Spectroscopy Signals Research Articles

A Comparative and Critical Analysis for In Vitro Cytotoxic Evaluation of Magneto-Crystalline Zinc Ferrite Nanoparticles Using MTT, Crystal Violet, LDH, and Apoptosis Assay.

Zinc ferrite nanoparticles (ZFO NPs) are a promising magneto-crystalline platform for nanomedicine-based cancer theranostics. ZFO NPs synthesized using co-precipitation method are characterized using different techniques. UV-visible spectroscopy exhibits absorption peaks specific for ZFO. Raman spectroscopy identifies Raman active, infrared active, and silent vibrational modes while Fourier transforms infrared spectroscopic (FTIR) spectra display IR active modes that confirm the presence of ZFO. X-ray diffraction pattern (XRD) exhibits the crystalline planes of single-phase ZFO with a face-centered cubic structure that coincides with the selected area electron diffraction pattern (SAED). The average particle size according to high-resolution transmission electron microscopy (HR-TEM) is 5.6 nm. X-ray photoelectron spectroscopy (XPS) signals confirm the chemical states of Fe, Zn, and O. A superconducting quantum interference device (SQUID) displays the magnetic response of ZFO NPs, showing a magnetic moment of 45.5 emu/gm at 70 kOe. These ZFO NPs were then employed for comparative cytotoxicity evaluation using MTT, crystal violet, and LDH assays on breast adenocarcinoma epithelial cell (MCF-7), triple-negative breast cancer lines (MDA-MB 231), and human embryonic kidney cell lines (HEK-293). Flow cytometric analysis of all the three cell lines were performed in various concentrations of ZFO NPs for automated cell counting and sorting based on live cells, cells entering in early or late apoptotic phase, as well as in the necrotic phase. This analysis confirmed that ZFO NPs are more cytotoxic towards triple-negative breast cancer cells (MDA-MB-231) as compared to breast adenocarcinoma cells (MCF-7) and normal cell lines (HEK-293), thus corroborating that ZFO can be exploited for cancer therapeutics.

Identification and Detection of Intracellular Reactive Sulfur Species Using a Reaction-Mediated Dual-Recognition Strategy.

Reactive sulfur species (RSS) are emerging as a potential key gasotransmitter in diverse physiological processes linking two signaling molecules H2S and SO2. However, the exact roles of H2S and SO2 remain unclear. A major hurdle is the shortage of accurate and robust approaches for sensing of H2S and SO2 in biological systems. Herein, we report a reaction-mediated dual-recognition strategy-based nanosensor, silver nanoparticles (AgNPs)-loaded MIL-101 (Fe) (ALM) hybrids, for the simultaneous detection of H2S and SO2 in a living cell. Upon exposure to H2S, AgNPs can be oxidized to form Ag2S, causing a decrease of surface enhanced Raman spectroscopy (SERS) signals of p,p'-dimercaptoazobenzene. Moreover, SO2 reacts with the amino moiety of MIL-101 to form charge-transfer complexes, resulting in an increment of fluorescent (FL) intensity. The ALM with dual-modal signals can simultaneously analyze H2S and SO2 at a concentration as low as 2.8 × 10-6 and 0.003 μM, respectively. Most importantly, the ALM sensing platform enables targeting mitochondria and detection multiple RSS simultaneously in living cells under external stimulation, as well as displays indiscernible crosstalk between SERS and FL signals, which is very beneficial for the comprehension of physiological issues related with RSS.

Exploring the feasibility of tensor decomposition for analysis of fNIRS signals: a comparative study with grand averaging method.

The analysis of functional near-infrared spectroscopy (fNIRS) signals has not kept pace with the increased use of fNIRS in the behavioral and brain sciences. The popular grand averaging method collapses the oxygenated hemoglobin data within a predefined time of interest window and across multiple channels within a region of interest, potentially leading to a loss of important temporal and spatial information. On the other hand, the tensor decomposition method can reveal patterns in the data without making prior assumptions of the hemodynamic response and without losing temporal and spatial information. The aim of the current study was to examine whether the tensor decomposition method could identify significant effects and novel patterns compared to the commonly used grand averaging method for fNIRS signal analysis. We used two infant fNIRS datasets and applied tensor decomposition (i.e., canonical polyadic and Tucker decompositions) to analyze the significant differences in the hemodynamic response patterns across conditions. The codes are publicly available on GitHub. Bayesian analyses were performed to understand interaction effects. The results from the tensor decomposition method replicated the findings from the grand averaging method and uncovered additional patterns not detected by the grand averaging method. Our findings demonstrate that tensor decomposition is a feasible alternative method for analyzing fNIRS signals, offering a more comprehensive understanding of the data and its underlying patterns.

Investigating Perampanel Antiepileptic Drug by DFT Calculations and SERS with Custom Spinning Cell.

SERS, a clinical practice where medical doctors can monitor the drug concentration in biological fluids, has been proposed as a viable approach to therapeutic drug monitoring (TDM) of the antiepileptic drug Perampanel. The adoption of an acidic environment during the SERS experiments was found to be effective in enhancing the spectroscopic signal. In this work, we combine SERS experiments, conducted with a custom spinning cell in controlled acidic conditions, with DFT calculations aimed at investigating the possible protonated forms of Perampanel. The DFT-simulated Raman spectra of protonated Perampanel accounts for most of the observed SERS signals, thus explaining the effective role of protonation of the analyte. Our results suggest protonation as a viable approach to fostering SERS of alkaline drugs.

Efficient dual sensor based on modified NH2-UiO-66(Zr) MOF for sensitive and rapid monitoring of ultra-trace arsenic (III) in aqueous media

This research aimed to develop a new zirconium-based sensitive material called the 5Cl2HA=(Zr) MOF sensor. The sensor was created by condensing 5-Chloro-2-hydroxyacetophenone with NH2-UiO-66(Zr). Its effectiveness, selectivity, and sensitivity were demonstrated by successfully detecting ultra-trace concentrations of As3+ ions in real water samples using spectrophotometric and fluorometric methods. The characterization of the 5Cl2HA=(Zr) MOF sensor involved various techniques, including SEM, XRD, XPS, BET, and FT-IR, which confirmed its successful synthesis and provided insights into its structural and surface properties. Optimization experiments were conducted to determine the best conditions for detecting As3+ ions using colorimetric and fluorometric methods. The sensor exhibited a rapid detection capability, achieving a stable spectroscopic signal in less than 10 s. The proposed methods adhered to (ICH) guidelines, and parameters such as precision, the limit of detection (LOD), the limit of quantification (LOQ), and linearity were evaluated. The spectrophotometric technique demonstrated the lowest LOD for As3+ ions, with a value of 0.0607 ppm. The recyclability of the sensor was investigated, showing successful reuse up to five times with 85% efficiency by employing 0.01 M HCl as the eluent for regeneration. Furthermore, the performance of the 5Cl2HA=(Zr) MOF sensor was evaluated using various real samples, including groundwater and city water samples, demonstrating high sensitivity and selectivity in detecting As3+ ions in these complex matrices. These findings highlight the sensor's potential for real-world applications.

Non-invasive monitoring of intracranial pressure changes: healthy volunteers study.

Objective: This research aims to evaluate the possible association between pulsatile near infrared spectroscopic waveform features and induced changes in intracranial pressure in healthy volunteers. Methods: An optical intracranial pressure sensor was attached to the forehead of 16 healthy volunteers. Pulsatile near infrared spectroscopic signals were acquired from the forehead during body position changes and Valsalva manoeuvers. Features were extracted from the pulsatile signals and analyses were carried out to investigate the presence of statistical differences in the features when intracranial pressure changes were induced. Classification models were developed utilizing the features extracted from the pulsatile near-infrared spectroscopic signals to classify between different body positions and Valsalva manoeuvre. Results: The presence of significant differences in the majority of the analyzed features (p 0.05) indicates the technique's ability to distinguish between variations in intracranial pressure. Furthermore, the disparities observed in the optical signal features captured by the proximal and distal photodetectors support the hypothesis that alterations in back-scattered light directly correspond to brain-related changes. Further research is required to subtract distal and proximal signals and construct predictive models employing a gold standard measurement for non-invasive, continuous monitoring of intracranial pressure. Conclusion: The study investigated the use of pulsatile near infrared spectroscopic signals to detect changes in intracranial pressure in healthy volunteers. The results revealed significant differences in the features extracted from these signals, demonstrating a correlation with ICP changes induced by positional changes and Valsalva manoeuvre. Classification models were capable of identifying changes in ICP using features from optical signals from the brain, with a sensitivity ranging from 63.07% to 80% and specificity ranging from 60.23% to 70% respectively. These findings underscored the potential of these features to effectively identify alterations in ICP. Significance: The study's results demonstrate the feasibility of using features extracted from optical signals from the brain to detect changes in ICP induced by positional changes and Valsalva manoeuvre in healthy volunteers. This represents a first step towards the non-invasive monitoring of intracranial pressure.

Gate-modulated reflectance spectroscopy for detecting excitonic states in two-dimensional semiconductors

We have developed a microspectroscopy technique for measuring gate-modulated reflectance to probe excitonic states in two-dimensional transition metal dichalcogenides. Through the use of gate-modulated spectroscopy, we were able to detect excited states of excitons and trions, which would typically have weak optical signals in traditional reflectance spectroscopy. Using a transfer matrix method for spectral analysis, the binding energy of trion 2s was determined to be 26 meV. As observing the Rydberg series of excitonic states provides fruitful information on the strong Coulomb interaction in two-dimensional systems, gate-modulated spectroscopy can be a versatile tool for understanding underlying many-body physics as well as designing next-generation quantum optoelectronics based on two-dimensional materials.

Photoacoustic and absorption spectroscopy imaging analysis of human blood.

Photoacoustic and absorption spectroscopy imaging are safe and non-invasive molecular quantification techniques, which do not utilize ionizing radiation and allow for repeated probing of samples without them being contaminated or damaged. Here we assessed the potential of these techniques for measuring biochemical parameters. We investigated the statistical association between 31 time and frequency domain features derived from photoacoustic and absorption spectroscopy signals and 19 biochemical blood parameters. We found that photoacoustic and absorption spectroscopy imaging features are significantly correlated with 14 and 17 individual biochemical parameters, respectively. Moreover, some of the biochemical blood parameters can be accurately predicted based on photoacoustic and absorption spectroscopy imaging features by polynomial regression. In particular, the levels of uric acid and albumin can be accurately explained by a combination of photoacoustic and absorption spectroscopy imaging features (adjusted R-squared > 0.75), while creatinine levels can be accurately explained by the features of the photoacoustic system (adjusted R-squared > 0.80). We identified a number of imaging features that inform on the biochemical blood parameters and can be potentially useful in clinical diagnosis. We also demonstrated that linear and non-linear combinations of photoacoustic and absorption spectroscopy imaging features can accurately predict some of the biochemical blood parameters. These results demonstrate that photoacoustic and absorption spectroscopy imaging systems show promise for future applications in clinical practice.

Ultrasensitive Electrochemical Immunosensors Using Nanobodies as Biocompatible Sniffer Tools of Agricultural Contaminants and Human Disease Biomarkers.

Nanobodies (Nbs) are known as camelid single-domain fragments or variable heavy chain antibodies (VHH) that in vitro recognize the antigens (Ag) similar to full-size antibodies (Abs) and in vivo allow immunoreactions with biomolecule cavities inaccessible to conventional Abs. Currently, Nbs are widely used for clinical treatments due to their remarkably improved performance, ease of production, thermal robustness, superior physical and chemical properties. Interestingly, Nbs are also very promising bioreceptors for future rapid and portable immunoassays, compared to those using unstable full-size antibodies. For all these reasons, Nbs are excellent candidates in ecological risk assessments and advanced medicine, enabling the development of ultrasensitive biosensing platforms. In this review, immobilization strategies of Nbs on conductive supports for enhanced electrochemical immune detection of food contaminants (Fcont) and human biomarkers (Hbio) are discussed. In the case of Fcont, the direct competitive immunoassay detection using coating antigen solid surface is the most commonly used approach for efficient Nbs capture which was characterized with cyclic voltammetry (CV) and differential pulse voltammetry (DPV) when the signal decays for increasing concentrations of free antigen prepared in aqueous solutions. In contrast, for the Hbio investigations on thiolated gold electrodes, increases in amperometric and electrochemical impedance spectroscopy (EIS) signals were recorded, with increases in the antigen concentrations prepared in PBS or spiked real human samples.

Stroke analysis and recognition in functional near-infrared spectroscopy signals using machine learning methods.

Stroke is a high-incidence disease with high disability and mortality rates. It is a serious public health problem worldwide. Shortened onset-to-image time is very important for the diagnosis and treatment of stroke. Functional near-infrared spectroscopy (fNIRS) is a noninvasive monitoring tool with real-time, noninvasive, and convenient features. In this study, we propose an automatic classification framework based on cerebral oxygen saturation signals to identify patients with hemorrhagic stroke, patients with ischemic stroke, and normal subjects. The reflected fNIRS signals were used to detect the cerebral oxygen saturation and the relative value of oxygen and deoxyhemoglobin concentrations of the left and right frontal lobes. The wavelet time-frequency analysis-based features from these signals were extracted. Such features were used to analyze the differences in cerebral oxygen saturation signals among different types of stroke patients and healthy humans and were selected to train the machine learning models. Furthermore, an important analysis of the features was performed. The accuracy of the models trained was greater than 85%, and the accuracy of the models after data augmentation was greater than 90%, which is of great significance in distinguishing patients with hemorrhagic stroke or ischemic stroke. This framework has the potential to shorten the onset-to-diagnosis time of stroke.

Biomimetic Ag/ZnO@PDMS Hybrid Nanorod Array-Mediated Photo-induced Enhanced Raman Spectroscopy Sensor for Quantitative and Visualized Analysis of Microplastics.

Microplastics are persistent pollutants that accumulate in the environment and can cause serious toxicity to mammals. At present, few technologies are able to quantitatively detect chemicals and provide morphological information simultaneously. Herein, we developed a dragonfly-wing-mimicking ZnO nanorod array decorated with AgNPs on polydimethylsiloxane (PDMS) as a surface-enhanced Raman spectroscopy (SERS) and photo-induced enhanced Raman spectroscopy (PIERS) substrate for trace analysis of microplastics. The Ag/ZnO@PDMS hybrid nanorod array endows the sensor with high sensitivity and signal repeatability (RSD ∼ 5.89%), ensuring the reliable quantitative analysis of microplastics. Importantly, when the noble metal-semiconductor substrate was pre-radiated with ultraviolet light, a surprising PIERS was attained, achieving an additional enhancement of 11.3-fold higher than the normal SERS signal. By combining the PIERS technology with the "coffee ring effect", the sensor successfully discerned microplastics of polyethylene (PE) and polystyrene (PS) at a trace level of 25 μg/mL even with a portable Raman device. It was capable of identifying PS microspheres in contaminated tap water, lake water, river water, and seawater with detection limits of 25, 28, 35, and 60 μg/mL, respectively. The recovery rates of PS microspheres in four water environments ranged from 94.8 to 102.4%, with the RSD ranging from 2.40 to 6.81%. Moreover, quantitative and visualized detection of microplastics was readily realized by our sensor. This portable PIERS sensor represents a significant step toward the generalizability and practicality of quantitative and visual sensing technology.

Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b

Extremely low-density exoplanets are tantalizing targets for atmospheric characterization because of their promisingly large signals in transmission spectroscopy. We present the first analysis of the atmosphere of the lowest-density gas giant currently known, HAT-P-67b. This inflated Saturn-mass exoplanet sits at the boundary between hot and ultrahot gas giants, where thermal dissociation of molecules begins to dominate atmospheric composition. We observed a transit of HAT-P-67b at high spectral resolution with CARMENES and searched for atomic and molecular species using cross-correlation and likelihood mapping. Furthermore, we explored potential atmospheric escape by targeting Hα and the metastable helium line. We detect Ca ii and Na i with significances of 13.2σ and 4.6σ, respectively. Unlike in several ultrahot Jupiters, we do not measure a day-to-night wind. The large line depths of Ca ii suggest that the upper atmosphere may be more ionized than models predict. We detect strong variability in Hα and the helium triplet during the observations. These signals suggest the possible presence of an extended planetary outflow that causes an early ingress and late egress. In the averaged transmission spectrum, we measure redshifted absorption at the ∼3.8% and ∼4.5% level in the Hα and He i triplet lines, respectively. From an isothermal Parker wind model, we derive a mass-loss rate of and an outflow temperature of T ∼ 9900 K. However, due to the lack of a longer out-of-transit baseline in our data, additional observations are needed to rule out stellar variability as the source of the Hα and He signals.

Efficient and cost-effective determination of Cr(VI) ions using immobilized 4,5-dihydroxynaphthalene-2,7-disulfonic acid disodium salt dihydrate: a novel analytical reagent with high selectivity and sensitivity

ABSTRACT The determination of Cr(VI) ions in real-world samples is of utmost importance due to the critical role it plays in assessing and monitoring the levels of Cr(VI) contamination in water. The present study introduces a time-saving, cost-effective, selective, highly efficient and eco-friendly analytical reagent to detect Cr(VI) ions. This study presents fabrication of immobilised 4,5-dihydroxynaphthalene-2,7-disulfonic acid disodium salt dihydrate (DHNDSADNa) on a polyethylene-polyamine polyacrylonitrile (PPA) matrix for the determination of Cr(VI) ions using X-ray fluorescence, spectroscopic analysis and potentiodynamic titration methods. The findings revealed that the immobilised DHNDSADNa on the PPA matrix is a highly selective, accurate, easily regenerable, and rapid analytical reagent for the determination of Cr(VI) ions. The DHNDSADNa-anchored PPA framework demonstrated distinctive spectroscopic signals at 460 nm and 590 nm for the reagent and its complex with Cr(VI) ions, respectively. The detection limit for Cr(VI) ions was found to be 2 μg/mL, which is lower than that of DHNDSADNa in solution. Furthermore, the reagent exhibited high selectivity and efficiency for Cr(VI) ions even in the presence of other ions such as Co(II), Ni(II), Al(III), Fe(II), Ti(IV), Zr(IV), Ta(V), Mn(II), and W(VI). Last but not least, the immobilised DHNDSADNa on the PPA matrix exhibits superior analytical characteristics compared to previously utilised methods, making it a promising tool for various applications in medicine, pharmacology, biology, chemistry, and engineering.

Extending the range of metal ions SERS detection using hybrid plasmonic/ZIF-8 particles

Nanosensors based on surface-enhanced Raman spectroscopy (SERS) have emerged as a class of promising optical tools for the ultrasensitive quantification of metal ions of environmental and biological interest. A central bottleneck in this field is the availability of suitable surface receptors able to convert the selective binding with these vibrationless analytes into measurable SERS signals. In this work, we tackle this issue by employing a hybrid substrate comprising a highly SERS-active plasmonic core and a ZIF-8 metal-organic framework (MOF) shell. The ZIF-8 shell firmly captures aromatic receptors close to the plasmonic structure regardless of their intrinsic affinity for the metallic surface and without altering their ability to coordinate metal ions. Furthermore, it imparts molecular sieving abilities enabling the direct use of the SERS sensing platform in complex media such as biological fluids. This was demonstrated by using different classes of chromogenic reagents (bathocuproine, a 2,6':2′,2″-terpyridine derivative, and Arsenazo III) which were exploited for the SERS detection of both transition and alkaline earth metal ions (i.e., divalent copper, cobalt and calcium ions). Notably, we successfully applied this approach for the detection of Cu(II) in untreated urine samples for Wilson's disease diagnosis. Overall, we believe this class of multifunctional hybrid substrates will serve as a valuable material for expanding the applicability of SERS spectroscopy in real-life environmental and biomedical metal ions analysis.

C-STIM: Protocol for a randomized, single-blind, crossover study of cerebellar repetitive transcranial magnetic stimulation (rTMS) for postural instability in people with progressive supranuclear palsy (PSP)

BackgroundMethods for modulating the cerebellum with transcranial magnetic stimulation (TMS) are well established, and preliminary data from our group and others has shown evidence of transient improvements in balance after cerebellar repetitive transcranial magnetic stimulation (rTMS) in progressive suprancuclear palsy (PSP). This study examines extensive posturography measures before and after 10 sessions of cerebellar rTMS and sham TMS in PSP. MethodsThirty subjects with PSP and postural instability will undergo cerebellar active and sham rTMS in a single-blind, crossover design with a randomized order of a 10-day intervention. Primary outcomes will be changes in sway area and medio-lateral range of sway with eyes open while standing on a stationary force-plate, and safety, tolerability, and blindedness. Secondary outcomes will include posturography and gait analysis with body-worn, triaxial inertial sensors, clinical balance scales and questionnaires, and a bedside test of vestibular function. Exploratory outcomes are changes in functional near infrared spectroscopy (fNIRS) signal over the prefrontal, supplementary motor, and primary motor cortices while standing and walking, and speech samples for future analysis. DiscussionThe C-STIM crossover intervention study adds a longer duration of stimulation and extensive posturography measures to more finely measure the improvements in balance and exploratory functional near-infrared spectroscopy (fNIRS) over the prefronal, supplementary motor, and primary motor cortices during balance assessments before and after 10 sessions of cerebellar rTMS and 10 sessions of sham cerebellar TMS. This project will improve our understanding of the importance of the cerebellum for control of postural stability in PSP.

Benefits and Meaning of Lipids Profile in Relation to Oxidative Balance and Brain Morphology in Schizophrenia.

Schizophrenia is characterized by complex metabolic dysregulations and their consequences. Until now, numerous theories have explained its pathogenesis, using a spectrum of available technologies. We focused our interest on lipid profile-periphery high-density cholesterol level and lipoproteins in the human brain and compared magnetic resonance imaging (MRI) scans of patients with schizophrenia and the healthy group. Detailed analysis of biochemical parameters was performed using magnetic resonance spectroscopy. Our study aimed to reveal correlations between periphery high-density lipoproteins levels and lipoproteins in the brain, depicted in MRI scans, and parameters of peripheral oxidative stress expressed as paraoxonase. Patients with schizophrenia have decreased levels of high-density lipoproteins, low paraoxonase activity, and slightly raised sodium in the blood. Positive significant correlations between serum high-density cholesterol and anterior cingulate cortex, unique brain area for schizophrenia pathophysiology, MR spectroscopy signals, and diffusion have been revealed. To our knowledge, this is the first study to describe the effect of an anterior cingulate disorder on high-density cholesterol levels on the development of schizophrenia.

2D vanadium disulfide nanosheets assisted ultrasensitive, rapid, and label-free electrochemical quantification of cancer biomarker (MMP-2)

Cancer is one of the most tormenting global health burdens reporting high mortality and morbidity worldwide. Matrix metalloproteinase 2 (MMP-2) protein has elevated expression for most types of cancers, including prostate and breast cancer. Therefore, accurate and specific detection of MMP-2 biomarker is crucial for screening, treatment, and prognosis of related cancer. In this work, we have proposed a label-free electrochemical biosensor for the detection of MMP-2 protein. This biosensor was fabricated using hydrothermally synthesized vanadium disulfide (VS2) nanosheets with monoclonal anti-MMP2 antibodies biofunctionalized using a suitable linker. The VS2 nanomaterials were synthesized hydrothermally at different reaction temperatures (140 °C, 160 °C, 180 °C and 200 °C) generating different morphologies from a 3D bulk cubic structure at 140 °C to 2D nanosheets at 200 °C. Owing to the advantages of 2D VS2 nanosheets with high surface-to-volume ratio, excellent electrochemical response and high antibody loading possibility, it was selected for fabricating an MMP-2 specific biosensor. The antibody–antigen binding event is analyzed by recording electrochemical impedance spectroscopy signals for different target MMP-2 protein concentrations. The sensitivity and lower limit of detection were 7.272 (ΔR/R)(ng ml)−1 cm−2 and 0.138 fg ml−1, respectively in 10 mM phosphate buffer saline for this proposed sensor. Further, interference studies were also performed which demonstrates the sensor to be highly selective against non-specific target proteins. This 2D VS2 nanosheet-based electrochemical biosensor is a sensitive, cost-effective, accurate, and selective solution for cancer diagnosis.

Integrated Imaging and Spectroscopic Analysis of Painted Fresco Surfaces Using Terahertz Time-Domain Technique

Terahertz time-domain (THz-TD) imaging plays an increasingly significant role in the study of solid-state materials by enabling the simultaneous extraction of spectroscopic composition and surface topography in the far-infrared region (3–300 cm−1). However, when applied to works of art in reflection configuration, significant challenges arise, including weak signal intensity, multiple signal losses, and surface distortion. This study proposes a practical solution to overcome these limitations and conducts an integrated imaging and spectroscopic analysis on painted fresco surfaces, allowing for the retrieval of surface thicknesses, material distribution, and pigment spectroscopic signals. The study addresses the issue of surface geometrical distortion, which hampers the accurate determination of the THz phase signal. By tackling this challenge, this work successfully determines the absorption coefficient for each point on the surface and retrieves spectroscopic signatures. Additionally, the temporal deconvolution technique is employed to separate different layers of the sample and differentiate between outer and inner surface topography. The objective of this study is to demonstrate the advantages and limitations of THz-TD imaging in determining surface thicknesses, material distribution, and pigment spectroscopic signals. The results obtained highlight the potential of THz-TD imaging in investigating painted works of art, offering new possibilities for routine analysis in the field of cultural heritage preservation.

Modified paper-based substrates fabricated via electrostatic attraction of gold nanospheres for non-destructive detection of pesticides based on surface-enhanced Raman spectroscopy.

Flexible surface-enhanced Raman spectroscopy (SERS) substrates such as paper-based substrates show great potential for rapid detection of residual chemicals on food surfaces. However, controlling the density and distribution of metallic nanoparticles adsorbed on the paper is still challenging. The amount of gold (Au) nanospheres (51 ± 4 nm) attached on the filter paper modified with 3-aminopropyltriethoxysilane (APTES) was tunable, increasing as the level of APTES (2.5-15.0 g kg-1 ) applied for paper modification increased. Moreover, the Au nanospheres were relative evenly distributed on the filter paper modified with 2.5-10.0 g kg-1 of APTES, which resulted in excellent intra- and inter-reproducibility of SERS signals for pesticides including thiram, diquat dibromide, and paraquat dichloride (relative standard deviation = 2.2-10.1%). The modified paper-based substrate could be used to detect as low as 0.05-0.2 mg L-1 of pesticides in standard solutions, and as low as 5-20 ng cm-2 of residual pesticides on apple skins with minimum sample pretreatment. This paper-based substrate with tunable feature for the density and distribution of nanoparticles is applicable for rapid SERS detection of residual pesticides in fruits and vegetables. © 2023 Society of Chemical Industry.

Feature extraction of time series data on functional near-infrared spectroscopy and comparison of deep learning performance for classifying patients with Alzheimer's-related mild cognitive impairment: a post-hoc analysis of a diagnostic interventional trial.

This study aimed to define a method of classifying patients with mild cognitive impairment caused by Alzheimer's disease by the retrieval of functional near-infrared spectroscopy (fNIRS) signal characteristics obtained during olfactory stimulation and the validation of deep learning findings. Participants were recruited for the study from March 02 and August 30, 2021. A total of 78 participants met the criteria for categorization. The Mini-Mental State Examination and the Seoul Neuropsychological Scale were used to distinguish between patients with mild Alzheimer's disease-related cognitive impairment and healthy controls. fNIRS data received during olfactory stimulation were used to create 1,680 time-series sample values. A total of 150 indices with a p-value ≤ 0.1 were used as deep learning features to construct the result values for 120 models accounting for all conceivable combinations of data ratios. For this trial, 78 participants were recruited for the original intervention trial. The average accuracy of the 120 deep-learning models for classifying patients with Alzheimer's-related mild cognitive impairment ranged from 0.78 to 0.90. Sensitivity ranged from 0.88 to 0.96 for the 120 models, while specificity ranged from 0.86 to 0.94. The F1 scores ranged from 0.74 to 0.88. At 0.78 to 0.90, the precision and recall were equivalent. This trial using a deep-learning model found that the representative value extracted from the time series data of each channel could distinguish between healthy people and patients with mild cognitive impairment caused by Alzheimer's disease.