The cost-efficient techniques to evaluate the age-based structural heterogeneity and morphological analysis of multicellular tumor spheroids.
Tumor tissues being resistant to penetration of drug molecules causing hindrance to cancer therapy. Multicellular tumor spheroids (MCTSs) are used as an in-vitro tumor model and its detailed exploration is the need of the day. MCTSs were generated by liquid overlay technique, their penta-physical characteristics including diameter, cell number, volume per cell, viability status and estimated shell of viable and core of dead cells, were determined via confocal microscopy and haemocytometry. The growth of spheroids was linear over the first week but declined in the 2<sup>nd</sup> week. Compaction of spheroids occurs from day 3 to day 7, with the mature spheroids having a lower amount of extra cellular space compared to intracellular volume. Age-oriented growth of MCTSs provides a rationale to predict less rapid penetration as spheroids get older and could be correlated with in-vivo tumors to predict pharmaceutical and therapeutic intervention.
Authors
- Imran Tariq (ORCID: https://orcid.org/0000-0001-6662-8819)
- Sheikh Abdur Rashid (ORCID: https://orcid.org/0000-0003-2731-2138)
- Muhammad Asif Khan (ORCID: https://orcid.org/0000-0002-3572-3001)
- Shujaat Ahmad (ORCID: https://orcid.org/0000-0002-4449-6525)
- Abidullah
- Maqsood Ur Rehman (ORCID: https://orcid.org/0000-0002-3969-9555)
- Attiqa Naz (ORCID: https://orcid.org/0000-0002-6125-5643)
- Nauman Rahim Khan (ORCID: https://orcid.org/0000-0002-5698-3662)
- Jahangir Khan (ORCID: https://orcid.org/0000-0003-0292-0276)
- Aziz-Ur-Rahman
- Sumbal Kibria
Institutions
- Shaheed Benazir Bhutto University (PK)
- University of the Punjab (PK)
- Kohat University of Science and Technology (PK)
- Sarhad University of Science and Information Technology (PK)
- University of Manchester (GB)
- Abasyn University (PK)
- University of Malakand (PK)
Publication Details
- Journal
- PubMed
- Published
- 2025-05-15
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00