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viernes, 31 de diciembre de 2010

Platelet-Rich Fibrin Matrix Doesn't Boost Healing of Rotator Cuff Repair

From Reuters Health Information

Platelet-Rich Fibrin Matrix Doesn't Boost Healing of Rotator Cuff Repair

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By Will Boggs MD

NEW YORK (Reuters Health) Dec 29 - An autologous platelet-rich fibrin matrix (PRFM) does not enhance healing after arthroscopic rotator cuff repair, European researchers reported online December 15th in The American Journal of Sports Medicine.

"The pressure to use the latest available products is great, and often we embrace new technologies in a relatively acritical fashion, simply because 'if it is new, it must be better,'" Dr. Nicola Maffulli from Barts and The London School of Medicine and Dentistry told Reuters Health. "We have shown that, in the context of surgical repair of fairly chronic small and moderate tears of the rotator cuff in mature patients, this particular form of platelet rich augmentation does not offer any advantages."

"Our healing abilities have evolved over millions of years, and we have been selected for our capacity and ability to heal," Dr. Maffulli added. "To think that a few shots of a not yet well characterized product injected at a fairly random time point of the healing process will work a miracle is probably expecting too much."

In a randomized trial conducted at Ospedale Civile, Jesi, Italy, Dr. Maffulli and colleagues assessed the efficacy and safety of autologous PRFM augmentation during arthroscopic rotator cuff repair in 88 patients.

At 16 months after surgery, on the 100-point Constant-Murley shoulder rating scale, patients had improved from an average score of 42.7 before surgery to 88.6 without PRFM and from a mean of 42.1 to a mean of 88.58 with PRFM.

Among 78 patients (38 without PRFM and 40 with PRFM) with MRI results, there were no significant differences in tendon thickness or size of the tendon footprint. There was a significant difference between the groups in tendon signal intensity, but the researchers say "it is difficult to give a clinical significance to this finding."

"It may well be that other formulations of platelet rich augmentation may work, or that platelet rich augmentation is effective in large and massive tears," Dr. Maffulli added. "The point that we make is that science has to progress through carefully planned studies. Injecting platelet rich augmentation products in 10,000 patients and then saying 'it works!' is not advancing science, though it may advance one's career and make their business prosper."

SOURCE: http://link.reuters.com/cuf24r

Am J Sports Med 2010.

Amygdala volume and social network size in humans

NATURE NEUROSCIENCE | BRIEF COMMUNICATION

Amygdala volume and social network size in humans

Kevin C Bickart,

Christopher I Wright,

Rebecca J Dautoff,

Bradford C Dickerson

& Lisa Feldman Barrett

Nature Neuroscience

(2010)

doi:10.1038/nn.2724

Received: 06 October 2010
Accepted: 24 November 2010
Published online: 26 December 2010

We found that amygdala volume correlates with the size and complexity of social networks in adult humans. An exploratory analysis of subcortical structures did not find strong evidence for similar relationships with any other structure, but there were associations between social network variables and cortical thickness in three cortical areas, two of them with amygdala connectivity. These findings indicate that the amygdala is important in social behavior.

Main

For many species, but particularly for primates, living in groups is a major adaptive advantage¹. But living in a social group also presents its own challenges. To get along while getting ahead, it is necessary to learn who is who, who is friend and who is foe. It might be productive to form an alliance with certain group members in one context, but to outmaneuver them in another. The 'social brain hypothesis' suggests that, evolutionarily, living in larger, more complex social groups selected for larger brain regions with a greater capacity for performing relevant computations². On the basis of its central functional role^3, 4 and anatomic position⁵ in the social brain, investigators have proposed that amygdala volume should be related to the size of social groups, in part because the size of a brain region is one indicator of its processing capacity⁶.

Comparative neuroanatomical studies in nonhuman primates strongly support a link between amygdala volume and social network size⁷ and social behavior⁸. Species characterized by larger social groups have a larger corticobasolateral complex within the amygdala. The corticobasolateral complex conjointly expanded with evolutionarily newer cortex and the lateral geniculate nucleus, particularly the layers of the lateral geniculate nucleus that project to the ventral stream visual system⁷. Taken together, these comparative findings suggest that a larger amygdala provides for the increased processing demands required by a complex social life.

In this study we examined whether amygdala volume varies with individual variation in the size and complexity of social groupings within a single primate species, humans. In 58 healthy adults (22 females; mean age M = 52.6, s.d. = 21.2, range = 19–83 years) with confirmed absence of DSM-IV Axis I diagnoses and normal performance on cognitive testing, we examined social network size and complexity with two subscales of the Social Network Index (SNI⁹). One SNI subscale (Number of People in Social Network) measures the total number of regular contacts that a person maintains, reflecting overall network size. A second subscale (Number of Embedded Networks) measured the number of different groups these contacts belong to, reflecting network complexity. Despite the fact that the two social network variables were strongly correlated within the present sample (r = 0.86, P < 0.001), we opted to consider their separate relation to amygdala and hippocampal volumes. (For more details, see Supplementary Results.)

To assess amygdala (and, as a control region, hippocampal) volume, we performed quantitative morphometric analysis of T1-weighted MRI data using an automated segmentation and probabilistic region-of-interest (ROI) labeling technique (FreeSurfer, http://surfer.nmr.mgh.harvard.edu/). For methodological details, seeSupplementary Methods. To adjust for differences in head size, amygdala and hippocampal volumes were divided by total intracranial volume, as performed previously^10, 11.

Linear regression analyses revealed that individuals with larger and more complex social networks had larger amygdala volumes (Fig. 1). These relationships held when controlling for the age of the participant (because older individuals have, on average, smaller amygdala volumes than do younger individuals; Table 1). These relationships held when left and right amygdala volumes were analyzed separately (Table 1), indicating no lateralization of the effect.

Figure 1: Amygdala volume correlates with social network size and complexity.

Table 1: Linear regressions using amygdala and hippocampal volumes as independent variables and social network characteristics as dependent variables

Full table

To assess discriminant validity, we performed a linear regression using right and left hippocampal volumes (corrected for total intracranial volume) as independent variables and social network size and complexity as dependent variables while controlling for age (because hippocampal volume typically diminishes with age). For the whole group, these analyses showed no significant relationship between hippocampal volume and either of the social network variables (Table 1). For the young and older subgroups, linear regressions showed a significant relationship only for older participants between left hippocampal volume and social network complexity (Table 1). Because hippocampal and amygdala volumes were themselves strongly correlated (left:r = 0.831, P < 0.001; right: r = 0.727, P < 0.001; combined: r = 0.815, P < 0.001), we conducted hierarchical linear regressions using amygdala and hippocampal volumes (corrected for total intracranial volume) as independent variables and social network characteristics as dependent variables. Increased amygdala volume remained significant when controlling for hippocampal volume (Supplementary Table 1).

To further investigate the specificity of the relationship between amygdala volume and social network characteristics, we conducted an exploratory analysis assessing the relationship between social network variables and all other subcortical volumes segmented by FreeSurfer. Linear regressions revealed that none of the other subcortical regions significantly correlated with either social network variable when controlling for age and correcting for multiple comparisons. (For more details, see Supplementary Methods and Supplementary Results.) Also supporting the discriminant validity of our primary finding, we found that amygdala volume did not relate to other measures of social functioning such as perceived social support^12, 13 and life satisfaction¹⁴. (r values ranged from −0.26 to 0.27, P < 0.15 to P < 0.98; for more details about these measures, seeSupplementary Methods.)

Finally, to explore the association between social network variables and cortical thickness throughout the cerebral cortex, we conducted a whole brain surface–based analysis (see Supplementary Methods); this analysis did not include subcortical structures (such as the amygdala). In the first fully corrected test, we found no regions that were correlated with the social network variables at conventional levels of statistical significance. In the second, more exploratory analysis, with a more lenient threshold (P < 0.01, uncorrected for multiple comparisons) we found that social network variables correlated significantly with the caudal inferior temporal sulcus, caudal superior frontal gyrus and subgenual anterior cingulate cortex. Separate analyses of young and older participants showed very consistent findings, supporting the reliability of these observations (for more details, see Supplementary Results, Supplementary Fig. 1 and Supplementary Tables 2 and 3).

To our knowledge, these findings demonstrate the first link between amygdala volume and social network characteristics within a single species. Although our findings do not test an evolutionary hypothesis specifically, they, along with cross-species studies in nonhuman primates^7, 15, are consistent with the hypothesis that the primate amygdala evolved, in part, under the pressures of increasingly complex social life (for more details, see Supplementary Discussion). In addition, that individuals with larger subgenual anterior cingulate cortex and caudal inferior temporal sulcus volumes also reported larger and more complex social networks supports the hypothesis that the amygdala expanded in conjunction with some other brain regions to which it is densely connected⁷. The correlation found for the caudal superior frontal gyrus requires further investigation. Results from the exploratory analysis should be taken as preliminary findings that could guide future work aimed at examining the distributed network of brain regions that might support social network size and complexity.

Humans are inherently social animals. We play, work, eat and fight with one another. A larger amygdala might enable us to more effectively identify, learn about and recognize socioemotional cues in conspecifics³, allowing us to develop complex strategies to cooperate and compete¹.

References

Acknowledgments

Author information

Affiliations

Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA.
- Kevin C Bickart
Psychiatric Neuroimaging Research Program, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
- Bradford C Dickerson
Department of Psychology, Northeastern University, Boston, Massachusetts, USA.
- Lisa Feldman Barrett

Contributions

C.I.W. and L.F.B. designed the study. R.J.D. and L.F.B. performed the research. K.C.B., R.J.D., B.C.D. and L.F.B. analyzed the data. K.C.B., B.C.D., C.I.W. and L.F.B. wrote the manuscript. B.C.D., C.I.W. and L.F.B. contributed to grant funding.

Competing financial interests

The authors declare no competing financial interests.

Modelos matemáticos del lenguaje y la cultura

LINGÜÍSTICA
Modelos matemáticos del lenguaje y la cultura
22/12/2010

Al igual que los seres vivos, las lenguas también evolucionan: aparecen palabras nuevas, otras se extinguen y la gramática va cambiando poco a poco. ¿Es posible analizar con precisión cómo se transforma el lenguaje? ¿Podrían aplicarse métodos de estadística y de biología evolutiva para estudiar sus cambios?

Jean-Baptiste Michel y Erez Lieberman Aiden, matemáticos y doctores en biología de la Universidad de Harvard, han encontrado una herramienta que quizá les permita responder cuantitativamente a estas preguntas: Google Books, el polémico proyecto de la multinacional para digitalizar todos los libros publicados desde que Gutenberg fabricase la primera imprenta de tipos móviles en el siglo XV.

Junto con otros colaboradores, los autores recopilaron en una base de datos todas las palabras contenidas en unos 5 millones de libros digitalizados por Google Books. El texto acumulado asciende a un 4% de todos los libros publicados a lo largo de la historia y suma unos 500.000 millones de palabras en 7 idomas: inglés (361.000 millones de palabras), francés (45.000 millones), español (45.000 millones), alemán (37.000 millones), ruso (35.000 millones), chino (13.000 millones) y hebreo (2000 millones).

Para evitar problemas con los derechos de autor, los investigadores agruparon todo el texto en una única base de datos que no permite reconstruir las obras, pero sí hacer estadística fiable sobre la frecuencia de uso de las palabras. En partícular, resulta posible extraer la frecuencia relativa con la que, entre los años 1800 y 2000, han aparecido publicadas cadenas de hasta 5 palabras. Por ejemplo: ¿qué porcentaje de veces, relativo a todas las palabras publicadas ese año, apareció la palabra "Cuba" en las obras en español aparecidas en 1898? ¿Y cómo evolucionó la frecuencia relativa de empleo de la palabra desde entonces hasta nuestros días? El siguiente gráfico nos da la respuesta:

Frecuencia de aparición de la palabra "Cuba" en las obras publicadas en lengua castellana entre 1800 y 2000. El eje de ordenadas indica el porcentaje de menciones respectivas sobre el total de vocablos registrados cada año. Los dos máximos principales coinciden en el tiempo con la independencia y la revolución cubanas.

En su artículo, publicado la semana pasada en la edición online de la revista Science, los autores investigan varios aspectos relacionados con la frecuencia de uso de ciertos términos o cadenas de términos. Por ejemplo, concluyen que casi de la mitad de las palabras registradas en inglés (tras eliminar cadenas alfanuméricas, erratas o extranjerismos) no se hallan recogidas en ningún diccionario. También estudian algunos aspectos referentes a la evolución de la gramática, como el proceso de desaparición de los verbos irregulares, un tema que ya les mereció un artículo en Nature en 2007. Por ejemplo, es fácil ver cómo, desde el punto de vista léxico, algunas palabras han sido reemplazadas por otras a lo largo del tiempo:

Evolución entre 1800 y 2000 de la frecuencia de uso de las palabras "obscuro" y "oscuro" en la literatura en español incluida en la base de datos. La suma de ambas se mantiene aproximadamente constante, pero se aprecia cómo un vocablo reemplaza al otro.

Al margen de los aspectos puramente lingüísticos, los autores analizan otros factores que interpretan como relacionados con la memoria colectiva o con las tendencias culturales. Por ejemplo, razonan que el número de menciones a un año ("1982") probablemente pueda interpretarse como reflejo del interés colectivo por lo que sucedió en ese año. Como cabría esperar, la gráfica del uso de "1982" exhibe un pico en torno a 1982. Pero más interesante es preguntarse cómo evoluciona con el paso del tiempo el interés por un año concreto. El siguiente gráfico lo ilustra muy bien:

Evolución entre 1800 y 2000 de la frecuencia con que se han registrado menciones escritas a diferentes años.

Hay una tendencia muy clara: 1) La altura de los picos aumenta con el tiempo. 2) Con relación a la altura del pico, la curva decae cada vez más deprisa. Al respecto, los autores concluyen que "existe un aumento de interés por el momento presente", pero también que "olvidamos nuestro pasado cada vez más rápido".

Otras inferencias curiosas pueden extraerse a partir de las menciones a personajes o fenómenos concretos, que se corresponderían con el auge y declive de su fama. El mismo procedimiento también permite identificar episodios de censura histórica.

Lieberman Aiden y Michel ya se habían ocupado de estos temas en el pasado. En 2007 publicaron un artículo en Nature en el que analizaban la frecuencia con la que, en inglés, los verbos irregulares tienden a convertirse en regulares. Su conclusión fue que la "vida media" de un verbo irregular es proporcional a la raíz cuadrada de su frecuencia de uso: un verbo que se emplea 100 veces menos que otro se "regulariza" 10 veces más rápido.

Los autores interpretan su estudio como un primer paso en una disciplina que, en analogía con la genómica, bautizan como "culturómica": el estudio estadístico y la modelización matemática de fenómenos lingüísticos y culturales. Quizá pueda sonar algo pretencioso, al menos por el momento (por ejemplo, no sabemos cuán representativa es la muestra del 4% incluida en la base de datos), pero puede que la idea sí represente un primer paso hacia la obtención de modelos evolutivos del lenguaje.

En cualquier caso, la iniciativa y la sola posibilidad de analizar semejantes cantidades de datos no dejan de sorprender. Al igual que hace unos días reseñábamos otro trabajo que relacionaba las fluctuaciones bursátiles con las búsquedas de ciertos términos en Google, también en este caso se trata de una investigación que solo la gran cantidad de datos almacenados por el gigante informático ha hecho posible. Parece que las herramientas del buscador sirven para mucho más que para resolver las necesidades momentáneas de sus usuarios.

La base de datos es de acceso libre. Cualquiera puede acceder y experimentar con ella a través de www.culturomics.org

Más información en Science.

Artículo de los mismos autores sobre la evolución de los verbos irregulares en Nature.