Background/Aims: Pancreatic stenting reduces post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP) and aids in cannulation in difficult cases. However, conservative endoscopists may stop at this step, resulting in suboptimal outcomes. This study assessed the efficacy of transpancreatic precut sphincterotomy (TPS) as a rescue procedure following pancreatic stenting. Methods: Between March 2013 and November 2018, 82 patients underwent pancreatic stenting at our institution prior to successful biliary cannulation. TPS was introduced in April 2016, and patients were divided into Before TPS and After TPS groups. The outcomes included cannulation success, PEP incidence, and predictors of TPS conversion. Results: There were 43 and 39 patients in the Before TPS and After TPS groups, respectively. Twenty-two patients (56.4%) underwent conversion to TPS in the After TPS group. The After TPS group had a higher bile duct cannulation rate (89.7% vs. 72.1%) than the Before TPS group, but this difference was not statistically significant (p=0.054). Multivariate analysis showed that age >50 years (odds ratio [OR], 0.181; p=0.021) and being in the After TPS group (OR, 0.712; p=0.039) were independently associated with reduced PEP risk. Haraldsson Type 2 and Type 4 papillae carried a relatively high TPS conversion rate. Conclusions A stepwise cannulation strategy that incorporates TPS after pancreatic stenting minimizes the need for advanced techniques and improves PEP outcomes.

Background: Cerebral hypoxia-ischemia impairs brain development in extremely preterm infants and is associated with poor neurological outcomes. Near-infrared spectroscopy (NIRS) is a noninvasive continuous monitoring method for regional cerebral oxygen saturation (rcSO2).Purpose: This study evaluated the clinical feasibility and neurological impact of a neurocritical care bundle that incorporates prolonged multidisciplinary hemodynamic monitoring and a stepwise management algorithm. Methods: Preterm infants with a gestational age (GA) ≤28 weeks or birth weight (BW) ≤1,000 g were prospectively enrolled in a bundle group subjected to NIRS for rcSO2, electrical cardiometry for cardiac output, and daily brain and cardiac echography during the first 72 hours of life. Monitoring was repeated weekly in the first month and then monthly until discharge or the term-equivalent age (TEA) was reached. We implemented a stepwise management algorithm for treating cerebral hypoxia. The primary outcome was a composite of mortality and adverse neurological events (structural abnormalities or electroencephalogram-confirmed seizures) before discharge. The secondary outcomes were the physiological pattern of rcSO2 within the initial 72 hours and up to discharge or TEA. Results: Thirty preterm infants (GA, 27.1±2.0 weeks; BW, 830±225 g) were enrolled in the bundle group. The mean time-averaged rcSO2 (66.8%±10.3%) was not associated with GA or BW. However, postnatal age appeared to influence physiological rcSO2 changes, given that rcSO2 values were higher during the initial 72 hours than at subsequent intervals. Seven infants (23.3%) had poor outcomes and significantly lower time-averaged rcSO2 (51.1% [50.0%–65.2%] vs. 71.8% [67.1%–73.1%], P=0.002). Multivariate regression indicated that a lower rcSO2 was an independent risk factor, and a 65% threshold showed an optimal predictive value for poor outcomes. Conclusion The neurocritical care bundle helped identify preterm infants at risk of cerebral hypoxia, and lower rcSO2 was an independent risk factor for composite mortality and adverse neurological outcomes.

The zebrafish has emerged as a powerful model organism in life science owing to its remarkable biological characteristics and wide-ranging applications. This review provides a comprehensive overview of the recent advancements in research on zebrafish within the field of environmental toxicology, highlighting specific studies where this species was used to investigate various pollutants to elucidate their impacts and underlying mechanisms. The findings of these studies underscore the significant potential of zebrafish as a model to gain crucial insights into the ecological consequences of environmental contamination and toxicity pathways. By incorporating cutting-edge technologies such as artificial intelligence (AI), high-throughput screening, and omics approaches, the use of zebrafish as a model organism is poised to significantly accelerate toxicological investigations, promote environmental conservation efforts, contribute to safeguarding human health, and advance sustainable development objectives.
Animals ; Zebrafish ; Ecotoxicology/methods* ; Artificial Intelligence ; Humans ; Models, Animal ; Environmental Pollutants/toxicity*

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.

Objectives: The effectiveness of combining romosozumab (ROMO) with denosumab (Dmab) remains uncertain.We compare the six-month effects of Dmab plus monthly ROMO versus Dmab plus daily teriparatide (TPTD) on bone mineral density (BMD) in treatment-naïve postmenopausal women with osteoporosis. Methods: This retrospective cohort study analyzed 26 treatment-naïve postmenopausal women with primary osteoporosis. Participants received either a monthly regimen of ROMO and Dmab (N = 14) or a daily regimen of TPTD plus Dmab (N = 12). BMD at the lumbar spine, total hip, and femoral neck was measured at baseline, 3 months, and 6 months by dual-energy X-ray absorptiometry. Serum levels of C-terminal telopeptide (CTX) and procollagen type I N-terminal propeptide (P1NP) were assessed at the same intervals. Results: Both regimens significantly improved lumbar spine BMD at 6 months (ROMO + Dmab: +9.75%; TPTD +Dmab: +7.42%). Improvements in total hip and femoral neck BMD were modest and similar between groups (~2%). Serum CTX and P1NP were significantly suppressed in both groups at 3 months, but P1NP suppression waned in the TPTD + Dmab group by 6 months. No statistically significant differences in BMD or marker changes were detected between the two regimens. Conclusions Both combination therapies effectively improve lumbar spine BMD over 6 months. The ROMO +Dmab regimen yielded numerically greater increases with fewer injections.

Epstein-Barr virus (EBV)-associated lymphomas can, on rare occasions, involve body cavities, making effusion cytol‑ ogy an important diagnostic tool. This mini-review explores the spectrum of EBV-related lymphomas that may be detected in serous fluids, including EBV-positive nodal T/NK-cell lymphoma (EBV + nT/NKCL), extranodal NK/T-cell lym‑ phoma, primary effusion lymphoma, EBV-positive diffuse large B-cell lymphoma, and classic Hodgkin lymphoma. We present an index case of EBV + nT/NKCL with lymphomatous pleural effusion and discuss the cytologic features, differ‑ ential diagnoses, and role of ancillary studies such as immunocytochemistry, EBER in situ hybridization, and molecular assays. Accurate diagnosis requires the integration of cytomorphologic, immunophenotypic, and molecular findings with clinical information to establish a definitive diagnosis and distinguish these aggressive lymphomas from reactive and non-hematologic mimics.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.