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This study aims to undertake a comprehensive assessment of the effectiveness and safety profile of Mahuang Fuzi and Shenzhuo Decoction (MFSD) in the management of primary membranous nephropathy (PMN), within the context of a prospective clinical investigation. A multicenter, open-label clinical trial was executed on patients diagnosed with PMN. These individuals were subjected to MFSD therapy for a duration of at least 24 months, with primary outcome of clinical remission rates. The Cox regression analysis was employed to discern the pertinent risk factors exerting influence on the efficacy of MFSD treatment, with scrupulous monitoring of any adverse events. The study comprised 198 participants in total. Following 24 months of treatment, the remission rate was 58.6% (116/198). Among the subgroup of 130 participants subjected to a 36-month follow-up, the remission rate reached 70% (91/130). Subgroup analysis revealed that neither a history of immunosuppressive therapy (HIST) nor an age threshold of ≥60 years exhibited a statistically significant impact on the remission rate at the 24-month mark (p >.05). Multivariate Cox regression analyses elucidated HIST, nephrotic syndrome, or mass proteinuria, and a high-risk classification as noteworthy risk factors in the context of MFSD treatment. Remarkably, no fatalities resulting from side effects were documented throughout the study's duration. This trial establishes the efficacy of MFSD as a treatment modality for membranous nephropathy. MFSD demonstrates a favorable side effect profile, and remission rates are consistent across patients, irrespective of HIST and age categories. [ABSTRACT FROM AUTHOR]

To give a better assistance to users in the understanding and analyzing of data, this paper proposes a new color mapping method based on global statistical and focal spacial characteristics, aiming at emphasis of both the global patterns and the boundary information of data. Our method employs Jenks natural break method to minimize control points in the color map and maximizes the color differences between boundary points and its neighbors to obtain the color napping. The effectiveness of our method Is verified via qualitative experiments and user researches on the 2D scalar field data. [ABSTRACT FROM AUTHOR]

Background: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs., Purpose: To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs., Study Type: Retrospective., Subjects: 395 patients with 1149 FLLs., Field Strength/sequence: The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging., Assessment: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists., Statistical Tests: McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant., Results: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174)., Data Conclusion: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions., Technical Efficacy: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

Background: Long COVID impacts ∼10% of people diagnosed with COVID-19, yet the pathophysiology driving ongoing symptoms is poorly understood. We hypothesised that 129 Xe magnetic resonance imaging (MRI) could identify unique pulmonary phenotypic subgroups of long COVID, therefore we evaluated ventilation and gas exchange measurements with cluster analysis to generate imaging-based phenotypes., Methods: COVID-negative controls and participants who previously tested positive for COVID-19 underwent 129 XeMRI ∼14-months post-acute infection across three centres. Long COVID was defined as persistent dyspnea, chest tightness, cough, fatigue, nausea and/or loss of taste/smell at MRI; participants reporting no symptoms were considered fully-recovered. 129 XeMRI ventilation defect percent (VDP) and membrane (Mem)/Gas, red blood cell (RBC)/Mem and RBC/Gas ratios were used in k-means clustering for long COVID, and measurements were compared using ANOVA with post-hoc Bonferroni correction., Results: We evaluated 135 participants across three centres: 28 COVID-negative (40±16yrs), 34 fully-recovered (42±14yrs) and 73 long COVID (49±13yrs). RBC/Mem (p=0.03) and FEV 1 (p=0.04) were different between long- and COVID-negative; FEV 1 and all other pulmonary function tests (PFTs) were within normal ranges. Four unique long COVID clusters were identified compared with recovered and COVID-negative. Cluster1 was the youngest with normal MRI and mild gas-trapping; Cluster2 was the oldest, characterised by reduced RBC/Mem but normal PFTs; Cluster3 had mildly increased Mem/Gas with normal PFTs; and Cluster4 had markedly increased Mem/Gas with concomitant reduction in RBC/Mem and restrictive PFT pattern., Conclusion: We identified four 129 XeMRI long COVID phenotypes with distinct characteristics. 129 XeMRI can dissect pathophysiologic heterogeneity of long COVID to enable personalised patient care., Competing Interests: Conflicts of interest: Rachel Eddy reports grants from Michael Smith Health Research BC, Canadian Respiratory Research Network and Natural Sciences and Engineering Research Council Canada, consulting fees from VIDA Diagnostics Inc., payment or honoraria for lectures from Thorasys Thoracic Medical Systems Inc., support for attending meetings and/or travel from Canadian Institutes of Health Research – Institute of Circulatory and Respiratory Health, and a leadership or fiduciary role for the Xenon MRI Clinical Trials Consortium (Steering Committee Member). Conflicts of interest: David Mummy reports consultancy fees from Polarean Imaging plc. Conflicts of interest: Firoozeh Gerayeli reports grants from MITACS Accelerate. Conflicts of interest: Jonathon Leipsic reports grants from GE Healthcare, consultancy fees and support for attending meetings and/or travel from Heartflow, and owns stock/stock options in Heartflow. Conflicts of interest: Janice Leung reports grants from Canadian Institutes of Health Research and BC Lung Foundation, payment or honoraria for lectures, presentations, manuscript writing or educational events from BC Lung Foundation, participation on a data safety monitoring board or advisory board for Enhance Quality Safety, and Patient experience in Chronic Obstructive Pulmonary Disorder (EQuiP COPD), and leadership or fiduciary roles for Canadian Respiratory Research Network and CanCOLD Study. Conflicts of interest: Bastiaan Driehuys reports grants from Translating Duke Health (Duke Internal Award), royalties or licenses from Polarean Imaging, a leadership or fiduciary role with Polarean Imaging, and owns stock/stock options in Polarean Imaging. Conflicts of interest: Loretta G. Que reports grants from Translating Duke Health (Duke Internal Award), and a leadership or fiduciary role as member of XeMRI Consortium. Conflicts of interest: Mario Castro reports grants from NIH, ALA, PCORI, AstraZeneca, Gala Therapeutics, Genentech, GSK, Novartis, Pulmatrix, Sanofi-Aventis, Shionogi and Theravance, consulting fees from Genentech, Teva, Sanofi-Aventis, Merck, Novartis, Arrowhead Pharmaceuticals, Allakos, Amgen, OM Pharma, Pfizer, Pioneering Medicines and GSK, payment or honoraria for lectures, presentations, manuscript writing or educational events from Amgen, AstraZeneca, Genentech, Regeneron, Sanofi-Aventis and Teva, and owns stock/stock options in Aer Therapeutics. Conflicts of interest: Don D. Sin reports payment or honoraria for lectures, presentations, manuscript writing or educational events from GSK, AZ and BI, and participation on a data safety monitoring board or advisory board for NHLBI. Conflicts of interest: Peter Niedbalski reports grants from Scleroderma Foundation (new investigator grant) and American Heart Association (CDA 930177), and consultancy fees, payment or honoraria for lectures, and support for attending meetings and/or travel from Polarean Imaging Plc. Conflicts of interest: The remaining authors have no potential conflicts of interest to disclose., (Copyright ©The authors 2024.)

Environmental concerns associated with the efficient defluoridation of contaminated water remain a substantial challenge. In this work, a new nanocomposite, UiO-66-NH 2 @PS + , was successfully fabricated via in situ precipitation of a water-stable metal-organic framework (UiO-66-NH 2 ) inside a commercial polystyrene anion exchanger PS + . The as-formed nanocomposite UiO-66-NH 2 @PS + was characterized using various morphological methods, which demonstrated that nanosized UiO-66-NH 2 was homogenously dispersed within the inner pores of PS + . Batch adsorption experiments indicated that UiO-66-NH 2 @PS + exhibited outstanding adsorption performance for fluoride over a broad pH range of 3.0-8.0. The saturated adsorption capacity of fluoride at 298 K was 27.5 and 32.8 mg/g for pH 6.5 and 4.5 with the adsorbent dosage of 0.5 g/L and initial concentration of 5-80 mg/L. Moreover, the utilization rate of active adsorption sites of UiO-66-NH 2 was greatly improved after encapsulation. The XPS study indicated that the integrated effects of specific inner-sphere coordination and ligand exchange between fluoride and UiO-66-NH 2 might be the dominant adsorption mechanism. Fixed-bed tests indicated that the UiO-66-NH 2 @PS + column could successively produce clean water with bed volumes of 350 and 70 ([F - ] <1.5 mg/L) from simulated fluoride-pollution water at pH 4.5 and 8.0, with a liquid velocity of 20 mL/h, and an empty bed contact time (EBCT) of 15 min, which was higher than that of the other materials. In addition, the exhausted UiO-66-NH 2 @PS + was regenerated and reused for 5 times through complete regeneration, highlighting the potential feasibility of defluorination in practical applications., (Copyright © 2022 Elsevier Ltd. All rights reserved.)