ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

Objective:To evaluate the sample preparation proficiency and storage proficiency of 11 clinical biobanks in Beijing through simulated experiments, and to establish an assessment method for the quality comparability of biological samples.Methods:An exploratory research design was adopted. In November 2023, artificial composite serum quality control materials containing six recombinant human protein markers—recombinant human alanine aminotransferase (rhALT), recombinant human aspartate aminotransferase (rhAST), recombinant human creatine kinase (rhCK), recombinant human creatine kinase-MB (rhCK-MB), recombinant human B-type natriuretic peptide (rhBNP), and recombinant human troponin I (rhTNI)—were distributed to 11 clinical biobanks in Beijing City. Sample preparation and storage followed the standardized operating procedures. Proficiency differences were assessed through statistical analysis.Results:Three-way repeated measures ANOVA revealed all six protein markers showed a declining trend over storage time in ultra-low-temperature environments ( F values 11.68-4 179.66, all P<0.01). However, neither long-term/temporary refrigerator types ( F values 0.01-1.23, all P>0.05)nor placement locations within refrigerators significantly affected the stability of these six proteins ( F valus 0.03-1.47, all P>0.05). The biases in detection results for rhALT, rhAST, rhTNI, and rhBNP at different storage time points were within the allowable bias limits for each item, supporting their use as markers for protein stability in biobank samples. All 11 institutions passed the storage proficiency assessment. In the preparation proficiency assessment, deviations were observed in post-preparation sample results, with a notably high out-of-control rate for rhCK (36.36%). Conclusion:Sample preparation proficiency can serve as a quality control metric for clinical biobanks. Future external quality assessment systems for biobanks should focus on sample preparation rather than storage processes.

Objectives:Comparative analysis of the beagles acute-phase electrocardiographic,hemodynamic,left ventricular flow field status,and energy consumption characteristics of pacing at different sites of conduction system may help to elucidate the scientific mechanism of left bundle branch pacing(LBBP)as a option of physiological pacing therapy.Methods:Eight healthy adult beagles were used in this study.Initially,an active fixation lead was implanted in the right atrial appendage,followed by implantation of another active fixation lead at the right ventricular apex,distal His bundle,and left bundle septal branch,respectively.After connecting a dual-chamber pacemaker,electrocardiographic and acute phase hemodynamic parameters under sinus rhythm,right ventricular apex pacing(RVAP),distal His bundle pacing(DHBP),and LBBP states were collected and analyzed.Three complete cardiac cycles of standard apical three-chamber color Doppler dynamic images were acquired under vector flow mapping(VFM)mode.Offline analysis was performed on obtained parameters including isovolumic contraction period,rapid ejection period,isovolumic relaxation period,rapid filling period,atrial contraction period,and left ventricular intracavitary energy consumption.These parameters were compared under pacing at different sites and the linear correlations of major parameters were analyzed.Results:The QRS duration of baseline intrinsic sinus rhythm,RVAP,DHBP and LBBP were(45.0±4.0)ms,(98.4±6.2)ms,(50.0±4.5)ms and(62.0±4.7)ms,respectively.The LBBP-QRS duration was significantly wider than intrinsic sinus rhythm and DHBP,but significantly narrower than RVAP(all P<0.01).Compared with baseline AOO mode(the pacing rate was performed at 10 beats/min above the intrinsic heart rate),the change of acute phase maximum left ventricular pressure rise rate(LVdP/dtmax)in RVAP,DHBP and LBBP was([-7.89±5.67]% ),([0.74±2.05]% )and([-0.14±3.59]% ),respectively.There was no significant difference in LVdP/dtmax changes between DHBP and LBBP(P=0.667),but both pacing modalities were significantly better than RVAP(all P<0.01).The average energy consumption of the left ventricle under RVAP was significantly higher than that of intrinsic sinus rhythm,DHBP,and LBBP in isovolumic contraction period,rapid ejection period,isovolumic relaxation period,rapid filling period,and atrial contraction period(all P<0.01).However,there was no statistically significant difference in energy consumption among intrinsic sinus rhythm,DHBP,and LBBP during the above five phases(all P>0.05).DHBP and LBBP did not show a significant increase in the number of left ventricular vortices,vortex area,and circulation intensity compared to intrinsic sinus rhythm,and LBBP did not show a significant increase in vortex area and circulation intensity compared to DHBP.Conclusions:Although LBBP canines significantly prolonged the paced QRS duration,it showed no significant differences in acute phase left ventricular hemodynamics,left ventricular flow field status,and energy consumption compared to intrinsic sinus rhythm and DHBP.Performance of LBBP was superior to RVAP.This study may contribute to revealing the theoretical basis of LBBP as a feasible physiological pacing therapy.

Purpose To measure the spatial resolution in MRI quality control testing automatically based on the American College of Radiology(ACR)phantom using the support vector machine(SVM)method,and the feasibility,accuracy and measurement speed of this method are explored.Materials and Methods Quality control tests were performed using eight MRI devices at Beijing Friendship Hospital of Capital Medical University.A retrospective study was conducted on 71 MRI quality control test images collected based on ACR phantoms between 2017 and 2019.The images were preprocessed by binarization,extraction region of interest and so on.An SVM-based classification model was constructed for analyzing the spatial resolution of dot arrays in row and column directions.The dataset was randomly split into a training set and a test set.The generalization performance of the classification model in this study was evaluated through accuracy,precision,recall and F1 score on the test set.Comparing the results of spatial resolution measurements obtained by both manual and automatic method,we demonstrated the feasibility and accuracy of the method.Additionally,the time taken for the automatic spatial resolution measurement was recorded.Results In this study,the proposed method of automatically measuring the spatial resolution of ACR phantom test images using SVM was feasible,high accuracy and short time.In classification performance test,the accuracy of the spatial resolution of the row directional latices was 95%,the precision was 100%.The accuracy of the spatial resolution of the column directional latices was 97%,the precision was 100%.Among the test cases,the results of automatic measurements matched those of manual measurements in 13 out of 14 cases.On average,automatic spatial resolution measurement took 0.158 seconds per case.Conclusion This study achieves automatic measurement of spatial resolution in MRI quality control based on the ACR phantom using SVM method.The method demonstrates high accuracy and fast measurement speeds,holding significant implications for future rapid MRI quality control stability testing.

Purpose To explore an automatic detection method for quality control performance indicators of radio frequency coils based on American College of Radiology(ACR)phantom,and verify its accuracy stability and computational efficiency.Materials and Methods A retrospective study was conducted on 50 quality control images collected based on ACR phantom in Beijing Friendship Hospital,Capital Medical University from May 2017 to July 2019.The measurement and calculation methods of signal noise ratio(SNR),percent image uniformity(PIU)and percent signal ghosting(PSG)were used to automatically calculate the above indicators using a self-designed program in Python.A simple linear regression analysis on the automatically calculated SNR,PSG and PIU values compared to the manually measured results was performed,and Bland-Altman analysis was used to calculate the percentage difference to evaluate the consistency and bias between the performance indicators calculated by the two methods.The time consumption of two detection methods was compared to verify their computational complexity and efficiency.Results There was a strong correlation between the performance indicators SNR,PSG and PIU of radio frequency coils measured and calculated automatically and manually(r=0.991 4,0.992 8 and 0.909 8,all P<0.0001).The Bland-Altman results showed that most of the data fall within the 95%confidence interval and were evenly distributed.In terms of computational complexity and efficiency,compared to the complex manual delineation and calculation of 2-3 minutes per case,automatic detection could simultaneously obtain SNR,PSG and PIU values in less than 1 second.Conclusion The automatic and manual measurement methods have good consistency,and the automatic detection method is easy to operate,which is helpful for the daily quality control work and performance monitoring of radio frequency coils.

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

BACKGROUND: Electroacupuncture (EA) may affect the severity of hot flashes (HFs) associated with natural menopause and provide additional benefits for postmenopausal women. However, the evidence for its effectiveness in the management of early postmenopausal HFs remains inadequately understood. OBJECTIVE: We designed this trial to assess the efficacy and safety of EA for relieving early postmenopausal HFs. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS: This randomized sham-controlled trial involved 72 women with HFs. The participants were divided equally into the intervention and control groups. The intervention group was treated with EA, while the control group was treated with sham acupuncture. The main acupoints used were Hegu (LI4), Guanyuan (RN4), Sanyinjiao (SP6), Taixi (KI3), Fuliu (KI7) and Shenshu (BL23). All participants received 18 treatment sessions, distributed across a 6-week period. The treatment was administered on three occasions per week, adhering to a fixed weekday schedule (Monday, Wednesday, Friday or Tuesday, Thursday, Saturday) with a minimum interval of one day between sessions. Each patient received a 12-week follow-up. MAIN OUTCOME MEASURES: The HF score was the primary outcome. Participants documented the frequency and severity of HFs in a 7-day symptom diary, which provided data for calculating the HF score. Secondary outcomes were the Menopause Rating Scale (MRS), Menopause-Specific Quality of Life Questionnaire (MENQOL), Pittsburgh Sleep Quality Index (PSQI) and Traditional Chinese Medicine Syndrome Score Scale (TCMSSS), as well as estradiol (E₂), luteinizing hormone (LH) and follicle-stimulating hormone (FSH) levels. RESULTS: Both groups demonstrated significant reductions in HF scores after the treatment and during the follow-up (P < 0.001). Immediately after completion of the 6-week treatment cycle and at 12 weeks post-intervention, the HF scores were similar in both groups. At week 6, the intervention group showed significantly greater improvements in MRS, MENQOL (vasomotor, psychosocial, and physical), PSQI and TCMSSS scores (P < 0.05). The improvements in the MENQOL (vasomotor, and psychosocial) and PSQI total scores persisted through the follow-up (P < 0.05). However, the results showed no significant inter- or intragroup differences in sexual scores on the MENQOL (P > 0.05). EA did not significantly decrease E₂, LH or FSH levels compared to placebo. The incidence of adverse events was similar in both groups. CONCLUSION: EA does not significantly improve HFs in early postmenopausal patients. However, it enhances the quality of sleep and decreases menopausal symptoms across vasomotor, psychosocial and physical domains. TRIAL REGISTRATION Chinese Clinical Trial Registry (http://www.chictr.org.cn); Trial ID: ChiCTR2300072002. Please cite this article as: Wang HX, Yu XT, Hu J, Chen JJ, Mei YT, Chen YF. Electroacupuncture for hot flashes in early menopause: A randomized sham-controlled trial. J Integr Med. 2025; 23(5):519-527.
Humans ; Female ; Electroacupuncture ; Hot Flashes/therapy* ; Middle Aged ; Acupuncture Points ; Quality of Life ; Menopause ; Treatment Outcome ; Adult

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 examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis

OBJECTIVE: This study aimed to investigate possible serum 25-hydroxyvitamin D [25(OH)D] cutoffs for the associations between 25(OH)D and Bone turnover markers (BTMs), and how GC gene variation influences such cutoffs in Chinese women of childbearing age. METHODS: In total, 1,505 non-pregnant or non-lactating women (18-45 years) were recruited from the 2015 Chinese Adult Chronic Disease and Nutrition Surveillance. Serum 25(OH)D, osteocalcin (OC), procollagen type 1 N-terminal propeptide (P1NP), β-CrossLaps of type 1 collagen containing cross-linked C-telopeptide (β-CTX), and single nucleotide polymorphisms were determined. Locally weighted regression and smoothing scatterplot and segmented regression were performed to estimate the 25(OH)D thresholds. RESULTS: The median serum 25(OH)D was 16.63 (11.96-22.55) ng/mL and the prevalence of low serum 25(OH)D (< 12 ng/mL) was 25.2%. Women with the lowest 25(OH)D had the highest β-CTX. After adjustment for the confounders, 25(OH)D cutoffs for OC [14.04 (12.84-15.23) ng/mL], β-CTX [13.94 (12.49-15.39) ng/mL], and P1NP [13.87 (12.37-15.37) ng/mL] in the whole population, cutoffs for OC [12.30 (10.68-13.91) ng/mL], β-CTX [12.23 (10.22-14.23) ng/mL], and P1NP [11.85 (10.40-13.31) ng/mL] in women with the GC rs2282679 G allele, and cutoffs for OC [12.75 (11.81-13.68) ng/mL], β-CTX [13.05 (11.78-14.32) ng/mL], and P1NP [12.81 (11.57-14.06) ng/mL] in women with the GC rs2282679 T allele, were observed. Below these cutoffs, BTMs were negatively associated with 25(OH)D, while above these cutoffs, BTMs plateaued. CONCLUSION In Chinese women of childbearing age, there were thresholds effect of serum 25(OH)D concentrations on BTMs. The results indicated that serum 25(OH)D concentrations < 13.87 ng/mL in this population had adverse influences on maintaining bone remodeling. BTMs were suppressed at a relatively lower serum 25(OH)D in women with the GC rs2282679 G allele compared with those with the T allele.
Humans ; Female ; Vitamin D/blood* ; Adult ; Middle Aged ; Polymorphism, Single Nucleotide ; Adolescent ; Young Adult ; China ; Biomarkers/blood* ; Bone Remodeling/genetics* ; Vitamin D-Binding Protein/genetics* ; Procollagen/blood* ; Osteocalcin/blood* ; Peptide Fragments/blood* ; East Asian People