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In this article, we review recent advances in the technology of writing fiber Bragg gratings (FBGs) in selected cores of multicore fibers (MCFs) by using femtosecond laser pulses. The writing technology of such a key element as the FBG opens up wide opportunities for the creation of next generation fiber lasers and sensors based on MCFs. The advantages of the technology are shown by using the examples of 3D shape sensors, acoustic emission sensors with spatially multiplexed channels, as well as multicore fiber Raman lasers.

We present experimental results on the reconstruction of the 2D temperature field on the surface of a 250 × 250 mm sensor panel based on the distributed frequency shift measured by an optical backscatter reflectometer. A linear regression and a feed-forward neural network algorithm, trained by varying the temperature field and capturing thermal images of the panel, are used for the reconstruction. In this approach, we do not use any information about the exact trajectory of the fiber, material properties of the sensor panel, and a temperature sensitivity coefficient of the fiber. Mean absolute errors of 0.118 °C and 0.086 °C are achieved in the case of linear regression and feed-forward neural network, respectively.

In this paper, we demonstrate the application of deep neural networks (DNNs) for processing the reflectance spectrum from a fiberoptic temperature sensor composed of densely inscribed fiber bragg gratings (FBG). Such sensors are commonly avoided in practice since close arrangement of short FBGs results in distortion of the spectrum caused by mutual interference between gratings. In our work the temperature sensor contained 50 FBGs with the length of 0.95 mm, edge-to-edge distance of 0.05 mm and arranged in the 1500–1600 nm spectral range. Instead of solving the direct peak detection problem for distorted signal, we applied DNNs to predict temperature distribution from entire reflectance spectrum registered by the sensor. We propose an experimental calibration setup where the dense FBG sensor is located close to an array of sparse FBG sensors. The goal of DNNs is to predict the positions of the reflectance peaks of the reference sparse FBG sensors from the reflectance spectrum of the dense FBG sensor. We show that a convolution neural network is able to predict the positions of FBG reflectance peaks of sparse sensors with mean absolute error of 7.8 pm that is slightly higher than the hardware reused interrogator equal to 5 pm. We believe that dense FBG sensors assisted with DNNs have a high potential to increase spatial resolution and also extend the length of a fiber optical sensors.

Thermal ablation (TA) is known as an alternative therapy to surgery to treat tumors. However, TA-based therapy requires advanced approaches in order to prevent causing damage to healthy tissue around the tumor and selectively target the desired area. Nanoparticles are considered as a promising tool in biomedicine to fulfill these requirements. This study was carried out in order to analyze the effect of iron oxide nanoparticles on the temperature increment during radiofrequency ablation therapy of porcine liver. In addition, this research aimed to experimentally evaluate the impact of two solvents such as agarose and chitosan on the temperature change, when magnetic nanoparticles were dispersed in them. The iron oxide nanoparticles were synthesized by the solvothermal method demonstrating the magnetic properties by acting to the external magnetic field. To increase the local heat superparamagnetic nanoparticles (iron oxide magnetic nanoparticle (IONPs)) of the average size of 20 nm in size and the concentrations from 1 to 10 mg/mL of MNPs with a step size of 1 mg/mL were tested in 10 replicates for each concentration and solvent. Moreover, the temperature changes for dry liver, and 0 mg/mL concentration was checked for calibration and reference purposes. As a sensing system, advanced 16-FBG optical fiber sensors connected to an interrogator were employed allowing the temperature change to be monitored accurately in real time. A maximum temperature of about 142 °C was recorded by a 5 mg/mL concentration of iron oxide nanoparticles dispersed in the agarose solvent.

The increasing recognition of minimally invasive thermal treatment of tumors motivate the development of accurate thermometry approaches for guaranteeing the therapeutic efficacy and safety. Magnetic Resonance Thermometry Imaging (MRTI) is nowadays considered the gold-standard in thermometry for tumor thermal therapy, and assessment of its performances is required for clinical applications. This study evaluates the accuracy of fast MRTI on a synthetic phantom, using dense ultra-short Fiber Bragg Grating (FBG) array, as a reference. Fast MRTI is achieved with a multi-slice gradient-echo echo-planar imaging (GRE-EPI) sequence, allowing monitoring the temperature increase induced with a 980 nm laser source. The temperature distributions measured with 1 mm-spatial resolution with both FBGs and MRTI were compared. The root mean squared error (RMSE) value obtained by comparing temperature profiles showed a maximum error of 1.2 °C. The Bland-Altman analysis revealed a mean of difference of 0.1 °C and limits of agreement 1.5/−1.3 °C. FBG sensors allowed to extensively assess the performances of the GRE-EPI sequence, in addition to the information on the MRTI precision estimated by considering the signal-to-noise ratio of the images (0.4 °C). Overall, the results obtained for the GRE-EPI fully satisfy the accuracy (~2 °C) required for proper temperature monitoring during thermal therapies.

Summary: The therapeutic use of RNAi has grown but often faces several hurdles related to delivery systems, compound stability, immune activation, and on-target/off-tissue effects. Self-delivering RNAi (sdRNA) molecules do not require delivery agents or excipients. Here we demonstrate the ability of sdRNA to reduce the expression of PTEN (phosphatase and tensin homolog) to stimulate regenerative axon regrowth in the injured adult CNS. PTEN-targeting sdRNA compounds were tested for efficacy in vivo by intravitreal injection after adult rat optic nerve injury. We describe critical steps in lead compound generation through the optimization of nucleotide modifications, enhancements for stability in biological matrices, and screening for off-target immunostimulatory activity. The data show that PTEN expression in vivo can be reduced using sdRNA and this enhances regeneration in adult CNS neurons after injury, raising the possibility that this method could be utilized for other clinically relevant nervous system indications.

The interrogation of proteomes ("proteomics") in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (~100 fM-1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.