Design and Verification of Integrated Circuitry for Real-Time Frailty Monitoring.

In this study, a new wireless electronic circuitry to analyze weight distribution was designed and incorporated into a chair to gather data related to common human postures (sitting and standing up). These common actions have a significant impact on various motor capabilities, including gait parameters, fall risk, and information on sarcopenia. The quality of these actions lacks an absolute measurement, and currently, there is no qualitative and objective metric for it. To address this, the designed analyzer introduces variables like Smoothness and Percussion to provide more information and objectify measurements in the assessment of stand-up/sit-down actions. Both the analyzer and the proposed variables offer additional information that can objectify assessments depending on the clinical eye of the physicians.

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics.

This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system's effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

Enhanced refractometer for aqueous solutions based on perfluorinated polymer optical fibres.

The use of the new CYTOP (Cyclized Transparent Optical Polymer) fibres for the inscription of optical structures and the detection of different parameters has started to gain importance in the past decade. This work presents the design, simulation and manufacture of a CYTOP-based surrounding refractive index sensor for aqueous solutions, given its high sensitivity in the range 1.315 - 1.333 (at 1550 nm wavelength). The structure is based on a bent and polished fibre (in order to increase its sensitivity), the polished area being the surface on which a diffraction grating is inscribed with a femtosecond laser. The interaction of the field propagated by the fibre with the grating causes diffraction of certain orders towards the outside, depending, among other things, on the refractive index of the fluid. In addition to a maximum sensitivity of -208.8 nm/RIU and a remarkable insensitivity to temperature, it offers a spectral fingerprint of each sensed fluid.

A Dual-Wavelength Fiber Laser Sensor with Temperature and Strain Discrimination.

This work presents a dual-wavelength C-band erbium-doped fiber laser assisted by an artificial backscatter reflector. This fiber-based reflector, inscribed by femtosecond laser direct writing, was fabricated into a single mode fiber with a length of 32 mm. The dual-wavelength laser obtained, centered at 1527.7 nm and 1530.81 nm, showed an optical signal-to-noise ratio over 46 dB when pumped at 150 mW. Another feature of this laser was that the power difference between the two channels was just 0.02 dB, regardless of the pump power, resulting in a dual emission laser with high equalization. On the other hand, an output power level and a central wavelength instability as low as 0.3 dB and 0.01 nm were measured, in this order for both channels. Moreover, the threshold pump power was 40 mW. Finally, the performance of this dual-wavelength fiber laser enhanced with a random reflector for sensing applications was studied, achieving the simultaneous measurement of strain and temperature with sensitivities around 1 pm/µÎµ and 9.29 pm/°C, respectively.

Recent Advances in Biomedical Photonic Sensors: A Focus on Optical-Fibre-Based Sensing.

In this invited review, we provide an overview of the recent advances in biomedical photonic sensors within the last five years. This review is focused on works using optical-fibre technology, employing diverse optical fibres, sensing techniques, and configurations applied in several medical fields. We identified technical innovations and advancements with increased implementations of optical-fibre sensors, multiparameter sensors, and control systems in real applications. Examples of outstanding optical-fibre sensor performances for physical and biochemical parameters are covered, including diverse sensing strategies and fibre-optical probes for integration into medical instruments such as catheters, needles, or endoscopes.

Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System.

Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper will use the Kinect v2 device, which incorporates a random forest algorithm for 25 joints detection in the human body. However, although Kinect v2 is a powerful tool, there are circumstances in which the device's design does not allow the extraction of such data or the accuracy of the data is low, as is usually the case with foot position. We propose a method of acquiring this data in circumstances where the Kinect v2 device does not recognize the body when only the lower limbs are visible, improving the ankle angle's precision employing projection lines. Using a region-based convolutional neural network (Mask RCNN) for body recognition, raw data extraction for automatic ankle angle measurement has been achieved. All angles have been evaluated by inertial measurement units (IMUs) as gold standard. For the six tests carried out at different fixed distances between 0.5 and 4 m to the Kinect, we have obtained (mean ± SD) a Pearson's coefficient, r = 0.89 ± 0.04, a Spearman's coefficient, ρ = 0.83 ± 0.09, a root mean square error, RMSE = 10.7 ± 2.6 deg and a mean absolute error, MAE = 7.5 ± 1.8 deg. For the walking test, or variable distance test, we have obtained a Pearson's coefficient, r = 0.74, a Spearman's coefficient, ρ = 0.72, an RMSE = 6.4 deg and an MAE = 4.7 deg.

Reflection-based lab-in-fiber sensor integrated in a surgical needle for biomedical applications.

Recently, lab-in-fiber (LIF) sensors have offered a new paradigm in many different scenarios, such as optofluidics, due to their ability to integrate different multiphysics sensor elements in a small space. In this Letter, the design and manufacture of a multiparameter sensing device is proposed, through the combination of an in-fiber air microcavity and a plane-by-plane fiber Bragg grating (FBG). The reflection-based sensor, with a length of less than 300 µm, is located at the end of a single-mode fiber and integrated into a surgical needle for exploitation in biomedical applications. Here we present the first (to our knowledge) ultra-short LIF sensor reported under the "touch and measure" approach. In this first prototype, the detection of axial tensile strain (6.69pm/µÎµ in air cavity) and surrounding refractive index (11.5 nm/RIU in FBG) can be achieved simultaneously.

Optical Fiber Sensors by Direct Laser Processing: A Review.

The consolidation of laser micro/nano processing technologies has led to a continuous increase in the complexity of optical fiber sensors. This new avenue offers novel possibilities for advanced sensing in a wide set of application sectors and, especially in the industrial and medical fields. In this review, the most important transducing structures carried out by laser processing in optical fiber are shown. The work covers different types of fiber Bragg gratings with an emphasis in the direct-write technique and their most interesting inscription configurations. Along with gratings, cladding waveguide structures in optical fibers have reached notable importance in the development of new optical fiber transducers. That is why a detailed study is made of the different laser inscription configurations that can be adopted, as well as their current applications. Microcavities manufactured in optical fibers can be used as both optical transducer and hybrid structure to reach advanced soft-matter optical sensing approaches based on optofluidic concepts. These in-fiber cavities manufactured by femtosecond laser irradiation followed by chemical etching are promising tools for biophotonic devices. Finally, the enhanced Rayleigh backscattering fibers by femtosecond laser dots inscription are also discussed, as a consequence of the new sensing possibilities they enable.

Virtual FBGs Using Saturable Absorbers for Sensing with Fiber Lasers.

The spectral narrowing of Fiber Bragg Gratings (FBGs) introduced by unpumped Er-doped fiber (EDF) was analyzed for fiber lasers (FL). Owing to spatial hole burning (SHB), the spectral response of a virtual FBG can be employed for narrowing the band pass filter employed to determine the lasing wavelength of laser cavities. A common FL was mounted to analyze the spectral stability of the method, and a FL sensor for strain and temperature measurements was experimentally characterized to determine the stability of the narrowing effect achieved by the unpumped EDF, which acts as a virtual FBG. The results exhibited remarkably good narrowing effects of the spectral response of uniform FBGs.

Identification of hypermucoviscous Klebsiella pneumoniae K1, K2, K54 and K57 capsular serotypes by Raman spectroscopy.

Antimicrobial resistance poses a significant challenge in modern medicine, affecting public health. Klebsiella pneumoniae infections compound this issue due to their broad range of infections and the emergence of multiple antibiotic resistance mechanisms. Efficient detection of its capsular serotypes is crucial for immediate patient treatment, epidemiological tracking and outbreak containment. Current methods have limitations that can delay interventions and increase the risk of morbidity and mortality. Raman spectroscopy is a promising alternative to identify capsular serotypes in hypermucoviscous K. pneumoniae isolates. It provides rapid and in situ measurements with minimal sample preparation. Moreover, its combination with machine learning tools demonstrates high accuracy and reproducibility. This study analyzed the viability of combining Raman spectroscopy with one-dimensional convolutional neural networks (1-D CNN) to classify four capsular serotypes of hypermucoviscous K. pneumoniae: K1, K2, K54 and K57. Our approach involved identifying the most relevant Raman features for classification to prevent overfitting in the training models. Simplifying the dataset to essential information maintains accuracy and reduces computational costs and training time. Capsular serotypes were classified with 96 % accuracy using less than 30 Raman features out of 2400 contained in each spectrum. To validate our methodology, we expanded the dataset to include both hypermucoviscous and non-mucoid isolates and distinguished between them. This resulted in an accuracy rate of 94 %. The results obtained have significant potential for practical healthcare applications, especially for enabling the prompt prescription of the appropriate antibiotic treatment against infections.

LAM Test: A New Cognitive Marker for Early Detection in Preclinical Alzheimer's Disease.

Background: With the arrival of disease-modifying treatments, it is mandatory to find new cognitive markers that are sensitive to Alzheimer's disease (AD) pathology in preclinical stages. Objective: To determine the utility of a newly developed Learning and Associative Memory face test: LAM test. This study examined the relationship between AD cerebrospinal fluid (CSF) biomarkers and performance on LAM test, and assessed its potential clinical applicability to detect subtle changes in cognitively healthy subjects at risk for AD. Methods: We studied eighty cognitively healthy volunteers from the Valdecilla cohort. 61% were women and the mean age was 67.34 years (±6.416). All participants underwent a lumbar puncture for determination of CSF biomarkers and an extensive neuropsychological assessment, including performance on learning and associative memory indices of the LAM-test after 30 min and after 1 week, and two classic word lists to assess verbal episodic memory: the Rey Auditory Verbal Learning Test (RAVLT) and the Free and Cued Selective Reminding Test (FCSRT). We analyzed cognitive performance according to amyloid status (A+ versus A-) and to ATN model (A-T-N-; A+T-N-; A+T+N-/A+T+N+). Results: Performance on the LAM-test was significantly correlated with CSF Aß ratio. A+ participants performed worse on both learning (mean difference = 2.19, p = 0.002) and memory LAM measures than A- (mean difference = 2.19, p = 0.004). A decline in performance was observed along the Alzheimer's continuum, with significant differences between ATN groups. Conclusions: Our findings suggest that LAM test could be a useful tool for the early detection of subjects within the AD continuum, outperforming classical memory tests.

Advanced Light Source Technologies for Photodynamic Therapy of Skin Cancer Lesions.

Photodynamic therapy (PDT) is an increasingly popular dermatological treatment not only used for life-threatening skin conditions and other tumors but also for cosmetic purposes. PDT has negligible effects on underlying functional structures, enabling tissue regeneration feasibility. PDT uses a photosensitizer (PS) and visible light to create cytotoxic reactive oxygen species, which can damage cellular organelles and trigger cell death. The foundations of modern photodynamic therapy began in the late 19th and early 20th centuries, and in recent times, it has gained more attention due to the development of new sources and PSs. This review focuses on the latest advancements in light technology for PDT in treating skin cancer lesions. It discusses recent research and developments in light-emitting technologies, their potential benefits and drawbacks, and their implications for clinical practice. Finally, this review summarizes key findings and discusses their implications for the use of PDT in skin cancer treatment, highlighting the limitations of current approaches and providing insights into future research directions to improve both the efficacy and safety of PDT. This review aims to provide a comprehensive understanding of PDT for skin cancer treatment, covering various aspects ranging from the underlying mechanisms to the latest technological advancements in the field.

Polarisation-independent ultrafast laser selective etching processing in fused silica.

In fused silica, ultrafast laser assisted etching enables high chemical etching rates (>300 µm h-1) by setting a light polarisation linear and perpendicular to the beam writing direction. However, for many non-planar surfaces and 3D structures, dynamic polarisation control is difficult or not yet possible to implement. In this contribution, we identify a laser inscription regime in which high etching rates are accomplished independently of the light polarisation. In this regime (<15 pulses per µm), we measure etching rates ∼300 µm h-1 (4 hours in NaOH) including femtosecond-pulse energies corresponding to type II modifications. Few pulse inscriptions show a low degree of anisotropy as compared to higher number of pulses, thus enabling the polarisation insensitivity whose mechanisms are discussed. To demonstrate the capabilities of the processing, we fabricate curved and square-wave microchannels together with a complex 3D geometrical structure (stellated octahedron) containing an inter-plane arrangement with challenging angles (45°), which are difficult to achieve even employing dynamic polarisation control.

Automatic classification of Candida species using Raman spectroscopy and machine learning.

One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities.

Normalization of laser-induced breakdown spectroscopy spectra using a plastic optical fiber light collector and acoustic sensor device.

To estimate the acoustic plasma energy in laser-induced breakdown spectroscopy (LIBS) experiments, a light collecting and acoustic sensing device based on a coil of plastic optical fiber (POF) is proposed. The speckle perturbation induced by the plasma acoustic energy was monitored using a CCD camera placed at the end of a coil of multimode POF and processed with an intraimage contrast ratio method. The results were successfully verified with the acoustic energy measured by a reference microphone. The proposed device is useful for normalizing LIBS spectra, enabling a better estimation of the sample's chemical composition.

Photonic Microfluidic Technologies for Phytoplankton Research.

Phytoplankton is a crucial component for the correct functioning of different ecosystems, climate regulation and carbon reduction. Being at least a quarter of the biomass of the world's vegetation, they produce approximately 50% of atmospheric O2 and remove nearly a third of the anthropogenic carbon released into the atmosphere through photosynthesis. In addition, they support directly or indirectly all the animals of the ocean and freshwater ecosystems, being the base of the food web. The importance of their measurement and identification has increased in the last years, becoming an essential consideration for marine management. The gold standard process used to identify and quantify phytoplankton is manual sample collection and microscopy-based identification, which is a tedious and time-consuming task and requires highly trained professionals. Microfluidic Lab-on-a-Chip technology represents a potential technical solution for environmental monitoring, for example, in situ quantifying toxic phytoplankton. Its main advantages are miniaturisation, portability, reduced reagent/sample consumption and cost reduction. In particular, photonic microfluidic chips that rely on optical sensing have emerged as powerful tools that can be used to identify and analyse phytoplankton with high specificity, sensitivity and throughput. In this review, we focus on recent advances in photonic microfluidic technologies for phytoplankton research. Different optical properties of phytoplankton, fabrication and sensing technologies will be reviewed. To conclude, current challenges and possible future directions will be discussed.

Light Technology for Efficient and Effective Photodynamic Therapy: A Critical Review.

Photodynamic therapy (PDT) is a cancer treatment with strong potential over well-established standard therapies in certain cases. Non-ionising radiation, localisation, possible repeated treatments, and stimulation of immunological response are some of the main beneficial features of PDT. Despite the great potential, its application remains challenging. Limited light penetration depth, non-ideal photosensitisers, complex dosimetry, and complicated implementations in the clinic are some limiting factors hindering the extended use of PDT. To surpass actual technological paradigms, radically new sources, light-based devices, advanced photosensitisers, measurement devices, and innovative application strategies are under extensive investigation. The main aim of this review is to highlight the advantages/pitfalls, technical challenges and opportunities of PDT, with a focus on technologies for light activation of photosensitisers, such as light sources, delivery devices, and systems. In this vein, a broad overview of the current status of superficial, interstitial, and deep PDT modalities-and a critical review of light sources and their effects on the PDT process-are presented. Insight into the technical advancements and remaining challenges of optical sources and light devices is provided from a physical and bioengineering perspective.

Photodynamic Therapy: A Compendium of Latest Reviews.

Photodynamic therapy (PDT) is a promising therapy against cancer. Even though it has been investigated for more than 100 years, scientific publications have grown exponentially in the last two decades. For this reason, we present a brief compendium of reviews of the last two decades classified under different topics, namely, overviews, reviews about specific cancers, and meta-analyses of photosensitisers, PDT mechanisms, dosimetry, and light sources. The key issues and main conclusions are summarized, including ways and means to improve therapy and outcomes. Due to the broad scope of this work and it being the first time that a compendium of the latest reviews has been performed for PDT, it may be of interest to a wide audience.

Liquid level sensor based on dynamic Fabry-Perot interferometers in processed capillary fiber.

In this work, a novel optical fiber sensor capable of measuring both the liquid level and its refractive index is designed, manufactured and demonstrated through simulations and experimentally. For this, a silica capillary hollow-core fiber is used. The fiber, with a sensing length of 1.55 mm, has been processed with a femtosecond laser, so that it incorporates four holes in its structure. In this way, the liquid enters the air core, and it is possible to perform the sensing through the Fabry-Perot cavities that the liquid generates. The detection mode is in reflection. With a resolution of 4 µm (liquid level), it is in the state of the art of this type of sensor. The system is designed so that in the future it will be capable of measuring the level of immiscible liquids, that is, liquids that form stratified layers. It can be useful to determine the presence of impurities in tanks.

Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection.

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.