Background: Influenza A virus (IAV) is a negative-sense RNA virus of the Orthomyxoviridae family and is the etiological agent of a highly contagious acute respiratory disease that can lead to acute lung injury. Objective: To elucidate the molecular mechanisms of IAV infection, an integrative research approach combining gene expression profiling, multinetwork analysis, and in vivo experimental validations was employed. Methods: First, a series of network-based analyses were performed, including protein-protein interaction network construction, weighted gene co-expression network analysis, and subsequent gene set enrichment analysis, to identify the major underlying mechanisms of IAV infection. Following gene expression analysis, core targets, both direct and indirect regulators, were screened. An IAV (H1N1) strain A/PR/8/34-induced acute lung injury mouse model was constructed for in vivo validations. Batch one included two groups to evaluate findings from the multi-network analysis: Mock (n = 10; 5 males and 5 females) and IAV (n = 10; 5 males and 5 females). Batch two included three groups to assess the role of toll-like receptor 3 (TLR3) in IAV infection: Mock (n = 6; 3 males and 3 females), IAV (n = 6; 3 males and 3 females), and TLR3 inhibitor (n = 6; 3 males and 3 females). Body weight was measured on days 0, 3, and 5 after infection. On day 5, lung tissues were collected to assess viral load and histopathological changes. Key targets were examined using enzyme-linked immunosorbent assay, Western blotting, and immunofluorescence staining, both in sera and lung tissues. Results: IAV infection was significantly associated with dysregulation of the immune-inflammation system, such as the LTR, nucle-otide-binding oligomerization domain-(NOD) like receptor, retinoic acid-inducible gene I-like receptor, and nuclear factor kappa-B signaling pathways. Gene set enrichment analysis further indicated that the TLR and necroptosis signaling pathways played crucial roles in the progression of IAV infection (TLR signaling pathway normalized enrichment score = 2.3941, P = 1.00 × 10 ⁻¹⁰; necroptosis normalized enrichment score = 1.9421, P = 6.21 × 10 ⁻⁷). Among the core targets, TLR3 and mixed lineage kinase domain-like protein (MLKL) may regulate gene expression at the transcriptional level (all P < 0.05). In vivo validation using an IAV (PR8) infected acute lung injury mouse model demonstrated increased viral load and lung index, alveolar structural damage, and inflammatory cell infiltration. Immunofluorescence staining exhibited large gaps in Lamin B1 staining and breaches in Emerin signals following IAV-PR8 infection. Expression levels of TLR3, p-receptor-interacting serine/threonine-protein kinase 3 (RIPK3)/RIPK3, and p-mixed lineage kinase domain-like protein (MLKL)/MLKL proteins in lung tissues, as well as proinflammatory factors and mediators in sera, were significantly elevated after IAV infection. Moreover, enhanced neutrophil infiltration (myeloperoxidase) and citrullinated histone H3 (a neutrophil extracellular trap-specific marker), both established indicators of neutrophil extracellular trap formation, were observed. Notably, treatment with a TLR3 inhibitor significantly ameliorated IAV-induced acute lung injury by regulating necroptosis-related targets. Conclusion: Our study provides network-based in vivo evidence that TLR3-receptor-interacting serine/threonine-protein kinase 3-MLKL-mediated necroptosis may underlie IAV-induced acute lung injury and could serve as a potential therapeutic target in severe influenza cases.

Lung cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide, and its treatment continues to face major challenges such as therapeutic resistance and tumor recurrence. Pyroptosis, a newly characterized form of programmed cell death, induces tumor cell death through gasdermin-mediated membrane pore formation and is accompanied by the release of inflammatory mediators, thereby playing complex roles in lung cancer initiation, progression, and modulation of the tumor microenvironment. Active components and herbal formulas derived from traditional Chinese medicine can modulate pyroptosis-related signaling pathways through multi-target mechanisms, showing potential advantages in inducing lung cancer cell death, inhibiting proliferation and migration, and reversing chemoresistance. This review systematically summarizes relevant studies from domestic and international sources, focusing on the molecular mechanisms of pyroptosis, its roles in lung cancer development and tumor microenvironment remodeling, and the current research progress on traditional Chinese medicine-based interventions targeting pyroptosis, with the aim of providing references for the prevention and treatment of lung cancer using traditional Chinese medicine.

BACKGROUND/OBJECTIVES: Unhealthy diets are associated with chronic stress. An allostatic load (AL) is a comprehensive physiological index that measures the chronic stress response of the body. As a healthy dietary pattern, the role of Dietary Approaches to Stop Hypertension (DASH) in the development of an AL during pregnancy remains unclear. This study examined the relationship between the DASH dietary pattern and AL during pregnancy. SUBJECTS/METHODS: This study was a prospective study. From January 2024 to November 2024, 134 pregnant women in the second trimester (23–27 weeks) and third trimester (32–36 weeks) of pregnancy who met the inclusion and exclusion criteria in the obstetrics clinic of a tertiary general hospital in Anhui Province were selected for a questionnaire survey, physical examination, and laboratory examination. The DASH dietary pattern was assessed using the DASH scoring tool, and the total AL score was calculated using nine biomarkers that represent the cardiovascular, metabolic, and immune systems. Logistic regression was used to analyze the relationship between the DASH score and AL. RESULTS: One hundred and thirty-four pregnant women were included in this study. At the second and third trimesters, 41.8% and 37% of pregnant women, respectively, were in the high AL group. The binary logistic regression results showed that the DASH score was negatively correlated with the AL in the unadjusted model (odds ratio [OR], 0.878; 95% confidence interval [CI], 0.807–0.957; P = 0.003) and adjusted model (OR, 0.878; 95% CI, 0.792–0.973; P = 0.013) in the second trimester, the DASH score was negatively correlated with the AL in the unadjusted model (OR, 0.832; 95% CI, 0.758–0.913; P < 0.001) and adjusted model (OR, 0.806; 95% CI, 0.716–0.908; P < 0.001) in the third trimester. CONCLUSION The DASH score was negatively correlated with the AL in pregnant women.A low DASH score may increase the risk of a high AL, which may have adverse effects on physical and mental health.

OBJECTIVE To clarify the current development status of clinical pharmacist training bases and the pharmacist workforce in China， and to provide evidence for standardizing base construction and promoting the high-quality development of hospital pharmacy. METHODS Questionnaires were distributed to all clinical pharmacist training bases for urgently-needed health professionals and clinical pharmacist training bases under the Chinese Hospital Association that had been approved by the end of 2023 to collect data including base profiles and pharmacist staffing conditions. Expert teams carried out field investigations to verify the collected data. Descriptive statistical analyses were conducted on the quantity， type and regional distribution of training bases， and the influencing factors of pharmacist staffing in the bases were analyzed. RESULTS Field surveys were completed covering 297 training bases across 31 provincial-level administrative regions. Among the base hospitals， 87.88% were Grade A tertiary general hospitals， and 65.32% were located in provincial capitals. The eastern region had the largest number of bases （136， accounting for 45.79%）， followed by the western region （79， 26.60%）. Great disparities existed among provinces in terms of base quantity and hospital scale. The median proportion of pharmaceutical technical personnel in base hospitals was 4.05%， and the median number of clinical pharmacists per 100 hospital beds was 0.53. Both indicators reached the highest in the eastern region （0.57， 4.43%） and the lowest in the northeastern region （0.45， 3.01%）. A total of 3 627 full-time clinical pharmacists were employed in all surveyed bases， among whom 83.68% held clinical pharmacist training certificates， 35.43% possessed senior professional titles， and 78.19% had postgraduate or higher educational background. The total annual training capacity of the surveyed bases was 4 291 trainees， with obvious differences in annual training capacity across regions and provinces. Training specialties covered 19 specialized disciplines plus one general discipline， and no province could deliver training for all 20 specialties simultaneously. Multivariate Logistic regression analysis showed that geographic region exerte d a significant impact on the proportion of pharmaceutical technical personnel （≥4%） （ P ＜0.05）， while the approval time of training bases and hospital scale had significant effects on the number of clinical pharmacists per 100 beds （≥0.6） （ P ＜0.05）. CONCLUSIONS China’s clinical pharmacist training system has basically matured and taken initial shape. The distribution of clinical pharmacist training resources is generally consistent with regional population and economic development levels. However， the allocation of pharmaceutical staff and clinical pharmacists has not yet met national standards and clinical service demands.

OBJECTIVE To clarify the current development status of clinical pharmacist training bases and the pharmacist workforce in China， and to provide evidence for standardizing base construction and promoting the high-quality development of hospital pharmacy. METHODS Questionnaires were distributed to all clinical pharmacist training bases for urgently-needed health professionals and clinical pharmacist training bases under the Chinese Hospital Association that had been approved by the end of 2023 to collect data including base profiles and pharmacist staffing conditions. Expert teams carried out field investigations to verify the collected data. Descriptive statistical analyses were conducted on the quantity， type and regional distribution of training bases， and the influencing factors of pharmacist staffing in the bases were analyzed. RESULTS Field surveys were completed covering 297 training bases across 31 provincial-level administrative regions. Among the base hospitals， 87.88% were Grade A tertiary general hospitals， and 65.32% were located in provincial capitals. The eastern region had the largest number of bases （136， accounting for 45.79%）， followed by the western region （79， 26.60%）. Great disparities existed among provinces in terms of base quantity and hospital scale. The median proportion of pharmaceutical technical personnel in base hospitals was 4.05%， and the median number of clinical pharmacists per 100 hospital beds was 0.53. Both indicators reached the highest in the eastern region （0.57， 4.43%） and the lowest in the northeastern region （0.45， 3.01%）. A total of 3 627 full-time clinical pharmacists were employed in all surveyed bases， among whom 83.68% held clinical pharmacist training certificates， 35.43% possessed senior professional titles， and 78.19% had postgraduate or higher educational background. The total annual training capacity of the surveyed bases was 4 291 trainees， with obvious differences in annual training capacity across regions and provinces. Training specialties covered 19 specialized disciplines plus one general discipline， and no province could deliver training for all 20 specialties simultaneously. Multivariate Logistic regression analysis showed that geographic region exerte d a significant impact on the proportion of pharmaceutical technical personnel （≥4%） （ P ＜0.05）， while the approval time of training bases and hospital scale had significant effects on the number of clinical pharmacists per 100 beds （≥0.6） （ P ＜0.05）. CONCLUSIONS China’s clinical pharmacist training system has basically matured and taken initial shape. The distribution of clinical pharmacist training resources is generally consistent with regional population and economic development levels. However， the allocation of pharmaceutical staff and clinical pharmacists has not yet met national standards and clinical service demands.

OBJECTIVE To investigate the current status of quality management in clinical pharmacist training bases in China， and to provide references for the standardized development and improvement of these bases. METHODS A combined approach of questionnaire survey and on-site investigation was adopted， targeting the healthcare highly sought-after talent （clinical pharmacist） training bases in 31 provinces of China as well as the clinical pharmacist training bases affiliated with the Chinese Hospital Association. A training quality management evaluation index system consisting of 25 tertiary indicators was established. Investigations were conducted from two dimensions： structural quality of training and quality management of training processes. Data were statistically analyzed using descriptive statistical methods. RESULTS On-site investigations were completed for 297 clinical pharmacist training bases across the 31 provinces， among which 284 were affiliated with the Chinese Hospital Association and 271 were healthcare highly sought-after talent （clinical pharmacist） training bases. In terms of structural quality， the core indicator compliance rate for hospital-level training systems exceeded 80%， the rate of special fund utilization for designated purposes reached 80.13%， and the overall compliance rate for software and hardware facilities surpassed 88%. A total of 1 348 preceptors were employed across the training bases， among whom those with senior professional titles and full-time specialist clinical pharmacists as lead preceptors accounted for 62.91% and 94.36%， respectively. Regarding process quality， the compliance rate for “establishment of clinical practice teaching groups in accordance with regulations during clinical department rotations” was 85.19%， and 79.80% and 76.09% of the bases were found to implement strict confidentiality in theoretical examinations and meet the required scale of assessment cases， respectively. However， formal documents of corresponding training management regulations were formulated in only 57.91% of the bases， and the completeness and standardization rate of training manual completion was merely 47.47%. In addition， considerable disparities in quality management levels were observed among provinces， with issues in training process quality being particularly prominent. CONCLUSIONS The management system， hardware facilities， and preceptor staffing of clinical pharmacist training bases in China are relatively well-established， yet notable variations in quality management exist among training bases across different provinces.

OBJECTIVE To evaluate the efficacy and safety of cadonilimab plus chemotherapy in patients with advanced gastric adenocarcinoma or adenocarcinoma of gastroesophageal junction （AEG） with intermediate， low， or no human epidermal growth factor receptor 2 （HER2） expression. METHODS Patients with advanced gastric adenocarcinoma or AEG with intermediate， low， or no HER2 expression who were treated at Liaoning Cancer Hospital between October 1， 2024 and June 30， 2025 were retrospectively included. According to the treatment regimen received， the patients were divided into group A [cadonilimab plus oxaliplatin and capecitabine （XELOX）， n ＝54 ] ， group B （sintilimab plus XELOX， n ＝68）， and group C （tislelizumab plus XELOX， n ＝49）. Short-term efficacy after 4 cycles of treatment， survival outcomes， and adverse events were compared among the three groups. Cox regression and Logistic regression analyses were further performed to evaluate the effects of the treatment regimen and baseline data on survival outcomes and the risk of grade 3-4 treatment related adverse events （TRAEs）. RESULTS No statistically significant differences in objective response rate or disease control rate were observed among the three groups （ P ＞0.05）. The median progression-free survival was 7.56 （5.62， 8.11） months in group A， 7.23 （5.33， 8.23） months in group B， and 7.33 （5.42， 8.19） months in group C， with no statistically significant difference among the three groups （ P ＞0.05）. The median overall survival （mOS） was 11.67 （8.82， 13.88） months in group A， 9.78 （6.82， 12.73） months in group B， and 9.69 （6.78， 12.66） months in group C. The mOS was significantly longer in group A than in group B and group C （ P ＜0.05）， whereas no statistically significant difference was observed between group B and group C （ P ＞0.05）. The correlation analysis indicated that treatment regimen， Eastern Cooperative Oncology Group performance status （ECOG PS）， number of metastatic organs， and peritoneal metastasis may be associated with overall survival （OS）. No significant association was observed between treatment regimen and the risk of grade 3-4 TRAEs. The incidences of grade 1-2 or grade 3-4 TRAEs did not differ significantly among the three groups （ P ＞0.05）. CONCLUSIONS In patients with advanced gastric adenocarcinoma or AEG with intermediate， low， or no HER2 expression， cadonilimab plus XELOX may provide a more significant OS benefit with a safety profile comparable to that of sintilimab or tislelizumab plus XELOX. Further studies with larger sample sizes are required to validate these findings.

OBJECTIVE To investigate the mechanism of Shenling baizhu san （SLBZS） in ameliorating ulcerative colitis （UC）. METHODS Mice were randomly divided into normal group， model group， and low-， medium-， high-dose SLBZS groups （3， 6， 12 g/kg）， with 10 mice in each group. Except for the normal group， mice in other groups were given free access to 3% dextran sulfate sodium solution for 7 days to establish UC models. Simultaneously， mice in each group were intragastrically administered corresponding medicinal liquid or double-distilled water once a day for consecutive 7 days. After the last administration， the histopathological morphology of colon tissues was observed； the serum levels of inflammatory factors including tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β） and IL-18 were detected； the total bile acid contents in liver， gallbladder and colon tissues were determined； the expression level of NOD-like receptor family pyrin domain-containing protein 3 （NLRP3） in colon tissues was tested. The expression levels of flora metabolism-related proteins [G protein-coupled receptor 41（GPR41），GPR43，farnesoid X receptor （FXR）， G protein-coupled bile acid receptor 1 （TGR5）， flavin-containing monooxygenase 3 （FMO3）， cytochrome P450 family 27 subfamily A member 1 （CYP27A1）， CYP7A1] in liver or colon tissues， as well as the proteins related to Toll-like receptor 4 （TLR4）/nuclear factor-κB （NF-κB）/NLRP3 inflammatory axis [TLR4， NF-κB， phosphorylated NF-κB （p-NF-κB）， myeloid differentiation factor 88 （MyD88）， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， caspase-1] in colon tissues were measured. RESULTS Compared with the model group， the colon tissue injury and inflammatory cell infiltration were alleviated in medium- and high-dose SLBZS groups. The serum levels of TNF-α， IL-1β and IL-18， total bile acid contents in liver and colon tissues， as well as the protein expression levels of FMO3 and CYP7A1 in liver tissues， TLR4， p-NF-κB， NF-κB， MyD88， NLRP3， ASC and caspase-1 in colon tissues were significantly decreased （P＜0.05）. Meanwhile， the total bile acid content in gallbladder， the protein expression levels of GPR41 and GPR43 in colon tissues， along with CYP27A1， FXR and TGR5 in liver tissues were significantly increased （P＜0.05）. CONCLUSIONS SLBZS may relieve colonic inflammatory injury and ameliorate UC by regulating the expression of flora metabolism-related proteins and inhibiting the abnormal activation of TLR4/NF-κB/NLRP3 inflammatory axis.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.