the-application-point-care-ultrasonography-pocus-management-acute

The Application of Point-of-Care Ultrasonography (POCUS) in the Management of Acute Respiratory Distress Syndrome (ARDS) in the Intensive Care Unit

Acute respiratory distress syndrome (ARDS) is a life-threatening condition that necessitates dynamic, non-invasive monitoring for optimal management. ...

Zhejiang University School of Medicine

Mixed phenotype acute leukemia (MPAL) is a subtype of leukemia in which lymphoid and myeloid markers are co-expressed. Knowledge regarding the genetic features of MPAL is lacking due to its rarity and heterogeneity. Here, we applied an integrated genomic and transcriptomic approach to explore the molecular characteristics of 176 adult patients with MPAL, including 86 patients with T-lymphoid/myeloid MPAL (T/My MPAL-NOS), 42 with Ph+ MPAL, 36 with B-lymphoid/myeloid MPAL (B/My MPAL-NOS), 4 with t(v;11q23), and 8 with MPAL, NOS, rare types. Genetically, T/My MPAL-NOS was similar to B/T MPAL-NOS but differed from Ph+ MPAL and B/My MPAL-NOS. T/My MPAL-NOS exhibited higher CEBPA, DNMT3A, and NOTCH1 mutations. Ph+ MPAL demonstrated higher RUNX1 mutations. B/T MPAL-NOS showed higher NOTCH1 mutations. By integrating next-generation sequencing and RNA sequencing data of 89 MPAL patients, we defined eight molecular subgroups (G1-G8) with distinct mutational and gene expression characteristics. G1 was associated with CEBPA mutations, G2 and G3 with NOTCH1 mutations, G4 with BCL11B rearrangement and FLT3 mutations, G5 and G8 with BCR::ABL1 fusion, G6 with KMT2A rearrangement/KMT2A rearrangement-like features, and G7 with ZNF384 rearrangement/ZNF384 rearrangement-like characteristics. Subsequently, we analyzed single-cell RNA sequencing data from five patients. Groups G1, G2, G3, and G4 exhibited overexpression of hematopoietic stem cell disease-like and common myeloid progenitor disease-like signatures, G5 and G6 had high expression of granulocyte-monocyte progenitor disease-like and monocyte disease-like signatures, and G7 and G8 had common lymphoid progenitor disease-like signatures. Collectively, our findings indicate that integrative genomic and transcriptomic profiling may facilitate more precise diagnosis and develop better treatment options for MPAL.

Integrative genomic and transcriptomic profiling reveals distinct molecular subsets in adult mixed phenotype acute leukemia.

Sepsis, a syndrome of life-threatening organ dysfunction caused by dysregulated host responses to infection, exhibits profound pathobiological heterogeneity, hindering the development of effective therapies. Current subtyping approaches, often reliant on single-omics data or unsupervised clustering, yield poorly reproducible and therapeutically misaligned classifications. Here, we introduce a goal-directed subgroup identification (GD-SI) framework that optimizes patient stratification for differential treatment responses, integrating longitudinal multi-omics data (transcriptomic, proteomic, metabolomic, phenomic) from 1327 subjects across 43 hospitals. While supervised multi-omics integration frameworks (e.g., DIABLO) effectively capture shared biological signals, our approach anchors subgroup discovery directly to treatment-effect optimization. This strategy achieves substantial cross-omic concordance and, crucially, generalizes to predict differential treatment response across international critical care databases. Patients stratified by GD-SI-derived benefit scores for restrictive versus liberal fluid resuscitation exhibited marked survival differences, with similar advantages observed for ulinastatin immunomodulation. External validations in MIMIC-IV and ZiGongDB confirm prognostic generalizability. This framework reconciles biological heterogeneity with clinical actionability, offering a scalable infrastructure for precision trial design and personalized sepsis management. Our findings underscore the translational potential of omics-driven, goal-directed stratification to overcome decades of therapeutic stagnation in critical care.

Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study.

Sepsis poses a significant global health burden, with millions of cases and high mortality rates annually, largely due to challenges in early diagnosis and monitoring. Traditional methods, reliant on nonspecific clinical manifestations and limited biomarkers like C-reactive protein and procalcitonin, often fail to distinguish infection from non-infectious inflammation or capture disease heterogeneity. This review synthesizes recent progress in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-for advancing sepsis management. Genomics, via metagenomic next-generation sequencing, enables rapid pathogen identification and genetic variant analysis for susceptibility and prognosis. Transcriptomics reveals molecular subtypes and immune dynamics through RNA sequencing and single-cell approaches. Proteomics and metabolomics uncover protein and metabolite profiles linked to immune imbalance, organ damage, and metabolic disorders. Multi-omics integration, enhanced by artificial intelligence and machine learning, facilitates biomarker discovery, patient stratification, and predictive modeling, bridging laboratory findings to bedside applications like rapid diagnostic tools and clinical decision support systems. Despite advancements, challenges including data heterogeneity, high costs, and ethical concerns persist. Future directions emphasize single-cell and spatial omics, AI-driven personalization, and ethical frameworks to transform sepsis care from reactive to proactive, ultimately improving outcomes.

Article	Year
Integrative genomic and transcriptomic profiling reveals distinct molecular subsets in adult mixed phenotype acute leukemia. American journal of hematology\| PubMed ID: 36219502	2023
Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study. Nature communications\| PubMed ID: 41285725	2025
Research Progress on Sepsis Diagnosis and Monitoring Based on Omics Technologies: A Review. Diagnostics (Basel, Switzerland)\| PubMed ID: 41300910	2025

Hongjie Shen

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