Recent advances in optical methods for biological sample characterization reflect a profound shift driven by the convergence of photonic innovation, computational intelligence, and increasing biological complexity. In this Perspective, we present a concise overview and a forward-looking vision of five core domains that structure this Research Topic: advanced bioimaging technologies, next-generation optical biosensors, optical tweezers for nanoscale force measurements, particle tracking techniques, and artificial intelligence-driven data analysis. Rather than offering an exhaustive review, we highlight selected conceptual and technological developments, identify current limitations, and discuss emerging opportunities where integration across optical modalities and computational approaches may prove decisive. Particular emphasis is placed on multimodal and quantitative platforms, in situ and real-time measurements, high-throughput methodologies, and the growing role of physics-informed and on-the-fly artificial intelligence. By articulating common challenges and shared future directions across these five areas, this article aims to stimulate interdisciplinary dialogue, provide a unifying framework for the contributions collected in this Research Topic, and encourage further advances in optical technologies for probing complex biological systems.

Electrochemical biosensors are promising tools for clinical diagnostics, yet challenges remain in extending sensitivity, linear range, and stability, particularly in complex biological matrices. Here, we report an electrochemical aptasensor for dengue NS1 protein detection based on a self-assembled monolayer (SAM) of DNA aptamers, 6-mercapto-1-hexanol, and 6-ferrocenyl-hexanethiol, characterized using electrochemical capacitance spectroscopy (ECS). The aptamer:thiol ratio was optimized, with the 1:50 condition providing the best analytical performance. The platform achieved sensitivities of 0.18% ± 0.02% per decade in PBS and 0.21% ± 0.01% per decade in commercial human serum, within a linear range of 0.01–1,000 ng/mL. Limits of detection were 24.9 ng/mL in PBS and 25.8 ng/mL in serum. Although long-term stability decreased after 7–14 days, the sensor demonstrated robustness in both simple and complex medium. These results confirm the viability of aptamer-based ECS platforms for clinically relevant NS1 detection and represent a step toward integrating quantum-scale concepts into bioelectrochemical sensing.

One particularly fruitful research in the fields of integrated photonics, carried out by a good number of physicists and engineers, concerns the study of different types of materials to be used to control the detection of photons, if not just single photons, in interferometers. In a Mach–Zehnder interferometer, which consists of two beam-splitters, two mirrors, and two detectors, a material that can cause a controlled change in the phase of light in one of the two arms of the interferometer consequently allows control of the probability of detection at the two detectors. In this work, we use an electrochromic molecule, N,N′-bis(cysteine)pyromellitic diimide (BCPD), that has a refractive index dependent on the applied electric field. We simulate the single-photon detection probability in a Mach–Zehnder interferometer with direct light transmission and a waveguide-based Mach–Zehnder interferometer, consisting of two 3-dB couplers connected by two optical channel waveguides. With the employment of the non-equilibrium Green’s function formalism, we have simulated the conductance of BCPD. The results could be of interest in quantum communication.

Neuropathic pain (NP) is a chronic and disabling condition resulting from injury or disease of the somatosensory system. Characterized by sensory disturbances such as allodynia, hyperalgesia, and spontaneous pain, NP remains a major clinical challenge due to the limited efficacy and significant side effects of conventional pharmacological treatments. In recent years, photobiomodulation therapy (PBMT), also referred to as low-level laser therapy (LLLT), has emerged as a promising non-pharmacological strategy for managing NP. PBMT involves the application of red or near-infrared light to biological tissues, triggering a range of photochemical and photophysical responses that enhance mitochondrial function, reduce oxidative stress, modulate inflammation, and support neural repair. This review provides a comprehensive synthesis of the current evidence on PBMT for NP, integrating mechanistic insights with preclinical findings. We discuss the biological underpinnings of PBMT, including mitochondrial activation via cytochrome c oxidase, modulation of cytokines and oxidative stress markers, and upregulation of neurotrophic factors such as BDNF. Preclinical studies in well-established NP models (e.g., chronic constriction injury, spared nerve injury, diabetic neuropathy) demonstrate consistent analgesic effects and neuroprotective outcomes following both local and remote/systemic PBMT applications. We also highlight key limitations and knowledge gaps in the field, including the need for standardized protocols, greater exploration of remote PBMT strategies, and improved consideration of sex-based responses. Finally, we outline future directions, such as integration with multimodal therapies, personalized dosimetry, and the development of wearable and transcranial PBMT technologies. Together, the existing body of evidence supports PBMT as a safe and potentially effective tool for NP management, while underscoring the need for more rigorous and translational research.

Dopamine (DA) is one of the most important neurotransmitters in the human body, which is becoming a key breakthrough for addressing myopia, neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease, and mental diseases such as depression and schizophrenia. However, the activity of DA shows diurnal and seasonal variations, which may be due to the influence of solar activity time on the biological clock of the suprachiasmatic nucleus. By irradiating ARPE-19 cells with red and near-infrared light of different wavelengths, we studied and confirmed that the secretion and transformation of the light-induced neurotransmitter DA significantly depend on light wavelength and light dose. LED-chip light sources with emission peaks at 620, 680, 730, 800, and 850 nm and phosphor-converted LED light sources with emission peaks at 710 and 830 nm were used. It was confirmed that both the red and near-infrared light with variant wavelengths and doses can induce DA secretion to some extent. Yet, the concentrations of DA induced by the wideband spectral light of W710 and SW830 are higher than those induced by the narrowband single-LED-chip light and remain relatively stable under variant light doses. Among all the light sources, the model SW830 light source is the best one. This paper proposes a noninvasive way to induce the secretion of neurotransmitter DA and paves a reliable way to treat myopia, neurodegenerative diseases, and other diseases by using the neurotransmitter DA and the basic knowledge of photophysiology.

IntroductionOptical property retrieval in diffuse reflectance imaging, like diffuse reflectance spectroscopy (DRS) and hyperspectral imaging (HSI), often involves fitting measured spectra to analytical solutions using approximations such as Diffusion Theory (DT). This method, while accurate, is not always generalizable due to the assumptions inherent in DT and results in non-unique solutions for optical properties and physiological parameters. In addition, it is computationally intensive. Physics-inspired deep learning offers generalizable data descriptions guided by physical principles but requires extensive labelled data, which is hard to obtain, especially in medical contexts.MethodsWe propose a deep learning approach to retrieve physiological parameters from DRS and HSI spectra using DT-simulated training data. The DT-simulated data is synthesised using a range for the optical properties: Blood Volume Fraction (BVF), Saturation, water-fat ratio (WFR), average blood vessel radius (R), scattering amplitude (SA), and scattering slope (SL). The range for these parameters we have extracted from literature.ResultsOur feed-forward neural network achieved median relative errors of 4% and 2% for DRS and HSI, respectively.DiscussionResults suggest that the proposed method is robust and that retrieval of optical properties is possible with similar results to DT but also reducing operation time.

Reaching nanometric spatial resolution in terahertz (THz) nanoimaging provides a powerful tool for the characterization of photonic devices. Here, we couple a THz source to a conductive atomic force microscope to measure the THz photo-induced current with nanometric spatial resolution. We aim at measuring the THz photo-induced current of few-layer graphene flakes with a platinum nanometric probe that acts both as THz field-enhancement antenna and as metal counter-electrode that forms a nanojunction. The THz beam is generated at 0.61 THz by an amplifier-multiplier chain. THz photo-induced current signals are detected and compared with the current-voltage characteristics. With this method, we map nanometric charge puddles in few-layer graphene flakes, and observe evidence of THz rectification at the platinum-graphene nanojunction. The local junction characteristic can be used to assess the surface quality of 2D-material flakes.

Introduction:Surface-emitting terahertz quantum cascade lasers (THz QCLs) are highly promising for applications requiring high-quality far-field beams and controlled beam divergence. However, limited brightness and output power in conventional surface-emitting designs remain significant barriers to practical implementation. Although photonic crystal structures and distributed Bragg reflectors have been explored to enhance surface emission, intrinsic limitations in emission area scaling and brightness improvement persist. Thus, new strategies are essential to advance the performance of surface-emitting THz QCLs.Methods:This study proposes a plasma-assisted photonic crystal waveguide design to improve surface emission efficiency in THz QCLs. A three-dimensional TM-mode coupled wave theory (3D TM-mode CWT) model was developed, incorporating effective permittivity enhancement and a self-consistent iterative scheme to accurately simulate optical field distribution and interaction within the structure.Results:Simulations reveal that the introduction of a plasma layer effectively disrupts the optical field symmetry characteristic of conventional double-metal waveguides, promoting vertical emission. Through systematic optimization, a plasma layer thickness of 0.8 μm was identified as having the potential to achieve surface emission efficiencies exceeding those of conventional structures by over two orders of magnitude. This enhancement is realized without significantly increasing fabrication complexity.Discussion:The plasma-assisted photonic crystal waveguide design offers a viable pathway toward realizing high-brightness surface-emitting THz QCLs. Although challenges such as material growth control and thermal management remain, the substantial improvement in surface emission efficiency underscores the potential of this approach for future high-performance terahertz applications.

This Research Topic on "Ocular Imaging Technology and Application," comprising submissions from Frontiers in Photonics and Frontiers in Medicine, was expertly organized by Associate Editors Dr. Xinyu Liu (Peking University, China), Dr. Binyao Tan (Singapore Eye Research Institute, Singapore), and Dr. Xiaojun Yu (Northwestern Polytechnical University, China), under the supervision of Dr. Leopold Schmetterer (Medical University of Vienna, Austria). This collection has been highly successful, publishing 14 articles-including original research (10), minireviews (2), and case reports (2)-selected from a total of 31 submissions. Overall, this collection featured the application of novel ocular imaging technologies, including OCT, OCTA, confocal microscopy, and two-photon imaging across various eye conditions. The emerging applications of artificial intelligence (AI) in ocular imaging analysis are particularly noteworthy, highlighting the field's progression toward more precise, efficient, and personalized vision care.