Complete genome sequence and genome-wide transposon mutagenesis enable the determination of genes required for sodium hypochlorite tolerance and drug resistance in pathogen Aeromonas veronii GD2019.

Aeromonas veronii, a significant pathogen in aquatic environments, poses a substantial threat to both human and animal health, particularly in aquaculture. In this study, we isolated A. veronii strain GD2019 from diseased largemouth bass (Micropterus salmoides) during a severe outbreak of aeromonad septicemia in Guangdong Province, China. The complete genome sequence of A. veronii GD2019 revealed that GD2019 contains a single chromosome of 4703,168 bp with an average G+C content of 58.3%. Phylogenetic analyses indicated that GD2019 forms a separate sub-branch in A. veronii and comparative genomic analyses identified the existence of an intact Type III secretion system. Moreover, to investigate the genes that are required for the conditional fitness of A. veronii under various stresses, a high-density transposon insertion library in GD2019 was generated by a Tn5-based transposon and covers 6311 genomic loci including 4155 genes and 2156 intergenic regions. Leveraging this library, 630 genes were classified as essential genes for growth in rich-nutrient LB medium. Furthermore, the genes GE001863/NtrC and GE002550 were found to confer tolerance to sodium hypochlorite in A. veronii. GE002562 and GE002614 were associated with the resistance to carbenicillin. Collectively, our results provide abundant genetic information on A. veronii, shedding light on the pathogenetic mechanisms of Aeromonas.

Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interaction.

The development and function of the immune system are controlled by temporospatial gene expression programs, which are regulated by cis-regulatory elements, chromatin structure, and trans-acting factors. In this study, we cataloged the dynamic histone modifications and chromatin interactions at regulatory regions during T helper (Th) cell differentiation. Our data revealed that the H3K4me1 landscape established by MLL4 in naive CD4+ T cells is critical for restructuring the regulatory interaction network and orchestrating gene expression during the early phase of Th differentiation. GATA3 plays a crucial role in further configuring H3K4me1 modification and the chromatin interaction network during Th2 differentiation. Furthermore, we demonstrated that HSS3-anchored chromatin loops function to restrict the activity of the Th2 locus control region (LCR), thus coordinating the expression of Th2 cytokines. Our results provide insights into the mechanisms of how the interplay between histone modifications, chromatin looping, and trans-acting factors contributes to the differentiation of Th cells.

The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome.

The mechanisms underlying the many phenotypic manifestations of post-acute COVID-19 syndrome (PACS) are poorly understood. Herein, we characterized the gut microbiome in heterogeneous cohorts of subjects with PACS and developed a multi-label machine learning model for using the microbiome to predict specific symptoms. Our processed data covered 585 bacterial species and 500 microbial pathways, explaining 12.7% of the inter-individual variability in PACS. Three gut-microbiome-based enterotypes were identified in subjects with PACS and associated with different phenotypic manifestations. The trained model showed an accuracy of 0.89 in predicting individual symptoms of PACS in the test set and maintained a sensitivity of 86% and a specificity of 82% in predicting upcoming symptoms in an independent longitudinal cohort of subjects before they developed PACS. This study demonstrates that the gut microbiome is associated with phenotypic manifestations of PACS, which has potential clinical utility for the prediction and diagnosis of PACS.

Aging induces region-specific dysregulation of hormone synthesis in the primate adrenal gland.

Adrenal glands, vital for steroid secretion and the regulation of metabolism, stress responses and immune activation, experience age-related decline, impacting systemic health. However, the regulatory mechanisms underlying adrenal aging remain largely uninvestigated. Here we established a single-nucleus transcriptomic atlas of both young and aged primate suprarenal glands, identifying lipid metabolism and steroidogenic pathways as core processes impacted by aging. We found dysregulation in centripetal adrenocortical differentiation in aged adrenal tissues and cells in the zona reticularis region, responsible for producing dehydroepiandrosterone sulfate (DHEA-S), were highly susceptible to aging, reflected by senescence, exhaustion and disturbed hormone production. Remarkably, LDLR was downregulated in all cell types of the outer cortex, and its targeted inactivation in human adrenal cells compromised cholesterol uptake and secretion of dehydroepiandrosterone sulfate, as observed in aged primate adrenal glands. Our study provides crucial insights into endocrine physiology, holding therapeutic promise for addressing aging-related adrenal insufficiency and delaying systemic aging.

Phase Separation of Chromatin Structure-related Biomolecules: A Driving Force for Epigenetic Regulations.

Intracellularly, membrane-less organelles are formed by spontaneous fusion and fission of macro-molecules in a process called phase separation, which plays an essential role in cellular activities. In certain disease states, such as cancers and neurodegenerative diseases, aberrant phase separations take place and participate in disease progression. Chromatin structure-related proteins, based on their characteristics and upon external stimuli, phase separate to exert functions like genome assembly, transcription regulation, and signal transduction. Moreover, many chromatin structure-related proteins, such as histones, histone-modifying enzymes, DNA-modifying enzymes, and DNA methylation binding proteins, are involved in epigenetic regulations through phase separation. This review introduces phase separation and how phase separation affects epigenetics with a focus on chromatin structure-related molecules.

Sexually dimorphic renal expression of Klotho is directed by a kidney-specific distal enhancer responsive to HNF1b.

Transcription enhancers are genomic sequences regulating common and tissue-specific genes and their disruption can contribute to human disease development and progression. Klotho, a sexually dimorphic gene specifically expressed in kidney, is well-linked to kidney dysfunction and its deletion from the mouse genome leads to premature aging and death. However, the sexually dimorphic regulation of Klotho is not understood. Here, we characterize two candidate Klotho enhancers using H3K27ac epigenetic marks and transcription factor binding and investigate their functions, individually and combined, through CRISPR-Cas9 genome engineering. We discovered that only the distal (E1), but not the proximal (E2) candidate region constitutes a functional enhancer, with the double deletion not causing Klotho expression to further decrease. E1 activity is dependent on HNF1b transcription factor binding site within the enhancer. Further, E1 controls the sexual dimorphism of Klotho as evidenced by qPCR and RNA-seq. Despite the sharp reduction of Klotho mRNA, unlike germline Klotho knockouts, mutant mice presented normal phenotype, including weight, lifespan, and serum biochemistry. Lastly, only males lacking E1 display more prominent acute, but not chronic kidney injury responses, indicating a remarkable range of potential adaptation to isolated Klotho loss, especially in female E1 knockouts, retaining renoprotection despite over 80% Klotho reduction.

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. OBJECTIVE: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. METHODS: We retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. RESULTS: A total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. CONCLUSIONS: By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.

RGS4 controls airway hyperresponsiveness through GAP-independent mechanisms.

Regulators of G protein signaling (RGS) proteins constrain G protein-coupled receptor (GPCR)-mediated and other responses throughout the body primarily, but not exclusively, through their GTPase-activating protein activity. Asthma is a highly prevalent condition characterized by airway hyper-responsiveness (AHR) to environmental stimuli resulting in part from amplified GPCR-mediated airway smooth muscle contraction. Rgs2 or Rgs5 gene deletion in mice enhances AHR and airway smooth muscle contraction, whereas RGS4 KO mice unexpectedly have decreased AHR because of increased production of the bronchodilator prostaglandin E2 (PGE2) by lung epithelial cells. Here, we found that knockin mice harboring Rgs4 alleles encoding a point mutation (N128A) that sharply curtails RGS4 GTPase-activating protein activity had increased AHR, reduced airway PGE2 levels, and augmented GPCR-induced bronchoconstriction compared with either RGS4 KO mice or WT controls. RGS4 interacted with the p85α subunit of PI3K and inhibited PI3K-dependent PGE2 secretion elicited by transforming growth factor beta in airway epithelial cells. Together, these findings suggest that RGS4 affects asthma severity in part by regulating the airway inflammatory milieu in a G protein-independent manner.

A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients.

BACKGROUND: Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers. METHODS: We analyzed electronic medical records of 200,859 and 50,920 hospitalized patients within 24 h after being diagnosed with ARDS from the Philips eICU Institute (eICU-CRD) and the Medical Information Mart for Intensive Care (MIMIC-IV) dataset, respectively. Patients were sorted into three groups, including rapid death, long stay, and recovery, based on their condition or outcome between 24 and 72 h after ARDS diagnosis. To improve prediction performance and generalizability, a "pretrain-finetune" approach was applied, where we pretrained models on the eICU-CRD dataset and performed model finetuning using only a part (35%) of the MIMIC-IV dataset, and then tested the finetuned models on the remaining data from the MIMIC-IV dataset. Well-known machine-learning algorithms, including logistic regression, random forest, extreme gradient boosting, and multilayer perceptron neural networks, were employed to predict ARDS outcomes. Prediction performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: Results show that, in general, multilayer perceptron neural networks outperformed the other models. The use of pretrain-finetune yielded improved performance in predicting ARDS outcomes achieving a micro-AUC of 0.870 for the MIMIC-IV dataset, an improvement of 0.046 over the pretrain model. CONCLUSIONS: The proposed pretrain-finetune approach can effectively improve model generalizability from one to another dataset in ARDS prediction.

Prediction of survival and analysis of prognostic factors for patients with AFP negative hepatocellular carcinoma: a population-based study.

PURPOSE: Hepatocellular carcinoma (HCC) has a poor prognosis, and alpha-fetoprotein (AFP) is widely used to evaluate HCC. However, the proportion of AFP-negative individuals cannot be disregarded. This study aimed to establish a nomogram of risk factors affecting the prognosis of patients with AFP-negative HCC and to evaluate its diagnostic efficiency. PATIENTS AND METHODS: Data from patients with AFP-negative initial diagnosis of HCC (ANHC) between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and validation. We randomly divided overall cohort into the training or validation cohort (7:3). Univariate and multivariate Cox regression analysis were used to identify the risk factors. We constructed nomograms with overall survival (OS) and cancer-specific survival (CSS) as clinical endpoint events and constructed survival analysis by using Kaplan-Meier curve. Also, we conducted internal validation with Receiver Operating Characteristic (ROC) analysis and Decision curve analysis (DCA) to validate the clinical value of the model. RESULTS: This study included 1811 patients (1409 men; 64.7% were Caucasian; the average age was 64 years; 60.7% were married). In the multivariate analysis, the independent risk factors affecting prognosis were age, ethnicity, year of diagnosis, tumor size, tumor grade, surgery, chemotherapy, and radiotherapy. The nomogram-based model related C-indexes were 0.762 (95% confidence interval (CI): 0.752-0.772) and 0.752 (95% CI: 0.740-0.769) for predicting OS, and 0.785 (95% CI: 0.774-0.795) and 0.779 (95% CI: 0.762-0.795) for predicting CSS. The nomogram model showed that the predicted death was consistent with the actual value. The ROC analysis and DCA showed that the nomogram had good clinical value compared with TNM staging. CONCLUSION: The age(HR:1.012, 95% CI: 1.006-1.018, P-value < 0.001), ethnicity(African-American: HR:0.946, 95% CI: 0.783-1.212, P-value: 0.66; Others: HR:0.737, 95% CI: 0.613-0.887, P-value: 0.001), tumor diameter(HR:1.006, 95% CI: 1.004-1.008, P-value < 0.001), year of diagnosis (HR:0.852, 95% CI: 0.729-0.997, P-value: 0.046), tumor grade(Grade 2: HR:1.124, 95% CI: 0.953-1.326, P-value: 0.164; Grade 3: HR:1.984, 95% CI: 1.574-2.501, P-value < 0.001; Grade 4: HR:2.119, 95% CI: 1.115-4.027, P-value: 0.022), surgery(Liver Resection: HR:0.193, 95% CI: 0.160-0.234, P-value < 0.001; Liver Transplant: HR:0.102, 95% CI: 0.072-0.145, P-value < 0.001), chemotherapy(HR:0.561, 95% CI: 0.471-0.668, P-value < 0.001), and radiotherapy(HR:0.641, 95% CI: 0.463-0.887, P-value:0.007) were independent prognostic factors for patients with ANHC. We developed a nomogram model for predicting the OS and CSS of patients with ANHC, with a good predictive performance.

The Microtubule End Binding Protein Mal3 Is Essential for the Dynamic Assembly of Microtubules during Magnaporthe oryzae Growth and Pathogenesis.

Cytoskeletal microtubules (MTs) play crucial roles in many aspects of life processes in eukaryotic organisms. They dynamically assemble physiologically important MT arrays under different cell conditions. Currently, aspects of MT assembly underlying the development and pathogenesis of the model plant pathogenic fungus Magnaporthe oryzae (M. oryzae) are unclear. In this study, we characterized the MT plus end binding protein MoMal3 in M. oryzae. We found that knockout of MoMal3 results in defects in hyphal polar growth, appressorium-mediated host penetration and nucleus division. Using high-resolution live-cell imaging, we further found that the MoMal3 mutant assembled a rigid MT in parallel with the MT during hyphal polar growth, the cage-like network in the appressorium and the stick-like spindle in nuclear division. These aberrant MT organization patterns in the MoMal3 mutant impaired actin-based cell growth and host infection. Taken together, these findings showed that M. oryzae relies on MoMal3 to assemble elaborate MT arrays for growth and infection. The results also revealed the assembly mode of MTs in M. oryzae, indicating that MTs are pivotal for M. oryzae growth and host infection and may be new targets for devastating fungus control.

Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients.

Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (BIDMC) were first included in building the model. Two external validation populations included 3025 patients from the Philips eICU Research Institute and 2625 patients from Zhongda Hospital Southeast University. A total of 152 intelligently engineered predictors were extracted on an hourly basis. The prediction model referred to as DeepAKI was designed with the basic framework of squeeze-and-excitation networks with dilated causal convolution embedded. The integrated gradients method was utilized to explain the prediction model. When performed on the internal validation set (3175 [15 %] patients from BIDMC) and the two external validation sets, DeepAKI obtained the area under the curve of 0.799 (95 % CI 0.791-0.806), 0.763 (95 % CI 0.755-0.771) and 0.676 (95 % CI 0.668-0.684) for continuousAKI prediction, respectively. For model interpretability, clinically relevant important variables contributing to the model prediction were informed, and individual explanations along the timeline were explored to show how AKI risk arose. The potential threats to generalisability in deep learning-based models when deployed across health systems in real-world settings were analyzed.

Advancing injection force modeling and viscosity-dependent injectability evaluation for prefilled syringes.

The development of PFS requires a detailed understanding of the forces occurring during the drug administration process and patient's capability. This research describes an advanced mathematic injection force model that consisting hydrodynamic force and friction force. The hydrodynamic force follows the basic law of Hagen-Poiseuille but refines the modeling approach by delving into specific properties of drug viscosity (Newtonian and Shear-thinning) and syringe shape constant, while the friction force was accounted from empty barrel injection force. Additionally, we take actual temperature of injection into consideration, providing more accurate predication. The results show that the derivation of the needle dimension constant and the rheological behavior of the protein solutions are critical parameters. Also, the counter pressure generated by the tissue has been considered in actual administration to address the issue of the inaccuracies of current injection force evaluation preformed in air, especially when the viscosity of the injected drug solution is below 9.0 cP (injecting with 1 mL L PFS staked with 29G ½ inch needle). Human factor studies on patients' capability against medication viscosity filled the gap in design space of PFS drug product and available viscosity data in very early phase.

Integrin ß4 Regulates Cell Migration of Lung Adenocarcinoma Through FAK Signaling.

The role of the integrin family in malignancy has received increasing attention. Many studies have confirmed that ITGB4 could activate multiple signal pathways and promote cell migration in various cancers. However, the regulatory role of integrin ß4 (ITGB4) in lung adenocarcinoma (LUAD) is still unclear. Examination of the expression or survival analysis of ITGB4 in cells, pathological samples, and bioinformatics lung adenocarcinoma databases showed ITGB4 was highly expressed in LUAD and significantly associated with poor prognosis. Small interfering RNA and plasmids were performed to investigate the effect of changes in ITGB4 expression on lung adenocarcinoma. Focal adhesion kinase (FAK) inhibitor defactinib was used to further explore the molecular mechanism of ITGB4. The results showed depletion of ITGB4 inhibited migration and activation of FAK signaling pathways in lung adenocarcinoma cells. Moreover, increased ITGB4 expression activated FAK signaling and promoted cell migration, which can be reversed by defactinib. In addition, ITGB4 could interact with FAK in lung adenocarcinoma cells. ITGB4 may promote cell migration of lung adenocarcinoma through FAK signaling pathway and has the potential to be a biomarker for lung adenocarcinoma.

Statin therapy: a potential adjuvant to immunotherapies in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide and accounts for more than 90% of primary liver cancer. The advent of immune checkpoint inhibitor (ICI)-related therapies combined with angiogenesis inhibition has revolutionized the treatment of HCC in late-stage and unresectable HCC, as ICIs alone were disappointing in treating HCC. In addition to the altered immune microenvironment, abnormal lipid metabolism in the liver has been extensively characterized in various types of HCC. Stains are known for their cholesterol-lowering properties and their long history of treating hypercholesterolemia and reducing cardiovascular disease risk. Apart from ICI and other conventional therapies, statins are frequently used by advanced HCC patients with dyslipidemia, which is often marked by the abnormal accumulation of cholesterol and fatty acids in the liver. Supported by a body of preclinical and clinical studies, statins may unexpectedly enhance the efficacy of ICI therapy in HCC patients through the regulation of inflammatory responses and the immune microenvironment. This review discusses the abnormal changes in lipid metabolism in HCC, summarizes the clinical evidence and benefits of stain use in HCC, and prospects the possible mechanistic actions of statins in transforming the immune microenvironment in HCC when combined with immunotherapies. Consequently, the use of statin therapy may emerge as a novel and valuable adjuvant for immunotherapies in HCC.

Student Loss: Towards the Probability Assumption in Inaccurate Supervision.

Noisy labels are often encountered in datasets, but learning with them is challenging. Although natural discrepancies between clean and mislabeled samples in a noisy category exist, most techniques in this field still gather them indiscriminately, which leads to their performances being partially robust. In this paper, we reveal both empirically and theoretically that the learning robustness can be improved by assuming deep features with the same labels follow a student distribution, resulting in a more intuitive method called student loss. By embedding the student distribution and exploiting the sharpness of its curve, our method is naturally data-selective and can offer extra strength to resist mislabeled samples. This ability makes clean samples aggregate tightly in the center, while mislabeled samples scatter, even if they share the same label. Additionally, we employ the metric learning strategy and develop a large-margin student (LT) loss for better capability. It should be noted that our approach is the first work that adopts the prior probability assumption in feature representation to decrease the contributions of mislabeled samples. This strategy can enhance various losses to join the student loss family, even if they have been robust losses. Experiments demonstrate that our approach is more effective in inaccurate supervision. Enhanced LT losses significantly outperform various state-of-the-art methods in most cases. Even huge improvements of over 50% can be obtained under some conditions. An implementation of the main codes is available at https://github.com/Zhangshuojackpot/Student-Loss.

Fimbrin associated with Pmk1 to regulate the actin assembly during Magnaporthe oryzae hyphal growth and infection.

The dynamic assembly of the actin cytoskeleton is vital for Magnaporthe oryzae development and host infection. The actin-related protein MoFim1 is a key factor for organizing the M. oryzae actin cytoskeleton. Currently, how MoFim1 is regulated in M. oryzae to precisely rearrange the actin cytoskeleton is unclear. In this study, we found that MoFim1 associates with the M. oryzae mitogen-activated protein (MAP) kinase Pmk1 to regulate actin assembly. MoFim1 directly interacted with Pmk1, and the phosphorylation level of MoFim1 was decreased in Δpmk1, which led to a change in the subcellular distribution of MoFim1 in the hyphae of Δpmk1. Moreover, the actin cytoskeleton was aberrantly organized at the hyphal tip in the Δpmk1, which was similar to what was observed in the Δmofim1 during hyphal growth. Furthermore, phosphorylation analysis revealed that Pmk1 could phosphorylate MoFim1 at serine 94. Loss of phosphorylation of MoFim1 at serine 94 decreased actin bundling activity. Additionally, the expression of the site mutant of MoFim1 S94D (in which serine 94 was replaced with aspartate to mimic phosphorylation) in Δpmk1 could reverse the defects in actin organization and hyphal growth in Δpmk1. It also partially rescues the formation of appressorium failure in Δpmk1. Taken together, these findings suggest a regulatory mechanism in which Pmk1 phosphorylates MoFim1 to regulate the assembly of the actin cytoskeleton during hyphal development and pathogenesis.

Development and validation of a machine learning-based early prediction model for massive intraoperative bleeding in patients with primary hepatic malignancies.

BACKGROUND: Surgical resection remains the primary treatment for hepatic malignancies, and intraoperative bleeding is associated with a significantly increased risk of death. Therefore, accurate prediction of intraoperative bleeding risk in patients with hepatic malignancies is essential to preventing bleeding in advance and providing safer and more effective treatment. AIM: To develop a predictive model for intraoperative bleeding in primary hepatic malignancy patients for improving surgical planning and outcomes. METHODS: The retrospective analysis enrolled patients diagnosed with primary hepatic malignancies who underwent surgery at the Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University between 2010 and 2020. Logistic regression analysis was performed to identify potential risk factors for intraoperative bleeding. A prediction model was developed using Python programming language, and its accuracy was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Among 406 primary liver cancer patients, 16.0% (65/406) suffered massive intraoperative bleeding. Logistic regression analysis identified four variables as associated with intraoperative bleeding in these patients: ascites [odds ratio (OR): 22.839; P < 0.05], history of alcohol consumption (OR: 2.950; P < 0.015), TNM staging (OR: 2.441; P < 0.001), and albumin-bilirubin score (OR: 2.361; P < 0.001). These variables were used to construct the prediction model. The 406 patients were randomly assigned to a training set (70%) and a prediction set (30%). The area under the ROC curve values for the model's ability to predict intraoperative bleeding were 0.844 in the training set and 0.80 in the prediction set. CONCLUSION: The developed and validated model predicts significant intraoperative blood loss in primary hepatic malignancies using four preoperative clinical factors by considering four preoperative clinical factors: ascites, history of alcohol consumption, TNM staging, and albumin-bilirubin score. Consequently, this model holds promise for enhancing individualised surgical planning.

A single-nucleus transcriptomic atlas of primate liver aging uncovers the pro-senescence role of SREBP2 in hepatocytes.

Aging increases the risk of liver diseases and systemic susceptibility to aging-related diseases. However, cell type-specific changes and the underlying mechanism of liver aging in higher vertebrates remain incompletely characterized. Here, we constructed the first single-nucleus transcriptomic landscape of primate liver aging, in which we resolved cell type-specific gene expression fluctuation in hepatocytes across three liver zonations and detected aberrant cell-cell interactions between hepatocytes and niche cells. Upon in-depth dissection of this rich dataset, we identified impaired lipid metabolism and upregulation of chronic inflammation-related genes prominently associated with declined liver functions during aging. In particular, hyperactivated sterol regulatory element-binding protein (SREBP) signaling was a hallmark of the aged liver, and consequently, forced activation of SREBP2 in human primary hepatocytes recapitulated in vivo aging phenotypes, manifesting as impaired detoxification and accelerated cellular senescence. This study expands our knowledge of primate liver aging and informs the development of diagnostics and therapeutic interventions for liver aging and associated diseases.

A Review on Atrial Fibrillation Detection From Ambulatory ECG.

Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical symptoms of AF, the status of AF diagnosis and treatment is not optimal. Early AF screening or detection is essential. Internet of Things (IoT) and artificial intelligence (AI) technologies have driven the development of wearable electrocardiograph (ECG) devices used for health monitoring, which are an effective means of AF detection. The main challenges of AF analysis using ambulatory ECG include ECG signal quality assessment to select available ECG, the robust and accurate detection of QRS complex waves to monitor heart rate, and AF identification under the interference of abnormal ECG rhythm. Through ambulatory ECG measurement and intelligent detection technology, the probability of postoperative recurrence of AF can be reduced, and personalized treatment and management of patients with AF can be realized. This work describes the status of AF monitoring technology in terms of devices, algorithms, clinical applications, and future directions.