In this paper, we demonstrate the development of plasmonically active PMMA optical fiber probes by the attachment of gold nanoparticles to the probe surface functionalized by means of flowing post-discharges from dielectric barrier discharge (DBD) plasmas for the first time. Polymer optical fiber (POF) probes (U shape to improve absorbance sensitivity) were subjected to reactive gas atmospheres in the post-discharge region of a coaxial DBD plasma reactor run at atmospheric pressure in different gases (Ar, Ar ? 10 % O 2 , O 2 , N 2 , N 2 ? 0.5 % H 2 ). Plasma treatments in Ar or N 2 gave rise to waterstable electrophilic functional groups on PMMA surface, whereas the amine groups generated by N 2 -containing plasmas were not stable. Subsequently, PMMA surfaces were treated with hexamethylene diamine (HMDA) to obtain stable amine groups through the reaction of electrophilic groups. Gold nanoflowers (AuNF, 37 nm, peak 570 nm) binding to the amine functionalized fiber probes was monitored in real-time by recording the optical absorbance changes at 570 nm with the help of a UV-vis spectrometer. Absorbance response from Ar or N 2 plasma treated probes are 100 and 60 times, respectively, that of untreated control probes. A 25 fold improvement in absorbance response was obtained for Ar plasma treated POF in comparison with only HMDA treated POF. The shelf life of the hence fabricated plasmonically active probes was found to be at least 3 months. In addition, plasmonic activity of U-bent fiber probes treated in Ar plasma is better than the conventional wet-chemical activation by environmentally hazardous acid pre-treatment approaches.Electronic supplementary material The online version of this article (

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. Significance: Accurate early diagnosis of malignant skin lesions is critical in providing adequate and timely treatment; unfortunately, initial clinical evaluation of similar-looking benign and malignant skin lesions can result in missed diagnosis of malignant lesions and unnecessary biopsy of benign ones. Aim: To develop and validate a label-free and objective image-guided strategy for the clinical evaluation of suspicious pigmented skin lesions based on multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy. Approach: We tested the hypothesis that maFLIM-derived autofluorescence global features can be used in machine-learning (ML) models to discriminate malignant from benign pigmented skin lesions. Clinical widefield maFLIM dermoscopy imaging of 41 benign and 19 malignant pigmented skin lesions from 30 patients were acquired prior to tissue biopsy sampling. Three different pools of global image-level maFLIM features were extracted: multispectral intensity, time-domain biexponential, and frequency-domain phasor features. The classification potential of each feature pool to discriminate benign versus malignant pigmented skin lesions was evaluated by training quadratic discriminant analysis (QDA) classification models and applying a leave-one-patient-out cross-validation strategy. Results: Classification performance estimates obtained after unbiased feature selection were as follows: 68% sensitivity and 80% specificity with the phasor feature pool, 84% sensitivity, and 71% specificity with the biexponential feature pool, and 84% sensitivity and 32% specificity with the intensity feature pool. Ensemble combinations of QDA models trained with phasor and biexponential features yielded sensitivity of 84% and specificity of 90%, outperforming all other models considered. Conclusions: Simple classification ML models based on time-resolved (biexponential and phasor) autofluorescence global features extracted from maFLIM dermoscopy images have the potential to provide objective discrimination of malignant from benign pigmented lesions. ML-assisted maFLIM dermoscopy could potentially assist with the clinical evaluation of suspicious lesions and the identification of those patients benefiting the most from biopsy examination.

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Predictive modeling holds great promise for improving personalized cancer treatment and efficiency of drug development. In recent years, deep learning (DL) has been extensively explored for drug response prediction (DRP), outperforming classical machine learning in prediction generalization to new data. Despite the considerable interest in DRP, no agreed-upon methodology for evaluating and comparing the diverse DL models yet exists. Existing papers generally demonstrate the performance of proposed models using cross-validation within a single cell line dataset and compare with baseline models of their choice, substantially limiting the scope and validity of model evaluation and comparison. In this work, we investigate the ability of DRP models for generalizing predictions across datasets of multiple drug screening studies, a more challenging scenario mimicking practical applications of DRP models. Five cell line datasets and six community DRP models with advanced DL architectures have been explored. Public cell line drug screening datasets have been curated and processed for this analysis, including CCLE, CTRP, GDSC1, GDSC2, and GCSI. For each dataset, the same preprocessing pipeline was used to generate cell line gene expressions, drug representations, and drug response values. The six DRP models include advanced architectures and feature engineering methods such as transformer, graph neural network, and image representation of tabular data. Systematic model curation and training have been applied, including consistent training and testing data splits across models and hyperparameter optimization (HPO). To cope with the large-scale model training and HPO, automatic workflows have been implemented and executed on high-performance computing systems. A 5-by-5 matrix of prediction scores, corresponding to the five datasets in both row and column dimensions, has been generated for each model, with off-diagonal values representing the cross-dataset generalization. Despite the advanced DL techniques, all models exhibit substantially inferior performance in cross-dataset analysis as compared with cross-validation within a single dataset. This result demonstrates the challenge of cross-dataset generalization for DRP and motivates the need for rigorous and systematic evaluation of DRP models, which simulates real-world applications. Citation Format: Alexander Partin, Thomas S. Brettin, Yitan Zhu, Jamie Overbeek, Oleksandr Narykov, Priyanka Vasanthakumari, Austin Clyde, Sara E. Jones, Satishkumar Ranganathan Ganakammal, Justin M. Wozniak, Andreas Wilke, Jamaludin Mohd-Yusof, Michael R. Weil, Alexander T. Pearson, Rick L. Stevens. Systematic evaluation and comparison of drug response prediction models: a case study of prediction generalization across cell lines datasets. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5380.

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