Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

Objective To understand the current status of low-density lipoprotein cholesterol (LDL-C) control in patients after coronary artery bypass grafting (CABG). Methods Clinical data of patients who underwent isolated CABG in Beijing Anzhen Hospital in 2023 were collected. All patients returned to our hospital approximately one year after surgery (10-13 months) for a lipid level recheck. We analyzed their LDL-C attainment status and influencing factors. Patients were categorized into two groups based on whether their LDL-C met the target: a LDL-C attainment group and a LDL-C non-attainment group. Results This study included 1456 patients who underwent CABG, including 320 females and 1136 males, with an average age of (61.41±9.12) years. One year post-surgery, 234 patients achieved the LDL-C target, with an attainment rate of 16.07%. The proportion of patients in the LDL-C attainment group who were ultra-high risk (77.35% vs. 92.06%, P＜0.001), female (16.24% vs. 23.08%, P=0.021), and those with comorbid hypertension (55.98% vs. 63.18%, P=0.038) was significantly lower than those in the LDL-C non-attainment group. Additionally, the baseline body mass index (BMI) [(25.37±3.24) kg/m2 vs. (26.03±3.56) kg/m2, P=0.017], total cholesterol levels [(3.30±0.84) mmol/L vs. (4.01±1.03) mmol/L, P＜0.001], LDL-C [(1.62±0.63) mmol/L vs. (2.25±0.85) mmol/L, P＜0.001], and high-density lipoprotein cholesterol [(0.98±0.26) mmol/L vs. (1.02±0.24) mmol/L, P=0.049] upon admission in the attainment group were all lower than those in the non-attainment group. Moreover, the lipid-lowering drug usage rate in the attainment group (100.00% vs. 96.24%, P=0.003) and the proportion using two types of drugs together (25.21% vs. 10.72%, P＜0.001) were both higher than those in the non-attainment group, while the statin monotherapy rate was lower than that in the non-attainment group (74.79% vs. 85.19%, P＜0.001). Logistic regression analysis showed that baseline BMI (OR=0.928, P=0.012) and baseline LDL-C levels (OR=0.207, P<0.001), patient cardiovascular risk stratification (OR=0.155, P<0.001) and lipid-lowering drug treatment regimen (OR=3.758, P<0.001) are significant factors affecting the LDL-C control status. Conclusion The LDL-C compliance rate of patients undergoing CABG is at a relatively low level 1 year after surgery. Patients with very high risk of atherosclerotic cardiovascular disease, high baseline LDL-C levels, and overweight or obesity should be strengthened lipid management. For these patients, the intensity of lipid-lowering drug use or combination medication should be increased upon discharge.

ObjectiveThis paper aims to observe the syndrome improvement and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome）. MethodsThrough a multi-center randomized controlled methodology design，confirmed influenza cases were collected from October 2022 to April 2023 in the pediatrics department of eight hospitals，such as Dongfang Hospital of Beijing University of Chinese Medicine. A total of 180 children with influenza and heat accumulation in the lung and stomach syndrome conforming to the standard were recruited through the clinic. The sick children meeting the inclusion criteria were randomly divided into groups by a block-randomized method. The children in the experimental group were treated with Qingxuan Daozhi formula for five days，and those in the control group were treated with Oseltamivir Phosphate Granules for five days. The primary efficacy indicator was the negative conversion rate of influenza antigen detection. Secondary efficacy indicators were the Canadian acute respiratory illness and flu scale （CARIFS） and the incidence of complications，severe cases， and critical cases. Follow-up observation was conducted on the day of enrollment，48 hours after medication，72 hours after medication， and （6+1） d after medication. ResultsOne hundred and eighty participants were randomly assigned to the experimental group （90 cases） or the control group （90 cases）. All participants were followed up during the study. Comparison of influenza antigen detection results in the primary efficacy indicators showed that the average time of negative influenza antigen conversion in the experimental group was （5.29±1.25） d，and that in the control group was （5.40±1.68） d，without a statistically significant difference. After five days of intervention，52 cases in the experimental group and 51 cases in the control group converted to negative，without a statistically significant difference. CARIFS score results in the secondary efficacy indicators showed that during 72 hours after intervention，there were statistically significant differences between the experimental group and the control group in three dimensions， including headache，muscle soreness， and the need for extra care （P<0.05）. On the （6+1） days after the intervention，the differences in both the experimental group and the control group were statistically significant in 10 dimensions， including sore throat，bad sleep，uncomfortable feeling，poor spirit and fatigue，crying more than usual，the need for extra care，symptom，function，influence on parents，and total score （P<0.05）. The comparison results within the group in the dimensional scores of symptom， function， and influence on parents，as well as the CARIFS total score showed that with the delay of follow-up time，scores of both groups decreased significantly，with a statistically significant difference （P<0.01）. Inter-group comparison results showed that the mean score of the experimental group was higher than that of the control group at the time of enrollment. With the progress of intervention，the score of the experimental group was significantly decreased compared with that of the control group. At the end of follow-up，the mean score of the experimental group was lower than that of the control group，with no statistically significant difference. In terms of the incidence of complications，severe cases， and critical cases， there were no complications，severe cases， and critical cases in the two groups，without a statistically significant difference. ConclusionThe symptom improvement effect and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome） are not inferior to Oseltamivir Phosphate granules， and children's acceptance is better. It can be more widely used in clinical treatment of influenza in children （heat accumulation in the lung and stomach syndrome）.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Objective: To analyze the change trends in the body shape indicators and proportions of students in Harbin from 2010 to 2024, and to investigate ergonomic compatibility of functional dimensions of school desks and chairs with current student shape indicators, so as to provide a reference for revising furniture standards of desks and chairs. Methods: Between September and November of both 2010 and 2024, a combination of convenience sampling and stratified cluster random sampling was conducted across three districts in Harbin, yielding samples of 6 590 and 6 252 students, respectively. Anthropometric shape indicators cluding height, sitting height, crus length, and thigh length-and their proportional changes were compared over the 15-year period. The 2024 data were compared with current standard functional dimensions of school furniture. The statistical analysis incorporated t-test and Mann-Whitney U- test. Results: From 2010 to 2024, average height increased by 1.8 cm for boys and 1.5 cm for girls; sitting height increased by 1.5 cm for both genders; crus length increased by 0.3 cm for boys and 0.4 cm for girls; and thigh length increased by 0.5 cm for both genders. The ratios of sitting height to height, and sitting height to leg length increased by less than 0.1 . The difference between desk chair height and 1/3 sitting height ranged from 0.4-0.8 cm. Among students matched with size 0 desks and chairs, 22.0% had a desk to chair height difference less than 0, indicating that the desk to chair height difference might be insufficient for taller students. The differences between seat height and fibular height ranged from -1.4 to 1.1 cm; and the differences between seat depth and buttock popliteal length ranged from -9.8 to 3.4 cm. Among obese students, the differences between seat width and 1/2 hip circumference ranged from -20.5 to -8.7 cm, while it ranged from -12.2 to -3.8 cm among non obese students. Conclusion Current furniture standards basically satisfy hygienic requirements; however, in the case of exceptionally tall and obese students, ergonomic accommodations such as adaptive seating allocation or personalized adjustments are recommended to meet hygienic requirements.

Disorders of consciousness (DOC) are pathological conditions characterized by severely suppressed brain function and the persistent interruption or loss of consciousness. Accurate diagnosis and evaluation of DOC are prerequisites for precise treatment. Traditional assessment methods are primarily based on behavioral scales, which are inherently subjective and rely on observable behaviors. Moreover, traditional methods have a high misdiagnosis rate, particularly in distinguishing minimally conscious state (MCS) from vegetative state/unresponsive wakefulness syndrome (VS/UWS). This diagnostic uncertainty has driven the exploration of objective, reliable, and efficient assessment tools. Among these tools, electroencephalography (EEG) has garnered significant attention for its non-invasive nature, portability, and ability to capture real-time neurodynamics. This paper systematically reviews the application of EEG biomarkers in DOC assessment. These biomarkers are categorized into 3 main types: resting-state EEG features, task-related EEG features, and features derived from transcranial magnetic stimulation-EEG (TMS-EEG). Resting-state EEG biomarkers include features based on spectrum, microstates, nonlinear dynamics, and brain network metrics. These biomarkers provide baseline representations of brain activity in DOC patients. Studies have shown their ability to distinguish different levels of consciousness and predict clinical outcomes. However, because they are not task-specific, they are challenging to directly associate with specific brain functions or cognitive processes. Strengthening the correlation between resting-state EEG features and consciousness-related networks could offer more direct evidence for the pathophysiological mechanisms of DOC. Task-related EEG features include event-related potentials, event-related spectral modulations, and phase-related features. These features reveal the brain’s responses to external stimuli and provide dynamic information about residual cognitive functions, reflecting neurophysiological changes associated with specific cognitive, sensory, or behavioral tasks. Although these biomarkers demonstrate substantial value, their effectiveness rely on patient cooperation and task design. Developing experimental paradigms that are more effective at eliciting specific EEG features or creating composite paradigms capable of simultaneously inducing multiple features may more effectively capture the brain activity characteristics of DOC patients, thereby supporting clinical applications. TMS-EEG is a technique for probing the neurodynamics within thalamocortical networks without involving sensory, motor, or cognitive functions. Parameters such as the perturbational complexity index (PCI) have been proposed as reliable indicators of consciousness, providing objective quantification of cortical dynamics. However, despite its high sensitivity and objectivity compared to traditional EEG methods, TMS-EEG is constrained by physiological artifacts, operational complexity, and variability in stimulation parameters and targets across individuals. Future research should aim to standardize experimental protocols, optimize stimulation parameters, and develop automated analysis techniques to improve the feasibility of TMS-EEG in clinical applications. Our analysis suggests that no single EEG biomarker currently achieves an ideal balance between accuracy, robustness, and generalizability. Progress is constrained by inconsistencies in analysis methods, parameter settings, and experimental conditions. Additionally, the heterogeneity of DOC etiologies and dynamic changes in brain function add to the complexity of assessment. Future research should focus on the standardization of EEG biomarker research, integrating features from resting-state, task-related, and TMS-EEG paradigms to construct multimodal diagnostic models that enhance evaluation efficiency and accuracy. Multimodal data integration (e.g., combining EEG with functional near-infrared spectroscopy) and advancements in source localization algorithms can further improve the spatial precision of biomarkers. Leveraging machine learning and artificial intelligence technologies to develop intelligent diagnostic tools will accelerate the clinical adoption of EEG biomarkers in DOC diagnosis and prognosis, allowing for more precise evaluations of consciousness states and personalized treatment strategies.

OBJECTIVE: To study the clinical efficacy of Wenyang Lishui Formula (WYLSF) in preventing ovarian hyperstimulation syndrome (OHSS) and explore the suitable range of estradiol (E₂) on the human chorionic gonadotropin (HCG) day in patients with OHSS using WYLSF. METHODS: Part I: eligible patients at high risk for OHSS undergoing ovulation induction between January and December, 2023 were randomized into 2 groups based on the actual treatment. The treatment group received 200 mL WYLSF formula twice daily for 5 days after oocyte retrieval in a combination of lifestyle coaching (LC) intervention including regular diet and exercise, whereas the LC group received LC intervention alone. The incidence of OHSS, OHSS self-assessment scales, changes in E₂ levels on HCG day and 5 days after oocyte retrieval, ovarian morphology changes, and menstrual recovery were compared between the two groups. Part II: patients at high risk for OHSS treated with WYLSF were studied. The optimal E₂ threshold on the HCG day was determined using the maximum selection test, and a multivariate analysis was adopted to compare the relationship between different E₂ levels on HCG day and hospitalization rate, incidence of moderate to severe OHSS, and self-assessment scales, to explore the preventive effect of WYLSF on OHSS in patients with varying E₂ levels. RESULTS: A total of 120 patients were included in the Part I analysis. The treatment group (60 cases) showed a significant reduction in the incidence, duration, and severity of abdominal distension, as well as the incidence of vomiting compared with the LC group (P<0.05). The post-retrieval E₂ levels in the treatment group decreased significantly more (P=0.032). Among 1,652 patients treated with WYLSF in the Part II, 90 patients with ⩽ 10092 pmol/L, 159 with >31074 pmol/L, and 1,403 in the middle range group were formed based on E₂ levels on HCG day in Part two analysis. Univariate and regression analyses showed that patients with E₂ levels >31073 pmol/L had a significantly higher incidence of moderate to severe OHSS compared to those with E₂ levels ⩽ 10092 pmol/L (P<0.05). CONCLUSIONS WYLSF can effectively reduce specific symptoms in high-risk OHSS patients after ovulation induction and significantly lower E₂ levels. It may be more suitable for high-risk OHSS patients with E₂ levels <31073 pmol/L on HCG day. (Registration No. MR-11-23-032493, https://www.medicalresearch.org.cn/login ).
Humans ; Ovarian Hyperstimulation Syndrome/blood* ; Female ; Adult ; Prospective Studies ; Drugs, Chinese Herbal/pharmacology* ; Estradiol/blood* ; Ovulation Induction ; Chorionic Gonadotropin

Objective: To investigate the iodine nutrition status of children in water borne iodine excess areas in Hebei Province, so as to provide references for scientific prevention and control of water borne iodine excess hazards. Methods: From March to September each year during 2018 to 2023, a cross sectional survey was conducted in 39 water borne iodine excess counties (measured in 2017) from 5 cities (Cangzhou, Hengshui, Xingtai, Handan and Langfang) in Hebei Province. The survey included the detection of iodine content in residents drinking water, the measurement of thyroid volume in children aged 6-12, the detection of salt iodine and urinary iodine. The iodine nutrition status and water iodine distribution of 6-12 year-old children were evaluated from different perspectives such as years, gender, and age. Kruskal-Wallis H- test, Mann-Whitney U test and Chi square test were used for group comparison. Results: A total of 38 755 children were surveyed from 2018 to 2023, and 1 270 drinking water samples were tested across the province. The mass volume concentration of iodine in water showed a decreasing trend over the years ( Z= -30.87, P <0.01). Among 38 470 salt samples monitored from children s home, 24 790 were not non iodized salt, with a non iodized salt rate of 64.44%. A total of 31 989 urine samples were collected from children aged 8-10 years, with the median urine iodine was 245.94 μg/L. Comparing the results of urinary iodine in children from different years, the median urinary iodine from 2018 to 2023 were 328.0, 339.3, 267.8, 279.1, 291.3, 186.5 μg/L, respectively, with statistically significant differences ( H= 4 138.40 , P <0.01). Further pairwise comparisons showed that the median urinary iodine of children in 2023 was lower than in all other years ( Z =-51.59 to -11.41, all P <0.01). Among children aged 6-12 years, 1 150 cases of goiter were detected and the rate of goiter was 3.0%; and the goiter rates in boys and girls were 2.8% and 3.1%, with no significant difference between the sexes ( χ 2= 2.76, P >0.05). There were significant differences in the rate of goiter among different years and ages ( χ 2=324.02, 191.61, both P <0.05). Conclusions With the progress of water reform in water borne iodine excess areas of Hebei Province, children s iodine nutrition has reduced from excessive state to suitable state. It is necessary to continue to expand the coverage of water based iodine reduction projects, and strengthen the monitoring of iodine nutrition status of key populations in water borne iodine excess areas.

ObjectiveTo evaluate the efficacy and safety of Xiaoji Hufei Formula in protecting children with close contact exposure to influenza， and to provide reference and evidence-based support for better clinical prevention and treatment of influenza in children. MethodsA multicenter， prospective， non-randomized， parallel， controlled trial was conducted from October 2021 to May 2022 in five hospitals， including Dongfang Hospital of Beijing University of Chinese Medicine. Confirmed influenza cases and influenza-like illness （ILI） cases were collected， and eligible children with close contact exposure to these cases were recruited in the outpatient clinics. According to whether the enrolled close contacts were willing to take Xiaoji Hufei formula for influenza prevention， they were assigned to the observation group （108 cases） or the control group （108 cases）. Follow-up visits were conducted on days 7 and 14 after enrollment. The primary outcomes were the incidence of ILI and the rate of laboratory-confirmed influenza. Secondary outcomes included traditional Chinese medicine （TCM） symptom score scale for influenza， influenza-related emergency （outpatient） visit rate， influenza hospitalization rate， and time to onset after exposure to influenza cases. ResultsA total of 216 participants were enrolled， with 108 in the observation group and 108 in the control group. Primary outcomes： （1） Incidence of ILI： The incidence was 12.0% （13/108） in the observation group and 23.1% （25/108） in the control group， with the observation group showing a significantly lower incidence （χ²=4.6， P<0.05）. （2） Influenza confirmation rate： 3.7% （4/108） in the observation group and 4.6% （5/108） in the control group， with no statistically significant difference. Secondary outcomes： （1） TCM symptom score scale： after onset， nasal congestion and runny nose scores differed significantly between the two groups （P<0.05）， while other symptoms such as fever， sore throat， and cough showed no significant differences. （2） Influenza-related emergency （outpatient） visit rate： 84.6% （11 cases） in the observation group and 96.0% （24 cases） in the control group， with no significant difference. （3） Time to onset after exposure： The median onset time after exposure to index patients was 7 days in the observation group and 4 days in the control group， with a statistically significant difference （P<0.05）. ConclusionIn previously healthy children exposed to infectious influenza cases under unprotected conditions， Xiaoji Hufei formula prophylaxis significantly reduced the incidence of ILI. Xiaoji Hufei Formula can be recommended as a specific preventive prescription for influenza in children.