Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach.

Respiratory viruses' detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an 'unknown' dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.

Making Sense of Electrical Stimulation: A Meta-analysis for Wound Healing.

Electrical stimulation as a mode of external enhancement factor in wound healing has been explored widely. It has proven to have multidimensional effects in wound healing including antibacterial, galvanotaxis, growth factor secretion, proliferation, transdifferentiation, angiogenesis, etc. Despite such vast exploration, this modality has not yet been established as an accepted method for treatment. This article reviews and analyzes the approaches of using electrical stimulation to modulate wound healing and discusses the incoherence in approaches towards reporting the effect of stimulation on the healing process. The analysis starts by discussing various processes adapted in in vitro, in vivo, and clinical practices. Later it is focused on in vitro approaches directed to various stages of wound healing. Based on the analysis, a protocol is put forward for reporting in vitro works in such a way that the outcomes of the experiment are replicable and scalable in other setups. This work proposes a ground of unification for all the in vitro approaches in a more sensible manner, which can be further explored for translating in vitro approaches to complex tissue stimulation to establish electrical stimulation as a controlled clinical method for modulating wound healing.

Assessment of the Complex Refractive Indices of Xenopus Laevis Sciatic Nerve for the Optimization of Optical (NIR) Neurostimulation.

Despite an increasing interest in the use of light for neural stimulation, there is little information on how it interacts with neural tissue. The choice of wavelength in most of the optical stimulation literature is based on already available light sources designed for other applications. This paper is the first one to report the complex refractive index of the sciatic nerve of Xenopus laevis, which is a crucial parameter for identifying the optimal wavelength of optical stimuli. The Xenopus laevis neural tissue is the most widely used tissue type in peripheral neurostimulation studies. In this paper, the reflectance ( ) and the transmittance ( ) of the sciatic nerve were measured over a wavelength range of 860-2250 nm, and the corresponding real ( ) and the imaginary ( ) refractive indices were calculated using appropriate formulae in a novel way. The reported values were between 1.3-1.44 and the values are of the order of over the full wavelength range. The absorption coefficient was found to be 100-500 cm . Several localized wavelength ranges were identified that can offer a maximized power coupling between potential optical stimuli and the neural tissue (1150-1200 nm, 1500-1700 nm, and 1900-2050 nm). The narrower regions of 1400-1600 nm and 1850-2150 nm were found to exhibit maximized absorbance. Separately, three regions were identified, where the penetration depths are the greatest (950-1000 nm, 1050-1350 nm, and 1600-1900 nm). This paper provides, for the first time, the fundamental specifications for optimizing the parameters of optical neurostimulation systems.