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 This study aimed to investigate the seasonal variation of mosquitoes in Yantai, Shandong Province, and provide a scientific basis for mosquito and mosquito-borne disease control. Methods From March to November for each of the years between 2020 and 2023, adult mosquitoes were collected from different habitats, including parks, hospitals, urban residential areas, rural households and livestock sheds, in 13 counties of Yantai City using a mosquito trap lamp method, among which, female mosquitoes were separated and identified, and the numbers were counted. We used the circular distribution method to analyze the temporal distribution characteristics of mosquito density. Results The γ values obtained for mosquitoes in Yantai from 2020 to 2023, the distribution of mosquitoes in different habitats, and dominant mosquito species in different months showed strong seasonality, with values ranging from 0.7461 to 0.8531(P ＜ 0.05). The peak period of seasonal variation in mosquitoes occurred from June 28thto September 13th, with August 5thbeing identified as the peak day. The peak periods of seasonal variation in mosquitoes in residential areas, parks, hospitals, peasant households, and animals sheds were observed from June 24thto September 14th, June 25thto September 10th, June 22ndto September 12th, July 1stto September 9th, and July 2ndto September 17th, respectively, with corresponding peak days on August 4th,3rd, 2nd, 5th, and 9th, respectively. The peak periods of Culex pipiens pallens, Aedes albopictus, and Anopheles sinensis were from June 26thto September 11th, June 28thto September 17th, and July 12thto September 17th, respectively, with corresponding peak days on August 3rd,7th, and 14th, respectively. Conclusions Our findings in this study revealed that the peak of mosquito activity in Yantai was concentrated in early August, with a peak period extending from June 28thto September 13th, and there were differences between different habitats(with peak periods for rural households and livestock sheds occurring later than those in residential areas, parks, and hospitals). Moreover, with respect to the mosquito species, populations of Cx. pipiens pallens and Ae. albopictus peaked earlier than those of An. sinensis. Based on these findings, we recommend that city-wide mosquito control work should commence in late June, and include appropriate early prevention and control measures for key habitats, such as livestock sheds, and dominant mosquito species, such as Cx. pipiens pallens, to minimize the density of mosquito vectors and the risk of disease transmission.

Ulcerative colitis（UC） is a chronic non-specific inflammatory bowel disease characterized by inflammation and ulceration of the colonic mucosa and submucosa， and its complex pathogenesis involves immune abnormality， oxidative stress and other factors. The nuclear transcription factor E₂-related factor 2（Nrf2）， encoded by the Nfe212 gene， plays a central role in antioxidant responses. It not only activates various antioxidant response elements such as heme oxygenase-1（HO-1） and quinone oxidoreductase 1（NQO1）， but also enhances the activity of glutathione-S-transferase（GST） and superoxide dismutase 1（SOD1）， effectively eliminating reactive oxygen species（ROS） accumulated in the body， and mitigating oxidative stress-induced damage to intestinal mucosa. In addition， Nrf2 can reduce the release of inflammatory factors and infiltration of immune cells by regulating immune response， cell apoptosis and autophagy pathways， thereby alleviating intestinal inflammation and promoting the repair and regeneration of damaged mucosa. Based on this， this paper reviews the research progress of Chinese materia medica in the prevention and treatment of UC by modulating the Nrf2 signaling pathway. It deeply explores the physiological role of Nrf2， the molecular mechanism of activation， the protective effect in the pathological process of UC， and how active ingredients in Chinese materia medica regulate the Nrf2 signaling pathway through multiple pathways to exert their potential mechanisms. These studies have revealed in depth that Chinese materia medica can effectively combat oxidative stress by regulating the Nrf2 signaling pathway. It can also play a role in anti-inflammatory， promoting autophagy， inhibiting apoptosis， protecting the intestinal mucosal barrier， and promoting intestinal mucosal repair， providing new ideas and methods for the multi-faceted treatment of UC.

ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties with communication and social interaction, restricted and repetitive behaviors. Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits, which are closely related to the core symptoms of ASD. Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities. Therefore, this study explores the behavior of children with ASD in capturing attention to changes in topological properties. MethodsOur study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing (TD) age-matched controls. In an attention capture task, we recorded the saccadic behaviors of children with ASD and TD in response to topological change (TC) and non-topological change (nTC) stimuli. Saccadic reaction time (SRT), visual search time (VS), and first fixation dwell time (FFDT) were used as indicators of attentional bias. Pearson correlation tests between the clinical assessment scales and attentional bias were conducted. ResultsThis study found that TD children had significantly faster SRT (P<0.05) and VS (P<0.05) for the TC stimuli compared to the nTC stimuli, while the children with ASD did not exhibit significant differences in either measure (P>0.05). Additionally, ASD children demonstrated significantly less attention towards the TC targets (measured by FFDT), in comparison to TD children (P<0.05). Furthermore, ASD children exhibited a significant negative linear correlation between their attentional bias (measured by VS) and their scores on the compulsive subscale (P<0.05). ConclusionThe results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection. This atypical attention may affect the child’s cognitive and behavioral development, thereby impacting their social communication and interaction. In sum, our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.

Epilepsy is a chronic neurological disease caused by abnormal synchronous discharge of the brain, which is characterized by recurrent and transient neurological abnormalities, mainly manifested as loss of consciousness and limb convulsions, and can occur in people of all ages. At present, anti-epileptic drugs (AEDs) are still the main means of treatment, but their efficacy is limited by the problem of drug resistance, and long-term use can cause serious side effects, such as cognitive dysfunction and vital organ damage. Although surgical resection of epileptic lesions has achieved certain results in some patients, the high cost and potential risk of neurological damage limit its scope of application. Therefore, the development of safe, accurate and personalized non-invasive treatment strategies has become one of the key directions of epilepsy research. In recent years, photobiomodulation (PBM) has gained significant attention as a promising non-invasive therapeutic approach. PBM uses light of specific wavelengths to penetrate tissues and interact with photosensitive molecules within cells, thereby modulating cellular metabolic processes. Research has shown that PBM can enhance mitochondrial function, promote ATP production, improve meningeal lymphatic drainage, reduce neuroinflammation, and stimulate the growth of neurons and synapses. These biological effects suggest that PBM not only holds the potential to reduce the frequency of seizures but also to improve the metabolic state and network function of neurons, providing a novel therapeutic avenue for epilepsy treatment. Compared to traditional treatment methods, PBM is non-invasive and avoids the risks associated with surgical interventions. Its low risk of significant side effects makes it particularly suitable for patients with drug-resistant epilepsy, offering new therapeutic options for those who have not responded to conventional treatments. Furthermore, PBM’s multi-target mechanism enables it to address a variety of complex etiologies of epilepsy, demonstrating its potential in precision medicine. In contrast to therapies targeting a single pathological mechanism, PBM’s multifaceted approach makes it highly adaptable to different types of epilepsy, positioning it as a promising supplementary or alternative treatment. Although animal studies and preliminary clinical trials have shown positive outcomes with PBM, its clinical application remains in the exploratory phase. Future research should aim to elucidate the precise mechanisms of PBM, optimize light parameters, such as wavelength, dose, and frequency, and investigate potential synergistic effects with other therapeutic modalities. These efforts will be crucial for enhancing the therapeutic efficacy of PBM and ensuring its safety and consistency in clinical settings. This review summarizes the types of epilepsy, diagnostic biomarkers, the advantages of PBM, and its mechanisms and potential applications in epilepsy treatment. The unique value of PBM lies not only in its multi-target therapeutic effects but also in its adaptability to the diverse etiologies of epilepsy. The combination of PBM with traditional treatments, such as pharmacotherapy and neuroregulatory techniques, holds promise for developing a more comprehensive and multidimensional treatment strategy, ultimately alleviating the treatment burden on patients. PBM has also shown beneficial effects on neural network plasticity in various neurodegenerative diseases. The dynamic remodeling of neural networks plays a critical role in the pathogenesis and treatment of epilepsy, and PBM’s multi-target mechanism may promote brain function recovery by facilitating neural network remodeling. In this context, optimizing optical parameters remains a key area of research. By adjusting parameters such as wavelength, dose, and frequency, researchers aim to further enhance the therapeutic effects of PBM while maintaining its safety and stability. Looking forward, interdisciplinary collaboration, particularly in the fields of neuroscience, optical engineering, and clinical medicine, will drive the development of PBM technology and facilitate its transition from laboratory research to clinical application. With the advancement of portable devices, PBM is expected to provide safer and more effective treatments for epilepsy patients and make a significant contribution to personalized medicine, positioning it as a critical component of precision therapeutic strategies.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective:To explore the clinical characteristics and prognosis risk factors of aggressive NK-cell leukemia(ANKL).Methods:The clinical and laboratory data of 34 patients with ANKL and 15 patients with chronic lymphoproliferative disorders of NK cells(CLPD-NK)admitted to the First Affiliated Hospital of Zhengzhou University from September 2019 to December 2024 were retrospectively analyzed.The Kaplan-Meier method was used to calculate survival rates,and the Cox proportional hazards regression model was used to analyze prognostic factors.Results:Compared with CLPD-NK patients,ANKL patients had a younger median age of onset,a higher proportion patients with EBV-DNA≥500 copies/ml,hepatosplenomegaly and hemophagocytic syndrome.They also presented with a higher peak of fever,a shorter median survival time,lower WBC count,PLT count,ALB and Fib values,while having higher LDH,AST,TG,ferritin,CRP and PCT levels.There were statistically significant differences in the morphology and expression of HLA-DR,CD56,CD57,CD16 and CD158 on abnormall cells between ANKL patients and CLPD-NK patients.Multivariate survival analysis revealed that combined with asparaginase treatment could improve patients' survival,and CRP≥ 15 mg/L and Fib＜2.0 g/L were independent risk factors affecting the overall survival of patients with ANKL.Conclusion:The differences in clinical features and laboratory tests between patients with ANKL and CLPD-NK aid in the diagnosis of ANKL.CRP and Fib levels can be used to predict the prognosis of patients,and combined asparaginase therapy can enhance the overall survival of patients.

Objective To investigate the predictive value of cerebroplacental ratio(CPR),cerebro-uterine ratio(CUR)combined with cystatin C(CysC)for adverse pregnancy outcomes in preeclampsia(PE)fetuses.Methods A retrospective analysis was conducted on the clinical data of 150 PE patients admitted to the department of obstetrics and gynecology of Nuclear Industry 416 Hospital from January 2019 to December 2024,and patients were divided into the adverse-outcome group and the favorable-outcome group according to pregnancy outcomes.The clinical data,CPR,CUR and CysC level were compared between the two groups.Multivariate Logistic regression was used to analyze the independent influencing factors of adverse pregnancy outcome in PE patients;then,receiver operating characteristic(ROC)curve analysis was performed to evaluate the predictive efficiency of each index on adverse pregnancy outcome.Results The adverse-outcome group had shorter/lower gestational age at diagnosis,estimated fetal body weight,CUR,and CPR,but higher body mass index and CysC level compared to those in the favorable-outcome group,with significant differences(P<0.05).Multivariate Logistic regression identified that the elevated CysC level,and decreased CUR and CPR were related influencing factors for adverse pregnancy outcome(P<0.05).ROC curve analysis demonstrated that CUR,CPR and CysC had strong predictive value for adverse pregnancy outcome,with the area under the curve(AUC)of 0.802(95%CI:0.729 to 0.863),0.834(95%CI:0.764 to 0.890),and 0.791(95%CI:0.717 to 0.853),respectively;the combined prediction of CUR,CPR and CysC had grater AUC of 0.909(95%CI:0.851 to 0.950)than the individual prediction of the above three indicators(P<0.05).Conclusion CUR,CPR and CysC are influencing factors of adverse pregnancy outcome in PE patients,and their combined detection demonstrates good predictive value for adverse pregnancy outcome in PE patients.