High glucose microenvironment and human mesenchymal stem cell behavior.

High glucose (HG) culture conditions in vitro and persistent exposure to hyperglycemia in diabetes patients are detrimental to stem cells, analogous to any other cell type in our body. It interferes with diverse signaling pathways, i.e. mammalian target of rapamycin (mTOR)-phosphoinositide 3-kinase (PI3K)-Akt signaling, to impact physiological cellular functions, leading to low cell survival and higher cell apoptosis rates. While elucidating the underlying mechanism responsible for the apoptosis of adipose tissue-derived mesenchymal stem cells (MSCs), a recent study has shown that HG culture conditions dysregulate mTOR-PI3K-Akt signaling in addition to mitochondrial malfunctioning due to defective mitochondrial membrane potential (MtMP) that lowers ATP production. This organelle-level dysfunction energy-starves the cells and increases oxidative stress and ultrastructural abnormalities. Disruption of the mitochondrial electron transport chain produces an altered mitochondrial NAD+/NADH redox state as evidenced by a low NAD+/NADH ratio that primarily contributes to the reduced cell survival in HG. Some previous studies have also reported altered mitochondrial membrane polarity (causing hyperpolarization) and reduced mitochondrial cell mass, leading to perturbed mitochondrial homeostasis. The hostile microenvironment created by HG exposure creates structural and functional changes in the mitochondria, altering their bioenergetics and reducing their capacity to produce ATP. These are significant data, as MSCs are extensively studied for tissue regeneration and restoring their normal functioning in cell-based therapy. Therefore, MSCs from hyperglycemic donors should be cautiously used in clinical settings for cell-based therapy due to concerns of their poor survival rates and increased rates of post engraftment proliferation. As hyperglycemia alters the bioenergetics of donor MSCs, rectifying the loss of MtMP may be an excellent target for future research to restore the normal functioning of MSCs in hyperglycemic patients.

Two-stage adversarial learning based unsupervised domain adaptation for retinal OCT segmentation.

BACKGROUND: Deep learning based optical coherence tomography (OCT) segmentation methods have achieved excellent results, allowing quantitative analysis of large-scale data. However, OCT images are often acquired by different devices or under different imaging protocols, which leads to serious domain shift problem. This in turn results in performance degradation of segmentation models. PURPOSE: Aiming at the domain shift problem, we propose a two-stage adversarial learning based network (TSANet) that accomplishes unsupervised cross-domain OCT segmentation. METHODS: In the first stage, a Fourier transform based approach is adopted to reduce image style differences from the image level. Then, adversarial learning networks, including a segmenter and a discriminator, are designed to achieve inter-domain consistency in the segmentation output. In the second stage, pseudo labels of selected unlabeled target domain training data are used to fine-tune the segmenter, which further improves its generalization capability. The proposed method was tested on cross-domain datasets for choroid or retinoschisis segmentation tasks. For choroid segmentation, the model was trained on 400 images and validated on 100 images from the source domain, and then trained on 1320 unlabeled images and tested on 330 images from target domain I, and also trained on 400 unlabeled images and tested on 200 images from target domain II. For retinoschisis segmentation, the model was trained on 1284 images and validated on 312 images from the source domain, and then trained on 1024 unlabeled images and tested on 200 images from the target domain. RESULTS: The proposed method achieved significantly improved results over that without domain adaptation, with improvement of 8.34%, 55.82% and 3.53% in intersection over union (IoU) respectively for the three test sets. The performance is better than some state-of-the-art domain adaptation methods. CONCLUSIONS: The proposed TSANet, with image level adaptation, feature level adaptation and pseudo-label based fine-tuning, achieved excellent cross-domain generalization. This alleviates the burden of obtaining additional manual labels when adapting the deep learning model to new OCT data.

Adsorption of NO x (x = 1, 2) Molecules on the CoFeMnSi(001) Surface: First-Principles Insights.

In this article, the adsorption of NO x (x = 1, 2) gas molecules on the (001) surface of CoFeMnSi quaternary Heusler alloys has been investigated theoretically with density functional theory (DFT) calculations. The adsorption strength was estimated with adsorption energy (E a), magnitude of charge transfer (ΔQ), charge density difference (CDD), minimum distance between molecule and surface (d), and adsorption mechanism was analyzed with density of states. The results showed that unlike half-metallic nature of the bulk phase, the pristine CoFeMnSi(001) surface exhibited metallic character caused by the emergence of electronic states of the atoms in the top-most layer of the surface. It was found that both NO and NO2 molecules undergo chemical adsorption and strongly interact with the surface evidenced by the large value of E a and ΔQ. In particular, the NO x molecule dissociates into N and O atoms for some adsorption configurations. Bader charge analysis reveals that NO x molecules act as charge acceptors by drawing charge from the surface atoms through p-d hybridization. Such findings might be useful in the development of Heusler alloys based gas sensors.

Graded 2D/3D Perovskite Hetero-Structured Films with Suppressed Interfacial Recombination for Efficient and Stable Solar Cells via DABr Treatment.

Several strategies and approaches have been reported for improving the resilience and optoelectronic properties of perovskite films. However, fabricating a desirable and stable perovskite absorber layer is still a great challenge due to the optoelectronic and fabrication limitations of the materials. Here, we introduce diethylammonium bromide (DABr) as a post-treatment material for the pre-deposited methylammonium lead iodide (MAPbI3) film to fabricate a high-quality two-dimensional/three-dimensional (2D/3D) stacked hetero-structure perovskite film. The post-treatment method of DABr not only induces the small crystals of MAPbI3 perovskite secondary growth into a large crystal, but also forms a 2D capping layer on the surface of the 3D MAPbI3 film. Meanwhile, the grains and crystallization of 3D film with DABr post-treatment are significantly improved, and the surface defect density is remarkably reduced, which in turn effectively suppressed the charge recombination in the interface between the perovskite layer and the charge transport layer. The perovskite solar cell based on the DABr-treatment exhibited a significantly enhanced power conversion efficiency (PCE) of 19.10% with a notable improvement in the open circuit voltage (VOC) of 1.06 V and good stability, advocating the potential of this perovskite post-treatment approach.

Deep Learning Approach for Automatic Microaneurysms Detection.

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely "E-Ophtha" and "DIARETDB1", and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.

On m-polar Diophantine Fuzzy N-soft Set with Applications.

INTRODUCTION: In this paper, we present a novel hybrid model m-polar Diophantine fuzzy N-soft set and define its operations. METHODS: We generalize the concepts of fuzzy sets, soft sets, N-soft sets, fuzzy soft sets, intuitionistic fuzzy sets, intuitionistic fuzzy soft sets, Pythagorean fuzzy sets, Pythagorean fuzzy soft sets and Pythagorean fuzzy N-soft sets by incorporating our proposed model. Additionally, we define three different sorts of complements for Pythagorean fuzzy N-soft sets and examine few outcomes, which do not hold in Pythagorean fuzzy N-soft sets complements unlike to crisp set. We further discuss (α, ß, γ) -cut of m-polar Diophantine fuzzy N-soft sets and their properties. Lastly, we prove our claim that the defined model is a generalization of the soft set, N-soft set, fuzzy Nsoft set, intuitionistic fuzzy N soft set, and Pythagorean fuzzy N-soft set. RESULTS: m-polar Diophantine fuzzy N-soft set is more efficient and an adaptable model to manage uncertainties as it also overcomes drawbacks of existing models, which are to be generalized. CONCLUSION: We introduced the novel concept of m-polar Diophantine fuzzy N-soft sets (MPDFNS sets).

Cobalt Single Atom Incorporated in Ruthenium Oxide Sphere: A Robust Bifunctional Electrocatalyst for HER and OER.

The development of highly active and stable bifunctional noble-metal-based electrocatalysts for the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) is a crucial goal for clean and renewable energy, which still remains challenging. Herein, we report an efficient and stable catalyst comprising a Co single atom incorporated in an RuO2 sphere for HER and OER, in which the Co single atom in the RuO2 sphere was confirmed by XAS, AC-STEM, and DFT. This tailoring strategy uses a Co single atom to modify the electronic structures of the surrounding Ru atoms and thereby remarkably elevates the electrocatalytic activities. The catalyst requires ultralow overpotentials, 45 mV for HER and 200 mV for OER, to deliver a current density of 10 mA cm-2 . The theoretical calculations reveal that the energy barriers for HER and OER are lowered after incorporation of a cobalt single atom.

A Novel Algebraic Structure of (α,ß)-Complex Fuzzy Subgroups.

A complex fuzzy set is a vigorous framework to characterize novel machine learning algorithms. This set is more suitable and flexible compared to fuzzy sets, intuitionistic fuzzy sets, and bipolar fuzzy sets. On the aspects of complex fuzzy sets, we initiate the abstraction of (α,ß)-complex fuzzy sets and then define α,ß-complex fuzzy subgroups. Furthermore, we prove that every complex fuzzy subgroup is an (α,ß)-complex fuzzy subgroup and define (α,ß)-complex fuzzy normal subgroups of given group. We extend this ideology to define (α,ß)-complex fuzzy cosets and analyze some of their algebraic characteristics. Furthermore, we prove that (α,ß)-complex fuzzy normal subgroup is constant in the conjugate classes of group. We present an alternative conceptualization of (α,ß)-complex fuzzy normal subgroup in the sense of the commutator of groups. We establish the (α,ß)-complex fuzzy subgroup of the classical quotient group and show that the set of all (α,ß)-complex fuzzy cosets of this specific complex fuzzy normal subgroup form a group. Additionally, we expound the index of α,ß-complex fuzzy subgroups and investigate the (α,ß)-complex fuzzification of Lagrange's theorem analog to Lagrange' theorem of classical group theory.

Fused tetraphenylethylene-triphenylamine as an efficient hole transporting material in perovskite solar cells.

A fused tetraphenylethylene-based hole transporting material shows higher power conversion efficiency and better stability compared with its non-fused counterpart, and the former molecule even outperforms the conventional spiro-OMeTAD.

MACl-Induced Intermediate Engineering for High-Performance Mixed-Cation Perovskite Solar Cells.

Recently, mixed-cation perovskites have been extensively used for high-performance solar cells. Nevertheless, the mixed-cation perovskite based on formamidinium methylammonium lead tri-iodide (FAxMA1-xPbI3) fabricated through the existing methods often suffers from phase stability and trap density. Herein, we demonstrate a facile intermediate engineering approach to improve the quality of the mixed-cation perovskite based on FAxMA1-xPbI3. Varying concentrations of methylammonium chloride (MACl) are used to treat the FA-MA-PbI3-solvent intermediate. It is noted that MACl has a strong impact on the crystallization kinetics and charge carrier dynamics as well as the defect density of the obtained perovskite. The mixed-cation perovskite treated with 20 mg mL-1 MACl yields a large grain size, highly uniform morphology, and better crystalline stability. Subsequently, the device with an acquired high-quality mixed-cation perovskite shows a high efficiency of 20.40%, which is obviously higher than that obtained from the traditional nontreated method. Moreover, the device prepared through the developed method could retain over 85% of the initial efficiency after 860 h at room temperature.

Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies.

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder-decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases-especially metastatic cancers. The deep learning model for nodules' detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

Low-Temperature Annealed Perovskite Films: A Trade-Off between Fast and Retarded Crystallization via Solvent Engineering.

Currently, in the field of photovoltaics, researchers are working hard to produce efficient, stable, and commercially feasible devices. The prime objective behind the innovation of any photovoltaic device is to yield more energy with easy manufacture and less process cost. Perovskite solar cells (PSCs) are prominent in the field of photovoltaics, owing to its low material cost, simple fabrication process, and ideal optoelectronic properties. Despite rapid augmentation in progress of PSCs, it is still a bottleneck to produce a high-quality perovskite layer at low temperatures in a short time. Herein, a facile solvent engineering technique is used to produce a high-quality perovskite layer at 50 °C in just 30 min. We employed solvent coordination strength to form the intermediate state as well as their sensitive behavior against antisolvent to establish a trade-off between fast and retarded crystallization. Dimethylsulphoxide (DMSO), a traditional co-solvent is used as an additive instead of co-solvent; in contrast, mixed 1-methyl-2-pyrrolidinone (NMP) and dimethylacetamide are employed as principal solvents for perovskite precursors. Different volume ratios of DMSO as a fraction of NMP are added to examine the evolution of the perovskite layer at low temperatures. It is noted that the mixed solvent with 30% DMSO shows a pin-hole free, uniform, and compact layer with a strong absorption spectrum. Promisingly, the corresponding device with 30% DMSO shows a high efficiency of 18.19%, which is even comparable to traditionally high-temperature annealed PSCs. These findings may provide a way to produce low-temperature annealed, high-quality perovskite films and subsequently facilitate the production of cost-effective and efficient devices.

Hyperglycemia associated blood viscosity can be a nexus stimuli.

BACKGROUND: The viscosity of a fluid is a measure of its resistance. It is the thickness and stickiness of blood, and a direct measure of the resistance of blood to flow through the vessels. Various factors in the blood have direct or indirect impact on blood viscosity. These hemorheological factors play an important role in the pathogenesis of many diseases. Glucose is one such factor, which, when increased in the blood, causes resistance in the blood flow. OBJECTIVE: The present study is aimed to assess the changes in blood viscosity associated with hyperglycemia in rodents. METHODS: Diabetic patients were grouped, depending on the duration of their diabetic status assessed by their increased HbA1c. Similarly rodents were subjected to acute or chronic hyperglycemic conditions in various experiments. In vivo, perfusion study was performed using micro probe in diabetic mice. Flow cytometry was used to assess the expression of VCAM-1 on endothelial surface. RESULTS: An approximate 40% increase in blood viscosity is observed in individual who were diabetic for the past 15 years than those who were diagnosed just one year back. Similarly such increase in blood viscosity was evident in different experiments of rodents. Our in vivo perfusion study did not showed conclusive finding however long term hyperglycemia can have deleterious effect on flow rate. Vascular pathology which was evident from the data of flow cytometry, where increase in VCAM-1 expression on the endothelial surface was observed in response to glucose and in diabetic mice. CONCLUSIONS: Hyperglycemia implicates the blood viscosity which in turn can have tedious effect on metabolic syndromes thus causing the serious effect in the tissue perfusion of an organs.

Hexa-kis-(N,N'-dimethyl-thio-urea-κS)nickel(II) nitrate.

The title complex salt, [Ni(C(3)H(8)N(2)S)(6)](NO(3))(2), consists of an [Ni(Dmtu)(6)](2+) (Dmtu is N,N'-dimethyl-thio-urea) dication and two nitrate counter-anions. The Ni(II) atom (site symmetry ) is coordinated by the S atoms of six Dmtu ligands within a slightly distorted octa-hedral environment. The crystal structure is characterized by weak intra-molecular N-H⋯S inter-actions and by inter-molecular N-H⋯O hydrogen bonds involving the nitrate anion (site symmetry 3.). These inter-molecular inter-actions lead to the formation of two-dimensional networks lying parallel to the ab plane. The networks are linked via non-classical inter-molecular C-H⋯O hydrogen bonds, forming a three-dimensional arrangement.