Clinical Decision Support for Alzheimer's: Challenges in Generalizable Data-Driven Approach.

Report on the Barbados Workshop

Deep learning (DL) has become a powerful tool for the recognition and classification of biological sequences. However, conventional single-architecture models often struggle with suboptimal predictive performance and high computational costs. To address these challenges, we present EnsembleDL-Lipo, an innovative ensemble deep learning framework that combines Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to enhance the identification of lipocalin sequences. Lipocalins are multifunctional extracellular proteins involved in various diseases and stress responses, and their low sequence similarity and occurrence in the 'twilight zone' of sequence alignment present significant hurdles for accurate classification. These challenges necessitate efficient computational methods to complement traditional, labor-intensive experimental approaches. EnsembleDL-Lipo overcomes these issues by leveraging a set of PSSM-based features to train a large ensemble of deep learning models. The framework integrates multiple feature representations derived from position-specific scoring matrices (PSSMs), optimizing classification performance across diverse sequence patterns. The model achieved superior results on the training dataset, with an accuracy (ACC) of 97.65%, recall of 97.10%, Matthews correlation coefficient (MCC) of 0.95, and area under the curve (AUC) of 0.99. Validation on an independent test set further confirmed the robustness of the model, yielding an ACC of 95.79%, recall of 90.48%, MCC of 0.92, and AUC of 0.97. These results demonstrate that EnsembleDL-Lipo is a highly effective and computationally efficient tool for lipocalin sequence identification, significantly outperforming existing methods and offering strong potential for applications in biomarker discovery.

Proteomics has revealed itself as a powerful tool in the identification and determination of proteins and their biological significance. More recently, several groups have taken advantage of the high-throughput nature of proteomics in order to gain a more in-depth understanding of the human brain. In turn, this information has provided researchers with invaluable insight into the potential pathways and mechanisms involved in the pathogenesis of several neurodegenerative disorders, e.g., Alzheimer and Parkinson disease. Furthermore, these findings likely will improve methods to diagnose disease and monitor disease progression as well as generate novel targets for therapeutic intervention. Despite these advances, comprehensive understanding of the human brain proteome remains challenging, and requires development of improved sample enrichment, better instrumentation, and innovative analytic techniques. In this review, we will focus on the most recent progress related to identification of proteins in the human brain under normal as well as pathological conditions, mainly Alzheimer and Parkinson disease, their potential application in biomarker discovery, and discuss current advances in protein identification aimed at providing a more comprehensive understanding of the brain.

3D convolutional neural networks with hybrid attention mechanism for early diagnosis of Alzheimer’s disease

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.

Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems

MRomicsNet: A morphomics-radiomics-driven adaptive topological model for AD diagnosis on clinically routine T1-weighted images.

Over large areas, land cover classification has conventionally been undertaken using satellite time series. Typically temporal metric percentiles derived from single pixel location time series have been used to take advantage of spectral differences among land cover classes over time and to minimize the impact of missing observations. Deep convolutional neural networks (CNNs) have demonstrated potential for land cover classification of single date images. However, over large areas and using time series their application is complicated because they are sensitive to missing observations and they may misclassify small and spatially fragmented surface features due to their spatial patch-based implementation. This study demonstrates, for the first time, a one-dimensional (1D) CNN single pixel time series land classification approach that uses temporal percentile metrics and that does not have these issues. This is demonstrated for all the Conterminous United States (CONUS) considering two different 1D CNN structures with 5 and 8 layers, respectively. CONUS 30 m land cover classifications were derived using all the available Landsat-5 and -7 imagery over a seven-month growing season in 2011 with 3.3 million 30 m land cover class labelled samples extracted from the contemporaneous CONUS National Land Cover Database (NLCD) 16 class land cover product. The 1D CNNs and, a conventional random forest model, were trained using 10%, 50% and 90% samples, and the classification accuracies were evaluated with an independent 10% proportion. Temporal metrics were classified using 5, 7 and 9 percentiles for each of five Landsat reflective wavelength bands and their eight band ratios. The CONUS and detailed 150 × 150 km classification results demonstrate that the approach is effective at scale and locally. The 1D CNN classification land cover class boundaries were preserved for small axis dimension features, such as roads and rivers, with no stripes or anomalous spatial patterns. The 8-layer 1D CNN provided the highest overall classification accuracies and both the 5-layer and 8-layer 1D CNN architectures provided higher accuracies than the random forest by 1.9% - 2.8% which as all the accuracies were > 83% is a meaningful increase. The CONUS overall classification accuracies increased marginally with the number of percentiles (86.21%, 86.40%, and 86.43% for 5, 7 and 9 percentiles, respectively) using the 8-layer 1D-CNN. Class specific producer and user accuracies were quantified, with lower accuracies for the developed land, crop and pasture/hay classes, but no systematic pattern among classes with respect to the number of temporal percentiles used. Application of the trained model to a different year of CONUS Landsat ARD showed moderately decreased accuracy (80.79% for 7 percentiles) that we illustrate is likely due to different intra-annual surface variations between years. These encouraging results are discussed with recommended research for deep learning using temporal metric percentiles.

Membrane bound vesicles, including microvesicles and exosomes, are secreted by both normal and cancerous cells into the extracellular space and in blood circulation. These circulating extracellular vesicles (cirEVs) and exosomes in particular are recognized as a potential source of disease biomarkers. However, to exploit the use of circulatory exosomes as a biomarker, a rapid, high-throughput and reproducible method is required for their isolation and molecular analysis. We have developed a simple, low cost microfluidic-based platform to isolate cirEVs enriched in exosomes directly from blood serum allowing simultaneous capture and quantification of exosomes in a single device. To capture specific exosomes, we employed "ExoChip", a microfluidic device fabricated in polydimethylsiloxane (PDMS) and functionalized with antibodies against CD63, an antigen commonly overexpressed in exosomes. Subsequent staining with a fluorescent carbocyanine dye (DiO) that specifically labels the exosomes, we quantitated exosomes using a standard plate-reader. Ten independent ExoChip experiments performed using serum obtained from five pancreatic cancer patients and five healthy individuals revealed a statistically significant increase (2.34 ± 0.31 fold, p < 0.001) in exosomes captured in cancer patients when compared to healthy individuals. Exosomal origins of ExoChip immobilized vesicles were further confirmed using immuno-electron-microscopy and Western blotting. In addition, we demonstrate the ability of ExoChip to recover exosomes with intact RNA enabling profiling of exosomal-microRNAs through openarray analysis, which has potential applications in biomarker discovery. Based on our findings, ExoChip is a well suited platform to be used as an exosome-based diagnostic and research tool for molecular screening of human cancers.

Immunoproteomics is the branch of proteomics with an emphasis on the study of functional peptides and proteins related to the immune system. Combining proteomics techniques with immunological research aims to uncover the complex interactions of proteins involved in immune responses. This review discusses the methods, applications, and recent advancements in immunoproteomics, highlighting its critical role in understanding immune responses, discovering biomarkers, and developing vaccines and therapeutics. This study offers a comprehensive exploration of the methodologies, applications, and advancements within immunoproteomics. Techniques such as mass spectrometry, antibody-based assays, and computational analysis are pivotal in unraveling the complexities of the immune system at the protein level. Immunoproteomics finds diverse applications in biomarker discovery, vaccine development, autoimmune disease research, infectious disease diagnostics, and cancer immunotherapy. Challenges, including data integration, sample heterogeneity, and biomarker validation, persist, necessitating innovative approaches and interdisciplinary collaborations. In the future, immunoproteomics will likely play a major role in expanding our knowledge of immune-related diseases and accelerating the creation of targeted and precise immunotherapies.

In the era of quantitative proteomics, where mass spectrometry plays a pivotal role, in particular associated with the use of data-independent acquisition, it is time to perform an overview of this growing field with special focus on one of the most promising approaches: SWATH-MS, and to present future perspectives for its application as a translational tool. Therefore, a summary of this technique is presented focusing on two key relevant concepts associated with its application in biomarker discovery: the protein library and the individual digital maps concepts. It is also the purpose of this review to document the likely impact of SWATH-MS in both fundamental and translational research including biomarker identification and creation of diagnostic tools. To that end, the two concepts referred above were integrated with ongoing technical developments. Finally, some of the current restrictions for the implementation of SWATH-MS on a large scale are identified, and potential solutions presented, namely protocol standardization combined with the use of the proper standards.

Mass spectrometry (MS)-based blood proteomics is a crucial research focus in identifying disease biomarkers. Blood serum or plasma is the most commonly used sample for such analysis; however, it presents challenges due to the complexity and dynamic range of protein abundance. Despite these difficulties, the development of high-resolution MS instruments has made comprehensive investigation of blood proteomics possible. The evolution of time-of-flight (TOF) or Orbitrap MS instruments has played a significant role in the field of blood proteomics. These instruments are now among the most prominent techniques for blood proteomics due to their sensitivity, selectivity, fast response, and stability. For optimal results, it is necessary to eliminate high-abundance proteins from the blood sample, to maximize the depth coverage of the blood proteomics analysis. This can be achieved through various methods, including commercial kits, chemically synthesized materials, and MS technologies. This paper reviews recent advancements in MS technology and its remarkable applications in biomarker discovery, particularly in the areas of cancer and COVID-19 studies.

Gliomas represent the most frequent primary brain tumors in adults and are marked by considerable molecular and clinical heterogeneity. The 2021 WHO classification of central nervous system tumors identifies three major adult-type diffuse glioma subtypes: astrocytoma IDH-mutant, oligodendroglioma IDHmutant with 1p/19q-codeletion, and glioblastoma IDH-wildtype.1 Despite ongoing advances, treatment resistance and poor prognosis remain major challenges, largely due to intratumoral heterogeneity and the complexity of the tumor microenvironment.2 A promising yet underexplored avenue for patient-centered glioma profiling is the tumor secretome, which includes soluble factors, extracellular vesicles, and metabolites released by both neoplastic and stromal cells.3 Existing studies rely on in vitro models that fail to recapitulate the complexity of tumor-host interactions. To bridge this gap, we established an innovative, standardized ex vivo protocol for secretome isolation directly from glioma tissue. The protocol preserves microenvironmental cues from the operating room to laboratory analysis, ensuring spontaneous secretome release under controlled conditions. Feasibility and performance were assessed in a preliminary cohort of seven adult diffuse glioma patients. Transmission electron microscopy confirmed tissue structural integrity. Proteomic profiling identified 84 cancer-associated proteins within the secretome. Additionally, metabolomic analysis revealed a distinctive signature of small molecules. Notably, glioblastoma IDH-wildtype samples exhibited increased lactate levels, consistent with hypoxia-driven metabolism, and decreased N-acetylaspartate, indicating neuronal loss. Overall, this ex vivo secretome protocol offers a robust and physiologically relevant platform for glioma characterization, with potential applications in biomarker discovery, patient stratification, and the development of personalized therapeutic strategies.

Membrane bound extracellular vesicles, including microvesicles and exosomes, are secreted by both normal and cancerous cells into the extracellular space and in blood circulation. These circulating extracellular vesicles (cirEVs) and exosomes in particular, are recognized as a potential source of disease biomarkers and also carry signature molecules for early stage cancer detection. However, to exploit the use of circulatory exosomes as a new diagnostic tool, a rapid, high-throughput and reproducible method is required for their isolation and to allow their molecular analysis. We have developed a simple and a low cost microfluidic-based platform to analyze cirEVs enriched in exosomes directly from blood serum in a single step allowing simultaneous capture and quantification of exosomes. To capture exosomes with specificity, we employed a microfluidic device “ExoChip” functionalized with antibodies against exosomes to capture cirEVs. Subsequent staining with a fluorescent dye that specifically labels the exosomes, we quantitated exosomes using a standard plate-reader. Ten independent ExoChip experiments performed using serum obtained from five pancreatic cancer patients and five healthy individuals revealed a statistically significant increase (2.34±0.31 fold, p &amp;lt;0.001) in exosomes captured in cancer patients when compared to healthy individuals. Exosomal origin of ExoChip immobilized vesicles were further confirmed using immuno-electron-microscopy and Western blotting. In addition, we demonstrate the ability of ExoChip to recover exosomes with intact RNA enabling profiling of exosomal-microRNAs through openarray analysis, which has potential applications in biomarker discovery. Based on our findings, ExoChip is a well suited platform to be used as an exosome-based diagnostic and research tool for molecular screening of human cancers. Citation Format: Shailender S. Kanwar, Christopher J. Dunlay, Diane M. Simeone, Sunitha Nagrath. Simultaneous isolation and quantification of circulating exosomes for cancer biomarker discovery. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5591. doi:10.1158/1538-7445.AM2014-5591