Background and Aims Gliomas account for over half of all primary brain tumours and have a very poor prognosis, with a median survival of less than two years. There is an urgent and unmet clinical need to develop new therapies against glioma. Recent reports have indicated the overexpression of FPR1 in gliomas particularly in high grade gliomas. The aim of this project was to identify and synthesise small molecule FPR1 antagonists, and to demonstrate a proof of principle in preclinical in vitro and in vivo models that small molecule FPR1 antagonism can retard expansion of glioma. Methods A number of small molecule FPR1 antagonists were identified by in silico design, or from the literature and then were prepared using chemical synthesis. FPR1 antagonists were evaluated in vitro for their ability to abrogate FPR1-induced cellular responses in a range of models including calcium mobilisation, cell migration, and invasion. The efficacy of FPR1 antagonist ICT12035 in vivo was assessed in a U-87 MG subcutaneous xenograft model. Results Virtual high throughput screening using a homology model of FPR1 led to the identification of two small molecule FPR1 antagonists. At the same time chemical synthesis of two other antagonists, ICT5100 and ICT12035 as well as their analogues were carried out. The FPR1 antagonists were assessed in calcium flux assay which gave an insight into their structure-activity relationship. Further investigation of both ICT5100 and ICT12035 demonstrated that both small molecule FPR1 antagonists were effective at abrogating FPR1-induced calcium mobilisation, migration, and invasion in U- 87 MG in vitro models in a dose-dependent manner. ICT12035 is a particularly selective and potent inhibitor of FPR1 with an IC50 of 37.7 nM in calcium flux assay. Additionally, it was shown that the FPR1 antagonist ICT12035 was able to arrest the growth rate of U-87 MG xenografted tumours in mice. Conclusion The results demonstrate that targeting FPR1 by a small molecule antagonist such as ICT12035, could provide a potential new therapy for the treatment of glioblastoma. / Yorkshire Cancer Research

<p dir="ltr">Deep learning models have demonstrated remarkable success across diverse domains, including computer vision and natural language processing. These models heavily rely on resources, encompassing annotated data, computational power, and storage. However, mobile devices, particularly in scenarios like medical or multilingual contexts, often face constraints with computing power, making ample data annotation prohibitively expensive. Developing deep learning models for such resource-constrained scenarios presents a formidable challenge. Our primary goal is to enhance the efficiency of state-of-the-art neural network models tailored for resource-limited scenarios. Our commitment lies in crafting algorithms that not only mitigate annotation requirements but also reduce computational complexity and alleviate storage demands. Our dissertation focuses on two key areas: Parameter-efficient Learning and Data-efficient Learning. In Part 1, we present our studies on parameter-efficient learning. This approach targets the creation of lightweight models for efficient storage or inference. The proposed solutions are tailored for diverse tasks, including text generation, text classification, and text/image retrieval. In Part 2, we showcase our proposed methods for data-efficient learning, concentrating on cross-lingual and multi-lingual text classification applications. </p>

Over the last decade, chronic inflammatory enteropathies (CIE) in dogs have received great attention in the basic and clinical research arena. The 2010 ACVIM Consensus Statement, including guidelines for the diagnostic criteria for canine and feline CIE, was an important milestone to a more standardized approach to patients suspected of a CIE diagnosis. Great strides have been made since understanding the pathogenesis and classification of CIE in dogs, and novel diagnostic and treatment options have evolved. New concepts in the microbiome-host-interaction, metabolic pathways, crosstalk within the mucosal immune system, and extension to the gut-brain axis have emerged. Novel diagnostics have been developed, the clinical utility of which remains to be critically evaluated in the next coming years. New directions are also expected to lead to a larger spectrum of treatment options tailored to the individual patient. This review offers insights into emerging concepts and future directions proposed for further CIE research in dogs for the next decade to come.

京都大学 / 新制・論文博士 / 博士(医科学) / 乙第13611号 / 論医科博第12号 / 九州大学大学院薬学府創薬科学専攻 / (主査)教授 井上 治久, 教授 松田 秀一, 教授 萩原 正敏 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

The purpose of the study was to investigate and analyze the role of school counselors in detecting and preventing juvenile delinquency. To address this purpose, a qualitative method was used in the form of an interview study, where eight middle school counselors were interviewed. The data was processed through thematic analysis. Subsequently, the results were analyzed in relation to system theory and collaboration theory. The results indicate that school counselors currently work on crime detection and prevention, albeit not to the extent they desire. The lack of preventive work is a consequence of resource scarcity, weak school structure, and ambiguity regarding the role of school counselors in legislation. Additionally, the sense of professional isolation experienced by school counselors and the resulting heavy workload hinder crime prevention efforts. To effectively detect and prevent juvenile delinquency, school counselors collaborate with other stakeholders. Building relationships with students also allows school counselors to work on detecting and preventing juvenile delinquency. The study concludes that school counselors have a significant, but often overlooked role in crime preventative work. / Syftet med studien var att undersöka och analysera skolkuratorns roll för att upptäcka och förebygga ungdomsbrottslighet. För att besvara syftet användes en kvalitativ metod i form av en intervjustudie, där åtta högstadiekuratorer intervjuades. Empirin bearbetades genom en tematisk analys. Därefter analyserades resultatet i relation till systemteori och samverkansteori. Av resultatet framkommer att skolkuratorer idag arbetar brottsupptäckande och förebyggande, dock inte i den utsträckning de önskar. Bristen på förebyggande arbete är en konsekvens av resursbrist, svag struktur i skolan samt en otydlighet gällande skolkuratorns roll i skollagen. Vidare visar resultatet att skolkuratorerna upplever en ensamhet i professionen och har därmed en stor arbetsbelastning vilket hämmar det brottsförebyggande arbetet. För att på bästa sätt upptäcka och förebygga ungdomsbrottslighet arbetar skolkuratorer i samverkan med andra aktörer. Även genom relationsskapande med eleverna kan skolkuratorn arbeta för att upptäcka och förebygga ungdomsbrottslighet. Studiens slutsats är att skolkuratorer har en betydelsefull men ofta förbisedd roll i det brottsförebyggande arbetet.

Na área de inteligência artificial existem algoritmos de aprendizado, notavelmente aqueles pertencentes à área de aprendizado de máquina AM , capazes de automatizar a extração do conhecimento implícito de um conjunto de dados. Dentre estes, os algoritmos de AM simbólico são aqueles que extraem um modelo de conhecimento inteligível, isto é, que pode ser facilmente interpretado pelo usuário. A utilização de AM simbólico é comum no contexto de classificação, no qual o modelo de conhecimento extraído é tal que descreve uma correlação entre um conjunto de atributos denominados premissas e um atributo particular denominado classe. Uma característica dos algoritmos de classificação é que, em geral, estes são utilizados visando principalmente a maximização das medidas de cobertura e precisão, focando a construção de um classificador genérico e preciso. Embora essa seja uma boa abordagem para automatizar processos de tomada de decisão, pode deixar a desejar quando o usuário tem o desejo de extrair um modelo de conhecimento que possa ser estudado e que possa ser útil para uma melhor compreensão do domínio. Tendo-se em vista esse cenário, o principal objetivo deste trabalho é pesquisar métodos de computação evolutiva multiobjetivo para a construção de regras de conhecimento individuais com base em critérios definidos pelo usuário. Para isso utiliza-se a biblioteca de classes e ambiente de construção de regras de conhecimento ECLE, cujo desenvolvimento remete a projetos anteriores. Outro objetivo deste trabalho consiste comparar os métodos de computação evolutiva pesquisados com métodos baseado em composição de rankings previamente existentes na ECLE. É mostrado que os métodos de computação evolutiva multiobjetivo apresentam melhores resultados que os métodos baseados em composição de rankings, tanto em termos de dominância e proximidade das soluções construídas com aquelas da fronteira Pareto-ótima quanto em termos de diversidade na fronteira de Pareto. Em otimização multiobjetivo, ambos os critérios são importantes, uma vez que o propósito da otimização multiobjetivo é fornecer não apenas uma, mas uma gama de soluções eficientes para o problema, das quais o usuário pode escolher uma ou mais soluções que apresentem os melhores compromissos entre os objetivos / Machine Learning algorithms are notable examples of Artificial Intelligence algorithms capable of automating the extraction of implicit knowledge from datasets. In particular, Symbolic Learning algorithms are those which yield an intelligible knowledge model, i.e., one which a user may easily read. The usage of Symbolic Learning is particularly common within the context of classification, which involves the extraction of knowledge such that the associated model describes correelation among a set of attributes named the premises and one specific attribute named the class. Classification algorithms usually target into creating knowledge models which maximize the measures of coverage and precision, leading to classifiers that tend to be generic and precise. Althought this constitutes a good approach to creating models that automate the decision making process, it may not yield equally good results when the user wishes to extract a knowledge model which could assist them into getting a better understanding of the domain. Having that in mind, it has been established as the main goal of this Masters thesis the research of multi-objective evolutionary computing methods to create individual knowledge rules maximizing sets of arbitrary user-defined criteria. This is achieved by employing the class library and knowledge rule construction environment ECLE, which had been developed during previous research work. A second goal of this Masters thesis is the comparison of the researched evolutionary computing methods against previously existing ranking composition methods in ECLE. It is shown in this Masters thesis that the employment of multi-objective evolutionary computing methods produces better results than those produced by the employment of ranking composition-based methods. This improvement is verified both in terms of solution dominance and proximity of the solution set to the Pareto-optimal front and in terms of Pareto-front diversity. Both criteria are important for evaluating the efficiency of multi-objective optimization algorithms, for the goal of multi-objective optimization is to provide a broad range of efficient solutions, so the user may pick one or more solutions which present the best trade-off among all objectives

Finally, by basing its analysis on theories of institutional change, this study provides explanation for two phenomena that emerge from the process of curriculum implementation: formalism in the implementation strategies that attempt to cope with policy requirements and the lack of change in the curriculum deal to difficulties encountered. In light of the aforesaid phenomena, policy recommendations are given for the advancement of curriculum reform. / Secondly, the said pattern of behavior can be explained by institutional factors that have played a substantial role in the implementation process. Guided by a theoretical framework proposed by Scott (2001), this study uses its concepts and propositions to illustrate the institutional resources that are present in the practices of sampled schools and illuminates the institutional mechanisms that are at work. / Since 2001, a new round of curriculum reform was initiated in the Chinese Mainland. In the process of implementation, the reform endeavor has encountered a lot of difficulties. For those scholars who are interested in the reform of Chinese education, achieving an understanding of these difficulties has become their common pursuit. / Thirdly, the nature of the institution that influences the implementation of school curriculum is constituted by the interaction of stakeholders, namely, schools, government, higher institutions, parents, and the media. Through interaction, these stakeholders reach compromises in an "organization field" which in turn contribute to the formation of the institution. / This study approaches education reform from the perspective of policy implementation. It adopts the theoretical lens of neo-institutionalism and examines the implementation of "research based learning" in the schools of District A. As a new form of learning, "research based learning" is heralded as one of the major theme of the new school curriculum. Through the analysis of data collected through interviews and field observation, this study yields results that are presented in the paragraphs below. / To begin with, research has unearthed an obvious "isomorphism" in the reform practices in different schools. The study finds that there is a stable pattern of behavior that is discernible in the implementation process of curriculum reform. This pattern of behavior is commonly shared by different schools engaged in the reform process. / 柯政. / Advisers: Leslie Lo; Wing-kwong Tsang. / Source: Dissertation Abstracts International, Volume: 70-06, Section: A, page: 1903. / Thesis (doctoral)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 243-261). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in Chinese and English. / School code: 1307. / Ke Zheng.

Data dependencies, or integrity constraints, are used to improve the quality of a database schema, to optimize queries, and to ensure consistency in a database. In the last years conditional dependencies have been introduced to analyze and improve data quality. In short, a conditional dependency is a dependency with a limited scope defined by conditions over one or more attributes. Only the matching part of the instance must adhere to the dependency. In this paper we focus on conditional inclusion dependencies (CINDs). We generalize the definition of CINDs, distinguishing covering and completeness conditions. We present a new use case for such CINDs showing their value for solving complex data quality tasks. Further, we define quality measures for conditions inspired by precision and recall. We propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. Our algorithms choose not only the condition values but also the condition attributes automatically. Finally, we show that our approach efficiently provides meaningful and helpful results for our use case. / Datenabhängigkeiten (wie zum Beispiel Integritätsbedingungen), werden verwendet, um die Qualität eines Datenbankschemas zu erhöhen, um Anfragen zu optimieren und um Konsistenz in einer Datenbank sicherzustellen. In den letzten Jahren wurden bedingte Abhängigkeiten (conditional dependencies) vorgestellt, die die Qualität von Daten analysieren und verbessern sollen. Eine bedingte Abhängigkeit ist eine Abhängigkeit mit begrenztem Gültigkeitsbereich, der über Bedingungen auf einem oder mehreren Attributen definiert wird. In diesem Bericht betrachten wir bedingte Inklusionsabhängigkeiten (conditional inclusion dependencies; CINDs). Wir generalisieren die Definition von CINDs anhand der Unterscheidung von überdeckenden (covering) und vollständigen (completeness) Bedingungen. Wir stellen einen Anwendungsfall für solche CINDs vor, der den Nutzen von CINDs bei der Lösung komplexer Datenqualitätsprobleme aufzeigt. Darüber hinaus definieren wir Qualitätsmaße für Bedingungen basierend auf Sensitivität und Genauigkeit. Wir stellen effiziente Algorithmen vor, die überdeckende und vollständige Bedingungen innerhalb vorgegebener Schwellwerte finden. Unsere Algorithmen wählen nicht nur die Werte der Bedingungen, sondern finden auch die Bedingungsattribute automatisch. Abschließend zeigen wir, dass unser Ansatz effizient sinnvolle und hilfreiche Ergebnisse für den vorgestellten Anwendungsfall liefert.

Temporal data mining is concerned with the exploration of large sequential (or temporally ordered) data sets to discover some nontrivial information that was previously unknown to the data owner. Sequential data sets come up naturally in a wide range of application domains, ranging from bioinformatics to manufacturing processes. Pattern discovery refers to a broad class of data mining techniques in which the objective is to unearth hidden patterns or unexpected trends in the data. In general, pattern discovery is about finding all patterns of 'interest' in the data and one popular measure of interestingness for a pattern is its frequency in the data. The problem of frequent pattern discovery is to find all patterns in the data whose frequency exceeds some user-defined threshold. Discovery of temporal patterns that occur frequently in sequential data has received a lot of attention in recent times. Different approaches consider different classes of temporal patterns and propose different algorithms for their efficient discovery from the data. This thesis is concerned with a specific class of temporal patterns called episodes and their discovery in large sequential data sets. In the framework of frequent episode discovery, data (referred to as an event sequence or an event stream) is available as a single long sequence of events. The ith event in the sequence is an ordered pair, (Et,tt), where Et takes values from a finite alphabet (of event types), and U is the time of occurrence of the event. The events in the sequence are ordered according to these times of occurrence. An episode (which is the temporal pattern considered in this framework) is a (typically) short partially ordered sequence of event types. Formally, an episode is a triple, (V,<,9), where V is a collection of nodes, < is a partial order on V and 9 is a map that assigns an event type to each node of the episode. When < is total, the episode is referred to as a serial episode, and when < is trivial (or empty), the episode is referred to as a parallel episode. An episode is said to occur in an event sequence if there are events in the sequence, with event types same as those constituting the episode, and with times of occurrence respecting the partial order in the episode. The frequency of an episode is some measure of how often it occurs in the event sequence. Given a frequency definition for episodes, the task is to discover all episodes whose frequencies exceed some threshold. This is done using a level-wise procedure. In each level, a candidate generation step is used to combine frequent episodes from the previous level to build candidates of the next larger size, and then a frequency counting step makes one pass over the event stream to determine frequencies of all the candidates and thus identify the frequent episodes. Frequency counting is the main computationally intensive step in frequent episode discovery. Choice of frequency definition for episodes has a direct bearing on the efficiency of the counting procedure. In the original framework of frequent episode discovery, episode frequency is defined as the number of fixed-width sliding windows over the data in which the episode occurs at least once. Under this frequency definition, frequency counting of a set of |C| candidate serial episodes of size N has space complexity O(N|C|) and time complexity O(ΔTN|C|) (where ΔT is the difference between the times of occurrence of the last and the first event in the data stream). The other main frequency definition available in the literature, defines episode frequency as the number of minimal occurrences of the episode (where, a minimal occurrence is a window on the time axis containing an occurrence of the episode, such that, no proper sub-window of it contains another occurrence of the episode). The algorithm for obtaining frequencies for a set of |C| episodes needs O(n|C|) time (where n denotes the number of events in the data stream). While this is time-wise better than the the windows-based algorithm, the space needed to locate minimal occurrences of an episode can be very high (and is in fact of the order of length, n, of the event stream). This thesis proposes a new definition for episode frequency, based on the notion of, what is called, non-overlapped occurrences of episodes in the event stream. Two occurrences are said to be non-overlapped if no event corresponding to one occurrence appears in between events corresponding to the other. Frequency of an episode is defined as the maximum possible number of non-overlapped occurrences of the episode in the data. The thesis also presents algorithms for efficient frequent episode discovery under this frequency definition. The space and time complexities for frequency counting of serial episodes are O(|C|) and O(n|C|) respectively (where n denotes the total number of events in the given event sequence and |C| denotes the num-ber of candidate episodes). These are arguably the best possible space and time complexities for the frequency counting step that can be achieved. Also, the fact that the time needed by the non-overlapped occurrences-based algorithm is linear in the number of events, n, in the event sequence (rather than the difference, ΔT, between occurrence times of the first and last events in the data stream, as is the case with the windows-based algorithm), can result in considerable time advantage when the number of time ticks far exceeds the number of events in the event stream. The thesis also presents efficient algorithms for frequent episode discovery under expiry time constraints (according to which, an occurrence of an episode can be counted for its frequency only if the total time span of the occurrence is less than a user-defined threshold). It is shown through simulation experiments that, in terms of actual run-times, frequent episode discovery under the non-overlapped occurrences-based frequency (using the algorithms developed here) is much faster than existing methods. There is also a second frequency measure that is proposed in this thesis, which is based on, what is termed as, non-interleaved occurrences of episodes in the data. This definition counts certain kinds of overlapping occurrences of the episode. The time needed is linear in the number of events, n, in the data sequence, the size, N, of episodes and the number of candidates, |C|. Simulation experiments show that run-time performance under this frequency definition is slightly inferior compared to the non-overlapped occurrences-based frequency, but is still better than the run-times under the windows-based frequency. This thesis also establishes the following interesting property that connects the non-overlapped, the non-interleaved and the minimal occurrences-based frequencies of an episode in the data: the number of minimal occurrences of an episode is bounded below by the maximum number of non-overlapped occurrences of the episode, and is bounded above by the maximum number of non-interleaved occurrences of the episode in the data. Hence, non-interleaved occurrences-based frequency is an efficient alternative to that based on minimal occurrences. In addition to being superior in terms of both time and space complexities compared to all other existing algorithms for frequent episode discovery, the non-overlapped occurrences-based frequency has another very important property. It facilitates a formal connection between discovering frequent serial episodes in data streams and learning or estimating a model for the data generation process in terms of certain kinds of Hidden Markov Models (HMMs). In order to establish this connection, a special class of HMMs, called Episode Generating HMMs (EGHs) are defined. The symbol set for the HMM is chosen to be the alphabet of event types, so that, the output of EGHs can be regarded as event streams in the frequent episode discovery framework. Given a serial episode, α, that occurs in the event stream, a method is proposed to uniquely associate it with an EGH, Λα. Consider two N-node serial episodes, α and β, whose (non-overlapped occurrences-based) frequencies in the given event stream, o, are fα and fβ respectively. Let Λα and Λβ be the EGHs associated with α and β. The main result connecting episodes and EGHs states that, the joint probability of o and the most likely state sequence for Λα is more than the corresponding probability for Λβ, if and only if, fα is greater than fβ. This theoretical connection has some interesting consequences. First of all, since the most frequent serial episode is associated with the EGH having the highest data likelihood, frequent episode discovery can now be interpreted as a generative model learning exercise. More importantly, it is now possible to derive a formal test of significance for serial episodes in the data, that prescribes, for a given size of the test, a minimum frequency for the episode needed in order to declare it as statistically significant. Note that this significance test for serial episodes does not require any separate model estimation (or training). The only quantity required to assess significance of an episode is its non-overlapped occurrences-based frequency (and this is obtained through the usual counting procedure). The significance test also helps to automatically fix the frequency threshold for the frequent episode discovery process, so that it can lead to what may be termed parameterless data mining. In the framework considered so far, the input to frequent episode discovery process is a sequence of instantaneous events. However, in many applications events tend to persist for different periods of time and the durations may carry important information from a data mining perspective. This thesis extends the framework of frequent episodes to incorporate such duration information directly into the definition of episodes, so that, the patterns discovered will now carry this duration information as well. Each event in this generalized framework looks like a triple, (Ei, ti, τi), where Ei, as earlier, is the event type (from some finite alphabet) corresponding to the ith event, and ti and τi denote the start and end times of this event. The new temporal pattern, called the generalized episode, is a quadruple, (V, <, g, d), where V, < and g, as earlier, respectively denote a collection of nodes, a partial order over this collection and a map assigning event types to nodes. The new feature in the generalized episode is d, which is a map from V to 2I, where, I denotes a collection of time interval possibilities for event durations, which is defined by the user. An occurrence of a generalized episode in the event sequence consists of events with both 'correct' event types and 'correct' time durations, appearing in the event sequence in 'correct' time order. All frequency definitions for episodes over instantaneous event streams are applicable for generalized episodes as well. The algorithms for frequent episode discovery also easily extend to the case of generalized episodes. The extra design choice that the user has in this generalized framework, is the set, I, of time interval possibilities. This can be used to orient and focus the frequent episode discovery process to come up with temporal correlations involving only time durations that are of interest. Through extensive simulations the utility and effectiveness of the generalized framework are demonstrated. The new algorithms for frequent episode discovery presented in this thesis are used to develop an application for temporal data mining of some data from car engine manufacturing plants. Engine manufacturing is a heavily automated and complex distributed controlled process with large amounts of faults data logged each day. The goal of temporal data mining here is to unearth some strong time-ordered correlations in the data which can facilitate quick diagnosis of root causes for persistent problems and predict major breakdowns well in advance. This thesis presents an application of the algorithms developed here for such analysis of the faults data. The data consists of time-stamped faults logged in car engine manufacturing plants of General Motors. Each fault is logged using an extensive list of codes (which constitutes the alphabet of event types for frequent episode discovery). Frequent episodes in fault logs represent temporal correlations among faults and these can be used for fault diagnosis in the plant. This thesis describes how the outputs from the frequent episode discovery framework, can be used to help plant engineers interpret the large volumes of faults logged, in an efficient and convenient manner. Such a system, based on the algorithms developed in this thesis, is currently being used in one of the engine manufacturing plants of General Motors. Some examples of the results obtained that were regarded as useful by the plant engineers are also presented.

Les biothérapies (anticorps monoclonaux, récepteurs solubles) ciblant les cytokines IL-6 etTNF-alpha pour le traitement des maladies inflammatoires chroniques ont constitué un succèsmajeur de l’industrie pharmaceutique. Elles présentent néanmoins des inconvénientsimportants : résistances, mode d’administration contraignant, coût élevé.Notre équipe travaille sur l’identification de petites molécules inhibant directement cescytokines, afin d’élargir l’offre thérapeutique existante. Administrées par voie orale, ellesconstitueraient une alternative particluièrement favorable aux patients.Durant ma thèse, j’ai réalisé le criblage expérimental (tests cellulaires et tests biochimiquesde liaison) des meilleurs composés identifiés par criblage virtuel d’un grande chimiothèque dediversité, ainsi que de composés dérivés de pyridazine issus d’une chimiothèque médicinale. J’aiainsi pu identifier plusieurs inhibiteurs directs du TNF-alpha et de l’IL-6. De plus, mon travail apermis d’affiner les procédures de criblage du Laboratoire.Ces travaux ouvrent de nouvelles pistes pour le développement de médicaments anti-cytokines. / Anti-cytokine biologics (monoclonal antibodies, soluble receptors) targeting TNF-alpha and IL-6in chronic inflammatory diseases have been a major success for pharmaceutical industry.However, they exhibit several drawbacks : resistance, difficult administration, high costs.Our team works on the discovery of small molecule inhibitors of cytokines suck as TNF-alphaand IL-6, in order to widen the range of therapeutic drugs. Orally active drugs would represent ahighly beneficial alternative for patients.During my PhD, I have performed an experimental screening (using cellular and biochemicalbinding testings) of the best compounds identified through virtual screening of a large chemicallibrary, and on pyridazine compounds of a medicinal chemical library. I have been able toidentify several small molecules inhibiting the interaction of TNF-! and IL-6 with their receptor.Moreover, my work will have an impact on the laboratory screening strategies.Overall, this work opens new avenues for anti-cytokine drug discovery.