Study on Crack Behavior of GH3230 Superalloy Fabricated via High-Throughput Additive Manufacturing.

This study utilized Fe, Co, Ni elemental powders alongside GH3230 pre-alloyed powder as raw materials, employing high-throughput additive manufacturing based on laser powder bed fusion in situ to alloying technology to fabricate the bulk samples library for GH3230 superalloy efficiently. A quantitative identification algorithm for detecting crack and hole defects in additive manufacturing samples was developed. The primary focus was to analyze the composition variations in specimens at varying Fe, Co, and Ni elemental compositions and their impact on crack formation. Experimental results demonstrated that increased laser power improved element distribution uniformity but it proved to be not significantly effective in reducing crack defects. Moreover, augmented Fe and Co alloying content could not eliminate these defects. However, elevated Ni content led to a decrease in the alloy's solidification cracking index and carbide reduction in solidification products. Notably, a significant reduction in cracks was observed when the Ni content of the alloy reached 63 wt.%, and these defects were nearly eliminated at 67 wt.% Ni content.

Rapid and sensitive detection of glucocorticoids using engineered magnetosomes functionalized protein A conjugated broad-spectrum monoclonal antibody.

Engineered bacterial magnetic nanoparticles (BMPs) fused with protein A (BMP-PA) can bind antibodies, creating immunomagnetic beads that offer an attractive tool for targets screening. In the study, BMP-PA-IgG was formed by attaching broad-spectrum monoclonal antibodies against glucocorticoids (GCs) to BMP-PA. Immunomagnetic assay was developed for analysis of GCs, using the BMP-PA-IgG and hydrocortisone-horseradish peroxidase. The developed assay exhibited broad specificity for GCs, including hydrocortisone (HCS), betamethasone (BMS), dexamethasone (DMS), prednisolone (PNS), beclomethasone (BCMS), cortisone (CS), 6-α-methylprednisone (6-α-MPNS), and fludrocortisone acetate (HFCS), with half inhibitory concentrations (IC50) ranging from 0.88 to 6.57 ng/mL. The proposed assay showed average recoveries of HCS and DMS ranging from 75.6% to 105.2% in chicken and pork samples, which were correlated well with those obtained by LC-MS/MS. This study indicated that the integration of engineered immunomagnetic beads into immunoassay systems offer possibilities for the sensitive and selective detection of GCs.

Numerical Simulation of the Evaporation Behavior of Fe-Mn Heterogeneous Powder in Selective Laser Melting Process.

Multi-material additive manufacturing using heterogeneous powders as raw materials is one of the important development directions of metal additive manufacturing technology. The evaporation behavior of heterogeneous powders in the selective laser melting (SLM) process has a significant influence on the accuracy of chemical composition control and the quality of the final product. In this paper, the fusion process of Fe20Mn (80 wt.% Fe and 20 wt.% Mn) heterogeneous powder, Fe and Mn elemental powders, and Fe20Mn pre-alloyed powder is numerically simulated using FLOW-3D® software and partially validated through SLM experimental results. The morphology and the characteristics of the flow field and temperature field in the melt pool for four kinds of powder materials are analyzed. The influence of the elemental evaporation behavior of different powders on the mass loss of the Mn element is discussed. The results show that the excessive accumulation of heat increases the maximum temperature of the melt pool, thus increasing mass loss. The Fe20Mn heterogeneous powder has a wider heat-affected zone and a higher peak value of temperature, nearly 400 K higher than that of the Fe20Mn pre-alloyed powders, which exhibits an intensive evaporation behavior. The mass loss of the Mn element obtained from the SLM experiment for Fe20Mn heterogeneous powders forming parts is more than the Fe20Mn pre-alloyed powders' forming parts for different laser powers, up to 17 wt.% at P = 120 W. This tendency is consistent with the numerical analysis of the effect of evaporation behavior of Fe-Mn heterogeneous powder during the SLM process. This study provides the necessary theoretical reference and process guidance for realizing the precise control of the SLM composition of a heterogeneous powder in multi-material additive manufacturing caused by evaporation behavior.

Development of a broad-spectrum one-step immunoassay for detection of glucocorticoids in milk using magnetosome-based immunomagnetic beads.

Immunomagnetic beads provide novel tools for high-throughput immunoassay techniques. In this study, protein G (PG) was immobilized onto bacterial magentic particles (BMPs) using an additional cysteine residue at the C-terminus. A broad-spectrum monoclonal antibody against glucocorticoids (GCs) was attached to BMPs through PG-Fc interaction, generating BMP-PG-mIgG immunomagentic beads. A sensitive one-step immunoassay was developed for GCs based on combination of BMP-PG-mIgG and dexamethasone-horseradish peroxidase tracer (DMS-HRP). The developed assay exhibited half inhibitory concentrations (IC50) for dexamethasone (DMS), betamethasone (BMS), prednisolone (PNS), hydrocortisone (HCS), beclomethasone (BCMS), cortisone (CS), 6-α-methylprednisone (6-α-MPNS), fludrocortisone acetate (HFCS) of 0.98, 1.49, 2.42, 9.29, 1.63, 6.13, 7.3, and 4.89 ng/mL, respectively. The method showed recoveries ranging rates from 86.5 % to 117 % with a coefficient of variation less than 12.3 % in milk sample, which showed a good correlation with LC-MS/MS. Thus, the proposed assay offers a rapid and broad-spectrum screening tool for simultaneous detection of GCs in milk.

Mendelian randomization studies of depression: evidence, opportunities, and challenges.

BACKGROUND: Major depressive disorder (MDD) poses a significant social and economic burden worldwide. Identifying exposures, risk factors, and biological mechanisms that are causally connected to MDD can help build a scientific basis for disease prevention and development of novel therapeutic approaches. METHODS: In this systematic review, we assessed the evidence for causal relationships between putative causal risk factors and MDD from Mendelian randomization (MR) studies, following PRISMA. We assessed methodological quality based on key elements of the MR design: use of a full instrumental variable analysis and validation of the three key MR assumptions. RESULTS: We included methodological details and results from 52 articles. A causal link between lifestyle, metabolic, inflammatory biomarkers, particular pathological states and MDD is supported by MR investigations, although results for each category varied substantially. CONCLUSIONS: While this review shows how MR can offer useful information for examining prospective treatment targets and better understanding the pathophysiology of MDD, some methodological flaws in the existing literature limit reliability of results and probably underlie their heterogeneity. We highlight perspectives and recommendations for future works on MR in psychiatry.

Adaptive kernel selection network with attention constraint for surgical instrument classification.

Computer vision (CV) technologies are assisting the health care industry in many respects, i.e., disease diagnosis. However, as a pivotal procedure before and after surgery, the inventory work of surgical instruments has not been researched with the CV-powered technologies. To reduce the risk and hazard of surgical tools' loss, we propose a study of systematic surgical instrument classification and introduce a novel attention-based deep neural network called SKA-ResNet which is mainly composed of: (a) A feature extractor with selective kernel attention module to automatically adjust the receptive fields of neurons and enhance the learnt expression and (b) A multi-scale regularizer with KL-divergence as the constraint to exploit the relationships between feature maps. Our method is easily trained end-to-end in only one stage with few additional calculation burdens. Moreover, to facilitate our study, we create a new surgical instrument dataset called SID19 (with 19 kinds of surgical tools consisting of 3800 images) for the first time. Experimental results show the superiority of SKA-ResNet for the classification of surgical tools on SID19 when compared with state-of-the-art models. The classification accuracy of our method reaches up to 97.703%, which is well supportive for the inventory and recognition study of surgical tools. Also, our method can achieve state-of-the-art performance on four challenging fine-grained visual classification datasets.

3D asymmetric expectation-maximization attention network for brain tumor segmentation.

Automatic brain tumor segmentation on MRI is a prerequisite to provide a quantitative and intuitive assistance for clinical diagnosis and treatment. Meanwhile, 3D deep neural network related brain tumor segmentation models have demonstrated considerable accuracy improvement over corresponding 2D methodologies. However, 3D brain tumor segmentation models generally suffer from high computation cost. Motivated by a recently proposed 3D dilated multi-fiber network (DMF-Net) architecture that pays more attention to reduction of computation cost, we present in this work a novel encoder-decoder neural network, ie a 3D asymmetric expectation-maximization attention network (AEMA-Net), to automatically segment brain tumors. We modify DMF-Net by introducing an asymmetric convolution block into a multi-fiber unit and a dilated multi-fiber unit to capture more powerful deep features for the brain tumor segmentation. In addition, AEMA-Net further incorporates an expectation-maximization attention (EMA) module into the DMF-Net by embedding the EMA block in the third stage of skip connection, which focuses on capturing the long-range dependence of context. We extensively evaluate AEMA-Net on three MRI brain tumor segmentation benchmarks of BraTS 2018, 2019 and 2020 datasets. Experimental results demonstrate that AEMA-Net outperforms both 3D U-Net and DMF-Net, and it achieves competitive performance compared with the state-of-the-art brain tumor segmentation methods.

p38 Inhibitor Protects Mitochondrial Dysfunction by Induction of DJ-1 Mitochondrial Translocation After Subarachnoid Hemorrhage.

p38 mitogen-activated protein kinase (MAPK) is a major player in mitochondrial dysfunction after subarachnoid hemorrhage (SAH). Moreover, DJ-1, which responds to oxidative stress and translocates to mitochondria, maintains mitochondrial homeostasis. Although a few studies have demonstrated that DJ-1 indirectly regulates p38 activation, the relationship between DJ-1 and p38 in mitochondrial dysfunction after SAH has not been delineated. Using an in vitro SAH model, alterations in p38, p-p38, DJ-1, and autophagic-related protein expression were detected. As expected, p38 inhibitor significantly blocked excessive expression of p38 and p-p38 after SAH, whereas total DJ-1 expression and mitochondrial DJ-1 were up-regulated. Further analysis showed that p38 inhibitor significantly blocked oxyhemoglobin (OxyHb) induced mitochondrial dysfunction, including mitochondrial membrane potential depolarization and reactive oxygen species (ROS) release. In addition, p38 inhibitor restored OxyHb-induced abnormal autophagic flux at the initiation and formation stage by regulating Atg5, beclin-1, the ratio of LC3-II/LC3-I, and p62 expression. This study suggested that overexpression of p38 induced the accumulation of mitochondrial dysfunction partly due to abnormal activation of autophagy, which largely relied on DJ-1 mitochondrial translocation.