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In settings with high tuberculosis (TB) endemicity, distinct genotypes of the Mycobacterium tuberculosis complex (MTBC) often differ in prevalence. However, the factors leading to these differences remain poorly understood. Here we studied the MTBC population in Dar es Salaam, Tanzania over a six-year period, using 1,082 unique patient-derived MTBC whole-genome sequences (WGS) and associated clinical data. We show that the TB epidemic in Dar es Salaam is dominated by multiple MTBC genotypes introduced to Tanzania from different parts of the world during the last 300 years. The most common MTBC genotypes deriving from these introductions exhibited differences in transmission rates and in the duration of the infectious period, but little differences in overall fitness, as measured by the effective reproductive number. Moreover, measures of disease severity and bacterial load indicated no differences in virulence between these genotypes during active TB. Instead, the combination of an early introduction and a high transmission rate accounted for the high prevalence of L3.1.1, the most dominant MTBC genotype in this setting. Yet, a longer co-existence with the host population did not always result in a higher transmission rate, suggesting that distinct life-history traits have evolved in the different MTBC genotypes. Taken together, our results point to bacterial factors as important determinants of the TB epidemic in Dar es Salaam., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Zwyer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: During the emerging COVID-19 pandemic, radiology departments faced a substantial increase in chest CT admissions coupled with the novel demand for quantification of pulmonary opacities. This article describes how our clinic implemented an automated software solution for this purpose into an established software platform in 10 days. The underlying hypothesis was that modern academic centers in radiology are capable of developing and implementing such tools by their own efforts and fast enough to meet the rapidly increasing clinical needs in the wake of a pandemic., Method: Deep convolutional neural network algorithms for lung segmentation and opacity quantification on chest CTs were trained using semi-automatically and manually created ground-truth (N total  = 172). The performance of the in-house method was compared to an externally developed algorithm on a separate test subset (N = 66)., Results: The final algorithm was available at day 10 and achieved human-like performance (Dice coefficient = 0.97). For opacity quantification, a slight underestimation was seen both for the in-house (1.8 %) and for the external algorithm (0.9 %). In contrast to the external reference, the underestimation for the in-house algorithm showed no dependency on total opacity load, making it more suitable for follow-up., Conclusions: The combination of machine learning and a clinically embedded software development platform enabled time-efficient development, instant deployment, and rapid adoption in clinical routine. The algorithm for fully automated lung segmentation and opacity quantification that we developed in the midst of the COVID-19 pandemic was ready for clinical use within just 10 days and achieved human-level performance even in complex cases., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

Nowadays, amyotrophic lateral sclerosis (ALS) is considered as a multisystem disorder, characterized by a primary degeneration of motor neurons as well as neuropathological changes in non-motor regions. Neurodegeneration in subcortical areas, such as the thalamus, are believed to contribute to cognitive and behavioral abnormalities in ALS patients. In the present study, we investigated neurodegenerative changes including neuronal loss and glia pathology in the anterodorsal thalamic nucleus (AD) of SOD1(G93A) mice, a widely used animal model for ALS. We detected massive dendrite swelling and neuronal loss in SOD1(G93A) animals, which was accompanied by a mild gliosis. Furthermore, misfolded SOD1 protein and autophagy markers were accumulating in the AD. Since innate immunity and activation inflammasomes seem to play a crucial role in ALS, we examined protein expression of Nod-like receptor protein 3 (NLRP3), apoptosis-associated speck-like protein containing a caspase-1 recruitment domain (ASC) and the cytokine interleukin 1 beta (IL1β) in AD glial cells and neurons. NLRP3 and ASC were significantly up-regulated in the AD of SOD1(G93A) mice. Finally, co-localization studies revealed expression of NLRP3, ASC and IL1β in neurons. Our study yielded two main findings: (i) neurodegenerative changes already occur at an early symptomatic stage in the AD and (ii) increased inflammasome expression may contribute to neuronal cell death. In conclusion, neurodegeneration in the anterior thalamus may critically account for cognitive changes in ALS pathology., (© 2016 International Society of Neuropathology.)