Ulinastatin, a broad-spectrum serine protease inhibitor widely used in Asia, has attracted increasing interest for its potential role in critical care. This review summarizes current evidence on its efficacy and safety in acute pancreatitis, severe acute pancreatitis, sepsis, acute respiratory distress syndrome, and perioperative management in cardiac surgery with cardiopulmonary bypass. Meta-analyses suggest that ulinastatin may improve outcomes by reducing mortality, shortening intensive care unit and hospital stays, and attenuating inflammatory responses. In severe acute pancreatitis, its use has been associated with reduced mortality and shorter hospitalization. In sepsis and septic shock, ulinastatin appears to lower all-cause mortality, decrease organ dysfunction scores, and reduce inflammatory markers. Evidence in acute respiratory distress syndrome indicates improvements in the oxygenation index and possible mortality reduction. Perioperative administration during cardiac surgery may mitigate postoperative inflammation and shorten the duration of mechanical ventilation. Despite these encouraging findings, most available studies originate from Asia and are limited by small sample sizes, heterogeneous designs, and inconsistent dosing regimens, which restrict generalizability and prevent standardized recommendations. Additionally, although ulinastatin demonstrates a favorable safety profile with a low incidence of adverse drug reactions, long-term and multinational pharmacovigilance data remain limited. Well-designed international, multicenter randomized controlled trials are required to clarify optimal dosing strategies, confirm clinical efficacy across diverse populations, and determine its independent effects compared with combination therapies. Overall, ulinastatin shows promise as a potential adjunctive therapy in critical care through modulation of inflammation and organ protection, but broader global adoption will depend on higher-quality evidence addressing current methodological gaps.

Luteolin is a dietary flavonoid widely distributed in fruits and vegetables. It has attracted substantial preclinical interest due to its pleiotropic hepatoprotective effects against hepatic steatosis, inflammation, fibrosis, and hepatocellular carcinoma. By reviewing data from in vitro and in vivo studies, this review comprehensively synthesizes the full spectrum of liver-directed pharmacology of luteolin, covering metabolic and toxic liver injury, fibrosis, cancer, and viral hepatitis, while critically mapping each mechanism to specific disease contexts and systematically identifying the key challenges limiting its clinical translation. The underlying mechanisms of luteolin action involve activation of Nrf2-mediated antioxidant defense, suppression of NF-κB- and NLRP3-driven inflammatory responses, inhibition of hepatic stellate cell activation via the TGF-β/Smad and STAT3 pathways, and regulation of metabolic homeostasis through liver X receptor (LXR)/SREBP-1c and AMPK signaling. Despite well-characterized mechanisms in preclinical models, several critical gaps hinder its clinical translation: (1) Rigorous randomized controlled trials in well-defined patient populations are scarce, with only one combination supplement study reported. (2) The relative contribution of luteolin metabolites to its overall bioactivity remains poorly understood, even though derivatives such as luteolin-7-diglucuronide exhibit distinct pharmacological properties. Cell-type-specific delivery systems, which show promise in preclinical fibrosis and cancer models, have not been evaluated clinically. (3) Systematic studies on the synergistic effects of luteolin with standard-of-care drugs remain largely exploratory. Overall, luteolin is a promising multi-target nutraceutical for liver diseases, and its clinical translation requires optimized delivery strategies, investigation of metabolite activity, and well-designed human clinical trials.

The immunosuppressive tumor microenvironment (TME) limits immunotherapy efficacy in intrahepatic cholangiocarcinoma (ICC). Understanding the molecular drivers of this TME is essential for developing new therapies. This study aimed to identify novel oncogenes that modulate the immune landscape of ICC using a multi-omics approach.

We integrated transcriptomic and proteomic data from our ICC cohorts with public datasets (TCGA-CHOL, GSE107943, OEP002768) to identify genes co-upregulated with PD-L1 (CD274). Single-cell RNA sequencing (scRNA-seq) was used to analyze cell-type-specific expression and intercellular communication. Clinical significance was validated through tissue microarrays and multiplex immunofluorescence in an independent ICC cohort.

Multi-omics screening identified TACC3 as a key candidate in ICC. Elevated TACC3 expression in ICC tissues correlated with poor prognosis and promoted tumor cell proliferation and migration. TACC3 activated the STAT3 pathway, increasing PD-L1 transcription. scRNA-seq showed TACC3/PD-L1 interaction in malignant epithelial cells, with PD-L1 co-expressed with FOXP3 in regulatory T cells (Tregs). Cell–cell communication analysis predicted strong interactions between malignant cells and Tregs. TACC3 knockdown reduced PD-L1 expression and inhibited STAT3 and AKT phosphorylation. Clinical validation confirmed co-expression of TACC3, PD-L1, and FOXP3, with high TACC3 levels linked to worse clinicopathological features and shorter progression-free survival.

Our study defines a TACC3-STAT3-PD-L1 axis driving immunosuppression in ICC. TACC3 fosters an immunosuppressive TME by upregulating PD-L1 and is associated with a Treg-rich contexture, suggesting that TACC3 may serve as a potential therapeutic target to overcome ICC immunosuppression.

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.

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.

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.

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.

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.