Brain Aquaporin 4 in Hyperammonemia

In liver failure, congenital enzymopathies of the urea cycle, and other disorders, ammonia may not be properly detoxified and thus hyperammonemia ensues. Hyperammonemia is considered one of the main factors leading to cerebral edema and related consequences (increased intracranial pressure, brain herniation, and death). Cerebral edema is a critical component of neurological status impairment in patients with hyperammonemia and hepatic encephalopathy (HE). Although cerebral edema is generally classified as cytotoxic (cellular) and vasogenic (extracellular), both components often coexist in the same patient. Both types of edema can occur in acute hyperammonemia and liver failure with cytotoxic edema being a consequence of astrocytes swelling and vasogenic edema, mainly due to blood-brain barrier (BBB) disruption. It is well known that hyperammonemia is a crucial factor in astrocytes swelling and increased BBB permeability; however, the molecular mechanisms by which ammonia causes these alterations are not completely understood. Aquaporins (water channels) are one of the main pathways leading to water influx into the brain and efflux from the brain; consequently, it is conceivable brain aquaporins disturbances are involved in the pathophysiology of cerebral edema in hyperammonemia and HE. This review summarizes brain aquaporins main functions and distribution, and particularly, the aquaporin 4 (AQP-4) alterations induced under hyperammonemia and acute liver failure (ALF)

Mechanical Plantar Foot Stimulation in Parkinson's Disease: A Scoping Review

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease in older individuals. Neurorehabilitation-based interventions such as those improving gait are crucial for a holistic approach and to limit falls. Several studies have recently shown that mechanical plantar foot stimulation is a beneficial intervention for improving gait impairment in PD patients. The objective of this scoping review is to evaluate the beneficial effects of this stimulation on gait parameters, and to analyse protocols of foot stimulation and other effects in non-motor symptoms. Relevant articles were searched in the Medline database using Pubmed and Scopus, using the primary search terms 'foot stimulation' OR 'plantar stimulation' AND 'Parkinson's disease*'. Several protocols have been used for mechanical plantar foot stimulation (ranging from medical devices to textured insoles). The gait parameters that have been shown to be improved are stride length and walking speed. The beneficial effects are achieved after both acute and repeated plantar foot stimulation. Beneficial effects are observed in other organs and systems, such as muscle activation, brain connectivity, cardiovascular control in the central nervous system, and the release of brain-derived neurotrophic factor and cortisol in blood added evidence about this intervention's impact on brain function. Mechanical plantar foot stimulation is a safe and effective add-on treatment able for improving gait impairments in PD patients during the L-dopa off state. Randomized and controlled clinical trials to study its eventual potentiating effect with different pharmacotherapy regimens are warranted

A survey on sleep assessment methods

[EN] Purpose. A literature review is presented that aims to summarize and compare curren methods to evaluate sleep. Methods. Current sleep assessment methods have been classified according to different. criteria; e.g., objective (polysomnography actigraphy) vs.subjective (sleep questionnaires, diaries...), contact vs. contactless devices, and need for medical assistance vs. self-assessment. A comparison of validation studies is carried out for each method, identifying their sensitivity and specificity reported in the literature. Finally, the state of the market has also been reviewed with respect to customers' opinions about current sleep apps. Results. A taxonomy that classifies the sleep, detection methods. IA deseriPtion of each method that includes the tendencies of their underlying technologies lanalyzed in accordance with the literature. A comparison in terms, of precision of existing validation studies and reports. Discussion. In order of accuracy, sleep detection methods may be arranged as follows: Questionnaire < Sleep diary < Contactless devices < Contact devices < Polysotnnography A literature review suggests that current subjective methods present a sensitivity between 73% and 97.7%, while their specificity ranges in the interval 50%-96%. Objective methods such as actigraphy present a sensibility higher than 90%. However, their specificity is low compared to their sensitivity, being one of the limitations of such technology. Moreover, there are other factors, such as the Patients Perception of her or his sleep, that can be provided only by subjective methods. Therefore, sleep detection methods should be combined to produce a synergy between objective and subjective methods. The review of the market indicates the most valued sleep apps, but it also identifies problems and gaps, e.g., many hardware devices have not been validated and (especially software apps) should be studied before their clinical use.Ibáñez, V.; Silva, J.; Cauli, O. (2018). A survey on sleep assessment methods. PeerJ. 6:1-26. https://doi.org/10.7717/peerj.4849S1266Baandrup, L., & Jennum, P. (2015). A validation of wrist actigraphy against polysomnography in patients with schizophrenia or bipolar disorder. Neuropsychiatric Disease and Treatment, 2271. doi:10.2147/ndt.s88236Bhat, S., Ferraris, A., Gupta, D., Mozafarian, M., DeBari, V. A., Gushway-Henry, N., … Chokroverty, S. (2015). Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography. Journal of Clinical Sleep Medicine, 11(07), 709-715. doi:10.5664/jcsm.4840Bobes, J., González, M. P., Vallejo, J., Sáiz, J., Gibert, J., Ayuso, J. L., & Rico, F. (1998). Oviedo Sleep Questionnaire (OSQ): A new semistructured Interview for sleep disorders. European Neuropsychopharmacology, 8, S162. doi:10.1016/s0924-977x(98)80198-3Boyne, K., Sherry, D. D., Gallagher, P. R., Olsen, M., & Brooks, L. J. (2012). Accuracy of computer algorithms and the human eye in scoring actigraphy. Sleep and Breathing, 17(1), 411-417. doi:10.1007/s11325-012-0709-zBuysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193-213. doi:10.1016/0165-1781(89)90047-4Carney, C. E., Buysse, D. J., Ancoli-Israel, S., Edinger, J. D., Krystal, A. D., Lichstein, K. L., & Morin, C. M. (2012). The Consensus Sleep Diary: Standardizing Prospective Sleep Self-Monitoring. Sleep, 35(2), 287-302. doi:10.5665/sleep.1642Carskadon, M. A. (1986). Guidelines for the Multiple Sleep Latency Test (MSLT): A Standard Measure of Sleepiness. Sleep, 9(4), 519-524. doi:10.1093/sleep/9.4.519Chai-Coetzer, C. L., Antic, N. A., Rowland, L. S., Catcheside, P. G., Esterman, A., Reed, R. L., … McEvoy, R. D. (2011). A simplified model of screening questionnaire and home monitoring for obstructive sleep apnoea in primary care. Thorax, 66(3), 213-219. doi:10.1136/thx.2010.152801Chasens, E. R., Ratcliffe, S. J., & Weaver, T. E. (2009). Development of the FOSQ-10: A Short Version of the Functional Outcomes of Sleep Questionnaire. Sleep, 32(7), 915-919. doi:10.1093/sleep/32.7.915Chung, F., Yegneswaran, B., Liao, P., Chung, S. A., Vairavanathan, S., Islam, S., … Shapiro, C. M. (2008). STOP Questionnaire. Anesthesiology, 108(5), 812-821. doi:10.1097/aln.0b013e31816d83e4Cruz, S., Littner, M., & Zeidler, M. (2014). Home Sleep Testing for the Diagnosis of Obstructive Sleep Apnea—Indications and Limitations. Seminars in Respiratory and Critical Care Medicine, 35(05), 552-559. doi:10.1055/s-0034-1390066De Zambotti, M., Baker, F. C., & Colrain, I. M. (2015). Validation of Sleep-Tracking Technology Compared with Polysomnography in Adolescents. Sleep, 38(9), 1461-1468. doi:10.5665/sleep.4990De Zambotti, M., Claudatos, S., Inkelis, S., Colrain, I. M., & Baker, F. C. (2015). Evaluation of a consumer fitness-tracking device to assess sleep in adults. Chronobiology International, 32(7), 1024-1028. doi:10.3109/07420528.2015.1054395Douglass, A. B., Bomstein, R., Nino-Murcia, G., Keenan, S., Miles, L., Zarcone, V. P., … Dement, W. C. (1994). The Sleep Disorders Questionnaire I: Creation and Multivariate Structure of SDQ. Sleep, 17(2), 160-167. doi:10.1093/sleep/17.2.160El-Sayed, I. H. (2012). Comparison of four sleep questionnaires for screening obstructive sleep apnea. Egyptian Journal of Chest Diseases and Tuberculosis, 61(4), 433-441. doi:10.1016/j.ejcdt.2012.07.003Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(1). doi:10.1186/s12966-015-0314-1FIRAT, H., YUCEEGE, M., DEMIR, A., & ARDIC, S. (2012). Comparison of four established questionnaires to identify highway bus drivers at risk for obstructive sleep apnea in Turkey. Sleep and Biological Rhythms, 10(3), 231-236. doi:10.1111/j.1479-8425.2012.00566.xWARD FLEMONS, W., & REIMER, M. A. (1998). Development of a Disease-specific Health-related Quality of Life Questionnaire for Sleep Apnea. American Journal of Respiratory and Critical Care Medicine, 158(2), 494-503. doi:10.1164/ajrccm.158.2.9712036Flemons, W. W., Whitelaw, W. A., Brant, R., & Remmers, J. E. (1994). Likelihood ratios for a sleep apnea clinical prediction rule. American Journal of Respiratory and Critical Care Medicine, 150(5), 1279-1285. doi:10.1164/ajrccm.150.5.7952553Ibáñez, V., Silva, J., & Cauli, O. (2018). A survey on sleep questionnaires and diaries. Sleep Medicine, 42, 90-96. doi:10.1016/j.sleep.2017.08.026Jungquist, C. R., Pender, J. J., Klingman, K. J., & Mund, J. (2015). Validation of Capturing Sleep Diary Data via a Wrist-Worn Device. Sleep Disorders, 2015, 1-6. doi:10.1155/2015/758937Kelly, J. M., Strecker, R. E., & Bianchi, M. T. (2012). Recent Developments in Home Sleep-Monitoring Devices. ISRN Neurology, 2012, 1-10. doi:10.5402/2012/768794Lee, J., Hong, M., & Ryu, S. (2015). Sleep Monitoring System Using Kinect Sensor. International Journal of Distributed Sensor Networks, 2015, 1-9. doi:10.1155/2015/875371Lorenz, C. P., & Williams, A. J. (2017). Sleep apps. Current Opinion in Pulmonary Medicine, 23(6), 512-516. doi:10.1097/mcp.0000000000000425Marino, M., Li, Y., Rueschman, M. N., Winkelman, J. W., Ellenbogen, J. M., Solet, J. M., … Buxton, O. M. (2013). Measuring Sleep: Accuracy, Sensitivity, and Specificity of Wrist Actigraphy Compared to Polysomnography. Sleep, 36(11), 1747-1755. doi:10.5665/sleep.3142Martin, J. L., & Hakim, A. D. (2011). Wrist Actigraphy. Chest, 139(6), 1514-1527. doi:10.1378/chest.10-1872Meira, L., van Zeller, M., Eusébio, E., Clara, E. S., Viana, P., & Drummond, M. (2017). Maintenance of Wakefulness Test in clinical practice. Chronobiology and other sleep disorders. doi:10.1183/23120541.sleepandbreathing-2017.p5Meltzer, L. J., Hiruma, L. S., Avis, K., Montgomery-Downs, H., & Valentin, J. (2015). Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents. Sleep, 38(8), 1323-1330. doi:10.5665/sleep.4918Meltzer, L. J., Wong, P., Biggs, S. N., Traylor, J., Kim, J. Y., Bhattacharjee, R., … Marcus, C. L. (2016). Validation of Actigraphy in Middle Childhood. Sleep, 39(6), 1219-1224. doi:10.5665/sleep.5836Min, J.-K., Doryab, A., Wiese, J., Amini, S., Zimmerman, J., & Hong, J. I. (2014). Toss «n» turn. Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI ’14. doi:10.1145/2556288.2557220MONK, T. H., REYNOLDS, C. F., KUPFER, D. J., BUYSSE, D. J., COBLE, P. A., HAYES, A. J., … RITENOUR, A. M. (1994). The Pittsburgh Sleep Diary. Journal of Sleep Research, 3(2), 111-120. doi:10.1111/j.1365-2869.1994.tb00114.xUsing a Questionnaire to Help Identify Patients with Sleep Apnea. (1999). Annals of Internal Medicine, 131(7), 485. doi:10.7326/0003-4819-131-7-199910050-00041Pandi-Perumal, S. R., Spence, D. W., & BaHammam, A. S. (2014). Polysomnography: An Overview. Primary Care Sleep Medicine, 29-42. doi:10.1007/978-1-4939-1185-1_4Sateia, M. J. (2014). International Classification of Sleep Disorders-Third Edition. Chest, 146(5), 1387-1394. doi:10.1378/chest.14-0970Silva, G., Goodwin, J., Vana, K., & Quan, S. (2016). Obstructive sleep apnea and quality of life: comparison of the SAQLI, FOSQ, and SF-36 questionnaires. Southwest Journal of Pulmonary and Critical Care, 13(3), 137-149. doi:10.13175/swjpcc082-16Silva, G. E., Vana, K. D., Goodwin, J. L., Sherrill, D. L., & Quan, S. F. (2011). 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Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation

[EN] Introduction: Sleep assessment devices are essential for the detection, diagnosis, and monitoring of sleep disorders. This paper provides a state-of-the-art review and comparison of sleep assessment devices and a market analysis. Areas covered: Hardware devices are classified into contact and contactless devices. For each group, the underlying technologies are presented, paying special attention to their limitations. A systematic literature review has been carried out by comparing the most important validation studies of sleep tracking devices in terms of sensitivity and specificity. A market analysis has also been carried out in order to list the most used, best-selling, and most highly-valued devices. Software apps have also been compared with regards to the market. Expert opinion: Thanks to technological advances, the reliability and accuracy of sensors has been significantly increased in recent years. According to validation studies, some actigraphs present a sensibility higher than 90%. However, the market analysis reveals that many hardware devices have not been validated, and especially software devices should be studied before their clinical use.Ibáñez, V.; Silva, J.; Navarro, E.; Cauli, O. (2019). Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation. Expert Review of Medical Devices. 16(12):1041-1052. https://doi.org/10.1080/17434440.2019.1693890S104110521612El-Sayed, I. H. (2012). Comparison of four sleep questionnaires for screening obstructive sleep apnea. Egyptian Journal of Chest Diseases and Tuberculosis, 61(4), 433-441. doi:10.1016/j.ejcdt.2012.07.003FIRAT, H., YUCEEGE, M., DEMIR, A., & ARDIC, S. (2012). Comparison of four established questionnaires to identify highway bus drivers at risk for obstructive sleep apnea in Turkey. Sleep and Biological Rhythms, 10(3), 231-236. doi:10.1111/j.1479-8425.2012.00566.xPataka, A., Daskalopoulou, E., Kalamaras, G., Fekete Passa, K., & Argyropoulou, P. (2014). Evaluation of five different questionnaires for assessing sleep apnea syndrome in a sleep clinic. Sleep Medicine, 15(7), 776-781. doi:10.1016/j.sleep.2014.03.012Ibáñez, V., Silva, J., & Cauli, O. (2018). A survey on sleep questionnaires and diaries. Sleep Medicine, 42, 90-96. doi:10.1016/j.sleep.2017.08.026Ong, A. A., & Gillespie, M. B. (2016). Overview of smartphone applications for sleep analysis. World Journal of Otorhinolaryngology-Head and Neck Surgery, 2(1), 45-49. doi:10.1016/j.wjorl.2016.02.001Kolla, B. P., Mansukhani, S., & Mansukhani, M. P. (2016). Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Review of Medical Devices, 13(5), 497-506. doi:10.1586/17434440.2016.1171708Ibáñez, V., Silva, J., & Cauli, O. (2018). A survey on sleep assessment methods. PeerJ, 6, e4849. doi:10.7717/peerj.4849Bianchi, M. T. (2018). Sleep devices: wearables and nearables, informational and interventional, consumer and clinical. Metabolism, 84, 99-108. doi:10.1016/j.metabol.2017.10.008Blackwell, T., Redline, S., Ancoli-Israel, S., Schneider, J. L., Surovec, S., Johnson, N. L., … Stone, K. L. (2008). Comparison of Sleep Parameters from Actigraphy and Polysomnography in Older Women: The SOF Study. Sleep, 31(2), 283-291. doi:10.1093/sleep/31.2.283Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., … Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1). doi:10.1186/2046-4053-4-1Wu, H., Kato, T., Numao, M., & Fukui, K. (2017). Statistical sleep pattern modelling for sleep quality assessment based on sound events. Health Information Science and Systems, 5(1). doi:10.1007/s13755-017-0031-zLee, J., Hong, M., & Ryu, S. (2015). Sleep Monitoring System Using Kinect Sensor. International Journal of Distributed Sensor Networks, 2015, 1-9. doi:10.1155/2015/875371Patel, P., Kim, J. Y., & Brooks, L. J. (2016). Accuracy of a smartphone application in estimating sleep in children. Sleep and Breathing, 21(2), 505-511. doi:10.1007/s11325-016-1425-xCHEN, K. Y., & BASSETT, D. R. (2005). The Technology of Accelerometry-Based Activity Monitors: Current and Future. Medicine & Science in Sports & Exercise, 37(Supplement), S490-S500. doi:10.1249/01.mss.0000185571.49104.82Öberg, P. Å., Togawa, T., & Spelman, F. A. (Eds.). (2004). Sensors in Medicine and Health Care. doi:10.1002/3527601414Godfrey, A., Conway, R., Meagher, D., & ÓLaighin, G. (2008). Direct measurement of human movement by accelerometry. Medical Engineering & Physics, 30(10), 1364-1386. doi:10.1016/j.medengphy.2008.09.005Yang, C.-C., & Hsu, Y.-L. (2010). A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring. Sensors, 10(8), 7772-7788. doi:10.3390/s100807772Marino, M., Li, Y., Rueschman, M. N., Winkelman, J. W., Ellenbogen, J. M., Solet, J. M., … Buxton, O. M. (2013). Measuring Sleep: Accuracy, Sensitivity, and Specificity of Wrist Actigraphy Compared to Polysomnography. Sleep, 36(11), 1747-1755. doi:10.5665/sleep.3142Meltzer, L. J., Wong, P., Biggs, S. N., Traylor, J., Kim, J. Y., Bhattacharjee, R., … Marcus, C. L. (2016). Validation of Actigraphy in Middle Childhood. Sleep, 39(6), 1219-1224. doi:10.5665/sleep.5836De Souza, L., Benedito-Silva, A. A., Pires, M. L. N., Poyares, D., Tufik, S., & Calil, H. M. (2003). Further Validation of Actigraphy for Sleep Studies. Sleep, 26(1), 81-85. doi:10.1093/sleep/26.1.81Sivertsen, B., Omvik, S., Havik, O. E., Pallesen, S., Bjorvatn, B., Nielsen, G. H., … Nordhus, I. H. (2006). A Comparison of Actigraphy and Polysomnography in Older Adults Treated for Chronic Primary Insomnia. Sleep, 29(10), 1353-1358. doi:10.1093/sleep/29.10.1353Paquet, J., Kawinska, A., & Carrier, J. (2007). Wake Detection Capacity of Actigraphy During Sleep. Sleep, 30(10), 1362-1369. doi:10.1093/sleep/30.10.1362Sitnick, S. L., Goodlin-Jones, B. L., & Anders, T. F. (2008). The Use of Actigraphy to Study Sleep Disorders in Preschoolers: Some Concerns about Detection of Nighttime Awakenings. Sleep, 31(3), 395-401. doi:10.1093/sleep/31.3.395Natale, V., Plazzi, G., & Martoni, M. (2009). Actigraphy in the Assessment of Insomnia: A Quantitative Approach. Sleep, 32(6), 767-771. doi:10.1093/sleep/32.6.767Nakazaki, K., Kitamura, S., Motomura, Y., Hida, A., Kamei, Y., Miura, N., & Mishima, K. (2014). Validity of an algorithm for determining sleep/wake states using a new actigraph. Journal of Physiological Anthropology, 33(1), 31. doi:10.1186/1880-6805-33-31Matsuo, M., Masuda, F., Sumi, Y., Takahashi, M., Yamada, N., Ohira, M. H., … Kadotani, H. (2016). Comparisons of Portable Sleep Monitors of Different Modalities: Potential as Naturalistic Sleep Recorders. Frontiers in Neurology, 7. doi:10.3389/fneur.2016.00110Pigeon, W. R., Taylor, M., Bui, A., Oleynk, C., Walsh, P., & Bishop, T. M. (2018). Validation of the Sleep-Wake Scoring of a New Wrist-Worn Sleep Monitoring Device. Journal of Clinical Sleep Medicine, 14(06), 1057-1062. doi:10.5664/jcsm.7180Toon, E., Davey, M. J., Hollis, S. L., Nixon, G. M., Horne, R. S. C., & Biggs, S. N. (2016). Comparison of Commercial Wrist-Based and Smartphone Accelerometers, Actigraphy, and PSG in a Clinical Cohort of Children and Adolescents. Journal of Clinical Sleep Medicine, 12(03), 343-350. doi:10.5664/jcsm.5580Lee, X. K., Chee, N. I. Y. N., Ong, J. L., Teo, T. B., van Rijn, E., Lo, J. C., & Chee, M. W. L. (2019). Validation of a Consumer Sleep Wearable Device With Actigraphy and Polysomnography in Adolescents Across Sleep Opportunity Manipulations. Journal of Clinical Sleep Medicine, 15(09), 1337-1346. doi:10.5664/jcsm.7932Roomkham, S., Hittle, M., Cheung, J., Lovell, D., Mignot, E., & Perrin, D. (2019). Sleep monitoring with the Apple Watch: comparison to a clinically validated actigraph. F1000Research, 8, 754. doi:10.12688/f1000research.19020.1De Zambotti, M., Claudatos, S., Inkelis, S., Colrain, I. M., & Baker, F. C. (2015). Evaluation of a consumer fitness-tracking device to assess sleep in adults. Chronobiology International, 32(7), 1024-1028. doi:10.3109/07420528.2015.1054395Montgomery-Downs, H. E., Insana, S. P., & Bond, J. A. (2011). Movement toward a novel activity monitoring device. Sleep and Breathing, 16(3), 913-917. doi:10.1007/s11325-011-0585-yDe Zambotti, M., Baker, F. C., & Colrain, I. M. (2015). Validation of Sleep-Tracking Technology Compared with Polysomnography in Adolescents. Sleep, 38(9), 1461-1468. doi:10.5665/sleep.4990Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(1). doi:10.1186/s12966-015-0314-1Cole, R. J., Kripke, D. F., Gruen, W., Mullaney, D. J., & Gillin, J. C. (1992). Automatic Sleep/Wake Identification From Wrist Activity. Sleep, 15(5), 461-469. doi:10.1093/sleep/15.5.461Boyne, K., Sherry, D. D., Gallagher, P. R., Olsen, M., & Brooks, L. J. (2012). Accuracy of computer algorithms and the human eye in scoring actigraphy. Sleep and Breathing, 17(1), 411-417. doi:10.1007/s11325-012-0709-zSadeh, A., Sharkey, M., & Carskadon, M. A. (1994). Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues. Sleep, 17(3), 201-207. doi:10.1093/sleep/17.3.201Shin, M., Swan, P., & Chow, C. M. (2015). The validity of Actiwatch2 and SenseWear armband compared against polysomnography at different ambient temperature conditions. Sleep Science, 8(1), 9-15. doi:10.1016/j.slsci.2015.02.003Quante, M., Kaplan, E. R., Cailler, M., Rueschman, M., Wang, R., Weng, J., … Redline, S. (2018). Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms. Nature and Science of Sleep, Volume 10, 13-20. doi:10.2147/nss.s151085Meltzer, L. J., Hiruma, L. S., Avis, K., Montgomery-Downs, H., & Valentin, J. (2015). Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents. Sleep, 38(8), 1323-1330. doi:10.5665/sleep.4918Sørensen, K., Van den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., … Brand, H. (2012). Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health, 12(1). doi:10.1186/1471-2458-12-80Baron, K. G., Abbott, S., Jao, N., Manalo, N., & Mullen, R. (2017). Orthosomnia: Are Some Patients Taking the Quantified Self Too Far? Journal of Clinical Sleep Medicine, 13(02), 351-354. doi:10.5664/jcsm.647

Differences and Similarities in Neuropathy in Type 1 and 2 Diabetes: A Systematic Review

Background: Diabetic neuropathy is defined as the dysfunction of the peripheral nervous system in diabetic patients. It is considered a microvascular complication of diabetes mellitus. Its presence is associated with increased morbidity and mortality. Although several studies have found alterations at somatic motor, sensory levels and at the level of autonomic nervous system in diabetic patients, there is not a systematic approach regarding the differences in neuropathy between the major variants of diabetes, e.g., type 1 and 2 diabetes at both neurological and molecular level. Data sources: we systematically (Medline, Scopus, and Cochrane databases) evaluated the literature related to the difference of neuropathy in type 1 and 2 diabetes, differences in molecular biomarkers. Study characteristics: seventeen articles were selected based on pre-defined eligibility criteria. Conclusions: both superficial sensitivity (primarily thermal sensitivity to cold) and deep sensitivity (such as vibratory sensitivity), have been reported mainly in type 2 diabetes. Cardiac autonomic neuropathy is one of the diabetic complications with the greatest impact at a clinical level but is nevertheless one of the most underdiagnosed. While for type 1 diabetes patients most neuropathy alterations have been reported for the Valsalva maneuver and for the lying-to-standing test, for type 2 diabetes patients, alterations have been reported for deep-breathing test and the Valsalva test. In addition, there is a greater sympathetic than parasympathetic impairment, as indicated by the screening tests for autonomic cardiac neuropathy. Regarding subclinical inflammation markers, patients with type 2 diabetes showed higher blood levels of inflammatory markers such as high-sensitivity C-reactive protein, proinflammatory cytokines IL-6, IL-18, soluble cell adhesion molecules and E-selectin and ICAM-1, than in type 1 diabetes patients. By contrast, the blood levels of adiponectin, an adipocyte-derived protein with multiple paracrine and endocrine activities (anti-inflammatory, insulin-sensitizing and proangiogenic effects) are higher in type 1 than in type 2 diabetic patients. This review provides new insights into the clinical differences in type 1 and 2 diabetes and provide future directions in this research field

A Survey on Sleep Questionnaires and Diaries

[EN] Sleep assessment is a fundamental part of health evaluation. In fact, many diseases (such as obesity, diabetes, or hypertension, as well as psychiatric, neurological, and cardiovascular diseases) produce sleep disorders that are often used as indicators, diagnosis (symptoms), or even as predictors (eg, for depression) of health. For this reason, many efforts have been devoted to designing methods to control and report on sleep quality. Two of the most used sleep assessment tools are sleep questionnaires and sleep diaries. Both methods have a very low cost are easy to administer do not require a sleep centre (unlike, eg, polysomnography), and can be self-administered. Most important, as it has been shown in recent studies, their accuracy is relatively high. In this survey, we systematically review and compare these tools. We examine the evolution of sleep questionnaires and diaries over time, and compare their structure and usage. We also review the validation studies and comparatives performed in previous studies. This allows us to compare the relative sensitivities and specificities of these methods. Modern sleep diaries come in the form of an app. Therefore, we also present the most advanced and used apps, and discuss their advantages over classical paper diaries.Ibáñez Del Valle, V.; Silva, J.; Cauli, O. (2017). A Survey on Sleep Questionnaires and Diaries. Sleep Medicine. 42:90-96. doi:10.1016/j.sleep.2017.08.026S90964

Kaempferol as a dietary anti-inflammatory agent: current therapeutic standing

Inflammation is a physiological response to different pathological, cellular or vascular damages due to physical, chemical or mechanical trauma. It is characterized by pain, redness, heat and swelling. Current natural drugs are carefully chosen as a novel therapeutic strategy for the management of inflammatory diseases. Different phytochemical constituents are present in natural products. These phytochemicals have high efficacy both in vivo and in vitro. Among them, flavonoids occur in many foods, vegetables and herbal medicines and are considered as the most active constituent, having the ability to attenuate inflammation. Kaempferol is a polyphenol that is richly found in fruits, vegetables and herbal medicines. It is also found in plant-derived beverages. Kaempferol is used in the management of various ailments but there is no available review article that can summarize all the natural sources and biological activities specifically focusing on the anti-inflammatory effect of kaempferol. Therefore, this article is aimed at providing a brief updated review of the literature regarding the anti-inflammatory effect of kaempferol and its possible molecular mechanisms of action. Furthermore, the review provides the available updated literature regarding the natural sources, chemistry, biosynthesis, oral absorption, metabolism, bioavailability and therapeutic effect of kaempferol

Foot Orthosis and Sensorized House Slipper by 3D Printing

In clinical practice, specific customization is needed to address foot pathology, which must be disease and patient-specific. To date, the traditional methods for manufacturing custom functional Foot Orthoses (FO) are based on plaster casting and manual manufacturing, hence orthotic therapy depends entirely on the skills and expertise of individual practitioners. This makes the procedures difficult to standardize and replicate, as well as expensive, time-consuming and material-wasting, as well as difficult to standardize and replicate. 3D printing offers new perspectives in the development of patient-specific orthoses, as it permits addressing all the limitations of currently available technologies, but has been so far scarcely explored for the podiatric field, so many aspects remain unmet, especially for what regards customization, which requires the definition of a protocol that entails all stages from patient scanning to manufacturing