We conducted an observational retrospective study of all adults hospitalized for documented varicella-zoster virus (VZV) meningitis or encephalitis during years 2000-2015 in one referral centre. Thirty-six patients (21 males, 15 females) were included, with meningitis (n = 21), or meningoencephalitis (n = 15). Median age was 51 years [interquartile range, 35-76], and 6 patients (17%) were immunocompromised. Aciclovir was started in 32 patients (89%), with a median dose of 11 mg/kg/8 h [10-15]. No patient died, but 12 (33%) had neurological sequelae at discharge. Age was the only variable associated with adverse outcome (OR 1.98 [1.17-3.35] per 10-year increment, P = 0.011).

Characteristics and outcome of varicella-zoster virus central nervous system infections in adults.


 We present the result of a spectroscopic campaign targeting active galactic nucleus (AGN) candidates selected using a novel unsupervised machine-learning (ML) algorithm trained on optical and mid-infrared photometry. AGN candidates are chosen without incorporating prior AGN selection criteria and are fainter, redder, and more numerous, ∼340 AGN deg−2, than comparable photometric and spectroscopic samples. In this work, we obtain 178 rest-optical spectra from two candidate ML-identified AGN classes with the Hectospec spectrograph on the MMT Observatory. We find that our first ML-identified group is dominated by Type I AGNs (85%) with a <3% contamination rate from non-AGNs. Our second ML-identified group is mostly comprised of Type II AGNs (65%), with a moderate contamination rate of 15% primarily from star-forming galaxies. Our spectroscopic analyses suggest that the classes recover more obscured AGNs, confirming that ML techniques are effective at recovering large populations of AGNs at high levels of extinction. We demonstrate the efficacy of pairing existing WISE data with large-area and deep optical/near-infrared photometric surveys to select large populations of AGNs and recover obscured growth of supermassive black holes. This approach is well suited to upcoming photometric surveys, such as Euclid, Rubin, and Roman.

pdf/spectroscopic-confirmation-of-obscured-agn-populations-from-2z34qgci1e.pdf

Spectroscopic Confirmation of Obscured AGN Populations from Unsupervised Machine Learning

Considering that little is known about the etiology of half of the cases of viral meningoencephalitis and given the clinical relevance of these infections, this thesis aimed to evaluate the etiology of viral infections in the CNS through polymerase chain reaction (PCR) testing for 28 pathogens in 195 patients who attended the Ribeirão Preto Medical School Hospital (HCFMRP/USP) from January 1, 2018, to December 31, 2022.DNA and RNA were extracted from cerebrospinal fluid (CSF) samples, and for the diagnosis of RNA viruses, reverse transcription (RT) of RNA was performed to obtain complementary DNA (cDNA).Nucleic acid extracts and cDNA were subjected to the following tests: Real-time polymerase chain reaction (qPCR) using a commercial kit for the detection of common CNS pathogens; In-house developed conventional PCR for the detection of flaviviruses and alphaviruses; qPCR using a commercial kit for SARS-CoV-2 detection; Investigation of JC virus (JCV).The results of virological tests were correlated with cell counts and cerebrospinal fluid biochemistry.Additionally, clinical and epidemiological data of all patients were collected.Among the 195 patients with suspected viral CNS infections, viruses were found in the cerebrospinal fluid of 65 patients (33.3% of cases).This viral positivity was higher than that observed in other studies.The viruses found in the cerebrospinal fluid were: HAdV = 14(20.5%),EBV = 12(17.5%),HHV7 = 11(16.2%),SARS-CoV-2 = 9(13.2%),VZV = 5(7.4%),HSV1 = 4(5.9%),CMV = 4(5.9%),JCV = 4(5.9%),HHV6 = 2(3%), HEV, EB19, and DENV3 each had 1(1.5%) infected patient.A higher occurrence of viral CNS infections was observed in younger patients, aged 0 to 18 years (p<0.002647),and 19 to 35 years (p<0.017061).Immunodepression was also observed as a risk factor for viral CNS infections (p<0.00785).Furthermore, individuals engaged in home-based activities, institutions, schools, detention facilities, or those who consistently interacted with the same people were at a higher risk of CNS viral infections (p<0.0000593).Interestingly, a correlation was observed between viral diagnosis and cerebrospinal fluid with low cellularity (p<0.00908). 

Diagnóstico etiológico em pacientes com infecção viral do sistema nervoso central, por PCR, no período de 2018 a 2022

Biphasic evolution of varicella-zoster virus meningoencephalitis : Diagnostic challenges.

. Optical spectra of galaxies and quasars from large cosmological surveys are used to measure redshifts and infer distances. They are also rich with information on the intrinsic properties of these astronomical objects. However, their physical in-terpretation can be challenging due to the substantial number of degrees of freedom, various sources of noise, and degeneracies between physical parameters that cause similar spectral characteristics. To gain deeper insights into these degeneracies, we apply two unsupervised machine learning frameworks to a sample from the Sloan Digital Sky Survey data release 16 (SDSS DR16). The ﬁrst framework is a Probabilistic Auto-Encoder (PAE), a two-stage deep learning framework consisting of a data compression stage from 1000 elements to 10 parameters and a density estimation stage. The second framework is a Uniform Manifold Approximation and Projection (UMAP), which we apply to both the uncompressed and compressed data. Exploring across regions on the compressed data UMAP, we construct sequences of stacked spectra which show a gradual transition from star-forming galaxies with narrow emission lines and blue spectra to passive galaxies with absorption lines and red spectra. Focusing on galaxies with broad emission lines produced by quasars, we ﬁnd a sequence with varying levels of obscuration caused by cosmic dust. The experiments we present here inform future applications of neural networks and dimensionality reduction algorithms for large astronomical spectroscopic surveys.

Reconstructing and Classifying SDSS DR16 Galaxy Spectra with Machine-Learning and Dimensionality Reduction Algorithms

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Biphasic evolution of varicella-zoster virus meningoencephalitis : Diagnostic challenges.

Reconstructing and Classifying SDSS DR16 Galaxy Spectra with Machine-Learning and Dimensionality Reduction Algorithms