Synthesis and Biological Activities of Luminescent 5,6-Membered Bis(Metallacyclic) Platinum(II) Complexes.

Four couples of 5,6-membered bis(metallacyclic) Pt(II) complexes with acetylide and isocyanide auxiliary ligands have been prepared and characterized. The structures of (-)-2 and (-)-3 are confirmed by single-crystal X-ray diffraction, showing a distorted square-planar coordination environment around the Pt(II) nucleus. Both solutions and solid samples of all complexes are emissive at RT. Acetylide-coordinated Pt(II) complexes have a lower energy emission than those isocyanide-coordinated ones. The emission spectra of N^N'*C-coordinated Pt(II) derivatives show a lower energy emission maximum relative to N^C*N'-coordinated complexes with the same auxiliary ligand. Moreover, the difference between cyclometalated N^N'*C and N^C*N' ligands exerts a more remarkable effect on the emission than the auxiliary ligands acetylide and isocyanide. Cytotoxicity and cell imaging of luminescent 5,6-membered bis(metallacyclic) Pt(II) complexes have been evaluated.

A Fluorescent Probe Based on the Hydrazone Schiff Base for the Detection of Zn2+ and its Application on Test Strips.

A novel fluorescent probe SHK for Zn2+ detection was designed based on the hydrazone Schiff base, successfully synthesized by Suzuki coupling and condensation reactions. The probe SHK in DMSO/H2O showed extremely weak fluorescence. However, the solution exhibited an intensive yellow-green emission with the introduction of Zn2+. In contrast, negligible fluorescence change was observed when other metal ions were added, suggesting a high selectivity of SHK for Zn2+ detection. The Job's Plot analysis revealed that a 1:1 stoichiometric adduct SHK-Zn2+ formed during the Zn2+ sensing. The binding constant of the complex was determined to be 184 M- 1, and the detection limit for Zn2+ was calculated to be 112 µM. Moreover, the probe SHK achieved selective fluorescence sensing for Zn2+ on test strips, which guaranteed its practical application prospect.

Circularly polarized luminescence of pinene-modified tetradentate platinum(II) enantiomers containing fused 5/6/6 metallocycles.

In this study, a couple of tetradentate Pt(II) enantiomers ((-)-1 and (+)-1) and a couple of tetradentate Pt(IV) enantiomers ((-)-2 and (+)-2) containing fused 5/6/6 metallocycles have been synthesized by controlling reaction conditions. Two valence forms could transform into each other through mild chemical oxidants and reductants. Single-crystal X-ray diffraction confirms the structures of (-)-1 and (-)-2. The coordination sphere of the Pt(II) cation in (-)-1 displays a distorted square-planar geometry and a platinum centroid helix chirality. In contrast, the structure of (-)-2 reveals a distorted octahedral geometry. The solution and the solid of (-)-1 are highly luminescent. Complex (-)-1 shows a prominent aggregation-induced emission enhancement (AIEE) behavior in DMSO/water solution with emission quantum yield (Φ em) up to 73.2%. Furthermore, highly phosphorescent Pt(II) enantiomers exhibit significant circularly polarized luminescence (CPL) with a dissymmetry factor (g lum) of order 10-3 in CH2Cl2 solutions at room temperature. Symmetrically appreciable CPL signals are observed for the enantiomers (-)-1 and (+)-1.

Longitudinal subcortical segmentation with deep learning.

Longitudinal information is important for monitoring the progression of neurodegenerative diseases, such as Huntington's disease (HD). Specifically, longitudinal magnetic resonance imaging (MRI) studies may allow the discovery of subtle intra-subject changes over time that may otherwise go undetected because of inter-subject variability. For HD patients, the primary imaging-based marker of disease progression is the atrophy of subcortical structures, mainly the caudate and putamen. To better understand the course of subcortical atrophy in HD and its correlation with clinical outcome measures, highly accurate segmentation is important. In recent years, subcortical segmentation methods have moved towards deep learning, given the state-of-the-art accuracy and computational efficiency provided by these models. However, these methods are not designed for longitudinal analysis, but rather treat each time point as an independent sample, discarding the longitudinal structure of the data. In this paper, we propose a deep learning based subcortical segmentation method that takes into account this longitudinal information. Our method takes a longitudinal pair of 3D MRIs as input, and jointly computes the corresponding segmentations. We use bi-directional convolutional long short-term memory (C-LSTM) blocks in our model to leverage the longitudinal information between scans. We test our method on the PREDICT-HD dataset and use the Dice coefficient, average surface distance and 95-percent Hausdorff distance as our evaluation metrics. Compared to cross-sectional segmentation, we improve the overall accuracy of segmentation, and our method has more consistent performance across time points. Furthermore, our method identifies a stronger correlation between subcortical volume loss and decline in the total motor score, an important clinical outcome measure for HD.

MRI subcortical segmentation in neurodegeneration with cascaded 3D CNNs.

The subcortical structures of the brain are relevant for many neurodegenerative diseases like Huntington's disease (HD). Quantitative segmentation of these structures from magnetic resonance images (MRIs) has been studied in clinical and neuroimaging research. Recently, convolutional neural networks (CNNs) have been successfully used for many medical image analysis tasks, including subcortical segmentation. In this work, we propose a 2-stage cascaded 3D subcortical segmentation framework, with the same 3D CNN architecture for both stages. Attention gates, residual blocks and output adding are used in our proposed 3D CNN. In the first stage, we apply our model to downsampled images to output a coarse segmentation. Next, we crop the extended subcortical region from the original image based on this coarse segmentation, and we input the cropped region to the second CNN to obtain the final segmentation. Left and right pairs of thalamus, caudate, pallidum and putamen are considered in our segmentation. We use the Dice coefficient as our metric and evaluate our method on two datasets: the publicly available IBSR dataset and a subset of the PREDICT-HD database, which includes healthy controls and HD subjects. We train our models on only healthy control subjects and test on both healthy controls and HD subjects to examine model generalizability. Compared with the state-of-the-art methods, our method has the highest mean Dice score on all considered subcortical structures (except the thalamus on IBSR), with more pronounced improvement for HD subjects. This suggests that our method may have better ability to segment MRIs of subjects with neurodegenerative disease.

Mechanochromic luminescence properties of fluoro-substituted pinene-containing cyclometalated platinum(II) complexes with multiple triplet excited states.

The structure-mechanochromism relationship is explored with respect to packing patterns and corresponding intermolecular interactions that are affected by the number and location of -F. The distinct and reversible mechanochormic luminescence (Δλem up to ca. 90 nm) of yellow solids (-)-1-Yg, (-)-2-Yg, and (-)-3-Yg was displayed with a simultaneous crystal-to-amorphous transformation. The change of multiple triplet excited states accounted for the mechanochormic luminescence, and a switch from the 3π,π* monomer to the excimer/3MMLCT occurred in the grinding process. The mechanical force led to perturbation in the molecular packing, and aggregates with effective PtPt and π-π interactions were formed in the amorphous phase, leading to the variation of excited states. The mechanochromic luminescence could be reverted by dropping in CH2Cl2 and could be cycled multiple times without perceivable performance degradation. This work gives a reference for designing mechanochromic luminescent materials toward multicolor and multicomponent responses.

Anatomical Context Protects Deep Learning from Adversarial Perturbations in Medical Imaging.

Deep learning has achieved impressive performance across a variety of tasks, including medical image processing. However, recent research has shown that deep neural networks are susceptible to small adversarial perturbations in the image. We study the impact of such adversarial perturbations in medical image processing where the goal is to predict an individual's age based on a 3D MRI brain image. We consider two models: a conventional deep neural network, and a hybrid deep learning model which additionally uses features informed by anatomical context. We find that we can introduce significant errors in predicted age by adding imperceptible noise to an image, can accomplish this even for large batches of images using a single perturbation, and that the hybrid model is much more robust to adversarial perturbations than the conventional deep neural network. Our work highlights limitations of current deep learning techniques in clinical applications, and suggests a path forward.

Enhanced Circularly Polarized Luminescence Activity in Chiral Platinum(II) Complexes With Bis- or Triphenylphosphine Ligands.

Distinct circularly polarized luminescence (CPL) activity was observed in chiral (C∧N∧N)Pt(II) [(C∧N∧N) = 4,5-pinene-6'-phenyl-2,2'-bipyridine] complexes with bis- or triphenylphosphine ligands. Compared to the pseudo-square-planar geometry of chiral (C∧N∧N)Pt(II) complexes with chloride, phenylacetylene (PPV) and 2,6-dimethylphenyl isocyanide (Dmpi) ligands, the coordination configuration around the Pt(II) nucleus of chiral (C∧N∧N)Pt(II) complexes with bulk phosphine ligands is far more distorted. The geometry is straightforwardly confirmed by X-ray crystallography. The phosphines' participation enhanced the CPL signal of Pt(II) complexes profoundly, with the dissymmetry factor (g lum) up to 10-3. The distorted structures and enhanced chiroptical signals were further confirmed by time-dependent density functional theory (TD-DFT) calculations.

Generalizing MRI Subcortical Segmentation to Neurodegeneration.

Many neurodegenerative diseases like Huntington's disease (HD) affect the subcortical structures of the brain, especially the caudate and the putamen. Automated segmentation of subcortical structures from MRI scans is thus important in HD studies. LiviaNET [2] is the state-of-the-art deep learning approach for subcortical segmentation. As all learning-based models, this approach requires appropriate training data. While annotated healthy control images are relatively easy to obtain, generating such annotations for each new disease population can be prohibitively expensive. In this work, we explore LiviaNET variants using well-known strategies for improving performance, to make it more generalizable to patients with substantial neurodegeneration. Specifically, we explored Res-blocks in our convolutional neural network, and we also explored manipulating the input to the network as well as random elastic deformations for data augmentation. We tested our method on images from the PREDICT-HD dataset, which includes control and HD subjects. We trained on control subjects and tested on both controls and HD patients. Compared to the original LiviaNET, we improved the accuracy of most structures, both for controls and for HD patients. The caudate has the most pronounced improvement in HD subjects with the proposed modifications to LiviaNET, which is noteworthy since caudate is known to be severely atrophied in HD. This suggests our extensions may improve the generalization ability of LiviaNET to cohorts where significant neurodegeneration is present, without needing to be retrained.

Robust Multiple Sclerosis Lesion Inpainting with Edge Prior.

Inpainting lesions is an important preprocessing task for algorithms analyzing brain MRIs of multiple sclerosis (MS) patients, such as tissue segmentation and cortical surface reconstruction. We propose a new deep learning approach for this task. Unlike existing inpainting approaches which ignore the lesion areas of the input image, we leverage the edge information around the lesions as a prior to help the inpainting process. Thus, the input of this network includes the T1-w image, lesion mask and the edge map computed from the T1-w image, and the output is the lesion-free image. The introduction of the edge prior is based on our observation that the edge detection results of the MRI scans will usually contain the contour of white matter (WM) and grey matter (GM), even though some undesired edges appear near the lesions. Instead of losing all the information around the neighborhood of lesions, our approach preserves the local tissue shape (brain/WM/GM) with the guidance of the input edges. The qualitative results show that our pipeline inpaints the lesion areas in a realistic and shape-consistent way. Our quantitative evaluation shows that our approach outperforms the existing state-of-the-art inpainting methods in both image-based metrics and in FreeSurfer segmentation accuracy. Furthermore, our approach demonstrates robustness to inaccurate lesion mask inputs. This is important for practical usability, because it allows for a generous over-segmentation of lesions instead of requiring precise boundaries, while still yielding accurate results.

Angiotensin-converting enzyme insertion/deletion polymorphism and susceptibility to Henoch-Schönlein purpura: a meta-analysis.

OBJECTIVE:: Meta-analysis was performed in the current study to evaluate the relationship of the angiotensin-converting enzyme insertion/deletion polymorphism with the risk of the incidence of Henoch-Schönlein purpura. METHODS:: The electronic databases, including Embase, PubMed and Google scholar, were systemically retrieved to search for related articles. Meanwhile, statistical analysis was performed using the odds ratio and the corresponding 95% confidence interval. RESULTS:: A total of six articles enrolling 504 patients and 706 healthy controls was enrolled into the current meta-analysis. Results of the meta-analysis suggested that the angiotensin-converting enzyme D allele was markedly correlated with the risk of the incidence of Henoch-Schönlein purpura among the general population (deletion (D) vs. insertion (I): odds ratio (OR) 1.42, 95% confidence interval (CI) 1.05-1.93; DD vs. II: OR 2.23, 95% CI 1.06-4.70; DI vs. II: OR 1.36, 95% CI 1.00-1.85; dominant model: OR 1.56, 95% CI 1.00-2.42; recessive model: OR 1.83, 95% CI 1.06-3.16). Moreover, such a polymorphism was found to correlate with the susceptibility to Henoch-Schönlein purpura when studies were stratified according to the sample size of over 200. In addition, such a polymorphism was recognised to be remarkably associated with the susceptibility to Henoch-Schönlein purpura in the Caucasian population, which was not found in the Asian population. CONCLUSIONS:: The results of the current meta-analysis indicate that the angiotensin-converting enzyme D allele might be a risk factor against the risk of Henoch-Schönlein purpura, especially in Caucasians.

Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices.

In this paper, we present a fully convolutional densely connected network (Tiramisu) for multiple sclerosis (MS) lesion segmentation. Different from existing methods, we use stacked slices from all three anatomical planes to achieve a 2.5D method. Individual slices from a given orientation provide global context along the plane and the stack of adjacent slices adds local context. By taking stacked data from three orientations, the network has access to more samples for training and can make more accurate segmentation by combining information of different forms. The conducted experiments demonstrated the competitive performance of our method. For an ablation study, we simulated lesions on healthy controls to generate images with ground truth lesion masks. This experiment confirmed that the use of 2.5D patches, stacked data and the Tiramisu model improve the MS lesion segmentation performance. In addition, we evaluated our approach on the Longitudinal MS Lesion Segmentation Challenge. The overall score of 93.1 places the L 2-loss variant of our method in the first position on the leaderboard, while the focal-loss variant has obtained the best Dice coefficient and lesion-wise true positive rate with 69.3% and 60.2%, respectively.