Machine Learning-Assisted SHG Morphometry Reveals Distinct Collagen Microarchitectures of Trabecular Bone and Fibrosis in Bone Marrow Biopsies
Zakhar P. Asaulenko, Anton A. Egorchev, Dmitry A. Peshekhonov, Leniz F. Nurullin, Anastasiia A. Melnikova, Alexander A. Rosin, Daria S. Vedischeva, Samat M. Shaidullin, Ilsaf I. Vafin, Anton S. Buchaka, Nikita S. Gladyshev, Maxim E. Fedchenko, Dmitry E. Chickrin, Yuriy A. Krivolapov, Dmitry V. Samig · 2026
Read Machine Learning-Assisted SHG Morphometry Reveals Distinct Collagen Microarchitectures of Trabecular Bone and Fibrosis in Bone Marrow Biopsies by Zakhar P. Asaulenko, Anton A. Egorchev, Dmitry A. Peshekhonov, Leniz F. Nurullin, Anastasiia A. Melnikova, Alexander A. Rosin, Daria S. Vedischeva, Samat M. Shaidullin, Ilsaf I. Vafin, Anton S. Buchaka, Nikita S. Gladyshev, Maxim E. Fedchenko, Dmitry E. Chickrin, Yuriy A. Krivolapov, Dmitry V. Samig (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13566618.txt.
Description
Collagen's weave in a bone marrow sample carries clues about scarring and bone change, yet pathologists rarely measure it. Zakhar Asaulenko and co-authors combine in 2026 a laser imaging method that lights collagen with machine learning measurement tools. They compared collagen in firm and crumbly bone beams and in scar tissue from marrow biopsies of myelofibrosis patients.
Scar collagen ran thinner and simpler than the collagen of firm bone, while crumbly bone branched wildly in short segments, a sign of remodeling. How trainees marked the training images swayed the results strongly. The team offers the workflow as a repeatable way to grade marrow change.
Catalog record
| CREATOR | Zakhar P. Asaulenko, Anton A. Egorchev, Dmitry A. Peshekhonov, Leniz F. Nurullin, Anastasiia A. Melnikova, Alexander A. Rosin, Daria S. Vedischeva, Samat M. Shaidullin, Ilsaf I. Vafin, Anton S. Buchaka, Nikita S. Gladyshev, Maxim E. Fedchenko, Dmitry E. Chickrin, Yuriy A. Krivolapov, Dmitry V. Samig |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 48 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |