Acute kidney injury on chronic kidney disease: From congestive heart failure to light chain deposition disease and cast nephropathy in multiple myeloma

Acute on chronic renal failure is a common but notably broad diagnosis. We present a 64-year-old man with a history of diastolic heart failure and chronic kidney disease, admitted for an elevated creatinine. History and physical examination were suggestive of decompensated heart failure; however, the careful interpretation of urinalysis rendered the diagnosis of multiple myeloma. On renal biopsy, the patient was found to have light chain deposition disease with cast nephropathy. Combination lesions in multiple myeloma are rare and require diligent histopathology for detection, including light microscopy, immunofluorescence and electron microscopy. These patients portray different demographics, renal manifestations, oncologic characteristics and outcomes, and hence, further studies isolating these combined lesions are warranted

La création de la détention pour enquête en common law : dérive jurisprudentielle ou évolution nécessaire ? Un point de vue pragmatique

L’article qui suit examine l’évolution relativement récente ayant donné lieu à la création d’un pouvoir de détention pour enquête en common law. Il en découle un changement fondamental de l’état du droit au Canada qui semble répondre à un besoin légitime des forces de l’ordre. Bien qu’il eût été préférable qu’une loi reconnaisse et régule le pouvoir policier de détention pour enquête, il valait mieux que la jurisprudence pallie le silence du législateur plutôt que d’ignorer une réalité sur le terrain dépourvue de normes juridiques. Aussi perfectible soit-il, le cadre juridique fourni par le droit prétorien constitue une évolution souhaitable du droit pénal qui n’est cependant pas à l’abri des dérives, d’où un appel à une action législative pour compléter et préciser les normes jurisprudentielles.This article explores the relatively recent evolution that gave birth to the common law power of investigative detention. This resulted in a fundamental modification of Canadian law which recognized the legitimate need of such police power for law enforcement purposes. Admittedly, an enactment formally recognizing and regulating investigative detention would have been desirable. But considering the inaction of Parliament in this area, it was necessary for courts to take over this task instead of ignoring a practical reality devoid of any legal framework. Although still incomplete, the judicially created scheme amounts to an improvement of the criminal law. However, in view of the need to amplify and further detail the rules governing investigative detention as well as to prevent potential abuses, the authors call for the adoption of a complete legislative framework

Bornes PAC-Bayes et algorithmes d'apprentissage

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2010-2011L’objet principale de cette thèse est l’étude théorique et la conception d’algorithmes d’apprentissage concevant des classificateurs par vote de majorité. En particulier, nous présentons un théorème PAC-Bayes s’appliquant pour borner, entre autres, la variance de la perte de Gibbs (en plus de son espérance). Nous déduisons de ce théorème une borne du risque du vote de majorité plus serrée que la fameuse borne basée sur le risque de Gibbs. Nous présentons également un théorème permettant de borner le risque associé à des fonctions de perte générale. À partir de ce théorème, nous concevons des algorithmes d’apprentissage construisant des classificateurs par vote de majorité pondérés par une distribution minimisant une borne sur les risques associés aux fonctions de perte linéaire, quadratique, exponentielle, ainsi qu’à la fonction de perte du classificateur de Gibbs à piges multiples. Certains de ces algorithmes se comparent favorablement avec AdaBoost.The main purpose of this thesis is the theoretical study and the design of learning algorithms returning majority-vote classifiers. In particular, we present a PAC-Bayes theorem allowing us to bound the variance of the Gibbs’ loss (not only its expectation). We deduce from this theorem a bound on the risk of a majority vote tighter than the famous bound based on the Gibbs’ risk. We also present a theorem that allows to bound the risk associated with general loss functions. From this theorem, we design learning algorithms building weighted majority vote classifiers minimizing a bound on the risk associated with the following loss functions : linear, quadratic and exponential. Also, we present algorithms based on the randomized majority vote. Some of these algorithms compare favorably with AdaBoost

Approche algébrique pour la prévention d'intrusions

Dans ce travail, nous définissons une nouvelle algèbre de processus basée sur CCS. Cette algèbre, qui est destinée à la sécurisation formelle de réseaux, est munie d'un opérateur de surveillance qui permet de contrôler les entrées et les sorties d'un processus, ou d'un sous-processus, à l'image d'un pare-feu dans un réseau informatique. L'algèbre permet donc de modéliser des réseaux munis de moniteurs, et également, n'importe quel système communicant devant être contrôlé par des moniteurs. Avant d'entrer dans le vif du sujet, nous débutons par une revue des approches globales en détection d'intrusions, soient l'approche comportementale et l'approche par scénarios. Nous parcourons par la suite différents langages formels destinés à la modélisation de systèmes communicants, en prêtant une attention particulière aux algèbres de processus

Active nuclear import and cytoplasmic retention of activation-induced deaminase

The enzyme activation-induced deaminase (AID) triggers antibody diversification in B cells by catalyzing deamination and consequently mutation of immunoglobulin genes. To minimize off-target deamination, AID is restrained by several regulatory mechanisms including nuclear exclusion, thought to be mediated exclusively by active nuclear export. Here we identify two other mechanisms involved in controlling AID subcellular localization. AID is unable to passively diffuse into the nucleus, despite its small size, and its nuclear entry requires active import mediated by a conformational nuclear localization signal. We also identify in its C terminus a determinant for AID cytoplasmic retention, which hampers diffusion to the nucleus, competes with nuclear import and is crucial for maintaining the predominantly cytoplasmic localization of AID in steady-state conditions. Blocking nuclear import alters the balance between these processes in favor of cytoplasmic retention, resulting in reduced isotype class switching.This work was supported by the Canadian Institutes of Health Research (MOP 84543) and a Canada Research Chair (to J.M.D.). A.O. was supported by a fellowship from the Canadian Institutes of Health Research Cancer Training Program at the IRCM. V.A.C. was supported in part by a Michel Saucier fellowship from the Louis-Pasteur Canadian Fund through the University of Montreal

COVID-19 infection and its deadly cytokine storm in a young obese adult

SARS-CoV-2 is a novel coronavirus that was initially described in Wuhan China in December 2019. In the USA (US), the person to be diagnosed with the novel Coronavirus infection (COVID) was on 19 January 2020. On 18 March 2020, a 31-year-old morbidly obese African American woman presented with severe dyspnea with associated hypoxemia, fever and bilateral interstitial pulmonary ground glass infiltrates consistent with viral pneumonitis. Nasopharyngeal PCR testing was positive for SARS-CoV-2. Despite initiation of hydroxychloroquine and azithromycin along with supplemental oxygen therapy, rapid disease progression consistent with cytokine release syndrome ensued, leading to initiation of mechanical ventilatory support. Anti-Interleukin (IL)-6 receptor monoclonal antibody (tocilizumab) was administered. Acute respiratory distress syndrome (ARDS) leads to refractory hypoxemia and demise. Severe morbid obesity as well as race may be unidentified risk factors for the development of severe Illness in patients with COVID-19

Invasive Group B streptococcus: multiloculated perinephric abscesses treated with percutaneous drainage

Group B streptococcus infections (GBSI) are commonly associated with neonates and pregnant women, but may also affect nonpregnant adults. Among its spectrum of manifestations, perinephric abscess (PA) is exceedingly rare. Comorbid conditions such as diabetes mellitus (DM) and immunosuppression increase the risk of GBSI. We describe a 61-year-old Vietnamese man with compensated alcoholic cirrhosis, who presented with acute encephalopathy following subacute, progressive abdominal pain. He was afebrile and hemodynamically stable. Laboratory data were remarkable for leukocytosis, thrombocytopenia, azotemia, and pyuria. He was found to have two right-sided PA measuring 15 × 10 × 11 cm and 4.6 × 2.7 × 7.8 cm, requiring interval placement of multiple percutaneous drains. Culture from abscesses revealed beta-hemolytic Group B streptococcus (GBS). His course was complicated by contiguous spread to abdominal wall and paraspinal musculature, as well as a new diagnosis of type 2 DM. Along with drainage, a prolonged course of intravenous antimicrobial treatment led to abscess resolution. Given the rising number of unusual clinical presentations of GBSI, this bacteria should be considered as a part of the microbiological differential diagnosis of PA, especially in conditions leading to immunosuppression

PAC-Bayesian Learning of Linear Classifiers

We present a general PAC-Bayes theorem from which all known PAC-Bayes risk bounds are obtained as particular cases. We also propose different learning algorithms for finding linear classifiers that minimize these bounds. These learning algorithms are generally competitive with both AdaBoost and the SVM. 1. Intoduction For the classification problem, we are given a training set of examples—each generated according to the same (but unknown) distribution D, and the goal is to find a classifier that minimizes the true risk (i.e., the generalization error or the expected loss). Since the true risk is defined only with respect to the unknown distribution D, we are automatically confronted with the problem of specifying exactly what we should optimize on the training data to find a classifier having the smallest possible true risk. Many different specifications (of what should be optimized on the training data) have been provided by using different inductive principles but the final guarantee on the true risk, however, always comes with a so-called risk bound that holds uniformly over a set of classifiers. Hence, the formal justification of a learning strategy has always come a posteriori via a risk bound. Since a risk bound can be computed from what a classifier achieves on the training data, it automatically suggests the following optimization problem for learning algorithms: given a risk (upper) bound, find a classifier that minimizes it. Despite the enormous impact they had on our understanding of learning, the VC bounds are generally very loose. These bounds are characterized by the fact tha

Hugh-Stovin syndrome: the ‘incomplete Behcet’s disease’. A case study of a young adult with recurrent pulmonary embolism and pulmonary arterial aneurysms

Hugh-Stovin Syndrome (HSS) is characterized by recurrent thrombophlebitis and multiple pulmonary and/or bronchial artery aneurysms indistinguishable from the cardiovascular features seen in Behcet’s disease (BD). Our case describes a 30-year-old male with recurrent pulmonary embolism and bilateral pulmonary aneurysms. Autoimmune, hypercoagulable, and infectious work up were negative. Elevated inflammatory markers and absence of the typical clinical findings seen in BD led to the diagnosis of Hugh-Stovin syndrome (HSS). Immunosuppression using steroids and azathioprine led to clinical response. Anticoagulation was continued based on risk/benefit ratio