Non-invasive Optical Technique Research Articles

Evaluation of Diffuse Reflectance Spectroscopy Vegetal Phantoms for Human Pigmented Skin Lesions.

Pigmented skin lesions have increased considerably worldwide in the last years, with melanoma being responsible for 75% of deaths and low survival rates. The development and refining of more efficient non-invasive optical techniques such as diffuse reflectance spectroscopy (DRS) is crucial for the diagnosis of melanoma skin cancer. The development of novel diagnostic approaches requires a sufficient number of test samples. Hence, the similarities between banana brown spots (BBSs) and human skin pigmented lesions (HSPLs) could be exploited by employing the former as an optical phantom for validating these techniques. This work analyses the potential similarity of BBSs to HSPLs of volunteers with different skin phototypes by means of several characteristics, such as symmetry, color RGB tonality, and principal component analysis (PCA) of spectra. The findings demonstrate a notable resemblance between the attributes concerning spectrum, area, and color of HSPLs and BBSs at specific ripening stages. Furthermore, the spectral similarity is increased when a fiber-optic probe with a shorter distance (240 µm) between the source fiber and the detector fiber is utilized, in comparison to a probe with a greater distance (2500 µm) for this parameter. A Monte Carlo simulation of sampling volume was used to clarify spectral similarities.

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study.

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contribute significantly to global cancer mortality. Routine detection methods, including medical imaging, endoscopic examination, and pathological biopsy, often suffer from drawbacks such as low sensitivity and laborious and complex procedures. Raman spectroscopy is a non-invasive and label-free optical technique that provides highly sensitive biomolecular information to facilitate effective tumor identification. In this work, we report the use of fiber-optic Raman spectroscopy for the accurate and rapid diagnosis of gastric and esophageal cancers. Using a database of 14,000 spectra from 140 ex vivo tissue pieces of both tumor and normal tissue samples, we compare the random forest (RF) and our established Euclidean distance Raman spectroscopy (EDRS) model. The RF analysis achieves a sensitivity of 85.23% and an accuracy of 83.05% in diagnosing gastric tumors. The EDRS algorithm with improved diagnostic transparency further increases the sensitivity to 92.86% and accuracy to 89.29%. When these diagnostic protocols are extended to esophageal tumors, the RF and EDRS models achieve accuracies of 71.27% and 93.18%, respectively. Finally, we demonstrate that fewer than 20 spectra are sufficient to achieve good Raman diagnostic accuracy for both tumor tissues. This optimizes the balance between acquisition time and diagnostic performance. Our work, although conducted on ex vivo tissue models, offers valuable insights for in vivo in situ endoscopic Raman diagnosis of gastric and esophageal cancer lesions in the future. Our study provides a robust, rapid, and convenient method as a new paradigm in in vivo endoscopic medical diagnostics that integrates spectroscopic techniques and aRaman probe for detecting upper gastrointestinal malignancies.

Luciferase-Loaded Calcium Phosphate Nanoparticles for Persistent Bioluminescence Imaging of Orthotopic Breast Tumors.

Bioluminescence imaging (BLI) is an important noninvasive optical imaging technique that has been widely used to monitor many biological processes due to its high sensitivity, resolution, and signal-to-noise ratio. However, the BLI technique based on the firefly luciferin-luciferase system is limited by the expression of exogenous luciferase and the short half-life of firefly luciferin, which pose challenges for long-term tracking in vivo. To solve the problems, here we rationally designed an intelligent strategy for persistent BLI in tumors by combining luciferase-loaded calcium phosphate nanoparticles (Luc@CaP NPs) to provide luciferase and the probe Cys(SEt)-Lys-CBT (CKCBT) to slowly produce the luciferase substrate amino luciferin (Am-luciferin). Luc@CaP NPs constructed with CaP as a carrier could enable luciferase activity to be maintained in vivo for at least 12 h. And compared to the conventional substrate luciferin, CKCBT apparently prolonged the BL time by up to 2 h through GSH-induced intracellular self-assembly and subsequent protease degradation-induced release of Am-luciferin. We anticipate that this strategy could be applied for clinical translation in more disease diagnosis and treatment in the near future.

Raman spectroscopy - a visit to the literature on plant, food, and agricultural studies.

Raman spectroscopy, a fast, non-invasive, and label-free optical technique, has significantly advanced plant and food studies and precision agriculture by providing detailed molecular insights into biological tissues. Utilizing the Raman scattering effect generates unique spectral fingerprints that comprehensively analyze tissue composition, concentration, and molecular structure. These fingerprints are obtained without chemical additives or extensive sample preparation, making Raman spectroscopy particularly suitable for in-field applications. Technological enhancements such as surface-enhanced Raman scattering, Fourier-transform-Raman spectroscopy, and chemometrics have increased Raman spectroscopy sensitivity and precision. These and other advancements enable real-time monitoring of compound translocation within plants and improve the detection of chemical and biological contaminants, essential for food safety and crop optimization. Integrating Raman spectroscopy into agronomic practices is transformative and marks a shift toward more sustainable farming activities. It assesses crop quality - as well as the quality of the food that originated from crop production - early plant stress detection and supports targeted breeding programs. Advanced data processing techniques and machine learning integration efficiently handle complex spectral data, providing a dynamic and detailed view of food conditions and plant health under varying environmental and biological stresses. As global agriculture faces the dual challenges of increasing productivity and sustainability, Raman spectroscopy stands out as an indispensable tool, enhancing farming practices' precision, food safety, and environmental compatibility. This review is intended to select and briefly comment on outstanding literature to give researchers, students, and consultants a reference for works of literature in Raman spectroscopy mainly focused on plant, food, and agronomic sciences. © 2024 Society of Chemical Industry.

Neuroplasticity of Speech-in-Noise Processing in Older Adults Assessed by Functional Near-Infrared Spectroscopy (fNIRS)

Functional near-infrared spectroscopy (fNIRS), a non-invasive optical neuroimaging technique that is portable and acoustically silent, has become a promising tool for evaluating auditory brain functions in hearing-vulnerable individuals. This study, for the first time, used fNIRS to evaluate neuroplasticity of speech-in-noise processing in older adults. Ten older adults, most of whom had moderate-to-mild hearing loss, participated in a 4-week speech-in-noise training. Their speech-in-noise performances and fNIRS brain responses to speech (auditory sentences in noise), non-speech (spectrally-rotated speech in noise) and visual (flashing chequerboards) stimuli were evaluated pre- (T0) and post-training (immediately after training, T1; and after a 4-week retention, T2). Behaviourally, speech-in-noise performances were improved after retention (T2 vs. T0) but not immediately after training (T1 vs. T0). Neurally, we intriguingly found brain responses to speech vs. non-speech decreased significantly in the left auditory cortex after retention (T2 vs. T0 and T2 vs. T1) for which we interpret as suppressed processing of background noise during speech listening alongside the significant behavioural improvements. Meanwhile, functional connectivity within and between multiple regions of temporal, parietal and frontal lobes was significantly enhanced in the speech condition after retention (T2 vs. T0). We also found neural changes before the emergence of significant behavioural improvements. Compared to pre-training, responses to speech vs. non-speech in the left frontal/prefrontal cortex were decreased significantly both immediately after training (T1 vs. T0) and retention (T2 vs. T0), reflecting possible alleviation of listening efforts. Finally, connectivity was significantly decreased between auditory and higher-level non-auditory (parietal and frontal) cortices in response to visual stimuli immediately after training (T1 vs. T0), indicating decreased cross-modal takeover of speech-related regions during visual processing. The results thus showed that neuroplasticity can be observed not only at the same time with, but also before, behavioural changes in speech-in-noise perception. To our knowledge, this is the first fNIRS study to evaluate speech-based auditory neuroplasticity in older adults. It thus provides important implications for current research by illustrating the promises of detecting neuroplasticity using fNIRS in hearing-vulnerable individuals.

Significance of Saliva as a Sensor Detector in Marking Oral Cancer

One of the most deadly forms of cancer is oral cancer. Since the screening methods currently in use are poor, a consistent portion of oral health issues are diagnosed at any late stage. Because existing diagnostic processes are complex and imprecise, it would be imperative to enhance them. The possibility of diagnosis by the discovery of biomarkers from biological fluids. The salivary glands secrete protein, peptides, electrolytes, organic and inorganic salts, and gingival crevicular fluids and mucosal transudates, which contribute in a complementary manner to human whole mouth saliva. This study focuses on biosensors that work at the molecular level, such as DNA, RNA, and protein biosensors, and it discusses tactics that use diverse biosensors to target different types of biomarkers. We also looked at non-invasive electrochemical techniques, optical techniques, and nanobiosensors for analyzing the cancer biomarkers found in bodily fluids including serum and saliva. Consequently, this review clarifies the advancements made in the creation of novel biosensors for the early identification and diagnosis of oral cancer. To clearly clarify the clinical situation, an introduction to oral cancer diagnosis, prognosis, and treatment is provided. Saliva is then offered as an alternative biofluid, along with its benefits, drawbacks, and methods of collection. It is suggested that salivary biomarkers be used as non-invasive diagnostic instruments.

Advancements in non-invasive optical imaging techniques for precise diagnosis of skin disorders

Advancements in non-invasive optical imaging techniques for precise diagnosis of skin disorders

Optical imaging (HandScan) can identify ultrasound remission in rheumatoid arthritis

BackgroundIdentifying remission is of high importance in rheumatoid arthritis (RA) because remission is associated with less structural progression. We investigated the efficacy of a new optical imaging device, HandScan, to identify RA remission, as defined by ultrasound (US).Methods61 RA patients were included. Disease activity was evaluated by clinical assessment and US, using gray-scale (GS) and Power Doppler (PD). HandScan determined unitary optical spectral transmission (OST) values for wrists, metacarpophalangeal and proximal interphalangeal joints. At the patient level, three composite HandScan (HS) scores were calculated: total HS score; disease activity score OST (DAS-OST) and DAS-OST without patient global assessment (PtGA). Using ROC curves, we determined HS cut-offs to identify US-defined remission.ResultsAt the joint level, unitary OST values significantly correlated with GS synovitis [odds ratio (OR) 2.43, p < 0.0001] and PD positivity (OR 3.72, p = 0.0002 ). At the patient level, total HS score and DAS-OST were significantly associated with all gray-scale US (GSUS) and power doppler US (PDUS) parameters evaluated (synovitis number and grade, synovial thickness, PD grade) (p < 0.05). The cut-off to identify US-defined remission at the joint level was of 0.92, giving an 81% sensitivity and a 96% positive predictive value (PPV). At the patient level, ROC-curves failed to identify a robust cut-off for the total HS score, but did identify a cut-off (3.68) for DAS-OST to identify US-defined remission, but with lower sensitivity (75%), specificity (56%) and PPV (67%).ConclusionsHandScan is a non-invasive optical imaging technique providing OST values that correlate with GSUS and PDUS parameters. In addition, HandScan is able to reliably identify US-defined remission in RA at the joint level, with a good sensitivity and high PPV. At the patient level, HandScan DAS-OST can also determine US remission (while total HS score failed to do so), but with lower performance.

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View.

The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.

PyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Objective. Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. Approach. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter. Main results. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Significance. Based on these fiducial points, pyPPG engineered a set of 74 PPG biomarkers. Studying PPG time-series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on https://physiozoo.com/.

Peroperační využití potencované kontaktní endoskopie k hodnocení maligního potenciálu slizničních lézí hrtanu, validace ELS klasifikace

Summary Introduction: Enhanced contact endoscopy (ECE) – combination of contact endoscopy and NBI (narrow-band imaging) or IMAGE1 S, is a noninvasive optical technique used for assessment of superfi cial vascular changes of mucosal lesions in high magnifi cation. Aim: The aim of our study was to evaluate the dia gnostic value of ECE in an intraoperative settlement and validation of the ELS classifi cation. Methods: Patients with laryngeal lesions underwent direct laryngoscopy with a structured assessment of the lesion using white light, NBI and ECE. Lesions were classified according to the European Laryngological Society Classifi cation that divides the vascular pattern changes into longitudinal (unsuspicious) and perpendicular (suspicious). Evaluation was correlated with histopathology. Results: 60 patients with 76 lesions were enrolled. Sensitivity, specifi city, positive predictive value (PPV), negative predictive value (NPV) and accuracy for NBI assessment reached 71.4%, 100%, 100%, 53.8% and 78.6%, resp.,  index of 0.556. Sensitivity, specifi city, PPV, NPV and accuracy for ECE reached 86.4%, 89.5%, 95.0%, 73.9% and 87.3%,  index of 0.716. Additional 20% (9/ 45) of the leukoplakias could be assessed with ECE compared to NBI. Conclusions: Our data support the assumption that ECE is a useful tool for pre-histological examination of mucosal lesions, however it cannot fully replace bio psy sampling. ECE shows higher accuracy in detecting malignant lesions compared to NBI and can be especially helpful in the assessment of vocal fold leukoplakia. Key words enhanced contact endoscopy – narrow-band imaging – laryngeal mucosal lesions – squamous cell carcinoma – leukoplakia

Cold-Water Immersion and Lower Limb Muscle Oxygen Consumption as Measured by Near-Infrared Spectroscopy in Trained Endurance Athletes.

Cold-water immersion (CWI) has been reported to reduce tissue metabolism postimmersion, but physiological data are lacking regarding the muscle metabolic response to its application. Near-infrared spectroscopy (NIRS) is a noninvasive optical technique that can inform muscle hemodynamics and tissue metabolism. To investigate the effects of CWI at 2 water temperatures (10°C and 15°C) on NIRS-calculated measurements of muscle oxygen consumption (mVO2). Crossover study. University sports rehabilitation center. A total of 11 male National Collegiate Athletic Association Division II long-distance runners (age = 23.4 ± 3.4 years, height = 1.8 ± 0.1 m, mass = 68.8 ± 10.7 kg, mean adipose tissue thickness = 6.7 ± 2.7 mm). Cold-water immersion at 10°C and 15°C for 20 minutes. We calculated mVO2 preimmersion and postimmersion at water temperatures of 10°C and 15°C. Changes in tissue oxyhemoglobin (O2Hb), deoxyhemoglobin (HHb), total hemoglobin (tHb), hemoglobin difference (Hbdiff), and tissue saturation index (TSI %) were measured during the 20-minute immersion at both temperatures. We observed a decrease in mVO2 after immersion at both 10°C and 15°C (F1,9 = 27.7801, P = .001). During the 20-minute immersion at both temperatures, we noted a main effect of time for O2Hb (F3,27 = 14.227, P = .001), HHb (F3,27 = 5.749, P = .009), tHb (F3,27 = 24.786, P = .001), and Hbdiff (F3,27 = 3.894, P = .020), in which values decreased over the course of immersion. Post hoc pairwise comparisons showed that these changes occurred within the final 5 minutes of immersion for tHb and O2Hb. A 20-minute CWI at 10°C and 15°C led to a reduction in mVO2. This was greater after immersion at 10°C. The reduction in mVO2 suggests a decrease in muscle metabolic activity (ie, O2 use after CWI). Calculating mVO2 via the NIRS-occlusion technique may offer further insight into muscle metabolic responses beyond what is attainable from observing the NIRS primary signals.

Multi-scale representation of surface-enhanced Raman, spectroscopy data for deep learning-based liver, cancer detection

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Optimizing clinical O2 saturation mapping using hyperspectral imaging and diffuse reflectance spectroscopy in the context of epinephrine injection.

Clinical determination of oxygen saturation (sO2) in patients is commonly performed via non-invasive optical techniques. However, reliance on a few wavelengths and some form of pre-determined calibration introduces limits to how these methods can be used. One example involves the assessment of sO2 after injection of local anesthetic using epinephrine, where some controversy exists around the time it takes for the epinephrine to have an effect. This is likely caused by a change in the tissue environment not accounted for by standard calibrated instruments and conventional analysis techniques. The present study aims to account for this changing environment by acquiring absorption spectra using hyperspectral imaging (HSI) and diffuse reflectance spectroscopy (DRS) before, during, and after the injection of local anesthesia containing epinephrine in human volunteers. We demonstrate the need to account for multiple absorbing species when applying linear spectral unmixing in order to obtain more clinically relevant sO2 values. In particular, we demonstrate how the inclusion of water absorption greatly affects the rate at which sO2 seemingly drops, which in turn sheds light on the current debate regarding the time required for local anesthesia with epinephrine to have an effect. In general, this work provides important insight into how spectral analysis methods need to be adapted to specific clinical scenarios to more accurately assess sO2.

Determination of the Ion Concentrations in VRFB by Non-Invasive Optical Techniques Due to Chemical Reactions, Complexes, and Side Reactions

The Vanadium redox flow battery has been intensively examined since the 1970s, with researchers looking at its electrochemical time varying electrolyte concentration time variation (both tank and cells, for negative and positive half cells), its thermal time variation, and fluid flow behavior. Chemistry behavior of the electrolyte ions have also been intensively examined too. Our focus in this paper is to examine the disturbance and effect of other chemical reactions and constituents to those typically found in the positive electrode electrolyte, while handling the less problematic negative electrode. Appropriate formulas are developed which allow assessment of the size of the effect, and their evaluation indicates something on the order of up to 14%. Therefore, for unambiguous non-invasive optical measurements, knowledge of the disturbance effect, and taking it into account will allow proper concentration determination. This treatment includes a comprehensive examination of identified side reactions which occur due to crossover ions between the electrodes.

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method.

Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient and reduced scattering coefficient ) and scalp and skull thicknesses. We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance. We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor () estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30mm) on BFi and estimations among DCS-NET, semi-infinite, and three-layer fitting models. DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of and at a source-detector separation of 30mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively. DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

Oxygen Assessment in Tumors In Vivo Using Phosphorescence Lifetime Imaging Microscopy.

The oxygen level in a tumor is a crucial factor for its development and response to therapies. Phosphorescence lifetime imaging (PLIM) with the use of phosphorescent oxygen probes is a highly sensitive, noninvasive optical technique for the assessment of molecular oxygen in living cells and tissues. Here, we present a protocol for microscopic mapping of oxygen distribution in a mouse tumor model in vivo. We demonstrate that PLIM microscopy, in combination with an Ir(III)-based probe, enables visualization of cellular-level heterogeneity of tumor oxygenation.

Lipid nanoparticle encapsulated large peritoneal macrophages migrate to the lungs via the systemic circulation in a model of clodronate-mediated lung-resident macrophage depletion.

Rationale: A mature tissue resident macrophage (TRM) population residing in the peritoneal cavity has been known for its unique ability to migrate to peritoneally located injured tissues and impart wound healing properties. Here, we sought to expand on this unique ability of large peritoneal macrophages (LPMs) by investigating whether these GATA6+ LPMs could also intravasate into systemic circulation and migrate to extra-peritoneally located lungs upon ablating lung-resident alveolar macrophages (AMs) by intranasally administered clodronate liposomes in mice. Methods: C12-200 cationic lipidoid-based nanoparticles were employed to selectively deliver a small interfering RNA (siRNA)-targeting CD-45 labeled with a cyanine 5.5 (Cy5.5) dye to LPMs in vivo via intraperitoneal injection. We utilized a non-invasive optical technique called Diffuse In Vivo Flow Cytometry (DiFC) to then systemically track these LPMs in real time and paired it with more conventional techniques like flow cytometry and immunocytochemistry to initially confirm uptake of C12-200 encapsulated siRNA-Cy5.5 (siRNA-Cy5.5 (C12-200)) into LPMs, and further track them from the peritoneal cavity to the lungs in a mouse model of AM depletion incited by intranasally administered clodronate liposomes. Also, we stained for LPM-specific marker zinc-finger transcription factor GATA6 in harvested cells from biofluids like broncho-alveolar lavage as well as whole blood to probe for Cy5.5-labeled LPMs in the lungs as well as in systemic circulation. Results: siRNA-Cy5.5 (C12-200) was robustly taken up by LPMs. Upon depletion of lung-resident AMs, these siRNA-Cy5.5 (C12-200) labeled LPMs rapidly migrated to the lungs via systemic circulation within 12-24 h. DiFC results showed that these LPMs intravasated from the peritoneal cavity and utilized a systemic route of migration. Moreover, immunocytochemical staining of zinc-finger transcription factor GATA6 further confirmed results from DiFC and flow cytometry, confirming the presence of siRNA-Cy5.5 (C12-200)-labeled LPMs in the peritoneum, whole blood and BALF only upon clodronate-administration. Conclusion: Our results indicate for the very first time that selective tropism, migration, and infiltration of LPMs into extra-peritoneally located lungs was dependent on clodronate-mediated AM depletion. These results further open the possibility of therapeutically utilizing LPMs as delivery vehicles to carry nanoparticle-encapsulated oligonucleotide modalities to potentially address inflammatory diseases, infectious diseases and even cancer.

Transfer Contrastive Learning for Raman Spectroscopy Skin Cancer Tissue Classification.

Using Raman spectroscopy (RS) signals for skin cancer tissue classification has recently drawn significant attention, because of its non-invasive optical technique nature using molecular structures and conformations within biological tissue for diagnosis. In reality, RS signals are noisy and unstable for training machine learning models. The scarcity of tissue samples also makes it challenging to learn reliable deep-learning networks for clinical usages. In this paper, we advocate a Transfer Contrasting Learning Paradigm (TCLP) to address the scarcity and noisy characteristics of the RS for skin cancer tissue classification. To overcome the challenge of limited samples, TCLPleverages transfer learning to pre-train deep learning models using RS data from similar domains (but collected from different RS equipments for other tasks). To tackle the noisy nature of the RS signals, TCLPuses contrastive learning to augment RS signals to learn reliable feature representation to represent RS signals for final classification. Experiments and comparisons, including statistical tests, demonstrate that TCLPoutperforms existing deep learning baselines for RS signal-based skin cancer tissue classification. Both code and data used in our study are available at: https://github.com/yeyimilk/tclp.

A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers.

Biometric authentication is a widely used method for verifying individuals' identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the system can accurately identify and authenticate the user despite these variations is a significant challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D image that visually represents the time-varying frequency content of multiple PPG signals from the same human using the scalogram technique. Afterward, the features fusion approach is developed by combining features from the hybrid convolution vision transformer (CVT) and convolutional mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms for the classification of human identity. This hybrid model has the potential to provide more accurate and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP), F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model's performance in accurately distinguishing genuine individuals. The results of extensive experiments on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of 97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system. These results suggest that the proposed method performs well in accurately classifying or identifying patterns within the PPG signals to perform continuous human authentication.