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Introduction. Until now, there has been limited evidence, primarily from US cohorts, focusing on frailty as a patient-oriented outcome after liver transplantation (LT). Our study aimed to explore the relationship between pre- and post-LT frailty in a multicenter European cohort of outpatients with cirrhosis undergoing LT. Methods. We conducted a prospective analysis of data from 180 LT recipients recruited between 2018 and 2020 from 5 Spanish centers. Participants underwent objective and subjective frailty assessments using the Liver Frailty Index (LFI) and the Subjective Clinician Assessment (SCA) pretransplant and at 3- and/or 6-mo posttransplant. Results. The median pretransplant LFI was 3.9, showing minimal change at 3 mo (3.8; P = 0.331) and improvement at 6-mo post-LT (3.6; P = 0.001). Conversely, the SCA significantly improved early post-LT: at 3 mo, poor SCA decreased from 11% to 1%, and good SCA increased from 54% to 89% (P

Background & Aims: Frailty is prevalent in liver transplant (LT) candidates. It is considered an independent predictor of adverse outcomes pre- and post-transplant according to data obtained in the United States. We aimed to externally validate the liver frailty index (LFI) in a multicenter cohort of LT candidates. Methods: Outpatients with cirrhosis were prospectively recruited from five Spanish centers (2018-2020). Patients were defined as “frail” by an optimal cut-off of LFI ≥4.5. Patients were followed for at least 6 months to study associations of pre-LT frailty with pre- and post-transplant mortality, length of hospital and intensive care unit (ICU) stays, risk of early (

Indoor cultivation of Cannabis sativa is essential for precise environmental control, maximizing cannabinoid yield, ensuring year-round product availability, and allowing genetic enhancement, making it indispensable for commercial producers and cannabis consumers alike. SmartGrow DataControl presents a cutting-edge computational tool designed specifically for monitoring, collecting, and controlling microclimatic factors in indoor crop cultivation. This system utilizes Model-View-Controller (MVC) architecture patterns and layered architectural patterns to seamlessly combine wireless devices with open-source databases. It focuses on energy efficiency by implementing standardized communication protocols like as TCP/IP, MQTT, HTTP, and Web Socket. The platform intends to optimize data collection for agricultural physiology studies, specifically in crops like Cannabis sativa, by utilizing advanced optical and Internet of Things (IoT) technologies. It is designed with a user-friendly interface to facilitate ease of use. Experimental validation verifies the effectiveness of SmartGrow DataControl, enabling farmers to effectively oversee crops and increase productivity through gradual technical improvements.

OptiGUI DataCollector is a Python 3.8-based graphical user interface that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including spectrometers and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and analysis. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies but can be expanded to other R&D processes.

AnIMAGE is a MATLAB-based software tool that empowers designers to create microstrip antennas with complex shapes, enhancing their performance and expanding design possibilities. With its user-friendly interface, the software simplifies the design process and streamlines it by automating calculations. Furthermore, it offers the functionality to export the shapes of the images, enabling CST Studio to simulate their electromagnetic behavior effectively. By leveraging AnIMAGE, designers gain the capability to fabricate antennas with complex forms, such as fractals or structures derived from photographs. As a valuable resource for antenna designers, AnIMAGE introduces innovative approaches to the development of complex microstrip antennas.

The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

In this study, a novel system was designed to enhance the efficiency of data acquisition in a portable and compact instrument dedicated to the spectral analysis of various surfaces, including plant leaves, and materials requiring characterization within the 410 to 915 nm range. The proposed system incorporates two nine-band detectors positioned on the top and bottom of the target surface, each equipped with a digitally controllable LED. The detectors are capable of measuring both reflection and transmission properties, depending on the LED configuration. Specifically, when the upper LED is activated, the lower detector operates without its LED, enabling the precise measurement of light transmitted through the sample. The process is reversed in subsequent iterations, facilitating an accurate assessment of reflection and transmission for each side of the target surface. For reliability, the error estimation utilizes a color checker, followed by a multi-layer perceptron (MLP) implementation integrated into the microcontroller unit (MCU) using TinyML technology for real-time refined data acquisition. The system is constructed with 3D-printed components and cost-effective electronics. It also supports USB or Bluetooth communication for data transmission. This innovative detector marks a significant advancement in spectral analysis, particularly for plant research, offering the potential for disease detection and nutritional deficiency assessment.

Mode division multiplexing (MDM) is currently one of the most attractive multiplexing techniques in optical communications, as it allows for an increase in the number of channels available for data transmission. Optical modal converters are one of the main devices used in this technique. Therefore, the characterization and improvement of these devices are of great current interest. In this work, we present a dataset of 49,736 near-field intensity images of a modal converter based on a long-period fiber grating (LPFG) written on a few-mode fiber (FMF). This characterization was performed experimentally at various wavelengths, polarizations, and temperature conditions when the device converted from LP01 mode to LP11 mode. The results show that the modal converter can be tuned by adjusting these parameters, and that its operation is optimal under specific circumstances which have a great impact on its performance. Additionally, the potential application of the database is validated in this work. A modal decomposition technique based on the particle swarm algorithm (PSO) was employed as a tool for determining the most effective combinations of modal weights and relative phases from the spatial distributions collected in the dataset. The proposed dataset can open up new opportunities for researchers working on image segmentation, detection, and classification problems related to MDM technology. In addition, we implement novel artificial intelligence techniques that can help in finding the optimal operating conditions for this type of device.

A CO2 laser-based system was studied and implemented to produce asymmetric long period fiber gratings (LPFG) with a large attenuation peak, high reproducibility, and high stability. The first half of this study provides a mathematical uncertainty model of the CO2 laser-based approach that takes into account various mechanical and thermal effects that impact this production technique. This is the first time that metrological analysis and modeling are performed on the CO2 laser-based engraving technique. Following that, the engraved system’s quality was assessed using a microscopic approach to confirm mechanical characteristics such as grating period, engraved spot width, and penetration depth, demonstrating that, if the thermal and mechanical components of the overall system are correctly managed, it is feasible to have very low inaccuracy. Lastly, the LPFG performance as temperature and strain sensors was tested, and the findings show that they had good linearity in both circumstances. Thus, the temperature sensor had a maximal sensitivity of 58 pm/°C when measuring temperature changed from 20 to 97 °C, but the strain sensor had sensitivity of 43 pm/με when measuring strain variations from 5.59 to 25 με. As a result, the model and results presented in this paper can be utilized to create a platform for the metrological management of lengths involved in the process of manufacturing LPFGs, devices that are widely employed in the creation of sensors and communications devices.

Pneumocystis jirovecii pneumonia is one of the diseases that most affects immunocompromised patients today, and under certain circumstances, it can be fatal. On the other hand, more and more automatic tools based on artificial intelligence are required every day to help diagnose diseases and thus optimize the resources of the healthcare system. It is therefore important to develop techniques and mechanisms that enable early diagnosis. One of the most widely used techniques in diagnostic laboratories for the detection of its etiological agent, Pneumocystis jirovecii, is optical microscopy. Therefore, an image dataset of 29 different patients is presented in this work, which can be used to detect whether a patient is positive or negative for this fungi. These images were taken in at least four random positions on the specimen holder. The dataset consists of a total of 137 RGB images. Likewise, it contains realistic, annotated, and high-quality microscope images. In addition, we provide image segmentation and labeling that can also be used in numerous studies based on artificial intelligence implementation. The labeling was also validated by an expert, allowing it to be used as a reference in the training of automatic algorithms with supervised learning methods and thus to develop diagnostic assistance systems. Therefore, the dataset will open new opportunities for researchers working in image segmentation, detection, and classification problems related to Pneumocystis jirovecii pneumonia diagnosis.

The oxidative degradation of omega-3 fatty acids-rich fish oil was investigated by 1H Nuclear Magnetic Resonance (NMR) Spectroscopy. The intensities of key distinct spectral signals corresponding to bis-allylic (RBAG) and terminal methyl groups (RO3TG) from omega-3 fatty acid residues were monitored in relation to the sn-2 glycerol peak from triacylglycerols, in fish oil oxidized under various conditions. Under oxygen-rich conditions and accelerated oxidation, RBAG and RO3TG decreased sharply over time, whereas under oxygen-limited conditions, they remained unchanged throughout the entire length of the storage, demonstrating that the method has a practical applicability for comparing storage conditions and antioxidants behaviors. At ambient temperature and limited oxygen availability, the NMR indicators declined steadily only after the initial period in which they increased. It was hypothesized that sn-2 hydrolysis followed by initiation of oxidation via electron transfer and decarboxylation may be responsible for these observations.

This paper presents the design and analysis of a surface plasmon resonance (SPR) sensor in a photonic crystal fiber (PCF) platform, where graphene is used externally to attain improved sensing performance for an aqueous solution. The performance of the proposed sensor was analyzed using the finite element method-based simulation tool COMSOL Multiphysics. According to the simulation results, the proposed sensor exhibits identical linear characteristics as well as a very high figure of merit (FOM) of 2310.11 RIU−1 in the very low detection limit of 10−3. The analysis also reveals the maximum amplitude sensitivity of 14,847.03 RIU−1 and 7351.82 RIU−1 for the x and y polarized modes, respectively, which are high compared to several previously reported configurations. In addition, the average wavelength sensitivity is 2000 nm/RIU which is comparatively high for the analyte refractive index (RI) ranging from 1.331 to 1.339. Hence, it is highly expected that the proposed PCF-based SPR sensor can be a suitable candidate in different sensing applications, especially for aqueous solutions.

A petrol adulteration sensor based on a rectangular shaped hollow-core photonic crystal fiber is proposed and numerically analyzed in the terahertz regime. The performance of the proposed sensor was evaluated when it is employed to characterize different kerosene mixtures. In this research, the adulterated fuel sample is filled in the rectangular hollow channel and the electromagnetic signal of the terahertz band is also driven through the same channel. The received signal after the interaction of fuel with the terahertz signal will advise the refractive index of the fuel oil inside the core, which will also bear the information of how much extrinsic component is present in the fuel. The finite element method based simulation shows that the proposed sensor can reach a high relative sensitivity of 89% and presents low confinement losses at 2.8 THz. The reported sensing structure is easily realizable with the conventional manufacturing techniques. Consequently, this proposed fiber may be treated as an essential part of real-life applications of petrol adulteration measurements.

In this paper, we propose and numerically analyze a novel design for a high sensitivity refractive index (RI) sensor based on long-range surface plasmon resonance in H-shaped microstructured optical fiber with symmetrical dielectric–metal–dielectric waveguide (DMDW). The influences of geometrical and optical characteristics of the DMDW on the sensor performance are investigated theoretically. A large RI analyte range from 1.33 to 1.39 is evaluated to study the sensing characteristics of the proposed structure. The obtained results show that the DMDW improves the coupling between the fiber core mode and the plasmonic mode. The best configuration shows 27 nm of full width at half maximum with a resolution close to 1.3 × 10 − 5 nm, a high sensitivity of 7540 nm/RIU and a figure of merit of 280 RIU − 1 . Additionally, the proposed device has potential for multi-analyte sensing and self-reference when dissimilar DMDWs are deposited on the inner walls of the side holes. The proposed sensor structure is simple and presents very competitive sensing parameters, which demonstrates that this device is a promising alternative and could be used in a wide range of application areas.

This work presents a non-invasive, reusable and submersible permittivity sensor that uses a microwave technique for the dielectric characterization of liquid materials. The proposed device consists of a compact split ring resonator excited by two integrated monopole antennas. The sensing principle is based on the notch introduced by the resonators in the transmission coefficient, which is affected due to the introduction of the sensor in a new liquid material. Then, a frequency shift of the notch and the Q-factor of the proposed sensor are related with the changes in the surrounding medium. By means of a particular experimental procedure, commercial liquids are employed to obtain the calibration curve. Thus, a mathematical equation is obtained to extract the dielectric permittivity of liquid materials with unknown dielectric properties. A good match between simulated and experimental results is obtained, as well as a high Q-factor, compact size, good sensitivity and high repeatability for use in sensing applications. Sensors like the one here presented could lead to promising solutions for characterizing materials, particularly in determining material properties and quality in the food industry, bio-sensing and other applications.

Aims. To define the prevalence and clinical characteristics of glucose metabolism disorders (GMD) in patients with compensated liver cirrhosis (LC).Material and methods. Fasting plasma glucose (FPG) levels were measured to 130 patients with clinically stable LC. Oral glucose tolerance tests (OGTT) and fasting plasma insulin determinations were performed to patients with normal FPG. Insulin resistance (IR) was calculated with HOMA2-IR index. GMD were classified according to FPG and OGTT tests results and to the chronologic relation between diagnosis of diabetes mellitus (DM) and LC as follows: type-2 DM (T2DM), hepatogenous diabetes (HD) and impaired glucose tolerance. Patients from all groups were compared.Results. The prevalence of GMD were as follows: T2DM in 25 patients (19.2%, 95% CI 12.5-25.9), HD in 28 (21.5%, 95% CI 14.5-28.5) and IGT in 36 (38.5%, 95% CI 30.1-46.7). The total of patients with GMD was 79.2% (95% CI 72.3-86.1). In 41% of cases GMD were subclinical and 48.7% of patients had IR. Patients with T2DM had a higher number of variables with significant differences compared with the other groups (more marked compared to the patients without GMD). The only differences between the patients with T2DM and HD were hypercreatininemia: 1.14 ± 0.53 vs. 0.84 ± 0.22 mg/dL (p = 0.005) and family history of DM: 8 (32%) vs. 2 (7%) (p = 0.02).Conclusion. Almost 80% of patients with compensated LC had GMD. Half of them were subclinical. The patients with T2DM had marked clinical differences compared to patients from the other groups, particularly renal impairment.

Background. It has been suggested that DM may reduce survival of patients with liver cirrhosis (LC). Nevertheless only few prospective studies assessing the impact of DM on mortality of cirrhotic patients have been published, none in compensated LC.Aims. (i) to study the impact of DM on mortality and (ii) to identify predictors of death.Methods. Patients with compensated LC with and without DM were studied. Survival was analyzed by the Kaplan-Meier Method. Univariate and multivariate analysis was performed to determine independent predictors of mortality.Results. 110 patients were included: 60 without DM and 50 with DM. Diabetic patients had significantly higher frequency of cryptogenic cirrhosis, anemia, hypoalbuminemia, and hypercreatininemia. They also had significantly higher BMI and Child-Pugh score. The 2.5-years cumulative survival was significantly lower in patients with DM (48 vs. 69%, p < 0.05). By univariate analysis: DM, female gender, serum creatinine > 1.5 mg/dL, Child-Pugh score class C and cryptogenic cirrhosis were significant. However, only serum creatinine > 1.5 mg/dL and Child-Pugh score class C were independent predictors of death.Conclusion: DM was associated with a significant increase in mortality in patients with compensated liver cirrhosis. Serum creatinine > 1.5 mg/dL and Child-Pugh score class C were independent predictors of death.

En este trabajo se muestra el desarrollo de una sonda con tres fi bras ópticas para estudiar el comportamiento de la burbuja en lechos gas-sólido fl uidizados burbujeantes. Su estructura compacta permite minimizar las perturbaciones inducidas al campo del fl ujo, lográndose resultados más exactos frentes a otros tipos de sonda. Los lechos fl uidizados burbujeantes son ampliamente usados en procesos petroquímicos, metalúrgicos y en termoeléctricas. El comportamiento de la burbuja es uno de los parámetros más importantes para la simulación y diseño de reactores termoeléctricos, dado que se relaciona con los saltos de fase, la interacción entre las fases sólida y gaseosa del lecho y el comportamiento de la transferencia de masa. Conocer tanto la velocidad como el tamaño de burbuja, ayudan a caracterizar estos sistemas, llevando un mejor diseño de reactores a gran escala.

La Espectroscopia de Emisión Atómica con Plasma Inductivamente Acoplado ocupa un lugar destacado entre las técnicas que actualmente se emplean en laboratorios nacionales e internacionales para el análisis de diversas muestras del medio ambiente. En particular constituye una técnica establecida para el análisis de aguas naturales debido, entre otras ventajas, a: fácil introducción en el plasma, tratamiento previo mínimo que no exige para muchos analitos un proceso de preconcentración, determinación de elementos mayoritarios y traza debido a los amplios intervalos de linealidad de las curvas de calibración, así como bajos límites de detección y adecuada precisión. Sin embargo, para ciertos tipos de aguas, la variabilidad entre muestras y relativa elevada concentración de los elementos Ca, Mg y Na introduce errores sistemáticos en la determinación. En este trabajo se realizó un estudio de la influencia del Ca, Mg y Na (0 a 2 000mg · L-1), y sus posibles combinaciones, sobre la intensidad de la señal de 10 analitos (Li, Cu, Zn, Ba, Fe, Cr, Co, Ni, Cd y Pb), los cuales tienen potencial total de excitación (PTE) variable en el intervalo 1,85a14,79eV. La magnitud del efecto aumentó con el PTE del analito en presencia de calcio y magnesio. Sin embargo, para el sodio ocurre el efecto contrario. La aplicación de diseños experimentales factoriales 2n permitió la adecuada descripción y diagnóstico del efecto; así como el desarrollo de un procedimiento de corrección que aumenta la exactitud de los resultados hasta en un orden de magnitud según el elemento que se determina.