Search for correlations in raman, diffuse reflectance, and fluorescence spectroscopy data from intracranial tumors

Use of diffuse reflectance spectroscopy and Nix pro color sensor in combination for rapid prediction of soil organic carbon

.SignificanceMachine 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, and reduced scattering coefficient, .AimWe aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra.ApproachWe 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 to , and to . 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.ResultsWhen compounding all use-errors on simulated data, the WIR model best balanced accuracy and speed, yielding errors of 1.75% for and 1.53% for , compared to the MCI’s 50.9% for and 24.6% for . Regarding experimental data, WIR model had mean errors of 13.2% and 6.1% for and , respectively. The errors for MCI were about eight times higher.ConclusionsThe WIR model presents reliable use-error-robust optical property predictions from DRS data.

There is a strong need to develop clinical instruments that can perform rapid tissue assessment at the tip of smart clinical instruments for a variety of oncological applications. This study presents the first in vivo real-time tissue characterization during 24 liver biopsy procedures using diffuse reflectance (DR) spectroscopy at the tip of a core biopsy needle with integrated optical fibers. DR measurements were performed along each needle path, followed by biopsy of the target lesion using the same needle. Interventional imaging was coregistered with the DR spectra. Pathology results were compared with the DR spectroscopy data at the final measurement position. Bile was the primary discriminator between normal liver tissue and tumor tissue. Relative differences in bile content matched with the tissue diagnosis based on histopathological analysis in all 24 clinical cases. Continuous DR measurements during needle insertion in three patients showed that the method can also be applied for biopsy guidance or tumor recognition during surgery. This study provides an important validation step for DR spectroscopy-based tissue characterization in the liver. Given the feasibility of the outlined approach, it is also conceivable to make integrated fiber-optic tools for other clinical procedures that rely on accurate instrument positioning.

Dispensability of the conventional Tauc’s plot for accurate bandgap determination from UV–vis optical diffuse reflectance data

A series of CaMoO4-BO33−:Eu3+ phosphors with different concentrations of Eu3+ (0, 0.5, 1.0, 1.5, 2.0, and 2.5 mol. %) were prepared using the solid-state reaction method. The crystal structure, particle morphology, and photoluminescence (PL) were characterized by x-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), Fourier transform infrared spectrometry, ultraviolet–vis diffuse reflectance, and PL spectroscopy. The XRD patterns showed that CaMoO4−BO33−:Eu3+ phosphor consisted of a pure phase with a tetragonal structure. The introduction of BO33− ions did not affect the crystal structure of the CaMoO4:Eu3+ phosphors, but it slightly influenced the particle morphology and the peak intensity of the red PL from Eu3+. As confirmed from the FE-SEM data, the shapes and sizes of the particles were modified by the incorporation of BO33− ions. The bandgap was influenced by the incorporation of BiO33− and Eu3+ as confirmed from the diffuse reflectance data. The PL intensity was dependent on the concentration of the Eu3+, and the red emission due to the f → f transitions of Eu3+ was enhanced considerably by the incorporation of BO33− ions when the samples were excited at the wavelength of 395 nm using a monochromatized xenon lamp. The effect of BO33− ions on the PL intensity is discussed.

Diffuse reflection data are presented for ethyl methylphosphonate in a fine Utah dirt sample as a model system for organophosphate-contaminated soil. The data revealed a chemometric artifact when the spectra were represented in Kubelka-Munk units that manifests as a linear dependence of spectral peak height on variations in the observed baseline position (i.e., the position of the observed transmission intensity where no absorption features occur in the sample spectrum). We believe that this artifact is the result of the mathematical process by which the raw data are converted into Kubelka-Munk units, and we developed a numerical strategy for compensating for the observed effect and restoring chemometric precision to the diffuse reflection data for quantitative analysis while retaining the benefits of linear calibration afforded by the Kubelka-Munk approach. We validated our Kubelka-Munk correction strategy by repeating the experiment using a simpler system--pure caffeine in potassium bromide. The numerical preprocessing includes conventional multiplicative scatter correction coupled with a baseline offset correction that facilitates the use of quantitative diffuse reflection data in the Kubelka-Munk formalism for the quantitation of contaminants in a complex soil matrix, but is also applicable to more fundamental diffuse reflection quantitative analysis experiments.

The predictive properties of methods aimed for estimating the water content in skin from the spectral diffuse reflection characteristics near the water absorption line in the near-IR spectral range are analysed. Numerical simulation data, experimental data on diffuse reflection from human skin phantoms, and data from the reference data set of human skin reflectance spectra are used to consider the possibility of gaining additional information about the water distribution in skin. The influence of variations in the scattering coefficient and oxyhaemoglobin concentration on the water content estimates is investigated.

An iron-porphyrin-based metal organic framework PCN-222(Fe) is investigated upon postsynthetic reduction with piperidine. Fe K-edge X-ray absorption and Kβ mainline emission spectroscopy measurements reveal the local coordination geometry, oxidation, and spin state changes experienced by the Fe sites upon reaction with this axially coordinating reducing agent. Analysis and fitting of these data confirm the binding pattern predicted by a space-filling model of the structurally constrained pore environments. These results are further supported by UV-vis diffuse reflectance, IR, and resonance Raman spectroscopy data.

A non-contact Fourier transform interferometric imaging system was used to collect hyperspectral images of the steady-state diffuse reflectance from a point source in turbid media for the spectral range of 550–850 nm. Steady-state diffuse reflectance profiles were generated from the hyperspectral images, and partial least-squares (PLS) regression was performed on the diffuse reflectance profiles to quantify absorption (μ a) and reduced scattering (μ' s) properties of turbid media. The feasibility of using PLS regression to predict optical properties was examined for two different sets of spatially-resolved diffuse reflectance data. One set of data was collected from 40 turbid phantoms, while the second set was generated by convolving Monte Carlo simulations with the instrument response of the imaging system. Study results show that PLS prediction of μ a and μ' s was accurate to within ±8% and ±5%, respectively, when the model was trained on turbid phantom data. Moreover, PLS prediction of optical properties was considerably faster and more efficient than direct least-squares fitting of spatially-resolved profiles. When the PLS model was trained on Monte Carlo simulated data and subsequently used to predict μ a and μ' s from the diffuse reflectance of turbid phantom, the percent accuracies degraded to ± 12% and ± 5%, respectively. These accuracy values are applicable to homogenous, semi-infinite turbid phantoms with optical property ranges comparable to tissues.

Tumor phenotypes can be characterized by radiomics features extracted from images. However, the prediction accuracy is challenged by difficulties such as small sample size and data imbalance. The purpose of the study was to evaluate the performance of machine learning strategies for the prediction of cancer prognosis. A total of 422 patients diagnosed with non-small cell lung carcinoma (NSCLC) were selected from The Cancer Imaging Archive (TCIA). The gross tumor volume (GTV) of each case was delineated from the respective CT images for radiomic features extraction. The samples were divided into 4 groups with survival endpoints of 1 year, 3 years, 5 years, and 7 years. The radiomic image features were analyzed with 6 different machine learning methods: decision tree (DT), boosted tree (BT), random forests (RF), support vector machine (SVM), generalized linear model (GLM), and deep learning artificial neural networks (DL-ANNs) with 70:30 cross-validation. The overall average prediction performance of the BT, RF, DT, SVM, GLM and DL-ANNs was AUC with 0.912, 0.938, 0.793, 0.746, 0.789 and 0.705 respectively. The RF and BT gave the best and second performance in the prediction. The DL-ANN did not show obvious advantage in predicting prognostic outcomes. Deep learning artificial neural networks did not show a significant improvement than traditional machine learning methods such as random forest and boosted trees. On the whole, the accurate outcome prediction using radiomics serves as a supportive reference for formulating treatment strategy for cancer patients.

High-Efficiency Generative Adversarial Network Model for Chemical Process Fault Diagnosis

In this work, the generation of Y-zeolite-entrapped zinc tetra(3,4-pyridine)porphyrazine (ZnPz-Z) at several loading levels is reported. The ZnPz-Z samples were obtained using the template synthesis method involving condensation of 3,4-dicyanopyridine (DCP) around Zn(2+) cations which had been ion-exchanged into the Y-zeolite supercages (Zn(2+)-Z). The integrity of the sample was documented by diffuse reflectance (DR), emission (Em) and resonance Raman (RR) spectroscopic data. The Raman spectrum for the ground-state ZnPz-Z intrazeolitic complex, with the laser excitation at lambda(ex) = 568.2 nm, closely matched that obtained for the free ZnPz porphyrazine, with some bands exhibiting slight frequency shifts of 1-5 cm(-)(1). The DR spectrum of ZnPz-Z exhibited maxima at lambda(max) = 414 and 682 nm, and the emission spectrum of ZnPz-Z revealed fluorescence at lambda(em) = 705 nm, with lambda(ex) = 350.9, 406.7, or 647.1 nm. This emission was 22 nm red shifted with respect to that recorded for the free ZnPz complex in pyridine solution and 24 nm red shifted relative to that acquired for the ZnPz complex adsorbed on the outer surface of the zeolite. All these spectroscopic data suggest that the ZnPz molecules within the zeolite supercages were only slightly distorted. The ZnPz-Z samples contain a small amount of luminescent (lambda(em) = 500 nm) impurities assigned to products of 3,4-dicyanopyridine noncyclic condensation.

Intensive care units (ICUs) are challenging environments for healthcare professionals (HCPs), patients, and families. Up to 75% of HCPs experience psychosomatic symptoms and 30% intend to leave their job. To address this, a German ICU implemented an integrated psychosocial care model. Its implementation and evaluation are described. In a quasi-experimental design, a psychologist was integrated part-time into an ICU team for 7 months (June 2022-January 2023), tasked to deliver low-threshold psychosocial support to HCPs. Additional components were one-on-one interactions, clinical supervisions, a feedback survey and health circles. Pre- and post-measures from a voluntary sample of the intervention ward (N = 65) and a control ward receiving usual psychosocial care (N = 33) were taken via online surveys assessing the psychosocial safety climate (PSC), emotional and cognitive irritation and intention to leave. Groups were compared using linear mixed-effect models. The intervention was implemented as planned, and HCPs expressed satisfaction with the changes experienced on the ward. Statistical testing revealed no significant interaction effects between point of measurement and group allocation on irritation and intention to leave (P > .01). The PSC interaction effect did not reach the significance (α = .01, P = .02) but indicated a trend towards improvement in the intervention group. The intervention successfully provided accessible psychosocial support and fostered a more positive PSC within the ICU team. Statistical effects were limited, likely due to the quasi-experimental study design, small sample size and group imbalances, highlighting the need for larger, more controlled studies.

This chapter appraises a noninferiority trial that tested whether a 3-day course of antibiotics is as effective as an 8-day regimen in hospitalized adults with mild to moderate-severe community-acquired pneumonia (CAP). Researchers conducted a double-blinded, placebo-controlled study across nine Dutch hospitals, randomizing 121 patients who showed substantial clinical improvement after 3 days of intravenous amoxicillin. Both groups achieved similar outcomes in clinical cure, bacteriological and radiological success, symptom resolution, and adverse events at 10 and 28 days. Although the chapter includes limitations—such as small sample size, limited generalizability, and baseline imbalances—it still confirms that shorter antibiotic therapy does not compromise effectiveness. Finally, the chapter emphasizes that early antibiotic discontinuation aligns with stewardship goals by reducing unnecessary exposure, limiting adverse effects, and helping prevent antimicrobial resistance.

Biopsy with same-session MRI-guided laser interstitial thermal therapy versus biopsy alone in patients with primary unresectable glioblastoma: a multicentre randomised controlled trial.