Spatial metabolic differences found in glioblastoma (GBM) tumor core (contrast enhancing) and peritumoral (T2/FLAIR hyperintense) edge tissue regions have recently enabled stratification of patient overall survival. However, the association between metabolic dysregulation and survival duration remains poorly understood. To gain further insight into the biological characteristics underlying longer vs. shorter survival, this study employed an interdisciplinary approach to analyze GBM region-specific metabolic signatures predictive of patient overall survival.

Patient survival data were paired with core and edge biopsy tumor metabolomic data (n = 37 pairs) obtained by 2D liquid chromatography-mass spectrometry/mass spectrometry. Metabolite expression was compared between patients with short (≤ median) and long (> median) overall survival using relative abundance analysis. Metabolic signatures predictive of patient survival were identified via a comprehensive machine learning (ML) workflow, including repeated nested cross validation and test (holdout) set evaluation. Pathways associated with key metabolites were identified from the KEGG database.

Core metabolite levels generally were increased and edge metabolite levels were decreased in patients with longer survival, Edge tissue metabolic signatures reflected survival duration better than signatures from core tissue. Metabolites differentiating short vs. long survival were associated with metabolic pathway dysfunction related to fatty acid and amino acid metabolism, glycolysis and gluconeogenesis, and ATP synthesis.

Interdisciplinary analysis of GBM region-specific metabolic signatures predictive of patient short vs. long survival can yield insight into the local and global metabolic dysfunction associated with survival duration.

Patients with obstructive sleep apnea (OSA) frequently seek information online, yet the comparative quality of content delivered by web search engines versus generative AI systems is unclear. This study evaluated how different digital information sources perform in answering common patient questions about OSA.

Thirty high-volume, patient-facing OSA questions were identified using Google Trends. Each question was submitted verbatim to four general-purpose large language models (GPT-4, GPT-5, DeepSeek, Mistral), a medically specialized retrieval-augmented model (OpenEvidence), and Google Search. Seven otolaryngologists with clinical experience in OSA independently rated each response for accuracy, clarity, completeness, relevance, and usefulness using a five-point rubric. Composite and domain scores were analyzed using one-way analysis of variance with multiple-comparison correction; inter-rater reliability was assessed with two-way random-effects intraclass correlation coefficients.

A total of 180 question-system pairs received 6295 domain-level ratings. OpenEvidence achieved the highest mean composite score (4.33), followed by a tightly clustered group of LLMs (means 4.00-4.04). Google Search scored significantly lower (3.15). Differences among systems were statistically significant across all domains (p < 0.001), with large effect sizes for comparisons of OpenEvidence and general LLMs versus Google. Composite average-rater reliability was good (ICC = 0.70).

For common OSA questions, generative AI systems-particularly a retrieval-augmented medical model-produced higher-quality patient-facing information than standard web search. These findings support cautious consideration of GenAI tools to supplement patient education in OSA, while underscoring the need for ongoing evaluation across diseases, disciplines, and patient populations.

Patients with obstructive sleep apnea (OSA) frequently rely on online sources such as Google Search to understand symptoms, testing, and treatment, yet the quality of patient-facing information varies widely. As generative artificial intelligence tools are increasingly used for health questions, their comparative performance for OSA education has not been systematically evaluated using blinded expert review.

In this blinded comparative study, generative AI systems, particularly a retrieval-augmented medical model, provided more accurate, clear, complete, and useful answers to common OSA questions than standard web search. These findings highlight that the choice of digital information source can meaningfully influence the quality of patient education in sleep medicine and support further evaluation of AI tools within clinical practice.

Objective: To investigate the molecular sensitization profiles of inhalant allergens in Chinese patients with airway allergies using component-resolved diagnosis (CRD) and to analyze their distributional characteristics. Methods: In this cross-sectional study, allergen component testing data were collected from 225 patients diagnosed with allergic rhinitis (AR) and/or allergic asthma (AS) across 20 medical centers in 13 provinces (Northern, Southern, and Northwestern China) between October 2019 and January 2023. Specific immunoglobulin E (sIgE) levels for 132 components were analyzed. Sensitization differences were compared across clinical phenotypes, age stratifications, and geographical regions. Spearman correlation analysis was employed to evaluate molecular cross-reactivity. Results: Among the 225 patients [mean age: (19.40±17.53) years; 119 males, 106 females], children aged 0-6 and 7-14 years accounted for 58.67% (132/225). Regarding overall distribution, the predominant sensitizing components were Artemisia vulgaris (Art v 1, 43.11%), dust mites (Der p 2, 39.11%; Der f 2, 38.22%), and Phleum pratense (Phl p 12, 39.11%). Phenotypically, while the AR combined with AS group exhibited the broadest polysensitization, the AR only group was distinct for its significantly higher sensitization to cat (Fel d 1, 68.42%) and dog (Can f 1,36.84%) dander (both P<0.05). Conversely, pollen sensitization was relatively lower in the AS only group. Regarding age, children (0-6 and 7-14 years) showed early sensitization to pan-allergens (e.g., Phl p 12, Bet v 2) and fungi (Alt a 1), whereas dust mite components (Der f 2, Der p 2, Der p 23) showed lower prevalence in the 0-6 group (23.19%, 23.19%, and 10.14%, respectively). Regional analysis highlighted a "Northern Pollen vs Southern Mite" dichotomy: weeds (Artemisia>60%) and tree pollen dominated the Northwest and North, whereas mites (>60%) and cockroaches dominated the South (P<0.001). Furthermore, extensive molecular cross-reactivity was confirmed within the Serum Albumin and Profilin families. Conclusions: Significant heterogeneity exists in sensitization profiles across phenotypes, ages, and regions. Mugwort and mites are core allergens. CRD facilitates the identification of cross-reactivity and guides precise, regionalized management.

Objective: Preliminary analysis of dynamic expression characteristics of plasma proteins in children with allergic asthma during subcutaneous immunotherapy (SCIT) with dust mite allergens based on multi-time-point proteomics technology. Methods: A cross-sectional study was conducted, enrolling 84 children with allergic asthma primarily sensitized to house dust mites (HDM) who visited the Children's Hospital of Soochow University between November 2024 and May 2025. Participants were categorized into five groups based on the duration of SCIT: 0M, 3M, 6M, 12M, and 24M. To detect the levels of specific immunoglobulin E (sIgE) and specific immunoglobulin G4 (sIgG4) against dust mite components (Der p1, Der f1, Der p2, Der f2, Der p5, Der p7, Der p10, Der p21, Der p23). Five subjects were randomly selected from each group using simple random sampling, and an additional 5 healthy children (HC) were included as the control group for plasma proteomic analysis. The temporal expression patterns of differentially expressed proteins were clustered via the Mfuzz algorithm and subjected to functional annotation. Results: A total of 84 children were enrolled, with a mean age of (10.12±2.15) years, including 57 males and 27 females. Statistically significant differences were observed in Der p1-, Der f1-, Der p2-, and Der f2-specific IgG4 levels among the five groups (all P<0.05). The 24M group exhibited significantly higher levels of Der p1-, Der f1-, Der p2-, and Der f2-specific IgG4 compared with the 0M group [Der p1: 695.20 (57.85, 1 894.60) vs 44.35 (32.10, 51.10); Der f1: 70.15 (51.35, 1 141.03) vs 46.65 (35.30, 63.68); Der p2: 3 440.20 (892.20, 4 183.00) vs 66.85 (43.08, 189.98); Der f2: 2 015.50 (704.60, 2 523.10) vs 69.10 (45.80, 159.35), all P<0.05]. Enrichment analysis of differentially expressed proteins in each group using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database showed that: compared with the healthy control group, the 0M group exhibited upregulation of cholesterol metabolism and complement/coagulation cascades, and downregulation of cornified envelope formation and ECM-receptor interaction pathways. compared with the 0M group, the 3M group showed upregulation of ECM-receptor interaction and focal adhesion and downregulation of fatty acid metabolism. The 6M group showed upregulation of cell adhesion molecules and downregulation of cholesterol metabolism; the 12M and 24M groups showed upregulation of TGF-β and PI3K-Akt pathways. Fuzzy C-means clustering analysis revealed that proteins highly expressed in the 6M group were enriched in glutathione metabolism, while those in the 24M group were significantly enriched in B cell-mediated immune responses. Six potential regulatory proteins (ARPC3, LPL, ILF3, FARSB, USP14, and SLIT2) were initially identified. Furthermore, Spearman correlation analysis revealed that ARPC3 was significantly negatively correlated with the levels of Der f2-sIgE, Der p2-sIgG4, and Der f2-sIgG4 (r=-0.44, -0.57, -0.54, respectively, all P<0.05). Additionally, ILF3 was positively correlated with Der p21-sIgE (r=0.57, P<0.01), FARSB was negatively correlated with Der p2-sIgG4 (r=-0.44, P<0.01), and USP14 was negatively correlated with Der f1-sIgE (r=-0.46, P<0.05). Conclusions: Differences exist in allergen antibody and plasma protein expression profiles among children with allergic asthma across groups with different durations of SCIT. The differentially expressed proteins may be involved in pathways related to airway epithelial barrier, metabolic homeostasis and immune regulation.

Objective: To investigate the efficacy and optimal timing of prophylactic medication before pollen season for children with seasonal allergic rhinitis (AR) and allergic asthma (AA) in Hohhot. Methods: A prospective, self-controlled study was conducted. Children aged 4-14 years who were diagnosed with AR or AR+AA in Hohhot First Hospital from January to August 2024 were enrolled, with the same patients undergoing self-medication in 2024 pollen season as the controls. Before 2025 pollen season, children were stratified into eight groups based on disease type (AR/AR+AA), severity of disease (mild/moderate-severe), and onset season (spring/autumn). For each group, prophylactic medication was initiated at 5, 10, 15, and 20 days before the season, respectively. Symptom onset time, symptomatic days, visual analogue scale (VAS) score, total medication score (TMS), and childhood asthma control test (C-ACT) score were compared between the two groups. Results: A total of 312 children were included in the study, consisting of 183 males (58.7%) and 129 females (41.3%), with a mean age of (8.6±3.0) years. After initiating medication 5, 10, and 15 days before the season for mild spring, moderate-severe spring and mild autumn, and moderate-severe autumn AR cases, respectively, children experienced significantly shorter symptomatic periods and lower VAS scores than those in the self-medication group (all P<0.01). For children with AR+AA, clinical indicators (symptomatic days, VAS score, and C-ACT score) were significantly improved after initiating medication 15 days in advance for mild spring cases and 20 days in advance for other subgroups (all P<0.01). The proportions of pre-season symptoms in the prophylactic medication groups were lower, and TMS scores were higher (all P<0.01). Conclusion: Stratified prophylactic medication based on disease severity before the pollen season can delay symptom onset, alleviate symptom severity, and shorten the duration of symptoms in children with AR and AA.

Objective: To investigate the characteristics of 14 arachidonic acid (AA) metabolites in the serum of patients with allergic bronchopulmonary aspergillosis (ABPA), screen for differential metabolic biomarkers that can distinguish it from severe allergic asthma (SA), and construct a diagnostic model. Methods: A cross-sectional study was conducted. A total of 19 patients with ABPA, 19 patients with SA, and 19 healthy controls (HC) from the First Affiliated Hospital of Guangzhou Medical University between December 2024 and July 2025 were enrolled as the training set. Additionally, a validation set consisting of 12 SA patients and 12 ABPA patients was recruited from the same center between September to December 2025. Peripheral blood cell counts and immunological and biochemical parameters were measured in all participants. Absolute quantification of 14 AA metabolites in serum was performed using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) based on 5-(diisopropylamino) pentylamine derivative. Differences among groups were compared to identify significantly altered metabolites. In the training set, a combined clinical and metabolic indicator model was constructed using binary logistic regression. The diagnostic performance, calibration and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis, respectively. The model was further validated in the validation set. Results: A total of 19 patients in the ABPA group were enrolled, aged 45 (33, 60) years, with 12 males; 19 patients in the SA group were aged 39 (29, 47) years, with 11 males; and 19 subjects in the HC group were aged 43 (34, 52) years, with 11 males. Compared with the SA group, patients with ABPA exhibited significantly higher levels of total IgE [1 578.00 (1 346.00, 2 538.00) U/ml vs 599.30 (382.20, 1 462.00) U/ml] and Aspergillus fumigatus-specific IgE (Af.sIgE) [4.93 (0.95, 8.41) kUA/L vs 0.27 (0.13, 2.17) kUA/L] (both P<0.05). Serum levels of arachidonic acid (AA), prostaglandin D₂ (PGD₂), leukotriene B₄ (LTB₄), 5-hydroxyeicosatetraenoic acid (HETE), 12-HETE, and 15-HETE were significantly higher in the ABPA group than in the HC group (all P<0.05). Moreover, PGD₂ [1.80 (0.82, 2.13) μg/L vs 0.63 (0.37, 1.15) μg/L] and 15-HETE levels [16.39 (11.15, 20.41) μg/L vs 9.31 (6.57, 10.86) μg/L] were significantly elevated in the ABPA group compared with the SA group. A four-indicator combined diagnostic model incorporating total IgE, Af.sIgE, PGD₂, and 15-HETE was constructed. In the training set, the area under the curve (AUC) of the four-indicator combined model was 0.96 (95%CI: 0.92-1.00), with a sensitivity of 0.93 and specificity of 0.95. In the validation set, the AUC was 0.91 (95%CI: 0.81-1.00), with a sensitivity of 0.92 and specificity of 0.83. Calibration curves showed good consistency for the four-indicator model across all datasets, and decision curve analysis indicated favorable clinical applicability. Conclusions: ABPA patients exhibit characteristic AA metabolic disorders, and their characteristically elevated serum PGD2 and 15-HETE are potential specific biomarkers for differentiating ABPA from SA. The constructed four-indicator combined model including total IgE, Af.sIgE, PGD₂, and 15-HETE has good diagnostic efficacy in differentiating ABPA from SA.

Benzodiazepine receptor agonists (BZRAs) are well-known hypnotics. Although the research is scarce, relatively newer hypnotics such as melatonin receptor agonists (MRAs) and dual orexin receptor antagonists (DORAs), can be associated with respiratory depression. The association between hypnotic use and respiratory depression in clinical practice was investigated in this study.

A total of 733,296 reports from the Japanese Adverse Event Reporting Database published between April 2004 and September 2023 were analyzed. The reporting odds ratios (RORs) and 95% confidence intervals (CIs) for respiratory depression associated with each hypnotic condition were calculated after adjusting for potential confounders.

Among all the reports, 5,147 involved respiratory depression. After adjustments for sex, age, reporting year, concomitant medications, and medical history the associations with non-benzodiazepines (adjusted ROR=1.06; 95% CI=0.92-1.23) and DORAs (adjusted ROR=0.94; 95% CI=0.70-1.27) were not significant. In contrast, significant associations remained for BZRAs (adjusted ROR=1.34; 95% CI=1.20-1.49) and MRAs (adjusted ROR=2.03; 95% CI=1.56-2.64). The stratified analyses showed that BZRAs were associated with respiratory depression regardless of age, whereas MRAs were associated regardless of opioid use.

The associations between hypnotics and respiratory depression varied depending on the drug administered. In addition to BZRAs, MRAs may also have a potential risk for respiratory depression.

The management of Stage II empyema complicated by chronic inflammatory demyelinating polyneuropathy (CIDP) presents a formidable clinical challenge due to the precarious balance required between aggressive infection control and the modulation of immune-mediated neurological frailty. We report a complex case of comorbid empyema and CIDP successfully managed with a multidisciplinary approach and adjuvant molecular hydrogen (H₂) therapy.

A 76-year-old male with CIDP presented with acute respiratory failure secondary to Stage II empyema. Following video-assisted thoracoscopic surgery (VATS) decortication, the patient exhibited systemic hyperinflammation and immune exhaustion. Adjuvant oral molecular hydrogen therapy was initiated during the critical phase. Longitudinal immunophenotyping was performed to monitor the therapeutic response. Clinical recovery coincided with profound immunological remodeling. We observed a cell-specific up-regulation of fas cell surface death receptor (Fas) expression in hyper-activated naïve and effector cytotoxic T cells (Tc), suggesting a H₂-facilitated apoptotic clearance of pro-inflammatory lineages. Conversely, the helper T-cell (Th) reservoir was preserved. Recovery was further characterized by the consistent restoration of intermediate and post-germinal center memory B-cell populations, coupled with a substantial augmentation of suppressive T and B-cell subsets (Tregs and Bregs). This shift toward immune homeostasis prevented secondary autoimmune flares and facilitated successful discharge.

Molecular hydrogen may act as a systemic bioregulator that fosters immune homeostasis by selectively targeting hyper-activated effector cells while promoting regulatory cell recovery. This dual antioxidant and immunomodulatory capacity positions H₂ as a promising adjuvant therapy for complex infectious and neuroinflammatory conditions.

Pneumocystis jirovecii pneumonia (PCP) remains a life-threatening opportunistic infection in patients receiving chemotherapy and other immunosuppressive cancer treatments. Accurate identification of true PCP cases within real-world electronic health record (EHR) databases is essential for epidemiological research and optimization of prophylactic strategies in oncology practice. The aim of this study was to develop and validate a practical, EHR-based algorithm for reliably identifying PCP cases.

This retrospective, single-center validation study used EHR data from a Japanese university hospital between April 2022 and March 2024. Adult patients (≧20 years) who were assigned an ICD-10 code for PCP were extracted, and true cases were confirmed by a detailed review of the patient records. Seven candidate algorithms combining diagnostic codes, therapeutic-dose anti-PCP prescriptions, laboratory testing, chemotherapy exposure, and prescription duration were evaluated. The positive predictive value (PPV) and capture rate were then calculated using chart-confirmed PCP as the reference standard.

Among 617 ICD-coded patients, 11 (1.8%) were confirmed as true PCP cases. The PPV of diagnostic codes alone was 1.8%. A prescription-enhanced algorithm (A1) identified 12 patients, including 11 true cases (PPV=91.7%; capture rate 100%). Algorithms incorporating β-D-glucan or PCR testing achieved PPVs of 100% with lower capture rates (63.6-81.8%). Incorporation of concurrent chemotherapy also resulted in a PPV of 100% with reduced capture. An algorithm requiring therapeutic-dose prescription for ≥21 days showed equivalent performance to A1.

Prescription-based algorithms substantially improve the accuracy of PCP case identification in EHR data compared with diagnostic codes alone. This straightforward, scalable approach offers a robust framework for real-world oncology research, enabling a more reliable evaluation of PCP incidence and informing future prophylaxis strategies for patients receiving anticancer treatment.

Sarcoidosis is a multisystem disorder with variable presentation and disease course. Diagnosis requires the exclusion of other causes of granulomatous inflammation. Current clinical management is often fraught with diagnostic uncertainy and the lack of tools to predict pulmonary disease progression. To address these challenges, we designed a study using data from bronchoalveolar lavage (BAL) fluid and cells to develop diagnostic and prognostic tools in patients with pulmonary sarcoidosis.

This multicentre study will include discovery and validation cohorts of healthy controls, interstitial lung disease controls and pulmonary sarcoidosis cases from three study sites. Sarcoidosis participants will be grouped into progressive and non-progressive pulmonary disease based on changes in pulmonary function testing, chest radiographs or treatment requirements. The discovery cohort consists of participants with existing BAL fluid, BAL cells, and clinical datasets; the validation cohort will be prospectively enrolled and participants will consent for BAL collection from either a clinical or research bronchoscopy. Untargeted proteomic profiling of BALF along with statistical modelling with variable selection techniques will generate a classifier for diagnosis and prognosis. Targeted proteomics using parallel reaction monitoring-mass spectrometry will be used for internal and external validation. Additionally, BAL cell single-cell gene-expression analysis using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) will be integrated with proteome-wide data to elucidate cell-specific pathways implicated in the development and progression of sarcoidosis.

The study will be conducted in accordance with Good Clinical Practice and the Declaration of Helsinki. The protocol has been approved by the Biomedical Research Alliance of New York Institutional Review Board (IRB), which serves as the single IRB across all study sites. The findings of this study will be presented as abstracts at scientific meetings and summarised in peer-reviewed journal manuscripts.