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ε-CL-20 is prepared from raw CL-20 by solvent evaporation and precipitation methods. Experiments were also done using solvent evaporation coupled with in-situ ultrasonication method. Using precipitation method, ε-CL-20 is scaled up to 500 g batch. Raw CL-20 was assigned to α-CL-20. The chemical and polymorphic purity of prepared ε-CL-20 was found to be about 98 per cent and > 95 per cent, respectively. ε-CL-20 was obtained agglomeration free with well defined geometry in comparison with raw CL-20 and its crystal morphology is dominantly bi-pyramidal or lozenge crystal shapes. The obtained mean particle size of prepared ε-CL-20 by solvent evaporation method with and without in-situ ultrasonication and also by precipitation methods is about 30 µm - 40 µm, 150 µm - 200 µm and 150 µm - 300 µm, respectively. The measured true density of prepared ε-CL-20 by precipitation method with 100 g and 500 g batch scale using Helium gas pycnometer was 2.038 g/cm³ and 2.043 g/cm³, respectively. The lower value of calculated void percentage of ε-CL-20 (0.05-0.29%) indicate better crystal quality. Conclusively, prepared ε-CL20 has high true density with less percentage of voids, less total moisture content and free from agglomeration as compared with the starting raw CL-20 material. [ABSTRACT FROM AUTHOR]

Potassium, rubidium, and cesium salts of 5,7-diamino- 4,6-dinitrobenzofuroxan (CL-14) have been prepared by reacting sodium salt of 5,7-diamino-4,6-dinitrobenzofuroxan with alkali metal nitrate in an aqueous medium. The structure of the compounds was unequivocally confirmed by spectra data, elemental analyses, and estimation of metal content. Further, the compounds have been evaluated for explosive and thermal properties and found more suitable as compared to alkali metal salts of 4,6-dinitrobenzofuroxan (DNBF). [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has shifted mental health care delivery to digital platforms, videoconferencing, and other mobile communications. However, existing reviews of digital health interventions are narrow in scope and focus on a limited number of mental health conditions., Objective: To address this gap, we conducted a comprehensive systematic meta-review of the literature to assess the state of digital health interventions for the treatment of mental health conditions., Methods: We searched MEDLINE for secondary literature published between 2010 and 2021 on the use, efficacy, and appropriateness of digital health interventions for the delivery of mental health care., Results: Of the 3022 records identified, 466 proceeded to full-text review and 304 met the criteria for inclusion in this study. A majority (52%) of research involved the treatment of substance use disorders, 29% focused on mood, anxiety, and traumatic stress disorders, and >5% for each remaining mental health conditions. Synchronous and asynchronous communication, computerized therapy, and cognitive training appear to be effective but require further examination in understudied mental health conditions. Similarly, virtual reality, mobile apps, social media platforms, and web-based forums are novel technologies that have the potential to improve mental health but require higher quality evidence., Conclusions: Digital health interventions offer promise in the treatment of mental health conditions. In the context of the COVID-19 pandemic, digital health interventions provide a safer alternative to face-to-face treatment. However, further research on the applications of digital interventions in understudied mental health conditions is needed. Additionally, evidence is needed on the effectiveness and appropriateness of digital health tools for patients who are marginalized and may lack access to digital health interventions., (©Tristan J Philippe, Naureen Sikder, Anna Jackson, Maya E Koblanski, Eric Liow, Andreas Pilarinos, Krisztina Vasarhelyi. Originally published in JMIR Mental Health (https://mental.jmir.org), 12.05.2022.)

Pneumonia is a fatal disease responsible for almost one in five child deaths worldwide. Many developing countries have high mortality rates due to pneumonia because of the unavailability of proper and timely diagnostic measures. Using machine learning-based diagnosis methods can help to detect the disease early and in less time and cost. In this study, we proposed a novel method to determine the presence of pneumonia and identify its type (bacterial or viral) through analyzing chest radiographs. We performed a three-class classification based on features containing diverse information of the samples. After using an augmentation technique to balance the dataset's sample sizes, we extracted the chest X-ray images' statistical features, as well as global features by employing a deep learning architecture. We then combined both sets of features and performed the final classification using the RandomForest classifier. A feature selection method was also incorporated to identify the features with the highest relevance. We tested the proposed method on a widely used (but relabeled) chest radiograph dataset to evaluate its performance. The proposed model can classify the dataset's samples with an 86.30% classification accuracy and 86.03% F-score, which assert the model's efficacy and reliability. However, results show that the classifier struggles while distinguishing between viral and bacterial pneumonia samples. Implementing this method will provide a fast and automatic way to detect pneumonia in a patient and identify its type., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2021 Mehedi Masud et al.)

The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers., Competing Interests: The authors declare that there is no conflict of interest.

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient's chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

Multiphase growth during crystallization severely affects deliverable output of explosive materials. Appearance and incomplete transformation of metastable phases are a major source of polymorphic impurities. This article presents a methodical and molecular level understanding of the metastable phase transformation mechanism during crystallization of cyclic nitramine explosives, viz. RDX, HMX and CL-20. Instantaneous reverse precipitation yielded metastable γ-HMX and β-CL-20 which undergo solution mediated transformation to the respective thermodynamic forms, β-HMX and ε-CL-20, following 'Ostwald's rule of stages'. However, no metastable phase, anticipated as β-RDX, was evidenced during precipitation of RDX, which rather directly yielded the thermodynamically stable α-phase. The γ→β-HMX and β→ε-CL-20 transformations took 20 and 60 minutes respectively, whereas formation of α-RDX was instantaneous. Density functional calculations were employed to identify the possible transition state conformations and to obtain activation barriers for transformations at wB97XD/6-311++G(d,p)(IEFPCM)//B3LYP/6-311G(d,p) level of theory. The computed activation barriers and lattice energies responsible for transformation of RDX, HMX and CL-20 metastable phases to thermodynamic ones conspicuously supported the experimentally observed order of phase stability. This precise result facilitated an understanding of the occurrence of a relatively more sensitive and less dense β-CL-20 phase in TNT based melt-cast explosive compositions, a persistent and critical problem unanswered in the literature. The crystalline material recovered from such compositions revealed a mixture of β- and ε-CL-20. However, similar compositions of RDX and HMX never showed any metastable phase. The relatively long stability with the highest activation barrier is believed to restrict complete β→ε-CL-20 transformation during processing. Therefore a method is suggested to overcome this issue.

Energetic materials used extensively both for civil and military applications. There are continuous research programmes worldwide to develop new materials with higher performance and enhanced insensitivity to thermal or shock insults than the existing ones in order to meet the requirements of future military and space applications. This review concentrates on recent advances in syntheses, potential formulations and space applications of potential compounds with respect to safety, performance and stability.

Two new initiatory molecules, e.g. rubidium and cesium salts of 4,6-dinitrobenzofuroxan (DNBF) have been prepared by reacting sodium salt of 4,6-dinitrobenzofuroxan (DNBF) with rubidium nitrate and cesium nitrate, respectively, at 60 degrees C in aqueous medium. The characterisation of compounds by IR, (1)H-NMR, elemental analysis and metal content is described along with some of the evaluated thermal and explosive properties. The results indicate that cesium salt of DNBF (Cs-DNBF) appears promising initiatory and may suitably replace potassium salt of DNBF (K-DNBF), being used currently in initiatory compositions.

Three isomers of N, N-diethyltolylacetamides were synthesized and evaluated against Aedes aegypti and Culex quinquefasciatus for repellency and with mice for LD50, as an indicator of mammalian toxicity. Of these, N, N-diethyl-p-tolylacetamide (DEPTA) showed comparatively better repellency against C. quinquefasciatus. All the compounds showed higher repellency against C. quinquefasciatus than Ae. aegypti. In addition, N, N-diethyl-m-tolylacetamide (DEMTA) was found to be least toxic to mice.