Comparison of 3D Easy Box cone-beam computed tomography analysis with 2D Modified Easy Box on OPG as a prognostic tool for impacted maxillary canines: A pilot study.

OBJECTIVE: To assess and compare the validity of 2D modified Easy Box and measurement of the Beta angle on standard conventional orthopantomogram (OPG) versus 3D cone-beam computed tomography (CBCT) OPG-constructed view. DESIGN: A retrospective agreement study. METHODS: The aim of this study was to construct an Easy Box on a standard conventional OPG and to validate this novel method by comparing it with the Easy Box method on 3D CBCT. After approval from the Ethics Committee, OPG and CBCT radiographs were obtained for the study from departmental records and five private practices in the same location (Indore, India). The radiographs were selected based on record availability and with written consent from the participants before the commencement of the study. The records were analysed to enable a comparison and to assess the accuracy of Easy Box construction on both 3D CBCT and standard conventional OPG radiographs. The location of the impacted canine within the Easy Box boundaries and the measurement of the Beta angle were determined on both views. RESULTS: A perfect agreement was obtained for the comparison of 3D Easy Box CBCT analysis with 2D modified Easy Box on OPG for impacted maxillary canines (Kappa = 1.0). A Bland-Altman (LoA) analysis showed no proportional bias in the comparison of the Beta angle on 3D and 2D OPG radiographs. CONCLUSION: Beta angle and 2D modified Easy Box on a conventional OPG yield similar results when compared to Easy Box on 3D CBCT OPG-constructed view. The standard OPG was valuable and cost-effective, particularly in the early stages of diagnosis and treatment planning, either as a substitute or when CBCT was unavailable.

Thermochromic Nanocellulose Films for Temperature-Adaptive Passive Cooling.

Energy efficiency in habitation spaces is a pivotal topic for maintaining energy sufficiency, cutting climate impact, and facilitating economic savings; thus, there is a critical need for solutions aimed at tackling this problem. One viable approach involves complementing active cooling methods with powerless or passive cooling ones. Moreover, considerable scope remains for the development of passive radiative cooling solutions based on sustainable materials. Cellulose, characterized by its abundance, renewability, and biodegradability, emerges as a promising material for this purpose due to its notable radiative cooling potential exploiting the mid-infrared (MIR) atmospheric transmission window (8-13 µm). In this work, we propose the utilization of thermochromic (TC) materials in conjunction with cellulose nanofibrils (CNF) to confer temperature-dependent adaptivity to hybrid CNF films. We employ a concept where high reflection, coupled with MIR emission in the heated state, facilitates cooling, while high visible light absorption in the cold state allows heating, thus enabling adaptive thermal regulation. CNF films were doped with black-to-leuco TC particles, and a thin silver layer was optionally applied to the films. The films exhibited a rapid transition (within 1 s) in their optical properties at ∼22 °C, becoming transparent above the transition temperature. Visible range transmittance of all samples ranged from 60 to 90%, with pronounced absorption in the 8-13 µm range. The cooling potential of the films was measured at 1-4 °C without any Ag layer and ∼10 °C with a Ag layer. In outdoor field testing, a peak cooling value of 12 °C was achieved during bright sunshine, which is comparable to a commercial solar film. A simulation model was also built based on the experimental results. The concept presented in this study extends beyond applications as standalone films but has applicability also in glass coatings. Overall, this work opens the door for a novel application opportunity for green cellulose-based materials.

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy.

Carboxymethyl Cellulose (CMC) Optical Fibers for Environment Sensing and Short-Range Optical Signal Transmission.

Optical fibers are a key component in modern photonics, where conventionally used polymer materials are derived from fossil-based resources, causing heavy greenhouse emissions and raising sustainability concerns. As a potential alternative, fibers derived from cellulose-based materials offer renewability, biocompatibility, and biodegradability. In the present work, we studied the potential of carboxymethyl cellulose (CMC) to prepare optical fibers with a core-only architecture. Wet-spun CMC hydrogel filaments were cross-linked using aluminum ions to fabricate optical fibers. The transmission spectra of fibers suggest that the light transmission window for cladding-free CMC fibers was in the range of 550-1350 nm, wherein the attenuation coefficient for CMC fibers was measured to be 1.6 dB·cm-1 at 637 nm. CMC optical fibers were successfully applied in touch sensing and respiratory rate monitoring. Finally, as a proof-of-concept, we demonstrate high-speed (150 Mbit/s) short-distance signal transmission using CMC fibers (at 1310 nm) in both air and water media. Our results establish the potential of carboxymethyl cellulose-based biocompatible optical fibers for highly demanding advanced sensor applications, such as in the biomedical domain.

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.

Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.

Thermoresponsive Nanocellulose Films as an Optical Modulation Device: Proof-of-Concept.

Flexible optoelectronic technologies are becoming increasingly important with the advent of concepts such as smart-built environments and wearable systems, where they have found applications in displays, sensing, healthcare, and energy harvesting. Parallelly, there is also a need to make these innovations environmentally sustainable by design. In the present work, we employ nanocellulose and its excellent film-forming properties as a basis to develop a green flexible photonic device for sensing applications. Cellulose nanofibrils (CNFs) and cellulose nanocrystals (CNCs) were used as matrix materials along with a black thermochromic pigment to prepare thermoresponsive hybrid films. Optical properties of nanocellulose films such as transparency and haze were tuned by varying pigment loading. Nearly 90% transparent CNF and CNC films could be tuned to reduce the transmission to as low as 4 and 17%, respectively. However, the films regained transparency to up to 60% when heated above the thermochromic transition temperature (31 °C). The thermoresponsive behavior of the prepared films was exploited to demonstrate an all-optical modulation device. Continuous infrared light (1300 nm) was modulated by using a 660 nm visible diode laser. The laser intensity was sufficient to cause a localized thermochromic transition in the films. The laser was pulsed at 0.3 Hz and a uniform cyclic modulation depth of 0.3 dB was achieved. The demonstrated application of functional nanocellulose hybrid films as a light switch (modulator) could be harnessed in various thermally stimulated sensing systems such as temperature monitoring, energy-saving, and anti-counterfeiting.