The Early Postnatal Nonhuman Primate Neocortex Contains Self-Renewing Multipotent Neural Progenitor Cells

The postnatal neocortex has traditionally been considered a non-neurogenic region, under non-pathological conditions. A few studies suggest, however, that a small subpopulation of neural cells born during postnatal life can differentiate into neurons that take up residence within the neocortex, implying that postnatal neurogenesis could occur in this region, albeit at a low level. Evidence to support this hypothesis remains controversial while the source of putative neural progenitors responsible for generating new neurons in the postnatal neocortex is unknown. Here we report the identification of self-renewing multipotent neural progenitor cells (NPCs) derived from the postnatal day 14 (PD14) marmoset monkey primary visual cortex (V1, striate cortex). While neuronal maturation within V1 is well advanced by PD14, we observed cells throughout this region that co-expressed Sox2 and Ki67, defining a population of resident proliferating progenitor cells. When cultured at low density in the presence of epidermal growth factor (EGF) and/or fibroblast growth factor 2 (FGF-2), dissociated V1 tissue gave rise to multipotent neurospheres that exhibited the ability to differentiate into neurons, oligodendrocytes and astrocytes. While the capacity to generate neurones and oligodendrocytes was not observed beyond the third passage, astrocyte-restricted neurospheres could be maintained for up to 6 passages. This study provides the first direct evidence for the existence of multipotent NPCs within the postnatal neocortex of the nonhuman primate. The potential contribution of neocortical NPCs to neural repair following injury raises exciting new possibilities for the field of regenerative medicine

Analysis of the Human Kinome Using Methods Including Fold Recognition Reveals Two Novel Kinases

Background: Protein sequence similarity is a commonly used criterion for inferring the unknown function of a protein from a protein of known function. However, proteins can diverge significantly over time such that sequence similarity is difficult, if not impossible, to find. In some cases, a structural similarity remains over long evolutionary time scales and once detected can be used to predict function. Methodology/Principal Findings: Here we employed a high-throughput approach to assign structural and functional annotation to the human proteome, focusing on the collection of human protein kinases, the human kinome. We compared human protein sequences to a library of domains from known structures using WU-BLAST, PSI-BLAST, and 123D. This approach utilized both sequence comparison and fold recognition methods. The resulting set of potential protein kinases was cross-checked against previously identified human protein kinases, and analyzed for conserved kinase motifs. Conclusions/Significance: We demonstrate that our structure-based method can be used to identify both typical and atypical human protein kinases. We also identify two potentially novel kinases that contain an interesting combination o

Exploring the Role of Explicit and Implicit Self-Esteem and Self-Compassion in Anxious and Depressive Symptomatology Following Acquired Brain Injury

[EN] Objectives Acquired brain injury (ABI) can lead to the emergence of several disabilities and is commonly associated with high rates of anxiety and depression symptoms. Self-related constructs, such as self-esteem and self-compassion, might play a key role in this distressing symptomatology. Low explicit (i.e., deliberate) self-esteem is associated with anxiety and depression after ABI. However, implicit (i.e., automatic) self-esteem, explicit-implicit self-discrepancies, and self-compassion could also significantly contribute to this symptomatology. The purpose of the present study was to examine whether implicit self-esteem, explicit-implicit self-discrepancy (size and direction), and self-compassion are related to anxious and depressive symptoms after ABI in adults, beyond the contribution of explicit self-esteem. Methods The sample consisted 38 individuals with ABI who were enrolled in a long-term rehabilitation program. All participants completed the measures of explicit self-esteem, implicit self-esteem, self-compassion, anxiety, and depression. Pearson's correlations and hierarchical regression models were calculated. Results Findings showed that both self-compassion and implicit self-esteem negatively accounted for unique variance in anxiety and depression when controlling for explicit self-esteem. Neither the size nor direction of explicit-implicit self-discrepancy was significantly associated with anxious or depressive symptomatology. Conclusions The findings suggest that the consideration of self-compassion and implicit self-esteem, in addition to explicit self-esteem, contributes to understanding anxiety and depression following ABI.Lorena Desdentado is supported by a FPU doctoral scholarship (FPU18/01690) from the Spanish Ministry of Universities. This work was supported by CIBEROBN, an initiative of the ISCIII (ISC III CB06 03/0052).Desdentado, L.; Cebolla, A.; Miragall, M.; Llorens Rodríguez, R.; Navarro, MD.; Baños, RM. (2021). Exploring the Role of Explicit and Implicit Self-Esteem and Self-Compassion in Anxious and Depressive Symptomatology Following Acquired Brain Injury. Mindfulness. 12(4):899-910. https://doi.org/10.1007/s12671-020-01553-wS899910124Anson, K., & Ponsford, J. (2006). Coping and emotional adjustment following traumatic brain injury. The Journal of Head Trauma Rehabilitation, 21(3), 248–259. https://doi.org/10.1097/00001199-200605000-00005.Baños, R. M., & Guillén, V. (2000). Psychometric characteristics in normal and social phobic samples for a Spanish version of the Rosenberg Self-Esteem Scale. Psychological Reports, 87(1), 269–274. https://doi.org/10.2466/pr0.2000.87.1.269.Beadle, E. J., Ownsworth, T., Fleming, J., & Shum, D. (2016). The impact of traumatic brain injury on self-identity: a systematic review of the evidence for self-concept changes. The Journal of Head Trauma Rehabilitation, 31(2), E12–E25. https://doi.org/10.1097/HTR.0000000000000158.Beck, A. T. (1979). Cognitive therapy of depression. New York: Guilford Press.Beevers, C. G. (2005). Cognitive vulnerability to depression: A dual process model. Clinical Psychology Review, 25(7), 975–1002. https://doi.org/10.1016/j.cpr.2005.03.003.Bos, A. E. R., Huijding, J., Muris, P., Vogel, L. R. R., & Biesheuvel, J. (2010). Global, contingent and implicit self-esteem and psychopathological symptoms in adolescents. Personality and Individual Differences, 48(3), 311–316. https://doi.org/10.1016/j.paid.2009.10.025.Bowerman, B. L., & O’Connell, R. T. (1990). Linear statistical models: An applied approach (2nd ed.). Belmont, CA: Duxbury.Brenner, R. E., Heath, P. J., Vogel, D. L., & Credé, M. (2017). Two is more valid than one: examining the factor structure of the self-compassion scale (SCS). Journal of Counseling Psychology, 64(6), 696–707. https://doi.org/10.1037/cou0000211.Brysbaert, M. (2019). How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. Journal of Cognition, 2(1), 1–38. https://doi.org/10.5334/joc.72.Carroll, E., & Coetzer, R. (2011). Identity, grief and self-awareness after traumatic brain injury. Neuropsychological Rehabilitation, 21(3), 289–305. https://doi.org/10.1080/09602011.2011.555972.Corrigan, P. W., & Watson, A. C. (2002). The paradox of self-stigma and mental illness. Clinical Psychology: Science and Practice, 9(1), 35–53. https://doi.org/10.1093/clipsy/9.1.35.Creemers, D. H. M., Scholte, R. H. J., Engels, R. C. M. E., Prinstein, M. J., & Wiers, R. W. (2012). Implicit and explicit self-esteem as concurrent predictors of suicidal ideation, depressive symptoms, and loneliness. Journal of Behavior Therapy and Experimental Psychiatry, 43(1), 638–646. https://doi.org/10.1016/j.jbtep.2011.09.006.Creemers, D. H. M., Scholt, R. H. J., Engels, R. C. M. E., Prinstein, M. J., & Wiers, R. W. (2013). Damaged self-esteem is associated with internalizing problems. Frontiers in Psychology, 4, 152. https://doi.org/10.3389/fpsyg.2013.00152.Curvis, W., Simpson, J., & Hampson, N. (2018). Factors associated with self-esteem following acquired brain injury in adults: a systematic review. Neuropsychological Rehabilitation, 28(1), 142–183. https://doi.org/10.1080/09602011.2016.1144515.Elbaum, J., & Benson, D. (Eds.). (2007). Acquired brain injury: an integrative neuro-rehabilitation approach. New York: Springer. https://doi.org/10.1007/978-0-387-37575-5.Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149.FEDACE. (2015). Las personas con daño cerebral adquirido en España. Ministerio de Sanidad, Servicios Sociales e Igualdad. Retrieved May 21, 2020, from: https://fedace.org/index.php?V_dir=MSC&V_mod=download&f=2016-9/26-16-4-11.admin.Informe_FEDACE_RPD_para_DDC-1.pdf.Feigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., Moran, A. E., Sacco, R. L., Anderson, L., Truelsen, T., O’Donnell, M., Venketasubramanian, N., Barker-Collo, S., Lawes, C. M. M., Wang, W., Shinohara, Y., Witt, E., Ezzati, M., & Naghavi, M. (2014). Global and regional burden of stroke during 1990-2010: findings from the Global Burden of Disease Study 2010. The Lancet, 383(9913), 245–254. https://doi.org/10.1016/S0140-6736(13)61953-4.Fennell, M. J. V. (1997). Low self-esteem: a cognitive perspective. Behavioural and Cognitive Psychotherapy, 25(1), 1–26. https://doi.org/10.1017/s1352465800015368.Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. https://doi.org/10.1016/0022-3956(75)90026-6.Garcia-Campayo, J., Navarro-Gil, M., Andrés, E., Montero-Marin, J., López-Artal, L., Marcos, M., & Demarzo, P. (2014). Validation of the Spanish versions of the long (26 items) and short (12 items) forms of the Self-Compassion Scale (SCS). Health and Quality of Life Outcomes, 12(4). https://doi.org/10.1186/1477-7525-12-4.GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators. (2018). Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology, 18(1), 56–87. https://doi.org/10.1016/S1474-4422(18)30415-0.Gould, K. R., Ponsford, J. L., Johnston, L., & Schönberger, M. (2011). Relationship between psychiatric disorders and 1-year psychosocial outcome following traumatic brain injury. The Journal of Head Trauma Rehabilitation, 26(1), 79–89. https://doi.org/10.1097/HTR.0b013e3182036799.Gracey, F., Palmer, S., Rous, B., Psaila, K., Shaw, K., O’Dell, J., Cope, J., & Mohamed, S. (2008). “Feeling part of things”: personal construction of self after brain injury. Neuropsychological Rehabilitation, 18(5–6), 627–650. https://doi.org/10.1080/09602010802041238.Gracey, F., Evans, J. J., & Malley, D. (2009). Capturing process and outcome in complex rehabilitation interventions: a “Y-shaped” model. Neuropsychological Rehabilitation, 19(6), 867–890. https://doi.org/10.1080/09602010903027763.Greenwald, A. G., & Farnham, S. D. (2000). Using the Implicit Association Test to measure self-esteem and self-concept. Journal of Personality and Social Psychology, 79(6), 1022–1038. https://doi.org/10.1037/0022-3514.79.6.1022.Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: the Implicit Association Test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464.Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197–216. https://doi.org/10.1037/0022-3514.85.2.197.Hackett, M. L., Yapa, C., Parag, V., & Anderson, C. S. (2005). Frequency of depression after stroke: a systematic review of observational studies. Stroke, 36(6), 1330–1340. https://doi.org/10.1161/01.STR.0000165928.19135.35.Haeffel, G. J., Abramson, L. Y., Brazy, P. C., Shah, J. Y., Teachman, B. A., & Nosek, B. A. (2007). Explicit and implicit cognition: a preliminary test of a dual-process theory of cognitive vulnerability to depression. Behaviour Research and Therapy, 45(6), 1155–1167. https://doi.org/10.1016/j.brat.2006.09.003.Ingram, R. E. (1984). Toward an information-processing analysis of depression. Cognitive Therapy and Research, 8(5), 443–477. https://doi.org/10.1007/BF01173284.Izuma, K., Kennedy, K., Fitzjohn, A., Sedikides, C., & Shibata, K. (2018). Neural activity in the reward-related brain regions predicts implicit self-esteem: a novel validity test of psychological measures using neuroimaging. Journal of Personality and Social Psychology, 114(3), 343–357. https://doi.org/10.1037/pspa0000114.Khan-Bourne, N., & Brown, R. G. (2003). Cognitive behaviour therapy for the treatment of depression in individuals with brain injury. Neuropsychological Rehabilitation, 13(1–2), 89–107. https://doi.org/10.1080/09602010244000318.Kim, H. S., & Moore, M. T. (2019). Symptoms of depression and the discrepancy between implicit and explicit self-esteem. Journal of Behavior Therapy and Experimental Psychiatry, 63, 1–5. https://doi.org/10.1016/j.jbtep.2018.12.001.Lane, K. A., Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2007). Understanding and using the Implicit Association Test: IV. What we know (so far) about the method. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp. 59–102). New York: The Guildford Press.Leary, M. R., Tate, E. B., Adams, C. E., Batts Allen, A., & Hancock, J. (2007). Self-compassion and reactions to unpleasant self-relevant events: the implications of treating oneself kindly. Personality Processes and Individual Differences, 92(5), 887–904. https://doi.org/10.1037/0022-3514.92.5.887.Lennon, A., Bramham, J., Carroll, À., McElligott, J., Carton, S., Waldron, B., Fortune, D., Burke, T., Fitzhenry, M., & Benson, C. (2014). A qualitative exploration of how individuals reconstruct their sense of self following acquired brain injury in comparison with spinal cord injury. Brain Injury, 28(1), 27–37. https://doi.org/10.3109/02699052.2013.848378.Longworth, C., Deakins, J., Rose, D., & Gracey, F. (2018). The nature of self-esteem and its relationship to anxiety and depression in adult acquired brain injury. Neuropsychological Rehabilitation, 28(7), 1078–1094. https://doi.org/10.1080/09602011.2016.1226185.MacBeth, A., & Gumley, A. (2012). Exploring compassion: a meta-analysis of the association between self-compassion and psychopathology. Clinical Psychology Review, 32(6), 545–552. https://doi.org/10.1016/j.cpr.2012.06.003.McDonald, S., Saad, A., & James, C. (2011). Social dysdecorum following severe traumatic brain injury: loss of implicit social knowledge or loss of control? Journal of Clinical and Experimental Neuropsychology, 33(6), 619–630. https://doi.org/10.1080/13803395.2011.553586.Milne, E., & Grafman, J. (2001). Ventromedial prefrontal cortex lesions in humans eliminate implicit gender stereotyping. The Journal of Neuroscience, 21(12), 1–6.Moors, A., & De Houwer, J. (2006). Automaticity: a theoretical and conceptual analysis. Psychological Bulletin, 132(2), 297–326. https://doi.org/10.1037/0033-2909.132.2.297.Muris, P., & Petrocchi, N. (2017). Protection or vulnerability? A meta-analysis of the relations between the positive and negative components of self-compassion and psychopathology. Clinical Psychology & Psychotherapy, 24(2), 373–383. https://doi.org/10.1002/cpp.2005.Myers, R. (2000). Classical and modern regression with applications (2nd ed.). Belmont, CA: Duxbury.Neff, K. D. (2003). Self-compassion: an alternative conceptualization of a healthy attitude toward oneself. Self and Identity, 2(2), 85–101. https://doi.org/10.1080/15298860309032.Neff, K. D., & Vonk, R. (2009). Self-compassion versus global self-esteem: two different ways of relating to oneself. Journal of Personality, 77, 23–50. https://doi.org/10.1111/j.1467-6494.2008.00537.x.Neff, K. D., Tóth-Király, I., Yarnell, L. M., Arimitsu, K., Castilho, P., Ghorbani, N., Guo, H. X., Hirsch, J. K., Hupfeld, J., Hutz, C. S., Kotsou, I., Lee, W. K., Montero-Marin, J., Sirois, F. M., De Souza, L. K., Svendsen, J. L., Wilkinson, R. B., & Mantzios, M. (2019). Examining the factor structure of the Self-Compassion Scale in 20 diverse samples: support for use of a total score and six subscale scores. Psychological Assessment, 31(1), 27–45. https://doi.org/10.1037/pas0000629.Norton, P. J., & Paulus, D. J. (2017). Transdiagnostic models of anxiety disorder: theoretical and empirical underpinnings. Clinical Psychology Review, 56, 122–137. https://doi.org/10.1016/j.cpr.2017.03.004.Nosek, B. A., & Banaji, M. R. (2001). The go/no-go association task. Social Cognition, 19(6), 625–664. https://doi.org/10.1521/soco.19.6.625.20886.Oddy, M., & Herbert, C. (2003). Intervention with families following brain injury: evidence-based practice. Neuropsychological Rehabilitation, 13(1–2), 259–273. https://doi.org/10.1080/09602010244000345.Ouimet, A. J., Gawronski, B., & Dozois, D. J. A. (2009). Cognitive vulnerability to anxiety: a review and an integrative model. Clinical Psychology Review, 29(6), 459–470. https://doi.org/10.1016/j.cpr.2009.05.004.Ponsford, J., Kelly, A., & Couchman, G. (2014). Self-concept and self-esteem after acquired brain injury: a control group comparison. Brain Injury, 28(2), 146–154. https://doi.org/10.3109/02699052.2013.859733.Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the Self-Compassion Scale. Clinical Psychology & Psychotherapy, 18(3), 250–255. https://doi.org/10.1002/cpp.702.Romero, M., Sánchez, A., Marín, C., Navarro, M. D., Ferri, J., & Noé, E. (2012). Clinical usefulness of the Spanish version of the Mississippi Aphasia Screening Test (MASTsp): validation in stroke patients. Neurología (English Edition), 27(4), 216–224. https://doi.org/10.1016/j.nrleng.2011.06.001.Rosenberg, M. (1965). Rosenberg Self-Esteem Scale (RSE). Acceptance and Commitment Therapy. Measures Package, 61, 52 /S0034-98872009000600009.Sandstrom, M. J., & Jordan, R. (2008). Defensive self-esteem and aggression in childhood. Journal of Research in Personality, 42(2), 506–514. https://doi.org/10.1016/j.jrp.2007.07.008.Schönberger, M., & Ponsford, J. (2010). The factor structure of the Hospital Anxiety and Depression Scale in individuals with traumatic brain injury. Psychiatry Research, 179(3), 342–349. https://doi.org/10.1016/j.psychres.2009.07.003.Schröder-Abé, M., Rudolph, A., & Schütz, A. (2007). High implicit self-esteem is not necessarily advantageous: discrepancies between explicit and implicit self-esteem and their relationship with anger expression and psychological health. European Journal of Personality, 21(3), 319–339. https://doi.org/10.1002/per.626.Scoglio, A. A. J., Rudat, D. A., Garvert, D., Jarmolowski, M., Jackson, C., & Herman, J. L. (2018). Self-compassion and responses to trauma: the role of emotion regulation. Journal of Interpersonal Violence, 33(13), 2016–2036. https://doi.org/10.1177/0886260515622296.Sloan, E., Hall, K., Moulding, R., Bryce, S., Mildred, H., & Staiger, P. K. (2017). Emotion regulation as a transdiagnostic treatment construct across anxiety, depression, substance, eating and borderline personality disorders: a systematic review. Clinical Psychology Review, 57, 141–163. https://doi.org/10.1016/j.cpr.2017.09.002.Smeijers, D., Vrijsen, J. N., van Oostrom, I., Isaac, L., Speckens, A., Becker, E. S., & Rinck, M. (2017). Implicit and explicit self-esteem in remitted depressed patients. Journal of Behavior Therapy and Experimental Psychiatry, 54, 301–306. https://doi.org/10.1016/j.jbtep.2016.10.006.Smith, E. R., & DeCoster, J. (2000). Dual-process models in social and cognitive psychology: conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4(2), 108–131. https://doi.org/10.1207/S15327957PSPR0402_01.Sowislo, J. F., & Orth, U. (2013). Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychological Bulletin, 139(1), 213–240. https://doi.org/10.1037/a0028931.Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8(3), 220–247. https://doi.org/10.1207/s15327957pspr0803_1.Terol-Cantero, M. C., Cabrera-Perona, V., & Martín-Aragón, M. (2015). Hospital Anxiety and Depression Scale (HADS) review in Spanish samples. Anales de Psicología, 31(2), 494–503. https://doi.org/10.6018/analesps.31.2.172701.Tóth-Király, I., & Neff, K. D. (2020). Is self-compassion universal? Support for the measurement invariance of the Self-Compassion Scale across populations. Assessment. Advance online publication. https://doi.org/10.1177/1073191120926232.Turner-Stokes, L., & Wade, D. (2003). Rehabilitation following acquired brain injury: National Clinical Guidelines. Clinical Medicine, 4(1), 61–65. https://doi.org/10.7861/clinmedicine.4-1-61.Tyerman, A., & Humphrey, M. (1984). Changes in self-concept following severe head injury. International Journal of Rehabilitation Research, 7(1), 11–23. https://doi.org/10.1097/00004356-198403000-00002.Valiente, C., Cantero, D., Vázquez, C., Sanchez, Á., Provencio, M., & Espinosa, R. (2011). Implicit and explicit self-esteem discrepancies in paranoia and depression. Journal of Abnormal Psychology, 120(3), 691–699. https://doi.org/10.1037/a0022856.Vickery, C. D., Sepehri, A., & Evans, C. C. (2008). Self-esteem in an acute stroke rehabilitation sample: a control group comparison. Clinical Rehabilitation, 22(2), 179–187. https://doi.org/10.1177/0269215507080142.Whelan-Goodinson, R., Ponsford, J., & Schönberger, M. (2009). Validity of the Hospital Anxiety and Depression Scale to assess depression and anxiety following traumatic brain injury as compared with the Structured Clinical Interview for DSM-IV. Journal of Affective Disorders, 114(1–3), 94–102. https://doi.org/10.1016/j.jad.2008.06.007.Zeigler-Hill, V. (2006). Discrepancies between implicit and explicit self-esteem: Implications for narcissism and self-esteem instability. Journal of Personality, 74(1), 119–144. https://doi.org/10.1111/j.1467-6494.2005.00371.x.Zessin, U., Dickhäuser, O., & Garbade, S. (2015). The relationship between self-compassion and well-being: a meta-analysis. Applied Psychology. Health and Well-Being, 7(3), 340–364. https://doi.org/10.1111/aphw.12051.Zhang, J. W., Chen, S., & Tomova Shakur, T. K. (2020). From me to you: Self-compassion predicts acceptance of own and others’ imperfections. Personality and Social Psychology Bulletin, 46(2), 228–242. https://doi.org/10.1177/0146167219853846.Zigmond, A. S., & Snaith, R. P. (1983). The Hospital Anxiety and Depression Scale. Acta Psychiatrica Scandinavica, 67(6), 361–370. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x

Effectiveness of preoperative staging in rectal cancer: digital rectal examination, endoluminal ultrasound or magnetic resonance imaging?

In rectal cancer, preoperative staging should identify early tumours suitable for treatment by surgery alone and locally advanced tumours that require therapy to induce tumour regression from the potential resection margin. Currently, local staging can be performed by digital rectal examination (DRE), endoluminal ultrasound (EUS) or magnetic resonance imaging (MRI). Each staging method was compared for clinical benefit and cost-effectiveness. The accuracy of high-resolution MRI, DRE and EUS in identifying favourable, unfavourable and locally advanced rectal carcinomas in 98 patients undergoing total mesorectal excision was compared prospectively against the resection specimen pathological as the gold standard. Agreement between each staging modality with pathology assessment of tumour favourability was calculated with the chance-corrected agreement given as the kappa statistic, based on marginal homogenised data. Differences in effectiveness of the staging modalities were compared with differences in costs of the staging modalities to generate cost effectiveness ratios. Agreement between staging and histologic assessment of tumour favourability was 94% for MRI (kappa=0.81, s.e.=0.05; kappa(W)=0.83), compared with very poor agreements of 65% for DRE (kappa=0.08, s.e.=0.068, kappa(W)=0.16) and 69% for EUS (kappa=0.17, s.e.=0.065, kappa(W)=0.17). The resource benefits resulting from the use of MRI rather than DRE was 67164 UK pounds and 92244 UK pounds when MRI was used rather than EUS. Magnetic resonance imaging dominated both DRE and EUS on cost and clinical effectiveness by selecting appropriate patients for neoadjuvant therapy and justifies its use for local staging of rectal cancer patients

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

Overweight and obesity in urban Africa: A problem of the rich or the poor?

BACKGROUND: Obesity is a well recognized risk factor for various chronic diseases such as cardiovascular diseases, hypertension, and type 2 diabetes mellitus. The aim of this study was to shed light on the patterns of overweight and obesity in sub-Saharan Africa, with special interest in differences between the urban poor and the urban non-poor. The specific goals were to describe trends in overweight and obesity among urban women; and examine how these trends vary by education and household wealth. METHODS: The paper used Demographic and Health Surveys data from seven African countries where two surveys had been carried out with an interval of at least 10 years between them. Among the countries studied, the earliest survey took place in 1992 and the latest in 2005. The dependent variable was body mass index coded as: Not overweight/obese; Overweight; Obese. The key covariates were time lapse between the two surveys; woman's education; and household wealth. Control variables included working status, age, marital status, parity, and country. Multivariate ordered logistic regression in the context of the partial proportional odds model was used. RESULTS: Descriptive results showed that the prevalence of urban overweight/obesity increased by nearly 35% during the period covered. The increase was higher among the poorest (+50%) than among the richest (+7%). Importantly, there was an increase of 45-50% among the non-educated and primary-educated women, compared to a drop of 10% among women with secondary education or higher. In the multivariate analysis, the odds ratio of the variable time lapse was 1.05 (p < 0.01), indicating that the prevalence of overweight/obesity increased by about 5% per year on average in the countries in the study. While the rate of change in urban overweight/obesity did not significantly differ between the poor and the rich, it was substantially higher among the non-educated women than among their educated counterparts. CONCLUSION: Overweight and obesity are on the rise in Africa and might take epidemic proportions in the near future. Like several other public health challenges, overweight and obesity should be tackled and prevented early as envisioned in the WHO Global strategy on diet, physical activity and health

Molecular characterization and expression analysis of five different elongation factor 1 alpha genes in the flatfish Senegalese sole (Solea senegalensis Kaup): Differential gene expression and thyroid hormones dependence during metamorphosis

<p>Abstract</p> <p>Background</p> <p>Eukaryotic elongation factor 1 alpha (eEF1A) is one of the four subunits composing eukaryotic translation elongation factor 1. It catalyzes the binding of aminoacyl-tRNA to the A-site of the ribosome in a GTP-dependent manner during protein synthesis, although it also seems to play a role in other non-translational processes. Currently, little information is still available about its expression profile and regulation during flatfish metamorphosis. With regard to this, Senegalese sole (<it>Solea senegalensis</it>) is a commercially important flatfish in which <it>eEF1A </it>gene remains to be characterized.</p> <p>Results</p> <p>The development of large-scale genomics of Senegalese sole has facilitated the identification of five different <it>eEF1A </it>genes, referred to as <it>SseEF1A1</it>, <it>SseEF1A2</it>, <it>SseEF1A3</it>, <it>SseEF1A4</it>, and <it>Sse42Sp50</it>. Main characteristics and sequence identities with other fish and mammalian eEF1As are described. Phylogenetic and tissue expression analyses allowed for the identification of <it>SseEF1A1 </it>and <it>SseEF1A2 </it>as the Senegalese sole counterparts of mammalian <it>eEF1A1 </it>and <it>eEF1A2</it>, respectively, and of <it>Sse42Sp50 </it>as the ortholog of <it>Xenopus laevis </it>and teleost <it>42Sp50 </it>gene. The other two elongation factors, <it>SseEF1A3 </it>and <it>SseEF1A4</it>, represent novel genes that are mainly expressed in gills and skin. The expression profile of the five genes was also studied during larval development, revealing different behaviours. To study the possible regulation of <it>SseEF1A </it>gene expressions by thyroid hormones (THs), larvae were exposed to the goitrogen thiourea (TU). TU-treated larvae exhibited lower <it>SseEF1A4 </it>mRNA levels than untreated controls at both 11 and 15 days after treatment, whereas transcripts of the other four genes remained relatively unchanged. Moreover, addition of exogenous T4 hormone to TU-treated larvae increased significantly the steady-state levels of <it>SseEF1A4 </it>with respect to untreated controls, demonstrating that its expression is up-regulated by THs.</p> <p>Conclusion</p> <p>We have identified five different <it>eEF1A </it>genes in the Senegalese sole, referred to as <it>SseEF1A1</it>, <it>SseEF1A2</it>, <it>SseEF1A3</it>, <it>SseEF1A4</it>, and <it>Sse42Sp50</it>. The five genes exhibit different expression patterns in tissues and during larval development. TU and T4 treatments demonstrate that <it>SseEF1A4 </it>is up-regulated by THs, suggesting a role in the translational regulation of the factors involved in the dramatic changes that occurs during Senegalese sole metamorphosis.</p

Structural genomics is the largest contributor of novel structural leverage

The Protein Structural Initiative (PSI) at the US National Institutes of Health (NIH) is funding four large-scale centers for structural genomics (SG). These centers systematically target many large families without structural coverage, as well as very large families with inadequate structural coverage. Here, we report a few simple metrics that demonstrate how successfully these efforts optimize structural coverage: while the PSI-2 (2005-now) contributed more than 8% of all structures deposited into the PDB, it contributed over 20% of all novel structures (i.e. structures for protein sequences with no structural representative in the PDB on the date of deposition). The structural coverage of the protein universe represented by today’s UniProt (v12.8) has increased linearly from 1992 to 2008; structural genomics has contributed significantly to the maintenance of this growth rate. Success in increasing novel leverage (defined in Liu et al. in Nat Biotechnol 25:849–851, 2007) has resulted from systematic targeting of large families. PSI’s per structure contribution to novel leverage was over 4-fold higher than that for non-PSI structural biology efforts during the past 8 years. If the success of the PSI continues, it may just take another ~15 years to cover most sequences in the current UniProt database

Using Structure to Explore the Sequence Alignment Space of Remote Homologs

Protein structure modeling by homology requires an accurate sequence alignment between the query protein and its structural template. However, sequence alignment methods based on dynamic programming (DP) are typically unable to generate accurate alignments for remote sequence homologs, thus limiting the applicability of modeling methods. A central problem is that the alignment that is “optimal” in terms of the DP score does not necessarily correspond to the alignment that produces the most accurate structural model. That is, the correct alignment based on structural superposition will generally have a lower score than the optimal alignment obtained from sequence. Variations of the DP algorithm have been developed that generate alternative alignments that are “suboptimal” in terms of the DP score, but these still encounter difficulties in detecting the correct structural alignment. We present here a new alternative sequence alignment method that relies heavily on the structure of the template. By initially aligning the query sequence to individual fragments in secondary structure elements and combining high-scoring fragments that pass basic tests for “modelability”, we can generate accurate alignments within a small ensemble. Our results suggest that the set of sequences that can currently be modeled by homology can be greatly extended