Study on energy evolution and fractal characteristics of sandstone with different fracture dip angles under uniaxial compression.

In order to investigate the failure modes and instability mechanism of fractured rock. Uniaxial compression tests were conducted on sandstone specimens with different dip angles. Based on rock energy dissipation theory and fractal theory, the energy evolution characteristics and fragmentation fractal characteristics in the process of deformation and failure of specimens were analyzed. The results show that the peak strength and elastic modulus of fractured rock mass are lower than those of intact samples, and both show an exponential increase with the increase of fracture dip angle. The energy evolution laws of different fracture specimens are roughly similar and can be classified into four stages based on the stress-strain curve: pressure-tight, elastic, plastic, and post-destructive. The total strain energy, elastic strain energy, and dissipated strain energy of the specimen at the peak stress point increased exponentially with crack inclination, and the dissipated strain energy and compressive strength conformed to a power function growth relationship. The distribution of the fragments after the failure of the fracture sample has fractal characteristics, and the fractal dimension increases with the increase of the fracture dip angle. In addition, the higher the compressive strength of the specimen, the greater the energy dissipation, the more serious the degree of fragmentation, and the greater the fractal dimension. The data fitting further shows that there is a power function relationship between the dissipated strain energy and the fractal dimension. The research results can provide a theoretical basis for the stability of rock mass engineering and structural deformation control.

Quality of life and its association with predictors in lung transplant recipients: a latent profile analysis.

Backgrounds: Improving quality of life (QOL) is one of the main aims of lung transplantation (LTx). There is a need to identify those who have poor quality of life early. However, research addressing inter individual quality of life variability among them is lacking. This study aims to identify group patterns in quality of life among lung transplant recipients and examine the predictors associated with quality of life subgroups. Methods: In total, 173 lung transplant recipients were recruited from one hospital in Guangdong Province between September 2022 and August 2023. They were assessed using the Lung Transplant Quality of Life scale (LT-QOL), Mindful Attention Awareness Scale (MAAS), Life Orientation Test-Revised scale (LOT-R), and Positive and Negative Affect Scale (PANAS). Latent profile analysis was used to identify QOL subtypes, and logistic regression analysis was used to examine the associations between latent profiles and sociodemographic and psychosocial characteristics. Results: Two distinct QOL profiles were identified: "low HRQOL" profile [N = 53 (30.94%)] and "high HRQOL" profile [N = 120 (69.06%)]. Single lung transplant recipients, and patients who reported post-transplant infection, high levels of negative emotion or low levels of mindfulness and optimism were significantly correlated with the low QOL subgroup. Conclusion: Using the domains of the LT-QOL scale, two profiles were identified among the lung transplant recipients. Our findings highlighted that targeted intervention should be developed based on the characteristics of each latent class, and timely attention must be paid to patients who have undergone single lung transplantation, have had a hospital readmission due to infection, exhibit low levels of optimism, low levels of mindfulness or high negative emotions.

HMGA1 is a Prognostic Biomarker and Correlated with Glycolysis in Lung Adenocarcinoma.

Purpose: Lung cancer is one of the leading causes with high morbidity and mortality. High mobility group A1 (HMGA1) protein participates in the process of tumorigenesis. This study seeks to explore the specific role of HMGA1 in prognostic value based on The Cancer Genome Atlas (TCGA) database of Lung adenocarcinoma (LUAD) and glycolysis progression in LUAD cells. Patients and Methods: In this research, we compared HMGA1 mRNA expression between tumor tissues and normal samples and evaluated the correlations with clinical characteristics in LUAD patients based on the data of TCGA database. The survival outcome with overall survival (OS), disease-specific survival (DSS) and clinicopathologic characteristics associated were performed using the Kaplan-Meier method and Cox regression. In addition, gene-set enrichment analysis (GSEA) was carried out to explore the biological pathways that related to HMGA1. Cell experiments including cell proliferation assay and glycolysis proteins were performed with A549 and H1299 cells. Results: Our results revealed that HMGA1 mRNA expression was higher in LUAD tissues than in normal tissues. Increased HMGA1 expression in LUAD was associated with Gender (p<0.01), Pathologic stage I&II vs stage III&IV (p<0.001), T1&T2 vs T3&T4 stage (p<0.05), N0 vs N2 stage (p<0.01). Furthermore, multivariate analysis revealed that HMGA1 was an independent risk factor of OS and DSS for LUAD patients (p<0.05). HMGA1 were positively correlated with glycolysis gluconeogenesis pathway and glycolysis markers (HK2, GLUT1, PKM2, LDHA) based on GSEA and Gene Expression Profiling Interactive Analysis (GEPIA) database. At the cellular level, the results of qRT-PCR and western blot assays showed that si-HMGA1 markedly decreased the expression of glycolysis markers. HMGA1 promoted cell glycolysis progression via PI3K/AKT pathway transfected with HMGA1-plasmid and the treatment with 20 µM LY294002. Relevant animal experiments were also synchronously validated and si-HMGA1 groups down-regulated xenograft growth including the weights and size in tumor xenografts. Conclusions: In conclusion, our results suggested that HMGA1 was significantly correlated with poor survival for LUAD tissues and involved in the process of glycolysis in LUAD cells.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

BACKGROUND: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. OBJECTIVE: This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. METHODS: In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. RESULTS: A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. CONCLUSIONS: The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Efficacy of endovascular therapy for cerebral vasospasm following aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.

Background: Cerebral vasospasm (CV) is a common complication of aneurysmal subarachnoid hemorrhage (aSAH), leading to increased morbidity and mortality rates. Endovascular therapy, particularly intra-arterial vasodilator infusion (IAVI), has emerged as a potential alternative treatment for CV. Methods: A systematic review and meta-analysis were conducted to compare the efficacy of endovascular therapy with standard treatment in patients with CV following aSAH. The primary outcomes assessed were in-hospital mortality, discharge favorable outcome, and follow-up favorable outcome. Secondary outcomes included major infarction on CT, ICU stay duration, and total hospital stay. Results: Regarding our primary outcomes of interest, patients undergoing intervention exhibited a significantly lower in-hospital mortality compared to the standard treatment group, with the intervention group having only half the mortality risk (RR = 0.49, 95% CI [0.29, 0.83], p = 0.008). However, there were no significant differences between the two groups in terms of discharge favorable outcome (RR = 0.99, 95% CI [0.68, 1.45], p = 0.963) and follow-up favorable outcome (RR = 1.09, 95% CI [0.86, 1.39], p = 0.485). Additionally, there was no significant difference in major infarction rates (RR = 0.79, 95% CI [0.34, 1.84], p = 0.588). It is important to note that patients undergoing endovascular treatment experienced longer stays in the ICU (MD = 6.07, 95% CI [1.03, 11.12], p = 0.018) and extended hospitalization (MD = 5.6, 95% CI [3.63, 7.56], p < 0.001). Subgroup analyses based on the mode of endovascular treatment further supported the benefits of IAVI in lowering in-hospital mortality (RR = 0.5, 95% CI [0.27, 0.91], p = 0.023). Conclusion: Endovascular therapy, particularly IAVI, holds promising potential in reducing in-hospital mortality for patients with CV following aSAH. However, it did not show significant improvement in long-term prognosis and functional recovery. Further research with larger sample sizes and randomized controlled trials is necessary to validate these findings and optimize the treatment strategy for cerebral vasospasm in aSAH patients. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42023451741.

A Multimode Optical Sensor for Selective and Sensitive Detection of Harmful Heavy Metal Cr(VI) in Fresh Water and Sea Water.

Water pollution originating from heavy metals has shown great impacts on the ecological environment and human health due to their extremely low biodegradability. Hexavalent chromium Cr(VI), as one harmful heavy metal with strong oxidation, high biological permeability, and high carcinogenicity, is becoming an increasingly serious threat to human health. Therefore, conveniently but accurately, monitoring the Cr(VI) level in water to maintain its normal level and ensuring the stability of the ecosystem and human health become very valuable. However, most of these heavy metal sensors reported are turn-off type single-emission sensors. In this work, a ratiometric fluorescence/colorimetry/smartphone triple-mode turn-on optical sensor for Cr(VI) was developed based on a multifunctional metal-organic framework platform. The detection limits for these three mutual verification modes were only 1.28, 4.89, and 68.4 nM, respectively. Additionally, the color changes of the detection system under sunlight can also be observed directly by the naked eye. The accuracy and practicability of this multimode sensor were further proved by the detection of Cr(VI) in actual water and seawater samples, and the recovery rate ranged from 97.308 to 104.041%.

Effects of tumor marker regression load score on long-term prognosis of gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy.

BACKGROUND: The effects of the dynamics of serum tumor markers (CA72-4, CEA, CA19-9, CA125 and AFP) before and after neoadjuvant chemotherapy (NACT) on the prognosis of gastric cancer(GC) patients remain unclear. METHODS: The training set contained 334 GC patients from Fujian Medical University Union Hospital (FJMUUH) and 113 GC patients in Qinghai University Affiliated Hospital (QhUAH) were used as an external validation set. Tumor marker regression load (ΔTMRL) indicator, including ΔCA72-4, ΔCEA, ΔCA19-9, ΔCA125, and ΔAFP, is defined as [(postNACT marker- preNACT marker)/preNACT marker]. Tumor marker regression load score (TMRLS) consists of ΔCA72-4, ΔCEA and ΔCA125. The predictive performance of the nomogram-TMRLS was evaluated using the area under the receiver operating characteristic(ROC) curve(AUC), decision curve analysis(DCA), and C-index. RESULTS: Patients from FJMUUH were divided into two groups, TMRLS-low and TMRLS-high, determined by R package maxstat. Survival analysis revealed a higher 3-year overall survival(OS) in the TMRLS-low than in the TMRLS-high group. The TMRLS-high group who received postoperative adjuvant chemotherapy(AC) showed a significantly higher 3-year OS rate than those who did not. Multivariate COX regression analysis indicated that TMRLS was an independent prognostic factor for OS. A nomogram for predicting OS based on TMRLS showed a significantly higher C-index and AUC than the ypTNM stage. The above results were also found in the QhUAH external validation cohort. CONCLUSION: TMRLS is a novel independent prognostic factor for GC who underwent NACT and a radical gastrectomy. Furthermore, the TMRLS-high group, who received postoperative AC, may achieve better survival outcomes. Notably, the predictive performance of the nomogram-TMRLS significantly outperformed that of the ypTNM stage.

Heart Sound Abnormality Detection From Multi-Institutional Collaboration: Introducing a Federated Learning Framework.

OBJECTIVE: Early diagnosis of cardiovascular diseases is a crucial task in medical practice. With the application of computer audition in the healthcare field, artificial intelligence (AI) has been applied to clinical non-invasive intelligent auscultation of heart sounds to provide rapid and effective pre-screening. However, AI models generally require large amounts of data which may cause privacy issues. Unfortunately, it is difficult to collect large amounts of healthcare data from a single centre. METHODS: In this study, we propose federated learning (FL) optimisation strategies for the practical application in multi-centre institutional heart sound databases. The horizontal FL is mainly employed to tackle the privacy problem by aligning the feature spaces of FL participating institutions without information leakage. In addition, techniques based on deep learning have poor interpretability due to their "black-box" property, which limits the feasibility of AI in real medical data. To this end, vertical FL is utilised to address the issues of model interpretability and data scarcity. CONCLUSION: Experimental results demonstrate that, the proposed FL framework can achieve good performance for heart sound abnormality detection by taking the personal privacy protection into account. Moreover, using the federated feature space is beneficial to balance the interpretability of the vertical FL and the privacy of the data. SIGNIFICANCE: This work realises the potential of FL from research to clinical practice, and is expected to have extensive application in the federated smart medical system.

The role of circular RNAs in regulating resistance to cancer immunotherapy: mechanisms and implications.

Cancer immunotherapy has rapidly transformed cancer treatment, yet resistance remains a significant hurdle, limiting its efficacy in many patients. Circular RNAs (circRNAs), a novel class of non-coding RNAs, have emerged as pivotal regulators of gene expression and cellular processes. Increasing evidence indicates their involvement in modulating resistance to cancer immunotherapy. Notably, certain circRNAs function as miRNA sponges or interact with proteins, influencing the expression of immune-related genes, including crucial immune checkpoint molecules. This, in turn, shapes the tumor microenvironment and significantly impacts the response to immunotherapy. In this comprehensive review, we explore the evolving role of circRNAs in orchestrating resistance to cancer immunotherapy, with a specific focus on their mechanisms in influencing immune checkpoint gene expression. Additionally, we underscore the potential of circRNAs as promising therapeutic targets to augment the effectiveness of cancer immunotherapy. Understanding the role of circRNAs in cancer immunotherapy resistance could contribute to the development of new therapeutic strategies to overcome resistance and improve patient outcomes.

Association between gut microbiota dysbiosis and poor functional outcomes in acute ischemic stroke patients with COVID-19 infection.

Acute ischemic stroke (AIS) patients with active COVID-19 infection often have more severe symptoms and worse recovery. COVID-19 infection can cause gut microbiota dysbiosis, which is also a risk factor for poor outcomes in AIS patients. However, the association between gut microbiota and functional outcomes among AIS patients with COVID-19 infection has not been fully clarified yet. In this study, we performed 16S rRNA gene sequencing to characterize the gut microbial community among AIS patients with acute COVID-19 infection, AIS patients with post-acute COVID-19 infection, and AIS patients without COVID-19 infection. We found that AIS patients with acute COVID-19 experienced poorer recovery and significant gut dysbiosis, characterized by higher levels of Enterobacteriaceae and lower levels of Ruminococcaceae and Lachnospiraceae. Furthermore, a shorter time window (less than 28 days) between COVID-19 infection and stroke was identified as a risk factor for poor functional outcomes in AIS patients with COVID-19, and the enrichment of Enterobacteriaceae was indicated as a mediator in the relationship between infection time window and poor stroke outcomes. Our findings highlight the importance of early intervention after COVID-19 infection, especially by regulating the gut microbiota, which plays a role in the prognosis of AIS patients with COVID-19 infection.IMPORTANCEThe gut microbiota plays an important role in the association between respiratory system and cerebrovascular system through the gut-lung axis and gut-brain axis. However, the specific connection between gut bacteria and the functional outcomes of acute ischemic stroke (AIS) patients with COVID-19 is not fully understood yet. In our study, we observed a significant decrease in bacterial diversity and shifts in the abundance of key bacterial families in AIS patients with acute COVID-19 infection. Furthermore, we identified that the time window was a critical influence factor for stroke outcomes, and the enrichment of Enterobacteriaceae acted as a mediator in the relationship between the infection time window and poor stroke outcomes. Our research provides a new perspective on the complex interplay among AIS, COVID-19 infection, and gut microbiota dysbiosis. Moreover, recognizing Enterobacteriaceae as a potential mediator of poor stroke prognosis offers a novel avenue for future exploration and therapeutic interventions.

Effect of free and bound proanthocyanidins from Chinese quince on heterocyclic aromatic amine formation and quality in fried chicken.

The abilities of Chinese quince free proanthocyanidins (FP) and bound proanthocyanidins (BP) at different levels (0.1%, 0.15%, and 0.3%) to mitigate heterocyclic aromatic amine (HAA) formation in fried chicken patties were investigated for the first time and compared with vitamin C (Vc). FP and BP reduced HAAs in a dose-dependent manner. Significantly, high concentrations of FP (0.3%) resulted in a reduction of PhIP, harman, and norharman levels by 59.84%, 22.91%, and 38.21%, respectively, in chicken patties. The addition of proanthocyanidins significantly (p < 0.05) reduced the weight loss of fried chicken patties. Furthermore, a positive correlation was observed among pH, weight loss, and total HAA formation in all three groups (FP, BP, and Vc). Multivariate analysis showed that FP had a more pronounced effect than BP from the perspective of enhancing the quality of fried chicken patties and reducing the formation of HAAs. These results indicate that proanthocyanidins, both BP and FP, but especially FP, from Chinese quince can inhibit the formation of carcinogenic HAAs when added to protein-rich foods that are subsequently fried.

Absolute Structures of a Mirror Pair of Infinite Na(H2O)4 + -Connected ε-Keggin-Al13 Species.

The absolute structures of a pair of infinite Na(H2O)4+-connected ε-Keggin-Al13 species (Na-ε-K-Al13) that were inversion structures and mirror images of each other were determined. Single crystals obtained by adding A2SO4 (A = Li, Na, K, Rb, or Cs) solution to NaOH-hydrolyzed AlCl3 solution were subjected to X-ray structure analyses. The statistical results for 36 single crystals showed that all the crystals had almost the same unit cell parameter, belonged to the same F4̅3m space group, and possessed the same structural formula [Na(H2O)4AlO4Al12(OH)24(H2O)12](SO4)4·10H2O. However, the crystals had two inverse absolute structures (denoted A and B), which had a crystallization ratio of 1:1. From Li+ to Cs+, with increasing volume of the cation coexisting in the mother solution, the degree of disorder of the four H2O molecules in the Na(H2O)4+ hydrated ion continuously decreased; they became ordered when the cation was Cs+. Absolute structures A and B are the first two infinite aluminum polycations connected by statistically occupied [(Na1/4)4(H2O)4]+ hydrated ions. The three-dimensional structure of the infinite Na-ε-K-Al13 species can be regarded as the assembly of finite ε-K-Al13 species linked by [(Na1/4)4(H2O)4]+ in a 1:1 ratio. In this assembly, each [(Na1/4)4(H2O)4]+ is connected to four ε-K-Al13 and each ε-K-Al13 is also connected to four [(Na1/4)4(H2O)4]+ in tetrahedral orientations to form a continuous rigid framework structure, which has an inverse spatial orientation between absolute structure A and B. This discovery clarifies that the ε-K-Al13 (or ε-K-GaAl12) species in Na[MO4Al12(OH)24(H2O)12](XO4)4·nH2O (M = Al, Ga; X = S, Se; n = 10-20) exists as discrete groups and deepens understanding of the formation and evolution process of polyaluminum species in forcibly hydrolyzed aluminum salt solution. The reason why Na+ statistically occupies the four sites was examined, and a formation and evolution mechanism of the infinite Na-ε-K-Al13 species was proposed.

Associations of per- and polyfluoroalkyl substances (PFAS) and their mixture with risk of rheumatoid arthritis in the U.S. adult population.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are known environmental contaminants with immunosuppressive properties. Their connection to rheumatoid arthritis (RA), a condition influenced by the immune system, is not well studied. This research explores the association between PFAS exposure and RA prevalence. METHODS: This research utilized data from the NHANES, encompassing a sample of 10,496 adults from the 2003-2018 cycles, focusing on serum levels of several PFAS. The presence of RA was determined based on self-reports. This study used multivariable logistic regression to assess the relationship between individual PFAS and RA risk, adjusting for covariates to calculate odds ratios (ORs). The combined effects of PFAS mixtures were evaluated using BKMR, WQS regression, and quantile g-computation. Additionally, sex-specific associations were explored through stratified analysis. RESULTS: Higher serum PFOA (OR = 0.88, 95% CI: 0.79, 0.98), PFHxS (OR = 0.91, 95% CI: 0.83, 1.00), PFNA (OR = 0.87, 95% CI: 0.77, 0.98), and PFDA (OR = 0.89, 95% CI: 0.81, 0.99) concentration was related to lower odds of RA. Sex-specific analysis in single chemical models indicated the significant inverse associations were only evident in females. BKMR did not show an obvious pattern of RA estimates across PFAS mixture. The outcomes of sex-stratified quantile g-computation demonstrated that an increase in PFAS mixture was associated with a decreased odds of RA in females (OR: 0.76, 95% CI: 0.62, 0.92). We identified a significant interaction term of the WQS*sex in the 100 repeated hold out WQS analysis. Notably, a higher concentration of the PFAS mixture was significantly associated with reduced odds of RA in females (mean OR = 0.93, 95% CI: 0.88, 0.98). CONCLUSIONS: This study indicates potential sex-specific associations of exposure to various individual PFAS and their mixtures with RA. Notably, the observed inverse relationships were statistically significant in females but not in males. These findings contribute to the growing body of evidence indicating that PFAS may have immunosuppressive effects.

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network.

Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.

High-Energy Symmetric Li-Ion Battery Enabled by Binder-Free FeOF-MXene Heterostructure with Doubly Matched Capacity and Kinetics.

Fluorides are viewed as promising conversion-type Li-ion battery cathodes to meet the desired high energy density. FeOF is a typical member of conversion-type fluorides, but its major drawback is sluggish kinetics upon deep discharge. Herein, a heterostructured FeOF-MXene composite (FeOF-MX) is demonstrated to overcome this limitation. The rationally designed FeOF-MX electrode features a microsphere morphology consisting of closely packed FeOF nanoparticles, providing fast transport pathways for lithium ions while a continuous wrapping network of MXene nanosheets ensures unobstructed electron transport, thus enabling high-rate lithium storage with enhanced pseudocapacitive contribution. In/ex situ characterization techniques and theoretical calculations, both reveal that the lithium storage mechanism in FeOF arises from a hybrid intercalation-conversion process, and strong interfacial interactions between FeOF and MXene promote Li-ion adsorption and migration. Remarkably, through demarcating the conversion-type reaction with a controlled potential window, a symmetric full battery with prelithiated FeOF-MX as both cathode and anode is fabricated, achieving a high energy density of 185.5 Wh kg-1 and impressive capacity retention of 88.9% after 3000 cycles at 1 A g-1. This work showcases an effective route toward high-performance MXene engineered fluoride-based electrodes and provides new insights into constructing symmetric batteries yet with high-energy/power densities.

Transcriptional Determinism and Stochasticity Contribute to the Complexity of Autism Associated SHANK Family Genes.

Precision of transcription is critical because transcriptional dysregulation is disease causing. Traditional methods of transcriptional profiling are inadequate to elucidate the full spectrum of the transcriptome, particularly for longer and less abundant mRNAs. SHANK3 is one of the most common autism causative genes. Twenty-four Shank3 mutant animal lines have been developed for autism modeling. However, their preclinical validity has been questioned due to incomplete Shank3 transcript structure. We applied an integrative approach combining cDNA-capture and long-read sequencing to profile the SHANK3 transcriptome in human and mice. We unexpectedly discovered an extremely complex SHANK3 transcriptome. Specific SHANK3 transcripts were altered in Shank3 mutant mice and postmortem brains tissues from individuals with ASD. The enhanced SHANK3 transcriptome significantly improved the detection rate for potential deleterious variants from genomics studies of neuropsychiatric disorders. Our findings suggest the stochastic transcription of genome associated with SHANK family genes.

Rydberg atom electric field sensing for metrology, communication and hybrid quantum systems.

Rydberg atoms-based electric field sensing has developed rapidly over the past decade. A variety of theoretical proposals and experiment configurations are suggested and realized to improve the measurement metrics, such as intensity sensitivity, bandwidth, phase, and accuracy. The Stark effect and electromagnetically induced transparency (EIT) or electromagnetically induced absorption (EIA) are fundamental physics principles behind the stage. Furthermore, various techniques such as amplitude- or frequency-modulation, optical homodyne read-out, microwave superheterodyne and frequency conversion based on multi-wave mixing in atoms are utilized to push the metrics into higher levels. In this review, different technologies and the corresponding metrics they had achieved were presented, hoping to inspire more possibilities in the improvement of metrics of Rydberg atom-based electric field sensing and broadness of application scenarios.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma.

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.

Autophagy-dependent lysosomal calcium overload and the ATP5B-regulated lysosomes-mitochondria calcium transmission induce liver insulin resistance under perfluorooctane sulfonate exposure.

Perfluorooctane sulfonate (PFOS), an officially listed persistent organic pollutant, is a widely distributed perfluoroalkyl substance. Epidemiological studies have shown that PFOS is intimately linked to the occurrence of insulin resistance (IR). However, the detailed mechanism remains obscure. In previous studies, we found that mitochondrial calcium overload was concerned with hepatic IR induced by PFOS. In this study, we found that PFOS exposure noticeably raised lysosomal calcium in L-02 hepatocytes from 0.5 h. In the PFOS-cultured L-02 cells, inhibiting autophagy alleviated lysosomal calcium overload. Inhibition of mitochondrial calcium uptake aggravated the accumulation of lysosomal calcium, while inhibition of lysosomal calcium outflowing reversed PFOS-induced mitochondrial calcium overload and IR. Transient receptor potential mucolipin 1 (TRPML1), the calcium output channel of lysosomes, interacted with voltage-dependent anion channel 1 (VDAC1), the calcium intake channel of mitochondria, in the PFOS-cultured cells. Moreover, we found that ATP synthase F1 subunit beta (ATP5B) interacted with TRPML1 and VDAC1 in the L-02 cells and the liver of mice under PFOS exposure. Inhibiting ATP5B expression or restraining the ATP5B on the plasma membrane reduced the interplay between TRPML1 and VDAC1, reversed the mitochondrial calcium overload and deteriorated the lysosomal calcium accumulation in the PFOS-cultured cells. Our research unveils the molecular regulation of the calcium crosstalk between lysosomes and mitochondria, and explains PFOS-induced IR in the context of activated autophagy.