Deaths registered in 2019 in England and Wales due to dementia and Alzheimer's disease, by sex, age group, ethnicity, region and place of occurrence. Includes analysis of comorbidities.

BackgroundWith global aging, Alzheimer’s disease (AD) and other dementias have emerged as significant health threats to the older adults, garnering considerable attention due to their impact on public health. Despite the substantial burden of dementia in Asia, targeted research remains limited. This study aims to assess the current burden, future trends, risk factors, and inequalities in Asia.MethodThe GBD 2021 study was utilized to evaluate the numbers and age-standardized rates (ASRs) of prevalence, mortality, and disability-adjusted life-years (DALYs) of AD and other dementias from 1990 to 2021. Joinpoint regression analysis was performed to assess the trends during this period, while the Autoregressive Integrated Moving Average (ARIMA) model was employed to predict future trends. Additionally, the relationship between disease burden and sociodemographic index (SDI) was also analyzed.ResultsIn 2021, Asia experienced a 250.44% increase in prevalent cases, a 297.34% rise in mortality, and a 249.54% surge in DALYs for AD and other dementias compared to 1990. Meanwhile, the age-standardized prevalence rate, age-standardized mortality rate, and age-standardized DALY rate also exhibited varying degrees of rise from 1990 to 2021. Demographically, the disease burden was higher in women and those aged 65 and above. Regionally, the burden was highest in East Asia and relatively low in South and Central Asia. Nationally, China, India, Japan, and Indonesia reported the most cases. Over the next 15 years, the age-standardized prevalence rate in Asia is expected to peak in 2028 before declining, while the age-standardized mortality rate is anticipated to keep rising. An overall “V” shaped association was found between sociodemographic index (SDI) and the age-standardized DALY rate in Asia. Only smoking, high fasting plasma glucose (FPG), and high BMI were identified as causal risk factors within the GBD framework.ConclusionThe burden of AD and other dementias in Asia has significantly increased over the past three decades and is expected to persistently impact Asian populations, particularly in developing countries experiencing rapid demographic shifts. Women and the older adult should be a focus of attention. It is imperative to implement targeted prevention and intervention strategies, enhance chronic disease management, and control risk factors.

In the United States, around 39 percent of people with Alzheimer’s are 75 to 84 years old. Additionally, around 26 percent of those with Alzheimer’s are aged 65 to 74 years. Alzheimer’s disease is a form of dementia which impacts memory, behavior, and thinking and can lead to symptoms becoming so severe that those with the disease require support with basic daily tasks. Alzheimer’s remains a relevant problem around the world. Alzheimer’s disease deaths Alzheimer’s is currently the sixth leading cause of death in the United States, causing more deaths than diabetes and kidney disease. While advances in medicine and increased access to treatment and care have caused decreases in many major causes of death, deaths from Alzheimer’s have risen over the past couple of decades. For example, from 2000 to 2022, deaths from stroke in the U.S. declined by 1.4 percent, while deaths from Alzheimer’s increased 142 percent. Alzheimer’s disease worldwide Alzheimer’s is not only a problem in the United States but impacts every country around the globe. In 2018, there were an estimated 50 million people living with dementia worldwide. This figure is predicted to increase to some 152 million by the year 2050. Alzheimer’s does not only cause a significant amount of death but also has a significant economic impact. In 2018, cost estimates for Alzheimer’s care worldwide totaled around one trillion U.S. dollars, with this figure predicted to double by the year 2030.

BackgroundAccording to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is rising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data.ObjectiveTo solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer’s disease.MethodFor predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work.Results and conclusionsThe performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer’s disease, cognitively normal, non-Alzheimer’s dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work.

BackgroundAccording to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is rising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data.ObjectiveTo solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer’s disease.MethodFor predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work.Results and conclusionsThe performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer’s disease, cognitively normal, non-Alzheimer’s dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work.

Neuron Tracing Software Market Outlook

The global neuron tracing software market size was valued at approximately USD 250 million in 2023 and is projected to reach around USD 620 million by 2032, growing at a Compound Annual Growth Rate (CAGR) of 10.8% during the forecast period. The increasing prevalence of neurological disorders and the expanding field of neuroscience research are major growth factors propelling the market forward. Additionally, advancements in imaging technologies and the growing availability of large datasets are driving market expansion.

One of the primary growth factors for the neuron tracing software market is the rising incidence of neurological disorders globally. Conditions such as Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis are becoming more common, necessitating advanced diagnostic and research tools. Neuron tracing software aids in the accurate mapping of neural pathways, which is crucial for understanding the pathology of these disorders. This increased need for precise diagnostic tools is significantly contributing to market growth.

Another significant growth driver is the continuous advancement in imaging technologies, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). These technologies generate detailed images of the brain, which can be further analyzed using neuron tracing software. The integration of Artificial Intelligence (AI) and machine learning algorithms into these software solutions has enhanced their accuracy and speed, making them indispensable tools in both clinical and research settings. Furthermore, the increasing adoption of these advanced imaging techniques in various medical institutions is fueling the demand for neuron tracing software.

The availability of large datasets, generated from advanced imaging techniques and various research initiatives, is also driving the growth of the neuron tracing software market. Big data analytics allows for the comprehensive analysis of these datasets, providing deeper insights into neural structures and functions. The incorporation of cloud computing in neuron tracing software solutions has made it easier to store and analyze large volumes of data efficiently. This accessibility to vast amounts of data is fostering research and development activities, further propelling market growth.

From a regional perspective, North America is expected to hold the largest market share due to the presence of well-established healthcare infrastructure and significant investment in neuroscience research. Europe follows closely, driven by increasing government initiatives and funding for neurological research. The Asia Pacific region is anticipated to witness the fastest growth rate due to the rising healthcare expenditure and growing awareness of neurological disorders. Latin America and the Middle East & Africa are also expected to register substantial growth, albeit at a slower pace, as these regions are gradually adopting advanced medical technologies.

Component Analysis

The neuron tracing software market is segmented into software and services. The software segment dominates the market due to the increasing demand for advanced and sophisticated tools for neural pathway mapping. The software solutions available in the market offer a variety of features such as 3D visualization, automated tracing, and data integration capabilities. The continuous innovation and development in software solutions to enhance accuracy and efficiency are driving the segment’s growth. Additionally, the integration of AI and machine learning algorithms into software solutions has significantly improved their performance, making them indispensable in both clinical and research settings.

On the other hand, the services segment, which includes installation, maintenance, and training, is also witnessing significant growth. The complexity of neuron tracing software necessitates specialized training and support services to ensure optimal usage. Many software providers offer comprehensive service packages that include regular updates, technical support, and user training. The increasing adoption of these services is driving the overall growth of the segment. Furthermore, the trend of outsourcing IT services in the healthcare sector is propelling the demand for professional services related to neuron tracing software.

The integration of cloud computing in neuron tracing software solutions is also contributing to the growth of the services segment. Cloud-based solutions require ongoing service agreements for data storage,

Examine predictors of clinical and resource utilization outcomes associated with Alzheimer’s disease and related dementias (ADRD), stratified by patient severity profiles. Cross-sectional study of adults (30+ year old) with ADRD discharged from US hospitals to home health care (HHC) and identified from the 2010–2015 Nationwide Readmissions Database (NRD) using ICD 9th-10th codes. Outcomes of interest included 30-day hospital readmissions, in-hospital mortality, and hospital length of stay (LOS). Covariates consisted of sociodemographic and clinical variables. Multiple logistic regressions (for readmissions and mortality) and generalized linear regressions (for LOS) were used to examine associations between outcomes and study covariates, stratified by patient severity profiles. Of 164,598 ADRD patients, 3,848 were mild, 68803 were moderate, 72428 were severe, and 19,519 were extreme. The 30-day readmission rate was 3.2%, death rate was 14.5%, and LOS was 3.0 days, (95%, CI: 15.0, 17.0) to 5.0 days, (95%, CI: 18.0, 19.0), all with a p-value

BackgroundPsoriasis is an immune-related skin disease notable for its chronic inflammation of the entire system. Alzheimer’s disease (AD) is more prevalent in psoriasis than in the general population. Immune-mediated pathophysiologic processes may link these two diseases, but the mechanism is still unclear. This article aimed to explore potential molecular mechanisms in psoriasis and AD.MethodsGene expression profiling data of psoriasis and AD were acquired in the Gene Expression Omnibus (GEO) database. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were first applied in two datasets. Differentially expressed genes (DEGs) of two diseases were identified, and common DEGs were selected. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to explore common biological pathways. Signature transcription factors (STFs) were identified and their diagnostic values was calculated by receiver operating characteristic (ROC) curve analysis in the exploration cohort and verified in the validation cohort. The expression levels of STFs were further investigated in the validation cohort and the GTEx Portal Database. Additionally, four kinds of interaction analysis were performed: correlation analysis among STFs, gene-gene, chemical-protein, and protein-ligand interaction analyses. In the end, we predicted the transcription factor that potentially regulates STFs.ResultsBiosynthesis and metabolic pathways were enriched in GSEA analysis. In ssGSEA analysis, most immunoreaction gene lists exhibited differential enrichment in psoriasis cases, whereas three receptor-related gene lists did in AD. The KEGG analysis of common DEGs redetermined inflammatory and metabolic pathways essential in both diseases. 5 STFs (PPARG, ZFPM2, ZNF415, HLX, and ANHX) were screened from common DEGs. The ROC analysis indicated that all STFs have diagnostic values in two diseases, especially ZFPM2. The correlation analysis, gene-gene, chemical-protein, and protein-ligand interaction analyses suggested that STFs interplay and involve inflammation and aberrant metabolism. Eventually, ZNF384 was the predicted transcription factor regulating PPARG, ZNF415, HLX, and ANHX.ConclusionsThe STFs (PPARG, ZFPM2, ZNF415, HLX, and ANHX) may increase the morbidity rate of AD in psoriasis by initiating a positive feedback loop of excessive inflammation and metabolic disorders. ZNF384 is a potential therapeutic target for psoriasis and AD by regulating PPARG, ZNF415, HLX, and ANHX.

Demographic data for AD patients and older controls.

All-age numbers and ASR per 100,000 of incidence, prevalence, deaths, DALYs, YLDs and YLLs for ADD and total percentage change by sex globally, 1990-2021.

Characteristics statistics of Alzheimer’s disease and normal subjects.

Selected baseline characteristics among patients with Alzheimer’s disease by phenotype algorithm in Optum® EHR.

Objective: Previous studies have shown that gabapentin or pregabalin use is associated with cognitive decline. Herein, we aimed to evaluate the association between gabapentin or pregabalin use and the risk of dementia.Methods: In this retrospective, population-based matched cohort study, all research data were collected from the 2005 Longitudinal Health Insurance Database, which contains data of 2 million people randomly selected from the National Health Insurance Research Database of Taiwan in 2005. The study extracted data from 1 January 2000, to 31 December 2017. Adult patients taking gabapentin or pregabalin were included in the exposure group, and patients not using gabapentin or pregabalin matched to exposure subjects in a 1:5 ratio by propensity scores composed of age, sex and index date were included in the non-exposure group.Results: A total of 206,802 patients were enrolled in the study. Of them, 34,467 gabapentin- or pregabalin-exposure and 172,335 non-exposure patients were used for analysis. The mean follow-up day (±standard deviation) after the index date was 1724.76 (±1282.32) and 1881.45 (±1303.69) in the exposure and non-exposure groups, respectively; the incidence rates of dementia were 980.60 and 605.48 per 100,000 person-years, respectively. The multivariate-adjusted hazard ratio of risk of dementia for gabapentin or pregabalin exposure versus the matched non-exposed group was 1.45 (95% confidence interval [CI], 1.36–1.55). The risk of dementia increased with higher cumulative defined daily doses during the follow-up period. Moreover, the stratification analysis revealed that the risk of dementia associated with gabapentin or pregabalin exposure was significant in all age subgroups; however, it was higher in younger patients (age