Surface plasmons have proven their ability to boost the sensitivity of mid-infrared hyperspectral imaging by enhancing light-matter interactions. Surface phonons, a counterpart technology to plasmons, present unclear contributions to hyperspectral imaging. Here, we investigate this by developing a plasmon-phonon hyperspectral imaging system that uses asymmetric cross-shaped nanoantennas composed of stacked plasmon-phonon materials. The phonon modes within this system, controlled by light polarization, capture molecular refractive index intensity and lineshape features, distinct from those observed with plasmons, enabling more precise and sensitive molecule identification. In a deep learning–assisted imaging demonstration of severe acute respiratory syndrome coronavirus (SARS-CoV), phonons exhibit enhanced identification capabilities (230,400 spectra/s), facilitating the de-overlapping and observation of the spatial distribution of two mixed SARS-CoV spike proteins. In addition, the plasmon-phonon system demonstrates increased identification accuracy (93%), heightened sensitivity, and enhanced detection limits (down to molecule monolayers). These findings extend phonon polaritonics to hyperspectral imaging, promising applications in imaging-guided molecule screening and pharmaceutical analysis. 

Surface plasmons-phonons for mid-infrared hyperspectral imaging

Abstract The application of infrared thermography technology (IRT) in flap has become a major focus of research, as it provides a non‐invasive, real‐time, and quantitative approach for monitoring flap perfusion. In this regard, we conducted a comprehensive visualization and scientometric analysis to systematically summarize and discuss the current state of research in this field. We systematically reviewed publications on the application of IRT in flap procedures from 1999 to 2022, using the Web of Science Core Collection (WoSCC). Through scientometric analysis, we examined annual trends, affiliations, countries, journals, authors, and their relationships, providing insights into current hotspots and future developments in this area. We analysed 522 English studies and found a steady increase in annual publications. The United States and Germany had the highest publication rates, with Beth Israel Deaconess Medical Center and Shanghai Jiaotong University being leading institutions. Notably, Lee BT and Alex Keller emerged as influential authors in this field. Compared to existing techniques, infrared‐based technology offers significant advantages for non‐invasive monitoring of flap perfusion, including simplicity of operation and objective results. Future trends should focus on interdisciplinary collaborations to develop new infrared devices and achieve intelligent image processing, enabling broader application in various clinical scenarios. This bibliometric study summarizes the progress and landscape of research on ‘the Application of infrared thermography technology in flap’ over the past two decades, providing valuable insights and serving as a reliable reference to drive further advancements and spark researchers' interest in this field. 

The application of infrared thermography technology in flap: A perspective from bibliometric and visual analysis

This study presents a robust approach for the classification of ovarian cancer subtypes through the integration of deep learning and k-nearest neighbor (KNN) methods. The proposed model leverages the powerful feature extraction capabilities of EfficientNet-B0, utilizing its deep features for subsequent fine-grained classification using the fine-KNN approach. The UBC-OCEAN dataset, encompassing histopathological images of five distinct ovarian cancer subtypes, namely, high-grade serous carcinoma (HGSC), clear-cell ovarian carcinoma (CC), endometrioid carcinoma (EC), low-grade serous carcinoma (LGSC), and mucinous carcinoma (MC), served as the foundation for our investigation. With a dataset comprising 725 images, divided into 80% for training and 20% for testing, our model exhibits exceptional performance. Both the validation and testing phases achieved 100% accuracy, underscoring the efficacy of the proposed methodology. In addition, the area under the curve (AUC), a key metric for evaluating the model's discriminative ability, demonstrated high performance across various subtypes, with AUC values of 0.94, 0.78, 0.69, 0.92, and 0.94 for MC. Furthermore, the positive likelihood ratios (LR 

Deep fine-KNN classification of ovarian cancer subtypes using efficientNet-B0 extracted features: a comprehensive analysis

This tutorial reviews the rapidly growing field of optical photothermal infrared (O-PTIR) spectroscopy and chemical imaging. O-PTIR is an infrared super-resolution measurement technique where a shorter wavelength visible probe is used to measure and map infrared (IR) absorption with spatial resolution up to 30× better than conventional techniques such as Fourier transform infrared and direct IR laser imaging systems. This article reviews key limitations of conventional IR instruments, the O-PTIR technology breakthroughs, and their origins that have overcome the prior limitations. This article also discusses recent developments in expanding multi-modal O-PTIR approaches that enable complementary Raman spectroscopy and fluorescence microscopy imaging, including wide-field O-PTIR imaging with fluorescence-based detection of IR absorption. Various practical subjects are covered, including sample preparation techniques, optimal measurement configurations, use of IR tags/labels and techniques for data analysis, and visualization. Key O-PTIR applications are reviewed in many areas, including biological and biomedical sciences, environmental and microplastics research, (bio)pharmaceuticals, materials science, cultural heritage, forensics, photonics, and failure analysis. 

A tutorial on optical photothermal infrared (O-PTIR) microscopy

The promising horizon of deep learning and artificial intelligence in flap monitoring.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being lever-aged for digital histopathology. Optical photothermal infrared (O-PTIR) is based on vibrational absorbance imaging using a pump-probe architecture capable of a 10 × enhancement in spatial resolution relative to FTIR imaging. This allows truly sub-cellular spectroscopic investigation of tissue at biochemically important ﬁngerprint wavelengths. Modern histopathologic identiﬁcation of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming, subjective, and requires extensive expertise. In this paper, we present the ﬁrst label-free automated histological classiﬁcation of ovarian tissue sub-types using MIRSI. We demonstrate that enhanced resolution of sub-cellular features, combined with spectroscopic information, enables reliable classiﬁcation ( 0 . 98 AUC) of ovarian cell sub-types. Moreover, we present statistically robust validation from 74 patient samples with over 60 million data points. This demonstrates that sub-cellular resolution from ﬁve wavenumbers is suﬃcient to outperform state-of-the-art diﬀraction-limited techniques from up to 374 diﬀerent wavenumbers. O-PTIR also performs measurements in back-reﬂection geometry, opening the door to future in vivo studies on glass slides.

Leveraging high-resolution spatial features in mid-infrared spectroscopic imaging to classify tissue subtypes in ovarian cancer

<h3>Background and Importance</h3> Excessive waiting time is one of the main causes of patient dissatisfaction in oncologic daily care unit (DCU). Lean management, dose banding, advanced prescription and automatisation are usually used in our hospital to improve patient care pathway. In our adult DCU (&gt;26 000 patients/years), patients have to wait for their treatment less than an hour. <h3>Aim and Objectives</h3> The aim of this work is to reassess the time of availability in this DCU and to identify the factors influencing this time. <h3>Material and Methods</h3> It is an ambispective monocentric study in which human factors (n=2), equipment factors (n=7), organisational factors (n=4), productivity factors (n=16) and time-related factors (n=6) were recorded randomly between September 2021 and April 2022 (i.e. 15 days studied). Data were also extracted from CHIMIO<sup>®</sup> software and from our institutional ‘LEAN tool’ for real-time monitoring of patients in oncologic DCU, in order to calculate time between the prescription of the day and the dispensation of the treatment. <h3>Results</h3> The average number of patients and preparations manufactured per day were respectively 105 (+/-7) and 146 (+/-12); 52% of these preparations prepared the day before. The average number of preparations not prescribed in advance is 49 [18-62] (34%) for an average number of 31 patients [14-43] (30%). The average time to availability was 54 min (+/- 16) with a median of 60 min. On average, 12 [0-24] patients per day waited more than an hour after the prescription with a maximum waiting time of 360 min. Four days (27%) were identified with an average dispensing time greater than 60 min. During these critical days, a percentage of anticipated preparations less than 50%, with a high number of prescriptions (&gt;30 patients) and particularly before 9:45 a.m. or between 12:00 and 14:00 p.m. were observed. We noticed also a higher productivity ([174-214] preparations), the lack of coordination (2 of 4 days), or additional productions (analgesic syringe preparations). <h3>Conclusion and Relevance</h3> Main impacting factors seem to be human factors and productivity. Time to availability became an essential quality indicator of our compounding anti-cancer unit. This study showed that our working procedures are efficient for a majority of patients, but not for all. <h3>References and/or Acknowledgements</h3> <h3>Conflict of Interest</h3> No conflict of interest 

/pdf/3pc-018-time-to-availability-of-injectable-anticancer-drugs-3v12vid3.pdf

3PC-018 Time to availability of injectable anticancer drugs for outpatients: reassessment in a French comprehensive cancer centre

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming and subjective and requires extensive expertise. This paper presents the first label-free, quantitative, and automated histological recognition of ovarian tissue subtypes using a new MIRSI technique. This optical photothermal infrared (O-PTIR) imaging technique provides a 10× enhancement in spatial resolution relative to prior instruments. It enables sub-cellular spectroscopic investigation of tissue at biochemically important fingerprint wavelengths. We demonstrate that the enhanced resolution of sub-cellular features, combined with spectroscopic information, enables reliable classification of ovarian cell subtypes achieving a classification accuracy of 0.98. Moreover, we present a statistically robust analysis from 78 patient samples with over 60 million data points. We show that sub-cellular resolution from five wavenumbers is sufficient to outperform state-of-the-art diffraction-limited techniques with up to 235 wavenumbers. We also propose two quantitative biomarkers based on the relative quantities of epithelia and stroma that exhibit efficacy in early cancer diagnosis. This paper demonstrates that combining deep learning with intrinsic biochemical MIRSI measurements enables quantitative evaluation of cancerous tissue, improving the rigor and reproducibility of histopathology.

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.

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Leveraging high-resolution spatial features in mid-infrared spectroscopic imaging to classify tissue subtypes in ovarian cancer

3PC-018 Time to availability of injectable anticancer drugs for outpatients: reassessment in a French comprehensive cancer centre

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.