UNASSIGNED: Regional citrate anticoagulation is a preferred option for renal replacement therapy in critically ill patients. However, current implementations ignore individual differences that may exist in the fluctuation of patients\' ionized calcium levels. To address this problem, individualized citrate and calcium supplementation models were established based on the pharmacokinetic and clearance characteristics of citrate, and an automated regional citrate anticoagulation system was built with these models as its core to facilitate the treatment of clinical patients. This study was designed to preliminarily evaluate the safety and efficacy of this system, the SuperbMed® RCA-SP100 automated regional citrate anticoagulation system, in prolonged intermittent renal replacement therapy.
UNASSIGNED: Seven patients undergoing prolonged intermittent renal replacement therapy completed treatment with the SuperbMed® RCA-SP100 system. In vivo and in vitro ionized calcium levels were measured every hour before and after the start of dialysis. The accuracy and alarm sensitivity of the pumps were also monitored.
UNASSIGNED: During seven treatments, the average extracorporeal ionized calcium level was 0.34 ± 0.02 mmol/L, and the mean ionized calcium level in vivo was 1.09 ± 0.07 mmol/L. No patient required intervention, and there was no filter coagulation. The pumps all had an absolute accuracy less than 5%, and alarms could be triggered precisely.
UNASSIGNED: We reported on an automated system that allows for individualized citrate and calcium supplementation in prolonged intermittent renal replacement therapy and enables the precise and secure implementation of regional citrate anticoagulation.

OBJECTIVE: Electronic medical records (EMRs) have streamlined workflows for health care professionals, yet their full potential is not always actualized. Modern EMRs are often capable of generating automated prepopulated inpatient lists, however if these capabilities are not made available to inpatient teams or not designed with the end user in mind, resident physicians may be left to create alternative, manual solutions to ensure reliable and efficient care. The purpose of the current study was to longitudinally compare the impact of both manual and automated inpatient lists on resident education, wellness, and patient safety.
METHODS: Retrospective standardized surveys were administered to resident physicians in the orthopedic surgery department at a level I trauma center over a 3-year period to evaluate the impact of various automated and manual list iterations coinciding with changes to the EMR. Data collected included post graduate year (PGY) status, arrival time to work, daily time spent preparing and updating the list, perceived impact on patient safety, resident workload, education, and sleep. We compared manual versus automated list data with unpaired t-tests and chi-squared tests.
METHODS: The study was conducted at Brooke Army Medical Center, a level 1 trauma center in San Antonio, Texas. It is an Academic Medical Center and the Department of Defense\'s largest medical facility.
METHODS: A total of 71 surveys were collected from 33 orthopedic surgery residents in all levels of training.
RESULTS: Intern list prep time in the morning was 27 ± 16 minutes for the automated list (n = 17) vs 72 ± 21 minutes for the manual lists (n = 23) (p < 0.0001). Total time spent by interns updating the list for the entire day was on average 83 minutes for the automated list (n = 17) vs 196 minutes for the manual lists (n = 23) (p < 0.0001). In addition, 86% of interns felt the time spent on the manual list impacted their education, with 96% stating that it directly impacted the amount of time they had to study and 100% agreed that it negatively impacted their sleep (n = 23). Only 48% of interns (n = 23) were satisfied with the performance of the manual lists compared to 94% satisfaction (n = 17) with the automated list. Further, 87% of interns felt the manual list negatively impacted patient care and negatively affected their job satisfaction. In comparison, 59% of interns felt the automated list improved their job satisfaction. Ultimately, for an intern an automated list versus a manual list affords them an extra 106 minutes per day for education, sleep, or other activities. PGY2 residents and above noted similar trends regarding their experience with the lists.
CONCLUSIONS: The benefits of utilizing automated inpatient lists as determined by our study are improved efficiency in the morning with less preparation and maintenance throughout the day. Additionally, with automated lists there was more perceived time for education and sleep, with improved job satisfaction. Most importantly, respondents felt that automated lists were safer for patient care when compared to manual lists. This should compel further research and efforts into improving automated EMR tracking lists in hospitals. In summary, as compared to the automated electronic medical record inpatient list, manual lists resulted in substantially more preparation time and maintenance throughout the day thereby negatively impacting resident education and quality of life, while raising concerns for patient safety.

BACKGROUND: There is an increasing demand for automated surgical skill assessment to solve issues such as subjectivity and bias that accompany manual assessments. This study aimed to verify the feasibility of assessing surgical skills using a surgical phase recognition model.
METHODS: A deep learning-based model that recognizes five surgical phases of laparoscopic sigmoidectomy was constructed, and its ability to distinguish between three skill-level groups-the expert group, with a high Endoscopic Surgical Skill Qualification System (ESSQS) score (26 videos); the intermediate group, with a low ESSQS score (32 videos); and the novice group, with an experience of < 5 colorectal surgeries (27 videos)-was assessed. Furthermore, 1 272 videos were divided into three groups according to the ESSQS score: ESSQS-high, ESSQS-middle, and ESSQS-low groups, and whether they could be distinguished by the score calculated by multiple regression analysis of the parameters from the model was also evaluated.
RESULTS: The time for mobilization of the colon, time for dissection of the mesorectum plus transection of the rectum plus anastomosis, and phase transition counts were significantly shorter or less in the expert group than in the intermediate (p = 0.0094, 0.0028, and < 0.001, respectively) and novice groups (all p < 0.001). Mesorectal excision time was significantly shorter in the expert group than in the novice group (p = 0.0037). The group with higher ESSQS scores also had higher AI scores.
CONCLUSIONS: This model has the potential to be applied to automated skill assessments.

UNASSIGNED: Despite significant progress in our understanding of depression, prevalence rates have substantially increased in recent years. Thus, there is an imperative need for more cost-effective and scalable mental health treatment options, including digital interventions that minimize therapist burden.
UNASSIGNED: This study focuses on a fully automated digital implementation of behavioral activation (BA)-a core behavioral component of cognitive behavioral therapy for depression. We examine the efficacy of a 1-month fully automated SMS text message-based BA intervention for reducing depressive symptoms and anhedonia.
UNASSIGNED: To this end, adults reporting at least moderate current depressive symptoms (8-item Patient Health Questionnaire score ≥10) were recruited online across the United States and randomized to one of three conditions: enjoyable activities (ie, BA), healthy activities (ie, an active control condition), and passive control (ie, no contact). Participants randomized to enjoyable and healthy activities received daily SMS text messages prompting them to complete 2 activities per day; participants also provided a daily report on the number and enjoyment of activities completed the prior day.
UNASSIGNED: A total of 126 adults (mean age 32.46, SD 7.41 years) with current moderate depressive symptoms (mean score 16.53, SD 3.90) were recruited. Participants in the enjoyable activities condition (BA; n=39) experienced significantly greater reductions in depressive symptoms compared to participants in the passive condition (n=46). Participants in both active conditions-enjoyable activities and healthy activities (n=41)-reported reduced symptoms of anxiety compared to those in the control condition.
UNASSIGNED: These findings provide preliminary evidence regarding the efficacy of a fully automated digital BA intervention for depression and anxiety symptoms. Moreover, reminders to complete healthy activities may be a promising intervention for reducing anxiety symptoms.

Clostridioides difficile detection in community settings is time-intensive, resulting in delays in diagnosing and quarantining infected individuals. However, with the advent of semi-automated devices and improved algorithms in recent decades, the ability to discern CDI infection from asymptomatic carriage has significantly improved. This, in turn, has led to efficiently regulated monitoring systems, further reducing endemic risk, with recent concerns regarding a possible surge in hospital-acquired Clostridioides difficile infections post-COVID failing to materialize. This review highlights established and emerging technologies used to detect community-acquired Clostridioides difficile in research and clinical settings.

Background/Objectives: Radiography is an essential and low-cost diagnostic method in pulmonary medicine that is used for the early detection and monitoring of lung diseases. An adequate and consistent image quality (IQ) is crucial to ensure accurate diagnosis and effective patient management. This pilot study evaluates the feasibility and effectiveness of the International Atomic Energy Agency (IAEA)\'s remote and automated quality control (QC) methodology, which has been tested in multiple imaging centers. Methods: The data, collected between April and December 2022, included 47 longitudinal data sets from 22 digital radiographic units. Participants submitted metadata on the radiography setup, exposure parameters, and imaging modes. The database comprised 968 exposures, each representing multiple image quality parameters and metadata of image acquisition parameters. Python scripts were developed to collate, analyze, and visualize image quality data. Results: The pilot survey identified several critical issues affecting the future implementation of the IAEA method, as follows: (1) difficulty in accessing raw images due to manufacturer restrictions, (2) variability in IQ parameters even among identical X-ray systems and image acquisitions, (3) inconsistencies in phantom construction affecting IQ values, (4) vendor-dependent DICOM tag reporting, and (5) large variability in SNR values compared to other IQ metrics, making SNR less reliable for image quality assessment. Conclusions: Cross-comparisons among radiography systems must be taken with cautious because of the dependence on phantom construction and acquisition mode variations. Awareness of these factors will generate reliable and standardized quality control programs, which are crucial for accurate and fair evaluations, especially in high-frequency chest imaging.

Chicken production has increased over the past decade, resulting in a concomitant rise in the demand for more humane options for poultry products including cage-free, free-range, and organic meat and eggs. These husbandry changes, however, have come hand-in-hand with increased prevalence of Ascaridia galli infection, which can cause clinical disease in chickens as well as the occasional appearance of worms in eggs. Additionally, development of anthelmintic resistance in closely related helminths of turkeys highlights the need for closely monitored anthelmintic treatment programs. Manual faecal egg counts (FECs) can be time-consuming and require specialist training. As such, this study sought to validate an automated FEC system for use in detection and quantification of A. galli eggs in chicken faeces. Automated counts using the Parasight System (PS) were compared to traditional manual McMaster counting for both precision and correlation between methods. Overall, ten repeated counts were performed on twenty individual samples for a total of 200 counts performed for each method. A strong, statistically significant correlation was found between methods (R2 = 0.7879, P < 0.0001), and PS counted more eggs and performed with statistically significant higher precision (P = 0.0391) than manual McMaster counting. This study suggests that PS is a good alternative method for performing A. galli FECs and provides a new tool for use in helminth treatment and control programs in chicken operations.

An improved automated bloodstain pattern analysis method has been developed and validated, which utilises computer vision techniques to identify bloodstains on a plain background within a digital image. The method generates metrics relating to the individual stains as well as the overall pattern, including bloodstain pattern specific metrics such as the gamma angle, circularity, solidity, area of convergence, stain density and pattern linearity. This method provides an objective approach to the analysis of bloodstains and bloodstain patterns and can generate a wealth of quantitative data that is currently not obtainable using manual techniques or other image-based programs currently utilised in the discipline. This method will be useful to analysts and researchers investigating the application of quantitative methods to bloodstain pattern analysis.

This work describes the development of a new automated parallel dispersive tip microextraction method (Au-Pa-DPX) for the determination of eleven polycyclic aromatic hydrocarbons (PAHs) in four samples of Brazilian sugarcane spirit beverages, with separation and detection done by the HPLC-DAD. The results obtained with the Au-Pa-DPX approach were also compared with those obtained via the conventional parallel manual DPX method with the same samples and optimized extraction process. Desorption solvent and cycles of desorption, cleaning and extraction were optimized using response surface methodology and univariate approaches. For the Au-Pa-DPX method, the coefficient of determination (R2) ranged from 0.9948 to 0.9997. The limits of detection and quantification were all 0.303 μg l-1 and 1.00 μg l-1, respectively. Interday and intraday precision ranged from 7.6 % to 31.7 % and 0.40 % to 15.8 %, respectively. For the manual parallel DPX method, the interday and intraday precision ranged from 8.2 % to 38.1 % and 5.40 % to 18.7 %, respectively. The relative recovery values obtained with the proposed method ranged from 53.29 to 124.94 %. The enrichment factors ranged from 15.13 to 22.35. The sum of PAH concentrations in the four samples ranged from undetected to 25.58 μg l-1. These results, when correlated to other methods, highlight the gains in regards to precision obtained with the automated apparatus. Furthermore, when compared to other methods from the literature, it is an interesting green alternative for the determination of these analytes and this sample, with high throughput (4.67 min per sample), low consumption of solvents and samples, generating less waste and reducing health risks to the analyst.

OBJECTIVE: To develop a standardised, automated protocol for detecting protein gene product 9.5 (PGP9.5) positive intra-epidermal nerve fibres (IENFs) in skin biopsies, transitioning from the established manual technique to an automated platform. This automated method, although currently intended for research applications, may improve the accessibility of this diagnostic test for small fibre neuropathy in clinical settings.
METHODS: Skin biopsies (n = 274) from 100 participants (fibromyalgia syndrome n = 62; idiopathic small fibre neuropathy: n = 16; healthy volunteers: n = 22) were processed using an automated immunohistochemistry platform. IENF quantification was performed by blinded examiners, with reliability assessed via a two-way mixed-effects model to evaluate inter- and intra-observer variability.
RESULTS: The automated staining system reproduced intra-epidermal nerve fibre density (IENFD) counts consistent with free-floating sections (mean ± standard deviation: free-floating: 5.6 ± 3.4 fibres/mm; automated: 5.9 ± 3.2 fibres/mm). A median difference of 0.3 with a lower bound 95% Confidence Interval (CI) at -0.00005 established non-inferiority against a margin of -0.4 (p = .08). Specifically, the inter-class correlation coefficient (class denotes consistency in measured observations) was 99% (95% CI: 0.9-1), indicating excellent agreement between free-floating and automated methods. The inter- and intra-class coefficient between examiners were both 99% (95% CI: 0.9-0.1) for IENFD, demonstrating high reliability using sections stained using the automated method.
CONCLUSIONS: Automated immunohistochemistry provides high-throughput reliable and reproducible intra-epidermal nerve fibre quantification. This method, although currently proof-of-concept, for research use only, may be more widely deployed in histopathology laboratories to increase the adoption of IENFD assessment for the diagnosis of peripheral neuropathies.