Quantitative Validation Research Articles

Evaluation of self-collected dried blood spots for detection of SARS-CoV-2 nucleocapsid antibodies shows low sensitivity.

Dried blood spots (DBS) have been proposed as a cost-effective surveillance method for population-wide screening of SARS-CoV-2 immunity but sensitivity of DBS based on self-collected DBS samples is unknown. To evaluate the success of vaccination strategies, it is necessary to differentiate vaccination from natural infection. Therefore, a test for antibodies against the viral nucleocapsid protein (anti-N) is desirable. In our prospectively followed cohort of healthcare workers (HCW) in eastern Switzerland, we assessed SARS-CoV-2-anti-N-seroprevalence using DBS on a biweekly basis from March to September 2020. Phlebotomy samples were collected in March and September and tested for anti-N-seropositivity, as well as SARS-CoV-2 spike antibodies for quantitative validation. Venous antibody testing was compared with DBS results for anti-N using the Roche Elecsys electro-chemiluminescence immunoassay. 792 HCW (median age 38.3years) were included, 35 (4.4%) were SARS-CoV-2-anti-N-seropositive. Of 43 matching DBS, 25 tested positive for anti-N, accounting for a sensitivity of 58.1% (95%CI 43.3-71.6%). We found a significant correlation of anti-N from DBS with results from phlebotomy samples (r=0.77;p<0.0001), with higher levels being associated with a higher true-positive rate. Anti-N in DBS correlated significantly with quantitatively validated anti-S obtained from serum (r=0.67;p<0.0001). Although home DBS collection was feasible in a larger cohort and we found a high correlation between anti-N detection in DBS and phlebotomy samples, the sensitivity of self-collected DBS samples was significantly impaired for the Roche Elecsys anti-N assay. Therefore, we cannot recommend this method for DBS when testing from venous blood is possible.

Translation and assessment of encultured meaning of the Multi-Dimensional Scale of Perceived Social Support in Diné bizaad (Navajo) using community-based participatory action research methods.

Perceived social support is a psychological construct that is used to describe the 'perception of adequacy' of the support being provided by a person's social network. Higher perceived social support has been linked to multiple benefits across numerous studies over the past several decades and among multiple populations. The Multidimensional Scale of Perceived Social Support (MSPSS) is a 12-item scale to assess the construct of perceived social support. The instrument has been translated to approximately 35 languages and dialects, but it has rarely been translated into tribal languages, which may be commonly spoken in rural areas. Further, such translations have not always been accompanied by cultural adaptation. Assessment of the encultured meaning of terms from a validated instrument is important alongside translation because words and terms related to perceived social support can be culturally specific. As such, this article presents a community-engaged research approach to develop a translation of the MSPSS into Din&eacute; bizaad (Navajo), along with a qualitative assessment of the meaning and implications of key terminology from the instrument. This study was led by a faculty member at a Native American-Serving Nontribal Institution (NASNTI) in south-western Colorado, US. Additional research collaborators included the original developer of the English MSPSS, a researcher with experience in methodology, and a member of the local Navajo community who was a student at the NASNTI. Using convenience and snowball sampling, a 2.5-hour focus group discussion was conducted and audio-recorded in May 2023 with eight Navajo community members who met eligibility criteria (including fluency in both Din&eacute; and English). All participants provided consent and received gratitude gifts for completion. Participants were four males and four females with an age range of 30-60 years. Since the discussion and recording were bilingual, a written English and Din&eacute; transcription was produced and anonymized, then reviewed by researchers. It was then backward-forward translated to English, then checked with discussion participants to validate accuracy. Using the general inductive method, key concepts and codes were separately identified and documented using NVivo 21 by two researchers. Full consensus as to coding was achieved over a sequence of six iterative consensus meetings among the coders. The project was able to produce a harmonized version of the MSPSS translated into Din&eacute; bizaad that accounted for variation in meaning and intent of multiple core concepts of perceived social support. For example, concepts of 'family' and 'friend' were often characterized by ethnic clan-based close-knit bonds. The concept of 'significant other' often meant 'spouse' but sometimes also meant someone who was highly trusted, especially among unmarried discussants. 'Social support' was trust-based rather than need-based and often relied on gender-concordant bonding. Many additional nuances were discussed and are outlined in the full study. Navajo community members were excited to collaboratively work on making this screening tool available in Din&eacute; bizaad. The translation that emerged from this process likely differed from what would have emerged from a direct translation without community input. Next steps for the tool should include quantitative reliability and validity analyses.

Toward Equitable Access to Psychosocial Screening in Canada: Validation of the French–Canadian Psychosocial Assessment Tool

ABSTRACTObjectiveThe Psychosocial Assessment Tool (PAT) is a brief caregiver report, family‐centered, psychosocial risk screening tool widely used in pediatrics and available in many languages. Although French is an official language of Canada, a French–Canadian version of the PAT has not yet been validated, which impedes access to this tool for family psychosocial screening. This study aimed to translate, adapt as necessary, and validate the French–Canadian version of the PAT.MethodsThe PAT 3.0 was translated into French using the forward–backwards method. Interviews with healthcare workers (n = 5) and a focus group of parents of children newly diagnosed with cancer (n = 4) led to minor modifications to improve cultural adaptation and comprehensibility. Subsequently, 66 French‐speaking parents of children newly diagnosed with cancer participated in a quantitative validation study; completing the French–Canadian PAT and related caregiver measures.ResultsThe French–Canadian PAT has an overall internal consistency of KR‐20 = 0.64, with subscales ranging from 0.57 to 0.86. The total PAT score significantly correlated with the Distress Thermometer (r = 0.54). Congruent validity was demonstrated for most PAT subscales, except for Family Beliefs and Family Problems. The sample followed this distribution on the Pediatric Psychosocial Preventative Health Model: 21.2% universal, 53% targeted, and 25.8% clinical.ConclusionThe French–Canadian PAT provides insight into family psychosocial risk. This study is a step toward equitable access to care by providing French‐speaking families with access to a tool aligned with the psychosocial standards of care in pediatric oncology. Future research should focus on implementing the PAT in Canadian clinical settings.

Transcriptomic analysis of the inhibition mechanisms against Pseudomonas plecoglossicida by antibacterial aptamer B4.

Pseudomonas plecoglossicida is a common bacterial pathogen in aquaculture, often leading to visceral white spot disease in large yellow croakers (Pseudosciaena crocea). Previous studies have found that certain aptamers show an efficient antibacterial effect against this pathogen. In this study, we analyzed the transcriptome of P. plecoglossicida to get insights into the antibacterial and inhibitions mechanisms following exposure to the aptamer B4. The results showed seven differentially expressed genes (DEGs) associated with the antibacterial effect of the aptamer, namely sad gene encoding aldehyde dehydrogenase, the paaB gene of phenylacetyl coenzyme A cyclooxygenase, the metN1 gene of ABC transporter proteins, two transposase genes with different positions but identical sequences involved in cutting and splicing DNA sequences, and two hypothetical protein genes with unknown functions. Gene Ontology (GO) analysis showed that the DEGs were mainly involved in DNA-mediated translocation, phenylacetic acid catabolism, growth hormone catabolism, polyamine transporter ATPase activity, betaine aldehyde dehydrogenase activity, ABC transporter protein complex, and other related pathways. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the metabolic pathway of niacin and niacinamide mediated through the sad gene was the most significant and relevant, followed by the metabolism of phenylalanine, alanine, aspartic acid and glutamic acid. Real-time quantitative PCR validation showed that the changes in the DEGs were consistent with the transcriptome analysis. These results suggest that the antibacterial aptamer B4 may inhibit P. plecoglossicida by blocking the synthesis of essential nucleic acids and proteins through the modulation of these DEGs and inhibiting their metabolic pathways.

Translation and psychometric properties of the comfort behavioral scale-Persian version

BackgroundManaging pain and the level of sedation of critically ill children admitted to the intensive care unit is a complex process and one of the most basic problems of nurses, which requires the use of an available valid, and reliable scale. The present study aimed to translate the comfort behavioral scale into Persian and check its validity and reliability.MethodsThis methodological research was carried out in the children’s hospitals of Tehran Province (Iran) from Nov 2023 to Apr 2024. After forward and backward translation, face validity was performed qualitatively and quantitatively. Then, qualitative content validity was checked and the content validity ratio and content validity index were measured. The discriminant validity method was used to determine construct validity, and the nurse interpretation sedation scale and nurse report pain scale were used to check the criterion validity. The concordance correlation coefficient method was used to determine reliability.ResultsIn quantitative face validity, all 30 items received an impact score of more than 1.5. In qualitative content validity, some words were replaced with more concrete words. In the content validity ratio, considering the minimum value of 0.62 for 10 experts, none of the items were excluded. The content validity index for all items was higher than 0.8 and S-CVI/Ave for the whole scale was 0.96. There was a significant difference between the pain score in the painful state (24.87 ± 2.5) and the resting state (20.9714 ± 2.4) (P = 0.000). The correlation between Comfort and criterion instruments (NISS and NRS) in a resting state and during a painful procedure was positively significant (P < 0.05). The kappa agreement coefficient showed values higher than 0.4 and reliability was confirmed.ConclusionsThe Persian version of the COMFORT behavioral scale has good validity and reliability and can be used as a suitable tool for pain management of hospitalized children with a low level of consciousness and lack of verbal communication. The use of this tool not only helps the clinical, educational, and research process but also leads to an increase in the quality of care and satisfaction of the child and her parents in Iranian hospitals.

On the Finite Element Modeling of Metal/Ceramic Functionally Graded Beams Subjected to Non-Uniform Static Bending

Currently, several approaches focused on the optimization of materials design and solutions to respond to the ecological and economic concerns through the development of novel materials and structures. In this connection, functionally graded materials (FGM) known as a new generation of composites with optimized mechanical properties which vary according to a continuous function-law to avoid interfacial debonding and stresses concentration were largely investigated. Accordingly, the main purpose of this work is to advocate a new way to simulate the mechanical behavior of simply supported FGM beams submitted to non-uniform static bending through a finite element modeling (FEM). The power-law function which governs the stiffness distribution of the used metal and ceramic materials are explicitly presented. In addition, a mesh sensitivity was established to assess the optimal mesh size of the modeled FGM based-beams. The simulation outcomes presented in the form stress and strain cartographies were correlated with a good agreement to the analytical results from the literature in term of qualitative and quantitative validations.

Developing an Emergency Medical Services Resilience Assessment Tool in Climate Change: A Study Protocol

Abstract Background: Climate change poses significant threats to the health and safety of communities worldwide. Extreme weather events can disrupt critical infrastructure and overwhelm emergency medical services (EMS) systems. As the frequency and intensity of these climate-related disasters continue to increase, it is essential that EMS organizations develop robust strategies to enhance their resilience. One important step in building climate-resilient EMS systems is the development of comprehensive assessment tools. Developing a comprehensive tool for assessing the resilience of EMS in the face of climate change. Methods: This research employs an applied, mixed-methods approach to investigate the development and validation of the Emergency Medical Services Resilience Assessment (EMSRA) tool. The study will be executed across four key phases: (a) A scoping review will be conducted to identify the global dimensions and components of EMS systems that demonstrate resilience to the impacts of climate change. (b) A qualitative study, involving semistructured interviews, will be undertaken to explore the specific dimensions and components of EMS resilience within the Iranian context. (c) Based on the findings from the scoping review and qualitative inquiry, assessment items related to EMS resilience in climate change will be extracted and synthesized to develop a pilot version of the EMSRA tool. (d) The pilot EMSRA tool will undergo a rigorous process of quantitative and qualitative validation, including an evaluation of its psychometric properties, to assess the resilience of EMS systems in the face of climate-related challenges. Conclusions: The development of EMSRA tool will enable the assessment and enhancement of climate change resilience within the national and provincial EMS systems, providing critical data to inform evidence-based strategies and plans for strengthening EMS against the impacts of severe climate changes.

Machine Learning—Supported Geotechnical Interpretation of Rock Slopes at the Zentrum Am Berg (ZaB) Using SAM

The Segment Anything Model (SAM) introduces advanced transformer-based capabilities for geological image segmentation. While traditional geoscience applications rely on machine learning models like random forests and support vector machines, SAM’s attention mechanisms enable it to adapt to image data. This contribution evaluates SAM’s performance in segmenting rock outcrop images into three geological classes, using ground truth masks as references. Segmentation accuracy was assessed via intersection over union (IoU) scores across prompt types, including points and bounding boxes. A combination of bounding box and mask prompts provided the best results, particularly for large, distinct textures. Initial findings indicate SAM’s potential in geological segmentation, though further prompt refinement and expanded datasets are needed to address rock heterogeneity. Future work will focus on fine-tuning SAM for complex textures and integrating Laserscan-derived data for quantitative validation. This contribution underscores SAM’s promise in advancing automated geological segmentation applications.

Cross cultural adaptation and validation of audiovisual educational material for use in indigenous patients with rheumatoid arthritis.

Culturally appropriate educational materials are necessary to improve health literacy among Indigenous populations. However, practically no such materials have been cross-culturally adapted and validated for Indigenous peoples based on compliance with efficacy components. To perform a cross-cultural adaptation and validation of audiovisual educational materials for adult patients with rheumatoid arthritis belonging to Indigenous communities in Chiapas, Mexico. Mixed-methods study consisting of three phases: 1) Spanish-Tzotzil translation and cross-cultural adaptation of seven previously designed and validated audiovisual educational materials; 2) qualitative validation; and 3) quantitative validation based on the efficacy components (attraction, understanding, induction to action, involvement, and acceptance). The information collected during the validation phases was recorded and transcribed for content analysis. A total of 31 patients with rheumatoid arthritis participated in the study. Patients had a mean age of 49years, ≥5years since disease onset, low adherence to pharmacological treatment (<20%), and a high level of illiteracy (>80%). After three versions of the educational material, where elements of cultural identification were added, the efficacy components increased significantly to reach scores higher than 90%. This suggests that culturally-adapted materials could promote greater patient participation in treatment. This study shows the importance of cross-cultural adaptation in the design and validation of audiovisual educational materials for Indigenous populations; this aspect should be considered when implementing educational strategies for patients with chronic diseases. First educational audiovisual material translated and adapted from Spanish to Tzotzil, with a cultural sensitivity approach to achieve educational goals and improve therapeutic adherence.

Perceived Worries in the Adoption of Artificial Intelligence Among Healthcare Professionals in Saudi Arabia: A Cross-Sectional Survey Study.

The use of AI in the healthcare sector is facing some formidable concerns raised by the practitioners themselves. This study aimed to establish the concerns that surround the adoption of AI among Saudi Arabian healthcare professionals. Materials and methods: This was a cross-sectional study using stratified convenience sampling from September to November 2024 across health facilities. This study included all licensed healthcare professionals practicing for at least one year, whereas interns and administrative staff were excluded from the research. Data collection was conducted through a 33-item validated questionnaire that was provided in paper form and online. The questionnaire measured AI awareness with eight items, past experience with five items, and concerns in four domains represented by 20 items. Four hundred questionnaires were distributed, and the response rate was 78.5% (n = 314). The majority of the participants were females (52.5%), Saudis (89.2%), and employees of MOH (77.1%). The mean age for the participants was 35.6 ± 7.8 years. Quantitative analysis revealed high AI awareness scores with a mean of 3.96 ± 0.167, p < 0.001, and low previous experience scores with a mean of 2.65 ± 0.292. Data management-related worries came out as the top worry, with a mean of 3.78 ± 0.259, while the poor data entry impact topped with a mean of 4.15 ± 0.801; healthcare provider-related worries with a mean of 3.71 ± 0.182; and regulation/ethics-related worries with a mean of 3.67 ± 0.145. Health professionals' main concerns about AI adoption were related to data reliability and impacts on clinical decision-making, which significantly hindered successful AI integration in healthcare. These are the particular concerns that, if addressed through robust data management protocols and enhanced processes for clinical validation, will afford the best implementation of AI technology in an optimized way to bring better quality and safety to healthcare. Quantitative validation of AI outcomes and the development of standardized integration frameworks are subjects for future research.

Development and Validation of a Self-Administered Semiquantitative Food Frequency Questionnaire Focused on Gut Microbiota: The Stance4Health-FFQ.

Background/Objectives: Diet significantly influences gut microbiota (GM), with variations in GM responses linked to the type and quantity of food consumed. These variations underscore the need for personalized nutrition. The Stance4Health (S4H) project developed the S4H Food Frequency Questionnaire (S4H-FFQ) and the i-Diet S4H app to assess dietary intake of foods affecting GM. This study aimed to validate the S4H-FFQ against the validated I.Family-FFQ and the i-Diet S4H app; Methods: The S4H-FFQ, with 200 food items across 14 food groups, evaluates dietary intake over the past month. Qualitative validation compared food group consumption frequencies from the S4H-FFQ and the I.Family-FFQ, while quantitative validation assessed nutrient and energy intake using the i-Diet S4H app. The S4H-GM score, a measure of GM-relevant food consumption, was evaluated through the S4H-FFQ and i-Diet S4H app; Results: Pearson correlations between the S4H-FFQ and the I.Family-FFQ ranged from 0.3 to 0.7 and were statistically significant across all the food groups. Quantitative validation showed lower but consistent correlations, comparable with other studies, confirming the S4H-FFQ's ability to estimate food intake. A positive correlation was also found between the S4H-GM scores from the S4H-FFQ and the i-Diet S4H app (p < 0.001); Conclusions: The S4H-FFQ is a reliable tool for assessing dietary patterns that influence GM. Its application in nutritional studies can enhance personalized nutrition and support future research aimed at optimizing GM and improving health outcomes.

Evolution-Aware Constraint Derivation Approach for Software Remodularization

Existing software clustering techniques tend to ignore prior knowledge from domain experts, leading to results (suggested big-bang remodularization actions) that cannot be acceptable to developers. Incorporating domain experts knowledge or constraints during clustering ensures the obtained modularization aligns with developers’ perspectives, enhancing software quality. However, manual review by knowledgeable domain experts for constraint generation is time-consuming and labor-intensive. In this article, we propose an evolution-aware constraint derivation approach, Escort , which automatically derives clustering constraints based on the evolutionary history from the analyzed software. Specifically, Escort can serve as an alternative approach to derive implicit and explicit constraints in situations where domain experts are absent. In the subsequent constrained clustering process, Escort can be considered as a framework to help supplement and enhance various unconstrained clustering techniques to improve their accuracy and reliability. We evaluate Escort based on both quantitative and qualitative analysis. In quantitative validation, Escort , using generated clustering constraints, outperforms seven classic unconstrained clustering techniques. Qualitatively, a survey with developers from five IT companies indicates that 89% agree with Escort ’s clustering constraints. We also evaluate the utility of refactoring suggestions from our constrained clustering approach, with 54% acknowledged by project developers, either implemented or planned for future releases.

How to validate UV-C based air cleaners using viruses containing aerosols in a test room.

UV-C based air cleaners may reduce the transmission of infectious diseases. However, microbiological validation is necessary to quantify their efficiency. In this study, the stability of aerosolized bacteriophages for validation purposes was investigated in a test room, before a UV-C based air cleaner was exemplarily evaluated regarding the inactivation of airborne bacteriophages. The bacteriophage Phi6 was selected as virus surrogate and aerosolized in a room of 30 m³ volume. The recovery of infectious bacteriophages was first analyzed under variation of the relative humidity (20%-55% RH) and sampling time. The aerosol studies showed that a low humidity between 20% RH and 30% RH provides a high and stable recovery of bacteriophages Phi6 over 1h. However, with increasing humidity, the number of infectious airborne bacteriophages Phi6 decreased significantly. At 50% RH, the recovery of Phi6 was 4 orders of magnitude lower compared to 20% RH. The validation of a UV-C based air cleaner was then demonstrated in the test room whereat the decline of infectious airborne bacteriophages was recorded over time. The nonenveloped bacteriophage MS2 was used as a reference. The validation results were significantly different for Phi6 when the humidity in the test room was either 40% RH or 30% RH, whereas comparable results were obtained for MS2 at both humidities. A rising humidity in the test room caused a significant decline in the recovery of infectious airborne bacteriophages Phi6. The result of a quantitative validation of UV-C based air cleaners may therefore be affected by the respective humidity.

NIMG-23. A NOVEL APPROACH TO PERFUSION MRI STUDIES IN BRAIN TUMOR PATIENTS USING ENDOGENOUS DEOXYHEMOGLOBIN AS A CONTRAST AGENT: A PROOF-OF-CONCEPT STUDY

Abstract BACKGROUND Assessment of resting perfusion in brain tumors is clinically relevant and relies on dynamic susceptibility contrast magnetic resonance imaging using gadolinium contrast. Preliminary studies have introduced the use of controlled hemoglobin desaturation by means of a transient hypoxic stimulus during echo-planar imaging(EPI) as a surrogate for gadolinium for resting perfusion assessment in healthy subjects. In this proof of concept study, we investigated whether this approach is feasible in brain tumors and warrant further validation. METHODS A computer controlled gas blender was used to induce transient and standardized hypoxic stimulus in isocapnic conditions during EPI acquisition in a heterogenous cohort of tumor patients. The induced blood-oxygen-level-dependent(BOLD) signal changes determined by transit of a bolus of deoxygenated blood in the brain vasculature were used as a surrogate of gadolinium to calculate cerebral blood volume(CBV), cerebral blood flow(CBF) and mean transit time(MTT) using a global arterial input functions obtained through the BOLD signal measured over the middle cerebral artery as previously described by Poublanc et al. Perfusion parameters were calculated in the whole-brain voxel-by-voxel and in automatically segmented region of interest(ROI) using SPM, AFNI, Verbena softwares and MATLAB in-house written scripts. RESULTS Nine patients were included. MTT, rCBV, rCBF were calculated voxel-per-voxel. Obtained maps were overlayed on the co-registered T1 MRI scans and compared to T1CE and FLAIR to identify perfusion patterns in tumor ROIs (contrast enhancement, edema, necrosis). The deoxy-hemoglobin BOLD MRI perfusion were compared to clinical DSC-MRI when available and showed high qualitative agreement. CONCLUSION Transient and precise hemoglobin desaturation by controlled hypoxic gas modulation is safe and repeatable in brain tumor patients. The induced BOLD signal change allows measurement of resting perfusion, which qualitatively closely mimic gadolinium perfusion in different brain tumors. Deoxyhemoglobin-based perfusion warrant further quantitative validation against gadolinium in a larger cohort of heterogenous tumor patients.

A Planar Feature-Preserving Texture Defragmentation Method for 3D Urban Building Models

Oblique photogrammetry-based 3D modeling is widely used for large-scale urban reconstruction. However, textures generated with photogrammetric techniques often exhibit scattered and irregular characteristics, which lead to significant challenges with texture seams and UV map discontinuities, increasing storage requirements and affecting rendering quality. In this paper, we propose a planar feature-preserving texture defragmentation method designed specifically for urban building models. Our approach leverages the multi-planar topology of buildings to optimize texture merging and reduce fragmentation. The proposed approach is composed of three main stages: the extraction of planar features from texture fragments; the employment of these planar elements as guiding constraints for merging adjacent texture fragments within a two-dimensional texture space; and an enhanced texture-packing algorithm designed for more regular texture charts to systematically generate refined texture atlas. Experiments on various urban building models demonstrate that our method significantly improves texture continuity and storage efficiency compared to traditional approaches, with both quantitative and qualitative validation.

Quantitative Validation of Hematocrit Percentage Drop Cutoff Points in Postpartum Hemorrhage: An Automated Gravimetric Approach

AbstractObjective:To validate the hematocrit percentage drop cutoff points for blood loss in patients with postpartum hemorrhage (PPH) using the automated gravimetric method.Methods:A prospective cohort study was conducted from January 2023 to July 2023, in which 107 patients 18 years of age and above were scheduled for elective cesarean with obstetrical indications. We excluded cases with difficulty quantifying blood loss, those with incomplete data, and those of patients who did not consent to participate. Blood loss was measured by an automated gravimetric system integrated into a suction blood collector and surgical gauze weighing systems to automatically sum up blood loss immediately after hysterectomy and fetal delivery. The percentage drop in hematocrit was determined by subtracting the 8-hour postsurgical from presurgical hematocrit, divided by presurgical hematocrit. We performed the Pearson correlation test, and the receiver operating characteristic curve was used to determine cutoff points, their sensitivity, and their specificity. The κ index was used to determine the diagnostic agreement between the two methods.Results:A positive correlation was observed between the volume of blood loss and the percentage drop in hematocrit, with a Pearson correlation index of 0.70 andP &lt; 0.0001. A 14% decrease in hematocrit had an 81.7% agreement rate, with a good κ index of 0.62, a sensitivity of 82.5%, and a specificity of 80.0%. A 10% drop in hemoglobin was sensitive (93%) but not very specific (56%) for blood loss greater than 1000 mL.Conclusion:The automated gravimetric method strongly correlates with hematocrit changes, providing an accurate real-time diagnosis of postpartum hemorrhage. Additionally, a hematocrit percentage drop can retrospectively indicate significant blood loss, aiding in managing patients at risk for long-term PPH complications.

A mechanistic systems biology model of brain microvascular endothelial cell signaling reveals dynamic pathway-based therapeutic targets for brain ischemia

Ischemic stroke is a significant threat to human health. Currently, there is a lack of effective treatments for stroke, and progress in new neuron-centered drug target development is relatively slow. On the other hand, studies have demonstrated that brain microvascular endothelial cells (BMECs) are crucial components of the neurovascular unit and play pivotal roles in ischemic stroke progression. To better understand the complex multifaceted roles of BMECs in the regulation of ischemic stroke pathophysiology and facilitate BMEC-based drug target discovery, we utilized a transcriptomics-informed systems biology modeling approach and constructed a mechanism-based computational multipathway model to systematically investigate BMEC function and its modulatory potential. Extensive multilevel data regarding complex BMEC pathway signal transduction and biomarker expression under various pathophysiological conditions were used for quantitative model calibration and validation, and we generated dynamic BMEC phenotype maps in response to various stroke-related stimuli to identify potential determinants of BMEC fate under stress conditions. Through high-throughput model sensitivity analyses and virtual target perturbations in model-based single cells, our model predicted that targeting succinate could effectively reverse the detrimental cell phenotype of BMECs under oxygen and glucose deprivation/reoxygenation, a condition that mimics stroke pathogenesis, and we experimentally validated the utility of this new target in terms of regulating inflammatory factor production, free radical generation and tight junction protection in vitro and in vivo. Our work is the first that complementarily couples transcriptomic analysis with mechanistic systems-level pathway modeling in the study of BMEC function and endothelium-based therapeutic targets in ischemic stroke.

A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.

Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured type, which is one of the shell texture types of breast implants, has been identified as a possible etiologic factor for lymphoma, specifically breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Identifying the shell texture type of the implant is critical to diagnosing BIA-ALCL. However, distinguishing the shell texture type can be difficult due to the loss of human memory and medical history. An alternative approach is to use ultrasonography, but this method also has limitations in quantitative assessment. This study aims to determine the feasibility of using a deep learning model to classify the shell texture type of breast implants and make robust predictions from ultrasonography images from heterogeneous sources. A total of 19,502 breast implant images were retrospectively collected from heterogeneous sources, including images captured from both Canon and GE devices, images of ruptured implants, and images without implants, as well as publicly available images. The Canon images were trained using ResNet-50. The model's performance on the Canon dataset was evaluated using stratified 5-fold cross-validation. Additionally, external validation was conducted using the GE and publicly available datasets. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PRAUC) were calculated based on the contribution of the pixels with Gradient-weighted Class Activation Mapping (Grad-CAM). To identify the significant pixels for classification, we masked the pixels that contributed less than 10%, up to a maximum of 100%. To assess the model's robustness to uncertainty, Shannon entropy was calculated for 4 image groups: Canon, GE, ruptured implants, and without implants. The deep learning model achieved an average AUROC of 0.98 and a PRAUC of 0.88 in the Canon dataset. The model achieved an AUROC of 0.985 and a PRAUC of 0.748 for images captured with GE devices. Additionally, the model predicted an AUROC of 0.909 and a PRAUC of 0.958 for the publicly available dataset. This model maintained the PRAUC values for quantitative validation when masking up to 90% of the least-contributing pixels and the remnant pixels in breast shell layers. Furthermore, the prediction uncertainty increased in the following order: Canon (0.066), GE (0072), ruptured implants (0.371), and no implants (0.777). We have demonstrated the feasibility of using deep learning to predict the shell texture type of breast implants. This approach quantifies the shell texture types of breast implants, supporting the first step in the diagnosis of BIA-ALCL.

Toroidal injection angle dependence of EC assisted plasma initiation at DIII-D

Abstract An experimental scan of the electron cyclotron waves (EC) toroidal injection angle in plasma breakdown is performed at the DIII-D tokamak. The second harmonic, extraordinary mode EC is used for the study. The dependence of n e and T e on the EC injection angle cannot be conclusively drawn from this study due to the large error bars in the n e and T e measurements. On the other hand, high T e data points are observed in some discharges which can be explained by nonlinear heating. The D α emission measurement shows a clear relation between the breakdown time and the injection angle. An experimental investigation of the cause of the dependence of breakdown delay on the EC injection angle suggests that when the injection angle is oblique, the EC heating after the reflection at the inboard wall may become ineffective and cause the breakdown delay even when the EC heating before and directly upon injection remains effective. A preliminary run of the heat and transport balance code DYON indicates that the obtained dataset is suitable for a quantitative validation of EC absorption models.

Determination of the Optimal Method for Measuring Malondialdehyde in Human Saliva

While few methodological studies have been published on the salivary measurement of malondialdehyde (MDA), none have precisely detailed the analytical method. This work presents the development of an analytical method for MDA measurement in microvolumes of saliva samples from healthy individuals, using thiobarbituric acid derivatization and fluorescence reading of the formed compound. This method was progressively designed to meet specific constraints such as the limited sample volume available, cost-effectiveness of each assay, time required for analysis and costs. After studying the impact of various parameters, quantitative validation of this method was conducted using the accuracy profile. The study demonstrated that the fluorometric analysis method for salivary MDA accurately quantifies the concentration of this molecule within a validity range from 0.031 μM to 2.3 μM. Additionally, the accuracy profile allowed estimation of measurement uncertainty for each concentration level. It was determined that the calibration method using raw data was most appropriate as it introduced the least uncertainty in the obtained results.