Triple Isomerism in 3D Covalent Organic Frameworks.

Isomerism in covalent organic frameworks (COFs) has scarcely been known. Here, for the first time we show 3D COFs with three framework isomers or polymorphs constructed from the same building blocks. All isomers were obtained as large (>10 µm) crystals; although their crystal shapes were distinctly different, they showed identical FT-IR and solid-state NMR spectra. Our structural analyses revealed unprecedented triple isomerism in 3D COFs (noninterpenetrated dia, qtz, and 3-fold interpenetrated dia-c3 nets). Furthermore, this Communication reports the first known COF with qtz topology for which the structure determination was based on Rietveld analysis. We achieved triple framework isomerism by reticulating a tetrahedral building block with a flexible junction and a linear building block with PEO side chains and by varying solution compositions. Our energy calculations, along with the discovery of interisomer transition, revealed that the isomer with qtz topology was a kinetic isomer. Thus, this simple yet little-explored concept of reticulating only flexible building blocks is an effective pathway to significantly broaden the diversity of 3D COFs, which have been proposed for a myriad of applications.

Magnetic and thermodynamic control of coordination network crystallization using a hexaazaphenalene-based ligand.

Assembly of coordination networks from Cd(II) and a multi-interactive hexaazaphenalene-based ligand was successfully modulated using magnetic fields and thermodynamic control. A relatively weak field of only 320 mT was able to perturb the orientational distribution of the ligand in solution nudging the reaction down a different path. The underlying mechanism involved alignment of the ligands along the field lines, which was supported by DFT calculations. This crystallization technique could be extended to the synthesis of other networks and facilitate a deeper exploration of the reaction landscapes.

Artificial intelligence-assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection.

Background: Joint bleeding can lead to synovitis and arthropathy in people with hemophilia, reducing quality of life. Although early diagnosis is associated with improved therapeutic outcomes, diagnostic ultrasonography requires specialist experience. Artificial intelligence (AI) algorithms may support ultrasonography diagnoses. Objectives: This study will research, develop, and evaluate the diagnostic precision of an AI algorithm for detecting the presence or absence of hemarthrosis and synovitis in people with hemophilia. Methods: Elbow, knee, and ankle ultrasound images were obtained from people with hemophilia from January 2010 to March 2022. The images were used to train and test the AI models to estimate the presence/absence of hemarthrosis and synovitis. The primary endpoint was the area under the curve for the diagnostic precision to diagnose hemarthrosis and synovitis. Other endpoints were the rate of accuracy, precision, sensitivity, and specificity. Results: Out of 5649 images collected, 3435 were used for analysis. The area under the curve for hemarthrosis detection for the elbow, knee, and ankle joints was ≥0.87 and for synovitis, it was ≥0.90. The accuracy and precision for hemarthrosis detection were ≥0.74 and ≥0.67, respectively, and those for synovitis were ≥0.83 and ≥0.74, respectively. Analysis across people with hemophilia aged 10 to 60 years showed consistent results. Conclusion: AI models have the potential to aid diagnosis and enable earlier therapeutic interventions, helping people with hemophilia achieve healthy and active lives. Although AI models show potential in diagnosis, evidence is unclear on required control for abnormal findings. Long-term observation is crucial for assessing impact on joint health.