This study presents the design and characterization of a highly Q-Factor and ultrasensitive THz refractive-index-based metamaterial biosensor for detecting coronaviruses at electronic infusion device (EID) concentrations $$0.01$$0.01 and $$1000$$1000 . The proposed biosensor is constructed using a gold plane perforated by a star shape. Moreover, the developed structure is polarization insensitive due to the rotatory symmetry and is angularly stable up to 90°. The proposed biosensor achieves near-perfect absorption at $$1.9656$$1.9656 THz and $$3.3692$$3.3692 THz. The full width at half-maximum is $$5.276\%$$5.276 % and $$0.641\%$$0.641 % comparative to the absorption frequency. In addition, the estimated free space absorptivity is 97.2% and 99.1% with a Q-Factor of 19.08 and 155.98 at 1.9656 THz and 3.3692 THz, respectively, when transverse electromagnetic mode (TEM) was selected. The perforated star-shaped was evaluated for IBV (Family of COVID-19) regarding frequency deviation, sensitivity, and figure of merit. Results show that at 1.9656 THz, the proposed design gives 30.8 GHz, 940.49 GHz/RIU, and 8.6, respectively, for 0.01 (EID/5 µL concentration) and 4.4 GHz, 2200 × 10³ GHz/RIU, and 20,215.014, respectively at 1.9612 THz for 1000 (EID/5 µL concentration). Although the obtained results demonstrate the efficiency of the proposed THz metamaterial biosensor in coronavirus detection, it has also been extended for other types of viruses, including H5N1, H5N2, H9N2, H4N6, and FAdV, based on the slight variations in their refractive indices. Additionally, the influence of the design parameters is optimized in order to achieve better performance.

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.

Optical differentiation is a promising tool in biomedical diagnosis mainly because of its safety. The optical parameters’ values of biological tissues differ according to the histopathology of the tissue and hence could be used for differentiation. The optical fluence rate distribution on tissue boundaries depends on the optical parameters. So, providing image displays of such distributions can provide a visual means of biomedical diagnosis. In this work, an experimental setup was implemented to measure the spatially-resolved steady state diffuse reflectance and transmittance of native and coagulated chicken liver and native and boiled breast chicken skin at 635 and 808 nm wavelengths laser irradiation. With the measured values, the optical parameters of the samples were calculated in vitro using a combination of modified Kubelka-Munk model and Bouguer-Beer-Lambert law. The estimated optical parameters values were substituted in the diffusion equation to simulate the fluence rate at the tissue surface using the finite element method. Results were verified with Monte-Carlo simulation. The results obtained showed that the diffuse reflectance curves and fluence rate distribution images can provide discrimination tools between different tissue types and hence can be used for biomedical diagnosis.

Benign prostatic hyperplasia (BPH) is a chronic, progressive disease characterized by mesenchymal cell-predominance and stromal and glandular cell-hyperproliferation. Although, the precise cause of BPH is unknown, it is believed to be associated with hormonal changes in aging men. Despite androgens and ageing are likely to play a role in the development of BPH, the pathophysiology of BPH remains uncertain. This paper aims to evaluate the diagnostic efficacy of platelet-to-lymphocyte ratio (PLR), neutrophil–lymphocyte ratio (NLR) and systemic immune-inflammation index in in diagnosing BPH. A single-center-randomized-retrospective study was carried out at Alzahraa university hospital between January 2022 and November 2022 on 80 participants (40 non-BPH subjects and 40 patients with symptomatic enlarged prostate) who visited the outpatient clinic or admitted to the urology department. The BPH cases were evaluated by digital rectal examination (DRE), International Prostate Symptom Score (IPSS), prostate size, prostate specific antigen (PSA), TRUS biopsy in elevated PSA > 4 ng/ml, PLR, NLR and systemic immune inflammatory (SII). The diagnosing efficiency of the selected parameters was evaluated using Receiver Operating Characteristic (ROC) and Artificial Neural Network (ANN) showing excellent discrimination with 100% accuracy and AUC = 1 in the ROC curves. Moreover, the accuracy rate of the ANN exceeds 99%. Conclusion: PLR, NLR and SII can be significantly employed for diagnosing BPH.