J Proteome Res. 2025 Jan 9. doi: 10.1021/acs.jproteome.4c00757. Online ahead of print.

ABSTRACT

Proteomics has become a powerful approach for the identification and characterization of type III effectors (T3Es). Members of the Ralstonia solanacearum species complex (RSSC) deploy T3Es to manipulate host cells and to promote root infection of, among others, a wide range of solanaceous plants such as tomato, potato, and tobacco. Here, we used TurboID-mediated proximity labeling (PL) in tomato hairy root cultures to explore the proxeomes of the core RSSC T3Es RipU, RipD, and RipB. The RipU proxeome was enriched for multiple protein kinases, suggesting a potential impact on the two branches of the plant immune surveillance system, being the membrane-localized PAMP-triggered immunity (PTI) and the RIN4-dependent effector-triggered immunity (ETI) complexes. In agreement, a transcriptomics analysis in tomato revealed the potential involvement of RipU in modulating reactive oxygen species (ROS) signaling. The proxeome of RipB was putatively enriched for mitochondrial and chloroplast proteins and that of RipD for proteins potentially involved in the endomembrane system. Together, our results demonstrate that TurboID-PL in tomato hairy roots represents a promising tool to study Ralstonia T3E targets and functioning and that it can unravel potential host processes that can be hijacked by the bacterial pathogen.

PMID:39786355 | DOI:10.1021/acs.jproteome.4c00757

Alzheimers Dement. 2024 Dec;20 Suppl 7:e091942. doi: 10.1002/alz.091942.

ABSTRACT

BACKGROUND: The Coaching for Cognition in Alzheimer's (COCOA) Trial was a prospective RCT testing a remotely coached multimodal lifestyle intervention for participants early on the Alzheimer's disease spectrum. Intervention focused on diet, exercise, cognitive training, sleep, stress, and social engagement. Enrollment criteria targeted individuals with cognitive decline who were able to engage remotely with a professional coach. COCOA demonstrated cognitive and functional benefits. Dense omics data were collected on 53 individuals (≥ 58 years).

METHODS: We sought to identify blood analytes that mediated the effects of specific elements of the multimodal intervention on specific outcomes. Outcomes were assessed with the MCI Screen (MCIS) and the Functional Assessment Staging Tool (FAST). We combined these and other measures with proteomics and metabolomics data. We analyzed the resulting dataset of over 300,000 distinct molecular data points-reflecting over 1400 measures- assayed over a period of two years. We used MEGENA to hierarchically multiscale cluster analytes based on correlated responses and identified individual metabolites and functional clusters associated with each intervention and outcome. We analyzed individual time courses of key analyte mediators to illustrate personalized effects of interventions and individualized functional and cognitive outcomes.

RESULTS: Distinct sets of correlated serum analytes ("communities") convey effects to functional (FAST) outcome and to cognitive (MCIS) outcome. Distinct communities respond to different modalities of intervention. Participants followed different aspects of the multimodal recommendations to different extents, and the analytes in their blood also responded idiosyncratically; analyte trajectories in different individuals show distinct dynamics. We made personalized predictions of future inflections in outcome based on observed changes in key serum mediators. We validated results with data from the Precision Recommendations for Environmental Variables, Exercise, Nutrition and Training Interventions to Optimize Neurocognition (PREVENTION) Trial.

CONCLUSIONS: Lifestyle interventions have profound effects on blood metabolites (Figure 1). These in turn convey subtler specific effects to cognition and broad-based effects to function. Pathways that ameliorate the impact of AD via lifestyle interventions in some individuals include nitrogen subsystems, kidney function, and mitochondrial metabolism. These highlight the importance of clinical attention to overall health spanning multiple organ systems in individuals across the Alzheimer's disease spectrum.

PMID:39784630 | DOI:10.1002/alz.091942

Alzheimers Dement. 2024 Dec;20 Suppl 7:e092449. doi: 10.1002/alz.092449.

ABSTRACT

BACKGROUND: Medical management and lifestyle are potentially crucial interventions for Alzheimer's disease (AD). In this study, we present the relationship between adherence of a personalized multi-modal intervention for AD on change in cerebral blood flow (CBF) after 12-months.

METHODS: The PREVENTION study is an ongoing randomized clinical trial (McEwen, 2001). Thirty-three participants with biomarker evidence of amyloidosis had completed the study at the time of the analysis (Table 1). While both arms received personalized multi-modal lifestyle recommendations and four medical visits, the active arm also received dietary counseling, group physical and cognitive exercise, health coaching, and nutritional supplements free of charge. We examined the effects of the 1) the intervention and 2) adherence on CBF. We hypothesized that 1) the active arm and 2) higher intervention adherence would have improved CBF in regions related to level of physical activity (Kleinloog, 2019; Chapman, 2013) and those pertinent to AD. CBF was assessed using arterial spin labeling (ASL). Adherence was measured using the clinician rating scale (CRS), which uses a scale of 1-7 (Kemp, 1998). Participants were divided into two groups based on a cutoff of 5 (passive acceptance). One participant was excluded from this analysis due to missing CRS data. Effects were assessed using a two-tailed t-test.

RESULTS: Treatment arms did not differ in any demographic measures at baseline or CRS. Preliminary findings indicate that regional blood flow declined over one year across the whole sample (Table 2). However, individuals with higher adherence experienced increased blood flow in the fusiform gyrus and less blood flow reduction, compared to those with lower adherence, in the anterior cingulate and hippocampus. Findings were borderline significant in the fusiform and anterior cingulate, but not the hippocampus.

CONCLUSIONS: In this small sample, we found evidence that higher adherence increased or attenuated decline in CBF in regions impacted by physical activity, one modality of the PREVENTION intervention. We did not see an effect in the hippocampus, possibly due to small sample size. We did not find an effect of treatment arm, potentially because both receive recommendations and medical management, and did not differ in adherence.

PMID:39784611 | DOI:10.1002/alz.092449

Alzheimers Dement. 2024 Dec;20 Suppl 2:e092688. doi: 10.1002/alz.092688.

ABSTRACT

BACKGROUND: Faced with a rapidly aging population and the rising prevalence of Alzheimer's disease (AD) and related dementias, the field needs to urgently consider screening tools that utilize widely accessible data modalities. We have previously shown that lower-cost data, operationalized as data modalities accessible at primary care visits, can indeed accurately predict AD clinical diagnosis and that clustering these data can provide useful information. Here, we apply a similar approach to predicting histopathological status.

METHOD: We first applied our previously-developed feature extraction method based on a supervised encoder (SE) to transform potentially noisy input features while maintaining or amplifying relevant information. We next performed classification and clustering to stratify subjects by their neuropathology. Here, we compared two traditional classification methods with a novel Bayesian clustering-classification algorithm called an Infinite Mixture Classifier (IMC). We identified distinct trajectories of subjects based upon changes in cluster assignment over time. Data for this study come from the National Alzheimer's Coordinating Center, funded by NIA/NIH Grant U24 AG072122 and contributed to by NIA-funded ADRCs.

RESULT: We found that relatively high classification accuracy of neuropathologic lesions was possible using widely accessible, lower cost clinical data. In addition, the supervised clusters, derived from using the SE's latent features and from the IMC, held meaningful clinical diagnostic information that differentiates subjects along the clinical and pathologic continuum. When clusters were derived using longitudinal clinical data, we further observed distinct trajectories of subjects across time as their cluster assignments changed. These trajectory subgroups have significantly different risk of showcasing each type of neuropathologic lesion obtained from postmortem neuropathology.

CONCLUSION: Our framework benefits from the combined strengths of clustering and classification methods while avoiding drawbacks of unsupervised methods. By using lower cost features, which could be obtained at Medicare annual wellness visits, this work is broadly generalizable and has direct implications for screening of neuropathologic lesions of AD and related dementias for the public. As blood biomarkers become more accessible, our framework can be easily extended to include additional data to improve screening for neuropathology using widely accessible data.

PMID:39784083 | DOI:10.1002/alz.092688

Alzheimers Dement. 2024 Dec;20 Suppl 6:e086778. doi: 10.1002/alz.086778.

ABSTRACT

BACKGROUND: Despite increasing knowledge of the etiology of neurodegenerative diseases, translation of these benefits into therapeutic advances for Alzheimer's Disease and related diseases (ADRD) has been slow. Drug repurposing is a promising strategy for identifying new uses for approved drugs beyond their initial indications. We developed a high-throughput drug screening platform aimed at identifying drugs capable of reducing proteotoxicity in vivo (Aß toxicity in Caenorhabditis elegans) AND inhibiting microglial inflammation (TNF-alpha IL-6), both implicated in driving AD(figure attached with sample of results in C. elegans). These screens led us to prioritize 50 potentially protective FDA-approved drugs. We propose to test our screening results in humans using administrative claims data collected from the Centers for Medicare and Medicaid Services METHOD: This is an observational retrospective pharmaco-epidemiological longitudinal cohort study. The cohort is a random sample of 1,000,000 beneficiaries, aged 65-75 years, followed for 10 consecutive years, requested from CMS. Files include MedPar, Outpatient, Carrier, Hospice to maximize inclusion of AD beneficiaries according to Bynum algorithm, and Part D event for drug prescription details. We will use Cox regression, to compute Hazard Ratios and associated 95% confidence intervals, of the association between drug exposure status and the risk of ADRD. We will examine potential confounding by indication, drug target, and competing risks.

RESULT: 1/We propose to assess if drugs which reduce Ab toxicity in a C. elegans model of AD AND reduce microglial inflammation reduce the risk of developing ADRD in humans, using Medicare claims. 2/We propose to assess if drugs which reduce inflammation, reduce the risk of developing ADRD in humans, using the Medicare claims.

CONCLUSION: The end goal of the study is to identify drugs to be repurposed to treat ADRD and to accumulate strong epidemiological evidence in addition to the existing evidence from the model organism C. elegans and cell culture studies.

PMID:39782522 | DOI:10.1002/alz.086778

Alzheimers Dement. 2024 Dec;20 Suppl 6:e087380. doi: 10.1002/alz.087380.

ABSTRACT

BACKGROUND: Global epidemiological studies involving over nine million participants have shown a 35% lower incidence of Alzheimer's Disease (AD) in older cancer survivors compared to those without a history of cancer. This inverse relationship, consistent across recent studies with methodological controls, suggests that cancer itself, rather than cancer treatments, may offer protective factors against AD. This insight opens avenues for novel therapeutic strategies targeting early AD by harnessing cancer-associated protective factors.

METHODS: To investigate the potential protective effect of cancer against Alzheimer's Disease (AD), we developed "cancer-in-AD" mouse models. These models involved injecting a small number of breast cancer cells into young AD-model mice (5xFAD) and monitoring amyloid plaque progression. Additionally, we introduced extracellular vesicles (EVs) from breast tumor-bearing mice into similar AD models. Using spatial transcriptomics, we analyzed brain tissue gene expression and cell-cell interactions, focusing on the astrocyte-microglia-oligodendrocyte network near amyloid plaques. This approach helped identify potential drugs for repurposing in AD treatment.

RESULTS: The study found a significant reduction in amyloid burden within the brains of the cancer-in-AD mouse models compared to age-matched cancer-free AD mice. The administration of EVs from cancer animal's plasma to the AD mice prompted the release of various inflammatory cytokines and chemokines. A key discovery was an activated astrocyte-microglia-oligodendrocyte signaling network that regulates amyloid-beta homeostasis in these mouse brains. Out of 49 FDA-approved drugs identified to induce this cancer-induced signaling, 11 showed promise in improving AD symptoms and reducing amyloid and tau accumulations, in both preclinical and clinical studies.

CONCLUSIONS: The study reveals a notable decrease in amyloid levels in AD mice with cancer or exposed to tumor-derived EVs, linked to immune system reprogramming and glial network activation. This supports the study's drug repositioning approach and sets the stage for further research into the anti-AD properties of these drugs, focusing on identifying crucial signaling elements for enhanced drug repositioning and combination treatment strategies.

PMID:39782516 | DOI:10.1002/alz.087380

Alzheimers Dement. 2024 Dec;20 Suppl 6:e089290. doi: 10.1002/alz.089290.

ABSTRACT

BACKGROUND: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.

METHOD: The graph neural network framework integrates four layers of input data including transcriptome, proteome, drug, and subject to connect to bipartite graphs in the United Kingdom Biobank (UKBB). Medication usage, clinical, and GWAS data were downloaded for 48187 subjects with first and second cognitive exams from the UKBB. The protein and transcriptome layers were constructed using the String database and gene coexpression networks generated from single nuclei RNA data (Sahelijo et al. 2022). Layers were connected by binary bipartite graphs constructed using drug information from the UniProt and DrugCentral databases. Sequential Graph Attention Networks convoluted embedded features generated by each layer in a hierarchical order: 1. gene-gene, 2. gene-protein, 3. protein-protein, 4. protein-drug, 5. drug-subj. Feature embeddings were decoded using a multilayer perceptron to predict cognitive improvement between the first and second cognitive exams. Two models compared methods of data aggregation. The first model follows a strict hierarchy, aggregating inter-layer data in a single direction (gene > protein > drug > subj). The second model allows for fluid message passing between inter-layers. We used 60% of the UKBB subjects for training and 30% for validation. We assessed the model with the greatest training accuracy using the remaining 10% of subjects.

RESULT: We observed that the performance of the strict message-passing model attained validation and test accuracy of 57.7% and 51.2%, respectively. Performance of the fluid message passing model improved prediction accuracy with 61.3% and 58.1% in the test set.

CONCLUSION: Our investigation suggests the feasibility of the PreSiBOGNN framework to infer cognitive improvement of existing drugs by integrating medication, multi-omics, and clinical data. Future work will focus on model optimizations and the integration of additional modalities including compound-specific fingerprint data.

PMID:39782415 | DOI:10.1002/alz.089290

Alzheimers Dement. 2024 Dec;20 Suppl 6:e090350. doi: 10.1002/alz.090350.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) presents challenges with its complex neurodegenerative mechanisms, leading to a high failure rate in clinical trials. While drug repositioning offers a cost-effective solution, the lack of a subtype-driven strategy hinders success. Previously, we defined genetic subtypes and their prioritized genes for each genetic subtype (Sahelijo et al., 2022). This study evaluated unsupervised learning algorithms to characterize existing compounds targeting prioritized genes for the genetic subtypes.

METHOD: Compounds in at least Phase 2 clinical trials were gathered from PubChem. Active compounds against subtype-specific genes were selected, and their structural data were transformed into pharmacophore fingerprints using ChemmineR. Unsupervised algorithms (Agglomerative Clustering, Ensemble Clustering, Gaussian Mixture Models, Bayesian Gaussian Mixture Models) were optimized using evaluation metrics (Calinski-Harabasz, Davies-Bouldin, Silhouette) to cluster compounds within each subtype with the optimal cluster number and optimal algorithm. The finalized clusters of compounds were evaluated using significance values and mean within-cluster Jaccard similarity scores and were characterized with target genes and mechanisms of action.

RESULT: Four of the nine genetic subtypes generated compound clusters, including 3 clusters using Agglomerative Clustering for Ast-M2 with 11 targets and 180 compounds, 2 clusters using BGMM for Ast-M9 with 14 targets and 341 compounds, and 4 clusters using Ensemble Clustering for Oli-M45 with 11 targets and 66 compounds and for Oli-M50 with 18 genes and 431 compounds. We observed common structural signatures between Ast-M2, Ast-M9, and Oli-M50 clusters, while Oli-M45 clusters did not share any signatures with other clusters. The most significant cluster was found for the Oli-M45 subtype, with a cluster significance value of 8.49 and the highest mean compound similarity score of 0.96. The top-ranked cluster primarily contained Vinblastine formulations-microtubule and tubulin polymerization inhibitors-targeting TUBA1A and TUBA1B.

CONCLUSION: We demonstrated a novel drug repositioning framework for AD using unsupervised learning algorithms, enabling precision medicine and subtype-driven repositioning. This framework will be implemented in our future software tools.

PMID:39782372 | DOI:10.1002/alz.090350

J Biomed Semantics. 2025 Jan 8;16(1):1. doi: 10.1186/s13326-024-00322-1.

ABSTRACT

BACKGROUND: TogoID ( https://togoid.dbcls.jp/ ) is an identifier (ID) conversion service designed to link IDs across diverse categories of life science databases. With its ability to obtain IDs related in different semantic relationships, a user-friendly web interface, and a regular automatic data update system, TogoID has been a valuable tool for bioinformatics.

RESULTS: We have recently expanded TogoID's ability to represent semantics between datasets, enabling it to handle multiple semantic relationships within dataset pairs. This enhancement enables TogoID to distinguish relationships such as "glycans bind to proteins" or "glycans are processed by proteins" between glycans and proteins. Additional new features include the ability to display labels corresponding to database IDs, making it easier to interpret the relationships between the various IDs available in TogoID, and the ability to convert labels to IDs, extending the entry point for ID conversion. The implementation of URL parameters, which reproduces the state of TogoID's web application, allows users to share complex search results through a simple URL.

CONCLUSIONS: These advancements improve TogoID's utility in bioinformatics, allowing researchers to explore complex ID relationships. By introducing the tool's multi-semantic and label features, TogoID expands the concept of ID conversion and supports more comprehensive and efficient data integration across life science databases.

PMID:39780290 | DOI:10.1186/s13326-024-00322-1

Ann Pharmacother. 2025 Jan 8:10600280241310595. doi: 10.1177/10600280241310595. Online ahead of print.

ABSTRACT

BACKGROUND: Among people with cystic fibrosis (PwCF), methicillin-resistant Staphylococcus aureus (MRSA)-associated acute pulmonary exacerbations (APEs) have been increasing in prevalence and can cause rapid declines in lung function and increased mortality. Fortunately, since 2019, incidence has started to decline.

OBJECTIVE: The purpose of this study was to evaluate if doxycycline has comparable efficacy to vancomycin for the treatment of APEs in PwCF. Given the potential toxicities and intolerances associated with vancomycin, evaluating alternative therapies such as doxycycline is warranted.

METHODS: A multicenter retrospective cohort study was conducted in adult and pediatric PwCF who received greater than 48 hours of either vancomycin or doxycycline to treat MRSA-associated APEs between May 1, 2014, and August 31, 2021. The primary outcome was the number of PwCF with a return to ≥90% of baseline forced expiratory volume in the first second (FEV1).

RESULTS: There were 229 PwCF encounters screened, of which 89 met inclusion criteria (n = 26, vancomycin; n = 63, doxycycline). There were no differences between vancomycin and doxycycline for the primary outcome: 18/26 (69.2%) in the vancomycin group vs 51/63 (81.0%) in the doxycycline group (P = 0.23). Secondary outcomes were similar between groups, including no difference in incidence of acute kidney injury (AKI), although a significantly higher incidence of adverse events occurred in the vancomycin arm.

CONCLUSION AND RELEVANCE: The findings of this study suggest doxycycline may be a reasonable alternative to vancomycin for MRSA-associated APEs, particularly in PwCF who may not tolerate vancomycin or who require concomitant nephrotoxins such as intravenous (IV) aminoglycosides.

PMID:39780358 | DOI:10.1177/10600280241310595

Alzheimers Dement. 2024 Dec;20 Suppl 6:e086486. doi: 10.1002/alz.086486.

ABSTRACT

BACKGROUND: Pivotal Alzheimer's Disease (AD) trials typically require thousands of participants, resulting in long enrollment timelines and substantial costs. We leverage deep learning predictive models to create prognostic scores (forecasted control outcome) of trial participants and in combination with a linear statistical model to increase statistical power in randomized clinical trials (RCT). This is a straightforward extension of the traditional RCT analysis, allowing for ease of use in any clinical program. We demonstrate the application of these methods retrospectively on 3 pivotal Phase III clinical trials in mild-to-moderate AD (NCT00236431, NCT00574132, and NCT00575055).

METHOD: A probabilistic deep learning model was trained on the trajectories of nearly 7000 participants who had varying degrees of cognitive impairment, ranging from mild cognitive impairment (MCI) to moderate AD. These trajectories were collected observational studies and the control arms of RCTs. This trained model was used to forecast the control outcomes of participants in the three trials retrospectively, by entering their individual trial baseline data. The resultant forecasts are known as prognostic scores and represent comprehensive predictions across a broad range of AD outcomes. We evaluated the potential reduction in estimated variance and how this could translate to required sample size by incorporating the prognostic score as a covariate in the primary linear statistical model of each study, analyzing the 11-component Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog11) and the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) endpoints as applicable.

RESULT: Prognostic scores have the potential to decrease estimated variance between 5% to 10% and placebo arm sample size between 7% and 17% in the 3 studies when comparing standard + prognostic score vs. standard adjustment.

CONCLUSION: Prognostic scores have the potential to increase the statistical power in clinical trials; this would enable a reduced number of subjects required to detect a significant treatment effect. Potential sample size reduction during trial planning must be carefully estimated using independent validation studies to reduce the risk of under-powering the trial.

PMID:39782540 | DOI:10.1002/alz.086486

Alzheimers Dement. 2024 Dec;20 Suppl 4:e087844. doi: 10.1002/alz.087844.

ABSTRACT

BACKGROUND: Clinical Dementia Rating (CDR) and its evaluation have been important nowadays as its prevalence in older ages after 60 years. Early identification of dementia can help the world to take preventive measures as most of them are treatable. The cellular Automata (CA) framework is a powerful tool in analyzing brain dynamics and modeling the prognosis of Alzheimer's disease.

METHOD: The proposed algorithm uses the CA framework to construct features for the classifier for the classification of classes in the dataset. A subject is assigned to a CA cell grid based on its feature values as rows and the specified number of cells in each row. When a CA grid receives a feature from a subject, the feature values are distributed among the cells using a transfer function. The distribution of featured values in the CA grid from the initialized cell to the neighboring cells in the row with a diffusion rate of 20%. Hence, redistributed CA images have been obtained for all the subjects. Deep learning architecture constituted with 4 layered Conv2d has been modeled for the classification of the CA images to classify low, moderate, and severe cognitive impairment.

RESULT: CDR from the ADNI dataset comprising 1948 subjects has been preprocessed for the six features and three classes (i.e., Low, moderate, and severe cognitive impairment) with 70% train sets and 30% test sets. A balanced dataset of 89 subjects for moderate and severe cognitive impairment has given the classification accuracy of 96%. A balanced dataset of 363 subjects for low and moderate cognitive impairment has given the classification accuracy of 95%.

CONCLUSION: A CA framework for the classification of cognitive impairment has been achieved with good accuracy. The implementation of the CA approach and its runtime performance has an advantage over the well-known algorithms by giving a good pathway in contributing to the classification problems.

PMID:39782453 | DOI:10.1002/alz.087844

Alzheimers Dement. 2024 Dec;20 Suppl 4:e083965. doi: 10.1002/alz.083965.

ABSTRACT

BACKGROUND: With the advent of new media, more people - possibly including caregivers of persons with dementia - are turning to social media platforms to share their thoughts and emotions related to personal life experiences. This may potentially serve as an opportunity to leverage on social media to gain insights into the key issues faced by dementia caregivers. We examined salient concerns of dementia caregivers through Twitter posts, aiming to shed light on how to better support and engage such caregivers.

METHOD: English tweets related to "dementia" and "caregiver" (or related terms such as "Alzheimer's disease" and "carer") were extracted between 1st January 2013 and 31st December 2022. A supervised deep learning model (Bidirectional Encoder Representations from Transformers, BERT) was trained to select tweets describing individual's accounts related to dementia caregiving. Then, an unsupervised deep learning approach (BERT-based topic modelling) was applied to identify topics from selected tweets, with each topic further grouped into themes manually using thematic analysis.

RESULT: A total of 44,527 tweets were analysed, and stratified using the emergence of COVID-19 pandemic as a threshold. Three themes were derived: challenges of caregiving in dementia, positive aspects related to dementia caregiving, and dementia-related stigmatization. Over time, there is a rising trend of tweets relating to dementia caregiving. Post-pandemic, challenges of caregiving remained the top discussed topic; followed by an increase in tweets related to dementia-related stigmatization; and a decrease in tweets related to positive aspects of caregiving (p-value<.001).

CONCLUSION: Social media is increasingly being used by dementia caregivers to share their thoughts. The findings uncover the worrying trend of growing dementia-related stigmatization among the caregivers, and its manifestation in the form of devaluing others. The persistence of these issues post-pandemic underscores the need for caregiver's support and resources.

PMID:39782315 | DOI:10.1002/alz.083965

J Magn Reson Imaging. 2025 Jan 9. doi: 10.1002/jmri.29687. Online ahead of print.

ABSTRACT

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines. This review aims to provide a comprehensive overview of the current state of CADe systems in breast MRI, focusing on the technical details of pipelines and segmentation models including classical intensity-based methods, supervised and unsupervised machine learning (ML) approaches, and the latest deep learning (DL) architectures. It highlights recent advancements from traditional algorithms to sophisticated DL models such as U-Nets, emphasizing CADe implementation of multi-parametric MRI acquisitions. Despite these advancements, CADe systems face challenges like variable false-positive and negative rates, complexity in interpreting extensive imaging data, variability in system performance, and lack of large-scale studies and multicentric models, limiting the generalizability and suitability for clinical implementation. Technical issues, including image artefacts and the need for reproducible and explainable detection algorithms, remain significant hurdles. Future directions emphasize developing more robust and generalizable algorithms, integrating explainable AI to improve transparency and trust among clinicians, developing multi-purpose AI systems, and incorporating large language models to enhance diagnostic reporting and patient management. Additionally, efforts to standardize and streamline MRI protocols aim to increase accessibility and reduce costs, optimizing the use of CADe systems in clinical practice. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

PMID:39781684 | DOI:10.1002/jmri.29687

J Magn Reson Imaging. 2025 Jan 9. doi: 10.1002/jmri.29708. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is the deadliest malignant tumor, with a grim 5-year overall survival rate of about 12%. As its incidence and mortality rates rise, it is likely to become the second-leading cause of cancer-related death. The radiological assessment determined the stage and management of PDAC. However, it is a highly heterogeneous disease with the complexity of the tumor microenvironment, and it is challenging to adequately reflect the biological aggressiveness and prognosis accurately through morphological evaluation alone. With the dramatic development of artificial intelligence (AI), multiparametric magnetic resonance imaging (mpMRI) using specific contrast media and special techniques can provide morphological and functional information with high image quality and become a powerful tool in quantifying intratumor characteristics. Besides, AI has been widespread in the field of medical imaging analysis. Radiomics is the high-throughput mining of quantitative image features from medical imaging that enables data to be extracted and applied for better decision support. Deep learning is a subset of artificial neural network algorithms that can automatically learn feature representations from data. AI-enabled imaging biomarkers of mpMRI have enormous promise to bridge the gap between medical imaging and personalized medicine and demonstrate huge advantages in predicting biological characteristics and the prognosis of PDAC. However, current AI-based models of PDAC operate mainly in the realm of a single modality with a relatively small sample size, and the technical reproducibility and biological interpretation present a barrage of new potential challenges. In the future, the integration of multi-omics data, such as radiomics and genomics, alongside the establishment of standardized analytical frameworks will provide opportunities to increase the robustness and interpretability of AI-enabled image biomarkers and bring these biomarkers closer to clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

PMID:39781607 | DOI:10.1002/jmri.29708

J Surg (Lisle). 2024;9(15):11189. doi: 10.29011/2575-9760.11189. Epub 2024 Nov 27.

ABSTRACT

PURPOSE: Appropriate opioid management is crucial to reduce opioid overdose risk for ICU surgical patients, which can lead to severe complications. Accurately predicting postoperative opioid needs and understanding the associated factors can effectively guide appropriate opioid use, significantly enhancing patient safety and recovery outcomes. Although machine learning models can accurately predict postoperative opioid needs, lacking interpretability hinders their adoption in clinical practice.

METHODS: We developed an interpretable deep learning framework to evaluate individual feature's impact on postoperative opioid use and identify important factors. A Permutation Feature Importance Test (PermFIT) was employed to assess the impact with a rigorous statistical inference for machine learning models including Support Vector Machines, eXtreme Gradient Boosting, Random Forest, and Deep Neural Networks (DNN). The Mean Squared Error (MSE) and Pearson Correlation Coefficient (PCC) were used to evaluate the performance of these models.

RESULTS: We conducted analysis utilizing the electronic health records of 4,912 surgical patients from the Medical Information Mart for Intensive Care database. In a 10-fold cross-validation, the DNN outperformed other machine learning models, achieving the lowest MSE (7889.2 mcg) and highest PCC (0.283). Among 25 features, 13-including age, surgery type, and others-were identified as significant predictors of postoperative opioid use (p < 0.05).

CONCLUSION: The DNN proved to be an effective model for predicting postoperative opioid consumption and identifying significant features through the PermFIT framework. This approach offers a valuable tool for precise opioid prescription tailored to the individual needs of ICU surgical patients, improving patient outcomes and enhancing safety.

PMID:39781484 | PMC:PMC11709741 | DOI:10.29011/2575-9760.11189

Int J Clin Pediatr Dent. 2024 Nov;17(11):1289-1295. doi: 10.5005/jp-journals-10005-2971. Epub 2024 Dec 19.

ABSTRACT

AIM AND BACKGROUND: The applications of artificial intelligence (AI) are escalating in all frontiers, specifically healthcare. It constitutes the umbrella term for a number of technologies that enable machines to independently solve problems they have not been programmed to address. With its aid, patient management, diagnostics, treatment planning, and interventions can be significantly improved. The aim of this review is to analyze the current data to assess the applications of artificial intelligence in pediatric dentistry and determine their clinical effectiveness.

MATERIALS AND METHODS: A search of published studies in PubMed, Web of Science, Scopus, and Google Scholar databases was included till January 2024.

RESULTS: This review consisted of 30 published studies in the English language. The use of AI has been employed in the detection of dental caries, dental plaque, behavioral science, interceptive orthodontics, predicting the dental age, and identification of teeth which can enhance patient care.

CONCLUSION: Artificial intelligence models can be used as an aid to the clinician as they are of significant help at individual and community levels in identifying an increased risk to dental diseases.

CLINICAL SIGNIFICANCE: Artificial intelligence can be used as an asset in preventive school health programs, dental education for students and parents, and to assist the clinician in the dental practice. Further advancements in technology will give rise to newer potential innovations and applications.

HOW TO CITE THIS ARTICLE: Tanna DA, Bhandary S, Hegde SK. Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review. Int J Clin Pediatr Dent 2024;17(11):1289-1295.

PMID:39781392 | PMC:PMC11703760 | DOI:10.5005/jp-journals-10005-2971

Cureus. 2024 Dec 9;16(12):e75389. doi: 10.7759/cureus.75389. eCollection 2024 Dec.

ABSTRACT

Alzheimer's disease (AD) and other neurodegenerative illnesses place a heavy strain on the world's healthcare systems, particularly among the aging population. With a focus on research from January 2022 to September 2023, this scoping review, which adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-Scr) criteria, examines the changing landscape of artificial intelligence (AI) applications for early AD detection and diagnosis. Forty-four carefully chosen articles were selected from a pool of 2,966 articles for the qualitative synthesis. The research reveals impressive advancements in AI-driven approaches, including neuroimaging, genomics, cognitive tests, and blood-based biomarkers. Notably, AI models focusing on deep learning (DL) algorithms demonstrate outstanding accuracy in early AD identification, often even before the onset of clinical symptoms. Multimodal approaches, which combine information from various sources, including neuroimaging and clinical assessments, provide comprehensive insights into the complex nature of AD. The study also emphasizes the critical role that blood-based and genetic biomarkers play in strengthening AD diagnosis and risk assessment. When combined with clinical or imaging data, genetic variations and polygenic risk scores help to improve prediction models. In a similar vein, blood-based biomarkers provide non-invasive instruments for detecting metabolic changes linked to AD. Cognitive and functional evaluations, which include neuropsychological examinations and assessments of daily living activities, serve as essential benchmarks for monitoring the course of AD and directing treatment interventions. When these evaluations are included in machine learning models, the diagnosis accuracy is improved, and treatment monitoring is made more accessible. In addition, including methods that support model interpretability and explainability helps in the thorough understanding and valuable implementation of AI-driven insights in clinical contexts. This review further identifies several gaps in the research landscape, including the need for diverse, high-quality datasets to address data heterogeneity and improve model generalizability. Practical implementation challenges, such as integrating AI systems into clinical workflows and clinician adoption, are highlighted as critical barriers to real-world application. Moreover, ethical considerations, particularly surrounding data privacy and informed consent, must be prioritized as AI adoption in healthcare accelerates. Performance metrics (e.g., sensitivity, specificity, and area under the curve (AUC)) for AI-based approaches are discussed, with a need for clearer reporting and comparative analyses. Addressing these limitations, alongside methodological clarity and critical evaluation of biases, would strengthen the credibility of AI applications in AD detection. By expanding its scope, this study highlights areas for improvement and future opportunities in early detection, aiming to bridge the gap between innovative AI technologies and practical clinical utility.

PMID:39781179 | PMC:PMC11709138 | DOI:10.7759/cureus.75389

Biomed Eng Lett. 2024 Dec 2;15(1):37-55. doi: 10.1007/s13534-024-00436-6. eCollection 2025 Jan.

ABSTRACT

Robotic systems rely on spatio-temporal information to solve control tasks. With advancements in deep neural networks, reinforcement learning has significantly enhanced the performance of control tasks by leveraging deep learning techniques. However, as deep neural networks grow in complexity, they consume more energy and introduce greater latency. This complexity hampers their application in robotic systems that require real-time data processing. To address this issue, spiking neural networks, which emulate the biological brain by transmitting spatio-temporal information through spikes, have been developed alongside neuromorphic hardware that supports their operation. This paper reviews brain-inspired learning rules and examines the application of spiking neural networks in control tasks. We begin by exploring the features and implementations of biologically plausible spike-timing-dependent plasticity. Subsequently, we investigate the integration of a global third factor with spike-timing-dependent plasticity and its utilization and enhancements in both theoretical and applied research. We also discuss a method for locally applying a third factor that sophisticatedly modifies each synaptic weight through weight-based backpropagation. Finally, we review studies utilizing these learning rules to solve control tasks using spiking neural networks.

PMID:39781065 | PMC:PMC11704115 | DOI:10.1007/s13534-024-00436-6

Biomed Eng Lett. 2024 Oct 10;15(1):1-35. doi: 10.1007/s13534-024-00437-5. eCollection 2025 Jan.

ABSTRACT

Osteoarthritis (OA) is a musculoskeletal disorder that affects weight-bearing joints like the hip, knee, spine, feet, and fingers. It is a chronic disorder that causes joint stiffness and leads to functional impairment. Knee osteoarthritis (KOA) is a degenerative knee joint disease that is a significant disability for over 60 years old, with the most prevalent symptom of knee pain. Radiography is the gold standard for the evaluation of KOA. These radiographs are evaluated using different classification systems. Kellgren and Lawrence's (KL) classification system is used to classify X-rays into five classes (Normal = 0 to Severe = 4) based on osteoarthritis severity levels. In recent years, with the advent of artificial intelligence, machine learning, and deep learning, more emphasis has been given to automated medical diagnostic systems or decision support systems. Computer-aided diagnosis is needed for the improvement of health-related information systems. This survey aims to review the latest advances in automated radiographic classification and detection of KOA using the KL system. A total of 85 articles are reviewed as original research or survey articles. This survey will benefit researchers, practitioners, and medical experts interested in X-rays-based KOA diagnosis and prediction.

PMID:39781063 | PMC:PMC11704124 | DOI:10.1007/s13534-024-00437-5