Aim To evaluate the effectiveness and safety of remimazolam tosylate for administering general anesthesia in morbidly obese patients.Methods This clinical trial was conducted at a single center from De-cember 2021 to October 2023.It assessed 108 morbid-ly obese patients(body mass index,BMI≥40)who underwent laparoscopic sleeve gastrectomy.Patients were randomly assigned to either the remimazaolam group(Group R)or the propofol group(Group P)for general anesthesia induction and maintenance.The primary outcome was to compare the incidence of ad-verse events and postoperative recovery characteristics between the two groups.Results During induction pe-riod,the incidence of adverse events was higher in group P,including hypotension(P＜0.01),hypox-emia(P＜0.05),bradycardia(P＜0.01),and in-creased vasopressor requirement(P＜0.05).The time to loss of consciousness and BIS falling to 60 was shor-ter in group P than in group R(P＜0.01).There were no statistically significant differences between the two groups in terms of postoperative quality of recovery(QoR-40 score),24-hour postoperative pain visual an-alogue scale(VAS)scores and morphine consump-tion.In conclusion,remimazolam tosylate,utilized for anesthesia induction in morbidly obese patients,signif-icantly reduced hypotension and hypoxemia compared to propofol,while it could also maintain similar postop-erative recovery quality.Conclusions Remimazolam is effective in reducing the incidence of hypotension and hypoxaemia during the induction period of general anaesthesia in morbidly obese patients and it is compa-rable to propofol in terms of quality of postoperative re-covery.

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.

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.

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.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

Purpose: This study was conducted to evaluate the efficacy of bladder outlet surgery in patients with detrusor underactivity (DU) and to identify factors associated with successful outcomes. Methods: We conducted a retrospective review of men diagnosed with DU in urodynamic studies who underwent bladder outlet surgery for lower urinary tract symptoms between May 2018 and April 2023. The International Prostate Symptom Score (IPSS) questionnaire, uroflowmetry (UFM), and multichannel urodynamic studies were administered. Successful treatment outcomes were defined as either an IPSS improvement of at least 50% or the regaining of spontaneous voiding in patients urethral catheterization prior to surgery. Results: The study included 93 male patients. Men diagnosed with significant or equivocal bladder outlet obstruction (BOO) experienced significant postoperative improvements in IPSS (from 20.6 to 6.0 and from 17.4 to 6.5, respectively), maximum urine flow rate (from 5.0 mL/sec to 14.4 mL/sec and from 8.8 mL/sec to 12.2 mL/sec, respectively) and voiding efficiency (from 48.8% to 86.0% and from 61.2% to 85.1%, respectively). However, in the group without obstruction, the improvements in IPSS and UFM results were not significant. The presence of detrusor overactivity (odds ratio [OR], 3.152; P=0.025) and preoperative urinary catheterization (OR, 2.756; P=0.040) were associated with favorable treatment outcomes. Conversely, an unobstructed bladder outlet was identified as a negative prognostic factor. Conclusions In men with DU accompanied by equivocal or significant BOO, surgical intervention to alleviate the obstruction may enhance the IPSS, quality of life, and UFM results. However, those with DU and an unobstructed bladder outlet face a comparatively high risk of treatment failure. Preoperative detrusor overactivity and urinary catheterization are associated with more favorable surgical outcomes. Consequently, active deobstructive surgery should be considered for patients with DU who are experiencing urinary retention.

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.

As arsenic widely exists in nature and has been used in the pharmaceutical preparations, the traditional Chinese medicine(TCM) with arsenic include realgar(As_2S_2 or As_4S_4), orpiment(As_2S_3), and white arsenic(As_2O_3). Among the above representative medicine, the TCM compound formulas with realgar are utilized extensively. Just in Chinese Pharmacopoeia(2020 edition), there are 37 Chinese patent medicines including realgar. The traditional element analysis focuses on the detection of the total amount of elements, which neglects the study on the speciation and valence of elements. The activity, toxicity, bioavailability, and metabolic pathways of arsenic in vivo are closely related to the existence of its form, and different forms of arsenic have different effects on organisms. Therefore, the study on the speciation and valence of arsenic is of great importance for arsenic-containing TCMs and their compound formulas. This paper reviewed four aspects of the speciation and valence of arsenic, including property, absorption and metabolism, toxicity, and analytical assay.
Arsenic/analysis* ; Arsenicals/analysis* ; Sulfides ; Arsenic Trioxide ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/analysis* ; Biological Products