Due to the lack of specific Western medicine therapies for post-coronavirus disease 2019 (COVID-19) syndrome in clinical practice, this study aimed to investigate the efficacy of traditional Chinese medicine (TCM) for post-COVID-19 syndrome using a cohort study design and to explore its clinical value in alleviating patients’ symptoms and improving clinical outcomes.

In this cohort study, patients were divided into two groups according to clinical treatment. The control group received conventional Western medicine, and the treatment group received additional TCM syndrome differentiation–based treatment. Propensity score matching methods were used to reduce selection bias by equating groups based on observed covariates. Clinical data, including TCM symptom scores, the Short Form 36 Health Survey, clinical efficacy, and adverse events at Day 7, were collected. The primary outcome was the efficacy rate, defined by improvement at Day 7 compared with the Day 0 score. Data were processed and analyzed using SPSS 23.0 and R 4.5.0 software.

A total of 434 patients were enrolled in the cohort, including 306 patients in the control group and 128 in the treatment group. After 1:1 matching, 94 matched pairs were analyzed. For the primary outcome, the effective rate in the treatment group was higher than that in the control group (30.8% vs. 17.2%; odds ratio (OR) = 2.17, 95% confidence interval (CI): 1.09–4.35, P = 0.003). After seven days of treatment, the TCM syndrome score improved more in the treatment group than in the control group (median difference (MD) = 2.00, 95% CI: 0.50–3.50, P = 0.009). Subgroup analyses showed generally favorable efficacy in the treatment group across subgroups, though not all reached statistical significance.

TCM syndrome differentiation–based therapy effectively relieves clinical symptoms in patients with post-COVID-19 syndrome.

Chronic pelvic pain remains a significant clinical challenge, often refractory to conservative and interventional treatments. Superior hypogastric plexus block is an established technique; however, conventional anterior and posterior approaches may be limited by anatomical variability and potential risks to adjacent structures. Based on these anatomical findings, we propose that a posterior transosseous S1 pedicular approach represents a novel and anatomically robust corridor for accessing the superior hypogastric plexus. We hypothesize that the highly reproducible osseous anatomy of the S1 pedicle, combined with its consistent spatial relationship to the anterior sacral cortex and retroperitoneal compartment, may enable precise and fluoroscopically reproducible instrument guidance toward the plexus. Furthermore, this trajectory may mitigate the anatomical variability and procedural limitations associated with conventional anterior or paravertebral techniques while potentially reducing the risk of inadvertent injury to adjacent visceral, vascular, and neural structures. This concept is based on anatomical reasoning and fluoroscopic observations obtained during cadaveric anatomical orientation, suggesting that a transosseous trajectory through the S1 pedicle toward the anterior sacral cortex may offer improved spatial control and reproducibility compared with soft-tissue-based approaches. The proposed pathway remains conceptual and is not intended for clinical application at this stage. Further cadaveric, imaging-based, and clinical studies are required to evaluate its anatomical validity, safety, and potential clinical relevance.

The aberrant activation of the mTOR pathway and its crosstalk with other signaling cascades represent key drivers of hepatocellular carcinoma (HCC) progression. mTOR-mediated ferroptosis suppression has been implicated in HCC resistance to chemotherapy. This study aimed to elucidate the mechanisms underlying mTOR inhibitor resistance and to evaluate the therapeutic potential of multidrug combinations in β-catenin-mutant HCC.

MHCC97H and SNU449 cells were transfected with 4EBP1WT, 4EBP1A4, or HSP90β expression plasmids and then treated with rapamycin to assess their effects on ferroptosis and rapamycin sensitivity. The role of 4EBP1 in regulating ferroptosis was further explored by Western blotting, co-immunoprecipitation, and immunofluorescence. The inhibitory effects of mTOR inhibitors (rapamycin, MLN0128), ERK inhibitors (PD901), and their combination (MLN0128 + PD901) on tumor cells were evaluated. HCC mouse models were generated via hydrodynamic tail vein injection of c-Met/β-cateninΔN90 or c-Met/β-cateninΔN90/4EBP1A4 plasmids to evaluate the therapeutic effects of the four treatment regimens.

Rapamycin more potently inhibited mTOR/RPS6 than mTOR/4EBP1 and concurrently induced ferroptosis. 4EBP1A4 promoted ferroptosis and potentiated rapamycin efficacy. Mechanistically, 4EBP1A4 competitively bound HSP90β, displacing Keap1, thereby increasing Keap1–Nrf2 complex formation and promoting Nrf2 degradation. Furthermore, rapamycin, MLN0128, PD901, and their combination reduced p-4EBP1 levels, induced ferroptosis, and inhibited HCC cell proliferation, thereby suppressing tumor growth, with the combination exhibiting the strongest effect.

4EBP1A4 enhances Nrf2 ubiquitination and degradation via the HSP90β/Keap1 axis, relieving mTOR-mediated ferroptosis suppression and synergistically improving rapamycin efficacy. Additionally, rapamycin, MLN0128, and PD901 suppress HCC progression by inducing ferroptosis, with their combination showing superior potency.

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.

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.

Digital pathology (DP) is transitioning from an adjunct technology to an enterprise diagnostic platform in the United States. Despite accelerating clinical adoption, many laboratories face persistent barriers, including high capital and operating costs, workflow disruption, interoperability challenges, and a complex regulatory and reimbursement environment. This narrative review proposes a practical lifecycle framework for implementing and sustaining DP programs, with an emphasis on defining and operationalizing institutional artificial intelligence (AI) readiness for safe and sustainable adoption.

We performed a targeted narrative review informed by searches of PubMed/MEDLINE and Google Scholar for English-language publications from January 1, 2014 through December 31, 2025. Core search concepts included DP, whole slide imaging, image management/viewing systems, laboratory information system integration, validation, reimbursement, U.S. Food and Drug Administration clearance, Clinical Laboratory Improvement Amendments oversight, College of American Pathologists accreditation, interoperability standards, cybersecurity, and AI. We supplemented database searches with reference screening and review of primary guidance and public databases from regulatory and professional organizations in the United States. We prioritized peer-reviewed literature and used web-based regulatory sources when they represented the authoritative primary reference. We also incorporated our professional experience and knowledge in DP and AI.

Key implementation domains span foundational infrastructure (scanners, storage/networking, and integrated image management platforms), workflow redesign across pre-analytic, analytic, and post-analytic phases, validation and quality management, regulatory compliance and accreditation, cost capture, interoperability strategy, cybersecurity and access control, education and change management, and long-term governance. We also describe an institution-level AI readiness model that can be assessed across data quality, integration, validation, monitoring, governance, and workforce capabilities to support safe clinical AI deployment.

Successful DP implementation requires a lifecycle approach that couples technical build-out with workflow redesign and institutional governance. Early planning for compliance, interoperability, reimbursement strategy, and AI readiness can reduce implementation risk and position laboratories for sustained clinical and computational innovation.

Metabolic dysfunction–associated steatotic liver disease (MASLD), defined by the American Association for the Study of Liver Diseases as hepatic steatosis with at least one cardiometabolic risk factor, affects approximately 30–40% of adults worldwide. This condition may progress to fibrosis, cirrhosis, and hepatocellular carcinoma. The rising prevalence, alongside obesity and type 2 diabetes, underscores the need for early risk stratification and integrated therapeutic strategies. Circadian homeostasis, orchestrated by the suprachiasmatic nucleus and core clock gene feedback loops, synchronizes hepatic metabolic pathways with environmental light–dark cycles. The objective of this review is to evaluate the role of circadian disruption and metabolic dysfunction in the development of hepatic steatosis, as well as to assess current and potential treatment modalities for both disorders. Circadian disruption through shift work, artificial light at night, sleep restriction, and chrono-nutritional misalignment destabilizes hepatic clocks, promoting insulin resistance, dyslipidemia, inflammation, and steatosis. Experimental models demonstrate that clock gene dysfunction alone can induce steatohepatitis, while progressive MASLD further impairs central circadian regulation, establishing a self-reinforcing chrono-metabolic cycle. Pharmacologic therapies, including glucagon-like peptide-1 receptor agonists and thyroid hormone receptor-β agonists, improve histologic endpoints and fibrosis regression, although heterogeneity among clinical trials precludes direct comparison. Recent evidence characterizing MASLD as a predominantly nocturnal metabolic disorder further highlights persistent nighttime insulin dysregulation despite weight loss, emphasizing the potential role of circadian-targeted interventions such as melatonin. In conclusion, the peripheral circadian clock is intricately linked with MASLD pathogenesis, and metabolic dysfunction, in turn, disrupts circadian pathways. Several pharmacologic therapies offer potential for the treatment of MASLD and circadian dysfunction.

Colorectal polyp detection from endoscopic images is critical for the early diagnosis of colorectal cancer. However, traditional deep learning methods often suffer from limited generalization when deployed across datasets containing different polyp morphologies. This work aimed to investigate whether vision-language foundation models can facilitate zero-shot generalization across multiple polyp datasets without target-domain fine-tuning.

We introduced a zero-shot colorectal polyp detection framework based on Contrastive Language-Image Pretraining (CLIP) to improve cross-dataset detection performance. Key innovations include: (1) a background patch contrastive loss using pseudo-normal tissue patches to teach the model to distinguish normal mucosa from polyps; (2) attribute-enhanced text prompts that incorporate domain-specific descriptors of polyp appearance, improving the model’s semantic generalization to novel polyp morphologies; and (3) an enhanced CLIP visual adapter with per-layer adaptive feature fusion and generalized mean pooling to capture multi-scale features for better polyp localization. During training, we use one annotated colorectal polyp dataset (e.g., CVC-ColonDB) to learn patch-level image-text correspondence. The model is then evaluated in a zero-shot manner on different polyp datasets (CVC-ClinicDB, Kvasir-SEG, and CVC-300), where we evaluate pixel-level anomaly detection performance.

The framework demonstrated robust zero-shot generalization on unseen test cohorts. Without any dataset-specific fine-tuning, the model achieved a mean pixel-level AUROC of 0.94 and a mean average precision of 0.81 across the 12 leave-one-dataset-out zero-shot transfer settings. In the CVC-ColonDB-source benchmark, the model achieved a mean Dice coefficient of 0.84 across CVC-ClinicDB, Kvasir-SEG, and CVC-300. This high level of performance was consistent across datasets with distinct visual characteristics, underscoring the ability of the model to detect diverse polyp morphologies that it had not been explicitly trained to recognize.

Our findings demonstrate that an anomaly-aware vision-language model significantly improves cross-dataset polyp detection generalization without requiring normal images for training. This multimodal strategy may facilitate the robust deployment of artificial intelligence-based colorectal screening systems by enabling reliable detection of diverse polyp morphologies across different clinical settings. Extension to non-polyp colorectal pathologies (e.g., ulcerative colitis and colorectal tumors) remains an important direction for future work, pending the availability of pixel-level annotated datasets for these lesion categories.

Primary biliary cholangitis (PBC) significantly impairs health-related quality of life (HRQL), yet the impact of disease stage and fatigue on HRQL and psychological status remains insufficiently quantified. This study aimed to investigate differences in HRQL across disease stages and the impact of fatigue in patients with PBC.

This cross-sectional study recruited 219 patients with PBC from two Chinese tertiary hospitals (2011–2024). After excluding one preclinical case, 218 patients were analyzed. Quality of life was assessed using the validated Chinese versions of the SF-36 and Chronic Liver Disease Questionnaire (CLDQ); psychological status was assessed using the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Between-group differences were quantified by mean differences (MDs) and odds ratios (ORs) with 95% confidence intervals (CIs). Baseline characteristics were balanced across stages (all P > 0.05).

Of the 218 patients (90.4% female; mean age, 57.2 ± 10.3 years), 41 were in the clinical stage, 75 in the fibrosis stage, and 102 in the cirrhosis stage. SF-36 scores were lowest in the cirrhosis stage (e.g., Physical Functioning MD, 17.26; 95% CI, 6.93–27.59 vs. clinical stage), with similar declines in CLDQ domains. Anxiety was highest in the clinical stage (58.5%; OR vs. cirrhosis, 4.13; 95% CI, 1.92–8.92), whereas depression was highest in the cirrhosis stage (55.9%; OR vs. clinical stage, 4.50; 95% CI, 1.95–10.38). Fatigue prevalence was 66.1% and increased with disease stage. Patients with fatigue had lower SF-36 scores in Physical Functioning, Bodily Pain, Vitality, Mental Health, and Physical Component Summary (e.g., Physical Component Summary MD, 38.22; 95% CI, 10.41–66.02).

HRQL declines progressively with PBC stage. Fatigue is strongly associated with impaired HRQL and is closely interrelated with anxiety and depression. Stage-specific psychological patterns suggest the need for tailored supportive interventions.