Exploring Inherent Consistency for Semi-supervised Anatomical Structure Segmentation in Medical Imaging.

Due to the exorbitant expense of obtaining labeled data in the field of medical image analysis, semi-supervised learning has emerged as a favorable method for the segmentation of anatomical structures. Although semi-supervised learning techniques have shown great potential in this field, existing methods only utilize image-level spatial consistency to impose unsupervised regularization on data in label space. Considering that anatomical structures often possess inherent anatomical properties that have not been focused on in previous works, this study introduces the inherent consistency into semi-supervised anatomical structure segmentation. First, the prediction and the ground-truth are projected into an embedding space to obtain latent representations that encapsulate the inherent anatomical properties of the structures. Then, two inherent consistency constraints are designed to leverage these inherent properties by aligning these latent representations. The proposed method is plug-and-play and can be seamlessly integrated with existing methods, thereby collaborating to improve segmentation performance and enhance the anatomical plausibility of the results. To evaluate the effectiveness of the proposed method, experiments are conducted on three public datasets (ACDC, LA, and Pancreas). Extensive experimental results demonstrate that the proposed method exhibits good generalizability and outperforms several state-of-the-art methods.

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets.

BACKGROUND: Convolutional neural networks (CNNs) have produced state-of-the-art results in meningioma segmentation on magnetic resonance imaging (MRI). However, images obtained from different institutions, protocols, or scanners may show significant domain shift, leading to performance degradation and challenging model deployment in real clinical scenarios. OBJECTIVE: This research aims to investigate the realistic performance of a well-trained meningioma segmentation model when deployed across different health care centers and verify the methods to enhance its generalization. METHODS: This study was performed in four centers. A total of 606 patients with 606 MRIs were enrolled between January 2015 and December 2021. Manual segmentations, determined through consensus readings by neuroradiologists, were used as the ground truth mask. The model was previously trained using a standard supervised CNN called Deeplab V3+ and was deployed and tested separately in four health care centers. To determine the appropriate approach to mitigating the observed performance degradation, two methods were used: unsupervised domain adaptation and supervised retraining. RESULTS: The trained model showed a state-of-the-art performance in tumor segmentation in two health care institutions, with a Dice ratio of 0.887 (SD 0.108, 95% CI 0.903-0.925) in center A and a Dice ratio of 0.874 (SD 0.800, 95% CI 0.854-0.894) in center B. Whereas in the other health care institutions, the performance declined, with Dice ratios of 0.631 (SD 0.157, 95% CI 0.556-0.707) in center C and 0.649 (SD 0.187, 95% CI 0.566-0.732) in center D, as they obtained the MRI using different scanning protocols. The unsupervised domain adaptation showed a significant improvement in performance scores, with Dice ratios of 0.842 (SD 0.073, 95% CI 0.820-0.864) in center C and 0.855 (SD 0.097, 95% CI 0.826-0.886) in center D. Nonetheless, it did not overperform the supervised retraining, which achieved Dice ratios of 0.899 (SD 0.026, 95% CI 0.889-0.906) in center C and 0.886 (SD 0.046, 95% CI 0.870-0.903) in center D. CONCLUSIONS: Deploying the trained CNN model in different health care institutions may show significant performance degradation due to the domain shift of MRIs. Under this circumstance, the use of unsupervised domain adaptation or supervised retraining should be considered, taking into account the balance between clinical requirements, model performance, and the size of the available data.

Consistent representation via contrastive learning for skin lesion diagnosis.

BACKGROUND: Skin lesions are a prevalent ailment, with melanoma emerging as a particularly perilous variant. Encouragingly, artificial intelligence displays promising potential in early detection, yet its integration within clinical contexts, particularly involving multi-modal data, presents challenges. While multi-modal approaches enhance diagnostic efficacy, the influence of modal bias is often disregarded. METHODS: In this investigation, a multi-modal feature learning technique termed "Contrast-based Consistent Representation Disentanglement" for dermatological diagnosis is introduced. This approach employs adversarial domain adaptation to disentangle features from distinct modalities, fostering a shared representation. Furthermore, a contrastive learning strategy is devised to incentivize the model to preserve uniformity in common lesion attributes across modalities. Emphasizing the learning of a uniform representation among models, this approach circumvents reliance on supplementary data. RESULTS: Assessment of the proposed technique on a 7-point criteria evaluation dataset yields an average accuracy of 76.1% for multi-classification tasks, surpassing researched state-of-the-art methods. The approach tackles modal bias, enabling the acquisition of a consistent representation of common lesion appearances across diverse modalities, which transcends modality boundaries. This study underscores the latent potential of multi-modal feature learning in dermatological diagnosis. CONCLUSION: In summation, a multi-modal feature learning strategy is posited for dermatological diagnosis. This approach outperforms other state-of-the-art methods, underscoring its capacity to enhance diagnostic precision for skin lesions.

Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs.

Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents' diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents' performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.

A Feature Space-Restricted Attention Attack on Medical Deep Learning Systems.

Deep neural network has shown a powerful performance in the medical image analysis of a variety of diseases. However, a number of studies over the past few years have demonstrated that these deep learning systems can be vulnerable to well-designed adversarial attacks, with minor disruptions added to the input. Since both the public and academia have focused on deep learning in the health information economy, these adversarial attacks would prove more important and raise security concerns. In this article, adversarial attacks on deep learning systems in medicine are analyzed from two different points of view: 1) white box and 2) black box. A fast adversarial sample generation method, Feature Space-Restricted Attention Attack is proposed to explore more confusing adversarial samples. It is based on a generative adversarial network with bound classification space to generate perturbations to achieve attacks. Meanwhile, it can employ an attention mechanism to focus this perturbation on the lesion region. This enables the perturbation closely associated with the classification information making the attack more efficient and invisible. The performance and specificity of the proposed attack method are demonstrated by conducting extensive experiments on three different types of medical images. Finally, it is expected that this work can assist practitioners become being of current weaknesses in the deployment of deep learning systems in clinical settings. And, it further investigates domain-specific features of medical deep learning systems to enhance model generalization and resistance to attacks.

Contrastive domain adaptation with consistency match for automated pneumonia diagnosis.

Pneumonia can be difficult to diagnose since its symptoms are too variable, and the radiographic signs are often very similar to those seen in other illnesses such as a cold or influenza. Deep neural networks have shown promising performance in automated pneumonia diagnosis using chest X-ray radiography, allowing mass screening and early intervention to reduce the severe cases and death toll. However, they usually require many well-labelled chest X-ray images for training to achieve high diagnostic accuracy. To reduce the need for training data and annotation resources, we propose a novel method called Contrastive Domain Adaptation with Consistency Match (CDACM). It transfers the knowledge from different but relevant datasets to the unlabelled small-size target dataset and improves the semantic quality of the learnt representations. Specifically, we design a conditional domain adversarial network to exploit discriminative information conveyed in the predictions to mitigate the domain gap between the source and target datasets. Furthermore, due to the small scale of the target dataset, we construct a feature cloud for each target sample and leverage contrastive learning to extract more discriminative features. Lastly, we propose adaptive feature cloud expansion to push the decision boundary to a low-density area. Unlike most existing transfer learning methods that aim only to mitigate the domain gap, our method instead simultaneously considers the domain gap and the data deficiency problem of the target dataset. The conditional domain adaptation and the feature cloud generation of our method are learning jointly to extract discriminative features in an end-to-end manner. Besides, the adaptive feature cloud expansion improves the model's generalisation ability in the target domain. Extensive experiments on pneumonia and COVID-19 diagnosis tasks demonstrate that our method outperforms several state-of-the-art unsupervised domain adaptation approaches, which verifies the effectiveness of CDACM for automated pneumonia diagnosis using chest X-ray imaging.

Novel Meso-Benzothiazole-Substituted BODIPY-Based AIE Fluorescent Rotor for Imaging Lysosomal Viscosity and Monitoring Autophagy.

Meso-substituted boron dipyrromethenes (BODIPYs) provide a potential and innovative strategy for the synergistic construction of aggregation-induced emission (AIE) probes and fluorescent rotors for monitoring cellular viscosity changes, which play critical roles in understanding the function of viscosity in its closely associated diseases. Therefore, for the first time, a BODIPY-based fluorescent probe (1) with a rotatable meso-benzothiazole group was rationally designed and synthesized, showing both good viscosity-responsive and AIE properties. Probe 1 through direct linkage with the thiazole group, showed nearly no emission in low viscous solvents; however, a strong emission at 534 nm appeared and increased gradually with the increase in viscosity, attributing to the efficient restriction of the rotatable meso-benzothiazole group. The intensity (log I534) displayed a good linear relationship with viscosity (log η) in the viscous range of 0.59-945 cP in methanol/glycerol mixtures. Interestingly, 1 showed enhanced emission at 534 nm in 70% water compared to pure acetonitrile due to the aggregation-induced inhibited rotations. Cellular imaging suggested that 1 could successfully sense lysosomal viscosity changes induced by lipopolysaccharide, nystatin, low temperature, and dexamethasone in living cells, which could be further applied in autophagy monitoring by tracing viscosity changes. As a comparison, its analogue 2 directly linking with the phenyl group showed no viscosity-responsive or AIE properties. Therefore, for the first time, we reported a meso-benzothiazole-BODIPY-based fluorescent rotor with AIE and lysosomal viscosity-responsive properties in nervous cells, which was further applied in monitoring autophagy, and this work thus could provide an innovative strategy for the design of potential AIE and viscosity-responsive probes.

Uncertainty-Guided Voxel-Level Supervised Contrastive Learning for Semi-Supervised Medical Image Segmentation.

Semi-supervised learning reduces overfitting and facilitates medical image segmentation by regularizing the learning of limited well-annotated data with the knowledge provided by a large amount of unlabeled data. However, there are many misuses and underutilization of data in conventional semi-supervised methods. On the one hand, the model will deviate from the empirical distribution under the training of numerous unlabeled data. On the other hand, the model treats labeled and unlabeled data differently and does not consider inter-data information. In this paper, a semi-supervised method is proposed to exploit unlabeled data to further narrow the gap between the semi-supervised model and its fully-supervised counterpart. Specifically, the architecture of the proposed method is based on the mean-teacher framework, and the uncertainty estimation module is improved to impose constraints of consistency and guide the selection of feature representation vectors. Notably, a voxel-level supervised contrastive learning module is devised to establish a contrastive relationship between feature representation vectors, whether from labeled or unlabeled data. The supervised manner ensures that the network learns the correct knowledge, and the dense contrastive relationship further extracts information from unlabeled data. The above overcomes data misuse and underutilization in semi-supervised frameworks. Moreover, it favors the feature representation with intra-class compactness and inter-class separability and gains extra performance. Extensive experimental results on the left atrium dataset from Atrial Segmentation Challenge demonstrate that the proposed method has superior performance over the state-of-the-art methods.

WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification.

The early detection and timely treatment of breast cancer can save lives. Mammography is one of the most efficient approaches to screening early breast cancer. An automatic mammographic image classification method could improve the work efficiency of radiologists. Current deep learning-based methods typically use the traditional softmax loss to optimize the feature extraction part, which aims to learn the features of mammographic images. However, previous studies have shown that the feature extraction part cannot learn discriminative features from complex data using the standard softmax loss. In this paper, we design a new architecture and propose respective loss functions. Specifically, we develop a double-classifier network architecture that constrains the extracted features' distribution by changing the classifiers' decision boundaries. Then, we propose the double-classifier constraint loss function to constrain the decision boundaries so that the feature extraction part can learn discriminative features. Furthermore, by taking advantage of the architecture of two classifiers, the neural network can detect the difficult-to-classify samples. We propose a weighted double-classifier constraint method to make the feature extract part pay more attention to learning difficult-to-classify samples' features. Our proposed method can be easily applied to an existing convolutional neural network to improve mammographic image classification performance. We conducted extensive experiments to evaluate our methods on three public benchmark mammographic image datasets. The results showed that our methods outperformed many other similar methods and state-of-the-art methods on the three public medical benchmarks. Our code and weights can be found on GitHub.

A novel methylenemalononitrile-BODIPY-based fluorescent probe for highly selective detection of hydrogen peroxide in living cells.

Hydrogen peroxide (H2O2) plays vital roles in oxidative stress and signal transduction in living organisms, and its abnormal levels could be linked to many diseases. Despite numerous efforts spent, it is still urgent and of high importance to develop better H2O2 probes with good selectivity, high sensitivity and low backgrounds. To this end, a novel boron dipyrromethene (BODIPY)-based fluorescent probe with an electron-withdrawing methylenemalononitrile at the meso position has been rationally designed, successfully synthesized and investigated for detection of H2O2 in aqueous solutions and living cells, which exhibited high selectivity and sensitivity, fluorescent "turn-on" phenomenon at 540 nm, and ratiometric changes from 506 to 540 nm. Upon exposure to H2O2, a strong fluorescent emission at 540 nm appeared and the corresponding quantum yields changed from 0.009 to 0.13. The detection limit towards H2O2 was calculated to be 31 nM by the linear fluorescence change at 540 nm in the H2O2-concentration ranging from 2 to 10 µM. This probe was applicable in a pH range from 6 to 10. Meanwhile, the sensing mechanism was also confirmed by the 1H NMR and mass spectrometry, suggesting that the above changes might be ascribed to the quick addition and oxidization of the double bond. Furthermore, confocal imaging results also showed great enhancement of intracellular fluorescence upon exposure to H2O2 and PMA in RAW264.7 cells, unambiguously confirming its great potentials as a fluorescent probe for highly sensitive detection of both exogenous and endogenous H2O2 in living cells.

Synthesis, molecular docking, and evaluation of antibacterial activity of 1,2,4-triazole-norfloxacin hybrids.

A series of 1,2,4-triazole-norfloxacin hybrids was designed, synthesized, and evaluated for in vitro antibacterial activity against common pathogens. All the newly synthesized compounds were characterized by Fourier-transform infrared spectrophotometry, proton and carbon nuclear magnetic resonance, and electrospray ionization-mass spectrometry. Representative compounds from each step of the synthesis were further characterized by X-ray crystallography. Many of the compounds synthesized exhibited antibacterial activity superior to that of norfloxacin toward both, gram-positive and gram-negative bacteria. The toxicity of the 1,2,4-triazole-norfloxacin hybrids toward bacterial cells was 32-512 times higher than that toward mouse fibroblast cells. Moreover, hemolysis was not observed at concentrations of 64 µg/mL, suggesting good biocompatibility. Molecular docking showed a least binding energy of -9.4 to -9.7 kcal/mol, and all compounds were predicted to show remarkable affinity for the bacterial topoisomerase IV.

Deep adversarial domain adaptation for breast cancer screening from mammograms.

The early detection of breast cancer greatly increases the chances that the right decision for a successful treatment plan will be made. Deep learning approaches are used in breast cancer screening and have achieved promising results when a large-scale labeled dataset is available for training. However, they may suffer from a dramatic decrease in performance when annotated data are limited. In this paper, we propose a method called deep adversarial domain adaptation (DADA) to improve the performance of breast cancer screening using mammography. Specifically, our aim is to extract the knowledge from a public dataset (source domain) and transfer the learned knowledge to improve the detection performance on the target dataset (target domain). Because of the different distributions of the source and target domains, the proposed method adopts an adversarial learning technique to perform domain adaptation using the two domains. Specifically, the adversarial procedure is trained by taking advantage of the disagreement of two classifiers. To evaluate the proposed method, the public well-labeled image-level dataset Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) is employed as the source domain. Mammography samples from the West China Hospital were collected to construct our target domain dataset, and the samples are annotated at case-level based on the corresponding pathological reports. The experimental results demonstrate the effectiveness of the proposed method compared with several other state-of-the-art automatic breast cancer screening approaches.

Crystal structures and biological properties of aroylhydrazone Ni(II) complexes.

Three aroylhydrazone ligands ((Z)-N'-([2,2'-bithiophen]-5-ylmethylene)-2-hydroxybenzohydrazide, HL1; (Z)-N'-([2,2'-bithiophen]-5-ylmethylene)-3-hydroxybenzohydrazide, HL2; and (Z)-N'-([2,2'-bithiophen]-5-ylmethylene)-4-hydroxybenzohydrazide, HL3) and their complexes with nickel (Ni(L1)2, 1; Ni(L2)2, 2; Ni(L3)2∙DMF, 3) were synthesized and characterized by ESI-MS, NMR, IR, UV-vis and elemental analysis techniques. The molecular structure of ligand (HL2) and complexes 1-3 was confirmed by single crystal X-ray crystallography. The single crystal X-ray structure of complexes 1-3 showed a distorted square planar geometry around the metal center, and the ligands adopt a bidentate chelating mode. The interaction of calf thymus (ctDNA) with nickel(II) complexes was explored using absorption, emission spectrum, viscosity, and circular dichroism methods. These complexes exhibited moderate affinity for ctDNA through groove binding modes. The most efficient DNA binder was complex 2. The interaction of the complexes with DNA has also been supported by molecular docking study and molecular dynamics simulation. An in vitro cytotoxicity study of the complexes found low activity against human cervical (Hela) and breast (MCF-7) cancer cell lines, with the best results for complex 2, where IC50 values are 86 µM and 92 µM respectively.

Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification.

Breast cancer is one of the most frequently diagnosed solid cancers. Mammography is the most commonly used screening technology for detecting breast cancer. Traditional machine learning methods of mammographic image classification or segmentation using manual features require a great quantity of manual segmentation annotation data to train the model and test the results. But manual labeling is expensive, time-consuming, and laborious, and greatly increases the cost of system construction. To reduce this cost and the workload of radiologists, an end-to-end full-image mammogram classification method based on deep neural networks was proposed for classifier building, which can be constructed without bounding boxes or mask ground truth label of training data. The only label required in this method is the classification of mammographic images, which can be relatively easy to collect from diagnostic reports. Because breast lesions usually take up a fraction of the total area visualized in the mammographic image, we propose different pooling structures for convolutional neural networks(CNNs) instead of the common pooling methods, which divide the image into regions and select the few with high probability of malignancy as the representation of the whole mammographic image. The proposed pooling structures can be applied on most CNN-based models, which may greatly improve the models' performance on mammographic image data with the same input. Experimental results on the publicly available INbreast dataset and CBIS dataset indicate that the proposed pooling structures perform satisfactorily on mammographic image data compared with previous state-of-the-art mammographic image classifiers and detection algorithm using segmentation annotations.

Nickel complexes of aroylhydrazone ligand: synthesis, crystal structure and DNA binding properties.

In this work, three aroylhydrazone ligands ((E)-2-hydroxy-N'-(1-(pyrazin-2-yl)ethylidene)benzohydrazide, HL1; (E)-3-hydroxy-N'-(1-(pyrazin-2-yl)ethylidene)benzohydrazide, HL2; and (E)-4-hydroxy-N'-(1-(pyrazin-2-yl)ethylidene)benzohydrazide, HL3) and their complexes with nickel (Ni(L1)2, NiL1; Ni(L2)2∙2DMF, NiL2; Ni(L3)2∙2DMF, NiL3) were prepared. The single crystal X-ray structures analysis of three compounds showed that they were neutral. The ligand adopts tridentate chelating mode. The nickel ion is six-coordinate with two O atoms and four N atoms from two ligands, and forms an octahedral arrangement. The investigation of DNA binding ability by ultraviolet and fluorescence titrations showed that NiL2 and NiL3 exhibit moderate binding affinity toward calf Thymus DNA. Spectroscopy, molecular docking, and molecular dynamics simulation indicated that NiL2 and NiL3 bind at the minor groove of DNA through intercalation.

Copper(ii) complexes with NNO ligands: synthesis, crystal structures, DNA cleavage, and anticancer activities.

Three novel copper(ii) complexes, Cu(L1)2 (1), Cu(L2)2·2DMF (2), and Cu(L3)2·2DMF (3), were synthesized using three aroylhydrazone ligands, (E)-2-hydroxy-N'-(1-(pyrazin-2-yl)ethylidene)benzohydrazide (HL1), (E)-3-hydroxy-N'-(1-(pyrazin-2-yl)ethylidene)benzohydrazide (HL2) and (E)-4-hydroxy-N'-(1-(pyrazin-2-yl)ethylidene)benzohydrazide (HL3). The complexes were characterized by elemental analysis, infrared (IR), and Ultraviolet-visible light (UV-vis) spectroscopy. The X-ray crystal structures of the complexes all possess a distorted octahedral coordination geometry. Both an absorption spectral titration and a competitive binding assay (ethidium bromide, 4',6-diamidino-2-phenylindole (DAPI), and methyl green) revealed that complexes 2 and 3 bind readily to calf thymus DNA (ctDNA) through intercalative and minor groove binding modes. Complexes 2 and 3 also exhibited oxidative cleavage of supercoiled plasmid DNA (pUC19) in the presence of ascorbic acid as an activator. Cytotoxicity studies showed that complexes 2 and 3 possessed high cytotoxicities toward the HeLa human cervical cancer cell line, but weak toxicities toward the L929 normal mouse fibroblast cell line. We therefore have reason to believe that complexes 2 and 3 both show potential as promising anticancer candidate drugs.

Reversible crystal-to-crystal transformation from a trinuclear cluster to a 1D chain and the corresponding spin crossover (SCO) behaviour change.

Reversible crystal-to-crystal transformation between a linear trinuclear Fe(ii) complex [Fe3(NH2-trz)6(SCN)5(H2O)] (SCN)·4H2O (1, NH2-trz = 4-amino-1,2,4-triazole) and a 1D chain [Fe3(NH2-trz)6(SCN)5]n(SCN)n (1a) and the SCO behaviour change have been studied by X-ray single-crystal diffraction, magnetic measurements and DSC. Complex 1a exhibits one more SCO step at a low temperature.

In-situ nano-crystal-to-crystal transformation synthesis of energetic materials based on three 5,5'-azotetrazolate Cr(III) salts.

The in-situ nano-crystal-to-crystal transformation (SCCT) synthesis provides a powerful approach for tailoring controllable feature shapes and sizes of nano crystals. In this work, three nitrogen-rich energetic nano-crystals based on 5,5'-azotetrazolate(AZT2-) Cr(III) salts were synthesized by means of SCCT methodology. SEM and TEM analyses show that the energetic nano-crystals feature a composition- and structure-dependent together with size-dependent thermal stability. Moreover, nano-scale decomposition products can be obtained above 500 °C, providing a new method for preparing metallic oxide nano materials.

Chiral resolution of a racemic macrocyclic complex by recognition of one enantiomer over the other: structures and DFT calculations.

The enantiopure agents d- and l-leucine, selectively bind RR and SS enantiomers from a racemate [Ni(alpha-rac-L)](2+) to give {[Ni(RR-L)(d-HLeu)](ClO(4))(2)}(n) (Delta-) and {[Ni(SS-L)(l-HLeu)](ClO(4))(2)}(n) (Lambda-), respectively, and leave the corresponding uninteracted SS and RR enantiomers of [Ni(alpha-SS-L)](ClO(4))(2) (S-) and [Ni(alpha-RR-L)](ClO(4))(2) (R-). Occasionally, a few crystals of {[Ni(RR-L)(l-HLeu)](ClO(4))(2)}(n) (Delta-) and {[Ni(SS-L)(d-HLeu)](ClO(4))(2)}(n) (Lambda-) were found to have accreted with the crystals of Lambda-/R-, and Delta-/S-, respectively (the yields are less than 2%). The results of X-ray crystal structural analysis reveal that Delta- and Lambda-, S- and R-, and Delta- and Lambda- are enantiomers, in which Delta- and Delta- possess 1D right-handed helical chains, while Lambda- and Lambda- exhibit a motif of 1D left-handed helical chains. The results of DFT calculations reveal that the single-point energies of [Ni(RR-L)(d-HLeu)](2+)/[Ni(SS-L)(l-HLeu)](2+) in Delta-/Lambda- are 582 kJ mol(-1) lower than those of [Ni(RR-L)(l-HLeu)](2+)/[Ni(SS-L)(d-HLeu)](2+) in Delta-/Lambda-, demonstrating the favorable stereo-coordination environments of [Ni(alpha-RR-L)](2+) and [Ni(alpha-SS-L)](2+) towards d and l-HLeu, respectively.