Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.

Background: Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accurate prediction to optimize patient care. Methods: A comprehensive search was conducted across PubMed, Web of Science, Scopus, Embase, and Google Scholar, covering studies from inception until July 10, 2024. Studies were included if they used machine learning (ML) or deep learning algorithms to predict HT in AIS patients treated with thrombolysis. Exclusion criteria included studies involving endovascular treatments and those not evaluating model effectiveness. Data extraction and quality assessment were performed following PRISMA guidelines and using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) tools. Results: Out of 1943 identified records, 12 studies were included in the final analysis, encompassing 18 007 AIS patients who received thrombolytic therapy. The ML models demonstrated high predictive performance, with pooled area under the curve (AUC) values ranging from 0.79 to 0.95. Specifically, XGBoost models achieved AUCs of up to 0.953 and Artificial Neural Network (ANN) models reached up to 0.942. Sensitivity and specificity varied significantly, with the highest sensitivity at 0.90 and specificity at 0.99. Significant predictors of HT included age, glucose levels, NIH Stroke Scale (NIHSS) score, systolic and diastolic blood pressure, and radiomic features. Despite these promising results, methodological disparities and limited external validation highlighted the need for standardized reporting and further rigorous testing. Conclusion: ML techniques, especially XGBoost and ANN, show great promise in predicting HT following thrombolysis in AIS patients, enhancing risk stratification and clinical decision-making. Future research should focus on prospective study designs, standardized reporting, and integrating ML assessments into clinical workflows to improve AIS management and patient outcomes.

A Predictive Nomogram of In-Hospital Mortality After 48 h for Atrial Fibrillation Patients in the Coronary Care Unit.

BACKGROUND: Patients with atrial fibrillation (AF) suffer a higher risk of death, and it is necessary to develop prediction tools for mortality risk in critically ill patients with AF. This study aimed to develop a novel predictive nomogram of in-hospital mortality after 48 h in the coronary care unit (CCU) for patients with AF. METHODS: We collected information on CCU patients with AF from the "Medical Information Mart for Intensive Care-III" database and developed a nomogram model for predicting the all-cause mortality risk after 48 h in the hospital. Key variables were selected by univariate logistic and least absolute shrinkage and selection operator regression. The independent predictors with p < 0.05 were screened out by multivariate logistic regression. A predictive nomogram was constructed using these independent predictors, and the model calibration and discrimination were evaluated. RESULTS: This study finally enrolled 1248 CCU patients with AF, and the in-hospital mortality was 17% (209/1248). The predictive nomogram was constructed by 13 selected independent predictors, including age, smoking status, acute kidney injury, chronic obstructive pulmonary disease, ventricular arrhythmia, shock, urea, red cell distribution width, leucocytosis, continuous renal replacement therapy, continuous positive airway pressure, anticoagulation, and heart rate. The area under the curve of the nomogram was 0.803 (95% confidence interval 0.771-0.835). The nomogram was verified to have good accuracy and calibration. CONCLUSIONS: This study developed a novel nomogram containing age, acute kidney injury, and heart rate that can be a good predictor of potential in-hospital mortality after 48 h in CCU patients with AF.

Ab Initio Kinetic Pathway of Diborane Decomposition on Transition Metal Surfaces in Borophene Chemical Vapor Deposition Growth.

The chemical vapor deposition (CVD) method holds promise for the scalable and controlled synthesis of high-quality borophene. However, the current lack of an atomistic understanding of intricate kinetic pathways from precursors to borophene impedes process optimization. Here, we employ first-principles simulations to systematically explore the pyrolytic decomposition pathways of the most used precursor diborane (B2H6) to borophene on various transition metal surfaces. Our results reveal that B2H6 on various metal substrates exhibits different dissociation behaviors. Meanwhile, the activity of the examined metal substrates is quite anisotropic and surface direction-dependent, where the estimated overall catalytic activity order of these metals is found to be Pd ≈ Pt ≈ Rh > Ir ≈ Ru ≈ Cu > Au ≈ Ag. Our study provides atomistic insights into the dissociation kinetics of diborane precursors on various transition metal surfaces, serving as a guide for experimental growth of borophene.

Long-working-distance high-collection-efficiency three-photon microscopy for in vivo long-term imaging of zebrafish and organoids.

Zebrafish and organoids, crucial for complex biological studies, necessitate an imaging system with deep tissue penetration, sample protection from environmental interference, and ample operational space. Traditional three-photon microscopy is constrained by short-working-distance objectives and falls short. Our long-working-distance high-collection-efficiency three-photon microscopy (LH-3PM) addresses these challenges, achieving a 58% fluorescence collection efficiency at a 20 mm working distance. LH-3PM significantly outperforms existing three-photon systems equipped with the same long working distance objective, enhancing fluorescence collection and dramatically reducing phototoxicity and photobleaching. These improvements facilitate accurate capture of neuronal activity and an enhanced detection of activity spikes, which are vital for comprehensive, long-term imaging. LH-3PM's imaging of epileptic zebrafish not only showed sustained neuron activity over an hour but also highlighted increased neural synchronization and spike numbers, marking a notable shift in neural coding mechanisms. This breakthrough paves the way for new explorations of biological phenomena in small model organisms.

Progranulin inhibits autophagy to facilitate intracellular colonization of Helicobacter pylori through the PGRN/mTOR/DCN axis in gastric epithelial cells.

Helicobacter pylori (H. pylori) infection is the primary risk factor for the progress of gastric diseases. The persistent stomach colonization of H. pylori is closely associated with the development of gastritis and malignancies. Although the involvement of progranulin (PGRN) in various cancer types has been well-documented, its functional role and underlying mechanisms in gastric cancer (GC) associated with H. pylori infection remain largely unknown. This report demonstrated that PGRN was up-regulated in GC and associated with poor prognosis, as determined through local and public database analysis. Additionally, H. pylori induced the up-regulation of PGRN in gastric epithelial cells both in vitro and in vivo. Functional studies have shown that PGRN promoted the intracellular colonization of H. pylori. Mechanistically, H. pylori infection induced autophagy, while PGRN inhibited autophagy to promote the intracellular colonization of H. pylori. Furthermore, PGRN suppressed H. pylori-induced autophagy by down-regulating decorin (DCN) through the mTOR pathway. In general, PGRN inhibited autophagy to facilitate intracellular colonization of H. pylori via the PGRN/mTOR/DCN axis. This study provides new insights into the molecular mechanisms underlying the progression of gastric diseases, suggesting PGRN as a potential therapeutic target and prognostic predictor for these disorders.

Intraoperative assessment of microimplantation-induced acute brain inflammation with titanium oxynitride-based plasmonic biosensor.

Implantable devices for brain-machine interfaces and managing neurological disorders have experienced rapid growth in recent years. Although functional implants offer significant benefits, issues related to transient trauma and long-term biocompatibility and safety are of significant concern. Acute inflammatory reaction in the brain tissue caused by microimplants is known to be an issue but remains poorly studied. This study presents the use of titanium oxynitride (TiNO) nanofilm with defined surface plasmon resonance (SPR) properties for point-of-care characterizing of acute inflammatory responses during robot-controlled micro-neuro-implantation. By leveraging surface-enriched oxynitride, TiNO nanofilms can be biomolecular-functionalized through silanization. This label-free TiNO-SPR biosensor exhibits a high sensitivity toward the inflammatory cytokine interleukin-6 with a detection limit down to 6.3 fg ml-1 and a short assay time of 25 min. Additionally, intraoperative monitoring of acute inflammatory responses during microelectrode implantation in the mice brain has been accomplished using the TiNO-SPR biosensors. Through intraoperative cerebrospinal fluid sampling and point-of-care plasmonic biosensing, the rhythm of acute inflammatory responses induced by the robot-controlled brain microelectrodes implantation has been successfully depicted, offering insights into intraoperative safety assessment of invasive brain-machine interfaces.

Black ginseng: a novel medicine for treating heart failure.

Introduction: Black ginseng (BG) was processed by "steaming and drying" (generally nine times) repeatedly to produce "rare saponins" and secondary ginsenosides. Both ginseng (GS) and red ginseng (RG) were commonly used in treating heart failure (HF), and the latter was confirmed to be more potent, implying the presence of rare ginsenosides that contribute positively to the treatment of heart failure. Previous research indicated that rare ginsenosides are more abundant in BG than in RG. Consequently, this study aims to investigate the effects of BG and its components on HF to elucidate the active substances and their underlying mechanisms in the treatment of HF. Methods: The effects of BG and its fractions (water-eluted fraction (WEF), total saponin fraction (TSF), and alcohol-eluted fraction (AEF)) on rats with isoproterenol (ISO)-induced HF were explored, and steroids belonging to the hypothalamic-pituitary-adrenal (HPA) and hypothalamic-pituitary-gonadal (HPG) axes were determined quantitatively using the ultra-performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-QqQ-MS/MS) method. In addition, 16S rDNA sequencing was performed on the gut microbiota, followed by GC-MS analysis of short-chain fatty acids (SCFAs), and the biochemical indexes related to energy metabolism and the serum cyclic nucleotide system were also analyzed by ELISA. Results: Based on a thorough evaluation of energy metabolism and the endocrine system, it was observed that the effects of BG components on the hypothalamic-pituitary-thyroid (HPT) and HPA axes were more pronounced. Notably, the treatment efficacy of the low dose of the total saponin fraction (TSFL), water decoction (WD), and high dose of the polysaccharide fraction (PSFH) was superior based on pharmacodynamic indicators such as brain natriuretic peptide (BNP), creatine kinase (CK), and estradiol (E2)/T). Furthermore, the WD and BG components exhibited significant effects on androgens (T and androstenedione (A4)). The TSFL group exerts an anti-inflammatory effect by regulating Lactobacillus/Erysipelotrichales. The WD, PSFH, and TSFL may impact inflammatory cytokines through the gut microbiota (Lactobacillus/Erysipelotrichales) and their metabolites (acetate and butyrate), exerting an anti-inflammatory effect. Discussion: The BG and all its split components demonstrated varying levels of efficacy in alleviating HF, and TSF and PSF exhibited a significant protective effect on HF. The main active components in TSF were revealed to be ginsenosides Rk1, Rk3, 20-(S)-Rg3, and 20-(S)-Rh2 by the H9C2 cell experiment. The decoction of BG and its components exhibited a potent impact on androgen hormones, with an elevation trend. This phenomenon may be attributed to the activation of the eNOS-NO pathway through androgen regulation, thereby contributing to its anti-HF activities. The WD, PSFH, and TSFL may exert anti-inflammatory effects through the intestinal flora (Lactobacillaceae/Erysipelotrichaceae) and its metabolites (acetic acid and butyric acid), which affect the inflammatory factors. The different mechanisms of action of each component of HF also reflect the significance and necessity of the overall role of traditional Chinese medicine (TCM). Our research was the first to report that the E2/T is related to HF and can be used as an indicator to evaluate HF.

Defying Gravity to Enhance Power Output and Conversion Efficiency in a Vertically Oriented Four-Electrode Microfluidic Microbial Fuel Cell.

High power output and high conversion efficiency are crucial parameters for microbial fuel cells (MFCs). In our previous work, we worked with microfluidic MFCs to study fundamentals related to the power density of the MFCs, but nutrient consumption was limited to one side of the microchannel (the electrode layer) due to diffusion limitations. In this work, long-term experiments were conducted on a new four-electrode microfluidic MFC design, which grew Geobacter sulfurreducens biofilms on upward- and downward-facing electrodes in the microchannel. To our knowledge, this is the first study comparing electroactive biofilm (EAB) growth experiencing the influence of opposing gravitational fields. It was discovered that inoculation and growth of the EAB did not proceed as fast at the downward-facing anode, which we hypothesize to be due to gravity effects that negatively impacted bacterial settling on that surface. Rotating the device during the growth phase resulted in uniform and strong outputs from both sides, yielding individual power densities of 4.03 and 4.13 W m-2, which increased to nearly double when the top- and bottom-side electrodes were operated in parallel as a single four-electrode MFC. Similarly, acetate consumption could be doubled with the four electrodes operated in parallel.

LSTM-Based Prediction Model for Tuberculosis Among HIV-Infected Patients Using Structured Electronic Medical Records: A Retrospective Machine Learning Study.

Background: Both HIV and TB are chronic infectious diseases requiring long-term treatment and follow-up, resulting in extensive electronic medical records. With the exponential growth of health and medical big data, effectively extracting and analyzing these data has become the research hotspot. As a fundamental aspect of artificial intelligence, machine learning has been extensively applied in medical research, encompassing diagnosis, treatment, patient monitoring, drug development, and epidemiological investigations. This significantly enhances medical information systems and facilitates the interoperability of medical data. Methods: In our study, we analyzed longitudinal data from the electronic health records of 4540 patients, gathered from the National Clinical Research Center for Infectious Diseases in Shenzhen, China, spanning from 2017 to 2021. Initially, we employed the fine-tuned ChatGLM to structure the electronic medical records. Subsequently, we utilized a multi-layer perceptron to classify each patient and determined the presence of tuberculosis in HIV patients. Using machine learning-based natural language processing, we structured these records to build a specialized database for HIV and TB co-infection. We studied the epidemiological characteristics, focusing on incidence patterns, patient characteristics, and influencing factors, to uncover the transmission characteristics of these diseases in Shenzhen. Additionally, we used Long Short-Term Memory to create a predictive model for TB co-infection among HIV patients, based on their medical records. This model predicted the risk of TB co-infection, providing scientific evidence for clinical decision-making and enabling early detection and precise intervention. Results: Based on the refined ChatGLM model tailored for structured electronic health records, the accuracy of symptom extraction consistently surpassed 0.95 precision. Key symptoms such as diarrhea and normal showed precision rates exceeding 0.90. High scores were also achieved in recall and F1 scores. Among 4540 HIV patients, 758 were diagnosed with concurrent tuberculosis, indicating a 16.7% co-infection rate, while syphilis co-infection affected 25.1%, underscoring the prevalence of concurrent infections among HIV patients. Utilizing electronic health records, a Multilayer Perceptron classifier was developed as a benchmark against Long Short-Term Memory to predict high-risk groups for HIV and tuberculosis co-infections. The Multilayer Perceptron classifier demonstrated predictive ability with AUROC values ranging from 0.616 to 0.682 on the test set, suggesting opportunities for further optimization and generalization despite its accuracy in identifying HIV-TB co-infections. In tuberculosis intelligent diagnosis based on laboratory results, the Long Short-Term Memory showed consistent performance across 5-fold cross-validation, with AUROC values ranging from 0.827 to 0.850, indicating reliability and consistency in tuberculosis prediction. Furthermore, by optimizing classification thresholds, the model achieved an overall accuracy of 81.18% in distinguishing HIV co-infected tuberculosis from simple HIV infection. Conclusion: Combining the Multilayer Perceptron classifier with Long Short-Term Memory represented an advanced approach for effectively extracting electronic health records and utilizing it for disease prediction. This underscored the superior performance of deep learning techniques in managing both structured and unstructured medical data. Models leveraging laboratory time-series data demonstrated notably better performance compared to those relying solely on electronic health records for predicting tuberculosis incidence. This emphasized the benefits of deep learning in handling intricate medical data and provided valuable insights for healthcare providers exploring the use of deep learning in disease prediction and management.

Ten ring-B aromatized ergosterols from Aspergillus spectabilis.

Spectasterols F-O (1-10), ten interesting ergosterols with an aromatized B ring, were obtained from Aspergillus spectabilis. Their structures and absolute configurations were determined using a combination of high-resolution electrospray ionization mass spectrometry (HR-ESI-MS), nuclear magnetic resonance (NMR) spectroscopy, single-crystal X-ray diffraction analyses, and electronic circular dichroism (ECD) calculations. Structurally, these aromatic ergosterols feature versatile side chains. Notably, compound aromatic ergosterols featured versatile side chains, and compound 4 is an unusual C23 ergosterol characterized by a shorter side chain due to oxidative cleavage between C-23 and C-24. All compounds were evaluated for their neuroprotective activities, with compound 8 showing a dose-dependent ability to reduce apoptosis and protect mitochondrial function in glutamate-induced SH-SY5Y cells.

A Comprehensive Review on Harnessing Soy Proteins in the Manufacture of Healthy Foods through Extrusion.

The global development of livestock production systems, accelerated by the growing demand for animal products, has greatly contributed to land-use change, greenhouse gas emissions, and pollution of the local environment. Further, excessive consumption of animal products has been linked with cardiovascular diseases, digestive system diseases, diabetes, and cancer. On the other hand, snacks, pasta, and bread available on the market are made from wheat, fat, salt, and sugar, which contribute to the risk of cardiovascular diseases. To counter these issues, a range of plant protein-based food products have been developed using different processing techniques, such as extrusion. Given the easy scalability, low cost of extrusion technology, and health benefits of soy proteins, this review focuses on the extrusion of soy protein and the potential application of soy protein-based extrudates in the manufacture of healthy, nutritious, and sustainable meat analogs, snacks, pasta products, and breakfast cereals. This review discusses the addition of soy protein to reformulate hypercaloric foods through extrusion technology. It also explores physical and chemical changes of soy proteins/soy protein blends during low and high moisture extrusion. Hydrogen bonds, disulfide bonds, and hydrophobic interactions influence the properties of the extrudates. Adding soy protein to snacks, pasta, breakfast cereals, and meat analogs affects their nutritional value, physicochemical properties, and sensory characteristics. The use of soy proteins in the production of low-calorie food could be an excellent opportunity for the future development of the soybean processing industry.

Soy Protein Isolate Gel Subjected to Freezing Treatment: Influence of Methylcellulose and Sodium Hexametaphosphate on Gel Stability, Texture and Structure.

Freezing affects texture and induces the loss of gel quality. This study investigated the effects of methylcellulose (MC) (0.2%, 0.4%, 0.6%) and sodium hexametaphosphate (SHMP) (0.15%, 0.3%) on the gel textural and structural properties of SPI gels before and after freezing, and explores the synergistic enhancement of gel texture and the underlying mechanisms resulting from the simultaneous addition of SHMP and MC to SPI gels. It was revealed that MC improved the strength of SPI gels through its thickening properties, but it could not inhibit the reduction of SPI gels after freezing. The 0.4% MC-SPI gel exhibited the best gel strength (193.2 ± 2.4 g). SHMP inhibited gel reduction during freezing through hydrogen bonding and ionic interactions; it enhanced the freezing stability of SPI gels. The addition of 0.15% SHMP made the water-holding capacity in SPI gels reach the highest score after freezing (58.2 ± 0.32%). The synergistic effect of MC and SHMP could improve the strength and the freezing stability of SPI gels. MC facilitated the release of ionizable groups within SPI, causing negatively charged SHMP groups to aggregate on the SPI and inhibit the freezing aggregation of proteins. These results provide a strong basis for the improvement of cryogenic soy protein gel performance by SHMP and MC.

Self-Sensing Electromechanical System Integrated with the Embedded Displacement Sensor.

Conventionally, the electromechanical system requires the installation of auxiliary displacement sensors and only the amount on the drive part and motion end, which increases volume, cost, and measurement error in the system. This paper presents an integrated measurement method with a sensing head, which takes the equal division characteristics of mechanical structures as part of the sensor, thus, the so-called self-sensing system. Moreover, the displacement is measured by counting the time pulses. The sensing head is integrated with the entire electromechanical system, including the driving, transmitting, and moving parts. Thus, the integration of the sensing part is greatly improved. Taking the rotary table as a special example, and the sensing head embedded into each part of the system, displacement information is obtained by the common processing system and fused by the adaptive weighted average method. The results of the experiment show that the fusion precision of each component is higher than only the motor position information as the feedback. The proposed method is a practical self-sensing technology with significant volume reduction and intelligent control benefits in the industry, especially suitable for extremely small and narrow spaces.

A pH-triggered N-oxide polyzwitterionic nano-drug loaded system for the anti-tumor immunity activation research.

Triple negative breast cancer (TNBC) has the characteristics of low immune cell infiltration, high expression of tumor programmed death ligand 1 (PD-L1), and abundant cancer stem cells. Systemic toxicity of traditional chemotherapy drugs due to poor drug selectivity, and chemotherapy failure due to tumor drug resistance and other problems, so it is particularly important to find new cancer treatment strategies for TNBC with limited treatment options. Both the anti-tumor natural drugs curcumin and ginsenoside Rg3 can exert anti-tumor effects by inducing immunogenic cell death (ICD) of tumor cells, reducing PD-L1 expression, and reducing cancer stem cells. However, they have the disadvantages of poor water solubility, low bioavailability, and weak anti-tumor effect of single agents. We used vinyl ether bonds to link curcumin (Cur) with N-O type zwitterionic polymers and at the same time encapsulated ginsenoside Rg3 to obtain hyperbranched zwitterionic drug-loaded micelles OPDEA-PGED-5HA@Cur@Rg3 (PPH@CR) with pH response. In vitro cell experiments and in vivo animal experiments have proved that PPH@CR could not only promote the maturation of dendritic cells (DCs) and increase the CD4+ T cells and CD8+ T cells by inducing ICD in tumor cells but also reduce the expression of PD-L1 in tumor tissues, and reduce cancer stem cells and showed better anti-tumor effects and good biological safety compared with free double drugs, which is a promising cancer treatment strategy.

Genome-Wide Identification and Bioinformatics Analysis of the FK506 Binding Protein Family in Rice.

The FK506 Binding Protein (FKBP), ubiquitously present across diverse species, is characterized by its evolutionarily conserved FK506 binding domain (FKBd). In plants, evidence suggests that this gene family plays integral roles in regulating growth, development, and responses to environmental stresses. Notably, research on the identification and functionality of FKBP genes in rice remains limited. Therefore, this study utilized bioinformatic tools to identify 30 FKBP-encoding genes in rice. It provides a detailed analysis of their chromosomal locations, evolutionary relationships with the Arabidopsis thaliana FKBP family, and gene structures. Further analysis of the promoter elements of these rice FKBP genes revealed a high presence of stress-responsive elements. Quantitative PCR assays under drought and heat stress conditions demonstrated that genes OsFKBP15-2, OsFKBP15-3, OsFKBP16-3, OsFKBP18, and OsFKBP42b are inducible by these adverse conditions. These findings suggest a significant role for the rice FKBP gene family in stress adaptation. This research establishes a critical foundation for deeper explorations of the functional roles of the OsFKBP genes in rice.

Magnesium status modulating the effect of serum vitamin D levels on retinopathy: National Health and Nutrition Examination Survey 2005 to 2008.

Aim: Magnesium levels may influence the effect of vitamin D levels on the body. This study aimed to assess the combined effect of magnesium status as reflected by magnesium depletion score (MDS) and vitamin D status on the risk of retinopathy. Methods: This cross-sectional study included participants aged 40 years and older with complete information on vitamin D, MDS, and retinopathy assessment from the 2005-2008 National Health and Nutrition Examination Survey (NHANES). Logistic regression analysis was utilized to analyze the relationship of MDS and vitamin D with retinopathy and expressed as odds ratio (OR) and 95% confidence interval (CI). Results: Of these 4,953 participants included, 602 (9.53%) participants had retinopathy. Serum vitamin D levels ≤30 nmol/L (vs. >30 nmol/L) (OR = 1.38, 95%CI: 1.05-1.81) and MDS >2 points (vs. ≤2 points) (OR = 1.47, 95%CI: 1.01-2.16) were associated with higher odds of retinopathy. There was an interaction between MDS and vitamin D on the increased odds of retinopathy (OR = 2.29, 95%CI: 1.12-4.68, P interaction = 0.025). In different MDS groups, serum vitamin D levels ≤30 nmol/L increased the odds of retinopathy only in the MDS >2 group (OR = 2.90, 95%CI: 1.16-7.24), but not in the MDS ≤2 group (p = 0.293). Subgroups analyses demonstrated that the interaction between MDS and serum vitamin D on retinopathy was observed in males (OR = 6.88, 95%CI: 1.41-33.66, P interaction = 0.019), people with diabetes (OR = 3.43, 95%CI: 1.78-6.63, P interaction < 0.001), and people with body mass index (BMI) ≥25 kg/m2 (OR = 2.46, 95%CI: 1.11-5.44, P interaction = 0.028). Conclusion: Magnesium plays a moderating role in the relationship between serum vitamin D and retinopathy. The protective effect of vitamin D against retinopathy was primarily present among those with inadequate magnesium levels.

Corrigendum: Sesquiterpene lactones attenuate paclitaxel resistance via inhibiting MALAT1/STAT3/ FUT4 axis and P-Glycoprotein transporters in lung cancer cells.

[This corrects the article DOI: 10.3389/fphar.2022.795613.].

Effective cryopreservation of human brain tissue and neural organoids.

Human brain tissue models and organoids are vital for studying and modeling human neurological disease. However, the high cost of long-term cultured organoids inhibits their wide-ranging application. It is therefore urgent to develop methods for the cryopreservation of brain tissue and organoids. Here, we establish a method using methylcellulose, ethylene glycol, DMSO, and Y27632 (termed MEDY) for the cryopreservation of cortical organoids without disrupting the neural cytoarchitecture or functional activity. MEDY can be applied to multiple brain-region-specific organoids, including the dorsal/ventral forebrain, spinal cord, optic vesicle brain, and epilepsy patient-derived brain organoids. Additionally, MEDY enables the cryopreservation of human brain tissue samples, and pathological features are retained after thawing. Transcriptomic analysis shows that MEDY can protect synaptic function and inhibit the endoplasmic reticulum-mediated apoptosis pathway. MEDY will enable the large-scale and reliable storage of diverse neural organoids and living brain tissue and will facilitate wide-ranging research, medical applications, and drug screening.

WTAP-mediated m6A modification of lncRNA Snhg1 improves myocardial ischemia-reperfusion injury via miR-361-5p/OPA1-dependent mitochondrial fusion.

BACKGROUND: Myocardial ischemia-reperfusion injury (MIRI) is caused by reperfusion after ischemic heart disease. LncRNA Snhg1 regulates the progression of various diseases. N6-methyladenosine (m6A) is the frequent RNA modification and plays a critical role in MIRI. However, it is unclear whether lncRNA Snhg1 regulates MIRI progression and whether the lncRNA Snhg1 was modified by m6A methylation. METHODS: Mouse cardiomyocytes HL-1 cells were utilized to construct the hypoxia/reoxygenation (H/R) injury model. HL-1 cell viability was evaluated utilizing CCK-8 method. Cell apoptosis, mitochondrial reactive oxygen species (ROS), and mitochondrial membrane potential (MMP) were quantitated utilizing flow cytometry. RNA immunoprecipitation and dual-luciferase reporter assays were applied to measure the m6A methylation and the interactions between lncRNA Snhg1 and targeted miRNA or target miRNAs and its target gene. The I/R mouse model was constructed with adenovirus expressing lncRNA Snhg1. HE and TUNEL staining were used to evaluate myocardial tissue damage and apoptosis. RESULTS: LncRNA Snhg1 was down-regulated after H/R injury, and overexpressed lncRNA Snhg1 suppressed H/R-stimulated cell apoptosis, mitochondrial ROS level and polarization. Besides, lncRNA Snhg1 could target miR-361-5p, and miR-361-5p targeted OPA1. Overexpressed lncRNA Snhg1 suppressed H/R-stimulated cell apoptosis, mitochondrial ROS level and polarization though the miR-361-5p/OPA1 axis. Furthermore, WTAP induced lncRNA Snhg1 m6A modification in H/R-stimulated HL-1 cells. Moreover, enforced lncRNA Snhg1 repressed I/R-stimulated myocardial tissue damage and apoptosis and regulated the miR-361-5p and OPA1 levels. CONCLUSION: WTAP-mediated m6A modification of lncRNA Snhg1 regulated MIRI progression through modulating myocardial apoptosis, mitochondrial ROS production, and mitochondrial polarization via miR-361-5p/OPA1 axis, providing the evidence for lncRNA as the prospective target for alleviating MIRI progression.

Polycyclic aromatic hydrocarbons exposure and arterial stiffness-related plasma miRNAs: A panel study.

The underlying mechanisms between polycyclic aromatic hydrocarbons (PAHs) exposure and arterial stiffness are poorly understood. We carried out a panel study involving three repeated surveys to examine the associations of individual and mixture of PAHs exposure with arterial stiffness-related miRNAs among 123 community adults. In linear mixed-effect (LME) models, we found that urinary 9-hydroxyfluorene (9-OHFlu), 2-hydroxyphenanthrene (2-OHPh), 9-hydroxyphenanthrene (9-OHPh) at lag 0 day were positively linked to miR-146a and/or miR-222. The Bayesian kernel machine regression (BKMR) analyses revealed positive overall associations of PAHs mixture at lag 0 day with miR-146a and miR-222, and urinary 9-OHFlu contributed the most. In addition, an inter-quartile range (IQR) increase in urinary 9-OHFlu at lag 0 day was associated with elevated miR-146a and miR-222 by 0.16 (95% CI: 0.02, 0.30) to 0.34 (95% CI: 0.13, 0.54). Accordingly, exposure to PAHs, especially 9-OHFlu at lag 0 day, was related to elevated arterial stiffness-related plasma miRNAs.