Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.

Purpose: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs). Material and methods: The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model. Results: Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively. Conclusion: The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

Machine learning identifies SLC6A14 as a novel biomarker promoting the proliferation and metastasis of pancreatic cancer via Wnt/ß-catenin signaling.

Pancreatic cancer (PC) has the poorest prognosis compared to other common cancers because of its aggressive nature, late detection, and resistance to systemic treatment. In this study, we aimed to identify novel biomarkers for PC patients and further explored their function in PC progression. We analyzed GSE62452 and GSE28735 datasets, identifying 35 differentially expressed genes (DEGs) between PC specimens and non-tumors. Based on 35 DEGs, we performed machine learning and identified eight diagnostic genes involved in PC progression. Then, we further screened three critical genes (CTSE, LAMC2 and SLC6A14) using three GEO datasets. A new diagnostic model was developed based on them and showed a strong predictive ability in screen PC specimens from non-tumor specimens in GEO, TCGA datasets and our cohorts. Then, clinical assays based on TCGA datasets indicated that the expression of LAMC2 and SLC6A14 was associated with advanced clinical stage and poor prognosis. The expressions of LAMC2 and SLC6A14, as well as the abundances of a variety of immune cells, exhibited a significant positive association with one another. Functionally, we confirmed that SLC6A14 was highly expressed in PC and its knockdown suppressed the proliferation, migration, invasion and EMT signal via regulating Wnt/ß-catenin signaling pathway. Overall, our findings developed a novel diagnostic model for PC patients. SLC6A14 may promote PC progression via modulating Wnt/ß-catenin signaling. This work offered a novel and encouraging new perspective that holds potential for further illuminating the clinicopathological relevance of PC as well as its molecular etiology.

HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAM.

The early lesions of Hashimoto's thyroiditis are inconspicuous, and the ultrasonic features of these early lesions are indistinguishable from other thyroid diseases. This paper proposes a Hashimoto Thyroiditis ultrasound image classification model HT-RCM which consists of a Residual Full Convolution Transformer (Res-FCT) model and a Residual Channel Attention Module (Res-CAM). To collect the low-order information caused by hypoechoic signals accurately, the residual connection is injected between FCTs to form Res-FCT which helps HT-RCM superimpose the low-order input information and high-order output information together. Res-FCT can make HT-RCM focus more on hypoechoic information while avoiding gradient dispersion. The initial feature map is inserted into Res-FCT again through a down-sampling component, which further helps HT-RCM exact multi-level original semantic information in the ultrasound image. Res-CAM is constructed by implementing a residual connection between a channel attention module and a convolution layer. Res-CAM can effectively increase the weights of the lesion channels while suppressing the weights of the noise channels, which makes HT-RCM focus more on the lesion regions. The experimental results on our collected dataset show that HT-RCM outperforms the mainstream models and obtains state-of-the-art performance in HT ultrasound image classification.

Constructing a Deep Learning Radiomics Model Based on X-ray Images and Clinical Data for Predicting and Distinguishing Acute and Chronic Osteoporotic Vertebral Fractures: A Multicenter Study.

RATIONALE AND OBJECTIVES: To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs). METHODS: A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA). RESULTS: Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models. CONCLUSION: Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram.

Repair of soft magnetic properties of wasted silicon steel and Co7Fe3 alloy deposition mechanism.

In order to repair the soft magnetic properties of wasted silicon steel, a theoretical process of co-depositing Co-Fe soft magnetic alloy on the surface of wasted silicon steel is proposed. The results show that the co-deposited Co-Fe alloy coatings can serve to repair the soft magnetic properties of wasted silicon as detected by the vibrating sample magnetometer, and the alloy coatings with Co7Fe3 as the main phase structure can provide surface protection for silicon steel. Subsequently, the mechanism of co-deposited Co-Fe alloys was investigated, and it was concluded that Co2+ and Fe2+ undergo a one-step two-electron co-deposition reaction, as studied using cyclic voltammetry. The chronoamperometric analysis and its fitting results indicated that the deposition of Co2+ and Fe2+ was a diffusion-controlled transient nucleation process, and the AC impedance indicated that higher voltages were favorable for the deposition of Co-Fe alloys but were accompanied by hydrogen precipitation reactions.

Targeted deprivation of methionine with engineered Salmonella leads to oncolysis and suppression of metastasis in broad types of animal tumor models.

The strong dependency of almost all malignant tumors on methionine potentially offers a pathway for cancer treatment. We engineer an attenuated strain of Salmonella typhimurium to overexpress an L-methioninase with the aim of specifically depriving tumor tissues of methionine. The engineered microbes target solid tumors and induce a sharp regression in several very divergent animal models of human carcinomas, cause a significant decrease in tumor cell invasion, and essentially eliminate the growth and metastasis of these tumors. RNA sequencing analyses reveal that the engineered Salmonella reduce the expression of a series of genes promoting cell growth, cell migration, and invasion. These findings point to a potential treatment modality for many metastatic solid tumors, which warrants further tests in clinical trials.

HTC-Net: Hashimoto's thyroiditis ultrasound image classification model based on residual network reinforced by channel attention mechanism.

Convolutional neural network (CNN) is efficient in extracting and aggregating local features in the spatial dimension of the images. However, obtaining the inapparent texture information of the low-echo area in the ultrasound images is not easy, and it is especially challenging for the early lesion recognition in Hashimoto's thyroiditis (HT) ultrasound images. In this paper, a HT ultrasound image classification model HTC-Net based on residual network reinforced by channel attention mechanism is proposed. HTC-Net strengthens the features of the important channels by reinforced channel attention mechanism through which the high-level semantic information is enchanced and the low-level semantic information is suppressed. Residual network assists HTC-Net focus on the key local areas of the ultrasound images while pay attention to the global semantic information. Furthermore, in order to solve the problem of uneven distribution caused by large amount of difficult-to-classify samples in the data sets, a new feature loss function TanCELoss with weight factor dynamically adjusting is constructed. TanCELoss function can better assist HTC-Net to transform difficult-to-classify samples into easy-to-classify samples gradually, and improve the balancing distribution of the samples. The experiments are implemented based on data sets collected by the Endocrinology Department of four branches from Guangdong Provincial Hospital of Chinese Medicine. Both quantitative testing and visualization results show that HTC-Net obtains STOA performance for early lesions recognition in HT ultrasound images. HTC-Net has great application value especially under the condition of owning only small data samples.

Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features.

BACKGROUND: We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). METHODS: A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). RESULTS: Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996-0.999) and 0.946 (95% CI, 0.906-0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. CONCLUSION: The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.

A nomogram based on radiological features of MRI for predicting the risk of severe pain in patients with osteoarthritis of the knee.

Methods: This study aimed to develop and validate a nomogram for predicting the risk of severe pain in patients with knee osteoarthritis. A total of 150 patients with knee osteoarthritis were enrolled from our hospital, and nomogram was established through a validation cohort (n = 150). An internal validation cohort (n = 64) was applied to validate the model. Results: Eight important variables were identified using the Least absolute shrinkage and selection operator (LASSO) and then a nomogram was developed by Logistics regression analysis. The accuracy of the nomogram was determined based on the C-index, calibration plots, and Receiver Operating Characteristic (ROC) curves. Decision curves were plotted to assess the benefits of the nomogram in clinical decision-making. Several variables were employed to predict severe pain in knee osteoarthritis, including sex, age, height, body mass index (BMI), affected side, Kellgren-Lawrance (K-L) degree, pain during walking, pain going up and down stairs, pain sitting or lying down, pain standing, pain sleeping, cartilage score, Bone marrow lesion (BML) score, synovitis score, patellofemoral synovitis, bone wear score, patellofemoral bone wear, and bone wear scores. The LASSO regression results showed that BMI, affected side, duration of knee osteoarthritis, meniscus score, meniscus displacement, BML score, synovitis score, and bone wear score were the most significant risk factors predicting severe pain. Conclusions: Based on the eight factors, a nomogram model was developed. The C-index of the model was 0.892 (95% CI: 0.839-0.945), and the C-index of the internal validation was 0.822 (95% CI: 0.722-0.922). Analysis of the ROC curve of the nomogram showed that the nomogram had high accuracy in predicting the occurrence of severe pain [Area Under the Curve (AUC) = 0.892] in patients with knee osteoarthritis (KOA). The calibration curves showed that the prediction model was highly consistent. Decision curve analysis (DCA) showed a higher net benefit for decision-making using the developed nomogram, especially in the >0.1 and <0.86 threshold probability intervals. These findings demonstrate that the nomogram can predict patient prognosis and guide personalized treatment.

Pathological and Molecular Characterization of a Duck Plague Outbreak in Southern China in 2021.

Duck plague (DP) is a highly contagious viral disease in ducks caused by the duck plague virus (DPV). The DPV, a member of Herpesviridae, poses a severe threat to the waterfowl farming industry worldwide. In this study, we reported a recent outbreak of DPV in domestic laying ducks at 310 days of age from southern China in December 2021. The gross lesion, histopathologic examination, molecular detection, and genetic characterization studies of DPV are described here. As a result, gross lesions such as an enlarged congestive spleen and liver were observed. Liver with vacuolar degeneration and small vacuoles and spleen with hemosiderosis were remarkable microscopic findings. Our results suggested that the liver had the highest viral load, followed by the trachea, pancreas, kidney, brain, spleen, and heart. In addition, DPV was successfully isolated in chicken embryo fibroblast cell culture and designated as DP-GD-305-21. The UL2, UL12, UL41, UL47, and LORF11 genes of DP-GD-305-21 shared a high nucleotide homology with the Chinese virulent (CHv) strain and the Chinese variant (CV) strain. In conclusion, this study reports the isolation and molecular characterization of DPV from a recent outbreak in southern China. Our results contributed to the understanding of the pathological and molecular characterization of currently circulating DPV in China.

Construction of Benzenesulfonamide Derivatives via Copper and Visible Light-induced Azides and S(O)2-H Coupling.

We here have developed an S(O)2-N coupling between phenylsulfinic acid derivatives and aryl azides by dual copper and visible light catalysis. In this efficient and mild pathway, the reaction produces sulfonamide compounds under redox-neutral condition, which is mechanistically different from the nitrogen nucleophilic substitution reactions. Significantly, this transformation intends to utilize the property of visible light-induced azides to generate triplet nitrene and followed coupling with sulfonyl radicals in situ to achieve structurally diverse benzenesulfinamides in good yields.

Detection and genetic characterization of novel infectious bronchitis viruses from recent outbreaks in broiler and layer chicken flocks in southern China, 2021.

Avian infectious bronchitis virus (IBV) is a prevalent RNA virus that causes respiratory distress, nephritis, salpingitis, and egg production decline in chickens, resulting in significant economic loss. IBV is composed of complex genotypes and serotypes, which poses a great challenge for disease control. The current study reports 2 IBV outbreaks which were characterized by respiratory symptoms in IBV vaccinated commercial broilers and layers in Guangdong, China, in 2021. Two IBV strains, ZH01 and HH09, were identified via a RT-PCR assay through targeting the N gene and further characterization through full-length spike (S) gene sequence analysis. Phylogenetic analysis of S1 gene revealed that both ZH01 and HH09 belonged to the GI-19 lineage but contained a certain genetic distance from the GI-19 strain. Of note, the ZH01 and HH09 strains share a low homology of 70 and 86%, respectively, with common vaccine strains (H120), resulting in low vaccine protection. Further recombination analysis based on the S1 sequence suggested the newly identified IBV strains emerged through an intragroup recombination events between CK/CH/SCDY2003-2 and I0305/19 from G1-19 lineage. In addition, a number of novel mutations such as T273I, T292A, and S331K were found in the emerging IBV strains. Taken together, this study reports the genetic characteristics of 2 recent IBV outbreaks in southern China and emphasizes the urgent need for enhanced surveillance and development of novel vaccines for the control of IBV.

Tunable Dual-Wavelength with Twin-Pulse Dissipative Solitons in All-Normal Dispersion Yb-Doped Fiber Laser.

A tunable dual-wavelength with two separated twin-pulse dissipative solitons (DSs) of Yb-doped mode-locked fiber laser in the all-normal-dispersion (ANDi) regime is firstly reported and demonstrated in this paper. A Sagnac loop is used as an all-fiber format spectral filter in the laser cavity, and stable twin-pulse DSs with different wavelength mode-locked lasers are achieved by the nonlinear polarization evolution (NPE) effect. By adjusting the polarization state of the Sagnac loop, the spectral ranges of the dual-wavelength can be tuned from 1031.3 nm to 1041.5 nm and from 1067.1 nm to 1080.9 nm, respectively. However, the pulse space between the two separated twin-pulse DSs is maintained, i.e., 41.63 ns. Furthermore, the twin-pulse can regress to the single-pulse when the pump power keeps dropping. It has been observed that the highest energy of the two twin-pulse DSs output is 23.36 nJ at a repetition rate of 2.282 MHz with a maximum pump power of 560 mW.

Improved polar-code-based efficient post-processing algorithm for quantum key distribution.

Combined with one-time pad encryption scheme, quantum key distribution guarantees the unconditional security of communication in theory. However, error correction and privacy amplification in the post-processing phase of quantum key distribution result in high time delay, which limits the final secret key generation rate and the practicability of quantum key distribution systems. To alleviate this limitation, this paper proposes an efficient post-processing algorithm based on polar codes for quantum key distribution. In this algorithm, by analyzing the channel capacity of the main channel and the wiretap channel respectively under the Wyner's wiretap channel model, we design a codeword structure of polar codes, so that the error correction and privacy amplification could be completed synchronously in a single step. Through combining error correction and privacy amplification into one single step, this efficient post-processing algorithm reduces complexity of the system and lower the post-processing delay. Besides, the reliable and secure communicaiton conditions for this algorithm has been given in this paper. Simulation results show that this post-processing algorithm satisfies the reliable and secure communication conditions well.

Leonurine-Repressed miR-18a-5p/SOCS5/JAK2/STAT3 Axis Activity Disrupts CML malignancy.

Leonurine, an active natural alkaloid compound isolated from Herba leonuri, has been reported to exhibit promising anticancer activity in solid tumors. The aim of this study was to explore whether leonurine is able to inhibit chronic myeloid leukemia (CML) malignancy. Here, we found that leonurine dose dependently inhibited the proliferation, migration, colony formation and promoted apoptosis of CML cells. Furthermore, leonurine markedly reduced CML xenograft growth in vivo. Mechanically, leonurine upregulated SOCS5 expression, thus leading JAK2/STAT3 signaling suppression. Silencing of SOCS5 by its siRNA abrogated the effect of leonurine on CML cells, demonstrating that SOCS5 mediates the anti-leukemia effect of leonurine. Notably, we observed that miR-18a-5p was remarkably increased in CML cells. Treating CML cells with leonurine significantly decreased miR-18a-5p expression. Moreover, we found miR-18a-5p repressed SOCS5 by directly targeting its 3'-UTR. miR-18a-5p downregulation induced by leonurine reduced the biological activity of CML cells by relieving miR-18a-5p repression of SOCS5 expression. Taken together, leonurine exerts significant anti-leukemia efficacy in CML by regulating miR-18a-5p/SOCS5/JAK2/STAT3 axis.

An Elastic Mineralized 3D Electrospun PCL Nanofibrous Scaffold for Drug Release and Bone Tissue Engineering.

Complex shaped and critical-sized bone defects have been a clinical challenge for many years. Scaffold-based strategies such as hydrogels provide localized drug release while filling complex defect shapes, but ultimately possess weaknesses in low mechanical strength alongside a lack of macroporous and collagen-mimicking nanofibrous structures. Thus, there is a demand for mechanically strong, extracellular matrix (ECM) mimicking scaffolds that can robustly fit complex shaped critical sized defects and simultaneously provide localized, sustained, multiple growth factor release. We therefore developed a composite, bi-phasic PCL/hydroxyapatite (HA) 3D nanofibrous (NF) scaffold for bone tissue regeneration by using our innovative electrospun-based thermally induced self-agglomeration (TISA) technique. One intriguing feature of our ECM-mimicking TISA scaffolds is that they are highly elastic and porous even after evenly coated with minerals and can easily be pressed to fit different defect shapes. Furthermore, the bio-mimetic mineral deposition technique allowed us to simultaneously encapsulate different type of drugs, e.g., proteins and small molecules, on TISA scaffolds under physiologically mild conditions. Compared to scaffolds with physically surface-adsorbed phenamil, a BMP2 signaling agonist, incorporated phenamil composite scaffolds indicated less burst release and longer lasting sustained release of phenamil with subsequently improved osteogenic differentiation of cells in vitro. Overall, our study indicated that the innovative press-fit 3D NF composite scaffold may be a robust tool for multiple-drug delivery and bone tissue engineering.

Simultaneous Electrospinning and Electrospraying for the Preparation of a Precursor Membrane Containing Hydrothermally Generated Biochar Particles to Produce the Value-Added Product of Carbon Nanofibrous Felt.

Biochar is a byproduct generated from the hydrothermal liquefaction of biomass, such as corn stover, in an anaerobic environment. This work aims to convert biochar into a value-added product of carbon nanofibrous felt. First, the biochar-containing precursor membrane was prepared from simultaneous electrospinning and electrospraying. After thermal stabilization in air and carbonization in argon, the obtained precursor membrane was converted into a mechanically flexible and robust carbon nanofibrous felt. Electrochemical results revealed that the biochar-derived carbon nanofibrous felt might be a good candidate as a supercapacitor electrode with a good rate capability and high kinetic performance.

Iodosobenzene-Mediated Three-Component Selenofunctionalization of Olefins.

A three-component reaction of olefin, diselenide and water, alcohols, phenol, carboxylic acid, or amine by a commercially available hypervalent iodine(III) reagent, PhIO, was developed. This method provides access to a wide range of vicinally functionalized selenoderivatives under ambient conditions with mostly excellent yields and high diastereoselectivity. The developed reaction displays high levels of functional group compatibility and is suitable for the late-stage functionalization of styrene-functionalized biomolecules. Preliminary investigations on the mechanism of the reaction are also presented.

Upregulating miR-637 aggravates endoplasmic reticulum stress-induced apoptosis in gastric cancer cells by suppressing Calreticulin.

Gastric cancer is a leading cause of cancer death worldwide. Endoplasmic reticulum (ER) stress-induced apoptosis has been confirmed to be important in the treatment of gastric cancer. MiR-637 has recently been found to exert inhibitory effects on gastric cancer, and this study aimed to investigate whether miR-637 could regulate apoptosis through ER stress. The results showed that tunicamycin (TM) induced downregulation of miR-637 in gastric cancer cells (AGS) and increase of apoptosis and ER stress. Overexpression of miR-637 promoted TM-induced apoptosis and expression of ER stress associated proteins (GRP78 and CHOP), but inhibited expression of Calreticulin. MiR-637 could bind with the 3'-UTR of CALR, and negatively regulated the expression of CALR. The co-transfection of miR-637 and CALR in AGS cells show that, CALR overexpression could reverse the pro-apoptosis effects of miR-637 in TM-treated cells. In conclusion, the present study suggests that miR-637 participates in ER stress-induced apoptosis in gastric cancer cells by suppressing CALR expression. miR-637 or CALR may be a future potential target for gastric cancer treatment.