Search

Fe3O4 magnetic nanoparticles exhibit significant potential for cancer detection due to their unique magnetic properties and versatility. This review examines their role in enhancing cancer biomarker detection, focusing on synthesis methods, surface modifications and diagnostic accuracy. Fe3O4 nanoparticles serve as sensitive MRI contrast agents, facilitating early cancer diagnosis and improving patient prognosis. Synthesis techniques influence their properties, morphology and dimensions, which affect performance. Surface modifications enhance targeting and reduce immunological reactions, enabling precise biomarker detection and effective drug delivery to tumours, minimizing damage to healthy tissue. Despite these advantages, challenges remain in optimizing delivery efficiency and overcoming medication resistance. Further research is required to understand the pharmacokinetics and pharmacodynamics of these nanoparticles, including their distribution, metabolism and physiological impacts. This review provides insights into the potential of Fe3O4 magnetic nanoparticles as cancer detection agents, aiming to guide future research towards developing safer and more efficient applications in medical diagnostics and treatment. [ABSTRACT FROM AUTHOR]

Lung carcinoids (L‐CDs) are rare, poorly characterised neuroendocrine tumours (NETs). L‐CDs are more common in women and are not the consequence of cigarette smoking. They are classified histologically as typical carcinoids (TCs) or atypical carcinoids (ACs). ACs confer a worse survival. Histological classification is imperfect, and there is increasing interest in molecular markers. We therefore investigated global transcriptomic and epigenomic profiles of 15 L‐CDs resected with curative intent at Royal Brompton Hospital. We identified underlying mutations and structural abnormalities through whole‐exome sequencing (WES) and single nucleotide polymorphism (SNP) genotyping. Transcriptomic clustering algorithms identified two distinct L‐CD subtypes. These showed similarities either to pancreatic or neuroendocrine tumours at other sites and so were named respectively L‐CD‐PanC and L‐CD‐NeU. L‐CD‐PanC tumours featured upregulation of pancreatic and metabolic pathway genes matched by promoter hypomethylation of genes for beta cells and insulin secretion (p < 1 × 10−6). These tumours were centrally located and showed mutational signatures of activation‐induced deaminase/apolipoprotein B editing complex activity, together with genome‐wide DNA methylation loss enriched in repetitive elements (p = 2.2 × 10−16). By contrast, the L‐CD‐NeU group exhibited upregulation of neuronal markers (adjusted p < 0.01) and was characterised by focal spindle cell morphology (p = 0.04), peripheral location (p = 0.01), high mutational load (p = 2.17 × 10−4), recurrent copy number alterations, and enrichment for ACs. Mutations affected chromatin remodelling and SWI/SNF complex pathways. L‐CD‐NeU tumours carried a mutational signature attributable to aflatoxin and aristolochic acid (p = 0.05), suggesting a possible environmental exposure in their pathogenesis. Immunologically, myeloid and T‐cell markers were enriched in L‐CD‐PanC and B‐cell markers in L‐CD‐NeU tumours. The substantial epigenetic and non‐coding differences between L‐CD‐PanC and L‐CD‐NeU open new possibilities for biomarker selection and targeted treatment of L‐CD. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

The isolation of pure compounds from extracts represents a key step common to all investigations of natural product (NP) research. Isolation methods have gone through a remarkable evolution. Current approaches combine powerful metabolite profiling methods for compounds annotation with omics mining results and/or bioassay for bioactive NPs/biomarkers priorisation. Targeted isolation of prioritized NPs is performed using high-resolution chromatographic methods that closely match those used for analytical profiling. Considerable progress has been made by the introduction of innovative stationary phases providing remarkable selectivity for efficient NPs isolation. Today, efficient separation conditions determined at the analytical scale using high- or ultra-high-performance liquid chromatography can be optimized via HPLC modelling software and efficiently transferred to the semi-preparative scale by chromatographic calculation. This ensures similar selectivity at both the analytical and preparative scales and provides a precise separation prediction. High-resolution conditions at the preparative scale can notably be granted using optimized sample preparation and dry load sample introduction. Monitoring by ultraviolet, mass spectrometry, and or universal systems such as evaporative light scattering detectors and nuclear magnetic resonance allows to precisely guide the isolation or trigger the collection of specific NPs with different structural scaffolds. Such approaches can be applied at different scales depending on the amounts of NPs to be isolated. This review will showcase recent research to highlight both the potential and constraints of using these cutting-edge technologies for the isolation of plant and microorganism metabolites. Several strategies involving their application will be examined and critically discussed. [ABSTRACT FROM AUTHOR]

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Abstract Background Machine Learning (ML) plays a crucial role in biomedical research. Nevertheless, it still has limitations in data integration and irreproducibility. To address these challenges, robust methods are needed. Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive cancer with low early detection rates and survival rates, is used as a case study. PDAC lacks reliable diagnostic biomarkers, especially metastatic biomarkers, which remains an unmet need. In this study, we propose an ML-based approach for discovering disease biomarkers, apply it to the identification of a PDAC metastatic composite biomarker candidate, and demonstrate the advantages of harnessing data resources. Methods We utilised primary tumour RNAseq data from five public repositories, pooling samples to maximise statistical power and integrating data by correcting for technical variance. Data were split into train and validation sets. The train dataset underwent variable selection via a 10-fold cross-validation process that combined three algorithms in 100 models per fold. Genes found in at least 80% of models and five folds were considered robust to build a consensus multivariate model. A random forest model was constructed using selected genes from the train dataset and tested in the validation set. We also assessed the goodness of prediction by recalibrating a model using only the validation data. The biological context and relevance of signals was explored through enrichment and pathway analyses using QIAGEN Ingenuity Pathway Analysis and GeneMANIA. Results We developed a pipeline that can detect robust signatures to build composite biomarkers. We tested the pipeline in PDAC, exploiting transcriptomics data from different sources, proposing a composite biomarker candidate comprised of fifteen genes consistently selected that showed very promising predictive capability. Biological contextualisation revealed links with cancer progression and metastasis, underscoring their potential relevance. All code is available in GitHub. Conclusion This study establishes a robust framework for identifying composite biomarkers across various disease contexts. We demonstrate its potential by proposing a plausible composite biomarker candidate for PDAC metastasis. By reusing data from public repositories, we highlight the sustainability of our research and the wider applications of our pipeline. The preliminary findings shed light on a promising validation and application path.

Abstract Background Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. Method We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. Results We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. Conclusion The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .

Jian Hu,1,2,&ast; Xiaoyun Liu,1,2,&ast; Peng Yao1,2 1First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Peng Yao, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine/National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, 150006, People’s Republic of China, Email 75232745@qq.com

Background: Machine Learning (ML) plays a crucial role in biomedical research. Nevertheless, it still has limitations in data integration and irreproducibility. To address these challenges, robust methods are needed. Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive cancer with low early detection rates and survival rates, is used as a case study. PDAC lacks reliable diagnostic biomarkers, especially metastatic biomarkers, which remains an unmet need. In this study, we propose an ML-based approach for discovering disease biomarkers, apply it to the identification of a PDAC metastatic composite biomarker candidate, and demonstrate the advantages of harnessing data resources. Methods: We utilised primary tumour RNAseq data from five public repositories, pooling samples to maximise statistical power and integrating data by correcting for technical variance. Data were split into train and validation sets. The train dataset underwent variable selection via a 10-fold cross-validation process that combined three algorithms in 100 models per fold. Genes found in at least 80% of models and five folds were considered robust to build a consensus multivariate model. A random forest model was constructed using selected genes from the train dataset and tested in the validation set. We also assessed the goodness of prediction by recalibrating a model using only the validation data. The biological context and relevance of signals was explored through enrichment and pathway analyses using QIAGEN Ingenuity Pathway Analysis and GeneMANIA. Results: We developed a pipeline that can detect robust signatures to build composite biomarkers. We tested the pipeline in PDAC, exploiting transcriptomics data from different sources, proposing a composite biomarker candidate comprised of fifteen genes consistently selected that showed very promising predictive capability. Biological contextualisation revealed links with cancer progression and metastasis, underscoring their potential relevance. All code is available in GitHub. Conclusion: This study establishes a robust framework for identifying composite biomarkers across various disease contexts. We demonstrate its potential by proposing a plausible composite biomarker candidate for PDAC metastasis. By reusing data from public repositories, we highlight the sustainability of our research and the wider applications of our pipeline. The preliminary findings shed light on a promising validation and application path. [ABSTRACT FROM AUTHOR]

Precise targeting has become the main direction of anti-cancer drug development. Trophoblast cell surface antigen 2 (Trop-2) is highly expressed in different solid tumors but rarely in normal tissues, rendering it an attractive target. Trop-2-targeted antibody-drug conjugates (ADCs) have displayed promising efficacy in treating diverse solid tumors, especially breast cancer and urothelial carcinoma. However, their clinical application is still limited by insufficient efficacy, excessive toxicity, and the lack of biological markers related to effectiveness. This review summarizes the clinical trials and combination therapy strategies for Trop-2-targeted ADCs, discusses the current challenges, and provides new insights for future advancements. [ABSTRACT FROM AUTHOR]

The identification of prototypical waveforms, such as sleep spindles and epileptic spikes, is crucial for the diagnosis of neurological disorders. These prototypical waveforms are usually recurrently presented in certain brain states, serving as potential biomarkers for clinical evaluations. Convolutional sparse coding (CSC) approaches have demonstrated strength in identifying recurrent patterns in time-series. However, existing CSC approaches do not explicitly explore state-specific patterns, making it difficult to identify state-related biomarkers. To address this problem, we propose state-sensitive CSC to learn state-specific prototypical waveforms. Specifically, we model signals of a certain state with specific waveforms that only appear frequently in this state and background waveforms that are independent of states. Based on this, state-sensitive CSC separates state-specific waveforms from background ones explicitly by incorporating incoherence constraints into optimizations. Experiments with epilepsy brain signals demonstrate that our approach can effectively identify prototypical waveforms in pre-ictal states, providing potential biomarkers for seizure prediction. Our approach provides a promising tool for automatic biomarker candidate identification. [ABSTRACT FROM AUTHOR]

Background: Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. Method: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. Results: We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. Conclusion: The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks. [ABSTRACT FROM AUTHOR]

Abstract Background Breast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy. Materials and methods Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers. Results The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples. Conclusions These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.

Integrating and analyzing multiple omics data sets, including genomics, proteomics and radiomics, can significantly advance researchers' comprehensive understanding of Alzheimer's disease (AD). However, current methodologies primarily focus on the main effects of genetic variation and protein, overlooking non-additive effects such as genotype–protein interaction (GPI) and correlation patterns in brain imaging genetics studies. Importantly, these non-additive effects could contribute to intermediate imaging phenotypes, finally leading to disease occurrence. In general, the interaction between genetic variations and proteins, and their correlations are two distinct biological effects, and thus disentangling the two effects for heritable imaging phenotypes is of great interest and need. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we propose |$\textbf{M}$| ulti- |$\textbf{T}$| ask |$\textbf{G}$| enotype- |$\textbf{P}$| rotein |$\textbf{I}$| nteraction and |$\textbf{C}$| orrelation disentangling method (⁠|$\textbf{MT-GPIC}$|⁠) to identify GPI and extract correlation patterns between them. To ensure stability and interpretability, we use novel and off-the-shelf penalties to identify meaningful genetic risk factors, as well as exploit the interconnectedness of different brain regions. Additionally, since computing GPI poses a high computational burden, we develop a fast optimization strategy for solving MT - GPIC, which is guaranteed to converge. Experimental results on the Alzheimer's Disease Neuroimaging Initiative data set show that MT - GPIC achieves higher correlation coefficients and classification accuracy than state-of-the-art methods. Moreover, our approach could effectively identify interpretable phenotype-related GPI and correlation patterns in high-dimensional omics data sets. These findings not only enhance the diagnostic accuracy but also contribute valuable insights into the underlying pathogenic mechanisms of AD. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithmto identify for each subject which parameters are important in the context of CRC. Results: The proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state. Discussion: These findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks. [ABSTRACT FROM AUTHOR]

Background: CTLA-4 impedes the immune system's antitumor response. There are two Food and Drug Administration-approved anti-CTLA-4 agents – ipilimumab and tremelimumab – both used together with anti-PD-1/PD-L1 agents. Objective: To assess the prognostic implications and immunologic correlates of high CTLA-4 in tumors of patients on immunotherapy and those on non-immunotherapy treatments. Design/methods: We evaluated RNA expression levels in a clinical-grade laboratory and clinical correlates of CTLA-4 and other immune checkpoints in 514 tumors, including 489 patients with advanced/metastatic cancers and full outcome annotation. A reference population (735 tumors; 35 histologies) was used to normalize and rank transcript abundance (0–100 percentile) to internal housekeeping gene profiles. Results: The most common tumor types were colorectal (140/514, 27%), pancreatic (55/514, 11%), breast (49/514, 10%), and ovarian cancers (43/514, 8%). Overall, 87 of 514 tumors (16.9%) had high CTLA-4 transcript expression (⩾75th percentile rank). Cancers with the largest proportion of high CTLA-4 transcripts were cervical cancer (80% of patients), small intestine cancer (33.3%), and melanoma (33.3%). High CTLA-4 RNA independently/significantly correlated with high PD-1, PD- L2, and LAG3 RNA levels (and with high PD-L1 in univariate analysis). High CTLA-4 RNA expression was not correlated with survival from the time of metastatic disease [ N = 272 patients who never received immune checkpoint inhibitors (ICIs)]. However, in 217 patients treated with ICIs (mostly anti-PD-1/anti-PD- L1), progression-free survival (PFS) and overall survival (OS) were significantly longer among patients with high versus non-high CTLA-4 expression [hazard ratio, 95% confidence interval: 0.6 (0.4–0.9) p = 0.008; and 0.5 (0.3–0.8) p = 0.002, respectively]; results were unchanged when 18 patients who received anti-CTLA-4 were omitted. Patients whose tumors had high CTLA-4 and high PD-L1 did best; those with high PD-L1 but non-high CTLA-4 and/or other expression patterns had poorer outcomes for PFS (p = 0.004) and OS (p = 0.009) after immunotherapy. Conclusion: High CTLA-4, especially when combined with high PD-L1 transcript expression, was a significant positive predictive biomarker for better outcomes (PFS and OS) in patients on immunotherapy. Plain language summary: High CTLA-4 expression and immunotherapy outcome High CTLA-4 expression was not a prognostic factor for survival in patients not receiving ICIs but was a significant positive predictive biomarker for better outcome (PFS and OS) in patients on immunotherapy, perhaps because it correlated with expression of other checkpoints such as PD-1 and PD-L2. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA–disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases. [ABSTRACT FROM AUTHOR]

Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature. We applied DeepBiomarker2 to analyze the EMR data of 38,807 patients from the University of Pittsburgh Medical Center diagnosed with post-traumatic stress disorder (PTSD) to determine their risk of developing alcohol and substance use disorder (ASUD). DeepBiomarker2 predicted whether a PTSD patient would have a diagnosis of ASUD within the following 3 months with an average c-statistic (receiver operating characteristic AUC) of 0.93 and average F1 score, precision, and recall of 0.880, 0.895, and 0.866 in the test sets, respectively. Our study found that the medications clindamycin, enalapril, penicillin, valacyclovir, Xarelto/rivaroxaban, moxifloxacin, and atropine and the SDoH parameters access to psychotherapy, living in zip codes with a high normalized vegetative index, Gini index, and low-income segregation may have potential to reduce the risk of ASUDs in PTSD. In conclusion, the integration of SDoH information, coupled with the refined feature contribution analysis, empowers DeepBiomarker2 to accurately predict ASUD risk. Moreover, the model can further identify potential indicators of increased risk along with medications with beneficial effects. [ABSTRACT FROM AUTHOR]

BackgroundThe human immunodeficiency virus (HIV) remains a critical global health issue, with a pressing need for effective diagnostic and monitoring tools.MethodologyThis study explored distinctions in salivary metabolome among healthy individuals, individuals with HIV, and those receiving highly active antiretroviral therapy (HAART). Utilizing LC–MS/MS for exhaustive metabolomics profiling, we analyzed 90 oral saliva samples from individuals with HIV, categorized by CD4 count levels in the peripheral blood.ResultsOrthogonal partial least squares-discriminant analysis (OPLS-DA) and other analyses underscored significant metabolic alterations in individuals with HIV, especially in energy metabolism pathways. Notably, post-HAART metabolic profiles indicated a substantial presence of exogenous metabolites and changes in amino acid pathways like arginine, proline, and lysine degradation. Key metabolites such as citric acid, L-glutamic acid, and L-histidine were identified as potential indicators of disease progression or recovery. Differential metabolite selection and functional enrichment analysis, combined with receiver operating characteristic (ROC) and random forest analyses, pinpointed potential biomarkers for different stages of HIV infection. Additionally, our research examined the interplay between oral metabolites and microorganisms such as herpes simplex virus type 1 (HSV1), bacteria, and fungi in individuals with HIV, revealing crucial interactions.ConclusionThis investigation seeks to contribute understanding into the metabolic shifts occurring in HIV infection and following the initiation of HAART, while tentatively proposing novel avenues for diagnostic and treatment monitoring through salivary metabolomics.

BackgroundColorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC.ResultsThe proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state.DiscussionThese findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.

Having a previous history of sexually transmitted diseases (STDs) such as gonorrhea and chlamydia increases the chance of developing prostate cancer, the second most frequent malignant cancer among men. However, the molecular functions that cause the development of prostate cancer in persons with gonorrhea and chlamydia are yet unknown. In this study, we studied RNA-seq gene expression profiles using computational biology methods to find out potential biomarkers that could help us in understanding the patho-biological mechanisms of gonorrhea, chlamydia, and prostate cancer. Using statistical methods on the Gene Expression Omnibus (GEO) data sets, it was found that a total of 22 distinct differentially expressed genes were shared among these 3 diseases of which 14 were up-regulated (PGRMC1, TSC22D1, SH3BGRL, NNT, CTSC, FRMD3, CCR2, FAM210B, VCL, PTGS1, SLFN11, SLC40A1, PROS1, and DSE) and the remaining 8 genes were down-regulated (PRNP, HINT3, MARCKSL1, TMED10, SH3KBP1, ENSA, DERL1, and KMT2B). Investigation on these 22 unique dysregulated genes using Gene Ontology, BioCarta, KEGG, and Reactome revealed multiple altered molecular pathways, including regulation of amyloid precursor protein catabolic process, ferroptosis, effects on gene expression of Homo sapiens PPAR pathway, and innate immune system R-HSA-168249. Four significant hub proteins namely VCL, SH3KBP1, PRNP, and PGRMC1 were revealed by protein-protein interaction network analysis. By analyzing gene-transcription factors and gene-miRNAs interactions, significant transcription factors (POU2F2, POU2F1, GATA6, and HIVEP1) and posttranscriptional regulator microRNAs (hsa-miR-7-5p) were also identified. Three potential therapeutic compounds namely INCB3284, CCX915, and MLN-1202 were found to interact with up-regulated protein C-C chemokine receptor type 2 (CCR2) in protein-drug interaction analysis. The proposed biomarkers and therapeutic potential molecules could be investigated for potential pharmacological targets and activity in the fight against in patients with gonorrhea, chlamydia, and prostate cancer. [ABSTRACT FROM AUTHOR]

With the inexorable aging of the global populace, neurodegenerative diseases (NDs) like Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) pose escalating challenges, which are underscored by their socioeconomic repercussions. A pivotal aspect in addressing these challenges lies in the elucidation and application of biomarkers for timely diagnosis, vigilant monitoring, and effective treatment modalities. This review delineates the quintessence of biomarkers in the realm of NDs, elucidating various classifications and their indispensable roles. Particularly, the quest for novel biomarkers in AD, transcending traditional markers in PD, and the frontier of biomarker research in ALS are scrutinized. Emergent susceptibility and trait markers herald a new era of personalized medicine, promising enhanced treatment initiation especially in cases of SOD1-ALS. The discourse extends to diagnostic and state markers, revolutionizing early detection and monitoring, alongside progression markers that unveil the trajectory of NDs, propelling forward the potential for tailored interventions. The synergy between burgeoning technologies and innovative techniques like -omics, histologic assessments, and imaging is spotlighted, underscoring their pivotal roles in biomarker discovery. Reflecting on the progress hitherto, the review underscores the exigent need for multidisciplinary collaborations to surmount the challenges ahead, accelerate biomarker discovery, and herald a new epoch of understanding and managing NDs. Through a panoramic lens, this article endeavors to provide a comprehensive insight into the burgeoning field of biomarkers in NDs, spotlighting the promise they hold in transforming the diagnostic landscape, enhancing disease management, and illuminating the pathway toward efficacious therapeutic interventions. [ABSTRACT FROM AUTHOR]

The gut microbiome has been regarded as one of the fundamental determinants regulating human health, and multi-omics data profiling has been increasingly utilized to bolster the deep understanding of this complex system. However, stemming from cost or other constraints, the integration of multi-omics often suffers from incomplete views, which poses a great challenge for the comprehensive analysis. In this work, a novel deep model named Incomplete Multi-Omics Variational Neural Networks (IMOVNN) is proposed for incomplete data integration, disease prediction application and biomarker identification. Benefiting from the information bottleneck and the marginal-to-joint distribution integration mechanism, the IMOVNN can learn the marginal latent representation of each individual omics and the joint latent representation for better disease prediction. Moreover, owing to the feature-selective layer predicated upon the concrete distribution, the model is interpretable and can identify the most relevant features. Experiments on inflammatory bowel disease multi-omics datasets demonstrate that our method outperforms several state-of-the-art methods for disease prediction. In addition, IMOVNN has identified significant biomarkers from multi-omics data sources. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment. [ABSTRACT FROM AUTHOR]

Metabolic disease is a significant risk factor for severe COVID-19 infection, but the contributing pathways are not yet fully elucidated. Using data from two randomized controlled trials across 13 U.S. academic centers, our goal was to characterize metabolic features that predict severe COVID-19 and define a novel baseline metabolomic signature. Individuals (n = 133) were dichotomized as having mild or moderate/severe COVID-19 disease based on the WHO ordinal scale. Blood samples were analyzed using the Biocrates platform, providing 630 targeted metabolites for analysis. Resampling techniques and machine learning models were used to determine metabolomic features associated with severe disease. Ingenuity Pathway Analysis (IPA) was used for functional enrichment analysis. To aid in clinical decision making, we created baseline metabolomics signatures of low-correlated molecules. Multivariable logistic regression models were fit to associate these signatures with severe disease on training data. A three-metabolite signature, lysophosphatidylcholine a C17:0, dihydroceramide (d18:0/24:1), and triacylglyceride (20:4_36:4), resulted in the best discrimination performance with an average test AUROC of 0.978 and F1 score of 0.942. Pathways related to amino acids were significantly enriched from the IPA analyses, and the mitogen-activated protein kinase kinase 5 (MAP2K5) was differentially activated between groups. In conclusion, metabolites related to lipid metabolism efficiently discriminated between mild vs. moderate/severe disease. SDMA and GABA demonstrated the potential to discriminate between these two groups as well. The mitogen-activated protein kinase kinase 5 (MAP2K5) regulator is differentially activated between groups, suggesting further investigation as a potential therapeutic pathway. [ABSTRACT FROM AUTHOR]

Abstract Background Cold-dampness Syndrome (RA-Cold) and Hot-dampness Syndrome (RA-Hot) are two distinct groups of rheumatoid arthritis (RA) patients with different clinical symptoms based on traditional Chinese medicine (TCM) theories and clinical empirical knowledge. However, the biological basis of the two syndromes has not been fully elucidated, which may restrict the development of personalized medicine and drug discovery for RA diagnosis and therapy. Methods An integrative strategy combining clinical transcriptomics, phenomics, and metabolomics data based on clinical cohorts and adjuvant-induced arthritis rat models was performed to identify novel candidate biomarkers and to investigate the biological basis of RA-Cold and RA-Hot. Results The main clinical symptoms of RA-Cold patients are joint swelling, pain, and contracture, which may be associated with the dysregulation of T cell-mediated immunity, osteoblast differentiation, and subsequent disorders of steroid biosynthesis and phenylalanine metabolism. In contrast, the main clinical symptoms of RA-Hot patients are fever, irritability, and vertigo, which may be associated with various signals regulating angiogenesis, adrenocorticotropic hormone release, and NLRP3 inflammasome activation, leading to disorders of steroid biosynthesis, nicotinamide, and sphingolipid metabolism. IL17F, 5-HT, and IL4I1 were identified as candidate biomarkers of RA-Cold, while S1P and GLNS were identified as candidate biomarkers of RA-Hot. Conclusions The current study presents the most comprehensive metabonomic and transcriptomic profiling of serum, urine, synovial fluid, and synovial tissue samples obtained from RA-Cold and RA-Hot patients and experimental animal models to date. Through the integration of multi-omics data and clinical independent validation, a list of novel candidate biomarkers of RA-Cold and RA-Hot syndromes were identified, that may be useful in improving RA diagnosis and therapy.

Precise targeting has become the main direction of anti-cancer drug development. Trophoblast cell surface antigen 2 (Trop-2) is highly expressed in different solid tumors but rarely in normal tissues, rendering it an attractive target. Trop-2-targeted antibody-drug conjugates (ADCs) have displayed promising efficacy in treating diverse solid tumors, especially breast cancer and urothelial carcinoma. However, their clinical application is still limited by insufficient efficacy, excessive toxicity, and the lack of biological markers related to effectiveness. This review summarizes the clinical trials and combination therapy strategies for Trop-2-targeted ADCs, discusses the current challenges, and provides new insights for future advancements.

Background: Multiple sclerosis (MS) is a chronic, inflammatory and neurodegenerative disease that leads to irreversible damage to the brain and spinal cord. The goal of so-called "immune reconstitution therapies" (IRTs) is to achieve long-term disease remission by eliminating a pathogenic immune repertoire through intense short-term immune cell depletion. B cells are major targets for effective immunotherapy in MS. Objectives: The aim of this study was to analyze the gene expression pattern of B cells before and during IRT (i.e., before B-cell depletion and after B-cell repopulation) to better understand the therapeutic effects and to identify biomarker candidates of the clinical response to therapy. Methods: B cells were obtained from blood samples of patients with relapsing–remitting MS (n = 50), patients with primary progressive MS (n = 13) as well as healthy controls (n = 28). The patients with relapsing MS received either monthly infusions of natalizumab (n = 29) or a pulsed IRT with alemtuzumab (n = 15) or cladribine (n = 6). B-cell subpopulation frequencies were determined by flow cytometry, and transcriptome profiling was performed using Clariom D arrays. Differentially expressed genes (DEGs) between the patient groups and controls were examined with regard to their functions and interactions. We also tested for differences in gene expression between patients with and without relapse following alemtuzumab administration. Results: Patients treated with alemtuzumab or cladribine showed on average a > 20% lower proportion of memory B cells as compared to before IRT. This was paralleled by profound transcriptome shifts, with > 6000 significant DEGs after adjustment for multiple comparisons. The top DEGs were found to regulate apoptosis, cell adhesion and RNA processing, and the most highly connected nodes in the network of encoded proteins were ESR2, PHB and RC3H1. Higher mRNA levels of BCL2, IL13RA1 and SLC38A11 were seen in patients with relapse despite IRT, though these differences did not pass the false discovery rate correction. Conclusions: We show that B cells circulating in the blood of patients with MS undergoing IRT present a distinct gene expression signature, and we delineated the associated biological processes and gene interactions. Moreover, we identified genes whose expression may be an indicator of relapse risk, but further studies are needed to verify their potential value as biomarkers. [ABSTRACT FROM AUTHOR]

Background: Cold-dampness Syndrome (RA-Cold) and Hot-dampness Syndrome (RA-Hot) are two distinct groups of rheumatoid arthritis (RA) patients with different clinical symptoms based on traditional Chinese medicine (TCM) theories and clinical empirical knowledge. However, the biological basis of the two syndromes has not been fully elucidated, which may restrict the development of personalized medicine and drug discovery for RA diagnosis and therapy. Methods: An integrative strategy combining clinical transcriptomics, phenomics, and metabolomics data based on clinical cohorts and adjuvant-induced arthritis rat models was performed to identify novel candidate biomarkers and to investigate the biological basis of RA-Cold and RA-Hot. Results: The main clinical symptoms of RA-Cold patients are joint swelling, pain, and contracture, which may be associated with the dysregulation of T cell-mediated immunity, osteoblast differentiation, and subsequent disorders of steroid biosynthesis and phenylalanine metabolism. In contrast, the main clinical symptoms of RA-Hot patients are fever, irritability, and vertigo, which may be associated with various signals regulating angiogenesis, adrenocorticotropic hormone release, and NLRP3 inflammasome activation, leading to disorders of steroid biosynthesis, nicotinamide, and sphingolipid metabolism. IL17F, 5-HT, and IL4I1 were identified as candidate biomarkers of RA-Cold, while S1P and GLNS were identified as candidate biomarkers of RA-Hot. Conclusions: The current study presents the most comprehensive metabonomic and transcriptomic profiling of serum, urine, synovial fluid, and synovial tissue samples obtained from RA-Cold and RA-Hot patients and experimental animal models to date. Through the integration of multi-omics data and clinical independent validation, a list of novel candidate biomarkers of RA-Cold and RA-Hot syndromes were identified, that may be useful in improving RA diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Aim: Depression is a psychiatric disease which is accompanied by metabolic disorder. Though depression has been widely studied, its metabolism is yet to be illustrated. We aimed to manifest the underlying mechanisms to diagnose depression. Methods: One hundred thirty serum samples, including 65 patients and 65 healthy controls from different hospitals (training and validation cohorts), were recruited into the research. Sensitive Profiling for ChemoSelective Derivatization Carboxylomics (SPCSDCarboxyl) was applied to deeply hunt for the differential metabolites. Then, the serum, CSF, and hippocampus from depression rat models (CUMS group) were used to further confirm the results. Additionally, the co‐occurrence between enzymes and biomarkers, as well as the combinatorial marker panel and the correlation of biomarkers among serum, CSF, or hippocampus were elucidated. Results: Two hundred eight metabolites were identified from the sera of patients. Proline, 1‐pyrroline‐5‐carboxylate (P5C), and glutamic acid could discriminate patients from healthy humans and were confirmed to be the potential biomarkers. After further validation through CUMS rats, proline, and P5C were enriched, while glutamic acid was depleted in the CUMS group. The co‐occurrence analysis of enzymes and biomarkers indicated that they could be used for the diagnosis of depression. Moreover, the combinatorial marker panel and the correlation analysis of biomarkers between serum and CSF or between serum and hippocampus revealed that serum could be an alternative approach to directly reflect the potential physiological mechanisms and diagnose depression instead of brain samples. Conclusion: These integrated methods may facilitate the identification of biomarkers and help manifest the underlying mechanisms of depression. [ABSTRACT FROM AUTHOR]

Purpose: Finding the biomarkers associated with autism spectrum disorder (ASD) is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatments. In essence, we are faced with two challenges (i) how to learn a node representation and a clean graph structure from original graph data with high dimensionality and (ii) how to jointly model the procedure of node representation learning, structure learning and graph classification. Methods: We propose FSL-BrainNet, an interpretable graph convolution network (GCN) model for jointly Learning of node Features and clean Structures in brain networks for automatic brain network classification and interpretation. We formulate an end-to-end trainable and interpretable framework for graph classification and biomarkers (salient brain regions and potential subnetworks) identification. Results: The experimental results on the ABIDE dataset show that our proposed methods not only achieve improved prediction performance compared with the state-of-the-art methods, but also find a compact set of highly suggestive biomarkers including relevant brain regions and subnetworks to ASD. Conclusion: Through node feature learning and structure learning, our model can simultaneously select important brain regions and identify subnetworks. [ABSTRACT FROM AUTHOR]

The objective of our study is to discern the aberrant functional and structural brain connectivity in mental disorder patients, ultimately to develop quality therapeutic strategies. We developed a bivariate mixed-effects model to ascertain the differences in brain connectivity between healthy individuals and affected patients. The model accounts for subject-specific variability and heteroscedastic errors, enabling it to handle correlations within and across different connectivity measures. Such comprehensive accounting allowed us to identify notable connectivity differences between groups. To validate the performance of our model, we conducted a simulation study. Then, we applied our method to neuroimaging data for late-life depression patients, assessing biomarker identification compared to univariate mixed-effects models. [ABSTRACT FROM AUTHOR]

Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs. [ABSTRACT FROM AUTHOR]

Dimensionality reduction is an essential step in the processing of analytical chemistry data. When this reduction is carried out by variable selection, it can enable the identification of biochemical pathways. CovSel has been developed to meet this requirement, through a parsimonious selection of non-redundant variables. This article presents the g-CovSel method, which modifies the CovSel algorithm to produce highly complementary groups containing highly correlated variables. This modification requires the theoretical definition of the groups' construction and of the deflation of the data with respect to the selected groups. Two applications, on two extreme case studies, are presented. The first, based on near-infrared spectra related to four chemicals, demonstrates the relevance of the selected groups and the method's ability to handle highly correlated variables. The second, based on genomic data, demonstrates the method's ability to handle very highly multivariate data. Most of the groups formed can be interpreted from a functional point of view, making g-CovSel a tool of choice for biomarker identification in omics. Further work will be carried out to generalize g-CovSel to multi-block and multi-way data. • The CovSel algorithm has been extended to g-CovSel, in order to select groups of variables linked to multiple responses. • g-CovSel produces highly complementary groups containing highly correlated variables. • CovSel algorithm has been extended to g-CovSel, to select groups of variables linked to multiple responses. • g-CovSel appears as a promising tool for biochemical pathway identification. [ABSTRACT FROM AUTHOR]

The presented study evaluated the impact of six land management practices on soil bacterial and fungal communities under apple trees: green field belts, forests, gardens with trees and ornamental plants, gardens with farm animals, and uncultivated and cultivated orchards. The study explored soil microbial properties including the enzymatic activity of dehydrogenases (tested by colorimetric reaction with 2,3,5-triphenyl-tetrazolium chloride), metabolic profile (using Biolog™ ECO Plates), metataxonomy structure (Next Generation Sequencing using Illumina®), followed by physicochemical properties (pH, N, P, K, microelements concentrations, organic matter, and C org content). The hypothesis was that different land management practices would influence soil microbial properties, with cultivated orchards expected to show significantly lower dehydrogenases activity, and higher substrate-based respiratory than biomass response, within substrate stress occurrence, when testing metabolic profiles, but also different composition and lower relative abundances of specific microbial taxa and different biomarker genera, compared to other treatments. We aimed to identify practices promoting diverse microbial substrate-based metabolic and taxa diversity responses, with a focus on Bacillus and Trichoderma abundances, which are potential biological agents against fungal pathogens. As expected the presented research revealed significant statistical variations in microbial communities among different land management practices in soil beneath apple trees. It was accordingly noted that cultivated orchards, but also green belts, clearly exhibited reduced microbial activity (3.59 and 4.76 TPF kg−1 d−1, respectively) compared to gardens and uncultivated orchards (12.08 and 9.89 TPF kg−1 d−1). Cultivated orchards notably showed higher respiration levels and substrate stress compared, especially to forests and other land management practices represented by a clear separation of observed according to Sneath's criteria in cluster analysis. Different land management practices induce unique stress responses in microbial communities: forests struggled with B-Methyl- d -Glucoside, gardens with Serine and Putrescine, cultivated orchards with d -Glucosaminic Acid and Cyclodextrin, and bounds with 2-Hydroxy-Benzoic Acid. Substantial differences were also observed in the relative abundance of the top ten bacterial and fungal orders, and biomarker genera representatives. In cultivated orchards, there was a significant decrease in the relative abundance of many bacterial taxa such as e.g. Rhizobiales, Burkholderiales, Vivinamibacterales, and fungal taxa including Eurotiales, and Saccharomycetales. Notably, no significant differences were noted for Bacillus abundance among tested management practices. Forests favored Trichoderma abundance the most among tested practices (relative abundance 0.05 %). In turn, Trichoderma representatives were revealed as biomarker genera in gardens with animals. Williamsia representatives, as found in uncultivated orchards were suggested to be a biomarker of less disturbance, resulting from area restoration. Overall, the study discussed how different land management practices influence soil microbial communities and their functional roles, emphasizing the potential impacts of use on soil health and biodiversity within its implications. The most important recommendation bullet points: • using Williamsia representatives as a soil biomarker microorganism to indicate successful area restoration processes in apple orchards, • using d -Glucosaminic Acid metabolic stress test to reveal early difficulties in controlling fungal pathogens in soil microbial communities, • promoting diverse plant covers and reasonably reduced agrochemical inputs in apple orchards for enhancing soil microbial resilience, • preserving habitats of wild apple trees in forests or green belts, as these environments exhibit reduced metabolic stress and support the occurrence of fungi like Trichoderma , in the context of locations from which such isolates should be sought for further biocontrol use. [Display omitted] • Williamsia representatives as restoration biomarker in orchards • d -Glucosaminic Acid stress test for early reveal fungal pathogen challenges • Diverse plant covers, reduced agrochemicals boost resilience. • Preserve wild apple tree habitats for microbial diversity • Forests and green belts support Trichoderma for potential isolate sources for biocontrol. [ABSTRACT FROM AUTHOR]

Metabolic disease is a significant risk factor for severe COVID-19 infection, but the contributing pathways are not yet fully elucidated. Using data from two randomized controlled trials across 13 U.S. academic centers, our goal was to characterize metabolic features that predict severe COVID-19 and define a novel baseline metabolomic signature. Individuals (n = 133) were dichotomized as having mild or moderate/severe COVID-19 disease based on the WHO ordinal scale. Blood samples were analyzed using the Biocrates platform, providing 630 targeted metabolites for analysis. Resampling techniques and machine learning models were used to determine metabolomic features associated with severe disease. Ingenuity Pathway Analysis (IPA) was used for functional enrichment analysis. To aid in clinical decision making, we created baseline metabolomics signatures of low-correlated molecules. Multivariable logistic regression models were fit to associate these signatures with severe disease on training data. A three-metabolite signature, lysophosphatidylcholine a C17:0, dihydroceramide (d18:0/24:1), and triacylglyceride (20:4_36:4), resulted in the best discrimination performance with an average test AUROC of 0.978 and F1 score of 0.942. Pathways related to amino acids were significantly enriched from the IPA analyses, and the mitogen-activated protein kinase kinase 5 (MAP2K5) was differentially activated between groups. In conclusion, metabolites related to lipid metabolism efficiently discriminated between mild vs. moderate/severe disease. SDMA and GABA demonstrated the potential to discriminate between these two groups as well. The mitogen-activated protein kinase kinase 5 (MAP2K5) regulator is differentially activated between groups, suggesting further investigation as a potential therapeutic pathway.

Background: Intake of sweet and fatty snacks may partly contribute to the occurrence of obesity and other health conditions in childhood. Traditional dietary assessment methods may be limited in accurately assessing the intake of sweet and fatty snacks in children. Metabolite biomarkers may aid the objective assessment of children's food intake and support establishing diet-disease relationships., Objectives: The present study aimed to identify biomarkers of sweet and fatty snack intake in 2 independent cohorts of European children., Methods: We used data from the IDEFICS/I.Family cohort from baseline (2007/2008) and 2 follow-up examination waves (2009/2010 and 2013/2014). In total, 1788 urine samples from 599 children were analyzed for untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry. Short-term dietary intake was assessed by 24-h dietary recalls, and habitual dietary intake was calculated with the National Cancer Institute method. Data from the Dortmund Nutritional and Anthropometric Longitudinal Designed (DONALD) cohort of 24-h urine samples (n = 567) and 3-d weighted dietary records were used for external replication of results. Multivariate modeling with unbiased variable selection in R algorithms and linear mixed models were used to identify novel biomarkers. Metabolite features significantly associated with dietary intake were then annotated., Results: In total, 66 metabolites were discovered and found to be statistically significant for chocolate candy; cakes, puddings, and cookies; candy and sweets; ice cream; and crisps. Most of the features (n = 62) could not be annotated. Short-term and habitual chocolate intake were positively associated with theobromine, xanthosine, and cyclo(L-prolyl-L-valyl). These results were replicated in the DONALD cohort. Short-term candy and sweet intake was negatively associated with octenoylcarnitine., Conclusions: Of the potential metabolite biomarkers of sweet and fatty snacks in children, 3 biomarkers of chocolate intake, namely theobromine, xanthosine, and cyclo(L-prolyl-L-valyl), are externally replicated. However, these potential biomarkers require further validation in children., Competing Interests: Conflict of interest AF reports financial support was provided by German Research Foundation. PK-R reports financial support was provided by French National Research Agency. All other authors report no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Random Forest models are widely used in genomic data analysis and can offer insights into complex biological mechanisms, particularly when features influence the target in interactive, nonlinear, or nonadditive ways. Currently, some of the most efficient Random Forest methods in terms of computational speed are implemented in Python. However, many biologists use R for genomic data analysis, as R offers a unified platform for performing additional statistical analysis and visualization. Here, we present an R package, pyRforest, which integrates Python scikit-learn "RandomForestClassifier" algorithms into the R environment. pyRforest inherits the efficient memory management and parallelization of Python, and is optimized for classification tasks on large genomic datasets, such as those from RNA-seq. pyRforest offers several additional capabilities, including a novel rank-based permutation method for biomarker identification. This method can be used to estimate and visualize P-values for individual features, allowing the researcher to identify a subset of features for which there is robust statistical evidence of an effect. In addition, pyRforest includes methods for the calculation and visualization of SHapley Additive exPlanations values. Finally, pyRforest includes support for comprehensive downstream analysis for gene ontology and pathway enrichment. pyRforest thus improves the implementation and interpretability of Random Forest models for genomic data analysis by merging the strengths of Python with R. pyRforest can be downloaded at: https://www.github.com/tkolisnik/pyRforest with an associated vignette at https://github.com/tkolisnik/pyRforest/blob/main/vignettes/pyRforest-vignette.pdf., (© The Author(s) 2024. Published by Oxford University Press.)

In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such dataset describing patients with bladder cancer, mostly non-muscle-invasive (NMIBC), and propose a novel approach to feature selection. This approach returns high-quality features for prediction and yet allows interpretability as well as a certain level of insight into the analyzed data. As a result, we obtain a small set of seven of the most-useful biomarkers for diagnostics. They can also be used to build tests that avoid the costly and time-consuming existing methods. We summarize the current biological knowledge of the chosen biomarkers and contrast it with our findings. [ABSTRACT FROM AUTHOR]

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI). • Pioneering traumatic seizure classification with multimodal neuroimaging data (dMRI, EEG, and fMRI). • Bayesian estimator for direct classification, handling missing modality in data. • Novel fusion algorithms for integrating multivariate information. • Modality-specific potential biomarkers using statistics and explainable machine learning. [ABSTRACT FROM AUTHOR]