Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder.

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides.

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.

Causal relationships between atopic dermatitis and psychiatric disorders: a bidirectional two-sample Mendelian randomization study.

BACKGROUND: Observational studies have suggested the potential associations between atopic dermatitis (AD) and psychiatric disorders. However, the causal relationship between them remains uncertain. This study aimed to evaluate the potential bidirectional causal relationship between AD and psychiatric disorders, including autism spectrum disorder (ASD), major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), anorexia nervosa (AN), Tourette syndrome (TS), schizophrenia, and anxiety. METHODS: Bidirectional two-sample Mendelian randomization (MR) was employed to elucidate the causality between AD and psychiatric disorders, using summary statistics from the most comprehensive genome-wide association studies conducted on AD (Ncases = 60,653, Ncontrols = 804,329). Psychiatric disorders were derived from the Psychiatric Genomics Consortium and were independent of AD data sources. The MR analysis entailed the implementation of multiple methods, including the inverse variance weighted method, MR-Egger regression method, weighted median method, simple mode method, and weighted mode method. RESULTS: Bidirectional two-sample MR analysis uncovered significant causal associations between AD and severe psychiatric disorders. Specifically, liability to AD was associated with increased risk of ADHD (OR = 1.116; 95% CI: [1.009, 1.234]; P = 0.033) and ASD (OR = 1.131; 95% CI: [1.023, 1.251]; P = 0.016). Additionally, evidence suggested that liability to ADHD (OR = 1.112; 95% CI: [1.094, 1.130]; P = 9.20e-40), liability to AN (OR = 1.1; 95% CI: [1.068, 1.134]; P = 4.45e-10) and liability to BD (OR = 1.067; 95% CI: [1.009, 1.128]; P = 0.023) were associated with an increased risk of AD. Only the causal association between AD and ASD was independent of the reverse effect bias. These causal associations were robust and not affected by biases of heterogeneity and horizontal pleiotropy. CONCLUSIONS: Our study emphasizes the significant causal association between AD and an increased risk of ASD, and also identifying BD and AN as risk factors for AD.

NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.

Host-microbiota interactions contributing to the heterogeneous tumor microenvironment in colorectal cancer.

Colorectal cancer (CRC) exhibits pronounced heterogeneity and is categorized into four widely accepted consensus molecular subtypes (CMSs) with unique tumor microenvironments (TMEs). However, the intricate landscape of the microbiota and host-microbiota interactions within these TMEs remains elusive. Using RNA-sequencing data from The Cancer Genome Atlas, we analyzed the host transcriptomes and intratumoral microbiome profiles of CRC samples. Distinct host genes and microbial genera were identified among the CMSs. Immune microenvironments were evaluated using CIBERSORTx and ESTIMATE, and microbial coabundance patterns were assessed with FastSpar. Through LASSO penalized regression, we explored host-microbiota associations for each CMS. Our analysis revealed distinct host gene signatures within the CMSs, which encompassed ferroptosis-related genes and specific immune microenvironments. Moreover, we identified 293, 153, 66, and 109 intratumoral microbial genera with differential abundance, and host-microbiota associations contributed to distinct TMEs, characterized by 829, 1,270, 634, and 1,882 robust gene-microbe associations for each CMS in CMS1-CMS4, respectively. CMS1 featured inflammation-related HSF1 activation and gene interactions within the endothelin pathway and Flammeovirga. Integrin-related genes displayed positive correlations with Sutterella in CMS2, whereas CMS3 spotlighted microbial associations with biosynthetic and metabolic pathways. In CMS4, genes involved in collagen biosynthesis showed positive associations with Sutterella, contributing to disruptions in homeostasis. Notably, immune-rich subtypes exhibited pronounced ferroptosis dysregulation, potentially linked to tissue microbial colonization. This comprehensive investigation delineates the diverse landscapes of the TME within each CMS, incorporating host genes, intratumoral microbiota, and their complex interactions. These findings shed light on previously uncharted mechanisms underpinning CRC heterogeneity and suggest potential therapeutic targets.NEW & NOTEWORTHY This study determined the following: 1) providing a comprehensive landscape of consensus molecular subtype (CMS)-specific tumor microenvironments (TMEs); 2) constructing CMS-specific networks, including host genes, intratumoral microbiota, and enriched pathways, analyzing their associations to uncover unique patterns that demonstrate the intricate interplay within the TME; and 3) revealing a connection between immune-rich subtypes and ferroptosis activation, suggesting a potential regulatory role of the microbiota in ferroptosis dysregulation of the colorectal cancer TME.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer.

Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

Evaluation of Chinese healthcare organizations' innovative performance in the digital health era.

Background: Healthcare workers' relationship with industry is not merely an agent mediating between consumer and vendor, but they are also inventors of the interventions they exist to deliver. Driven by the background of the digital health era, scientific research and technological (Sci-tech) innovation in the medical field are becoming more and more closely integrated. However, scholars shed little light on Sci-tech relevance to evaluate the innovation performance of healthcare organizations, a distinctive feature of healthcare organizations' innovation in the digital health era. Methods: Academic publications and patents are the manifestations of scientific research outputs and technological innovation outcomes, respectively. The study extracted data from publications and patents of 159 hospitals in China to evaluate their innovation performance. A total of 18 indicators were constructed, four of which were based on text similarity match and represented the Sci-tech relevance. We then applied factor analyses, analytical hierarchy process, and logistic regression to construct an evaluation model. We also examined the relationship between hospitals' innovation performance and their geographical locations. Finally, we implemented a mediation analysis to show the influence of digital health on hospital innovation performance. Results: A total of 16 indicators were involved, four of which represented the Sci-tech including the number of articles matched per patent (NAMP), the number of patents matched per article (NPMA), the proportion of highly matched patents (HMP), and the proportion of highly matched articles (HMA). Indicators of HMP (r = 0.52, P = 2.40 × 10-12), NAMP (r = 0.52, P = 2.54 × 10-12), and NPMA (r = 0.51, P = 5.53 × 10-12) showed a strong positive correlation with hospital innovation performance score. The evaluation model in this study was different from other Chinese existing hospital ranking systems. The regional innovation performance index (RIP) of healthcare organizations is highly correlated with per capita disposable income (r = 0.58) and regional GDP (r = 0.60). There was a positive correlation between digital health innovation performance scores and overall hospital innovation performance scores (r = 0.20). In addition, the hospitals' digital health innovation performance affected the hospital's overall innovation score with the mediation of Sci-tech relevance indicators (NPMA and HMA). The hospitals' digital health innovation performance score showed a significant correlation with the number of healthcare workers (r = 0.44). Conclusion: This study constructed an assessment model with four invented indicators focusing on Sci-tech relevance to provide a novel tool for researchers to evaluate the innovation performance of healthcare organizations in the digital health era. The regions with high RIP were concentrated on the eastern coastal areas with a higher level of economic development. Therefore, the promotion of scientific and technological innovation policies could be carried out in advance in areas with better economic development. The innovations in the digital health field by healthcare workers enhance the Sci-tech relevance in hospitals and boost their innovation performance. The development of digital health in hospitals depends on the input of medical personnel.

Microbial genes outperform species and SNVs as diagnostic markers for Crohn's disease on multicohort fecal metagenomes empowered by artificial intelligence.

Dysbiosis of gut microbial community is associated with the pathogenesis of CD and may serve as a promising noninvasive diagnostic tool. We aimed to compare the performances of the microbial markers of different biological levels by conducting a multidimensional analysis on the microbial metagenomes of CD. We collected fecal metagenomic datasets generated from eight cohorts that altogether include 870 CD patients and 548 healthy controls. Microbial alterations in CD patients were assessed at multidimensional levels including species, gene, and SNV level, and then diagnostic models were constructed using artificial intelligence algorithm. A total of 227 species, 1047 microbial genes, and 21,877 microbial SNVs were identified that differed between CD and controls. The species, gene, and SNV models achieved an average AUC of 0.97, 0.95, and 0.77, respectively. Notably, the gene model exhibited superior diagnostic capability, achieving an average AUC of 0.89 and 0.91 for internal and external validations, respectively. Moreover, the gene model was specific for CD against other microbiome-related diseases. Furthermore, we found that phosphotransferase system (PTS) contributed substantially to the diagnostic capability of the gene model. The outstanding performance of PTS was mainly explained by genes celB and manY, which demonstrated high predictabilities for CD with metagenomic datasets and was validated in an independent cohort by qRT-PCR analysis. Our global metagenomic analysis unravels the multidimensional alterations of the microbial communities in CD and identifies microbial genes as robust diagnostic biomarkers across geographically and culturally distinct cohorts.

The Microbial and Metabolic Signatures of Patients with Stable Coronary Artery Disease.

Growing evidence indicates an association between gut dysbiosis and coronary artery disease (CAD). However, the underlying mechanisms relevant to stable CAD (SCAD) pathogenesis, based on microbe-host metabolism interactions, are poorly explored. Here, we constructed a quasi-paired cohort based on the metabolic background of metagenomic samples by the propensity score matching (PSM) principle. Compared to healthy controls (HCs), gut microbiome disturbances were observed in SCAD patients, accompanied by differences in serum metabolome, mainly including elevated acylcarnitine and decreased unsaturated fatty acids in SCAD patients, which implicated the reduced cardiac fatty acid oxidation. Moreover, we identified Ralstonia pickettii as the core strain responsible for impaired microbial homeostasis in SCAD patientsm and may be partly responsible for the decrease of host unsaturated fatty acid levels. These findings highlight the importance of unsaturated fatty acids, R. pickettii, and their interaction in the pathogenesis of SCAD. IMPORTANCE Stable coronary artery disease (SCAD) is an early stage of CAD development. It is important to understand the pathogenesis of SCAD and find out the possible prevention and control targets for delaying the progression of CAD. We observed reduced levels of unsaturated fatty acids (USFAs) in SCAD patients. However, the reduced USFAs may be related to Ralstonia Pickettii, which was the core strain responsible for the impaired gut microbial function in SCAD patients, and further affected the host's cardiovascular health by altering amino acids, vitamin B metabolism, and LPS biosynthesis. These findings not only emphasized the importance of USFAs for cardiovascular health, but also R. Pickettii for maintaining microbial function homeostasis. More importantly, our study revealed, for the first time, that enriched R. Pickettii might be responsible for the reduced USFAs in SCAD patients, which adds new evidence on the role of altered gut microbiota for SCAD formation.

IBD Subtype-Regulators IFNG and GBP5 Identified by Causal Inference Drive More Intense Innate Immunity and Inflammatory Responses in CD Than Those in UC.

Background: The pathological differences between Crohn's disease (CD) and ulcerative colitis (UC) are substantial and unexplained yet. Here, we aimed to identify potential regulators that drive different pathogenesis of CD and UC by causal inference analysis of transcriptome data. Methods: Kruskal-Wallis and Dunnett's tests were performed to identify differentially expressed genes (DEGs) among CD patients, UC patients, and controls. Subsequently, differentially expressed pathways (DEPs) between CD and UC were identified and used to construct the interaction network of DEPs. Causal inference was performed to identify IBD subtype-regulators. The expression of the subtype-regulators and their downstream genes was validated by qRT-PCR with an independent cohort. Results: Compared with the control group, we identified 1,352 and 2,081 DEGs in CD and UC groups, respectively. Multiple DEPs between CD and UC were closely related to inflammation-related pathways, such as NOD-like receptor signaling, IL-17 signaling, and chemokine signaling pathways. Based on the priori interaction network of DEPs, causal inference analysis identified IFNG and GBP5 as IBD subtype-regulators. The results with the discovery cohort showed that the expression level of IFNG, GBP5, and NLRP3 was significantly higher in the CD group than that in the UC group. The regulation relationships among IFNG, GBP5, and NLRP3 were confirmed with transcriptome data from an independent cohort and validated by qRT-PCR. Conclusion: Our study suggests that IFNG and GBP5 were IBD subtype-regulators that trigger more intense innate immunity and inflammatory responses in CD than those in UC. Our findings reveal pathomechanical differences between CD and UC that may contribute to personalized treatment for CD and UC.

Multi-kingdom microbiota analyses identify bacterial-fungal interactions and biomarkers of colorectal cancer across cohorts.

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.

Putative Familial Transmissible Bacteria of Various Body Niches Link with Home Environment and Children's Immune Health.

Owing to their significant impact on children's long-term health, familial factors in the microbiomes of children have attracted increasing attention. However, the mechanism underlying microbiome transmission across generations remains unclear. A significantly lower alpha diversity was observed in the gut flora of children than in the gut flora of parents and grandparents; the alpha diversity of oral and skin microbiota was relatively higher in children than in their predecessors. Gut, oral, and skin microbiome was more similar between family members than between unrelated individuals. Meanwhile, 55.05%, 61.09%, and 76.73% of amplicon sequence variants (ASVs) in children's gut, oral, and skin microbiomes, respectively, were transmitted from all family members. Among these, the most transmissible ASVs belonged to Methylophilaceae, Solimonadaceae, Neisseriaceae, and Burkholderiaceae, which were defined as "putative familial transmissible bacteria." Furthermore, we found that the time spent with parents/grandparents and children's dietary preferences were important factors that influenced the proportion of the transmissible microbiome. Moreover, the majority of transmissible ASVs (85.06%), especially those of Ruminococcaceae and Lachnospiraceae, were significantly associated with the immune indices, such as CD3+, CD4+, CD8+, IgG, and IgA. IMPORTANCE Our study revealed that the children's microbiota was partially transmitted from their family members and specific putative transmissible ASVs were associated with the immune system of children. These findings suggest that home life plays a key role in the shaping of young children's microbiomes and has long-term health benefits.

Alterations in bile acid metabolizing gut microbiota and specific bile acid genes as a precision medicine to subclassify NAFLD.

Multiple mechanisms for the gut microbiome contributing to the pathogenesis of nonalcoholic fatty liver disease (NAFLD) have been implicated. Here, we aim to investigate the contribution and potential application for altered bile acids (BA) metabolizing microbes in NAFLD by post hoc analysis of whole metagenome sequencing (WMS) data. The discovery cohort consisted of 86 well-characterized patients with biopsy-proven NAFLD and 38 healthy controls. Assembly-based analysis was performed to identify BA-metabolizing microbes. Statistical tests, feature selection, and microbial coabundance analysis were integrated to identify microbial alterations and markers in NAFLD. An independent validation cohort was subjected to similar analyses. NAFLD microbiota exhibited decreased diversity and microbial associations. We established a classifier model with 53 differential species exhibiting a robust diagnostic accuracy [area under the receiver-operator curve (AUC) = 0.97] for detecting NAFLD. Next, eight important differential pathway markers including secondary BA biosynthesis were identified. Specifically, increased abundance of 7α-hydroxysteroid dehydrogenase (7α-HSDH), 3α-hydroxysteroid dehydrogenase (baiA), and bile acid-coenzyme A ligase (baiB) was detected in NAFLD. Furthermore, 10 of 50 BA-metabolizing metagenome-assembled genomes (MAGs) from Bacteroides ovatus and Eubacterium biforme were dominant in NAFLD and interplayed as a synergetic ecological guild. Importantly, two subtypes of patients with NAFLD were observed according to secondary BA metabolism potentials. Elevated capability for secondary BA biosynthesis was also observed in the validation cohort. These bacterial BA-metabolizing genes and microbes identified in this study may serve as disease markers. Microbial differences in BA-metabolism and strain-specific differences among patients highlight the potential for precision medicine in NAFLD treatment.

Identification of microbial markers across populations in early detection of colorectal cancer.

Associations between gut microbiota and colorectal cancer (CRC) have been widely investigated. However, the replicable markers for early-stage adenoma diagnosis across multiple populations remain elusive. Here, we perform an integrated analysis on 1056 public fecal samples, to identify adenoma-associated microbial markers for early detection of CRC. After adjusting for potential confounders, Random Forest classifiers are constructed with 11 markers to discriminate adenoma from control (area under the ROC curve (AUC) = 0.80), and 26 markers to discriminate adenoma from CRC (AUC = 0.89), respectively. Moreover, we validate the classifiers in two independent cohorts achieving AUCs of 0.78 and 0.84, respectively. Functional analysis reveals that the altered microbiome is characterized with increased ADP-L-glycero-beta-D-manno-heptose biosynthesis in adenoma and elevated menaquinone-10 biosynthesis in CRC. These findings are validated in a newly-collected cohort of 43 samples using quantitative real-time PCR. This work proves the validity of adenoma-specific markers across multi-populations, which would contribute to the early diagnosis and treatment of CRC.

Dr AFC: drug repositioning through anti-fibrosis characteristic.

Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into predictive modeling may lead to more success in drug repositioning. In this study, anti-fibrosis knowledge base was first built by collecting data from multiple resources. Both structural and biological profiles were then derived from the knowledge base and used for constructing machine learning models including Structural Profile Prediction Model (SPPM) and Biological Profile Prediction Model (BPPM). Three external public data sets were employed for validation purpose and further exploration of potential repositioning drugs in wider chemical space. The resulting SPPM and BPPM models achieve area under the receiver operating characteristic curve (area under the curve) of 0.879 and 0.972 in the training set, and 0.814 and 0.874 in the testing set. Additionally, our results also demonstrate that substantial amount of multi-targeting natural products possess notable anti-fibrosis characteristics and might serve as encouraging candidates in fibrosis treatment and drug repositioning. To leverage our methodology and findings, we developed repositioning prediction platform, drug repositioning based on anti-fibrosis characteristic that is freely accessible via https://www.biosino.org/drafc.

Risk factors for the critical illness in SARS-CoV-2 infection: a multicenter retrospective cohort study.

BACKGROUND: Prior studies reported that 5 ~ 32% COVID-19 patients were critically ill, a situation that poses great challenge for the management of the patients and ICU resources. We aim to identify independent risk factors to serve as prediction markers for critical illness of SARS-CoV-2 infection. METHODS: Fifty-two critical and 200 non-critical SARS-CoV-2 nucleic acid positive patients hospitalized in 15 hospitals outside Wuhan from January 19 to March 6, 2020 were enrolled in this study. Multivariable logistic regression and LASSO logistic regression were performed to identify independent risk factors for critical illness. RESULTS: Age older than 60 years, dyspnea, respiratory rate > 24 breaths per min, leukocytosis > 9.5 × 109/L, neutrophilia > 6.3 × 109/L, lymphopenia < 1.1 × 109/L, neutrophil-to-lymphocyte ratio > 3.53, fibrinogen > 4 g/L, d-dimer > 0.55 µg/mL, blood urea nitrogen > 7.1 mM, elevated aspartate transaminase, elevated alanine aminotransferase, total bilirubin > 21 µM, and Sequential Organ Failure Assessment (SOFA) score ≥ 2 were identified as risk factors for critical illness. LASSO logistic regression identified the best combination of risk factors as SOFA score, age, dyspnea, and leukocytosis. The Area Under the Receiver-Operator Curve values for the risk factors in predicting critical illness were 0.921 for SOFA score, 0.776 for age, 0.764 for dyspnea, 0.658 for leukocytosis, and 0.960 for the combination of the four risk factors. CONCLUSIONS: Our findings advocate the use of risk factors SOFA score ≥ 2, age > 60, dyspnea and leukocytosis > 9.5 × 109/L on admission, alone or in combination, to determine the optimal management of the patients and health care resources.

Phylogenetic analyses with systematic taxon sampling show that mitochondria branch within Alphaproteobacteria.

Though it is well accepted that mitochondria originated from an alphaproteobacteria-like ancestor, the phylogenetic relationship of the mitochondrial endosymbiont to extant Alphaproteobacteria is yet unresolved. The focus of much debate is whether the affinity between mitochondria and fast-evolving alphaproteobacterial lineages reflects true homology or artefacts. Approaches such as site exclusion have been claimed to mitigate compositional heterogeneity between taxa, but this comes at the cost of information loss, and the reliability of such methods is so far unproven. Here we demonstrate that site-exclusion methods produce erratic phylogenetic estimates of mitochondrial origin. Thus, previous phylogenetic hypotheses on the origin of mitochondria based on pretreated datasets should be re-evaluated. We applied alternative strategies to reduce phylogenetic noise by systematic taxon sampling while keeping site substitution information intact. Cross-validation based on a series of trees placed mitochondria robustly within Alphaproteobacteria, sharing an ancient common ancestor with Rickettsiales and currently unclassified marine lineages.