Workflow Efficiency Research Articles

Workflow interruptions in an era of instant messaging: A detailed analysis

IntroductionThe complex practice environment and responsibilities incumbent on diagnostic radiologists creates a workflow susceptible to disruption. While interruptions have been shown to contribute to medical errors in the healthcare delivery environment, the exact impact on highly subspecialized services such as diagnostic radiology is less certain. One potential source of workflow disruption is the use of a departmental instant messaging system (Webex), to facilitate communications between radiology faculty, residents, fellows, and technologists. A retrospective review was conducted to quantify the frequency of interruption experienced by our neuroradiology fellows. Materials and methodsData logs were gathered comprising all instant messages sent and received within the designated group chats from July 5–December 31, 2021, during weekday shifts staffed by neuroradiology fellows. Interruptions per shift were calculated based on month, week, and day of the week. Results14,424 messages were sent across 289 total shifts. The 6 fellows assigned to the main neuroradiology reading room sent 3258 messages and received 10,260 messages from technologists and other staff. There was an average of 50 interruptions per shift when examined by month (range 48–53), and 52 interruptions per shift when examined by day of the week (range 40–60). ConclusionNeuroradiology fellows experience frequent interruptions from the departmental instant messaging system. These disruptions, when considered in conjunction with other non-interpretative tasks, may have negative implications for workflow efficiency, requiring iterative process improvements when incorporating new technology into the practice environment of diagnostic radiology.

Impact of automated dispensing solutions in long-term care facilities and closed-door pharmacies: A mixed methods study of medication management

BackgroundFinancial, operational, and clinical workflow impacts of deploying an automated dispensing cabinet (ADC) in long-term care (LTC) facilities based on actual observations have not been documented in peer-reviewed literature. ObjectivesTo evaluate the impact of a closed-door pharmacy (CDP) implementing an ADC with unique secure, removable, and transportable locked pockets in an unstudied setting (LTC facilities) for management of first and emergency dose medications. Practice DescriptionThis study was conducted in 1 CDP and 2 LTC facilities. Practice InnovationEnhancing emergency medication management and inventory tracking in an unstudied setting through implementation of an ADC system featuring unique electronically encoded medication storage pockets that can be prepared in the CDP, locked and securely transported to the LTC, and when inserted into ADC it informs staff of its presence, position, and contents. Evaluation MethodsMixed methods, pre- and poststudy to assess the impact of replacing manual emergency medication kits with an ADC. Outcomes were evaluated using rapid ethnography with workflow modeling; inventory and delivery reports; a nursing perception survey; and transactional data from the ADC during postimplementation phase. ResultsPharmacy technician preparation time and pharmacist checking time decreased by 59% and 80%, respectively, and standing inventory was reduced by more than $10,000 combined for the CDP and 2 LTCs by replacing emergency medication kits with the ADC. In the LTCs, this change led to a 71% reduction in emergency medication retrieval time, an increase in emergency medication utilization, and a 96% reduction in the cost of unscheduled deliveries. Over 70% of the nurses surveyed favored replacement of the emergency medication kits with the ADC system. ConclusionReplacing manual emergency medication kit with the described ADC system improved workflow efficiency in the CDP and LTC. It also significantly reduced unscheduled (STAT) deliveries and standing inventory and increased the availability of medications commonly used.

Improving Immunization Health Care Data Quality using Two-Dimensional Barcoding and Barcode Scanning Practices.

Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience. Data entry practices and entered records from routine and mass vaccination settings were analyzed. Data quality improvement opportunities were identified. A sample of 50 million emergency use authorization (EUA) coronavirus disease 2019 (COVID-19) vaccine records were analyzed for accuracy and completeness across three data fields: lot number, expiration date, and National Drug Code (NDC). The EUA COVID-19 vaccines lacked a 2D barcode containing these data fields, which necessitated manual data entry at administration. A CDC pilot at clinic compared scanned and manually entered data for routine vaccines across these same data fields. Analysis of 50 million manually entered EUA COVID-19 vaccine administration records indicated significant gaps in data accuracy and completeness across three data fields. Over half of the analyzed EUA vaccine NDCs (53%) and one-third of the expiration dates (35%) had missing or inaccurate data recorded. Pilot data also showed many errors when manually entered. However, when the pilot's routine vaccines were scanned (out of 71,969 records), nearly all entries were complete and accurate across all three data fields (ranging from 99.7% to 99.999% accurate). Vaccine 2D barcode scanning practices increased data accuracy and completeness (up to 99.999% accurate) across data fields assessed. When used consistently, vaccine 2D barcode scanning can resolve issues demonstrated in manually entered data. To realize these benefits, the immunization community should widely use scanning practices. To increase use, CDC developed a Vaccine 2D Barcode National Adoption Strategy and implementation resources.

Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.

A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.

Automated knee degenerative arthritis reports generation through visual data analysis

Knee degenerative arthritis is a common medical condition that affects the knee joint.Knee Degenerative arthritis causes major disability in patients all over the world. The computerized reporting procedure requires effort and expertise. Manual diagnosis, segmentation of knee joints are still used in clinical practice. Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren – Lawrence (KL) system. Despite the fact that they are time-consuming and sensitive to user variance. As a result, we have the proposed system employing the CNN model with Computer Vision to increase the clinical workflow efficiency and overcome the constraints of the generally used method. We can also implement the report generation system that generates medical reports based on X-ray image features. By extracting the relevant features such as joint space narrowing, and cartilage degeneration, the system generates detailed and objective reports.

An Exploration of Dutch Dermatologists' Experience and Satisfaction With Teledermatology: Sociotechnical and Complex Adaptive System Perspective.

Despite the global upscale of teledermatology during the COVID-19 pandemic, persistent barriers, such as the poor anamnesis and photo quality, hinder its effective use in practice. Understanding Dutch dermatologists' experiences and satisfaction with using the teledermatology system in the Dutch health care system is needed. A holistic evaluation may provide valuable insight to understand how barriers interrelate which is deemed necessary for the innovation of teledermatology in practice. Guided by a complex adaptive system perspective, this study aims to understand Dutch dermatologists' experience and satisfaction with their training, support communication, interaction, and usage of a teledermatology platform of a Dutch digital hospital during the COVID-19 pandemic, uncovering insights to improve teledermatology services for the future. A web-based questionnaire was sent in December 2021 to Dutch dermatologists who (1) had an active teledermatology platform account, and (2) responded to a teledermatology consultation between October 1, 2019, and September 30, 2021. The questionnaire consisted of the validated Store-and-Forward Telemedicine Service User-satisfaction Questionnaire (SAF-TSUQ) questionnaire, and new questions regarding; demographics of teledermatologists, the use of teledermatology during the COVID-19 pandemic, the performance of teledermatology by general practitioners (GP), and the role of dermatologists in the teledermatology process. The open-ended questions were analyzed by a grounded theory approach guided by a sociotechnical model and complemented by a complex adaptive system perspective. A panel discussion with 3 dermatologists was performed to provide additional insight into the responses to the questionnaire. We obtained responses from 25 out of the 249 (10%) invited dermatologists. Overall, dermatologists had a positive experience with teledermatology. Interestingly, teledermatology use frequency remained unaffected by the COVID-19 pandemic. However, the insufficient quality and incompleteness of the clinical content (photos and anamneses information) of the teledermatology consultation impacted the efficiency of the teledermatology workflow. Dermatologists expressed the need for improvement to avoid time-consuming processes or physical referrals. The panel discussion enriched and confirmed the responses, suggesting solutions like mandatory fields for the GPs for a complete anamnesis. Dutch Dermatologists view teledermatology as a valuable tool to provide access to dermatology care. However, improvements regarding the quality and completeness of the provided clinical content are necessary for the effectiveness and efficiency of the complex teledermatology system in Dutch health care. This could increase both the dermatologists' satisfaction and the quality of teledermatology services. Managing trade-offs, such as time investments versus image quality, is crucial for teledermatology implementation and should be assessed from a complexity perspective to understand trade-offs and prevent unintended consequences.

Clinicians’ experience with technology within the physical environment of trauma rooms: a focus group study

PurposeThis study aims to investigate how trauma team members perceive technological equipment and tools in the trauma room (TR) environment and to identify how the technological equipment could be optimized in relation to the TR’s space.Design/methodology/approachA total of 21 focus group sessions were conducted with 69 trauma team members, all of whom worked in Level I TRs from six teaching hospitals in the USA.FindingsThe collected data was analyzed and categorized into three parent themes: imaging equipment, assistive devices and room features. The results of the study suggest that trauma team members place high importance on the availability and versatility of the technological equipment in the TR environment. Although CT scans are a usual procedure necessity in TRs, few facilities were optimized for easy access to CT-scanners for the TR. The implementation of cameras and screens was suggested as an improvement to accommodate situational awareness. Rapid sharing of data, such as imaging results, was highly sought after. Unorthodox approaches, such as the use of automatic doors, were associated with slowing down the course of actions.Practical implicationsThis study provides health-care designers with the knowledge they need to make informed decisions when designing TRs. It will cover key considerations such as room layout, equipment selection, lighting and controls. Implementing the strategies will help minimize negative patient outcomes.Originality/valueLevel I TRs are a critical element of emergency departments and designing them correctly can significantly impact patient outcomes. However, designing a TR can be a complex process that requires careful consideration of various factors, including patient safety, workflow efficiency, equipment placement and infection control. This study suggests multiple considerations when designing TRs.

A deep learning approach for projection and body-side classification in musculoskeletal radiographs

BackgroundThe growing prevalence of musculoskeletal diseases increases radiologic workload, highlighting the need for optimized workflow management and automated metadata classification systems. We developed a large-scale, well-characterized dataset of musculoskeletal radiographs and trained deep learning neural networks to classify radiographic projection and body side.MethodsIn this IRB-approved retrospective single-center study, a dataset of musculoskeletal radiographs from 2011 to 2019 was retrieved and manually labeled for one of 45 possible radiographic projections and the depicted body side. Two classification networks were trained for the respective tasks using the Xception architecture with a custom network top and pretrained weights. Performance was evaluated on a hold-out test sample, and gradient-weighted class activation mapping (Grad-CAM) heatmaps were computed to visualize the influential image regions for network predictions.ResultsA total of 13,098 studies comprising 23,663 radiographs were included with a patient-level dataset split, resulting in 19,183 training, 2,145 validation, and 2,335 test images. Focusing on paired body regions, training for side detection included 16,319 radiographs (13,284 training, 1,443 validation, and 1,592 test images). The models achieved an overall accuracy of 0.975 for projection and 0.976 for body-side classification on the respective hold-out test sample. Errors were primarily observed in projections with seamless anatomical transitions or non-orthograde adjustment techniques.ConclusionsThe deep learning neural networks demonstrated excellent performance in classifying radiographic projection and body side across a wide range of musculoskeletal radiographs. These networks have the potential to serve as presorting algorithms, optimizing radiologic workflow and enhancing patient care.Relevance statementThe developed networks excel at classifying musculoskeletal radiographs, providing valuable tools for research data extraction, standardized image sorting, and minimizing misclassifications in artificial intelligence systems, ultimately enhancing radiology workflow efficiency and patient care.Key points• A large-scale, well-characterized dataset was developed, covering a broad spectrum of musculoskeletal radiographs.• Deep learning neural networks achieved high accuracy in classifying radiographic projection and body side.• Grad-CAM heatmaps provided insight into network decisions, contributing to their interpretability and trustworthiness.• The trained models can help optimize radiologic workflow and manage large amounts of data.Graphical

Automated inversion time selection for late gadolinium–enhanced cardiac magnetic resonance imaging

ObjectivesTo develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement cardiac MRI.Materials and methodsRetrospective multicentre study conducted on 1136 1.5-T and 3-T cardiac MRI examinations from four centres and three scanner vendors. Deep learning models, comprising a convolutional neural network (CNN) that provides input to a long short-term memory (LSTM) network, were trained on TI scout pixel data from centres 1 to 3 to identify optimal TI, using ground truth annotations by two readers. Accuracy within 50 ms, mean absolute error (MAE), Lin’s concordance coefficient (LCCC) and reduced major axis regression (RMAR) were used to select the best model from validation results, and applied to holdout test data. Robustness of the best-performing model was also tested on imaging data from centre 4.ResultsThe best model (SE-ResNet18-LSTM) produced accuracy of 96.1%, MAE 22.9 ms and LCCC 0.47 compared to ground truth on the holdout test set and accuracy of 97.3%, MAE 15.2 ms and LCCC 0.64 when tested on unseen external (centre 4) data. Differences in vendor performance were observed, with greatest accuracy for the most commonly represented vendor in the training data.ConclusionA deep learning model was developed that can identify optimal inversion time from TI scout images on multi-vendor data with high accuracy, including on previously unseen external data. We make this model available to the scientific community for further assessment or development.Clinical relevance statementA robust automated inversion time selection tool for late gadolinium–enhanced imaging allows for reproducible and efficient cross-vendor inversion time selection.Key Points• A model comprising convolutional and recurrent neural networks was developed to extract optimal TI from TI scout images.• Model accuracy within 50 ms of ground truth on multi-vendor holdout and external data of 96.1% and 97.3% respectively was achieved.• This model could improve workflow efficiency and standardise optimal TI selection for consistent LGE imaging.

Comparative Perspectives: Exploring Workflow Efficiencies of BIM and CAD in 3D Modelling

Building Information Modelling (BIM) is a new technology to make genuine project contributions, especially in the industry, architecture, and construction sectors. However, in practice, many professional workforces choose computer-aided design (CAD) as the primary technology, which has formerly appeared and is further comprehensible. Building Information Modelling offered different perspectives on purpose to facilitate designers or architects to expertise in various fields. Building Information Modelling (BIM) contains informative data that can use some expertise as a solution to make the occupation more coherent. This research aims to comprehend the workflow differences between Computer Aided Design (CAD) and Building Information Modelling (BIM) system technology. We collated methods by reviewing some literature and reviewing case studies. Then, we compare the workflow and conclude which is more effective for architectural design. In this research, we explore the difference between BIM's workflow and Cad's workflow to know which is more effective. So, we conclude and can apply the technology appropriately in the workflow needed so that it can simplify the workflow.

Downstream Processing of Secondary Metabolite Extraction from Medicinal Plants (Tinospora cordifolia, Ocimum tenuiflorum, Asparagus racemosus, Bacopa monnieri): Optimization and Equipment Utilization

Tinosoporacordifolia, Ocimum tenuiflorum,Asparagus racemosus and Bacopa monnieri are all important and plants with significant medicinal propertiesfrom these plants secondary metabolites extracts were derived and TLC characterization was done. This paper delves into the intricate downstream processing steps and the utilization of specialized equipment in the extraction and purification of secondary metabolites from four medicinal plants: Tinospora cordifolia, Ocimum tenuiflorum, Asparagus racemosus, and Bacopa monnieri. The study emphasizes the optimization of extraction methods, purification techniques, and the role of specific equipment in enhancing the efficiency of the downstream processing workflow.

Bioassay-Driven, Fractionation-Empowered, Focused Metabolomics for Discovering Bacterial Activators of Aryl Hydrocarbon Receptor.

Aryl hydrocarbon receptor (AhR) is a transcription factor that regulates gene expression upon ligand activation, enabling microbiota-dependent induction, training, and function of the host immune system. A spectrum of metabolites, encompassing indole and tryptophan derivatives, have been recognized as activators. This work introduces an integrated, mass spectrometry-centric workflow that employs a bioassay-guided, fractionation-based methodology for the identification of AhR activators derived from human bacterial isolates. By leveraging the workflow efficiency, the complexities inherent in metabolomics profiling are significantly reduced, paving the way for an in-depth and focused mass spectrometry analysis of bioactive fractions isolated from bacterial culture supernatants. Validation of AhR activator candidates used multiple criteria─MS/MS of the synthetic reference compound, bioassay of AhR activity, and elution time confirmation using a C-13 isotopic reference─and was demonstrated for N-formylkynurenine (NFK). The workflow reported provides a roadmap update for improved efficiency of identifying bioactive metabolites using mass spectrometry-based metabolomics. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (PR001479, Project DOI: 10.21228/M8JM7Q).

Validation of low-pass genome sequencing for prenatal diagnosis.

Chromosomal microarray (CMA), while considered the gold standard for detecting copy number variants (CNVs) in prenatal diagnostics, has its limitations, including the necessity to replace aging microarray equipment, low throughput, a static design, and an inefficient multi-day workflow. This study evaluates the feasibility of low-pass genome sequencing (LP-GS) as a potential replacement for CMA in prenatal diagnostics. We comprehensively compared LP-GS at 10x and 5x average depths with CMA in a prenatal laboratory. We examined parameters, including concordance, sensitivity, specificity, workflow efficiency, and cost-effectiveness. We found a high degree of agreement between LP-GS and CMA for detecting CNVs and absence of heterozygosity. Furthermore, compared to CMA, LP-GS increased workflow efficiency and proved to be cost-neutral at 10x and cost-effective at 5x. Our study suggests that LP-GS is a promising alternative to CMA in prenatal diagnostics, offering advantages, including a more efficient workflow and scalability for larger testing volumes. Importantly, for clinical laboratories that have adopted next-generation sequencing in a separate capacity, LP-GS facilitates a unified NGS-centric approach, enabling workflow consolidation. By offering a single, streamlined platform for detecting a broad range of genetic variants, LP-GS may represent a critical step toward enhancing the diagnostic capabilities of prenatal laboratories.

Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography

BackgroundAutomated identification of spectral domain optical coherence tomography (SD-OCT) features can improve retina clinic workflow efficiency as they are able to detect pathologic findings. The purpose of this study was to test a deep learning (DL)-based algorithm for the identification of Idiopathic Full Thickness Macular Hole (IFTMH) features and stages of severity in SD-OCT B-scans.MethodsIn this cross-sectional study, subjects solely diagnosed with either IFTMH or Posterior Vitreous Detachment (PVD) were identified excluding secondary causes of macular holes, any concurrent maculopathies, or incomplete records. SD-OCT scans (512 × 128) from all subjects were acquired with CIRRUS™ HD-OCT (ZEISS, Dublin, CA) and reviewed for quality. In order to establish a ground truth classification, each SD-OCT B-scan was labeled by two trained graders and adjudicated by a retina specialist when applicable. Two test sets were built based on different gold-standard classification methods. The sensitivity, specificity and accuracy of the algorithm to identify IFTMH features in SD-OCT B-scans were determined. Spearman’s correlation was run to examine if the algorithm’s probability score was associated with the severity stages of IFTMH.ResultsSix hundred and one SD-OCT cube scans from 601 subjects (299 with IFTMH and 302 with PVD) were used. A total of 76,928 individual SD-OCT B-scans were labeled gradable by the algorithm and yielded an accuracy of 88.5% (test set 1, 33,024 B-scans) and 91.4% (test set 2, 43,904 B-scans) in identifying SD-OCT features of IFTMHs. A Spearman’s correlation coefficient of 0.15 was achieved between the algorithm’s probability score and the stages of the 299 (47 [15.7%] stage 2, 56 [18.7%] stage 3 and 196 [65.6%] stage 4) IFTMHs cubes studied.ConclusionsThe DL-based algorithm was able to accurately detect IFTMHs features on individual SD-OCT B-scans in both test sets. However, there was a low correlation between the algorithm’s probability score and IFTMH severity stages. The algorithm may serve as a clinical decision support tool that assists with the identification of IFTMHs. Further training is necessary for the algorithm to identify stages of IFTMHs.

Influence of Contrast Media Temperature and Concentration on Patient Comfort and Safety in Computed Tomography: CATCHY II Trial.

Previous research on the necessity to reduce the viscosity of contrast media (CM) by either prewarming CM before injection during computed tomography (CT) or by using less concentrated CM has yielded conflicting results. In addition, there is limited evidence on patient comfort. The aim of the study was to examine if prewarming CM, with varying CM concentrations, is superior to CM at room temperature, with respect to patient comfort and safety in CT. All elective patients scheduled for contrast-enhanced CT scans at Maastricht University Medical Center+ between October 27, 2021 and October 31, 2022 were eligible for inclusion when a questionnaire evaluating patient comfort was completed. This 1-year period was divided into 4 intervals (4 groups): group 1 (370 mg I/mL, 37°C), group 2 (370 mg I/mL, room temperature), group 3 (300 mg I/mL, 37°C), and group 4 (300 mg I/mL, room temperature). All CT scans were performed using state of the art equipment (Siemens Healthineers; SOMATOM Force and SOMATOM Definition AS, Forchheim, Germany). Contrast media injections were performed using a dual-head power injector (Stellant; Bayer Healthcare, Berlin, Germany) and individualized to body weight and/or tube voltage, depending on the CM protocols. After the CT scan, patients completed a questionnaire covering the primary outcomes comfort, pain, and adverse events such as feelings of heat, nausea, vomiting, itchiness, urticaria, difficulty breathing, dizziness, goosebumps, or an odd taste. Technicians were asked to report any adverse events, including extravasation and allergic-like reactions. The secondary outcome involved attenuation (in Hounsfield unit, HU), which was evaluated by assessing the HU of the coronary arteries for vascular CT, and liver enhancement in portal venous CT. The Kruskal-Wallis test was used for continuous scale outcomes and χ 2 tests for examining adverse events. Results showed no significant differences examining comfort score ( P = 0.054), pain sensation ( P = 0.469), extravasation ( P = 0.542), or allergic-like reaction ( P = 0.253). Significant differences among the 4 groups were found with respect to heat sensation and dizziness ( P = 0.005 and P = 0.047, respectively), showing small effect sizes. All other adverse effects showed no significant results. No significant differences were observed in coronary attenuation among the 4 groups in coronary CT angiography ( P = 0.113). When analyzing attenuation in portal venous CT scans, significant differences were found among the 4 groups ( P = 0.008). Administrating prewarmed CM is nonsuperior compared with CM at room temperature in relation to patient comfort and safety, regardless of CM concentration. These findings suggest that prewarming CM before usage is unnecessary, which will improve the efficiency of daily clinical workflow and brings environmentally friendly benefits.

Toward a solid microneedle patch for rapid and enhanced local analgesic action.

Analgesic creams find widespread application as adjuncts for localized anesthesia prior to surgical procedures. Nevertheless, the onset of analgesic action is protracted due to the skin barrier's inherent characteristics, which necessitates prolonged intervals of patient and clinician waiting, consequently impinging upon patient compliance and clinician workflow efficiency. In this work, a biodegradable microneedles (MNs) patch was introduced to enhance the intradermal administration of lidocaine cream to achieve rapid analgesia through a minimally invasive and conveniently accessible modality. The polylactic acid (PLA) MNs were mass-produced using a simple hot-pressing method and served the purpose of creating microchannels across the skin's surface for rapid absorption of lidocaine cream. Optical and electron microscopes were applied to meticulously scrutinize the morphology of the fabricated MNs, and the comprehensive penetration tests involving dynamometer tests, evaluation on porcine cadaver skin, artificial film, optical coherence tomography (OCT), transepidermal water loss, and analysis on rats' skins, demonstrated the robust mechanical strength of PLA MNs for successful intradermal penetration. The behavioral pain sensitivity tests on living rats using Von Frey hair filaments revealed that the MN-assisted lidocaine treatment expeditiously accelerated the onset of action from 40 to 10min and substantially enhanced the efficacy of localized anesthesia. Furthermore, different treatment protocols encompassing the sequence of drug application relative to MN treatment, MN dimensions, and the frequency of MN insertions exhibited noteworthy influence on the resultant local anesthesia efficacy. Together, these results demonstrated that the lidocaine cream followed by diverse PLA MN treatments would be a promising strategy for rapid clinical local anesthesia with wide-ranging applications.

Magnetic Bead-Based Workflow for Sensitive and Streamlined Cell Surface Proteomics.

Cell surface proteins represent an important class of molecules for therapeutic targeting and cellular phenotyping. However, their enrichment and detection via mass spectrometry-based proteomics remains challenging due to low abundance, post-translational modifications, hydrophobic regions, and processing requirements. To improve their identification, we optimized a Cell-Surface Capture (CSC) workflow that incorporates magnetic bead-based processing. Using this approach, we evaluated labeling conditions (biotin tags and catalysts), enrichment specificity (streptavidin beads), missed cleavages (lysis buffers), nonenzymatic deamidation (digestion and deglycosylation buffers), and data acquisition methods (DDA, DIA, and TMT). Our findings support the use of alkoxyamine-PEG4-biotin plus 5-methoxy-anthranilic acid, SDS/urea-based lysis buffers, single-pot solid-phased-enhanced sample-preparation (SP3), and streptavidin magnetic beads for maximal surfaceome coverage. Notably, with semiautomated processing, sample handling was simplified and between ∼600 and 900 cell surface N-glycoproteins were identified from only 25-200 μg of HeLa protein. CSC also revealed significant differences between in vitro monolayer cultures and in vivo tumor xenografts of murine CT26 colon adenocarcinoma samples that may aid in target identification for drug development. Overall, the improved efficiency of the magnetic-based CSC workflow identified both previously reported and novel N-glycosites with less material and high reproducibility that should help advance the field of surfaceomics by providing insight in cellular phenotypes not previously documented.

Smile renaissance: Monolithic marvels with cad-cam crown

Progress in materials science, bonding protocols, and innovative manufacturing techniques promotes the creation of new ceramic materials. These advancements aim to address the growing need for fixed prosthodontics restorations that are not only highly aesthetic but also biocompatible and durable. The recently launched ALD ceramic, designed for the "Chairside Economical Restoration of Esthetic Ceramics" (CEREC) system, yields restorations that are both aesthetically satisfying and superior clinically. The reduced processing time, coupled with elevated flexural strength, enhances efficiency of chairside workflows. In this particular case report, restoration was deemed necessary for anterior tooth. This tooth underwent preparation, scanning, and design processes using CEREC-Omnicam, with subsequent fabrication using MCXL The rehabilitation accomplished with the ALD blocks resulted in a visually pleasing and functionally sound outcome. Notably, this approach contributed to a more efficient overall treatment time, resulting in heightened satisfaction for both the patient and the practitioner.

A robust autofocusing method for microscopic imaging based on an event camera

Autofocusing is a crucial method for enhancing the efficiency of biological detection workflows. Traditional methods involve calculating various focus metrics on images captured by standard frame-based cameras at multiple static positions to determine the focal point, which can be time-consuming. In contrast, event cameras have the capability to asynchronously detect brightness changes within the field of view of the microscope and provide a continuous stream of events triggered by axial motion. A completely automated coarse-to-fine method is proposed for event based autofocusing, independent of the objective-sample distance. Two criteria for autofocusing in sufficient and low illumination are respectively proposed. The results show that the dynamic accuracy and dynamic repeatability is less than 5 mm.

Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.

To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD). Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index. In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively (p<0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively (p< 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9-97.7%) and 94.3% (89.5-97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%). FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events. Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events. • Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. • Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. • Full automation can facilitate the application of CCTA in early detection of coronary artery disease.