Cladribine and Fludarabine Nucleoside Change the Levels of CD Antigens on B-Lymphoproliferative Disorders

The purine analogs, fludarabine nucleoside (FdA), and cladribine (CdA) (1 μM, 24 hours), significantly changed the levels of some surface antigens on the human B-cell lines MEC2 and Raji. Changes in the surface proteins were identified using a Cluster of Differentiation (CD) antibody microarray that captures live cells and confirmed by flow cytometry. For Raji cells, CdA up-regulated CD10, CD54, CD80, and CD86, with repression of CD22, while FdA up-regulated CD20, CD54, CD80, CD86 and CD95. For MEC2 cells, CdA up-regulated CD11a, CD20, CD43, CD45, CD52, CD54, CD62L, CD80, CD86, and CD95, but FdA had no effect. Up-regulation of particular CD antigens induced on a B-cell lymphoproliferative disorder by a purine analog could provide targets for therapeutic antibodies with synergistic cell killing

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E. Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported by a French National Research Agency ‘Investissements d’Avenir’ programme grant (ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research Training Program Scholarship. The Raipur Group is thankful to: (1) the University Grants Commission, New Delhi, India for the research grants received through its SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science; and (2) the Center for Translational Chronobiology at the School of Studies in Life Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was supported by grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported by the National Science Foundation Graduate Research Fellowship (R010138018). We acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E. Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova); S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R. C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T. Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera); J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B. Todorova (University of Vienna, Austria). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

Cell surface markers in colorectal cancer prognosis

The classification of colorectal cancers (CRC) is currently based largely on histologically determined tumour characteristics, such as differentiation status and tumour stage, i.e., depth of tumour invasion, involvement of regional lymph nodes and the occurrence of metastatic spread to other organs. These are the conventional prognostic factors for patient survival and often determine the requirement for adjuvant therapy after surgical resection of the primary tumour. However, patients with the same CRC stage can have very different disease-related outcomes. For some, surgical removal of early-stage tumours leads to full recovery, while for others, disease recurrence and metastasis may occur regardless of adjuvant therapy. It is therefore important to understand the molecular processes that lead to disease progression and metastasis and to find more reliable prognostic markers and novel targets for therapy. This review focuses on cell surface proteins that correlate with tumour progression, metastasis and patient outcome, and discusses some of the challenges in finding prognostic protein markers in CRC

Colorectal cancer therapeutic antibodies

Colorectal cancer (CRC) may occur in the colon, rectum or appendix. It is the third most common form of cancer and the third leading cause of cancer-related death in the Western world. CRC is the fourth most common cancer in men and the third in women, though significant international variations in the distribution of CRC have been observed. World-wide, nearly 1.2 million new cases of CRC were diagnosed in 2007, resulting in about 630,000 deaths (8 percent of all cancer deaths). Many CRC are thought to arise from adenomatous polyps in the colon, which are usually benign, but may develop into cancer over time. Localized CRC is generally detected by colonoscopy. Therapy is usually through surgery, often followed by chemotherapy. Recently, antibody-based therapies have also been used

Antibody microarrays and multiplexing

This chapter presents a range of statistical methods for antibody microarray normalization and data analysis. Commonly used techniques for cluster generation, differential analysis, and classification are covered. The focus is on the implementation of each technique to the technology and its suitability in relation to sample types and experiment design

Biomarkers of breast cancer apoptosis induced by chemotherapy and TRAIL

Treatment of breast cancer is complex and challenging due to the heterogeneity of the disease. To avoid significant toxicity and adverse side-effects of chemotherapy in patients who respond poorly, biomarkers predicting therapeutic response are essential. This study has utilized a proteomic approach integrating 2D-DIGE, LC-MS/MS, and bioinformatics to analyze the proteome of breast cancer (ZR-75-1 and MDA-MB-231) and breast epithelial (MCF-10A) cell lines induced to undergo apoptosis using a combination of doxorubicin and TRAIL administered in sequence (Dox-TRAIL). Apoptosis induction was confirmed using a caspase-3 activity assay. Comparative proteomic analysis between whole cell lysates of Dox-TRAIL and control samples revealed 56 differentially expressed spots (≥2-fold change and p < 0.05) common to at least two cell lines. Of these, 19 proteins were identified yielding 11 unique protein identities: CFL1, EIF5A, HNRNPK, KRT8, KRT18, LMNA, MYH9, NACA, RPLP0, RPLP2, and RAD23B. A subset of the identified proteins was validated by selected reaction monitoring (SRM) and Western blotting. Pathway analysis revealed that the differentially abundant proteins were associated with cell death, cellular organization, integrin-linked kinase signaling, and actin cytoskeleton signaling pathways. The 2D-DIGE analysis has yielded candidate biomarkers of response to treatment in breast cancer cell models. Their clinical utility will depend on validation using patient breast biopsies pre- and post-treatment with anticancer drugs.11 page(s