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.

A unified consensus statement on medication-related osteonecrosis of the jaw (MRONJ) has not yet been established among the Asian member countries or regions of the Asian Federation of Osteoporosis Societies (AFOS). This study aimed to develop a consensus on MRONJ in patients with osteoporosis across these countries and regions. In this study, the term “Asia-Pacific” refers specifically to the Asian member countries and regions of AFOS. A structured survey consisting of nine MRONJ-related questions was distributed across 10 countries and regions to assess the level of agreement and summarize regional perspectives. In addition, a manual literature review and voting were conducted to evaluate the current evidence on MRONJ. The key aspects of MRONJ, including definition, staging, diagnosis, pathogenesis, risk factors, management, and prevention, were generally consistent among the AFOS countries and regions. The annual incidence and incidence rate of MRONJ associated with low-dose antiresorptive therapy in patients with osteoporosis ranged from 0.025% to 0.136% and 21 to 283 cases per 100,000 person-years, respectively. However, evidence regarding the benefits of drug discontinuation before dental surgery, such as tooth extraction, remains insufficient. Large-scale, multinational studies across AFOS countries and regions are warranted to determine the incidence of MRONJ better and evaluate the impact of antiresorptive drug discontinuation before dental procedures. These findings may contribute to the devel opment of effective evidence-based strategies for preventing MRONJ in patients with osteoporosis.

Background/Aims: Acetyl coenzyme A (acetyl-CoA) is one of the most essential metabolites in cell metabolism but its function and concentration in hepatocellular carcinoma (HCC) remain elusive and controversial. Methods: A comprehensive analysis of acetyl-CoA levels and acetyl-CoA synthetase 2 (ACSS2) expression across a range of samples, including patient specimens from both hepatitis B virus (HBV) positive and HBV negative HCC individuals, HBV-transgenic mouse HCC models, and multiple cell lines. Furthermore, to evaluate the functional significance of ACSS2 in HBV-related HCC, we implemented both genetic and pharmacological inhibition strategies targeting ACSS2. Molecular mechanism and mitophagy assessment were revealed by cleavage under target and tagmentation sequencing, RNA sequencing, bioinformatic analyses, transmission electron microscopy and JC-1 staining. Results: Our study revealed a distinct metabolic signature of HBV-related HCC, marked by elevated acetyl-CoA, which was driven by ACSS2. ACSS2 was upregulated by the carbohydrate response element-binding protein in HBV-related HCC. Furthermore, ACSS2 improved tumor cell proliferation, an effect that was dependent on its enzymatic activity. Mechanistically, ACSS2-induced acetyl-CoA accumulation activated voltage-dependent anion channels 1 transcription through increased H3K27ac occupancy, which subsequently promoted mitophagy and HBV-related HCC tumorigenesis. Notably, targeting ACSS2 by depletion or inhibition with a catalytic inhibitor significantly suppressed tumor growth. Conclusions These findings not only illustrate the interplay between metabolic reprogramming, epigenetic modification, and tumorigenesis in the context of HBV infection, but also highlight ACSS2 as a novel metabolic vulnerability in HBV-related HCC. Therefore, targeting ACSS2 could be a novel strategy against HBV-related HCC.

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.

ObjectiveTo evaluate the comparative clinical outcomes of zero-profile interbody fusion （Zero-P） and traditional anterior cervical discectomy and fusion with titanium plate （ACDF-plate） for three-level cervical spondylosis， providing a reference to optimize surgical strategies for three-level cervical spondylosis. MethodsIn this retrospective comparative study， 113 patients were included and assigned to either the Zero-P group （n=51） or the ACDF-plate group （n=62） according to their surgical approaches. Clinical outcomes were evaluated and compared between the two groups using operative time， intraoperative blood loss， length of hospital stay， visual analog scale （VAS） scores， cervical range of motion （ROM）， neck disability index （NDI）， Cervical Disability Index （CDI）， Japanese orthopaedic association （JOA） scores， and postoperative complication rates. ResultsCompared with the ACDF-plate group， the Zero-P group had a shorter operative time and less intraoperative blood loss （P<0.05）； the neck pain VAS score was lower on the 1st day and 1st week after surgery （P<0.05）； the cervical flexion range of motion was higher at 1 month after surgery （P<0.05）； the JOA score was higher at 3 months after surgery， while the NDI and CDI scores were lower （P<0.05）； the total incidence of complications （11.76%） was significantly lower than that in the plate fixation group （35.48%）， and the incidence of neck foreign body sensation/stiffness was lower （P<0.05）. ConclusionZero-P not only demonstrates safety and efficacy comparable to the ACDF-plate， but also highlights the significant value of the minimally invasive concept in facilitating early recovery. Both procedures have their respective emphasis on biomechanical maintenance and minimally invasive benefits， providing a clear evidence-based reference for individualized and precise surgical decision-making in three-level cervical spondylosis.

Objective To explore the reproductive health literacy of young female sailors,investigate the influence of long voyage on the health,and propose strategies on health education.Methods A total of 49 young female sailors were enrolled.Their healthy status was evaluated by annual medical records.The questionnaire survey was also conducted to evaluate general status related to reproductive health,knowledge pertaining to reproductive health,and health status after long voyage.Results Some female sailors had symptoms of thyroid diseases,such as hypothyroidism(32.65%),benign thyroid nodules(59.18%),and suspected papillary carcinoma(in one sailor,2.04%).Ultrasonography revealed breast nodules in 5 subjects(10.00%),mammary gland hyperplasia in 46 subjects(93.88%)and reproductive benign diseases in 6 subjects(12.24%).The physical and mental states significantly changed in the late stage of long voyage.The changes in work and living environment were the main factors affecting the emotional and psychological status in the long voyage.The discomfort in menstrual cycle piled up with the above-mentioned changes during a long voyage,and the menstruation significantly affected the duty of 36.73%of female sailors during a long voyage.Female sailors had little knowledge about the prevention and examination of breast cancer and thyroid cancer.Conclusion The living and work environment during a long voyage may affect the health of the thyroid,mammary gland and reproductive system of young female sailors.Active health education via online and offline ways is helpful to improve the health literacy and self-care of female sailors,subsequently enhancing the combat effectiveness.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

OBJECTIVE: To explore the immunophenotypic characteristics of bone marrow granulocytes (G) and their clinical significance in patients with multiple myeloma (MM). METHODS: The granulocyte immunophenotypes of bone marrow in 70 MM patients (MM group) and 40 anemia patients (control group) were detected by flow cytometry, and its correlation with clinical characteristics was further analyzed. Univariate and multivariate regression analysis were used to screen factors that affected prognosis. RESULTS: The CD56⁺G%, CD13⁺G%, CD22⁺G% and CD117⁺G% in MM group were higher than those in the control group (all P <0.05). CD56⁺G% and CD117⁺G% in CR⁺VGPR group were significantly lower than those in PR+MR+PD group (both P <0.05). The CD10⁺G% in RISS Ⅲ stage and Ca²⁺ ≥2.65 mmol/L groups were increased (both P <0.05). The CD56⁺G% in elevated lactate dehydrogenase, β₂-microglobulin≥5.5 mg/L and hemoglobin <85 g/L groups were increased (all P <0.05), while the CD117⁺G% in high-risk cytogenetic positive group was decreased (P <0.05). The expression rate of CD molecules on granulocytes was divided into low (L) and high (H) groups according to the median value. The overall survival (OS) of the LCD56⁺G%, LCD13⁺G% and LCD22⁺G% groups was significantly prolonged (all P <0.05). CD13⁺G% and CD22⁺G% were independent risk factors for OS in MM patients (HR=0.443, 0.410, both P <0.05). CONCLUSION The CD56⁺G%, CD10⁺G% and CD117⁺G% are closely correlated with clinical features in MM patients, while CD13⁺G% and CD22⁺G% are closely correlated with prognosis. Detection of CD molecules expression on granulocytes may be used to evaluate prognosis and guide treatment.
Humans ; Multiple Myeloma/immunology* ; Granulocytes/immunology* ; Prognosis ; Immunophenotyping ; Male ; Bone Marrow ; Female ; Flow Cytometry ; Middle Aged ; Aged ; Clinical Relevance

OBJECTIVE: To investigate the effect of non-chemotherapy strategy of retinoic acid (ATRA) combined with arsenic trioxide (ATO) on the survival of patients with acute promyelocytic leukemia (APL). METHODS: The data of APL patients with complete information diagnosed in the hematology department of our hospital from June 2009 to November 2024 were retrospective analyzed. All patients in the non-CHT group received ATRA-ATO induction, consolidation and maintenance therapy. Patients in the CHT group received ATRA-ATO+chemotherapy induction therapy, followed by 3 cycles of ATRA-ATO+CHT consolidation therapy and 6-10 cycles of ATRA-ATO maintenance therapy. The primary endpoint was event-free survival (EFS). Secondary endpoints included overall survival (OS), remission rate, differentiation syndrome (DS) and safety. RESULTS: There were 182 patients with APL and 15 patients with early death (ED), accounting for 8.24%, which was related to age and risk stratification. There was no significant difference in remission rate between the non-CHT group and the CHT group (P =0.486). As of February 2025, the median follow-up time of patients was 39.5 months. The EFS of the non-CHT group was significantly better than that of the CHT group (P =0.038). There was no significant difference in OS between the two groups (P =0.442). Subgroup analysis showed that EFS in the non-CHT was longer in standard-risk patients (P =0.012). There was no significant difference in EFS (P =0.585) and OS (P =0.473) between the CHT and non-CHT groups in high-risk patients. The incidence of mild DS was 23.6% in the non-CHT group and 23.1% in the CHT group, respectively, with no statistically significant difference(P =0.937). Compared with CHT group, the incidence of serious adverse events was lower in the non-CHT group. CONCLUSION The non-chemotherapy regimen of ATRA combined with ATO is a feasible method to cure APL patients.
Humans ; Leukemia, Promyelocytic, Acute/drug therapy* ; Arsenic Trioxide/therapeutic use* ; Tretinoin/administration & dosage* ; Retrospective Studies ; Female ; Treatment Outcome ; Male ; Adult ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Middle Aged ; Remission Induction

OBJECTIVE: To screen anti-tumor drugs that improve antigen processing and presentation in acute myeloid leukemia (AML) cells. METHODS: A TCR-like or TCR mimic antibody that can specifically recognize HLA-A*0201:WT1_126-134 ( RMFPNAPYL) complex (hereafter referred to as HLA-A2:WT1) was synthesized to evaluate the function of antigen processing and presentation machinery (APM) in AML cells. AML cell line THP1 was incubated with increasing concentrations of IFN-γ, hypomethylating agents (HMA), immunomodulatory drugs (IMiD), proteasome inhibitors (PI) and γ-secretase inhibitors (GSI), followed by measuring of HLA-ABC, HLA-A2 and HLA-A2:WT1 levels by flow cytometry at consecutive time points. RESULTS: The TCR-like antibody we generated only binds to HLA-A*0201⁺WT1⁺ cells, indicating the specificity of the antibody. HLA-A2:WT1 level of THP-1 cells detected with the TCR-like antibody was increased significantly after co-incubation with IFN-γ, showing that the HLA-A2:WT1 TCR like antibody could evaluate the function of APM. Among the anti-tumor agents screened in this study, GSI (LY-411575) and HMA (decitabine and azacitidine) could significantly increase the HLA-A2:WT1 level. The IMiD lenalidomide and pomalidomide could aslo upregulate the expression of HLA-A2:WT1 complex under certain concentrations of the drugs and incubation time. As proteasome inhibitors, carfilzomib could significantly decreased the expression of HLA-A2:WT1, while bortezomib had no significant effect on HLA-A2:WT1 expression. CONCLUSION HLA-A2:WT1 TCR-like antibody can effectively reflect the APM function. Some of the anti-tumor drugs can affect the APM function and immunogenicity of tumor cells.
Humans ; Leukemia, Myeloid, Acute/immunology* ; Antineoplastic Agents/pharmacology* ; Antigen Presentation/drug effects* ; HLA-A2 Antigen/immunology* ; Receptors, Antigen, T-Cell/immunology* ; Cell Line, Tumor ; Interferon-gamma