Recent concepts are changing the management of ankle instability. These include concurrent medial and lateral instabilities, use of ankle arthroscopy, use of suture anchors, all-arthroscopic stabilization, synthetic augmentation, and early postoperative rehabilitation. Medial sided injuries occur in up to 72% of the lateral ankle sprains, and concomitant repair may provide greater stability. Suture anchors are equally as strong as transosseous tunnels, and the technique is simple, reproducible, and may decrease complications, but anchors do increase costs. Synthetic augmentation demonstrates greater strength than Broström alone in cadaver-based biomechanical testing. Although clinical studies of synthetic augmentation have demonstrated equivocal stability and pain compared with Broström alone, synthetic augmentation may expedite rehabilitation. All-arthroscopic ankle stabilization is gaining popularity with increasing publications. Early findings demonstrate comparable biomechanical and clinical data compared with open techniques. Early postoperative weight-bearing within 2 weeks seems to be safe and may shorten time to return to play. Surgeons may consider using these novel techniques in the management of lateral ankle instability.
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http://dx.doi.org/10.5435/JAAOS-D-20-00176 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Urogynecology (Phila)
January 2025
Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA.
Importance: Tobacco smoking is linked to poor surgical outcomes, leading many physicians to avoid synthetic implants like mesh in smokers due to concerns about impaired healing. While long-term outcomes for smokers have been studied, the effect of smoking on 30-day postoperative complications, especially related to surgical mesh, is less understood.
Objectives: This study aimed to quantify the association between tobacco smoking and risk of postoperative infection, readmission, and reoperation within 30 days of minimally invasive apical prolapse repair.
Eur J Med Chem
January 2025
Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, 201210, China. Electronic address:
Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy with poor prognosis. Antibody-drug conjugates (ADCs) and their combinations with various anti-tumor drugs have made great progress. Camptothecin, and its derivatives (Dxd, SN-38 or exatecan) targeted TOP1 are effective payloads due to their potent anti-tumor activity.
View Article and Find Full Text PDFPolymers (Basel)
January 2025
Institute of Environmental Sciences, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, H-2100 Gödöllő, Hungary.
This study attempted to isolate and identify pedospheric microbes originating in dumpsites and utilized them for the degradation of selected synthetic polymers for the first time in a cost-effective, ecologically favorable and sustainable manner. Specifically, low-density polyethylene (LDPE) and polyurethane (PUR) were converted by the isolated fungi, i.e.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance.
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