Musculoskeletal infection remains a great challenge in orthopedic and trauma surgery. Despite best medical and surgical practice and significant advances in research and development, bone and implant associated infections are still difficult to diagnose, impossible to prevent in all cases and require invasive and debilitating treatment. The development and safe clinical implementation of novel preventative, therapeutic or diagnostic strategies requires the use of animal models of infection, which provide crucial evidence regarding performance, cytocompatibility, biocompatibility, and safety prior to clinical implementation.
Many animal models of musculoskeletal infection have been described in the literature; however, there remains a dearth of fully standardized or universally accepted reference models hindering advancement in the field. The following chapter provides an overview of the animal models available for the study of musculoskeletal infection, the latest advances that are expected to improve them, and some of the most important scientific output achieved using these models.

Autoimmune rheumatic diseases as a group are characterized by common features such as multisystem involvement, polypharmacy, and the presence of comorbidities. Recent large epidemiological studies shed light on their role as risk factors for thromboembolic complications. The pathogenic factors are now better understood. The proposed mechanisms for this increased risk include increased hypercoagulability, lack of inhibitors, and other factors. The group of vasculitis which includes Behçet’s disease, giant cell vasculitis, Takayasu’s arteritis, and ANCA-associated vasculitis deserves special attention. However other diseases such as systemic lupus erythematosus, Sjögren syndrome, scleroderma, rheumatoid and psoriatic arthritis, myositis, and sarcoidosis are also associated with significantly increased risk of thrombosis.

Numerous endeavors have been made to improve the retrieval procedure in Textual Case-Based Reasoning (TCBR) utilizing clustering and feature selection strategies. SOPHisticated Information Analysis (SOPHIA) approach is one of the most successful efforts which is characterized by its ability to work without the domain of knowledge or language dependency. SOPHIA is based on the conditional probability, which facilitates an advanced Knowledge Discovery (KD) framework for case-based retrieval. SOPHIA attracts clusters by themes which contain only one word in each. However, using one word is not sufficient to construct cluster attractors because the exclusion of the other words associated with that word in the same context could not give a full picture of the theme. The main contribution of this chapter is to introduce an enhanced clustering approach called GloSOPHIA (GloVe SOPHIA) that extends SOPHIA by integrating word embedding technique to enhance KD in TCBR. A new algorithm is proposed to feed SOPHIA with similar terms vector space gained from Global Vector (GloVe) embedding technique. The proposed approach is evaluated on two different language corpora and the results are compared with SOPHIA, K-means, and Self- Organizing Map (SOM) in several evaluation criteria. The results indicate that GloSOPHIA outperforms the other clustering methods in most of the evaluation criteria.

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This chapter offers a discussion of three texts by bicultural Egyptian writers: Waguih Ghali’s Beer in the Snooker Club (1964), Radwa Ashour’s Specters (1999), and Miral al-Tahawy’s Brooklyn Heights (2010). The three works are read via an autofictional lens, with focus on Ghali’s autofictional identity, Ashour’s autofictional threads, and al-Tahawy’s autofictionalizing experience. The study suggests that autofictionality can be identified in the texts in terms of genre as well as technique, demonstrating the potential of the autofictional as a literary strategy in negotiating identity, memory, and experience in the writing of Egyptian literature. Our reading of the three texts testifies to the affordance of an autofictional lens in reading Arabic literature and allows new insights into writing at the intersection of reality and the imagination.

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The main purpose of bioremediation processes is to eliminate or even to reduce the risk of the toxic pollutants to reach safe and acceptable limits. Although environmental pollutants have complex nature and composition, most of the performed studies reported the application of methods for biodegradation of pollutants and xenobiotics in environment, especially wastewater, as a black box without considering sequestered risks. In this sense, chemical analysis alone is not sufficient for complete assessment of water quality while biological toxicity assays can estimate the effect of wastewater on the biota and assess the actual direct and sequestered environmental risks. This was the main driving force to teamwork in our lab to adopt, develop, and optimize biological testing methods and bioassays for better process control and real monitoring of the bioremediation efficiency. Several methods and bioassays were used such as BOD5 and algal toxicity (to assess the degree of wastewater toxicity), Artemia toxicity (to assess impact on the aquatic creatures), phytotoxicity (to assess the impact on terrestrial plants). Also endocrine disruptors’ assays and cytotoxicity (to assess the impact on mammalians and humans) were optimized. The authors discuss and transfer the knowledge and the step by step experience gained with these methods and bioassays. Moreover, this chapter elaborates all necessary practical tricks and precautions required to achieve accurate and reproducible measurements.