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Spontaneous Pneumothorax in the setting of coronavirus disease 19 (COVID-19) has been rarely described and is a potentially lethal complication. We report our institutional experience. Patients with confirmed COVID-19 who were admitted at 5 hospitals within the Inova health system between February 21 and May 2020 were included in the study. We identified 1619 patients, 22 patients (1.4%) developed spontaneous pneumothorax during their hospitalization without evidence of traumatic injury.In the present study, the role of 3-hydroxy group of a series of epoxymorphinan derivatives in their binding affinity and selectivity profiles toward the opioid receptors (ORs) has been investigated. It was found that the 3-hydroxy group was crucial for the binding affinity of these derivatives for all three ORs due to the fact that all the analogues 1a-e exhibited significantly higher binding affinities compared to their counterpart 3-dehydroxy ones 6a-e. Meanwhile most compounds carrying the 3-hydroxy group possessed similar selectivity profiles for the kappa opioid receptor over the mu opioid receptor as their corresponding 3-dehydroxy derivatives. Crenolanib mouse [35S]-GTPγS functional assay results indicated that the 3-hydroxy group of these epoxymorphinan derivatives was important for maintaining their potency on the ORs with various effects. Further molecular modeling studies helped comprehend the remarkably different binding affinity and functional profiles between compound 1c (NCP) and its 3-dehydroxy analogue 6c.Benzodiazepines (BZDs) have been widely used in neurological disorders such as insomnia, anxiety, and epilepsy. The use of classical BZDs, e.g., diazepam, has been limited due to adverse effects such as interaction with alcohol, ataxia, amnesia, psychological and physical dependence, and tolerance. In the quest for new benzodiazepine agonists with more selectivity and low adverse effects, novel derivatives of 4,6-diphenylpyrimidin-2-ol were designed, synthesized, and evaluated. In this series, compound 2, 4-(2-(benzyloxy)phenyl)-6-(4-fluorophenyl)pyrimidin-2-ol, was the most potent analogue in radioligand binding assay with an IC50 value of 19 nM compared to zolpidem (IC50 = 48 nM), a nonbenzodiazepine central BZD receptor (CBR) agonist. Some compounds with a variety of affinities in radioligand receptor binding assay were selected for in vivo evaluations. Compound 3 (IC50 = 25 nM), which possessed chlorine instead of fluorine in position 4 of the phenyl ring, exhibited an excellent ED50 value in most in vivo tests. Proper sedative-hypnotic effects, potent anticonvulsant activity, appropriate antianxiety effect, and no memory impairment probably served compound 3, a desirable candidate as a benzodiazepine agonist. The pharmacological effects of compound 3 were antagonized by flumazenil, a selective BZD receptor antagonist, confirming the BZD receptors' involvement in the biological effects of the novel ligand.The amyloid state of protein aggregation is associated with neurodegenerative and systemic diseases but can play physiological roles in many organisms, including as stress granules and virulence determinants. The recent resolution revolution in cryogenic electron microscopy (cryo-EM) has significantly expanded the repertoire of high-resolution amyloid structures, to include, for the first-time, fibrils extracted ex vivo in addition to those formed, or seeded, in vitro. Here, we review recently solved cryo-EM amyloid structures, and compare amino acid prevalence, in efforts to systematically distinguish between pathological and functional amyloids, even though such structural classification is hindered by extensive polymorphism even among fibrils of the same protein, and by dual functioning of some human amyloids in both physiological activities and disease mechanisms. Forthcoming structures of bacterial amyloids may expose specific, evolutionary-designed properties specific to functional fibrils.Global organic waste is increasing, bioconversion of organic waste arises because it can recover valuable nutrients and produce bioactive substances. Betaines are important bioactive substances in plants under environmental stress, but have received limited attention in vermicompost/larvae bioconversion compost. In this study, betaines in organic waste and vermicompost/larvae bioconversion compost were identified and quantified by HPLC-ESI-MS/MS. We observed the existence of glutamine betaine in all samples, which was first found in natural sources recently. Valine betaine was the highest among all detected betaines followed by GABA betaine, and both were rare in plants. The existence of tyrosine betaine in cow dung (CD) and vermicompost (CDV) was found, which was previously shown to be in fungi. Most importantly, we found larvae bioconversion could increase betaines by 5.56-99.75%, while vermicomposting decreased them. Bioconversion of larvae can effectively increase betaines in compost and can be used to produce potential novel functional organic fertilizers.Fluoroquinolone antibiotics like ofloxacin (OFL) have been frequently detected in the aquatic environment. Recently manganese-oxidizing bacteria (MOB) have attracted research efforts on the degradation of recalcitrant pollutants with the aid of their biogenic manganese oxides (BioMnOx). Herein, the degradation of OFL with a strain of MOB (Pseudomonas sp. F2) was investigated for the first time. It was found that the bacteria can degrade up to 100% of 5 μg/L OFL. BioMnOx and Mn(III) intermediates significantly contributed to the degradation. Moreover, the degradation was clearly declined when the microbial activity was inactivated by heat or ethanol, indicating the importance of bioactivity. Possible transformation products of OFL were identified by HPLC-MS and the degradation pathway was proposed. In addition, the toxicity of OFL was reduced by 66% after the degradation.Protein structural class prediction for low similarity sequences is a significant challenge and one of the deeply explored subjects. This plays an important role in drug design, folding recognition of protein, functional analysis and several other biology applications. In this paper, we worked with two benchmark databases existing in the literature (1) 25PDB and (2) 1189 to apply our proposed method for predicting protein structural class. Initially, we transformed protein sequences into DNA sequences and then into binary sequences. Furthermore, we applied symmetrical recurrence quantification analysis (the new approach), where we got 8 features from each symmetry plot computation. Moreover, the machine learning algorithms such as Linear Discriminant Analysis (LDA), Random Forest (RF) and Support Vector Machine (SVM) are used. In addition, comparison was made to find the best classifier for protein structural class prediction. Results show that symmetrical recurrence quantification as feature extraction method with RF classifier outperformed existing methods with an overall accuracy of 100% without overfitting.