The Isotropic Fractionator as a Tool for Quantitative Analysis in Central Nervous System Diseases

One major aim in quantitative and translational neuroscience is to achieve a precise and fast neuronal counting method to work on high throughput scale to obtain reliable results.Here we tested the Isotropic Fractionator (IF) method for evaluating neuronal and non-neuronal cell loss in different models of central nervous system (CNS) pathologies.Sprague-Dawley rats underwent: (i) ischemic brain damage; (ii) intraperitoneal injection with kainic acid (KA) to induce epileptic seizures; and (iii) monolateral striatal injection with quinolinic acid (QA) mimicking human Hungtington’s disease.All specimens were processed for IF method and cell loss assessed.Hippocampus from KA-treated rats and striatum from QA-treated rats were carefully dissected using a dissection microscope and a rat brain matrix. Ischemic rat brains slices were first processed for TTC staining and then for IF.In the ischemic group the cell loss corresponded to the neuronal loss suggesting that hypoxia primarily affects neurons. Combining IF with TTC staining we could correlate the volume of lesion to the neuronal loss; by IF, we could assess that neuronal loss also occurs contralaterally to the ischemic side.In the epileptic group we observed a reduction of neuronal cells in treated rats, but also evaluated the changes in the number of non-neuronal cells in response to the hippocampal damage

A role for hemopexin in oligodendrocyte differentiation and myelin formation.

Myelin formation and maintenance are crucial for the proper function of the CNS and are orchestrated by a plethora of factors including growth factors, extracellular matrix components, metalloproteases and protease inhibitors. Hemopexin (Hx) is a plasma protein with high heme binding affinity, which is also locally produced in the CNS by ependymal cells, neurons and glial cells. We have recently reported that oligodendrocytes (OLs) are the type of cells in the brain that are most susceptible to lack of Hx, as the number of iron-overloaded OLs increases in Hx-null brain, leading to oxidative tissue damage. In the current study, we found that the expression of the Myelin Basic Protein along with the density of myelinated fibers in the basal ganglia and in the motor and somatosensory cortex of Hx-null mice were strongly reduced starting at 2 months and progressively decreased with age. Myelin abnormalities were confirmed by electron microscopy and, at the functional level, resulted in the inability of Hx-null mice to perform efficiently on the Rotarod. It is likely that the poor myelination in the brain of Hx-null mice was a consequence of defective maturation of OLs as we demonstrated that the number of mature OLs was significantly reduced in mutant mice whereas that of precursor cells was normal. Finally, in vitro experiments showed that Hx promotes OL differentiation. Thus, Hx may be considered a novel OL differentiation factor and the modulation of its expression in CNS may be an important factor in the pathogenesis of human neurodegenerative disorders

A Metachronous splenic metastases from esophageal cancer: a case report

The spleen is an infrequent site for metastatic lesions, and solitary splenic metastases from squamous cell carcinoma of the esophagus are very rare: only 4 cases have been reported thus far. These lesions are whitish nodules that are macroscopically and radiologically similar to primary splenic lymphomas. We report a case of metachronous splenic metastases from esophageal cancer and multiple splenic abscesses, which developed nine months after apparently curative esophagectomy without adjuvant chemotherapy. The patient underwent splenectomy dissection followed by adjuvant chemotherapy, but liver and skin metastases developed, and the patient died 9 months later

The My Active and Healthy Aging (My-AHA) ICT platform to detect and prevent frailty in older adults: Randomized control trial design and protocol

[EN] Introduction Frailty increases the risk of poor health outcomes, disability, hospitalization, and death in older adults and affects 7%¿12% of the aging population. Secondary impacts of frailty on psychological health and socialization are significant negative contributors to poor outcomes for frail older adults. Method The My Active and Healthy Aging (My-AHA) consortium has developed an information and communications technology¿based platform to support active and healthy aging through early detection of prefrailty and provision of individually tailored interventions, targeting multidomain risks for frailty across physical activity, cognitive activity, diet and nutrition, sleep, and psychosocial activities. Six hundred adults aged 60 years and older will be recruited to participate in a multinational, multisite 18-month randomized controlled trial to test the efficacy of the My-AHA platform to detect prefrailty and the efficacy of individually tailored interventions to prevent development of clinical frailty in this cohort. A total of 10 centers from Italy, Germany, Austria, Spain, United Kingdom, Belgium, Sweden, Japan, South Korea, and Australia will participate in the randomized controlled trial. Results Pilot testing (Alpha Wave) of the My-AHA platform and all ancillary systems has been completed with a small group of older adults in Europe with the full randomized controlled trial scheduled to commence in 2018. Discussion The My-AHA study will expand the understanding of antecedent risk factors for clinical frailty so as to deliver targeted interventions to adults with prefrailty. Through the use of an information and communications technology platform that can connect with multiple devices within the older adult's own home, the My-AHA platform is designed to measure an individual's risk factors for frailty across multiple domains and then deliver personalized domain-specific interventions to the individual. The My-AHA platform is technology-agnostic, enabling the integration of new devices and sensor platforms as they emerge.This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 689582 and the Australian National Health and Medical Research Council (NHRMC) European Union grant scheme (1115818). M.J.S. reports personal fees from Eli Lilly (Australia) Pty Ltd and grants from Novotech Pty Ltd, outside the submitted work. All other authors report nothing to disclose.Summers, MJ.; Rainero, I.; Vercelli, AE.; Aumayr, GA.; De Rosario Martínez, H.; Mönter, M.; Kawashima, R. (2018). The My Active and Healthy Aging (My-AHA) ICT platform to detect and prevent frailty in older adults: Randomized control trial design and protocol. Alzheimer's and Dementia: Translational Research and Clinical Interventions. 4:252-262. https://doi.org/10.1016/j.trci.2018.06.004S2522624Blair, S. N. (1995). Changes in Physical Fitness and All-Cause Mortality. JAMA, 273(14), 1093. doi:10.1001/jama.1995.03520380029031Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D., & Anderson, G. (2004). Untangling the Concepts of Disability, Frailty, and Comorbidity: Implications for Improved Targeting and Care. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 59(3), M255-M263. doi:10.1093/gerona/59.3.m255Gillick, M. (2001). Guest Editorial: Pinning Down Frailty. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 56(3), M134-M135. doi:10.1093/gerona/56.3.m134Hamerman, D. (1999). Toward an Understanding of Frailty. Annals of Internal Medicine, 130(11), 945. doi:10.7326/0003-4819-130-11-199906010-00022Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J., … McBurnie, M. A. (2001). Frailty in Older Adults: Evidence for a Phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 56(3), M146-M157. doi:10.1093/gerona/56.3.m146Panza, F., Solfrizzi, V., Barulli, M. R., Santamato, A., Seripa, D., Pilotto, A., & Logroscino, G. (2015). Cognitive Frailty: A Systematic Review of Epidemiological and Neurobiological Evidence of an Age-Related Clinical Condition. Rejuvenation Research, 18(5), 389-412. doi:10.1089/rej.2014.1637Soong, J., Poots, A., Scott, S., Donald, K., Woodcock, T., Lovett, D., & Bell, D. (2015). Quantifying the prevalence of frailty in English hospitals. BMJ Open, 5(10), e008456. doi:10.1136/bmjopen-2015-008456Varadhan, R., Walston, J., Cappola, A. R., Carlson, M. C., Wand, G. S., & Fried, L. P. (2008). Higher Levels and Blunted Diurnal Variation of Cortisol in Frail Older Women. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 63(2), 190-195. doi:10.1093/gerona/63.2.190BROWN, I., RENWICK, R., & RAPHAEL, D. (1995). Frailty. International Journal of Rehabilitation Research, 18(2), 93-102. doi:10.1097/00004356-199506000-00001Buchner, D. M., & Wagner, E. H. (1992). Preventing Frail Health. Clinics in Geriatric Medicine, 8(1), 1-18. doi:10.1016/s0749-0690(18)30494-4Kojima, G., Iliffe, S., Jivraj, S., & Walters, K. (2016). Association between frailty and quality of life among community-dwelling older people: a systematic review and meta-analysis. Journal of Epidemiology and Community Health, 70(7), 716-721. doi:10.1136/jech-2015-206717Ory, M. G., Schechtman, K. B., Miller, J. P., Hadley, E. C., Fiatarone, M. A., … Province, M. A. (1993). Frailty and Injuries in Later Life: The FICSIT Trials. Journal of the American Geriatrics Society, 41(3), 283-296. doi:10.1111/j.1532-5415.1993.tb06707.xShamliyan, T., Talley, K. M. C., Ramakrishnan, R., & Kane, R. L. (2013). Association of frailty with survival: A systematic literature review. Ageing Research Reviews, 12(2), 719-736. doi:10.1016/j.arr.2012.03.001Woodhouse, K. W., & O’Mahony, M. S. (1997). Frailty and ageing. Age and Ageing, 26(4), 245-246. doi:10.1093/ageing/26.4.245CAMPBELL, A. J., & BUCHNER, D. M. (1997). Unstable disability and the fluctuations of frailty. Age and Ageing, 26(4), 315-318. doi:10.1093/ageing/26.4.315Drey, M., Pfeifer, K., Sieber, C. C., & Bauer, J. M. (2011). The Fried Frailty Criteria as Inclusion Criteria for a Randomized Controlled Trial: Personal Experience and Literature Review. Gerontology, 57(1), 11-18. doi:10.1159/000313433Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., … Phelps, C. H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270-279. doi:10.1016/j.jalz.2011.03.008Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild Cognitive Impairment. Archives of Neurology, 56(3), 303. doi:10.1001/archneur.56.3.303Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.-O., … Petersen, R. C. (2004). Mild cognitive impairment - beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256(3), 240-246. doi:10.1111/j.1365-2796.2004.01380.xDubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., … Andrieu, S. (2016). Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimer’s & Dementia, 12(3), 292-323. doi:10.1016/j.jalz.2016.02.002Moher, D., Hopewell, S., Schulz, K. F., Montori, V., Gotzsche, P. C., Devereaux, P. J., … Altman, D. G. (2010). CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. BMJ, 340(mar23 1), c869-c869. doi:10.1136/bmj.c869Gray, L. C., Bernabei, R., Berg, K., Finne-Soveri, H., Fries, B. E., Hirdes, J. P., … Ariño-Blasco, S. (2008). Standardizing Assessment of Elderly People in Acute Care: The interRAI Acute Care Instrument. Journal of the American Geriatrics Society, 56(3), 536-541. doi:10.1111/j.1532-5415.2007.01590.xRadloff, L. S. (1977). The CES-D Scale. Applied Psychological Measurement, 1(3), 385-401. doi:10.1177/014662167700100306Guralnik, J. M., Simonsick, E. M., Ferrucci, L., Glynn, R. J., Berkman, L. F., Blazer, D. G., … Wallace, R. B. (1994). A Short Physical Performance Battery Assessing Lower Extremity Function: Association With Self-Reported Disability and Prediction of Mortality and Nursing Home Admission. Journal of Gerontology, 49(2), M85-M94. doi:10.1093/geronj/49.2.m85Powell, L. E., & Myers, A. M. (1995). The Activities-specific Balance Confidence (ABC) Scale. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 50A(1), M28-M34. doi:10.1093/gerona/50a.1.m28Kendzierski, D., & DeCarlo, K. J. (1991). Physical Activity Enjoyment Scale: Two Validation Studies. Journal of Sport and Exercise Psychology, 13(1), 50-64. doi:10.1123/jsep.13.1.50Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). «Mini-mental state». Journal of Psychiatric Research, 12(3), 189-198. doi:10.1016/0022-3956(75)90026-6Brandt, J. (1991). The hopkins verbal learning test: Development of a new memory test with six equivalent forms. Clinical Neuropsychologist, 5(2), 125-142. doi:10.1080/13854049108403297Lubben, J. E. (1988). Assessing social networks among elderly populations. Family & Community Health, 11(3), 42-52. doi:10.1097/00003727-198811000-00008Russell, D., Peplau, L. A., & Cutrona, C. E. (1980). The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39(3), 472-480. doi:10.1037/0022-3514.39.3.472De Vries, O. J., Peeters, G. M. E. E., Lips, P., & Deeg, D. J. H. (2013). Does frailty predict increased risk of falls and fractures? A prospective population-based study. Osteoporosis International, 24(9), 2397-2403. doi:10.1007/s00198-013-2303-zTheou, O., Stathokostas, L., Roland, K. P., Jakobi, J. M., Patterson, C., Vandervoort, A. A., & Jones, G. R. (2011). The Effectiveness of Exercise Interventions for the Management of Frailty: A Systematic Review. Journal of Aging Research, 2011, 1-19. doi:10.4061/2011/569194Cadore, E. (2014). Strength and Endurance Training Prescription in Healthy and Frail Elderly. Aging and Disease, 5(3), 183. doi:10.14336/ad.2014.0500183Cadore, E. L., Rodríguez-Mañas, L., Sinclair, A., & Izquierdo, M. (2013). Effects of Different Exercise Interventions on Risk of Falls, Gait Ability, and Balance in Physically Frail Older Adults: A Systematic Review. Rejuvenation Research, 16(2), 105-114. doi:10.1089/rej.2012.1397Gardner, M. M. (2001). Practical implementation of an exercise-based falls prevention programme. Age and Ageing, 30(1), 77-83. doi:10.1093/ageing/30.1.77Eng, J. J. (2010). Fitness and Mobility Exercise Program for Stroke. Topics in Geriatric Rehabilitation, 26(4), 310-323. doi:10.1097/tgr.0b013e3181fee736Wadlinger, H. A., & Isaacowitz, D. M. (2008). Looking happy: The experimental manipulation of a positive visual attention bias. Emotion, 8(1), 121-126. doi:10.1037/1528-3542.8.1.121MacLeod, C. (2012). Cognitive bias modification procedures in the management of mental disorders. Current Opinion in Psychiatry, 25(2), 114-120. doi:10.1097/yco.0b013e32834fda4aMensink, R. P., & Katan, M. B. (1989). Effect of a Diet Enriched with Monounsaturated or Polyunsaturated Fatty Acids on Levels of Low-Density and High-Density Lipoprotein Cholesterol in Healthy Women and Men. New England Journal of Medicine, 321(7), 436-441. doi:10.1056/nejm19890817321070

Mutant Prourokinase with Adjunctive C1-Inhibitor Is an Effective and Safer Alternative to tPA in Rat Stroke

A single-site mutant (M5) of native urokinase plasminogen activator (prouPA) induces effective thrombolysis in dogs with venous or arterial thrombosis with a reduction in bleeding complications compared to tPA. This effect, related to inhibition of two-chain M5 (tcM5) by plasma C1-inhibitor (C1I), thereby preventing non-specific plasmin generation, was augmented by the addition of exogenous C1I to plasma in vitro. In the present study, tPA, M5 or placebo +/− C1I were administered in two rat stroke models. In Part-I, permanent MCA occlusion was used to evaluate intracranial hemorrhage (ICH) by the thrombolytic regimens. In Part II, thromboembolic occlusion was used with thrombolysis administered 2 h later. Infarct and edema volumes, and ICH were determined at 24 h, and neuroscore pre (2 h) and post (24 h) treatment. In Part I, fatal ICH occurred in 57% of tPA and 75% of M5 rats. Adjunctive C1I reduced this to 25% and 17% respectively. Similarly, semiquantitation of ICH by neuropathological examination showed significantly less ICH in rats given adjunctive C1I compared with tPA or M5 alone. In Part-II, tPA, M5, and M5+C1I induced comparable ischemic volume reductions (>55%) compared with the saline or C1I controls, indicating the three treatments had a similar fibrinolytic effect. ICH was seen in 40% of tPA and 50% of M5 rats, with 1 death in the latter. Only 17% of the M5+C1I rats showed ICH, and the bleeding score in this group was significantly less than that in either the tPA or M5 group. The M5+C1I group had the best Benefit Index, calculated by dividing percent brain salvaged by the ICH visual score in each group. In conclusion, adjunctive C1I inhibited bleeding by M5, induced significant neuroscore improvement and had the best Benefit Index. The C1I did not compromise fibrinolysis by M5 in contrast with tPA, consistent with previous in vitro findings

Deep Machine Learning Application to the Detection of Preclinical Neurodegenerative Diseases of Aging

Artificial intelligence (AI) deep learning protocols offer solutions to complex data processing and analysis. Increasingly these solutions are being applied in the healthcare field, most commonly in processing complex medical imaging data used for diagnosis. Current models apply AI to screening populations of patients for markers of disease and report detection accuracy rates exceeding those of human data screening. In this paper, we explore an alternate model for AI deployment, that of monitoring and analysing an individual’s level of function over time. In adopting this approach, we propose that AI may provide highly accurate and reliable detection of preclinical disease states associated with aging-related neurodegenerative diseases. One of the key challenges facing clinical detection of preclinical phases of diseases such as dementia is the high degree of inter-individual variability in aging-related changes to cognitive function. AI based monitoring of an individual over time offers the potential for the early detection of change in function for the individual, rather than relying on comparing the individual’s performance to population norms. We explore an approach to developing AI platforms for individual monitoring and preclinical disease detection and examine the potential benefits to the stakeholders in this technological development

Role of JNK isoforms in the development of neuropathic pain following sciatic nerve transection in the mouse

Background: Current tools for analgesia are often only partially successful, thus investigations of new targets for pain therapy stimulate great interest. Consequent to peripheral nerve injury, c-Jun N-terminal kinase (JNK) activity in cells of the dorsal root ganglia (DRGs) and spinal cord is involved in triggering neuropathic pain. However, the relative contribution of distinct JNK isoforms is unclear. Using knockout mice for single isoforms, and blockade of JNK activity by a peptide inhibitor, we have used behavioral tests to analyze the contribution of JNK in the development of neuropathic pain after unilateral sciatic nerve transection. In addition, immunohistochemical labelling for the growth associated protein (GAP)-43 and Calcitonin Gene Related Peptide (CGRP) in DRGs was used to relate injury related compensatory growth to altered sensory function. Results: Peripheral nerve injury produced pain-related behavior on the ipsilateral hindpaw, accompanied by an increase in the percentage of GAP43-immunoreactive (IR) neurons and a decrease in the percentage of CGRP-IR neurons in the lumbar DRGs. The JNK inhibitor, D-JNKI-1, successfully modulated the effects of the sciatic nerve transection. The onset of neuropathic pain was not prevented by the deletion of a single JNK isoform, leading us to conclude that all JNK isoforms collectively contribute to maintain neuropathy. Autotomy behavior, typically induced by sciatic nerve axotomy, was absent in both the JNK1 and JNK3 knockout mice. Conclusions: JNK signaling plays an important role in regulating pain threshold: the inhibition of all of the JNK isoforms prevents the onset of neuropathic pain, while the deletion of a single splice JNK isoform mitigates established sensory abnormalities. JNK inactivation also has an effect on axonal sprouting following peripheral nerve injury

Functional anatomy of cortical areas characterized by Von Economo neurons

Von Economo's neurons (VENs) are large, bipolar or corkscrew-shaped neurons located in layers III and V of the frontoinsular and the anterior cingulate cortices. VENs are reported to be altered in pathologies such as frontotemporal dementia and autism, in which the individual's self control is seriously compromised. To investigate the role of VENs in the active human brain, we have explored the functional connectivity of brain areas containing VENs by analyzing resting state functional connectivity (rsFC) in 20 healthy volunteers. Our results show that cortical areas containing VENs form a network of frontoparietal functional connectivity. With the use of fuzzy clustering techniques, we find that this network comprises four sub-networks: the first network cluster resembles a "saliency detection" attentional network, which includes superior frontal cortex (Brodmann's Area, BA 10), inferior parietal lobe, anterior insula, and dorsal anterior cingulate cortex; the second cluster, part of a "sensory-motor network", comprises the superior temporal, precentral and postcentral areas; the third cluster consists of frontal ventromedial and ventrodorsal areas constituted by parts of the "anterior default mode network"; and the fourth cluster encompasses dorsal anterior cingulate cortex, dorsomedial prefrontal, and superior frontal (BA 10) areas, resembling the anterior part of the "dorsal attentional network". Thus, the network that emerges from analyzing functional connectivity among areas that are known to contain VENs is primarily involved in functions of saliency detection and self-regulation. In addition, parts of this network constitute sub-networks that partially overlap with the default mode, the sensory-motor and the dorsal attentional networks