Proportion Of Correct Classification Research Articles

Validation of a Discharge Risk Calculator for Rural Patients Following Total Joint Arthroplasty

BackgroundAs the volume of total joint arthroplasty in the US continues to grow, new challenges surrounding appropriate discharge surface. Arthroplasty literature has demonstrated discharge disposition to postacute care facilities carries major risks regarding the need for revision surgery, patient comorbidities, and financial burden. To quantify, categorize, and mitigate risks, a decision tool that uses preoperative patient variables has previously been published and validated using an urban patient population. The aim of our investigation was to validate the same predictive model using patients in a rural setting undergoing total knee arthroplasty (TKA) and total hip arthroplasty. MethodsAll TKA and THA procedures that were performed between January 2012 and September 2022 at our institution were collected. A total of 9,477 cases (39.6% TKA, 60.4% THA) were included for the validation analysis. There were 9 preoperative variables that were extracted in an automated fashion from the electronic medical record. Included patients were then run through the predictive model, generating a risk score representing that patient’s differential risk of discharge to a skilled nursing facility versus home. Overall accuracy, sensitivity and specificity were calculated after obtaining risk scores. ResultsScore cutoff equally maximizing sensitivity and specificity was 0.23, and the proportion of correct classifications by the predictive tool in this study population was found to be 0.723, with an area under the curve of 0.788 — both higher than previously published accuracy levels. With the threshold of 0.23, sensitivity and specificity were found to be 0.720 and 0.723, respectively. ConclusionsThe risk calculator showed very good accuracy, sensitivity, and specificity in predicting discharge location for rural patients undergoing TKA and THA, with accuracy even higher than in urban populations. The model provides an easy-to-use interface, with automation representing a viable tool in helping with shared decision-making regarding postoperative discharge plans.

Actual Clinical Practice Assessment: A Rapid and Easy-to-Use Tool for Evaluating Cognitive Decline Equivalent to Dementia.

Background Screening tests reveal the early signs of cognitive decline, enabling better self-care and preparation for the future. We developed and evaluated the accuracy of a rapid (20 s) and easy-to-use tool called ONSEI, assessing the cognitive decline equivalent to dementia in actual clinical practice by correlating clinical diagnoses with the ONSEI classification. Methods In this retrospective observational study, data were collected from individuals who visited three neurosurgical clinics in neighboring prefectures of Tokyo, Japan. ONSEI analysis was performed using a smartphone or tablet. The tool adopts a machine-learning algorithm using the speaker's age, time-orientation task score, and acoustic features of spoken responses to that task. Significant differences in accuracy, sensitivity, and specificity were evaluated by Fisher's exact test. Results The overall classification accuracy of ONSEI was 98.1% (p<0.001). The sensitivity and specificity were 97.3% (p<0.001) and 98.5% (p<0.001), respectively. The proportion of correct classifications was consistent across different age groups. Conclusion ONSEI showed high classification accuracy for dementia in cognitively normal individuals in actual clinical practice, regardless of the facility at which the tests were conducted or the age of the participants. Thus, ONSEI can be useful for dementia screening and self-care.

Fitting mathematical functions to extended lactation curves and forecasting late-lactation milk yields of dairy cows

A 305-d lactation followed by a 60-d dry period has traditionally been considered economically optimal, yet dairy cows in modern intensive dairy systems are frequently dried off while still producing significant quantities of milk. Managing cows for an extended lactation has reported production, welfare, and economic benefits, but not all cows are suitable for an extended lactation. Implementation of an extended lactation strategy on-farm could benefit from use of a decision support system, based on a mathematical lactation model, that can identify suitable cows during early lactation that have a high likelihood of producing above a target milk yield (MY) at 305 d in milk (DIM). Therefore, our objectives were (1) to compare the suitability of 3 commonly used lactation models for modeling extended lactations (Dijkstra, Wood, and Wilmink) in primiparous and multiparous cows under a variety of lactation lengths, and (2) to determine the amount of early-lactation daily MY data needed to accurately forecast MY at d 305 by using the most suitable model and determine whether this is sufficient for identifying cows suitable for an extended lactation before the end of a typical voluntary waiting period (50-90 d). Daily MY data from 467 individual Holstein-Friesian lactations (DIM >305 d; 379 ± 65-d lactation length [mean ± SD]) were fitted by the 3 lactation models using a nonlinear regression procedure. The parameter estimates of these models, lactation characteristics (peak yield, time to peak yield, and persistency), and goodness-of-fit were compared between parity and different lactation lengths. The models had similar performance, and differences between parity groups were consistent with previous literature. Then, data from only the first i DIM for each individual lactation, where i was incremented by 30 d from 30 to 150 DIM and by 50 d from 150 to 300 DIM, were fitted by each model to forecast MY at d 305. The Dijkstra model was selected for further analysis, as it had superior goodness-of-fit statistics for i= 30 and 60. The data set was fit twice by the Dijkstra model, with parameter bounds either unconstrained or constrained. The quality of predictions of MY at d 305 improved with increasing data availability for both models and assisting the model fitting procedure with more biologically relevant constraints on parameters improved the predictions, but neither was reliable enough for practical use on-farm due to the high uncertainty of forecasted predictions. Using 90 d of data, the constrained model correctly classified 66% of lactations as being above or below a target MY at d 305 of 25 kg/d, with a probability threshold of 0.95. The proportion of correct classifications became smaller at lower targets of MY at d 305 and became greater when using more lactation days. Overall, further work is required to develop a model that can forecast late-lactation MY with sufficient accuracy for practical use. We envisage that a hybridized machine learning and mechanistic model that incorporates additional historical and genetic information with early-lactation MY could produce meaningful lactation curve forecasts.

Soluble interleukin-2 receptor combined with interleukin-8 is a powerful predictor of future adverse cardiovascular events in patients with acute myocardial infarction.

Little is known about the role of interleukin (IL) in patients with acute myocardial infarction (MI), especially soluble IL-2 receptor (sIL-2R) and IL-8. We aim to evaluate, in MI patients, the predictive value of serum sIL-2R and IL-8 for future major adverse cardiovascular events (MACEs), and compare them with current biomarkers reflecting myocardial inflammation and injury. This was a prospective, single-center cohort study. We measured serum concentrations of IL-1β, sIL-2R, IL-6, IL-8 and IL-10. Levels of current biomarkers for predicting MACEs were measured, including high-sensitivity C reactive protein, cardiac troponin T and N-terminal pro-brain natriuretic peptide. Clinical events were collected during 1-year and a median of 2.2 years (long-term) follow-up. Twenty-four patients (13.8%, 24/173) experienced MACEs during 1-year follow-up and 40 patients (23.1%, 40/173) during long-term follow-up. Of the five interleukins studied, only sIL-2R and IL-8 were independently associated with endpoints during 1-year or long-term follow-up. Patients with high sIL-2R or IL-8 levels (higher than the cutoff value) had a significantly higher risk of MACEs during 1-year (sIL-2R: HR 7.7, 3.3-18.0, p < 0.001; IL-8: HR 4.8, 2.1-10.7, p < 0.001) and long-term (sIL-2R: HR 7.7, 3.3-18.0, p < 0.001; IL-8: HR 4.8, 2.1-10.7, p < 0.001) follow-up. Receiver operator characteristic curve analysis regarding predictive accuracy for MACEs during 1-year follow-up showed that the area under the curve for sIL-2R, IL-8, sIL-2R combined with IL-8 was 0.66 (0.54-0.79, p = 0.011), 0.69 (0.56-0.82, p < 0.001) and 0.720 (0.59-0.85, p < 0.001), whose predictive value were superior to that of current biomarkers. The addition of sIL-2R combined with IL-8 to the existing prediction model resulted in a significant improvement in predictive power (p = 0.029), prompting a 20.8% increase in the proportion of correct classifications. High serum sIL-2R combined with IL-8 levels was significantly associated with MACEs during follow-up in patients with MI, suggesting that sIL-2R combined with IL-8 may be a helpful biomarker for identifying the increased risk of new cardiovascular events. IL-2 and IL-8 would be promising therapeutic targets for anti-inflammatory therapy.

The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers.

The area under a receiver operating characteristic (ROC) curve (AUC) is a popular measure of pure diagnostic accuracy that is independent from the proportion of diseased subjects in the analysed sample. However, its actual usefulness in the clinical context has been questioned, because it does not seem to be directly related to the actual performance of a diagnostic marker in identifying diseased and non-diseased subjects in real clinical settings. This study evaluates the relationship between the AUC and the proportion of correct classifications (global diagnostic accuracy, GDA) in relation to the shape of the corresponding ROC curves. We demonstrate that AUC represents an upward-biased measure of GDA at an optimal accuracy cut-off for balanced groups. The magnitude of bias depends on the shape of the ROC plot and on the proportion of diseased and non-diseased subjects. In proper curves, the bias is independent from the diseased/non-diseased ratio and can be easily estimated and removed. Moreover, a comparison between 2 partial AUCs can be replaced by a more powerful test for the corresponding whole AUCs. Applications to 3 real datasets are provided: a marker for a hormone deficit in children, 2 tumour markers for malignant mesothelioma, and 2 gene expression profiles in ovarian cancer patients. The AUC is a measure of accuracy with potential clinical relevance for the evaluation of disease markers. The clinical meaning of ROC parameters should always be evaluated with an analysis of the shape of the corresponding ROC curve.

Validation of a Predictive Tool for Discharge to Rehabilitation or a Skilled Nursing Facility After TJA.

Cost excess in bundled payment models for total joint arthroplasty (TJA) is driven by discharge to rehabilitation or a skilled nursing facility (SNF). A recently published preoperative risk prediction tool showed very good internal accuracy in stratifying patients on the basis of likelihood of discharge to an SNF or rehabilitation. The purpose of the present study was to test the accuracy of this predictive tool through external validation with use of a large cohort from an outside institution. A total of 20,294 primary unilateral total hip (48%) and knee (52%) arthroplasty cases at a tertiary health system were extracted from the institutional electronic medical record. Discharge location and the 9 preoperative variables required by the predictive model were collected. All cases were run through the model to generate risk scores for those patients, which were compared with the actual discharge locations to evaluate the cutoff originally proposed in the derivation paper. The proportion of correct classifications at this threshold was evaluated, as well as the sensitivity, specificity, positive and negative predictive values, number needed to screen, and area under the receiver operating characteristic curve (AUC), in order to determine the predictive accuracy of the model. A total of 3,147 (15.5%) of the patients who underwent primary, unilateral total hip or knee arthroplasty were discharged to rehabilitation or an SNF. Despite considerable differences between the present and original model derivation cohorts, predicted scores demonstrated very good accuracy (AUC, 0.734; 95% confidence interval, 0.725 to 0.744). The threshold simultaneously maximizing sensitivity and specificity was 0.1745 (sensitivity, 0.672; specificity, 0.679), essentially identical to the proposed cutoff of the original paper (0.178). The proportion of correct classifications was 0.679. Positive and negative predictive values (0.277 and 0.919, respectively) were substantially better than those of random selection based only on event prevalence (0.155 and 0.845), and the number needed to screen was 3.6 (random selection, 6.4). A previously published online predictive tool for discharge to rehabilitation or an SNF performed well under external validation, demonstrating a positive predictive value 79% higher and number needed to screen 56% lower than simple random selection. This tool consists of exclusively preoperative parameters that are easily collected. Based on a successful external validation, this tool merits consideration for clinical implementation because of its value for patient counseling, preoperative optimization, and discharge planning. Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Performance of the 2021 Race-Free CKD-EPI Creatinine- and Cystatin C–Based Estimated GFR Equations Among Kidney Transplant Recipients

Race-free estimated glomerular filtration rate (eGFR) equations incorporating creatinine with and without cystatin C were recently developed and recommended for routine use. However, the performance of these equations among kidney transplant recipients (KTRs) remains unknown. Cross-sectional study to validate the 2021 race-free Chronic Kidney Disease (CKD) Epidemiology Collaboration (CKD-EPI) eGFR equation based on creatinine alone (eGFRcr) or based on creatinine and cystatin C (eGFRcr-cys) among KTRs. KTRs in stable condition (N= 415) from Canada and New Zealand with same-day measurements of creatinine, cystatin C, and glomerular filtration rate (GFR) usingradiolabeled diethylenetriaminepentaacetic acid. The 2009 CKD-EPI eGFRcr, 2021 CKD-EPI eGFRcr, 2012 CKD-EPI eGFRcr-cys, 2021 CKD-EPI eGFRcr-cys, 2012 CKD-EPI eGFRcys, and Modification of Diet in Renal Disease (MDRD) Study eGFR equations were compared with measured GFR. Bias, precision, accuracy, and correct classification by CKD stage. Bias was defined as the difference between estimated and measured GFR. Precision was represented by the interquartile range. Accuracy was defined as the percentages of participants with eGFRs within 10%/20%/30% (P10/P20/P30) of measured GFR, root mean square error, and mean absolute error. 87% of patients studied were White, 3% Black, and 10% other races. Mean measured GFR was 53 ± 19 (SD) mL/min/1.73 m2. The 2009 and 2021 CKD-EPI eGFRcr equations demonstrated similar median bias (-2.3 vs-0.2 mL/min/1.73 m2, respectively), precision (14.5 vs 14.9 mL/min/1.73 m2), and accuracy (P10/P20/P30, 32%/65%/84% vs 33%/63%/84%). The 2012 and 2021 CKD-EPI eGFRcr-cys equations also demonstrated similar median bias (-3.6 vs 0.3 mL/min/1.73 m2, respectively), precision (13.3 vs 14.3 mL/min/1.73 m2), and accuracy (P10/P20/P30, 32%/63%/80% vs 32%/67%/83%). No clear difference in performance was detected between the 2021 CKD-EPI eGFRcr and eGFRcr-cys equations among KTRs. The proportion of correct classification by CKD stage was similar across all eGFR equations. Moderate sample size, few patients had a GFR<30 mL/min/1.73 m2, and the large majority of patients were White. Among KTRs, the 2021 race-free CKD-EPI eGFR equations perform similarly to the previous CKD-EPI equations that included race correction terms. No significant difference in performance was observed between the 2021 CKD-EPI eGFRcr and eGFRcr-cys equations in the kidney transplant population.

Exploratory Study on Application of MALDI-TOF-MS to Detect SARS-CoV-2 Infection in Human Saliva.

SARS-CoV-2 has caused a large outbreak since its emergence in December 2019. COVID-19 diagnosis became a priority so as to isolate and treat infected individuals in order to break the contamination chain. Currently, the reference test for COVID-19 diagnosis is the molecular detection (RT-qPCR) of the virus from nasopharyngeal swab (NPS) samples. Although this sensitive and specific test remains the gold standard, it has several limitations, such as the invasive collection method, the relative high cost and the duration of the test. Moreover, the material shortage to perform tests due to the discrepancy between the high demand for tests and the production capacities puts additional constraints on RT-qPCR. Here, we propose a PCR-free method for diagnosing SARS-CoV-2 based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling and machine learning (ML) models from salivary samples. Kinetic saliva samples were collected at enrollment and ten and thirty days later (D0, D10 and D30), to assess the classification performance of the ML models compared to the molecular tests performed on NPS specimens. Spectra were generated using an optimized protocol of saliva collection and successive quality control steps were developed to ensure the reliability of spectra. A total of 360 averaged spectra were included in the study. At D0, the comparison of MS spectra from SARS-CoV-2 positive patients (n = 105) with healthy healthcare controls (n = 51) revealed nine peaks that significantly distinguished the two groups. Among the five ML models tested, support vector machine with linear kernel (SVM-LK) provided the best performance on the training dataset (accuracy = 85.2%, sensitivity = 85.1%, specificity = 85.3%, F1-Score = 85.1%). The application of the SVM-LK model on independent datasets confirmed its performances with 88.9% and 80.8% of correct classification for samples collected at D0 and D30, respectively. Conversely, at D10, the proportion of correct classification had fallen to 64.3%. The analysis of saliva samples by MALDI-TOF MS and ML appears as an interesting supplementary tool for COVID-19 diagnosis, despite the mitigated results obtained for convalescent patients (D10).

Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow &amp;amp; Ice Monitoring Service

Abstract. The High Resolution Snow &amp;amp; Ice Monitoring Service was launched in 2020 to provide near-real-time, pan-European snow and ice information at 20 m resolution from Sentinel-2 observations. Here we present an evaluation of the snow detection using a database of snow depth observations from 1764 stations across Europe over the hydrological year 2016–2017. We find a good agreement between both datasets with an accuracy (proportion of correct classifications) of 94 % and kappa of 0.81. More accurate (+6 % kappa) retrievals are obtained by excluding low-quality pixels at the cost of a reduced coverage (−13 % data).

Assessment of morphological variation between stocks of bluefish, Pomatomus saltatrix (Actinopterygii, Perciformes, Pomatomidae), in the Aegean Sea, Black Sea, and Sea of Marmara

The population structure of the bluefish, Pomatomus saltatrix (Linnaeus, 1766), in Turkish waters is scarcely described in the literature. To identify any distinct population units of bluefish, and reaffirm the findings of a previous study, four areas were selected: the Aegean Sea, western Black Sea, eastern Black Sea, and the Sea of Marmara. In this study, truss network morphometrics, meristics, and otolith shape analyses were successfully applied for different population identification of the bluefish. Multivariate analysis of variance (MANOVA) revealed no differences for truss network morphometrics, meristic, and otolith shape characters between males and females. Hence, both sexes were combined for the discriminant function (DFA) and the Principal Component Analysis (PCA). Using univariate ANOVA based on the stepwise method revealed a highly significant difference among different locations for each truss-morphometrics and otolith shape characters. Furthermore, six out of seven meristic characters also showed significant differences between different areas. Based on PCA, 25 out of 27 truss-morphometric characters had a loading value above 0.70, which was considered significant in this study. The results of DFA show clear patterns of truss-morphometric character variations, forming four distinct clusters that were well separated from each other, indicating the existence of four morphologically differentiated populations of the bluefish. The proportion of the correctly classified Aegean Sea, western Black Sea, and eastern Black Sea bluefish samples to their original groups were 100%, demonstrating clear separation of these stocks from each other. Whereas up to 5% of the total samples of the Sea of Marmara were incorrectly classified, assigning to the eastern Black Sea. These findings were supported by meristic and otolith shape characters that also indicated four morphologically differentiated populations of the bluefish. However, their overall proportion of correct classification was relatively lower than the truss-morphometric traits method. The findings suggest the requirement of strategic assessment and management of each bluefish stock separately to use them sustainably in the future.

Standard Minimum Dietary Diversity Indicators for Women or Infants and Young Children Are Good Predictors of Adequate Micronutrient Intakes in 24–59-Month-Old Children and Their Nonpregnant Nonbreastfeeding Mothers in Rural Burkina Faso

ABSTRACTBackgroundSimple proxy indicators are needed to assess and monitor micronutrient intake adequacy of vulnerable populations. Standard dichotomous indicators exist for nonpregnant women of reproductive age and 6–23-mo-old children in low-income countries, but not for 24–59-mo-old children or pregnant or breastfeeding women.ObjectivesThis study aimed to evaluate the performance of 2 standard food group scores (FGSs) and related dichotomous indicators to predict micronutrient adequacy of the diet of rural Burkinabe 24–59-mo-old children and women of reproductive age by physiological status.MethodsA 24-h recall survey was conducted at dry season among 1066 pairs of children and caregivers. Micronutrient adequacy was evaluated by the mean probability of adequacy (MPA) of intake over 11 micronutrients. Proxy indicators were FGS-10 [10 food groups based on the FAO/FHI360 minimum dietary diversity for women (MDD-W) guidelines] and related MDD-W (FGS-10 ≥5); and FGS-7 [7 groups based on the WHO infant and young child (IYC) feeding MDD guidelines] and related MDD-IYC (FGS-7 ≥4).ResultsFGS-10 and FGS-7 were similar across children and women (∼3 groups). FGS-10 performed better than FGS-7 to predict MPA in children (Spearman rank correlation = 0.59 compared with 0.50) and women of all 3 physiological statuses (Spearman rank correlation = 0.53–0.55 compared with 0.42–0.52). MDD-W and MDD-IYC performed well in predicting MPA >0.75 in children and MPA >0.6 in nonpregnant nonbreastfeeding (NPNB) women, but a 4-group cutoff for FGS-10 allowed a better balance between sensitivity, specificity, and proportion of correct classification. MPA levels for pregnant and breastfeeding women were too low to assess best cutoff points.ConclusionsMDD-IYC or an adapted MDD-W (FGS-10 ≥4 instead of FGS-10 ≥5) can be extended to 24–59-mo-old children and NPNB women in similar-diet settings. The inadequacy of micronutrient intakes in pregnant and breastfeeding women warrants urgent action. Micronutrient adequacy predictors should be validated in populations where a higher proportion of these women do meet dietary requirements.

NUMERICAL SIMULATION AND EXPERIMENTAL STUDY OF A HARDWARE PULSE NEURAL NETWORK WITH MEMRISTOR SYNAPSES

This article presents the results of a numerical simulation and an experimental study of the electrical circuit of a hardware spiking perceptron based on a memristor-diode crossbar. That has required developing and manufacturing a measuring bench, the electrical circuit of which consists of a hardware perceptron circuit and an input peripheral electrical circuit to implement the activation functions of the neurons and ensure the operation of the memory matrix in a spiking mode. The authors have performed a study of the operation of the hardware spiking neural network with memristor synapses in the form of a memory matrix in the mode of a single-layer perceptron synapses. The perceptron can be considered as the first layer of a biomorphic neural network that performs primary processing of incoming information in a biomorphic neuroprocessor. The obtained experimental and simulation learning curves show the expected increase in the proportion of correct classifications with an increase in the number of training epochs. The authors demonstrate generating a new association during retraining caused by the presence of new input information. Comparison of the results of modeling and an experiment on training a small neural network with a small crossbar will allow creating adequate models of hardware neural networks with a large memristor-diode crossbar. The arrival of new unknown information at the input of the hardware spiking neural network can be related with the generation of new associations in the biomorphic neuroprocessor. With further improvement of the neural network, this information will be comprehended and, therefore, will allow the transition from weak to strong artificial intelligence.

Circulating amino acid signature in older people with Parkinson's disease: A metabolic complement to the EXosomes in PArkiNson Disease (EXPAND) study.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in old age. Neurotoxicity of dopaminergic neurons triggered by aggregation of misfolded α-synuclein is a major pathogenic trait of PD. However, growing evidence indicates that peripheral processes, including metabolic changes, may precede and contribute to neurodegeneration. The present study was undertaken to identify a metabolic signature of PD through the quantification of serum amino acids and derivatives. Twenty older adults with PD (11 men and 9 women; mean age 73.1 ± 10.2 years) and 30 age-matched controls (14 men and 16 women; mean age 74.6 ± 4.3 years) were enrolled. A panel of 37 serum amino acids and derivatives was assessed by ultra-performance liquid chromatography/mass spectrometry. Partial least squares - discriminant analysis (PLS-DA) followed by double cross-validation was used to characterize the relationship between amino acid profiles and PD. The optimal complexity of the PLS-DA model was found to be three latent variables. The proportion of correct classifications was 99.3 ± 2.5% for participants with PD and 94.7 ± 3.0% for non-PD controls. Higher levels of β-amino butyric acid, cystine, ornithine, phosphoethanolamine, and proline defined the circulating amino acid profile of older people with PD. Controls were characterized by higher concentrations of 3-methyl-histidine, citrulline, and serine. Our findings indicate the existence of a distinct metabotype in older persons with PD. Future studies will have to establish whether changes in amino acid metabolism are involved in the pathogenesis of PD. This knowledge may be harnessed to identify novel disease biomarkers as well as new targets for interventions.

Inflammatory signatures in older persons with physical frailty and sarcopenia: The frailty "cytokinome" at its core.

The construct of physical frailty and sarcopenia (PF&S) identifies an age-related pre-disability condition defined by reduced physical performance and low muscle mass. Whether PF&S is characterized by perturbations of the cytokine network is presently unclear. Furthermore, the existence of gender-specific inflammatory profiles of PF&S is unknown. This study was designed to explore the association between a large panel of inflammatory biomolecules and PF&S in older adults through a multivariate statistical approach. Gender-specific inflammatory patterns were also explored. One-hundred community-dwellers aged 70 years and older with PF&S and 100 non-sarcopenic, non-frail controls (nonPF&S) were enrolled. A panel of 30 circulating inflammatory biomarkers was assayed. Partial least squares discriminant analysis (PLS-DA) was employed to explore the relationship between inflammatory molecules and PF&S. Separate PLS-DA models were built for the whole sample and the two genders. Double cross-validation procedures were used to validate the predictive ability of PLS-DA models. The optimal complexity of the PLS-DA model built on the whole sample was found to be four latent variables. The proportion of correct classification was 75.6 ± 1.3% (82.3 ± 1.6% for enrollees with PF&S and 68.7 ± 2.5% for nonPF&S controls). The inflammatory profile of people with PF&S was defined by higher levels of P-selectin, C-reactive protein (CRP), and interferon γ-induced protein 10. NonPF&S participants were characterized by higher levels of myeloperoxidase (MPO), interleukin (IL) 8, monocyte chemoattractant protein 1 (MCP-1), macrophage inflammatory protein 1-α, platelet-derived growth factor (PDGF) BB. Gender-specific PLS-DA allowed identifying a "core" inflammatory signature of PF&S, composed by higher levels of CRP, and lower concentrations of MPO, IL8, MCP-1, and PDGF-BB, with peculiar patterns of relationships for men and women. A core inflammatory profile was identified in people with PF&S with a gender-specific signature. The dissection of the PF&S "cytokinome" will provide novel insights into the role played by inflammation in the disabling cascade and allow designing personalized treatment strategies.

A Distinct Pattern of Circulating Amino Acids Characterizes Older Persons with Physical Frailty and Sarcopenia: Results from the BIOSPHERE Study.

Physical frailty and sarcopenia (PF&S) are hallmarks of aging that share a common pathogenic background. Perturbations in protein/amino acid metabolism may play a role in the development of PF&S. In this initial report, 68 community-dwellers aged 70 years and older, 38 with PF&S and 30 non-sarcopenic, non-frail controls (nonPF&S), were enrolled as part as the “BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons” (BIOSPHERE) study. A panel of 37 serum amino acids and derivatives was assayed by UPLC-MS. Partial Least Squares–Discriminant Analysis (PLS-DA) was used to characterize the amino acid profile of PF&S. The optimal complexity of the PLS-DA model was found to be three latent variables. The proportion of correct classification was 76.6 ± 3.9% (75.1 ± 4.6% for enrollees with PF&S; 78.5 ± 6.0% for nonPF&S). Older adults with PF&S were characterized by higher levels of asparagine, aspartic acid, citrulline, ethanolamine, glutamic acid, sarcosine, and taurine. The profile of nonPF&S participants was defined by higher concentrations of α-aminobutyric acid and methionine. Distinct profiles of circulating amino acids and derivatives characterize older people with PF&S. The dissection of these patterns may provide novel insights into the role played by protein/amino acid perturbations in the disabling cascade and possible new targets for interventions.

Specific Profiles Of Circulating Mediators Characterize Older Persons With Physical Frailty And Sarcopenia

Chronic inflammation, changes in body composition, muscle loss (sarcopenia) and decreasing homeostatic reserve (frailty) are hallmarks of aging. Several circulating inflammatory markers have been associated with these age‐related conditions. However, a gold standard biomarker able to predict functional impairment in older adults is currently unavailable. Muscle is crucial for several metabolic processes, including protein/aminoacid metabolism. Perturbations in protein/aminoacid metabolism may play also a role in the development of physical frailty and sarcopenia (PF&amp;S). The simultaneous analysis of an array of circulating aminoacid/metabolites and their relationship with inflammatory mediators may help gain insights in the pathophysiology of PF&amp;S.To this aim, we analyzed the profile of circulating inflammatory mediators and amino acids in older people with and without PF&amp;S.More than five hundred persons aged 70+ years were screened. Of these, sixty (20 men and 40 women; mean age 76.9±4.8 years) were diagnosed with PF&amp;S. Thirty (14 men and 16 women) non‐sarcopenic, non‐frail persons were enrolled in the control group. A panel of 27 cytokines, chemokines and growth factors was analyzed via a multiplex, magnetic bead‐based immunoassay on a Bio‐Plex® System with Luminex xMap Technology. A panel of 37 serum amino acids and derivatives was assessed by UPLC‐MS. Multi‐block partial least squares discriminant analysis (PLS‐DA) was employed to explore the relationship among inflammatory and amino acid profiles of people with PF&amp;S. Double cross‐validation procedures were used to validate the predictive ability of the PLS‐DA model.The optimal complexity of the PLS‐DA model was found to be three latent variables. The proportion of correct classification was 85.5 ± 4.4 for persons with PF&amp;S and 88.3 ± 3.6 for controls. As for the amino acid profile, people with PF&amp;S showed higher levels of aspartic acid, asparagine, taurine, citrulline, alpha‐aminobutyric acid, methionine, and glutamic acid. Increased levels of interleukin‐8, myeloperoxidase, and platelet derived growth factor‐BB were also found in people with PF&amp;S.The dissection of these patterns may provide novel insights into the role played by inflammatory mediators and protein/amino acid perturbations in the disabling cascade associated with PF&amp;S and possible new targets for interventions.Support or Funding InformationThis study was supported by the “Centro Studi Achille e Linda Lorenzon”, intramural research grants from the Catholic University of the Sacred Heart (D3.2 2013 and D3.2 2015), Innovative Medicines Initiative Joint Undertaking (IMI‐JU 115621), and Fondazione Roma (NCDs Call for Proposal 2013)This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Systemic inflammation, body composition, and physical performance in old community-dwellers.

BackgroundChronic inflammation, changes in body composition, and declining physical function are hallmarks of the ageing process. The aim of the present study was to provide a preliminary characterisation of the relationship among these age‐related phenomena via multivariate modelling.MethodsThirty‐five old adults (OAs) and 17 young adults (YAs) were enrolled. The volume of skeletal muscle, subcutaneous adipose tissue (SAT), and intermuscular adipose tissue (IMAT) of the thigh was quantified by three‐dimensional magnetic resonance imaging. Muscle strength was measured by knee extension strength testing. In OAs, physical performance was further assessed via the Short Physical Performance Battery (SPPB). Multi‐block partial least squares‐discriminant analysis (PLS‐DA) was employed to explore the relationship among inflammatory profiles and functional and imaging parameters. Double cross‐validation procedures were used to validate the predictive ability of the PLS‐DA model.ResultsThe optimal complexity of the PLS‐DA model was found to be two latent variables. The proportion of correct classification was 92.3% in calibration (94.1% in YAs and 91.4% in OAs), 84.6% in internal validation (95.3% in YAs and 78.5% in OAs), and 82.6% in external validation (94% in YAs and 76.9% in OAs). Relative to YAs, OAs were characterised by smaller muscle volume, greater IMAT volume, lower muscle strength, and higher levels of myeloperoxidase, P‐selectin, soluble intercellular adhesion molecule 1, and vascular cell adhesion molecule 1. Compared with OAs with SPPB >8, those scoring ≤8 were characterised by smaller muscle volume, greater SAT volume, lower muscle strength, and higher levels of interleukin 1 beta, 6, 10, 12, 13, tumour necrosis factor alpha, and granulocyte‐macrophage colony‐stimulating factor.ConclusionsMulti‐block PLS‐DA identified distinct patterns of relationships among circulating cytokines and functional and imaging parameters in persons of different ages and varying levels of physical performance. The longitudinal implementation of such an innovative strategy could allow for the tracking of health status over time, the early detection of deviations in health trajectories, and the monitoring of response to treatments.

Comparison of model-based and expert-rule based electrocardiographic identification of the culprit artery in patients with acute coronary syndrome

Background and purposeCulprit coronary artery assessment in the triage ECG of patients with suspected acute coronary syndrome (ACS) is relevant a priori knowledge preceding percutaneous coronary intervention (PCI). We compared a model-based automated method (Olson method) with an expert-rule based method for the culprit artery assessment. MethodsIn each of the 53 patients who were admitted with the working diagnosis of suspected ACS, scheduled for emergent angiography with a view on revascularization as initial treatment and subsequently found to have an angiographically documented completely occluded culprit artery, culprit artery location was assessed in the preceding ECG by both the model-based Olson method and the expert-rule based method that considered either visual or computer-measured J-point amplitudes. ECG culprit artery estimations were compared with the angiographic culprit lesion locations. Proportions of correct classifications were compared by a Z test at the 5% significance level. ResultsThe Olson method performed slightly, but not significantly, better, when the expert-rule based method used visual assessment of J-point amplitudes (88.7% versus 81.1% correct; P=0.28). However, the Olson method performed significantly better when the expert-rule based method used computer-measured J-point amplitudes (88.7% versus 71.7% correct; P<0.05). ConclusionThe automated model-based Olson method performed at least at the level of expert cardiologists using a manual rule-based method.

Patterns of circulating inflammatory biomarkers in older persons with varying levels of physical performance: a partial least squares-discriminant analysis approach.

Background: Chronic, low-grade inflammation and declining physical function are hallmarks of the aging process. However, previous attempts to correlate individual inflammatory biomarkers with physical performance in older people have produced mixed results. Given the complexity of the inflammatory response, the simultaneous analysis of an array of inflammatory mediators may provide more insights into the relationship between inflammation and age-related physical function decline. This study was designed to explore the association between a panel of inflammatory markers and physical performance in older adults through a multivariate statistical approach.Methods: Community-dwelling older persons were categorized into “normal walkers” (NWs; n = 27) or “slow walkers” (SWs; n = 11) groups using 0.8 m s−1 as the 4-m gait speed cutoff. A panel of 14 circulating inflammatory biomarkers was assayed by multiplex analysis. Partial least squares-discriminant analysis (PLS-DA) was used to identify patterns of inflammatory mediators associated with gait speed categories.Results: The optimal complexity of the PLS-DA model was found to be five latent variables. The proportion of correct classification was 88.9% for NW subjects (74.1% in cross-validation) and 90.9% for SW individuals (81.8% in cross-validation). Discriminant biomarkers in the model were interleukin 8, myeloperoxidase, and tumor necrosis factor alpha (all higher in the SW group), and P-selectin, interferon gamma, and granulocyte–macrophage colony-stimulating factor (all higher in the NW group).Conclusion: Distinct profiles of circulating inflammatory biomarkers characterize older subjects with different levels of physical performance. The dissection of these patterns may provide novel insights into the role played by inflammation in the disabling cascade and possible new targets for interventions.

Parasites as biological tags of fish stocks: a meta-analysis of their discriminatory power

The use of parasites as biological tags to discriminate among marine fish stocks has become a widely accepted method in fisheries management. Here, we first link this approach to its unstated ecological foundation, the decay in the similarity of the species composition of assemblages as a function of increasing distance between them, a phenomenon almost universal in nature. We explain how distance decay of similarity can influence the use of parasites as biological tags. Then, we perform a meta-analysis of 61 uses of parasites as tags of marine fish populations in multivariate discriminant analyses, obtained from 29 articles. Our main finding is that across all studies, the observed overall probability of correct classification of fish based on parasite data was about 71%. This corresponds to a two-fold improvement over the rate of correct classification expected by chance alone, and the average effect size (Zr = 0·463) computed from the original values was also indicative of a medium-to-large effect. However, none of the moderator variables included in the meta-analysis had a significant effect on the proportion of correct classification; these moderators included the total number of fish sampled, the number of parasite species used in the discriminant analysis, the number of localities from which fish were sampled, the minimum and maximum distance between any pair of sampling localities, etc. Therefore, there are no clear-cut situations in which the use of parasites as tags is more useful than others. Finally, we provide recommendations for the future usage of parasites as tags for stock discrimination, to ensure that future applications of the method achieve statistical rigour and a high discriminatory power.