M4X3 MXenes: Application in Energy Storage Devices.

MXene has garnered widespread recognition in the scientific community due to its remarkable properties, including excellent thermal stability, high conductivity, good hydrophilicity and dispersibility, easy processability, tunable surface properties, and admirable flexibility. MXenes have been categorized into different families based on the number of M and X layers in Mn+1Xn, such as M2X, M3X2, M4X3, and, recently, M5X4. Among these families, M2X and M3X2, particularly Ti3C2, have been greatly explored while limited studies have been given to M5X4 MXene synthesis. Meanwhile, studies on the M4X3 MXene family have developed recently, hence, demanding a compilation of evaluated studies. Herein, this review provides a systematic overview of the latest advancements in M4X3 MXenes, focusing on their properties and applications in energy storage devices. The objective of this review is to provide guidance to researchers on fostering M4X3 MXene-based nanomaterials, not only for energy storage devices but also for broader applications.

Trace polymer coated clarithromycin spherulites: Formation mechanism, improvement in pharmaceutical properties and development of high-drug-loading direct compression tablets.

Clarithromycin (CLA) is a high dose antibiotic drug exhibiting poor flowability and tabletability, making the tablet development challenging. This study aims to develop spherulitic CLA by introducing trace amount of polymer in crystallization solution. Its formation mechanism, physicochemical properties and potential for the direct compression (DC) tablets development were also investigated. Morphological analyses and the in situ observation on crystallization process revealed that the CLA spherulites are formed by fractal branching growth from both sides of the threadlike precursor fibers. 1H NMR analysis and nucleation time monitoring indicated that the existence of hydroxypropyl cellulose in solution slowed down the crystal nucleation and growth rate by forming hydrogen bonding interactions with CLA molecules, making the system maintain high supersaturation, providing high driving forces for CLA spherulitic growth. In comparison to commercial CLA, the CLA spherulites exhibit profoundly improved flowability, tabletability and dissolution behaviors. XPS, contact angle and Raman mapping analysis confirmed the presence of a thin HPC layer on the surfaces and interior of CLA spherulitic particles, resulting in increasing powder plasticity, interparticulate bonding strength and powder wettability, thus better tabletability and dissolution performances. The improved flowability and tabletability of CLA spherulites also enabled the successful development of DC tablet formulation with a high CLA loading (82.8 wt%) and similar dissolution profiles to reference listed drug. This study provides a novel solid form of CLA with superior manufacturability for further development.

Functional characteristics of DNA N6-methyladenine modification based on long-read sequencing in pancreatic cancer.

Abnormalities of DNA modifications are closely related to the pathogenesis and prognosis of pancreatic cancer. The development of third-generation sequencing technology has brought opportunities for the study of new epigenetic modification in cancer. Here, we screened the N6-methyladenine (6mA) and 5-methylcytosine (5mC) modification in pancreatic cancer based on Oxford Nanopore Technologies sequencing. The 6mA levels were lower compared with 5mC and upregulated in pancreatic cancer. We developed a novel method to define differentially methylated deficient region (DMDR), which overlapped 1319 protein-coding genes in pancreatic cancer. Genes screened by DMDRs were more significantly enriched in the cancer genes compared with the traditional differential methylation method (P < 0.001 versus P = 0.21, hypergeometric test). We then identified a survival-related signature based on DMDRs (DMDRSig) that stratified patients into high- and low-risk groups. Functional enrichment analysis indicated that 891 genes were closely related to alternative splicing. Multi-omics data from the cancer genome atlas showed that these genes were frequently altered in cancer samples. Survival analysis indicated that seven genes with high expression (ADAM9, ADAM10, EPS8, FAM83A, FAM111B, LAMA3 and TES) were significantly associated with poor prognosis. In addition, the distinction for pancreatic cancer subtypes was determined using 46 subtype-specific genes and unsupervised clustering. Overall, our study is the first to explore the molecular characteristics of 6mA modifications in pancreatic cancer, indicating that 6mA has the potential to be a target for future clinical treatment.

Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation.

Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system. In this paper, we combine a canonical variable analysis (CVA) and adaptive kernel density estimation (AKDE) for the early fault detection of nonlinear dynamic hydraulic systems. The collected hydraulic system data set was used to construct the typical variable space, and the state space and residual space are divided to represent the characteristics of different correlations between the two variables, which are quantitatively described using Hotelling's T2 and Q. In order to investigate the proper upper control limits, AKDE was utilised to estimate the underlying probability density functions of T2 and Q by taking the nonlinearity of the hydraulic system variables into consideration. The advantages of the proposed approach for incipient fault detection are illustrated via a marine power plant lubrication system.

Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images.

The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer.

Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tools in predicting cancer drug sensitivity. In this study, we proposed a novel method called Multi-Omics Integrated Collective Variational Autoencoders (MOICVAE), which leverages integrated omics knowledge, including genomic and transcriptomic data, to fill in missing cancer-drug associations and enhance drug sensitivity prediction. Our method employs an encoder-decoder network to learn latent feature representations from cell lines. These learned feature vectors are then fed into a collective variational autoencoder network to train an association matrix. We evaluated MOICVAE on the GDSC and CCLE benchmark datasets using 10-fold cross-validation and achieved impressive AUCs of 0.856 and 0.808, respectively, outperforming state-of-the-art methods. Furthermore, on the TCGA dataset, consisting of 25 drugs across 7 cancer types, MOICVAE exhibited an average AUC of 0.91 in predicting drug sensitivity. Additionally, significant differences were observed in survival, tumor inflammatory assessment, and tumor microenvironment between the predicted drug-sensitive and drug-resistant groups. These results are consistent with predictions made on the METABRIC dataset. Moreover, we discovered that fusing omics data based on mRNA and CNV (copy number variations) yielded superior results in drug sensitivity prediction. MOICVAE not only achieved higher accuracy in drug sensitivity prediction but also provided additional value for combining immunotherapy with chemotherapy, offering patients with more precise treatment options. The code and dataset for MOICVAE are freely available at https://github.com/wanggnoc/MOICVAE.

Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer.

A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the "Hot" tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.

Micro-mechanism study on tissue removal behavior under medical waterjet impact using coupled SPH-FEM.

To fully grasp the numerical characteristics of the interaction process between medical waterjet and soft tissue, the smoothed particle hydrodynamics (SPH)-finite element method (FEM) was used in the simulation of this complex process to avoid the unstable error caused by indirect measurement in experiments. The SPH was applied to the numerical simulation of medical waterjet, and a three-dimensional model of gelatin sample was proposed with the FEM. The impact process between two extremely deformed materials was reproduced, and the established model was verified by comparison with experimental data; the comparison showed relatively consistent results. The separation effect under three operating modes was deduced with the stress and strain range. For the vertical impact condition, the higher the waterjet impact pressure is, the higher the biological tissue deformation bulge height is. For oblique intrusion, the longitudinal separation rate decreases and the kerf width increases with the increase of the incident angle. For the moving impact condition, with the increase of the waterjet moving speed, the longitudinal high-stress distribution range of the impact object decreases slightly.

Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer.

Luminal breast cancer (BC) accounts for a large proportion of patients in BC, with high heterogeneity. Determining the precise subtype and optimal selection of treatment options for luminal BC is a challenge. In this study, we proposed an MSBR framework that integrate DNA methylation profiles and transcriptomes to identify immune subgroups of luminal BC. MSBR was implemented both on a key module scoring algorithm and "Boruta" feature selection method by DNA methylation. Luminal A was divided into two subgroups and luminal B was divided into three subgroups using the MSBR. Furthermore, these subgroups were defined as different immune subgroups in luminal A and B respectively. The subgroups showed significant differences in DNA methylation levels, immune microenvironment (immune cell infiltration, immune checkpoint PD1/PD-L1 expression, immune cell cracking activity (CYT)) and pathology features (texture, eccentricity, intensity and tumor-infiltrating lymphocytes (TILs)). The results also showed that there is a subgroup in both luminal A and B that has the benefit from immunotherapy. This study proposed a classification of luminal BC from the perspective of epigenetics and immune characteristics, which provided individualized treatment decisions.

DNA Methylation-Specific Analysis of G Protein-Coupled Receptor-Related Genes in Pan-Cancer.

Tumor heterogeneity presents challenges for personalized diagnosis and treatment of cancer. The identification method of cancer-specific biomarkers has important applications for the diagnosis and treatment of cancer types. In this study, we analyzed the pan-cancer DNA methylation data from TCGA and GEO, and proposed a computational method to quantify the degree of specificity based on the level of DNA methylation of G protein-coupled receptor-related genes (GPCRs-related genes) and to identify specific GPCRs DNA methylation biomarkers (GRSDMs) in pan-cancer. Then, a ridge regression-based method was used to discover potential drugs through predicting the drug sensitivities of cancer samples. Finally, we predicted and verified 8 GRSDMs in adrenocortical carcinoma (ACC), rectum adenocarcinoma (READ), uveal Melanoma (UVM), thyroid carcinoma (THCA), and predicted 4 GRSDMs (F2RL3, DGKB, GRK5, PIK3R6) which were sensitive to 12 potential drugs. Our research provided a novel approach for the personalized diagnosis of cancer and informed individualized treatment decisions.

Toward Exempting from Sentinel Lymph Node Biopsy in T1 Breast Cancer Patients: A Retrospective Study.

Background and Objective: Sentinel lymph node biopsy (SLNB) is used to assess the status of axillary lymph node (ALN), but it causes many adverse reactions. Considering the low rate of sentinel lymph node (SLN) metastasis in T1 breast cancer, this study aims to identify the characteristics of T1 breast cancer without SLN metastasis and to select T1 breast cancer patients who avoid SLNB through constructing a nomogram. Methods: A total of 1,619 T1 breast cancer patients with SLNB in our hospital were enrolled in this study. Through univariate and multivariate logistic regression analysis, we analyzed the tumor anatomical and clinicopathological factors and constructed the Heilongjiang Medical University (HMU) nomogram. We selected the patients exempt from SLNB by using the nomogram. Results: In the training cohort of 1,000 cases, the SLN metastasis rate was 23.8%. Tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes were found to be independent predictors for SLN metastasis in multivariate regression analysis. Distance from nipple or surface and position of tumor have no effect on SLN metastasis. A regression model based on the results of the multivariate analysis was developed to predict the risk of SLN metastasis, indicating an AUC of 0.798. It showed excellent diagnostic performance (AUC = 0.773) in the validation cohort. Conclusion: The HMU nomogram for predicting SLN metastasis incorporates four variables, including tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes. The SLN metastasis rates of intraductal carcinoma and HER2 enriched are 2.05% and 6.67%. These patients could be included in trials investigating the SLNB exemption.

Inertial microfluidics: Recent advances.

Inertial microfluidics has attracted significant attentions in last decade due to its superior advantages of high throughput, label- and external field-free operation, simplicity, and low cost. A wide variety of channel geometry designs were demonstrated for focusing, concentrating, isolating, or separating of various bioparticles such as blood components, circulating tumor cells, bacteria, and microalgae. In this review, we first briefly introduce the physics of inertial migration and Dean flow for allowing the readers with diverse backgrounds to have a better understanding of the fundamental mechanisms of inertial microfluidics. Then, we present a comprehensive review of the recent advances and applications of inertial microfluidic devices according to different channel geometries ranging from straight channels, curved channels to contraction-expansion-array channels. Finally, the challenges and future perspective of inertial microfluidics are discussed. Owing to its superior benefit for particle manipulation, the inertial microfluidics will play a more important role in biology and medicine applications.

Study on the Similarity of Biomechanical Behavior between Gelatin and Porcine Liver.

As a natural polymer, gelatin is increasingly being used as a substitute for animals or humans for the simulation and testing of surgical procedures. In the current study, the similarity verification was neglected and a 10 wt.% or 20 wt.% gelatin sample was used directly. To compare the mechanical similarities between gelatin and biological tissues, different concentrations of gelatin samples were subjected to tensile, compression, and indentation tests and compared with porcine liver tissue. The loading rate in the three tests fully considered the surgical application conditions; notably, a loading speed up to 12 mm/s was applied in the indentation testing, the tensile test was performed at a speed of 1 mm/s until fracture, and the compression tests were compressed at a rate of 0.16 mm/s and 1 mm/s. A comparison of the results shows that the mechanical behaviors of low-concentration gelatin samples involved in the study are similar to the mechanical behavior of porcine liver tissue. The results of the gelatin material were mathematically expressed by the Mooney-Rivlin model and the Prony series. The results show that the material properties of gelatin can mimic the range of mechanical characteristics of porcine liver, and gelatin can be used as a matrix to further improve the similarity between substitute materials and biological tissues.

Investigation into the effect of upstream obstacles and hazardous sources on dispersion in the urban environment with LES model.

The release of hazardous materials originated from industrial/natural accidents or intentional attacks within a compact urban environment can be disastrous. Once released, the hazardous pollutants will be dispersed through the atmosphere instantaneously and threaten human health. This study aims at evaluating the overall effect of upstream obstacles and hazardous sources on the downstream targets through parametric investigations. A complex building morphology composed of obstacle group, target group, one main street canyon and several subsidiary street canyons is proposed. The dimensional parameters along with the sources are set as variables. An orthogonal numerical test with Large Eddy Simulation (LES) model is carried out, indicating that the source conditions play a more important role than the obstacles dimension in pollutants accumulation around targets. The flow and dispersion structures are categorized into five patterns, which are strongly relevant to the dimension parameters. Due to the upstream perturbation, the sidewalls of targets are found to be contaminated more seriously than the obstacles, while both their windward and leeward walls have a lower concentration of pollutants.

Reveal the Deformation Mechanism of (110) Silicon from Cryogenic Temperature to Elevated Temperature by Molecular Dynamics Simulation.

Silicon undergoes a brittle-to-ductile transition as its characteristic dimension reduces from macroscale to nanoscale. The thorough understanding of the plastic deformation mechanism of silicon at the nanoscale is still challenging, although it is essential for developing Si-based micro/nanoelectromechanical systems (MEMS/NEMS). Given the wide application of silicon in extreme conditions, it is, therefore, highly desirable to reveal the nanomechanical behavior of silicon from cryogenic temperature to elevated temperature. In this paper, large-scale molecular dynamics (MD) simulations were performed to reveal the spherical nanoindentation response and plastic deformation mechanism of (110)Si at the temperature range of 0.5 K to 573 K. Special attention was paid to the effect of temperature. Multiple pop-ins detected in load/pressure-indentation strain curves are impacted by temperature. Four featured structures induced by nanoindentation, including high-pressure phases, extrusion of α-Si, dislocations, and crack, are observed at all temperatures, consistent with experiment results. The detailed structure evolution of silicon was revealed at the atomic scale and its dependence on temperature was analyzed. Furthermore, structure changes were correlated with pop-ins in load/pressure-indentation strain curves. These results may advance our understanding of the mechanical properties of silicon.

Ventilation system arrangement in large cross-section tunnels：a CFD simulation based on direct initial concentration assessment.

The dust removal efficiency is essential for safety protection in tunnels. For the cutting head working in a large cross-section tunnel, the extensive movement of the cutting head and large drift space decrease the accuracy of deduced initial dust concentrations. To ascertain the initial dust concentration, cutting models were employed. Then, computational fluid dynamics (CFD) models were established to study the dust movement regularity. Comparison of the best arrangement of the ventilation system, illustrates that some of the rules governing the layout of the ventilation system in small cross-section tunnels can be used in large cross-section tunnels. The results indicate the best range of a ventilation system for distance from forcing and exhausting outlet to working face and airflow ratio. Finally, the dust concentration in different height was analysed and optimized. The study is meaningful for further research and the rules could applied in other hard rock larger cross-section tunnels.

LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction.

An increasing number of evidences indicate microbes are implicated in human physiological mechanisms, including complicated disease pathology. Some microbes have been demonstrated to be associated with diverse important human diseases or disorders. Through investigating these disease-related microbes, we can obtain a better understanding of human disease mechanisms for advancing medical scientific progress in terms of disease diagnosis, treatment, prevention, prognosis and drug discovery. Based on the known microbe-disease association network, we developed a semi-supervised computational model of Laplacian Regularized Least Squares for Human Microbe-Disease Association (LRLSHMDA) by introducing Gaussian interaction profile kernel similarity calculation and Laplacian regularized least squares classifier. LRLSHMDA reached the reliable AUCs of 0.8909 and 0.7657 based on the global and local leave-one-out cross validations, respectively. In the framework of 5-fold cross validation, average AUC value of 0.8794 +/-0.0029 further demonstrated its promising prediction ability. In case studies, 9, 9 and 8 of top-10 predicted microbes have been manually certified to be associated with asthma, colorectal carcinoma and chronic obstructive pulmonary disease by published literature evidence. Our proposed model achieves better prediction performance relative to the previous model. We expect that LRLSHMDA could offer insights into identifying more promising human microbe-disease associations in the future.

Malondialdehyde: A novel predictive biomarker for post-stroke depression.

BACKGROUND: There is evidence that stroke is accompanied by oxidative stress. However, the links between oxidative stress and depression in stroke patients are poorly understood. This study examines whether post-stroke depression (PSD) is associated with oxidative stress. METHODS: Overall, 216 acute stroke patients were consecutively recruited and followed up for 1 month. Blood specimens were collected within 24h after admission and measured for the following oxidative stress biomarkers: malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX). All enrolled patients were divided into the PSD group or the non-PSD group according to an assessment of clinical depression. One hundred normal control subjects were also recruited. RESULTS: There was a positive correlation between serum MDA levels and HAMD scores in stroke patients (r=0.536, p<0.001). Based on the Receiver-operating characteristic (ROC) curve, the optimal cutoff value of serum MDA levels as an indicator for an auxiliary diagnosis of PSD was projected to be 2.898 nmol/ml, which yielded a sensitivity of 77.9% and a specificity of 81.1%, with an area under the curve of 0.883 (95% CI, 0.836-0.929). Elevated MDA (≥2.898 nmol/ml) was an independent predictive marker of PSD (odds ratio OR=24.295; 95% CI, 9.461-62.388; p<0.001, adjusted for relevant confounders). LIMITATIONS: We excluded patients with severe aphasia or with serious conditions. In addition, the information for dietary intake was not recorded, which may influence oxidative stress levels. CONCLUSION: Our study demonstrated that an elevated serum MDA level at admission was positively associated with an increased risk of developing depression after acute stroke, especially minor stroke.

Brain-derived neurotrophic factor levels and depression during methamphetamine withdrawal.

BACKGROUND: Depression symptoms is highly comorbid with methamphetamine (METH) dependence. Except for the role in the pathophysiology of depression symptoms, brain-derived neurotrophic factor (BDNF) is also involved in the METH dependence. The present study aims to explore whether BDNF plays a role in the development of depression symptoms during METH withdrawal. METHODS: We recruited 179 patients with METH dependence who were followed up for two weeks. Ultimately, 131 (73.2%) patients finished the follow-up. Besides, 90 healthy controls were also recruited. Serum BDNF levels were measured by DuoSet ELISA Development System upon admission. The short form (13 items) of the Beck Depression Inventory (BDI) and Amphetamine Withdrawal Questionnaire (AWQ) were used to measure the depression and withdrawal symptoms. Patients with BDI score ≥ 8 were identified to have depression symptoms. RESULTS: Of the 131 patients, 64 (48.9%) were identified to have depression symptoms at the two-week endpoint. Patients with depression symptoms showed significantly lower BDNF levels than those with no depression symptoms. Serum BDNF levels (≤ 1251.0pg/ml) were independently associated with the development of depression symptoms during METH withdrawal (OR = 3.50, 95% CI, 1.14-10.73, p = 0.028). LIMITATIONS: BDNF levels were tested in serum but not in brain and the baseline BDI and AWQ scores between the depression and non-depression groups were not matched. Besides, the follow-up time was relatively short. CONCLUSIONS: Our study demonstrated that patients with serum BDNF levels ≤ 1251.0pg/ml had higher risk of depression symptoms during METH withdrawal.

The levels of triglyceride and total cholesterol in methamphetamine dependence.

The serum triglyceride (TG) and total cholesterol (TC) levels have been reported altered in the traditional drug-dependence (such as marijuana and heroin). However, studies assessing the relationships among serum TC, TG, and methamphetamine (MA)-dependence have not been described well. In this study, our aim is to explore the serum TG and TC levels in large sample of MA-dependent patients. A retrospective study was conducted in 938 MA-dependent patients who were recruited between February 2, 2008 and March 11, 2013, with social characteristics and drug-dependence history (duration of MA use, routes of drug administration, and daily dose were collected). Then, the serum levels of TC, TG, glucose (GLU), body mass index (BMI), and blood pressure were measured among the participants. Meanwhile, 985 age- and gender-matched healthy people in the physical examination center were selected as control group. Compared with the control group, significant decreases of TC, TG, GLU, and BMI were observed in MA-dependent patients (P < 0.05). Besides, we found that the daily dose of MA use was associated with TC (ß = -0.079, P = 0.015) and the duration of MA use was independently related to BMI (ß = -0.071, P = 0.031). This study demonstrated that the levels of TC, TG, GLU, and BMI factors altered in the MA-dependent patients. In addition, there is a negative association between MA dependence and TC and BMI.