Complementary Tool Research Articles

Gender Differences in the Predictive Value of Obesity Indices for Insulin Resistance in Adult Mexican Individuals.

Obesity-linked insulin resistance (IR) is an important risk factor for metabolic diseases, and anthropometric indices are commonly used for risk assessment. The study aimed to assess possible differences between women and men in the predictive value and association of nine obesity indices with IR, as assessed by HOMA-IR, in a nondiabetic adult population. The cross-sectional study included individuals recruited from a hospital in Mexico City. Indices evaluated were waist circumference (WC), hip circumference (HC), body mass index (BMI), waist-to-hip ratio, waist-to-height ratio, visceral adiposity index, body adiposity index (BAI), relative fat mass (RFM), and conicity index (CI). Fasting plasma glucose and insulin were measured to calculate HOMA-IR. Correlation analysis was performed between obesity indices and HOMA-IR. Receiver operating characteristics curve analyses were performed to determine predictive accuracy and cut-off values of obesity indices for IR. A binary logistic regression (BLR) analysis with OR calculation was performed to determine the strength of association with HOMA-IR. We included 378 individuals (59% females, mean age 46.38 ±12.25 years). The highest Pearson coefficient value was observed for BMI among women, while among men, the highest values were found for BMI and BAI. WC among women, and BAI and RFM among men showed the highest sensitivity, while the highest specificity was observed for WHR among women and WC among men with respect to insulin prediction. In the adjusted BLR model, BMI, WC, and WHR among women and WC and RFM and BAI among men were independently associated with IR, showing the highest odds ratio (OR). In Mexican adults, WC, WHR, RFM and BAI could be complementary tools for BMI in screening for IR.

Computational models as catalysts for investigating redoxin systems.

Thioredoxin, glutaredoxin and peroxiredoxin systems play central roles in redox regulation, signaling and metabolism in cells. In these systems, reducing equivalents from NAD(P)H are transferred by coupled thiol-disulfide exchange reactions to redoxins which then reduce a wide array of targets. However, the characterization of redoxin activity has been unclear, with redoxins regarded as enzymes in some studies and redox metabolites in others. Consequently, redoxin activities have been quantified by enzyme kinetic parameters in vitro, and redox potentials or redox ratios within cells. By analyzing all the reactions within these systems, computational models showed that many kinetic properties attributed to redoxins were due to system-level effects. Models of cellular redoxin networks have also been used to estimate intracellular hydrogen peroxide levels, analyze redox signaling and couple omic and kinetic data to understand the regulation of these networks in disease. Computational modeling has emerged as a powerful complementary tool to traditional redoxin enzyme kinetic and cellular assays that integrates data from a number of sources into a single quantitative framework to accelerate the analysis of redoxin systems.

The Memory Binding Test Detects Early Subtle Episodic Memory Decline in Preclinical Alzheimer's Disease: A Longitudinal Study.

The asymptomatic at-risk phase might be the optimal time-window to establish clinically meaningful endpoints in Alzheimer's disease (AD). We investigated whether, compared with the Free and Cued Selective Reminding Test (FCSRT), the Memory Binding Test (MBT) can anticipate the diagnosis of emergent subtle episodic memory (EM) deficits to an at-risk phase. Five-year longitudinal FCSRT and MBT scores from 45 individuals matched for age, education, and gender, were divided into 3 groups of 15 subjects: Aβ-/controls, Aβ+/stable, and Aβ+/progressors (preclinical-AD). The MBT adds an associative memory component (binding), particularly sensitive to subtle EM decline. In the MBT, EM decline started in the Aβ+/progressors (preclinical-AD) up to 4 years prior to diagnosis in delayed free recall (FR), followed by decline in binding-associated scores 1 year later. Conversely, in the FCSRT, EM-decline began later, up to 3 years prior to diagnosis, in the same group on both immediate and delayed versions of FR, while on total recall (TR) and intrusions decline started only 1 year prior to diagnosis. The MBT seems more sensitive than the FCSRT for early EM-decline detection, regarding the year of diagnosis and the number of scores showing AD-linked EM deficits (associated with the AD-characteristic amnesic hippocampal syndrome). Considering the MBT as a detection tool of early subtle EM-decline in an asymptomatic at-risk phase, and the FCSRT as a classification tool of stages of EM-decline from a preclinical phase, these tests ought to potentially become complementary diagnostic tools that can foster therapies to delay cognitive decline. Clinical trial registration title: Electrophysiological markers of the progression to clinical Alzheimer disease in asymptomatic at-risk individuals: a longitudinal event-related potential study of episodic memory in the INSIGHT pre-AD cohort (acronym: ePARAD).

Navigation by magnetic signatures in a realistic model of Earth's magnetic field.

Certain animal species use the Earth's magnetic field (i.e. magnetoreception) alongside their other sensory modalities to navigate long distances that include continents and oceans. It is hypothesized that several animals use geomagnetic parameters, such as field intensity and inclination, to recognize specific locations or regions, potentially enabling migration without a pre-surveyed map. However, it is unknown how animals use geomagnetic information to generate guidance commands, or where in the world this type of strategy would maximize an animal's fitness. While animal experiments have been invaluable in advancing this area, the phenomenon is difficult to studyin vivoorin situ, especially on the global scale where the spatial layout of the geomagnetic field is not constant. Alongside empirical animal experiments, mathematical modeling and simulation are complementary tools that can be used to investigate animal navigation on a global scale, providing insights that can be informative across a number of species. In this study, we present a model in which a simulated animal (i.e. agent) navigates via an algorithm which determines travel heading based on local and goal magnetic signatures (here, combinations of geomagnetic intensity and inclination) in a realistic model of Earth's magnetic field. By varying parameters of the navigation algorithm, different regions of the world can be made more or less reliable to navigate. We present a mathematical analysis of the system. Our results show that certain regions can be navigated effectively using this strategy when these parameters are properly tuned, while other regions may require more complex navigational strategies. In a real animal, parameters such as these could be tuned by evolution for successful navigation in the animal's natural range. These results could also help with developing engineered navigation systems that are less reliant on satellite-based methods.

Use of sleep quality questionary and cortisol awakening response as complementary tools for the evaluation of major depression progression

Use of sleep quality questionary and cortisol awakening response as complementary tools for the evaluation of major depression progression

Evaluation of the Educaplus website and its influence on education

This study aimed to evaluate and determine if the Educaplus website contributes to the learning of mathematics in primary school students. The methodology consisted of a pretest to know the initial status of the teachers who made up the study sample and their skills and perceptions about the use of digital tools in mathematics. Then, training in Educaplus was applied as a complementary tool for mathematics training, and then a final evaluation to determine the teachers' receptivity to the incorporation of new methodologies in teaching. The research design allowed us to observe that the teachers' initial lack of knowledge prevented them from having a clear vision of the use of computer applications for mathematics. However, the training managed to change the panorama in the teaching community and improve perceptions.

AutoML‐based predictive framework for predictive analysis in adsorption cooling and desalination systems

AbstractAdsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods.

Bacterial hypothetical proteins may be of functional interest

Genomic analysis is part of the daily routine for many microbiology researchers. These analyses frequently unveil genes that encode proteins with uncertain functions, and for many bacterial species, these unknown genes constitute a significant proportion of their genomic coding sequences. Because these genes do not have defined functions, they are often overlooked in analyses. Experimentally determining the function of a gene can be challenging; however, ongoing advancements in bioinformatics tools, especially in protein structural analysis, are making it progressively easier to assign functions to hypothetical sequences. Leveraging various complementary tools and automated pipelines for annotating hypothetical sequences could ultimately enhance our comprehension of microbial functions and provide direction for new laboratory experiments.

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring.

Analyzing morphometry among extant and extinct species: A case study of genus Agassizia (Spatangoida: Echinoidea)

Introduction: The genus Agassizia in Mexico is represented both in the fossil record with the species Agassizia regia† during the Miocene of Chiapas and in the extant species Agassizia excentrica on the Atlantic coast and Agassizia scrobiculata on the Pacific coast. Qualitative diagnosis and descriptions make it hard to distinguish morphological boundaries between species, especially in groups with fossils and recent representatives, increasing the level of complexity by having samples of disparate qualities and quantities.
 Objective: We propose the use of little explored statistical methods in the comparison of paleontological and biological populations. This methodology allowed us to resolve issues of missing values in a morphometric data set for the genus Agassizia.
 Methods: Using samples recently collected and specimens already housed in collections, we explore a routine of recovery of missing data MICE and the numerical and graphic analyses PERMANOVA, PCA, and SIMPER to compare morphometric parameters between these species for recognizing diagnostic characters.
 Results: Our results show a clear morphological difference in the length of the ambulacrum II and the length and width of the periproct and peristome structures, these being greater in A. scrobiculata, with a consistent pattern in both population samples not previously described.
 Conclusions: Quantitative morphometric comparisons can be an assertive and complementary tool to determine distinctive differentiation characteristics in species of the same genus. Comparative morphology reviews should be an ongoing exercise to keep taxonomic knowledge on both extinct and extant species up to date. Our research encourage the scientific community studying fossil populations to utilize quantitative and multivariate methods to strengthen their investigations.

Application of Patient-Based Real-Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening.

Patient-based real-time quality control (PBRTQC) has gained attention because of its potential to continuously monitor the analytical quality insituations wherein internal quality control (IQC) is less effective. Therefore, we tried to investigate the application of PBRTQC method based on an artificial intelligence monitoring (AI-MA) platform in quality risk monitoring of Down syndrome (DS) serum screening. The DS serum screening item determination data and relative IQC data from January 4 to September 7 in 2021 were collected. Then, PBRTQC exponentially weighted moving average (EWMA) and moving average (MA) procedures were built and optimized in the AI-MA platform. The efficiency of the EWMA and MA procedures with intelligent and traditional control rules were compared. Next, the optimal EWMA procedures that contributed to the quality assurance of serum screening were run and generated early warning cases were investigated. Optimal EWMA and MA procedures on the AI-MA platform were built. Comparison results showed the EWMA procedure with intelligent QC rules but not traditional quality rules contained the best efficiency. Based on the AI-MA platform, two early warning cases were generated by using the optimal EWMA procedure, which finally found were caused by instrument failure. Moreover, the EWMA procedure could truly reflect the detection accuracy and quality insituations wherein traditional IQC products were unstable or concentrations were inappropriate. The EWMA procedure built by the AI-MA platform could be a good complementary control tool for the DS serum screening by truly and timely reflecting the detection quality risks.

Gait abnormalities in older adults with transthyretin cardiac amyloidosis

Background Transthyretin cardiac amyloidosis (ATTR cardiac amyloidosis) is caused by variant (ATTRv) or wild type (ATTRwt) transthyretin. While gait abnormalities have been studied in younger patients with ATTRv amyloidosis, research on gait in older adults with ATTR cardiac amyloidosis is lacking. Given ATTR cardiac amyloidosis’ association with neuropathy and orthopedic manifestations, we explore the gait in this population. Methods Twenty-eight older male ATTR cardiac amyloidosis patients and 11 healthy older male controls walked overground with and without a dual cognitive task. Gait parameters: stride width, length, velocity and stance time percentage were measured using an instrumented mat. ATTR amyloidosis patients were further categorized based on clinical and functional assessments. Results We found significant gait differences between ATTR cardiac amyloidosis patients and healthy controls; patients had more variable, slower, narrower and shorter strides, with their feet spending more time in contact with the ground as opposed to in swing. However, the observed gait differences did not correlate with clinical and functional measures of ATTR cardiac amyloidosis severity. Conclusions Our results suggest that gait analysis could be a complementary tool for characterizing ATTR cardiac amyloidosis patients and may inform clinical care as it relates to falls, management of anticoagulation, and functional independence.

Loss-of-function of activity-dependent neuroprotective protein (ADNP) by a splice-acceptor site mutation causes Helsmoortel-Van der Aa syndrome.

Mutations in ADNP result in Helsmoortel-Van der Aa syndrome. Here, we describe the first de novo intronic deletion, affecting the splice-acceptor site of the first coding ADNP exon in a five-year-old girl with developmental delay and autism. Whereas exome sequencing failed to detect the non-coding deletion, genome-wide CpG methylation analysis revealed an episignature suggestive of a Helsmoortel-Van der Aa syndrome diagnosis. This diagnosis was further supported by PhenoScore, a novel facial recognition software package. Subsequent whole-genome sequencing resolved the three-base pair ADNP deletion c.[-5-1_-4del] with transcriptome sequencing showing this deletion leads to skipping of exon 4. An N-terminal truncated protein could not be detected in transfection experiments with a mutant expression vector in HEK293T cells, strongly suggesting this is a first confirmed diagnosis exclusively due to haploinsufficiency of the ADNP gene. Pathway analysis of the methylome indicated differentially methylated genes involved in brain development, the cytoskeleton, locomotion, behavior, and muscle development. Along the same line, transcriptome analysis identified most of the differentially expressed genes as upregulated, in line with the hypomethylated CpG episignature and confirmed the involvement of the cytoskeleton and muscle development pathways that are also affected in patient cell lines and animal models. In conclusion, this novel mutation for the first time demonstrates that Helsmoortel-Van der Aa syndrome can be caused by a loss-of-function mutation. Moreover, our study elegantly illustrates the use of EpiSignatures, WGS and Phenoscore as novel complementary diagnostic tools in case a of negative WES result.

COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

Appearance of facet joint syndrome in a case of chronic sacroiliitis: Evidenced from quantitative sacroiliac scintigraphy and dual images of SPECT-CT

Lower back pain (LBP) is a global health concern with increasing prevalence, projected to reach 843 million cases by 2050. Lumbar facet joint (LFJ) syndrome contributes significantly to LBP, impacting patient quality of life. Despite its prevalence, a gold standard for accurate and noninvasive diagnosis of LBP has not yet been established, leading to underdiagnosis and inadequate treatment. This case report presents a 57-year-old woman diagnosed with LFJ syndrome and mild sacroiliitis through single-photon emission computed tomography- computed tomography (SPECT-CT) and quantitative sacroiliac scintigraphy (QSS). The patient presented with persistent LBP and hip pain without history of trauma or surgery. Clinical examination revealed lumbar tenderness, limited mobility, and positive Fortin and STORK tests. Whole body bone scan (WBBS) revealed tracer uptake at the sacroiliac (SI) joints and multiple spinal levels, particularly L3-L4. Hybrid SPECT-CT imaging showed heightened bone activity on lumbar spine, confirming degenerative arthropathy of the L3-L4 facet joints, in contrast with minimal evidence obtained from plain radiography and CT images. Furthermore, combining results from SPECT-CT and borderline sacroiliac indices from QSS indicate the presence of chronic degenerative sacroiliitis. Identifying the source of LBP remains challenging. SPECT-CT emerges as a valuable tool for comprehensive evaluation of LBP by combining anatomical and functional imaging of target tissues, allowing increased foci localization and accuracy of diagnosis. QSS can also be utilized as a complement in identifying the presence of chronic lesions of the SI joints. These promising results open opportunities for more research on the efficacy of combined morphological and functional imaging, as well as sacroiliac indices as a complementary diagnostic tool.

Thematic Analysis through Artificial Intelligence (AI)

Thematic analysis, a well-enforced qualitative analytic method, is likely to continue evolving with the adoption of AI technologies. This how-to report does not delve into the details of thematic analysis itself, as there are ample existing studies on the topic. Instead, it acknowledges the potential impacts, dynamics, and pitfalls of AI in thematic analysis while offering valuable advice, particularly for novice analysts, on how to incorporate and document AI tools in each phase of a thematic analysis. The author underscores the importance of not allowing AI to overshadow the analyst's critical evaluative and interpretive skills but instead supporting the use of AI as an aid in thematic analysis, enhancing the depth and breadth of analysis, provided certain criteria are adhered to. This approach ensures that AI serves as a complementary tool, augmenting rather than replacing human analytical inquiry.

Atrial fibrillation in the elderly - the geriatric perspective

Atrial fibrillation is the most common cardiac arrhythmia in older adults. As a result of the positive data on ablation, the focus has shifted to rhythm control. The method of choice is pulmonary vein isolation. Even older patients may benefit from ablation. Oral anticoagulation remains an essential component of pharmacological therapy for atrial fibrillation. Older adults require an individualised treatment approach, which should be based on current guidelines and complementary tools. The assessment of functionality should be taken into account in therapy and goal planning.

Building Information Modeling and Geographic Information System: Integrated Framework in Support of Facility Management (FM)

Currently, the management of public real estate stocks is one of the most challenging activities. In this case, facility management emerges as a strategic but complex discipline that requires a great amount of integrated and structured data and information of heterogeneous origin and nature. Starting from this premise, this paper focuses on how FM discipline can be supported by Building Information Modeling (BIM) and Geographic Information System (GIS) integrated methodologies and tools achieving high levels of effectiveness and efficiency in public building stock management. This led to the definition and development of a framework that mainly takes advantage of open-source tools (e.g., Quantum Geographic Information System—QGIS, and Dynamo) and non-proprietary data exchange formats like the COBie (Construction Operations Building Information Exchange) international standard. This methodological framework is capable of integrating different but strongly complementary tools, data, information, and expert knowledge for a multiscale approach. The methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform but leads instead to an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. The findings of experimental applications of the methodological approach to the former Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are presented and discussed. Finally, the analysis about potentials and criticalities of the developed methodological framework aims to trigger the widest future development and dissemination of this research.

Occlusal caries detection and monitoring using a 3D intraoral scanner system. An in vivo assessment

ObjectiveTo assess the agreement in detecting and monitoring occlusal caries over thirty months using conventional visual and radiographic assessment and an intraoral scanner system which supports automated caries scoring. MethodsNinety-one young participants aged 12–19 years were included in the study. All occlusal surfaces were examined visually, radiographically (when indicated), and scanned with the TRIOS 4 intraoral scanner. TRIOS Patient Monitoring software (vers. 2.3, 3Shape TRIOS A/S, Denmark) was used for automated caries detection on the 3D digital models. ResultsFifty-five of the study participants were re-examined after 30-months. Significant differences regarding caries detection were found between the conventional methods and the automated caries scoring system (p < 0.01), with moderate positive percent agreement (49–61%) and high negative percent agreement (87–98%). All methods reported significant caries progression over the follow-up period (p < 0.01). However, the automated system showed significantly more caries progression than the other methods (p < 0.01). ConclusionsThe software for automated caries detection and classification showed moderate positive agreement and strong negative agreement with the conventional methods considering both the baseline and the follow-up assessments. The automated caries scoring system detected significantly fewer caries lesions and tended to underestimate the caries severity. All methods indicated significant caries progression over the follow-up period, while the automated system detected more caries progression. Clinical significanceThe TRIOS system supporting automated occlusal caries detection and classification can assist in detecting and monitoring occlusal caries on permanent teeth as a complementary tool to the conventional methods. However, the operator should be aware that the automated system shows a tendency to underestimate the caries presence and lesion severity.

Simultaneous high-definition transcranial direct current stimulation and robot-assisted gait training in stroke patients

This study investigates whether simultaneous high-definition transcranial direct current stimulation (HD-tDCS) enhances the effects of robot-assisted gait training in stroke patients. Twenty-four participants were randomly allocated to either the robot-assisted gait training with real HD-tDCS group (real HD-tDCS group) or robot-assisted gait training with sham HD-tDCS group (sham HD-tDCS group). Over four weeks, both groups completed 10 sessions. The 10 Meter Walk Test, Timed Up and Go, Functional Ambulation Category, Functional Reach Test, Berg Balance Scale, Dynamic Gait Index, Fugl-Meyer Assessment, and Korean version of the Modified Barthel Index were conducted before, immediately after, and one month after the intervention. The real HD-tDCS group showed significant improvements in the 10 Meter Walk Test, Timed Up and Go, Functional Reach Test, and Berg Balance Scale immediately and one month after the intervention, compared with before the intervention. Significant improvements in the Dynamic Gait Index and Fugl-Meyer Assessment were also observed immediately after the intervention. The sham HD-tDCS group showed no significant improvements in any of the tests. Application of HD-tDCS during robot-assisted gait training has a positive effect on gait and physical function in chronic stroke patients, ensuring long-term training effects. Our results suggest the effectiveness of HD-tDCS as a complementary tool to enhance robotic gait rehabilitation therapy in chronic stroke patients.