scRNA seq on detecting early Alzheimer's disease (AD).

 

Single-cell RNA sequencing of blood cells shows promise in detecting early Alzheimer's disease (AD). Several studies have demonstrated that this technique can identify specific cellular and molecular changes associated with AD:

1. A study using single-cell RNA sequencing on peripheral blood mononuclear cells (PBMCs) from AD patients and controls revealed significant alterations in immune cell subsets, particularly a decrease in B cells that correlated with disease progression[1].

2. Another study profiling 36,849 PBMCs from AD patients with amyloid-positive status identified changes in cell subset proportions and gene expression patterns, as well as alterations in T and B cell receptor diversity[5].

3. Researchers found 31 cell type-specific key genes, including human leukocyte antigen genes, and multiple immune-related pathways that were altered in AD patients' blood cells[5].

4. Single-cell sequencing has uncovered B cell-related molecular biomarkers in PBMCs that could potentially be used for early AD detection[1].

5. The technique has also revealed changes in adaptive immune responses, particularly those mediated by T cells, which may play a role in AD pathogenesis[5].

While these findings are promising, it's important to note that further research and validation are needed before single-cell sequencing of blood cells can be used as a reliable early detection method for Alzheimer's disease.

Citations:

[1] https://www.nature.com/articles/s12276-021-00714-8

[2] https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.645666/full

[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC9955959/

[4] https://www.nature.com/articles/s41467-020-19737-2

[5] https://pubmed.ncbi.nlm.nih.gov/34447367/

[6] https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.060629

[7] https://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-022-00517-z

[8] https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.13691

Yes, there are relevant single-cell datasets available for early Alzheimer's disease (AD) detection. Several studies and databases have been developed to facilitate research in this area:

1. ssREAD (single-cell and spatial RNA-seq database for Alzheimer's disease) is a comprehensive repository that includes:

   - 1,053 samples from 67 AD-related scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells

   - 381 spatial transcriptomics datasets from 18 human and mouse brain studies

   - Samples annotated with details such as species, gender, brain region, disease/control status, age, and AD Braak stages[2][3]

2. A study profiling 36,849 peripheral blood mononuclear cells (PBMCs) from AD patients with amyloid-positive status and normal controls using single-cell transcriptome and immune repertoire sequencing[5]

3. A large-scale single-cell transcriptomic atlas covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease, examining six different brain regions[6]

4. A dataset (GEO GSE157827) composed of single-nucleus RNA-sequencing of prefrontal cortex from AD patients and matched healthy controls, including 169,496 nuclei from 12 AD patients and 9 neurological control subjects[4]

These datasets provide valuable resources for researchers to investigate transcriptomic alterations in AD compared to controls at various resolutions: sub-cellular, cellular, and spatial levels. They enable the exploration of early molecular changes associated with AD, potentially leading to the development of early detection methods.

Citations:

[1] https://elifesciences.org/articles/90214

[2] https://www.biorxiv.org/content/10.1101/2023.09.08.556944v2

[3] https://www.nature.com/articles/s41467-024-49133-z

[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC9955959/

[5] https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.645666/full

[6] https://www.nature.com/articles/s41586-024-07606-7

[7] https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2023.1157792/full

[8] https://www.nature.com/articles/s12276-021-00714-8

bytedance speech to text

 

https://github.com/bytedance/neurst

https://github.com/llmsystem/

generative AI teaching collections

https://llmsystem.github.io/llmsystem2025spring/docs/Syllabus  

https://www.cmu.edu/online/gai-llm/

https://github.com/llmsystem

multivew cost funciton

 Multi-view cost functions are used in multi-view stereo (MVS) and multi-view learning algorithms to aggregate information from multiple views or data sources. The cost function typically aims to optimize the matching or reconstruction process across different views.

Key Components of Multi-view Cost Functions

1. Photo-consistency: This measures the similarity of corresponding pixels or features across different views
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2. Spatial regularization: This enforces smoothness and coherence in the reconstructed 3D geometry or learned representations
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3. View aggregation: Costs from individual views are combined, often using weighted sums or other fusion strategies
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Examples of Multi-view Cost Functions

1. MVS Cost Aggregation:
   $S(p,j)={\sum }_i{w}_i(p)\cdot S_i(p,j)$
   Where $S(p,j)$ is the aggregated cost for pixel $p$ at depth hypothesis $j$, $w_i(p)$ is the weight for view $i$, and $S_i(p,j)$ is the cost from view $i$
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2. Multi-view Tracking Cost:
   $c_{i_1{i}_2\mathrm{.}\mathrm{.}\mathrm{.}{i}_N}=-\ln \frac{p(Z_{i_1{i}_2\mathrm{.}\mathrm{.}\mathrm{.}{i}_N}\mathrm{\mid }a)}{p(Z_{i_1{i}_2\mathrm{.}\mathrm{.}\mathrm{.}{i}_N})}$
   This cost function compares the likelihood of measurements belonging to an object versus being false positives
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3. Multi-view Learning Reconstruction Error:
   ${\sum }_{i=1}^n\mathrm{\mid }\mathrm{\mid }{x}_i-g(v_i)\mathrm{\mid }{\mathrm{\mid }}_F^2+{\sum }_{i=1}^n\mathrm{\mid }\mathrm{\mid }{v}_i-f(x_i)\mathrm{\mid }{\mathrm{\mid }}_F^2$
   Used in auto-encoders to minimize reconstruction error across multiple views
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Multi-view cost functions often incorporate terms for data fidelity, regularization, and view consistency. They are designed to leverage complementary information from different views while handling challenges such as occlusions and varying viewpoints.