Invasive fungal infections have been described throughout the COVID-19 pandemic. Cryptococcal disease after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been ...reported in several isolated case reports and 1 larger case series. We sought to describe cryptococcal infections following SARS-CoV-2 through establishing a database to investigate underlying risk factors, disease manifestations, and outcomes.
We created a crowdsourced call for cases solicited through the Mycoses Study Group Education and Research Consortium, the Centers for Disease Control and Prevention Emerging Infectious Diseases Network, and infectious diseases Twitter groups. Data were collected in a web-based and secure REDCap survey without personal identifiers.
Sixty-nine cases were identified and submitted by 29 separate institutional sites. Cryptococcosis was diagnosed a median of 22 days (interquartile range, 9-42 days) after SARS-CoV-2 infection. Mortality among those with available follow-up was 72% (26/36) for the immunocompetent group and 48% (15/31) for the immunocompromised group (likelihood ratio, 4.01; P = .045). We observed a correlation between disease manifestation (central nervous system infection, proven/probable disseminated disease, and respiratory) and mortality (P = .002).
The mortality rate of 59% for patients with cryptococcosis following SARS-CoV-2 is higher than that of modern Cryptococcus cohorts. There was an association between immunocompromised status and cryptococcal disease manifestations as well as mortality. Moreover, our series emphasizes the need for clinical and laboratory assessment of opportunistic infections beyond 30 days when concerning symptoms develop.
A sub(11) diencephalospinal dopamine (DA) neurons provide the major source of DA innervation to the spinal cord. DA in the dorsal and ventral horns modulates sensory, motor, nociceptive, and sexual ...functions. Previous studies from our laboratory revealed a sex difference in the density of DA innervation in the lumbar spinal cord. The purpose of this study was to determine whether sex differences in spinal cord DA are androgen dependent, influenced by adult or perinatal androgens, and whether a sex difference in the number of lumbar-projecting A sub(11) neurons exists. Adult male mice have significantly higher DA concentrations in the lumbar spinal cord than either females or males carrying the testicular feminization mutation (tfm) in the androgen receptor (AR) gene, suggesting an AR-dependent origin. Spinal cord DA concentrations are not changed following orchidectomy in adult male mice or testosterone administration to ovariectomized adult female mice. Administration of exogenous testosterone to postnatal day 2 female mice results in DA concentrations in the adult lumbar spinal cord comparable to those of males. Male mice display significantly more lumbar-projecting A sub(11) DA neurons than females, particularly in the caudal portion of the A sub(11) cell body region, as determined by retrograde tract tracing and immunohistochemistry directed toward tyrosine hydroxylase. These results reveal an AR-dependent sex difference in both the number of lumbar-projecting A sub(11) DA neurons and the lumbar spinal cord DA concentrations, organized by the presence of androgens early in life. The AR-dependent sex difference suggests thyat this system serves a sexually dimorphic function in the lumbar spinal cord. J. Comp. Neurol. 518:2423-2436, 2010. copyright 2010 Wiley-Liss, Inc.
To generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input's class. Many problems have "class-agnostic" nuisance transformations that apply ...similarly to all classes, such as lighting and background changes for image classification. Neural networks can learn these invariances given sufficient data, but many real-world datasets are heavily class imbalanced and contain only a few examples for most of the classes. We therefore pose the question: how well do neural networks transfer class-agnostic invariances learned from the large classes to the small ones? Through careful experimentation, we observe that invariance to class-agnostic transformations is still heavily dependent on class size, with the networks being much less invariant on smaller classes. This result holds even when using data balancing techniques, and suggests poor invariance transfer across classes. Our results provide one explanation for why classifiers generalize poorly on unbalanced and long-tailed distributions. Based on this analysis, we show how a generative approach for learning the nuisance transformations can help transfer invariances across classes and improve performance on a set of imbalanced image classification benchmarks. Source code for our experiments is available at https://github.com/AllanYangZhou/generative-invariance-transfer.
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