Magnetic iron-based waterworks sludge modified by chitosan and FeS for aqueous Cr(vi) adsorption and reduction.

Heavy metals have been considered an evolving environmental concern due to their harmful and long-lasting impacts. We synthesized a composite of FeS/CS@MIBWS for aqueous Cr(vi) adsorption and reduction utilizing the iron-based waterworks sludge modified by chitosan and FeS. After determining the optimal conditions for the FeS/CS@MIBWS preparation, its Cr(vi) removal capability was evaluated using material characterisation and static Cr(vi) adsorption assays. Cr(vi) elimination by the composite was a pH-dependent process, with pH 2 being the optimum in the range of 2-10. The adsorption process was befitted a pseudo-second-order model, and the equilibrium results agreed well with the Langmuir model. The thermodynamics investigation showed that Cr(vi) removal by the composite has both spontaneous and endothermic nature. Considering the ionic effects, Cl-, SO4 2- and PO4 3- decreased Cr(vi) elimination in the sequence of Cl- < SO4 2- < PO4 3-. The key mechanisms for Cr(vi) elimination were physical and chemical adsorption, chelation, and Cr(vi) reduction into Cr(iii). Furthermore, FeS/CS@MIBWS demonstrated steady reusability (removal effectiveness of 70% after 5 cycles). FeS/CS@MIBWS's rapid, high-performance, reusable, and easily separable adsorption properties make it a promising choice for heavy metal environmental cleaning.

In-depth learning of automatic segmentation of shoulder joint magnetic resonance images based on convolutional neural networks.

OBJECTIVE: Magnetic resonance imaging (MRI) is gradually replacing computed tomography (CT) in the examination of bones and joints. The accurate and automatic segmentation of the bone structure in the MRI of the shoulder joint is essential for the measurement and diagnosis of bone injuries and diseases. The existing bone segmentation algorithms cannot achieve automatic segmentation without any prior knowledge, and their versatility and accuracy are relatively low. For this reason, an automatic segmentation algorithm based on the combination of image blocks and convolutional neural networks is proposed. METHODS: First, we establish 4 segmentation models, including 3 U-Net-based bone segmentation models (humeral segmentation model, joint bone segmentation model, humeral head and articular bone segmentation model as a whole) and a block-based Alex Net segmentation model; Then we use 4 segmentation models to obtain the candidate bone area, and accurately detect the location area of the humerus and joint bone by voting. Finally, the Alex Net segmentation model is further used in the detected bone area to segment the bone edge with the accuracy of the pixel level. RESULTS: The experimental data is obtained from 8 groups of patients in the orthopedics department of our hospital. Each group of scan sequence includes about 100 images, which have been segmented and labeled. Five groups of patients were used for training and five-fold cross-validation, and three groups of patients were used to test the actual segmentation effect. The average accuracy of Dice Coefficient, Positive Predicted Value (PPV) and Sensitivity reached 0.91 ±â€¯0.02, respectively. 0.95 ±â€¯0.03 and 0.95 ±â€¯0.02. CONCLUSIONS: The method in this paper is for a small sample of patient data sets, and only through deep learning on 2D medical images, very accurate shoulder joint segmentation results can be obtained, provide clinical diagnostic guidance to orthopedics. At the same time, the proposed algorithm framework has a certain versatility and is suitable for the precise segmentation of specific organs and tissues in MRI based on a small sample data.

[Comprehensive analysis of genomic detection for a patient with myelodysplastic syndrome].

This study was purposed to investigate the significance of genomic comprehensive analysis information in diagnosis, therapy and prognosis of MDS through comprehensive analysis of a patient with MDS. The bone marrow specimen from a patient with MDS was comprehensively analyzed by a combination of genomic approaches, including chromosomal karyotyping, fluorescence in situ hybridization (FISH), genome scanning using Affymetrix high density SNP microarray platform, and next-generation sequencing (NGS) analysis using IonTorrent Cancer Gene Panel. The results showed that an abnormal clone was identified by standard G-banding karyotyping and confirmed by FISH, which contains interstitial deletions on the long arms of chromosome 5 and 11 respectively. SNP-array analysis defined the two genomic deletions to be an 81 Mb interstitial deletion on the long are of chromosome 5 and a 24 Mb interstitial deletion on the long are of chromosome 11. Meanwhile, SNP-array detected two genomic regions with acquired loss of heterozygosity (LOH), a 58 Mb region on the short arm of chromosome 1 and a 39 Mb region on the distal end of the long arm of chromosome 14. In addition, SNP-array identified multiple genomic regions with long stretch of absence of heterozygosity, representing about 5.3% of autosomal genome, indication a certain level of consanguinity between the parents. No clinically significant gene mutation was identified using IonTorrent 50 Cancer Gene Panel while 6 polymorphisms within 6 genes were observed including APC, FGFR3, KDR, KIT, PDGFRA, and RET. It is concluded that the combined genomic techniques are necessary to provide a full picture of the patient's genomic alterations. Some of the acquired genomic findings are important for the diagnosis and therapy selection. Germline genomic alterations warrant genetic counseling and are useful for further studies to explore the mechanisms leading to tumorigenesis of MDS patient.

RANKL is a downstream mediator for insulin-induced osteoblastic differentiation of vascular smooth muscle cells.

Several reports have shown that circulating insulin level is positively correlated with arterial calcification; however, the relationship between insulin and arterial calcification remains controversial and the mechanism involved is still unclear. We used calcifying vascular smooth muscle cells (CVSMCs), a specific subpopulation of vascular smooth muscle cells that could spontaneously express osteoblastic phenotype genes and form calcification nodules, to investigate the effect of insulin on osteoblastic differentiation of CVSMCs and the cell signals involved. Our experiments demonstrated that insulin could promote alkaline phosphatase (ALP) activity, osteocalcin expression and the formation of mineralized nodules in CVSMCs. Suppression of receptor activator of nuclear factor κB ligand (RANKL) with small interfering RNA (siRNA) abolished the insulin-induced ALP activity. Insulin induced the activation of extracellular signal-regulated kinase (ERK)1/2, mitogen-activated protein kinase (MAPK) and RAC-alpha serine/threonine-protein kinase (Akt). Furthermore, pretreatment of human osteoblasts with the ERK1/2 inhibitor PD98059, but not the phosphoinositide 3-kinase (PI3K) inhibitor, LY294002, or the Akt inhibitor, 1L-6-hydroxymethyl-chiro-inositol 2-(R)-2-O-methyl-3-O-octadecylcarbonate (HIMO), abolished the insulin-induced RANKL secretion and blocked the promoting effect of insulin on ALP activities of CVSMCs. Recombinant RANKL protein recovered the ALP activities decreased by RANKL siRNA in insulin-stimulated CVSMCs. These data demonstrated that insulin could promote osteoblastic differentiation of CVSMCs by increased RANKL expression through ERK1/2 activation, but not PI3K/Akt activation.