Objective To explore the differences in heartbeat-evoked response (HER) under drug-related cues and neutral cues in individuals with heroin use disorder (HUD), and analyze the correlation between HER potentials and immediate cue-induced craving scores. Methods Fifty HUD participants were recruited from the Chang’an Compulsory Isolation Drug Rehabilitation Center in Shaanxi Province from June to September 2024. Simultaneous acquisition of 64-channel electroencephalography (EEG) and electrocardiogram signals was performed. Twenty alternating segments of drug-related and neutral cue videos were presented, and participants rated their subjective craving after each segment using visual analogue scale (VAS) scores. Scalp EEG data were source analyzed to obtain cortical EEG signals and corresponding HER. Short-time Fourier transform was used to calculate the power spectral density (PSD) of EEG within a time window from 100 ms before the R-peak to 500 ms after it, using the R-peak as the time zero point. Cluster-based permutation testing was used to analyze PSD differences between drug-related and neutral cues in the HUD individuals. Pearson correlation analysis was performed to evaluate the correlation between HER potentials and VAS scores. Results In the 350–420 ms time window, HER potentials in the left posterior parietal, temporal, and posterior cingulate cortices were significantly lower under drug-related cues compared to neutral cues (P＜0.01); in the 140–210 ms time window, HER potentials in the right prefrontal cortex were significantly higher under drug-related cues compared to neutral cues (P＜0.01). Correlation analysis showed that HER potentials in the left temporal and left posterior cingulate cortices were significantly negatively correlated with VAS scores (P＜0.05). Drug-related cues enhanced PSD of γ power (30–100 Hz) in salience network (fronto-insular), parietal and occipital regions (P＜0.05). PSD integrations of low-γ power (40–60 Hz) in parietal region (350–400 ms) and high-γ power (70–100 Hz) in left salience network (fronto-parietal) and occipital regions (300–350 ms) were positively correlated with VAS scores (P＜0.05). Conclusions Drug-related cues may modulate cortical activity related to heartbeat perception in HUD individuals, and such dynamic changes in both time and frequency domains are stably associated with subjective craving.

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.

Objective To evaluate the transition rules of normal body mass index(BMI),overweight and metabolic syndrome(MetS)in patients with major depressive disorder(MDD).Methods Patients with MDD who had multiple admission records between Jan 2016 and Nov 2021 in Beijing Anding Hospital,Capital Medical University were included.Based on the overweight and metabolic syndrome status assessed at each admission,the patients were categorized into three states:normal BMI,overweight and metabolic syndrome.A multi-state Markov model was used to analyze the transition intensity and transition frequency between three states and the influence of covariates on transitions.Results A total of 892 records of 398 subjects were included,with a median age of 56 years old and 31.4% males.The median follow-up period was 40 months.The multi-state model showed that there were 494 transitions between the three states,of which 5.1% moved from normal BMI to overweight and 5.5% moved from overweight to MetS.The intensity of transition was the highest from overweight to MetS,9.52 times greater than overweight to normal BMI.After 48.53 months,MDD patients with normal BMI began to transition to MetS.For overweight MDD patients,the transition to MetS started after 8.77 months.MDD patients with normal BMI or overweight had 31.4% and 50.4% probabilities of developing Mets after 36 months.For MDD patients comorbid with MetS,the probability of staying at MetS was 51.2% after 36 months.Multivariate analysis showed that being unmarried was a risk factor against developing overweight in normal BMI MDD patients,while a higher level of education was a protective factor against developing MetS in overweight MDD patients.Conclusion MDD patients exhibited a higher intensity and risk of developing MetS,and it is not easy to reverse MetS,suggesting that BMI management and MetS intervention should be strengthened in MDD patients.

This study analyzed the variations and evolution characteristics of influenza B Victoria(BV)virus in the Yellow River Delta region of China during 2021-2024.Throat swabs were collected from people with influenza-like illness(ILI)from 2021 to 2024 in Binzhou and Dongying,China.Viral isolation was performed,and 22 representative influenza BV isolates were selected for whole genome sequencing.Phylogenetic analysis of whole-genome sequences was performed in MegAlign and MEGA software.A total of 27 674 samples were obtained,and the overall positivity rate of influenza virus(A/H3N2,A/H1N1,BV)was 11.1％.Our surveillance data indicated that influenza B virus was detected in 2 years,which showed positivity rates of 28.2％and 1.7％,respectively.Statistically significant differences in the positivity rates of influenza BV viruses were observed(x2=3 641.791,P＜0.001).The median pairwise sequence identities ranged from 98.7％to 99.3％for eight segments of 22 viral sequences.The isolates for the monitoring years 2021-2024 were located in clades V1A.3a.1 and V1A.3a.2.Intra-lineage reassortments were discovered in B/shandongbincheng17/2022.The NA gene of one isolate exhibited an increase in the 488NLTV N-glycoproteome site.The K338R mutation occurred in the PA gene.Three locus deletion or insertion mutations occurred in the MP gene.The BV influenza epidemic was prevalent every other year;the intensity ranged from strong to weak;and the duration ranged from long to short in the Yellow River Delta region of China during 2021-2022.Influ-enza B virus formed intra-lineage reassortments,and showed significant mutations in NA gene,PA gene,and MP gene.

The immune microenvironment plays a pivotal role in maintaining ovarian homeostasis. Polycystic ovary syndrome (PCOS), a common endocrine and metabolic disorder, is closely associated with immune microenvironment imbalance. This review systematically describes the dysregulation of innate immune cells (e.g., macrophages, natural killer cells and dendritic cells) and adaptive immune cells (e.g., Th1, Th2, Treg and Th17) in PCOS, highlighting their impacts on ovarian function, insulin resistance, and hyperandrogenemia. These findings underscore the central role of immune microenvironment disturbances in PCOS pathogenesis. Additionally, the association between gut microbiota dysbiosis and PCOS is explored, emphasizing how gut microbiota influences metabolic byproducts and hormonal levels to contribute to PCOS development. Furthermore, therapeutic strategies targeting immune microenvironment imbalance such as modulating macrophage polarization, restoring Th1/Th2 and Th17/Treg balance, and ameliorating gut microbiota dysbiosis are discussed, offering novel insights for PCOS immunotherapy. In conclusion, this review comprehensively analyzes the pathogenesis of PCOS from the perspective of the immune microenvironment, aiming to provide a theoretical foundation and reference for future research and clinical practice.

Objective:To explore the changes in nutritional status and body composition of cervical cancer patients during concurrent chemoradiotherapy (CCRT) and their correlation with CCRT toxicities.Methods:In this prospective and observational clinical study, eligible treatment -na?ve patients with stage IB-IV primary cervical cancer were consecutively enrolled in the Department of Radiotherapy of Peking Union Medical College Hospital from September 2022 to August 2023. The patients were screened for nutritional risks, received dietary assessment, and were measured for body composition using multi-frequency bioelectrical impedance at baseline (prior to treatment), 4 weeks, and 8 weeks since treatment initiation. Insufficient muscle mass was diagnosed ccording to the Asian Working Group for Sarcopenia 2019 criteria. The severity of nausea, vomiting, abdominal pain, diarrhea, and hematological toxicity was assessed by the U.S. National Cancer Institute Common Terminology Criteria for Adverse Events (version 5.0).Results:A total of 109 patients were included. At baseline, there were 11 (10.1%) patients who were lean, 17 (15.6%) patients with insufficient muscle mass, and 28 patients (25.7%) at nutritional risk; at Week 8 of CCRT, patients at nutritional risk increased to 61 (56.0%). Compared to baseline, weight [(59.34±9.67) kg vs. (61.30±9.64) kg, P<0.001], skeletal muscle index [SMI, (6.15±0.74) kg/m 2vs. (6.39±0.74) kg/m 2, P<0.001], body fat percentage [(31.13±7.67) % vs. (32.07±7.70) %, P=0.004] were significantly decreased at Week 8 of CCRT. Besides, ≥10% SMI loss was only related to baseline body fat percentage ( HR=0.216, 95% CI: 0.001-0.724, P=0.038), but not related to age, nutritional status, or muscle mass (all P>0.05). At baseline and 8 weeks since CCRT, 8 (28.6%) and 40 (65.6%) patients at nutritional risk received nutritional support, respectively. During CCRT, the rates of grade ≥2 nausea and vomiting, diarrhea, and grade 3/4 hematological toxicity were 37.6%, 28.4% and 44.0%, respectively. Baseline nutritional risk was a risk factor for diarrhea ( HR=2.447, 95% CI: 1.017-6.068, P=0.047), and an advanced International Federation of Gynecology and Obstetrics (FIGO) stage was a risk factor for severe nausea and vomiting ( HR=1.735, 95% CI: 1.005-2.995, P=0.048). Patients presenting with severe nausea and vomiting had more significant reductions in body mass index [(-1.44±1.29) kg/m 2vs. (-0.59±0.84) kg/m 2, P<0.001] and SMI [(-0.37±0.41) kg/m 2vs. (-0.12±0.27) kg/m 2, P=0.013] compared to those without nausea and vomiting, while there was no significant difference in visceral fat area between these two groups [(-9.95±19.48) cm 2vs. (-5.12±15.79) cm 2, P=0.161]. Conclusions:Patients with cervical cancer have increased nutritional risk and more loss of body weight and muscle mass during CCRT. The presence of nutritional risk at baseline is a risk factor for diarrhea, while nausea and vomiting exacerbate the losses of body weight, muscle, and fat. Close monitoring, intensive symptomatic therapy, and appropriate nutritional interventions should be performed in the clinical setting to improve patients' tolerance of treatment and maintenance of body weight.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Lung cancer has the highest incidence and mortality among malignant tumors in China.Persistent subsolid nodules(SSNs)are closely associated with early-stage lung adenocarcinoma.Artificial intelligence(AI),as an emerging technology,is capable of performing in-depth analysis of large-scale imaging data through autonomous learning and possesses the ability to predict outcomes from new data,demonstrating great potential and application prospects in the assessment of SSNs.AI can not only effectively assist radiologists in diagnosis and treatment,but also improve work efficiency while reducing misdiagnosis and missed diagnosis rates.This review summarizes the recent applications and research progress of AI in the assessment of SSNs,to provide new insights for the diagnosis and treatment of SSNs.

Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.