Solid organ transplantation (SOT) is an effective treatment method for various end-stage diseases. However, due to the need for long-term use of immunosuppressive drugs to prevent rejection reactions after the surgery, SOT recipients are generally in a state of low immune function, resulting in a significant increase in the risk of infection. Post-transplant infection, especially infections caused by multi-drug resistant bacteria, is one of the common complications for SOT recipients and is also a major cause of graft dysfunction, graft loss and even death of the recipients. As the global situation of bacterial resistance becomes increasingly severe, the burden of infectious diseases continues to increase, seriously threatening the survival prognosis and graft function of SOT recipients. The exploration of new antibiotic research and rational application strategies worldwide has become crucial. The development and launch of various new antibiotics have provided more options for clinical practice. Therefore, systematically reviewing the drug characteristics of new antibiotics and their application status in this special population of SOT recipients is of great significance for guiding clinical practice and improving patients’ prognosis.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

OBJECTIVE: This study aimed to identify high-risk areas for type 2 diabetes mellitus (T2DM) mortality to provide relevant evidence for interventions in emerging economies. METHODS: Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality. The relationships between economic factors, air pollutants, and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model. RESULTS: A coastal district in East Guangdong, China, had the highest risk (Relative Risk [RR] = 4.58, P < 0.01), followed by the 10 coastal districts/counties in West Guangdong, China (RR = 2.88, P < 0.01). The coastal county in the Pearl River Delta, China (RR = 2.24, P < 0.01), had the third-highest risk. The remaining risk areas were two coastal counties in East Guangdong, 16 districts/counties in the Pearl River Delta, and two counties in North Guangdong, China. Mortality due to T2DM was associated with gross domestic product per capita (GDP per capita). In pilot assessments, T2DM mortality was significantly associated with carbon monoxide. CONCLUSION High mortality from T2DM occurred in the coastal areas of East and West Guangdong, especially where the economy was progressing towards the upper middle-income level.
Diabetes Mellitus, Type 2/epidemiology* ; China/epidemiology* ; Humans ; Risk Factors ; Spatio-Temporal Analysis ; Air Pollutants/analysis* ; Socioeconomic Factors ; Bayes Theorem ; Female ; Male ; Middle Aged

OBJECTIVE To explore the effects of meropenem exposure and degradation levels on clinical efficacy in patients with purulent meningitis （PM）. METHODS A total of 131 PM patients treated with meropenem at the Affiliated Suzhou Hospital of Nanjing Medical University from January 2022 to June 2025 were prospectively included. Relevant data were collected and divided into a cured group （91 cases） and a non-cured group （40 cases） based on the efficacy. High-performance liquid chromatography-tandem mass spectrometry was used to determine the concentration of meropenem and its open-loop metabolites. Risk factors that affect efficacy were screened， and their predictive power and correlation were evaluated by univariate analysis， and multivariate Logistic regression analysis， receiver operating characteristic （ROC） curves， and correlation analysis. RESULTS Univariate analysis showed that serum creatinine， creatinine clearance rate， minimum inhibitory concentration of meropenem ≥16 μg/mL， cerebrospinal fluid red blood cell count， cerebrospinal fluid white blood cell count， cerebrospinal fluid glucose content， blood trough concentration， blood open-loop metabolite concentration/trough concentration ratio， and intrathecal injection were all correlated with efficacy （P＜0.05）. The results of multiple Logistic regression analysis showed that serum creatinine blood open-loop metabolite concentration/trough concentration ratio， intrathecal injection， and cerebrospinal fluid glucose content were influencing factors for suboptimal anti-infective ltt efficacy （P＜0.05）. ROC curve analysis showed that when the blood open-loop metabolite concentration/trough concentration ratio was greater than 2.854 （AUC＝0.647）， serum creatinine was less than 59.5 μmol/L （AUC＝0.647）， and cerebrospinal fluid glucose content was less than 3.37 mmol/L （AUC＝0.709）， the risk of treatment failure significantly increased （P＜0.05）. Correlation analysis showed that the blood trough concentration of meropenem was positively correlated with the concentration of its open-loop metabolites （R 2＝0.134 5， P＜0.000 1）. CONCLUSIONS Insufficient exposure level and rapid degradation of meropenem are key mechanisms affecting the anti-infective efficacy of PM. Elevated blood open-loop metabolite concentration/ trough concentration ratio， low serum creatinine level， lack of intrathecal injection， and low cerebrospinal fluid glucose content are independent risk factors for poor efficacy.

Objective To explore the effect of management model of clinical pharmacists participating in multidisciplinary collaborative diagnosis and treatment(MDT)for new anti-tumor drugs in the national medical insurance drug negotiation(hereinafter referred to as"national negotiation"),including efficacy,safe-ty,economy and rationality.Methods The medical records of 326 cases using novel anti-tumor drugs by na-tional negotiation and conforming to the including and excluding standards in this hospital from July 2018 to June 2023 were retrospectively analyzed.The patients were divided into the MDT group(n=122)and non-MDT group(n=204).The patients diagnosed as non-small cell lung cancer(NSCLC)in the two groups were extracted and defined as the MDT-NSCLC subgroup(n=41)and non-MDT-NSCLC subgroup(n=77).The progression-free survival(PFS),overall survival(OS),disease control rate(DCR)and the indexes such as survival quality and medical quality control were compared between the groups.Results The median PFS in the two groups was 12.7 months and 8.0 months,the median OS was 75.2 months and 56.3 months,DCR was 96.72％and 81.86％respectively,and the differences were statistically significant(P＜0.05).The COX multivariate regression analysis indicated that the HR value of clinical pharmacists participating in MDT was higher than the other influencing factors.The median PFS time in the MDT-NSCLC subgroup and non-MDT-NSCLC subgroup was 10.5 months and 6.7 months,DCR was 97.30％and 75.64％respectively,and the differences were statistically significant(P＜0.05),the median OS time was 55.1 months and 40.3 months respectively,and the difference was statistically significant(P＞0.05).The COX multivariate regression anal-ysis indicated that the HR value with clinical pharmacists participating in MDT was higher than the other in-fluencing factors;The adverse reaction occurrence rate in the MDT group and non-MDT group was 45.9％and 58.3％respectively,and the difference was statistically significant(P＜0.05).The KPS score after treatment in the MDT group was higher than that in the non-MDT group,and the difference was statistically significant;in the aspect of medical quality control,the average drug proportion in the MDT group and non-MDT group was 63.93％and 64.54％respectively,the rational drug rate of comments on prescription was 98.36％and 88.73％respectively,the patient satisfaction average value was 90.69 points and 87.36 points respectively and the differences were statistically significant(P＜0.05).Conclusion Clinical pharmacists participating in MDT related to novel anti-tumor drugs by national negotiation is beneficial to improve the therapeutic effects,living quality and patient satisfaction,also benefit to management and control of off-label drug use and medical quality control indexes.

The continuous accumulation of plastic waste such as polyethylene in the environment has caused serious environmental pollution issues.Considering the high similarity in the molecular structure of petroleum and polyolefin,it is feasible to apply rare earth-zeolite catalysts in polyolefin plastic upcycling,which is commonly used in fluid catalytic cracking(FCC)in the field of petroleum refining.In this study,Ce-modified HY(Ce-HY)zeolites were synthesized and characterized by a series of analytical methods,such as high-angle annular dark-field scanning transmission electron microscopy(HAADF-STEM),Fourier infrared spectroscopy(FT-IR),X-ray photoelectron spectroscopy(XPS),etc.When introducing 5% Ce species into HY zeolites,the 5Ce-HY showed excellent catalytic performance in the catalytic cracking of low-density polyethylene(LDPE),which achieved 98.4% LDPE conversion with 91.5% selectivity of gaseous alkanes at 300℃,and 75.4% of them were isoparaffins.In addition,the effect of the location of rare earth species in Y zeolites on the catalytic performance was explored by fine X-ray diffraction(XRD)in the range of 11°-13°and in situ-Raman analyses.The Ce species located in the supercage of Y zeolites were more important,which enhanced the adsorption capacity and accessibility of substrate molecules,thus facilitating the entire catalytic cracking process.This method could be used to detect the location of rare earth elements in Y zeolites to understand the mechanism of rare earth catalysis.

Foodborne illnesses caused by Salmonella pose significant threats to worldwide public health safety.In this study,a rapid method for screening viable Salmonella in oyster sauce and milk was developed by utilizing an electronic microbial growth analyzer(EMGA).Target food samples were diluted 10-fold with RVS broth and loaded into test tubes.Test tubes were positioned in the EMGA to determine the bacterial growth curves and the time required to reach the maximum growth rate(Tmgr).Using Salmonella typhimurium(S.typhimurium)asan model species,there was linear relationship between the logarithmic value of viable bacterial concentration(lgC)and Tmgr over the range of 5×101-5×106 CFU/mL,with a detection limit of 10 CFU/mL.For oyster sauce,the regression equation was Tmgr(min)=-80.775lg[C/(CFU/mL)]+754.96(R2=0.9907),and the recovery rates of S.typhimurium ranged from 95.2%to 119.8%,with relative standard deviations(RSD)ranging from 3.5%to 16.3%.For milk,the regression equation was Tmgr(min)=-71.922 lg[C/(CFU/mL)]+618.65(R2=0.9985),with recovery rates ranging from 98.4%to 110.6%and RSD ranging from 6.4%to 12.8%.The EMGA method required only one portable instrument,and involving only three manual steps,i.e.,dilution,transfer,and insertion.When S.typhimurium contamination reached 106 CFU/mL,the total time consumption,from the unwrapping of samples to the readout of bacterial concentration,was no more than 7 h.When applied to detection of actual oyster sauce and milk samples,the new method demonstrated strong consistency with plate counting results in positive detection rates.This method was superior to the plate counting method,which was generally considered as a gold standard,in terms of accuracy,precision,simplicity and efficiency,representing a promising alternative for the on-site screening and quantification of viable Salmonella in oyster sauce and milk products.