Cardiorenal syndrome is associated with high morbidity and mortality and is characterized by bidirectional interactions between cardiac and renal dysfunction. The advent of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and the nonsteroidal mineralocorticoid receptor antagonist finerenone has substantially changed the therapeutic landscape. Combination therapy with SGLT2i and finerenone may provide additional benefits through complementary mechanisms, representing a potential paradigm shift in the management of cardiorenal syndrome. In this review, we examine the pathophysiological pathways that characterize cardiorenal syndrome, clinical data from major randomized controlled trials, and the rationale for the concomitant use of these two drug classes. SGLT2 inhibitors significantly reduce hospitalization for heart failure, slow renal function decline, and provide benefits in both heart failure with reduced ejection fraction and heart failure with preserved ejection fraction, irrespective of diabetes status. Finerenone has been shown to reduce the risk of cardiovascular events and chronic kidney disease progression in patients with type 2 diabetes and chronic kidney disease, with a more favorable safety profile than steroidal mineralocorticoid receptor antagonists. Emerging evidence suggests that combination therapy may reduce hospitalizations for heart failure and slow renal disease progression beyond the effects of either monotherapy. However, implementation of these therapeutic options requires careful patient selection, ongoing monitoring of renal function and electrolytes, and close collaboration between cardiologists and nephrologists.

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.

Sepsis is a life-threatening syndrome associated with high morbidity and mortality, underscoring the urgent need for early diagnostic biomarkers and therapeutic targets. However, current diagnostic strategies remain insufficiently precise because of the complex immune dysregulation and immune microenvironment heterogeneity that characterize sepsis. This study aimed to identify reliable diagnostic biomarkers for sepsis and explore their immune regulatory mechanisms together with potential therapeutic relevance using multidimensional bioinformatic analyses.

Single-cell transcriptomic and bulk RNA sequencing datasets were integrated to screen candidate diagnostic genes for sepsis. Immune infiltration, co-expression network and pathway enrichment analyses were performed to explore immune regulatory mechanisms. Machine-learning approaches were used to validate the diagnostic signature, and molecular docking was conducted to predict candidate targeted compounds.

A total of 346 differentially expressed genes were identified and were mainly enriched in immune, coagulation, and metabolic pathways. CIBERSORT and single-cell analyses revealed increased neutrophils, monocytes, and γδ T cells and reduced CD8+ T cells and resting natural killer cells. Four diagnostic genes (S100A12, CD22, CSTA, and UPP1) were prioritized. The four-gene model showed robust external performance (area under the receiver operating characteristic curve = 0.860; sensitivity = 0.781; specificity = 0.780), and interpretability analysis highlighted UPP1 and S100A12 as dominant predictors. Molecular docking suggested potential interactions between these targets and anti-inflammatory compounds.

This integrative framework identifies four immune-related diagnostic genes for sepsis and links them to immune-cell remodeling and candidate therapeutic interactions, providing a basis for future mechanistic and clinical validation.

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.

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.

Post-stroke dysphagia management research has primarily focused on screening, assessment, and intervention strategies, with limited objective indicators for evaluating nursing care quality. This study aimed to develop a dysphagia nursing quality evaluation index system for neurosurgical inpatients with stroke.

Using the “structure-process-outcome” three-dimensional quality model as the theoretical framework, a preliminary quality evaluation index system was constructed through literature analysis and group discussion. A two-round Delphi expert consultation was conducted among 25 purposively selected clinical experts from tertiary Class A hospitals, with inclusion criteria requiring a bachelor’s degree or higher, an intermediate professional title or above, and at least 10 years of clinical experience in stroke nursing or related fields. The analytic hierarchy process was used to determine indicator weights. Outcome measures included expert authority coefficients (Cr), Kendall’s W concordance coefficient, internal consistency reliability (Cronbach’s α), and the final indicator structure.

The Cr values were 0.87 and 0.88 across the two rounds. Kendall’s W concordance coefficient increased from 0.207 to 0.235 (P < 0.001), indicating statistically significant expert agreement. The final index system comprised 3 first-level indicators, 11 second-level indicators, and 44 third-level indicators, with all indicator definitions and weights determined. The overall Cronbach’s α was 0.86, indicating preliminary internal consistency.

This study developed a dysphagia nursing quality evaluation index system for neurosurgical inpatients with stroke using the three-dimensional quality model and the Delphi method. The system showed acceptable expert authority, statistically significant expert agreement, and preliminary internal consistency, suggesting potential applicability for nursing quality monitoring in neurosurgical wards and Neurosurgery Intensive Care Units. Further clinical validation is needed before routine implementation.

Cervical cancer is a major malignancy that threatens women’s health, and early screening is a core strategy for reducing its incidence and mortality. Multimodal fusion artificial intelligence (AI) pathological diagnosis models integrate multidimensional data—including cytological images, colposcopic images, whole-slide histopathological images, clinical data, and molecular testing results—and may enhance the detection sensitivity, grading accuracy, and screening efficiency for early cervical cancer and precancerous lesions. However, traditional cervical cancer screening methods face limitations such as high subjectivity, reliance on single-source information, relatively low efficiency, and insufficient primary care resources. Furthermore, existing reviews mostly focus on single-modal AI models or specific technical aspects, lacking a comprehensive analysis of the full technical framework and clinical translation pathways of multimodal fusion models. This review aims to comprehensively present the development and application of multimodal fusion AI models in pathological diagnosis for early cervical cancer screening. Specifically, it comprehensively details the technical architecture, data modalities, and fusion strategies—including deep learning, attention mechanisms, and cross-modal alignment techniques—that enable the complementary representation of morphological, clinical, and molecular information. Additionally, the review integrates recent advances in clinical applications and evaluates current translational challenges, providing insights into clinical validation pathways to bridge technological innovation and practical healthcare delivery. In conclusion, with further technological refinement and clinical validation, multimodal fusion AI may become a useful tool for improving the precision and efficiency of cervical cancer screening and prevention, and may inform the standardized application and translational research of AI technology in this field.