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Optical diagnostics methods are significantly appealing in biological applications since they are non-destructive, safe, and minimally invasive. Laser-induced fluorescence is a promising optical spectrochemical analytical technique widely employed for tissue classification through molecular analysis of the studied samples after excitation with appropriate short-wavelength laser light. On the other hand, diffuse optics techniques are used for tissue monitoring and differentiation based on their absorption and scattering characteristics in the red to the near-infrared spectra. Therefore, it is strongly foreseen to obtain promising results by combining these techniques. In the present work, tissues under different conditions (hydrated/dry skin and native/boiled adipose fat) were distinguished according to their fluorescence emission, absorption, and scattering properties. The selected tissues’ optical absorption and scattering parameters were determined via Kubelka–Munk mathematical model according to the experimental tissue reflectance and transmittance measurements. Such measurements were obtained using an optical configuration of integrating sphere and spectrometer at different laser wavelengths (808, 830, and 980 nm). Moreover, the diffusion equation was solved for the fluence rate at the sample surface using the finite element method. Furthermore, the accuracy of the obtained spectroscopic measurements was evaluated using partial least squares regression statistical analysis with 0.87 and 0.89 R-squared values for skin and adipose fat, respectively.

The present work introduces a detailed investigation to discriminate between some types of igneous and sedimentary rocks. The used experimental setup was a conventional single-pulse nanosecond system with a neodymium YAG laser at two excitation wavelengths, 1064 nm, and 355 nm. The laser-induced emission spectra of the rock samples were normalized to the irradiance for both utilized laser wavelengths to compensate for the different laser pulse energies used. The surface hardness values of the studied samples were spectroscopically determined via the intensity ratios of the ionic-to-atomic spectral lines of iron in the rock samples. Besides, the laser-induced plasma parameters, namely the plasma temperature and the electron density, were calculated from the obtained spectral data. It is found that the plasma temperature, in the infrared-laser case, was higher than that of the ultraviolet laser while the electron density values were the opposite. Multivariate statistical analysis of the spectroscopic data using the principal component analysis technique revealed the possibility of discrimination between different types of rocks. Furthermore, the laser-induced breakdown spectroscopy results have been validated by comparison to the results of the energy-dispersive X-ray analysis for the same samples.

Miniaturized microstrip antennas are efficiently utilized in MICS band wearable and implantable medical applications. However, the properties of the materials employed for antenna fabrication influence its resultant parameters and play a vital role in its performance. Rogers have been widely used as a substrate material in various antenna designs. In this work, a proof of concept study has been conducted to determine how altering the substrate used in antenna construction affects antenna performance. Using the laser-induced breakdown spectroscopy (LIBS) approach, the elements present in the two distinct substrate raw materials were compared to investigate potential effects on the antenna’s performance. Given their accessibility and widespread use, two types of Rogers’ substrates, RO 3210 and RO 4003, were selected. Furthermore, two identical antenna designs were modeled and fabricated using the two substrate materials. The reflection coefficient (S11) and other antenna parameters were determined and compared. Moreover, the recorded LIBS spectra were evaluated using principle component analysis and partial least square regression techniques. The LIBS spectra showed different copper and iron contents between the two Rogers (i.e., other dielectric properties), leading to a frequency shift. Additionally, impurities in the fabricated material increase the possible losses. Consequently, the elemental contents of the utilized Rogers control the antenna’s performance and can ensure its safety in wearable and implant applications.

Rapid bacterial detection is essential in clinical diagnostics, environmental monitoring, and food industry quality control, where sensitivity and speed are critical. This study evaluates four Laser-Induced Fluorescence (LIF) techniques, Conventional LIF, Reflection-Enhanced LIF (RELIF), Wavefront-Enhanced LIF (WELIF), and a combined approach (WERELIF), to improve sensitivity and lower detection limits for bacterial quantification. Using Pseudomonas aeruginosa as a model organism, fluorescence was excited at 405 nm, with a peak at approximately 500 nm. Calibration curves were constructed to determine the limit of detection (LOD) of each method and assess its performance in trace bacterial analysis. WERELIF demonstrated the highest fluorescence intensity and the lowest limit of detection (LOD) among the tested techniques, making it the most effective method for detecting low bacterial concentrations. RELIF exhibited significant signal enhancement due to reflective optimization. In contrast, WELIF provided moderate improvements, which were affected by sample inhomogeneity. The enhanced LIF techniques were superior to the conventional approach, particularly in terms of higher fluorescence intensity. This advantage is significant for applications such as early pathogen detection in clinical samples or monitoring bacterial contamination in water and food supplies. These findings provide a foundation for improving fluorescence-based bacterial quantification, with potential applications in point-of-care diagnostics, environmental surveillance, and industrial biosensing.

Rapid and successful clinical diagnosis and bacterial infection treatment depend on accurate identification and differentiation between different pathogenic bacterial species. A lot of efforts have been made to utilize modern techniques which avoid the laborious work and time-consuming of conventional methods to fulfill this task. Among such techniques, laser-induced breakdown spectroscopy (LIBS) can tell much about bacterial identity and functionality. In the present study, a sensitivity-improved version of LIBS, i.e. nano-enhanced LIBS (NELIBS), has been used to discriminate between two different bacteria ( Pseudomonas aeruginosa and Proteus mirabilis ) belonging to different taxonomic orders. Biogenic silver nanoparticles (AgNPs) are sprinkled onto the samples’ surface to have better discrimination capability of the technique. The obtained spectroscopic results of the NELIBS approach revealed superior differentiation between the two bacterial species compared to the results of the conventional LIBS. Identification of each bacterial species has been achieved in light of the presence of spectral lines of certain elements. On the other hand, the discrimination was successful by comparing the intensity of the spectral lines in the spectra of the two bacteria. In addition, an artificial neural network (ANN) model has been created to assess the variation between the two data sets, affecting the differentiation process. The results revealed that NELIBS provides higher sensitivity and more intense spectral lines with increased detectable elements. The ANN results showed that the accuracy rates are 88% and 92% for LIBS and NELIBS, respectively. In the present work, it has been demonstrated that NELIBS combined with ANN successfully differentiated between both bacteria rapidly with high precision compared to conventional microbiological discrimination techniques and with minimum sample preparation.

Inflammation detection in blood serum samples is commonly performed using clinical analyzers, which are expensive and complex and require specific labels or markers. Spectrochemical analytical techniques, such as laser-induced breakdown spectroscopy (LIBS) and laser-induced fluorescence (LIF), have emerged as alternative methods for qualitative and non-destructive analysis in various fields. This study explores applying LIBS and LIF techniques for label-free discrimination between normal and inflammatory blood serum samples. In the LIBS analysis, the serum samples are deposited on ashless filter paper and exposed to a high-power Nd:YAG laser source to induce plasma emission. The emitted light is dispersed in a spectrometer and an ICCD camera that captures the spectral lines. The LIF technique utilizes a diode-pumped solid-state laser source to excite the blood serum sample placed in a quartz cuvette. The resulting emission spectra are collected and analyzed using a spectrometer equipped with a CCD detector. The obtained spectroscopic data from both techniques is subjected to principal component analysis (PCA) and graph theory for classification and clustering. The PCA classified the two classes with a data variance of 85.4% and 92.8% based on the first two principal components (PCs) for LIBS and LIF spectra. The graph theory clustered the two classes with an accuracy of 76% and 100% based on LIBS and LIF spectra. The statistical methods effectively discriminate between normal and inflammatory serum samples, providing satisfactory results. The proposed spectrochemical methods offer several advantages over traditional clinical analyzers. They are cost-effective and rapid, making them suitable for the fast and reliable identification of serum samples in laboratories. The non-destructive nature of these techniques eliminates the need for specific labels or markers, further streamlining the analysis process. Graphical Abstract

Abstract We aimed to evaluate the efficacy of cognitive behavioral therapy (CBT) aided by biofeedback in rehabilitating Egyptian adolescents who were suffering from chronic fatigue syndrome (CFS). Out of 298 screened individuals with chronic fatigue, only 159 adolescents were eligible for study; of them 63 cases lost follow up and four cases were further excluded because of switch leaving only 92 cases with complete database. Age range of enrolled cases was 10–14 years and male/female ratio (1/2.5). They were recruited from private schools and polyclinics in Eastern province, Saudi Arabia; some cases were referred by psychiatrists in private hospitals of the same area. All cases were diagnosed as CFS according to the recommendations of International CFS Study Group. Patients were randomly allocated to one of two groups; interventional group comprised 50 cases and underwent CBT aided with biofeedback over a period of 18 months applying two protocols according to patient’s activity pattern. Forty-two cases were followed and treated symptomatically and used as control group. Data were processed and analyzed using SPSS version 10.0. The most common symptoms were unrefreshing sleep, headache and myalgia (95.8%, 67.7% and 50% respectively). Patients of interventional group showed marked improvement manifested by decrement of checklist individual strength (decreased 23.1%; 95% confidence interval 19.2–25.4%) and better school attendance (increased 31.5%; 95% confidence interval 29.8–36.6 hours/month) with the disappearance of some self-rated CFS symptoms. CBT aided by biofeedback could be very effective in treatment of adolescents suffering from CFS taking in consideration the stressors and precipitating factors during settings of psychotherapy.