Discriminatory Power Research Articles

Survival of Patients with Alcohol-Related Liver Disease Cirrhosis—Usefulness of the New Liver Mortality Inpatients Prognostic Score

Background/Objectives: Alcohol can directly damage the liver, causing steatosis, steatohepatitis, cirrhosis, and hepatocellular cancer. The aim of this study was to examine 28-day survival in hospitalized patients with alcohol-related liver disease (ALD) cirrhosis, as well as to develop and validate a new survival prediction model. Methods: A total of 145 patients with ALD cirrhosis were included; 107 were diagnosed with acute decompensation (AD) and 38 with acute-on-chronic liver failure (ACLF). The new liver mortality inpatients (LIV-IN) score was calculated using the following variables: hepatic encephalopathy (HE), hepatorenal syndrome (HRS), ascites, systemic inflammatory response syndrome (SIRS), community-acquired infection (CAI), and fibrinogen. The diagnostic accuracy of the LIV-IN score was tested, along with the model for end-stage liver disease (MELD), model for end-stage liver disease-sodium (MELD-Na), albumin-bilirubin (ALBI), neutrophil-to-lymphocyte ratio (NLR), chronic liver failure consortium-C acute decompensation (CLIF-C AD), and chronic liver failure consortium-acute-on-chronic liver failure (CLIF-C ACLF). Results: Lethal outcome occurred in 46 (31.7%) patients. The mortality rate was higher in the ACLF group (n = 22, 57.9%) compared to the AD group (n = 24, 22.4%) (p < 0.01). The highest predictive power for short-term mortality was observed for the LIV-IN score (AUC 73.4%, p < 0.01). In patients with AD, the diagnostic accuracy of the CLIF-C AD score was better than for the LIV-IN score (AUC 0.699; p = 0.004, AUC 0.686; p = 0.007, respectively). In patients with ACLF, only the LIV-IN score had statistically significant discriminative power in predicting 28-day survival. Conclusions: The liver mortality inpatients prognostic score is a new, reliable prognostic model in predicting 28-day mortality.

Assessing Teacher Accountability: Development and Validation of the Teacher’s Professional Accountability Scale (TPAS)

This study aims to develop and validate the Teachers’ Professional Accountability Scale (TPAS) constructed by the researcher, which addresses the need for a comprehensive tool to measure professional accountability among Secondary school teachers. Professional accountability is the obligation of educators to adhere established standards and practices with competence and integrity, is crucial for improving educational quality and ensuring favorable student outcomes. A descriptive survey method was employed, and the TPAS was constructed and standardized through a pilot study. The scale consists of 94 items across four dimensions: procedural responsibility, consequential responsibility, instructional responsibility, and school and classroom responsibilities for student outcomes. Item analysis and selection resulted in a final draft of 74 items with satisfactory discrimination power. Reliability analysis using split-half method and Cronbach’s alpha yielded coefficients of 0.85, indicating high internal consistency. Content validity was established through expert reviews, and norms were developed based on z-scores from a sample of 281 Secondary school teachers. The TPAS provides a validated measure for assessing professional accountability, contributing to the advancement of educational research and practice, The study reached some recommendations, including the commitment of the school administration and senior management to the accountability standards reached, and the provision of joint workshops to ensure the efficiency of the implementation of professional accountability standards for teachers.

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer.

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICIs). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an discriminative power (area under the curve [AUC]) exceeding 95% in identifying patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICIs). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an discriminative power (area under the curve [AUC]) exceeding 95% in identifying patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

A Novel Defect Segmentation Model via Integrating Enhanced U-Net with Convolutional Block Attention Mechanisms

Abstract. Defect segmentation in industrial quality control is pivotal for maintaining high product surface standards, but it remains a challenge due to complex defect patterns and varying backgrounds. Traditional segmentation methods often struggle with sensitivity and specificity, leading to either missed defects or false positives. In order to address the above issues this paper presents an advanced defect segmentation model that integrates a Convolutional Block Attention Module (CBAM) with an enhanced U-Net architecture. Our method leverages the spatial and channel-wise attention mechanisms to focus more accurately on subtle defects and remove background interference, enhancing the discriminative power of the feature maps generated by the U-Net. Experimental results on SD-saliency-900 industrial datasets demonstrate significant improvements in segmentation accuracy and robustness compared to existing state-of-the-art methods, confirming the effectiveness of integrating attention mechanisms in deep learning models for complex defect segmentation tasks. Specifically, our model achieved a mean Intersection over Union (mIoU) of 0.87, which is an increase of 5% over the traditional U-Net model and a 4% improvement compared to the DeepLabv3+ model. These results indicate a substantial enhancement in defect detection performance, validating the superiority of our approach.

Unipolar Voltage for Better Characterizing Left Atrium Substrates: Comparing the Predictive Efficacy for Recurrence Post Atrial Fibrillation Ablation in a Post Hoc Analysis of STABLE-SR-III Trial.

While bipolar voltage (BV) is acknowledged as an indicator of viable cardiomyocyte activation, unipolar recording has emerged as an alternative technique due to its advantage of providing a wider field of view. This study aims to compare the efficacy of unipolar voltage (UV) versus BV in predicting ablation recurrence in atrial fibrillation patients. In Substrate Ablation in the Left Atrium during Sinus Rhythm Trial III, 375 patients completed the follow-up with preserved mapping data were included in the analysis. For each patient, the mean UV and BV was obtained from the electrograms sampled in left atrium (LA). Totally 301 patients experience the primary endpoint within 23.0 ± 9.2 months. While both low UV and BV had significant associations with long-term recurrence of atrial tachyarrhythmia (ATa), only mean UV was independently associated with the outcome. The model by UV with ablation feature had higher discriminatory power to predict ATa recurrence compared with BV model (area under the curve [AUC]: 0.858 vs. 0.757, p < 0.001). In subgroup analysis, UV reveals more powerful predictive efficacy compared with BV, with the AUC 0.843 versus 0.751 (p < 0.001) in circumferential pulmonary vein isolation (CPVI) alone cohort and 0.882 versus 0.750 (p < 0.001) in CPVI plus cohort, respectively. UV exhibits higher efficacy for predicting long-term ATa recurrence after ablation compared with BV in elderly patients with atrial fibrillation regardless of whether the patient accepts substrate modification. The outcome suggests that unipolar recording may better characterize LA fibrosis by capturing more comprehensive transmural features than bipolar signals. ClinicalTrials.gov; URL: https://www. gov. Unique Identifier: NCT03462628.

Development of a nomogram for predicting pancreatic portal hypertension in patients with acute pancreatitis: a retrospective study

IntroductionPancreatic portal hypertension (PPH) is a rare complication of acute pancreatitis (AP) that can lead to severe gastrointestinal bleeding. The risk factors associated with PPH, as well as the overall...

Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches

Objectives: This study aims to assess the limitations of the height loss ratio (HLR) method and introduce a new approach that integrates a deep learning (DL) model to enhance vertebral compression fracture (VCF) detection performance. Methods: We conducted a retrospective study on 589 patients with chronic VCFs. We compared four different methods: HLR-only, DL-only, a combination of HLR and DL for positive VCF, and a combination of HLR and DL for negative VCF. The models were evaluated using dice similarity coefficient, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Results: The combined method (HLR + DL, positive) demonstrated the best performance with an AUROC of 0.968, sensitivity (94.95%), and specificity (90.59%). The HLR-only and the HLR + DL (negative) also showed strong discriminatory power, with AUROCs of 0.948 and 0.947, respectively. The DL-only model achieved the highest specificity (95.92%) but exhibited lower sensitivity (82.83%). Conclusions: Our study highlights the limitations of the HLR method in detecting chronic VCFs and demonstrates the improved performance of combining HLR with DL models.

Bacterial profile-based body fluid identification using a machine learning approach.

Identifying the origins of biological traces is critical for the reconstruction of crime scenes in forensic investigations. Traditional methods for body fluid identification rely on chemical, enzymatic, immunological, and spectroscopic techniques, which can be sample-consuming and depend on simple color-change reactions. However, these methods have limitations when residual samples are insufficient after DNA extraction. This study aimed to develop a method for body fluid identification by leveraging bacterial DNA profiling to overcome the limitations of the conventional approaches. Bacterial profiles were determined by sequencing the hypervariable region of the 16S rRNA gene,using DNA metabarcoding of evidence collected from criminal cases. Amplicon sequence variants (ASVs) were analyzed to identify significant microbial patterns in different body fluid samples. The bacterial profile-based method demonstrated high discriminatory power with a machine learning model trained using the naïve Bayes algorithm, achieving an accuracy of over 98% in classifying samples into one of four body fluid types: blood, saliva, vaginal secretion, and mixture traces of vaginal secretions and semen. Bacterial profiling enhances the accuracy and robustness of body fluid identification in forensic analysis, providing a valuable alternative to traditional methods by utilizing DNA and microbial community data despite the uncontrollable conditions. This approach offers significant improvements in the classification accuracy and practical applicability in forensic investigations.

Analysis of the Predictability of Postoperative Meningioma Resection Status Based on Clinical Features

Background: Our aim was to investigate the predictability of postoperative meningioma resection status based on clinical features. Methods: We examined 23 clinical features to assess their effectiveness in distinguishing gross total resections (GTR) from subtotal resections (STR). We analyzed whether GTR/STR cases are better predictable if the classification is based on the Simpson grading or the postoperative operative tumor volume (POTV). Results: Using a study cohort comprising a total of 157 patients, multivariate models for the preoperative prediction of GTR/STR outcome in relation to Simpson grading and POTV were developed and subsequently compared. Including only two clinical features, our models showed a notable discriminatory power in predicting postoperative resection status. Our final model, a straightforward decision tree applicable in daily clinical practice, achieved a mean AUC of 0.885, a mean accuracy of 0.866, a mean sensitivity of 0.889, and a mean specificity of 0.772 based on independent test data. Conclusions: Such models can be a valuable tool both for surgical planning and for early planning of postoperative treatment, e.g., for additional radiotherapy/radiosurgery, potentially required in case of subtotal resections.

Evaluation of seminal plasma levels of vaspin and visfatin in infertile males with elevated sperm DNA fragmentation index: a comparative study

BackgroundSperm DNA fragmentation (SDF) can significantly impact male fertility, especially in cases where there is a substantial level of DNA damage. We aimed in the current study to assess seminal plasma (SP) levels of vaspin and visfatin in infertile men with an elevated SDF index (SDFI ≥ 30%) compared to infertile males with a normal SDFI (SDFI < 30%).ResultsGroups with good and medium DNA integrity exhibited significantly higher total motile sperm count and sperm motility in comparison to the group with poor DNA integrity. Significant negative correlations were noticed between SDF index (SDFI) and numerous semen parameters. Similarly, a significant negative correlation was observed between SDFI and SP vaspin. On the other hand, a significant positive correlation was found between SDFI and abnormal forms percentage. A statistically significant negative correlation was identified SP vaspin with age (r = -0.305, P = 0.006) and infertility duration (r = -0.263, P = 0.019). Statistically significant negative correlation was also identified between SP visfatin and abnormal forms percentage (r = -0.239, P = 0.034). The receiver operating characterisitic curve for predicting poor DNA integrity (SDFI ≥ 30%) revealed fair discriminative power for SP vaspin, with a cutoff value of < 0.55 ng/ml. It demonstrated a sensitivity of 58.8% and a specificity of 64.5% (area under the cureve (AUC) 0.685, p = 0.008). Meanwhile, SP visfatin had little discriminative power (AUC 0.562, p = 0.408). Finally, the results of a linear regression analysis indicated that sperm motility and SP vaspin were significant independent predictors of poor DNA integrity (SDFI ≥ 30%). The analysis was done with a 95% confidence interval and showed upper and lower bounds of -0.302 and -0.623, and -1.362 and -16.101, p < 0.001, p = 0.021, respectively.ConclusionSP Level of vaspin had shown promise as potential biomarkers for sperm DNA integrity. However, vaspin appeared to have greater specificity than visfatin in this point. Future studies are required to validate these findings, evaluate the role of SP vaspin in maintaining sperm DNA integrity, and investigate the potential relationship between SP adipocytokines and other clinical-demographic variables.

Which Scoring System Best Predicts Long-term Survival in Patients with Spinal Metastasis in the Era of Targeted Systemic Treatment? A Comparative Study of Eight Prognostic Models.

Retrospective observational study. To evaluate the accuracy of eight scoring systems, including the Tomita, modified Tokuhashi, modified Bauer, Rades, Oswestry Spinal Risk index (OSRI), Lei, New England Spinal Metastasis Score, and Skeletal Oncology Research Group (SORG) nomogram, for predicting long-term survival of patients with spinal metastasis. Predicting the prognosis of spinal metastasis is vital for surgical decisions, yet the effectiveness of existing scoring systems in identifying long-term survival remains unclear. 456 cases were finally included. Prognostic scores were compared with survival outcomes. Receiver operating characteristic (ROC) curves were analyzed for the entire cohort and across three distinct time periods to evaluate the area under the curve (AUC) for 1-year and 2-year survival, alongside Harrell's C-statistic. The mean patient age was 58.9 years, and the median survival time was 8.6 months. For the entire cohort, the SORG nomogram, OSRI, and modified Tokuhashi scores yielded Harrell's C-index values of 0.64, 0.63, and 0.62, respectively. For 1-year survival prediction, the SORG nomogram, OSRI, and modified Tokuhashi score demonstrated moderate discriminative power, with AUC values of 0.72, 0.71, and 0.70, respectively. Similarly, for 2-year survival prediction, the modified Tokuhashi score, SORG nomogram, and OSRI also revealed moderate discriminative power, with AUC values of 0.73, 0.72, and 0.70, respectively. For patients who underwent surgery in the most recent period, OSRI demonstrated the highest predictive accuracy for 1-year survival, with a Harrell's C-index of 0.63 and an AUC of 0.68, and 2-year survival, with a Harrell's C-index of 0.63 and an AUC of 0.64. Most scoring systems exhibited low discriminative power, with only the SORG nomogram, OSRI, and modified Tokuhashi scores demonstrating moderate power for predicting long-term survival. In the most recent period, the OSRI demonstrated the highest predictive accuracy for both 1-year and 2-year survival.

Possible role of bone turnover markers in the diagnosis of adult hypophosphatasia.

Hypophosphatasia (HPP) is a rare disorder of the bone metabolism, characterized by genetically-determined low alkaline phosphatase (ALP) activity. Low ALP may also be observed in some common causes of bone fragility, such as in osteoporosis treated with antiresorptive drugs. This study aimed to verify whether differences in bone turnover markers (BTMs) could help differentiate adult patients with HPP from those with osteoporosis undergoing antiresorptive treatment. In this multicenter study, we enrolled 23 adult patients with a diagnosis of HPP and compared them with 46 osteoporotic subjects previously treated with zoledronic acid or denosumab. BTMs such as C-terminal telopeptide of type I collagen (CTX), N-terminal propeptide of type I procollagen (P1NP), total ALP, and bone ALP (bALP) were measured, and ratios between BTMs were also calculated. Considering that the control group included only females, in the primary analysis we compared their characteristics with that of the 16 female patients with HPP. Both individual BTMs (CTX and P1NP) and four BTM ratios (ALP/P1NP, bALP/P1NP, ALP/CTX, and bALP/CTX) showed satisfactory discriminatory power, outperforming ALP alone. P1NP, in particular, had an AUC of 0.962 with a cut-off of 32μg/L, while as for the BTMs ratios, the ALP/P1NP ratio had an AUC of 0.964 with a cut-off of 1.114. Similar results were confirmed when including male HPP patients, when adjusting for age and sex, and finally when performing a sensitivity analysis only in patients with ALP less than or equal to 32U/L (i.e., the median of the distribution of the entire population). In cases of low ALP and bone fragility, BTM and their ratios could help distinguish HPP patients from osteoporotic individuals treated with antiresorptive drugs, aiding in accurate diagnosis and reducing the risk of inappropriate treatment.

An integrated approach for decomposing time series data into trend, cycle and seasonal components

ABSTRACT This study presents a methodology for decomposing time series data into trend, seasonal, and cyclical components using the moving linear model approach by Kyo and Kitagawa (Journal of Business Cycle Research, 19(3): 373-397, 2023). Our approach integrates seasonal adjustment and decomposition into a single framework. We evaluated our approach with two case studies: examining daily COVID-19 case data and the Index of Industrial Production (IIP) in Japan, comparing the results to seasonally adjusted IIP data. Performance metrics included the discrimination power index and the variance of the adjusted cyclical component. Our findings show that our method effectively extracts business cycle information, achieving higher discrimination power and greater adjusted variance compared to seasonally adjusted IIP data, highlighting the superior performance of our integrated seasonal adjustment method. We compared the proposed approach with a state-space modelling method by introducing an overall stability as a new indicator. The results demonstrated the stability of the estimations obtained with our proposed method.

Associations between various anthropometric indices and hypertension and hyperlipidaemia: a cross-sectional study in China

BackgroundThis study aims to explore the association and determine the distinguished potential of anthropometric adiposity indices in screening for hypertension and hyperlipidaemia in the Chinese population.MethodsA recent nationwide cross-sectional study, called the Thyroid Disorders, Iodine State, and Diabetes Epidemiological Survey (TIDE 2015–2017), provided the newest data on the relationships between anthropometric adiposity indices and hypertension and hyperlipidaemia and included 65,231 subjects. The area under the curve (AUC) was used to assess the feasibility of using these indices to distinguish hypertension and hyperlipidaemia. After age stratification, a restricted cubic spline (RCS) fitted for generalized linear regression was used to visualize the relationships of the body mass index (BMI), waist circumference (WC), the waist-to-height ratio (WHtR), the body roundness index (BRI), and the “a body shape index” (ABSI) with hypertension and hyperlipidaemia.ResultsThe results showed that there were significant differences in the BMI, WC, the WHtR, the BRI, and the ABSI among the different age groups (P < 0.0001). After adjusting for sex, age, education, income, smoking status, urban or rural residence, and ethnicity in model, The WHtR and BRI had greater discriminatory power in identifying hypertension (AUC = 0.665, 95% confidence interval (CI) 0.660–0.671 for both), hypercholesterolaemia (AUC = 0.629, 95% CI 0.624–0.634 for both), and high low-density lipoprotein cholesterol (LDL-C; AUC = 0.659, 95% CI 0.653–0.664 for both) status in the overall population. When distinguishing hypertriglyceridaemia among the general population, the BMI (AUC = 0.711, 95% CI 0.706–0.716) and WC (AUC = 0.715, 95% CI 0.710–0.720) had greater discriminatory ability than the other anthropometric indices did. The BMI (AUC = 0.631, 95% CI 0.625–0.637) had the highest power for low high-density lipoprotein cholesterol (HDL-C) status in the general population.ConclusionsSeveral anthropometric indices show significant correlation with hypertension and hyperlipidaemia. The WHtR and BRI were better in distinguishing hypertension, hypercholesterolaemia and high LDL-C status, while the BMI was better in hypertriglyceridaemia and low HDL-C status. The use of combined indices, such as the BMI, WC, the WHtR and the BRI, can be included in an individual’s medical history and can be used as tools for cardiovascular health screening, which may yield superior results for public health.

Construction of a nomogram risk prediction model for depressive symptoms in elderly breast cancer patients

The primary objective of this study was to identify the factors associated with the development of depressive symptoms in elderly breast cancer (BC) patients and to construct a nomogram model for predicting these symptoms. We recruited 409 patients undergoing BC treatment in the breast departments of two tertiary-level hospitals in Jiangsu Province from November 2023 to April 2024 as our study cohort. Participants were categorized into depressed and non-depressed groups based on their clinical outcomes. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for depression among BC patients. Multivariate analysis revealed that monthly income, pain score, family support score, and physical activity score significantly influenced the onset of depression in older BC patients (P < 0.05).The risk prediction model, constructed using these identified factors, demonstrated excellent discriminatory power, as evidenced by an area under the ROC curve (AUC) of 0.824. The maximum Youden index was 0.627, with a sensitivity of 90.60%, specificity of 72.10%, and a diagnostic threshold value of 1.501. The results of the Hosmer-Lemeshow goodness-of-fit test (χ² = 3.181, P = 0.923) indicated that the model fit the data well. The calibration curve for the model closely followed the ideal curve, suggesting a strong fit and high predictive accuracy. Our nomogram model exhibited superior predictive performance, enabling healthcare professionals to identify high-risk patients early and implement preventative measures to mitigate the development of depressive symptoms. This study is a cross-sectional study that lacks longitudinal data and has a small sample size. Future research could involve larger samples, multicenter studies, and prospective designs to build better clinical predictive models.

Validation of the adult asthma epidemiological score: a secondary analysis of the EPI-ASTHMA population-based study.

The A2 score is an eight-question patient-reported outcome measure that has been validated for ruling in (score ≥4) and ruling out (score 0-1) asthma. However, this screening tool has been validated in a cohort similar to the derivation cohort used. This study aims to validate the predictive accuracy of the A2 score in a primary care population against general practitioner (GP) clinical assessment and to determine whether the proposed cut-offs are the most appropriate. This accuracy study is a secondary analysis of the EPI-ASTHMA population-based study. Primary care centres in Portugal. Random adult participants answered the A2 score by phone interview. Those with an A2 score ≥1 (plus 5% with an A2 score of 0) were invited to a diagnostic visit carried out by a GP to confirm or not a diagnosis of asthma. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves. A total of 1283 participants (median 54 (p25-p75 43-66) years; 60% women) were analysed. The A2 score showed high discriminatory power in identifying asthma, with an area under the ROC curve of 82.9% (95% CI 80.4% to 85.4%). The proposed cut-off ≥4 was the most appropriate to rule in asthma (specificity 83.1%, positive predictive value 62.4%, accuracy 78%). Similarly, the proposed cut-off<2 was the most suitable for excluding asthma (sensitivity 92.7%, negative predictive value 93.7%, accuracy 60.5%). The A2 score is a useful tool to identify patients with asthma in a primary care population. NCT0516961.

Time to positivity of Coagulase Negative Staphylococcus In Neonatal Blood Cultures as an adjunct tool to help discriminate between sepsis and contamination.

To assess the usefulness of time to positivity (TTP) to distinguish between sepsis and contamination in coagulase-negative staphylococci (CoNS) isolates. Unicentric retrospective observational. Medical records of 168 patients with suspected sepsis and positive blood culture for CoNS were reviewed. Patients were subdivided into sepsis (29%) and probable contamination (71%). Logistic regression analyses were performed to evaluate different risk factors and clinical signs and symptoms associated with sepsis. TTP cut-off value that best discriminated sepsis from contamination was found to be 18 h. Regression analysis revealed that TTP ≤ 18 h, gestational age ≤32 weeks, taquycardia/bradycardia and hypoactivity/lethargy were independent predictors of sepsis. TTP is useful in distinguishing sepsis from contamination, especially in neonates with lower gestational age (<32 weeks). The clinical signs that most increase the discriminatory power of TTP are the presence of tachycardia/bradycardia or hypoactivity.

Pleural effusion in acute pulmonary embolism: characteristics and relevance

IntroductionThe characteristics and clinical relevance of pleural effusion (PLEF) in acute pulmonary embolism (APE) are not fully understood.MethodsA single-centre, retrospective study was performed of patients admitted with APE classified according...

Validation and implementation of whole-genome sequencing-based analytical methods for molecular surveillance and relatedness analysis of Mycobacterium tuberculosis Complex isolates at a National Reference laboratory

Whole genome sequencing-based methodologies have become extremely relevant for the molecular surveillance of human pathogens and are being increasingly introduced into national reference laboratory services. In this study, we describe the validation and implementation of core-genome Multi-Locus Sequence Typing (cgMLST) and whole genome single-nucleotide polymorphism (wgSNP) analysis at the Irish Mycobacteria Reference Laboratory, as a replacement for Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR) typing. Concordance of clustering, discriminatory power, and ease-of-use of both WGS analytical methods were evaluated. Although wgSNP analysis (MTBseq) was the most discriminatory method (p<0,001), we recommend cgMLST (SeqSphere+), as the first-line approach for molecular typing of Mycobacterium tuberculosis isolates in the context of routine surveillance work due to its ease of use and decreased turnaround time, while reserving wgSNP-analysis for a more in-depth cluster analysis of new isolates that show a distance of ≤ 12 alleles to any other isolate(s) in the cgMLST database.