mtPGS: Leverage multiple correlated traits for accurate polygenic score construction.

Accurate polygenic scores (PGSs) facilitate the genetic prediction of complex traits and aid in the development of personalized medicine. Here, we develop a statistical method called multi-trait assisted PGS (mtPGS), which can construct accurate PGSs for a target trait of interest by leveraging multiple traits relevant to the target trait. Specifically, mtPGS borrows SNP effect size similarity information between the target trait and its relevant traits to improve the effect size estimation on the target trait, thus achieving accurate PGSs. In the process, mtPGS flexibly models the shared genetic architecture between the target and the relevant traits to achieve robust performance, while explicitly accounting for the environmental covariance among them to accommodate different study designs with various sample overlap patterns. In addition, mtPGS uses only summary statistics as input and relies on a deterministic algorithm with several algebraic techniques for scalable computation. We evaluate the performance of mtPGS through comprehensive simulations and applications to 25 traits in the UK Biobank, where in the real data mtPGS achieves an average of 0.90%-52.91% accuracy gain compared to the state-of-the-art PGS methods. Overall, mtPGS represents an accurate, fast, and robust solution for PGS construction in biobank-scale datasets.

Microbiota derived D-malate inhibits skeletal muscle growth and angiogenesis during aging via acetylation of Cyclin A.

Metabolites derived from the intestinal microbiota play an important role in maintaining skeletal muscle growth, function, and metabolism. Here, we found that D-malate (DMA) is produced by mouse intestinal microorganisms and its levels increase during aging. Moreover, we observed that dietary supplementation of 2% DMA inhibits metabolism in mice, resulting in reduced muscle mass, strength, and the number of blood vessels, as well as the skeletal muscle fiber type I/IIb ratio. In vitro assays demonstrate that DMA decreases the proliferation of vascular endothelial cells and suppresses the formation of blood vessels. In vivo, we further demonstrated that boosting angiogenesis by muscular VEGFB injection rescues the inhibitory effects of D-malate on muscle mass and fiber area. By transcriptomics analysis, we identified that the mechanism underlying the effects of DMA depends on the elevated intracellular acetyl-CoA content and increased Cyclin A acetylation rather than redox balance. This study reveals a novel mechanism by which gut microbes impair muscle angiogenesis and may provide a therapeutic target for skeletal muscle dysfunction in cancer or aging.

l-2-Hydroxyglutarate contributes to tumor radioresistance through regulating the hypoxia-inducible factor-1α signaling pathway.

l-2-Hydroxyglutarate (l-2-HG) has been regarded as a tumor metabolite, and it plays a crucial role in adaptation of tumor cells to hypoxic conditions. However, the role of l-2-HG in tumor radioresistance and the underlying mechanism have not yet been revealed. Here, we found that l-2-HG exhibited to have radioresistance effect on U87 human glioblastoma cells, which could reduce DNA damage and apoptosis caused by irradiation, promote cell proliferation and migration, and impair G2/M phase arrest. Mechanistically, l-2-HG upregulated the protein level of hypoxia-inducible factor-1α (HIF-1α) and the expression levels of HIF-1α downstream target genes. The knockdown of l-2-hydroxyglutarate dehydrogenase (L2HGDH) gene promoted the tumor growth and proliferation of U87 cells in nude mice by increasing HIF-1α expression level in vivo. In addition, the low expression level of L2HGDH gene was correlated with the short survival of patients with glioma or kidney cancer. In conclusion, our study revealed the role and mechanism of l-2-HG in tumor radioresistance and may provide a new perspective for overcoming tumor radioresistance and broaden our comprehension of the role of metabolites in tumor microenvironment.

A Sequentially Activated Probe for Imaging of Superoxide Anion and Peroxynitrite in PC12 Cells under Oxidative Stress.

Superoxide anion (O2·-) and peroxynitrite (ONOO-), two important oxidants under oxidative stress, coexist in complex cell and organism systems, playing crucial roles in various physiological and pathological processes, particularly in neurodegenerative diseases. Despite the absence of robust molecular tools capable of simultaneously visualizing O2·- and ONOO- in biosystems, the relationship between these two species remains understudied. Herein, we present sequentially activated fluorescent probe, DHX-SP, which exhibits exceptional selectivity and sensitivity toward O2·- and ONOO-. This probe enables precise imaging of these species in living PC12 cells under oxidative stress conditions using distinct fluorescence signal combinations. Furthermore, the probe DHX-SP has the ability to visualize changes in O2·- and ONOO- levels during ferroptosis of PC12 cells and in the Parkinson's disease model. These findings establish a connection between the crosstalk of the phosphorus group of O2·- and ONOO- in PC12 cells under oxidative stress.

Exploration of Novel Meso-C═N-BODIPY-Based AIE Fluorescent Rotors with Large Stokes Shifts for Organelle-Viscosity Imaging.

The research on fluorescent rotors for viscosity has attracted extensive interest to better comprehend the close relationships of microviscosity variations with related diseases. Although scientists have made great efforts, fluorescent probes for cellular viscosity with both aggregation-induced emissions (AIEs) and large Stokes shifts to improve sensing properties have rarely been reported. Herein, we first report four new meso-C═N-substituted BODIPY-based rotors with large Stokes shifts, investigate their viscosity/AIE characteristics, and perform cellular imaging of the viscosity in subcellular organelles. Interestingly, the meso-C═N-phenyl group-substituted probe 6 showed an obvious 594 nm fluorescence enhancement in glycerol and a moderate 650 nm red AIE emission in water. Further, on attaching CF3 to the phenyl group, a similar phenomenon was observed for 7 with red-shifted emissions, attributed to the introduction of a phenyl group, which plays a key role in the red AIE emissions and large Stokes shifts. Comparatively, for phenyl-group-free probes, both the meso-C═N-trifluoroethyl group and thiazole-substituted probes (8 and 9) exhibited good viscosity-responsive properties, while no AIE was observed due to the absence of phenyl groups. For cellular experiments, 6 and 9 showed good lysosomal and mitochondrial targeting properties, respectively, and were further successfully used for imaging viscosity through the preincubation of monensin and lipopolysaccharide (LPS), indicating that C═N polar groups potentially work as rotatable moieties and organelle-targeting groups, and the targeting difference might be ascribed to increased charges of thiazole. Therefore, in this study, we investigated the structural relationships of four meso-C═N BODIPY-based rotors with respect to their viscosity/AIE characteristics, subcellular-targeting ability, and cellular imaging for viscosity, potentially serving as AIE fluorescent probes with large Stokes shifts for subcellular viscosity imaging.

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials.

BACKGROUND: Empirical evidence suggests that lack of blinding may be associated with biased estimates of treatment benefit in randomized controlled trials, but the influence on medication-related harms is not well-recognized. We aimed to investigate the association between blinding and clinical trial estimates of medication-related harms. METHODS: We searched PubMed from January 1, 2015, till January 1, 2020, for systematic reviews with meta-analyses of medication-related harms. Eligible meta-analyses must have contained trials both with and without blinding. Potential covariates that may confound effect estimates were addressed by restricting trials within the comparison or by hierarchical analysis of harmonized groups of meta-analyses (therefore harmonizing drug type, control, dosage, and registration status) across eligible meta-analyses. The weighted hierarchical linear regression was then used to estimate the differences in harm estimates (odds ratio, OR) between trials that lacked blinding and those that were blinded. The results were reported as the ratio of OR (ROR) with its 95% confidence interval (CI). RESULTS: We identified 629 meta-analyses of harms with 10,069 trials. We estimated a weighted average ROR of 0.68 (95% CI: 0.53 to 0.88, P < 0.01) among 82 trials in 20 meta-analyses where blinding of participants was lacking. With regard to lack of blinding of healthcare providers or outcomes assessors, the RORs were 0.68 (95% CI: 0.53 to 0.87, P < 0.01 from 81 trials in 22 meta-analyses) and 1.00 (95% CI: 0.94 to 1.07, P = 0.94 from 858 trials among 155 meta-analyses) respectively. Sensitivity analyses indicate that these findings are applicable to both objective and subjective outcomes. CONCLUSIONS: Lack of blinding of participants and health care providers in randomized controlled trials may underestimate medication-related harms. Adequate blinding in randomized trials, when feasible, may help safeguard against potential bias in estimating the effects of harms.

Impact of mutations in SARS-COV-2 spike on viral infectivity and antigenicity.

Since the outbreak of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, the viral genome has acquired numerous mutations with the potential to alter the viral infectivity and antigenicity. Part of mutations in SARS-CoV-2 spike protein has conferred virus the ability to spread more quickly and escape from the immune response caused by the monoclonal neutralizing antibody or vaccination. Herein, we summarize the spatiotemporal distribution of mutations in spike protein, and present recent efforts and progress in investigating the impacts of those mutations on viral infectivity and antigenicity. As mutations continue to emerge in SARS-CoV-2, we strive to provide systematic evaluation of mutations in spike protein, which is vitally important for the subsequent improvement of vaccine and therapeutic neutralizing antibody strategies.

CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data.

The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

Proteomic analysis of mitochondria associated membranes in renal ischemic reperfusion injury.

BACKGROUND: The mitochondria and endoplasmic reticulum (ER) communicate via contact sites known as mitochondria associated membranes (MAMs). Many important cellular functions such as bioenergetics, mitophagy, apoptosis, and calcium signaling are regulated by MAMs, which are thought to be closely related to ischemic reperfusion injury (IRI). However, there exists a gap in systematic proteomic research addressing the relationship between these cellular processes. METHODS: A 4D label free mass spectrometry-based proteomic analysis of mitochondria associated membranes (MAMs) from the human renal proximal tubular epithelial cell line (HK-2 cells) was conducted under both normal (N) and hypoxia/reperfusion (HR) conditions. Subsequent differential proteins analysis aimed to characterize disease-relevant signaling molecules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was applied to total proteins and differentially expressed proteins, encompassing Biological Process (BP), Cell Component (CC), Molecular Function (MF), and KEGG pathways. Further, Protein-Protein Interaction Network (PPI) exploration was carried out, leading to the identification of hub genes from differentially expressed proteins. Notably, Mitofusion 2 (MFN2) and BCL2/Adenovirus E1B 19-kDa interacting protein 3(BNIP3) were identified and subsequently validated both in vitro and in vivo. Finally, the impact of MFN2 on MAMs during hypoxia/reoxygenation was explored through regulation of gene expression. Subsequently, a comparative proteomics analysis was conducted between OE-MFN2 and normal HK-2 cells, providing further insights into the underlying mechanisms. RESULTS: A total of 4489 proteins were identified, with 3531 successfully quantified. GO/KEGG analysis revealed that MAM proteins were primarily associated with mitochondrial function and energy metabolism. Differential analysis between the two groups showed that 688 proteins in HR HK-2 cells exhibited significant changes in expression level with P-value < 0.05 and HR/N > 1.5 or HR/N < 0.66 set as the threshold criteria. Enrichment analysis of differentially expressed proteins unveiled biological processes such as mRNA splicing, apoptosis regulation, and cell division, while molecular functions were predominantly associated with energy metabolic activity. These proteins play key roles in the cellular responses during HR, offering insights into the IRI mechanisms and potential therapeutic targets. The validation of hub genes MFN2 and BNIP3 both in vitro and vivo was consistent with the proteomic findings. MFN2 demonstrated a protective role in maintaining the integrity of mitochondria associated membranes (MAMs) and mitigating mitochondrial damage following hypoxia/reoxygenation injury, this protective effect may be associated with the activation of the PI3K/AKT pathway. CONCLUSIONS: The proteins located in mitochondria associated membranes (MAMs) are implicated in crucial roles during renal ischemic reperfusion injury (IRI), with MFN2 playing a pivotal regulatory role in this context.

Protein aggregation in plant mitochondria lacking Lon1 inhibits translation and induces unfolded protein responses.

Loss of Lon1 led to stunted plant growth and accumulation of nuclear-encoded mitochondrial proteins including Lon1 substrates. However, an in-depth label-free proteomics quantification of mitochondrial proteins in lon1 revealed that the majority of mitochondrial-encoded proteins decreased in abundance. Additionally, we found that lon1 mutants contained protein aggregates in the mitochondrial that were enriched in metabolic enzymes, ribosomal subunits and PPR-containing proteins of the translation apparatus. These mutants exhibited reduced general mitochondrial translation as well as deficiencies in RNA splicing and editing. These findings support the role of Lon1 in maintaining a functional translational apparatus for mitochondrial-encoded gene translation. Transcriptome analysis of lon1 revealed a mitochondrial unfolded protein response reminiscent of the mitochondrial retrograde signalling dependent on the transcription factor ANAC017. Notably, lon1 mutants exhibited transiently elevated ethylene production, and the shortened hypocotyl observed in lon1 mutants during skotomorphogenesis was partially alleviated by ethylene inhibitors. Furthermore, the short root phenotype was partially ameliorated by introducing a mutation in the ethylene receptor ETR1. Interestingly, the upregulation of only a select few target genes was linked to ETR1-mediated ethylene signalling. Together this provides multiple steps in the link between loss of Lon1 and signalling responses to restore mitochondrial protein homoeostasis in plants.

Repeatability and reproducibility comparisons of liver IVIM imaging with free-breathing or respiratory-triggered sequences.

For liver intravoxel incoherent motion (IVIM) data acquisition, respiratory-triggering (RT) MRI is commonly used, and there are strong motivations to shorten the scan duration. For the same scan duration, more b values or higher numbers of excitations can be allowed for free-breathing (FB) imaging than for RT. We studied whether FB can be used to replace RT when careful IVIM image acquisition and image processing are conducted. MRI data of 22 healthy participants were acquired using a 3.0 T scanner. Diffusion imaging was based on a single-shot spin-echo-type echo-planar sequence and 16 b values of 0, 2, 4, 7, 10, 15, 20, 30, 46, 60, 72, 100, 150, 200, 400, and 600 s/mm2 . Each subject attended two scan sessions with an interval of 10-20 days. For each scan session, a subject was scanned twice, first with RT and then with FB. The mean image acquisition time was 5.4 min for FB and 10.8 min for RT. IVIM parameters were calculated with bi-exponential model segmented fitting with a threshold b value of 60 s/mm2 , and fitting started from b = 2 s/mm2 . There was no statistically significant difference between IVIM parameters measured with FB imaging or RT imaging. Perfusion fraction ICC (intraclass correlation coefficient) for FB imaging and RT imaging in the same scan session was 0.824. For perfusion fraction, wSD (within-subject standard deviation), BA (Bland-Altman) difference, BA 95% limit, and ICC were 0.022, 0.0001, -0.0635~0.0637, and 0.687 for FB and 0.031, 0.0122, -0.0723~0.0967, and 0.611 for RT. For Dslow (×10-3  s/mm2 ), wSD, BA difference, BA 95% limit, and ICC were 0.057, 0.0268, -0.1258~0.1793, and 0.471 for FB and 0.073, -0.0078, -0.2170-0.2014, and <0.4 for RT. The Dfast coefficient of variation was 0.20 for FB imaging and 0.28 for RT imaging. All reproducibility indicators slightly favored FB imaging.

Porcine HERC6 acts as major E3 ligase for ISGylation and is auto-ISGylated for effective ISGylation in porcine.

Interferon-stimulated gene product 15 (ISG15) can be conjugated to substrates through ISGylation. Currently, the E3 ligase for porcine ISGylation remains unclear. Here, we identified porcine HERC5 and HERC6 (pHERC5/6) as ISGylation E3 ligases with pHERC6 acting as a major one by reconstitution of porcine ISGylation system in HEK-293 T cell via co-transfecting E1, E2 and porcine ISG15(pISG15) genes. Meanwhile, our data demonstrated that co-transfection of pISG15 and pHERC5/6 was sufficient to confer ISGylation, suggesting E1 and E2 of ISGylation are interchangeable between human and porcine. Using an immunoprecipitation based ISGylation analysis, our data revealed pHERC6 was a substrate for ISGylation and confirmed that K707 and K993 of pHERC6 were auto-ISGylation sites. Mutation of these sites reduced pHERC6 half-life and inhibited ISGylation, suggesting that auto-ISGylation of pHERC6 was required for effective ISGylation. Conversely, sustained ISGylation induced by overexpression of pISG15 and pHERC6 could be inhibited by a well-defined porcine ISGylation antagonist, the ovarian tumor (OTU) protease domain of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV)-nsp2 and PRRSV-nsp1ß, further indicating such method could be used for identification of virus-encoded ISG15 antagonist. In conclusion, our study contributes new insights towards porcine ISGylation system and provides a novel tool for screening viral-encoded ISG15 antagonist.

Identification of triangular single crystals of transition metal dichalcogenides based on the detection algorithm.

The distinctive properties and facile integration of 2D materials hold the potential to offer promising avenues for the on-chip photonic devices, and the expeditious and nondestructive identification and localization of diverse fundamental building blocks become key prerequisites. Here, we present a methodology grounded in digital image processing and deep learning, which effectively achieves the detection and precise localization of four monolayer-thick triangular single crystals of transition metal dichalcogenides with the mean average precision above 90%, and the approach demonstrates robust recognition capabilities across varied imaging conditions encompassing both white light and monochromatic light. This stands poised to serve as a potent data-driven tool enhancing the characterizing efficiency and holds the potential to expedite research initiatives and applications founded on the utilization of 2D materials.

Liquid Crystalline Nanoparticles via Polymerization-Induced Self-Assembly: Morphology Evolution and Function Regulation.

Liquid crystalline nanoparticles (LC NPs) are a kind of polymer NPs with LC mesogens, which can form special anisotropic morphologies due to the influence of LC ordering. Owing to the stimuli-responsiveness of the LC blocks, LC NPs show abundant morphology evolution behaviors in response to external regulation. LC NPs have great application potential in nano-devices, drug delivery, special fibers and other fields. Polymerization-induced self-assembly (PISA) method can synthesize LC NPs at high solid content, reducing the harsh demand for reaction solvent of the LC polymers, being a better choice for large-scale production. In this review, we introduced recent research progress of PISA-LC NPs by dividing them into several parts according to the LC mesogen, and discussed the improvement of experimental conditions and the potential application of these polymers.

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer.

BACKGROUND: The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC). METHODS: Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis. RESULTS: A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001). CONCLUSION: Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.

The ADAM17 inhibitor ZLDI-8 sensitized hepatocellular carcinoma cells to sorafenib through Notch1-integrin ß-talk.

ZLDI-8 is an A disintegrin and metalloproteinase domain 17 (ADAM17) inhibitor that suppresses the shedding of Notch1 to the Notch1 intracellular domain (NICD). In previous studies, we found that ZLDI-8 was able to sensitize HCC to sorafenib, but the mechanism of action remains unclear. The sensitizing effects of ZLDI-8 were tested both in vitro and in vivo. EMT-related factors, sorafenib sensitivity-related proteins and ECM-related gene expression were assessed using immunohistochemistry, RTPCR and Western blotting. Knockdown assays were conducted to determine the relationship between the Notch and Integrin pathways. CoIP assays, nuclear and cytoplasmic fractionation and immunofluorescence colocalization were applied to explore the interaction between the Notch and Integrin pathways. Appropriate statistical analysis methods were used to assess the significance of the experimental results and to ensure the scientific validity and reliability of the experimental design. We found that ECM- and EMT-related proteins were downregulated after ZLDI-8 treatment (P<0.05). ZLDI-8 significantly downregulated Integrinß1 and Integrinß3 in HCC in vitro and in vivo (P<0.05), possibly through Foxc2-dependent regulation. Mechanistically, interfering with the expression of both Integrin-linked kinase (ILK) and the NICD may downregulate the expression of proteins targeted by sorafenib, thereby sensitizing cells to sorafenib. The retroregulation of Integrinß by ILK may occur through the interaction between the NICD and ILK and may be the result of the translocation of the complexus. Our study indicates that blocking the Notch pathway may affect Integrinß through crosstalk between the Notch1 and Integrinß/ILK signaling pathways, thus providing a potential therapeutic strategy for HCC.

Anaerolentibacter hominis gen. nov. sp. nov., Diplocloster hominis sp. nov. and Pilosibacter fragilis gen. nov. sp. nov., isolated from human faeces.

Three Gram-positive, obligately anaerobic bacterial strains, namely CSJ-1T, CSJ-3T, and CSJ-4T, were isolated from faeces of healthy persons. They were characterized through a combination of whole-genome sequencing, phenotypic traits, and metabolomic analysis. The genome sizes of CSJ-1T, CSJ-4T, and CSJ-3T were 3.3, 3.8, and 6.1 Mbp, with DNA G+C contents of 47.2, 48.3, and 48.8 mol%, respectively. Strain CSJ-3T was identified as representing a novel species, Diplocloster hominis (type strain CSJ-3T=CGMCC 1.18033T=JCM 36512T) of the genus Diplocloster. The 16S rRNA gene sequence similarity and whole genome average nucleotide identity (gANI) of CSJ-4T to its closest related species, Diplocloster modestus ASD 4241T, were 98.3 and 91.4 %, respectively. Comparative analysis of 16S rRNA gene sequences showed 91.6 % similarity between CSJ-1T and its closest phylogenetic neighbour, Catenibacillus scindens DSM 106146T, and 93.3 % similarity between CSJ-4T and its closest relative strain, Clostridium fessum SNUG30386T. Based on the polyphasic taxonomic results, we proposed two novel genera and three novel species. Strain CSJ-1T was identified as representing a novel species of novel genus, Anaerolentibacter hominis gen. nov. sp. nov. (type strain CSJ-1T=CGMCC 1.18046T=JCM 36511T) of the family Lachnospiraceae, and strain CSJ-4T was identified as representing a novel species of novel genus Pilosibacter fragilis gen. nov. sp. nov. (type strain CSJ-4T=CGMCC 1.18026T= JCM 36513T) of the family Clostridiaceae.

Accelerating evidence synthesis for safety assessment through ClinicalTrials.gov platform: a feasibility study.

BACKGROUND: Standard systematic review can be labor-intensive and time-consuming meaning that it can be difficult to provide timely evidence when there is an urgent public health emergency such as a pandemic. The ClinicalTrials.gov provides a promising way to accelerate evidence production. METHODS: We conducted a search on PubMed to gather systematic reviews containing a minimum of 5 studies focused on safety aspects derived from randomized controlled trials (RCTs) of pharmacological interventions, aiming to establish a real-world dataset. The registration information of each trial from eligible reviews was further collected and verified. The meta-analytic data were then re-analyzed by using 1) the full meta-analytic data with all trials and 2) emulated rapid data with trials that had been registered and posted results on ClinicalTrials.gov, under the same synthesis methods. The effect estimates of the full meta-analysis and rapid meta-analysis were then compared. RESULTS: The real-world dataset comprises 558 meta-analyses. Among them, 56 (10.0%) meta-analyses included RCTs that were not registered in ClinicalTrials.gov. For the remaining 502 meta-analyses, the median percentage of RCTs registered within each meta-analysis is 70.1% (interquartile range: 33.3% to 88.9%). Under a 20% bias threshold, rapid meta-analyses conducted through ClinicalTrials.gov achieved accurate point estimates ranging from 77.4% (using the MH model) to 83.1% (using the GLMM model); 91.0% to 95.3% of these analyses accurately predicted the direction of effects. CONCLUSIONS: Utilizing the ClinicalTrials.gov platform for safety assessment with a minimum of 5 RCTs holds significant potential for accelerating evidence synthesis to support urgent decision-making.

The quality of reporting in case reports of permanent neonatal diabetes mellitus: a cross-sectional study.

BACKGROUND: Although randomized trials and systematic reviews provide the best evidence to guide medical practice, many permanent neonatal diabetes mellitus (PNDM) studies have been published as case reports. However, the quality of these studies has not been assessed. The purpose of this study was to assess the extent to which the current case reports for PNDM comply with the Case Report (CARE) guidelines and to explore variables associated with the reporting. METHOD: Six English and four Chinese databases were searched from their inception to December 2022 for PNDM case reports. The 23 items CARE checklist was used to measure reporting quality. Primary outcome was the adherence rate of each CARE item and second outcome was total reporting score for each included PNDM case report. Linear and logistic regression analyses were used to examine the connection between five pre-specified predictor variables and the reporting quality. The predictor variables were impact factor of the published journal (<3.4 vs. ≥3.4, categorized according to the median), funding (yes vs. no), language (English vs. other language), published journal type (general vs. special) and year of publication (>2013 vs. ≤ 2013). RESULT: In total, 105 PNDM case reports were included in this study. None of the 105 PNDM case reports fulfilled all 23 items of the CARE checklist. The response rate of 11 items were under 50%, including prognostic characteristics presentation (0%), patient perspective interpretation (0%), diagnostic challenges statement (2.9%), clinical course summary (21.0%), diagnostic reasoning statement (22.9%), title identification (24.8%), case presentation (33.3%), disease history description (34.3%), strengths and limitations explanation (41.0%), informed consent statement (45.7%), and lesson elucidation (47.6%). This study identified that the PNDM case reports published in higher impact factor journals were statistically associated with a higher reporting quality. CONCLUSION: The reporting of case reports for PNDM is generally poor. As a result, this information may be misleading to providers, and the clinical applications may be detrimental to patient care. To improve reporting quality, journals should encourage strict adherence to the CARE guidelines.