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.

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.

Background: We investigated osteoporosis management strategies and clinical outcomes following the occurrence of medicationrelated osteonecrosis of the jaw (MRONJ). Methods: We retrospectively studied individuals diagnosed with MRONJ during osteoporosis treatment who were managed in the Osteoporosis Center or the Oral Maxillofacial Surgery & Dental Unit at Queen Mary Hospital in Hong Kong between 2010 and 2022. We examined subsequent osteoporosis management plans, fracture events, and bone mineral density (BMD). Results: Thirty-six individuals were included (mean age, 78.5 years; 94.4% women). The estimated prevalence of MRONJ was 0.26%. All patients had been exposed to bisphosphonates, and seven had also received denosumab before MRONJ. Following MRONJ, only 14 individuals continued anti-osteoporosis treatment, a decision influenced by a higher fracture probability at MRONJ onset. The most common regimen was a teriparatide-raloxifene sequence (n=8): three patients achieved stable BMD, four achieved improving BMD, and one exhibited a mixed response. The patient with a mixed BMD response had also been treated with denosumab. Six patients sustained incident fractures after MRONJ, and these patients had lower BMD T-scores than their counterparts. Two patients experienced MRONJ recurrence, which was associated with the resumption of bisphosphonate or denosumab therapy after MRONJ. These patients had higher BMD T-scores than those who did not experience MRONJ recurrence. Conclusion MRONJ is challenging because high fracture risk necessitates discontinuation of antiresorptive agents. Teriparatide followed by raloxifene may be a reasonable regimen. Individualised decisions in osteoporosis management after MRONJ are required to balance fracture risk reduction with minimising MRONJ recurrence.

OBJECTIVE: To study the in vitro and in vivo antitumor effects of the polysaccharide of Alocasia cucullata (PAC) and the underlying mechanism. METHODS: B16F10 and 4T1 cells were cultured with PAC of 40 µg/mL, and PAC was withdrawn after 40 days of administration. The cell viability was detected by cell counting kit-8. The expression of Bcl-2 and Caspase-3 proteins were detected by Western blot and the expressions of ERK1/2 mRNA were detected by quantitative real-time polymerase chain reaction (qRT-PCR). A mouse melanoma model was established to study the effect of PAC during long-time administration. Mice were divided into 3 treatment groups: control group treated with saline water, positive control group (LNT group) treated with lentinan at 100 mg/(kg·d), and PAC group treated with PAC at 120 mg/(kg·d). The pathological changes of tumor tissues were observed by hematoxylin-eosin staining. The apoptosis of tumor tissues was detected by TUNEL staining. Bcl-2 and Caspase-3 protein expressions were detected by immunohistochemistry, and the expressions of ERK1/2, JNK1 and p38 mRNA were detected by qRT-PCR. RESULTS: In vitro, no strong inhibitory effects of PAC were found in various tumor cells after 48 or 72 h of administration. Interestingly however, after 40 days of cultivation under PAC, an inhibitory effect on B16F10 cells was found. Correspondingly, the long-time administration of PAC led to downregulation of Bcl-2 protein (P<0.05), up-regulation of Caspase-3 protein (P<0.05) and ERK1 mRNA (P<0.05) in B16F10 cells. The above results were verified by in vivo experiments. In addition, viability of B16F10 cells under long-time administration culture in vitro decreased after drug withdrawal, and similar results were also observed in 4T1 cells. CONCLUSIONS Long-time administration of PAC can significantly inhibit viability and promote apoptosis of tumor cells, and had obvious antitumor effect in tumor-bearing mice.
Mice ; Animals ; Alocasia/metabolism* ; MAP Kinase Signaling System ; Caspase 3/metabolism* ; Apoptosis ; RNA, Messenger/metabolism*

Slag is a typical metallurgical solid waste,mainly composed of magnesium oxide,iron oxide,alumina oxide and other metal oxides.The rapid quantitative analysis of slag components is helpful to determine the content of valuable elements or components in slag,and then choose a suitable resource utilization way to achieve efficient utilization and reduce environmental pollution.In this study,a quantitative analysis method of Fe,Si and Ti in slag was proposed based on laser induced breakdown spectroscopy(LIBS)combined with machine learning algorithm.Firstly,LIBS spectra of slag samples were collected,and the characteristic spectral lines of related elements were identified through the National Institute of Standards and Technology(NIST)database.Then,the influence of different spectral preprocessing methods on the predictive performance of PLS model was investigated,and the combined performance of spectral preprocessing methods was discussed.On this basis,a mixed variable selection algorithm combining variable importance in projection(VIP)and grey wolf algorithm(GWO)was proposed to screen LIBS spectral characteristic variables of slag samples.Based on cross-validation,the parameters,thresholds,input variables and model parameters of the preprocessing method and feature screening method were optimized.A quantitative analysis model of Fe,Si and Ti in slag based on LIBS technique was established based on the optimized parameters and input variables.The results showed that the optimized model had better prediction performance than the original spectral model,with R2p of 0.9525,0.9604 and 0.9972,and RMSEp of 0.0461,0.0141 and 0.1963,respectively.It was proved that LIBS combined with machine learning algorithm provided a feasible method for the field rapid detection of slag elements.The research is expected to provide some theoretical basis and technical reference for the resource utilization of metallurgical solid waste.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

Precision diagnosis is the keystone of clinical medicine. In East Asians, classical type 1 diabetes is uncommon in patients with youngonset diabetes diagnosed before age of 40, in whom a family history, obesity, and beta-cell and kidney dysfunction are key features. Young-onset diabetes affects one in five Asian adults with diabetes in clinic settings; however, it is often misclassified, resulting in delayed or non-targeted treatment. Complex aetiologies, long disease duration, aggressive clinical course, and a lack of evidence-based guidelines have contributed to variable care standards and premature death in these young patients. The high burden of comorbidities, notably mental illness, highlights the numerous knowledge gaps related to this silent killer. The majority of adult patients with youngonset diabetes are managed as part of a heterogeneous population of patients with various ages of diagnosis. A multidisciplinary care team led by physicians with special interest in young-onset diabetes will help improve the precision of diagnosis and address their physical, mental, and behavioral health. To this end, payors, planners, and providers need to align and re-design the practice environment to gather data systematically during routine practice to elucidate the multicausality of young-onset diabetes, treat to multiple targets, and improve outcomes in these vulnerable individuals.

The quality of moxa is a key factor affecting the efficacy of moxibustion. Traditional moxa grades are evaluated by the leaf-to-moxa ratio, but there is a lack of support from scientific data. Scanning electron microscopy(SEM), Image Pro Plus, Van Soest method, and stimultaneous thermal analysis(TGA/DSC) were used to characterize the scientific implication of the combustion differences between moxa with different leaf-to-moxa ratios(processed by crusher). The results showed that the median lengths from non-secretory trichomes(NSTs) of natural NSTs and moxa with leaf-to-moxa ratios of 3∶1, 5∶1, 10∶1, and 15∶1 were 542.46, 303.24, 291.18, 220.69, and 170.61 μm, respectively. The cellulose content of moxa increased significantly(P<0.05) with the increase in leaf-to-moxa ratio and the combustion parameters(T_i, t_i, D_i, C,-R_p,-R_v, S, D_b, and J_(total)) all showed an increasing trend. The correlation results showed that the burning properties of moxa(T_i,-R_v, t_i, and J_2) were significantly and positively correlated with cellulose content. NSTs with a length of 1-200 μm were significantly and positively correlated with J_2. NSTs with a length of 200-600 μm were significantly and positively correlated with J_1, T_(peak2), T_(peak1), and-R_v, and negatively correlated with J_(total), T_b, and t_b. As the leaf-to-moxa ratio increases, the NSTs in the moxa become shorter and the cellulose content increases, which is more conducive to ignition performance, heat release, and a milder, longer-lasting burn. The "NSTs-cellulose-TGA/DSC" quantitative evaluation method scientifically reveals the scientific connotation of the combustion of moxa with different leaf-to-moxa ratios and provides a scientific basis for the establishment of quality evaluation methods for moxa with different leaf-to-moxa ratios.
Trichomes ; Moxibustion ; Hot Temperature ; Plant Leaves