.
On the other hands, lectin microarrays offer a high throughput examination of the glycans on the proteinsurfaces, enabling a broader screening of a possible proteinglycosylation pattern.
So, they concluded that combining both approaches can provide acomprehensive understanding of glycoproteins, bridging the gapbetween structural characterization and functional implications