Eosinophilic esophagitis (EoE) is characterized by esophageal dysfunction and ≥15 eosinophils/high-power field on biopsy. The specific clinicopathologic characteristics and therapeutic outcomes of patients with concurrent gastric Helicobacter pylori infection and EoE remain poorly defined. This observational cohort study reexamines this relationship and evaluates the clinicopathologic features and outcomes of patients with concurrent diseases.

We retrospectively reviewed esophageal and gastric biopsies obtained between January 1, 2022, and December 30, 2025. Patients with a first-time diagnosis of EoE and concurrent treatment-naive gastric H. pylori infection were identified and confirmed by morphology and immunohistochemistry. Patients with a history of prior H. pylori eradication therapy or eosinophilic gastrointestinal disease were excluded. Clinical, pathologic, and follow-up data were analyzed.

Among 5,443 patients undergoing concurrent esophageal and gastric biopsy evaluation, 197 (3.6%, 95% confidence interval [CI], 3.1–4.1%) met the diagnostic criteria for EoE, and 286 (5.3%, 95% CI, 4.7–5.9%) had gastric H. pylori infection. Twenty-eight patients with concurrent EoE and gastric H. pylori infection were initially identified, corresponding to an H. pylori prevalence of 14.2% (28/197) among patients with EoE. A significant association was identified between EoE and H. pylori infection (odds ratio, 3.20; 95% CI, 2.11–4.87; Fisher’s exact test, P < 0.001). After exclusion of 1 patient with eosinophilic gastrointestinal disease and 3 patients with prior H. pylori treatment, 24 patients met the study inclusion criteria for the subsequent clinicopathologic analysis. Among these 24 patients (M:F = 7:1; mean age, 29.5 years; range, 8–82 years), 8 (33%) were children. The most common presentations (n = 18, 75%) were abdominal pain in children and dysphagia in adults. Atopy was present in 7 (29%) patients. Histologically, 5 (21%) cases exhibited classic features, while 19 (79%) showed nonclassic features with lower eosinophil density and a more uniform distribution of eosinophils within the epithelium. Among 10 patients with follow-up, 3/10 (30%) achieved resolution of both H. pylori infection and EoE after H. pylori eradication therapy with or without steroids; 5/10 (50%) had persistent H. pylori infection and persistent or recurrent EoE; 2(20%) achieved H. pylori-negative status but had persistent EoE; and fungal infection developed in 2 of 4 patients (50%) treated with steroids.

H. pylori infection may be associated with a nonclassic pattern of EoE. Recognition of this pattern may have implications for diagnosis and management.

Bioactive peptides encrypted within food proteins and released by enzymatic hydrolysis or gastrointestinal digestion represent potential functional ingredients. Amaranth is an underutilized pseudocereal with a balanced amino acid profile and a protein composition that may yield peptides with diverse biological activities. However, translation of amaranth-derived peptides remains limited by low or uncertain bioavailability, variable yields, extraction and purification challenges, incomplete sequence identification, insufficient genotype screening, limited understanding of structure-activity relationships, and scarce in vivo and clinical validation. This review summarizes current evidence on the production, characterization, and pharmacological potential of amaranth-derived bioactive peptides. Enzymatic hydrolysis, fermentation, gastrointestinal digestion, and protein engineering have generated peptide fractions or sequences with antioxidant, antimicrobial, angiotensin-converting enzyme-inhibitory, dipeptidyl peptidase IV-inhibitory, hypocholesterolemic, anti-inflammatory, antithrombotic, and anticancer activities, primarily in in silico, biochemical, cell-based, and animal models. Analytical workflows involving chromatographic separation, mass spectrometry, and bioinformatic prediction have improved peptide discovery, but results remain difficult to compare because processing conditions and activity assays are not standardized. Available evidence suggests that amaranth proteins are promising sources of multifunctional peptides; nevertheless, these findings do not yet establish clinical efficacy. Future work should optimize extraction and identification methods, clarify sequence-structure-activity relationships, evaluate stability and intestinal absorption, compare genotypes and non-seed tissues, and conduct well-designed in vivo studies, safety assessments, and clinical trials. Scalable processing and formulation strategies will also be required before amaranth-derived peptides can be developed as reliable functional food, nutraceutical, or pharmaceutical ingredients.

Hepatitis B virus (HBV) genotype C is common in East Asia and is associated with poor outcomes, particularly liver cirrhosis and hepatocellular carcinoma (HCC). Clinically, genotype C infection has been associated with persistent viral replication, delayed HBeAg seroconversion, more active hepatic inflammation, and an increased risk of HCC. Current evidence suggests that these features are driven by several key molecular events, including the A1762T/G1764A double mutation in the basal core promoter, the G1896A mutation in the precore region, abnormal hepatitis B virus X protein function, and viral integration. Together, these changes may reshape viral transcription, antigen expression, host immune interactions, and oncogenic signaling, thereby contributing to disease progression and hepatocarcinogenesis. Other factors, such as epigenetic changes, dysregulated DNA damage responses, impaired tumor protein p53 function, and disrupted autophagy, may also be involved, although their exact roles remain unclear. Notably, even after effective viral suppression with potent nucleos(t)ide analogs, patients with genotype C may still have a relatively high residual risk of HCC. This review summarizes the molecular virological features, pathogenic mechanisms, immune dysregulation, and clinical significance of HBV genotype C, and discusses the potential value of genotype information in risk stratification, long-term surveillance, and clinical assessment of chronic hepatitis B.

Bacterial infection is a key cause of mortality in patients with acute-on-chronic liver failure (ACLF). In this study, we aimed to identify metabolite biomarkers and develop a novel machine learning model for early identification of bacterial infection in ACLF.

Based on a prospective multicenter cohort from 14 centers, 1,314 patients with acute-on-chronic liver disease were enrolled, including those with ACLF and non-ACLF. Plasma samples at admission were collected for metabolomics profiling. Patients were randomly divided into discovery (n = 921) and validation (n = 393) sets. Machine learning was used to develop diagnostic models. The win ratio method was employed to assess the risk stratification capability of the models.

Bacterial infection occurred in 198 of the 451 ACLF patients and 132 of the 863 non-ACLF patients. Infection altered the plasma metabolome, especially in lipid, amino acid, and xenobiotic metabolic pathways. Models for bacterial infection in ACLF (five metabolites) and non-ACLF (six metabolites) demonstrated superior discrimination in the discovery (AUCs: 0.881 and 0.935, respectively) and validation sets (AUCs: 0.835 and 0.889, respectively) compared with C-reactive protein, white blood cell count, procalcitonin, and the best composite clinical model. Metabolic risk stratification based on the models effectively predicted 90-day outcomes (all-cause death, organ failure, sepsis, new-onset acute decompensation, and systemic inflammatory response syndrome).

Our models based on novel metabolic biomarkers enable identification of patients at high risk of bacterial infection and support risk stratification of 90-day outcomes.

In contemporary practice, elevated 24-hour urinary copper excretion (24-h UCE) often triggers referral for suspected Wilson disease (WD). In this hypercupriuric referral setting, interpretation of 24-h UCE may be distorted by spectrum effects. In this study, we aimed to compare conventional copper biomarkers in hypercupriuric referrals and evaluate whether a Leipzig-aligned ceruloplasmin (Cp) framework could provide a clinically useful triage approach.

We retrospectively studied consecutive, untreated patients evaluated for suspected WD with hypercupriuria between February 2017 and February 2025. The final diagnosis was established using a prespecified Leipzig-based algorithm, with ATP7B testing when indicated. Diagnostic performance of Cp and 24-h UCE was compared. A prespecified Cp three-zone framework was evaluated using <0.10 g/L, 0.10–0.20 g/L, and >0.20 g/L as high-probability, indeterminate, and low-probability zones, respectively.

Among 541 untreated hypercupriuric patients, 65 had WD and 476 had adjudicated non-WD liver disease. Cp outperformed 24-h UCE for diagnosing WD (AUROC, 0.988 vs. 0.762). The optimal Cp cutoff was 0.15 g/L, with 90.8% sensitivity and 97.7% specificity. Cp < 0.10 g/L defined a high-probability zone with 98.0% WD prevalence, whereas Cp > 0.20 g/L defined a low-probability zone with 0.5% WD prevalence. Among non-WD controls, higher urinary copper was independently associated with higher bilirubin, prolonged international normalized ratio, and lower albumin.

In hypercupriuric referrals for suspected WD, Cp retained strong diagnostic performance and outperformed 24-h UCE. A Leipzig-aligned Cp three-zone framework may support probability-based triage in contemporary referral practice.

The modern intensive care unit (ICU) inundates clinicians with large volumes of data, leading to cognitive overload and a gap between data availability and actionable insight. While artificial intelligence (AI) promises a solution, its clinical adoption is limited by systemic barriers, including algorithmic bias, a lack of trust, and validation failures. This paper argues that a design philosophy that envisions AI as an autonomous decision-maker, rather than an integrated collaborative tool, has hindered its clinical adoption. We propose an alternative: a collaborative framework designed to augment the intensivist’s expertise by offloading specific cognitive burdens. This framework redefines AI’s purpose as managing data-intensive tasks, illustrated through four collaborative example roles: a synthesizer to create coherent clinical narratives, a sentinel for proactive deterioration surveillance, a simulator to forecast patient responses to interventions, and a stratifier to identify meaningful subphenotypes within complex syndromes. By delegating these computational tasks, this collaborative model frees clinicians to focus on complex synthesis, nuanced judgment, and compassionate communication. Realizing this vision requires a deliberate translational pathway focused on robust data infrastructure, human-centered design, and rigorous validation through prospective clinical trials. Ultimately, the successful integration of AI in critical care depends not on replacing clinicians but on empowering them, creating a more functional ICU in which technology supports the delivery of safer, more precise, and more humane care.