Backscatter measurement of cancellous bone using the ultrasound transit time spectroscopy.

Recently, ultrasound transit time spectroscopy (UTTS) was proposed as a promising method for bone quantitative ultrasound measurement. Studies have showed that UTTS could estimate the bone volume fraction and other trabecular bone structure in ultrasonic through-transmission measurements. The goal of this study was to explore the feasibility of UTTS to be adapted in ultrasonic backscatter measurement and further evaluate the performance of backscattered ultrasound transit time spectrum (BS-UTTS) in the measurement of cancellous bone density and structure. First, taking ultrasonic attenuation into account, the concept of BS-UTTS was verified on ultrasonic backscatter signals simulated from a set of scatterers with different positions and intensities. Then, in vitro backscatter measurements were performed on 26 bovine cancellous bone specimens. After a logarithmic compression of the BS-UTTS, a linear fitting of the log-compressed BS-UTTS versus ultrasonic propagated distance was performed and the slope and intercept of the fitted line for BS-UTTS were determined. The associations between BS-UTTS parameters and cancellous bone features were analyzed using simple linear regression. The results showed that the BS-UTTS could make an accurate deconvolution of the backscatter signal and predict the position and intensity of the simulated scatterers eliminating phase interference, even the simulated backscatter signal was with a relatively low signal-to-noise ratio. With varied positions and intensities of the scatterers, the slope of the fitted line for the log-compressed BS-UTTS versus ultrasonic propagated distance (i.e., slope of BS-UTTS for short) yield a high agreement (r2 = 99.84%-99.96%) with ultrasonic attenuation in simulated backscatter signal. Compared with the high-density cancellous bone, the low-density specimen showed more abundant backscatter impulse response in the BS-UTTS. The slope of BS-UTTS yield a significant correlation with bone mineral density (r = 0.87; p < 0.001), BV/TV (r = 0.87; p < 0.001), and cancellous bone microstructures (r up to 0.87; p < 0.05). The intercept of BS-UTTS was also significantly correlated with bone densities (r = -0.87; p < 0.001) and trabecular structures (|r|=0.43-0.80; p < 0.05). However, the slope of the BS-UTTS underestimated attenuation when measurements were performed experimentally. In addition, a significant non-linear relationship was observed between the measured attenuation and the attenuation estimated by the slope of the BS-UTTS. This study demonstrated that the UTTS method could be adapted to ultrasonic backscatter measurement of cancellous bone. The derived slope and intercept of BS-UTTS could be used in the measurement of bone density and microstructure. The backscattered ultrasound transit time spectroscopy might have potential in the diagnosis of osteoporosis in the clinic.

Initial non-invasive in vivo sensing of the lung using time domain diffuse optics.

The in vivo diagnosis and monitoring of pulmonary disorders (caused for example by emphysema, Covid-19, immature lung tissue in infants) could be effectively supported by the non-invasive sensing of the lung through light. With this purpose, we investigated the feasibility of probing the lung by means of time-resolved diffuse optics, leveraging the increased depth (a few centimeters) attained by photons collected after prolonged propagation time (a few nanoseconds). We present an initial study that includes measurements performed on 5 healthy volunteers during a breathing protocol, using a time-resolved broadband diffuse optical spectroscopy system. Those measurements were carried out across the spectral range of 600-1100 nm at a source-detector distance of 3 cm, and at 820 nm over a longer distance (7-9 cm). The preliminary analysis of the in vivo data with a simplified homogeneous model revealed a maximum probing depth of 2.6-3.9 cm, suitable for reaching the lung. Furthermore, we observed variations in signal associated with respiration, particularly evident at long photon propagation times. However, challenges stemming from both intra- and inter-subject variability, along with inconsistencies potentially arising from conflicting scattering and absorption effects on the collected signal, hindered a clear interpretation. Aspects that require further investigation for a more comprehensive understanding are outlined.

Multidimensional discriminant analysis of species, strains and culture age of closely related entomopathogenic fungi using reflectance spectroscopy.

The diversity of fungal strains is influenced by genetic and environmental factors, growth conditions and mycelium age, and the spectral features of fungal mycelia are associated with their biochemical, physiological, and structural traits. This study investigates whether intraspecific differences can be detected in two closely related entomopathogenic species, namely Cordyceps farinosa and Cordyceps fumosorosea, using ultraviolet A to shortwave infrared (UVA-SWIR) reflectance spectra. Phylogenetic analysis of all strains revealed a high degree of uniformity among the populations of both species. The characteristics resulting from variation in the species, as well as those resulting from the age of the cultures were determined. We cultured fungi on PDA medium and measured the reflectance of mycelia in the 350-2500 nm range after 10 and 17 days. We subjected the measurements to quadratic discriminant analysis (QDA) to identify the minimum number of bands containing meaningful information. We found that when the age of the fungal culture was known, species represented by a group of different strains could be distinguished with no more than 3-4 wavelengths, compared to 7-8 wavelengths when the age of the culture was unknown. At least 6-8 bands were required to distinguish cultures of a known species among different age groups. Distinguishing all strains within a species was more demanding: at least 10 bands were required for C. fumosorosea and 21 bands for C. farinosa. In conclusion, fungal differentiation using point reflectance spectroscopy gives reliable results when intraspecific and age variations are taken into account.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was recently introduced as a rapid bone analysis technique in bone-infiltrating head and neck cancers. Research efforts on laser surgery systems with controlled tissue feedback are currently limited to animal specimens and the use of nontumorous tissues. Accordingly, this study aimed to characterize the electrolyte composition of tissues in human mandibular bone-infiltrating head and neck cancer. Mandible cross-sections from 12 patients with bone-invasive head and neck cancers were natively investigated with LIBS. Representative LIBS spectra (n = 3049) of the inferior alveolar nerve, fibrosis, tumor stroma, and cell-rich tumor areas were acquired and histologically validated. Tissue-specific differences in the LIBS spectra were determined by receiver operating characteristics analysis and visualized by principal component analysis. The electrolyte emission values of calcium (Ca) and potassium (K) significantly (p < 0.0001) differed in fibrosis, nerve tissue, tumor stroma, and cell-rich tumor areas. Based on the intracellular detection of Ca and K, LIBS ensures the discrimination between the inferior alveolar nerve and cell-rich tumor tissue with a sensitivity of ≥95.2% and a specificity of ≥87.2%. The heterogeneity of electrolyte emission values within tumorous and nontumorous tissue areas enables LIBS-based tissue recognition in mandibular bone-infiltrating head and neck cancer.

Comparison of single and double pulse laser-induced breakdown spectroscopy for the detection of biomolecules tagged with photon-upconversion nanoparticles.

BACKGROUND: Laser-induced breakdown spectroscopy (LIBS) is a well-recognized analytical technique used for elemental analysis. This method is gaining considerable attention also in biological applications thanks to its ability for spatial mapping and elemental imaging. The implementation of LIBS in the biomedical field is based on the detection of metals or other elements that either naturally occur in the samples or are present artificially. The artificial implementation of nanoparticle labels (Tag-LIBS) enables the use of LIBS as a readout technique for immunochemical assays. However, one of the biggest challenges for LIBS to meet immunoassay readout standards is its sensitivity. RESULTS: This paper focuses on the improvement of LIBS sensitivity for the readout of nanoparticle-based immunoassays. First, the LIBS setup was optimized on photon-upconversion nanoparticle (UCNP) droplets deposited on the microtiter plate wells. Two collection optics systems were compared, with single pulse (SP) and collinear double pulse (DP) LIBS arrangements. By deploying the second laser pulse, the sensitivity was improved up to 30 times. The optimized SP and DP setups were then employed for the indirect detection of human serum albumin based on immunoassay with UCNP-based labels. Compared to our previous LIBS study, the detection limit was enhanced by two orders of magnitude, from 10 ng mL-1 to 0.29 ng mL-1. In addition, two other immunochemical methods were used for reference, based on the readout of upconversion luminescence of UCNPs and absorbance measurement with enzyme labels. Finally, the selectivity of the assay was tested and the practical potential of Tag-LIBS was demonstrated by the successful analysis of urine samples. SIGNIFICANCE AND NOVELTY: In this work, we improved the sensitivity of the Tag-LIBS method by combining new labels based on UCNPs with the improved collection optics and collinear DP configuration. In the instrumental setup optimization, the DP LIBS showed better sensitivity and signal-to-noise ratio than SP. The optimizations allowed the LIBS readout to surpass the sensitivity of enzyme immunoassay, approaching the qualities of upconversion luminescence readout, which is nowadays a state-of-the-art readout technique.

Monitoring of Caffeine Consumption Effect on Skin Blood Properties by Diffuse Reflectance Spectroscopy.

Caffeine is the most widely consumed psychoactive substance worldwide, affecting numerous tissues and organs, with notable impacts on the central nervous system, heart, and blood vessels. The effect of caffeine on vascular smooth muscle cells is an initial transient contraction followed by significant vasodilatation. In this study we investigate the use of diffuse reflectance spectroscopy (DRS) for monitoring of vascular changes in human skin induced by caffeine consumption. DRS spectra were recorded on volar sides of the forearms of eight healthy volunteers at time intervals of 0, 30, 60, 120, and 180 min after consumption of caffeine, while one subject served as a negative control. Analytical diffusion approximation solutions for diffuse reflectance from three-layer structures were used to assess skin composition (e.g. dermal blood volume fraction and oxygen saturation) by fitting these solutions to experimental data. The results demonstrate that cutaneous vasodynamics induced by caffeine consumption can be monitored by DRS, while changes in the control subject not consuming caffeine were insignificant.

Real-Time Measurement of CH4 in Human Breath Using a Compact CH4/CO2 Sensor.

The presence of an elevated amount of methane (CH4) in exhaled breath can be used as a non-invasive tool to monitor certain health conditions. A compact, inexpensive and transportable CH4 sensor is thus very interesting for this purpose. In addition, if the sensor is also able to simultaneously measure carbon dioxide (CO2), one can extract the end-tidal concentration of exhaled CH4. Here, we report on such a sensor based on a commercial detection module using tunable diode laser absorption spectroscopy. It was found that the measured CH4/CO2 values exhibit a strong interference with water vapor. Therefore, correction functions were experimentally identified and validated for both CO2 and CH4. A custom-built breath sampler was developed and tested with the sensor for real-time measurements of CH4 and CO2 in exhaled breath. As a result, the breath sensor demonstrated the capability of accurately measuring the exhaled CH4 and CO2 profiles in real-time. We obtained minimum detection limits of ~80 ppbv for CH4 and ~700 ppmv for CO2 in 1.5 s measurement time.

A Portable Microplasma Optical Emission Spectrometric Device with Online Digestion Function for Field and Sensitive Determination of Lead in Biological Samples.

Accurate detection and screening of Pb in biological samples is helpful to assess the risk associated with lead pollution to human health. However, conventional atomic spectroscopic instruments are bulky and cumbersome, requiring additional sample pretreatment equipment, and difficult to perform field analysis with. Herein, a portable point discharge (PD) microplasma-optical emission spectrometric (OES) device with online digestion function is designed for field and sensitive determination of lead in biological samples. With rice as a model, online digestion of a batch of six 50 mg samples can be achieved in the HNO3 and H2O2 system within 25 min by a temperature control and timing module. Compared to the conventional microwave digestion, the digestion efficiency of this device reaches 97%. Pb in digestion solution is converted into volatile species by hydride generation (HG) and directly introduced into PD-OES for excitation and detection by a self-designed rotatable and telescopic cutoff gas sampling column. Six samples can be successively detected in 2 min, and argon consumption of the whole process is only <800 mL. Under the optimized conditions, the detection limit of Pb is 0.018 mg kg-1 (0.9 µg L-1) and precision is 3.6%. The accuracy and practicability of the present device are verified by measuring several certified reference materials and real biological samples. By virtue of small size (23.5 × 17 × 8.5 cm3), lightweight (2.5 kg), and low energy consumption (24.3 W), the present device provides a convenient tool for field analysis of toxic elements in biological samples.

Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery.

Significance: Accurately distinguishing tumor tissue from normal tissue is crucial to achieve complete resections during soft tissue sarcoma (STS) surgery while preserving critical structures. Incomplete tumor resections are associated with an increased risk of local recurrence and worse patient prognosis. Aim: We evaluate the performance of diffuse reflectance spectroscopy (DRS) to distinguish tumor tissue from healthy tissue in STSs. Approach: DRS spectra were acquired from different tissue types on multiple locations in 20 freshly excised sarcoma specimens. A k-nearest neighbors classification model was trained to predict the tissue types of the measured locations, using binary and multiclass approaches. Results: Tumor tissue could be distinguished from healthy tissue with a classification accuracy of 0.90, sensitivity of 0.88, and specificity of 0.93 when well-differentiated liposarcomas were included. Excluding this subtype, the classification performance increased to an accuracy of 0.93, sensitivity of 0.94, and specificity of 0.93. The developed model showed a consistent performance over different histological subtypes and tumor locations. Conclusions: Automatic tissue discrimination using DRS enables real-time intra-operative guidance, contributing to more accurate STS resections.

Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix.

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.

Low cost sample preparation method using ultrasound for the determination of environmentally critical elements in seaweed.

In this study, ultrasound (US) was evaluated for As, Cd, Pb, Mn, Sr and V extraction from seaweed samples. The following parameters of ultrasound-assisted extraction (UAE) using an US bath were: frequency (25 to 130 kHz), amplitude (30 to 100%), temperature (30 to 80 °C), sample mass (50 to 200 mg), extractant concentration (1 to 3 mol L-1 of HNO3) and treatment time (5 to 30 min). Acoustic density and power density distribution were calculated using the calorimetric method and mapping of the acoustic pressure distribution was also evaluated. The optimized UAE conditions were 200 mg of sample in 10 mL of 2 mol L-1 HNO3 and 30 min of sonication in a 25 kHz US bath (37.2 ± 4.0 W L-1) at 70% of amplitude and 70 °C. Analytes were quantified using inductively coupled plasma mass spectrometry and results were compared with values obtained using "silent" conditions (magnetic or mechanical stirring at 500 rpm, and without stirring), and a reference method based on microwave-assisted wet digestion (MAWD). The UAE method demonstrated the best extraction efficiency (higher than 95%) for all analytes, especially for As, Cd and V, with lower standard deviations (up to 5%) and lower blank values in comparison with the silent conditions. The proposed UAE method was more advantageous than the reference method, being faster, simpler, safer, more environmentally friendly, and with higher detectability (lower limits of quantification, from 0.0033 to 1.34 µg g-1). In addition, negligible blank values were obtained for UAE and no interference were observed in the determination step. Furthermore, the optimized UAE method was applied for Antarctic seaweed samples and comparison with results obtained by MAWD was satisfactory. In this sense, UAE is demonstrated to be a suitable option for sample preparation of seaweed samples and further determination of environmentally critical elements avoiding the use of concentrated reagents as in the MAWD reference method.

Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications.

Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355-1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives.

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra.

Significance: Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorption coefficient, µa and reduced scattering coefficient, µs'. Aim: We aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra. Approach: We developed a wavelength-independent regressor (WIR) to predict optical properties from DRS data. For validation, we generated 1520 simulated DRS spectra with the forward Monte Carlo model, where µa=0.44 to 2.45 cm-1, and µs'=6.53 to 9.58 cm-1. We introduced common use-errors, such as wavelength miscalibrations and intensity fluctuations. Finally, we collected 882 experimental DRS images from 170 tissue-mimicking phantoms and compared performances of the WIR model, a dense neural network, and the MCI model. Results: When compounding all use-errors on simulated data, the WIR model best balanced accuracy and speed, yielding errors of 1.75% for µa and 1.53% for µs', compared to the MCI's 50.9% for µa and 24.6% for µs'. Regarding experimental data, WIR model had mean errors of 13.2% and 6.1% for µa and µs', respectively. The errors for MCI were about eight times higher. Conclusions: The WIR model presents reliable use-error-robust optical property predictions from DRS data.

Analysis of polarization features of human breast cancer tissue by Mueller matrix visualization.

Significance: Breast cancer ranks second in the world in terms of the number of women diagnosed. Effective methods for its early-stage detection are critical for facilitating timely intervention and lowering the mortality rate. Aim: Polarimetry provides much useful information on the structural properties of breast cancer tissue samples and is a valuable diagnostic tool. The present study classifies human breast tissue samples as healthy or cancerous utilizing a surface-illuminated backscatter polarization imaging technique. Approach: The viability of the proposed approach is demonstrated using 95 breast tissue samples, including 35 healthy samples, 20 benign cancer samples, 20 grade-2 malignant samples, and 20 grade-3 malignant samples. Results: The observation results reveal that element m23 in the Mueller matrix of the healthy samples has a deeper color and greater intensity than that in the breast cancer samples. Conversely, element m32 shows a lighter color and reduced intensity. Finally, element m44 has a darker color in the healthy samples than in the cancer samples. The analysis of variance test results and frequency distribution histograms confirm that elements m23, m32, and m44 provide an effective means of detecting and classifying human breast tissue samples. Conclusions: Overall, the results indicate that surface-illuminated backscatter polarization imaging has significant potential as an assistive tool for breast cancer diagnosis and classification.

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.

Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of benign and malignant tumors differ significantly. Developing specialized treatment protocols tailored to various tumor types is essential for enhancing patient survival outcomes. Employing laser-induced breakdown spectroscopy (LIBS) in conjunction with a deep learning methodology, we attained a high-precision differential diagnosis of malignant and benign lung tumors. First, LIBS spectra of malignant tumors, benign tumors, and normal tissues were collected. The spectra were preprocessed and Z score normalized. Then, the intensities of the Mg II 279.6, Mg I 285.2, Ca II 393.4, Cu II 518.3, and Na I 589.6 nm lines were analyzed in the spectra of the three tissues. The analytical results show that the elemental lines have different contents in the three tissues and can be used as a basis for distinguishing between the three tissues. Finally, the RF-1D ResNet model was constructed by combining the feature importance assessment method of random forest (RF) and one-dimensional residual network (1D ResNet). The classification accuracy, precision, sensitivity, and specificity of the RF-1D ResNet model were 91.1%, 91.6%, 91.3%, and 91.3%, respectively. And the model demonstrates superior performance with an area under the curve (AUC) value of 0.99. The above results show that combining LIBS with deep learning is an effective way to diagnose malignant and benign tumors.

Rapid classification of whole milk powder and skimmed milk powder by laser-induced breakdown spectroscopy combined with feature processing method and logistic regression.

Whole milk powder and skimmed milk powder are suitable for different groups of people due to their differences in composition. Therefore, a rapid classification method for whole milk powder and skimmed milk powder is urgently needed. In this paper, a novel strategy based on laser-induced breakdown spectroscopy (LIBS) and feature processing methods combined with logistic regression (LR) was constructed for the classification of milk powder. A LR classification model based on mini-batch gradient descent (MGD) was employed first. As indicated by the research results, the accuracy of the MGD-LR model for the milk powder samples in the test set is 96.33% and the modeling time is 33.07 s. The modeling efficiency is low and needs to be improved. Principal components analysis (PCA) and mutual information (MI) were used as feature processing methods to reduce the high dimensional LIBS data into fewer features for improving the modeling efficiency of the classification model. The research results indicate that the accuracy of the PCA-MGD-LR model and the MI-MGD-LR model for the test set of milk powder samples was 99.33% and 99.67%, respectively. Compared with MGD-LR model, the modeling efficiency of PCA-MGD-LR and MI-MGD-LR models has increased by 89.7% and 74.8%, respectively. The results fully demonstrate the feasibility of rapid milk powder classification based on LIBS and feature processing methods combined with LR, and it will provide a new technology for the identification and classification of milk powder.

Direct Determination of Ca, K, and Mg in Soy Leaf Samples Using Laser-Induced Breakdown Spectroscopy.

This study was dedicated to developing analytical methods for determining macronutrients (Ca, K, and Mg) in soy leaf samples with and without petioles. The study's primary purpose was to present Laser-induced breakdown spectroscopy (LIBS) as a viable alternative for directly analyzing leaf samples using chemometric tools to interpret the data obtained. The instrumental condition chosen for LIBS was 70 mJ of laser pulse energy, 1.0 µs of delay time, and 100 µm of spot size, which was applied to 896 samples: 305 of soy without petioles and 591 of soy with petioles. The reference values of the analytes for the proposition of calibration models were obtained using inductively coupled plasma optical emission spectroscopy (ICP-OES) technique. Twelve normalization modes and two calibration strategies were tested to minimize signal variations and sample matrix microheterogeneity. The following were studied: multivariate calibration using partial least squares and univariate calibration using the area and height of several selected emission lines. The notable normalization mode for most models was the Euclidean norm. No analyte showed promising results for univariate calibrations. Micronutrients, P and S, were also tested, and no multivariate models presented satisfactory results. The models obtained for Ca, K, and Mg showed good results. The standard error of calibration ranged from 2.3 g/kg for Ca in soy leaves without petioles with two latent variables to 5.0 g/kg for K in soy leaves with petioles with two latent variables.

Single-cell force spectroscopy of fluid flow-tuned cell adhesion for dissecting hemodynamics in tumor metastasis.

Cell adhesion plays an important role in regulating the metastasis of cancer cells, and atomic force microscopy (AFM)-based single-cell force spectroscopy (SCFS) has become an important method to directly measure the adhesion forces of individual cells. Particularly, bodily fluid flow environments strongly affect the functions and behaviors of metastatic cells for successful dissemination. Nevertheless, the interactions between fluidic flow medium environment and cell adhesion remain poorly understood. In this work, AFM-based SCFS was exploited to examine the effects of fluidic flow environment on cellular adhesion. A fluidic cell culture medium device was used to simulate the fluidic flow environment experienced by cancer cells during metastasis, which was combined with AFM-based SCFS assay. A single living cancer cell was attached to the AFM tipless cantilever to prepare the single-cell probe for performing SCFS experiments on the mesothelial cells grown under the fluidic flow medium conditions, and the effects of experimental parameters (retraction speed, contact time, loading force) on the measured cellular adhesion forces were analyzed. Experimental results of SCFS assay show that cellular adhesion forces significantly decrease after growth in fluidic flow medium, whereas cellular adhesion forces increase after growth in static culture medium. Experiments performed with the use of spherical probes coated with cell adhesion-associated biomolecules also show the weakening of cell adhesion after growth in fluidic flow cell culture medium, which was subsequently confirmed by the confocal fluorescence microscopy experiments of cell adhesion molecules, vividly illustrating the remarkable effects of fluidic flow environment on cellular adhesion. The study provides a new approach to detect adhesion force dynamics involved in the interactions between cells and the fluidic flow environment at the single-cell level, which will facilitate dissecting the role of hemodynamics in tumor metastasis.

Analysis and optimization of aberration induced by oblique incidence for in-vivo tissue polarimetry.

For in-vivo polarimetry such as Mueller matrix endoscopy of human internal organ cavities, the complicated undulating tissue surfaces deliver an inescapable occurrence of oblique incidence, which induce a prominent aberration to backscattering tissue polarimetry. In this Letter, we quantitatively analyze such polarimetric aberration on polarization basic parameters derived from the Mueller matrix. A correlation heatmap is obtained as applicable criteria to select an appropriate incident angle for different polarization basic parameters. Based on the analyzing results, we propose two aberration optimization strategies of parameter selection and azimuth rotation, which are suitable for tissue samples with randomly and well-aligned fiber textures, respectively. Both strategies are demonstrated to be effective in the ex-vivo human gastric muscularis tissue experiment. The findings presented in this Letter can be useful to provide accurate polarization imaging results, widely applied on in-vivo polarimetric endoscopy for tissues with complicated surface topography.