Search Results (64)

B-cell acute lymphoblastic leukemia (B-ALL) is characterized by the abnormal proliferation of B-lineage lymphocytes in the bone marrow (BM). The roles of immune cells within the BM microenvironment remain incompletely understood. Single-cell RNA sequencing (scRNA-seq) provides the potential for groundbreaking insights into the pathogenesis of B-ALL. In this study, scRNA-seq was conducted on BM samples from 17 B-ALL patients (B-ALL cohorts) and 13 healthy controls (HCs). Bioinformatics analyses, including clustering, differential expression, pathway analysis, and gene set variation analysis, systematically identified immune cell types and assessed T-cell prognostic and metabolic heterogeneity. A metabolic-feature-based machine learning model was developed for B-ALL subtyping. Furthermore, T-cell–monocyte interactions, transcription factor (TF) activity, and drug enrichment analyses were performed to identify therapeutic targets. The results indicated significant increases in Pro-B cells, alongside decreases in B cells, NK cells, monocytes, and plasmacytoid dendritic cells (pDCs) among B-ALL patients, suggesting immune dysfunction. Clinical prognosis correlated significantly with the distribution of T-cell subsets. Metabolic heterogeneity categorized patients into four distinct groups (A–D), all exhibiting enhanced major histocompatibility class I (MHC-I)-mediated intercellular communication. The metabolic-based machine learning model achieved precise classification of B-ALL groups. Analysis of TF activity underscored the critical roles of MYC, STAT3, and TCF7 within the B-ALL immunometabolic network. Drug targeting studies revealed that dorlimomab aritox and palbociclib specifically target dysregulation in ribosomal and CDK4/6 pathways, offering novel therapeutic avenues. This study elucidates immunometabolic dysregulation in B-ALL, characterized by altered cellular composition, metabolic disturbances, and abnormal cellular interactions. Key TFs were identified, and targeted drug profiles were established, demonstrating the significant clinical potential of integrating immunological mechanisms with metabolic regulation for the treatment of B-ALL. Full article

Gamma-aminobutyric acid type A receptors (GABA_ARs) are pentameric ligand-gated ion channels essential for inhibitory neurotransmission in the central nervous system. Subtype-specific expression patterns of GABA_AR subunits underlie their diverse roles in regulating anxiety, motor function, and sedation. While non-selective GABA_AR positive allosteric modulators (PAMs), such as benzodiazepines, are clinically effective anxiolytic drugs, their non-selective activity across α1/2/3/5 subunit-containing GABA_ARs leads to sedation, cognitive impairment, and risk of dependence. To address this, we evaluated ENX-102, a novel GABA_AR PAM, which exhibits selectivity for α2/3/5 subunits. In rodents, ENX-102 demonstrated dose-dependent anxiolytic-like activity following acute and sub-chronic administration, without sedation. ENX-102 exhibited a dose-dependent quantitative electroencephalography (qEEG) spectral signature in rodents that was distinct from that of benzodiazepines. In a double-blind, placebo-controlled, multiple-ascending dose study in healthy human volunteers, ENX-102 was evaluated using the NeuroCart, a CNS test battery including saccadic peak velocity (SPV), adaptive tracking, pupillometry, body sway, the Bond and Lader Visual Analog Scale (VAS), the Visual Verbal Learning Task (VVLT), and qEEG. ENX-102 produced reductions in SPV that were indicative of central target engagement, with minimal effects on alertness and motor coordination, which is consistent with subtype-selective GABA_AR targeting. Notably, qEEG revealed increased β-band power and decreased δ- and θ-band activity, which were distinct from the spectral profile of non-selective PAMs, supporting translational alignment with preclinical findings. Across dose levels, ENX-102 was well tolerated and exhibited favorable pharmacokinetics. These results support further clinical development of ENX-102 as a next-generation GABA_AR subtype-selective anxiolytic drug. Full article

Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In C. elegans, chemotaxis assays on urine samples achieved sensitivities of 87–96% and specificities of 90–95% in case–control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70–76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94–95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82–100% classification accuracy within 250 ms in pilot studies (n ≈ 20–30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics. Full article

Dopamine has been increasingly implicated in shaping economic and financial decision-making, yet much of the evidence remains fragmented across paradigms and mechanistic levels, and heavily based on preclinical or clinical populations. This review synthesises pharmacological, neuroimaging, and genetic findings from studies involving healthy human participants, highlighting dopamine’s role in risk-taking, delay discounting, social fairness, reward sensitivity, and feedback learning. It distinguishes between transient state-related effects and stable trait-level influences, and clarifies how dopaminergic tone, receptor subtype activity—particularly D2—and corticostriatal circuitry modulate economic choices. In doing so, the review advances a mechanism-focused framework for understanding adaptive and biased decision strategies. Full article

Background/Objectives: We define severe postpartum hemorrhage (PPH) with macroscopic hematuria as clinical disseminated intravascular coagulation (DIC), a life-threatening condition. We also report a methodology using machine learning, a subtype of artificial intelligence, for developing the boundary criterion for predicting hematuria on the fibrinogen–fibrin/fibrinogen degradation product (FDP) plane. A positive FDP–fibrinogen/3–60 (mg/dL) value indicates hematuria; otherwise, non-hematuria is observed. We aimed to validate this criterion using severe placental abruption (PA), and to examine the activation of the coagulation–fibrinolytic system in clinical DIC. Methods: Of 17,285 deliveries across nine perinatal centers in Japan between 2020 and 2024, 13 had severe PA without hematuria, 18 had severe PPH without hematuria, and 3 had severe PPH with hematuria, i.e., clinical DIC. We calculated the values of the criterion formula for 13 cases of severe PA to validate the boundary criterion and compared the laboratory tests for coagulation–fibrinolytic activation among the three groups. Results: The calculated values using the criterion for the 13 PA without hematuria ranged from −108.91 to −5.87 and all were negative. In cases of clinical DIC, fibrinogen levels (median, 62 mg/dL) were lower (p < 0.05), while levels of FDP (96 mg/dL), the thrombin–antithrombin complex (120 ng/mL), and the plasmin-α₂–plasmin inhibitor complex (28.4 μg/mL) were significantly higher than in the other two groups. Conclusions: This study demonstrated the validity of the boundary criterion for predicting hematuria using severe PA. The coagulation–fibrinolytic test results suggested that PPH cases with hematuria were assumed to have clinical DIC, indicating that this criterion may be considered for diagnosing DIC during delivery. However, further additional patient data are needed to confirm the usefulness of this criterion because of the very low number of hematuria cases. Full article

This study identified 13 endoplasmic reticulum stress (ERS)-related biomarkers associated with multiple sclerosis (MS) through integrated bioinformatics analysis (including weighted gene co-expression network analysis and machine learning algorithms) and single-cell sequencing, combined with validation in an experimental autoimmune encephalomyelitis (EAE) mouse model. Among them, GPX1, RCN1, and UBE2D3 exhibited high diagnostic value (AUC > 0.7, p < 0.05), and the diagnostic potential of GPX1 and RCN1 was confirmed in the animal model. The study found that memory B cells, plasma cells, neutrophils, and M1 macrophages were significantly increased in MS patients, while naive B cells and activated NK cells decreased. Consensus clustering based on key ERS-related genes divided MS patients into two subtypes. Single-cell sequencing showed that microglia and pericytes were the cell types with the highest expression of key ERS-related genes, and the APP-CD74 pathway was enhanced in the brain tissue of MS patients. Mendelian randomization analysis suggested that GPX1 plays a protective role in MS. These findings reveal the mechanisms of ERS-related biomarkers in MS and provide potential targets for diagnosis and treatment. Full article

Significant challenges persist in diagnosing non-ischemic cardiomyopathies (NICMs) owing to early morphological overlap and subtle functional changes. While cardiac magnetic resonance (CMR) offers gold-standard structural assessment, current morphology-based AI models frequently overlook key biomechanical dysfunctions like diastolic/systolic abnormalities. To address this, we propose a dual-path hybrid deep learning framework based on CNN-LSTM and MLP, integrating anatomical features from cine CMR with biomechanical markers derived from intraventricular pressure gradients (IVPGs), significantly enhancing NICM subtype classification by capturing subtle biomechanical dysfunctions overlooked by traditional morphological models. Our dual-path architecture combines a CNN-LSTM encoder for cine CMR analysis and an MLP encoder for IVPG time-series data, followed by feature fusion and dense classification layers. Trained on a multicenter dataset of 1196 patients and externally validated on 137 patients from a distinct institution, the model achieved a superior performance (internal AUC: 0.974; external AUC: 0.962), outperforming ResNet50, VGG16, and radiomics-based SVM. Ablation studies confirmed IVPGs’ significant contribution, while gradient saliency and gradient-weighted class activation mapping (Grad-CAM) visualizations proved the model pays attention to physiologically relevant cardiac regions and phases. The framework maintained robust generalizability across imaging protocols and institutions with minimal performance degradation. By synergizing biomechanical insights with deep learning, our approach offers an interpretable, data-efficient solution for early NICM detection and subtype differentiation, holding strong translational potential for clinical practice. Full article

Background: Atherosclerosis is a progressive and complex vascular pathology characterized by cellular heterogeneity, metabolic dysregulation, and chronic inflammation. Despite extensive research, the intricate molecular mechanisms underlying its development and progression remain incompletely understood. Methods: Single-cell RNA sequencing (scRNA-seq) was employed to conduct a comprehensive mapping of immune cell enrichment and interactions within atherosclerotic plaques, aiming to investigate the cellular and molecular complexities of these structures. This approach facilitated a deeper understanding of the heterogeneities present in smooth muscle cells, which were subsequently analyzed using pseudotime trajectory analysis to monitor the developmental trajectories of smooth muscle cell (SMC) subpopulations. An integrative bioinformatics approach, primarily utilizing Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning techniques, identified Cathepsin C (CTSC), transforming growth factor beta-induced protein (TGFBI), and glia maturation factor-γ (GMFG) as critical biomarkers. A diagnostic risk score model was developed and rigorously tested through Receiver Operating Characteristic analysis. To illustrate the functional impact of CTSC on the regulation of plaque formation and SMC viability, both in vitro and in vivo experimental investigations were conducted. Results: An analysis revealed SMCs identified as the most prominent cellular type, exhibiting the highest density of disulfidptosis. Pseudotime trajectory analysis illuminated the dynamic activation pathways in SMCs, highlighting their significant role in plaque development and instability. Further characterization of macrophage subtypes demonstrated intercellular communication with SMCs, which exhibited specific signaling pathways, particularly between the proximal and core areas of plaques. The integrated diagnostic risk score model, which incorporates CTSC, TGFBI, and GMFG, proved to be highly accurate in distinguishing high-risk patients with elevated immune responses and systemic inflammation. Knockdown experiments of CTSC conducted in vitro revealed enhanced SMC survival rates, reduced oxidative stress, and inhibited apoptosis, while in vivo experiments confirmed a decrease in plaque burden and improvement in lipid profiles. Conclusions: This study emphasizes the significance of disulfidptosis in the development of atherosclerosis and identifies CTSC as a potential therapeutic target for stabilizing plaques by inhibiting SMC apoptosis and oxidative damage. Additionally, the risk score model serves as a valuable diagnostic tool for identifying high-risk patients and guiding precision treatment strategies. Full article

This study explored the proteomic landscape of inflammatory protein dysregulation in ankylosing spondylitis (AS), a chronic inflammatory disorder primarily affecting the axial skeleton and strongly associated with the HLA-B27 allele, particularly the HLA-B2705 and HLA-B2704 subtypes prevalent in Chinese populations. Blood samples from HLA-B27-positive AS patients and normal controls (NC) were analyzed using the Olink Target 96 inflammation panel to profile 92 inflammatory proteins. HLA-B27 subtyping was performed via PCR-SSP. To identify key proteins and stratify AS subtypes, we employed machine learning classifiers, including LightGBM models coupled with SHAP value interpretation, alongside traditional statistical analyses. The proteomic analysis revealed significant dysregulation of pro-inflammatory cytokines, such as IL-6 and IL-17A, in AS patients compared to NC, with CXCL9 and NRTN identified as potential biomarkers associated with disease activity. The combination of LightGBM classifiers and traditional statistical methods demonstrated high accuracy in distinguishing AS from NC and effectively stratifying subtypes. These findings provide valuable insights into the inflammatory mechanisms underlying AS pathogenesis and highlight potential biomarkers and therapeutic targets for improving diagnosis and treatment strategies. Future studies with larger and more diverse cohorts, as well as longitudinal designs, are warranted to validate these biomarkers and elucidate their dynamic changes during disease progression. Full article

Background/Objectives: Phosphodiesterase 7 (PDE7), a member of the PDE superfamily, selectively catalyzes the hydrolysis of cyclic adenosine 3′,5′-monophosphate (cAMP), thereby regulating the intracellular levels of this second messenger and influencing various physiological functions and processes. There are two subtypes of PDE7, PDE7A and PDE7B, which are encoded by distinct genes. PDE7 inhibitors have been shown to exert therapeutic effects on neurological and respiratory diseases. However, FDA-approved drugs based on the PDE7A inhibitor are still absent, highlighting the need for novel compounds to advance PDE7A inhibitor development. Methods: To address this urgent and important issue, we conducted a comprehensive cheminformatics analysis of compounds with potential for PDE7A inhibition using a curated database to elucidate the chemical characteristics of the highly active PDE7A inhibitors. The specific substructures that significantly enhance the activity of PDE7A inhibitors, including benzenesulfonamido, acylamino, and phenoxyl, were identified by an interpretable machine learning analysis. Subsequently, a machine learning model employing the Random Forest–Morgan pattern was constructed for the qualitative and quantitative prediction of PDE7A inhibitors. Results: As a result, six compounds with potential PDE7A inhibitory activity were screened out from the SPECS compound library. These identified compounds exhibited favorable molecular properties and potent binding affinities with the target protein, holding promise as candidates for further exploration in the development of potent PDE7A inhibitors. Conclusions: The results of the present study would advance the exploration of innovative PDE7A inhibitors and provide valuable insights for future endeavors in the discovery of novel PDE inhibitors. Full article

Background/Objectives: Massively parallel sequencing technologies have advanced chronic lymphocytic leukemia (CLL) diagnostics and precision oncology. Illumina platforms, while offering robust performance, require substantial infrastructure investment and a large number of samples for cost-efficiency. Conversely, third-generation long-read nanopore sequencing from Oxford Nanopore Technologies (ONT) can significantly reduce sequencing costs, making it a valuable tool in resource-limited settings. However, nanopore sequencing faces challenges with lower accuracy and throughput than Illumina platforms, necessitating additional computational strategies. In this paper, we demonstrate that integrating publicly available short-read data with in-house generated ONT data, along with the application of machine learning approaches, enables the characterization of the CLL transcriptome landscape, the identification of clinically relevant molecular subtypes, and the assignment of these subtypes to nanopore-sequenced samples. Methods: Public Illumina RNA sequencing data for 608 CLL samples were obtained from the CLL-Map Portal. CLL transcriptome analysis, gene module identification, and transcriptomic subtype classification were performed using the oposSOM R package for high-dimensional data visualization with self-organizing maps. Eight CLL patients were recruited from the Hematology Center After Prof. R. Yeolyan (Yerevan, Armenia). Sequencing libraries were prepared from blood total RNA using the PCR-cDNA sequencing-barcoding kit (SQK-PCB109) following the manufacturer’s protocol and sequenced on an R9.4.1 flow cell for 24–48 h. Raw reads were converted to TPM values. These data were projected into the SOMs space using the supervised SOMs portrayal (supSOM) approach to predict the SOMs portrait of new samples using support vector machine regression. Results: The CLL transcriptomic landscape reveals disruptions in gene modules (spots) associated with T cell cytotoxicity, B and T cell activation, inflammation, cell cycle, DNA repair, proliferation, and splicing. A specific gene module contained genes associated with poor prognosis in CLL. Accordingly, CLL samples were classified into T-cell cytotoxic, immune, proliferative, splicing, and three mixed types: proliferative–immune, proliferative–splicing, and proliferative–immune–splicing. These transcriptomic subtypes were associated with survival orthogonal to gender and mutation status. Using supervised machine learning approaches, transcriptomic subtypes were assigned to patient samples sequenced with nanopore sequencing. Conclusions: This study demonstrates that the CLL transcriptome landscape can be parsed into functional modules, revealing distinct molecular subtypes based on proliferative and immune activity, with important implications for prognosis and treatment that are orthogonal to other molecular classifications. Additionally, the integration of nanopore sequencing with public datasets and machine learning offers a cost-effective approach to molecular subtyping and prognostic prediction, facilitating more accessible and personalized CLL care. Full article

The brain serotonin (5-HT) system modulates glutamatergic and GABAergic transmission in almost every brain area, crucially regulating mood, food intake, body temperature, pain, hormone secretion, learning and memory. Previous studies suggest a disruption of the brain 5-HT system in Fragile X Syndrome, with abnormal activity of the 5-HT transporter leading to altered 5-HT brain levels. We provide an update on therapeutic effects exerted by drugs modulating serotonergic transmission on Fragile X patients and animal models. The enhancement of serotonergic transmission using Selective Serotonin Reuptake Inhibitors (SSRIs) corrected mood disorders and language deficits in Fragile X patients. In Fmr1 KO mice, a model of Fragile X Syndrome, selective 5-HT₇ receptor agonists rescued synaptic plasticity, memory and stereotyped behavior. In addition, drugs specifically acting on 5-HT_1A, 5-HT₂ and 5-HT₅ receptor subtypes were able to correct, respectively, epilepsy, learning deficits and hyperactivity in different Fragile X animal models. In conclusion, the SSRI treatment of Fragile X patients improves mood and language; in parallel, studies on animal models suggest that compounds selectively acting on distinct 5-HT receptor subtypes might provide a targeted correction of other Fragile X phenotypes, and thus should be further tested in clinical trials for future therapy. Full article

Immunotherapy is becoming a promising strategy for treating diverse cancers. However, it benefits only a selected group of gastric cancer (GC) patients since they have highly heterogeneous immunosuppressive microenvironments. Thus, a more sophisticated immunological subclassification and characterization of GC patients is of great practical significance for mRNA vaccine therapy. This study aimed to find a new immunological subclassification for GC and further identify specific tumor antigens for mRNA vaccine development. First, deep autoencoder (AE)-based clustering was utilized to construct the immunological profile and to uncover four distinct immune subtypes of GC, labeled as Subtypes 1, 2, 3, and 4. Then, in silico prediction using machine learning methods was performed for accurate discrimination of new classifications with an average accuracy of 97.6%. Our results suggested significant clinicopathology, molecular, and immune differences across the four subtypes. Notably, Subtype 4 was characterized by poor prognosis, reduced tumor purity, and enhanced immune cell infiltration and activity; thus, tumor-specific antigens associated with Subtype 4 were identified, and a customized mRNA vaccine was developed using immunoinformatic tools. Finally, the influence of the tumor microenvironment (TME) on treatment efficacy was assessed, emphasizing that specific patients may benefit more from this therapeutic approach. Overall, our findings could help to provide new insights into improving the prognosis and immunotherapy of GC patients. Full article

G protein-coupled receptors (GPCRs) constitute the largest and most frequently used family of molecular drug targets. The simplicity of GPCR drug design results from their common seven-transmembrane-helix topology and well-understood signaling pathways. GPCRs are extremely sensitive to slight changes in the chemical structure of compounds, which allows for the reliable design of highly selective and specific drugs. Only recently has the number of GPCR structures, both in their active and inactive conformations, together with their active ligands, become sufficient to comprehensively apply machine learning in decision support systems to predict compound activity in drug design. Here, we describe GPCRVS, an efficient machine learning system for the online assessment of the compound activity against several GPCR targets, including peptide- and protein-binding GPCRs, which are the most difficult for virtual screening tasks. As a decision support system, GPCRVS evaluates compounds in terms of their activity range, the pharmacological effect they exert on the receptor, and the binding mode they could demonstrate for different types and subtypes of GPCRs. GPCRVS allows for the evaluation of compounds ranging from small molecules to short peptides provided in common chemical file formats. The results of the activity class assignment and the binding affinity prediction are provided in comparison with predictions for known active ligands of each included GPCR. Multiclass classification in GPCRVS, handling incomplete and fuzzy biological data, was validated on ChEMBL and Google Patents-retrieved data sets for class B GPCRs and chemokine CC and CXC receptors. Full article

Background/Objectives: The aim of the present study was to assess the effectiveness of flexibility or strengthening exercises to improve flexibility, strength, muscle architecture, and functional performance in older adults with leg tightness and/or suspected sarcopenia. Methods: Ninety adults with leg tightness and/or suspected sarcopenia (age: 66.8 ± 4.9 years) were randomly allocated to two subtypes of intervention at home: resistance-band exercise (RE) or eccentric exercise (ECC) for those with weakness; static or dynamic stretching for those with tightness; and static stretching plus ECC or no exercise for those with both muscle tightness and weakness. The program consisted of 3–6 weekly sessions over eight weeks. Blinded outcome assessments before and after the eight-week program and at the three-month follow-up included mobility performance via Timed Up-and-Go (TUG), and flexibility and strength tests, as well as measurement of stiffness. Results: All groups had increased peak torque after eight weeks and improved TUG at the three-month follow-up (p < 0.05). Improved plantar flexor strength persisted at the three-month follow-up (p = 0.009). In addition, the RE and ECC groups had increased muscle thickness by 4.0 and 8.7% after eight weeks (p < 0.05). Hamstring flexibility increased in all exercise groups, except the RE group. Moreover, all six groups showed improved calf flexibility, whereas no changes in stiffness were noted. Conclusions: Increases in mobility performance, strength, and flexibility appeared due to learning effects and increased physical activity, rather than the specific training impact. However, strength-based programs may be recommended for older adults with suspected sarcopenia, as they provide additional benefits, such as short-lasting muscle hypertrophy. Full article