Sex differences in the functional topography of association networks in youth.

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Impact of three-dimensional-printing technology guidance on surgical outcomes for retroperitoneal sarcoma: A propensity score-matched study.

BACKGROUND: The surgical treatment of retroperitoneal sarcoma (RPS) is highly challenging because of its complex anatomy. In this study, the authors compared the surgical outcomes of patients with RPS who underwent surgical resection guided by three-dimensional (3D) printing technology versus traditional imaging. METHODS: This retrospective study included 251 patients who underwent RPS resection guided by 3D-printing technology or traditional imaging from January 2019 to December 2022. The main outcome measures were operative time, intraoperative blood loss, postoperative complications, and hospital stay. RESULTS: In total, 251 patients were enrolled in the study: 46 received 3D-printed navigation, and 205 underwent traditional surgical methods. Propensity score matching yielded 44 patients in the 3D group and 82 patients in the control group. The patients' demographics and tumor characteristics were comparable in the matched cohorts. The 3D group had significantly shorter operative time (median, 186.5 minutes [interquartile range (IQR), 130.0-251.3 minutes] vs. 210.0 minutes [IQR, 150.8-277.3 minutes]; p = .04), less intraoperative blood loss (median, 300.0 mL [IQR, 100.0-575.0 mL] vs. 375.0 mL [IQR, 200.0-925.0 mL]; p = .02), shorter postoperative hospital stays (median, 11.0 days [IQR, 9.0-13.0 days] vs. 14.0 days [IQR, 10.8-18.3 days]; p = .02), and lower incidence rate of overall postoperative complications than the control group (18.1% vs. 36.6%; p = .03). There were no differences with regard to the intraoperative blood transfusion rate, the R0/R1 resection rate, 30-day mortality, or overall survival. CONCLUSIONS: Patients in the 3D group had favorable surgical outcomes compared with those in the control group. These results suggest that 3D-printing technology might overcome challenges in RPS surgical treatment. PLAIN LANGUAGE SUMMARY: The surgical treatment of retroperitoneal sarcoma (RPS) is highly challenging because of its complex anatomy. The purpose of this study was to investigate whether three-dimensional (3D) printing technology offers advantages over traditional two-dimensional imaging (such as computed tomography and magnetic resonance imaging) for guiding the surgical treatment of RPS. In a group of patients who had RPS, surgery guided by 3D-printing technology was associated with better surgical outcomes, including shorter operative time, decreased blood loss, shorter hospital stays, and fewer postoperative complications. These findings suggested that 3D-printing technology could help surgeons overcome challenges in the surgical treatment of RPS. 3D-printing technology has important prospects in the surgical treatment of RPS.

KDM4B down-regulation facilitated breast cancer cell stemness via PHGDH upregulation in H3K36me3-dependent manner.

Despite recent advances have been made in clinical treatments of breast cancer, the general prognosis of patients remains poor. Therefore, it is imperative to develop a more effective therapeutic strategy. Lysine demethylase 4B (KDM4B) has been reported to participate in breast cancer development recently, but its exact biological role in breast cancer remains unclear. Here, we observed that KDM4B was down-regulated in human primary BRCA tissues and the low levels of KDM4B expression were correlated with poor survival. Gain- and loss-of-function experiments showed that KDM4B inhibited the proliferation and metastasis of breast cancer cells. Besides, knockdown of KDM4B promoted the epithelial-mesenchymal transition (EMT) and cell stemness in breast cancer cells. Mechanistically, KDM4B down-regulates PHGDH by decreasing the enrichment of H3K36me3 on the promoter region of PHGDH. Knockdown of PHGDH could significantly reversed proliferation, migration, EMT, and cell stemness induced by KDM4B silencing in breast cancer cells. Collectively, we propose a model for a KDM4B/PHGDH axis that provides novel insight into breast cancer development, which may serve as a potential factor for predicting prognosis and a therapeutic target for breast cancer.

Multimedia Mercury Recovery from Coal-Fired Power Plants Utilizing N-Containing Conjugated Polymer Functionalized Fly Ash.

To recover multimedia mercury from coal-fired power plants, a novel N-containing conjugated polymer (polyaniline and polypyrrole) functionalized fly ash was prepared, which could continuously adsorb 99.2% of gaseous Hg0 at a high space velocity of 368,500 h-1 and nearly 100% of aqueous Hg2+ in the solution pH range of 2-12. The adsorption capacities of Hg0 and Hg2+ reach 1.62 and 101.36 mg/g, respectively. Such a kind of adsorbent has good environmental applicability, i.e. good resistance to coexisting O2/NO/SO2 and coexisting Na+/K+/Ca2+/Mg2+/SO42-. This adsorbent has very low specific resistances (6 × 106-5 × 109 Ω·cm) and thus can be easily collected by an electrostatic precipitator under low-voltage (0.1-0.8 kV). The Hg-saturated adsorbent can desorb almost 100% Hg under relatively low temperature (<250 °C). Characterization and theoretical calculations reveal that conjugated-N is the critical site for adsorbing both Hg0 and Hg2+ as well as activating chlorine. Gaseous Hg0 is oxidized and adsorbed in the form of HgXClX(ad), while aqueous Hg2+ is adsorbed to form a complex with conjugated-N, and parts of Hg2+ are reduced to Hg+ by conjugated-N. This adsorbent can be easily large-scale manufactured; thus, this novel solid waste functionalization method is promising to be applied in coal-fired power plants and other Hg-involving industrial scenes.

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study.

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer's disease: deep learning analysis in 3238 participants worldwide.

OBJECTIVES: Hippocampal characterization is one of the most significant hallmarks of Alzheimer's disease (AD); rather, the single-level feature is insufficient. A comprehensive hippocampal characterization is pivotal for developing a well-performing biomarker for AD. To verify whether a comprehensive characterization of hippocampal features of gray matter volume, segmentation probability, and radiomics features could better distinguish AD from normal control (NC), and to investigate whether the classification decision score could serve as a robust and individualized brain signature. METHODS: A total of 3238 participants' structural MRI from four independent databases were employed to conduct a 3D residual attention network (3DRA-Net) to classify NC, mild cognitive impairment (MCI), and AD. The generalization was validated under inter-database cross-validation. The neurobiological basis of the classification decision score as a neuroimaging biomarker was systematically investigated by association with clinical profiles, as well as longitudinal trajectory analysis to reveal AD progression. All image analyses were performed only upon the single modality of T1-weighted MRI. RESULTS: Our study exhibited an outstanding performance (ACC = 91.6%, AUC = 0.95) of the comprehensive characterization of hippocampal features in distinguishing AD (n = 282) from NC (n = 603) in Alzheimer's Disease Neuroimaging Initiative cohort, and ACC = 89.2% and AUC = 0.93 under external validation. More importantly, the constructed score was significantly correlated with clinical profiles (p < 0.05), and dynamically altered over the AD longitudinal progression, provided compelling evidence of a solid neurobiological basis. CONCLUSIONS: This systemic study highlights the potential of the comprehensive characterization of hippocampal features to provide an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD. KEY POINTS: • The comprehensive characterization of hippocampal features exhibited ACC = 91.6% (AUC = 0.95) in classifying AD from NC under intra-database cross-validation, and ACC = 89.2% (AUC = 0.93) in external validation. • The constructed classification score was significantly associated with clinical profiles, and dynamically altered over the AD longitudinal progression, which highlighted its potential of being an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD.

Ångstrom-scale silver particles ameliorate collagen-induced and K/BxN-transfer arthritis in mice via the suppression of inflammation and osteoclastogenesis.

OBJECTIVE: Nanoparticles (NPs) hold a great promise in combating rheumatoid arthritis, but are often compromised by their toxicities because the currently used NPs are usually synthesized by chemical methods. Our group has previously fabricated Ångstrom-scale silver particles (AgÅPs) and demonstrated the anti-tumor and anti-sepsis efficacy of fructose-coated AgÅPs (F-AgÅPs). This study aimed to uncover the efficacy and mechanisms of F-AgÅPs for arthritis therapy. METHODS: We evaluated the efficacy of F-AgÅPs in collagen-induced arthritis (CIA) mice. We also compared the capacities of F-AgÅPs, the commercial AgNPs, and the clinical drug methotrexate (MTX) in protecting against K/BxN serum-transfer arthritis (STA) mice. Moreover, we evaluated the effects of F-AgÅPs and AgNPs on inflammation, osteoclast formation, synoviocytes migration, and matrix metalloproteinases (MMPs) production in vitro and in vivo. Meanwhile, the toxicities of F-AgÅPs and AgNPs in vitro and in vivo were also tested. RESULTS: F-AgÅPs significantly prevented bone erosion, synovitis, and cartilage damage, attenuated rheumatic pain, and improved the impaired motor function in mouse models of CIA or STA, the anti-rheumatic effects of which were comparable or stronger than AgNPs and MTX. Further studies revealed that F-AgÅPs exhibited similar or greater inhibitory abilities than AgNPs to suppress inflammation, osteoclast formation, synoviocytes migration, and MMPs production. No obvious toxicities were observed in vitro and in vivo after F-AgÅPs treatment. CONCLUSIONS: F-AgÅPs can effectively alleviate arthritis without notable toxicities and their anti-arthritic effects are associated with the inhibition of inflammation, osteoclastogenesis, synoviocytes migration, and MMPs production. Our study suggests the prospect of F-AgÅPs as an efficient and low-toxicity agent for arthritis therapy.

Preparation of Biomass Carbon Composites MgO@ZnO@BC and Its Adsorption and Removal of Cu(II) and Pb(II) in Wastewater.

The ternary composite MgO@ZnO@BC was synthesized and characterized for the adsorption of Cu2+, Pb2+ heavy metal ions from wastewater. The results show that the addition of the MgO@ZnO@BC composite results in higher adsorption properties for Cu2+ and Pb2+, with a molar ratio of 5% 0.1 g, and maximum adsorption capacity (50.63 mg/g for Cu2+ and 61.46 mg/g for Pb2+). The Langmuir adsorption isotherm of the adsorption complex and the kinetics of adsorption are secondary kinetics. The adsorption of Cu2+ and Pb2+ was mainly chemisorption, accompanied by physical adsorption. This adsorption method fully conforms to the concepts of clean production and efficient waste utilization, providing a reference for the removal of heavy metal ions from wastewater and waste recycling using ternary composite materials.

Momordicine I alleviates isoproterenol-induced cardiomyocyte hypertrophy through suppression of PLA2G6 and DGK-ζ.

This study aimed to observe the protective effect of momordicine I, a triterpenoid compound extracted from momordica charantia L., on isoproterenol (ISO)-induced hypertrophy in rat H9c2 cardiomyocytes and investigate its potential mechanism. Treatment with 10 µM ISO induced cardiomyocyte hypertrophy as evidenced by increased cell surface area and protein content as well as pronounced upregulation of fetal genes including atrial natriuretic peptide, ß-myosin heavy chain, and α-skeletal actin; however, those responses were markedly attenuated by treatment with 12.5 µg/ml momordicine I. Transcriptome experiment results showed that there were 381 and 447 differentially expressed genes expressed in comparisons of model/control and momordicine I intervention/model, respectively. GO enrichment analysis suggested that the anti-cardiomyocyte hypertrophic effect of momordicine I may be mainly associated with the regulation of metabolic processes. Based on our transcriptome experiment results as well as literature reports, we selected glycerophospholipid metabolizing enzymes group VI phospholipase A2 (PLA2G6) and diacylglycerol kinase ζ (DGK-ζ) as targets to further explore the potential mechanism through which momordicine I inhibited ISO-induced cardiomyocyte hypertrophy. Our results demonstrated that momordicine I inhibited ISO-induced upregulations of mRNA levels and protein expressions of PLA2G6 and DGK-ζ. Collectively, momordicine I alleviated ISO-induced cardiomyocyte hypertrophy, which may be related to its inhibition of the expression of glycerophospholipid metabolizing enzymes PLA2G6 and DGK-ζ.

Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning.

Electric fields (E-fields) induced by transcranial magnetic stimulation (TMS) can be modeled using partial differential equations (PDEs). Using state-of-the-art finite-element methods (FEM), it often takes tens of seconds to solve the PDEs for computing a high-resolution E-field, hampering the wide application of the E-field modeling in practice and research. To improve the E-field modeling's computational efficiency, we developed a self-supervised deep learning (DL) method to compute precise TMS E-fields. Given a head model and the primary E-field generated by TMS coils, a DL model was built to generate a E-field by minimizing a loss function that measures how well the generated E-field fits the governing PDE. The DL model was trained in a self-supervised manner, which does not require any external supervision. We evaluated the DL model using both a simulated sphere head model and realistic head models of 125 individuals and compared the accuracy and computational speed of the DL model with a state-of-the-art FEM. In realistic head models, the DL model obtained accurate E-fields that were significantly correlated with the FEM solutions. The DL model could obtain precise E-fields within seconds for whole head models at a high spatial resolution, faster than the FEM. The DL model built for the simulated sphere head model also obtained an accurate E-field whose average difference from the analytical E-fields was 0.0054, comparable to the FEM solution. These results demonstrated that the self-supervised DL method could obtain precise E-fields comparable to the FEM solutions with improved computational speed.

MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision.

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting. Particularly, a deep neural network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and warped moving images, similar to those adopted in conventional image registration algorithms. The network is implemented in a multi-resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self-supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields for diffeomorphic image registration. The spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used as input to the network for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high-resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state-of-the-art image registration algorithms.

Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks.

Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.

GLS1 depletion inhibited colorectal cancer proliferation and migration via redox/Nrf2/autophagy-dependent pathway.

Cancer cells can metabolize glutamine to replenish TCA cycle intermediates for cell survival. Glutaminase (GLS1) is over-expressed in multiple cancers, including colorectal cancer (CRC). However, the role of GLS1 in colorectal cancer development has not yet fully elucidated. In this study, we found that GLS1 levels were significantly increased in CRC cells. Knockdown of GLS1 by shRNAs as well as GLS1 inhibitor BPTES decreased DLD1 and SW480 cell proliferation, colony formation and migration. Knockdown of GLS1 as well as BPTES induced reactive oxygen species (ROS) production, down-regulation of GSH/GSSG ratio, an decrease in Nrf2 protein expression and an increase in cytoplasmic Nrf2 protein expression in DLD1 and SW480 cells. Furthermore, Knockdown of GLS1 as well as BPTES inhibited autophagy pathway, antioxidant NAC and Nrf2 activator could reversed inhibition of GLS1-mediated an decrease in autophagic flux in DLD1 and SW480 cells. Depletion of GLS1-induced inhibition of DLD1 and SW480 CRC cell proliferation, colony formation and migration was reversed by autophagy inducer rapamycin. These results suggest that targeting GLS1 might be a new potential therapeutic target for the treatment of CRC.

The Medial Border of Laparoscopic D3 Lymphadenectomy for Right Colon Cancer: Results from an Exploratory Pilot Study.

BACKGROUND: Opinions vary on the medial border of D3 lymphadenectomy for right colon cancer. Most surgeons place the medial border along the left side of the superior mesenteric vein, but some consider the left side of the superior mesenteric artery as the medial border. OBJECTIVES: This study investigated the clinical outcomes of laparoscopic D3 lymphadenectomy for right colon cancer with the medial border along the left side of superior mesenteric artery. DESIGN: This was a retrospective study. SETTINGS: The study was conducted in specialized colorectal cancer department of 5 tertiary hospitals. PATIENTS: Patients receiving laparoscopic D3 lymphadenectomy for right colon cancer from January 2013 to December 2018 were included. MAIN OUTCOME MEASURES: After propensity score matching, 307 patients receiving laparoscopic D3 lymphadenectomy along the left side of the superior mesenteric artery were assigned to the superior mesenteric artery group and 614 patients were assigned to the superior mesenteric vein group. Univariate, multivariate, and Kaplan-Meier analyses were performed to assess the clinical data. RESULTS: The short-term outcomes were similar between the 2 groups; however, the superior mesenteric artery group had a higher rate of chylous leakage (p < 0.001). More lymph nodes were harvested from the superior mesenteric artery group than from the superior mesenteric vein group (p = 0.001). The number (p = 0.005) of metastatic lymph nodes and the lymph node ratio (p = 0.041) in main nodes were both higher in the superior mesenteric artery group. The 2 groups had similar long-term survival, but the superior mesenteric artery group tended to show better disease-free survival in patients with stage disease III (p = 0.056). LIMITATIONS: This was a retrospective, nonrandomized study. CONCLUSION: Laparoscopic D3 lymphadenectomy along the left side of the superior mesenteric artery, except for a higher rate of chylous leakage, had short-term outcomes comparable to the superior mesenteric vein group. The superior mesenteric artery group tended to achieve better disease-free survival in patients with stage III disease, but further study is required to better elucidate differences in these approaches because risks/benefits do exist.

Development of a Flexible and Robust Synthesis of Tetrahydrofuro[3,4-b]furan Nucleoside Analogues.

In the context of a PRMT5 inhibitor program, we describe our efforts to develop a flexible and robust strategy to access tetrahydrofuro[3,4-b]furan nucleoside analogues. Ultimately, it was found that a Wolfe type carboetherification from an alkenol derived from d-glucofuranose diacetonide was capable of furnishing the B-ring and installing the desired heteroaryl group in a single step. Using this approach, key intermediate 1.3-A was delivered on a gram scale in a 62% yield and 9.1:1 dr in favor of the desired S-isomer. After deprotection of 1.3-A, a late-stage glycosylation was performed under Mitsunobu conditions to install the pyrrolopyrimidine base. This provided serviceable yields of nucleoside analogues in the range of 31-48% yield. Compound 1.1-C was profiled in biochemical and cellular assays and was demonstrated to be a potent and cellularly active PRMT5 inhibitor, with a PRMT5-MEP50 biochemical IC50 of 0.8 nM, a MCF-7 target engagement EC50 of 3 nM, and a Z138 cell proliferation EC50 of 15 nM. This work sets the stage for the development of new inhibitors of PRMT5 and novel nucleoside chemical matter for alternate drug discovery programs.

"Caudal-to-cranial" plus "artery first" technique with beyond D3 lymph node dissection on the right midline of the superior mesenteric artery for the treatment of right colon cancer: is it more in line with the principle of oncology?

OBJECTIVE: To explore the feasibility and application value of a "caudal-to-cranial" plus "artery first" technique with beyond D3 lymph node dissection on the right midline of the superior mesenteric artery (SMA) for the treatment of right colon cancer METHODS: Clinical data consisting of 168 right colon cancer cases under going laparoscopic D3 radical resection, including 84 cases of "caudal-to-cranial" plus "artery first" technique with beyond D3 lymph node dissection on the right midline of the SMA (CC + SMA group) and 84 cases of conventional medial approach plus dissection around the superior mesenteric vein (MA + SMV group), from January 2017 to March 2018 were retrospectively analyzed. For CC + SMA group, our surgical method was to isolate the mesocolon using a caudal-to-cranial pathway and ligate blood vessels along the midline of the SMA. RESULTS: The baseline data was not significantly different between the two groups (all p > 0.05). The mean operation time and intraoperative blood loss in the CC + SMA and the MA + SMV groups were 170.04 ± 43.10 versus 172.33 ± 41.84 min and 91.07 ± 55.12 versus 77.38 ± 40.21 ml, respectively, which has no significant difference (p > 0.05). The mean number of total and positive harvested lymph nodes in the two groups were 29.44 ± 5.90 versus 26.21 ± 6.64 (p < 0.05) and 2.57 ± 1.93 versus 2.51 ± 1.05, respectively (p > 0.05). Compared with the MA + SMV group, there was no significant difference in total postoperative complication rate in the CC + SMA group. The time to pull out drainage tube in the CC + SMA group was longer than MA + SMV group (4.05 ± 1.79 versus 3.38 ± 1.99 day; p = 0.022). CONCLUSION: It is safe and feasible for the "caudal-to-cranial" plus "artery first" technique with beyond D3 lymph node dissection on the right midline of the SMA in right colon cancer. It may have some advantages in the number of lymph nodes dissection, and the long-term prognosis remains to be expected.

[A multicenter survey of antibiotic use in very and extremely low birth weight infants in Hunan Province].

OBJECTIVE: To investigate the current status of antibiotic use for very and extremely low birth weight (VLBW/ELBW) infants in neonatal intensive care units (NICUs) of Hunan Province. METHODS: The use of antibiotics was investigated in multiple level 3 NICUs of Hunan Province for VLBW and ELBW infants born between January, 2017 and December, 2017. RESULTS: The clinical data of 1 442 VLBW/ELBW infants were collected from 24 NICUs in 2017. The median antibiotic use duration was 17 days (range: 0-86 days), accounting for 53.0% of the total length of hospital stay. The highest duration of antibiotic use was up to 91.4% of the total length of hospital stay, with the lowest at 14.6%. In 16 out of 24 NICUs, the antibiotic use duration was accounted for more than 50.0% of the hospitalization days. There were 113 cases with positive bacterial culture grown in blood or cerebrospinal fluid, making the positive rate of overall bacterial culture as 7.84%. The positive rate of bacterial culture in different NICUs was significantly different from 0% to 14.9%. The common isolated bacterial pathogens Klebsiella pneumoniae was 29 cases (25.7%); Escherichia coli 12 cases (10.6%); Staphylococcus aureus 3 cases (2.7%). The most commonly used antibiotics were third-generation of cephalosporins, accounting for 41.00% of the total antibiotics, followed by penicillins, accounting for 32.10%, and followed by carbapenems, accounting for 13.15%. The proportion of antibiotic use time was negatively correlated with birth weight Z-score and the change in weight Z-score between birth and hospital discharge (rs=-0.095, -0.151 respectively, P<0.01), positively correlated with death/withdrawal of care (rs=0.196, P<0.01). CONCLUSIONS: Antibiotics used for VLBW/ELBW infants in NICUs of Hunan Province are obviously prolonged in many NICUs. The proportion of routine use of third-generation of cephalosporins and carbapenems antibiotics is high among the NICUs.

Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks.

Decoding brain functional states underlying cognitive processes from functional MRI (fMRI) data using multivariate pattern analysis (MVPA) techniques has achieved promising performance for characterizing brain activation patterns and providing neurofeedback signals. However, it remains challenging to decode subtly distinct brain states for individual fMRI data points due to varying temporal durations and dependency among different cognitive processes. In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in intrinsic functional network modeling and sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, subject-specific intrinsic functional networks (FNs) are computed from resting-state fMRI data and are used to characterize functional signals of task fMRI data with a compact representation for building brain decoding models, and LSTM RNNs are adopted to learn brain decoding mappings between functional profiles and brain states. Validation results on fMRI data from the HCP dataset have demonstrated that brain decoding models built on training data using the proposed method could learn discriminative latent feature representations and effectively distinguish subtly distinct working memory tasks of different subjects with significantly higher accuracy than conventional decoding models. Informative FNs of the brain decoding models identified as brain activation patterns of working memory tasks were largely consistent with the literature. The method also obtained promising decoding performance on motor and social cognition tasks. Our results suggest that LSTM RNNs in conjunction with FNs could build interpretable, highly accurate brain decoding models.

Highly Diastereoselective Synthesis of a HCV NS5B Nucleoside Polymerase Inhibitor.

An asymmetric synthesis of HCV NS5B nucleoside polymerase inhibitor (1) is described. This novel route features several remarkably diastereoselective and high-yielding transformations, including construction of the all-carbon quaternary stereogenic center at C-2 via a thermodynamic aldol reaction. A subsequent glycosylation reaction with activated uracil via C-1 phosphate and installation of the cyclic phosphate group using an achiral phosphorus(III) reagent followed by oxidation provides 1.

Radiomic features from MRI distinguish myxomas from myxofibrosarcomas.

BACKGROUND: Myxoid tumors pose diagnostic challenges for radiologists and pathologists. All myxoid tumors can be differentiated from each other using fluorescent in-situ hybridization (FISH) or immunohistochemical markers, except for myxomas and myxofibrosarcomas. Myxomas and myxofibrosarcomas are rare tumors. Myxomas are benign and histologically bland, whereas myxofibrosarcomas are malignant and histologically heterogenous. Because of the histological heterogeneity, low grade myxofibrosarcomas may be mistaken for myxomas on core needle biopsies. We evaluated the performance of T1-weighted signal intensity (T1SI), tumor volume, and radiomic features extracted from magnetic resonance imaging (MRI) to differentiate myxomas from myxofibrosarcomas. METHODS: The MRIs of 56 patients (29 with myxomas, 27 with myxofibrosarcomas) were analyzed. We extracted 89 radiomic features. Random forests based classifiers using the T1SI, volume features, and radiomic features were used to differentiate myxomas from myxofibrosarcomas. The classifiers were validated using a leave-one-out cross-validation. The performances of the classifiers were then compared. RESULTS: Myxomas had lower normalized T1SI than myxofibrosaromas (p = 0.006) and the AUC using the T1SI was 0.713. However, the classification model using radiomic features had an AUC of 0.885 (accuracy = 0.839, sensitivity = 0.852, specificity = 0.828), and outperformed the classification models using T1SI (AUC = 0.713) and tumor volume (AUC = 0.838). The classification model using radiomic features was significantly better than the classifier using T1SI values (p = 0.039). CONCLUSIONS: Myxofibrosarcomas are on average higher in T1-weighted signal intensity than myxomas. Myxofibrosarcomas are larger and have shape differences compared to myxomas. Radiomic features performed best for differentiating myxomas from myxofibrosarcomas compared to T1-weighted signal intensity and tumor volume features.