Shefi, O. | bioRxiv Science

bioRxiv2021

An engineered nanocomplex with Photodynamic and Photothermal synergistic properties for cancer treatment

Photodynamic therapy (PDT) and photothermal therapy (PTT) are promising therapeutic methods for cancer treatment; however, as single modality therapies, either PDT or PTT are still limited in their success rate. A dual application of both PDT and PTT, in a combined protocol, has gained immense interest. In this study, gold nanoparticles (AuNPs) are conjugated with a PDT agent, meso-tetrahydroxyphenylchlorin (mTHPC) photosensitizer, designed as nanotherapeutic agents that can activate a dual photodynamic/photothermal therapy. The AuNP-mTHPC complex is biocompatible, soluable, and photostable. PDT efficiency is high because of immediate reactive oxygen species (ROS) production upon mTHPC activation by the 650 nm laser which decreased mitochondrial membrane potential ({Delta}{Psi}m). Likewise, the AuNP-mTHPC complex is used as a photoabsorbing (PTA) agent for PTT, due to efficient plasmon absorption and excellent photothermal conversion characteristics of AuNPs under laser irradiation at 532 nm. Under the laser irradiation of a PDT/PTT combination, a twofold phototoxicity outcome follows, compared to PDT-only or PTT-only treatment. This indicates that PDT and PTT have synergistic effects together as a combined therapeutic method. Hence, applying our AuNP-mTHPC may be a potential treatment of cancer in the biomedical field. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/471725v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@55fe3corg.highwire.dtl.DTLVardef@1a6468dorg.highwire.dtl.DTLVardef@161ea64org.highwire.dtl.DTLVardef@1638c91_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2018

Axonal tree morphology and signal propagation dynamics improve neuronal classification

Classification of neurons into specific subtypes is essential for better understanding of brain function and information transmission. Despite continuous progress, there is still no consensus regarding categorizing neuron taxonomy into proper subtypes. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the general axonal projection layout. In this study, we support the use of a morphology-based classification approach, focusing on the axonal tree. We demonstrate that utilizing the geometrical parameters of axonal tree structures significantly improves neuronal classification compared to the dendritic tree classification. Furthermore, we used neuronal activity patterns to classify interneurons into subtypes as well. Simulations of the activity along ramified axonal trees indicate that the axonal branching geometry may yield diverse responses in different subtrees. The classification schemes introduced here can be utilized to robustly classify neuronal subtypes in a functionally relevant manner. Our results open the door for deducing functionality from anatomical data.

neuroscience↗