Digital design of minimally invasive endodontic access cavity

New minimally invasive endodontic cavities have been described and proposed to preserve dentin (and enamel) through strategic access, including point endodontic access cavity (PEAC). There is no consensus to what extent PEAC contributes to tooth's resistance to fracture, because there is no agreement on how PEAC should be performed. The purpose of the present study is to describe and classify four different types of PEACs and to examine if a dynamic navigation system /DNS) could allow planning and precisely executing these cavities in vitro. Forty TrueTooth TM Replica # 3-001 models, were randomly divided into four identical groups of ten and scanned using a cone bean computed tomography (OP-Maxio 300, Instrumentarium-Kavo, Finland). Then, four different access cavities were planned and performed by using DNS (Navident dynamic navigation system, ClaroNav, Toronto, ON, Canada). For each tooth, a different PEAC was designed to obtain endodontic access to the main mesio-buccal canal (MB1), resulting in a different location of the entry point on the occlusal surface of the tooth. Precision was evaluated by comparing deviation in the inclinations between the planned and real cavity. Data were recorded and statistically analyzed. DNS allowed preparation of minimally invasive "straight line" cavities, with some differences in the accuracy

Self-supervised deep learning for highly efficient spatial immunophenotyping

Background: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. / Methods: This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. / Findings: With 1% annotations (18–114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828–11,459 annotated cells (−0.002 to −0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. / Interpretation: By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. / Funding: This study was funded by the Royal Marsden/ ICR National Institute of Health Research Biomedical Research Centre

A robust computational investigation on C₆₀ fullerene nanostructure as a novel sensor to detect SCNˉ

This study explored on the adsorption properties and electronic structure of SCNˉ via density functional theory analysis on the exterior surfaces of C₆₀ and CNTs using B3LYP functional and 6-31G** standard basis set. Then adsorption of SCNˉ through nitrogen atom on the C60 fullerene is electrostatic (₋48.02 kJ molˉ1) in comparison with the C₅₉Al fullerene that shows covalently attached to fullerene surface (₋389.10 kJ mol̄ˉ1). Our calculations demonstrate that the SCNˉ adsorption on the pristine and Al-doped single-walled CNTs are ₋173.13 and ₋334.43 kJ molˉ1, indicating that the SCNˉ can be chemically bonded on the surface of Al-doped CNTs. Moreover, the adsorption of SCNˉ on the C₆₀ surface is weaker in comparison with C₅₉B, C₅₉Al, and C₅₉Ga systems but its electronic sensitivity improved in comparison with those of C₅₉B, C₅₉Al, and C₅₉Ga fullerenes. The evaluation of adsorption energy, energy gap, and dipole moment demonstrates that the pure fullerene can be exploited in the design practice as an SCNˉ sensor and C₅₉Al can be used for SCNˉ removal application

Biomarkers for site-specific response to neoadjuvant chemotherapy in epithelial ovarian cancer: relating MRI changes to tumour cell load and necrosis.

Funder: We acknowledge funding from Cancer Research UK BIDD grant C1353/A12762 and Cancer Research UK and Engineering and Physical Sciences Research Council support to the Cancer Imaging Centre at the Institute of Cancer Research and Royal Marsden Hospital in association with the Medical Research Council and Department of Health C1060/A10334, C1060/A16464 and National Health Service funding to the National Institute for Health Research Biomedical Research Centres at Royal Marsden Hospital/Institute of Cancer Research and Cambridge, Experimental Cancer Medicine Centres, the Clinical Research Facility in Imaging, and the Cancer Research Network. We are also grateful for financial support from Addenbrooke’s Charitable Trust. The views expressed in this publication are those of the author(s) and not necessarily those of the National Health Service, the National Institute for Health Research or the Department of Health.BACKGROUND: Diffusion-weighted magnetic resonance imaging (DW-MRI) potentially interrogates site-specific response to neoadjuvant chemotherapy (NAC) in epithelial ovarian cancer (EOC). METHODS: Participants with newly diagnosed EOC due for platinum-based chemotherapy and interval debulking surgery were recruited prospectively in a multicentre study (n = 47 participants). Apparent diffusion coefficient (ADC) and solid tumour volume (up to 10 lesions per participant) were obtained from DW-MRI before and after NAC (including double-baseline for repeatability assessment in n = 19). Anatomically matched lesions were analysed after surgical excision (65 lesions obtained from 25 participants). A trained algorithm determined tumour cell fraction, percentage tumour and percentage necrosis on histology. Whole-lesion post-NAC ADC and pre/post-NAC ADC changes were compared with histological metrics (residual tumour/necrosis) for each tumour site (ovarian, omental, peritoneal, lymph node). RESULTS: Tumour volume reduced at all sites after NAC. ADC increased between pre- and post-NAC measurements. Post-NAC ADC correlated negatively with tumour cell fraction. Pre/post-NAC changes in ADC correlated positively with percentage necrosis. Significant correlations were driven by peritoneal lesions. CONCLUSIONS: Following NAC in EOC, the ADC (measured using DW-MRI) increases differentially at disease sites despite similar tumour shrinkage, making its utility site-specific. After NAC, ADC correlates negatively with tumour cell fraction; change in ADC correlates positively with percentage necrosis. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT01505829

The T cell differentiation landscape is shaped by tumour mutations in lung cancer

Tumour mutational burden (TMB) predicts immunotherapy outcome in non-small cell lung cancer (NSCLC), consistent with immune recognition of tumour neoantigens. However, persistent antigen exposure is detrimental for T cell function. How TMB affects CD4 and CD8 T cell differentiation in untreated tumours and whether this affects patient outcomes is unknown. Here, we paired high-dimensional flow cytometry, exome, single-cell and bulk RNA sequencing from patients with resected, untreated NSCLC to examine these relationships. TMB was associated with compartment-wide T cell differentiation skewing, characterized by loss of TCF7-expressing progenitor-like CD4 T cells, and an increased abundance of dysfunctional CD8 and CD4 T cell subsets with strong phenotypic and transcriptional similarity to neoantigen-reactive CD8 T cells. A gene signature of redistribution from progenitor-like to dysfunctional states was associated with poor survival in lung and other cancer cohorts. Single-cell characterization of these populations informs potential strategies for therapeutic manipulation in NSCLC

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting