Ethnic disparities in cardiovascular and renal responses to canagliflozin between Asian and White patients with type 2 diabetes mellitus: A post hoc analysis of the CANVAS Program.

AIM: To assess the potential heterogeneity in cardiovascular (CV), renal and safety outcomes of canagliflozin between Whites and Asians, as well as these outcomes in each subgroup. MATERIALS AND METHODS: The CANVAS Program enrolled 10 142 patients with type 2 diabetes, comprising 78.34% Whites and 12.66% Asians. CV, renal and safety outcomes were comprehensively analysed using Cox regression models, while intermediate markers were assessed using time-varying mixed-effects models. Racial heterogeneity was evaluated by adding a treatment-race interacion term. RESULTS: Canagliflozin showed no significant racial disparities in the majority of the CV, renal and safety outcomes. The heterogeneity (p = .04) was observed on all-cause mortality, with reduced risk in Whites (hazard ratio 0.84; 95% confidence interval 0.71-0.99) and a statistically non-significant increased risk in Asians (hazard ratio 1.64; 95% confidence interval 0.94-2.90). There was a significant racial difference in acute kidney injury (p = .04) and a marginally significant racial heterogeneity for the composite of hospitalization for heart failure and CV death (p = .06) and serious renal-related adverse events (p = .07). CONCLUSION: Canagliflozin reduced CV and renal risks similarly in Whites and Asians; however, there was a significant racial discrepancy in all-cause mortality. This distinction may be attributed to the fact that Asian patients exhibited diminished CV protection effects and more renal adverse events with canagliflozin, potentially resulting from the smaller reductions in weight and uric acid. These findings highlight the importance of investigating the impact of race on treatment response to sodium-glucose cotransporter-2 inhibitors and provide more precise treatment strategies.

Three-Dimensional Digital Reconstruction of the Cerebellar Cortex: Lobule Thickness, Surface Area Measurements, and Layer Architecture.

The cerebellum is ontogenetically one of the first structures to develop in the central nervous system; nevertheless, it has been only recently reconsidered for its significant neurobiological, functional, and clinical relevance in humans. Thus, it has been a relatively under-studied compared to the cerebrum. Currently, non-invasive imaging modalities can barely reach the necessary resolution to unfold its entire, convoluted surface, while only histological analyses can reveal local information at the micrometer scale. Herein, we used the BigBrain dataset to generate area and point-wise thickness measurements for all layers of the cerebellar cortex and for each lobule in particular. We found that the overall surface area of the cerebellar granular layer (including Purkinje cells) was 1,732 cm2 and the molecular layer was 1,945 cm2. The average thickness of the granular layer is 0.88 mm (± 0.83) and that of the molecular layer is 0.32 mm (± 0.08). The cerebellum (both granular and molecular layers) is thicker at the depth of the sulci and thinner at the crowns of the gyri. Globally, the granular layer is thicker in the lateral-posterior-inferior region than the medial-superior regions. The characterization of individual layers in the cerebellum achieved herein represents a stepping-stone for investigations interrelating structural and functional connectivity with cerebellar architectonics using neuroimaging, which is a matter of considerable relevance in basic and clinical neuroscience. Furthermore, these data provide templates for the construction of cerebellar topographic maps and the precise localization of structural and functional alterations in diseases affecting the cerebellum.

Identification of region-specific amino acid signatures for doxorubicin-induced chemo brain.

Doxorubicin (DOX) is a cornerstone of chemotherapy for solid tumors and leukemias. DOX-induced cognitive impairment, termed chemo brain, has been reported in cancer survivors, whereas its mechanism remains poorly understood. Here we initially evaluated the cognitive impairments of mice treated with clinically relevant, long-term, low-dosage of DOX. Using HILIC-MS/MS-based targeted metabolomics, we presented the changes of 21 amino acids across six anatomical brain regions of mice with DOX-induced chemo brain. By mapping the altered amino acids to the human metabolic network, we constructed an amino acid-based network module for each brain region. We identified phenylalanine, tyrosine, methionine, and γ-aminobutyric acid as putative signatures of three regions (hippocampus, prefrontal cortex, and neocortex) highly associated with cognition. Relying on the reported mouse brain metabolome atlas, we found that DOX might perturb the amino acid homeostasis in multiple brain regions, similar to the changes in the aging brain. Correlation analysis suggested the possible indirect neurotoxicity of DOX that altered the brain levels of phenylalanine, tyrosine, and methionine by causing metabolic disorders in the liver and kidney. In summary, we revealed the region-specific amino acid signatures as actionable targets for DOX-induced chemo brain, which might provide safer treatment and improve the quality of life among cancer survivors.

Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach.

OBJECTIVE: Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity among patients with this condition. Identification of AKI subphenotypes could enable targeted interventions and a deeper understanding of the injury's pathophysiology. While previous approaches based on unsupervised representation learning have been used to identify AKI subphenotypes, these methods cannot assess time series or disease severity. METHODS: In this study, we developed a data- and outcome-driven deep-learning (DL) approach to identify and analyze AKI subphenotypes with prognostic and therapeutic implications. Specifically, we developed a supervised long short-term memory (LSTM) autoencoder (AE) with the aim of extracting representation from time-series EHR data that were intricately correlated with mortality. Then, subphenotypes were identified via application of K-means. RESULTS: In two publicly available datasets, three distinct clusters were identified, characterized by mortality rates of 11.3%, 17.3%, and 96.2% in one dataset and 4.6%, 12.1%, and 54.6% in the other. Further analysis demonstrated that AKI subphenotypes identified by our proposed approach were statistically significant on several clinical characteristics and outcomes. CONCLUSION: In this study, our proposed approach could successfully cluster the AKI population in ICU settings into 3 distinct subphenotypes. Thus, such approach could potentially improve outcomes of AKI patients in the ICU, with better risk assessment and potentially better personalized treatment.

A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.

PURPOSE: This study aimed to construct a mortality model for the risk stratification of intensive care unit (ICU) patients with sepsis by applying a machine learning algorithm. METHODS: Adult patients who were diagnosed with sepsis during admission to ICU were extracted from MIMIC-III, MIMIC-IV, eICU, and Zigong databases. MIMIC-III was used for model development and internal validation. The other three databases were used for external validation. Our proposed model was developed based on the Extreme Gradient Boosting (XGBoost) algorithm. The generalizability, discrimination, and validation of our model were evaluated. The Shapley Additive Explanation values were used to interpret our model and analyze the contribution of individual features. RESULTS: A total of 16,741, 15,532, 22,617, and 1,198 sepsis patients were extracted from the MIMIC-III, MIMIC-IV, eICU, and Zigong databases, respectively. The proposed model had an area under the receiver operating characteristic curve (AUROC) of 0.84 in the internal validation, which outperformed all the traditional scoring systems. In the external validations, the AUROC was 0.87 in the MIMIC-IV database, better than all the traditional scoring systems; the AUROC was 0.83 in the eICU database, higher than the Simplified Acute Physiology Score III and Sequential Organ Failure Assessment (SOFA),equal to 0.83 of the Acute Physiology and Chronic Health Evaluation IV (APACHE-IV), and the AUROC was 0.68 in the Zigong database, higher than those from the systemic inflammatory response syndrome and SOFA. Furthermore, the proposed model showed the best discriminatory and calibrated capabilities and had the best net benefit in each validation. CONCLUSIONS: The proposed algorithm based on XGBoost and SHAP-value feature selection had high performance in predicting the mortality of sepsis patients within 24 h of ICU admission.

Automated prediction of HIV drug resistance from genotype data.

BACKGROUND: HIV/AIDS is a serious threat to public health. The emergence of drug resistance mutations diminishes the effectiveness of drug therapy for HIV/AIDS. Developing a computational prediction of drug resistance phenotype will enable efficient and timely selection of the best treatment regimens. RESULTS: A unified encoding of protein sequence and structure was used as the feature vector for predicting phenotypic resistance from genotype data. Two machine learning algorithms, Random Forest and K-nearest neighbor, were used. The prediction accuracies were examined by five-fold cross-validation on the genotype-phenotype datasets. A supervised machine learning approach for automatic prediction of drug resistance was developed to handle genotype-phenotype datasets of HIV protease (PR) and reverse transcriptase (RT). It predicts the drug resistance phenotype and its relative severity from a query sequence. The accuracy of the classification was higher than 0.973 for eight PR inhibitors and 0.986 for ten RT inhibitors, respectively. The overall cross-validated regression R(2)-values for the severity of drug resistance were 0.772-0.953 for 8 PR inhibitors and 0.773-0.995 for 10 RT inhibitors. CONCLUSIONS: Machine learning using a unified encoding of sequence and protein structure as a feature vector provides an accurate prediction of drug resistance from genotype data. A practical webserver for clinicians has been implemented.

Binding of Clinical Inhibitors to a Model Precursor of a Rationally Selected Multidrug Resistant HIV-1 Protease Is Significantly Weaker Than That to the Released Mature Enzyme.

We have systematically validated the activity and inhibition of a HIV-1 protease (PR) variant bearing 17 mutations (PR(S17)), selected to represent high resistance by machine learning on genotype-phenotype data. Three of five mutations in PR(S17) correlating with major drug resistance, M46L, G48V, and V82S, and five of 11 natural variations differ from the mutations in two clinically derived extreme mutants, PR20 and PR22 bearing 19 and 22 mutations, respectively. PR(S17), which forms a stable dimer (<10 nM), is ∼10- and 2-fold less efficient in processing the Gag polyprotein than the wild type and PR20, respectively, but maintains the same cleavage order. Isolation of a model precursor of PR(S17) flanked by the 56-amino acid transframe region (TFP-p6pol) at its N-terminus, which is impossible upon expression of an analogous PR20 precursor, allowed systematic comparison of inhibition of TFP-p6pol-PR(S17) and mature PR(S17). Resistance of PR(S17) to eight protease inhibitors (PIs) relative to PR (Ki) increases by 1.5-5 orders of magnitude from 0.01 to 8.4 µM. Amprenavir, darunavir, atazanavir, and lopinavir, the most effective of the eight PIs, inhibit precursor autoprocessing at the p6pol/PR site with IC50 values ranging from ∼7.5 to 60 µM. Thus, this process, crucial for stable dimer formation, shows inhibition ∼200-800-fold weaker than that of the mature PR(S17). TFP/p6pol cleavage, which occurs faster, is inhibited even more weakly by all PIs except darunavir (IC50 = 15 µM); amprenavir shows a 2-fold increase in IC50 (∼15 µM), and atazanavir and lopinavir show increased IC50 values of >42 and >70 µM, respectively.

Identifying representative drug resistant mutants of HIV.

BACKGROUND: Drug resistance is one of the most important causes for failure of anti-AIDS treatment. During therapy, multiple mutations accumulate in the HIV genome, eventually rendering the drugs ineffective in blocking replication of the mutant virus. The huge number of possible mutants precludes experimental analysis to explore the molecular mechanisms of resistance and develop improved antiviral drugs. RESULTS: In order to solve this problem, we have developed a new algorithm to reveal the most representative mutants from the whole drug resistant mutant database based on our newly proposed unified protein sequence and 3D structure encoding method. Mean shift clustering and multiple regression analysis were applied on genotype-resistance data for mutants of HIV protease and reverse transcriptase. This approach successfully chooses less than 100 mutants with the highest resistance to each drug out of about 10K in the whole database. When considering high level resistance to multiple drugs, the numbers reduce to one or two representative mutants. CONCLUSION: This approach for predicting the most representative mutants for each drug has major importance for experimental verification since the results provide a small number of representative sequences, which will be amenable for in vitro testing and characterization of the expressed mutant proteins.

Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure.

BACKGROUND: Drug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy. RESULTS: In order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance. CONCLUSION: The approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.

Fine-grained subphenotypes in acute kidney injury populations based on deep clustering: Derivation and interpretation.

BACKGROUND: Acute kidney injury (AKI) is associated with increased mortality in critically ill patients. Due to differences in the etiology and pathophysiological mechanism, the current AKI criteria put it an embarrassment to evaluate clinical therapy and prognosis. OBJECTIVE: We aimed to identify subphenotypes based on routinely collected clinical data to expose the unique pathophysiologic patterns. METHODS: A retrospective study was conducted based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD), and a deep clustering approach was conducted to derive subphenotypes. We conducted further analysis to uncover the underlying clinical patterns and interpret the subphenotype derivation. RESULTS: We studied 14,189 and 19,382 patients with AKI within 48 h of ICU admission in the two datasets, respectively. Through our approach, we identified seven distinct AKI subphenotypes with mortality heterogeneity in each cohort. These subphenotypes displayed significant variations in demographics, comorbidities, levels of laboratory measurements, and survival patterns. Notably, the subphenotypes could not be effectively characterized using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria alone. Therefore, we uncovered the unique underlying characteristics of each subphenotype through model-based interpretation. To assess the usability of the subphenotypes, we conducted an evaluation, which yielded a micro-Area Under the Receiver Operating Characteristic (AUROC) of 0.81 in the single-center cohort and 0.83 in the multi-center cohort within 48-hour of admission. CONCLUSION: We derived highly characteristic, interpretable, and usable AKI subphenotypes that exhibited superior prognostic values.

Construction of a Hantaan Virus Phage Antibody Library and Screening for Potential Neutralizing Activity.

China is one of the main epidemic areas for hemorrhagic fever with renal syndrome (HFRS). Currently, there is no human antibody specific to Hantaan virus (HTNV) for the emergency prevention and treatment of HFRS. To prepare human antibodies with neutralizing activity, we established an anti-HTNV phage antibody library using phage display technology by transforming peripheral blood mononuclear cells (PBMCs) of patients with HFRS into B lymphoblastoid cell lines (BLCLs) and extracting cDNA from BLCLs that secreted neutralizing antibodies. Based on the phage antibody library, we screened HTNV-specific Fab antibodies with neutralizing activities. Our study provides a potential way forward for the emergency prevention of HTNV and specific treatment of HFRS.

Expectation-maximization algorithm leads to domain adaptation for a perineural invasion and nerve extraction task in whole slide digital pathology images.

In addition to lymphatic and vascular channels, tumor cells can also spread via nerves, i.e., perineural invasion (PNI). PNI serves as an independent prognostic indicator in many malignancies. As a result, identifying and determining the extent of PNI is an important yet extremely tedious task in surgical pathology. In this work, we present a computational approach to extract nerves and PNI from whole slide histopathology images. We make manual annotations on selected prostate cancer slides once but then apply the trained model for nerve segmentation to both prostate cancer slides and head and neck cancer slides. For the purpose of multi-domain learning/prediction and investigation on the generalization capability of deep neural network, an expectation-maximization (EM)-based domain adaptation approach is proposed to improve the segmentation performance, in particular for the head and neck cancer slides. Experiments are conducted to demonstrate the segmentation performances. The average Dice coefficient for prostate cancer slides is 0.82 and 0.79 for head and neck cancer slides. Comparisons are then made for segmentations with and without the proposed EM-based domain adaptation on prostate cancer and head and neck cancer whole slide histopathology images from The Cancer Genome Atlas (TCGA) database and significant improvements are observed.

Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80.

The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.4T); however, recent studies show that regular 3T MR scanners can be sufficient for this purpose. This finding opens the door for the study of fine hippocampal morphometry for a large amount of clinical data. In particular, a characteristic bumpy and subtle feature on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation, presents a dramatic degree of variability between individuals from very smooth to highly dentated. In this report, we propose a combined method joining deep learning and sub-pixel level set evolution to efficiently obtain fine-scale hippocampal segmentation on 552 healthy subjects. Through non-linear dentation extraction and fitting, we reveal that the bumpiness of the inferior surface of the human hippocampus has a clear temporal trend. It is bumpiest between 40 and 50 years old. This observation should be aligned with neurodevelopmental and aging stages.

Assessing the impact of the 2018 tetanus guidelines on knowledge and practices of emergency physicians in trauma patients: a national survey study.

Background: Tetanus remains a significant public health issue in China, with the approach of anti-tetanus prophylaxis in the emergency department resulting in both overuse, particularly of human tetanus immune globulin (TIG), and underuse with the tetanus vaccine. This is largely due to the absence of updated guidelines on tetanus prophylaxis before 2018. Our study aimed to evaluate the effects of the 2018 Chinese tetanus guidelines on the knowledge and practices of emergency physicians about tetanus prevention in trauma patients. Methods: From November 2019 to April 2020, we conducted a web-based survey involving 499 emergency physicians. The survey included a questionnaire covering knowledge, attitudes, and practices related to tetanus. We assessed the influence of the 2018 tetanus guidelines on the knowledge and practices of emergency physicians related to tetanus prevention for patients with trauma using multiple regression analysis. Results: The survey results showed that only 45.3% of the participants had received formal training on tetanus immunization, despite 53.3% reporting the availability of tetanus vaccines at their institutions. Physicians typically prescribed tetanus antitoxin or human TIG instead of tetanus toxoid (TT) to treat injuries, regardless of the patient's TT vaccination history. Among the respondents, those who were aware of the 2018 tetanus guidelines had higher mean scores on the general knowledge, risk knowledge, and treatment knowledge scales, with increases of 6%, 13%, and 9%, respectively, compared to those who were unaware of the guidelines. Awareness of the 2018 tetanus guidelines was associated with a high level of knowledge, as indicated by the general knowledge score, recommendation knowledge score, and total knowledge score, after adjusting for the effects of all variables on the knowledge, attitudes, and practices of the participants. A high level of education was also associated with a high level of knowledge indicated by the recommendation knowledge score and total knowledge score. Conclusions: Our study highlights a substantial gap in the attitudes, knowledge, and practices of emergency physicians in China regarding tetanus immunization. The results suggest an urgent need to promote the Chinese Expert Consensus Guidelines on tetanus to improve emergency physicians' knowledge and competence in tetanus prophylaxis. The findings underscore the importance of enhancing physicians' awareness of the latest guidelines to ensure appropriate and effective treatment for patients with tetanus-prone injuries.

Capturing the reaction pathway in near-atomic-resolution crystal structures of HIV-1 protease.

Snapshots of three consecutive steps in the proteolytic reaction of HIV-1 protease (PR) were obtained in crystal structures at resolutions of 1.2-1.4 Å. Structures of wild-type protease and two mutants (PR(V32I) and PR(I47V)) with V32I and I47V substitutions, which are common in drug resistance, reveal the gem-diol tetrahedral intermediate, the separating N- and C-terminal products, and the C-terminal product of an autoproteolytic peptide. These structures represent three stages in the reaction pathway and shed light on the reaction mechanism. The near-atomic-resolution geometric details include a short hydrogen bond between the intermediate and the outer carboxylate oxygen of one catalytic Asp25 that is conserved in all three structures. The two products in the complex with mutant PR(I47V) have a 2.2 Å separation of the amide and carboxyl carbon of the adjacent ends, suggesting partial cleavage prior to product release. The complex of mutant PR(V32I) with a single C-terminal product shows density for water molecules in the other half of the binding site, including a partial occupancy water molecule interacting with the product carboxylate end and the carbonyl oxygen of one conformation of Gly27, which suggests a potential role of Gly27 in recycling from the product complex to the ligand-free enzyme. These structural details at near-atomic resolution enhance our understanding of the reaction pathway and will assist in the design of mechanism-based inhibitors as antiviral agents.

Plasma Pharmacokinetics and Tissue Distribution of Doxorubicin in Rats following Treatment with Astragali Radix.

Doxorubicin (DOX) is an essential component in chemotherapy, and Astragali Radix (AR) is a widely used tonic herbal medicine. The combination of DOX and AR offers widespread, well-documented advantages in treating cancer, e.g., reducing the risk of adverse effects. This study mainly aims to uncover the impact of AR on DOX disposition in vivo. Rats received a single intravenous dose of 5 mg/kg DOX following a single-dose co-treatment or multiple-dose pre-treatment of AR (10 g/kg × 1 or × 10). The concentrations of DOX in rat plasma and six tissues, including heart, liver, lung, kidney, spleen, and skeletal muscle, were determined by a fully validated LC-MS/MS method. A network-based approach was further employed to quantify the relationships between enzymes that metabolize and transport DOX and the targets of nine representative AR components in the human protein−protein interactome. We found that short-term (≤10 d) AR administration was ineffective in changing the plasma pharmacokinetics of DOX in terms of the area under the concentration−time curve (AUC, 1303.35 ± 271.74 µg/L*h versus 1208.74 ± 145.35 µg/L*h, p > 0.46), peak concentrations (Cmax, 1351.21 ± 364.86 µg/L versus 1411.01 ± 368.38 µg/L, p > 0.78), and half-life (t1/2, 31.79 ± 5.12 h versus 32.05 ± 6.95 h, p > 0.94), etc. Compared to the isotype control group, DOX concentrations in six tissues slightly decreased under AR pre-administration but only showed statistical significance (p < 0.05) in the liver. Using network analysis, we showed that five of the nine representative AR components were not localized to the vicinity of the DOX disposition-associated module. These findings suggest that AR may mitigate DOX-induced toxicity by affecting drug targets rather than drug disposition.

Open-source algorithm and software for computed tomography-based virtual pancreatoscopy and other applications.

Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases. However, the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures, such as gastroscopy and bronchoscopy, owing to its severe invasiveness. In comparison, virtual pancreatoscopy (VP) has shown notable advantages. However, because of the low resolution of current computed tomography (CT) technology and the small diameter of the pancreatic duct, VP has limited clinical use. In this study, an optimal path algorithm and super-resolution technique are investigated for the development of an open-source software platform for VP based on 3D Slicer. The proposed segmentation of the pancreatic duct from the abdominal CT images reached an average Dice coefficient of 0.85 with a standard deviation of 0.04. Owing to the excellent segmentation performance, a fly-through visualization of both the inside and outside of the duct was successfully reconstructed, thereby demonstrating the feasibility of VP. In addition, a quantitative analysis of the wall thickness and topology of the duct provides more insight into pancreatic diseases than a fly-through visualization. The entire VP system developed in this study is available at https://github.com/gaoyi/VirtualEndoscopy.git .

Point-of-Care Blood Coagulation Assay Based on Dynamic Monitoring of Blood Viscosity Using Droplet Microfluidics.

Monitoring of the coagulation function has applications in many clinical settings. Routine coagulation assays in the clinic are sample-consuming and slow in turnaround. Microfluidics provides the opportunity to develop coagulation assays that are applicable in point-of-care settings, but reported works required bulky sample pumping units or costly data acquisition instruments. In this work, we developed a microfluidic coagulation assay with a simple setup and easy operation. The device continuously generated droplets of blood sample and buffer mixture and reported the temporal development of blood viscosity during coagulation based on the color appearance of the resultant droplets. We characterized the relationship between blood viscosity and color appearance of the droplets and performed experiments to validate the assay results. In addition, we developed a prototype analyzer equipped with simple fluid pumping and economical imaging module and obtained similar assay measurements. This assay showed great potential to be developed into a point-of-care coagulation test with practical impact.