Biology subjects

Tecilla, M.

Publications and source records attributed to Tecilla, M..

3 recordsLinked to original sources

bioRxiv2024

Advanced Tissue Technologies of Blood-Brain Barrier Organoids as High Throughput Toxicity Readouts in Drug Development

Recent advancements in engineering Complex in vitro models (CIVMs) such as Blood-brain barrier (BBB) organoids offer promising platforms for preclinical drug testing. However, their application in drug development, and especially for the regulatory purposes of toxicity assessment, requires robust and reproducible techniques. Here, we developed an adapted set of orthogonal image-based tissue methods including hematoxylin and eosin staining (HE), immunohistochemistry (IHC), multiplex immunofluorescence (mIF), and Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) to validate CIVMs for drug toxicity assessments. We developed an artificial intelligence (AI) algorithm to increase the throughput and the reliability of histomorphologic evaluations of apoptosis for in vitro toxicity studies. Our data highlight the potential to integrate advanced morphology-based readouts such as histological techniques and digital pathology algorithms for use on CIVMs, as part of a standard preclinical drug development assessment. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/611987v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@190530eorg.highwire.dtl.DTLVardef@3cce5forg.highwire.dtl.DTLVardef@10ec3borg.highwire.dtl.DTLVardef@1051585_HPS_FORMAT_FIGEXP M_FIG C_FIG The graphical abstract was partially created with biorender.com. HighlightsO_LIAdvanced Complex in vitro models (CIVMs) like Blood-brain barrier (BBB) organoids show promise for preclinical drug testing. However, robust and reproducible techniques are crucial for the acceptance of CIVMs in drug development processes, especially for toxicity assessments which are highly regulated by health authorities. C_LIO_LIWe developed orthogonal image-based readouts on histological sections to enable the use of BBB organoids for future compound toxicity assessment. C_LIO_LIA newly established artificial intelligence (AI) algorithm provides automated and label-free detection of apoptotic cells in drug screening using BBB organoids and provides an alternative killing assay on single cell resolution (40x) to current standards. C_LI

pathology↗

bioRxiv2022

Modulation of motor vigour by expectation of reward probability trial-by-trial is preserved in healthy ageing and Parkinson's disease patients

Motor improvements, such as faster movement times or increased velocity, have been consistently associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigour dynamically remains undetermined. Here we investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial in healthy younger (HYA, 37) and older adults (HOA, 37), and in medicated Parkinsons Disease patients (PD, 20). We conducted an online study that coupled a standard one-armed bandit decision-making paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. Our results showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo on a trial-by-trial basis without modulating reaction times (RT). Using Bayesian linear mixed models, we demonstrated in HYA, HOA and PD a similar sensitivity (slope) of execution tempo to inferences about the reward probabilities, despite HOA and PD being generally slower than HYA (intercept). In a second experiment in HYA (39), we additionally showed that subjective inferences about credit assignment - whether lack of reward is associated with an incorrect decision or execution error - led to a similar modulation of motor vigour by reward expectation. Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster execution tempo, without affecting RT. We also provide novel evidence for a preserved sensitivity of motor vigour to inferences about the action-reward mapping in ageing and medicated PD. SIGNIFICANCE STATEMENTNavigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigour trial-by-trial in healthy younger and older adults, and in Parkinsons Disease patients on medication. We found an association between trial- by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding performance. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome invigorated motor performance. This positive bias was not compromised by age or Parkinsons disease.

neuroscience↗

bioRxiv2021

Computer-Assisted Mitotic Count Using a Deep Learning-based Algorithm Improves Inter-Observer Reproducibility and Accuracy in canine cutaneous mast cell tumors

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intra-observer discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying/classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, the computer-assisted review by pathologists may ensure reliability. In the present study we have compared partial (MC-ROI preselection) and full (additional visualization of MF candidate proposal and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MF and improving classification against imposters. The inter-observer consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study prove that computer assistance may lead to a more reproducible and accurate MCs in ccMCTs.

pathology↗