Sensitivity Of Criteria Research Articles

Field trauma triage criteria associated with need for dedicated trauma center care: a single-center retrospective cohort study.

Direct transport from the scene of injury to a trauma centre reduces saves lives. In Ontario,paramedics use the field trauma triage standard (FTTS) to determine if a patient meets trauma bypass criteria. Recent studies have questioned the efficacy of the FTTS in identifying severely injured patients. The objective of this study was to determine the predictive performance of the FTTS on the need for trauma center care in patients who were transported to a trauma center. This was a single-center health records study of patients transported by ambulance directly to a level 1 trauma center. Hospital based trauma center need and injury severity score-based needwere defined. Bivariate associations with one or more FTTS criteria were tested using the Wilcoxon two-sample test for continuous variables, and the Chi-square or Fisher's exact test for categorical indicators. The sensitivity and specificity of each category of the FTTS were calculated. There were 1427 patients included in the study, with76% men, mean age of 40, and 76% had a blunt mechanism. The overall sensitivity and specificity of the FTTS was 90.9% and 20.8% for hospital-based needand 91.6% and 20.3 for injury severity need. The most sensitive variable for hospital-based need was physiologic criteria (53.7).Mechanism of injury was the most sensitive criteria for injury severity need(54.8). Physiological criteria had the highest association with hospital-based and injury severity need (adjusted odds ratios 7.5[95% CI 5.8-9.8] and 5.1[95% CI 3.9-6.7]). The FTTS has fair performance in identifying the need for hospital-based and injury severity need. Systolic blood pressure less than 90mmHg, Glasgow Coma Scale (motor) less than 6,and falls greater than 6m were most predictive of trauma center need. Improving prehospital trauma triage is critical to ensure timely transport to a trauma centre.

Selective dual sensing strategy for free and vitamin D3 micelles in food samples based on S,N-GQDs photoinduced electron transfer.

A reliable nanotechnological sensing strategy, based on anS,N-co-doped graphene quantum dot (GQD) platform, has been developed to distinctly detect two key variants of vitamin D3, specifically the free (VD3) and the nanoencapsulated form (VD3Ms). For this purpose, food-grade vitamin D3 micelles were self-assembled using a low-energy procedure (droplet size: 49.6 nm, polydispersity index: 0.34, ζ-potential: -33 mV, encapsulation efficiency: 90 %) with an innovative surfactant mixture (Tween 60 and quillaja saponin). Herein, four fluorescent nanoprobes were also synthesized and thoroughly characterized: S,N-co-doped GQDs, α-cyclodextrin-GQDs, β-cyclodextrin-GQDs, and γ-cyclodextrin-GQDs. The goal was to achieve a selective dual sensing strategy for free VD3 and VD3Ms by exploiting their distinctive quenching behaviors. Thus, the four nanosensors allowed the individual sensing of both targetsto be performed (except α-CD-GQD for VD3Ms), but S,N-GQDs were finally selected due to selectivity and sensitivity (quantum yield, QY= 0.76) criteria. This choice led to a photoinduced electron transfer (PET) mechanism associated with static quenching, where differentiation was evidenced through a displayed 13-nm hypsochromic (blue) shift when interacting with VD3Ms. The reliability of this dual approach was demonstrated through an extensive evaluation of analytical performance characteristics. The feasibility and accuracy were proven in commercial food preparations and nutritional supplements containing declared nanoencapsulated and raw VD3, whose results were validated by a paired Student's t-test comparison with a UV-Vis method. To the best of our knowledge, this represents the first non-destructive analytical approach addressing the groundbreaking foodomic trend to distinctly detect different bioactive forms of vitamin D3, while also preserving their native nanostructures as achemical challenge, thus providing reliable information about their final stability and bioavailability.

Natural Characteristics of a Marine Two-Stage Tandem Hybrid Planetary System

This study focuses on a marine two-stage tandem hybrid planetary system. Natural frequencies and vibration modes are determined using a translational–torsional coupled dynamic model. Based on the motion characteristics of the transmission system, free vibration is categorized into three typical modes. The parameter sensitivity of natural frequencies is computed, and the effects of structural parameters such as unequally spaced planet, mesh stiffness, planet mass and rotational inertia on the natural frequencies are analyzed. Utilizing the coupling factor, the mode transition criterion for the natural frequencies response to parameters is formulated. The results demonstrate that the vibration modes of the two-stage tandem hybrid planetary system can be classified as the fixed-axis train vibration mode, the differential train vibration mode, and the coupled vibration mode. Unequally spaced planet primarily disrupts vibration modes without significantly affecting natural frequencies. In contrast, the effects of mesh stiffness, planet mass and rotational inertia on the modes are opposite to those of unequally spaced planets. The validity of the parameter sensitivity and mode transition criterion is substantiated through illustrative examples.

Dual CRISPR/Cas13a Cascade Strand Displacement-Triggered Transcription for Point-of-Care Detection of Plasmodium in Asymptomatic Malaria.

Asymptomatic infections of Plasmodium parasites are major obstacles to malaria control and elimination. A sensitive, specific, and user-friendly method is urgently needed for point-of-care (POC) Plasmodium diagnostics in asymptomatic malaria, especially in resource-limited settings. In this work, we present a POC method (termed Cas13a-SDT) based on the cascade sequence recognition and signal amplification of dual Cas13a trans-cleavage and strand displacement-triggered transcription (SDT). Cas13a-SDT not only achieves exceptional specificity in discriminating the target RNA from nontarget RNAs with any cross-interaction but also meets the sensitivity criterion set by the World Health Organization (WHO) for effective malaria detection. Remarkably, this novel method was successfully applied to screen malaria in asymptomatic infections from clinical samples. The proposed method provides a user-friendly and visually interpretable output mode while maintaining high accuracy and reliability comparable to RT-PCR. These excellent features demonstrate the significant potential of Cas13a-SDT for POC diagnosis of Plasmodium infections, laying a vital foundation for advancing malaria control and elimination efforts.

Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024.

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) standardizes the imaging technique, reporting lexicon, disease categorization, and management for patients with or at risk for hepatocellular carcinoma (HCC). LI-RADS encompasses HCC surveillance with US; HCC diagnosis with CT, MRI, or contrast-enhanced US (CEUS); and treatment response assessment (TRA) with CT or MRI. LI-RADS was recently expanded to include CEUS TRA after nonradiation locoregional therapy or surgical resection. This report provides an overview of LI-RADS CEUS Nonradiation TRA v2024, including a lexicon of imaging findings, techniques, and imaging criteria for posttreatment tumor viability assessment. LI-RADS CEUS Nonradiation TRA v2024 takes into consideration differences in the CEUS appearance of viable tumor and posttreatment changes within and in close proximity to a treated lesion. Due to the high sensitivity of CEUS to vascular flow, posttreatment reactive changes commonly manifest as areas of abnormal perilesional enhancement without washout, especially in the first 3 months after treatment. To improve the accuracy of CEUS for nonradiation TRA, different diagnostic criteria are used to evaluate tumor viability within and outside of the treated lesion margin. Broader criteria for intralesional enhancement increase sensitivity for tumor viability detection. Stricter criteria for perilesional enhancement limit miscategorization of posttreatment reactive changes as viable tumor. Finally, the TRA algorithm reconciles intralesional and perilesional tumor viability assessment and assigns a single LI-RADS treatment response (LR-TR) category: LR-TR nonviable, LR-TR equivocal, or LR-TR viable.

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning.

Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of waste generated require the development of automated waste management models. The development of smart technology offers a possible alternative to traditional waste management techniques that are proving insufficient to reduce the harmful effects of trash on the environment. This paper proposes an intelligent waste classification model to enhance the classification of waste materials, focusing on the critical aspect of waste classification. The proposed model leverages the InceptionV3 deep learning architecture, augmented by multi-objective beluga whale optimization (MBWO) for hyperparameter optimization. In MBWO, sensitivity and specificity evaluation criteria are integrated linearly as the objective function to find the optimal values of the dropout period, learning rate, and batch size. A benchmark dataset, namely TrashNet is adopted to verify the proposed model's performance. By strategically integrating MBWO, the model achieves a considerable increase in accuracy and efficiency in identifying waste materials, contributing to more effective waste management strategies while encouraging sustainable waste management practices. The proposed intelligent waste classification model outperformed the state-of-the-art models with an accuracy of 97.75%, specificity of 99.55%, F1-score of 97.58%, and sensitivity of 98.88%.

An Improved Ensemble Machine Learning Approach for Diabetes Diagnosis

Diabetes is recognized as one of the most detrimental diseases worldwide, characterized by elevated levels of blood glucose stemming from either insulin deficiency or decreased insulin efficacy. Early diagnosis of diabetes enables patients to initiate treatment promptly, thereby minimizing or eliminating the risk of severe complications. Although years of research in computational diagnosis have demonstrated that machine learning offers a robust methodology for predicting diabetes, existing models leave considerable room for improvement in terms of accuracy. This paper proposes an improved ensemble machine learning approach using multiple classifiers for diabetes diagnosis based on the Pima Indians Diabetes Dataset (PIDD). The proposed ensemble voting classifier amalgamates five machine learning algorithms: Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forests (RF), and XGBoost. We obtained the individual model accuracies and used the ensemble method to improve accuracy. The proposed approach uses a pre-processing stage of standardization and imputation and applies the Local Outlier Factor (LOF) to remove data anomalies. The model was evaluated using sensitivity, specificity, and accuracy criteria. With a reported accuracy of 81%, the proposed approach shows promise compared to prior classification techniques.

Revealing the compound interrelationships toward sustainable transition in semiconductor supply chain: A sensitivity analysis

This study provides specific guidelines for allocating resources by determining the positive and negative interrelationships as well as the sensitive criteria and factors of the semiconductor supply chain. However, revealing these compound interrelationships enables the semiconductor supply chain to accelerate the sustainable transition effectively and efficiently. Previous studies have introduced the sustainable transition without considering the compound interrelationships for guiding the semiconductor supply chain. The proposed hybrid digital twin sensitivity analysis method used in this study integrates clustering, machine learning, a cloud model, and the Monte Carlo simulation method. The contributions of the study are as follows: (1) the discovered factors strengthen the theoretical basis of the sustainable transition; (2) the proposed hybrid method reveals compound interrelationships by utilizing insufficient data; and (3) these compound interrelationships are presented in a visual diagram to provide guidelines for allocating resources to promote the sustainable transition. In practice, the total chromium and power consumption are the sensitive criteria influencing sustainable finance; technological innovations are influenced by the revenue and production area.

Brain magnetic resonance images segmentation via improved mixtures of factor analyzers based on dynamic co-clustering

The main goal of this paper is to attain accurate and automatic detection of brain tumors in gray magnetic resonance images using reducing the local dimensions. We propose a novel model called Improved Mixtures of Factor Analyzers based on Dynamic Co-Clustering (IMFADCC) for the detection and localization of brain tumors. After image preprocessing and enhancement, the optimal numbers of row clusters and column clusters corresponding to each row cluster, alongside other model parameters, are determined concurrently based on the size of the tumor by our model. Then, using the optimal values obtained, the image is co-clustered by simultaneously clustering rows and columns until the block containing the tumor is identified. The output image is divided into a certain number of blocks, one of which contains the tumor. For post-processing, the identified block is binarized using minimum error thresholding. The proposed model accurately detects and locates the block binarized within the original image while considering the remainder of the image as background. The effectiveness of the proposed method was assessed by evaluating its accuracy, Dice, intersection on union, geometric mean, receiver operating characteristic curve, specificity, and sensitivity criteria on the BraTS2018, BraTS2019, and BraTS2020 datasets. The results show that our method has significant performance and is highly accurate in detecting tumors of different sizes in complex and high-size images. Moreover, our method outperforms the existing diagnostic methods.

A spectrophotometric determination and the quantum chemical investigation of Pd(II)-3-hydroxy-2-(4-methoxyphenyl)-4-oxo-4H-1-benzopyran complex

An analytical approach has been instigated for spectrophotometric inquisition of trace amounts of palladium in its bivalent oxidation state employing a novel 1-benzopyran derivative viz. A 1:2 [Pd(II):HMPB] light yellow complex is formed by a spontaneous interaction between Pd(II) and HMPB. The complex is quantitatively extracted from a weakly basic medium into dichloromethane absorbing prevalently and consistently at 405-425 nm. The method coheres to linearity up to 1.5 µg mL-1 of palladium(II). Analytical parameters such as the molar absorption coefficient (ε=3.011 × 104 L mol-1 cm-1), Sandell's sensitivity [0.0035 µg Pd(II) cm-2], % RSD (0.62%) and the limit of detection [0.0147 µg Pd(II) mL-1] indicate that the study satisfies all the criteria for good sensitivity, precision and accuracy. In addition, intervention with regard to numerous anions/complexing agents and cations of primary analytical importance has been performed to assess the method's flexibility and usefulness. The results show that the majority of them do not cause any interference during determination. To better understand chemistry of the prepared complex, spectroscopic quantum chemical studies including DFT and MEP mapping based on examination of the electronic characteristics of the complex in its most stable least energy conformation are used.
 KEY WORDS: Palladium, 3-Hydroxy-2-(4-methoxyphenyl)-4-oxo-4H-1-benzopyran, Extractive spectrophotometric determination, DFT, MEP, Docking
 Bull. Chem. Soc. Ethiop. 2024, 38(3), 591-603. 
 DOI: https://dx.doi.org/10.4314/bcse.v38i3.4

Late-onset pulmonary complications following allogeneic hematopoietic cell transplantation in pediatric patients: a prospective multicenter study.

The primary objective of our multicenter prospective study was to describe the incidence of late-onset non-infectious pulmonary complications (LONIPCs) in children undergoing hematopoietic cell transplantation (HCT) using sensitive criteria for pulmonary function test (PFT) abnormalities including the non-specific pattern of airflow obstruction. Secondary objectives were to assess the factors associated with LONIPC occurrence and the sensitivity of the 2014 NIH-Consensus Criteria of bronchiolitis obliterans syndrome (BOS). PFT and clinical assessment were performed prior to HCT and at 6, 12, 24, and 36 months post-HCT. LONIPC diagnosis was validated by an Adjudication Committee. The study comprised 292 children from 12 centers. Thirty-two individuals (11%, 95% CI: 8-15%) experienced 35 LONIPCs: 25 BOS, 4 interstitial lung diseases, 4 organizing pneumonia and 2 pulmonary veno-occlusive diseases. PFT abnormalities were obstructive defects (FEV1/FVC z-score < -1.645; n = 12), restrictive defects (TLC < 80% predicted, FEV1 and FVC z-scores < -1.645; n = 7) and non-specific pattern (FEV1 and FVC z-score< -1.645, FEV1/FVC z-score > -1.645, and TLC > 80% predicted; n = 8). HCT for malignant disease was the only factor associated with LONIPC (P = 0.04). The 2014 NIH-Consensus Criteria would only diagnose 8/25 participants (32%) as having BOS. In conclusion, 11% of children experienced a LONIPC in a prospective design. Clinical Trials.gov identifier (NCT number): NCT02032381.

Hypoplastic Left Heart Syndrome with Mitral Stenosis and Aortic Atresia—Echocardiographic Findings and Early Outcomes

Mitral stenosis/aortic atresia (MS/AA) has been reported as a high-risk variant of hypoplastic left heart syndrome (HLHS), potentially related to ventriculocoronary connections (VCCs) or endocardial fibroelastosis (EFE) and myocardial hypoperfusion. We aimed to identify echocardiographic and clinical factors associated with early death or transplant in this group. Patients with HLHS MS/AA treated at our center between 2000 and 2020 were included. Pre-stage I palliation echocardiograms were reviewed. Certain imaging factors, such as determination of VCC, EFE, and measurement of tricuspid annular plane systolic excursion were measured from retrospective review of preoperative images; others were derived from clinical reports. Groups were compared according to primary outcome of death or transplant prior to stage II palliation. Of 141 patients included, 39 (27.7%) experienced a primary outcome. Ventriculocoronary connections were identified in 103 (73.0%) patients and EFE in 95 (67.4%) patients. Among imaging variables, smaller ascending aorta size (median, 2.2 [interquartile range (IQR) 1.7-2.8] vs 2.6 [2.2-3.4] mm, P=.01) was associated with primary outcome. There was similar frequency of VCC (74.4% vs 72.5%, P=.83), EFE (59.0% vs 72.5%, P=.19), moderate or greater tricuspid regurgitation (5.1% vs 5.9%, P=1.00), and similar right ventricular systolic function (indexed tricuspid annular plane systolic excursion 32.5±7.3 vs 31.4±7.2mm/m2, P=.47) in the primary outcome group compared to other patients. Clinical factors associated with primary outcome included lower birth weight (mean, 2.8±SD 0.8 vs 3.3±0.5kg, P=.0003), gestational age <37weeks (31.6% vs 4.9%, P<.0001), longer cardiopulmonary bypass time (median, 112 [IQR, 93-162] vs 82 [71-119] minutes, P=.001), longer intensive care unit length of stay (median, 19 [IQR, 10-30] vs 10 [7-15] days, P=.001), and extracorporeal membrane oxygenation following stage I palliation (43.6% vs 8.8%, P<.0001). Presence of VCCs and EFE was not associated with death or transplant after controlling for birth weight and era of stage I palliation. In one of the largest reported single-center cohorts of HLHS MS/AA, there were few pre-stage I palliation imaging characteristics associated with primary outcome. Imaging findings evaluated in this study, including the presence of VCC and/or EFE as determined using highly sensitive echocardiogram criteria, should not preclude intervention, although impact on long-term outcomes requires further evaluation.

Epidemiological Surveillance of Acute Flaccid Paralysis for Eradication of Poliomyelitis (A Brief Review)

Abstract In May 1988 the World Health Assembly set to World Health Organization (WHO) the goal to achieve a global eradication of poliomyelitis by the year 2000. Surveillance of the acute flaccid paralysis (AFP) remains the ‘gold standard’ for the detection of polio. The criterion of sensitivity of the surveillance system is the incidence from non-polio related AFP, in children under 15 years of age. The aim is to detect more than 1 case of AFP, per 100,000 children. In 2019, WHO announced the eradication of wild poliovirus 3, and poliovirus 2 was eradicated in 2015. Wild poliovirus 1 continues to circulate. The main goals of the WHO Polio Eradication Strategy for the period 2022-2026 are: permanently interrupt all poliovirus transmission in endemic countries (Afghanistan and Pakistan), stop cVDPV (circulating vaccine-derived poliovirus) transmission and prevent outbreaks in non-endemic countries.

Slimming Neural Networks Using Adaptive Connectivity Scores.

In general, deep neural network (DNN) pruning methods fall into two categories: 1) weight-based deterministic constraints and 2) probabilistic frameworks. While each approach has its merits and limitations, there are a set of common practical issues such as trial-and-error to analyze sensitivity and hyper-parameters to prune DNNs, which plague them both. In this work, we propose a new single-shot, fully automated pruning algorithm called slimming neural networks using adaptive connectivity scores (SNACS). Our proposed approach combines a probabilistic pruning framework with constraints on the underlying weight matrices, via a novel connectivity measure, at multiple levels to capitalize on the strengths of both approaches while solving their deficiencies. In SNACS, we propose a fast hash-based estimator of adaptive conditional mutual information (ACMI), that uses a weight-based scaling criterion, to evaluate the connectivity between filters and prune unimportant ones. To automatically determine the limit up to which a layer can be pruned, we propose a set of operating constraints that jointly define the upper pruning percentage limits across all the layers in a deep network. Finally, we define a novel sensitivity criterion for filters that measures the strength of their contributions to the succeeding layer and highlights critical filters that need to be completely protected from pruning. Through our experimental validation, we show that SNACS is faster by over 17× the nearest comparable method and is the state-of-the-art single-shot pruning method across four standard Dataset-DNN pruning benchmarks: CIFAR10-VGG16, CIFAR10-ResNet56, CIFAR10-MobileNetv2, and ILSVRC2012-ResNet50.

Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Abstract. Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers. Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in three European catchments (Spain, Finland, France) characterised by different climate, geology, and anthropogenic use. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in mesoscale river networks (between 120 and 350 km2). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a random forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, citizen science applications) are used to build the predictive model. This study aims to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativity) on the performance of the predictive model. Results show that the hybrid modelling approach developed in this study allows the spatio-temporal patterns of drying to be accurately predicted in the three catchments, with a sensitivity criterion above 0.9 for the prediction of dry events in the Finnish and French case studies and 0.65 in the Spanish case study. This study shows the value of combining different data sources of observed flow condition to reduce the uncertainty in predicting flow intermittence.

Therapeutic Monitoring of Palbociclib, Ribociclib, Abemaciclib, M2, M20, and Letrozole in Human Plasma: A Novel LC-MS/MS Method.

Therapeutic drug monitoring (TDM) using cyclin-dependent kinase inhibitors (CDK4/6is) is a novel approach for optimizing treatment outcomes. Currently, palbociclib, ribociclib, and abemaciclib are the available CDK4/6is and are primarily coadministered with letrozole. This study aimed to develop and validate an LC-MS/MS method for the simultaneous analysis of CDK4/6is, 2 active metabolites of abemaciclib (M2 and M20), and letrozole in human plasma for use in TDM studies. Sample pretreatment comprised protein precipitation with methanol and dilution of the supernatant with an aqueous mobile phase. Chromatographic separation was achieved using a reversed-phase XBridge BEH C18 column (2.5 μm, 3.0 × 75 mm XP), with methanol serving as the organic mobile phase and pyrrolidine-pyrrolidinium formate (0.005:0.005 mol/L) buffer (pH 11.3) as the aqueous mobile phase. A triple quadrupole mass spectrometer was used for the detection, with the ESI source switched from negative to positive ionization mode and the acquisition performed in multiple reaction monitoring mode. The complete validation procedure was successfully performed in accordance with the latest regulatory guidelines. The following analytical ranges (ng/mL) were established for the tested compounds: 6-300, palbociclib and letrozole; 120-6000, ribociclib; 40-800, abemaciclib; and 20-400, M2 and M20. All results met the acceptance criteria for linearity, accuracy, precision, selectivity, sensitivity, matrix effects, and carryover. A total of 85 patient samples were analyzed, and all measured concentrations were within the validated ranges. The percent difference for the reanalyzed samples ranged from -11.2% to 7.0%. A simple and robust LC-MS/MS method was successfully validated for the simultaneous quantification of CDK4/6is, M2, M20, and letrozole in human plasma. The assay was found to be suitable for measuring steady-state trough concentrations of the analytes in patient samples.

Comparing neuropsychological, typical, and ADNI criteria for the diagnosis of mild cognitive impairment in Vietnam-era veterans.

Neuropsychological criteria for mild cognitive impairment (MCI) more accurately predict progression to Alzheimer's disease (AD) and are more strongly associated with AD biomarkers and neuroimaging profiles than ADNI criteria. However, research to date has been conducted in relatively healthy samples with few comorbidities. Given that history of traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) are risk factors for AD and common in Veterans, we compared neuropsychological, typical (Petersen/Winblad), and ADNI criteria for MCI in Vietnam-era Veterans with histories of TBI or PTSD. 267 Veterans (mean age = 69.8) from the DOD-ADNI study were evaluated for MCI using neuropsychological, typical, and ADNI criteria. Linear regressions adjusting for age and education assessed associations between MCI status and AD biomarker levels (cerebrospinal fluid [CSF] p-tau181, t-tau, and Aβ42) by diagnostic criteria. Logistic regressions adjusting for age and education assessed the effects of TBI severity and PTSD symptom severity simultaneously on MCI classification by each criteria. Agreement between criteria was poor. Neuropsychological criteria identified more Veterans with MCI than typical or ADNI criteria, and were associated with higher CSF p-tau181 and t-tau. Typical and ADNI criteria were not associated with CSF biomarkers. PTSD symptom severity predicted MCI diagnosis by neuropsychological and ADNI criteria. History of moderate/severe TBI predicted MCI by typical and ADNI criteria. MCI diagnosis using sensitive neuropsychological criteria is more strongly associated with AD biomarkers than conventional diagnostic methods. MCI diagnostics in Veterans would benefit from incorporation of comprehensive neuropsychological methods and consideration of the impact of PTSD.

Optimization of parameters for a fringe projection measurement system by use of an improved differential evolution method.

Fringe projection 3D measurement is widely used for object surface reconstruction. While improving measurement accuracy is a crucial task. Measurement accuracy is profoundly affected by various optical structural parameters. However, the current practice of system construction lacks theoretical guidelines and often relies on the experience of the operator, inevitably leading to unpredictable error. This paper investigates a theoretical optimization model and proposes an automatic optimization method for qualitatively determining the multiple optimal optical structural parameters in fringe projection measurement system. The aim is to enhance measurement accuracy conducting a rational comprehensive optimal structural parameters design prior to the system construction. Firstly, the mathematical model of the measurement system is established based on the principle of optical triangulation, and the phase sensitivity criterion is defined as the optimization norm. Within the full measurement range, the optimization merit function is formulated by combing three positions: the center position, the left and right boundary of the CCD. The imaging effectiveness criteria and sensor geometric dimensions are taken into account as the constraint boundaries. Subsequently, a combined improved differential evolution and Levy flight optimization algorithm is applied to search for the optimal parameters. The optimal structural parameters of the system were designed based on the optimization process. Experimental results validated the improvement in measurement accuracy achieved by the optimized structural parameters.

Evaluation of 17 years of MERIN (Meningitis and Encephalitis register in Lower Saxony, Germany) surveillance system: participants acceptability survey, completeness and timeliness of data

BackgroundA Meningitis and Encephalitis Surveillance (MERIN) was implemented in 2003 in Lower Saxony, Germany as an alternative to acute flaccid paralyses surveillance, as the latter did not reach WHO sensitivity criteria. The system provides information on circulating enterovirus (EV) serotypes by focussing on patients with suspected aseptic meningitis, encephalitis or acute flaccid paralysis and contributes to the national surveillance in documenting polio free status. MERIN is based on voluntary participation of hospitals. Therefore, our evaluation focusses on acceptability of the system’s objectives and performance, and identifying areas for improvement.MethodsTo assess acceptability, 32 contributing hospitals were invited to an online-based survey (11/2021 to 01/2022) to rate the MERIN objectives, laboratory’s performance, their workload, modes of processes and communication. Ideas for improvement were collected in open fields. In addition, data completeness and timeliness of laboratory diagnostics were assessed.ResultsOf 32 hospitals, 21 responded (66% response rate), sending 30 questionnaires, 25 from pediatric and 5 from neurological departments. High levels of satisfaction with the communication (≥ 96%), timeliness (≥ 81%), and distribution of the results (≥ 85%) were reported, 97% of participants judged the required workload as adequate. The median proportion of eligible patients included in MERIN was 75%. Participants gave rapid and reliable diagnostic testing the highest priority (96%), while monitoring of Germany’s polio-free status was rated the lowest (61%). Providing medical reports digitally as well as regular updates about circulating EV serotypes were identified as areas for improvement. Data completeness of selected variables ranged from 78.3 to 99.9%. Median time between sample collection and arrival at laboratory was 2 days [IQR 1–3], EV diagnostics via PCR took one day [IQR 0–6] and EV isolation on cell culture 11 days [IQR 10–13].ConclusionMERIN is a highly accepted surveillance system. Its quality was enhanced further by addressing the suggested improvements such as regular reports on circulating EV serotypes and facilitating digital access to laboratory results.Our results emphasise the importance of recognizing and considering participants’ motivations and expectations, and addressing their priorities, even if this is not the surveillance system’s main focus.This approach can be applied to surveillance systems of other non-mandatory notifiable diseases.

Nursing care for people with Chagas disease: a scoping review

Introduction: Chagas disease is an infectious disease caused by the parasitism process of the protozoan Trypanosoma cruzi. Given its potential for chronicity, nursing care in the health care of patients with Chagas disease will provide an improvement in quality of life and the prognosis of the disease. Objective: Review scientific knowledge about nursing care for individuals with Chagas disease. Material and Methods: descriptive and exploratory research, carried out with two independent reviewers using high sensitivity criteria in databases and gray literature sources between June and July 2022. Results: The review identified 12 relevant publications that emphasized health care, education, relationships, disease prevention and health promotion. The most frequent and diverse nursing diagnoses were related to the Activity/Rest, Health Promotion and Coping/Stress domains. Discussion: To meet the care needs of Chagas disease, it is essential to ensure nursing care that recognizes individualities, highlighting the importance of creating tools that facilitate the nursing process. The main points highlighted were related to the health education process, longitudinal monitoring, healthy lifestyle habits, general nursing care during hospitalization and the use of nursing diagnoses. Conclusion: the need for comprehensive nursing care that meets the main needs of individuals with Chagas disease is emphasized, considering their unique circumstances. Developing tools to support the nursing process is essential to improve the results of care for this population.