Pemphigus autoimmunity: Hypotheses and realities

The goal of contemporary research in pemphigus vulgaris and pemphigus foliaceus is to achieve and maintain clinical remission without corticosteroids. Recent advances of knowledge on pemphigus autoimmunity scrutinize old dogmas, resolve controversies, and open novel perspectives for treatment. Elucidation of intimate mechanisms of keratinocyte detachment and death in pemphigus has challenged the monopathogenic explanation of disease immunopathology. Over 50 organ-specific and non-organ-specific antigens can be targeted by pemphigus autoimmunity, including desmosomal cadherins and other adhesion molecules, PERP cholinergic and other cell membrane (CM) receptors, and mitochondrial proteins. The initial insult is sustained by the autoantibodies to the cell membrane receptor antigens triggering the intracellular signaling by Src, epidermal growth factor receptor kinase, protein kinases A and C, phospholipase C, mTOR, p38 MAPK, JNK, other tyrosine kinases, and calmodulin that cause basal cell shrinkage and ripping desmosomes off the CM. Autoantibodies synergize with effectors of apoptotic and oncotic pathways, serine proteases, and inflammatory cytokines to overcome the natural resistance and activate the cell death program in keratinocytes. The process of keratinocyte shrinkage/detachment and death via apoptosis/oncosis has been termed apoptolysis to emphasize that it is triggered by the same signal effectors and mediated by the same cell death enzymes. The natural course of pemphigus has improved due to a substantial progress in developing of the steroid-sparing therapies combining the immunosuppressive and direct anti-acantholytic effects. Further elucidation of the molecular mechanisms mediating immune dysregulation and apoptolysis in pemphigus should improve our understanding of disease pathogenesis and facilitate development of steroid-free treatment of patients

Drug dosing during pregnancy—opportunities for physiologically based pharmacokinetic models

Drugs can have harmful effects on the embryo or the fetus at any point during pregnancy. Not all the damaging effects of intrauterine exposure to drugs are obvious at birth, some may only manifest later in life. Thus, drugs should be prescribed in pregnancy only if the expected benefit to the mother is thought to be greater than the risk to the fetus. Dosing of drugs during pregnancy is often empirically determined and based upon evidence from studies of non-pregnant subjects, which may lead to suboptimal dosing, particularly during the third trimester. This review collates examples of drugs with known recommendations for dose adjustment during pregnancy, in addition to providing an example of the potential use of PBPK models in dose adjustment recommendation during pregnancy within the context of drug-drug interactions. For many drugs, such as antidepressants and antiretroviral drugs, dose adjustment has been recommended based on pharmacokinetic studies demonstrating a reduction in drug concentrations. However, there is relatively limited (and sometimes inconsistent) information regarding the clinical impact of these pharmacokinetic changes during pregnancy and the effect of subsequent dose adjustments. Examples of using pregnancy PBPK models to predict feto-maternal drug exposures and their applications to facilitate and guide dose assessment throughout gestation are discussed

Fast machine-learning online optimization of ultra-cold-atom experiments.

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system

Computerized monitoring of patient-reported speech and swallowing problems in head and neck cancer patients in clinical practice

Purpose The purpose of this study is to evaluate computerized monitoring of speech and swallowing outcomes and its impact on quality of life (QoL) and emotional well-being in head and neck cancer patients in an outpatient clinic. Methods Sixty-seven patients, treated by single or multimodality treatment, completed the EORTC QLQ-C30 and QLQ-H&N35 questionnaires and the Hospital Anxiety and Depression Scale in an outpatient clinic, using a touch screen computer system (OncoQuest), at baseline (at time of diagnosis) and first follow-up (1 month after end of treatment). Results Tumor sites included oral cavity (n=12), oropharynx (n=18), hypopharynx (n=8), and larynx (n=29). Tumor stage included carcinoma in situ (n=3), stage I (n=21), stage II (n= 7), stage III (n=15), and IV (n=21). No speech or swallowing problems at baseline or follow-up were noted in 23%(speech) and 41 % (swallowing) of patients. Twenty-one percent (speech) and 19 % (swallowing) had problems at baseline and returned to normal scores at follow-up, while 16 % (speech) and 19%(swallowing) had normal scores at baseline and developed problems at follow-up. Forty percent (speech) and 21 % (swallowing) had persistent problems from baseline to follow-up. At baseline, speech problems were significantly related to tumor site and emotional distress. At baseline and follow-up, swallowing problems were significantly related to QoL and emotional distress. At follow-up, speech problems were significantly related to QoL, emotional distress, and swallowing problems. Conclusions Monitoring speech and swallowing problems through OncoQuest in an outpatient clinic is feasible. Many patients report speech and swallowing problems, negatively affecting their QoL and emotional well-being. © Springer-Verlag 2012