Search

As a high-k material, hafnium oxide (HfO2) has been used in gate dielectrics for decades. Since the discovery of polar phase in Si-doped HfO2 films, chemical doping has been widely demonstrated as an effective approach to stabilize the ferroelectric phase in HfO2 based thin films. However, the extra capping layer deposition, post-growth annealing and wake-up effect are usually required to arouse the ferroelectricity in HfO2 based thin films, resulting in the increase of complexity for sample synthesis and the impediment of device application. In this study, the ferroelectricity is observed in non-capped dopant-free HfO2 thin films prepared by pulsed laser deposition (PLD) without post-growth annealing. By adjusting the deposited temperature, oxygen pressure and thickness, the maximum polarization up to 14.7 μC/cm2 was obtained in 7.4 nm-thick film. The fraction of orthorhombic phase, concentrations of defects and size effects are considered as possible mechanisms for the influences of ferroelectric properties. This study indicates that PLD is an effective technique to fabricate high-quality ferroelectric HfO2 thin films in the absence of chemical doping, capping layer deposition and post-growth annealing, which may boost the process of nonvolatile memory device application.

Automatic International Classification of Diseases (ICD) coding is an important task in the future of artificial intelligence healthcare. In recent years, a lot of traditional machine learning-based methods have been proposed, and they achieved good results on this task. However, these traditional machine learning-based methods for automatic ICD coding only focus on the semantic features of clinical notes and ignore the feature extraction of ICD titles that are the descriptions of ICD codes. In this paper, we propose a knowledge attention-based deep learning framework called KAICD for automatic ICD coding. KAICD makes full use of the clinic notes and the ICD titles. The semantic features of clinic notes are extracted by a multi-scale convolutional neural network. For ICD titles, we use attention-based Bidirectional Gated Recurrent Unit (Bi-GRU) to build a knowledge database, which can offer additional information. Depending on input clinic notes, we can use the attention mechanism to obtain different knowledge vectors from the knowledge database where some ICD titles are more relevant to the input clinic notes. Last, we concatenate the knowledge vectors and the semantic features of clinic notes, and use them for the final prediction. KAICD is tested on a public dataset Medical Information Mart for Intensive Care III (MIMIC III); it achieves micro-precision of 0.502, micro-recall of 0.428, and micro-f1 of 0.462, which outperforms other competing methods. Furthermore, the results of the ablation study show that the knowledge database of ICD titles learned by the attention-based Bi-GRU enhances the feature expression and improves the prediction performance.

An ultra-sensitive capture of NO2 molecules and fast charge collection and transfer has been realized by constructing the model of artificial neuron-likegas sensing structure based on CuS quantum dots (QDs)/Bi2S3 nanosheets (NSs)realizes.Simulation analysis revealed that CuS QDs and Bi2S3NSs can be used, respectively, as the main adsorption sites and charge transport pathways, thus leading to a greatly enhanced gas capture ability and charge conduction performance of NO2. Supplementary Information The online version contains supplementary material available at 10.1007/s40820-021-00740-1., Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/Bi2S3 nanosheets (CuS QDs/Bi2S3 NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and Bi2S3 NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO2 can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO2 sensitivity due to the rich sensitive sites and quantum size effect. The Bi2S3 NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO2. Furthermore, the developed wearable device can also realize the visual detection of NO2 through real-time signal changes. Supplementary Information The online version contains supplementary material available at 10.1007/s40820-021-00740-1.

Knowledge of protein functions plays an important role in biology and medicine. With the rapid development of high-throughput technologies, a huge number of proteins have been discovered. However, there are a great number of proteins without functional annotations. A protein usually has multiple functions and some functions or biological processes require interactions of a plurality of proteins. Additionally, Gene Ontology provides a useful classification for protein functions and contains more than 40,000 terms. We propose a deep learning framework called DeepGOA to predict protein functions with protein sequences and protein-protein interaction (PPI) networks. For protein sequences, we extract two types of information: sequence semantic information and subsequence-based features. We use the word2vec technique to numerically represent protein sequences, and utilize a Bi-directional Long and Short Time Memory (Bi-LSTM) and multi-scale convolutional neural network (multi-scale CNN) to obtain the global and local semantic features of protein sequences, respectively. Additionally, we use the InterPro tool to scan protein sequences for extracting subsequence-based information, such as domains and motifs. Then, the information is plugged into a neural network to generate high-quality features. For the PPI network, the Deepwalk algorithm is applied to generate its embedding information of PPI. Then the two types of features are concatenated together to predict protein functions. To evaluate the performance of DeepGOA, several different evaluation methods and metrics are utilized. The experimental results show that DeepGOA outperforms DeepGO and BLAST.

We evaluated the modified anterior midline approach and its efficacy for hyperextension bicondylar tibial plateau (HEBTP) fractures.From 2015 to 2019, 18 patients with HEBTP fractures with just little posterior cortical displacement were treated using the modified anterior midline approach. The operative protocols are fully described in this article, and the following parameters: articular step-off height (ASH), posterior tibial slope angle (pTSA), and medial tibial plateau angle (mTPA) were measured perioperatively and at the final follow-up. We also recorded the Rasmussen score and range of motion (ROM) to assess knee joint function at the final follow-up.No complications, such as percutaneous nerve damage, infection, skin necrosis, and internal fixation breakage or loosening occurred perioperatively. The mean time for bony union was 13.7 weeks, and the mean preoperative ASH of the anterior cortex was 4.49 mm; this was restored to its normal height after surgery. The mean preoperative pTSA and mTPA were - 5.89° and 81.69°, respectively, compared with 3.89° and 87.91°, respectively, postoperatively. Comparing the postoperative and final follow-up radiographs, there were no significant differences in ASH, pTSA, and mTPA (P 0.05). The average Rasmussen score was 27.2 (range, 23-29) at the final follow-up. Excellent results were achieved in 14 (77.8%) patients and good in 4 (22.2%) patients. The mean ROM in flexion was 123.2° and 2.9° in extension at the final follow-up.This study suggested that the modified anterior midline approach is a reasonable alternative for HEBTP fracture repair.

To study the impact of total splenectomy (TS) on peripheral lymphocytes and their subsets in patients with hypersplenism associated with cirrhotic portal hypertension (CPH). We studied 102 consecutive patients who received TS from January 2008 to January 2020 due to CPH-related hypersplenism. A similar number of healthy individuals are used as healthy controls (HC). The total lymphocyte counts and their percentages of B lymphocytes, total T lymphocytes (cluster of differentiation (CD)3+) and their subsets (CD4+, CD8+), and natural killer (NK) cells in preoperative peripheral blood samples in hypersplenism patients were significantly lower than that of the HCs (both P P P > 0.05). However, the percentages of CD3+ cells was significantly higher 1 month after than before surgery (P +, and CD8+ T lymphocytes were significantly lower 1 week after surgery (P P +:CD8+ ratio was not significantly different from those before surgery, and 1 week or 1 month after surgery (P > 0.05). Patients with hypersplenism associated with CPH were significantly immunosuppressed preoperatively. After TS, the total lymphocyte count and percentages of B lymphocytes, and total T lymphocytes and their subsets increased significantly, resulting in improved immune functions.

Performing subtrochanteric osteotomy with cemented components in patients with Crowe IV developmental dysplasia of the hip (DDH) is technically challenging and not widely reported. This study aimed to evaluate the mid-term outcomes of cemented stem total hip arthroplasty (THA) with subtrochanteric femoral shortening and transverse derotational osteotomy in patients with Crowe IV DDH.Data collected from patients with Crowe IV DDH who underwent cemented stem THA with subtrochanteric femoral shortening and transverse derotational osteotomy between 2010 and 2018 were retrospectively evaluated. The cemented Lubinus SP II femoral component and the cementless CombiCup acetabular component were used together in all cases. These data, including Harris hip scores, limb length discrepancy (LLD), severity of limp, Trendelenburg test, bone union, length of the resected femur, limb lengthening, level of the osteotomy site, and length bridging the osteotomy site, as well as complications, were analyzed. A paired Student t-test was used to analyze continuous variables, categorical data were compared using Fisher's exact probability test, and correlation analysis was performed using Spearman's rank correlation coefficient.Among 14 included patients (10 females and four males), the mean age was 60.4 years (range, 47-73). The mean follow-up period was 49.1 months, and no patient was lost to follow-up. The mean Harris hip score improved from 40.7 to 87.7. The mean LLD decreased from 52 to 12.7 mm. The mean length of the excised femoral segment was 38.4 mm, and the mean length of limb lengthening was 27.1 mm. The mean distance between the osteotomy site and the lesser trochanter was 21.1 mm after surgery. The mean length of the femoral stem bridging the osteotomy site was 97.6 mm. Finally, the mean osteotomy union time was 10.6 months. No statistically significant correlation was found between the osteotomy union time and these factors. No neurological deficits were noted. Delayed union was observed in one patient, and postoperative dislocation was observed in two patients. Cement leakage into the osteotomy gap was observed in one patient, however, no revisions were required, and no signs of loosening or migration were observed.Cemented stem THA combined with subtrochanteric femoral shortening and transverse derotational osteotomy is safe and effective in treating patients with Crowe IV DDH. Rather than leading to nonunion, cement leakage may negatively affect bone healing.

Gastric cancer (GC) is one of the most common digestive system tumors in the world. Many circular RNAs (circRNAs) are involved in the progression of GC. The purpose of this study was to delve into the expression characteristics and biological functions of circ_0000064 in GC, and further study its mechanisms. Quantitative real‐time polymerase chain reaction (qRT‐PCR) was performed to detect circ_0000064 expression in 61 GC tissues and cell lines. Circ_0000064 knockdown was successfully modeled with siRNA. The effects of circ_0000064 on the biological functions of GC cells were analyzed by CCK‐8, BrdU, and Transwell assays. Bioinformatics and dual‐luciferase reporter gene assay were adopted to explore the relations between circ_0000064 and microRNA‐621 (miR‐621). Western blot was used to examine the regulatory function of circ_0000064 and miR‐621 on SYF2 pre‐mRNA splicing factor 2. Cric_0000064 expression was elevated in GC tissues and cell lines. Knocking down cric_0000064 could inhibit the viability, migration, and invasion of GC cells. Dual‐luciferase reporter gene assay showed that miR‐621 could bind circ_0000064 and SYF2 3'UTR; in addition, miR‐621 overexpression or SYF2 knockdown could partially weaken the cancer‐promoting effect of circ_0000064 on GC cells. Circ_0000064 expression was negatively correlated with miR‐621 expression in GC tissues while positively with SYF2 expression. Circ_0000064 can participate in the GC progression via modulating miR‐621/SYF2 axis. This implies that circ_0000064 may be a new diagnosed biomarker or a new therapeutic target of GC.

Background This study aimed to explore the clinical efficacy of treating a first metacarpal base fracture by closed reduction and percutaneous parallel K-wire interlocking fixation between the first and second metacarpals. Methods Twenty patients treated by the abovementioned modified technique (modified technique group) and ten patients treated by the traditional percutaneous K-wire fixation technique (traditional technique group) from October 2015 to November 2019 at our institution were retrospectively reviewed. The patients’ average age was 38 years (range, 16–61 years). The mean follow-up period was 13 months (range, 10–18 months). At the final follow-up, the functional recovery of the injured hand was assessed and compared between the modified and traditional technique groups. In addition, the functional recovery of the injured hand was compared with that of the uninjured hand within the modified technique group. Results All patients recovered well, with no cases of infection or nonunion. Compared with the traditional technique group, the modified technique group had a shorter operative time, lower postoperative visual analogue scale pain score, better effective range of motion score of the first carpometacarpal joint (Kapandji score), and had almost no need for auxiliary plaster fixation, enabling functional exercise to be started earlier. Within the modified technique group, the mean hand grip strength, pinch strength, and Kapandji score on the injured side did not significantly differ to the values on the uninjured side in both the extra-articular and intra-articular fracture subgroups. While the abduction and flexion–extension arcs of the thumb on the injured hand were significantly smaller than those on the uninjured hand in both the extra-articular and intra-articular fracture subgroups, the patients felt clinically well with respect to daily activities and strength. Conclusion The percutaneous parallel K-wire and interlocking fixation technique is simple, effective, and economical for first metacarpal base fractures.

An efficient formaldehyde (HCHO) gas sensor system based on nickel-doped tin oxide (Ni-doped SnO2) nanoparticle sensor and back-end circuit has been developed. Ni-doped SnO2 nanoparticles are prepared by a simple one-step hydrothermal method. To complete the function of detecting HCHO gas in real time, a back-end circuit suitable for resistance measurement of nanoparticle sensors and an organic light emitting diode screen which can display the output of gas concentration data are integrated as a portable device. Compared with the traditional HCHO detection device, this designed system exhibits better stability, excellent selectivity, and high response. Its detection limit is as low as 90 ppb. The gas response to 50 ppm HCHO is 67.63. Further for the quantitative evaluation, we refer to and modify the existing HCHO diffusion model, then carry out a practical test on the HCHO diffusion at the same time. The comparison between the theoretical fitting curve and the actual test curve matched well enough to reflect the potential of this system for the practical HCHO evaluation.

Machine learning pipelines for protein functional family prediction are urgently needed especially now that only 1% of raw protein sequences have been manually annotated. Although existing machine learning algorithms have achieved a decent performance in modeling and predicting the functional families of protein sequences, they still have two drawbacks. First, biological dependencies among nucleotides are not rich enough to describe motifs for these methods. Also, existing algorithms are not accurate enough to predict the functional families of newly discovered proteins. To address the above limitations simultaneously, we propose a novel deep learning framework for predicting protein family, DeepPPF, which employs the word2vec technique in capturing distributional dependencies among nucleotides and discovers rich features from diverse motif lengths to characterize proteins. The novelty of the DeepPPF is in utilizing distributional dependencies among nucleotides. Experimental results on G protein-coupled receptor hierarchical datasets show the effectiveness of DeepPPF in achieving the state-of-the-art performance in items of Mathew’s correlation coefficients (MCC) of 97.62%, 88.45% and, 83.09% for family, sub-family and, sub-subfamily hierarchical levels, respectively. Also, DeepPPF outperformed existing methods in terms of prediction accuracy and Mathew’s correlation coefficients on the cluster of orthologous groups (COG) and phage of orthologous groups (POG) datasets. Furthermore, we analyzed the ability of DeepPPF framework to discover rich motifs for functional classes with the least sets of protein sequences. The experimental results show that rich motif discovery is key to improving the modeling performance of protein families through deep learning techniques. Finally, we investigated the effect of transferring a low-level functional domain level to a high-level functional domain and results show that the target domain prediction can be improved with transfer learning. Therefore, our proposed deep learning framework can be useful in characterizing protein functional families. The codes and datasets are available at https://github.com/CSUBioGroup/DeepPPF .

In recent years, more and more evidence indicates that circular RNAs (circRNAs) with covalently closed loop play various roles in biological processes. Dysregulation and mutation of circRNAs may be implicated in diseases. Due to its stable structure and resistance to degradation, circRNAs provide great potential to be diagnostic biomarkers. Therefore, predicting circRNA-disease associations is helpful in disease diagnosis. However, there are few experimentally validated associations between circRNAs and diseases. Although several computational methods have been proposed, precisely representing underlying features and grasping the complex structures of data are still challenging. In this paper, we design a new method, called DMFCDA (Deep Matrix Factorization CircRNA-Disease Association), to infer potential circRNA-disease associations. DMFCDA takes both explicit and implicit feedback into account. Then, it uses a projection layer to automatically learn latent representations of circRNAs and diseases. With multi-layer neural networks, DMFCDA can model the non-linear associations to grasp the complex structure of data. We assess the performance of DMFCDA using leave-one cross-validation and 5-fold cross-validation on two datasets. Computational results show that DMFCDA efficiently infers circRNA-disease associations according to AUC values, the percentage of precisely retrieved associations in various top ranks, and statistical comparison. We also conduct case studies to evaluate DMFCDA. All results show that DMFCDA provides accurate predictions.

BACKGROUND Massive esophageal variceal bleeding can be catastrophic, leading to high morbidity and mortality. Patients experiencing massive esophageal variceal bleeding are at high risk of aspiration and hemorrhagic shock in acute episodes. Intubation and bleeding control are the two essential steps for resuscitation of these patients. CASE SUMMARY A 47-year-old male patient was admitted to our hospital with upper digestive tract bleeding. He was diagnosed with alcohol-induced liver cirrhosis and consequent esophagogastric varices. As he did not show a good response to somatostatin and Sengstaken-Blakemore tube placement, the patient was scheduled for endoscopic angiotherapy under anesthesia. Preoperative assessment showed an ASA physical status of III and Child-Pugh classification B. However, massive hemorrhage occurred just after induction of anesthesia. Intubation by video-guided laryngoscopy in the lateral decubitus position was attempted twice and was successful. After that, an experienced endoscopic ultrasound (EUS) specialist performed angiotherapy and occluded the culprit vessel. An ultra-thin gastroscope was then inserted into the endotracheal tube to extract the blood observed in the lobar bronchi. The patient suffered hemorrhagic shock with an estimated blood loss of 1500 mL in 20 min and remained in the intensive care unit for two days. The patient was discharged from our hospital eight days later without major complications. CONCLUSION Intubation in the lateral decubitus position and EUS-guided treatment can be life-saving procedures in patients with massive upper gastrointestinal hemorrhage.

Rationale: Acute liver failure (ALF) causes severe liver injury and a systemic inflammatory response, leading to multiorgan failure with a high short-term mortality. Bioartificial liver (BAL) therapy is a promising approach that is hampered by the lack of appropriate bioreactors and carriers to retain hepatic cell function and poor understanding of BAL treatment mechanisms in ALF and extrahepatic organ injury. Recently, we used a fiber scaffold bioreactor (FSB) for the high-density, three-dimensional (3D) culture of primary porcine hepatocytes (PPHs) combined with an absorption component to construct a BAL and verified its function in a D-galactosamine (D-gal)-induced ALF porcine model to evaluate its protective effects on the liver and extrahepatic organs. Methods: Male pigs were randomized into standard/supportive therapy (ST), ST+no-cell BAL (ST+Sham BAL) and ST+BAL groups and received treatment 48 h after receiving a D-gal injection. Changes in blood chemistry and clinical symptoms were monitored for 120 h. Tissues and plasma were collected for analysis by pathological examination, immunoblotting, quantitative PCR and immunoassays. Results: PPHs cultured in the FSB obtained sufficient aeration and nutrition for high-density, 3D culture and maintained superior viability and functionality (biosynthesis and detoxification) compared with those cultured in flasks. All the animals developed ALF, acute kidney injury (AKI) and hepatic encephalopathy (HE) 48 h after D-gal infusion and received corresponding therapies. Animals in the BAL group showed markedly improved survival (4/5; 80%) compared with those in the ST+Sham BAL (0/5; p < 0.001) and ST (0/5; p < 0.001) groups. The levels of blood ammonia and biochemical and inflammatory indices were alleviated after BAL treatment. Increased liver regeneration and attenuations in the occurrence and severity of ALF, AKI and HE were observed in the ST+BAL group compared with the ST (p = 0.0009; p = 0.038) and ST+Sham BAL (p = 0.011; p = 0.031) groups. Gut leakage, the plasma endotoxin level, bacterial translocation, and peripheral and neuroinflammation were alleviated in the ST+BAL group compared with those in the other groups. Conclusions: BAL treatment enhanced liver regeneration and alleviated the systemic inflammatory response and extrahepatic organ injury to prolong survival in the ALF model and has potential as a therapeutic approach for ALF patients.

Two-dimensional (2D) layered nanomaterials have attracted increasing attention in gas sensing due to their graphene-like properties. Although the gas sensing performances of 2D layered semiconductor transition metal dichalcogenides (TMDs), including MoS2, WS2, MoSe2 and WSe2, have been extensively studied, it has remained a grand challenge to develop a high-performance gas sensing material that can meet practical applications. Tantalum disulfide (TaS2), as a metallic TMD with low resistance and high current signal, has great promise in high-performance gas sensing. In stark contrast with Mo and W, Ta has a stronger positive charge, which contributes to a higher surface energy to capture gas molecules. Herein, through calculating the adsorption energy, charge transfer, electronic structure, and work function of the adsorption system with first-principles calculations, we first systematically studied the performance of noble metal atom substitution doping on a TaS2 monolayer for toxic nitrogen-containing gas (NH3, NO and NO2) sensing. We found that the TaS2 monolayer exhibits excellent NO sensing performance with an adsorption energy of 0.49 eV and a charge transfer of 0.17 e. However, it has a considerable adsorption energy (−0.22 and −0.39 eV) to NH3 and NO2 molecules, but a low charge transfer (−0.03 and 0.04 e) between the gas molecules and the TaS2 monolayer. To further enhance the gas-sensing performance of the TaS2 monolayer, noble metal atoms (Ag, Au, Pd and Pt) were substitutionally doped into the lattice of the TaS2 monolayer. The results showed that the values of adsorption energy and charge transfer can be significantly improved, and the electronic structure and work function of the doping system has also greatly changed, which makes it much easier to detect the changes in electrical signal due to gas adsorption. Our work indicates that the intrinsic as well as the noble metal doped TaS2 monolayers are promising candidates for high-performance gas sensors.

Metal phthalocyanine (MPc) has a great saturation response value, but its low conductivity and slow response speed limit its practical application. A novel hybrid material composed of graphene quantum dots (GQDs) and metal phthalocyanine derivatives has been obtained. GQDs can be anchored onto the surface of MPc nanofibers through π–π stacking. The response to NO2 can be significantly enhanced under certain component proportion matching, which is much better than their respective response to NO2. The introduction of GQDs greatly increases the conductivity of phthalocyanine fibers, leading to a faster response of the hybrid material. In addition, the reproducibility, selectivity and stability of the hybrid materials are excellent, and the minimum response concentration can reach 50 ppb. Ultra-low-power laser irradiation was used to solve the problem of slow recovery of metal phthalocyanine. Overall, we present the advantages of combining MPc nanofibers with GQDs and pave a new avenue for the application of MPc–GQD hybrids in the gas sensing field.

The hypoxic microenvironment was recognized as a major driving force of the malignant phenotype in hepatocellular carcinoma (HCC), which contributes to tumour immune microenvironment (TIM) remodeling and tumor progression. Dysregulated hypoxia-related genes (HRGs) result in treatment resistance and poor prognosis by reshaping tumor cellular activities and metabolism. Approaches to identify the relationship between hypoxia and tumor progression provided new sight for improving tumor treatment and prognosis. But, few practical tools, forecasting relationship between hypoxia, TIM, treatment sensitivity and prognosis in HCC were reported. Here, we pooled mRNA transcriptome and clinical pathology data from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), and later developed a hypoxia risk model including four HRGs (DCN, DDIT4, PRKCA and NDRG1). The high-risk group displayed poor clinical characteristics, a malignant phenotype with carcinogenesis/proliferation pathways activation (MTORC1 and E2F) and immunosuppressive TIM (decreased immune cell infiltrations and upregulated immunosuppressive cytokines). Meanwhile, activated B cells, effector memory CD8 T cells and EZH2 deregulation were associated with patient’s survival, which might be the core changes of HCC hypoxia. Finally, we validated the ability of the hypoxia risk model to predict treatment sensitivity and found high hypoxia risk patients had poor responses to HCC treatment, including surgical resection, Sorafenib, Transarterial Chemoembolization (TACE) and immunotherapy. In conclusion, based on 4 HRGs, we developed and validated a hypoxia risk model to reflect pathological features, evaluate TIM landscape, predict treatment sensitivity and compounds specific to hypoxia signatures in HCC patients.

A simple microwave-assisted method was applied to synthesize zinc oxide (ZnO) with controllable hierarchical structures. In a surfactant-free solvent system, the hierarchical structure of the ZnO precursor can be regulated by the concentration of urea at normal temperature and pressure. Upon annealing, ZnO with different morphologies shows its unique response towards six kinds of gases. The response data were clustered and analyzed by principal component analysis (PCA) to provide a basis for feature extraction. The classification to six kinds of gases was conducted through a model based on linear ridge classification (LRC), support vector machine (SVM). The prediction of ethanol concentration was achieved using backpropagation (BP) neural network and extreme learning machine (ELM). The results indicate that the six confusing gases can be distinguished clearly using SVM with an accuracy more than 0.99. Furthermore, the prediction of ethanol concentration shows a prominent performance (R2 > 0.98) by the ELM-based regressor, despite the nearly saturated response of the sensor array. This study explores the possibility of pattern recognition analysis based on machine learning to further improve the detection performance of the gas sensor array with different response characteristics regulated by the morphology.

Although numerous studies have revealed the structural and functional alterations in major depressive disorder (MDD) using unimodal diffusion magnetic resonance imaging (MRI) or functional MRI, however, the potential associations between changed microstructure and corresponding functional activities in the MDD has been largely uninvestigated. Herein, 27 medication-free MDD patients and 54 gender-, age-, and educational level-matched healthy controls (HC) were used to investigate the concurrent alterations of white matter microstructure and functional activities using tract-based spatial statistics (TBSS) analyses, fractional amplitude of low-frequency fluctuation (fALFF), and degree centrality (DC). The TBSS analyses revealed significantly decreased fractional anisotropy (FA) in the superior longitudinal fasciculus (SLF I) in the MDD patients compared to HC. Correlation analyses showed that decreased FA in the SLF I was significantly correlated with fALFF in left pre/postcentral gyrus and binary, weighted DC in right posterior cerebellum. Moreover, the fALFF in left pre/postcentral gyrus significantly reduced in MDD patients while binary and weighted DC in right posterior cerebellum significantly increased in MDD patients. Our results revealed concurrent structural and functional changes in MDD patients suggesting that the underlying structural disruptions are an important indicator of functional abnormalities.

Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr0.2Ti0.8)O3 layer, show not only multiple nonvolatile levels but also sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. In situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is demonstrated in the FE-PS-NET. Moreover, the FE-PS-NET is faultlessly competent for real-time image processing functionalities, including binary classification between ‘X’ and ‘T’ patterns with 100% accuracy and edge detection for an arrow sign with an F-Measure of 1 (under 365 nm ultraviolet light). This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.

The most critical aspect in the assembly of phosphor-converted white light-emitting diodes (pc-WLEDs) is how to stabilize the device in a practical environment. The high applied currents can generate enormous heat up to more than 100 °C, and such a continuous illumination process will lead to serious effects concerning the stability of the device. Therefore, the new search for examples to fully suppress thermal quenching effect is a real challenge. In this study, a novel Eu3+-activated CaMgGeO4 (CMGO) phosphor of olivine type is developed via a conventional solid-state reaction. The results reveal that Eu3+ occupies the low symmetric Ca2+ site of this host. Upon visible-light sensitization at 464 nm, a dominant red emission band with maximum at 612 nm is witnessed. Its full width at half-maximum (fwhm) is merely ∼4.37 nm, and a high color purity of around 94% is achieved. Their corresponding Commission Internationale de L'Eclairage (CIE) coordinates are very close to standard red color coordinates (0.666, 0.333). The influence of concentration and temperature on the optical property has been explored. It has been discovered that the optimized sample (CMGO:0.01Eu3+) is not influenced by the thermal quenching effect and its fluorescent intensity is improved even up to 473 K, which is mainly attributed to the incorporation of abundant trap sites generated by the nonequivalent substitution Eu3+ for Ca2+. After it is integrated into commercially available YAG:Ce3+ phosphor-based pc-WLEDs, the excellent optical parameters of the fabricated WLEDs are evaluated. The correlated color temperature (CCT) varies from cool white (6458 K) to warm (4370 K), and the color rendering index (CRI) increases from 78 to 86 under a high flux operating current of 200 mA. Furthermore, the chromaticity coordinates remain almost stable with the increasing drive current from 200 mA to 1000 mA. It is highly expected that CaMgGeO4:0.01Eu3+ will become a suitable red phosphor for the preparation of white LEDs with high efficiency.

Methylation of the O6-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of MGMT status in 87 glioblastoma patients with contrast-enhanced T1W images and 66 with fluid-attenuated inversion recovery(FLAIR) images. The end-to-end pipeline completes both tumor segmentation and status classification. The better tumor segmentation performance comes from FLAIR images (Dice score, 0.897±0.007) compared to contrast-enhanced T1WI (Dice score, 0.828±0.108), and the better status prediction is also from the FLAIR images (accuracy, 0.827±0.056; recall, 0.852±0.080; precision, 0.821±0.022; and F1 score, 0.836±0.072). This proposed pipeline not only saves the time in tumor annotation and avoids interrater variability in glioma segmentation but also achieves good prediction of MGMT methylation status. It would help find molecular biomarkers from routine medical images and further facilitate treatment planning.

This paper reviews the development of forced motion apparatuses (FMAs) and their applications in wind engineering. A kind of FMA has been developed to investigate nonlinear and nonstationary aerodynamic forces considering the coupled effects of multiple degrees of freedom (DOFs). This apparatus can make section models to vibrate in a prescribed displacement defined by a numerical signal in time domain, including stationary and nonstationary movements with time-variant amplitudes and frequencies and even stochastic displacements. A series of validation tests show that the apparatus can re-illustrate various motions with enough precision in 3D coupled states of two linear displacements and one torsional displacement. To meet the requirement of aerodynamic modeling, the flutter derivatives of a box girder section are identified, verifying its accuracy and feasibility by comparing with previously reported results. By simulating the nonstationary vibration with time-variant amplitude, the phenomena of frequency multiplication and memory effects are examined. In addition to studying the aerodynamics of a bluff body under large amplitudes and nonstationary vibrations, some potential applications of the proposed FMA are discussed in vehicle-bridge-wind dynamic analysis, pile-soil interaction, and line-tower coupled vibration aerodynamics in structural engineering.

In recent years, accumulating studies have shown that long non-coding RNAs (lncRNAs) not only play an important role in the regulation of various biological processes but also are the foundation for understanding mechanisms of human diseases. Due to the high cost of traditional biological experiments, the number of experimentally verified lncRNA-disease associations is very limited. Thus, many computational approaches have been proposed to discover the underlying associations between lncRNAs and diseases. However, the associations between lncRNAs and diseases are too complicated to model by using only traditional matrix factorization-based methods. In this study, we propose a hybrid computational framework (SDLDA) for the lncRNA-disease association prediction. In our computational framework, we use singular value decomposition and deep learning to extract linear and non-linear features of lncRNAs and diseases, respectively. Then we train SDLDA by combing the linear and non-linear features. Compared to previous computational methods, the combination of linear and non-linear features reinforces each other, which is better than using only either matrix factorization or deep learning. The computational results show that SDLDA has a better performance over existing methods in the leave-one-out cross-validation. Furthermore, the case studies show that 28 out of 30 cancer-related lncRNAs (10 for gastric cancer, 10 for colon cancer and 8 for renal cancer) are verified by mining recent biomedical literature. Code and data can be accessed at https://github.com/CSUBioGroup/SDLDA.

Different cellular constituents of the central nervous system occurring in encephaloceles or neuroglial heterotopias (NGHs) have been reported, but the ependymal morphology has rarely been described in the previous literature, let alone the related histological images. To determine the ependymal morphology in encephaloceles or NGHs, we report a rare case of encephalocele with numerous ependymal components. Radiological examination showed that a 6.2 × 3.1 cm nasal dorsum mass-forming encephalocele in a 24-year-old woman, who had an intracranial connection through a frontal bone defect. This patient underwent a resection of the encephalocele under nasal endoscopy and a reconstruction of the cranial base. The patient had a good prognosis with no postoperative complications during follow-up. Microscopically, the ependymal components entrapped in a collagenized background showed numerous slit-like spaces lined by columnar cells with abundant palely eosinophilic cytoplasm and apical surface microvilli. With immunohistochemistry, in addition to the expression of EMA along with the slit-like spaces, GFAP and S100 were diffusely expressed in the slit-like spaces. In conclusion, the ependymal component in either encephaloceles or NGHs may present slit-like spaces arranged in an anastomosing pattern. The unusual morphology of ependyma continues to be underrecognized by pathologists and is easily misdiagnosed; therefore, an awareness of the morphological change in ependyma is necessary.

Flexible sensors have been widely investigated due to their broad application prospects in various flexible electronics. However, most of the presently studied flexible sensors are only suitable for working at room temperature, and their applications at high or low temperatures are still a big challenge. In this work, we present a multimodal flexible sensor based on functional oxide La0.7Sr0.3MnO3 (LSMO) thin film deposited on mica substrate. As a strain sensor, it shows excellent sensitivity to mechanical bending and high bending durability (up to 3600 cycles). Moreover, the LSMO/Mica sensor also shows a sensitive response to the magnetic field, implying its multimodal sensing ability. Most importantly, it can work in a wide temperature range from extreme low temperature down to 20 K to high temperature up to 773 K. The flexible sensor based on the flexible LSMO/mica hetero-structure shows great potential applications for flexible electronics using at extreme temperature environment in the future.

Light-harvesting capacity and photoexcited charge carrier separation ability are two crucial requirements for high-efficiency semiconductor photocatalysis. Here, we report a plasmonic Z-scheme nanohybrid by hydrothermally in-situ growing two-dimensional (2D) oxygen-deficient molybdenum oxide (MoO3-x) nanoplates onto 2D graphitic carbon nitride (g-C3N4) nanosheets. The resultant 2D/2D MoO3-x/g-C3N4 nanohybrids not only construct a unique Z-scheme heterojunction, which improves the photogenerated charge carrier separation efficiency, but also possess numerous oxygen vacancies on the surface of MoO3-x, which could excite its plasmon resonance for extending spectrum adsorption. Importantly, the plasmon resonance can be readily designed by tailoring the oxygen vacancy concentration via an annealing in air. Benefiting from the synergetic effect of interfacial Z-scheme heterojunction and the tunable plasmon resonance of MoO3-x, the as-obtained nanohybrids achieve a remarkably improved photocatalytic H2 evolution efficiency. The optimal Z-scheme heterostructure presents 2.6 and 1.7 times higher of H2 evolution rate as compared to pure g-C3N4 and the annealing nanohybrid under visible light irradiation. Even under light irradiation with wavelength longer than 590 nm, the hybrid photocatalyst displays a H2 generation rate as high as 22.8 µmol h−1 due to the plasmonic sensitization effect. The result in our work can provide an alternative for fabricating Z-scheme heterostructures that take advantages of Z-scheme-induced charge carrier separation, accompanied with plasmon-enhanced light harvesting of semiconductor to advance the solar energy conversion efficiency in photocatalysis.

CsPb(Cl1–xBrx)3 perovskite nanocrystals (NCs) doped with Yb3+ ions have recently attracted large attention for their applications in photovoltaics in view of the high quantum yield, exceeding 100% ...

The most favorable treatments for advanced EGFR-mutant NSCLC are less indicated. Forty-one studies were eligible for this Bayesian network secondary analysis. For PFS, erlotinib (Erlo)+bevacizumab (Bev) (HR 0.26, 95% CrI: 0.08-0.75 vs placebo), osimertinib (Osi) (HR 0.29, 0.11-0.70 vs placebo), and afatinib (Afa) were top-ranking individual treatments, while immunotherapy (IT)+anti-VEGFR (aVEGFR)+platinum-based therapy (Plat) (HR 0.42, 0.06-2.63 vs placebo), EGFR-TKI (ET)+aVEGFR (HR 0.35, 0.14-0.85 vs placebo), and ET+aVEGFR+Plat were top-ranking medication classes. For OS, Osi (HR 0.52, 0.10-2.00 vs placebo), cetuximab (Cet)+Bev+Plat (HR 0.51, 0.06-3.38 vs placebo), and cilengitide (Cil)+Cet+Plat were top-ranking individual treatments, while ET+aVEGFR+Plat, ET+Plat, and third-generation EGFR-TKI (3rd ET) were top-ranking medication classes. For PFS regarding the EGFR genomic aberration status, Erlo+Bev, Osi, and Afa were superior for exon 19 deletion status, whereas ET+Bev, Osi, and gefitinib (Gef)+pemetrexed (Peme) were excellent for exon 21 L858Arg mutation status. The results were consistent in terms of the ORR and DoR and remained robust across sensitivity analyses. However, Erlo + Bev had the most grade 3 or higher adverse events. Osi, Erlo+Bev, and Erlo+Bev+Plat are reasonably recommended to balance PFS and OS, but adverse events should be considered. IT+aVEGFR+Plat shows potential superiority, but more clinical evidence is needed.

Objectives Although therapeutic drug monitoring (TDM) guidelines are available, systematic evaluations of their methodological quality are scarce. This study aimed to assess the quality of current TDM guidelines using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. Study Design and Setting We performed a systematic search to identify the relevant TDM guidelines in PubMed, EMBASE, CNKI, Wan Fang Database, CBM, VIP, four main guideline databases (NICE, NGC, GIN, and WHO guideline databases), and official websites of the governments and societies associated with TDM from the inception date to May 2018. Four independent appraisers rated the quality of each TDM guideline using the AGREE II instrument, and the mean score of each AGREE II item was calculated. The overall agreement among the appraisers was evaluated using the intraclass correlation coefficient (ICC). Results Twenty-eight TDM guidelines satisfied the eligibility criteria from among 12,235 references. The overall agreement among the appraisers was substantial (0.700–0.880). The mean scores for the six AGREE II domains were scope and purpose, 67.7% (95% CI, 64.0–71.4%); stakeholder involvement, 39.8% (95% CI, 33.3–46.3%); rigor of development, 36.0% (95% CI, 28.1–43.9%); clarity and presentation, 61.6% (95% CI, 55.7–67.4%); applicability, 30.6% (95% CI, 26.4–34.8%); and editorial independence, 49.2% (95% CI, 40.0–58.6%). The reviewers recommended only four guidelines, and most of the TDM guidelines were rated as “recommended with modifications.” Conclusion Overall, the quality of TDM guidelines is suboptimal according to the evaluation using the AGREE II instrument. The domains of applicability, rigor of development, stakeholder involvement, and editorial independence of the guidelines need to be reported. In addition, guideline developers closely adhering to the AGREE II instrument and the Grading of Recommendations Assessment Development and Evaluation system are required to draft high-quality and reliable TDM guidelines.

Few studies were reported on trace elements' alterations in uremic patients undergoing long-term (24 months) hemodialysis (HD), especially by using the whole blood as the biological fluid for the measuring purpose. Our objective was to develop an improved micro-sampling inductively coupled plasma-mass spectrometry (ICP-MS) method to determine the levels of Ca, Mg, Cu, Zn, Fe, Mn, Se and Pb in uremic patients receiving long-term HD. A ICP-MS method with a modified whole blood sample preparation procedure with small volumes was established and applied for the simultaneous quantification of the various elements in uremic patients undergoing long-term HD. 124 eligible uremic patients receiving long-term HD (75 males and 49 females) and 77 healthy subjects (54 males and 23 females) were recruited and Ca, Mg, Cu, Zn, Fe, Mn, Se and Pb levels were further determined. Our results revealed that uremic patients with HD had significantly higher blood levels of Ca, Mg, Zn and Pb and lower Cu, Fe, Se and Mn concentrations than healthy controls. In conclusion, a reproducible and reliable ICP-MS method using minimal whole blood sample volume (50 μL) with a simple dilution-based preparation procedure was successfully improved, validated and applied. Uremic patients undergoing long-term HD might be at increased risk of some essential trace elements deficiency (especially for Cu, Fe and Se) or toxic trace element excess (Pb) in respect to healthy subjects. Monitoring of blood levels and supplementation of some trace elements may be indicated in uremic patients undergoing long-term HD.

Low-power, form-free electronics require low-temperature and solution-processed high-κ dielectric materials with extremely low leakage current and good flexibility, which is still very challenging. For example, the large band-gap material La2O3 has been hampered from low-power electronics applications due to the poor stability and inability to be solution-processed. Here, we develop oxygen-incorporated solution-deposition to obtain a high-κ La2O3 dielectric film at a low temperature (120 °C). The thin film exhibits a uniform large area, a high-κ value (>12), a large band gap (>6.3 eV), a high breakdown electric field (>7 MV cm−1), and a very low leakage current (10−8 A cm−2 at 1 MV cm−1) with excellent and stable insulating characteristics comparable with ALD deposited films. This method efficiently improves the chemical reaction and wettability of the precursor solution for densification and is also applicable for other high-κ dielectric materials like HfO2 and ZrO2. The film endures compressive strain at the limit of the PET substrate (∼2.5%) and enables flexible CMOS circuits with stable organic thin-film transistors (OTFTs) that exhibit a high gain (>90), a low operation voltage (2 V), and a low static power consumption (∼0.5 nW). The excellent characteristics enable the presented film and method to generally advance low-power and high-performance electronics with printable and flexible properties.

Ternary I-III-VI2 semiconductors usually have narrow band gaps and large absorption coefficients arising from the unique characteristics of their outer-d valence electrons, which are intimately connected with the photothermal conversion properties. AgFeS2 is known as one such material that has the potential to absorb near-infrared light. In this work, we utilized density functional theory (DFT) calculations to evaluate the electronic structure and optical absorption properties of AgFeS2. Strong absorptions were predicted over a wide Vis-NIR region due to the localized 3d electron of Fe atoms, which agree quite well with the UV-Vis-NIR spectra measured by experiment. The as-prepared AgFeS2 nanoparticles were then modified with mPEG-DSPE, an efficient photothermal agent for artery stenosis therapy. Its photothermal conversion effect has been systematically studied, indicating the potential for causing the hyperthermia of macrophages, an essential part of the artery inflammation response. More importantly, both in vitro cell experiments and in vivo mouse-model studies show that the induction of hyperthermia in artery stenosis by using AgFeS2 nanoparticles is safe and effective when injected at a very low concentration. This study provides a novel photothermal platform derived from the inheritability of bandgap structure and also promotes the process of artery inflammation and stenosis therapy.

Feature analysis has been increasingly considered as an important way to enhance the discrimination performance of gas sensors. In this work, a new analytical method based on alternating current noise spectrum is developed to discriminate chemically and structurally similar gases with remarkable performance. Compared with the conventional analytics based on the maximum, integral, and time of response, the noise spectrum of electrical response introduces a new informative feature to discriminate chemical gases. In experiment, three chemically and structurally similar gases, mesitylene, toluene, and o-xylene, are tested on ZnO thin film gas sensors. The result indicated that the noise analytics assisted by the support vector machine algorithm discriminated these similar gases with 94.2% in precision, about 20% higher than those obtained by conventional methods. Such a new alternating current noise analytics is very promising for application in sensors for high discrimination precision.

Background Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.

Normal operation of gas turbines will be affected by deposition on turbine blades from particles mixed in fuels. This research shows that it is difficult to monitor the mass of the particles deposition on the wall surface in real time. With development of electronic technology, the antenna made of printed circuit board (PCB) has been widely used in many industrial fields. Microstrip antenna is first proposed for monitoring particles deposition to analyse the deposition law of the particles accumulated on the wall. The simulation software Computer Simulation Technology Microwave Studio (CST MWS) 2015 is used to conduct the optimization design of the PCB substrate antenna. It is found that the S11 of vivaldi antenna with arc gradient groove exhibits a monotonous increase with the increase of dielectric layer thickness, and this antenna is highly sensitive to the dielectric layer thickness. Moreover, a cold-state test is carried out by using atomized wax to simulate the deposition of pollutants. A relationship as a four number of times function is found between the capacitance and the deposited mass. These results provide an important reference for the mass detection of the particle deposition on the wall, and this method is suitable for other related engineering fields.

BackgroundLymph node inclusions are foci of ectopic tissue in lymph nodes, which were reported in different areas of the body. However, inclusions in the mediastinal lymph node are rare. Here, we report the first case of glandular inclusion within the parenchyma of the intrapulmonary lymph node in a patient with primary lung adenocarcinoma.Case presentationA computed tomography (CT) scan showed a solid pulmonary nodule in the right upper lobe in a 44-year-old man. After a fine needle aspiration biopsy diagnosis of adenocarcinoma, lobectomy and lymph dissection were performed. Histological sections of the lung demonstrated a papillary predominant adenocarcinoma and one intrapulmonary lymph node, which displayed glandular inclusion occupying the node parenchyma. The gland inclusion was very similar to metastasis, but was formed by two layers of epithelial cells, and the abluminal cells were positive for P63, P40, and CK5/6. The patient has remained alive without recurrence and metastasis at the last follow-up before publication.ConclusionsIt is very important to correctly diagnose a lymph node inclusion for proper clinical management.

Molybdenum disulfide (MoS 2 ) has sparked immense interests as a novel gas sensing material due to its inherent layered structure and large surface-to-volume ratio. However, the obstacles of insufficient sensitivity and poor recoverability for MoS 2 -based gas sensors need to be resolved for the further applications. Herein, we report the construction of MoS 2 nanosheets based p–n heterostructures for the purposes of achieving excellent NO 2 detection at room temperature. After functionalized with tin oxide (SnO 2 ) nanoparticles, the optimal MoS 2 /SnO 2 heterostructure-based gas sensor exhibits a response of 18.7 to 5 ppm nitrogen dioxide (NO 2 ), an outstanding selectivity compared with other gases, and an excellent long-term stability for 4 weeks. The enhanced sensing performance of MoS 2 /SnO 2 heterostructures can be ascribed to the unique 2D/0D nanostructures and the formation of numerous p–n heterojunctions. Therefore, construction of p–n heterostructures provides a versatile solution to overcome the sensing issues of MoS 2 -based gas sensors and also paves a new way for others room temperature sensor applications.

IntroductionPostoperative delirium (POD) is a common complication. The incidence of POD is about 25% in non-cardiac surgery and ranges from 10% to 30% in neurological procedures. A lot of trials show that dexmedetomidine might help to reduce the incidence of delirium in patients undergoing non-cardiac surgery. However, the impact of dexmedetomidine on POD for patients undergoing craniotomy and tumour resections remains unclear.Methods and analysisThe study is a prospective, single-centre, randomised, double-blinded, paralleled-group controlled trial. Patients undergoing elective frontotemporal tumour resections will be randomly assigned to the dexmedetomidine group and the control group. After endotracheal intubation, patients in the dexmedetomidine group will be administered with a loading dose of dexmedetomidine 0.6 µg/kg in 10 min followed by continuous infusion at a rate of 0.4 µg/kg/hour until the start of dural closure. In the control group, patients will receive the identical volume of normal saline in the same setting. The primary outcome will be the cumulative incidence of POD within 5 days. The delirium assessment will be performed by using the confusion assessment method in the first 5 consecutive days after surgery. Secondary outcomes include the pain severity assessed by Numerical Rating Scale pain score, quality of postoperative sleep assessed by the Richards Campbell sleep questionnaire and postoperative quality of recovery from anaesthesia by the Postoperative Quality Recovery Scale.Ethics and disseminationThe protocol (V.1.0, 10 November 2020) has been approved by the Ethics Review Committee of the Chinese Clinical Trial Registry (number ChiECRCT-20200436). The findings of the study will be disseminated in a peer-reviewed journal and at a scientific conference.Trial registration numberNCT04674241.

Background: Cancer is the second leading cause of death globally. However, most of the new anti-cancer agents screened by traditional drug screening methods fail in the clinic because of lack of efficacy. One of the reasons for this dilemma is that the two-dimensional (2D) culture cancer cell lines could not represent the in vivo cancer cells well. Fortunately, the development of a three-dimensional (3D) culture technique helps in this problem. Methods: The high-throughput spheroid culture plate was fabricated by using 3D print technique and agarose. 4 hepatocarcinoma (HCC) cell lines were 3D cultured to screen 19 small molecular agents based on the spheroid culture plate. 3D cultured primary HCC cells and tumor-bearing mice model were established to verify the candidate anti-hepatocarcinoma agent. Cell function experiments and western blotting were conducted to explore the anti-hepatocarcinoma mechanism of the candidate agent. Results: Based on the previous study, we established an in vitro 3D drug screening method by using our invented spheroid culture device and found that CUDC-907 can serve as a potent anti-hepatocarcinoma agent. The study data show that CUDC-907 (fimepinostat), a novel dual acting inhibitor of phosphoinositide 3-kinase (PI3K) and histone deacetylase (HDAC), has potent inhibitory effects on HCC cell lines and primary HCC cells in vitro, Animal studies have shown that CUDC-907 can also suppress HCC cells in vivo. Furthermore, we investigated the antitumor mechanism of CUDC-907 in HCC cells. We found that it inhibits the PI3K/AKT/mTOR pathway and downregulates the expression of c-Myc, leading to the suppression of HCC cells. Conclusion: Our results suggest that CUDC-907 can be a candidate anti-HCC drug, and the 3D in vitro drug screening method based on our novel spheroid culture device is promising for drug screening.

MotivationLong non-coding RNAs (IncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features.ResultsWe proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences, and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared to traditional machine learning models with k-mer features and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also provided a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks.AvailabilityThe DeepLncLoc web server, source code and datasets are freely available at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and https://github.com/CSUBioGroup/DeepLncLoc.Contactlimin@mail.csu.edu.cn

Purpose The purpose of this study was to determine the effects of polymethylmetnacrylate (PMMA) spacer loaded with different concentrations of vancomycin on the proliferative, osteogenic, and angiogenic capacity of the induced membrane. Methods Varying concentrations of vancomycin (0, 1, 2, 4, 6, 8, and 10 g) were fully mixed with bone cement powder (40 g), resulting in seven experimental groups. Hollow cylindrical PMMA spacers (10-mm height, 3-mm external diameter, and 0.8-mm internal diameter) were formed by a mold and submerged in phosphate-buffered saline for antibiotic release by spectrophotometry. Eighty-four New Zealand white rabbits were evenly randomized into seven groups, and segmental radius shaft defects (10-mm) were created. Defects were filled with cylindrical PMMA spacers containing different vancomycin concentrations, and subsequently underwent intramedullary fixation with a retrograde Kirschner's wire. Tissue toxicity was assessed and the proliferative, osteogenic, and angiogenic capacity of induced membranes were qualitatively analyzed by immunohistochemistry and real-time PCR. Results No obvious toxicity was observed in the animal model. Alizarin red s staining and qualitative detection of type I collagen, CD31, Ki67, and STRO-1 by immunohistochemistry revealed an obvious decrease in the percentage of positively stained cells and in osteogenic capacity when the concentration of vancomycin was more than 6 g per cement dose. Quantitation of gene expression related to osteogenesis (Col1a, Alp, and Runx2), vascularization (Vegf, Tgfb1, and vWF), and proliferation (Oct4 and Stro-1) by real-time PCR revealed slight increases in the expression of selected genes at low vancomycin concentrations (1–4 g per cement dose), and relatively lower gene expression when the concentration of vancomycin was more than 6 g per cement dose. Conclusion PMMA spacers loaded with relatively low concentrations of vancomycin (1–4 g per cement dose) did not interfere with the proliferative, osteogenic, and angiogenic capacity of induced membranes, and even promoted their capacity. In contrast, spacers loaded with relatively high concentrations of vancomycin (6–10 g per cement dose) had negative effects on osteoblast viability, angiogenesis, and proliferation.

The accessible emission spectral range of lead halide perovskite (LHP) CsPbX3 (X = Cl, Br, I) nanocrystals (NCs) has remained so far limited to wavelengths below 1 μm, corresponding to the emission line of Yb3+, whereas the direct sensitization of other near-infrared (NIR) emitting lanthanide ions is unviable. Herein, we present a general strategy to enable intense NIR emission from Er3+ at ∼1.5 μm, Ho3+ at ∼1.0 μm and Nd3+ at ∼1.06 μm through a Mn2+-mediated energy-transfer pathway. Steady-state and time-resolved photoluminescence studies show that energy-transfer efficiencies of about 39%, 35% and 70% from Mn2+ to Er3+, Ho3+ and Nd3+ are obtained, leading to photoluminescence quantum yields of ∼0.8%, ∼0.7% and ∼3%, respectively. This work provides guidance on constructing energy-transfer pathways in semiconductors and opens new perspectives for the development of lanthanide-functionalized LHPs as promising materials for optoelectronic devices operating in the NIR region. ispartof: NANOSCALE vol:13 issue:17 pages:8118-8125 ispartof: location:England status: published

Background: Alcohol consumption increases the risk of hepatocellular carcinoma (HCC), and associated with a high mortality rate and poor prognosis. N6-methyladenosine (m6A) methylations play key roles in tumorigenesis and progression. However, our current knowledge about m6A in alcohol-related HCC (A-HCC) remains elucidated. Herein, the authors construct an integrative m6A model based on A-HCC subtyping and mechanism exploration workflow. Methods: Based on the m6A expressions of A-HCC and in vivo experiment, different prognosis risk A-HCC subtypes are identified. Meanwhile, multiple interdependent indicators of prognosis including patient survival rate, clinical pathological prognosis and immunotherapy sensitivity. Results: The m6A model includes LRPPRC, YTHDF2, KIAA14219, and RBM15B, classified A-HCC patients into high/low-risk subtypes. The high-risk subtype compared to the low-risk subtype showed phenotypic malignancy, poor prognosis, immunosuppression, and activation of tumorigenesis and proliferation-related pathways, including the E2F target, DNA repair, and mTORC1 signalling pathways. The expression of Immunosuppressive cytokines DNMT1/EZH2 was up-regulated in A-HCC patients, and teniposide may be a potential therapeutic drug for A-HCC. Conclusion: Our model redefined A-HCC prognosis risk, identified potential m6As linking tumour progress and immune regulations and selected possible therapy target, thus promoting understanding and clinical applications about A-HCC.

Background. To investigate the chronic photodamage induced by the low-intensity blue light of phones, we carried out a clinical pilot study and established an animal model by irradiating SD rats with a homemade illuminator. Methods. Clinical investigation: A total of 25 clinical medical workers in our hospital were selected and divided into a control group and an observation group according to the daily video terminal use time. Multifocal electrophysiological system (Mf-ERG) was used for retinal functional examination. Animal experiment: A total of sixty SD rats were randomly divided into a control group ( n = 6 ) and an experimental group ( n = 54 ). The experimental rats were divided into nine groups, which were exposed to the blue light illuminator of the simulated cell phone array for different time. The visual electrophysiology of the rats was tested, and changes in structure were observed by H&E staining and transmission electron microscopy. Results. In clinical investigation, macular centers near the concave area retinal photoreceptor cells have reduced amplitude. In animal experiments, the amplitude of photoreceptor cells decreased, the peak time was delayed, and the amplitudes were lower in the experimental groups. H&E staining and transmission electron microscope showed retinal tissue structure and functional damage in experimental groups. Conclusions. Long-term exposure to low-illuminance blue light can cause retinal tissue structure and functional damage, and the chronic damage due to low-illuminance light warrants attention. The clinical registration number is 2018-KY-KS-LHL.

Gabapentin is an adjuvant antiepileptic agent and helps to reduce acute postoperative pain in several surgery settings. However, the effect of gabapentin on postoperative pain from suboccipital or subtemporal craniotomy is not clear.The study was a single-center, randomized, placebo-controlled, and double-blinded trial. A total of 122 patients undergoing elective craniotomy by a suboccipital or subtemporal approach were randomly allocated to a placebo group and gabapentin group. The patients received gabapentin (600 mg, orally) the night before surgery and 2 hours before anesthesia induction in the gabapentin group, and patients received vitamin B in the placebo group. The primary outcome was the postoperative pain score on movement at 24 hours. The secondary outcomes included the pain score at other time points, incidence of nausea and vomiting, sedation, and analgesic consumption.Gabapentin significantly decreased the postoperative acute pain scores at rest (P=0.001) and on movement (P=0.000) within 24 hours; however, it did not have an effect at 48 hours. Gabapentin decreased postoperative vomiting (P=0.047) and rescue antiemetic use (P=0.033), whereas it increased the postoperative sedation score at 2 hours (P0.05). Moreover, gabapentin decreased the mean consumption of intraoperative propofol (0.7 mg/kg/h; P=0.021) and remifentanil (1.3 µg/kg/h; P=0.025); however, it did not influence postoperative opioid consumption.Preoperative gabapentin significantly alleviated acute postoperative pain and decreased the incidence of vomiting in patients undergoing suboccipital or subtemporal craniotomy. However, more attention should be paid to early postoperative sedation when multimodal analgesia with gabapentin is administered.

Motivation Protein–protein interactions (PPIs) play important roles in many biological processes. Conventional biological experiments for identifying PPI sites are costly and time-consuming. Thus, many computational approaches have been proposed to predict PPI sites. Existing computational methods usually use local contextual features to predict PPI sites. Actually, global features of protein sequences are critical for PPI site prediction. Results A new end-to-end deep learning framework, named DeepPPISP, through combining local contextual and global sequence features, is proposed for PPI site prediction. For local contextual features, we use a sliding window to capture features of neighbors of a target amino acid as in previous studies. For global sequence features, a text convolutional neural network is applied to extract features from the whole protein sequence. Then the local contextual and global sequence features are combined to predict PPI sites. By integrating local contextual and global sequence features, DeepPPISP achieves the state-of-the-art performance, which is better than the other competing methods. In order to investigate if global sequence features are helpful in our deep learning model, we remove or change some components in DeepPPISP. Detailed analyses show that global sequence features play important roles in DeepPPISP. Availability and implementation The DeepPPISP web server is available at http://bioinformatics.csu.edu.cn/PPISP/. The source code can be obtained from https://github.com/CSUBioGroup/DeepPPISP. Supplementary information Supplementary data are available at Bioinformatics online.

Given the problem of key customer loss in e-commerce in the clutter environment, a Prediction Model of Key Customer Loss in E-commerce (hereinafter referred to as PMKCLE for short) based on Cemo re...