Multiple myeloma (MM) is a heterogeneous hematological malignancy characterized by the clonal proliferation of plasma cells in the bone marrow, with distinct subtypes including smoldering MM (SMM), newly diagnosed MM (NDMM), and relapsed/refractory MM (RRMM). Despite therapeutic advances, outcomes remain unsatisfactory, especially for RRMM, due to unclear heterogeneity, progression mechanisms, and crosstalk between tumor cells and the bone marrow microenvironment (BMME) via direct interactions or extracellular vesicles (EVs).

Single-cell RNA sequencing (scRNA-seq) was performed on bone marrow samples from 12 MM patients, and major cell types were identified. Plasma cells were subjected to re-clustering to explore subpopulation heterogeneity. Functional enrichment analysis of differentially expressed genes (DEGs) was conducted. Cellular stemness was evaluated by CytoTRACE, and developmental trajectories were inferred via Monocle and Slingshot. Cell-cell communication was analyzed by CellChat, while transcription factor (TF) regulatory networks were identified through SCENIC analysis. Metabolic pathway activity was also assessed. Finally, loss-of-function experiments (siRNA-mediated ASS1 knockdown) were conducted to validate its functional role.

The analysis identified 7 major cell types. Plasma cells were further stratified into 6 subpopulations (C0-PCSK1N+, C1-IGHGP+, C2-IGHA1+, C3-ASS1+, C4-CD27+, C5-STMN1+). C3 and C5 were enriched in RRMM, while C1 and C4 were dominant in SMM. C3 exhibited hyperactive EV signature and higher stemness-like scores. Pseudotime trajectory analysis identified C3 as poorly differentiated malignant progenitors driving disease progression. C3 showed strong crosstalk with monocytes/macrophages/conventional dendritic cells (cDCs) via MIF and ICAM signaling. Key TFs regulating C3 included ATF5, TP73, MYB, CEBPB, and NFIA. Metabolic pathway analysis indicated enhanced vitamin B6 metabolism, phenylalanine metabolism, and oxidative phosphorylation in C3 and RRMM. ASS1 silencing inhibited proliferation, clonogenic capacity and migration, while promoting apoptosis in MM cells.

Our study delineates MM heterogeneity, developmental dynamics, and regulatory networks at the single-cell level. ASS1+ plasma cells represent a highly malignant subpopulation associated with RRMM, driving disease progression through unique TF regulatory networks, metabolic reprogramming, and crosstalk with the BMME. These findings provide novel insights into MM pathogenesis and identify ASS1 as a potential therapeutic target for MM patients, particularly those with RRMM.

Regulatory T cells (Tregs) are central to immune regulation, preventing excessive immune responses, maintaining immune tolerance, and modulating inflammatory microenvironments. Dysregulation of Treg development, differentiation, proliferation, or function contributes significantly to autoimmune diseases, inflammatory disorders, and tumor immune evasion. Protein kinases, key mediators of cellular signaling, regulate diverse processes including motility, metabolism, transport, and cell cycle progression; their emerging roles in immune regulation make them promising therapeutic targets for inflammatory diseases, autoimmunity, and immunotherapy-treated cancers. Notably, protein kinases modulate Treg differentiation and function by controlling the expression of the lineage markers Forkhead box protein 3 (Foxp3, intracellular) and CD25 (cell surface). This mechanistic review addresses: (1) fundamental Treg characteristics, functions, and disease relevance; (2) protein kinase-mediated regulatory mechanisms in Tregs; and (3) progress on protein kinase inhibitors for Treg-related diseases. The review aims to outline the kinase regulatory network governing Treg biology and guide future identification of kinase targets for treating autoimmune, inflammatory, and malignant diseases.

Galectins, a structurally conserved family of β-galactoside-binding lectins, have emerged as key regulators of cancer progression. However, their integrated roles across tumor immunity, metabolic rewiring, and metastasis have yet to be fully defined. In this review, we synthesize current evidence on major galectin family members, particularly galectin (Gal)-1, Gal-3, Gal-4, Gal-7, Gal-9, and Gal-13, and delineate how they drive tumor progression through mechanistically distinct yet convergent pathways. Specifically, galectins induce T-cell dysfunction through T-cell immunoglobulin and mucin-domain containing-3 (TIM-3)- and programmed cell death protein 1 (PD-1)-associated signaling, reprogram macrophages and myeloid-derived suppressor cells (MDSCs) via phosphoinositide 3-kinase/protein kinase B (PI3K/AKT), Janus kinase/signal transducer and activator of transcription (JAK/STAT), and nuclear factor kappa B (NF-κB) pathways, and modulate innate immune effectors, including natural killer (NK) cells, neutrophils, and dendritic cells, in a context-dependent manner. Beyond immune regulation, galectins reshape tumor metabolism through effects on glycolysis, lipid metabolism, and glycan remodeling, and differentially regulate ferroptosis susceptibility, with the contrasting roles of Gal-1 and Gal-13 underscoring functional diversity within the family. Emerging evidence further implicates galectins in resistance to chemotherapy, targeted therapy, immune checkpoint blockade, and chimeric antigen receptor T-cell (CAR-T) therapy, positioning them as candidate therapeutic targets. We also discuss galectin-directed strategies, including small-molecule inhibitors, nanomedicine-based platforms, vaccination approaches, and rational combination therapies, and highlight the promise of multi-galectin biomarker panels for precision oncology.

Glioblastoma (GBM) is a highly malignant tumor with poor prognosis and limited effective treatment options. Emerging studies have suggested that gut microbiota may influence glioma progression through the gut-brain axis, though the precise mechanisms remain largely unclear. In this study, we employed a comprehensive multi-omics approach-encompassing single-cell transcriptomics, spatial transcriptomics, metagenomics, metabolomics, and m6A-seq-to investigate how antibiotic-induced gut microbiota disruption impacts glioma progression in a mouse model. Gene expression analysis revealed significant alterations in antibiotics-treated mice (ABX-treated mice), including reduced expression of Epha6 and upregulated expression of Tead1, key genes associated with glioma progression and immune modulation. Spatial transcriptomics and metabolomic profiling identified reduced methionine levels in gliomas of ABX-treated mice, linking gut-derived metabolite changes to epigenetic regulation via m6A methylation. Single-cell RNA sequencing further demonstrated an increased proportion of AC-like cells, disrupted intercellular communication, and aberrations in the EPHA and NRXN signaling pathways. These findings highlight the interplay between gut microbiota, immune signaling, and epigenetic modifications in shaping the glioma microenvironment. This study advances our understanding of the gut-brain axis in glioma biology and proposes the EPHA pathway as a promising biomarker for the immune-mediated modulation of tumor progression, thereby providing new insights into the role of the gut-brain axis in glioma regulation.

Protein S-palmitoylation, and more broadly protein S-acylation when acyl donors other than palmitoyl-CoA are involved, is an emerging post-translational mechanism in cancer that dynamically regulates protein stability, membrane localization, trafficking, and signaling output. Among the zinc finger DHHC-type palmitoyl acyltransferases, ZDHHC9 has attracted increasing attention because of its expanding roles across multiple malignancies. Accumulating evidence indicates that ZDHHC9 is frequently dysregulated in cancer and contributes to tumor progression through palmitoylation-dependent regulation of proteins involved in oncogenic signaling, stress adaptation, metabolic rewiring, and immune evasion. Functionally, ZDHHC9 has been linked to the modulation of substrates such as BiP/GRP78, CD38, STAT1, PD-L1, and PCBP1, thereby influencing unfolded protein response signaling, checkpoint-related immune suppression, ferroptosis-associated pathways, and tumor cell survival. Beyond its tumor-intrinsic effects, ZDHHC9 is increasingly recognized as a regulator of the tumor immune microenvironment, with emerging evidence showing that it can suppress effector CD8+ T-cell responses and reduce sensitivity to immune checkpoint blockade in selected cancer contexts. In this review, we summarize the molecular basis of ZDHHC9, discuss its roles in tumor progression and immune regulation, and highlight current advances and challenges in therapeutic targeting. A deeper understanding of ZDHHC9-dependent palmitoylation networks may support the development of new biomarkers and precision anticancer strategies.

Machine learning models for Obstructive Sleep Apnea (OSA) diagnosis have largely inherited some structural limitations: reliance on generic, opportunistically collected feature sets; use of the Apnea-Hypopnea Index (AHI) as the sole ground truth; poor performance in multi-class severity grading; and predictions that offer clinicians no mechanistic insight. This study addresses these gaps by prospectively assembling a multi-domain dataset that, alongside established demographic, anthropometric, and questionnaire-based predictors, incorporates a panel of craniofacial and intraoral metrics specifically designed to capture the structural-anatomical contributors to OSA - integrating these into an interpretable framework for three-class severity classification evaluated against both AHI and the Oxygen Desaturation Index (ODI).

In this single-center study, 233 treatment-naïve adults from a tertiary referral cohort (61.8% severe OSA prevalence) underwent in-laboratory polysomnography (PSG). All predictor variables were collected prior to PSG outcome disclosure through a standardized clinical examination, requiring no overnight recording or specialized equipment. An Artificial Neural Network (ANN) was independently trained for three-class severity classification (No/Mild, Moderate, Severe) for each index. Model performance was evaluated on an independent test set (n = 47; 20% of the sample), with interpretability assessed using SHapley Additive exPlanations (SHAP). Comparison with an anatomy-excluded ablation model was conducted to establish the added value of the full feature set.

The AHI-based model achieved 87.2% overall accuracy (sensitivity/specificity: No/Mild 0.93/0.97, Moderate 0.80/0.91, Severe 0.88/0.93). The ODI-based model achieved 76.6% accuracy, offering reliable exclusion of severe desaturation burden (No/Mild specificity: 0.94). Univariate analyses confirmed significant associations between OSA severity and STOP-BANG score, age, BMI, neck circumference, observed apnea, loud snoring, high blood pressure, Cervico-Mental Angle, Mentocervical Distance, and submental fat (all p ≤ .034 for both indices). SHAP analysis further identified V-shaped maxillary arch, Mallampati score, increased overjet, and alcohol use as influential model predictors - several reaching high model rankings despite modest univariate significance. Notably, AHI and ODI models diverged in their feature weighting - anatomy-driven features dominated AHI prediction while body habitus and comorbidity markers dominated ODI. Against a conventional demographic and questionnaire-based ablation model, the full anatomy-inclusive ANN achieved substantially higher accuracy (87.2% vs. 72.3%), with the largest gain at the Moderate-class boundary (sensitivity: 0.80 vs. 0.58).

As a proof-of-concept, this study demonstrates that an interpretable ML framework integrating craniofacial and intraoral assessments with standard clinical predictors can classify OSA severity across three classes and provide feature-level explanations to support clinical reasoning. By developing parallel AHI and ODI models, the framework moves beyond AHI-only paradigms, though both remain frequency-based surrogates; hypoxic burden - quantifying the cumulative oxygen desaturation load per sleep period - is the more physiologically complete target toward which this line of work should progress. Findings are limited by single-center design, spectrum bias from a tertiary referral cohort, modest sample size, and absence of inter-rater reliability data. External validation in larger, more representative populations is needed to confirm the robustness and clinical utility of this approach.

Pulmonary arterial hypertension (PAH) is a complex vascular disease characterized by endothelial dysfunction, pulmonary arterial smooth muscle cell (PASMC) hyperproliferation, metabolic reprogramming, and immune-inflammatory remodeling. These pathological features are not fully explained by isolated signaling abnormalities and increasingly point to post-translational modification (PTM) crosstalk as an important layer of protein regulation. In this review, we examine the interplay among phosphorylation, ubiquitination, and SUMOylation, with a focus on how these PTMs influence protein stability, subcellular localization, and degradation in the PAH microenvironment. To distinguish disease-supported mechanisms from broader biological extrapolation, we apply a tiered evidence framework that separates crosstalk axes validated in human PAH or relevant experimental pulmonary hypertension models from those inferred from hypoxia- or cancer-related systems. Across these studies, several recurring patterns emerge, including phosphodegron-dependent substrate recognition, PTM-dependent enzyme recruitment, and context-specific coupling between SUMOylation and ubiquitin-mediated turnover. These mechanisms help explain how PTM dysregulation may weaken vasculoprotective signaling, including BMPR2-related pathways, while sustaining proliferative, inflammatory, and hypoxia-responsive signaling programs. We also discuss a conceptual systems-level model in which chronic stress reshapes the effective PTM enzyme pool through changes in enzyme abundance, substrate allocation, and subcellular compartmentalization. Finally, we consider the translational implications of targeting PTM crosstalk, including opportunities for selective intervention and current barriers related to network redundancy, off-target toxicity, and drug delivery.

Dysfunctional breathing is an important cause of breathlessness, characterised by alterations in breathing patterns. Breathing retraining (BRtr) is believed to aid patients re-establish a normal diaphragmatic breathing pattern. Structured light plethysmography was used to assess the breathing pattern of 23 children and adolescents before and after attending a specialist BRtr clinic. It showed that a 3-month physiotherapist-led BRtr programme was associated with a significant reduction in the contribution of rib cage movement to the total change in thoracic volume and respiratory rate, which was maintained when reassessed 3 months later, despite having persisted for the 3 months prior to intervention. This study provides the first objective evidence that BRtr may result in sustained improvements in breathing patterns in children and adolescents with dysfunctional breathing.

ClinicalTrials.gov, TRN: NCT04215341. Date of registration: 23 April 2015.

• Dysfunctional breathing has significant effects on children and adolescents, affecting academic and sport performance, social interactions and activities of daily living. • Breathing retraining-based physiotherapy interventions are known to improve symptoms and quality of life in children and adolescents with dysfunctional breathing.

• Breathing retraining may result in positive objective changes in the biomechanics of breathing. • Objective improvements in breathing pattern are maintained for at least 3 months after intervention.

The European Cystic Fibrosis Society (ECFS) develops education resources to support members; however, these are almost exclusively in English. Many barriers to translation exist, including cost and time. Artificial intelligence (AI) provides an opportunity to support translation and address such barriers. This study aimed to pilot the use of AI-generated translation of ECFS e-learning modules and evaluate the quality.

An AI translation program was used to create subtitles of ECFS peer-reviewed education modules. Two independent native language speakers with extensive cystic fibrosis (CF) healthcare experience were identified and tasked with reviewing, editing, and validating. This was followed by the development and circulation of an online evaluation survey assessing users' views on quality.

Education packages, each consisting of six subtitled modules, were created in three languages: Ukrainian, Romanian, and Turkish. For each language, corrections to the AI-generated translation by the independent native speakers were essential. Evaluation was conducted in two countries. Eighteen completed surveys were received. Results indicated high levels of accuracy for the final modules, and feedback was very positive regarding the utility and range of topics.

The use of novel AI-generated translation shows promise and proved quick and affordable. However, quality of translation was variable, highlighting the critical role of collaborating with native-speaking CF experts to ensure linguistic accuracy. This project highlights the importance of interdisciplinary collaborative efforts between ECFS Education, the Twinning Project, CF Europe, and patient organizations. Further, it demonstrates both the feasibility and practicality of generating effective multilingual educational modules using AI.

COVID-19 severity and survival are influenced by the host immune response to SARS-CoV-2. Programmed cell death 1 (PD-1), a key immune checkpoint, regulates T-cell activation and antiviral immune balance. Since genetic variability can modulate these responses, we investigated whether the PDCD1 polymorphisms rs11568821 C > T, rs2227982 G > A, rs2227981 G > A and rs10204525 C > T are associated with COVID-19 severity and mortality in a Brazilian cohort. A total of 366 COVID-19 patients (165 mild, 72 moderate, and 129 severe cases) were genotyped for the four PDCD1 SNPs, and their haplotype structures were estimated. Significant differences were observed in the allele frequencies of rs11568821, and in genotypes and allele frequencies of the exonic rs2227982, among mild, moderate, and severe cases. Multinomial logistic regression identified associations between rs2227982 (dominant and overdominant models) and moderate COVID-19, and between the rs2227981 AA genotype (genotypic and recessive models) and severe COVID-19. The rs10204525 polymorphism (CT genotype under genotypic dominant and overdominant models) also presented an association with severe COVID-19. However, none of these associations remained independent. Haplotype analysis identified five major haplotypes with significantly different frequencies among the groups (p = 0.02); however, no association was found between the haplotypes and disease severity or outcome. This is the first study to evaluate SNP rs2227982 in COVID-19 patients, and the first to evaluate the four aforementioned SNPs in a Brazilian population. Overall, our findings suggest that these polymorphisms, although involved in COVID-19 immunopathogenesis, are not suitable biomarkers for predicting COVID-19 severity or clinical outcomes.