UNASSIGNED: Erythrocyte sedimentation rate (ESR) is a widely used screening test in clinical practice as an indicator of inflammatory and degenerative malignant diseases. The Westergren method, renowned as the gold standard, is valued for its accuracy and cost-effectiveness but demands considerable time and blood volume. Emerging automated methods offer quicker and more convenient alternatives, aiming to replace manual techniques. Nonetheless, validating these automated methods against the reference Westergren method is essential to ensure reliability. Therefore, this study aimed to evaluate ESR measurement results obtained from both the reference Westergren method and the automated (SFRI ESR 3000) method.
UNASSIGNED: A Hospital-based comparative cross-sectional study was conducted at Jigjiga University Sheik Hassen Yabare Referral Hospital from July 15 to September 16, 2023. Following the acquisition of informed consent, blood samples were obtained from 158 participants, five milliliters of blood from each participant. These samples were then subjected to ESR estimation using both the Westergren (reference) method and the automated (SFRI ESR 3000) method. Subsequently, the collected data were analyzed using SPSS version 20 and MedCalc version 12.3.0.0 statistical Softwares. Statistical analyses such as Paired t-tests, Pearson correlation, linear regression, and the Bland and Altman plot were employed. A p-value of < 0.05 was considered statistically significant.
UNASSIGNED: The paired sample t-test analysis revealed no significant difference between the use of the reference Westergren method and the automated method for ESR determination, with a mean difference (MD) of 0.7 ± 9.2 mm/h (P = 0.36). Additionally, a significant correlation was observed between the two methods, with a remarkable correlation coefficient (r = 0.94, p < 0.001). The Bland-Altman data analysis indicated no evidence of systematic bias and demonstrated good agreement of ESR values between the two methods, with a limit of agreement of -17.3 to +18.7. Moreover, within-run imprecision analysis for the automated method across a range of ESR values showed coefficient of variation of 27.08, 12.65, and 10.32% for low, medium, and high ESR levels, respectively.
UNASSIGNED: The SFRI ESR 300 automated method demonstrates the potential for interchangeable use with the Westergren method for determining ESR, given the strong correlation and good agreement. Additionally, the same reference range could be applied during interpretation.

BACKGROUND: Interleukin-6 (IL-6) is a proinflammatory cytokine that is associated with many inflammatory diseases. This validation study evaluates the automated Roche Elecsys IL-6 electrochemiluminescent immunoassay that has been granted emergency use authorization by the US Food and Drug Administration.
METHODS: The Elecsys IL-6 assay was evaluated for precision, linearity, interference (by hemoglobin, bilirubin, triglycerides, and biotin) and clinical performance was compared to the V-PLEX Human IL-6 immunoassay (Meso Scale Discovery), performed by a reference laboratory.
RESULTS: The Elecsys IL-6 assay is precise (intra-assay <3% coefficient of variation [CV], interassay <5% CV), exhibits an analytical measurable range of 1.5-4790 pg/mL, and is tolerant of significant interferences (H < 2522, I <62, L<2101, biotin <50 ng/mL). Comparison with the V-PLEX assay revealed a 2.95 slope bias in patient samples evaluated for IL-6 concentration (n = 43, range = 1.5-1891 pg/mL, y = 2.95x - 32.7, r2 = 0.84). Bland-Altman analysis revealed an absolute mean bias of 152 pg/mL (SD = 254 pg/mL), or a mean percentage difference of 73%.
CONCLUSIONS: The Roche IL-6 assay showed good analytical performance. The large systematic bias compared with another reference method precludes using multiple methods to monitor IL-6 response. The random-access nature of an automated IL-6 assay on the Roche platform makes the test available on demand.

BACKGROUND: Health care technology has the ability to change patient outcomes for the betterment when designed appropriately. Automation is becoming smarter and is increasingly being integrated into health care work systems.
OBJECTIVE: This study focuses on investigating trust between patients and an automated cardiac risk assessment tool (CRAT) in a simulated emergency department setting.
METHODS: A within-subjects experimental study was performed to investigate differences in automation modes for the CRAT: (1) no automation, (2) automation only, and (3) semiautomation. Participants were asked to enter their simulated symptoms for each scenario into the CRAT as instructed by the experimenter, and they would automatically be classified as high, medium, or low risk depending on the symptoms entered. Participants were asked to provide their trust ratings for each combination of risk classification and automation mode on a scale of 1 to 10 (1=absolutely no trust and 10=complete trust).
RESULTS: Results from this study indicate that the participants significantly trusted the semiautomation condition more compared to the automation-only condition (P=.002), and they trusted the no automation condition significantly more than the automation-only condition (P=.03). Additionally, participants significantly trusted the CRAT more in the high-severity scenario compared to the medium-severity scenario (P=.004).
CONCLUSIONS: The findings from this study emphasize the importance of the human component of automation when designing automated technology in health care systems. Automation and artificially intelligent systems are becoming more prevalent in health care systems, and this work emphasizes the need to consider the human element when designing automation into care delivery.

The clinical analysis of urine has classically focused on conventional chemical-based urinalysis and urine microscopy. Contemporary advances in both analysis subsets have started to employ new technologies such as automated image analysis, flow cytometry, and mass spectrometry. In addition to new detection technologies, current analyzers have incorporated more advanced imaging, automated sample handing, and machine learning analyses into their workflow. The most advanced semiautomated analyzers can be interfaced with hospital medical record systems, and in the point-of-care setting, smartphones can be used for image analysis. This review will discuss current technological advancements in the field of urinalysis and urine microscopy.

Automated planning has surged in popularity within external beam radiation therapy in recent times. Leveraging insights from previous clinical knowledge could enhance auto-planning quality. In this work, we evaluated the performance of Ethos automated planning with knowledge-based guidance, specifically using Rapidplan (RP). Seventy-four patients with head-and-neck (HN) cancer and 37 patients with prostate cancer were used to construct separate RP models. Additionally, 16 patients from each group (HN and prostate) were selected to assess the performance of Ethos auto-planning results. Initially, a template-based Ethos plan (Non-RP plan) was generated, followed by integrating the corresponding RP model\'s DVH estimates into the optimization process to generate another plan (RP plan). We compared the target coverage, OAR doses, and total monitor units between the non-RP and RP plans. Both RP and non-RP plans achieved comparable target coverage in HN and Prostate cases, with a negligible difference of less than 0.5% (p > 0.2). RP plans consistently demonstrated lower doses of OARs in both HN and prostate cases. Specifically, the mean doses of OARs were significantly reduced by 9% (p < 0.05). RP plans required slightly higher monitor units in both HN and prostate sites (p < 0.05), however, the plan generation time was almost similar (p > 0.07). The inclusion of the RP model reduced the OAR doses, particularly reducing the mean dose to critical organs compared to non-RP plans while maintaining similar target coverage. Our findings provide valuable insights for clinics adopting Ethos planning, potentially enhancing the auto-planning to operate optimally.

The work proposed here is an automated pre and post-processor integrated to PanAir that is is a high-order aerodynamic panel method-based software for flow analysis developed in 70s but still in active use especially for preliminary aircraft design. With the integrated environment proposed in this work, manipulation of input and output data to and from PanAir is bypassed successfully that is otherwise requires manual manipulations and use of third party software. The integrated environment is validated over a Cessna 210 aircraft with a modified NLF (1)-0414 airfoil. The flow around the aircraft is analyzed using PanAir together with the integrated environment and results show that pre and post processing times reduced and ease in PanAir use is increased significantly.

Due to the continuous rise in global incidence and severity of invasive fungal infections (IFIs), particularly among immunocompromised and immunodeficient patients, there is an urgent demand for swift and accurate fungal pathogen diagnosis. Therefore, the need for fungal-specific positron emission tomography (PET) imaging agents that can detect the infection in the early stages is increasing. Cellobiose, a disaccharide, is readily metabolized by fungal pathogens such as Aspergillus species. Recently, our group reported fluorine-18 labeled cellobiose, 2-deoxy-2-[18F]fluorocellobiose ([18F]FCB), for specific imaging of Aspergillus infection. The positive imaging findings with very low background signal on delayed imaging make this ligand a promising fungal-specific imaging ligand. Inspired by this result, the decision was made to automate the radiolabeling procedure for better reproducibility and to facilitate clinical translation. A Trasis AllInOne (Trasis AIO) automated module was used for this purpose. The reagent vials contain commercially available 2-deoxy-2-[18F]fluoroglucose ([18F]FDG), glucose-1-phosphate, and enzyme (cellobiose phosphorylase). A Sep-Pak cartridge was used to purify the tracer. The overall radiochemical yield was 50%-70% (n = 6, decay corrected) in 75-min synthesis time with a radiochemical purity of > 98%. This is a highly reliable protocol to produce current good manufacturing practice (cGMP)-compliant [18F]FCB for clinical PET imaging.

BACKGROUND: The occurrence of tics is the main basis for the diagnosis of Gilles de la Tourette syndrome (GTS). Video-based tic assessments are time consuming.
OBJECTIVE: The aim was to assess the potential of automated video-based tic detection for discriminating between videos of adults with GTS and healthy control (HC) participants.
METHODS: The quantity and temporal structure of automatically detected tics/extra movements in videos from adults with GTS (107 videos from 42 participants) and matched HCs were used to classify videos using cross-validated logistic regression.
RESULTS: Videos were classified with high accuracy both from the quantity of tics (balanced accuracy of 87.9%) and the number of tic clusters (90.2%). Logistic regression prediction probability provides a graded measure of diagnostic confidence. Expert review of about 25% of lower-confidence predictions could ensure an overall classification accuracy above 95%.
CONCLUSIONS: Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116 492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Human observations can only capture a portion of ongoing classroom social activity, and are not ideal for understanding how children\'s interactions are spatially structured. Here we demonstrate how social interaction can be investigated by modeling automated continuous measurements of children\'s location and movement using a commercial system based on radio frequency identification. Continuous location data were obtained from 16 five-year-olds observed during three 1-h classroom free play observations. Illustrative coordinate mapping indicated that boys and girls tended to cluster in different physical locations in the classroom, but there was no suggestion of gender differences in children\'s velocity (i.e., speed of movement). To detect social interaction, we present the radial distribution function, an index of when children were in social contact at greater than chance levels. Rank-order plots indicated that children were in social contact tens to hundreds of times more with some peers than others. We illustrate the use of social ties (higher than average levels of social contact) to visualize the classroom network. Analysis of the network suggests that transitivity is a potential lens through which to examine male, female, and mixed-sex cliques. The illustrative findings suggest the validity of the new measurement approach by re-examining well-established gender segregation findings from a new perspective.