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Journal articles on the topic 'Alzheimer's disease'

To see the other types of publications on this topic, follow the link: Alzheimer's disease.

Author: Grafiati

Published: 4 June 2021

Last updated: 31 July 2024

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1

Zarrouk, Amira, Meryam Debbabi, Maryem Bezine, El Mostafa Karym, Asmaa Badreddine, Olivier Rouaud, Thibault Moreau, et al. "Lipid Biomarkers in Alzheimer's Disease." Current Alzheimer Research 15, no. 4 (February 22, 2018): 303–12. http://dx.doi.org/10.2174/1567205014666170505101426.

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Background: There are now significant evidences that lipid metabolism is affected in numerous neurodegenerative diseases including Alzheimer’s disease. These dysfunctions lead to abnormal levels of certain lipids in the brain, cerebrospinal fluid and plasma. It is consequently of interest to establish lipid profiles in neurodegenerative diseases. This approach, which can contribute to identify lipid biomarkers of Alzheimers' disease, can also permit to identify new therapeutic targets. It was therefore of interest to focus on central and peripheral biomarkers in Alzheimer's disease. Methods: A review of the literature on 148 papers was conducted. Based on this literature, the involvement of lipids (cholesterol and oxysterols, fatty acids, phospholipids) in Alzheimer's disease has been proposed. Results: Of the 148 references cited for lipid biomarkers for Alzheimer's disease, 65 refer to cholesterol and oxysterols, 35 to fatty acids and 40 to phospholipids. Among these lipids, some of them such as 24S-hydroxyckolesterol, open up new therapeutic perspectives in gene therapy, in particular. The results on the very long-chain fatty acids suggest the potential of peroxisomal dysfunctions in Alzheimer's disease. As for the phospholipids, they could constitute interesting biomarkers for detecting the disease at the prodromal stage. Conclusion: There are now several lines of evidence that lipids play fundamental roles in the pathogenesis of AD and that some of them have a prognostic and diagnosis value. This may pave the way for the identification of new therapeutic targets, new effective drugs and / or new treatments.

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2

Graham, Nori. "Alzheimer's Disease International." International Psychogeriatrics 9, no. 1 (March 1997): 5–6. http://dx.doi.org/10.1017/s1041610297004146.

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Alzheimer's Disease International (ADI) aims to promote and support the work of national Alzheimer associations. Their main purpose is to support carers of people with Alzheimer's disease and related dementias, and to raise awareness of the impact of the disease on the individual and the carer.

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3

BAR, Hassan. "Various Infections and Alzheimer's Disease." Virology & Immunology Journal 7, no. 3 (July 7, 2023): 1–3. http://dx.doi.org/10.23880/vij-16000317.

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Alzheimer's disease (AD) is a major contributor to the worldwide prevalence of dementia. Extracellular-amyloid (A) senile plaques (SP) and intracellular neurofibrillary tangles (NFT) are the neuropathological hallmarks of Alzheimer's disease. Currently, it is believed that both hereditary and environmental variables interact to contribute to the pathophysiology of AD. Despite significant investments in neurological research, the precise molecular basis of AD pathogenesis remains unknown. Multiple studies point to the possibility that pathogenic microorganisms contribute to the development of AD. Microbes were formerly thought to have no connection to Alzheimer's disease, but a rising body of research suggests otherwise. Evidence that these microbes cause AD-specific cognitive and neuropathological deficits and changes is lacking, casting doubt on the hypothesis that AD is an infectious neurological illness. In addition, the gut flora may have a role in AD progression in humans.

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4

Zhao, Bin. "Statistical analysis on Alzheimer's disease." Journal of Infectious Diseases & Travel Medicine 7, no. 2 (October 31, 2023): 1–18. http://dx.doi.org/10.23880/jidtm-16000177.

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Alzheimer's disease is a progressive neurodegenerative disease that occurs mostly in the elderly and has memory impairment as the main clinical symptom. There is no ideal treatment for Alzheimer's disease, so early prevention is important. In this paper, we use brain structural information to diagnose Alzheimer's disease features and cognitive-behavioral characteristics, which is important for early and accurate diagnosis of mild cognitive impairment. To investigate the factors influencing Alzheimer's disease, a correlation analysis model was developed after preprocessing the missing values of the data. First, the data features were viewed, the missing values of the data were analyzed, and the useless features were removed and the missing values of the remaining features were filled with the average value. To verify the accuracy of the subsequent intelligent diagnosis model and clustering model, this paper divides the training set and test set according to PTID. Finally, the top ten important features are selected and the Spearman coefficients are chosen according to the distribution of the features for correlation analysis. Machine learning methods were utilized to build an Alzheimer's classification model to solve the problem of intelligent diagnosis of Alzheimer's disease. The pre-processed dataset in the above paper was trained with the model, and five methods of logistic regression, support vector machine, KNN classification, decision tree classification and XGB were utilized to build the classification model, and the accuracy, recall and F1 value of each model were visualized and compared, among which the accuracy of XGB model reached 83%, which is reasonable for the intelligent diagnosis of the disease. A K-Means-based clustering model for disease types was established using the K-Means clustering algorithm, clustering CN, MCI and AD into three major classes, and then refining MCI into three subclasses. The optimal K-values and random seeds were firstly found using the elbow principle, then the cluster analysis was performed using the feature values and data sets selected after preprocessing, and finally the MCI in MCI was extracted and sub-clustered into three subclasses SMC, EMCI and LMCI. In order to investigate the evolution pattern of different categories of diseases over time, patients with 3 categories of diseases are screened separately for analysis in this paper. Firstly, by combining the results above and reviewing the data, the features irrelevant to this task and columns containing a large number of missing values were removed, the remaining features were selected and probability density plots were drawn, and all discrete features and all features that were essentially zero were continued to be screened out. After that, the 15 features of CN, MCI and AD diseases were plotted separately over time to reveal their evolution patterns over time. We reviewed the relevant literature, sorted out and summarized the existing studies at home and abroad, and summarized the criteria for determining the five stages of Alzheimer's disease and the early intervention of the disease.

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5

Rajesh, Ravula, Singadi Akhil Reddy, Gandikota Varma Devraj, Raghuram Bhukya, Harika Dasari, and Naaram Srichandana. "Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (August 8, 2023): 465–70. http://dx.doi.org/10.17762/ijritcc.v11i6.7784.

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It's important to note that Alzheimer's disease can also affect individuals over the age of 60, and in fact, the risk of developing Alzheimer's increases with age. Additionally, while deep learning approaches have shown promising results in detecting Alzheimer's disease, they are not the only techniques available for diagnosis and treatment. That being said, using Region-based Convolutional Neural Network (RCNN) for efficient feature extraction and classification can be a valuable tool in detecting Alzheimer's disease. This new approach to identifying Alzheimer's disease could lead to a more accurate and personalized diagnosis. It can also help in early treatment and intervention. However, it's still important to continue developing new methods and techniques for this disorder. Considering this our work proposes an innovative Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction approach in this paper. The exhaustive experimental result carried out, which proves the efficacy of our Alzheimer prediction system.

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6

VanDongen, Antonius M. "Arc: A new target for treating alzheimer's disease." Open Access Government 43, no. 1 (July 8, 2024): 160–61. http://dx.doi.org/10.56367/oag-043-11454.

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Arc: A new target for treating alzheimer's disease Antonius M. VanDongen, Associate Professor from Duke University, walks us through Arc, a new target for treating Alzheimer’s disease. Alois Alzheimer is a German psychiatrist credited with identifying the first case of the debilitating disease named after him. In 1906, he described neurofibrillary tangles and amyloid plaques in his patient’s brain as unique hallmarks of her dementia. Advances in neuroimaging, genetics, and molecular biology have expanded our understanding of the mechanisms underlying Alzheimer’s disease (AD) significantly. But despite heroic efforts to find a cure, there is currently no therapy that prevents, stabilizes or reverses the progression of this disorder that is poised to take on epidemic proportions as the world ages.

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7

Rimmer, Elizabeth. "Alzheimer's Disease International." International Psychiatry 3, no. 4 (October 2006): 22–23. http://dx.doi.org/10.1192/s1749367600004999.

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Alzheimer's Disease International (ADI) is the international federation of 75 Alzheimer associations throughout the world and is in official relations with the World Health Organization. ADI was established to raise awareness about dementia, strengthen Alzheimer associations and provide a platform for the exchange of knowledge with the ultimate goal of improving the quality of life of people with dementia and their families.

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8

Conte, G. L., and M. Pomponi. "Alzheimer's or alzheimer-perusini's disease?" European Psychiatry 22 (March 2007): S297. http://dx.doi.org/10.1016/j.eurpsy.2007.01.1008.

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9

Corriveau, Roderick A., Walter J. Koroshetz, Jordan T. Gladman, Sophia Jeon, Debra Babcock, David A. Bennett, S. Thomas Carmichael, et al. "Alzheimer's Disease–Related Dementias Summit 2016: National research priorities." Neurology 89, no. 23 (November 8, 2017): 2381–91. http://dx.doi.org/10.1212/wnl.0000000000004717.

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Goal 1 of the National Plan to Address Alzheimer’s Disease is to prevent and effectively treat Alzheimer disease and Alzheimer disease–related dementias by 2025. To help inform the research agenda toward achieving this goal, the NIH hosts periodic summits that set and refine relevant research priorities for the subsequent 5 to 10 years. This proceedings article summarizes the 2016 Alzheimer's Disease–Related Dementias Summit, including discussion of scientific progress, challenges, and opportunities in major areas of dementia research, including mixed-etiology dementias, Lewy body dementia, frontotemporal degeneration, vascular contributions to cognitive impairment and dementia, dementia disparities, and dementia nomenclature.

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10

Ojetunde, Ayodeji Oluwatobi. "The Neuroprotective and Therapeutic Effects of Medicinal Plants and Natural Products against Aluminium Chloride-Induced Alzheimer’s Disease: Recent Update." Biology, Medicine, & Natural Product Chemistry 13, no. 1 (May 2, 2024): 7–33. http://dx.doi.org/10.14421/biomedich.2024.131.7-33.

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Alzheimer's disease currently affects more than 35 million individuals worldwide. Aluminium has been implicated in the pathogenesis of various cognitive disorders. Meanwhile, aluminium chloride (AlCl3) has a significant impact on the progression of neurodegenerative diseases including Alzheimer's disease. The majority of Alzheimer's disease medications now on the market are cholinesterase inhibitors. However, the effectiveness of these drugs is limited because they can't totally arrest the progression of the disease. The utilization of medicinal plants and natural products may present excellent prospective options for Alzheimer's disease prevention and therapy. This study summarized medicinal plants and natural products for the prevention and treatment of AlCl3-induced Alzheimer’s disease as an alternative therapy using published data in the literature from the years 2021-2023. The medicinal plants and natural products help to reduce Alzheimer’s disease pathogenesis by controlling different pathways and could be used as a therapeutic agent against the symptoms. The majority of the medicinal plants and natural products discussed in this review have been shown to have neuroprotective, antioxidant, anti-amyloid, anti-inflammatory, anticholinesterase, anti-apoptotic, and therapeutic actions. Therefore, medicinal plants and natural products may offer neuroprotective and therapeutic effects in the treatment of Alzheimer’s disease.

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11

Sree, M. Monika Bhargavi Sandhya. "Early detection of Alzheimer’s Disease using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 22, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34463.

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alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and causes cognitive impairment. It is the most common cause of dementia, a general term for a decline in cognitive abilities that interfere with daily life. Deep Learning, the subset of Artificial Intelligence is used in the early detection of Alzheimer's Disease. The human-level performance of the Deep Learning algorithm has been effectively shown in different disciplines. There isn’t a specific algorithm that is universal, but various Deep Learning algorithms, are used for the early detection of Alzheimer’s Disease. Researchers developed a blood test that could detect Alzheimer’s Disease promoting compounds in blood before the symptoms emerged. These findings may lead to early diagnostic tests for Alzheimer’s and other neurodegenerative diseases. Through research on the “Early detection of Alzheimer’s Disease using Deep Learning”, we can learn more about the potential of using advanced technology to identify the disease at its earliest stages. It also discusses the challenges and limitations of using Deep Learning for Alzheimer's Disease detection and highlights the need for future research in this area. Additionally, it can provide insights into the progression of the disease and potentially lead to the development of more accurate diagnostic tools. KEYWORDS: Alzheimer’s Disease, neurodegenerative, dementia, Early diagnosis, Deep Learning algorithms

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12

Tulbă, Delia, Liviu Cozma, Bogdan Ovidiu Popescu, and Eugenia Irene Davidescu. "Dysautonomia in Alzheimer's Disease." Medicina 56, no. 7 (July 8, 2020): 337. http://dx.doi.org/10.3390/medicina56070337.

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Alzheimer's disease is the most common neurodegenerative disorder, and its prevalence increases with age. Although there is a large amount of scientific literature focusing on Alzheimer's disease cardinal cognitive features, autonomic nervous system dysfunction remains understudied despite being common in the elderly. In this article, we reviewed the evidence for autonomic nervous system involvement in Alzheimer's disease. We identified four major potential causes for dysautonomia in Alzheimer's disease, out of which two are well-studied (comorbidities and medication) and two are rather hypothetical (Alzheimer's pathology and brain co-pathology). Although there appears to be some evidence linking Alzheimer's disease pathology to autonomic nervous system dysfunction, there is an important gap between two types of studies; histopathologic studies do not address dysautonomia manifestations, whereas clinical studies do not employ histopathologic diagnostic confirmation. Moreover, brain co-pathology is emerging as an important confounding factor. Therefore, we consider the correlation between dysautonomia and Alzheimer's disease to be an open question that needs further study. Nevertheless, given its impact on morbidity and mortality, we emphasize the importance of assessing autonomic dysfunction in patients with Alzheimer clinical syndrome.

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13

Heckmann, Bradlee. "Beyond amyloid: What's next for Alzheimers disease therapeutics?" Open Access Government 38, no. 1 (April 13, 2023): 116–17. http://dx.doi.org/10.56367/oag-038-10770.

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Beyond amyloid: What's next for Alzheimers disease therapeutics? Bradlee Heckmann, PhD, from USF Health Neuroscience Institute, Byrd Alzheimer's Center & Asha Therapeutics, in this discussion goes beyond amyloid, asking what's next for Alzheimer's Disease therapeutics. The recent approvals of aducanumab and lecanamab, targeting amyloid beta, a key pathogenic hallmark of Alzheimer's Disease (AD), represent an impressive step forward in developing new treatment options for one of the most devastating neurological diseases. AD impacts over 5 million individuals in the United States and an estimated 20-24 million people globally. Moreover, AD represents the sixth leading cause of death and according to the Alzheimer's Association approximately one in three individuals will die with either AD or a related dementia.

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14

Harshitha, Gowthami Chamarajan, and Charishma Y. "Alzheimer's Disease: A Survey." International Journal of Artificial Intelligence 8, no. 1 (June 22, 2021): 33–39. http://dx.doi.org/10.36079/lamintang.ijai-0801.220.

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Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease.

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15

Förstl, Hans, Alistair Burns, Phil Luthert, Nigel Cairns, and Raymond Levy. "The Lewy-Body Variant of Alzheimer's Disease." British Journal of Psychiatry 162, no. 3 (March 1993): 385–92. http://dx.doi.org/10.1192/bjp.162.3.385.

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At post-mortem, Lewy bodies (LBs) were found in the brainstem and neocortex of eight out of 65 patients who had been collected during a prospective long-term study on clinically diagnosed Alzheimer’s disease. All eight patients had accompanying Alzheimer pathology which was less severe than in a sample of eight age and sex-matched patients from the same study with neuropathologically verified Alzheimer's disease. Parkinsonian features were more common in patients with LBs. There were no particular differences in duration of illness, severity of cognitive impairment, presence of hallucinations, or fluctuations in the course of illness. Frontal cerebral atrophy was more marked in patients with LBs, as was the loss of neurons in the basal nucleus of Meynert and the substantia nigra. Cognitive performance correlated with the number of pigmented neurons in the substantia nigra. We conclude that the differential diagnosis of LB dementia should be considered in patients satisfying NINCDS-ADRDA criteria for Alzheimer-type dementia who show marked Parkinsonian features and a frontal accentuation of cerebral atrophy.

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16

Samborska, Julia, Paweł Więckowiak, Karina Stelmaszak, Katarzyna Stencel, Aleksandra Ogiegło-Kowalczyk, Monika Wojtasik, Michał Głodzik, Marek Miśkiewicz, Katarzyna Żak, and Martyna Łęcka. "The impact of the ketogenic diet on the health of patients with Alzheimer’s disease." Journal of Education, Health and Sport 52 (January 11, 2024): 98–110. http://dx.doi.org/10.12775/jehs.2024.52.007.

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INTRODUCTION: Diet is an integral element of every individual's health. Its impact on the functioning of the human body has fascinated scientists for years. One of the diets that alters the mechanism of the body's functioning is the ketogenic diet. The impact of the ketogenic diet on various disorders is still under investigation. It is known to have shown numerous benefits in reducing epileptic seizures, but its impact on other neurological disorders is less known. In this literature review, the efficacy of ketogenic therapies was assessed in Alzheimer's disease. AIM OF STUDY: Review of the current literature (since 2018) on the effects of implementing a ketogenic diet in patients with Alzheimer's disease. MATERIALS AND METHODS: The review was based on data gathered from the PubMed database using the keywords: 'ketogenic diet in Alzheimer’s disease,' 'ketogenic therapies Alzheimer disease,' and 'ketogenic diet in neurological disease’. SUMMARY: The ketogenic diet enhances the daily functioning of Alzheimer's patients. It significantly improves their cognitive functions, and changes in brain blood flow are visible in imaging studies. The ketogenic diet also positively modulates the gut microbiome in Alzheimer's patients. It represents a promising option in combating cognitive symptoms of Alzheimer's disease.

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17

Sianturi, Aditya Gloria Monalisa. "Stadium, Diagnosis, dan Tatalaksana Penyakit Alzheimer." Majalah Kesehatan Indonesia 2, no. 2 (October 25, 2021): 39–44. http://dx.doi.org/10.47679/makein.202132.

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Dementia is a general term for loss of memory that can occur along with behavioral or psychological symptoms in patients. The first cause of dementia patients is Alzheimer's disease. Alzheimer’s disease is a brain degenerative disease and the most common cause of dementia. In Alzheimer’s disease, there are three development stages, which is stage 1, stage 2, and stage 3 with different clinical symptoms at each stage. There are several clinical criteria for establishing a definitive diagnosis of Alzheimer’s disease and also support examinations have to be carried out. Until now, Alzheimer’s treatment has not been cured. Giving some pharmacotherapy only to reduce the progression of Alzheimer’s disease. Demensia merupakan hilangnya ingatan yang bisa timbul bersama dengan gejala gangguan perilaku maupun psikologis pada seseorang. Penyebab pertama penderita demensia adalah penyakit Alzheimer. Penyakit Alzheimer adalah penyakit degeneratif otak dan penyebab paling umum dari demensia. Pada penyakit Alzheimer terdapat beberapa stadium perkembangan penyakit Alzheimer yaitu stadium 1, stadium 2, dan stadium 3 dengan gejala klinik yang berbeda di setiap stadium. Terdapat beberapa kriteria klinis dalam penegakan diagnosis definitif penyakit Alzheimer serta harus dilakukan pemeriksaan penunjang. Pada tatalaksana penyakit Alzheimer hingga saat ini memang belum dapat disembuhkan, Pemberian obat-obatan hanya untuk mengurangi progresifitas penyakit Alzheimer.

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18

Koberskaya, N. N. "Alzheimer's disease." Neurology, Neuropsychiatry, Psychosomatics 11, no. 3S (June 24, 2019): 52–60. http://dx.doi.org/10.14412/2074-2711-2019-3s-52-60.

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Despite progress made in laboratory methods, genetic studies, and modern neuroimaging, the diagnosis of diseases that cause dementia makes difficulties. The review presents an update on the epidemiology, risk factors, pathogenesis, clinical presentation, diagnosis, and treatment of Alzheimer's disease (AD). It discusses the issues of symptomatic and pathogenetic treatments and combination therapy for AD. The efficacy of memantine (akatinol memantine) and the expediency of its use at different stages of the disease in patients with AD are noted. Non-pharmacological treatments for this disease, including physical activity and cognitive training, are considered.

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19

Lima de Araujo, Edivaldo. "Alzheimer's Disease and Frailty: A Complex Relationship." Open Access Journal of Frailty Science 2, no. 1 (2024): 1–2. http://dx.doi.org/10.23880/oajfs-16000102.

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The world’s population is ageing rapidly and in developing countries this is more evident. Cognitive decline and physical frailty are frequent situations in the ageing process, and so there is a progressive increase in neurodegenerative diseases, dementias, and new health conditions, such as frailty [1].

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20

Monisha, M., K. M. Harshitha, N. H. Dhanalakshmi, Kokatam Sai Prakash Reddy, C. R. Nagarathna, and M. Kusuma. "Early detection of Alzheimer’s: Modalities and Methods." March 2022 4, no. 1 (May 4, 2022): 69–79. http://dx.doi.org/10.36548/jaicn.2022.1.005.

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Alzheimer’s disease belongs to the group of neurodegenerative diseases and is considered as one of the most destructive and severe diseases of the human nervous system. There is presently no quick and cost-effective method for routinely screening individuals of age 65 and older for Alzheimer's disease, the most prevalent type of neurodegenerative dementia. Over 5.2 million Americans already suffer from this condition, with the number anticipated to rise to 7.7 million by 2030. This paper discusses how the use of Machine learning concepts has upgraded the detection of Alzheimer's disease in the early stage.

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21

Shua-Haim, Joshua R., and Joel S. Gross. "Alzheimer's Syndrome, Not Alzheimer's Disease." Journal of the American Geriatrics Society 44, no. 1 (January 1996): 96–97. http://dx.doi.org/10.1111/j.1532-5415.1996.tb05648.x.

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22

Hyman, Bradley T. "Alzheimer's disease or Alzheimer's diseases? clues from molecular epidemiology." Annals of Neurology 40, no. 2 (August 1996): 135–36. http://dx.doi.org/10.1002/ana.410400202.

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23

Kesken, Serdar. "Alzheimer's Disease." Journal of Tepecik Education and Research Hospital 5, no. 1 (1995): 11–20. http://dx.doi.org/10.5222/terh.1995.23722.

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24

Kicherova, O. A., and L. I. Reikhert. "Alzheimer's disease." Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova 118, no. 1 (2018): 77. http://dx.doi.org/10.17116/jnevro20181181177-81.

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25

Buckwalter, Kathleen Coen, Ivo L. Abraham, and Marcia M. Neundorfer. "Alzheimer's Disease." Nursing Clinics of North America 23, no. 1 (March 1988): 1–9. http://dx.doi.org/10.1016/s0029-6465(22)01361-5.

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26

Billig, Nathan. "Alzheimer's Disease." Nursing Clinics of North America 23, no. 1 (March 1988): 125–33. http://dx.doi.org/10.1016/s0029-6465(22)01369-x.

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27

Scheltens, Philip, Bart De Strooper, Miia Kivipelto, Henne Holstege, Gael Chételat, Charlotte E. Teunissen, Jeffrey Cummings, and Wiesje M. van der Flier. "Alzheimer's disease." Lancet 397, no. 10284 (April 2021): 1577–90. http://dx.doi.org/10.1016/s0140-6736(20)32205-4.

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28

Hanyu, Haruo. "Alzheimer's disease." Nippon Ronen Igakkai Zasshi. Japanese Journal of Geriatrics 50, no. 5 (2013): 622–24. http://dx.doi.org/10.3143/geriatrics.50.622.

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29

Cummings, Jeffrey L. "Alzheimer's Disease." New England Journal of Medicine 351, no. 1 (July 2004): 56–67. http://dx.doi.org/10.1056/nejmra040223.

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30

James, D. Geraint. "Alzheimer's disease." Journal of Medical Biography 18, no. 3 (August 2010): 132. http://dx.doi.org/10.1258/jmb.2009.009082.

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31

Querfurth, Henry W., and Frank M. LaFerla. "Alzheimer's Disease." New England Journal of Medicine 362, no. 4 (January 28, 2010): 329–44. http://dx.doi.org/10.1056/nejmra0909142.

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32

Gorelick, Philip B., and Fernando G. Bózzola. "Alzheimer's disease." Postgraduate Medicine 89, no. 4 (March 1991): 231–40. http://dx.doi.org/10.1080/00325481.1991.11700876.

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Skelton, William Paul, and Nadine Khouzam Skelton. "Alzheimer's disease." Postgraduate Medicine 90, no. 4 (September 15, 1991): 33–41. http://dx.doi.org/10.1080/00325481.1991.11701056.

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34

Walsh, Arthur C. "Alzheimer's Disease." Canadian Journal of Psychiatry 30, no. 3 (April 1985): 236. http://dx.doi.org/10.1177/070674378503000324.

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35

Leite, Cinthya, Terce Liana Mota de Menezes, Èrica Verônica de Vasconcelos Lyra, and Cláudia Marina Tavares de Araújo. "Alzheimer's disease." Arquivos de Neuro-Psiquiatria 71, no. 3 (March 2013): 201. http://dx.doi.org/10.1590/s0004-282x2013000300018.

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36

NAKAMURA, Shigenobu. "Alzheimer's disease." Japanese Journal of Medicine 28, no. 6 (1989): 793–95. http://dx.doi.org/10.2169/internalmedicine1962.28.793.

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37

Selkoe, D. J. "Alzheimer's Disease." Cold Spring Harbor Perspectives in Biology 3, no. 7 (May 16, 2011): a004457. http://dx.doi.org/10.1101/cshperspect.a004457.

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38

Hunter, R. Stephen. "Alzheimer's Disease." Science & Technology Libraries 6, no. 1-2 (October 4, 1985): 155–67. http://dx.doi.org/10.1300/j122v06n01_15.

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Gillen, Carol. "Alzheimer's Disease." Science & Technology Libraries 13, no. 2 (April 19, 1993): 37–55. http://dx.doi.org/10.1300/j122v13n02_03.

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40

Turner, R. "Alzheimer's Disease." Seminars in Neurology 26, no. 5 (November 2006): 499–506. http://dx.doi.org/10.1055/s-2006-951622.

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41

Enck, Robert E. "Alzheimer's disease." American Journal of Hospice and Palliative Medicine® 9, no. 5 (September 1992): 12–13. http://dx.doi.org/10.1177/104990919200900504.

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Features Submission, Haworth Continuing. "ALZHEIMER'S DISEASE." Journal of Nutrition For the Elderly 13, no. 3 (October 6, 1994): 75–82. http://dx.doi.org/10.1300/j052v13n03_06.

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Price, D. L., P. J. Whitehouse, and R. G. Struble. "Alzheimer's Disease." Annual Review of Medicine 36, no. 1 (February 1985): 349–56. http://dx.doi.org/10.1146/annurev.me.36.020185.002025.

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Yaari, Roy, and Jody Corey-Bloom. "Alzheimer's Disease." Seminars in Neurology 27, no. 1 (February 2007): 032–41. http://dx.doi.org/10.1055/s-2006-956753.

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Simpson, Steve, and Alistair Burns. "Alzheimer's disease." Current Opinion in Psychiatry 9, no. 1 (January 1996): 89–92. http://dx.doi.org/10.1097/00001504-199601000-00018.

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&NA;. "ALZHEIMER'S DISEASE." American Journal of Nursing 96, no. 11 (November 1996): 9. http://dx.doi.org/10.1097/00000446-199611000-00003.

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Oboudiyat, Carly, Hilary Glazer, Alon Seifan, Christine Greer, and Richard Isaacson. "Alzheimer's Disease." Seminars in Neurology 33, no. 04 (November 14, 2013): 313–29. http://dx.doi.org/10.1055/s-0033-1359319.

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Fox, Patrick J. "Alzheimer's Disease." American Journal of Alzheimer's Care and Related Disorders 1, no. 4 (October 1986): 18–24. http://dx.doi.org/10.1177/153331758600100408.

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Friedlander, Arthur H., Dean C. Norman, Michael E. Mahler, Keith M. Norman, and John A. Yagiela. "Alzheimer's disease." Journal of the American Dental Association 137, no. 9 (September 2006): 1240–51. http://dx.doi.org/10.14219/jada.archive.2006.0381.

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Haley, William E., and Marci I. Coleton. "Alzheimer's Disease:." Journal of Elder Abuse & Neglect 4, no. 4 (April 15, 1993): 71–86. http://dx.doi.org/10.1300/j084v04n04_07.

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