Objective To investigate the impact of two different algorithms, AAA and AXB, on the dose distribution of postoperative radiotherapy for left-sided breast cancer after breast-conserving surgery. Methods A total of 96 target volumes from patients who underwent breast-conserving surgery for left-sided breast cancer were selected for dose verification using a two-dimensional matrix system. The planned dose distributions were simulated using both AAA and AXB algorithms. Dosimetric differences in organs at risk and the target volumes were then compared to identify the algorithm that could reduce the radiation dose to organs at risk without compromising the dose distribution to the target volume. Dose verification was performed on the plans generated by both algorithms, and the pass rates of plans for each target volume using both algorithms were compared to provide a quantitative basis for the precise selection of subsequent radiotherapy plans. Results Both AAA and AXB plans met the radiotherapy requirements. The AXB algorithm demonstrated significant advantages in the D₉₈, D₂, homogeneity index, and conformity index for the planning target volume, as well as in the V₅ and V₂₀ for the left lung. The AXB algorithm showed advantages in the V₃₀ for the heart and the maximum and mean doses for the skin. With the 2 mm/2% criterion in dose verification, the gamma pass rate was higher for the AXB algorithm. Conclusion Through a comparative analysis of the two algorithms, this study revealed that the AXB algorithm offers certain advantages in the dose distribution of radiotherapy after breast-conserving surgery for left-sided breast cancer. These findings provide an important reference for the rational selection of algorithms in clinical practice and are expected to improve radiotherapy efficacy and patient prognosis.

Acute-on-chronic liver failure （ACLF） is a form of acute hepatic insufficiency that occurs in the context of a chronic liver disease， with a relatively high mortality rate. To improve the prognosis of ACLF patients， it is essential to early identify the patients with pre-ACLF and constantly optimize and innovate treatment regimens for the disease in the progressive stage. With more than 10 years of research， the Chinese CLIF consortium has developed an early warning model for ACLF and established a system for transferring high-risk patients to tertiary hospitals. At present， the real-world study has also confirmed the consistency between the early warning model and actual conditions in clinical practice， making contributions to the early screening， diagnosis， and treatment of ACLF. The treatment options for the progressive stage of ACLF are also expanding， from the development of innovative pharmaceuticals to the use of artificial liver support and stem cell therapy， and such treatment modalities have made significant achievements in clinical studies and are expected to be implemented in the near future. The development of a more efficient diagnostic system and novel treatment modalities has led to a significant improvement in the diagnosis and treatment of ACLF.

ObjectiveTo monitor the real-time changes in ammonia concentration in the laboratory animal facility environment before, during, and after the air conditioning system stops supplying air, so as to provide a basis and reference for developing emergency plans for the shutdown of the air conditioning system. MethodsThe laboratory animal facilities of the Wuhan Institute of Biological Products were used as the research object. Ammonia concentration detectors were used to monitor ammonia concentration continuously in the environment of conventional rabbit production facility, SPF hamster production facility, and SPF guinea pig experimental facility before and after the passive shutdown due to repairs and active maintenance shutdown of the air conditioning system, as well as the time for the ammonia concentration to return to daily levels after resuming air supply. ResultsUnder both shutdown modes of the air conditioning system, the trend of ammonia concentration changes in different laboratory animal facilities was consistent, showing a rapid increase after shutdown and a rapid decrease after resuming air supply. Under active maintenance shutdown, the maximum ammonia concentrations in the conventional rabbit production facilities, SPF hamster production facilities, and SPF guinea pig experimental facilities were 9.81 mg/m³, 14.27 mg/m³, and 6.98 mg/m³, respectively. Within 12 minutes after resuming air supply, ammonia concentration could return to normal daily levels. Under passive long-term shutdown, ammonia concentration value was positively correlated with the duration of air supply suspension. As the shutdown duration increased, ammonia concentration continued to increase. The maximum ammonia concentration values in the three facilities occurred at 88 minutes (38.06 mg/m³), 40 minutes (18.43 mg/m³), and 34 minutes (15.61 mg/m³) after air supply suspension, respectively.Within 11 minutes after resuming air supply, ammonia concentration could return to normal daily levels. ConclusionShutdown of the air conditioning system causes a rapid increase in ammonia concentration in laboratory animal facilities, and the rise in ammonia concentration is positively correlated with the duration of air supply suspension. Therefore, when an emergency shutdown of the air-conditioning system is required due to maintenance or other reasons, backup fans should be provided in accordance with the requirements of GB 50447-2008 "Architectural and Technical Code for Laboratory Animal Facilities". Older facilities should make adequate preparations and develop a scientifically sound emergency plan.

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 To explore the safety and effects of alpha-lipoic acid (ALA) in patients with ischemic heart failure (IHF). Methods A randomized, double-blind, placebo-controlled trial was designed (ClinicalTrial.gov registration number NCT03491969). From January 2019 to January 2023, 300 patients with IHF were enrolled in four medical centers in China, and were randomly assigned at a 1∶1 ratio to receive ALA (600 mg daily) or placebo on top of standard care for 24 months. The primary outcome was the composite outcome of hospitalization for heart failure (HF) or all-cause mortality events. The second outcome included non-fatal myocardial infarction (MI), non-fatal stroke, changes of left ventricular ejection fraction (LVEF) and 6-minute walking distance (6MWD) from baseline to 24 months after randomization. Results Finally, 138 patients of the ALA group and 139 patients of the placebo group attained the primary outcome. Hospitalization for HF or all-cause mortality events occurred in 32 patients (23.2%) of the ALA group and in 40 patients (28.8%) of the placebo group (HR＝0.753, 95%CI 0.473-1.198, P＝0.231; Figure 1A-1C). The absolute risk reduction (ARR) was 5.6%, the relative risk reduction (RRR) associated with ALA therapy was approximately 19.4% compared to placebo, corresponding to a number needed to treat (NNT) of 18 patients to prevent one event. In the secondary outcome analysis, the composite outcome of the major adverse cardiovascular events (MACE) including the hospitalization for HF, all-cause mortality events, non-fatal MI or non-fatal stroke occurred in 35 patients (25.4%) in the ALA group and 47 patients (33.8%) in the placebo group (HR＝0.685, 95%CI 0.442-1.062, P＝0.091; Figure 1D). Moreover, greater improvement in LVEF (β＝3.20, 95%CI 1.14-5.23, P＝0.002) and 6MWD (β＝31.7, 95%CI 8.3-54.7, P＝0.008) from baseline to 24 months after randomization were observed in the ALA group as compared to the placebo group. There were no differences in adverse events between the study groups. Conclusions These results show potential long-term beneficial effects of adding ALA to IHF patients. ALA could significantly improve LVEF and 6MWD compared to the placebo group in IHF patients.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome