Complex Interactions Research Articles

Challenging Yet Rewarding: Staff Experiences in Prolonged Disorders of Consciousness Rehabilitation.

Challenging Yet Rewarding: Staff Experiences in Prolonged Disorders of Consciousness Rehabilitation.

Pathological roles of NETs-platelet synergy in thrombotic diseases: From molecular mechanisms to therapeutic targeting.

Pathological roles of NETs-platelet synergy in thrombotic diseases: From molecular mechanisms to therapeutic targeting.

From quiescence to self-sustained activity: How astrocytes reshape neural dynamics.

From quiescence to self-sustained activity: How astrocytes reshape neural dynamics.

Electronic health records (EHR)-based machine learning (ML) approach to predict risk of progression to metastatic melanoma after initial diagnosis.

9560 Background: Strategies to predict progression to metastasis in early-stage melanoma patients have relied on a limited sample size and a limited set of clinical or genomic features. Prior studies were able to achieve good discrimination in small cohorts, but applying advanced machine learning techniques to large datasets with deep clinical and molecular data may yield tools with enhanced generalizability and clinical utility. Methods: We employed a machine learning approach to predict the likelihood of progression to metastatic melanoma for a cohort with an initial diagnosis of stage 0-3 (n=7477) using both structured and human-curated information in the ConcertAI Patient360 melanoma EHR dataset. Patients with uveal melanoma, a second primary malignancy, or clinical trial participation status were excluded. A total of 68 features including staging, demographic, testing, biomarker, and clinical tumor information recorded within 30 days of initial melanoma diagnosis were used to train several machine learning frameworks to predict the likelihood of progression to metastatic melanoma. A logistic regression, random forest classifier, gradient boosting decision tree, and XgBoost framework were compared using the AUC from a 20% hold-out set to determine the optimal framework after hyperparameter tuning. Additional evaluation metrics, which include accuracy, precision, recall, and F1 were computed for the final model. Feature importance measures were determined using Shapley Additive Explanation (SHAP) dependence plots. Permutation (N=1000) was utilized to evaluate the predictive power of the final model. Results: An XgBoost approach produced a test AUC of 0.708 with a pseudo-p value = 0.001 from permutation. Notably, the model produced a precision of 0.709 on the hold-out set. SHAP dependence measures showed that the most important features used for predictions include those involving initial staging and clinical measures of the tumor. Specifically, lower initial stage corresponded with lower predicted probability of metastatic progression. Similarly, higher values of mitotic rate and tumor thickness corresponded with higher predicted probability of progression. In addition, more complex interactions between features also contributed to the improved performance of the XGBoost framework. Conclusions: An XgBoost framework trained on a large set clinical features for 7477 melanoma patients predicted metastatic progression with significant predictive power (p = 0.001) yielding an AUC of 0.708. The model relied heavily on staging information at initial diagnosis and information on tumor size, mitotic rate, and ulceration status to make predictions, which were typically reported in unstructured EMR. These results indicate the clinical utility for machine learning models trained on real world data for both providers and patients.

Interactions between radiotherapy resistance mechanisms and the tumor microenvironment.

Interactions between radiotherapy resistance mechanisms and the tumor microenvironment.

Navigating the tumor landscape: VEGF, MicroRNAs, and the future of cancer treatment.

Navigating the tumor landscape: VEGF, MicroRNAs, and the future of cancer treatment.

NLP-driven integration of electrophysiology and traditional Chinese medicine for enhanced diagnostics and management of postpartum pain.

Postpartum pain encompasses a range of physical and emotional discomforts, often influenced by hormonal changes, physical recovery, and individual psychological states. The complex interactions between the variables can make it difficult for traditional diagnostic techniques to fully capture, creating inadequacies and inefficient management techniques. The aims to develop a comprehensive diagnostic and management framework for postpartum pain by integrating Natural Language Processing (NLP), electrophysiological data, and Traditional Chinese Medicine (TCM) principles. The seeks to enhance the accuracy of postpartum pain diagnosis, uncover meaningful correlations between TCM diagnoses and physiological markers, and optimize personalized treatment strategies. The focuses on analyzing textual data from patient-reported symptoms, medical records, and TCM diagnosis notes. Data pre-processing involves text cleaning and tokenization, followed by feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) to capture meaningful patterns. For diagnostics and management, a Refined Coyote Optimized Deep Recurrent Neural Network (RCO-DRNN) is employed to analyze and predict pain profiles, combining insights from TCM diagnoses with physiological markers. The results highlight the effectiveness of RCO-DRNN in accurately diagnosing pain types and offering personalized and holistic management strategies. This approach represents a significant advancement in integrating data-driven methodologies with traditional medical practices, providing a more comprehensive framework for postpartum pain management. The RCO-DRNN continuously beats the other models after thorough evaluation using metrics like MSE, MAE, and R2, obtaining the lowest MSE (0.005), the smallest MAE (0.04), and the highest R2 (0.98).

From wastewater to sludge: The role of microplastics in shaping anaerobic digestion performance and antibiotic resistance gene dynamics.

From wastewater to sludge: The role of microplastics in shaping anaerobic digestion performance and antibiotic resistance gene dynamics.

Transcranial alternating current stimulation for investigating complex oscillatory dynamics and interactions.

Transcranial alternating current stimulation for investigating complex oscillatory dynamics and interactions.

Integrated toxicity assessment of complex chemical mixtures in catalytic reactions.

Integrated toxicity assessment of complex chemical mixtures in catalytic reactions.

Predicting therapeutic response in stomach adenocarcinoma by integrating H&E and RNA-seq using deep learning.

e16088 Background: Recent breakthroughs in computational pathology have transformed how we analyze histopathology images, making it possible to predict clinical outcomes with incredible precision and speed. Yet, a key challenge remains: how do we turn these computational insights into something biologically meaningful that can guide real-world clinical decisions? That’s where human-interpretable image features (HIFs) come in. HIFs offer a detailed look at the tumor microenvironment (TME), helping us understand complex biological interactions in a way that makes sense for clinical care. In this study, we explored how HIFs derived from high-resolution histopathology images could predict the expression of genes linked to therapeutic response in stomach adenocarcinoma (STAD). Methods: Using whole-slide histopathology images and clinical data from The Cancer Genome Atlas, we analyzed the tumor microenvironment of therapeutically responsive and resistance patients. Expert pathologists annotated key features of the tissue samples were used to identify areas such as cancer, stroma, necrosis, and normal tissue, as well as different cell types like cancer cells, lymphocytes, macrophages, plasma cells, and fibroblasts. These annotations are used to train convolutional neural network for pattern recognition and HIF identification which are features that capture the biological makeup of the TME. We focussed on the genes that showed significant differences between therapeutic resistance vs responders (p < 0.01). We then analyzed how HIFs correlated with significantly expressed genes focusing only on strong correlations (ρ > 0.4). Results: One of the most intriguing findings was that lower expression of a gene called LMNB1 was strongly linked to therapeutic resistance in STAD (p < 0.01). Interestingly, LMNB1 also showed a strong correlation (ρ > 0.45, p = 0.005) with a specific HIF: the number of cancer cells within the detection range of lymphocytes. This relationship has significant biological and clinical implications. A high value for this HIF suggests that immune cells, such as lymphocytes, are actively engaging with a large portion of the tumor, which indicates a tumor environment well-penetrated by immune cells. Conclusions: This immune engagement may have major implications for treatment. High levels of immune infiltration often create a favorable environment for therapies like immune checkpoint inhibitors. The elevated LMNB1 expression was also associated with aggressive disease and, suggesting it could serve as a marker to identify high-risk patients. These findings demonstrate the power of HIFs to provide a window into the complex dynamics of the TME. By integrating this knowledge into AI-driven models, clinicians could potentially identify high-risk patients earlier in their treatment journey and design personalized treatment strategies tailored to their specific tumor biology.

Biochar amendment modulate microbial community assembly to mitigate saline-alkaline stress across soil depths.

Biochar amendment modulate microbial community assembly to mitigate saline-alkaline stress across soil depths.

Perivascular Adipose Tissue Niches for Modulating Immune Cell Function.

Perivascular adipose tissue is a unique fat depot surrounding most blood vessels with a significant role in vascular function. While adipocytes compose the vast majority of the perivascular adipose tissue by area, they only account for around 20% of the total cell number. Most of the cellular component belongs to resident immune cells, with macrophages and lymphoid cells representing ≈30% and 15% of total cells, respectively. Recently, new evidence has shown that aside from their well-known role in modulating the inflammatory tone, immune cells in perivascular adipose tissue can control adipogenesis, vessel integrity, and vascular contractility through complex cellular interactions. These interactions are spatially coordinated and influenced by the environmental state. Here, we review the mechanism by which immune cells regulate perivascular adipose tissue function with a special focus on the spatial organization of immune cells and their heterotypic interactions, supporting tissue function in health and disease.

Ethical Considerations in Psychiatric Genomics.

Ethical Considerations in Psychiatric Genomics.

Inosine-5'-monophosphate interacts with the TAS1R3 subunit to enhance sweet taste detection.

Umami and sweet taste detection is mediated by the activation of the TAS1R1/TAS1R3 and TAS1R2/TAS1R3 receptors, respectively. TAS1R2-Venus flytrap domain (VFT) constitutes the primary ligand-binding site for most of the sweeteners whereas TAS1R1-VFT contains the orthosteric binding site for umami compounds. Inosine-5'-monophosphate (IMP), previously known to potentiate umami taste, binds to a site of TAS1R1-VFT adjacent to the L-glutamate site leading to umami synergy. However, the involvement of the TAS1R3 subunit in umami receptor-ligand interactions or in synergy with IMP has never been demonstrated. To elucidate the VFT contribution to umami and sweet detection, we expressed human TAS1R1- and TAS1R3-VFTs in bacteria. Ligand binding studies quantified by intrinsic tryptophan fluorescence revealed that both TAS1R1- and TAS1R3-VFTs are able to interact with umami compounds. Cellular assays revealed that IMP is able, like cyclamate, to modulate the response of TAS1R2/TAS1R3 and TAS1R3 alone stimulated by calcium ions. IMP also acted as an enhancer of TAS1R2/TAS1R3 when stimulated with sucralose, neotame and cyclamate. Taking together, our data demonstrated that IMP modulates sweet compound detection at the receptor level acting via the TAS1R3 subunit. This research suggests more complex receptor interactions between umami and sweet taste qualities and paves the way for development of new sweetness enhancers.

Further negative effect of fibrous microplastics to the bioaccumulation and toxicity of decabromodiphenyl ethane on zebrafish.

Further negative effect of fibrous microplastics to the bioaccumulation and toxicity of decabromodiphenyl ethane on zebrafish.

Immune landscape of liver metastases in advanced lung cancer.

8540 Background: The liver is a frequent site of metastasis and carries a poor prognosis in patients with non-small cell (NSCLC) and small cell lung cancer (SCLC). Patients with liver metastases (LM) derive limited benefit from immune checkpoint inhibitors (ICI), due to hepatic myeloid derived suppressor cell (MDSC) mediated T cell elimination. Here, we used imaging mass cytometry (IMC) to perform single cell, highly multiplexed, analysis of LM and primary lung tumors to investigate how vascular endothelial growth factor (VEGF) influences T cell depletion within the tumor immune microenvironment (TIME) of LM. Methods: We comprehensively characterizedthe TIME in LM and primary lung tumors in 21 patients with NSCLC or SCLC using IMC. A panel of 40 antibodies was assembled to interrogate immune subsets and VEGF pathway markers. Each antibody was conjugated to a unique metal isotope. After validation, the antibody cocktail was used to stain the biopsies. Tissue images were segmented using Mesmer, and hierarchical clustering was applied to single-cell expression data to identify phenotypes. Similar clustering of cell neighbor profiles was applied to obtain spatial motifs. Phenotypic and motif frequencies, together with functional expression across phenotypes, were compiled from all samples and compared across conditions. Results: Initial visualization of the raw, unsegmented data revealed higher infiltration of CD8 + and CD4 + T cells in LM compared to the lung TIME. After segmentation, marker expression heatmaps uncovered complex cell-cell interaction ecosystems. The liver samples were enriched with M2 macrophages (CD163 + ), MDSC (CD11b + ), and proliferative endothelial cells (CD105 + ) whereas the lung samples were enriched in tumor cells (TTF1 + for NSCLC and INSM1 + /synaptophysin + for SCLC) and T cells (CD4 + , CD8 + ). Spatial neighborhood profiling of NSCLC liver tissues identified 12 neighborhood types, showing a general trend of mutual exclusivity between MDSCs and CD4 + /CD8 + T cells across neighborhoods. Notably, CD8 + T cells in MDSC-enriched neighborhoods exhibited consistently higher FAS expression, a key apoptotic marker. Heterogenous FAS and VEGF signaling across neighborhoods suggested a mixed immune-suppressive and vascularized response in the liver TIME, supporting VEGF’s role in mediating MDSC-driven hepatic CD8 + T cell depletion in patients with LM. Conclusions: Our findings highlight significant differences in the TIME between LM and primary lung tumors, with LM demonstrating a more immunosuppressive and VEGF-enriched milieu. The spatial association of MDSCs with CD8 + T cells, along with elevated FAS expression and VEGF signaling suggests a mechanistic role for VEGF in driving immune evasion within liver tumors. These results underscore the potential of VEGF blockade as a therapeutic strategy to overcome T cell suppression and improve ICI efficacy in patients with lung cancer metastatic to liver (NCT05588388, PI Sankar).

Valorization of crocodile head for anti-inflammatory peptides: In silico screening and cellular validation.

Valorization of crocodile head for anti-inflammatory peptides: In silico screening and cellular validation.

S-phase kinase-associated protein 1 inhibits orbital fibroblasts adipogenesis to improve thyroid-associated ophthalmopathy (TAO).

S-phase kinase-associated protein 1 inhibits orbital fibroblasts adipogenesis to improve thyroid-associated ophthalmopathy (TAO).

Fibromyalgia: are you a genetic/environmental disease?

Fibromyalgia, characterized by chronic widespread pain and fatigue, involves complex interactions between genetic predispositions and environmental triggers. This review delves into the multifaceted nature of fibromyalgia, emphasizing recent advances in understanding its pathogenesis through genetic, epigenetic, and environmental lenses. We explore the roles of specific genetic polymorphisms, such as those in the catechol-O-methyltransferase and serotonin transporter genes, and their correlation with the syndrome's susceptibility. The review also examines the significant impact of environmental factors, including physical trauma and stress, which potentiate the syndrome's severity. In addition, emerging research on the microbiome and epigenetic modifications provides new insights into the disease mechanisms, potentially guiding future therapeutic strategies. This article aims to synthesize current research findings and propose directions for future research, underscoring the necessity of a multidisciplinary approach to decipher the complexities of fibromyalgia.