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Except for the minority of patients with targetable driver mutations, HMAs - likely as part of combination therapies - will remain the backbone of frontline MDS treatment. However, the wider use of genetic testing may enable a more targeted and individualized therapy of MDS patients.Uveal melanoma is a rare disease. learn more Up to 50% of the patients will develop metastases for which the treatment options are limited. No randomized controlled data for the treatment of uveal melanoma patients are available. In this study we describe the clinical course of nine uveal melanoma patients included in the pembrolizumab expanded access program (EAP) in Belgium. Nine uveal melanoma patients were treated in the EAP with 2mg/kg pembrolizumab every 3 weeks. Patients received pembrolizumab as first or second-line treatment. Baseline characteristics and tumor responses were prospectively collected. During a median follow-up of 40 weeks, the estimated median PFS was 18 weeks (95% CI 0.7-35) and median OS was 46 weeks (95% CI 33-59%). Four patients had stable disease (SD) for more than 20 weeks (PFS of 21, 22, and 27 weeks respectively) and 1 patient presented with SD for 119 weeks. No objective responses according to irRC were observed. One grade 3 hepatitis occurred which was reversible with the administration of high doses oral corticosteroids. Even though treatment with pembrolizumab is well tolerated, clinical benefit is disappointing. Nevertheless long-term diseases control can be achieved in selected cases.Malignant melanoma of the lacrimal sac is an exceptionally rare tumor with a poor prognosis. We report two cases of malignant melanoma of the lacrimal sac a 73 year-old female treated with primary surgical resection and a 75 year-old male treated with surgical resection, adjuvant proton beam radiotherapy, and adjuvant immunotherapy. We discuss the role of post-operative proton beam therapy and recent advancements in immunotherapy. These cases highlight the importance of early diagnosis and multi-modality treatment in this aggressive malignancy.Exopolysaccharide is a key part of the extracellular matrix that contributes to important mechanisms of bacterial pathogenicity, most notably biofilm formation and immune evasion. In the human pathogens Staphylococcus aureus and S. epidermidis, as well as in many other staphylococcal species, the only exopolysaccharide is polysaccharide intercellular adhesin (PIA), a cationic, partially deacetylated homopolymer of N-acetylglucosamine, whose biosynthetic machinery is encoded in the ica locus. PIA production is strongly dependent on environmental conditions and controlled by many regulatory systems. PIA contributes significantly to staphylococcal biofilm formation and immune evasion mechanisms, such as resistance to antimicrobial peptides and ingestion and killing by phagocytes, and presence of the ica genes is associated with infectivity. Due to its role in pathogenesis, PIA has raised considerable interest as a potential vaccine component or target.Biological systems are typically composed of highly interconnected subunits and possess an inherent complexity that make monitoring, control and optimization of a bioprocess a challenging task. Today a toolset of modeling techniques can provide guidance in understanding complexity and in meeting those challenges. Over the last four decades, computational performance increased exponentially. This increase in hardware capacity allowed ever more detailed and computationally intensive models approaching a "one-to-one" representation of the biological reality. Fueled by governmental guidelines like the PAT initiative of the FDA, novel soft sensors and techniques were developed in the past to ensure product quality and provide data in real time. The estimation of current process state and prediction of future process course eventually enabled dynamic process control. In this review, past, present and envisioned future of models in biotechnology are compared and discussed with regard to application in process monitoring, control and optimization. In addition, hardware requirements and availability to fit the needs of increasingly more complex models are summarized. The major techniques and diverse approaches of modeling in industrial biotechnology are compared, and current as well as future trends and perspectives are outlined.Gastric cancer is one of the most common and clinically important diseases worldwide. The traditional Laeuren classification divides gastric cancer into two histopathological subtypes diffuse and intestinal. Recent cancer genomics research has led to the development of a new classification based on molecular characteristics. The newly defined genomically stable (GS) subtype shares many cases with the histopathologically diffuse type. In this study, we performed genetic profiling of recurrently and significantly mutated genes in diffuse type and GS subtype tumors. We observed significantly different genetic characteristics, although the two subtypes overlapped in many cases. In addition, based on the profiles of the significantly mutated genes, we identified molecular functions and mutational signatures characteristic of each subtype. These results will advance the clinical application of the diffuse type and GS subtype gastric cancer in precision medicine for treating gastric cancer.With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
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