Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.10.19.619201

Mathematical modeling of the frozen zone dynamics: towards using thermal imagers in cryotherapy

Abstract

Using thermal imaging for cryoablation and cryotherapy is a convenient technique but it does not allow for visualization of what is inside. We model thermal field distribution which can help relate surface thermal field images to in-depth temperature field dynamics that show freezing and thawing processes dynamics. We consider the spatio-temporal distribution of temperature using examples of hydrogel and living tissue subjected to a cryoprobe. Freezing in the hydrogel is compared with the measurements using visual morphometry. Such studies can be useful for comparing in-vitro and in-vivo thermal field dynamics and for estimating the correct timing for cryoapplication.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ivakhnenko, O. V., Todrin, O. F., Globa, V. Y., Chyzh, M. O., Kovalov, G. O., Shevchenko, S. N.. 2024-10-22. Mathematical modeling of the frozen zone dynamics: towards using thermal imagers in cryotherapy. https://doi.org/10.1101/2024.10.19.619201

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

Cracking the code of co-authorship networks geo-temporally using interpretable machine learning

An exponential growth in the scientific literature necessitates the development of highly scalable computational tools that can effectively analyze and distill insights from complex, interconnected research landscapes. We introduce Distributed, Interpretable, and Scalable computing for Co-authorship Networks (DISCo-Net), a robust and scalable tool engineered to curate and examine large-scale co-authorship networks by harnessing the power of distributed computing and advanced relational database queries. We use DISCo-Net to analyze co-authorship networks derived from millions of papers in the life sciences and physical sciences over more than two decades. Using a range of deep learning approaches, we surprisingly found that pre-trained zero-shot embeddings from a sentence transformer better captured global co-authorship relationships than a complex graphical attention transformer. Even more surprisingly, a simple interpretable Term Frequency-Inverse Document Frequency (TF-IDF) model performed as well as the Bidirectional Encoder Representations from Transformers (BERT) model. Through topic modeling on TF-IDF document descriptors, we identified nine major research areas prevalent globally over the past 24 years and captured topic-specific shifting trends in scientific output. Our study draws an innovative parallel between collaborative research networks and genomic regulatory structures, applying genomics data analysis methodologies to uncover patterns in global scientific collaboration. This approach reveals interpretable alignments between research interests and human developmental stages, while also identifying emerging influential players in the global research landscape. The findings highlight potential far-reaching consequences of current funding challenges, particularly in the U.S., and offer actionable insights for optimizing resource allocation and fostering innovation in an interconnected global scientific community.

scientific communication and education↗

Sketchy understandings: Drawings reveal where students may need additional support to understand scale and abstraction in common representations of DNA

Visual representations in molecular biology tend to follow a set of shared conventions for using certain shapes and symbols to convey information about the size and structure of nucleotides, genes, and chromosomes. Understanding how and why biologists use these conventions to represent DNA is a key part of visual literacy in molecular biology. Visual literacy, which is the ability to read and interpret visual representations, encompasses a set of skills that are necessary for biologists to effectively use models to communicate about molecular structures that cannot be directly observed. To gauge students visual literacy skills, we conducted semi-structured interviews with undergraduate students who had completed at least a year of biology courses. We asked students to draw and interpret figures of nucleotides, genes, and chromosomes, and we analyzed their drawings for adherence to conventions for representing scale and abstraction. We found that 77% of students made errors in representing scale and 86% of students made errors in representing abstraction. We also observed about half of the students in our sample using the conventional shapes and symbols to represent DNA in unconventional ways. These unconventional sketches may signal an incomplete understanding of the structure and function of DNA. Our findings indicate that students may need additional instructional support to interpret the conventions in common representations of DNA. We highlight opportunities for instructors to scaffold visual literacy skills into their teaching to help students better understand visual conventions for representing scale and abstraction in molecular biology.

scientific communication and education↗