An unusual case of anisocoria by vegetal intoxication: a case report

A 12 year old boy presented with an acute onset of anisocoria and blurred vision. Ocular motility was normal but his right pupil was dilated, round but sluggishly reactive to light. There was no history of trauma, eye drops' instillation, nebulised drugs or local ointments. His past medical history was negative

A deep phenotyping experience: up to date in management and diagnosis of Malan syndrome in a single center surveillance report

Background Malan syndrome (MALNS) is a recently described ultrarare syndrome lacking guidelines for diagnosis, management and monitoring of evolutive complications. Less than 90 patients are reported in the literature and limited clinical information are available to assure a proper health surveillance. Results A multidisciplinary team with high expertise in MALNS has been launched at the "Ospedale Pediatrico Bambino Gesu", Rome, Italy. Sixteen Italian MALNS individuals with molecular confirmed clinical diagnosis of MALNS were enrolled in the program. For all patients, 1-year surveillance in a dedicated outpatient Clinic was attained. The expert panel group enrolled 16 patients and performed a deep phenotyping analysis directed to clinically profiling the disorder and performing critical revision of previously reported individuals. Some evolutive complications were also assessed. Previously unappreciated features (e.g., high risk of bone fractures in childhood, neurological/neurovegetative symptoms, noise sensitivity and Chiari malformation type 1) requiring active surveillance were identified. A second case of neoplasm was recorded. No major cardiovascular anomalies were noticed. An accurate clinical description of 9 new MALNS cases was provided. Conclusions Deep phenotyping has provided a more accurate characterization of the main clinical features of MALNS and allows broadening the spectrum of disease. A minimal dataset of clinical evaluations and follow-up timeline has been proposed for proper management of patients affected by this ultrarare disorder

Influenza-Like Illness Surveillance on Twitter through Automated Learning of Naïve Language

<div><p>Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using naïve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems.</p> </div

Weekly reported ILI (CDC) and tweets including the words “flu” or “influenza”.

<p>The blue line represents in all graphs the z-scores of CDC’s reported ILI for the 14-week period starting in week 5 (January 2013) through week 18 (May 2013). The red line represents the z-scores of tweets including the words “flu” or “influenza”, geolocalized with the extended narrow localization pattern.</p

Weekly reported ILI (CDC) and tweets satisfying ILI query.

<p>The blue line represents in all graphs the z-scores of CDC’s reported ILI for the 14-week period starting in week 5 (January 2013) through week 18 (May 2013). The red line represents the z-scores of tweets satisfying the ECDC ILI query, selected with a different geolocalization strategy in each of the four graphs: a) all tweets (independently from geolocalization); b) US GEO(GPS localized tweets); c) Extended wide localization pattern; d) Extended narrow localization pattern.</p

Dominant {ARF}3 variants disrupt Golgi integrity and cause a neurodevelopmental disorder recapitulated in zebrafish

AbstractVesicle biogenesis, trafficking and signaling via Endoplasmic reticulum-Golgi network support essential developmental processes and their disruption lead to neurodevelopmental disorders and neurodegeneration. We report that de novo missense variants in ARF3, encoding a small GTPase regulating Golgi dynamics, cause a developmental disease in humans impairing nervous system and skeletal formation. Microcephaly-associated ARF3 variants affect residues within the guanine nucleotide binding pocket and variably perturb protein stability and GTP/GDP binding. Functional analysis demonstrates variably disruptive consequences of ARF3 variants on Golgi morphology, vesicles assembly and trafficking. Disease modeling in zebrafish validates further the dominant behavior of the mutants and their differential impact on brain and body plan formation, recapitulating the variable disease expression. In-depth in vivo analyses traces back impaired neural precursors’ proliferation and planar cell polarity-dependent cell movements as the earliest detectable effects. Our findings document a key role of ARF3 in Golgi function and demonstrate its pleiotropic impact on development