Rare presentation of renal failure related to tumor lysis syndrome

Background: Tumor lysis syndrome (TLS) which mostly occurs in lymphoproliferative malignancies after the start of chemotherapy is an oncologic emergency. Manifestations of metabolic imbalance including increasing hyperkalemia, hyperphosphatemia, hyperuricemia and hypocalcemia are common presentation of TLS. Case report: We present two cases of spontaneous TLS; a rare presentation of TLS before cytotoxic chemotherapy. These cases were admitted with presentation of TLS without any history of chemotherapy with mediastinal mass in chest X-ray (CXR) and subsequent diagnosis of lymphoblastic lymphoma and T-cell acute lymphocytic leukemia (ALL). After several hemodialysis sessions, their conditions were improved and they underwent chemotherapy. Conclusions: It was found that the presentation of mediastinal mass in cases of lymphoma and acute leukemia might be associated with TLS before chemotherapy. In addition, it is important to pay attention to CXR, when we face to a patient with acute renal failure related to TLS

Logic-based machine learning using a bounded hypothesis space: the lattice structure, refinement operators and a genetic algorithm approach

Rich representation inherited from computational logic makes logic-based machine learning a competent method for application domains involving relational background knowledge and structured data. There is however a trade-off between the expressive power of the representation and the computational costs. Inductive Logic Programming (ILP) systems employ different kind of biases and heuristics to cope with the complexity of the search, which otherwise is intractable. Searching the hypothesis space bounded below by a bottom clause is the basis of several state-of-the-art ILP systems (e.g. Progol and Aleph). However, the structure of the search space and the properties of the refinement operators for theses systems have not been previously characterised. The contributions of this thesis can be summarised as follows: (i) characterising the properties, structure and morphisms of bounded subsumption lattice (ii) analysis of bounded refinement operators and stochastic refinement and (iii) implementation and empirical evaluation of stochastic search algorithms and in particular a Genetic Algorithm (GA) approach for bounded subsumption. In this thesis we introduce the concept of bounded subsumption and study the lattice and cover structure of bounded subsumption. We show the morphisms between the lattice of bounded subsumption, an atomic lattice and the lattice of partitions. We also show that ideal refinement operators exist for bounded subsumption and that, by contrast with general subsumption, efficient least and minimal generalisation operators can be designed for bounded subsumption. In this thesis we also show how refinement operators can be adapted for a stochastic search and give an analysis of refinement operators within the framework of stochastic refinement search. We also discuss genetic search for learning first-order clauses and describe a framework for genetic and stochastic refinement search for bounded subsumption. on. Finally, ILP algorithms and implementations which are based on this framework are described and evaluated.Open Acces

A rare case of Kikuchi-Fujimoto disease (case report)

Background: Kikuchi disease is a rare, benign, self-limited disease characterized primarily by fever and cervical lymphadenopathy. Diagnosis is based on excisional biopsy and pathologic study. We report a case of an atypical axillary lymph node enlargement. Case report: This patient was a 12-year-old boy with clinical characteristics including axillary lymph adenopathy, fever and fatigue. He became asymptomatic after excisional biopsy. Histologic study reported necrotizing lymphadenitis without neutrophils (Kikuchi disease). Conclusions: These findings are important for diagnosis, because of the disease rarity, clinical features (such as lymphadenopathy, prolonged fever) and unidentified etiology

A rare presentation of actinomycosis: case report

Background: Anaerobic, nonsporulating, Gram-positive bacteria groups called actinomyces organisms are responsible for the so called actinomycosis. This chronic disease is rare in children and has tendency to mimic many other diseases. It also has wide variety of manifestations and non-specific symptoms. As a result, it is difficult to diagnose before the biopsy and microscopic examination. Although infection may involve any organ in the body, the significant sites of actinomyces infection include cervicofacial, abdominal, pelvic and pulmonary tissues. Case report: Here, we describe one case of unusual presentation; an 11-year-old girl with a soft tissue mass in the left lower lateral chest wall which was finally diagnosed actinomycosis based on the pathological findings. Conclusions: Actinomycosis may rarely present with chest wall mass

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Since the late 1990s predicate invention has been under-explored within inductive logic programming due to difficulties in formulating efficient search mechanisms. However, a recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of metalogical substitutions with respect to a modified Prolog meta-interpreter which acts as the learning engine. New predicate symbols are introduced as constants representing existentially quantified higher-order variables. The approach demonstrates that predicate invention can be treated as a form of higher-order logical reasoning. In this paper we generalise the approach of meta-interpretive learning (MIL) to that of learning higher-order dyadic datalog programs. We show that with an infinite signature the higher-order dyadic datalog class H2 2 has universal Turing expressivity though H2 2 is decidable given a finite signature. Additionally we show that Knuth–Bendix ordering of the hypothesis space together with logarithmic clause bounding allows our MIL implementation MetagolD to PAC-learn minimal cardinality H2 2 definitions. This result is consistent with our experiments which indicate that MetagolD efficiently learns compact H2 2 definitions involving predicate invention for learning robotic strategies, the East–West train challenge and NELL. Additionally higher-order concepts were learned in the NELL language learning domain. The Metagol code and datasets described in this paper have been made publicly available on a website to allow reproduction of results in this paper

Acoustical Methods for Cavitation Control in Shockwave Lithotripsy and Histotripsy

The overall goal of the work presented in this dissertation is to develop acoustic mechanisms to modulate, or manipulate, cavitation events in histotripsy and lithotripsy therapies in order to achieve efficient and fast histotripsy, high shock rate lithotripsy, and active tissue protection. We investigated the effects of applying properly tuned low pressure acoustic pulses before and during therapy in order to control the cavitation threshold, the shape of the resulting bubble cloud, and the behavior and interactions of residual microbubbles. Histotripsy is a tissue ablation method that utilizes focused high amplitude ultrasound to generate a cavitation bubble cloud that mechanically fractionates tissue. Effective histotripsy depends on initiation, control, and maintenance of cavitation bubble clouds in the targeted area. The work in this dissertation seeks to develop active tissue protection techniques by modulating the pressure threshold of bubble cloud initiation and focal sharpening using bubble suppressing pulses. We demonstrated that by applying a properly tuned low pressure pulse sequence before and/or during shock scattering histotripsy therapy, both the cavitation initiation pressure threshold and the growth of the cavitation bubble can be modified. This mechanism can be used to produce well defined lesions with minimal collateral damage. It can also be a way to actively protect soft tissue from cavitation damage during both lithotripsy and histotripsy by increasing the pressure threshold for bubble cloud initiation in the periphery zone. Cavitation also plays a significant role in the efficacy of stone comminution during shockwave lithotripsy (SWL). Although cavitation on the surface of urinary stones helps to improve stone fragmentation, cavitation bubbles along the propagation path may shield or block subsequent shockwaves and potentially induce collateral tissue damage. At low firing rates, there is sufficient time for the majority of the bubbles to passively dissolve, while at high firing rates the per shockwave efficacy is significantly reduced due to pre-focal persisting bubbles. We investigated acoustic methods for removing residual bubble nuclei in order to avoid shielding effects. Previous in vitro work has shown that applying low amplitude acoustic waves after each shockwave can force bubbles to consolidate and enhance SWL efficacy. In this work, the feasibility of applying acoustic bubble coalescence (ABC) in vivo was examined. We further optimized the parameters of bubble coalescing pulses, and conducted a feasibility investigation of bubble dispersion by forcing the residual bubble nuclei to disperse from the propagation path away or toward the targeted area before the arrival of the next therapy pulse. These results suggest that manipulation of residual bubbles after each shockwave can be further optimized by acoustic bubble coalescence and dispersion, which can reduce the shielding effect of residual bubble nuclei more efficiently than relying only on immediate coalescence of residual bubbles, resulting in a more efficient SWL treatment.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149949/1/alavi_1.pd

The effect of using partnership care model on the quality of life in the school-age children with β-thalassemia

زمینه و هدف: تالاسمی از شایع ترین بیماری های ژنتیک است که بر کیفیت زندگی اثر دارد. با توجه به لزوم یافتن بهترین روشهای اداره تالاسمی از طریق درگیری تمام ارکان مراقبتی، تحقیق حاضر با هدف تعیین تأثیر مدل مراقبت مشارکتی بر کیفیت زندگی کودکان سن مدرسه مبتلا به تالاسمی انجام گرفته است. روش بررسی: در این پژوهش تجربی 72 کودک مبتلا به تالاسمی ماژور به صورت در دسترس انتخاب و به صورت تصادفی در دو گروه آزمون و شاهد قرار گرفتند. پرسشنامه های دموگرافیک و کیفیت زندگی عمومی کودک (گزارش کودک و والدین) (Peds-QOL)، قبل از مداخله در هر دو گروه تکمیل و مدل مراقبت مشارکتی بر اساس مراحل انگیزش، آماده سازی، درگیرسازی و ارزشیابی، در گروه آزمون و با حضور مراقبت دهنده اصلی در منزل به مدت 2 ماه اجرا شد. میانگین نمرات کیفیت زندگی 3 ماه بعد از مداخله با اطلاعات قبل از مداخله به کمک آزمون های آماری کای دو، فیشر و t مستقل و t زوجی مقایسه گردید. یافته‌ها: نتایج نشان داد که تفاوت معنی‌داری در کیفیت زندگی کودکان بین دو گروه قبل از مداخله وجود ندارد، میانگین نمره کیفیت زندگی پس از مداخله در همه ابعاد (گزارش والدین و کودک) به جز عملکرد جسمی والدین در گروه آزمون بالاتر از گروه شاهد بود (001/0

An Evolutionary Algorithm for Discovering Multi-Relational Association Rules in the Semantic Web

International audienceIn the Semantic Web context, OWL ontologies represent the conceptualization of domains of interest while the corresponding assertional knowledge is given by RDF data referring to them. Because of its open, distributed, and collaborative nature, such knowledge can be incomplete, noisy, and sometimes inconsistent. By exploiting the evidence coming from the assertional data, we aim at discovering hidden knowledge patterns in the form of multi-relational association rules while taking advantage of the intensional knowledge available in ontological knowledge bases. An evolutionary search method applied to populated ontological knowledge bases is proposed for finding rules with a high inductive power. The proposed method, EDMAR, uses problem-aware genetic operators, echoing the refinement operators of ILP, and takes the intensional knowledge into account, which allows it to restrict and guide the search. Discovered rules are coded in SWRL, and as such they can be straightforwardly integrated within the ontology, thus enriching its expressive power and augmenting the assertional knowledge that can be derived. Additionally , discovered rules may also suggest new axioms to be added to the ontology. We performed experiments on publicly available ontologies, validating the performances of our approach and comparing them with the main state-of-the-art systems

Human-machine scientific discovery

International audienceHumanity is facing existential, societal challenges related to food security, ecosystem conservation, antimicrobial resistance, etc, and Artificial Intelligence (AI) is already playing an important role in tackling these new challenges. Most current AI approaches are limited when it comes to ‘knowledge transfer’ with humans, i.e. it is difficult to incorporate existing human knowledge and also the output knowledge is not human comprehensible. In this chapter we demonstrate how a combination of comprehensible machine learning, text-mining and domain knowledge could enhance human-machine collaboration for the purpose of automated scientific discovery where humans and computers jointly develop and evaluate scientific theories. As a case study, we describe a combination of logic-based machine learning (which included human-encoded ecological background knowledge) and text-mining from scientific publications (to verify machine-learned hypotheses) for the purpose of automated discovery of ecological interaction networks (food-webs) to detect change in agricultural ecosystems using the Farm Scale Evaluations (FSEs) of genetically modified herbicide-tolerant (GMHT) crops dataset. The results included novel food-web hypotheses, some confirmed by subsequent experimental studies (e.g. DNA analysis) and published in scientific journals. These machine-leaned food-webs were also used as the basis of a recent study revealing resilience of agro-ecosystems to changes in farming management using GMHT crops