Ruian People's Hospital

BACKGROUNDMalnutrition is highly prevalent in gastric cancer patients receiving adjuvant chemotherapy and may compromise treatment completion and survival outcomes. The comparative predictive value of various nutritional assessment tools in this clinical setting remains unclear.AIMTo investigate the impact of nutritional status on treatment completion and prognosis during adjuvant chemotherapy following gastric cancer surgery, providing scientific evidence for clinical nutritional intervention strategies.METHODSA retrospective analysis was conducted on clinical data of 80 patients who received adjuvant chemotherapy after gastric cancer surgery from January 2020 to June 2024. Nutritional status was assessed using Nutritional Risk Screening 2002 (NRS2002), Controlling Nutritional Status, Prognostic Nutritional Index (PNI), and Glasgow Prognostic Score before and after chemotherapy. Patients were divided into nutritional risk group (NRS2002 ≥ 3 points, n = 37) and non-nutritional risk group (< 3 points, n = 43) based on NRS2002 scores. Treatment completion, adverse reactions, and survival prognosis were evaluated. Logistic regression analysis was used to identify risk factors affecting treatment completion, and Cox regression analysis was used to analyze prognostic factors.RESULTSThirty-seven patients (46.2%) had nutritional risk before chemotherapy. The nutritional risk group had significantly lower treatment completion rate compared to the non-nutritional risk group (75.7% vs 95.3%, P = 0.009), insufficient relative dose intensity (78.6% ± 14.2% vs 92.1% ± 8.7%, P < 0.001), and significantly higher incidence rates of ≥ grade 3 hematologic and non-hematologic toxicities (P < 0.05). With a median follow-up of 28 months, the nutritional risk group had significantly lower 3-year disease-free survival (DFS) and overall survival (OS) rates compared to the non-nutritional risk group (62.1% vs 83.7%, P = 0.018; 72.4% vs 90.7%, P = 0.023). Multivariate analysis showed that NRS2002 ≥ 3 points was an independent risk factor for treatment completion [odds ratio = 4.829, 95% confidence interval (CI): 1.542-15.114, P = 0.007], DFS [hazard ratio (HR) = 2.847, 95%CI: 1.124-7.214, P = 0.027], and OS (HR = 3.524, 95%CI: 1.089-11.404, P = 0.036).CONCLUSIONNutritional status significantly affects treatment completion and prognosis in gastric cancer patients receiving postoperative adjuvant chemotherapy. Both NRS2002 and PNI demonstrate important predictive value, with NRS2002 showing the most consistent performance in predicting treatment completion and survival outcomes. Clinical practice should emphasize nutritional risk assessment and dynamic monitoring, and develop individualized nutritional intervention strategies to improve chemotherapy completion rates and patient outcomes.

Chronic Obstructive Pulmonary Disease (COPD) is a severe respiratory system disorder. In recent years, the combined therapy of inhaled corticosteroids/long-acting beta-agonists (ICS/LABA) has become the primary treatment for stable COPD patients. This study aimed to investigate the effects of ICS/LABA treatment on the airway microbiota and inflammatory profiles in COPD patients. Respiratory samples were collected from 18 individuals, including 2 healthy controls, 4 COPD patients, and 12 COPD patients receiving ICS/LABA treatment. Microbial diversity sequencing was employed to analyze the respiratory microbiota, with both diversity and functional predictions performed. Inflammatory factor levels were assessed using enzyme-linked immunosorbent assay (ELISA). The COPD group exhibited a dysregulated respiratory microbiota compared to the control group. Compared to the COPD group, patients in the ICS/LABA treatment group showed a trend toward decreased α-diversity of bacterial communities in the respiratory tract, while the α- diversity of fungi significantly increased. Post-treatment, the abundance of Streptococcus and Fusicolla decreased, whereas the abundance of Moraxella and Candida increased in the respiratory tract. These findings suggest that ICS/LABA treatment may help maintain a balanced respiratory microbiota. Furthermore, patients in the treatment group exhibited a significant decrease in IL-8 levels and a notable increase in IL-10 levels, indicating that ICS/LABA therapy may modulate cytokine levels by suppressing inflammatory responses and promoting anti-inflammatory reactions. The combined therapy of inhaled corticosteroids/long-acting beta-agonists (ICS/LABA) appears to regulate the gene functions of respiratory tract microbiota and IL-8/IL- 10 levels in stable COPD patients. These findings offer new insights into personalized COPD treatment and microbial interventions.

Friction-induced vibrations between the brake disc and pad represent a significant source of noise and vibrations in automotive systems. To mitigate these vibrations, this study investigates the influence of brake force on friction-induced vibrations and explores corresponding active suppression methods. Both simulation and experimental results indicate that the Hopf bifurcation curve can be used to prevent friction-induced vibrations; specifically, such vibrations can be avoided by maintaining the brake pressure below the Hopf bifurcation threshold. To optimize the trade-off between energy recovery efficiency and vibrations suppression, the energy recovery efficiency is defined as the objective function, while the Hopf bifurcation curve or the stick-slip curve is imposed as a constraint. Based on this framework, an IPSO-FC-based strategy is proposed to allocate regenerative and friction braking torques effectively. The results demonstrate that utilizing the Hopf bifurcation curve, rather than the stick-slip curve, significantly expands the feasible operational region and enhances regenerative braking efficiency. Furthermore, the extent of improvement in regenerative braking efficiency is shown to depend on the specific driving cycle.

ObjectivesThis study was designed to develop a multiregional MRI-based deep learning radiomics nomogram (DLRN) for predicting axillary pathological complete response (apCR) after neoadjuvant chemotherapy (NAC) in breast cancer.Materials and methodsIn total, 539 patients in our hospital were randomly split into a training cohort (TC; n = 431) and an internal validation cohort (IVC; n = 108), and 703 patients were recruited from three external centers as external validation cohorts (EVC1–3). Uni- and multivariate analyses were performed to select clinicopathological characteristics and establish a clinical model. DLR models were constructed based on DL and handcrafted radiomics features extracted from gross tumor volume (GTV) and GTV incorporating 3-, 5-, 7-, and 9-mm peritumoral regions (GPTV3, GPTV5, GPTV7, and GPTV9, respectively). A DLRN model incorporating the optimal DLR model and clinicopathological predictors was developed. Model performance was assessed employing the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis.ResultsThe GPTV5_DLR model surpassed the other DLR models, with an average AUC of 0.876 in the validation cohorts. The DLRN model better predicted apCR after NAC than the clinical model, demonstrating superior AUCs of 0.958 in the TC, 0.906 in the IVC, and 0.876–0.911 in EVC1–3. It also showed improved accuracy and clinical benefits for apCR prediction. Furthermore, the DLRN model achieved robust performance across different age, menstrual status, and clinical stage subgroups.ConclusionThe DLRN model, based on the GPTV5_DLR model and clinicopathological features, exhibited high predictive efficiency for apCR after NAC.Critical relevance statementThe deep learning radiomics nomogram based on intra- and peritumoral regions could noninvasively predict axillary pCR in breast cancer patients receiving NAC, which might prevent patients from undergoing unnecessary axillary lymph node dissection.Key PointsCombining intratumoral and 5-mm peritumoral region radiomics had the highest predictive efficiency for axillary pCR after NAC in breast cancer.The deep learning radiomics nomogram based on intra- and peritumoral regions outperformed the clinical model.The proposed model could provide a noninvasive and easy-to-use tool to offer decision support for optimizing treatments.Graphical

Understanding emotional states is fundamental to advancing next-generation AI systems with human-like attributes. Combining the complementary strengths of electroencephalog raphy (EEG) and facial expressions holds great promise for advancing multimodal emotion recognition (MER). EEG provides objective measurements of neural activity, while facial expressions convey rich, externally observable emotional cues. However, existing joint learning frameworks often fall short of fully exploiting the synergy between these modalities. Two key challenges remain unresolved: (1) insufficient cross-modal alignment and interaction prior to fusion which limits the semantic complementarity between modalities; and (2) modality unreliability caused by temporal fluctuations in signal quality and inconsistencies in emotional semantics. To address these limitations, we propose a novel framework that integrates a Step-wise Prompts (SwiP) module with an Uncertainty-Aware Dynamic Fusion (UADF) mechanism. SwiP enables progressive, fine-grained interaction by introducing sequential facial features as visual prompts to guide EEG representation learning, thereby enhancing cross modal complementarity. UADF dynamically adjusts modality contributions through a token- and modality-level uncertainty estimation scheme, enabling the model to selectively emphasize informative inputs and suppress noisy or irrelevant signals. Extensive experiments on benchmark datasets demonstrate that our method achieves state-of-the-art performance, consistently outperforming competitive baselines in both accuracy and stability. These results highlight the potential of our framework as a robust and generalizable solution for real-world affective computing applications.

An integrated, host-directed therapeutic strategy is urgently required to outpace the accelerating threat of antimicrobial resistance (AMR), because pathogen-centred antibiotics are losing efficacy worldwide. The growing threat of antimicrobial resistance has made traditional antibiotics less and less effective, and it has also shown that our pathogen-centered treatment model has systemic flaws. Bacterial immunotherapy offers an alternative that is directed at the host. Still, its many forms, such as innate immune agonists, monoclonal antibodies, engineered cells, CRISPR-based antimicrobials, and aptamer-guided nanoplatforms, have mostly been looked at separately. We put these different approaches together in this narrative review to create a new mechanistic taxonomy that shows how they can be used together to break down biofilms, stop efflux pumps, and get rid of intracellular reservoirs. We then put each modality on a translational continuum, from bench-top proof-of-concept to late-stage trials, and find the most essential delivery, safety, and regulatory problems. Lastly, we describe a precision-first vision that uses multi-omics profiling and theranostic platforms to help with patient stratification, adaptive dosing, and real-time monitoring. This review shows a straightforward way to turn narrative insights into context-sensitive, long-lasting interventions that will work with and maybe even change the future of infectious disease medicine by co-developing immunotherapeutic strategies with advanced diagnostics and stewardship frameworks.