Sensory processing deficiencies in patients with borderline personality disorder who experience auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are common in patients with borderline personality disorder (BPD). We examined two candidate mechanisms of AVH in patients with BPD, suggested to underlie sensory processing systems that contribute to psychotic symptoms in patients with schizophrenia; sensory gating (P50 ratio and P50 difference) and change detection (mismatch negativity; MMN). Via electroencephalographic recordings P50 amplitude, P50 ratio, P50 difference and MMN amplitude were compared between 23 borderline patients with and 25 without AVH, and 26 healthy controls. Borderline patients with AVH had a significantly lower P50 difference compared with healthy controls, whereas no difference was found between borderline patients without AVH and healthy controls. The groups did not differ on MMN amplitude. The impaired sensory gating in patients with borderline personality disorder who experience AVH implies that P50 sensory gating deficiencies may underlie psychotic vulnerability in this specific patient group. Patients with borderline personality disorder with or without AVH did not have problems with auditory change detection. This may explain why they are spared from the poor outcome associated with negative symptoms and symptoms of disorganization in patients with chronic schizophrenia

Optically active 6-acetyloxy-2H-pyran-3(6H)-one obtained by lipase catalyzed transesterification and esterification

Kinetic resolution of 6-acetyloxy-2H-pyran-3(6H)-one (1) is achieved by immobilized lipase PS on Hyflo Super Cell in organic solvents. Transesterification in hexane/n-butanol yields enantiomerically pure R-(-)-6-acetyloxy-2H-pyran-3(6H)-one, whereas esterification of 6-hydroxy-2H-pyran-3(6H)-one (2) with vinyl acetate by immobilized lipase PS gives the S-enantiomer with e.e.'s up to 76%. (C) 1997 Elsevier Science Ltd

Developing a Standard Set of Patient-Centred Outcomes for Inflammatory Bowel Disease—an International, Cross-disciplinary Consensus

Success in delivering value-based healthcare involves measuring outcomes that matter most to patients. Our aim was to develop a minimum Standard Set of patient-centred outcome measures for inflammatory bowel disease (IBD), for use in different healthcare settings.An international working group (n=25) representing patients, patient associations, gastroenterologists, surgeons, specialist nurses, IBD registries and patient-reported outcome measure (PROM) methodologists participated in a series of teleconferences incorporating a modified Delphi process. Systematic review of existing literature, registry data, patient focus groups and open review periods were used to reach consensus on a minimum set of standard outcome measures and risk adjustment variables. Similar methodology has been used in 21 other disease areas (www.ichom.org).A minimum Standard Set of outcomes was developed for patients (aged ≥16) with IBD. Outcome domains included survival and disease control (survival, disease activity/remission, colorectal cancer, anaemia), disutility of care (treatment-related complications), healthcare utilisation (IBD-related admissions, emergency room visits) and patient-reported outcomes (including quality of life, nutritional status and impact of fistulae) measured at baseline and at 6 or 12 month intervals. A single PROM (IBD-Control questionnaire) was recommended in the Standard Set and minimum risk adjustment data collected at baseline and annually were included: demographics, basic clinical information and treatment factors.A Standard Set of outcome measures for IBD has been developed based on evidence, patient input and specialist consensus. It provides an international template for meaningful, comparable and easy-to-interpret measures as a step towards achieving value-based healthcare in IBD

Indomethacin induces apoptosis via a MRP1-dependent mechanism in doxorubicin-resistant small-cell lung cancer cells overexpressing MRP1

Small-cell lung cancers (SCLCs) initially respond to chemotherapy, but are often resistant at recurrence. The non-steroidal anti-inflammatory drug indomethacin is an inhibitor of multidrug resistance protein 1 (MRP1) function. The doxorubicin-resistant MRP1-overexpressing human SCLC cell line GLC4-Adr was highly sensitive for indomethacin compared with the parental doxorubicin-sensitive line GLC4. The purpose of this study was to analyse the relationship between hypersensitivity to indomethacin and MRP1 overexpression. The experimental design involved analysis of the effect of MRP1 downregulation on indomethacin-induced cell survival and apoptosis in GLC4-Adr and GLC4, using siRNA. In addition the effect of indomethacin on glutathione levels and mitochondrial membrane potential was investigated. Small interfering RNAs directed against MRP1 reduced MRP1 mRNA levels twofold and reduced efflux pump function of MRP1, which was reflected by a 1.8-fold higher accumulation of MRP1 substrate carboxyfluorescein, in si-MRP1 versus si-Luciferase-transfected GLC4-Adr cells. Multidrug resistance protein 1 downregulation decreased initial high apoptosis levels 2-fold in GLC4-Adr after indomethacin treatment for 24 h, and increased cell survival (IC50) from 22.8±2.6 to 30.4±5.1 μM following continuous indomethacin exposure. Multidrug resistance protein 1 downregulation had no effect on apoptosis in GLC4 or on glutathione levels in both lines. Although indomethacin (20 μM) for 2 h decreased glutathione levels by 31.5% in GLC4-Adr, complete depletion of cellular glutathione by L-buthionine (S,R)-sulphoximine only resulted in a small increase in indomethacin-induced apoptosis in GLC4-Adr, demonstrating that a reduced cellular glutathione level is not the primary cause of indomethacin-induced apoptosis. Indomethacin exposure decreased mitochondrial membrane potential in GLC4-Adr cells, suggesting activation of the mitochondrial apoptosis pathway. Indomethacin induces apoptosis in a doxorubicin-resistant SCLC cell line through an MRP1-dependent mechanism. This may have implications for the treatment of patients with MRP1-overexpressing tumours

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Correction:How the COVID-19 pandemic highlights the necessity of animal research (vol 30, pg R1014, 2020)

(Current Biology 30, R1014–R1018; September 21, 2020) As a result of an author oversight in the originally published version of this article, a number of errors were introduced in the author list and affiliations. First, the middle initials were omitted from the names of several authors. Second, the surname of Dr. van Dam was mistakenly written as “Dam.” Third, the first name of author Bernhard Englitz was misspelled as “Bernard” and the surname of author B.J.A. Pollux was misspelled as “Pullox.” Finally, Dr. Keijer's first name was abbreviated rather than written in full. These errors, as well as various errors in the author affiliations, have now been corrected online

High-Order SNP Combinations Associated with Complex Diseases: Efficient Discovery, Statistical Power and Functional Interactions

There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations