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Abstract The human methyltransferase and transcriptional coactivator MLL4 and its paralog MLL3 are frequently mutated in cancer. MLL4 and MLL3 monomethylate histone H3K4 and contain a set of uncharacterized PHD fingers. Here, we report a novel function of the PHD2 and PHD3 (PHD2/3) fingers of MLL4 and MLL3 that bind to ASXL2, a component of the Polycomb repressive H2AK119 deubiquitinase (PR-DUB) complex. The structure of MLL4 PHD2/3 in complex with the MLL-binding helix (MBH) of ASXL2 and mutational analyses reveal the molecular mechanism which is conserved in homologous ASXL1 and ASXL3. The native interaction of the Trithorax MLL3/4 complexes with the PR-DUB complex in vivo depends solely on MBH of ASXL1/2, coupling the two histone modifying activities. ChIP-seq analysis in embryonic stem cells demonstrates that MBH of ASXL1/2 is required for the deubiquitinase BAP1 recruitment to MLL4-bound active enhancers. Our findings suggest an ASXL1/2-dependent functional link between the MLL3/4 and PR-DUB complexes.

Nb–Mo low-alloyed steels are promising advanced high strength steels (AHSS) because of the highly dislocated bainitic ferrite microstructure conferring an excellent combination of strength and toughness. In this study, the potential of precipitation strengthening during coiling for hot-strip Nb–Mo-bearing low-carbon steels has been investigated using hot-torsion and aging tests to simulate the hot-rolling process including coiling. The obtained microstructures were characterized using electron backscatter diffraction (EBSD), highlighting the effects of Nb and Mo additions on formation and tempering of the bainitic ferrite microstructures. Further, the evolution of nanometer-sized precipitates was quantified with high-resolution transmission electron microscopy (HR-TEM). The resulting age hardening kinetics have been modelled by combining a phenomenological precipitation strengthening model with a tempering model. Analysis of the model suggests a narrower coiling temperature window to maximize the precipitation strengthening potential in bainite/ferrite high strength low-alloyed (HSLA) steels than that for conventional HSLA steels with polygonal ferrite/pearlite microstructures.

Hericium erinaceus mycelium extract (HEM), containing erinacine A (HeA) and erinacine S (HeS), has shown promise in promoting the differentiation of oligodendrocyte precursor cells (OPCs) into mature oligodendrocytes (OLs), crucial for myelin production in the central nervous system (CNS). The main aim of this study was to characterize the protective effects of HEM and its components on OLs and myelin in demyelinating rodents by exposure to cuprizone (CPZ), a copper chelating agent commonly used to induce demyelination in the corpus callosum of the brain. Rats were fed by CPZ-containing diet and simultaneously orally administered HEM, HeA, or HeS on a daily basis for three weeks. We found that HEM and HeS preserved myelin and OLs in the corpus callosum of CPZ-fed rats, along with reduced microglia and astrocyte activation, and downregulated IL-1β expression. Furthermore, post-treatment with HeS, in mouse models with acute (6 weeks) or chronic (12 weeks) CPZ-induced demyelination demonstrated oral administration during the final 4 weeks (HeS4/6 or HeS4/12) effectively preserved myelin in the corpus callosum. Additionally, HeS4/6 and HeS4/12 inhibited anxious and depressive-like behaviors in CPZ-fed mice. In summary, simultaneous administration of HEM and HeS in rats during short-term CPZ intoxication preserved OLs and myelin. Furthermore, post-administration of HeS not only inhibited demyelination and gliosis but also alleviated anxiety and depression in both acute and chronic CPZ-fed mice. This study presents compelling evidence supporting the potential of HeS as a promising small active compound for protecting OLs and preserving myelin in demyelinating diseases associated with emotional disorders.

Biomaterials possess extraordinary properties due to intricate structures on the microscale. Learning from these microstructures is critical for the design of high-performance materials with multiple functions. However, explicit modeling of the microstructures is not always feasible. This study developed a deep generative network with a self-attention mechanism to generate three-dimensional (3D) bioinspired microstructures. The robustness of the model was first checked by generating a series of gyroids, a mathematically well-defined microstructure, which showed excellent consistency with the desired structures. The model was then applied to the microstructure of the elk antlers, which are complex and cannot be directly expressed mathematically. The results showed that the model also performs well in complex, ill-defined biological materials. The model learned the inherent patterns, generating different structures with similar geometric features. This study demonstrates the potential of using Transformer-based deep generative models that can be used to generate novel 3D microstructures from limited high-resolution X-ray micro-computed tomography data.

Abstract Background In recent years, several studies have demonstrated that stress hyperglycemia is significantly associated with poor prognosis in patients diagnosed with acute coronary syndrome (ACS). In the present study, we aimed to investigate the potential associations between various markers of stress hyperglycemia, such as admission blood glucose (ABG), fasting blood sugar (FBS), and stress hyperglycemia ratio (SHR) with different definitions, and the occurrence of adverse cardiovascular events in patients diagnosed with ST-elevation myocardial infarction (STEMI) who have undergone percutaneous coronary intervention (PCI). Methods Our study enrolled a total of 1099 patients diagnosed with STEMI who underwent PCI from 2016 to 2021. The primary outcomes of this study were in-hospital death and all-cause mortality. Results Stress hyperglycemia was associated with a higher incidence of in-hospital death (ABG OR: 1.27 95% CI 1.19–1.36; FBS OR: 1.25 95% CI 1.16–1.35; SHR1 OR: 1.61 95% CI 1.21–2.14; SHR2 OR: 1.57, 95%CI 1.22–2.01; SHR3 OR: 1.59, 95%CI 1.24–2.05) and all-cause mortality (ABG HR: 1.10, 95% CI 1.07–1.14; FBS HR: 1.12, 95 CI 1.07–1.17; SHR1 HR: 1.19 95% CI 1.03–1.39; SHR2 HR: 1.28, 95%CI 1.14–1.44; SHR3 HR: 1.29, 95%CI 1.14–1.45) after adjusting for ischemic time, age, gender, BMI, hypertension, hyperlipidemia, diabetes mellitus (DM), current smoking history, chronic kidney disease (CKD), previous history of coronary artery disease (CAD), atrial fibrillation (AF), heart failure (HF), stroke, cancer, culprit vessel, multi-vessel disease. These associations exhibited a non-linear, J-shaped pattern, wherein the risk significantly increased when the ABG and FBS levels exceeded 5mmol/L. Moreover, the inflection point for SHR was estimated to be 1.2. Conclusions Stress hyperglycemia was significantly associated with an increased risk of in-hospital death and all-cause mortality in STEMI patients treated with PCI. Stress hyperglycemia should be considered a high-risk prognostic marker in all STEMI patients, regardless of with or without diabetes.

An efficient carbon material was prepared using hydrothermal carbonization of birch-wood chips in the water under elevated pressure, subsequent chemical activation, and doping with nitrogen. The obtained catalyst had a large specific surface area of 2431 m2 g−1 and a high nitrogen content of 4.60 at.%. Most of the nitrogen was in the pyridinic-N (57.61 at.%) form. The synthesized material exhibits excellent electro-catalytic activity for the oxygen reduction reaction with an onset potential of 0.9 V versus the reversible hydrogen electrode, showing the 4e− electrons transfer path in 0.1 M KOH solution.

Abstract Background Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measures for activities and participation in stroke rehabilitation requires further investigation. Methods This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient-reported outcome measures of daily activities (i.e, the Motor Activity Log and the Nottingham Extended Activities of Daily Living) and participation (i.e, the Activities of Daily Living domain of the Stroke Impact Scale). Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k-nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection. Results Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper-limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best-performing models with 18 features ranged between 0.72 and 0.96. Those of the best-performing models with fewer features ranged between 0.72 and 0.84. Conclusions Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient-reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient-centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation.

One new compound with an isoindolinone skeleton, along with erinacines A, C, and S, was isolated from the mycelia of Hericium erinaceus, an edible fungus with a long history of use in traditional Chinese medicine. Based on analysis of MS and NMR spectral data, the structure of the compound was identified as (2E,6E)-8-(2-(1-carboxy-3-methylbutyl)-4,6-dihydroxy-1-oxoisoindolin-5-yl)-2,6-dimethylocta-2,6-dienoic acid. In light of this discovery, we have given this compound the name erinacerin W. Using a co-culture in vitro LPS-activated BV2 microglia-induced SH-SY5Y neuroinflammation model, the results showed that erinacerin W demonstrated protection against the LPS-activated BV-2 cell-induced overexpression of IL-6, IL-1β, and TNF-α on SH-SY5Y cells. This finding may provide potential therapeutic approaches for central nervous disorders.

Metabolic bone disease (MBD) predominantly affects preterm infants, particularly very-low-birth-weight (VLBW) infants weighing

In the present study, unmodified nitrogen-doped carbon material (N-doped C) and modified with copper nanoparticles (CuNPs) nitrogen-doped carbon material (Cu-N-doped C) have been synthesized using wood biomass as a carbon precursor, dicyandiamide as a nitrogen precursor, and anhydrous copper acetate as the precursor of Cu. The morphology, structure, and chemical composition of the obtained materials have been analyzed by performing nitrogen sorption experiments and described using transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and scanning-electron microscopy (SEM). The electrocatalytic performance of N-doped C and Cu-N-doped C materials has been evaluated for the electro-reduction of oxygen (ORR) in alkaline media using linear scan voltammetry (LSV) and the rotating disk and ring-disk electrode (RDE and RRDE) methods.It has been determined that the nitrogen-doped carbon material and that modified with CuNPs have been synthesized with graphene-like structures and especially high specific surface areas of 2631 m2 g−1 and 2243 m2 g−1, respectively. The onset potential (Eonset) and half-wave (E1/2) potential values are approximately 0.921 V and 0.828 V, respectively, for the N-doped carbon with the loading of 0.0889 mgcat cm−2 and 0.975 V and 0.843 V, respectively, for Cu–N-doped carbon (0.4044 mgcat cm−2), comparable with the commercial Pt/C catalyst.

ObjectiveNon-invasive methods for hemoglobin (Hb) monitoring can provide additional and relatively precise information between invasive measurements of Hb to help doctors' decision-making. We aimed to develop a new method for Hb monitoring based on mask R-CNN and MobileNetV3 with eye images as input.MethodsSurgical patients from our center were enrolled. After image acquisition and pre-processing, the eye images, the manually selected palpebral conjunctiva, and features extracted, respectively, from the two kinds of images were used as inputs. A combination of feature engineering and regression, solely MobileNetV3, and a combination of mask R-CNN and MobileNetV3 were applied for model development. The model's performance was evaluated using metrics such as R2, explained variance score (EVS), and mean absolute error (MAE).ResultsA total of 1,065 original images were analyzed. The model's performance based on the combination of mask R-CNN and MobileNetV3 using the eye images achieved an R2, EVS, and MAE of 0.503 (95% CI, 0.499–0.507), 0.518 (95% CI, 0.515–0.522) and 1.6 g/dL (95% CI, 1.6–1.6 g/dL), which was similar to that based on MobileNetV3 using the manually selected palpebral conjunctiva images (R2: 0.509, EVS:0.516, MAE:1.6 g/dL).ConclusionWe developed a new and automatic method for Hb monitoring to help medical staffs' decision-making with high efficiency, especially in cases of disaster rescue, casualty transport, and so on.

We herein report two cases involving children who died of influenza A (H3N2) virus infection-associated encephalopathy/encephalitis (IAE). Both children developed convulsions and impaired consciousness within a relatively short period and eventually died of brainstem failure. Patient 1 presented with high fever, vomiting, and diarrhea. Laboratory tests indicated persistently high lactate, alanine aminotransferase, and urea nitrogen concentrations in the blood as well as a high protein concentration in the cerebrospinal fluid. Patient 2 presented with persistent hyperthermia and progressive disturbance of consciousness, but the cerebrospinal fluid remained normal during the disease course. Both patients were actively given oseltamivir antiviral treatment after diagnosis of influenza virus infection. However, the disease progressed and invasive mechanical ventilation was performed. Both children’s condition quickly progressed to IAE, and they eventually died. IAE is a rare complication of influenza virus infection with high mortality, and its pathogenesis remains unclear. The purpose of this report is to draw attention to the serious central nervous system complications of influenza infection and raise awareness of the fatal consequences of this disease among pediatricians.

IntroductionPerioperative critical events will affect the quality of medical services and threaten the safety of patients. Using scientific methods to evaluate the perioperative risk of critical illness is of great significance for improving the quality of medical services and ensuring the safety of patients.MethodAt present, the traditional scoring system is mainly used to predict the score of critical illness, which is mainly dependent on the judgment of doctors. The result is affected by doctors' knowledge and experience, and the accuracy is difficult to guarantee and has a serious lag. Besides, the statistical prediction method based on pure data type do not make use of the patient's diagnostic text information and cannot identify comprehensive risk factor. Therefore, this paper combines the text features extracted by deep neural network with the pure numerical type features extracted by XGBOOST to propose a deep neural decision gradient boosting model. Supervised learning was used to train the risk prediction model to analyze the occurrence of critical illness during the perioperative period for early warning.ResultsWe evaluated the proposed methods based on the real data of critical illness patients in one hospital from 2014 to 2018. The results showed that the critical disease risk prediction model based on multiple modes had faster convergence rate and better performance than the risk prediction model based on text data and pure data type.DiscussionBased on the machine learning method and multi-modal data of patients, this paper built a prediction model for critical adverse events in patients, so that the risk of critical events can be predicted for any patient directly based on the preoperative and intraoperative characteristic data. At present, this work only classifies and predicts the occurrence of critical illness during or after operation based on the preoperative examination data of patients, but does not discuss the specific time when the patient was critical illness, which is also the direction of our future work.

Computer-assisted surgery (CAS) has occupied an important position in modern surgery, further stimulating the progress of methodology and technology. In recent years, a large number of computer vision-based methods have been widely used in surgical workflow recognition tasks. For training the models, a lot of annotated data are necessary. However, the annotation of surgical data requires expert knowledge and thus becomes difficult and time-consuming. In this paper, we focus on the problem of data deficiency and propose a knowledge transfer learning method based on artificial neural network to compensate a small amount of labeled training data. To solve this problem, we propose an unsupervised method for pre-training a Convolutional-De-Convolutional (CDC) neural network for sequencing surgical workflow frames, which performs neural convolution in space (for semantic abstraction) and neural de-convolution in time (for frame level resolution) simultaneously. Specifically, through neural convolution transfer learning, we only fine-tuned the CDC neural network to classify the surgical phase. We performed some experiments for validating the model, and it showed that the proposed model can effectively extract the surgical feature and determine the surgical phase. The accuracy (Acc), recall, precision (Pres) of our model reached 91.4, 78.9, and 82.5%, respectively.

White spot syndrome virus (WSSV) is a very large dsDNA virus. The accepted shape of the WSSV virion has been as ellipsoidal, with a tail-like extension. However, due to the scarcity of reliable references, the pathogenesis and morphogenesis of WSSV are not well understood. Here, we used transmission electron microscopy (TEM) and cryogenic electron microscopy (Cryo-EM) to address some knowledge gaps. We concluded that mature WSSV virions with a stout oval-like shape do not have tail-like extensions. Furthermore, there were two distinct ends in WSSV nucleocapsids: a portal cap and a closed base. A C14 symmetric structure of the WSSV nucleocapsid was also proposed, according to our Cryo-EM map. Immunoelectron microscopy (IEM) revealed that VP664 proteins, the main components of the 14 assembly units, form a ring-like architecture. Moreover, WSSV nucleocapsids were also observed to undergo unique helical dissociation. Based on these new results, we propose a novel morphogenetic pathway of WSSV.

(1) Background: Oral squamous cell carcinoma (OSCC) is a significant health burden worldwide. This study aimed to determine the potentials of Refametinib, an orally bioavailable selective MEK1/2 inhibitor, to increase the effectiveness of 5-Fluorouracil (5-FU), a common cytotoxic drug, in the SCC4 cell line. (2) Methods: SCC4 cells were treated with increasing concentrations of 5-FU, either alone or in combination with Refametinib. The chemosensitivity to treatment was assessed via cell viability assay, microscopic observation, colony formation assay, and detection of apoptotic markers using Western blotting. The whole-cell expression and surface expression of programmed death-ligand 1 (PD-L1), an immune checkpoint protein which contributes to chemoresistance and affects treatment response, were also determined using Western blotting and flow cytometry, respectively. (3) Results: The combined treatment suppressed cell proliferation and promoted apoptosis in a more potent way than 5-FU treatment alone did. Additionally, MEK/ERK inhibition mitigated 5-FU-induced PD-L1 upregulation. (4) Conclusions: This is the first report of an enhanced anticancer effect and reduced PD-L1 expression for the combination of 5-FU with Refametinib in OSCC, suggesting a new promising combination strategy.

Abstract Background Pneumococcus is one of the most common human airway pathogens that causes life-threatening infections. Ambient fine particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM2.5) is known to significantly contribute to respiratory diseases. PM2.5-induced airway inflammation may decrease innate immune defenses against bacterial infection. However, there is currently limited information available regarding the effect of PM2.5 exposure on molecular interactions between pneumococcus and macrophages. Results PM2.5 exposure hampered macrophage functions, including phagocytosis and proinflammatory cytokine production, in response to pneumococcal infection. In a PM2.5-exposed pneumococcus-infected mouse model, PM2.5 subverted the pulmonary immune response and caused leukocyte infiltration. Further, PM2.5 exposure suppressed the levels of CXCL10 and its receptor, CXCR3, by inhibiting the PI3K/Akt and MAPK pathways. Conclusions The effect of PM2.5 exposure on macrophage activity enhances pneumococcal infectivity and aggravates pulmonary pathogenesis.

Eisenmenger syndrome (ES) refers to congenital heart diseases (CHD) with reversal flow associated with increased pulmonary pressure and irreversible pulmonary vascular remodeling. Previous reports showed limited therapeutic strategies in ES. In this study, 5 ES patients (2 males and 3 females), who had been followed regularly at our institution from 2010 to 2019, were retrospectively reviewed. We adopted an add-on combination of sildenafil, bosentan, and iloprost and collected the clinical characteristics and outcomes as well as findings of echocardiography, computed tomography, pulmonary perfusion-ventilation scans, positron emission tomography, and biomarkers. The age of diagnosis in these ES patients ranged from 23 to 54 years (38.2 ± 11.1 years; mean ± standard deviation), and they were followed for 7 to 17 years. Their mean pulmonary arterial pressure and pulmonary vascular resistance index were 56.4 ± 11.3 mmHg and 24.7 ± 8.5 WU.m2, respectively. Intrapulmonary arterial thrombosis was found in 4 patients, ischemic stroke was noted in 2 patients, and increased glucose uptake of the right ventricle was observed in 4 patients. No patient mortality was seen within 5 years of follow-up. Subsequently, 2 patients died of right ventricular failure, 1 died of sepsis related to brain abscess, and another died of sudden death. The life span of these patients was 44–62 years. Although these patients showed longer survival, the beneficial data on specific-target pharmacologic interventions in ES is still preliminary. Thus, larger trials are warranted, and the study of cardiac remodeling in ES from various CHD should be explored.