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2,2-bis(chloromethyl) trimethylene bis[bis(2-chloroethyl) phosphate] (V6) has been widely used as an additive in a variety of plastics due to its extremely low toxicity. However, we showed in the study that once mixed with nanopolystyrene particles (NPs), the nontoxic V6 could exhibit significant toxicity to HeLa cells. The enhanced toxicity was much higher than the toxicity of NPs alone and was related to the size of NPs. The mixture of V6 and small polystyrene NPs (10 nm and 15 nm in radius) showed obvious toxicity to HeLa cells. The toxicity increased with the concentrations of both V6 and NPs. On the contrary, the mixture of V6 and larger NPs (25 nm, 50 nm, 100 nm, and 500 nm in radius) showed almost no toxicity even at extremely high concentrations (NPs: 100 mg/L; V6: 50 mg/L). The small NPs could enter the cells and accumulated in cytoplasm. However, the larger NPs did not distribute inside the cells. NPs efficiently adsorbed V6 on the surface. The mechanism of the enhanced toxicity was attributed to the increased intracellular reactive oxygen species (ROS) production and the regulation of gene expression concerning apoptosis and ROS scavenging. Our study not only showed that a safe chemical V6 could be turned to be toxic by NPs, but also pointed out a potential risk caused by the joint toxicity of ‘safe’ chemicals and plastic particles with small size.

A porous NiFe LDH with abundant cationic defects was synthesized to optimize interactions between active Ni species and adsorbates, exhibiting a highly efficient seawater electrolysis performance.

Bone disease and disorder treatment might be difficult because of its complicated nature. Millions of patients each year need bone substitutes that may help them recover quickly from a variety of illnesses. Synthetic bone replacements that mirror the structural, chemical, and biological features of bone matrix structure will be very helpful and in high demand. In this research, the inorganic bioactive glass nanoparticles matrixed with organic collagen and silk fibroin structure (COL/SF/CaO-SiO

This study aimed to investigate the role of computed tomography angiography (CTA) and three-dimensional (3D) reconstruction of renal arteries in the evaluation of bleeding after mini- percutaneous nephrolithotomy (PCNL).Thirty-one consecutive patients with continuous renal hemorrhage after mini-PCNL were enrolled from January 2015 to January 2022. Demographic and clinical data were retrospectively recorded and analyzed. All patients had received CTA evaluation and subsequently digital subtraction angiography (DSA) embolization to manage renal bleeding. CTA and 3D reconstruction of renal arteries were performed using the 320 multi-detector computed tomography technique and the images were evaluated by experienced radiologists. DSA embolization were performed by an interventional radiologist with more than 10 years of experiences.CTA and 3D construction of renal arteries showed 28 cases of vascular lesions (28/31, 90.3%), including 15 cases of pseudoaneurysm (15/28, 53.6%), 9 cases of arteriovenous fistula (9/28, 32.1%), and 4 cases of suspicious bleeding spot (4/28, 14.3%). While DSA revealed 31 cases of vascular lesions (100%), including 15 cases of pseudoaneurysm (15/31, 48.4%), 10 cases of arteriovenous fistula (10/31, 32.3%), 6 cases of bleeding spot and (6/31, 19.4%). The serum creatinine level was elevated slightly before mini-PCNL and after DSA embolization (73.1±18.1 vs 92.1±33.6, P.01). 15 patients (15/31, 48.4%) required blood transfusion, with mean blood transfusion volume of 700 ml ±660 ml (range, 400 ml-1800 ml). The bleeding was controlled without any further severe complications.CTA and 3D reconstruction of renal arteries were safe and effective in diagnosing renal arterial bleedings after mini-PCNL, with a sensitivity of 90.3% and a specificity of 100%.

In line with the global green steel development, a steel enterprise seeks to reduce its energy consumption by building a scientific, effective evaluation and optimization model for steel manufacturing’s energy utilizations. It highlights the enterprise and the steel sector’s green mainstream. This paper proposes a dynamic evaluation model for energy flow network operations and brings forward evaluation indicators based on analyses of each energy flow’s input and output, and utilization, conversion and loss during the steel manufacturing process. They provide support for making dynamic optimizations for the energy flow network to achieve an optimal operation efficiency. As a result, an optimization model for energy flow network operations has been built and plays a part in guiding the steel enterprise to contribute further to energy conservation and consumption reduction.

Damage-associated molecular pattern molecules (DAMPs) play a critical role in mediating cochlear cell death, which leads to noise-induced hearing loss (NIHL). High-mobility group box 1 (HMGB1), a prototypical DAMP released from cells, has been extensively studied in the context of various diseases. However, whether extracellular HMGB1 contributes to cochlear pathogenesis in NIHL and the potential signals initiating HMGB1 release from cochlear cells are not well understood. Here, through the transfection of the adeno-associated virus with HMGB1-HA-tag, we first investigated early cytoplasmic accumulation of HMGB1 in cochlear hair cells after noise exposure. We found that the cochlear administration of HMGB1-neutralizing antibody immediately after noise exposure significantly alleviated hearing loss and outer hair cells (OHCs) death induced by noise exposure. In addition, activation of signal transducer and activators of transcription 1 (STAT1) and cellular hyperacetylation were verified as potential canonical initiators of HMGB1 cytoplasmic accumulation. These findings reveal the adverse effects of extracellular HMGB1 on the cochlea and the potential signaling events mediating HMGB1 release in hair cells, indicating multiple potential pharmacotherapeutic targets for NIHL.

OBJECTIVE MRI-negative focal epilepsy is one of the most challenging cases in surgical epilepsy treatment. Many epilepsy centers recommend intracranial electroencephalography (EEG) for MRI-negative cases, especially neocortical epilepsy. This retrospective study aimed to explore whether intracranial monitoring is mandatory in MRI-negative neocortical epilepsy surgery and the factors that significantly influence the decision on whether to perform intracranial recording. METHODS In this study, consecutive surgical patients with focal MRI-negative neocortical epilepsy were recruited. All patients underwent routine preoperative evaluation according to the dedicated protocol of the authors’ epilepsy center to determine the treatment strategy. Patients were divided into two groups according to the surgical strategy, i.e., a direct group and a stereo-EEG (SEEG)–guided group. History of epilepsy, seizure frequency, interictal and ictal EEG data, PET data, PET/MRI coregistration data, neuropathological findings, and surgical outcomes were compared between the two groups. Multivariate analysis was performed to identify factors influencing the decision to perform SEEG monitoring. RESULTS Sixty-four patients were included in this study, 19 and 45 of whom underwent direct and SEEG-guided cortical resection, respectively. At an average follow-up of 3.9 years postoperatively, 56 patients (87.5%) had Engel class I results without permanent neurological deficits. Surgical outcomes were not significantly different between the direct and SEEG-guided groups (94.7% vs 84.4%). PET hypometabolic abnormalities were detected in all patients. There were significant differences between the two groups in the extent of hypometabolism (focal vs nonfocal, p < 0.01) and pathological subtype (focal cortical dysplasia type II vs others, p = 0.03). Multivariate analysis revealed that the extent of hypometabolism (OR 0.01, 95% CI 0.00–0.15; p = 0.001) was the only independent factor affecting the treatment strategy. CONCLUSIONS Careful selection of patients with MRI-negative neocortical epilepsy may yield favorable outcomes after direct cortical resection without intracranial monitoring. PET/MRI coregistration plays an essential role in the preoperative evaluation and subsequent resection of these patients. Intracranial monitoring is not a mandatory requirement for surgery if the focal hypometabolic areas are consistent with the findings of semiology and scalp EEG.

This study investigated the effect of sound therapy combined with drug therapy (SDT) on gap detection threshold and speech recognition scores in patients with sudden sensorineural hearing loss (SSNHL).Patients with SSNHL were grouped randomly into SDT and drug therapy (DT) groups. All patients received standard drug treatment and patients in the SDT group additionally received sound stimulation for the affected ears for 6 days. Pure tone audiogram, speech recognition scores at normal and time-compressed rates under quiet and noisy conditions, and the gap detection threshold of the SDT and DT groups before treatment and on day 6 and 30 after treatment were compared.There were 20 patients in the SDT group and 24 in the DT group. The pure tone thresholds of affected ears were significantly lower in the SDT group on day 6 after treatment than those in the DT group at 125 and 250 Hz. Significantly lower gap detection thresholds and higher speech recognition scores under noisy conditions were observed at the normal and time-compressed rates in the SDT group than those in the DT group on day 6 and 30 after treatment. Significant correlations were observed between the gap thresholds and speech recognition scores in a noisy environment at normal and time-compressed rates on day 6 and 30.SDT may improve the recovery of hearing abilities, such as the gap in noise thresholds and speech recognition in noise, in the case of SSNHL.ChiCTR-IOR-17012262.

Since 1995, photodynamic therapy (PDT) has been utilized as an effective method for cancer treatment. However, the residues of photosensitizers in the normal tissues after PDT can be activated by sunlight to cause severe skin phototoxicity, for which currently there are no clinical solutions. As a result, post-PDT patients need to remain out of sunlight for up to five weeks, which produces great living and mental burdens for patients. Herein, we report that a biocompatible porous organic polymer (POP) with average 3.1 nm porosity is able to suppress the skin phototoxicity of clinically used porphyrin-based photodynamic agents (PDAs), including Photofrin, Talaporfin and Hiporfin, through an adsorption-elimination mechanism. Fluorescence titration and dialysis experiments show that POP can adsorb and retain the PDAs at a micromolar concentration. In vivo experiments demonstrate that POP can significantly suppress the skin phototoxicity caused by all the three PDAs without reducing their PDT efficacy. STATEMENT OF SIGNIFICANCE: Up to now, no efficient clinical treatment for the inhibition of post-PDT phototoxicity of clinically used porphyrin-based PDAs is available. In the manuscript, a water-soluble cationic porous organic polymer has been revealed to include three clinically used PDAs. In vivo experiments show that this inclusion remarkably reduces the content of PDAs in mouse skins, leading to significant alleviation of their post-PDT phototoxicity without no negative effect on their PDT efficacy. Thus, this work provides a strategy for overcoming the drawback of clinically used photodynamic agents.

HLA-C*04:490 differs from HLA-C*04:01:01:01 by one nucleotide in exon 3. This article is protected by copyright. All rights reserved.

HLA-A*24:02:160 differs from HLA-A*24:02:01:01 by one nucleotide in exon 3. This article is protected by copyright. All rights reserved.

Qishen granules (QSG), a Chinese herbal formula, has been widely used in the treatment of myocardial ischemic chronic heart failure (CHF) for many years, but its mechanism of action is still unclear. In this study, comprehensive metabolomics was used to investigate the underlying protective mechanisms of QSG in an isoproterenol-induced CHF rat model. A total of 14 biomarkers were identified in serum and 34 biomarkers in urine, which were mainly related to fatty acid metabolism, bile acid metabolism, amino acid metabolism, purine metabolism, vitamin metabolism, and inflammation. Finally, 22 markers were selected for quantitative analysis of serum, urine, and fecal samples to verify the reliability of the results of untargeted metabolomics, and the results were similar to those of untargeted metabolomics. The correlation analysis showed that the targeted quantitative endogenous metabolites and CHF-related indexes were closely related. QSG might alleviate myocardial inflammatory response, oxidative stress, and amino acid metabolism disorder in CHF by regulating the level of endogenous metabolites. This study revealed QSG could regulate potential biomarkers and correlated metabolic pathway, which provided support for the further application of QSG.

This paper proposes a generalized deep continual learning-based cybertwin (GDC) modeling framework for developing one wind turbine (WT) cybertwin serving multiple modeling tasks in the wind farm operations and maintenance (O&M). A generalized WT cybertwin modeling problem is first formulated. Fully connected deep neural networks (DNNs) are adopted as the backbone for developing the GDC. The online elastic weight consolidation (OEWC) method is incorporated to mitigate the catastrophic forgetting phenomenon among different modeling tasks. Computational experiments are conducted to validate the effectiveness of the proposed GDC framework based on the supervisory control and data acquisition (SCADA) data. Three classical tasks, the WT gearbox failure detection, WT blade breakage detection, and wind power prediction, studied in the wind farm O&M are considered in the experiment. Compared with considered benchmarking models, the proposed GDC can achieve high accuracies on new tasks while maintain high accuracies on previous tasks.

Visual relationship detection (VRD) aims to locate objects and recognize their pairwise relationships for parsing scene graphs. To enable a higher understanding of the visual scene, we propose a symmetric fusion learning model for visual relationship detection and scene graph parsing. We integrate objects and relationship features at visual and semantic levels for better relations feature mapping. First, we apply a feature fusion for the construction of the visual module and introduce a semantic representation learning module combined with large-scale external knowledge. We minimize the loss by matching the visual and semantic embeddings using our designed symmetric learning module. The symmetric learning module based on reverse cross-entropy can boost cross-entropy symmetrically and perform reverse supervision for inaccurate annotations. Our model is compared with other state-of-the-art methods in two public data sets. Experiments show that our proposed model achieves encouraging performance in various metrics for the two data sets investigated. The further detailed analysis demonstrates that the proposed method performs better by partially alleviating the impact of inaccurate annotations.

Aromatic carboxylic acids (ACAs) may be as transformed key metabolites via gut microbiome for playing better pharmacological effects. However, it's rare to achieve high-specificity, high-sensitivity, and high-throughput detection simultaneously, especially, for tracing trace ACAs in gut microbiome. In this work, firstly, a novel dual-template and double-shelled molecularly imprinted 96-well microplates (DDMIPs) was designed and amplified signal for p-hydroxybenzoic acid (PBA) and 3,4,5-trimethoxycinnamic acid (TMA). Additionally, the DDMIPs and a stable isotope labeling derivatization (SILD) method combined with the ultra-high performance liquid chromatography triple quadrupole tandem mass spectrometry (UHPLC-TQ MS) was firstly stepwise integrated, achieving high-effective, high-sensitive, and high-throughput study of gut microbiome metabolism. The whole strategy showed lower limits of detections (LODs) up to 1000 folds than the traditional method, and revealed a more real metabolism-time profile of PBA and TMA by 3-step signal amplification. The platform also laid the foundation for fast, simple, high-selective, high-effective, and high-throughput metabolism and pharmacological research.