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A Bayesian optimization (BO) algorithm is employed to discover new Na3PS4 compositions with a high σion in a multi-dimensional co-doped search space.

Ultrasonography is an effective diagnostic tool for testicular torsion (TT), which is typically characterized by the absence of blood flow in the affected testicle on color Doppler mode. However, there are a few reported cases of TT with symmetrical preserved flow. We report a case of TT with the preserved intratesticular flow on color Doppler ultrasound.A 14-year-old boy was admitted due to sudden-onset right scrotal pain. Point-of-care ultrasound (POCUS) revealed that the right testicle was larger than the left. The intratesticular flow in both testicles was preserved. Radiology-performed ultrasound confirmed the preserved intratesticular flow observed on POCUS, but also demonstrated a whirlpool sign of the right spermatic cord. TT was confirmed surgically. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Emergency physicians should investigate the presence of intratesticular blood flow and the whirlpool sign of the spermatic cord or other ultrasound features suggestive of TT, even if testicular blood flow is preserved. Suspicion of TT from POCUS findings warrants further evaluation to preserve the patient's fertility.

When constructing a partially occupied model structure for use in density functional theory (DFT) and ab initio molecular dynamics (AIMD) calculations, the selection of appropriate configurations has been a vexing issue. We suggest a reasonable strategy to sort out this issue.

The development of eco-friendly flame retardants is crucial due to the hazardous properties of most conventional flame retardants. Herein, adenosine triphosphate (ATP) is reported to be a highly efficient "all-in-one" green flame retardant as it consists of three essential groups, which lead to the formation of char with extreme intumescence, namely, three phosphate groups, providing an acid source; one ribose sugar, working as a char source; and one adenine, acting as a blowing agent. Polyurethane foam was used as a model flammable material to demonstrate the exceptional flame retardancy of ATP. The direct flammability tests have clearly shown that the ATP-coated polyurethane (PU) foam almost did not burn upon exposure to the torch flame. Importantly, ATP exhibits an extreme volume increase, whereas general phosphorus-based flame retardants show a negligible increase in volume. The PU foam coated with 30 wt % of ATP (PU-ATP 30 wt %) exhibits a significant reduction in the peak heat release rate (94.3%) with a significant increase in the ignition time, compared to bare PU. In addition, PU-ATP 30 wt % exhibits a high limiting oxygen index (LOI) value of 31% and HF-1 rating in the UL94 horizontal burning foamed material test. Additionally, we demonstrated that ATP's flame retardancy is sufficient for other types of matrices such as cotton, as confirmed from the results of the standardized ASTM D6413 test; cotton-ATP 30 wt % exhibits an LOI value of 32% and passes the vertical flame test. These results strongly suggest that ATP has great potential to be used as an "all-in-one" green flame retardant.

Hybrid Organic-Inorganic Perovskites (HOIP) have received a great deal of attention as a key material for applications like solar cells and light emitting devices because of their many advantages, in spite of their stability and toxicity issues. Attempting to discover and characterize novel HOIPs using just an experimental approach would be prohibitively time-and-cost-consuming. Using theoretical or empirical calculations would greatly help. For these reasons, HOIP has been actively investigated using DFT (Density Functional Theory) calculations, which have significantly reduced research time and cost. However, the input model structure treatment needs to be standardized to avoid unnecessary complications. For this purpose, a sort of optimization of DFT calculation protocols for HOIPs is essential, because DFT calculation results are greatly affected by the input model structure arrangements and exchange-correlation functionals. In this paper, we used DFT to calculate the band gap, formation energy, and effective mass of the well-known cubic perovskite structure, methylammonium lead iodide (CH3NH3PbI3: MAPbI3) with and without the van der Waals function and SOC (Spin Orbit Coupling) and various geometrical molecule arrangements in the structure. In particular, the initial orientation of the ‘A’ site molecule in the input model structure was intensively investigated in terms of band gap, formation energy and effective mass. It was found that the relaxation-induced final structure was greatly influenced by the initial orientation of the molecule and thereby significantly affected the DFT-calculated result.

All solid-state batteries (ASSBs) are now anticipated to be an ultimate solution to the persistent safety issues of conventional lithium-ion batteries (LIBs). Contemporary society’s expanding power demands and growing energy consumption require energy storage with greater reliability, safety and capacity, which cannot be easily achieved with current state-of-the-art liquid-electrolyte-based LIBs. In contrast, these conditions are expected to be met by implementing ASSBs with high-performance solid-state electrolytes (SSEs). In this work, we altered the microscopic structure and Li diffusional behaviors of argyrodites (Li6-xPS5-xCl1+x), which were precisely monitored with cooling protocols. It was shown that, at the cooling speed of -3 oC·h-1, as the cooling rate decreased, impurities in Li5.5PS4.5Cl1.5 such as LiCl and Li3PO4 gradually diminished and eventually disappeared. At the same time, differences in the lattice sizes of Li5.5PS4.5Cl1.5 crystallites gradually decreased, resulting in a single phase Li5.5PS4.5Cl1.5. It was also found that the Cl content of the 4d crystallographic sites increased, eventually contributing to the improvement in ionic conductivity. This work also revealed the effect of cooling rates on the crystallographic atomic arrangements, which became weaker as a decrease in x. The correlations between ionic conductivities and structural features were experimentally verified via XRD and solid-state NMR studies.

A Y6-xCa1.5xSi11N20O:Ce3+(x=2.5) oxynitride phosphor is synthesized at 1,750 oC in a mixed gas atmosphere of 5% H2 and 95 % N2 by using YN, Ca3N2, Si3N4, and CeO2 as raw material reagents. The crystal structure is a trigonal crystal system that has a P31c (no.159) space group and has lattice parameters of a, b = 9.8876(3), and c = 10.6806(4). This structure is an Er6Si11N20O structure type in which a Y6-xCa1.5xSi11N20O structure is formed by substituting a trivalent Y3+ element and a bivalent Ca2+ element at the position of Er element having an oxidation number of +3. Here, the charge difference caused by different oxidation numbers is balanced by the occupancy of a partially vacant 2c site and an O/N anion ratio in the Er6Si11N20O structure type. The Y6-xCa1.5xSi11N20O:Ce3+ (x = 2.5) phosphor is yellow powder with yellow luminescence; performing Rietveld refinement on the phosphor on the basis of the data obtained by XRD measurement results in the lattice parameters as described above. The Y6-xCa1.5xSi11N20O:Ce3+ (x = 2.5) phosphor has a broad emission band due to Ce3+ as an activator with the center wavelength of 565 nm. This phosphor has a broader emission band than a YAG:Ce3+ phosphor, which is a representative LED phosphor, and thus extends further into the blue and red spectrum ranges. Accordingly, this phosphor is an interesting phosphor that can be used for 1pc-LED with an improved color rendering index.

A tandem (two-step) particle swarm optimization (PSO) algorithm is implemented in the argyrodite-based multidimensional composition space for the discovery of an optimal argyrodite composition, i.e., with the highest ionic conductivity (7.78 mS cm

Polymers constitute a diverse class of macromolecules that have demonstrated their unique advantages to be utilized for drug or gene delivery applications. In particular, polymers with a highly ordered, hyperbranched structure─"dendrons"─offer significant benefits to the design of such nanomedicines. The incorporation of dendrons into block copolymer micelles can endow various unique properties that are not typically observed from linear polymer counterparts. Specifically, the dendritic structure induces the conical shape of unimers that form micelles, thereby improving the thermodynamic stability and achieving a low critical micelle concentration (CMC). Furthermore, through a high density of highly ordered functional groups, dendrons can enhance gene complexation, drug loading, and stimuli-responsive behavior. In addition, outward-branching dendrons can support a high density of nonfouling polymers, such as poly(ethylene glycol), for serum stability and variable densities of multifunctional groups for multivalent cellular targeting and interactions. In this paper, we review the design considerations for dendron-lipid nanoparticles and dendron micelles formed from amphiphilic block copolymers intended for gene transfection and cancer drug delivery applications. These technologies are early in preclinical development and, as with other nanomedicines, face many obstacles on the way to clinical adoption. Nevertheless, the utility of dendron micelles for drug delivery remains relatively underexplored, and we believe there are significant and dramatic advancements to be made in tumor targeting with these platforms.

Protective surface coatings on Si anodes are promising for improving the electrochemical performance of lithium-ion batteries (LIBs). Nevertheless, most coating materials have severe issues, including low initial coulombic efficiency, structural fracture, morphology control, and complicated synthetic processing. In this study, a multifunctional TiO

The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic (PV) and light-emitting diode (LED) applications lags far behind their 3D counterparts. Here, we propose a computational strategy for discovering novel perovskites with as few computing resources as possible. A tandem optimization algorithm consisting of an elitism-reinforced nondominated sorting genetic algorithm (NSGA-II) and a multiobjective Bayesian optimization (MOBO) algorithm was used for density functional theory (DFT) calculations. The DFT-calculated band gap and effective mass were taken as objective functions to be optimized, and the constituent molecules and elements of a Ruddlesden–Popper (RP) structure (n = 2) were taken as decision variables. Fourteen previously unknown RP perovskite candidates for PV and LED applications were discovered as a result of the NSGA-II/MOBO algorithm. Thereafter, more accurate DFT calculations based on the HSE06 exchange correlation functional and ab initio molecular dynamics (AIMD) were conducted for the discovered 2D perovskites to ensure their validity.

The layered sodium transition metal oxide, NaTMO

A novel KGaS2 phosphor host that emits a cyan light was discovered to fill the cyan gap in the visible spectrum of phosphor-converted white light-emitting diodes (pc-wLEDs). KGaS2, belonging to the...

Hybrid organic/inorganic halide perovskites are considered to be a key material for high-end applications such as photovoltaic and light-emitting devices. Despite the phase stability and toxicity i...

The energy densities (EDs) of lithium-ion batteries have improved remarkably mostly via increases in the capacity of cathode materials, which suggests that the next quantum leap will require the simultaneous implementation of high-voltage cathodes and Li metal anodes for high-voltage operations. Herein, we describe for the first time that the compatibility of Li metal in adiponitrile (ADN) is ensured via the utilization of porous Li2O-coated Li (Li/Li2O). The optimal Li2O layer is constructed simply by immersing Li metal in ADN containing nitronium tetrafluoroborate (NO2BF4). When cycled in LiBF4/ADN, porous Li2O facilitates the formation of a stable LiF-enriched interfacial layer, which enables reversible Li plating/stripping during extended cycles with no dendrite formation. The sequential formation of LiF within the pores of Li2O is crucial, because massive formation of LiF precipitates is unavoidable without Li2O. Li/Li2O is also operable in LiTFSI/ADN (TFSI = bis(trifluoromethane)sulfonimide) with the addition of LiBF4. Finally, as a proof-of-concept experiment, we show the charge/discharge behaviors of LiNi0.5Mn1.5O4 when coupled with Li/Li2O in LiBF4:LiTFSI/ADN, which proves that a Li metal cell can be charged to 6.0 V. We hope that this work will expedite the development of Li metal batteries with a high level of ED in the dinitrile family.

In this research, dispersion of a new type of one-dimensional inorganic material Nb2Se9, composed of van der Waals bonds, in aqueous solution for bio-application study were studied. To disperse Nb2Se9, which exhibits hydrophobic properties in water, experiments were carried out using a block copolymer (poloxamer) as a dispersant. It was confirmed that PPO, the hydrophobic portion of Poloxamer, was adsorbed onto the surface of Nb2Se9, and PEO, the hydrophilic portion, induced steric hinderance to disperse Nb2Se9 to a size of 10 nm or less. To confirm the adaptability of muscle cells C2C12 to the dispersed Nb2Se9 using poloxamer 188 as dispersant, a MTT assay and a live/dead assay were performed, demonstrating improvement in the viability and proliferation of C2C12 cells.

Deep learning (DL) models trained with synthetic XRD data have never accomplished a satisfactory quantitative XRD analysis for the exact prediction of a constituent-phase fraction in unknown multiphase inorganic compounds, although DL-based phase identification has been successful. Here, we report a novel data-driven XRD analysis protocol involving a convolutional neural network (CNN) for exact phase identification and other machine learning (ML) techniques for accurate phase-fraction prediction. A key concept behind this reliable, pragmatic protocol is training with a huge amount of cheap synthetic data and testing with a small amount of expensive real-world experimental data. The protocol was applied to a Li–La–Zr–O quaternary compositional system that involves 218 ICSD-registered inorganic compounds, some of which are known as solid electrolyte materials. Synthetic data-driven XRD analysis has achieved a test accuracy of 96.47% for phase identification and a mean square error (MSE) of 0.0018 and an R2 of 0.9685 for phase-fraction regression. Real-world data tests have led to a phase-identification accuracy of 91.11% and a phase-fraction regression MSE of 0.0024 with an R2 of 0.9587.

The prediction of excitation band edge wavelength (EBEW) and peak emission wavelength (PEW) for Eu2+-activated phosphors is intricate in practice, although a theoretical interpretation has been well established. A data-driven approach could be of great help for EBEW and PEW prediction. We collected 91 Eu2+-activated phosphors, the host structures of which exhibit a single activator site and the EBEW and PEW of which are available at the critical activator concentration. We extracted 29 descriptors (input features) that implicate the elemental and structural traits of phosphor hosts, and set up an integrated machine-learning (ML) platform consisting of 18 ML algorithms that allowed prediction of the EBEW and PEW as well as the DFT-calculated band gap (Eg). The acquired dataset involving 91 phosphors was insufficient for the 29-input-feature problem and the real-world data collected from the literature have a so-called dirty nature due to inaccurate, unstandardized experiments. Despite an unavoidable paucity of data and the dirty-data problems of real-world data-based ML implementation, we obtained acceptable holdout dataset test results for PEW predications such as R2 > 0.6, MSE 0.77 for four ML algorithms. The EBEW and Eg predictions returned slightly better test results than these PEW examples.

Line-edge-roughness (LER) is one of undesirable process-induced random variation sources. LER is mostly occurred in the process of photo-lithography and etching, and it provokes random variation in performance of transistors such as metal oxide semiconductor field effect transistor (MOSFET), fin-shaped field effect transistor (FinFET), and gate-all-around field effect transistor (GAAFET). LER was analyzed/characterized with technology computer-aided design (TCAD), but it is fundamentally very time consuming. To tackle this issue, machine learning (ML)-based method is proposed in this work. LER parameters (i.e., amplitude, and correlation length X, Y) are provided as inputs. Then, artificial neural network (ANN) predicts 7-parameters [i.e., off-state leakage current (Ioff), saturation drain current (Idsat), linear drain current (Idlin), low drain current (Idlo), high drain current (Idhi), saturation threshold voltage (Vtsat), and linear threshold voltage (Vtlin)] which are usually used to evaluate the performance of FinFET. First, how datasets for training process of ANN were generated is explained. Next, the evaluation method for probabilistic problem is introduced. Finally, the architecture of ANN, training process and our new proposition is presented. It turned out that the prediction results (i.e., non-Gaussian distribution of device performance metrics) obtained from the ANN were very similar to that from TCAD in the respect of both qualitative and quantitative comparison.

Aim We presented the cut-off value of a diffusion-weighted image (DWI) scoring system to predict poor neurologic outcome using DWI taken 72−96 h after out-of-hospital cardiac arrest (OHCA) patients underwent target temperature management (TTM). Methods This was a prospective single-centre observational study, conducted from March 2018 to April 2020 in OHCA patients after TTM. Neurological status was assessed 6 months after return of spontaneous circulation (ROSC) using the Glasgow-Pittsburgh cerebral performance categories (CPC) scale. CPC of 1–2 demonstrated good neurologic outcomes whilst a CPC of 3–5 was related to poor neurologic outcomes. The receiver operating characteristic curves and DeLong method were used to evaluate the cut-off value of the DWI scoring system to predict poor neurologic outcome. Results The good and poor neurologic outcome groups consisted of 38 (54.3%) and 32 (45.7%) patients, respectively. The area under the receiver operating characteristic curve (AUROC) of the overall, cortex, deep grey nuclei, and cortex plus deep grey nuclei scores, white matter, brainstem, and cerebellum measured 72−96 h after ROSC were 0.96, 0.96, 0.97, 0.96, 0.95, 0.95, and 0.93 respectively. For 100.0% specificity to predict poor neurologic outcome, the overall scores of the DWI scoring system measured 72−96 h after ROSC with a cut-off value of 52 had a sensitivity of 81.3% (95% CI: 63.6–92.8). Conclusion This study demonstrated that the DWI scoring systems measured between 72 and 96 h after ROSC were valuable tools to predict poor neurologic outcome in post-OHCA patients treated with TTM.

Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis.

Aim In a previous study, low and high-normal arterial carbon dioxide tension (PaCO2) were not associated with serum neuron-specific enolase (NSE) in cardiac arrest survivors. We assessed the effect of PaCO2 on NSE in cerebrospinal fluid (CSF) and serum. Methods This was a retrospective study. PaCO2 for the first 24 h was analysed in four means, qualitative exposure state (qES), time-weighted average (TWA), median, and minimum–maximum (Min–Max). These subgroups were divided into low (LCO2) and high PaCO2 (HCO2) groups defined as PaCO2 ≤ 35.3 and PaCO2 > 43.5 mmHg, respectively. NSE was measured at 24, 48, and 72 h (sNSE24,48,72 and cNSE24,48,72) from return of spontaneous circulation (ROSC). The primary outcome was the association between PaCO2 and the NSE measured at 24 h after ROSC. Results Forty-two subjects (male, 33; 78.6%) were included in total cohort. PaCO2 in TWA subgroup was associated with cNSE24,48,72, while PaCO2 in the other subgroup were only associated with cNSE24. PaCO2 and cNSE in qES subgroup showed good correlation (r = −0.61; p Conclusion Association was found between NSE and PaCO2 using CSF, despite including normocapnic ranges; TWA of PaCO2 may be most strongly associated with CSF NSE levels. A prospective, multi-centre study is required to confirm our results.

Aim This study examined whether the presence of cortical necrosis (CN) on ultra-early diffusion-weighted imaging (DWI) and the severity of cytotoxic oedema (CytE) with cerebral oedema (CbrE), measured using quantitative analysis of apparent diffusion coefficient (ADC), could predict neurological outcomes before targeted temperature management in out-of-hospital cardiac arrest survivors (OHCAs). Methods In this retrospective study, the first DWI with ADC scans was performed within 6 h; the second was obtained between 72 and 96 h after return of spontaneous circulation. The primary outcome was neurological outcomes at 6 months after OHCA. The % voxels of ADC value (PV) was calculated; CbrE and CytE values were > or Results Thirty-six patients were included. CN (area under receiver operating characteristic curve [AUC] = 0.800), thld-CytE (PV420; AUC = 0.730), and thld-CbrE (PV1090; AUC = 0.775) showed meaningful performance, and the combined score showed best performance for poor outcome prediction (AUC = 0.956). DWI findings of CN patients was worse at the second DWI. ΔPV significantly increased in the poor outcome group, CN patients, and the group including both, thld-CytE and thld-CbrE. Conclusions In OHCAs, ultra-early DWI with ADC could successfully predict poor neurological outcomes by combining scores of CN, thld-CytE, and thld-CbrE.

Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification., Identifying the composition of multiphase inorganic compounds from XRD patterns is challenging. Here the authors use a convolutional neural network to identify phases in unknown multiphase mixed inorganic powder samples with an accuracy of nearly 90%.

NaCrO2 with high rate-capability is an attractive cathode material for sodium-ion batteries (NIBs). However, the amount of reversibly extractable Na+ ions is restricted by half, which results in relatively low energy density for practical NIB cathodes. Herein, we describe aliovalent-doped O3–Na0.9[Cr0.9Sn0.1]O2 (NCSnO) and O3–Na0.8[Cr0.9Sb0.1]O2 (NCSbO), both of which show high-voltage characteristics that translate to an increase in energy density. In contrast to NaCrO2, NCSnO and NCSbO can be reversibly charged to 3.80 and 3.95 V, respectively, delivering 0.5 Na+ along with Cr3+/4+ redox alone. The reversible chargeability to Na0.4[Cr0.9Sn0.1]O2 and Na0.3[Cr0.9Sb0.1]O2 is not associated with the suppression of Cr6+ formation. Both compounds show concentrations of Cr6+ that are higher than that of Na0.3CrO2, with an absence of O3′ phases. This implies that aliovalent-doping contributes to a suppression of the Cr6+ migration into tetrahedral sites in the interslab space, which reduces the possibility of irreversible comproportionation. NCSnO and NCSbO deliver capacities comparable to that of NaCrO2, but show a higher average discharge voltage (2.94 V for NaCrO2; 3.14 V for NCSnO; 3.21 V for NCSbO), which leads to a noticeable increase in energy densities. The high-voltage characteristics of NCSnO and NCSbO are also validated via density-functional-theory calculations.

Although silver (Ag)-based nanoparticles (NPs) are frequently used for bactericidal purposes, they have critical issues including excessive release of Ag+, severe oxidation, and cytotoxicity. In this study, we designed a multifunctional, on-demand antibacterial agent by successively encapsulating bimetallic gold/silver nanorods (Ag/AgNRs) with mesoporous silica (mSiO2) shells. Au/AuNRs were synthesized by coating Ag on AuNRs in a controlled manner, so that they exhibited a localized surface plasmon resonance peak in the near-infrared (NIR) region. When Au/AgNR@mSiO2 NPs were irradiated with an NIR laser under optimal conditions (0.4 W/cm2), they generated a small amount of heat (40–45 oC), which successively triggered the release of Ag+ and induced bacterial cell death. Here, mSiO2 shells play critical roles because they not only protect Ag from oxidation but also prevent the burst release of Ag+ and improve biocompatibility of the antibacterial agent against normal cells. We found that this multifunctional bacterial agent effectively kills gram-negative Escherichia coli and gram-positive Staphylococcus aureus without significantly increasing the temperature of the medium. Au/AgNR@mSiO2 NPs were also biologically benign with high biocompatibility against mammalian cells.

Organosilicon compounds have been actively used with nano- and micro-fillers to improve the adhesion and mechanical properties. However, studies on the adhesion properties of polymeric materials, such as polypropylene (PP), functionalized with organosilicon compounds are limited. Here, we investigated the adhesion of organosilanized PP substrates, functionalized using (3-glycidoxypropyl)trimethoxysilane (GPTMS) and (3-aminopropyl)trimethoxysilane (APTMS) as coupling agents, with epoxy adhesives. The curing of epoxy-functionalized PP (PP-EPOXY) with triethylenetetramine (TETA) hardener led to the chemical crosslinking of TETA with PP-EPOXY, as determined by X-ray photoelectron spectroscopy and vacuum Fourier-transform infrared spectroscopy. Similarly, the curing of amine-functionalized PP (PP-NH2) with bisphenol A diglycidyl ether (DGEBA) epoxy resin led to the chemical crosslinking of the resin with PP-NH2. Finally, we measured the adhesion properties of the functionalized PP substrates using an adhesive composed of DGEBA and TETA based on ASTM D3163 and observed that the shear strength of PP-EPOXY and PP-NH2 increased significantly up to 580% and 506% as compared with that of bare PP. These results strongly suggest that the functionalization of PP significantly contributes to the improvement of adhesion with an adhesive.

Studies on the prognostic performance of optic nerve sheath diameter (ONSD) in out-of-hospital cardiac arrest survivors (OHCA) have reported conflicting results. We aimed to investigate the usefulness of ONSD measured using magnetic resonance imaging (MRI) to estimate its association with intracranial pressure (ICP) and 6-month neurological outcomes in CA survivors treated with targeted temperature management (TTM).This retrospective study included 37 CA survivors who underwent TTM from January 2018 to December 2018. ICP was measured by lumbar catheter during TTM on Days 0, 1, 2, and 3. ONSD was measured using MRI on Days 0 and 3. The primary outcome was the correlation between ONSD and ICP associated with neurological outcomes obtained after 6 months.The median (interquartile range [IQR]) ONSD was not significantly different between the good and poor neurological outcome group on Day 0 (5.2 mm [4.8-5.8] vs 5.2 mm [4.8-5.6]; p = 0.948) and Day 3 (5.0 mm [4.8-5.2] vs 5.5 mm [4.4-5.9]; p = 0.105). ONSD and ICP had excellent correlation on Day 3 (r = 0.90, p 0.001). ONSD showed excellent correlation with increased ICP (IICP) defined as ICP above 20 mmHg (r = 0.89, p 0.001). ONSD cut-off of 5.99 mm was used with a sensitivity of 90.0% and specificity of 98.0% to identify IICP.The ONSD on Days 0 or 3 did not show differences in neurological outcomes in OHCA patients treated with TTM. However, ONSD had an excellent correlation with ICP on Day 3 and with IICP. Further studies are required to confirm our results.

Aim We evaluated the optimal timing of optic nerve sheath diameter (ONSD) measurement to predict neurologic outcome in post-cardiac arrest patients treated with target temperature management (TTM). Methods This was a prospective single-centre observational study from April 2018 to March 2019. Good outcome was defined as the Glasgow-Pittsburgh cerebral performance categories (CPC) 1 or 2, and poor outcome as a CPC between 3 and 5. ONSD was measured initially after return of spontaneous circulation (ROSC) (ONSDinitial), at 24 h (ONSD24), 48 h (ONSD48), and 72 h (ONSD72) using ultrasonography. The receiver operating characteristic (ROC) curves and DeLong method were used to compare the values for predicting neurologic outcomes. Results Out of the 36 patients enrolled, 18 had a good outcome. ONSD24, ONSD48, and ONSD72 were higher in the poor outcome group. The area under ROC curve of ONSD24 was 0.91 (95% confidence interval 0.77–0.98) in predicting poor neurologic outcomes. With a cut off value of 4.90 mm, ONSD24 had a sensitivity of 83.3% and a specificity of 94.4% in predicting poor neurologic outcomes. Conclusion Our findings demonstrate ONSD24 as a valuable tool to predict the neurologic outcome in post-cardiac arrest patients treated with TTM. Therefore, we recommend performing ONSD measurement using ultrasonography at 24 h after ROSC, rather than immediately after ROSC, to predict neurologic outcome in post-cardiac arrest patients treated with TTM.