Acute leukemias with chimeric fusion genes involving FET (FUS, EWSR1, and TAF15) family proteins and ETS (E26 transformation-specific)-like transcription factors often present with unique clinical and pathological characteristics. This mini-review aims to summarize the clinical and pathological features of acute leukemia cases harboring rearrangements involving the fused in sarcoma (FUS) or Ewing sarcoma breakpoint region 1 (EWSR1) genes.

An extensive literature review was performed on reported acute leukemia cases with fusions involving FUS or EWSR1. The details of the reported cases, as well as summarized information, are presented.

Rare cases of acute leukemia have been found to harbor either FUS or EWSR1 gene rearrangements with ETS or non-ETS proteins as partners and demonstrate heterogeneous clinical and pathological features. Acute leukemias carrying FUS gene rearrangements present with diverse immunophenotypes and are predominantly, but not exclusively, acute myeloid leukemia (AML), with ERG as the most frequent fusion partner. In contrast, acute leukemias with EWSR1 gene rearrangements more commonly present as B-cell acute lymphoblastic leukemia (ALL) and mixed phenotypic acute leukemia (MPAL), with ZNF384 as the predominant partner. At present, FUS::ERG-positive AML is the only specific entity with a FET::ETS fusion that is formally recognized in the World Health Organization 5th edition hematolymphoid tumor classification (WHO-HEM5) and the International Consensus Classification (ICC) systems. Cytogenetic karyotyping and fluorescence in situ hybridization remain crucial tools for detecting chromosomal translocations in over half of acute leukemias harboring FUS or EWSR1 gene rearrangements. However, a subset of patients may exhibit a normal karyotype and require advanced molecular diagnostic methods. EWSR1-rearranged leukemias can be difficult to distinguish from Ewing sarcoma and therefore require particular attention.

As more cases and additional data become available, it may be justified to expand this category of acute leukemias to include other specific acute leukemia entities with fusions involving FET::ETS, such as FUS::FLI1 and FUS::FEV, in addition to FUS::ERG-positive AML. However, additional data are required to support such subclassification. In contrast, AML cases with EWSR1 rearrangements are exceedingly rare and display considerable variability. Cases of B-ALL or B/myeloid MPAL with the EWSR1::ZNF384 fusion may be more appropriately classified together with other ZNF384-rearranged leukemia subtypes. Advanced molecular diagnostic methods, especially RNA-based next-generation sequencing, are suggested to improve the accurate diagnosis of acute leukemias with FUS or EWSR1 fusions. Additional pathologic workup, particularly immunohistochemical staining with hematopoietic markers, is highly recommended to differentiate EWSR1-rearranged leukemia from Ewing sarcoma.

While norepinephrine (NE) is the cornerstone of septic shock resuscitation, “macrocirculation–microcirculation decoupling” at high doses remains a critical clinical challenge. This study aimed to explore the quantitative tipping point where the NE dose shifts from a life-saving vasopressor to a microcirculatory toxin in septic intensive care unit patients.

In this prospective observational study (January–September 2025), we used handheld vital microscopy to monitor sublingual microcirculation (microvascular flow index [MFI], total vessel density, perfused vessel density, proportion of perfused vessels [PPV], and heterogeneity index [HI]) in adult septic patients within 24 hours of admission and on Day 3. Beyond standard linear analysis, generalized additive models were employed to identify the dose–response thresholds associated with microcirculatory deterioration, adjusted for Acute Physiology and Chronic Health Evaluation II, interleukin-6, and systemic hemodynamics.

Of 144 screened patients, 66 were analyzed. The NE dose showed strong linear correlations with lactate (r = 0.583, P < 0.001) and HI (r = 0.444, P < 0.001), and negative correlations with MFI (r = −0.492, P < 0.001). Crucially, generalized additive models analysis revealed a significant nonlinear “cliff effect”: when the NE dose exceeds the 0.71–0.80 µg/kg/min threshold (PPV: 0.71 µg/kg/min, HI: 0.72 µg/kg/min, MFI: 0.80 µg/kg/min), microcirculatory perfusion parameters deteriorate abruptly (all P < 0.05). Multivariable Cox regression identified an NE dose of 0.80 µg/kg/min as an independent predictor of increased mortality (hazard ratio = 1.32, 95% confidence interval: 1.28–3.10, P = 0.039).

In patients with septic shock, higher NE doses were associated with impaired microcirculatory perfusion and worse outcomes. These findings support individualized vasopressor titration and suggest that microcirculatory monitoring may help identify patients at risk of vasopressor-associated microvascular dysfunction.

Lipid metabolism reprogramming drives malignant proliferation and invasiveness in hepatocellular carcinoma (HCC). Beyond supplying energy and membrane components, lipids function as signaling molecules that modulate tumor cell epigenetics and the microenvironment. Accumulating research has clarified the implications of these metabolic alterations in HCC, providing a rationale for targeted therapies. This review summarizes key alterations in lipid metabolism within HCC and explores their mechanistic contributions to tumor progression. It further examines how lipid metabolic shifts in immune and stromal cells of the tumor microenvironment promote HCC advancement. Finally, we discuss the therapeutic potential of targeting lipid metabolism in liver cancer treatment.

The current criterion of biochemical response to ursodeoxycholic acid in primary biliary cholangitis is an alkaline phosphatase (ALP) level of ≤1.67 × the upper limit of normal (ULN) after 12 months of treatment. However, a proportion of patients who meet this parameter may still progress to liver decompensation. This study aimed to optimize the clinical management of primary biliary cholangitis by (1) establishing ALP normalization as a core treatment target, (2) identifying early intervention windows, and (3) developing risk stratification criteria.

This multicenter retrospective study included an internal cohort and an external validation cohort. We assessed the prognostic impact of ALP normalization with Kaplan-Meier and Cox regression. Sankey diagrams and segmented Poisson regression analysis mapped dynamic risk transitions to identify critical intervention windows. Predictive performance (sensitivity/specificity/positive predictive value/negative predictive value [NPV]) of Mayo, Paris II, and Toronto criteria for 12-month ALP normalization was compared.

Patients achieving ALP normalization showed significantly higher complication-free survival versus those with ALP 1.0–1.67 × ULN (89.8% vs. 79.8%; P = 0.016). Segmented Poisson regression identified significant change points at 3.73 and 5.5 months for high-to-medium and medium-to-low risk transitions, respectively. Failure to meet the Toronto criteria at month 3 predicted non-normalization with 95% NPV, whereas Paris II criteria at month 6 provided optimal specificity (73%) for identifying patients who failed to achieve ALP normalization.

ALP normalization significantly improves clinical outcomes. Two subgroups demonstrate low normalization probability and warrant early intervention: (1) patients with ALP ≥ 1.67 × ULN after 3 months and (2) those not meeting Paris II criteria by month 6.

Non-motor symptoms of Parkinson’s disease, including sleep disturbance, cognitive impairment, depression, and anxiety, are common and often undertreated, yet their responsiveness to mind-body exercises remains unclear. This scoping review evaluated the currently available evidence on the effects of Tai Chi and Qigong interventions on non-motor symptoms in patients with Parkinson’s disease.

We searched six databases (PubMed, Google Scholar, EMBASE, CINAHL, Web of Science, and PEDro) through February 28, 2026, for randomized controlled trials (RCTs). We included English-language RCTs that evaluated the effects of Qigong and Tai Chi interventions on non-motor outcomes in Parkinson’s disease and excluded non-RCTs, review articles, and protocol articles. We were predominantly interested in the following non-motor outcome measures: cognition, depression, anxiety, fatigue, and sleep quality.

This review identified 18 RCTs that met the inclusion criteria, including nine Tai Chi studies and nine Qigong studies. Most of the reviewed studies were of high quality according to the PEDro scale, but the small sample sizes limited our analysis to identifying trends in outcomes. A strong trend toward a beneficial effect was found for sleep quality and cognition, a moderate trend toward improvement was found in depression, anxiety and quality of life, and weak or unclear effects were found for other non-motor symptoms such as fatigue. Several studies also had high dropout rates.

Although these studies suggest that Tai Chi and Qigong may improve sleep quality and cognition, the evidence supporting their benefits in alleviating other non-motor symptoms is generally weak, primarily because of small sample sizes. The heterogeneity in methodologies across the reviewed studies and high dropout rates in some studies are significant limitations of previous RCTs.

Development of mixed histiocytosis (Langerhans cell histiocytosis (LCH))/Erdheim–Chester disease (ECD)) after treatment in patients with an initial skull LCH lesion has not been well recognized. An elderly woman initially developed LCH at the left temporal bone, preceded by polyuria and polydipsia five years earlier; the lesion was surgically removed. Two years thereafter, she experienced her first LCH relapse with a right parietal skull lesion, in which a BRAF V600E mutation was confirmed, and chemotherapy was initiated. After a second LCH relapse involving the left parietal bone, the patient presented with a third relapse at the L2 vertebra. This lesion was pathologically diagnosed as mixed histiocytosis (LCH/ECD), resulting in refractoriness to conventional chemotherapy, and was successfully treated with targeted therapy using BRAF and MEK inhibitors. Spinal mixed histiocytosis (LCH/ECD) may develop following relapses of skull LCH after chemotherapy, for which targeted therapy could be effective.

Predicting the malignant transformation of rectal precancerous lesions remains challenging because conventional Whole Slide Images (WSIs) capture morphological information but lack molecular insight. Multiomics data provide complementary biological signals that often precede visible morphological changes. This study aimed to develop an artificial intelligence (AI)-based multimodal framework integrating WSI and multiomics data for accurate early prediction of malignant transformation.

WSI patches (512×512 px at 20× magnification) and matched multiomics profiles were used for 450 rectal tissue samples from the publicly available The Cancer Genome Atlas dataset. A multimodal architecture was designed, employing a Vision Transformer (ViT-B/16) for WSI feature extraction and a Variational Autoencoder for multiomics representation learning. Features were fused via a cross-attention mechanism to capture inter-modality dependencies. Baseline models, including a convolutional neural network-only image model and an omics-only multilayer perceptron, were trained for comparison. Five-fold cross-validation was applied, with binary cross-entropy loss, the AdamW optimizer, early stopping, and hyperparameter tuning to ensure reproducibility.

The multimodal Vision Transformer–Variational Autoencoder fusion model outperformed unimodal baselines, achieving an accuracy of 0.892 ± 0.012 and an area under the receiver operating characteristic curve of 0.927 ± 0.009, corresponding to a 7–10% improvement over WSI-only and omics-only models. Cross-attention–based fusion improved prediction stability and classification performance, while interpretability analyses (Grad-CAM and SHAP) highlighted biologically meaningful histopathological regions and molecular feature contributions.

This study presents a robust and scalable AI-based framework for integrating WSI and multiomics data in rectal precancerous lesions. The model improves predictive precision compared with unimodal baselines and offers preliminary interpretability insights through attention mechanisms. These findings support the potential of multimodal AI for early cancer risk assessment and precision pathology.

Steatotic donor livers are highly susceptible to post-transplant dysfunction; however, the underlying mechanisms remain incompletely understood. This study aimed to investigate the role of galectin-3 (LGALS3)-mediated pyroptosis in steatotic liver graft injury and explore its therapeutic potential.

A mouse model of steatotic liver transplantation was established. Graft tissues were subjected to RNA sequencing to identify key regulators. In vitro, LGALS3 was modulated in steatotic hepatocytes under ischemia/reperfusion stress to assess its impact on the NLRP3 inflammasome and pyroptosis. The regulatory mechanism by which LGALS3 modulates NLRP3 ubiquitination was further examined. Finally, the therapeutic efficacy of LGALS3 inhibition was evaluated in an orthotopic liver transplantation model.

Transcriptomic analysis identified LGALS3 as a key upregulated molecule in steatotic grafts, associated with pyroptosis pathways. In vitro, LGALS3 overexpression enhanced NLRP3 inflammasome activation and pyroptotic cell death, whereas LGALS3 knockdown exerted protective effects. Mechanistically, LGALS3 modulated NLRP3 inflammasome activity by regulating its ubiquitination. In vivo, pharmacological inhibition of LGALS3 significantly improved graft function, reduced histological injury, suppressed pyroptosis, and prolonged recipient survival.

This study demonstrates that LGALS3 drives steatotic graft injury by promoting NLRP3-mediated pyroptosis through the regulation of ubiquitination. These findings identify LGALS3 as a promising therapeutic target for improving the outcomes of liver transplantation using steatotic donor organs.

Critical care medicine requires rapid, high-stakes decisions informed by dynamic and complex streams of patient data. Traditional predictive models have shown value in forecasting deterioration and identifying subphenotypes. However, this leaves a critical gap between anticipating adverse outcomes and guiding therapeutic interventions. Achieving true personalization demands moving beyond generalized protocols toward individualized strategies that account for patient heterogeneity and consequences of alternative clinical actions. Emerging methods in prescriptive artificial intelligence, particularly causal machine learning (causal ML) and reinforcement learning (RL), are beginning to bridge this gap. Causal ML enables estimation of individualized treatment effects by addressing confounding and enabling counterfactual reasoning, allowing clinicians to ask whether a specific intervention is likely to help or harm a given patient. RL can generate adaptive treatment policies that evolve with patient state. The objective of this review is to examine how critical care can progress from generalized prediction to true personalization through the development of prescriptive artificial intelligence. The review contributes by (1) surveying the achievements and limitations of current predictive models, (2) detailing how causal ML and RL can generate individualized treatment effects and sequential decision strategies, (3) identifying the major translational, technical, clinical, ethical, and regulatory barriers to implementation, and (4) outlining future pathways such as digital twins and clinician in the loop systems that may enable safe and actionable personalized decision support at the bedside.

Brain tumors represent a common class of life-threatening neoplastic conditions. The core objective of neurosurgery is to achieve maximal safe resection of tumors while preserving the patient’s neurological function. Intraoperative ultrasound (IOUS) assists surgeons in achieving complete lesion removal, helping to avoid insufficient resection or excessive excision of normal tissue, thereby reducing surgical morbidity. Contrast-enhanced ultrasound (CEUS), through harmonic imaging, enables more precise localization of lesions and intracranial structures. This review focuses on the synergistic value of IOUS and CEUS in brain tumor surgery. It traces the technological evolution from two-dimensional ultrasound to elastography, color Doppler flow imaging, microvascular flow imaging, artificial intelligence, and beyond, with an emphasis on CEUS for cranial tumors. It also examines the clinical applications of IOUS and CEUS in precise resection, residual tumor identification, vascular protection, boundary differentiation from peritumoral edema, and prognostic assessment. The review concludes by summarizing diagnostic performance, current limitations, and future directions, offering neurosurgeons a theoretical and practical framework for optimizing intraoperative guidance.