Similar Network Research Articles

Integrating multi-omics data reveals neuroblastoma subtypes in the tumor microenvironment

Neuroblastoma (NB) is a severe pediatric tumor originating from the developing sympathetic nervous system, characterized by diverse clinical outcomes, including spontaneous regression and aggressive metastasis. This variability suggests the existence of different NB subtypes, necessitating accurate classification for effective targeted treatment. In this study, we employed the similarity network fusion (SNF) algorithm and identified three NB subtypes, including mesenchymal-like (MES), MYCN-like (MYCN), and neurogenic-like (Neurogenic). The MES subtype exhibited the highest activation of immune-related pathways. The MYCN subtype demonstrated the worst prognosis, with enrichment in cell growth and proliferation pathways. Conversely, the Neurogenic subtype showed the best prognosis, with enrichment in sympathetic nervous system development processes. Through single-cell RNA sequencing (scRNA-seq) analysis, we examined the tumor microenvironments of these distinct NB subtypes, revealing divergent differentiation trajectories for adrenergic cells within the MYCN and Neurogenic subtypes. We also identified a significant presence of naïve T cells in the MES subtype, as well as mesenchymal cell subtypes associated with the unique plasticity observed in both the MES and MYCN subtypes. Drug sensitivity prediction analysis suggested that the MES subtype may respond favorably to MEK inhibitors, while the MYCN subtype may be susceptible to Bcl-2 inhibitors. Our integrative multi-omics approach enabled precise stratification of NB into biologically distinct subtypes, potentially facilitating the development of subtype-specific therapeutic strategies for improved patient management and survival outcomes.

Warm temperature perceived at the vegetative stage affects progeny seed germination in natural accessions of Arabidopsis thaliana.

Temperatures perceived early in the life cycle of mother plants can affect the germination of the offspring seeds. In Arabidopsis thaliana, vernalisation-insensitive mutants showed altered germination response to elevated maternal temperature, hence revealing a strong genetic determinism. However, the genetic control of this maternal effect and its prevalence across natural populations remain unclear. Here, we exposed a collection of European accessions of A. thaliana to increased temperature during the vegetative phase and assessed germination in their progeny to identify the genetic basis of transgenerational germination response. We found that genotypes with rapidly germinating progeny after early maternal exposure to elevated temperature originated from regions with low-light radiation. Combining genome-wide association, expression analysis and functional assays across multiple genetic backgrounds, we show a central role for PHYB in mediating the response to maternally perceived temperature at the vegetative stage. Differential gene expression analysis in leaves identified a similar genetic network as previously found in seed endosperm under elevated temperature, supporting the pleiotropic involvement of PHYB signalling across different tissues and stages. This provides evidence that complex environmental responses modulated by the maternal genotype can rely on a consistent set of genes yet produce different effects at the different stages of exposure.

Chemical diversity of cyanobacterial natural products.

Covering: 2010 to 2023Cyanobacterial natural products are a diverse group of molecules with promising biotechnological applications. This review examines the chemical diversity of 995 cyanobacterial metabolites reported from 2010 to 2023. A computational analysis using similarity networking was applied to visualize the chemical space and to compare the diversity of cyanobacterial metabolites among taxonomic orders and environmental sources. Key examples are highlighted, detailing their sources, biological activities, and discovery processes.

Prediction of miRNA-disease association based on multisource inductive matrix completion.

MicroRNAs (miRNAs) are endogenous non-coding RNAs approximately 23 nucleotides in length, playing significant roles in various cellular processes. Numerous studies have shown that miRNAs are involved in the regulation of many human diseases. Accurate prediction of miRNA-disease associations is crucial for early diagnosis, treatment, and prognosis assessment of diseases. In this paper, we propose the Autoencoder Inductive Matrix Completion (AEIMC) model to identify potential miRNA-disease associations. The model captures interaction features from multiple similarity networks, including miRNA functional similarity, miRNA sequence similarity, disease semantic similarity, disease ontology similarity, and Gaussian interaction kernel similarity between miRNAs and diseases. Autoencoders are used to extract more complex and abstract data representations, which are then input into the inductive matrix completion model for association prediction. The effectiveness of the model is validated through cross-validation, stratified threshold evaluation, and case studies, while ablation experiments further confirm the necessity of introducing sequence and ontology similarities for the first time.

DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction

In recent years, numerous studies have highlighted the pivotal importance of miRNAs in personalized healthcare, showcasing broad application prospects. miRNAs hold significant potential in disease diagnosis, prognosis assessment, and therapeutic target discovery, making them an integral part of precision medicine. They are expected to enable precise disease subtyping and risk prediction, thereby advancing the development of precision medicine. GNNs, a class of deep learning architectures tailored for graph data analysis, have greatly facilitated the advancement of miRNA-disease association prediction algorithms. However, current methods often fall short in leveraging network node information, particularly in utilizing global information while neglecting the importance of local information. Effectively harnessing both local and global information remains a pressing challenge. To tackle this challenge, we propose an innovative model named DGNMDA. Initially, we constructed various miRNA and disease similarity networks based on authoritative databases. Subsequently, we creatively design a dual heterogeneous graph neural network encoder capable of efficiently learning feature information between adjacent nodes and similarity information across the entire graph. Additionally, we develop a specialized fine-grained multi-layer feature interaction gating mechanism to integrate outputs from the neural network encoders to identify novel associations connecting miRNAs with diseases. We evaluate our model using 5-fold cross-validation and real-world disease case studies, based on the HMDD V3.2 dataset. Our method demonstrates superior performance compared to existing approaches in various tasks, confirming the effectiveness and potential of DGNMDA as a robust method for predicting miRNA-disease associations.

Similar PTSD symptom networks observed in male and female survivors of military sexual assault: implications for understanding trauma responses

Similar PTSD symptom networks observed in male and female survivors of military sexual assault: implications for understanding trauma responses

Whole-Genome Sequencing And Characterization Of Two Bacillus velezensis Strains from Termitarium and A Comprehensive Comparative Genomic Analysis of Biosynthetic Gene Clusters.

The speciesBacillus velezensisis known for its biosynthetic potential and metabolic versatility in producing several secondary metabolites and promoting plant growth. In this study, we isolated twoB. velezensisstrains, WGTg-8 and WGTm-299, from the termite gut and termitarium, which exhibited antimicrobial activity against multiple clinical and phytopathogens. The whole genomes of these strains were sequenced using the Illumina platform and annotated. The genome mining of the draft genome sequences revealed 48 biological gene clusters (BGCs) responsible for synthesizing various secondary metabolites. The construction of the similarity network of the BGCs and the comparative analysis with the genetically related organisms are aided in the identification of metabolites produced by these strains. We identified biosynthetic gene clusters (BGCs) coding for macrolactin H, bacilysin, bacillibactin, amylocyclin, comX4, and LCI, found in both strains with 100% similarity. The difficidin, bacillaene, thusin_alpha, and cericidin BGCs are exclusively found in strain WGTg-8, while the colicin BGC is exclusively present in the WGTm-299 strain. The fengycin and surfactin gene clusters are present in both strains with 80% similarity. Furthermore, 28 putative NRPS BGCs, NRPS-T1PKS hybrid clusters, a T1PKS, and a bacteriocin BGC were identified with very low similarity (≤ 25%) or no similarity with known antibiotics. In addition, we found several genes coding for plant growth-promoting properties, including nitrogen metabolism, hormone synthesis, sulfur metabolism, phosphate metabolism, and a few other properties.

Morphological similarity and white matter structural mapping of new daily persistent headache: a structural connectivity and tract-specific study

BackgroundNew daily persistent headache (NDPH) is a rare primary headache disorder characterized by daily and persistent sudden onset headaches. Specific abnormalities in gray matter and white matter structure are associated with pain, but have not been well studied in NDPH. The objective of this work is to explore the fiber tracts and structural connectivity, which can help reveal unique gray and white matter structural abnormalities in NDPH.MethodsThe regional radiomics similarity networks were calculated from T1 weighted (T1w) MRI to depict the gray matter structure. The fiber connectivity matrices weighted by diffusion metrics like fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD) were built, meanwhile the fiber tracts were segmented by anatomically-guided superficial fiber segmentation (Anat-SFSeg) method to explore the white matter structure from diffusion MRI. The considerable different neuroimaging features between NDPH and healthy controls (HC) were extracted from the connectivity and tract-based analyses. Finally, decision tree regression was used to predict the clinical scores (i.e. pain intensity) from the above neuroimaging features.ResultsT1w and diffusion MRI data were available in 51 participants after quality control: 22 patients with NDPH and 29 HCs. Significantly decreased morphological similarity was found between the right superior frontal gyrus and right hippocampus. The superficial white matter (SWM) showed significantly decreased FA in fiber tracts including the right superficial-frontal, left superficial-occipital, bilateral superficial-occipital-temporal (Sup-OT) and right superficial-temporal, meanwhile significant increased RD was found in the left Sup-OT. For the fiber connectivity, NDPH showed significantly decreased FA in the bilateral basal ganglion and temporal lobe, increased MD in the right frontal lobe, and increased RD in the right frontal lobe and left temporal-occipital lobe. Clinical scores could be predicted dominantly by the above significantly different neuroimaging features through decision tree regression.ConclusionsOur research indicates the structural abnormalities of SWM and the neural pathways projected between regions like right hippocampus and left caudate nucleus, along with morphological similarity changes between the right superior frontal gyrus and right hippocampus, constitute the pathological features of NDPH. The decision tree regression demonstrates correlations between these structural changes and clinical scores.

Text–video retrieval re-ranking via multi-grained cross attention and frozen image encoders

State-of-the-art methods for text–video retrieval generally leverage CLIP embeddings and cosine similarity for efficient retrieval. Meanwhile, recent advancements in cross-attention techniques introduce transformer decoders to facilitate attention computation between text queries and visual tokens extracted from video frames, enabling a more comprehensive interaction between textual and visual information. In this study, we combine the advantages of both approaches and propose a fine-grained re-ranking approach incorporating a multi-grained text–video cross attention module. Specifically, the re-ranker enhances the top K similar candidates identified by the cosine similarity network. To explore video and text interactions efficiently, we introduce frame and video token selectors to obtain salient visual tokens at both frame and video levels. Then, a multi-grained cross-attention mechanism is applied between text and visual tokens at these levels to capture multimodal information. To reduce the training overhead associated with the multi-grained cross-attention module, we freeze the vision backbone and only train the multi-grained cross attention module. This frozen strategy allows for scalability to larger pre-trained vision models such as ViT-G, leading to enhanced retrieval performance. Experimental evaluations on text–video retrieval datasets showcase the effectiveness and scalability of our proposed re-ranker combined with existing state-of-the-art methodologies.

Using Multiple Drug Similarity Networks to Promote Adverse Drug Event Detection

The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://drug-drug-sim.aimedlab.net/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring.

GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search

Biological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to the query networks. Great progress has been achieved in biological network similarity searches. However, it remains a challenge to mine the biological network information fully to improve the accuracy of query results without increasing time overheads. In this study, we propose a biological network similarity search method based on graph neural networks named GBNSS, which combines topological and biological information (GO annotations) of biological networks into graph neural networks to find topologically and biologically similar biological networks in the database. Additionally, GBNSS is a topology-free biological network similarity search method with an arbitrary network structure. The experimental results on four benchmark datasets show that GBNSS outperforms the existing methods in terms of computational efficiency and search accuracy. Case studies further demonstrate that GBNSS is capable of searching similar networks in real-world biological networks.

Prediction of QT interval in ECGs through baseline wander removal and deep learning technique

Abstract Background In electrocardiograms (ECGs), the QT interval, calculated from the onset of the Q-wave to the end of the T-wave, reflects the repolarization status of the heart. Currently, the QT interval automatically measured in 12-lead electrocardiograms is inaccurately derived as the median value, rather than utilizing the more precise mean value of QT intervals from all leads. This approach fails to provide information on heterogeneous QT prolongation crucial for predicting arrhythmias, as it does not accurately represent the values of QT intervals in each lead. Purpose Traditional analysis of ECGs based on QT intervals necessitates direct interpretation by cardiologists, leading to potential errors in diagnosis. In response to this challenge, we present a model that integrates low-pass filtering and continuous wavelet transformation to eliminate baseline wander noise occurring during ECG measurements. Moreover, to account for temporal features, we incorporate Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) to estimate QT intervals. Methods The dataset used in this study was the QT Database, comprising ECG data for 105 patients recorded at a sampling rate of 250Hz over a duration of 15 minutes. Each ECG was associated with annotations for two leads and information regarding the P, QRS, and T waves. We structured the dataset into a ratio of approximately 8:1:1, resulting in 51 patients for training (15,490 samples), 6 patients for validation (1,564 samples), and 7 patients for testing (1,682 samples). Results For the evaluation of deep learning, we selected Accuracy and F1-Score as metrics. We experimented with two configurations: the proposed model with two Bi-LSTM layers and an alternative with a single Bi-LSTM layer of 100 units. We also tested a similar recurrent neural network, GRU, with the same unit configuration. The results indicated that the method with two Bi-LSTM layers outperformed others in both Accuracy and F1-Score. Conclusion By incorporating CNN and Bi-LSTM, we conducted QT interval estimation, leveraging temporal features. This methodology facilitated the elimination of diverse noise types during ECG measurements, resulting in enhanced accuracy for QT interval estimation through the consideration of temporal ECG characteristics. In our future research endeavors, we plan to deploy this model for predicting arrhythmias, specifically targeting conditions like long QT syndrome and drug-induced QT prolongation.Methods and result

CMAGN: circRNA–miRNA association prediction based on graph attention auto-encoder and network consistency projection

Background: As noncoding RNAs, circular RNAs (circRNAs) can act as microRNA (miRNA) sponges due to their abundant miRNA binding sites, allowing them to regulate gene expression and influence disease development. Accurately identifying circRNA–miRNA associations (CMAs) is helpful to understand complex disease mechanisms. Given that biological experiments are time consuming and labor intensive, alternative computational methods to predict CMAs are urgently needed. Results: This study proposes a novel computational model named CMAGN, which incorporates several advanced computational methods, for predicting CMAs. First, similarity networks for circRNAs and miRNAs are constructed according to their sequences. Graph attention autoencoder is then applied to these networks to generate the first representations of circRNAs and miRNAs. The second representations of circRNAs and miRNAs are obtained from the CMA network via node2vec. The similarity networks of circRNAs and miRNAs are reconstructed on the basis of these new representations. Finally, network consistency projection is applied to the reconstructed similarity networks and the CMA network to generate a recommendation matrix. Conclusion: Five-fold cross-validation of CMAGN reveals that the area under ROC and PR curves exceed 0.96 on two widely used CMA datasets, outperforming several existing models. Additional tests elaborate the reasonability of the architecture of CMAGN and uncover its strengths and weaknesses.

Targeted discovery of polyketides with antioxidant activity through integrated omics and cocultivation strategies.

Fungi generate a diverse array of bioactive compounds with significant pharmaceutical applications. However, the chemical diversity of natural products in fungi remains largely unexplored. Here, we present a paradigm for specifically discovering diverse and bioactive compounds from fungi by integrating genome mining with building block molecular network and coculture analysis. Through pangenome and sequence similarity network analysis, we identified a rare type I polyketide enzyme from Penicillium sp. ZJUT-34. Subsequent building block molecular network and coculture strategy led to the identification and isolation of a pair of novel polyketides, (±)-peniphenone E [(±)-1], three known polyketides (2-4), and three precursor compounds (5-7) from a combined culture of Penicillium sp. ZJUT-34 and Penicillium sp. ZJUT23. Their structures were established through extensive spectroscopic analysis, including NMR and HRESIMS. Chiral HPLC separation of compound 1 yielded a pair of enantiomers (+)-1 and (-)-1, with their absolute configurations determined using calculated ECD methods. Compound (±)-1 is notable for its unprecedented structure, featuring a unique 2-methyl-hexenyl-3-one moiety fused with a polyketide clavatol core. We proposed a hypothetical biosynthetic pathway for (±)-1. Furthermore, compounds 2, 5, and 6 exhibited strong antioxidant activity, whereas (-)-1, (+)-1, 3, and four exhibited moderate antioxidant activity compared to the positive control, ascorbic acid. Our research demonstrates a pioneering strategy for uncovering novel polyketides by merging genome mining, metabolomics, and cocultivation methods. This approach addresses the challenge of discovering natural compounds produced by rare biosynthetic enzymes that are often silent under conventional conditions due to gene regulation.IMPORTANCEPolyketides, particularly those with complex structures, are crucial in drug development and synthesis. This study introduces a novel approach to discover new polyketides by integrating genomics, metabolomics, and cocultivation strategies. By combining genome mining, building block molecular networks, and coculturing techniques, we identified and isolated a unique polyketide, (±)-peniphenone E, along with three known polyketides and three precursor compounds from Penicillium sp. ZJUT-34 and Penicillium sp. ZJUT23. This approach highlights the potential of using combined strategies to explore fungal chemical diversity and discover novel bioactive compounds. The successful identification of (±)-peniphenone E, with its distinctive structure, demonstrates the effectiveness of this integrated method in enhancing natural product discovery and underscores the value of innovative approaches in natural product research.

Evidence of community structure in phonological networks of multiple languages.

Thousands of phonological word forms known to a speaker can be organised as a lexical network using the tools of network science. In these networks, nodes represent words and edges are placed between phonological neighbours. Previous work has shown that phonological networks of various languages have similar macrolevel network properties. The present study aimed to investigate if phonological networks of different languages also have similar mesolevel properties, specifically, the presence of robust community structure. Prior community detection analyses revealed robust community structure for English. Community detection analyses conducted on French, German, Dutch, and Spanish networks indicate that all networks showed strong evidence of community structure-mesolevel clustering of word forms whereby larger communities tended to contain shorter, frequent words with many phonological neighbours. Words of the same community tended to share similar phonotactic structures. Results suggest that the organisation of phonological word forms in language are governed by similar principles that could have important implications for lexical processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks

BackgroundLong non-coding RNAs (lncRNAs) can prevent, diagnose, and treat a variety of complex human diseases, and it is crucial to establish a method to efficiently predict lncRNA-disease associations.ResultsIn this paper, we propose a prediction method for the lncRNA-disease association relationship, named LDAGM, which is based on the Graph Convolutional Autoencoder and Multilayer Perceptron model. The method first extracts the functional similarity and Gaussian interaction profile kernel similarity of lncRNAs and miRNAs, as well as the semantic similarity and Gaussian interaction profile kernel similarity of diseases. It then constructs six homogeneous networks and deeply fuses them using a deep topology feature extraction method. The fused networks facilitate feature complementation and deep mining of the original association relationships, capturing the deep connections between nodes. Next, by combining the obtained deep topological features with the similarity network of lncRNA, disease, and miRNA interactions, we construct a multi-view heterogeneous network model. The Graph Convolutional Autoencoder is employed for nonlinear feature extraction. Finally, the extracted nonlinear features are combined with the deep topological features of the multi-view heterogeneous network to obtain the final feature representation of the lncRNA-disease pair. Prediction of the lncRNA-disease association relationship is performed using the Multilayer Perceptron model. To enhance the performance and stability of the Multilayer Perceptron model, we introduce a hidden layer called the aggregation layer in the Multilayer Perceptron model. Through a gate mechanism, it controls the flow of information between each hidden layer in the Multilayer Perceptron model, aiming to achieve optimal feature extraction from each hidden layer.ConclusionsParameter analysis, ablation studies, and comparison experiments verified the effectiveness of this method, and case studies verified the accuracy of this method in predicting lncRNA-disease association relationships.

Dual-Approach Co-expression Analysis Framework (D-CAF) Enables Identification of Novel Circadian Regulation From Multi-Omic Timeseries Data.

The circadian clock is a central driver of many biological and behavioral processes, regulating the levels of many genes and proteins, termed clock controlled genes and proteins (CCGs/CCPs), to impart biological timing at the molecular level. While transcriptomic and proteomic data has been analyzed to find potential CCGs and CCPs, multi-omic modeling of circadian data, which has the potential to enhance the understanding of circadian control of biological timing, remains relatively rare due to several methodological hurdles. To address this gap, a Dual-approach Co-expression Analysis Framework (D-CAF) was created to perform perturbation-robust co-expression analysis on time-series measurements of both transcripts and proteins. Applying this D-CAF framework to previously gathered transcriptomic and proteomic data from mouse macrophages gathered over circadian time, we identified small, highly significant clusters of oscillating transcripts and proteins in the unweighted similarity matrices and larger, less significant clusters of of oscillating transcripts and proteins using the weighted similarity network. Functional enrichment analysis of these clusters identified novel immunological response pathways that appear to be under circadian control. Overall, our findings suggest that D-CAF is a tool that can be used by the circadian community to integrate multi-omic circadian data to improve our understanding of the mechanisms of circadian regulation of molecular processes.

Characterization of two bacterial tyrosinases from the halophilic bacterium Hahella sp. CCB MM4 relevant for phenolic compounds oxidation in wetlands.

Tyrosinases (TYRs) are type-3 copper proteins that are widely distributed in nature. They can hydroxylate and oxidize phenolic molecules and are mostly known for producing melanins that confer protection against photo induced damage. TYRs are also thought to play an important role in the 'latch mechanism', where high concentrations of phenolic compounds inhibit oxidative decomposition of organic biomass and subsequent CO2 release, especially relevant in wetland environments. In the present study, we describe two TYRs, HcTyr1 and HcTyr2, from halophilic bacterium Hahella sp. CCB MM4 previously isolated at Matang mangrove forest in Perak, Malaysia. The structure of HcTyr1 was determined by X-ray crystallography at a resolution of 1.9 Å and represents an uncharacterized group of prokaryotic TYRs as demonstrated by a sequence similarity network analysis. The genes encoding the enzymes were cloned, expressed, purified and thoroughly characterized by biochemical methods. HcTyr1 was able to self-cleave its lid-domain (LID) in a protease independent manner, whereas the LID of HcTyr2 was essential for activity and stability. Both enzymes showed variable activity in the presence of different metals, surfactants and NaCl, and were able to oxidize lignin constituents. The high salinity tolerance of HcTyr1 indicates that the enzyme can be an efficient catalyst in the habitat of the host.

Cortical morphological changes and associated transcriptional signatures in post-traumatic stress disorder and psychological resilience.

Individuals who have experienced severe traumatic events are estimated to have a post-traumatic stress disorder (PTSD) prevalence rate ranging from 10 to 50%, while those not affected by trauma exposure are often considered to possess psychological resilience. However, the neural mechanisms underlying the development of PTSD, especially resilience after trauma, remain unclear. This study aims to investigate changes of cortical morphometric similarity network (MSN) in PTSD and trauma-exposed healthy individuals (TEHI), as well as the associated molecular alterations in gene expression, providing potential targets for the prevention and intervention of PTSD. We recruited PTSD patients and TEHI who had experienced severe earthquakes, and healthy controls who had not experienced earthquakes. We identified alterations in the whole-brain MSN changes in PTSD and TEHI, and established associations between these changes and brain-wide gene expression patterns from the Allen Human Brain Atlas microarray dataset using partial least squares regression. At the neuroimaging level, we found not only trauma-susceptible changes in TEHI same as those in PTSD, but also unique neurobiological alterations to counteract the deleterious impact of severe trauma. We identified 1444 and 2214 genes transcriptionally related to MSN changes in PTSD and TEHI, respectively. Functional enrichment analysis of weighted gene expression for PTSD and TEHI revealed distinct enrichments in Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, gene expression profiles of astrocytes, excitatory neurons, and microglial cells are highly related to MSN abnormalities in PTSD. The formation of resilience may be by an active compensatory process of the brain. The combination of macroscopic neuroimaging changes and microscopic human brain transcriptomics could offer a more direct and in-depth understanding of the pathogenesis of PTSD and psychological resilience, shedding light on new targets for the prevention and treatment of PTSD.

Optimizing clinical research in a regional cancer center network: A multidepartment survey-based approach.

27 Background: Memorial Sloan Kettering Cancer Center’s (MSKCC) regional care network (RCN) encompasses 6 sites across NY and NJ. Further expansion of clinical research (CR) in the RCN provides an opportunity to bring clinical research closer to patients. The CR RCN leadership group was created to identify and address barriers to research operations in the RCN. Methods: A survey was conducted among Medical, Surgical, Neurologic and Radiation Oncologists at the RCN sites (February 2022-April 2024) to assess perspectives on research-related challenges. The 23-question survey included multiple-choice, open-ended, and ranking questions regarding protocol availability, consenting processes, and staffing support. Results were used to inform interventions to address barriers in the RCN. Results: 92/108 (85%) medical oncologists, 16/31 (52%) radiation oncologists, 7/9 (77%) neurologists and 28/70 (40%) surgeons completed the survey, revealing three key challenges: 1) Limited protocol availability: Only 47% of the time was there a suitable medicine trial available in the RCN. Identified barriers to opening desired medicine protocols (n=23 in total) included trials that were closed to accrual, pending amendments, and sponsor approval issues. 2) Inadequate investigator representation: 278 medicine protocols lacked RCN physicians listed as consenting providers. 3) Insufficient research staff. Only 33% were satisfied with the research support, and 62% of physicians identified lack of clinical research support staff as the biggest barrier to accrual. 71% reported delays of more than one week in screening eligible patients due to insufficient support. Following these results, interventions were implemented across departments to overcome these challenges. Appropriate RCN physicians were added to the 278 protocols, and an analysis of staffing needs increased clinical trial nursing and administrative staff. Therapeutic and non-therapeutic accruals in the Department of Medicine rose by 10.5%, and total research visits increased by 21.4% compared to the previous year. Thirteen of the 23 studies in medicine, that were previously only open at main campus, were opened to accrual in the RCN. This survey highlighted research portfolio gaps in multiple departments that are now being addressed. Conclusions: This multi departmental survey-based approach identified key barriers to CR within MSKCC's RCN, and in turn, informed interventions that increased research activity. These findings offer a potential roadmap for optimizing research in similar networks by enhancing protocol accessibility, addressing staffing needs, and fostering collaboration. Further investigations are ongoing to address additional challenges and improve workflows across departments.