A systematic review and meta-analysis assessing the efficacy of Tuina for nocturnal enuresis in children.

Background: Desmopressin acetate (DDAVP) and behavioral interventions (BI) are cornerstone treatments for nocturnal enuresis (NE), a common pediatric urinary disorder. Despite the growing body of clinical studies on massage therapy for NE, comprehensive evaluations comparing the effectiveness of Tuina with DDAVP or BI are scarce. This study aims to explore the efficacy of Tuina in the management of NE. Methods: A systematic search of international databases was conducted using keywords pertinent to Tuina and NE. The inclusion criteria were limited to randomized controlled trials (RCTs) that evaluated NE treatments utilizing Tuina against DDAVP or BI. This meta-analysis included nine RCTs, comprising a total of 685 children, to assess both complete and partial response rates. Results: Tuina, used as a combination therapy, showed enhanced clinical efficacy and improved long-term outcomes relative to the control group. The therapeutic efficacy of Tuina was not directly associated with the number of acupoints used. Instead, employing between 11 and 20 acupoints appeared to have the most significant effect. Conclusion: The findings of this meta-analysis support the potential of Tuina as an adjunct therapy to enhance the sustained clinical efficacy of traditional treatments for NE. However, Tuina cannot completely replace DDAVP or BI in the management of NE. While this study illuminates some aspects of the effective acupoint combinations, further research is crucial to fully understand how Tuina acupoints contribute to the treatment of NE in children. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=442644, identifier CRD42023442644.

Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications.

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.

Hydrophilicity and Pore Structure Enhancement in Polyurethane/Silk Protein-Bismuth Halide Oxide Composite Films for Photocatalytic Degradation of Dye.

Polyurethane/silk protein-bismuth halide oxide composite films were fabricated using a blending-wet phase transformationin situsynthesis method. The crystal structure, micromorphology, and optical properties were conducted using XRD, SEM, and UV-Vis DRS characterize techniques. The results indicated that loaded silk protein enhanced the hydrophilicity and pore structure of the polyurethane composite films. The active species BiOX were observed to grow as nanosheets with high dispersion on the internal skeleton and silk protein surface of the polyurethane-silk protein film. The photocatalytic efficiency of BiOX/PU-SF composite films was assessed through the degradation of Rhodamine B under visible light irradiation. Among the tested films, the BiOBr/PU-SF composite exhibited the highest removal rate of RhB at 98.9%, surpassing the removal rates of 93.7% for the BiOCl/PU-SF composite and 85.6% for the BiOI/PU-SF composite. Furthermore, an active species capture test indicated that superoxide radical (•O2-) and hole (h+) species played a predominant role in the photodegradation process.

Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level.

Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80-90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.

Analysis of the association between urinary glyphosate exposure and fatty liver index: a study for US adults.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a prevalent condition that often goes unrecognized in the population, and many risk factors for this disease are not well understood. Glyphosate (GLY) is one of the most commonly used herbicides worldwide, and exposure to this chemical in the environment is significant. However, studies exploring the association between GLY exposure and NAFLD remain limited. Therefore, the aim of this study was to assess the association between urinary glyphosate (uGLY) level and fatty liver index (FLI) using data from the National Health and Nutrition Examination Survey (NHANES), which includes uGLY measurements. METHODS: The log function of uGLY was converted and expressed as Loge(uGLY) with the constant "e" as the base and used for subsequent analysis. The association between Loge(uGLY) (the independent variable) level and FLI (the dependent variable) was assessed by multiple linear regression analysis. Smoothing curve fitting and a generalized additive model were used to assess if there was a nonlinear association between the independent and the dependent variables. A subgroup analysis was used to find susceptible individuals of the association between the independent variable and the dependent variable. RESULTS: A final total of 2238 participants were included in this study. Participants were categorized into two groups (< -1.011 and ≥ -1.011 ng/ml) based on the median value of Loge(uGLY). A total of 1125 participants had Loge(uGLY) levels ≥ -1.011 ng/ml and higher FLI. The result of multiple linear regression analysis showed a positive association between Loge(uGLY) and FLI (Beta coefficient = 2.16, 95% CI: 0.71, 3.61). Smoothing curve fitting and threshold effect analysis indicated a linear association between Loge(uGLY) and FLI [likelihood ratio(LLR) = 0.364]. Subgroup analyses showed that the positive association between Loge(uGLY) and FLI was more pronounced in participants who were female, aged between 40 and 60 years, had borderline diabetes history, and without hypertension history. In addition, participants of races/ethnicities other than (Mexican American, White and Black) were particularly sensitive to the positive association between Loge(uGLY) and FLI. CONCLUSIONS: A positive linear association was found between Loge(uGLY) level and FLI. Participants who were female, 40 to 60 years old, and of ethnic backgrounds other than Mexican American, White, and Black, deserve more attention.

Enhancing cartilage regeneration and repair through bioactive and biomechanical modification of 3D acellular dermal matrix.

Acellular dermal matrix (ADM) shows promise for cartilage regeneration and repair. However, an effective decellularization technique that removes cellular components while preserving the extracellular matrix, the transformation of 2D-ADM into a suitable 3D scaffold with porosity and the enhancement of bioactive and biomechanical properties in the 3D-ADM scaffold are yet to be fully addressed. In this study, we present an innovative decellularization method involving 0.125% trypsin and 0.5% SDS and a 1% Triton X-100 solution for preparing ADM and converting 2D-ADM into 3D-ADM scaffolds. These scaffolds exhibit favorable physicochemical properties, exceptional biocompatibility and significant potential for driving cartilage regeneration in vitro and in vivo. To further enhance the cartilage regeneration potential of 3D-ADM scaffolds, we incorporated porcine-derived small intestinal submucosa (SIS) for bioactivity and calcium sulfate hemihydrate (CSH) for biomechanical reinforcement. The resulting 3D-ADM+SIS scaffolds displayed heightened biological activity, while the 3D-ADM+CSH scaffolds notably bolstered biomechanical strength. Both scaffold types showed promise for cartilage regeneration and repair in vitro and in vivo, with considerable improvements observed in repairing cartilage defects within a rabbit articular cartilage model. In summary, this research introduces a versatile 3D-ADM scaffold with customizable bioactive and biomechanical properties, poised to revolutionize the field of cartilage regeneration.

A human-specific insertion promotes cell proliferation and migration by enhancing TBC1D8B expression.

Human-specific insertions play important roles in human phenotypes and diseases. Here we reported a 446-bp insertion (Insert-446) in intron 11 of the TBC1D8B gene, located on chromosome X, and traced its origin to a portion of intron 6 of the EBF1 gene on chromosome 5. Interestingly, Insert-446 was present in the human Neanderthal and Denisovans genomes, and was fixed in humans after human-chimpanzee divergence. We have demonstrated that Insert-446 acts as an enhancer through binding transcript factors that promotes a higher expression of human TBC1D8B gene as compared with orthologs in macaques. In addition, over-expression TBC1D8B promoted cell proliferation and migration through "a dual finger" catalytic mechanism (Arg538 and Gln573) in the TBC domain in vitro and knockdown of TBC1D8B attenuated tumorigenesis in vivo. Knockout of Insert-446 prevented cell proliferation and migration in cancer and normal cells. Our results reveal that the human-specific Insert-446 promotes cell proliferation and migration by upregulating the expression of TBC1D8B gene. These findings provide a significant insight into the effects of human-specific insertions on evolution.

Construction and validation of a novel senescence-related risk score can help predict the prognosis and tumor microenvironment of gastric cancer patients and determine that STK40 can affect the ROS accumulation and proliferation ability of gastric cancer cells.

Background: In recent years, significant molecules have been found in gastric cancer research. However, their precise roles in the disease's development and progression remain unclear. Given gastric cancer's heterogeneity, prognosis prediction is challenging. This study aims to assess patient prognosis and immune therapy efficacy using multiple key molecules. Method: The WGCNA algorithm was employed to identify modules of genes closely related to immunity. A prognostic model was established using the Lasso-Cox method to predict patients' prognosis. Single-sample gene set enrichment analysis (ssGSEA) was conducted to quantify the relative abundance of 16 immune cell types and 13 immune functions. The relationship between risk score and TMB, MSI, immune checkpoints, and DNA repair genes was examined to predict the effectiveness of immune therapy. GO and KEGG analyses were performed to explore potential pathways and mechanisms associated with the genes of interest. Single-cell RNA sequencing was utilized to investigate the expression patterns of key genes in different cell types. Results: Through the WGCNA algorithm and Lasso-Cox algorithm selected KL, SERPINE1, and STK40 as key genes for constructing the prognostic model. The SSGSEA algorithm was employed to evaluate the infiltration of immune cells and immune functions in different patients, and their association with the risk score was investigated. The high-risk group exhibited lower TMB and MSI compared to the low-risk group. MMR and immune checkpoint analysis revealed a significant correlation between the risk score and multiple molecules. Finally, we also believe that STK40 is the most critical senescence-related gene affecting the progression of gastric cancer. In vitro experiments showed that ROS accumulation and cell proliferation ability of gastric cancer cells were impaired when STK40 was knocked down. Conclusion: In summary, we've constructed a prognostic model utilizing key genes for gastric cancer prognosis, while also showcasing its efficacy in predicting patient response to immunotherapy.

Oral microbiota disorder in GC patients revealed by 2b-RAD-M.

BACKGROUND: Microbiota alterations are linked with gastric cancer (GC). However, the relationship between the oral microbiota (especially oral fungi) and GC is not known. In this study, we aimed to apply 2b-RAD sequencing for Microbiome (2b-RAD-M) to characterize the oral microbiota in patients with GC. METHODS: We performed 2b-RAD-M analysis on the saliva and tongue coating of GC patients and healthy controls. We carried out diversity, relative abundance, and composition analyses of saliva and tongue coating bacteria and fungi in the two groups. In addition, indicator analysis, the Gini index, and the mean decrease accuracy were used to identify oral fungal indicators of GC. RESULTS: In this study, fungal imbalance in the saliva and tongue coating was observed in the GC group. At the species level, enriched Malassezia globosa (M. globosa) and decreased Saccharomyces cerevisiae (S. cerevisiae) were observed in saliva and tongue coating samples of the GC group. Random forest analysis indicated that M. globosa in saliva and tongue coating samples could serve as biomarkers to diagnose GC. The Gini index and mean decreases in accuracy for M. globosa in saliva and tongue coating samples were the largest. In addition, M. globosa in saliva and tongue coating samples classified GC from the control with areas under the receiver operating curve (AUCs) of 0.976 and 0.846, respectively. Further ecological analysis revealed correlations between oral bacteria and fungi. CONCLUSION: For the first time, our data suggested that changes in oral fungi between GC patients and controls may help deepen our understanding of the complex spectrum of the different microbiotas involved in GC development. Although the cohort size was small, this study is the first to use 2b-RAD-M to reveal that oral M. globosa can be a fungal biomarker for detecting GC.

Mild Photothermal Therapy Prevents Posterior Capsule Opacification through Cytoskeletal Remodeling.

Cataract is the first leading cause of blindness in the world and posterior capsule opacification (PCO) is the most common long-term complication after surgery. The primary pathogenic processes contributing to PCO are the proliferation and migration of residual lens epithelial cells (LECs). This study aimed to explore the mild photothermal effect on LECs. Interestingly, this work finds that the mild photothermal effect significantly inhibited the proliferation and migration of LECs. The live cell fluorescence imaging reveals that the remodeling of the actin cytoskeleton and cell morphology attributed to the inhibition effect. Further mechanistic studies at molecular level suggest that the mild photothermal effect can regulate the phosphorylation of ERM, YAP, and Cofilin and thereby affect the proliferation and migration of LECs. In order to explore the potential clinical application of mild photothermal therapy for PCO prevention, PDA/PVA gel rings with photothermal effect is prepared by the repeated freeze-thaw method and conducted experiments in vivo, which achieved favorable PCO prevention effect. Overall, this study shows that the mild photothermal effect can regulate the proliferation and migration of LECs through cytoskeletal remodeling and the results of experiments in vivo demonstrate that mild photothermal effect is a promising approach for PCO prevention.

Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in colon adenocarcinoma.

The interaction between the tumour and the surrounding microenvironment determines the malignant biological behaviour of the tumour. Cancer-associated fibroblasts (CAFs) coordinate crosstalk between cancer cells in the tumour immune microenvironment (TIME) and are extensively involved in tumour malignant behaviours, such as immune evasion, invasion and drug resistance. Here, we performed differential and prognostic analyses of genes associated with CAFs and constructed CAF-related signatures (CAFRs) to predict clinical outcomes in individuals with colon adenocarcinoma (COAD) based on machine learning algorithms. The CAFRs were further validated in an external independent cohort, GSE17538. Additionally, Cox regression, receiver operating characteristic (ROC) and clinical correlation analysis were utilised to systematically assess the CAFRs. Moreover, CIBERSORT, single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) analysis were utilised to characterise the TIME in patients with COAD. Microsatellite instability (MSI) and tumour mutation burden were also analysed. Furthermore, Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) elucidated the biological functions and signalling pathways involved in the CAFRs. Consensus clustering analysis was used for the immunological analysis of patients with COAD. Finally, the pRRophic algorithm was used for sensitivity analysis of common drugs. The CAFRs constructed herein can better predict the prognosis in COAD. The cluster analysis based on the CAFRs can effectively differentiate between immune 'hot' and 'cold' tumours, determine the beneficiaries of immune checkpoint inhibitors (ICIs) and provide insight into individualised treatment for COAD.

Association of weight-adjusted-waist index with non-alcoholic fatty liver disease and liver fibrosis: a cross-sectional study based on NHANES.

AIM: The purpose of this study was to explore the association of weight-adjusted-waist index (WWI) with non-alcoholic fatty liver disease (NAFLD) and liver fibrosis. METHODS: A cross-sectional study including 6587 participants was conducted in the National Health and Nutrition Examination Survey (NHANES). Multiple linear regression was used to validate the association of WWI with NAFLD and liver fibrosis, and smoothed curve fitting and threshold effect models were used to validate non-linear relationships. Subgroup analyses were used to verify the stability of the relationship between the independent and dependent variables in different populations. RESULTS: There was a positive association of WWI with NAFLD and liver fibrosis. In the model adjusted for all covariates, the effect values of WWI with NAFLD and liver fibrosis were (OR = 3.44, 95% CI: 3.09-3.82) and (OR = 2.40, 95% CI: 2.05-2.79), respectively. This positive correlation became more significant as WWI increased when WWI was presented in quartiles (P for trend < 0.01). Smoothed curve fitting and threshold effects analysis suggested a non-linear correlation between WWI and NAFLD (LLR < 0.01), with the positive correlation between WWI and NAFLD becoming more significant when WWI was less than 11.44 [5.93 (95% CI: 5.04-6.98)]. However, there was a linear correlation between WWI and liver fibrosis (LLR = 0.291). When subgroup analyses were performed by indicators such as age, race and gender, we found that the positive association between WWI and the dependent variables (NAFLD and liver fibrosis) was more pronounced in white male participants aged < 40 years. CONCLUSIONS: Among adults in the United States, WWI was positively associated with the prevalence of NAFLD and liver fibrosis. Participants with a WWI less than 11.44 should be cautious about the possibility of an increased risk of NAFLD development due to a higher WWI. Meanwhile, white males younger than 40 years of age should be more cautious about the higher risk of NAFLD and liver fibrosis that might be associated with an increased WWI.

Congenital cataracts affect the retinal visual cycle and mitochondrial function: A multi-omics study of GJA8 knockout rabbits.

Congenital cataracts are a threat to visual development in children, and the visual impairment persists after surgical treatment; however, the mechanisms involved remain unclear. Previous clinical studies have identified the effect of congenital cataracts on retinal morphology and function. To further understand the molecular mechanisms by which congenital cataracts affect retinal development, we analyzed retina samples from 7-week-old GJA8-knockout rabbits with congenital cataracts and controls by four-dimensional label-free quantification proteomics and untargeted metabolomics. Bioinformatics analysis of proteomic data showed that retinol metabolism, oxidative phosphorylation, and fatty acid degradation pathways were downregulated in the retinas of rabbits with congenital cataracts, indicating that their visual cycle and mitochondrial function were affected. Additional validation of differentially abundant proteins related to the visual cycle and mitochondrial function was performed using Parallel reaction monitoring and western blot experiments. Untargeted metabolome analysis showed significant upregulation of the antioxidant glutathione and ascorbic acid in the retinas of rabbits with congenital cataracts, indicating that their oxidative stress balance was not dysregulated. SIGNIFICANCE: Congenital cataracts in children can alter retinal structure and function, yet the mechanisms are uncertain. Here is the first study to use proteomics and metabolomics approaches to investigate the effects of congenital cataracts on retinal development in the early postnatal period. Our findings suggest that congenital cataracts have an impact on the retinal visual cycle and mitochondrial function. These findings give insight on the molecular pathways behind congenital cataract-induced visual function impairment in the early postnatal period.

N6AMT1 is a novel potential diagnostic, prognostic and immunotherapy response biomarker in pan-cancer.

BACKGROUND: The N-6-adenine-specific DNA methyltransferase 1 (N6AMT1) is the only writer responsible for DNA 6mA modifications. At present, its role in cancer is still unclear, and further systematic pan-cancer analysis is needed to explore its value in diagnosis, prognosis and immunological function. METHODS: The subcellular localization of N6AMT1 was explored by UniProt and HPA database. The expression data and prognosis data of N6AMT1 were downloaded from the UCSC (cohort: TCGA pan-cancer), and the diagnostic and prognostic value of N6AMT1 in pan-cancer was explored. The value of N6AMT1-guided immunotherapy was explored through three cohorts (GSE168204, GSE67501 and IMvigor210 cohort). The correlation between N6AMT1 expression and tumor immune microenvironment was explored using CIBERSORT and ESTIMATE calculation methods, combined with TISIDB database. The biological role of N6AMT1 in specific tumors was explored by GSEA method. Finally, we explored chemicals affecting N6AMT1 expression through the CTD. RESULTS: N6AMT1 is mainly localized in the nucleus and differentially expressed in 9 cancer types. In addition, N6AMT1 showed early diagnostic value in 7 cancers and showed potential prognostic value in multiple cancer types. We also demonstrated that N6AMT1 expression was significantly associated with immunomodulator-related molecules, infiltration of lymphocyte subsets, and biomarkers of immunotherapy response. Furthermore, we show that N6AMT1 is differentially expressed in the immunotherapy cohort. Finally, we explored 43 chemicals that can affect N6AMT1 expression. CONCLUSIONS: N6AMT1 has shown excellent diagnostic and prognostic capabilities in a variety of cancers, and it may reshape the tumor microenvironment and contribute to the ability to predict response to immunotherapy.

Combining WGCNA and machine learning to construct basement membrane-related gene index helps to predict the prognosis and tumor microenvironment of HCC patients and verifies the carcinogenesis of key gene CTSA.

Hepatocellular carcinoma (HCC) is a malignant tumor with high recurrence and metastasis rates and poor prognosis. Basement membrane is a ubiquitous extracellular matrix and is a key physical factor in cancer metastasis. Therefore, basement membrane-related genes may be new targets for the diagnosis and treatment of HCC. We systematically analyzed the expression pattern and prognostic value of basement membrane-related genes in HCC using the TCGA-HCC dataset, and constructed a new BMRGI based on WGCNA and machine learning. We used the HCC single-cell RNA-sequencing data in GSE146115 to describe the single-cell map of HCC, analyzed the interaction between different cell types, and explored the expression of model genes in different cell types. BMRGI can accurately predict the prognosis of HCC patients and was validated in the ICGC cohort. In addition, we also explored the underlying molecular mechanisms and tumor immune infiltration in different BMRGI subgroups, and confirmed the differences in response to immunotherapy in different BMRGI subgroups based on the TIDE algorithm. Then, we assessed the sensitivity of HCC patients to common drugs. In conclusion, our study provides a theoretical basis for the selection of immunotherapy and sensitive drugs in HCC patients. Finally, we also considered CTSA as the most critical basement membrane-related gene affecting HCC progression. In vitro experiments showed that the proliferation, migration and invasion abilities of HCC cells were significantly impaired when CTSA was knocked down.

Semi-surrogate modelling of droplets evaporation process via XGBoost integrated CFD simulations.

The applications of machine learning (ML) based approach are emerging as possible tools to accelerate CFD simulations. This study proposed a semi-surrogate model for CFD with integration of the cutting-edge ML algorithm, eXtreme Gradient Boosting (XGB), which enlightened a possible pathway to effectively and efficiently solve and predict those costly but highly repetitive fluid dynamics-related problems. Droplet evaporation, a complex but essential phenomenon in respiratory droplets transport, was studied as the practical case using the proposed model. Droplets evaporation and dynamic size distributions were firstly tracked under various combinations of indoor humidity and temperature using traditional Eulerian-Lagrangian CFD framework, followed by generating several datasets for XGB training. The trained XGB was then used to interpret the evaporated droplets size over time under new combinations of indoor conditions. Outcomes revealed that well-trained XGB-base semi-surrogate model was capable of interpreting complex non-linear relationships between droplets dynamic parameters (diameter and time) and indoor parameters (humidity and temperature). For each specific parameter, the predictive error of well-trained XGB could retain below 5 % and its prediction speed was found nearly 1 million times faster than that of new CFD simulations. Successful applications of XGB in conjunction with CFD demonstrated its great potential on providing rapid and more efficient predictions of complex, costly and repetitive fluid dynamics-related phenomenons (e.g. droplets evaporation). Also, the XGB predicted droplets evaporation data from this study could be further applied as initial conditions into new simulations via the User-defined function (UDF).

Validation of HIV-1 MA Shell Structural Arrangements and Env Protein Interactions Predict a Role of the MA Shell in Viral Maturation.

The molecular structure of the type 1 human immunodeficiency virus (HIV-1) is tightly linked to the mechanism of viral entry. The spike envelope (Env) glycoproteins and their interaction with the underlying matrix (MA) shell have emerged as key components of the entry mechanism. Microscopy evidence suggests that the MA shell does not span the entire inner lipid surface of the virus, producing a region of the virus that completely lacks an MA shell. Interestingly, evidence also suggests that Env proteins cluster during viral maturation and, thus, it is likely that this event takes place in the region of the virus that lacks an MA shell. We have previously called this part of the virus a fusion hub to highlight its importance during viral entry. While the structure of the MA shell is in contention due to the unaddressed inconsistencies between its reported hexagonal arrangement and the physical plausibility of such a structure, it is possible that a limited number of MA hexagons could form. In this study, we measured the size of the fusion hub by analysing the cryo-EM maps of eight HIV-1 particles and measured the size of the MA shell gap to be 66.3 nm ± 15.0 nm. We also validated the feasibility of the hexagonal MA shell arrangement in six reported structures and determined the plausible components of these structures that do not violate geometrical limitations. We also examined the cytosolic domain of Env proteins and discovered a possible interaction between adjacent Env proteins that could explain the stability of cluster formation. We present an updated HIV-1 model and postulate novel roles of the MA shell and Env structure.

An angiogenesis-related three-long non-coding ribonucleic acid signature predicts the immune landscape and prognosis in hepatocellular carcinoma.

The tumour microenvironment is a key determinant of the efficacy of immunotherapy. Angiogenesis is closely linked to tumour immunity. We aimed to screen long non-coding ribonucleic acids (lncRNAs) associated with angiogenesis to predict the prognosis of individuals with hepatocellular carcinoma (HCC) and characterise the tumour immune microenvironment (TIME). Patient data, including transcriptome and clinicopathological parameters, were retrieved from The Cancer Genome Atlas database. Moreover, co-expression algorithm was utilized to obtain angiogenesis-related lncRNAs. Additionally, survival-related lncRNAs were identified using Cox regression and the least absolute shrinkage and selection operator algorithm, which aided in constructing an angiogenesis-related lncRNA signature (ARLs). The ARLs was validated using Kaplan-Meier method, time-dependent receiver operating characteristic analyses, and Cox regression. Additionally, an independent external HCC dataset was used for further validation. Then, gene set enrichment analysis, immune landscape, and drug sensitivity analyses were implemented to explore the role of the ARLs. Finally, cluster analysis divided the entire HCC dataset into two clusters to distinguish different subtypes of TIME. This study provides insight into the involvement of angiogenesis-associated lncRNAs in predicting the TIME characteristics and prognosis for individuals with HCC. Furthermore, the developed ARLs and clusters can predict the prognosis and TIME characteristics in HCC, thereby aiding in selecting the appropriate therapeutic strategies involving immune checkpoint inhibitors and targeted drugs.

FBXO11 amplifies type I interferon signaling to exert antiviral effects by facilitating the assemble of TRAF3-TBK1-IRF3 complex.

As the key component of host innate antiviral immunity, type I interferons (IFN-Is) exert multiple antiviral effects by inducing hundreds of IFN-stimulated genes. However, the precise mechanism involved in host sensing of IFN-I signaling priming is particularly complex and remains incompletely resolved. This research identified F-box protein 11 (FBXO11), a component of the E3-ubiquitin ligase SKP/Cullin/F-box complex, acted as an important regulator of IFN-I signaling priming and antiviral process against several RNA/DNA viruses. FBXO11 functioned as an essential enhancer of IFN-I signaling by promoting the phosphorylation of TBK1 and IRF3. Mechanistically, FBXO11 facilitated the assembly of TRAF3-TBK1-IRF3 complex by mediating the K63 ubiquitination of TRAF3 in a NEDD8-dependent manner to amplify the activation of IFN-I signaling. Consistently, the NEDD8-activating enzyme inhibitor MLN4921 could act as a blocker for FBXO11-TRAF3-IFN-I axis of signaling. More significantly, examination of clinical samples of chronic hepatitis B virus (HBV) infection and public transcriptome database of severe acute respiratory syndrome coronavirus-2-, HBV-, and hepatitis C virus-infected human samples revealed that FBXO11 expression was positively correlated with the stage of disease course. Taken together, these findings suggest that FBXO11 is an amplifier of antiviral immune responses and might serve as a potential therapeutic target for a number of different viral diseases.

Metabolism-related signatures is correlated with poor prognosis and immune infiltration in hepatocellular carcinoma via multi-omics analysis and basic experiments.

Background: Metabolism is an ordered series of biological processes that occur in an organism. Altered cellular metabolism is often closely associated with the development of cancer. The aim of this research was to construct a model by multiple metabolism-related molecules to diagnose and assess the prognosis of patients. Method: WGCNA analysis was used to screen out differential genes. GO, KEGG are used to explore potential pathways and mechanisms. The lasso regression model was used to filter out the best indicators to construct the model. Single-sample GSEA (ssGSEA) assess immune cells abundance, immune terms in different Metabolism Index (MBI) groups. Human tissues and cells were used to verify the expression of key genes. Result: WGCNA clustering grouped genes into 5 modules, of which 90 genes from the MEbrown module were selected for subsequent analysis. GO analysis was found that BP mainly has mitotic nuclear division, while KEGG pathway is enriched to Cell cycle, Cellular senescence. Mutation analysis revealed that the frequency of TP53 mutations was much higher in samples from the high MBI group than in the low MBI group. Immunoassay revealed that patients with higher MBI have higher macrophage and Regulatory T cells (Treg) abundance, while NK cells were lowly expressed in the high MBI group. RT-qPCR and immunohistochemistry (IHC) revealed that the hub genes expression is higher in cancer tissues. The expression in hepatocellular carcinoma cells was also much higher than that in normal hepatocytes. Conclusion: In conclusion, a metabolism-related model was constructed that can be used to estimate the prognosis of hepatocellular carcinoma, and the clinical treatment of different hepatocellular carcinoma patients with medications was guided.