Background: The neutrophil to lymphocyte ratio (NLR) is a cost-effective and easily identifiable inflammatory biomarker that has been shown to be closely associated with tumor prognosis and predict survival in patients with multiple malignancies. However, the predictive value of NLR in patients with gastric cancer (GC) treated with immune checkpoint inhibitors (ICIs) has not been fully explored. Therefore, we conducted a meta-analysis to explore the potential of NLR as a predictor of survival in this population.Methods: We systematically searched the PubMed, Cochrane Library, and EMBASE databases from inception to the present for observational researches on NLR and its relationship with progression or survival in GC patients receiving ICIs. To assess the prognostic significance of NLR on overall survival (OS) or progression-free survival (PFS), we used fixed or random-effect models to derive and combine hazard ratios (HRs) with 95% confidence intervals (CIs). We also examined the relationship between NLR and treatment efficacy by calculating relative risks (RRs) with 95% CIs for objective response rate (ORR) and disease control rate (DCR) in patients with GC receiving ICIs.Results: Nine studies of 806 patients were eligible. OS and PFS data were obtained from 9 and 5 studies, respectively. In nine studies, NLR was associated with poor survival, the pooled HR was 1.98 (95% CI 1.67- 2.35, p < 0.001), indicating a significant association between high NLR and worse OS. We conducted subgroup analyses based on study characteristics to confirm the robustness of our findings. A relationship between NLR and PFS were reported in five studies with a HR of 1.49 (95% CI 0.99- 2.23, p = 0.056), which was not significantly associated. Pooling four studies that examined the correlation between NLR and ORR/DCR in GC patients, we observed a significant correlation between NLR and ORR (RR = 0.51, p = 0.003), but no significant correlation between NLR and DCR (RR = 0.48, p = 0.111).Conclusion: In summary, this meta-analysis indicates that increased NLR is significantly linked to worse OS in patients with GC receiving ICIs. In addition, lowering NLR can improve ORR. Thus, NLR can serve as a predictor for prognosis and treatment response in GC patients treated with ICIs. Nevertheless, further high-quality prospective studies are required to verify our findings in the future.

Background: Whooping cough caused by Bordetella pertussis is re-emerging despite high vaccination coverage, with rising incidence in adolescents and adults in the acellular vaccine (aP) era. This narrative review synthesizes evidence on the drivers of this paradox and their implications for pertussis control. Methods: We conducted a structured (but not fully systematic) literature search and narrative synthesis of PubMed, Web of Science, and Embase for publications from January 2000 to February 2025 using terms related to “Bordetella pertussis,” “pertussis resurgence,” “acellular vaccine,” “waning immunity,” “ptxP3,” “pertactin-deficient,” “macrolide resistance,” and “whole-genome sequencing.” English-language, peer-reviewed studies, surveillance reports, genomic analyses, and immunological investigations were included. About 1900 records met broad eligibility criteria and were screened, and key studies were selected for narrative synthesis. Results: The resurgence appears to result from three convergent factors: (1) waning and non-sterilizing aP-induced immunity, which allows bacterial colonization and transmission; (2) vaccine-driven genomic evolution of B. pertussis, marked by global dominance of the ptxP3 lineage and widespread pertactin-deficient (PRN−) strains; and (3) emergence of macrolide-resistant clones, exemplified by the MT28-Shanghai strain. Whole-genome sequencing (WGS) has been central for defining these processes and clonal sweeps under combined vaccine and antibiotic pressure, supporting a three-driver framework of waning aP immunity, vaccine-driven evolution, and macrolide resistance. Conclusions: Pertussis resurgence illustrates pathogen adaptation to human interventions. Effective mitigation requires WGS-integrated global surveillance, re-evaluation of vaccine formulations to keep pace with antigenic change, and strengthened antibiotic stewardship, alongside development of next-generation vaccines that induce durable mucosal immunity and block transmission.

To address the challenges of complex acoustic patterns and limited interpretability in pertussis cough sound recognition, this study proposes an interpretable deep learning framework based on adaptive time–frequency fusion and medically guided attention mechanisms. The framework first employs an Adaptive Time–Frequency Fusion Transformer to extract multi-scale temporal and spectral features of cough sounds, followed by a Medically-Guided Interpretable Attention Mapping module that aligns attention distributions with medically relevant acoustic features, achieving explicit interpretability in the diagnostic process. Experiments were conducted on three publicly available pertussis cough sound datasets from Kaggle, containing 68, 66, and 44 recordings, respectively. Under an 8:2 training–testing split with strict data leakage prevention, the proposed method achieved AUC scores of 0.994, 0.984, and 0.996 on the three datasets, outperforming the best existing baselines by an average of approximately 2%. Ablation studies demonstrated that the ATF module significantly enhances time–frequency dependency modeling, while the MGIAM module improves attention consistency in medically relevant regions. In noise robustness experiments, performance degradation remained below 3%, confirming the model’s reliability and generalization ability in clinical applications. Overall, the proposed framework achieves unified high accuracy and strong interpretability for pertussis sound recognition, providing a reusable modeling paradigm for medical acoustic analysis.