Progressive Pulmonary Fibrosis Is Caused by Elevated Mechanical Tension on Alveolar Stem Cells.

Fibrosis can develop in most organs and causes organ failure. The most common type of lung fibrosis is known as idiopathic pulmonary fibrosis, in which fibrosis starts at the lung periphery and then progresses toward the lung center, eventually causing respiratory failure. Little is known about the mechanisms underlying the pathogenesis and periphery-to-center progression of the disease. Here we discovered that loss of Cdc42 function in alveolar stem cells (AT2 cells) causes periphery-to-center progressive lung fibrosis. We further show that Cdc42-null AT2 cells in both post-pneumonectomy and untreated aged mice cannot regenerate new alveoli, resulting in sustained exposure of AT2 cells to elevated mechanical tension. We demonstrate that elevated mechanical tension activates a TGF-ß signaling loop in AT2 cells, which drives the periphery-to-center progression of lung fibrosis. Our study establishes a direct mechanistic link between impaired alveolar regeneration, mechanical tension, and progressive lung fibrosis.

Visualization of Membrane Pore in Live Cells Reveals a Dynamic-Pore Theory Governing Fusion and Endocytosis.

Fusion is thought to open a pore to release vesicular cargoes vital for many biological processes, including exocytosis, intracellular trafficking, fertilization, and viral entry. However, fusion pores have not been observed and thus proved in live cells. Its regulatory mechanisms and functions remain poorly understood. With super-resolution STED microscopy, we observed dynamic fusion pore behaviors in live (neuroendocrine) cells, including opening, expansion, constriction, and closure, where pore size may vary between 0 and 490 nm within 26 milliseconds to seconds (vesicle size: 180-720 nm). These pore dynamics crucially determine the efficiency of vesicular cargo release and vesicle retrieval. They are generated by competition between pore expansion and constriction. Pharmacology and mutation experiments suggest that expansion and constriction are mediated by F-actin-dependent membrane tension and calcium/dynamin, respectively. These findings provide the missing live-cell evidence, proving the fusion-pore hypothesis, and establish a live-cell dynamic-pore theory accounting for fusion, fission, and their regulation.

Non-syntrophic methanogenic hydrocarbon degradation by an archaeal species.

The methanogenic degradation of oil hydrocarbons can proceed through syntrophic partnerships of hydrocarbon-degrading bacteria and methanogenic archaea1-3. However, recent culture-independent studies have suggested that the archaeon 'Candidatus Methanoliparum' alone can combine the degradation of long-chain alkanes with methanogenesis4,5. Here we cultured Ca. Methanoliparum from a subsurface oil reservoir. Molecular analyses revealed that Ca. Methanoliparum contains and overexpresses genes encoding alkyl-coenzyme M reductases and methyl-coenzyme M reductases, the marker genes for archaeal multicarbon alkane and methane metabolism. Incubation experiments with different substrates and mass spectrometric detection of coenzyme-M-bound intermediates confirm that Ca. Methanoliparum thrives not only on a variety of long-chain alkanes, but also on n-alkylcyclohexanes and n-alkylbenzenes with long n-alkyl (C≥13) moieties. By contrast, short-chain alkanes (such as ethane to octane) or aromatics with short alkyl chains (C≤12) were not consumed. The wide distribution of Ca. Methanoliparum4-6 in oil-rich environments indicates that this alkylotrophic methanogen may have a crucial role in the transformation of hydrocarbons into methane.

The genomic origins of the Bronze Age Tarim Basin mummies.

The identity of the earliest inhabitants of Xinjiang, in the heart of Inner Asia, and the languages that they spoke have long been debated and remain contentious1. Here we present genomic data from 5 individuals dating to around 3000-2800 BC from the Dzungarian Basin and 13 individuals dating to around 2100-1700 BC from the Tarim Basin, representing the earliest yet discovered human remains from North and South Xinjiang, respectively. We find that the Early Bronze Age Dzungarian individuals exhibit a predominantly Afanasievo ancestry with an additional local contribution, and the Early-Middle Bronze Age Tarim individuals contain only a local ancestry. The Tarim individuals from the site of Xiaohe further exhibit strong evidence of milk proteins in their dental calculus, indicating a reliance on dairy pastoralism at the site since its founding. Our results do not support previous hypotheses for the origin of the Tarim mummies, who were argued to be Proto-Tocharian-speaking pastoralists descended from the Afanasievo1,2 or to have originated among the Bactria-Margiana Archaeological Complex3 or Inner Asian Mountain Corridor cultures4. Instead, although Tocharian may have been plausibly introduced to the Dzungarian Basin by Afanasievo migrants during the Early Bronze Age, we find that the earliest Tarim Basin cultures appear to have arisen from a genetically isolated local population that adopted neighbouring pastoralist and agriculturalist practices, which allowed them to settle and thrive along the shifting riverine oases of the Taklamakan Desert.

ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.

Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.

Monocytes Release Pro-Cathepsin D to Drive Blood-to-Brain Transcytosis in Diabetes.

BACKGROUND: Microvascular complications are the major outcome of type 2 diabetes progression, and the underlying mechanism remains to be determined. METHODS: High-throughput RNA sequencing was performed using human monocyte samples from controls and diabetes. The transgenic mice expressing human CTSD (cathepsin D) in the monocytes was constructed using CD68 promoter. In vivo 2-photon imaging, behavioral tests, immunofluorescence, transmission electron microscopy, Western blot analysis, vascular leakage assay, and single-cell RNA sequencing were performed to clarify the phenotype and elucidate the molecular mechanism. RESULTS: Monocytes expressed high-level CTSD in patients with type 2 diabetes. The transgenic mice expressing human CTSD in the monocytes showed increased brain microvascular permeability resembling the diabetic microvascular phenotype, accompanied by cognitive deficit. Mechanistically, the monocytes release nonenzymatic pro-CTSD to upregulate caveolin expression in brain endothelium triggering caveolae-mediated transcytosis, without affecting the paracellular route of brain microvasculature. The circulating pro-CTSD activated the caveolae-mediated transcytosis in brain endothelial cells via its binding with low-density LRP1 (lipoprotein receptor-related protein 1). Importantly, genetic ablation of CTSD in the monocytes exhibited a protective effect against the diabetes-enhanced brain microvascular transcytosis and the diabetes-induced cognitive impairment. CONCLUSIONS: These findings uncover the novel role of circulatory pro-CTSD from monocytes in the pathogenesis of cerebral microvascular lesions in diabetes. The circulatory pro-CTSD is a potential target for the intervention of microvascular complications in diabetes.

AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning.

Recently, peptide-based drugs have gained unprecedented interest in discovering and developing antifungal drugs due to their high efficacy, broad-spectrum activity, low toxicity and few side effects. However, it is time-consuming and expensive to identify antifungal peptides (AFPs) experimentally. Therefore, computational methods for accurately predicting AFPs are highly required. In this work, we develop AFP-MFL, a novel deep learning model that predicts AFPs only relying on peptide sequences without using any structural information. AFP-MFL first constructs comprehensive feature profiles of AFPs, including contextual semantic information derived from a pre-trained protein language model, evolutionary information, and physicochemical properties. Subsequently, the co-attention mechanism is utilized to integrate contextual semantic information with evolutionary information and physicochemical properties separately. Extensive experiments show that AFP-MFL outperforms state-of-the-art models on four independent test datasets. Furthermore, the SHAP method is employed to explore each feature contribution to the AFPs prediction. Finally, a user-friendly web server of the proposed AFP-MFL is developed and freely accessible at http://inner.wei-group.net/AFPMFL/, which can be considered as a powerful tool for the rapid screening and identification of novel AFPs.

Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data.

Due to the high heterogeneity and complexity of cancers, patients with different cancer subtypes often have distinct groups of genomic and clinical characteristics. Therefore, the discovery and identification of cancer subtypes are crucial to cancer diagnosis, prognosis and treatment. Recent technological advances have accelerated the increasing availability of multi-omics data for cancer subtyping. To take advantage of the complementary information from multi-omics data, it is necessary to develop computational models that can represent and integrate different layers of data into a single framework. Here, we propose a decoupled contrastive clustering method (Subtype-DCC) based on multi-omics data integration for clustering to identify cancer subtypes. The idea of contrastive learning is introduced into deep clustering based on deep neural networks to learn clustering-friendly representations. Experimental results demonstrate the superior performance of the proposed Subtype-DCC model in identifying cancer subtypes over the currently available state-of-the-art clustering methods. The strength of Subtype-DCC is also supported by the survival and clinical analysis.

STGIC: A graph and image convolution-based method for spatial transcriptomic clustering.

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors.

Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.

A noninvasive multianalytical approach establishment for risk assessment and gastric cancer screening.

Effective screening and early detection are critical to improve the prognosis of gastric cancer (GC). Our study aims to explore noninvasive multianalytical biomarkers and construct integrative models for preliminary risk assessment and GC detection. Whole genomewide methylation marker discovery was conducted with CpG tandems target amplification (CTTA) in cfDNA from large asymptomatic screening participants in a high-risk area of GC. The methylation and mutation candidates were validated simultaneously using one plasma from patients at various gastric lesion stages by multiplex profiling with Mutation Capsule Plus (MCP). Helicobacter pylori specific antibodies were detected with a recomLine assay. Integrated models were constructed and validated by the combination of multianalytical biomarkers. A total of 146 and 120 novel methylation markers were found in CpG islands and promoter regions across the genome with CTTA. The methylation markers together with the candidate mutations were validated with MCP and used to establish a 133-methylation-marker panel for risk assessment of suspicious precancerous lesions and GC cases and a 49-methylation-marker panel as well as a 144-amplicon-mutation panel for GC detection. An integrated model comprising both methylation and specific antibody panels performed better for risk assessment than a traditional model (AUC, 0.83 and 0.63, P < .001). A second model for GC detection integrating methylation and mutation panels also outperformed the traditional model (AUC, 0.82 and 0.68, P = .005). Our study established methylation, mutation and H. pylori-specific antibody panels and constructed two integrated models for risk assessment and GC screening. Our findings provide new insights for a more precise GC screening strategy in the future.

Large-Scale Chromosomal Changes Lead to Genome-Level Expression Alterations, Environmental Adaptation, and Speciation in the Gayal (Bos frontalis).

Determining the functional consequences of karyotypic changes is invariably challenging because evolution tends to obscure many of its own footprints, such as accumulated mutations, recombination events, and demographic perturbations. Here, we describe the assembly of a chromosome-level reference genome of the gayal (Bos frontalis) thereby revealing the structure, at base-pair-level resolution, of a telo/acrocentric-to-telo/acrocentric Robertsonian translocation (2;28) (T/A-to-T/A rob[2;28]). The absence of any reduction in the recombination rate or genetic introgression within the fusion region of gayal served to challenge the long-standing view of a role for fusion-induced meiotic dysfunction in speciation. The disproportionate increase noted in the distant interactions across pro-chr2 and pro-chr28, and the change in open-chromatin accessibility following rob(2;28), may, however, have led to the various gene expression irregularities observed in the gayal. Indeed, we found that many muscle-related genes, located synthetically on pro-chr2 and pro-chr28, exhibited significant changes in expression. This, combined with genome-scale structural variants and expression alterations in genes involved in myofibril composition, may have driven the rapid sarcomere adaptation of gayal to its rugged mountain habitat. Our findings not only suggest that large-scale chromosomal changes can lead to alterations in genome-level expression, thereby promoting both adaptation and speciation, but also illuminate novel avenues for studying the relationship between karyotype evolution and speciation.

Hydroxyphenylpyruvate Dioxygenase Is a Metabolic Immune Checkpoint for UTX-deficient Colorectal Cancer.

BACKGROUND & AIMS: Aberrant epigenetic events mediated by histone methyltransferases and demethylases contribute to malignant progression of colorectal cancer (CRC). However, the role of the histone demethylase ubiquitously transcribed tetratricopeptide repeat on chromosome X (UTX) in CRC remains poorly understood. METHODS: UTX conditional knockout mice and UTX-silenced MC38 cells were used to investigate UTX function in tumorigenesis and development of CRC. We performed time of flight mass cytometry to clarify the functional role of UTX in remodeling immune microenvironment of CRC. To investigate metabolic interaction between myeloid-derived suppressor cells (MDSCs) and CRC, we analyzed metabolomics data to identify metabolites secreted by UTX-deficient cancer cells and taken up by MDSCs. RESULTS: We unraveled a tyrosine-mediated metabolic symbiosis between MDSC and UTX-deficient CRC. Loss of UTX in CRC resulted in methylation of phenylalanine hydroxylase, preventing its degradation and subsequently increasing tyrosine synthesis and secretion. Tyrosine taken up by MDSCs was metabolized to homogentisic acid by hydroxyphenylpyruvate dioxygenase. Homogentisic acid modified protein inhibitor of activated STAT3 via carbonylation of Cys 176, and relieved the inhibitory effect of protein inhibitor of activated STAT3 on signal transducer and activator of transcription 5 transcriptional activity. This in turn, promoted MDSC survival and accumulation, enabling CRC cells to acquire invasive and metastatic traits. CONCLUSIONS: Collectively, these findings highlight hydroxyphenylpyruvate dioxygenase as a metabolic checkpoint to restrict immunosuppressive MDSCs and to counteract malignant progression of UTX-deficient CRC.

Targeted Phosphoproteomics of Human Saliva Extracellular Vesicles via Multiple Reaction Monitoring Cubed (MRM3).

Oral squamous cell carcinoma (OSCC) has become a global health problem due to its increasing incidence and high mortality rate. Early intervention through monitoring of the diagnostic biomarker levels during OSCC treatment is critical. Extracellular vesicles (EVs) are emerging surrogates in intercellular communication through transporting biomolecule cargo and have recently been identified as a potential source of biomarkers such as phosphoproteins for many diseases. Here, we developed a multiple reaction monitoring cubed (MRM3) method coupled with a novel sample preparation strategy, extracellular vesicles to phosphoproteins (EVTOP), to quantify phosphoproteins using a minimal amount of saliva (50 µL) samples from OSCC patients with high specificity and sensitivity. Our results established differential patterns in the phosphopeptide content of healthy, presurgery, and postsurgery OSCC patient groups. Notably, we discovered significantly increased salivary phosphorylated alpha-amylase (AMY) in the postsurgery group compared to the presurgery group. We hereby present the first targeted MS method with extremely high sensitivity for measuring endogenous phosphoproteins in human saliva EVs.

Spire2 and Rab11a synergistically activate myosin-5b motor function.

Cellular vesicle long-distance transport along the cytoplasmic actin network has recently been uncovered in several cell systems. In metaphase mouse oocytes, the motor protein myosin-5b (Myo5b) and the actin nucleation factor Spire are recruited to the Rab11a-positive vesicle membrane, forming a ternary complex of Myo5b/Spire/Rab11a that drives the vesicle long-distance transport to the oocyte cortex. However, the mechanism underlying the intermolecular regulation of the Myo5b/Spire/Rab11a complex remains unknown. In this study, we expressed and purified Myo5b, Spire2, and Rab11a proteins, and performed ATPase activity measurements, pulldown and single-molecule motility assays. Our results demonstrate that both Spire2 and Rab11a are required to activate Myo5b motor activity under physiological ionic conditions. The GTBM fragment of Spire2 stimulates the ATPase activity of Myo5b, while Rab11a enhances this activation. This activation occurs by disrupting the head-tail interaction of Myo5b. Furthermore, at the single-molecule level, we observed that the GTBM fragment of Spire2 and Rab11a coordinate to stimulate the Myo5b motility activity. Based on our results, we propose that upon association with the vesicle membrane, Myo5b, Spire2 and Rab11a form a ternary complex, and the inhibited Myo5b is synergistically activated by Spire2 and Rab11a, thereby triggering the long-distance transport of vesicles.

Monocytes release cystatin F dimer to associate with Aß and aggravate amyloid pathology and cognitive deficits in Alzheimer's disease.

BACKGROUND: Understanding the molecular mechanisms of Alzheimer's disease (AD) has important clinical implications for guiding therapy. Impaired amyloid beta (Aß) clearance is critical in the pathogenesis of sporadic AD, and blood monocytes play an important role in Aß clearance in the periphery. However, the mechanism underlying the defective phagocytosis of Aß by monocytes in AD remains unclear. METHODS: Initially, we collected whole blood samples from sporadic AD patients and isolated the monocytes for RNA sequencing analysis. By establishing APP/PS1 transgenic model mice with monocyte-specific cystatin F overexpression, we assessed the influence of monocyte-derived cystatin F on AD development. We further used a nondenaturing gel to identify the structure of the secreted cystatin F in plasma. Flow cytometry, enzyme-linked immunosorbent assays and laser scanning confocal microscopy were used to analyse the internalization of Aß by monocytes. Pull down assays, bimolecular fluorescence complementation assays and total internal reflection fluorescence microscopy were used to determine the interactions and potential interactional amino acids between the cystatin F protein and Aß. Finally, the cystatin F protein was purified and injected via the tail vein into 5XFAD mice to assess AD pathology. RESULTS: Our results demonstrated that the expression of the cystatin F protein was specifically increased in the monocytes of AD patients. Monocyte-derived cystatin F increased Aß deposition and exacerbated cognitive deficits in APP/PS1 mice. Furthermore, secreted cystatin F in the plasma of AD patients has a dimeric structure that is closely related to clinical signs of AD. Moreover, we noted that the cystatin F dimer blocks the phagocytosis of Aß by monocytes. Mechanistically, the cystatin F dimer physically interacts with Aß to inhibit its recognition and internalization by monocytes through certain amino acid interactions between the cystatin F dimer and Aß. We found that high levels of the cystatin F dimer protein in blood contributed to amyloid pathology and cognitive deficits as a risk factor in 5XFAD mice. CONCLUSIONS: Our findings highlight that the cystatin F dimer plays a crucial role in regulating Aß metabolism via its peripheral clearance pathway, providing us with a potential biomarker for diagnosis and potential target for therapeutic intervention.

Improving Buried Interface Contact by Bidentate Anchoring for Inverted Perovskite Solar Cells.

Nickel oxide (NiOx) is a promising hole transport layer (HTL) to fabricate efficient and large-scale inverted perovskite solar cells (PSCs) due to its low cost and superior chemical stability. However, inverted PSCs based on NiOx are still lagging behind that of other HTL because of the poor quality of buried interface contact. Herein, a bidentate ligand, 4,6-bis (diphenylphosphino) phenoxazine (2DPP), is used to regulate the NiOx surface and perovskite buried interface. The diphosphine Lewis base in the 2DPP molecule can coordinate both with NiOx and lead ions at NiOx/perovskite interface, leading to high-quality perovskite films with minimized defects. It is found that the inverted PSCs with 2DPP-modified buried interface exhibit double advantages of being both fast charge extraction and reduced nonradiative recombination, which is a combination of multiple factors including favorable energetic alignment, improved interface contact and strong binding between NiOx/2DPP and perovskite. The optimal PSC based on 2DPP modification yields a champion power conversion efficiency (PCE) of 21.9%. The unencapsulated PSC maintains above 75% of its initial PCE in the air with a relative humidity (RH) of 30-40% for 1000 h.

Population pharmacokinetics of daptomycin in critically ill patients receiving extracorporeal membrane oxygenation.

BACKGROUND: Daptomycin is widely used in critically ill patients for Gram-positive bacterial infections. Extracorporeal membrane oxygenation (ECMO) is increasingly used in this population and can potentially alter the pharmacokinetic (PK) behaviour of antibiotics. However, the effect of ECMO has not been evaluated in daptomycin. Our study aims to explore the effect of ECMO on daptomycin in critically ill patients through population pharmacokinetic (PopPK) analysis and to determine optimal dosage regimens based on both efficacy and safety considerations. METHODS: A prospective, open-label PK study was carried out in critically ill patients with or without ECMO. The total concentration of daptomycin was determined by UPLC-MS/MS. NONMEM was used for PopPK analysis and Monte Carlo simulations. RESULTS: Two hundred and ninety-three plasma samples were collected from 36 critically ill patients, 24 of whom received ECMO support. A two-compartment model with first-order elimination can best describe the PK of daptomycin. Creatinine clearance (CLCR) significantly affects the clearance of daptomycin while ECMO has no significant effect on the PK parameters. Monte Carlo simulations showed that, when the MICs for bacteria are  ≥1 mg/L, the currently recommended dosage regimen is insufficient for critically ill patients with CLCR > 30 mL/min. Our simulations suggest 10 mg/kg for patients with CLCR between 30 and 90 mL/min, and 12 mg/kg for patients with CLCR higher than 90 mL/min. CONCLUSIONS: This is the first PopPK model of daptomycin in ECMO patients. Optimal dosage regimens considering efficacy, safety, and pathogens were provided for critical patients based on pharmacokinetic-pharmacodynamic analysis.

SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants.

Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to predict the pathogenicity of deletions, insertions and duplications structural variations that occur in exons. We constructed three types of annotation features for each structural variation event in the ClinVar database. First, we treated complex structural variations as multiple consecutive single nucleotide polymorphisms events, and annotated them with correlation scores based on single nucleic acid substitutions, such as the impact on protein function. Second, we determined which genes the variation occurred in, and constructed gene-based annotation features for each structural variation. Third, we also calculated related features based on the transcriptome, such as histone signal, the overlap ratio of variation and genomic element definitions, etc. Finally, we employed a gradient boosting decision tree machine learning method, and used the deletions, insertions and duplications in the ClinVar database to train a structural variation pathogenicity prediction model SVPath. These structural variations are clearly indicated as pathogenic or benign. Experimental results show that our SVPath has achieved excellent predictive performance and outperforms existing state-of-the-art tools. SVPath is very promising in evaluating the clinical pathogenicity of structural variants. SVPath can be used in clinical research to predict the clinical significance of unknown pathogenicity and new structural variation, so as to explore the relationship between diseases and structural variations in a computational way.