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Background & Aims: The albumin-bilirubin (ALBI) score is calculated using serum levels of total bilirubin and albumin as a simple method to assess liver function. This study investigated the ability of baseline ALBI score/grade measurements to assess histological stage and disease progression in individuals with primary biliary cholangitis (PBC) in a large Japanese nationwide cohort. Methods: A total of 8,768 Japanese patients with PBC were enrolled between 1980 and 2016 from 469 institutions, among whom 83% received ursodeoxycholic acid (UDCA) only, 9% received UDCA and bezafibrate, and 8% were given neither drug. Baseline clinical and laboratory parameters were retrospectively retrieved and reviewed from a central database. Associations of ALBI score/grade with histological stage, mortality, and need for liver transplantation (LT) were evaluated using Cox proportional hazards models. Results: During the median follow-up period of 5.3 years, 1,227 patients died (including 789 from liver-related causes) and 113 underwent LT. ALBI score and ALBI grade were significantly associated with Scheuer's classification (both p <0.0001). ALBI grade 2 or 3 had significant associations with all-cause mortality or need for LT as well as liver-related mortality or need for LT according to Cox proportional hazards regression analysis (hazard ratio 3.453, 95% CI 2.942-4.052 and hazard ratio 4.242, 95% CI 3.421-5.260, respectively; both p <0.0001). Cumulative LT-free survival rates at 5 years in the ALBI grade 1, 2, and 3 groups were 97.2%, 82.4%, and 38.8%, respectively, while respective non-liver-related survival rates were 98.1%, 86.0%, and 42.0% (both p <0.0001, log-rank test). Conclusions: This large nationwide study of patients with PBC suggested that baseline measurements of ALBI grade were a simple non-invasive predictor of prognosis in PBC. Impact and implications: Primary biliary cholangitis (PBC) is an autoimmune liver disease characterized by progressive destruction of intrahepatic bile ducts. This study examined the ability of albumin-bilirubin (ALBI) score/grade to estimate histological findings and disease progression in PBC by means of a large-scale nationwide cohort in Japan. ALBI score/grade were significantly associated with Scheuer's classification stage. Baseline ALBI grade measurements may be a simple non-invasive predictor of prognosis in PBC.

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Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.

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Background: Visceral adipose tissue (VAT) is involved in the pathogenesis of Crohn's disease (CD). However, data describing its effects on CD progression remain scarce. We developed and validated a VAT-radiomics model (RM) using computed tomography (CT) images to predict disease progression in patients with CD and compared it with a subcutaneous adipose tissue (SAT)-RM. Methods: This retrospective study included 256 patients with CD (training, n = 156; test, n = 100) who underwent baseline CT examinations from June 19, 2015 to June 14, 2020 at three tertiary referral centres (The First Affiliated Hospital of Sun Yat-Sen University, The First Affiliated Hospital of Shantou University Medical College, and The First People's Hospital of Foshan City) in China. Disease progression referred to the development of penetrating or stricturing diseases or the requirement for CD-related surgeries during follow-up. A total of 1130 radiomics features were extracted from VAT on CT in the training cohort, and a machine-learning-based VAT-RM was developed to predict disease progression using selected reproducible features and validated in an external test cohort. Using the same modeling methodology, a SAT-RM was developed and compared with the VAT-RM. Findings: The VAT-RM exhibited satisfactory performance for predicting disease progression in total test cohort (the area under the ROC curve [AUC] = 0.850, 95% confidence Interval [CI] 0.764-0.913, P < 0.001) and in test cohorts 1 (AUC = 0.820, 95% CI 0.687-0.914, P < 0.001) and 2 (AUC = 0.871, 95% CI 0.744-0.949, P < 0.001). No significant differences in AUC were observed between test cohorts 1 and 2 (P = 0.673), suggesting considerable efficacy and robustness of the VAT-RM. In the total test cohort, the AUC of the VAT-RM for predicting disease progression was higher than that of SAT-RM (AUC = 0.786, 95% CI 0.692-0.861, P < 0.001). On multivariate Cox regression analysis, the VAT-RM (hazard ratio [HR] = 9.285, P = 0.005) was the most important independent predictor, followed by the SAT-RM (HR = 3.280, P = 0.060). Decision curve analysis further confirmed the better net benefit of the VAT-RM than the SAT-RM. Moreover, the SAT-RM failed to significantly improve predictive efficacy after it was added to the VAT-RM (integrated discrimination improvement = 0.031, P = 0.102). Interpretation: Our results suggest that VAT is an important determinant of disease progression in patients with CD. Our VAT-RM allows the accurate identification of high-risk patients prone to disease progression and offers notable advantages over SAT-RM. Funding: This study was supported by the National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Nature Science Foundation of Shenzhen, and Young S&T Talent Training Program of Guangdong Provincial Association for S&T. Translation: For the Chinese translation of the abstract see Supplementary Materials section.

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Rationale and objectives: Selecting region of interest (ROI) for left atrial appendage (LAA) filling defects assessment can be time consuming and prone to subjectivity. This study aimed to develop and validate a novel artificial intelligence (AI), deep learning (DL) based framework for automatic filling defects assessment on CT images for clinical and subclinical atrial fibrillation (AF) patients. Materials and methods: A total of 443,053 CT images were used for DL model development and testing. Images were analyzed by the AI framework and expert cardiologists/radiologists. The LAA segmentation performance was evaluated using Dice coefficient. The agreement between manual and automatic LAA ROI selections was evaluated using intraclass correlation coefficient (ICC) analysis. Receiver operating characteristic (ROC) curve analysis was used to assess filling defects based on the computed LAA to ascending aorta Hounsfield unit (HU) ratios. Results: A total of 210 patients (Group 1: subclinical AF, n = 105; Group 2: clinical AF with stroke, n = 35; Group 3: AF for catheter ablation, n = 70) were enrolled. The LAA volume segmentation achieved 0.931-0.945 Dice scores. The LAA ROI selection demonstrated excellent agreement (ICC ≥0.895, p < 0.001) with manual selection on the test sets. The automatic framework achieved an excellent AUC score of 0.979 in filling defects assessment. The ROC-derived optimal HU ratio threshold for filling defects detection was 0.561. Conclusion: The novel AI-based framework could accurately segment the LAA region and select ROIs while effectively avoiding trabeculae for filling defects assessment, achieving close-to-expert performance. This technique may help preemptively detect the potential thromboembolic risk for AF patients.

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Circular permutation (CP) is a protein sequence rearrangement in which the amino- and carboxyl-termini of a protein can be created in different positions along the imaginary circularized sequence. Circularly permutated proteins usually exhibit conserved three-dimensional structures and functions. By comparing the structures of circular permutants (CPMs), protein research and bioengineering applications can be approached in ways that are difficult to achieve by traditional mutagenesis. Most current CP detection algorithms depend on structural information. Because there is a vast number of proteins with unknown structures, many CP pairs may remain unidentified. An efficient sequence-based CP detector will help identify more CP pairs and advance many protein studies. For instance, some hypothetical proteins may have CPMs with known functions and structures that are informative for functional annotation, but existing structure-based CP search methods cannot be applied when those hypothetical proteins lack structural information. Despite the considerable potential for applications, sequence-based CP search methods have not been well developed. We present a sequence-based method, SeqCP, which analyzes normal and duplicated sequence alignments to identify CPMs and determine candidate CP sites for proteins. SeqCP was trained by data obtained from the Circular Permutation Database and tested with nonredundant datasets from the Protein Data Bank. It shows high reliability in CP identification and achieves an AUC of 0.9. SeqCP has been implemented into a web server available at: http://pcnas.life.nthu.edu.tw/SeqCP/.

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In transcriptomics, differentially expressed genes (DEGs) provide fine-grained phenotypic resolution for comparisons between groups and insights into molecular mechanisms underlying the pathogenesis of complex diseases or phenotypes. The robust detection of DEGs from large datasets is well-established. However, owing to various limitations (e.g., the low availability of samples for some diseases or limited research funding), small sample size is frequently used in experiments. Therefore, methods to screen reliable and stable features are urgently needed for analyses with limited sample size. In this study, MSPJ, a new machine learning approach for identifying DEGs was proposed to mitigate the reduced power and improve the stability of DEG identification in small gene expression datasets. This ensemble learning-based method consists of three algorithms: an improved multiple random sampling with meta-analysis, SVM-RFE (support vector machines-recursive feature elimination), and permutation test. MSPJ was compared with ten classical methods by 94 simulated datasets and large-scale benchmarking with 165 real datasets. The results showed that, among these methods MSPJ had the best performance in most small gene expression datasets, especially those with sample size below 30. In summary, the MSPJ method enables effective feature selection for robust DEG identification in small transcriptome datasets and is expected to expand research on the molecular mechanisms underlying complex diseases or phenotypes.

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Finding differentially expressed circular RNAs (circRNAs) is instrumental to understanding the molecular basis of phenotypic variation between conditions linked to circRNA-involving mechanisms. To date, several methods have been developed to identify circRNAs, and combining multiple tools is becoming an established approach to improve the detection rate and robustness of results in circRNA studies. However, when using a consensus strategy, it is unclear how circRNA expression estimates should be considered and integrated into downstream analysis, such as differential expression assessment. This work presents a novel solution to test circRNA differential expression using quantifications of multiple algorithms simultaneously. Our approach analyzes multiple tools' circRNA abundance count data within a single framework by leveraging generalized linear mixed models (GLMM), which account for the sample correlation structure within and between the quantification tools. We compared the GLMM approach with three widely used differential expression models, showing its higher sensitivity in detecting and efficiently ranking significant differentially expressed circRNAs. Our strategy is the first to consider combined estimates of multiple circRNA quantification methods, and we propose it as a powerful model to improve circRNA differential expression analysis.

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Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa. From the complete dataset, a subset reflecting solely known cancer driver genes was also constructed. Recursive Feature Elimination (RFE) was employed to identify top 20, 25, 30, 35, 40, 45, and 50 gene signatures that differentiate TNBC from the other BrCa subtypes. Five machine learning algorithms were employed on these selected features and on the basis of model performance evaluation, it was found that for the complete and driver dataset, XGBoost performs the best for a subset of 25 and 20 genes, respectively. Out of these 45 genes from the two datasets, 34 genes were found to be differentially regulated. The Kaplan-Meier (KM) analysis for Distant Metastasis Free Survival (DMFS) of these 34 differentially regulated genes revealed four genes, out of which two are novel that could be potential prognostic genes (POU2AF1 and S100B). Finally, interactome and pathway enrichment analyses were carried out to investigate the functional role of the identified potential prognostic genes in TNBC. These genes are associated with MAPK, PI3-AkT, Wnt, TGF-ß, and other signal transduction pathways, pivotal in metastasis cascade. These gene signatures can provide novel molecular-level insights into metastasis.

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The coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2 is ongoing. Individuals with sarcoidosis tend to develop severe COVID-19; however, the underlying pathological mechanisms remain elusive. To determine common transcriptional signatures and pathways between sarcoidosis and COVID-19, we investigated the whole-genome transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 and sarcoidosis and conducted bioinformatic analysis, including gene ontology and pathway enrichment, protein-protein interaction (PPI) network, and gene regulatory network (GRN) construction. We identified 33 abnormally expressed genes that were common between COVID-19 and sarcoidosis. Functional enrichment analysis showed that these differentially expressed genes were associated with cytokine production involved in the immune response and T cell cytokine production. We identified several hub genes from the PPI network encoded by the common genes. These hub genes have high diagnostic potential for COVID-19 and sarcoidosis and can be potential biomarkers. Moreover, GRN analysis identified important microRNAs and transcription factors that regulate the common genes. This study provides a novel characterization of the transcriptional signatures and biological processes commonly dysregulated in sarcoidosis and COVID-19 and identified several critical regulators and biomarkers. This study highlights a potential pathological association between COVID-19 and sarcoidosis, establishing a theoretical basis for future clinical trials.

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BACKGROUND: Hepatitis B e antigen (HBeAg) seroconversion is an important intermediate outcome in HBeAg-positive chronic hepatitis B patients. This study aimed to explore whether hepatitis B virus (HBV) RNA serum levels can predict HBeAg seroconversion treated with entecavir. METHODS: Serum samples from HBeAg-positive children previously treated with entecavir were retrospectively analyzed. HBV RNA levels were measured at baseline, weeks 12, 24, 48, 72 of therapy. Ability of individual biomarkers to predict HBeAg seroconversion was evaluated using receiver operating characteristics (ROC) analyzes. RESULTS: Serum HBV RNA was detectable in 51 children with a median of 6.05 (4.04-8.29) log10 IU/mL at baseline. Patients with subsequent HBeAg seroconversion showed a significantly larger decline in median HBV RNA levels during treatment from baseline to week 12 of 1.96 (0.30-3.38) and to week 24 of 2.27 (1.20-3.38) log10 IU/mL, respectively, in comparison to HBeAg-positive patients without HBeAg seroconversion (P < 0.001). Levels of HBV RNA at treatment weeks 12 and 24 showed good ability to predict HBeAg seroconversion (area under ROC scores > 0.85, P < 0.001). CONCLUSION: On-treatment HBV RNA dynamic predicts entecavir-induced HBeAg seroconversion in children with chronic hepatitis B living in China.

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PURPOSE: To determine the usefulness of T1 values measured using a phase-sensitive inversion recovery (PSIR) sequence for the diagnosis of focal liver lesions. METHOD: The study enrolled 87 patients who underwent gadoxetic acid-enhanced magnetic resonance imaging (MRI) for assessment of 38 hepatocellular carcinomas, 33 hepatic hemangiomas, 30 metastatic liver tumors, and 14 hepatic cysts. PSIR was performed before and 15 min after contrast agent administration, and then the respective T1 values were measured and the T1 reduction rate was calculated. Wilcoxon matched-pairs signed-rank test was used to compare T1 values pre- and post-contrast administration in each tumor. The Kruskal-Wallis test and Dunn's post-hoc test were used to compare T1 values among all tumors pre- and post-contrast administration and the T1 reduction rate among all tumors. RESULTS: The T1 values measured before and after contrast enhancement were 1056 ± 292 ms and 724 ± 199 ms for hepatocellular carcinoma, 1757 ± 723 ms and 1033 ± 406 ms for metastatic liver tumor, 2524 ± 908 ms and 1071 ± 390 ms for hepatic hemangioma, and 3793 ± 207 ms and 3671 ± 241 ms for liver cysts, respectively. The T1 values obtained before and after contrast administration showed significant differences for all tumors except liver cysts (P < 0.0001). T1 reduction rate was not significantly different between hepatocellular carcinoma and metastatic liver tumor, but was significantly different among other tumors (P < 0.05). CONCLUSIONS: T1 mapping using the PSIR sequence is useful to differentiate focal liver lesions.

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PURPOSE: To assess diagnostic performance of fat fractions (FF) from high-resolution 3D radial Dixon MRI for differentiating metastatic and non-metastatic axillary lymph nodes in breast cancer patients. METHOD: High-resolution 3D radial Dixon MRI was prospectively performed on 1.5 T in 70 biopsy-verified breast cancer patients. 35 patients were available for analysis with histopathologic and imaging data. FF images were calculated as fat / in-phase. Two radiologists measured lymph node FF and assessed morphological features in one ipsilateral and one contralateral lymph node in consensus. Diagnostic performance of lymph node FF and morphological criteria were compared using histopathology as reference. RESULTS: 22 patients had metastatic axillary lymph nodes. Mean lymph node FF were 0.20 ±â€¯0.073, 0.31 ±â€¯0.079, and 0.34 ±â€¯0.15 (metastatic, non-metastatic ipsi- and non-metastatic contralateral lymph nodes, respectively). Metastatic lymph node FF were significantly lower than non-metastatic ipsi- (p <  0.001) and contralateral lymph nodes (p <  0.001). Area under the receiver operating characteristics curve for lymph node FF was 0.80 compared to 0.76 for morphological criteria (p =  0.29). Lymph node FF yielded sensitivity 0.91, specificity 0.69, positive predictive value (PPV) 0.83, and negative predictive value (NPV) 0.82, while morphological criteria yielded sensitivity 0.91, specificity 0.62, PPV 0.80, and NPV 0.80 (p =  0.71). Combining lymph node FF and morphological criteria increased diagnostic performance with sensitivity 1.00, specificity 0.67, PPV 0.86, NPV 1.00, and AUC 0.83. CONCLUSIONS: Lymph node FF from high-resolution 3D Dixon images are a promising quantitative indicator of metastases in axillary lymph nodes.

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The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.

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Protein-protein interaction (PPI) is an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of average hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs, especially in obligate complexes. We also develop a neural network-based method, with an area under the receiver operating characteristic curve of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are <2.5 Šfor these fourteen cases. The structural analysis conducted in this work provides molecular details about the interface between α-helical IMPs and the WCN restraints represent an efficient means to score α-helical IMP docking candidates.

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Cerebrospinal fluid (CSF) tau and phospho-tau are well established biomarkers of Alzheimer's disease. While these measures are conventionally referred to as 'total tau' (T-tau) and 'phospho-tau' (P-tau), several truncated and modified tau forms exist that may relay additional diagnostic information. We evaluated the diagnostic performance of an endogenous tau peptide in CSF, tau 175-190, in the phosphorylated and non-phosphorylated state. A liquid chromatography-mass spectrometry (LC-MS) method was established to measure these peptides in CSF and was used to analyze two independent clinical cohorts; the first cohort included patients with Alzheimer's disease (AD, n = 15), Parkinson's disease (PD, n = 15), progressive supranuclear palsy (PSP, n = 15), and healthy controls (n = 15), the second cohort included AD patients (n = 16), and healthy controls (n = 24). In both cohorts T-tau and P-tau concentrations were determined by immunoassay. While tau 175-190 and P-tau 175-190 did not differentiate the study groups, the separation of AD and controls by T-tau (area under the ROC Curve (AUC) = 95%) and P-tau (AUC = 92%) was improved when normalizing the ELISA measurements to the concentrations of the endogenous peptides: T-tau/tau 175-190 (AUC = 100%), P-tau/P-tau 175-190 (AUC = 95%). The separation between patients and controls by T-tau (AUC = 88%) and P-tau (AUC = 82%) was similarly improved in the second cohort by taking the ratios of T-tau/tau 175-190 (AUC = 97%) and P-tau/P-tau 175-190 (AUC = 98%). In conclusion, our results suggest that the performance of the AD biomarkers T-tau and P-tau could be improved by normalizing their measurements to the endogenous peptides tau 175-190 and P-tau 175-190, possibly because these endogenous tau peptides serve to normalize for physiological, and disease-independent, secretion of tau from neurons to the extracellular space and the CSF. Finally, the observations made here add to the general applicability of mass spectrometry as a tool for rapid identification and accurate quantification of biomarker candidates.

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INTRODUCTION: Thrombotic and inflammatory mechanisms are involved in the pathophysiology of acute coronary syndrome (ACS). The aim of the study was the evaluation of inflammation (white blood cells count/WBC, C-reactive protein/CRP, interleukin-6/IL-6) and platelet (platelet count/PLT, mean platelet volume/MPV, large platelet/LPLT, beta-thromboglobulin/ß-TG) biomarkers in the groups of ACS patients depending on the severity of signs and symptoms and compared to controls without coronary artery disease. MATERIALS AND METHODS: The study group included 93 patients categorized into 3 subgroups depending on the severity of signs and symptoms of ACS. PLT, MPV, LPLT, and WBC were determined on hematological analyzer, IL-6 and ß-TG were measured using the ELISA method. RESULTS: In the whole group of ACS patients WBC, CRP, IL-6, MPV, and ß-TG were significantly higher as compared to controls. Analyzing the inflammation and platelet biomarkers depending on the severity of signs and symptoms in comparison to controls, statistically significant differences for above-mentioned parameters were also found. There were no significant differences between the advancement of coronary artery changes and inflammation as well as platelet parameters, except for CRP concentrations. The AUCs for all inflammation parameters tested were similar, however the highest AUCs showed WBC and CRP. Among platelet parameters the highest AUC revealed ß-TG. CONCLUSION: Markers of inflammation and platelet activation may be associated to myocardial ischemia and myocardial injury. WBC, CRP and IL-6 as inflammation parameters and MPV and ß-TG as platelet biomarkers may be useful indicators of the presence of coronary artery disease.

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Biotransformation is a process of the chemical modifications which may lead to the reactive metabolites, in particular the epoxides. Epoxide reactive metabolites may cause the toxic effects. The prediction of such metabolites is important for drug development and ecotoxicology studies. Epoxides are formed by some oxidation reactions, usually catalysed by cytochromes P450, and represent a large class of three-membered cyclic ethers. Identification of molecules, which may be epoxidized, and indication of the specific location of epoxide functional group (which is called SOE - site of epoxidation) are important for prediction of epoxide metabolites. Datasets from 355 molecules and 615 reactions were created for training and validation. The prediction of SOE is based on a combination of LMNA (Labelled Multilevel Neighbourhood of Atom) descriptors and Bayesian-like algorithm implemented in PASS software and MetaTox web-service. The average invariant accuracy of prediction (AUC) calculated in leave-one-out and 20-fold cross-validation procedures is 0.9. Prediction of epoxide formation based on the created SAR model is included as the component of MetaTox web-service ( http://www.way2drug.com/mg ).

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Modern medicine is overwhelmed by a plethora of both established risk factors and novel biomarkers for diseases. The majority of this information is expressed by probabilistic measures of association such as the odds ratio (OR) obtained by calculating differences in average "risk" between exposed and unexposed groups. However, recent research demonstrates that even ORs of considerable magnitude are insufficient for assessing the ability of risk factors or biomarkers to distinguish the individuals who will develop the disease from those who will not. In regards to coronary heart disease (CHD), we already know that novel biomarkers add very little to the discriminatory accuracy (DA) of traditional risk factors. However, the value added by traditional risk factors alongside simple demographic variables such as age and sex has been the subject of less discussion. Moreover, in public health, we use the OR to calculate the population attributable fraction (PAF), although this measure fails to consider the DA of the risk factor it represents. Therefore, focusing on CHD and applying measures of DA, we re-examine the role of individual demographic characteristics, risk factors, novel biomarkers and PAFs in public health and epidemiology. In so doing, we also raise a more general criticism of the traditional risk factors' epidemiology. We investigated a cohort of 6103 men and women who participated in the baseline (1991-1996) of the Malmö Diet and Cancer study and were followed for 18 years. We found that neither traditional risk factors nor biomarkers substantially improved the DA obtained by models considering only age and sex. We concluded that the PAF measure provided insufficient information for the planning of preventive strategies in the population. We need a better understanding of the individual heterogeneity around the averages and, thereby, a fundamental change in the way we interpret risk factors in public health and epidemiology.

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OBJECTIVES: To identify factors predicting renal recovery in patients presenting with renal failure secondary to bilateral obstructing urolithiasis. PATIENTS AND METHODS: Data from electronic records of consecutive adult patients presenting with bilateral obstructing urolithiasis between January 2007 and April 2011 were retrieved. Ultrasonography of the abdomen, and kidney, ureter, bladder (KUB study) X-ray or abdominal non-contrast computed tomography confirmed the diagnosis. Interventional radiologists placed bilateral nephrostomies. Definitive intervention was planned after reaching nadir creatinine. Renal recovery was defined as nadir creatinine of ⩽2 mg/dL. RESULTS: In all, 53 patients were assessed, 50 (94.3%) were male, and 18 (33.9%) were aged ⩽40 years. Renal recovery was achieved in 20 patients (37.7%). A symptom duration of ⩽25 days (P < 0.01), absence of hypertension (P = 0.018), maximum renal parenchymal thickness of >16.5 mm (P = 0.001), and haemoglobin >9.85 g/dL (P < 0.01) were significant on unadjusted analysis. Symptom duration of ⩽25 days alone remained significant after adjusted analysis. Symptom duration of ⩽25 days (hazard ratio (HR) 13.83, 95% confidence interval (CI) 4.52-42.26; P < 0.01), parenchymal thickness of ⩾16.5 mm (HR 5.91, 95% CI 1.94-17.99; P = 0.002), and absence of hypertension (HR 9.99, CI 95% 1.32-75.37; P = 0.026) were significantly related to time to nadir creatinine. Symptom duration of ⩽25 days (HR 17.44, 95% CI 2.48-122.79; P = 0.004) alone remained significant after adjusted analysis. A symptom duration of ⩽25 days (P = 0.007) was 22-times more likely to indicate renal recovery. CONCLUSIONS: Shorter symptom duration (⩽25 days) is predictive of renal recovery in renal failure secondary to bilateral obstructive urolithiasis.

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OBJECT The purpose of this study was to explore the use of super-resolution tract density images derived from probabilistic diffusion tensor imaging (DTI) tractography of the spinal cord as an imaging surrogate for microstructural integrity and functional impairment in patients with cervical spondylosis. METHODS Structural MRI and DTI images were collected for 27 patients with cervical spondylosis with (n= 21) and without (n= 6) functional impairment as defined by the modified Japanese Orthopaedic Association Scale (mJOA). DTI was performed axially through the site of compression in a total of 20 directions with 10 averages. Probabilistic tractography was performed at 0.5-mm isotropic spatial resolution using the streamline technique combined with constrained spherical deconvolution. The following measurements were calculated for each patient: maximum tract density at the site of compression, average tract density in rostral normal-appearing spinal cord, and the ratio of maximum density to normal density. RESULTS Compared with normal tissue, the site of compression exhibited elevated fiber tract density in all patients, and a higher fiber tract density was also noted in focal areas at the site of compression in patients with functional impairment. There was a strong negative correlation between maximum tract density and mJOA score (R(2)= 0.6324, p < 0.0001) and the ratio of maximum tract density to normal tract density (R(2)= 0.6647, p < 0.0001). When grouped according to severity of neurological impairment (asymptomatic, mJOA score of 18; mild, mJOA score of 15-17; moderate, mJOA score of 11-14; and severe, mJOA score < 11), the results showed a significant difference in the ratio between severe and both no impairment (p= 0.0009) and any impairment (p= 0.036). A ratio of maximum fiber tract density at the site of compression to fiber tract density at C-2 greater than 1.45 had 82% sensitivity and 70% specificity for identifying patients with moderate to severe impairment (ROC AUC= 0.8882, p= 0.0009). CONCLUSIONS These results support the use of DTI as a surrogate for determining spinal cord integrity in patients with cervical spondylosis. Probabilistic tractography provides spinal cord microstructural information that can help discern clinical status in cervical spondylosis patients with varying degrees of neurological impairment.

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