ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

Ferroptosis, a programmed cell death modality discovered and defined in the last decade, is primarily induced by iron-dependent lipid peroxidation. At present, it has been found that ferroptosis is involved in various physiological functions such as immune regulation, growth and development, aging, and tumor suppression. Especially its role in tumor biology has attracted extensive attention and research. Breast cancer is one of the most common female tumors, characterized by high heterogeneity and complex genetic background. Triple negative breast cancer (TNBC) is a special type of breast cancer, which lacks conventional breast cancer treatment targets and is prone to drug resistance to existing chemotherapy drugs and has a low cure rate after progression and metastasis. There is an urgent need to find new targets or develop new drugs. With the increase of studies on promoting ferroptosis in breast cancer, it has gradually attracted attention as a treatment strategy for breast cancer. Some studies have found that certain compounds and natural products can act on TNBC, promote their ferroptosis, inhibit cancer cells proliferation, enhance sensitivity to radiotherapy, and improve resistance to chemotherapy drugs. To promote the study of ferroptosis in TNBC, this article summarized and reviewed the compounds and natural products that induce ferroptosis in TNBC and their mechanisms of action. We started with the exploration of the pathways of ferroptosis, with particular attention to the System X_c^--cystine-GPX4 pathway and iron metabolism. Then, a series of compounds, including sulfasalazine (SAS), metformin, and statins, were described in terms of how they interact with cells to deplete glutathione (GSH), thereby inhibiting the activity of glutathione peroxidase 4 (GPX4) and preventing the production of lipid peroxidases. The disruption of the cellular defense against oxidative stress ultimately results in the death of TNBC cells. We have also our focus to the realm of natural products, exploring the therapeutic potential of traditional Chinese medicine extracts for TNBC. These herbal extracts exhibit multi-target effects and good safety, and have shown promising capabilities in inducing ferroptosis in TNBC cells. We believe that further exploration and characterization of these natural compounds could lead to the development of a new generation of cancer therapeutics. In addition to traditional chemotherapy, we discussed the role of drug delivery systems in enhancing the efficacy and reducing the toxicity of ferroptosis inducers. Nanoparticles such as exosomes and metal-organic frameworks (MOFs) can improve the solubility and bioavailability of these compounds, thereby expanding their therapeutic potential while minimizing systemic side effects. Although preclinical data on ferroptosis inducers are relatively robust, their translation into clinical practice remains in its early stages. We also emphasize the urgent need for more in-depth and comprehensive research to understand the complex mechanisms of ferroptosis in TNBC. This is crucial for the rational design and development of clinical trials, as well as for leveraging ferroptosis to improve patient outcomes. Hoping the above summarize and review could provide references for the research and development of lead compounds for the treatment for TNBC.

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.

People frequently struggle to juggle their work, family, and social life in today’s fast-paced environment, which can leave them exhausted and worn out. The development of technologies for detecting fatigue while driving is an important field of research since driving when fatigued poses concerns to road safety. In order to throw light on the most recent advancements in this field of research, this paper provides an extensive review of fatigue driving detection approaches based on electroencephalography (EEG) data. The process of fatigue driving detection based on EEG signals encompasses signal acquisition, preprocessing, feature extraction, and classification. Each step plays a crucial role in accurately identifying driver fatigue. In this review, we delve into the signal acquisition techniques, including the use of portable EEG devices worn on the scalp that capture brain signals in real-time. Preprocessing techniques, such as artifact removal, filtering, and segmentation, are explored to ensure that the extracted EEG signals are of high quality and suitable for subsequent analysis. A crucial stage in the fatigue driving detection process is feature extraction, which entails taking pertinent data out of the EEG signals and using it to distinguish between tired and non-fatigued states. We give a thorough rundown of several feature extraction techniques, such as topology features, frequency-domain analysis, and time-domain analysis. Techniques for frequency-domain analysis, such wavelet transform and power spectral density, allow the identification of particular frequency bands linked to weariness. Temporal patterns in the EEG signals are captured by time-domain features such autoregressive modeling and statistical moments. Furthermore, topological characteristics like brain area connection and synchronization provide light on how the brain’s functional network alters with weariness. Furthermore, the review includes an analysis of different classifiers used in fatigue driving detection, such as support vector machine (SVM), artificial neural network (ANN), and Bayesian classifier. We discuss the advantages and limitations of each classifier, along with their applications in EEG-based fatigue driving detection. Evaluation metrics and performance assessment are crucial aspects of any detection system. We discuss the commonly used evaluation criteria, including accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. Comparative analyses of existing models are conducted, highlighting their strengths and weaknesses. Additionally, we emphasize the need for a standardized data marking protocol and an increased number of test subjects to enhance the robustness and generalizability of fatigue driving detection models. The review also discusses the challenges and potential solutions in EEG-based fatigue driving detection. These challenges include variability in EEG signals across individuals, environmental factors, and the influence of different driving scenarios. To address these challenges, we propose solutions such as personalized models, multi-modal data fusion, and real-time implementation strategies. In conclusion, this comprehensive review provides an extensive overview of the current state of fatigue driving detection based on EEG signals. It covers various aspects, including signal acquisition, preprocessing, feature extraction, classification, performance evaluation, and challenges. The review aims to serve as a valuable resource for researchers, engineers, and practitioners in the field of driving safety, facilitating further advancements in fatigue detection technologies and ultimately enhancing road safety.

Hepatocellular carcinoma(HCC)accounts for more than 80％of primary liver cancer,and the prognosis of patients is very poor due to factors such as untimely diagnosis,failure of chemotherapy and frequent recurrence.MicroRNA is a kind of endogenous noncoding RNA,which can inhibit the translation of messenger RNA in liver malignant tumors,regulate the proliferation,apoptosis,migration and invasion of HCC cells,and play an important role in the development of HCC.Therefore,the mechanism of miRNAs in the development of HCC and its research progress in diagnosis and treatment are deeply discussed.

Objective:To investigate the perioperative impact of closure of mesenteric fissure in laparoscopic right hemicolectomy.Methods:The prospective randomized controlled trial was conducted. The clinical data of 320 patients who underwent laparoscopic right hemicolectomy in 11 medical centers, including The First Affiliated Hospital of China Medical University et al, from November 2022 to August 2023 were selected. Based on block randomization, patients were alloca-ted into the mesenteric fissure non-closure group and the mesenteric fissure closure group. Observa-tion indicators: (1) grouping of the enrolled patients; (2) intraoperative conditions; (3) postopera-tive conditions. Measurement data with skewed distribution were represented as M( Q1, Q3) and com-parison between groups was conducted using the Mann-Whitney U test. Count data were represen-ted as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test or Fisher's exact probability. Comparison of ordinal data was conducted using the rank sum test. Comparison of visual analog scores was analyzed using generalized estimating equations. Results:(1) Grouping of the enrolled patients. A total of 320 patients with colon cancer were screened for eligibility, including 156 males and 164 females, aged 68(59,73)years. All the 320 patients were allocated into the mesenteric fissure non-closure group with 164 cases and the mesenteric fissure closure group with 156 cases. There was no significant difference in the age, body mass index, American Society of Anesthesiologist score, maximum tumor diameter, anastomosis location, anastomosis method, surgical approach, range of lymph node dissection, tumor staging between the two groups ( P>0.05) and there was a significant difference in the sex between them ( P<0.05). (2) Intraoperative conditions. There was no significant difference between the mesenteric fissure closure group and the mesenteric fissure non-closure group in the volume of intraoperative blood loss, operation time, conversion to laparotomy, intraoperative complication ( P>0.05). Three patients in the mesenteric fissure non-closure group were converted to laparotomy. One patient in the mesenteric fissure closure group was converted to laparotomy, and 2 cases with intraoperative complication were mesenteric hematoma. (3) Postoperative conditions. There was no significant difference between the mesenteric fissure non-closure group and the mesenteric fissure closure group in the overall postoperative complications ( χ2=0.28, P>0.05). There was no significant difference in the occurrence of postoperative intestinal obstruction, abdominal distension, ascites, pleural effusion, gastric paralysis, anastomotic bleeding, anastomotic leakage, or surgical wound infection between the two groups ( P>0.05). There was no significant difference between the two groups in the reoperation, postoperative gastric tube replacement. There was no significant differ-ence in time to postoperative first flatus, time to postoperative initial liquid food intake, time to post-operative resumption of bowel movements, duration of postoperative hospital stay, total hospital expenses between the two groups ( Z=-0.01, 0.43, 1.04, -0.54, -0.36, P>0.05). One patient in the mesenteric fissure non-closure group received reoperation. No perioperative internal hernia or death occurred in either group. The visual analog score decreased with time in both groups. There was no significant difference in the visual analog score between the mesenteric fissure closure group and the mesenteric fissure non-closure group [ β=-0.20(-0.53,0.13), P>0.05]. Conclusion:Compared with closure of mesenteric fissure, non-closure of mesenteric fissure during laparoscopic right hemi-colectomy dose not increase perioperative complications or postoperative management risk.

Objective To compare the dosimetric effects of volume modulated arc therapy(VMAT)and intensity modulated radiation therapy(I M RT)on the anal sphincter(AS)and its sub-structures in neoadjuvant radiotherapy for rectal cancer to facilitate the selection of radiotherapy techniques.Methods Fifty rectal cancer patients receiving neoadjuvant radiotherapy were selected,and 2 types of radiotherapy plans,including coplanar double full-arc VMAT and coplanar seven-field homo-geneous IMRT,were designed based on the CT images of the patients,respectively.Under the premise of ensuring that the irradiated doses to the target area and the major organs at risk reached the standard and met the clinical requirements,AS and its substructures were added as the organs at risk for dosimetric evaluation.The absolute dose parameters and relative dose parameters of AS and its substructures were counted by dose-volume histograms.Statistical analysis was performed using IBM SPSS 22.0 software.Results The VMAT plan had the relative dose parameters V20,V30,and V40 of AS and its substructures lower those of the IMRT plan,the differences were statistically significant(P＜0.05),while the differences in V5 and V 10 were not statistically significant(P＞0.05).The VMAT plan had the absolute dose parameterDmeanlower while the D2％slightly higher than those of the IMRT plan,the differences were statistically significant(P＜0.05).The difference in D98％between the two plans was not statistically significant(P＞0.05).Conclusion During rectal cancer radiotherapy VMAT generally behaves better than IMRT in protecting AS and its sub-structures and decreasing injuries of AS and its sub-structures dosimetrically.[Chinese Medical Equipment Journal,2024,45(8):63-67]

Lithium(Li)salts are commonly used as psychotropic medications for the treatment of major depressive disorders.However,long-term use of Li salts poses a high risk of toxicity,necessitating continuous monitoring of Li concentration in patient blood to ensure medication safety,which is crucial for clinical treatment.Laser-induced breakdown spectroscopy(LIBS),as a rapid analytical technique,has been widely applied in the elemental analysis of complex matrices in various practical scenarios.In this study,LIBS technology combined with partial least squares(PLS)was employed for quantitative analysis of Li elements in blood matrix.A total of 45 clinical blood samples were utilized,and the quantitative models for plasma and whole blood matrices were separately investigated.The number of latent variables in the PLS algorithm was optimized using a five-fold cross-validation method.Results revealed that the PLS quantitative model constructed on the basis of plasma matrix achieved a predictive determination coefficient(R2)of 0.992,a predictive root mean square error(RMSEP)of 0.204 μg/mL,and a relative standard error(RSD)of 2.14%.In contrast,for the PLS quantitative model constructed on the basis of whole blood matrix,the R2 was 0.984,the RMSEP was 0.728 μg/mL,and the RSD was 3.45%Consequently,the LIBS model constructed on the basis of plasma calibration values demonstrated superior performance in quantitative analysis of Li element in whole blood,and LIBS technology provided a new possibility for rapid assessment of blood Li levels in clinical practice,with promising prospects for application.

Objective:To investigate the correlation of miR-155 expression with drug sensitivity of FLT3-ITD+acute myeloid leukemia(AML)cell line and its potential regulatory mechanism.Methods:By knocking out miR-155 gene in FLT3-ITD+AML cell line MV411 through CRISPR/Cas9 gene-editing technology,monoclonal cells were screened.The genotype of these monoclonal cells was validated by PCR and Sanger sequencing.The expression of mature miRNA was measured by RT-qPCR.The treatment response of doxorubicin,quizartinib and midostaurin were measured by MTT assay and IC50 of these drugs were calculated to identify the sensitivity.Transcriptome sequencing was used to analyze change of mRNA level in MV411 cells after miR-155 knockout,gene set enrichment analysis to analyze change of signaling pathway,and Western blot to verify expressions of key molecules in signaling pathway.Results:Four heterozygotes with gene knockout and one heterozygote with gene insertion were obtained through PCR screening and Sanger sequencing.RT-qPCR results showed that the expression of mature miR-155 in the monoclonal cells was significantly lower than wild-type clones.MTT results showed that the sensitivity of MV411 cells to various anti FLT3-ITD+AML drugs increased significantly after miR-155 knockout compared with wild-type clones.RNA sequencing showed that the mTOR signaling pathway and Wnt signaling pathway were inhibited after miR-155 knockout.Western blot showed that the expressions of key molecules p-mTOR,Wnt5α and β-catenin in signaling pathway were down-regulated.Conclusion:Drug sensitivity of MV411 cells to doxorubicin,quizartinib and midostaurin can be enhanced significantly after miR-155 knockout,which is related to the inhibition of multiple signaling pathways including mTOR and Wnt signaling pathways.

Objective To investigate the clinical characteristics of female migraine patients with patent foramen ovale(PFO)and design a risk prediction model for PFO in female migraine patients(migraineur patients PFO risk prediction model,MPRPM).Methods Female migraine patients who visited Zhongshan Hospital,Fudan University from Jun 1,2019 to Dec 31,2022 were included.Preoperative information and follow-up results after discontinuation of medication were collected.Patients were divided into PFO-positive and PFO-negative groups based on transesophageal echocardiography results.A multivariate Logistic regression model and a random forest model were constructed,and the random forest model was validated multidimensionally.Key features were selected based on the mean decrease accuracy(MDA)to construct MPRPM.Results A total of 305 female patients were included in the study,with 204 patients in the PFO-positive group and 101 patients in the PFO-negative group.Multivariate Logistic regression analysis showed that age at migraine onset,attack frequency,severe impact on life during attacks,exercise-related headaches,menstruation-induced headaches,aura migraines,and a history of cryptogenic stroke were predictive factors for PFO positivity.The random forest model effectively predicted the incidence of PFO in female migraine patients,with an AUC of 0.895(95%CI:0.847-0.943).MPRPM demonstrated a sensitivity of 71.6%and specificity of 91.1%(AUC:0.862,95%CI:0.818-0.906,P<0.001).The optimal cut-off value was 2.5 points.Patients correctly classified by the model showed a higher rate of symptom improvement compared to incorrectly classified patients(94.3%vs.82.0%,P=0.023).Conclusion We identified predictive factors for PFO in migraine patients.MPRPM can provide guidance in the diagnostic process and therapeutic decision-making for female migraine patients,assist in patient triage,and reduce the healthcare burden.