One-pot two-step mechanochemical synthesis of arylsulfonyl 4<i>H</i>-pyrans

In this paper, a simple and environment-friendly protocol was developed for the synthesis of arylsulfonyl 4H-pyrans via L-proline-catalyzed one-pot two-step reaction of aromatic aldehyde with phenylsulphonyl acetonitrile and dimedone under ball-milling conditions. Environmental acceptability, wide substrate scope, low cost and operational simplicity are the key features of this method.</p

Two-Dimensional Polyphenylene Networks with Tunable Micropores for Hydrogen Storage

Micropores, especially ultramicropores with pore size smaller than 1 nm, play a crucial role in hydrogen storage. In this contribution, we report on bulk production of two-dimensional (2D) polyphenylene networks (PPNs) through a solution-based Wurtz reaction. A self-assembled mechanism is proposed for the formation of 2D PPNs based on molecular dynamics simulations. The morphology, structure, surface chemistry, and textural properties of the PPNs are greatly influenced by anneal treatment at 450–550 °C in terms of deep thermal dechlorination and cyclodechlorination. The annealed PPNs are featured with moderate specific surface area (SBET) and wealthy micropores, which can be finely tuned by varying the anneal temperature. For PPNs, there is no direct correlation between the H2-uptake capacity and individual textural parameters such as SBET, Smicropore, Vtotal pore, Vmicropore, and Vmesopore. The H2-uptake capacity is highly dependent on the distribution of ultramicropores and the pore volume of ultramicropores in the range of 0.5–0.8 nm (Vultramicropore 0.5–0.8nm). The PPNs annealed at 500–520 °C, possessing moderate SBET (459.3–564.9 m2 g–1), relatively high Vultramicropore 0.5–0.8nm, and ultramicropores concentrated at 0.69–0.71 nm, exhibit superior H2-storage capacity (4.28–5.39 mmol g–1) at 77 K and 1 atm

Table_2_The correlation between modifications to corneal topography and changes in retinal vascular density and retinal thickness in myopic children after undergoing orthokeratology.DOCX

PurposeThis study aimed to investigate the relationship among changes in corneal topography, retinal vascular density, and retinal thickness in myopic children who underwent orthokeratology for 3 months.MethodThirty children with myopia wore orthokeratology lenses for 3 months. Using optical coherence tomography angiography (OCTA), the retina was imaged as 6 × 6 mm en-face images at baseline and 3 months after orthokeratology. Cornea data was acquired by topography and analyzed by customer MATLAB software. The cornea was divided into 3 zones and 9 sectors. The relative corneal refractive power shift (RCRPS) was used in this study. Changes in retinal vascular density (RVDC) and retinal thickness change (RTC) were associated with RCRPS by using spearman test. Statistical significance was set at p ResultA significant correlation was observed between the RVDC and the RCRPS in many regions (the r was 0.375 ~ 0.548, all p value  0.05) were observed at any regions.ConclusionThe correlation between the cornea and the retina was observed after orthokeratology. Cornea changes may affect regional retinal responses accordingly，which may explain how orthokeratology delays myopia progression partially.</p

Table_1_The correlation between modifications to corneal topography and changes in retinal vascular density and retinal thickness in myopic children after undergoing orthokeratology.DOCX

PurposeThis study aimed to investigate the relationship among changes in corneal topography, retinal vascular density, and retinal thickness in myopic children who underwent orthokeratology for 3 months.MethodThirty children with myopia wore orthokeratology lenses for 3 months. Using optical coherence tomography angiography (OCTA), the retina was imaged as 6 × 6 mm en-face images at baseline and 3 months after orthokeratology. Cornea data was acquired by topography and analyzed by customer MATLAB software. The cornea was divided into 3 zones and 9 sectors. The relative corneal refractive power shift (RCRPS) was used in this study. Changes in retinal vascular density (RVDC) and retinal thickness change (RTC) were associated with RCRPS by using spearman test. Statistical significance was set at p ResultA significant correlation was observed between the RVDC and the RCRPS in many regions (the r was 0.375 ~ 0.548, all p value  0.05) were observed at any regions.ConclusionThe correlation between the cornea and the retina was observed after orthokeratology. Cornea changes may affect regional retinal responses accordingly，which may explain how orthokeratology delays myopia progression partially.</p

Datas_Sheet_1_Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity.docx

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.</p

Table_1_Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity.XLSX

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.</p

Dual-Wavelength Ratiometric Electrochemiluminescence Immunosensor for Cardiac Troponin I Detection

Ratiometric electrochemiluminescence (ECL) has attracted special focus in the biological analysis field, because it could eliminate the environmental interference and allow for precise measurement. Herein, a dual-wavelength ratiometric ECL biosensor was designed for the detection of cardiac troponin I (cTnI), where (4,4′-dicarboxylic acid-2,2′-bipyridyl) ruthenium­(II) (Ru­(dcbpy)32+) and Au nanoparticle-loaded graphene oxide/polyethylenimine (GPRu–Au) nanomaterial acts as an acceptor, and Au nanoparticle-modified graphitic phase carbon nitride nanosheet composite (Au–CNN) acts as donor. Au–CNN shows a high and steady ECL signal centered at 455 nm, which is well-matched with the adsorption of GPRu–Au; thereby, a highly efficient electrochemiluminescent resonance energy transfer (ECL-RET) sensing platform is designed. AuNPs facilitate the immobilization of antibody on the nanomaterials through a Au–N bond. The high surface area of graphene oxide/polyethylenimine allows a large number of Ru­(dcbpy)32+ to be loaded, immensely amplifying the ECL signal. This sensing platform exhibits outstanding analytical performance toward cTnI with a detection limit of 3.94 fg/mL (S/N = 3). The high reliability, selectivity, and sensitivity of this ratiometric ECL biosensor provides a versatile sensing platform for the bioanalysis

Table_2_Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity.XLSX

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.</p

Scalable One-Pot Fabrication of Carbon-Nanofiber-Supported Noble-Metal-Free Nanocrystals for Synergetic-Dependent Green Hydrogen Production: Unraveling Electrolyte and Support Effects

Electrocatalytic hydrogen evolution reactions (HER) are envisaged as the most promising sustainable approach for green hydrogen production. However, the considerably high cost often associated with such reactions, particularly upon scale-up, poses a daunting challenge. Herein, a facile, effective, and environmentally benign one-pot scalable approach is developed to fabricate MnM (MCo, Cu, Ni, and Fe) nanocrystals supported over in situ formed carbon nanofibers (MnM/C) as efficient noble-metal-free electrocatalysts for HER. The formation of carbon nanofibers entails impregnating cellulose in an aqueous solution of metal precursors, followed by annealing the mixture at 550 °C. During the impregnation process, cellulose acts as a reactor for inducing the in situ reductions of MnM salts with the assistance of ether and hydroxyl groups to drive the mass production (several grams) of ultralong (5 ± 1 μM) carbon nanofibers ornamented with MnM nanoparticles (10–14 nm in size) at an average loading of 2.87 wt %. For better electrocatalytic HER benchmarking, the fabricated catalysts were tested over different working electrodes, i.e., carbon paper, carbon foam, and glassy carbon, in the presence of different electrolytes. All the fabricated MnM/C catalysts have demonstrated an appealing synergetic-effect-dependent HER activity, with MnCo/C exhibiting the best performance over carbon foam, close to that of the state-of-the-art commercial Pt/C (10 wt % Pt), with an overpotential of 11 mV at 10 mA cm–2, a hydrogen production rate of 2448 mol g–1 h–1, and a prolonged stability of 2 weeks. The HER performance attained by MnCo/C nanofibers is among the highest reported for Pt-free electrocatalysts, thanks to the mutual alloying effect, higher synergism, large surface area, and active interfacial interactions over the nanofibers. The presented findings underline the potential of our approach for the large-scale production of cost-effective electrocatalysts for practical HER

Data_Sheet_1_Quantitative collateral score for the prediction of clinical outcomes in stroke patients: Better than visual grading.PDF

ObjectivesTo identify preoperative prognostic factors for acute ischemic stroke (AIS) patients receiving mechanical thrombectomy (MT) and compare the performance of quantitative collateral score (qCS) and visual collateral score (vCS) in outcome prediction.MethodsFifty-five patients with AIS receiving MT were retrospectively enrolled. qCS was defined as the percentage of the volume of collaterals of both hemispheres. Based on the dichotomous outcome assessed using a 90-day modified Rankin Scale (mRS), we compared qCS, vCS, age, sex, National Institute of Health stroke scale score, etiological subtype, platelet count, international normalized ratio, glucose levels, and low-density lipoprotein cholesterol (LDL-C) levels between favorable and unfavorable outcome groups. Logistic regression analysis was performed to determine the effect on the clinical outcome. The discriminatory power of qCS, vCS, and their combination with cofounders for determining favorable outcomes was tested with the area under the receiver-operating characteristic curve (AUC).ResultsvCS, qCS, LDL-C, and age could all predict clinical outcomes. qCS is superior over vCS in predicting favorable outcomes with a relatively higher AUC value (qCS vs. vCS: 0.81 vs. 0.74) and a higher sensitivity rate (qCS vs. vCS: 72.7% vs. 40.9%). The prediction power of qCS + LDL-C + age was best with an AUC value of 0.91, but the accuracy was just increased slightly compared to that of qCS alone.ConclusionCollateral scores, LDL-C and age were independent prognostic predictors for patients with AIS receiving MT; qCS was a better predictor than vCS. Furthermore, qCS + LDL-C + age offers a strong prognostic prediction power and qCS alone was another good choice for predicting clinical outcome.</p