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ATP-induced cell death has emerged as a captivating realm of inquiry with profound ramifications in the context of osteoporosis. This study unveils a paradigm-shifting hypothesis that illuminates the prospective involvement of ATP-induced cellular demise in the etiology of osteoporosis. Initially, we explicate the morphological attributes of ATP-induced cell death and delve into the intricacies of the molecular machinery and regulatory networks governing ATP homeostasis and ATP-induced cell death. Subsequently, our focus pivots towards the multifaceted interplay between ATP-induced cellular demise and pivotal cellular protagonists, such as bone marrow-derived mesenchymal stem cells, osteoblasts, and osteoclasts, accentuating their potential contributions to secondary osteoporosis phenotypes, encompassing diabetic osteoporosis, glucocorticoid-induced osteoporosis, and postmenopausal osteoporosis. Furthermore, we probe the captivating interplay between ATP-induced cellular demise and alternative modalities of cellular demise, encompassing apoptosis, autophagy, and necroptosis. Through an all-encompassing inquiry into the intricate nexus connecting ATP-induced cellular demise and osteoporosis, our primary goal is to deepen our comprehension of the underlying mechanisms propelling this malady and establish a theoretical bedrock to underpin the development of pioneering therapeutic strategies.

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia.Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts.Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively.Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.