Film review: The Trial of Ratko Mladić

Reviewing the documentary by BBC on the trial of Ratko Mladić

Pregnancy in multiple sclerosis: clinical and self-report scales

Relapse rate is decreased during pregnancy in multiple sclerosis (MS). Risk for postpartum relapse is increased in the first 3 months after delivery. We aimed to study clinical course of MS around pregnancy, using clinical as well as self-report scales, including data on quality of life (QoL), and to identify clinical factors predisposing for postpartum relapse. We performed a prospective, longitudinal study among 35 MS patients and 20 controls. In patients we assessed expanded disability status scale (EDSS), the Guy’s neurological disability scale (GNDS) and the multiple sclerosis impact scale 29 (MSIS-29). In patients and controls we assessed the MOS 36 item short form health survey questionnaire (SF36), consisting of eight domains. The previously described surge in relapses after delivery was also obvious in this study (p = 0.005). At group level EDSS and MSIS-29 did not show overt fluctuations over time. The GNDS, however, improved during the third trimester, compared to the first trimester (p = 0.003). A concomitant improvement in the SF36 domains vitality (p < 0.001) and general health (p = 0.001) was found in patients. At the final visit, at least 9 months after delivery, no worsening of EDSS, GNDS, MSIS-29 or SF36 was observed compared with the (for MS, beneficial) third trimester. Duration of disease, relapses in the year preceding pregnancy or relapses during pregnancy were not associated with postpartum relapse. QoL is improved during pregnancy. Although relapse rate was increased directly after delivery, in the mid long term after delivery no adverse effects of pregnancy on MS were found

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores

Destruction of chemical warfare surrogates using a portable atmospheric pressure plasma jet

Today’s reality is connected with mitigation of threats from the new chemical and biological warfare agents. A novel investigation of cold plasmas in contact with liquids presented in this paper demonstrated that the chemically reactive environment produced by atmospheric pressure plasma jet (APPJ) is potentially capable of rapid destruction of chemical warfare agents in a broad spectrum. The decontamination of three different chemical warfare agent surrogates dissolved in liquid is investigated by using an easily transportable APPJ. The jet is powered by a kHz signal source connected to a low-voltage DC source and with He as working gas. The detailed investigation of electrical properties is performed for various plasmas at different distances from the sample. The measurements of plasma properties in situ are supported by the optical spectrometry measurements, whereas the high performance liquid chromatography measurements before and after the treatment of aqueous solutions of Malathion, Fenitrothion and Dimethyl Methylphosphonate. These solutions are used to evaluate destruction and its efficiency for specific neural agent simulants. The particular removal rates are found to be from 56% up to 96% during 10 min treatment. The data obtained provide basis to evaluate APPJ’s efficiency at different operating conditions. The presented results are promising and could be improved with different operating conditions and optimization of the decontamination process

Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses

Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers

<p>Abstract</p> <p>Methods</p> <p>We examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue.</p> <p>Results</p> <p>Based on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR.</p> <p>Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified.</p> <p>Conclusion</p> <p>The data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies.</p