High Discriminatory Capacity Research Articles

Arthroscopy has a higher discriminative capacity than MRI in detecting mild cartilage lesions.

To determine whether current magnetic resonance imaging (MRI) could detect superficial cartilage lesions that are observed during diagnostic arthroscopy in patients with knee pain who have not been diagnosed with joint disease. Adult patients with knee pain of unclear origin lasting more than three months, scheduled for a therapeutic/diagnostic arthroscopy were recruited. Demographic and clinical data, pain assessment, MRI imaging, and observations of cartilage damage in the medial femoral condyle during arthroscopic procedure were documented. Patients were categorized based on the presence of cartilage damage assessed via MRI and/or direct visualization. Concordance between these assessments and its variation with age and patient-reported pain were examined. Out of the 95 patients recruited, 48 exhibited lesions in the medial femoral condyle (MFC) during arthroscopic examination, while only 24 of them showed lesions on the MRI scans. The thickness of the cartilage in the MFC was significantly lower in patients with cartilage damage detected by MRI compared with those without. Among patients with cartilage lesions identified during arthroscopy, those also showing lesions on the MRI had lower cartilage thickness and higher Outerbridge score than those without lesions on the MRI. Patients with detectable cartilage damage on the MRI were significantly older and reported higher levels of pain than those with damage detected only by arthroscopic examination. Despite significant technological advancements in MRI, arthroscopy still proves superior in identifying mild structural cartilage lesions that are not identifiable by this technique.

Genetic characterisation of lymphogranuloma venereum in Spain: a multicentre study

IntroductionLymphogranuloma venereum (LGV) is a sexually transmitted infection caused by Chlamydia trachomatis genotypes L1–L3. A combination of techniques with high discriminatory capacity such as multilocus sequence typing (MLST) and the...

Quantitative analysis of aeolian dust incidence and diagnosis using a dynamic Bayesian network model: A case study of estuary area in central Taiwan

The presence of high concentrations of PM10 in aeolian dust within the estuary area of central Taiwan has been a focal point for air quality protection and remediation efforts over the past 15 years. This study explores the quantitative analysis of aeolian dust occurrences concerning various levels of PM10 concentration, aiming to offer early warning information to local communities and government agencies. Employing Dynamic Bayesian network (DBN) models, the research interrelates conditional probability dependencies among air quality and meteorological factors monitored in the downstream area of the Jhuoshuei River, predicting the probability distribution of aeolian dust events. The constructed DBNs demonstrate the inference of probability distributions of meteorological factors' states based on PM10 concentrations, enabling diagnostic analysis of meteorological conditions during high PM10 concentration dust events. The DBN models exhibit robust predictive ability, achieving an overall accuracy of 85.72% in validation and 85.8% in testing, with high discriminatory capacity that the area under the receiver operating characteristic curve (AUC) values of 0.924 and 0.915 during validation and testing, respectively. Sensitivity analysis highlights wind direction, wind speed, relative humidity as significant factors affecting PM10 concentrations. The probability of low PM10 concentrations was notably higher compared to higher concentrations, indicating effective aeolian dust remediation efforts. However, attention is warranted toward the 10% incidence of aeolian dust events deemed unhealthy or hazardous to human health. Diagnosis of meteorological conditions during high PM10 concentration events suggests low temperature, high wind speeds, and dry conditions, particularly with northeastern or southwestern winds, as conducive factors. This study underscores the physical relationship between meteorological factors and PM10 concentrations, employing DBNs to consider temporal relationships and uncertainties in data, aligning with human health impact standards proposed by the Ministry of Environment. Future research should incorporate soil and land surface conditions to provide a more comprehensive understanding of aeolian dust dynamics and mitigation strategies.

Associations of body adiposity index, body mass index, waist circumference, and percentage of body fat in young female Emirati adults.

Body Adiposity Index (BAI), which relies on an individual's hip circumference and height, was proposed as an alternative anthropometric measurement to Body Mass Index (BMI). Although this measure has been validated across different populations, its accuracy in predicting percent body fat (%BF) in the United Arab Emirates has not yet been assessed. The objective of this study was to examine the association between BAI, BMI, Waist Circumference (WC), and %BF in young female Emirati adults and determine the relative accuracy of BAI when predicting %BF. A retrospective cross-sectional study was conducted among 95 Emirati women between the ages of 17 and 27. The %BF was measured using the dual-energy X-ray absorption (DXA) scanner. Anthropometric measurements were collected, and BMI and BAI were calculated. BMI and %BF (r = 0.823, p <0.001) showed a greater association than that between BAI and %BF (r = 0.702, p <0.001). A linear regression analysis revealed that BMI was the single best predictor of %BF in the sample (r2 = 0.678, p<0.001). The variation around the regression line for BAI comparisons with %BF (standard error of estimate = 4.879) was greater than BMI comparisons (standard error of estimate = 3.889). BAI was found to significantly underestimate %BF at higher adiposity levels (mean difference = 8.7%). The ROC curve analysis demonstrated that BMI had a higher discriminatory capacity (AUC = 0.891) over WC and BAI. The results demonstrated that BMI was a better predictor of %BF in the sample than BAI and WC. Thus, BMI may be more useful in assessing adiposity in young female Emirati adults than BAI. However, the potential of BAI as an alternative measure of adiposity should continue to be examined.

Immunological biomarkers in patients with radiographic axial spondyloarthritis, an exploratory longitudinal Swedish study.

Objectives There is a need for more specific biomarkers to diagnose and predict disease course in patients with axial spondyloarthritis (axSpA). This study aimed to study immunological plasma biomarkers, at different time-points in radiographic (r)-axSpA patients overall and stratified by sex and compare these biomarker pattern in r-axSpA patients concerning disease phenotypes and disease activity. Methods Plasma samples were analysed from r-axSpA patients at and prior (Pre-Backbone) inclusion in the Backbone study. Interferon gamma, interleukin-10, -17A, -17F, -22, -23, -6, MCP-1, TNF-α, VEGF-A, MIF, IgA anti-CD74, zonulin, ESR, hsCRP, white blood cell count, and blood lipids were measured. Results Biomarker pattern discriminated significantly between r-axSpA patients in Backbone and Pre-Backbone compared with controls. When stratifying by sex, it was possible to discriminate between male and female r-axSpA patients in Backbone vs controls and between male r-axSpA patients in pre-Backbone and controls. In Backbone, markers with high discriminative capacity were MIF, IgA anti-CD74, and MCP-1. In Pre-Backbone, IL-6, TNF-α, MIF, triglycerides, cholesterol, IL-10, and zonulin displayed high discriminative capacity. Conclusion Based on their temporal pattern and mutual relationship, we suggest studying MIF, IgA anti-CD74, and MCP-1 in depth, at more time points, to further elucidate disease-driving mechanisms in this complex disease.

Comparison of two serological diagnosis tests for bovine paratuberculosis.

Mycobacterium avium subsp. paratuberculosis (MAP) is the causal agent of paratuberculosis (PTBC), a chronic infectious granulomatous enteritis of ruminants. The PTBC diagnosis with commercial ELISA has limitations in sensitivity and specificity, and its results depend on the state of progress of the disease. This research aimed to evaluate two different ELISAs: (a) an "in-house" ELISA with a sonicated antigen obtained from a MAP I47 strain, and (b) a commercial ELISA. In total, the evaluated sample consisted of 394 bovine serum samples from 12 farms in Argentina with high (5-9%) and low (≤ 0.05%) prevalence of PTBC. The evaluation of the new antigen (2.5µg/mL) was against a 1:50 dilution of the M. phlei faced sera. The cut-off point, sensitivity, and specificity determinations of both techniques were by ROC curve analysis. The area under the curve for the I47 ELISA was 0.9 (CI 95%, 0.93-0.97). With a cut-off point of 8.8%, the sensitivity was 84.3% and the specificity 96.6%. The agreement between both techniques was 0.7 (CI 95%, 0.6-0.8). These results indicate a high discriminative capacity to differentiate positive and negative bovine sera of MAP infection with the I47 ELISA. This result would represent an advantage to dispense with the imported kit.

Predictive effect of estimated glomerular filtrate rate by creatinine or cystatin C on mortality in patients with coronary artery disease

Background Renal dysfunction leads to poor prognosis of patients with coronary artery disease (CAD). Current studies have reported the prognosis or mortality of various diseases using different estimated glomerular filtrate rate (eGFR) formulas, while the performance of these equations is unclear in CAD patients. We aim to evaluate the predict effect of creatinine-based eGFR (eGFRcr), cystatin C-based eGFR (eGFRcys), and both creatinine and cystatin C-based eGFR (eGFRcr-cys) in CAD patients. Methods A total of 23,178 patients with CAD were included from CIN-II cohort study. The association of eGFRcr, eGFRcys and eGFRcr-cys with cardiovascular and all-cause mortality was detected by Cox regression analysis. The predictive effect of eGFRcr, eGFRcys and eGFRcr-cys on mortality was assessed. Results During a median follow up of 4.3 years, totally 2051 patients (8.8%) experience all-cause mortality, of which 1427 patients (6.2%) died of cardiovascular disease. For the detection of cardiovascular mortality among CAD patients, eGFRcr-cys had high discriminatory capacity with area under the curve (AUC) in receiver operator characteristic analysis of 0.730, which was significantly better than eGFRcr (AUC = 0.707, p < 0.001) and eGFRcys (AUC = 0.719, p < 0.001). Similar results were observed in all-cause mortality. Restricted cubic spline showed a U-shaped association between eGFRcr and all outcomes in patients with both reduced and supranormal eGFR levels, while a L-shaped association in eGFRcys and eGFRcr-cys. Conclusions Estimated GFR based on both creatinine and cystatin C has highest predictive effect for cardiovascular and all-cause mortality among CAD patients. Meanwhile, supranormal eGFRcr may indicate a higher risk of mortality.

Venous blood gases in the assessment of respiratory failure in patients undergoing sleep studies: a randomized study.

Venous blood gases (VBGs) are not consistently considered suitable surrogates for arterial blood gases (ABGs) in assessing acute respiratory failure due to variable measurement error. The physiological stability of patients with chronic ventilatory failure may lead to improved agreement in this setting. Adults requiring ABGs for sleep or ventilation titration studies had VBGs drawn before or after each ABG, in a randomized order. Veno-arterial correlation and agreement were examined for carbon dioxide tension (PCO2), pH, oxygen tension (PO2), and oxygen saturation (SO2). We analyzed 115 VBG-ABG pairs from 61 patients. Arterial and venous measures were correlated (P < .05) for PCO2 (r = .84) and pH (r = .72), but not for PO2 or SO2. Adjusted mean veno-arterial differences (95% limits of agreement) were +5.0 mmHg (-4.4 to +14.4) for PCO2; -0.02 (-0.09 to +0.04) for pH; -34.3 mmHg (-78.5 to +10.0) for PO2; and -23.9% (-61.3 to +13.5) for SO2. VBGs obtained from the dorsal hand demonstrated a lower mean PCO2 veno-arterial difference (P < .01). A venous PCO2 threshold of ≥ 45.8 mmHg was > 95% sensitive for arterial hypercapnia, so measurements below this can exclude the diagnosis without an ABG. A venous PCO2 threshold of ≥ 53.7 mmHg was > 95% specific for arterial hypercapnia, so such readings can be assumed diagnostic. The area under the receiver operating characteristic curve of 0.91 indicated high discriminatory capacity. A venous PCO2 < 45.8 mmHg or ≥ 53.7 mmHg would exclude or diagnose hypercapnia, respectively, in patients referred for sleep studies, but VBGs are poor surrogates for ABGs where precision is important. Registry: Australian New Zealand Clinical Trials Register; Name: A comparison of arterial and blood gas analyses in sleep studies; URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372717; Identifier: ACTRN12617000562370. Lindstrom SJ, McDonald CF, Howard ME, etal. Venous blood gases in the assessment of respiratory failure in patients undergoing sleep studies: a randomized study. J Clin Sleep Med. 2024;20(8):1259-1266.

AMter: An end‐to‐end model for transcriptional terminators prediction by extracting semantic feature automatically based on attention mechanism

SummaryThe Terminator, a specific DNA sequence, provides the transcriptional termination signal to RNA polymerase, making it a critical aspect of transcriptional regulation. This article proposes AMter, the first end‐to‐end model designed for predicting transcriptional terminators, leveraging attention mechanisms. In AMter, rather than manual feature engineering, two distinct modules based on attention mechanism, known as Frequency‐Attention and Allkmer‐Attention, are employed to automatically learn efficient features. Frequency‐Attention generates informative features by autonomously determining the significance of various frequency features, while Allkmer‐Attention aims to capture the relationships among all k‐mers within a DNA sequence. Features generated by Frequency‐Attention and Allkmer‐Attention demonstrate high informativeness and discriminative capacity, allowing precise discrimination of whether a DNA sequence is a terminator through a simple prediction network. The results of the 5‐fold cross‐validation test indicate the remarkable achievement of our proposed method, attaining 100% accuracy in both the training and validation datasets. Furthermore, AMter demonstrates outstanding prediction accuracy on two independent datasets, with 100% accuracy for Escherichia coli and 99.30% for Bacillus subtilis, marking a significant 94.4% relative improvement over prior methods. Experimental results conclusively demonstrate that AMter surpasses existing approaches, establishing a new state‐of‐the‐art in transcriptional terminator prediction.

The polygenic implication of clopidogrel responsiveness: Insights from platelet reactivity analysis and next-generation sequencing.

Clopidogrel is widely used worldwide as an antiplatelet therapy in patients with acute coronary disease. Genetic factors influence interindividual variability in response. Some studies have explored the polygenic contributions in the drug response, generating pharmacogenomic risk scores (PgxPRS). Importantly, these factors are less explored in underrepresented populations, such as Latin-American countries. Identifying patients at risk of high-on-treatment platelet reactivity (HTPR) is highly valuable in translational medicine. In this study we used a custom next-generation sequencing (NGS) panel composed of 91 single nucleotide polymorphisms (SNPs) and 28 genes related to clopidogrel metabolism, to analyze 70 patients with platelet reactivity values, assessed through closure time (CT). Our results demonstrated the association of SNPs with HTPR and non-HTPR, revealing the strongest associations with rs2286823 (OR: 5,0; 95% CI: 1,02-24,48; p: 0,03), rs2032582 (OR: 4,41; 95% CI: 1,20-16,12; p: 0,019), and rs1045642 (OR: 3,38; 95% CI: 0,96-11,9; p: 0,05). Bivariate regression analysis demonstrated the significant association of several SNPs with the CT value, a "surrogate" biomarker of clopidogrel response. Exploratory results from the LASSO regression model showed a high discriminatory capacity between HTPR and non-HTPR patients (AUC: 0,955), and the generated PgxPRS demonstrated a significant negative association between the risk score, CT value, and the condition of HTPR and non-HTPR. To our knowledge, our study addresses for the first time the analysis of the polygenic contribution in platelet reactivity using NGS and establishes PgxPRS derived from the LASSO model. Our results demonstrate the polygenic implication of clopidogrel response and offer insights applicable to the translational medicine of antiplatelet therapy in an understudied population.

Sequence-Guided Localization of DNA Hybridization Enables Highly Selective and Robust Genotyping.

Genetic variations are always related to human diseases or susceptibility to therapies. Nucleic acid probes that precisely distinguish closely related sequences become an indispensable requisite both in research and clinical applications. Here, a Sequence-guided DNA LOCalization for leaKless DNA detection (SeqLOCK) is introduced as a technique for DNA hybridization, where the intended targets carrying distinct "guiding sequences" act selectively on the probes. In silicon modeling, experimental results reveal considerable agreement (R2=0.9228) that SeqLOCK is capable of preserving high discrimination capacity at an extraordinarily wide range of target concentrations. Furthermore, SeqLOCK reveals high robustness to various solution conditions and can be directly adapted to nucleic acid amplification techniques (e.g., polymerase chain reaction) without the need for laborious pre-treatments. Benefiting from the low hybridization leakage of SeqLOCK, three distinct variations with a clinically relevant mutation frequency under the background of genomic DNA can be discriminated simultaneously. This work establishes a reliable nucleic acid hybridization strategy that offers great potential for constructing robust and programmable systems for molecular sensing and computing.

The Development of Thematic Core Collections in Cassava Based on Yield, Disease Resistance, and Root Quality Traits.

Thematic collections (TCs), which are composed of genotypes with superior agronomic traits and reduced size, offer valuable opportunities for parental selection in plant breeding programs. Three TCs were created to focus on crucial attributes: root yield (CC_Yield), pest and disease resistance (CC_Disease), and root quality traits (CC_Root_quality). The genotypes were ranked using the best linear unbiased predictors (BLUP) method, and a truncated selection was implemented for each collection based on specific traits. The TCs exhibited minimal overlap, with each collection comprising 72 genotypes (CC_Disease), 63 genotypes (CC_Root_quality), and 64 genotypes (CC_Yield), representing 4%, 3.5%, and 3.5% of the total individuals in the entire collection, respectively. The Shannon-Weaver Diversity Index values generally varied but remained below 10% when compared to the entire collection. Most TCs exhibited observed heterozygosity, genetic diversity, and the inbreeding coefficient that closely resembled those of the entire collection, effectively retaining 90.76%, 88.10%, and 88.99% of the alleles present in the entire collection (CC_Disease, CC_Root_quality, and CC_Disease, respectively). A PCA of molecular and agro-morphological data revealed well-distributed and dispersed genotypes, while a discriminant analysis of principal components (DAPC) displayed a high discrimination capacity among the accessions within each collection. The strategies employed in this study hold significant potential for advancing crop improvement efforts.

Identification of Vibrio parahemolyticus Isolated from Seafood via Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry

&#x0D; &#x0D; &#x0D; &#x0D; Background: Vibrio parahaemolyticus is the most common cause of human infections of all members of the Vibrio genus, accounting for between 31 and 50% of the food poisoning cases. Consumption of food contaminated with V. parahaemolyticus can cause severe digestive infection with symptoms of watery or bloody diarrhoea, stomach pain, vomiting, fever, and dehydration. The objective of the study was to establish the reliability of Matrix- Assisted Laser Desorption/Ionization Time оf Flight Mass Spectrometry (MALDI-TOF MS) for identifying V. parahaemolyticus isolated from seafood marketed for human consumption.&#x0D; Methods: A hundred and eighty seafood samples including mussels (Mytilus galloprovincialis), veined rapa whelks (Rapana venosа), bluefish (Pomatomus saltatrix), horse mackerel (Trachurus mediterraneus), gilthead seabream (Sparus aurata), sea bass (Dicentrarchus labrax), Atlantic salmon (Salmo salar), whiteleg shrimp (Litopenaeus vannamei), Argentine shortfin squid (Illex argentinus), and oysters (Ostreidae) were tested by Polymerase Chain Reaction (PCR) and MALDI-TOF MS for the presence of V. parahaemolyticus.&#x0D; Results: Of the tested 103 isolates, 44 (43%) samples were identified as V. parahaemolyticus by PCR, while 41 (40%) samples were confirmed as V. parahaemolyticus by MALDI-TOF MS. The PCR analysis using non-parametric t-test for comparison of the proportions confirmed 93% of the results obtained by MALDI-TOF MS.&#x0D; Conclusion: MALDI-TOF MS showed high discriminative capacity and can be used for high reliability fast identification of V. parahaemolyticus in seafood samples.&#x0D; &#x0D; &#x0D; &#x0D;

Prediction Model for Neurogenic Bladder Recovery One Year After Traumatic Spinal Cord Injury

Neurogenic bladder is a common complication after spinal cord injury (SCI) that carries substantial burdens on the inflicted individual. The objective of this study is to build a prediction model for neurogenic bladder recovery 1year after traumatic SCI. We queried the National Spinal Cord Injury Model Systems database for patients with traumatic SCI who had neurogenic bladder at the time of injury. The primary outcome of interest was the complete recovery of bladder function at 1year. Multiple imputations were performed to generate replacement values for missing data, and the final imputed data were used for our analysis. A multivariable odds logistic regression model was developed for complete bladder recovery at 1year. We identified a total of 2515 patients with abnormal bladder function at baseline who had an annual follow-up. A total of 417 patients (16.6%) recovered bladder function in 1year. Predictors of complete bladder recovery included the following baseline parameters: sacral sensation, American Spinal Injury Association (ASIA) impairment score, bowel function at baseline, voluntary sphincter contraction, anal sensation, S1 motor scores, and the number of days in the rehabilitation facility. The model performed with a discriminative capacity of 90.5%. We developed a prediction model for the probability of complete bladder recovery 1year after SCI. The model performed with a high discriminative capacity. This prediction model demonstrates potential utility in the counseling, research allocation, and management of individuals with SCI.

Diverticular Inflammation and Complication Assessment classification, CODA score and fecal calprotectin in clinical assessment of patients with diverticular disease: A decision curve analysis.

The Diverticular Inflammation and Complication Assessment (DICA) classification and the Combined Overview on Diverticular Assessment (CODA) were found to be effective in predicting the outcomes of Diverticular Disease (DD). We ascertain whether fecal calprotectin (FC) can further aid in improving risk stratification. A three-year international, multicentre, prospective cohort study was conducted involving 43 Gastroenterology and Endoscopy centres. Survival methods for censored observations were used to estimate the risk of acute diverticulitis (AD) in newly diagnosed DD patients according to basal FC, DICA, and CODA. The net benefit of management strategies based on DICA, CODA and FC in addition to CODA was assessed with decision curve analysis, which incorporates the harms and benefits of using a prognostic model for clinical decisions. At the first diagnosis of diverticulosis/DD, 871 participants underwent FC measurement. FC was associated with the risk of AD at 3years (HR per each base 10 logarithm increase: 3.29; 95% confidence interval, 2.13-5.10) and showed moderate discrimination (c-statistic: 0.685; 0.614-0.756). DICA and CODA were more accurate predictors of AD than FC. However, FC showed high discrimination capacity to predict AD at 3months, which was not maintained at longer follow-up times. The decision curve analysis comparing the combination of FC and CODA with CODA alone did not clearly indicate a larger net benefit of one strategy over the other. FC measurement could be used as a complementary tool to assess the immediate risk of AD. In all other cases, treatment strategies based on the CODA score alone should be recommended.

Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study

BackgroundOsteoarthritis is the most prevalent type of arthritis. Many approaches exist for characterising radiographic knee OA, including machine learning (ML).AimsTo examine Kellgren and Lawrence (K&L) scores from ML and expert observation, minimum joint space and osteophyte in relation to pain and function.MethodsParticipants from the Hertfordshire Cohort Study, comprising individuals born in Hertfordshire from 1931 to 1939, were analysed. Radiographs were assessed by clinicians and ML (convolutional neural networks) for K&L scoring. Medial minimum joint space and osteophyte area were ascertained using the knee OA computer-aided diagnosis (KOACAD) program. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was administered. Receiver operating characteristic analysis was implemented for minimum joint space, osteophyte, and observer- and ML-derived K&L scores in relation to pain (WOMAC pain score > 0) and impaired function (WOMAC function score > 0).Results359 participants (aged 71–80) were analysed. Among both sexes, discriminative capacity regarding pain and function was fairly high for observer-derived K&L scores [area under curve (AUC): 0.65 (95% CI 0.57, 0.72) to 0.70 (0.63, 0.77)]; results were similar among women for ML-derived K&L scores. Discriminative capacity was moderate among men for minimum joint space in relation to pain [0.60 (0.51, 0.67)] and function [0.62 (0.54, 0.69)]. AUC < 0.60 for other sex-specific associations.DiscussionObserver-derived K&L scores had higher discriminative capacity regarding pain and function compared to minimum joint space and osteophyte. Among women, discriminative capacity was similar for observer- and ML-derived K&L scores.ConclusionML as an adjunct to expert observation for K&L scoring may be beneficial due to the efficiency and objectivity of ML.

Association Between Clozapine Plasma Concentrations and Treatment Response: A Systematic Review, Meta-analysis and Individual Participant Data Meta-analysis.

Although therapeutic drug monitoring of clozapine is recommended, its optimisation is often adjusted only on the basis of dosage. The aim of this study was to assess the link between clozapine plasma concentrations and clinical response by a meta-analysis of published studies and by an individual participant data meta-analysis. We conducted a computerised search of bibliographic databases (EMBASE, PubMed, Clinical Trials, and Web of Science) to identify studies that assessed the relationship between clozapine serum or plasma concentrations and clinical efficacy. Using pooled data, we investigated the association between improvement of clinical outcome and clozapine or norclozapine plasma concentrations, the sum of clozapine and norclozapine plasma concentrations, and the coefficient of variation of clozapine plasma concentrations. Using available individual data, we assessed the relationship between clozapine plasma concentrations and clinical response (changes in the Brief Psychiatric Rating Scale score) and identified a threshold level for a favourable clinical response. Fifteen studies satisfied inclusion criteria. Our meta-analysis showed that responders had clozapine plasma concentrations that were, on average, 117 ng/mL higher than non-responders. The patients with plasma clozapine concentrations above the thresholds identified in each study had a higher likelihood of responding (odds ratio = 2.94, p < 0.001). Norclozapine plasma concentrations were not associated with a clinical response. The meta-analysis of individual data supported this result and confirmed the link between clozapine concentrations and a change in the Brief Psychiatric Rating Scale score and/or the probability of clinical response. Finally, with the analysis of the coefficient of variation of clozapine plasma concentrations, we found that a greater inter-individual fluctuation in plasma concentrations was associated with a loss of clinical response. Our work confirmed that, in contrast to clozapine doses, clozapine plasma concentrations were related to a favourable clinical response, with a mean difference between responders and non-responders of 117 ng/mL. A threshold for a treatment response of 407 ng/mL was determined, with a high discriminatory capacity,and a sensitivity and specificity of 71% and 89.1%, respectively.

Autoantibodies to Disease-Related Proteins in Joints as Novel Biomarkers for the Diagnosis of Rheumatoid Arthritis.

This study was undertaken to develop and characterize a multiplex immunoassay for detection of autoantibodies against peptides derived from proteins known to play a role in development of arthritis and that are also expressed in joints. We selected peptides from the human counterpart of proteins expressed in the joints, based on mouse models that showed these to be targeted by pathogenic or regulatory antibodies in vivo. Using bead-based flow immunoassays measuring IgG antibodies, we selected triple helical or cyclic peptides, containing the epitopes, to avoid collinear reactivity. We characterized the analytical performance of the immunoassay and then validated it in 3 independent rheumatoid arthritis (RA) cohorts (n=2,110), Swedish age- and sex-matched healthy controls, and patients with osteoarthritis (OA), patients with psoriatic arthritis (PsA), and patients with systemic lupus erythematosus (SLE). Screening assays showed 5 peptide antigens that discriminated RA patients from healthy controls with 99% specificity (95% confidence interval [CI] 98-100%). In our validation studies, we reproduced the discriminatory capacity of the autoantibodies in 2 other RA cohorts, showing that the autoantibodies had high discriminatory capacity for RA versus OA, PsA, and SLE. The novel biomarkers identified 22.5% (95% CI 19-26%) of early RA patients seronegative for anti-cyclic citrullinated peptide and rheumatoid factor. The usefulness of the biomarkers in identifying seronegative RA patients was confirmed in validation studies using 2 independent cohorts of RA patients and cohorts of patients with OA, PsA, and SLE. A multiplex immunoassay with peptides from disease-related proteins in joints was found to be useful for detection of specific autoantibodies in RA serum. Of note, this immunoassay had high discriminatory capacity for early seronegative RA.

On-farm evaluation of multiparametric models to predict subacute ruminal acidosis in dairy cows

This research aimed: (i) to evaluate on-farm (FARM data) multiparametric models developed under controlled experiment (INRAE data) and based on non-invasive indicators to detect subacute ruminal acidosis (SARA) in dairy cows. We also aimed to recover high discrimination capacity, if needed, by (ii) building new models with combined INRAE and FARM data; and (iii) enriching the models increasing from 2 to 5 indicators per model. For model enrichment, we focused on indicators determinable on-farm by quick and inexpensive routine analysis. Fifteen commercial dairy farms were selected to cover a wide range of SARA risk. In each farm, four Holstein early-lactating healthy primiparous cows were selected based on their last on-farm recording of milk yield and somatic cell count analysis. Cows were equipped with a reticulo-rumen pH sensor. The pH kinetics were analysed over a subsequent 7-day period. Relative pH indicators were used to classify cows with or without SARA. Milk, blood, faeces, and urine were collected for analysis of the indicators included in the models developed by Villot et al. (2020) on INRAE data that were externally evaluated using FARM data. Then, new models based on the same indicators were developed combining INRAE and FARM data to test whether a possible loss in performance was due to a limited validity domain of model by Villot et al (2020). Finally, the models developed combining INRAE and FARM data were adapted to the on-farm application and enriched by increasing indicators from 2 to 5 per model using linear discriminant analysis and leave-one-out cross-validation. The sensitivities (true-positive rate) in external evaluation on FARM data were substantially lower than those from cross-validation by Villot et al. (2020) (range: 0.1–0.75 vs 0.79–0.96, respectively), and the specificities (true-negative rate) showed a larger range with lower minimum values (range: 0.18–1.0 vs 0.62–0.97, respectively). The sensitivities of new models developed combining INRAE and FARM data ranged from 0.63 to 0.77. Models involving blood cholesterol, β-hydroxybutyrate, haptoglobin, milk and blood urea, and models involving milk fat/protein ratio, dietary starch proportion, and milk fatty acids had the highest performances, whereas models including sieved faecal residues and urine pH had the lowest. Enriching models to three indicators per model improved sensitivity and specificity, but the inclusion of more indicators was less or not effective. Larger field trials are required to validate our results and to increase variability and validity domain of models.

Preliminary Investigation of Essentially Derived Variety of Tea Tree and Development of SNP Markers.

The continuous emergence of Essentially Derived Varieties (EDVs) in the process of tea tree breeding will endanger and affect the innovation ability and development potential of tea tree breeding. In this study, genotyping by sequencing (GBS) technology was used to screen high-quality genomic SNPs for the first time to investigate the derived relationships of 349 tea trees from 12 provinces in China. A total of 973 SNPs uniformly covering 15 tea tree chromosomes with high discrimination capacity were screened as the core SNP set. A genetic similarity analysis showed that 136 pairs of tea trees had a genetic similarity coefficient (GS) > 90%, among which 60 varieties/strains were identified as EDVs, including 22 registered varieties (19 were indisputably EDVs). Furthermore, 21 SNPs with 100% identification of 349 tea trees were selected as rapid identification markers, of which 14 SNP markers could be used for 100% identification of non-EDV. These results provide the basis for the analysis of the genetic background of tea trees in molecular-assisted breeding.