Ratiometric Detection of Nanomolar Concentrations of Heparin in Serum and Plasma Samples Using a Fluorescent Chemosensor Based on Peptides

A peptidyl fluorescent chemosensor for heparin was synthesized by conjugating a pyrene fluorophore with the heparin-binding peptide. The fluorescent chemosensor (<b>Py12</b>; pyrene-RKRLQVQLSIRT) showed a highly sensitive ratiometric response to nanomolar concentrations of heparin in aqueous solutions at physiological pH by increasing excimer emission intensity at 500 nm with a concomitant decrease in monomer emission intensity at 400 nm. <b>Py12</b> showed a sensitive ratiometric response to heparin over a wide pH range (1.5 ≤ pH ≤ 11.5) and exhibited high selectivity for heparin compared to other biological competitors, such as hyaluronic acid and chondroitin sulfate. <b>Py12</b> sensitively and ratiometrically detected nanomolar concentrations of heparin in biologically relevant samples containing human serum and human plasma, respectively. The detection limit of <b>Py12</b> was 34 pM (<i>R</i>² = 0.997) for heparin in an aqueous buffer solutions containing 5% human serum and 33 pM (<i>R</i>² = 0.994) for heparin in aqueous buffer solutions containing 5% human plasma. <b>Py12</b> had sufficient sensitivity and selectivity for ratiometrically detecting a nanomolar concentration of heparin, indicating that the peptide-base chemosensor provides a potential tool for monitoring heparin levels in clinical plasma samples

Ruthenium–Cobalt Bimetallic Supramolecular Cages via a Less Symmetric Tetrapyridyl Metalloligand and the Effect of Spacer Units

The self-assembly of <i>C</i>_<i>s</i>-symmetric tetra­pyridyl cobalt–​metallo­ligand <b>2</b> with three half-sandwich diruthenium acceptors, <b>3</b>–<b>5</b>, led to the formation of A₄D₂ (A = acceptor, D = donor) metalla­cages <b>6</b>–<b>8</b>, as shown by ESI mass spectrometry, NMR spectroscopy, and X-ray crystallography. The solid-state structures of <b>6</b>–<b>8</b> revealed that the length of the acceptor unit greatly influences the molecular packing of these metalla­cages. Hence, in the solid state, <b>6</b>–<b>8</b> can be considered to have water­wheel-shaped, tweezer-shaped, and butterfly-like architectures, respectively

Ruthenium–Cobalt Bimetallic Supramolecular Cages via a Less Symmetric Tetrapyridyl Metalloligand and the Effect of Spacer Units

The self-assembly of <i>C</i>_<i>s</i>-symmetric tetra­pyridyl cobalt–​metallo­ligand <b>2</b> with three half-sandwich diruthenium acceptors, <b>3</b>–<b>5</b>, led to the formation of A₄D₂ (A = acceptor, D = donor) metalla­cages <b>6</b>–<b>8</b>, as shown by ESI mass spectrometry, NMR spectroscopy, and X-ray crystallography. The solid-state structures of <b>6</b>–<b>8</b> revealed that the length of the acceptor unit greatly influences the molecular packing of these metalla­cages. Hence, in the solid state, <b>6</b>–<b>8</b> can be considered to have water­wheel-shaped, tweezer-shaped, and butterfly-like architectures, respectively

Ruthenium–Cobalt Bimetallic Supramolecular Cages via a Less Symmetric Tetrapyridyl Metalloligand and the Effect of Spacer Units

The self-assembly of <i>C</i>_<i>s</i>-symmetric tetra­pyridyl cobalt–​metallo­ligand <b>2</b> with three half-sandwich diruthenium acceptors, <b>3</b>–<b>5</b>, led to the formation of A₄D₂ (A = acceptor, D = donor) metalla­cages <b>6</b>–<b>8</b>, as shown by ESI mass spectrometry, NMR spectroscopy, and X-ray crystallography. The solid-state structures of <b>6</b>–<b>8</b> revealed that the length of the acceptor unit greatly influences the molecular packing of these metalla­cages. Hence, in the solid state, <b>6</b>–<b>8</b> can be considered to have water­wheel-shaped, tweezer-shaped, and butterfly-like architectures, respectively

Ruthenium–Cobalt Bimetallic Supramolecular Cages via a Less Symmetric Tetrapyridyl Metalloligand and the Effect of Spacer Units

The self-assembly of <i>C</i>_<i>s</i>-symmetric tetra­pyridyl cobalt–​metallo­ligand <b>2</b> with three half-sandwich diruthenium acceptors, <b>3</b>–<b>5</b>, led to the formation of A₄D₂ (A = acceptor, D = donor) metalla­cages <b>6</b>–<b>8</b>, as shown by ESI mass spectrometry, NMR spectroscopy, and X-ray crystallography. The solid-state structures of <b>6</b>–<b>8</b> revealed that the length of the acceptor unit greatly influences the molecular packing of these metalla­cages. Hence, in the solid state, <b>6</b>–<b>8</b> can be considered to have water­wheel-shaped, tweezer-shaped, and butterfly-like architectures, respectively

A Ruthenium–Iron Bimetallic Supramolecular Cage with <i>D</i>₄ Symmetry from a Tetrapyridyl Iron(I) Metalloligand

The novel iron­(I) sandwich compound [Cp*Fe­(η⁴-C₄Py₄)] (<b>1</b>) was prepared and characterized by various methods, including X-ray crystallography. Coordination-driven self-assembly of a diruthenium acceptor with the tetrapyridyl metalloligand [Cp*Fe­(I)­(η⁴-C₄Py₄)] led to a <i>D</i>₄-symmetric three-dimensional M₄L₂ tetragonal supramolecular cage. This cage was characterized by IR and high-resolution electrospray ionization mass spectrometry. Its solid-state structure was confirmed by X-ray crystallography, showing a novel <i>D</i>₄ cage system

Selective Formation of Heterometallic Ru–Ag Supramolecules via Stoichiometric Control of Multiple Different Tectons

Stoichiometric control of Ru, Ag, and tetrazolyl ligands resulted in the formation of different heterometallic Ru–Ag supramolecular architectures. Although the reaction of Ru and 5-(2-hydroxyphenyl)-1<i>H</i>-tetrazolyl (<i><b>L</b></i>H₂) in a molar ratio of 2:1 or 6:4 resulted in the formation of dimeric or hexameric Ru complexes, Ag metal ions caused the Ru complexes to form three-dimensional cylindrical Ru₆Ag₆<i><b>L</b></i>₆ and double-cone-shaped Ru₆Ag₈<i><b>L</b></i>₆ complexes by occupying vacant coordination sites

Cationic Ti Complexes with Three [N,O]-Type Tetrazolyl Ligands: Ti↔Fe Transmetalation within Fe Metallascorpionate Complexes

Herein, we report the synthesis of two novel ionic Ti complexes possessing three [N,O]-type bidentate ligands from the reaction of Fe metallascorpionate ligands possessing extended alcohol groups and TiCl₄. The reaction of substituted hydroxyphenyl tetrazole and Fe­(ClO₄)₃ in a molar ratio of 3:1 afforded iron scorpionate metalloligands possessing extended arms, which were characterized by IR spectroscopy and ESI-TOF-MS spectrometry. Their molecular structures were also confirmed as neutral Fe-centered scorpionate complexes by X-ray crystallography, in which the extended alcohol groups adopted a tripodal geometry. Moreover, two different crystals of iron scorpionate metalloligand grown from CH₂Cl₂ and CH₃OH were studied, revealing that, in the latter crystal, the tripod arms are folded and aligned toward the <i>C</i>₃-rotational axis of the molecule, whereas the tripod arms are unfolded and spread outward from the rotational axis in the former crystal. These metalloligands are solvatochromatic; a bathochromic shift was observed as the solvent polarity increased. From the reaction, the aforesaid Fe complexes were further reacted with TiCl₄ in a molar ratio of 1:1 to produce ionic [Ti<b><i>L</i></b>₃]⁺[FeCl₄]⁻ (<i><b>L</b></i> = substituted hydroxyphenyl tetrazole) complexes from the transmetalation of Ti and Fe. The complexes were characterized by various analytical methods including UV/vis and IR spectroscopies, electrospray time-of-flight mass spectrometry (ESI-TOF-MS), and X-ray crystallography

Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): Development, external validation, and comparison to scoring systems

<div><p>The major challenge in the diagnosis of disseminated intravascular coagulation (DIC) comes from the lack of specific biomarkers, leading to developing composite scoring systems. DIC scores are simple and rapidly applicable. However, optimal fibrin-related markers and their cut-off values remain to be defined, requiring optimization for use. The aim of this study is to optimize the use of DIC-related parameters through machine learning (ML)-approach. Further, we evaluated whether this approach could provide a diagnostic value in DIC diagnosis. For this, 46 DIC-related parameters were investigated for both clinical findings and laboratory results. We retrospectively reviewed 656 DIC-suspected cases at an initial order for full DIC profile and labeled their evaluation results (Set 1; DIC, n = 228; non-DIC, n = 428). Several ML algorithms were tested, and an artificial neural network (ANN) model was established via independent training and testing using 32 selected parameters. This model was externally validated from a different hospital with 217 DIC-suspected cases (Set 2; DIC, n = 80; non-DIC, n = 137). The ANN model represented higher AUC values than the three scoring systems in both set 1 (ANN 0.981; ISTH 0.945; JMHW 0.943; and JAAM 0.928) and set 2 (AUC ANN 0.968; ISTH 0.946). Additionally, the relative importance of the 32 parameters was evaluated. Most parameters had contextual importance, however, their importance in ML-approach was different from the traditional scoring system. Our study demonstrates that ML could optimize the use of clinical parameters with robustness for DIC diagnosis. We believe that this approach could play a supportive role in physicians’ medical decision by integrated into electrical health record system. Further prospective validation is required to assess the clinical consequence of ML-approach and their clinical benefit.</p></div

Heat map presentation of the datasets used in this study.

<p>The x-axis denotes individual cases and the y-axis corresponds to the clinical variables. Each cell shows values of variables for each case. All cases are sorted horizontally by the labeled DIC status and predicted ANN model values. Rows 2–5 (ANN model, ISTH, JMHW, and JAAM criteria) show predictions of different DIC diagnostic classifiers based on the cut-off values (0.501 for ANN) or points (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195861#pone.0195861.t001" target="_blank">Table 1</a>).</p