ObjectiveStroke poses a heavy burden due to its high mortality and morbidity rates. Accurate and real-time detection of lesions is pivotal for prompt clinical intervention and favorable prognosis. Electrical impedance tomography (EIT) and microwave tomography (MWT) have emerged as compelling alternatives for stroke screening, owing to their non-ionizing, non-invasive and portable nature. EIT provides information on tissue conductivity, and MWT offers high sensitivity to changes in dielectric properties. However, single-modality imaging is inherently limited, EIT suffers from low sensitivity to deep-seated tissues and severe ill-posedness of inverse problems, whereas MWT is challenged by strong nonlinearity in inverse scattering and susceptibility to modeling errors. Consequently, the clinical utility of standalone EIT or MWT for stroke diagnosis remains constrained by poor spatial resolution and imaging artifacts. To improve the accuracy and robustness of stroke imaging, a dual-modality fusion conditional denoising diffusion probabilistic model (DM-DDPM) was proposed for high-precision dual-modality image reconstruction. MethodsA dual-encoder network with a symmetric architecture and independently trained parameters was constructed to extract heterogeneous features separately from EIT boundary voltage measurements and MWT scattered field signals. Attentional feature fusion (AFF) is employed to integrate complementary information from the two modalities adaptively, generating robust fused priors that suppress redundant noise while preserving key physical characteristics. Subsequently, the fused priors are embedded into a Transformer-based diffusion model via a cross attention mechanism to guide the reverse denoising process. This approach effectively reduces artifacts and enhances the stability of conductivity distribution reconstruction. Time step embedding is introduced to enable the network to perceive the diffusion stage and further improve the accuracy of noise prediction. ResultsSimulated experiments demonstrated that DM-DDPM significantly outperforms single-modality and multi-modality networks under various noise levels. A head model simulation dataset was constructed based on COMSOL Multiphysics, and tests were carried out under 50 dB, 40 dB and 30 dB signal-to-noise ratio levels. At 30 dB, the average relative error (RE) was below 0.20, while the structural similarity index measure (SSIM) and correlation coefficient (CC) remained above 0.90 and 0.89, respectively. Compared with single-modality and multi-modality networks, artifacts were significantly reduced, lesion edges were clearer, and localization was more accurate. The model maintains high reconstruction quality and strong robustness for single, double, and triple lesions simultaneously. Furthermore, physical experiments were conducted using a 16-electrode EIT system and a 16-antenna MWT system with asynchronous data acquisition. These experiments confirmed the feasibility of the method in real-world scenarios and demonstrated that it can robustly reconstruct simulated lesions despite environmental interference and measurement noise, validating its reliability for practical clinical applications. ConclusionThe proposed method effectively combines complementary dual-modality information with a conditional diffusion model. Low accuracy and poor noise resistance in single-modality imaging were effectively addressed, while the noise amplification issue caused by direct multimodal data fusion was avoided. The proposed algorithm exhibits strong anti-noise interference ability and high imaging stability in both simulation and physical experiments. Precise localization of stroke lesions with different quantities was achieved, providing a high-precision, and practical technical support for clinical stroke detection.

Objective To evaluate the effect of donor gender on short-term survival rate of lung transplant recipients. Methods A retrospective analysis was conducted on the data of 1 066 lung transplant recipients. The log-rank test was used to evaluate the differences in short-term fatality among different donor gender groups and donor-recipient gender combination groups. Multivariate Cox regression, propensity score (PS) regression, and propensity score matching (PSM) were employed to control for confounding factors and further assess the differences in fatality. Subgroup analyses were also performed based on donor gender. Results Multivariate Cox regression analysis showed no statistically significant differences in fatality at 30 days, 1 year, 2 years and 3 years postoperatively between male and female donor groups (all P>0.05). After PS regression and PSM, univariate Cox regression analysis indicated that recipients from female donors had a higher fatality at 2 years postoperatively compared to those from male donors, with hazard ratios (95% confidence intervals) of 1.29 (1.01-1.65) and 1.36 (1.03-1.80) respectively. Multivariate Cox regression analysis also revealed no statistically significant differences in fatality at various follow-up time points among different donor-recipient gender combination groups (all P>0.05). Subgroup analyses based on donor sex showed no statistically significant differences in fatality among recipients of different gender within either male or female donor groups (all P>0.05). Conclusions Female donors may reduce the short-term postoperative survival rate of lung transplant recipients, but this negative impact is not sustainable in the long term. At present, there is no evidence to support the inclusion of sex as a factor in lung allocation rules.

Objective To analyze the basic conditions and pathological characteristics of the samples in the Human Brain Bank of Hebei Medical University,which were pathologically diagnosed as cerebral amyloid angiopathy,and to provide reference for the research of related diseases.Methods The basic data of gender,age,apolipoprotein E genotype,pathological classification of cerebral amyloid angiopathy,Alzheimer's disease-related pathological change score,comorbidities and other pathological information were analyzed.Results Up to October 2024,twenty samples were confirmed by pathological diagnosis,with a male to female ratio of 3:1 and an average age of(80.90±8.08)years.Involve three kinds of apolipoprotein E subtype,5 kinds of genotypes(ε2/ε3 xε2/ε4、ε3/ε3 xε3/ε4、ε4/ε4);There were 2 pathologic types,including 6 cases of type 1 and 14 cases of type 2.The pathological grade included 3 grades.The severity grade and subtype classification of cerebral amyloid vascular disease were correlated with the degree of pathological changes of Alzheimer's disease.Cerebral amyloid angiopathy samples could coexist with other degenerative diseases with high comorbidity.Conclusion The incidence of cerebral amyloid angiopathy is higher in the aged samples collected based on Brain Bank,which coexists with conditions such as Alzheimer's disease and microbleeds,etc.It provides more detailed pathological diagnosis basis for further scientific research sharing of samples.

Lung transplantation is an important means to treat end-stage lung disease and improve the survival rate and quality of life of patients. However, many postoperative complications seriously affect the prognosis of recipients. Accurate identification of key prognostic factors and construction of individualized and accurate prediction models are of great significance for postoperative prognosis evaluation, treatment strategy formulation and clinical decision-making. In recent years, the clinical prediction model of lung transplantation has gradually changed from traditional statistical methods to machine learning-driven. Compared with traditional models such as Cox regression and Logistic regression, machine learning models such as random forest, support vector machine and artificial neural network have certain advantages in postoperative survival rate prediction, early warning of complications and pulmonary function evaluation. However, their application is also affected by insufficient sample size and poor interpretability of models. Under the condition of small samples, the traditional model still has important value in prediction accuracy. The appropriate prediction model should be selected according to the clinical status of lung transplantation in China, considering the factors such as sample size, variable complexity and model interpretability. In the future, a multi-center, large-sample lung transplantation database should be constructed to further optimize and tap the potential of machine learning algorithms to improve the robustness and clinical applicability of the model.

[Objective] To explore the relationship between neutrophil activation under cardiopulmonary bypass (CPB) and the incidence of cardiac surgery-associated acute kidney injury (CS-AKI). [Methods] This prospective cohort study enrolled adult patients who scheduled for cardiac surgery under CPB at West China Hospital between May 1, 2022 and March 31, 2023. The primary outcome was acute kidney injury (AKI). Blood samples (5 mL) were obtained from the central vein before surgery, at rewarming, at the end of CPB, and 24 hours after surgery. Neutrophils were labeled with CD11b, CD54 and other markers. To assess the effect of neutrophils activation on AKI, propensity score matching (PSM) was employed to equilibrate covariates between the groups. [Results] A total of 120 patients included into the study, and 17 (14.2%) developed AKI. Both CD11b⁺ and CD54⁺ neutrophils significantly increased during the rewarming phase and the increases were kept until 24 hours after surgery. During rewarming, the numbers of CD11b⁺ neutrophils were significantly higher in AKI compared to non-AKI (4.71×10⁹/L vs 3.31×10⁹/L, Z=-2.14, P<0.05). Similarly, the CD54⁺ neutrophils counts were also significantly higher in AKI than in non-AKI before surgery (2.75×10⁹/L vs 1.79×10⁹/L, Z=-2.99, P<0.05), during rewarming (3.12×10⁹/L vs 1.62×10⁹/L, Z=-4.34, P<0.05), and at the end of CPB (4.28×10⁹/L vs 2.14×10⁹/L, Z=-3.91, P<0.05). An analysis of 32 matched patients (16 in each group) revealed that CD11b⁺ and CD54⁺ neutrophil levels of AKI were 1.74 folds (4.83×10⁹/L vs 2.77×10⁹/L, Z=-2.72, P<0.05) and 2.34 folds (3.32×10⁹/L vs 1.42×10⁹/L, Z=-4.12, P<0.05), respectively, of non-AKI at rewarming phase. [Conclusion] Neutrophils are activated during CPB, and they can be identified by CD11b/CD54 markers. The activated neutrophils of AKI patients are approximately 2 folds of non-AKI during the rewarming phase, with disparity reached peak between groups during rewarming. These findings suggest the removal of 50% of activated neutrophils during the rewarming phase may be effective to reduce the risk of AKI.

To analyze the disease burden, temporal trends, and attributable risk factors of early-onset female breast cancer (EOBC) in China and globally from 1990 to 2021.

The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

From July 23, 2018, to December 31, 2019, a total of 299 219 children in Shandong Province received the fifth dose of the diphtheria, tetanus, and acellular pertussis combined vaccine (DTaP). Among these recipients, the distribution by age was as follows: 20 children under 2 years old (0.01%), 273 996 children aged 2 years (91.57%), 20 242 children aged 3 years (6.76%), 3 932 children aged 4 years (1.31%), 963 children aged 5 years (0.32%), and 66 children aged 6 years and above (0.02%). In total, 1 972 cases of adverse events following immunization (AEFI) were reported after the administration of the fifth dose of DTaP, resulting in an incidence rate of 659.05 per 100 000 doses. Among these, 1 718 cases were classified as common vaccine reactions, with an incidence rate of 574.16 per 100 000 doses, while 247 cases were identified as rare reactions, yielding an incidence rate of 82.55 per 100 000 doses. The incidence of AEFIs, as well as the rates of common and rare reactions, exhibited a significant increasing trend with the number of doses administered (all P<0.001). Among the rare reactions, there were 10 cases classified as severe, resulting in a reported incidence of 3.34 per 100 000 doses.

OBJECTIVE: To explore clinical efficacy of bone cement filling combined with lower extremity arterial balloon dilation in treating Wagner grade Ⅳ diabetic foot (DF). METHODS: From January to October 2024, 9 Wagner grade Ⅳ DF patients with lower extremity vascular occlusion were admitted, including 7 males and 2 females, aged from 51 to 87 years old;5 patients on the left side and 4 patients on the right side. All patients were underwent stageⅠdebridement of the affected foot and bone cement filling, and treated with lower extremity arterial balloon dilation after operation, they were. After the formation of the induced membrane, stageⅡwound repair was performed. The wound healing time and condition were observed. Ankle-brachial index (ABI) was used to evaluate the lower extremity vascular perfusion before operation and 3 months after operation, respectively. RESULTS: The wounds of all 9 patients healed completely, and the healing time ranged from 45 to 65 days. All patients were followed up for at least 6 months without recurrence. The skin of the affected foot wound healed with keratinization, and there was mild scar hyperplasia locally (1 patient had necrosis of the adjacent toe after stageⅠsurgery and was debridement and toe amputation again). The narrowed or occluded blood vessels of the lower extremities were all recanalized. ABI recovered from 0.3 to 0.5 before operation to 1.0 to 1.1 at 3 months after operation. CONCLUSION Bone cement filling combined with lower extremity arterial balloon dilation for the treatment of grade Wagner Ⅳ DF is conducive to promoting healing of the affected foot, effectively preventing secondary ulceration of the affected foot, and clinical therapeutic effect is satisfactory.
Humans ; Male ; Female ; Middle Aged ; Diabetic Foot/surgery* ; Aged ; Bone Cements/therapeutic use* ; Aged, 80 and over ; Lower Extremity/blood supply*