beta-Lactam antibiotics can induce severe neutropenia by a hitherto unknown mechanism. Fifty cases of beta-lactam antibiotic-induced neutropenia (less than 1,000 neutrophils/mm3) from 17 hospitals were analyzed and compared with 140 literature cases. The incidence of neutropenia was 5%-greater than 15% in patients treated for greater than or equal to 10 days with large doses of any beta-lactam antibiotic but less than 0.1% with shorter duration of therapy. In greater than 95% of cases recovery occurred between one to seven days after withdrawal of beta-lactam antibiotics. Bone marrow aspirates were characterized by a lack of well-differentiated myeloid elements in the presence of numerous immature granulocyte precursors. Nine penicillins and eight cephalosporins inhibited in vitro granulopoiesis in a dose-dependent manner. There was a good correlation between the inhibitory capacity of beta-lactam antibiotics in vitro and the doses inducing neutropenia in vivo. These observations may be relevant for therapy in the granulocytopenic patient.

The glucocorticoid methylprednisolone has clinically important anti;inflammatory effects at high concentrations through unknown mechanisms. Methylprednisolone at 0.2 mg/107 cells inhibits respiration in Concanavalin-A(ConA)-stimulated thymocytes from rats by about 20%. We have used topdown elasticity analysis to identify the blocks of reactions within oxidative phosphorylation in thymocytes whose kinetics are significantly affected by treatment with methylprednisolone. At this concentration methylprednisolone greatly inhibited the reactions of substrate oxidation and increased mitochondrial proton leak but did not significantly affect the synthesis and turnover of ATP by the phosphorylating system. Metabolic control analysis showed that oxygen consumption by ConAtreated thymocytes was controlled largely (0.51) by the phosphorylating system but also by proton leak (0.32) and substrate oxidation (0.17); this is similar to the distribution of control in hepatocytes, suggesting that this pattern may be general in cells. Methylprednisolone lowered control by the phosphorylating system to 0.26 and raised control by substrate oxidation to 0.37. From these results we conclude that the inhibition of respiration in ConA-stimulated thymocytes by methylprednisolone at this concentration results from an inhibition of substrate oxidation and a smaller stimulation of mitochondrial proton leak, with only a minor contribution of any effects within the phosphorylating system.The therapeutic effects of glucocorticoids are mostly receptor-mediated. However, clinical observations and experimental findings suggest that there are also rapid direct effects that are not mediated by induction or repression of specific genes. It is well known that the application of methylprednisolone in megadoses is an effective treatment in acute situations of autoimmune diseases (see e.g. Barile and La- Abbreviations. ConA, concanavalin A ; C, overall flux control coefficient; E , overall elasticity coefficient, J,,, or J,, total rate of oxygen consumption; J , rate of oxygen consumption required to pump protons out at a rate equal to their rate of return through the phosphorylating system ; JL, rate of oxygen consumption required to pump protons out at a rate equal to their rate of return through the proton leak; A tym, mitochondrial membrane potential; FCCP, carbonyl cyanide p-trifluoromethoxyphenylhydrazone; Ph,MeP+, triphenylmethylphosphonium cation ; Ph,MePBr, triphenylmethylphosphonium bromide ; P/O ratio, ATP molecules synthesized0 atom consumed; ADP/O ratio, ADP molecules consumed/O atom consumed. The superscripts and subscripts S, L and P refer to the three blocks of reactions that produce or consume dtyy,: substrate oxidation (cytosolic catabolic reactions, citric acid cycle, electron transport chain), proton leak (leak of protons and any proton-transporting cation cycles across the mitochondrial inner membrane) and the phosphorylation system (ATP synthesis and transport, all cellular ATP-consuming reactions) respectively. valle, 1992; de Gla...

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labeling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labeling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work describes the technical aspects of the platform, thereby covering the functionalities at its current state and exploring its future use cases and extensions.

IntroductionThis study presents COVID-Twitter-BERT (CT-BERT), a transformer-based model that is pre-trained on a large corpus of COVID-19 related Twitter messages. CT-BERT is specifically designed to be used on COVID-19 content, particularly from social media, and can be utilized for various natural language processing tasks such as classification, question-answering, and chatbots. This paper aims to evaluate the performance of CT-BERT on different classification datasets and compare it with BERT-LARGE, its base model.MethodsThe study utilizes CT-BERT, which is pre-trained on a large corpus of COVID-19 related Twitter messages. The authors evaluated the performance of CT-BERT on five different classification datasets, including one in the target domain. The model's performance is compared to its base model, BERT-LARGE, to measure the marginal improvement. The authors also provide detailed information on the training process and the technical specifications of the model.ResultsThe results indicate that CT-BERT outperforms BERT-LARGE with a marginal improvement of 10-30% on all five classification datasets. The largest improvements are observed in the target domain. The authors provide detailed performance metrics and discuss the significance of these results.DiscussionThe study demonstrates the potential of pre-trained transformer models, such as CT-BERT, for COVID-19 related natural language processing tasks. The results indicate that CT-BERT can improve the classification performance on COVID-19 related content, especially on social media. These findings have important implications for various applications, such as monitoring public sentiment and developing chatbots to provide COVID-19 related information. The study also highlights the importance of using domain-specific pre-trained models for specific natural language processing tasks. Overall, this work provides a valuable contribution to the development of COVID-19 related NLP models.