Structure of Plasmodium falciparum Rh5-CyRPA-Ripr invasion complex.

Plasmodium falciparum causes the severe form of malaria that has high levels of mortality in humans. Blood-stage merozoites of P. falciparum invade erythrocytes, and this requires interactions between multiple ligands from the parasite and receptors in hosts. These interactions include the binding of the Rh5-CyRPA-Ripr complex with the erythrocyte receptor basigin1,2, which is an essential step for entry into human erythrocytes. Here we show that the Rh5-CyRPA-Ripr complex binds the erythrocyte cell line JK-1 significantly better than does Rh5 alone, and that this binding occurs through the insertion of Rh5 and Ripr into host membranes as a complex with high molecular weight. We report a cryo-electron microscopy structure of the Rh5-CyRPA-Ripr complex at subnanometre resolution, which reveals the organization of this essential invasion complex and the mode of interactions between members of the complex, and shows that CyRPA is a critical mediator of complex assembly. Our structure identifies blades 4-6 of the ß-propeller of CyRPA as contact sites for Rh5 and Ripr. The limited contacts between Rh5-CyRPA and CyRPA-Ripr are consistent with the dissociation of Rh5 and Ripr from CyRPA for membrane insertion. A comparision of the crystal structure of Rh5-basigin with the cryo-electron microscopy structure of Rh5-CyRPA-Ripr suggests that Rh5 and Ripr are positioned parallel to the erythrocyte membrane before membrane insertion. This provides information on the function of this complex, and thereby provides insights into invasion by P. falciparum.

Bestrophin3 Deficiency in Vascular Smooth Muscle Cells Activates MEKK2/3-MAPK Signaling to Trigger Spontaneous Aortic Dissection.

BACKGROUND: Aortic dissection (AD) is a fatal cardiovascular disorder without effective medications due to unclear pathogenic mechanisms. Bestrophin3 (Best3), the predominant isoform of bestrophin family in vessels, has emerged as critical for vascular pathological processes. However, the contribution of Best3 to vascular diseases remains elusive. METHODS: Smooth muscle cell-specific and endothelial cell-specific Best3 knockout mice (Best3SMKO and Best3ECKO, respectively) were engineered to investigate the role of Best3 in vascular pathophysiology. Functional studies, single-cell RNA sequencing, proteomics analysis, and coimmunoprecipitation coupled with mass spectrometry were performed to evaluate the function of Best3 in vessels. RESULTS: Best3 expression in aortas of human AD samples and mouse AD models was decreased. Best3SMKO but not Best3ECKO mice spontaneously developed AD with age, and the incidence reached 48% at 72 weeks of age. Reanalysis of single-cell transcriptome data revealed that reduction of fibromyocytes, a fibroblast-like smooth muscle cell cluster, was a typical feature of human ascending AD and aneurysm. Consistently, Best3 deficiency in smooth muscle cells decreased the number of fibromyocytes. Mechanistically, Best3 interacted with both MEKK2 and MEKK3, and this interaction inhibited phosphorylation of MEKK2 at serine153 and MEKK3 at serine61. Best3 deficiency induced phosphorylation-dependent inhibition of ubiquitination and protein turnover of MEKK2/3, thereby activating the downstream mitogen-activated protein kinase signaling cascade. Furthermore, restoration of Best3 or inhibition of MEKK2/3 prevented AD progression in angiotensin II-infused Best3SMKO and ApoE-/- mice. CONCLUSIONS: These findings unveil a critical role of Best3 in regulating smooth muscle cell phenotypic switch and aortic structural integrity through controlling MEKK2/3 degradation. Best3-MEKK2/3 signaling represents a novel therapeutic target for AD.

Multimodal representation learning for predicting molecule-disease relations.

MOTIVATION: Predicting molecule-disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule-molecule, molecule-disease and disease-disease semantic dependencies can potentially improve prediction performance. METHODS: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule-disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. RESULTS: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Kupffer cell pyroptosis mediated by METTL3 contributes to the progression of alcoholic steatohepatitis.

Chronic alcohol consumption is a major risk factor for alcoholic steatohepatitis (ASH). Previous studies have shown that direct injury of hepatocytes is the key factor in its occurrence and development. However, our study shows that the role of Kupffer cells in ASH cannot be ignored. We isolated Kupffer cells from the livers of ASH mice and found that alcohol consumption induced Kupffer cell pyroptosis and increased the release of interleukin-1ß (IL-1ß). Furthermore, we screened the related m6A enzyme methyltransferase-like 3 (METTL3) from liver Kupffer cells, and found that silencing METTL3 alleviated inflammatory cytokine eruption by Kupffer cell pyroptosis in ASH mice. In vitro, we silenced METTL3 with lentivirus in BMDMs and RAW264.7 cells and confirmed that METTL3 could reduce pyroptosis by influencing the splicing of pri-miR-34A. Together, our results revealed a critical role of KC pyroptosis in ASH and highlighted the mechanism by which METLL3 relieves cell pyroptosis, which could be a promising therapeutic strategy for ASH.

Ruegeria marisflavi sp. nov. and Ruegeria aquimaris sp. nov., isolated from seawater of the Yellow Sea.

Two novel Gram-stain-negative, aerobic, non-motile and rod-shaped bacteria, designated as WL0004T and XHP0148T, were isolated from seawater samples collected from the coastal areas of Nantong and Lianyungang, PR China, respectively. Both strains were found to grow at 10-42 °C (optimum, 37 °C) and with 2.0-5.0 % (w/v) NaCl (optimum, 3.0 %). Strain WL0004T grew at pH 6.0-9.0 (optimum, pH 7.0-8.0), while XHP0148T grew at pH 6.0-10.0 (optimum, pH 7.0-8.0). The major cellular fatty acids (>10 %) of both strains included summed feature 8 (C18 : 1 ω6c and/or C18 : 1 ω7c). In addition, strain WL0004T contained 11-methyl C18 : 1 ω7c and strain XHP0148T contained C12 : 0 3-OH. The respiratory quinone of both strains was ubiquinone-10. The G+C content of genomic DNA of strains WL0004T and XHP0148T were 62.5 and 63.0 mol%, respectively. Strains WL0004T and XHP0148T showed the highest 16S rRNA gene sequence similarity to Ruegeria pomeroyi DSS-3T (99.4 and 99.0 %, respectively), and the 16S rRNA gene-based phylogenetic analysis indicated that the two strains were closely related to members of the genus Ruegeria. The average nucleotide identity and digital DNA-DNA hybridization values among the two strains and type strains of the genus Ruegeria were all below 95 and 70 %, respectively, and the phylogenetic tree reconstructed from the bac120 gene set indicated that the two strains are distinct from each other and the members of the genus Ruegeria. Based on this phenotypic and genotypic characterization, strains WL0004T (=MCCC 1K07523T=JCM 35565T=GDMCC 1.3083T) and XHP0148T (=MCCC 1K07543T=JCM 35569T=GDMCC 1.3089T) should be recognized as representing two novel species of the genus Ruegeria and the names Ruegeria marisflavi sp. nov. and Ruegeria aquimaris sp. nov. are proposed, respectively.

Trans-Balance: Reducing demographic disparity for prediction models in the presence of class imbalance.

INTRODUCTION: Risk prediction, including early disease detection, prevention, and intervention, is essential to precision medicine. However, systematic bias in risk estimation caused by heterogeneity across different demographic groups can lead to inappropriate or misinformed treatment decisions. In addition, low incidence (class-imbalance) outcomes negatively impact the classification performance of many standard learning algorithms which further exacerbates the racial disparity issues. Therefore, it is crucial to improve the performance of statistical and machine learning models in underrepresented populations in the presence of heavy class imbalance. METHOD: To address demographic disparity in the presence of class imbalance, we develop a novel framework, Trans-Balance, by leveraging recent advances in imbalance learning, transfer learning, and federated learning. We consider a practical setting where data from multiple sites are stored locally under privacy constraints. RESULTS: We show that the proposed Trans-Balance framework improves upon existing approaches by explicitly accounting for heterogeneity across demographic subgroups and cohorts. We demonstrate the feasibility and validity of our methods through numerical experiments and a real application to a multi-cohort study with data from participants of four large, NIH-funded cohorts for stroke risk prediction. CONCLUSION: Our findings indicate that the Trans-Balance approach significantly improves predictive performance, especially in scenarios marked by severe class imbalance and demographic disparity. Given its versatility and effectiveness, Trans-Balance offers a valuable contribution to enhancing risk prediction in biomedical research and related fields.

Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. METHOD: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of the explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). RESULTS: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. CONCLUSIONS: In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving model explanations. Our approach not only aligns model explanations more closely with clinical insights but also exhibits extensibility to other thoracic diseases. We anticipate that our template guidance will forge a novel approach to elucidating AI models by integrating clinical domain expertise.

Cryo-EM structure of an essential Plasmodium vivax invasion complex.

Plasmodium vivax is the most widely distributed malaria parasite that infects humans1. P. vivax invades reticulocytes exclusively, and successful entry depends on specific interactions between the P. vivax reticulocyte-binding protein 2b (PvRBP2b) and transferrin receptor 1 (TfR1)2. TfR1-deficient erythroid cells are refractory to invasion by P. vivax, and anti-PvRBP2b monoclonal antibodies inhibit reticulocyte binding and block P. vivax invasion in field isolates2. Here we report a high-resolution cryo-electron microscopy structure of a ternary complex of PvRBP2b bound to human TfR1 and transferrin, at 3.7 Å resolution. Mutational analyses show that PvRBP2b residues involved in complex formation are conserved; this suggests that antigens could be designed that act across P. vivax strains. Functional analyses of TfR1 highlight how P. vivax hijacks TfR1, an essential housekeeping protein, by binding to sites that govern host specificity, without affecting its cellular function of transporting iron. Crystal and solution structures of PvRBP2b in complex with antibody fragments characterize the inhibitory epitopes. Our results establish a structural framework for understanding how P. vivax reticulocyte-binding protein engages its receptor and the molecular mechanism of inhibitory monoclonal antibodies, providing important information for the design of novel vaccine candidates.

Architecture of the human GATOR1 and GATOR1-Rag GTPases complexes.

Nutrients, such as amino acids and glucose, signal through the Rag GTPases to activate mTORC1. The GATOR1 protein complex-comprising DEPDC5, NPRL2 and NPRL3-regulates the Rag GTPases as a GTPase-activating protein (GAP) for RAGA; loss of GATOR1 desensitizes mTORC1 signalling to nutrient starvation. GATOR1 components have no sequence homology to other proteins, so the function of GATOR1 at the molecular level is currently unknown. Here we used cryo-electron microscopy to solve structures of GATOR1 and GATOR1-Rag GTPases complexes. GATOR1 adopts an extended architecture with a cavity in the middle; NPRL2 links DEPDC5 and NPRL3, and DEPDC5 contacts the Rag GTPase heterodimer. Biochemical analyses reveal that our GATOR1-Rag GTPases structure is inhibitory, and that at least two binding modes must exist between the Rag GTPases and GATOR1. Direct interaction of DEPDC5 with RAGA inhibits GATOR1-mediated stimulation of GTP hydrolysis by RAGA, whereas weaker interactions between the NPRL2-NPRL3 heterodimer and RAGA execute GAP activity. These data reveal the structure of a component of the nutrient-sensing mTORC1 pathway and a non-canonical interaction between a GAP and its substrate GTPase.

Thalassospira aquimaris sp. nov. and Winogradskyella marincola sp. nov. two marine bacteria isolated from an agar-degrading co-culture.

Two novel Gram-stain-negative, aerobic, and non-motile strains, designated FZY0004T and YYF002T, were isolated from an agar-degrading co-culture, which was obtained from seawater of the intertidal zone of Yancheng City, the Yellow Sea of China. Strain FZY0004T optimally grew at 28 °C, pH 7.0, and 2-6% NaCl, while strain YYF002T optimally grew at 28 °C, pH 7.5, and 2-4% NaCl. Strain FZY0004T possessed Q-9 as the major respiratory quinone, and its major fatty acids (> 10%) were summed feature 8 (C18:1 ω7c), C16:0, and summed feature 3 (C16:1 ω7c/C16:1 ω6c). The polar lipids identified in strain FZY0004T were phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and several unidentified phospholipids (PL) and lipids (L). On the other hand, strain YYF002T had MK-6 as the predominant respiratory quinone and its major fatty acids consisted of iso-C15:0, iso-C15:1 G, and iso-C15:0 3-OH. The polar lipids identified in strain YYF002T were aminolipid (AL), PE, and several unidentified lipids. Strain FZY0004T shared 99.5% 16S rRNA gene sequence similarity and 90.1% average nucleotide identity (ANI) with T. povalilytica Zumi 95T, and strain YYF002T shared 99.2% 16S rRNA gene sequence similarity and 88.2% ANI with W. poriferorum JCM 12885T. The genomic DNA G + C contents of strains FZY0004T and YYF002T were 54.5% and 33.5%, respectively. The phylogenetic, phenotypic, and physiological characteristics permitted the distinction of the two strains from their neighbors, and we thus propose the names Thalassospira aquimaris sp. nov. (type strain FZY0004T = JCM 35895T = MCCC 1K08380T) and Winogradskyella marincola sp. nov. (type strain YYF002T = JCM 35950T = MCCC 1K08382T).

Description of Nesterenkonia aerolata sp. nov., an actinobacterium isolated from air of manufacturing shop in a pharmaceutical factory.

During our studies on the microorganism diversity from air of manufacturing shop in a pharmaceutical factory in Shandong province, China, a Gram-stain-positive, aerobic, cocci-shaped bacterium, designated LY-0111T, was isolated from a settling dish. Strain LY-0111T grew at temperature of 10-42 °C (optimum 35 °C), pH of 5.0-10.0 (optimum pH 7.0) and NaCl concentration of 1-12% (optimum 0.5-3%, w/v). Based on the 16S rRNA gene sequence analysis, the strain shared the highest sequence similarities to Nesterenkonia halophila YIM 70179T (96.2%), and was placed within the radiation of Nesterenkonia species in the phylogenetic trees. The genome of the isolate was sequenced, which comprised 2,931,270 bp with G + C content of 66.5%. A supermatrix tree based on the gene set bac120 indicated that LY-0111T was close related to Nesterenkonia xinjiangensis YIM 70097T (16S rRNA gene sequence similarity 95.3%). Chemotaxonomic analysis indicated that the main respiratory quinones were MK-7, MK-8, and MK-9, the predominant cellular fatty acids were anteiso-C15:0 and iso-C15:0, and the major polar lipids consisted of diphosphatidylglycerol, phosphatidylglycerol and phosphatidylinositol. According to the phenotypic, chemotaxonomic and phylogenetic features, strain LY-0111T is considered to represent a novel species, for which the name Nesterenkonia aerolata sp. nov. is proposed. The type strain is LY-0111T (= JCM 36375T = GDMCC 1.3945T). In addition, Nesterenkonia jeotgali was proposed as a later synonym of Nesterenkonia sandarakina, according to the ANI (96.8%) and dDDH (72.9%) analysis between them.

Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study.

The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (n = 200) was first used to establish 'gold standard' diagnosis labels for TBI ('Yes TBI' vs. 'No TBI'). To develop our algorithm, we used PheCAP, a semi-supervised pipeline that relied on the chart review diagnosis labels to train and create a prediction model for TBI. Cross-validation was used to train and evaluate the proposed algorithm, 'TBI-PheCAP.' TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (n = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. The TBI-PheCAP algorithm had an area under the receiver operating characteristic curve of 0.92, sensitivity of 84%, and positive predictive value (PPV) of 98% at specificity = 90%. TBI-PheCAP generally performed better than other classification methods, with equivalent or higher sensitivity and PPV than existing rules-based TBI algorithms and MVP TBI-related survey data. Given its strong classification metrics, the TBI-PheCAP algorithm is recommended for use in future population-based TBI research.

A divide-and-conquer method for sparse risk prediction and evaluation.

Divide-and-conquer (DAC) is a commonly used strategy to overcome the challenges of extraordinarily large data, by first breaking the dataset into series of data blocks, then combining results from individual data blocks to obtain a final estimation. Various DAC algorithms have been proposed to fit a sparse predictive regression model in the $L_1$ regularization setting. However, many existing DAC algorithms remain computationally intensive when sample size and number of candidate predictors are both large. In addition, no existing DAC procedures provide inference for quantifying the accuracy of risk prediction models. In this article, we propose a screening and one-step linearization infused DAC (SOLID) algorithm to fit sparse logistic regression to massive datasets, by integrating the DAC strategy with a screening step and sequences of linearization. This enables us to maximize the likelihood with only selected covariates and perform penalized estimation via a fast approximation to the likelihood. To assess the accuracy of a predictive regression model, we develop a modified cross-validation (MCV) that utilizes the side products of the SOLID, substantially reducing the computational burden. Compared with existing DAC methods, the MCV procedure is the first to make inference on accuracy. Extensive simulation studies suggest that the proposed SOLID and MCV procedures substantially outperform the existing methods with respect to computational speed and achieve similar statistical efficiency as the full sample-based estimator. We also demonstrate that the proposed inference procedure provides valid interval estimators. We apply the proposed SOLID procedure to develop and validate a classification model for disease diagnosis using narrative clinical notes based on electronic medical record data from Partners HealthCare.

Relevance and mechanism of STAT3/miR-221-3p/Fascin-1 axis in EGFR TKI resistance of triple-negative breast cancer.

The epidermal growth factor receptor 1 (EGFR) plays a crucial role in the progression of various malignant tumors and is considered a potential target for treating triple-negative breast cancer (TNBC). However, the effectiveness of representative tyrosine kinase inhibitors (TKIs) used in EGFR-targeted therapy is limited in TNBC patients. In our study, we observed that the TNBC cell lines MDA-MB-231 and MDA-MB-468 exhibited resistance to Gefitinib. Treatment with Gefitinib caused an upregulation of Fascin-1 (FSCN1) protein expression and a downregulation of miR-221-3p in these cell lines. However, sensitivity to Gefitinib was significantly improved in both cell lines with either inhibition of FSCN1 expression or overexpression of miR-221-3p. Our luciferase reporter assay confirmed that FSCN1 is a target of miR-221-3p. Moreover, Gefitinib treatment resulted in an upregulation of phosphorylated signal transducer and activator of transcription 3 (p-STAT3) in MDA-MB-231 cells. Using Stattic, a small-molecule inhibitor of STAT3, we observed a significant enhancement in the inhibitory effect of Gefitinib on the growth, migration, and invasion of MDA-MB-231 cells. Additionally, Stattic treatment upregulated miR-221-3p expression and downregulated FSCN1 mRNA and protein expression. A strong positive correlation was noted between the expression of STAT3 and FSCN1 in breast cancer tissues. Furthermore, patients with high expression levels of both STAT3 and FSCN1 had a worse prognosis. Our findings suggest that elevated FSCN1 expression is linked to primary resistance to EGFR TKIs in TNBC. Moreover, we propose that STAT3 regulates the expression of miR-221-3p/FSCN1 and therefore modulates resistance to EGFR TKI therapy in TNBC. Combining EGFR TKI therapy with inhibition of FSCN1 or STAT3 may offer a promising new therapeutic option for TNBC.

Copper-Catalyzed Aza-Benzyl Transfer Michael Addition via C-C Bond Cleavage.

A non-noble Cu-catalyzed transfer aza-benzyl Michael addition via the C-C bond cleavage of aza-benzyl alcohols has been disclosed. The unstrained C(sp3)-C(sp3) bond of an alcohol was selectively cleaved. This aza-benzyl transfer strategy provides a selective and environmentally benign approach for the C-alkylation of α,ß-unsaturated carbonyl compounds that employs readily available alcohols as carbon nucleophiles and is characterized by a wide range of substrates and good to excellent yields.

Engineered Superhydrophilic/Superaerophobic Catalyst: Two-Dimensional Co(OH)2-CeO2 Nanosheets Supported on Three-Dimensional Co Dendrites for Overall Water Splitting.

Efficient electrocatalysts require not only a tunable electronic structure but also great active site accessibility and favorable mass transfer. Here, a two-dimensional/three-dimensional (2D/3D) hierarchical electrocatalyst consisting of Co(OH)2-CeO2 nanosheet-decorated Co dendrites is proposed, named as Co(OH)2-CeO2/Co. Based on the strong electronic interaction of the Co(OH)2-CeO2 heterojunction, the electronic structure of the Co site is optimized, which facilitates the adsorption of intermediates and the dissociation of H2O. Moreover, the open 2D/3D structure formed by introducing the Co substrate further reduces the accumulation of heterogeneous nanosheets and promotes the radial diffusion of the electrolyte, significantly improving the utilization of active sites and shortening the electron transfer pathway. In addition, the superhydrophilic/superaerophobic interface achieved by constructing the hierarchical micro-nanostructure is beneficial to electrolyte infiltration and bubble desorption, thus ensuring favorable mass transfer. Therefore, Co(OH)2-CeO2/Co exhibits an excellent overall water-splitting activity in alkaline solution.

Semi-supervised calibration of noisy event risk (SCANER) with electronic health records.

OBJECTIVE: Electronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliably. Proxies of PFS time such as time to the first mention of progression in the notes are at best good approximations to the true event time. This leads to difficulty in efficiently estimating event rates for an EHR patient cohort. Estimating survival rates based on error-prone outcome definitions can lead to biased results and hamper the power in the downstream analysis. On the other hand, extracting accurate event time information via manual annotation is time and resource intensive. The objective of this study is to develop a calibrated survival rate estimator using noisy outcomes from EHR data. MATERIALS AND METHODS: In this paper, we propose a two-stage semi-supervised calibration of noisy event rate (SCANER) estimator that can effectively overcome censoring induced dependency and attains more robust performance (i.e., not sensitive to misspecification of the imputation model) by fully utilizing both a small-labeled set of gold-standard survival outcomes annotated via manual chart review and a set of proxy features automatically captured via EHR in the unlabeled set. We validate the SCANER estimator by estimating the PFS rates for a virtual cohort of lung cancer patients from one large tertiary care center and the ICU-free survival rates for COVID patients from two large tertiary care centers. RESULTS: In terms of survival rate estimates, the SCANER had very similar point estimates compared to the complete-case Kaplan Meier estimator. On the other hand, other benchmark methods for comparison, which fail to account for the induced dependency between event time and the censoring time conditioning on surrogate outcomes, produced biased results across all three case studies. In terms of standard errors, the SCANER estimator was more efficient than the KM estimator, with up to 50% efficiency gain. CONCLUSION: The SCANER estimator achieves more efficient, robust, and accurate survival rate estimates compared to existing approaches. This promising new approach can also improve the resolution (i.e., granularity of event time) by using labels conditioning on multiple surrogates, particularly among less common or poorly coded conditions.

FedScore: A privacy-preserving framework for federated scoring system development.

OBJECTIVE: We propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. MATERIALS AND METHODS: The FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore's performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a pre-existing score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. RESULTS: We compared the acquired FedScore model's performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. CONCLUSION: This study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Description of Limnobaculum eriocheiris sp. nov., an intestinal bacterium of Eriocheir sinensis, and reclassification of the genera Jinshanibacter and Insectihabitans as Limnobaculum.

A Gram-stain-negative, aerobic, non-motile and rod-shaped strain, designated LJY008T, was isolated from the intestinal of Eriocheir sinensis in Pukou base of Jiangsu Institute of Freshwater Fisheries. Strain LJY008T could grow at 4-37 â„ƒ (optimum, 30 â„ƒ), pH 6.0-8.0 (optimum, pH 7.0), and with 1.0-6.0% NaCl (w/v; optimum, 1.0%). Strain LJY008T shared highest 16S rRNA gene sequence similarity with Jinshanibacter zhutongyuii CF-458T (99.3%), followed by J. allomyrinae BWR-B9T (99.2%), Insectihabitans xujianqingii CF-1111T (97.3%), and Limnobaculum parvum HYN0051T (96.7%). The major polar lipids include phosphatidylethanolamine, phosphatidylglycerol, and diphosphatidylglycerol. The only respiratory quinone was Q8, and the main fatty acids (> 10%) were C16:0, summed feature 3 (C16:1ω7c/C16:1ω6c), summed feature 8 (C18:1ω7c), and C14:0. The genome-based phylogenies showed that strain LJY008T was closely associated with members of the genus Jinshanibacter, Insectihabitans, and Limnobaculum. The average nucleotide identities and average amino acid identities (AAI) among strain LJY008T and closely related neighbours were all below 95%, and the digital DNA-DNA hybridization values among them were all below 36%. The genomic DNA G + C content of strain LJY008T was 46.1%. Based on the phenotypic, phylogenetic, biochemical and chemotaxonomic analysis, strain LJY008T represents a novel species of the genus Limnobaculum, for which the name Limnobaculum eriocheiris sp. nov. is proposed. The type strain is LJY008T (= JCM 34675T = GDMCC 1.2436T = MCCC 1K06016T). In addition, the genera Jinshanibacter and Insectihabitans were reclassified as Limnobaculum, because there was no significant genome-scale divergence or diagnosable difference on phenotypic and chemotaxonomic traits, such as strains of Jinshanibacter and Insectihabitans sharing AAI values of 93.88-94.96%.