Comparisons of laparoscopic and robotic pancreaticoduodenectomy using barbed and conventional sutures for pancreaticojejunostomy: a propensity score matching study.

BACKGROUND: There are limited data on the effect of different sutures and surgical approaches on the quality of pancreaticojejunostomy in minimally invasive pancreaticoduodenectomy (MIPD). This study compares the incidence of clinically relevant postoperative pancreatic fistula (CR-POPF) between the use of barbed sutures (BSs) and conventional sutures (CSs). METHODS: A retrospective cohort study was conducted on 253 consecutive patients who had undergone MIPD from July 2016 to April 2023. Patients were excluded if conversion to open surgery or open anastomosis was necessary. 220 patients were enrolled and divided into BS (n = 148) and CS (n = 72) groups. After 1:1 propensity score matching (PSM), 67 cases remained in each group. Univariate and multivariate analyses identified factors associated with CR-POPF. Comparisons were also made between laparoscopic (LPD) and robotic (RPD) pancreaticoduodenectomy. RESULTS: After PSM, BSs were associated with significantly lower rates of CR-POPF (7.5 vs. 22.4%, P = 0.015) and severe complications (Clavien-Dindo ≥ III) (7.5vs. 19.4%, P = 0.043). No significant differences were found in operative time, length of postoperative hospital stay, or other major morbidities. Multivariate analyses revealed BMI ≥ 22 kg/m2 (OR = 5.048, 95% CI: 1.256-20.287, P = 0.023) and the use of BSs (OR = 0.196, 95% CI: 0.059-0.653, P = 0.008) as the independent predictors of CR-POPF. There were no significant differences in postoperative outcomes between the LPD and RPD groups, but RPD was associated with significantly shorter operative time (402.8 min vs. 429.4 min, P = 0.015). CONCLUSIONS: In conclusion, using BSs for PJ during MIPD is feasible and has the potential to reduce CR-POPF and severe complications.

Combining Protein Phase Separation and Bio-orthogonal Linking to Coimmobilize Enzymes for Cascade Biocatalysis.

The designed and ordered co-immobilization of multiple enzymes for vectorial biocatalysis is challenging. Here, a combination of protein phase separation and bioorthogonal linking is used to generate a zeolitic imidazole framework (ZIF-8) containing co-immobilized enzymes. Zn2+ ions induce the clustering of minimal protein modules, such as 6-His tag, proline-rich motif (PRM) and SRC homology 3 (SH3) domains, and allow for phase separation of the coupled aldoketoreductase (AKR) and alcohol dehydrogenase (ADH) at low concentrations. This is achieved by fusing SpyCatcher and PRM-SH3-6His peptide fragments to the C and N termini of AKR, respectively, and the SpyTag to ADH. Addition of 2-methylimidazole results in droplet formation and enables in situ spatial embedding the recombinant AKR and ADH to generate the cascade biocalysis system encapsulated in ZIF-8 (AAE@ZIF). In synthesizing (S)-1-(2-chlorophenyl) ethanol, ater 6 cycles, the yield can still reach 91%, with 99.99% enantiomeric excess (ee) value for each cycle. However, the yield could only reach 72.9% when traditionally encapsulated AKR and ADH in ZIF-8 are used. Thus, this work demonstrates that a combination of protein phase separation and bio-orthogonal linking enables the in situ creation of a stable and spatially organized bi-enzyme system with enhanced channeling effects in ZIF-8.

Tree-Inspired Aerogel Comprising Nonoxidized Graphene Flakes and Cellulose as Solar Absorber for Efficient Water Generation.

As global freshwater shortages worsen, solar steam generation (SSG) emerges as a promising, eco-friendly, and cost-effective solution for water purification. However, widespread SSG implementation requires efficient photothermal materials and solar evaporators that integrate enhanced light-to-heat conversion, rapid water transportation, and optimal thermal management. This study investigates using nonoxidized graphene flakes (NOGF) with negligible defects as photothermal materials capable of absorbing over 98% of sunlight. By combining NOGF with cellulose nanofibers (CNF) through bidirectional freeze casting, we created a vertically and radially aligned solar evaporator. The hybrid aerogel exhibited exceptional solar absorption, efficient solar-to-thermal conversion, and improved surface wettability. Inspired by tree structures, our design ensures rapid water supply while minimizing heat loss. With low NOGF content (∼10.0%), the NOGF/CNF aerogel achieves a solar steam generation rate of 2.39 kg m-2 h-1 with an energy conversion efficiency of 93.7% under 1-sun illumination, promising applications in seawater desalination and wastewater purification.

Unveiling the translational dynamics of lychee (Litchi chinesis Sonn.) in response to cold stress.

Cold stress poses a significant threat to the quality and productivity of lychee (Litchi chinensis Sonn.). While previous research has extensively explored the genomic and transcriptomic responses to cold stress in lychee, the translatome has not been thoroughly investigated. This study delves into the translatomic landscape of the 'Xiangjinfeng' cultivar under both control and low-temperature conditions using RNA sequencing and ribosome profiling. We uncovered a significant divergence between the transcriptomic and translatomic responses to cold exposure. Additionally, bioinformatics analyses underscored the crucial role of codon occupancy in lychee's cold tolerance mechanisms. Our findings reveal that the modulation of translation via codon occupancy is a vital strategy to abiotic stress. Specifically, the study identifies ribosome stalling, particularly at the E site AAU codon, as a key element of the translation machinery in lychee's response to cold stress. This work enhances our understanding of the molecular dynamics of lychee's reaction to cold stress and emphasizes the essential role of translational regulation in the plant's environmental adaptability.

Robotic Versus Laparoscopic Pancreaticoduodenectomy for Pancreatic Cancer: Evaluation and Analysis of Surgical Efficacy.

BACKGROUND: Evidence is limited for the treatment of pancreatic cancer among minimally invasive pancreatoduodenectomy. METHODS: This retrospective analysis evaluated patients who underwent robotic pancreaticoduodenectomy (RPD) or laparoscopic pancreaticoduodenectomy (LPD) from April 2016 to April 2023. Their baseline and perioperative data, including operative time, R0 resection rates, and severe complications rates, were analyzed, and the follow-up data, such as disease-free survival (DFS) and overall survival (OS), were collected. RESULTS: A total of 253 cases of LPD and RPD were performed, and 101 cases with pancreatic cancer were included, of which 54 were LPD and 47 were RPD. The conversion rate (4.3% vs. 29.6%, p = 0.001) and blood loss (400 vs. 575 mL, p < 0.05) were lower in the RPD group. No significant difference was observed between the two groups in terms of operative time, vessel resection rates, and TNM-stage diagnosis; however, R0 resection rates (80.9% vs. 70.4%) and lymph node harvest (24.2 vs. 21.9) had a higher tendency in the RPD group, and postoperative length of stay was shorter in the RPD cohort (11 vs. 13 days). Moreover, improved 1- to 3-years DFS (75.7%, 61.7%, and 36.0% vs. 59.0%, 35.6%, and 21.9%) and OS (94.7%, 84.7%, and 50.8% vs. 84.1%, 63.6%, and 45.5%) was found in the RPD group in comparison with the LPD group. CONCLUSIONS: RPD had advantages in surgical safety and oncological outcomes compared with LPD, but was similar to the latter in perioperative outcomes. Long-term outcomes require further study.

Learning curve of robotic pancreatoduodenectomy by a single surgeon with extensive laparoscopic pancreatoduodenectomy experience.

With the development of robotic systems, robotic pancreatoduodenectomies (RPDs) have been increasingly performed. However, the number of cases required by surgeons with extensive laparoscopic pancreatoduodenectomy (LPD) experience to overcome the learning curve of RPD remains unclear. Therefore, we aimed to analyze and explore the impact of different phases of the learning curve of RPD on perioperative outcomes. Clinical data were prospectively collected and retrospectively analyzed for 100 consecutive patients who underwent RPD performed by a single surgeon. This surgeon had previous experience with LPD, having performed 127 LPDs with low morbidity. The learning curve for RPD was analyzed using the cumulative sum (CUSUM) method based on operation time, and perioperative outcomes were compared between the learning and proficiency phases. Between April 2020 and November 2022, one hundred patients (56 men, 44 women) were included in this study. Based on the CUSUM curve of operation time, the learning curve for RPD was divided into two phases: phase I was the learning phase (cases 1-33) and phase II was the proficiency phase (cases 34-100). The operation time during the proficiency phase was significantly shorter than that during the learning phase. In the learning phase of RPD, no significant increases were observed in estimated blood loss, conversion to laparotomy, severe complications, postoperative pancreatic hemorrhage, clinical pancreatic fistula, or other perioperative complications compared to the proficiency phases of either RPD or LPD. A surgeon with extensive prior experience in LPD can safely surmount the RPD learning curve without increasing morbidity in the learning phase. The proficiency was significantly improved after accumulating experience of 33 RPD cases.

Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference.

Pelvic ring disruptions result from blunt injury mechanisms and are potentially lethal mainly due to associated injuries and massive pelvic hemorrhage. The severity of pelvic fractures in trauma victims is frequently assessed by grading the fracture according to the Tile AO/OTA classification in whole-body Computed Tomography (CT) scans. Due to the high volume of whole-body CT scans generated in trauma centers, the overall information content of a single whole-body CT scan and low manual CT reading speed, an automatic approach to Tile classification would provide substantial value, e. g., to prioritize the reading sequence of the trauma radiologists or enable them to focus on other major injuries in multi-trauma patients. In such a high-stakes scenario, an automated method for Tile grading should ideally be transparent such that the symbolic information provided by the method follows the same logic a radiologist or orthopedic surgeon would use to determine the fracture grade. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grading. To achieve interpretability despite processing high-dimensional whole-body CT images, we design a neurosymbolic algorithm that operates similarly to human interpretation of CT scans. The algorithm first detects relevant pelvic fractures on CTs with high specificity using Faster-RCNN. To generate robust fracture detections and associated detection (un)certainties, we perform test-time augmentation of the CT scans to apply fracture detection several times in a self-ensembling approach. The fracture detections are interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. We apply a Bayesian causal model to recover likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides fracture location and types, as well as information on important counterfactuals that would invalidate the system's recommendation. Our approach achieves an AUC of 0.89/0.74 for translational and rotational instability,which is comparable to radiologist performance. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box methods.

Synthesis, characterization and irradiation enhances anticancer activity of liposome-loaded iridium(III) complexes.

Herein, we synthesized and characterized two novel iridium (III) complexes: [Ir(bzq)2(PPD)](PF6) (4a, with bzq = deprotonated benzo[h]quinoline and PPD = pteridino[6,7-f][1,10]phenanthroline-11,13-diamine) and [Ir(piq)2(PPD)](PF6) (4b, with piq = deprotonated 1-phenylisoquinoline). The anticancer efficacy of these complexes, 4a and 4b, was investigated using 3-(4,5-dimethylthiazole)-2,5-diphenltetraazolium bromide (MTT). Complex 4a exhibited no cytotoxic activity, while 4b demonstrated moderate efficacy against SGC-7901, A549, and HepG2 cancer cells. To enhance their anticancer potential, we explored two strategies: (I) light irradiation and (II) encapsulation of the complexes in liposomes, resulting in the formation of 4alip and 4blip. Both strategies significantly increased the ability of 4a, 4b to kill cancer cells. The cellular studies indicated that both the free complexes 4a, 4b and their liposomal forms 4alip and 4blip effectively inhibited cell proliferation. The cell cycle arrest analysis uncovered 4alip and 4blip arresting cell growth in the S period. Additionally, we investigated apoptosis and ferroptosis pathways, observing an increase in malondialdehyde (MDA) levels, a reduction of glutathione (GSH), a down-regulation of GPX4 (glutathione peroxidase) expression, and lipid peroxidation. The effects on mitochondrial membrane potential and intracellular Ca2+ concentrations were also examined, revealing that both light-activated and liposomal forms of 4alip and 4blip caused a decline in mitochondrial membrane potential and an enhancement in intracellular Ca2+ levels. In conclusion, these complexes and them encapsulated liposomes induce cell death through apoptosis and ferroptosis.

Precision Engineering of the Co-immobilization of Enzymes for Cascade Biocatalysis.

The design and orderly layered co-immobilization of multiple enzymes on resin particles remain challenging. In this study, the SpyTag/SpyCatcher binding pair was fused to the N-terminus of an alcohol dehydrogenase (ADH) and an aldo-keto reductase (AKR), respectively. A non-canonical amino acid (ncAA), p-azido-L-phenylalanine (p-AzF), as the anchor for covalent bonding enzymes, was genetically inserted into preselected sites in the AKR and ADH. Employing the two bioorthogonal counterparts of SpyTag/SpyCatcher and azide-alkyne cycloaddition for the immobilization of AKR and ADH enabled sequential dual-enzyme coating on porous microspheres. The ordered dual-enzyme reactor was subsequently used to synthesize (S)-1-(2-chlorophenyl)ethanol asymmetrically from the corresponding prochiral ketone, enabling the in situ regeneration of NADPH. The reactor exhibited a high catalytic conversion of 74 % and good reproducibility, retaining 80 % of its initial activity after six cycles. The product had 99.9 % ee, which that was maintained in each cycle. Additionally, the double-layer immobilization method significantly increased the enzyme loading capacity, which was approximately 1.7 times greater than that of traditional single-layer immobilization. More importantly, it simultaneously enabled both the purification and immobilization of multiple enzymes on carriers, thus providing a convenient approach to facilitate cascade biocatalysis.

Near-infrared light-driven asymmetric photolytic reduction of ketone using inorganic-enzyme hybrid biocatalyst.

Effective photolytic regeneration of the NAD(P)H cofactor in enzymatic reductions is an important and elusive goal in biocatalysis. It can, in principle, be achieved using a near-infrared light (NIR) driven artificial photosynthesis system employing H2O as the sacrificial reductant. To this end we utilized TiO2/reduced graphene quantum dots (r-GQDs), combined with a novel rhodium electron mediator, to continuously supply NADPH in situ for aldo-keto reductase (AKR) mediated asymmetric reductions under NIR irradiation. This upconversion system, in which the Ti-O-C bonds formed between r-GQDs and TiO2 enabled efficient interfacial charge transfer, was able to regenerate NADPH efficiently in 64 % yield in 105 min. Based on this, the pharmaceutical intermediate (R)-1-(3,5-bis(trifluoromethyl)phenyl)ethan-1-ol was obtained, in 84 % yield and 99.98 % ee, by reduction of the corresponding ketone. The photo-enzymatic system is recyclable with a polymeric electron mediator, which maintained 66 % of its original catalytic efficiency and excellent enantioselectivity (99.9 % ee) after 6 cycles.

An ASER AI/ML expert panel formative user research study for an interpretable interactive splenic AAST grading graphical user interface prototype.

PURPOSE: The AAST Organ Injury Scale is widely adopted for splenic injury severity but suffers from only moderate inter-rater agreement. This work assesses SpleenPro, a prototype interactive explainable artificial intelligence/machine learning (AI/ML) diagnostic aid to support AAST grading, for effects on radiologist dwell time, agreement, clinical utility, and user acceptance. METHODS: Two trauma radiology ad hoc expert panelists independently performed timed AAST grading on 76 admission CT studies with blunt splenic injury, first without AI/ML assistance, and after a 2-month washout period and randomization, with AI/ML assistance. To evaluate user acceptance, three versions of the SpleenPro user interface with increasing explainability were presented to four independent expert panelists with four example cases each. A structured interview consisting of Likert scales and free responses was conducted, with specific questions regarding dimensions of diagnostic utility (DU); mental support (MS); effort, workload, and frustration (EWF); trust and reliability (TR); and likelihood of future use (LFU). RESULTS: SpleenPro significantly decreased interpretation times for both raters. Weighted Cohen's kappa increased from 0.53 to 0.70 with AI/ML assistance. During user acceptance interviews, increasing explainability was associated with improvement in Likert scores for MS, EWF, TR, and LFU. Expert panelists indicated the need for a combined early notification and grading functionality, PACS integration, and report autopopulation to improve DU. CONCLUSIONS: SpleenPro was useful for improving objectivity of AAST grading and increasing mental support. Formative user research identified generalizable concepts including the need for a combined detection and grading pipeline and integration with the clinical workflow.

Greater target or lure variability? An exploration on the effects of stimulus types and memory paradigms.

In recognition memory, the variance of the target distribution is almost universally found to be greater than that of the lure distribution. However, these estimates commonly come from long-term memory paradigms where words are used as stimuli. Two exceptions to this rule have found evidence for greater lure variability: a short-term memory task (Yotsumoto et al., Memory & Cognition, 36, 282-294 2008) and in an eyewitness memory paradigm (Wixted et al., Cognitive Psychology, 105, 81-114 2018). In the present work, we conducted a series of recognition memory experiments using different stimulus (faces vs. words) along with different paradigms (long-term vs. short-term paradigms) to evaluate whether either of these conditions would result in greater variability in lure items. Greater target variability was observed across stimulus types and memory paradigms. This suggests that factors other than stimuli and retention interval might be responsible for cases where variability is less for targets than lures.

A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.

Background: Reproducible approaches are needed to bring AI/ML for medical image analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical imaging segmentation algorithms on new studies in real-time will benefit from simple tools that integrate PACS with on-premises image processing, allowing visualization of DICOM-compatible segmentation results and volumetric data at the radiology workstation. Purpose: In this work, we develop and release a simple containerized and easily deployable pipeline for shadow testing of segmentation algorithms within the clinical workflow. Methods: Our end-to-end automated pipeline has two major components- 1. A router/listener and anonymizer and an OHIF web viewer backstopped by a DCM4CHEE DICOM query/retrieve archive deployed in the virtual infrastructure of our secure hospital intranet, and 2. An on-premises single GPU workstation host for DICOM/NIfTI conversion steps, and image processing. DICOM images are visualized in OHIF along with their segmentation masks and associated volumetry measurements (in mL) using DICOM SEG and structured report (SR) elements. Since nnU-net has emerged as a widely-used out-of-the-box method for training segmentation models with state-of-the-art performance, feasibility of our pipleine is demonstrated by recording clock times for a traumatic pelvic hematoma nnU-net model. Results: Mean total clock time from PACS send by user to completion of transfer to the DCM4CHEE query/retrieve archive was 5 min 32 s (± SD of 1 min 26 s). This compares favorably to the report turnaround times for whole-body CT exams, which often exceed 30 min, and illustrates feasibility in the clinical setting where quantitative results would be expected prior to report sign-off. Inference times accounted for most of the total clock time, ranging from 2 min 41 s to 8 min 27 s. All other virtual and on-premises host steps combined ranged from a minimum of 34 s to a maximum of 48 s. Conclusion: The software worked seamlessly with an existing PACS and could be used for deployment of DL models within the radiology workflow for prospective testing on newly scanned patients. Once configured, the pipeline is executed through one command using a single shell script. The code is made publicly available through an open-source license at "https://github.com/vastc/," and includes a readme file providing pipeline config instructions for host names, series filter, other parameters, and citation instructions for this work.

Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation.

Background: precision-medicine quantitative tools for cross-sectional imaging require painstaking labeling of targets that vary considerably in volume, prohibiting scaling of data annotation efforts and supervised training to large datasets for robust and generalizable clinical performance. A straight-forward time-saving strategy involves manual editing of AI-generated labels, which we call AI-collaborative labeling (AICL). Factors affecting the efficacy and utility of such an approach are unknown. Reduction in time effort is not well documented. Further, edited AI labels may be prone to automation bias. Purpose: In this pilot, using a cohort of CTs with intracavitary hemorrhage, we evaluate both time savings and AICL label quality and propose criteria that must be met for using AICL annotations as a high-throughput, high-quality ground truth. Methods: 57 CT scans of patients with traumatic intracavitary hemorrhage were included. No participant recruited for this study had previously interpreted the scans. nnU-net models trained on small existing datasets for each feature (hemothorax/hemoperitoneum/pelvic hematoma; n = 77-253) were used in inference. Two common scenarios served as baseline comparison- de novo expert manual labeling, and expert edits of trained staff labels. Parameters included time effort and image quality graded by a blinded independent expert using a 9-point scale. The observer also attempted to discriminate AICL and expert labels in a random subset (n = 18). Data were compared with ANOVA and post-hoc paired signed rank tests with Bonferroni correction. Results: AICL reduced time effort 2.8-fold compared to staff label editing, and 8.7-fold compared to expert labeling (corrected p < 0.0006). Mean Likert grades for AICL (8.4, SD:0.6) were significantly higher than for expert labels (7.8, SD:0.9) and edited staff labels (7.7, SD:0.8) (corrected p < 0.0006). The independent observer failed to correctly discriminate AI and human labels. Conclusion: For our use case and annotators, AICL facilitates rapid large-scale curation of high-quality ground truth. The proposed quality control regime can be employed by other investigators prior to embarking on AICL for segmentation tasks in large datasets.

Engineering ketoreductases for the enantioselective synthesis of chiral alcohols.

The use of engineered ketoreductases (KREDS), both as whole microbial cells and isolated enzymes, in the highly enantiospecific reduction of prochiral ketones is reviewed. The homochiral alcohol products are key intermediates in, for example, pharmaceuticals synthesis. The application of sophisticated protein engineering and enzyme immobilisation techniques to increase industrial viability are discussed.

A vendor-agnostic, PACS integrated, and DICOMcompatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.

Background: Reproducible approaches are needed to bring AI/ML for medical image analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical imaging segmentation algorithms on new studies in real-time will benefit from simple tools that integrate PACS with on-premises image processing, allowing visualization of DICOM-compatible segmentation results and volumetric data at the radiology workstation. Purpose: In this work, we develop and release a simple containerized and easily deployable pipeline for shadow testing of segmentation algorithms within the clinical workflow. Methods: Our end-to-end automated pipeline has two major components-1. a router/listener and anonymizer and an OHIF web viewer backstopped by a DCM4CHEE DICOM query/retrieve archive deployed in the virtual infrastructure of our secure hospital intranet, and 2. An on-premises single GPU workstation host for DICOM/NIfTI conversion steps, and image processing. DICOM images are visualized in OHIF along with their segmentation masks and associated volumetry measurements (in mL) using DICOM SEG and structured report (SR) elements. Feasibility is demonstrated by recording clock times for a traumatic pelvic hematoma cascaded nnU-net model. Results: Mean total clock time from PACS send by user to completion of transfer to the DCM4CHEE query/retrieve archive was 5 minutes 32 seconds (+/- SD of 1 min 26 sec). This compares favorably to the report turnaround times for whole-body CT exams, which often exceed 30 minutes. Inference times accounted for most of the total clock time, ranging from 2 minutes 41 seconds to 8 minutes 27 seconds. All other virtual and on-premises host steps combined ranged from a minimum of 34 seconds to a maximum of 48 seconds. Conclusion: The software worked seamlessly with an existing PACS and could be used for deployment of DL models within the radiology workflow for prospective testing on newly scanned patients. Once configured, the pipeline is executed through one command using a single shell script. The code is made publicly available through an open-source license at "https://github.com/vastc/", and includes a readme file providing pipeline config instructions for host names, series filter, other parameters, and citation instructions for this work.

Mechanochromic Optical/Electrical Skin for Ultrasensitive Dual-Signal Sensing.

Following earlier research efforts dedicated to the realization of multifunctional sensing, recent developments of artificial skins endeavor to go beyond human sensory functions by integrating interactive visualization of strain and pressure stimuli. Inspired by the microcracked structure of spider slit organs and the mechanochromic mechanism of chameleons, this work aims to design a flexible optical/electrical skin (OE-skin) capable of responding to complex stimuli with interactive feedback of human-readable structural colors. The OE-skin consists of an ionic electrode combined with an elastomer dielectric layer, a chromotropic layer containing photonic crystals and a conductive carbon nanotube/MXene layer. The electrode/dielectric layers function as a capacitive pressure sensor. The mechanochromic photonic crystals of ferroferric oxide-carbon magnetic arrays embedded in the gelatin/polyacrylamide stretchable hydrogel film perceive strain and pressure stimuli with bright color switching outputs in the full visible spectrum. The underlying microcracked conductive layer is devoted to ultrasensitive strain sensing with a gauge factor of 191.8. The multilayered OE-skin delivers an ultrafast, accurate response for capacitive pressure sensing with a detection limit of 75 Pa and long-term stability of 5000 cycles, while visualizing complex deformations in the form of high-resolution spatial colors. These findings offer deep insights into the rational design of OE-skins as multifunctional sensing devices.

Toward automated interpretable AAST grading for blunt splenic injury.

BACKGROUND: The American Association for the Surgery of Trauma (AAST) splenic organ injury scale (OIS) is the most frequently used CT-based grading system for blunt splenic trauma. However, reported inter-rater agreement is modest, and an algorithm that objectively automates grading based on transparent and verifiable criteria could serve as a high-trust diagnostic aid. PURPOSE: To pilot the development of an automated interpretable multi-stage deep learning-based system to predict AAST grade from admission trauma CT. METHODS: Our pipeline includes 4 parts: (1) automated splenic localization, (2) Faster R-CNN-based detection of pseudoaneurysms (PSA) and active bleeds (AB), (3) nnU-Net segmentation and quantification of splenic parenchymal disruption (SPD), and (4) a directed graph that infers AAST grades from detection and segmentation results. Training and validation is performed on a dataset of adult patients (age ≥ 18) with voxelwise labeling, consensus AAST grading, and hemorrhage-related outcome data (n = 174). RESULTS: AAST classification agreement (weighted κ) between automated and consensus AAST grades was substantial (0.79). High-grade (IV and V) injuries were predicted with accuracy, positive predictive value, and negative predictive value of 92%, 95%, and 89%. The area under the curve for predicting hemorrhage control intervention was comparable between expert consensus and automated AAST grading (0.83 vs 0.88). The mean combined inference time for the pipeline was 96.9 s. CONCLUSIONS: The results of our method were rapid and verifiable, with high agreement between automated and expert consensus grades. Diagnosis of high-grade lesions and prediction of hemorrhage control intervention produced accurate results in adult patients.

Direct Gene Expression Profile Prediction for Uveal Melanoma from Digital Cytopathology Images via Deep Learning and Salient Image Region Identification.

Objective: To demonstrate that deep learning (DL) methods can produce robust prediction of gene expression profile (GEP) in uveal melanoma (UM) based on digital cytopathology images. Design: Evaluation of a diagnostic test or technology. Subjects Participants and Controls: Deidentified smeared cytology slides stained with hematoxylin and eosin obtained from a fine needle aspirated from UM. Methods: Digital whole-slide images were generated by fine-needle aspiration biopsies of UM tumors that underwent GEP testing. A multistage DL system was developed with automatic region-of-interest (ROI) extraction from digital cytopathology images, an attention-based neural network, ROI feature aggregation, and slide-level data augmentation. Main Outcome Measures: The ability of our DL system in predicting GEP on a slide (patient) level. Data were partitioned at the patient level (73% training; 27% testing). Results: In total, our study included 89 whole-slide images from 82 patients and 121 388 unique ROIs. The testing set included 24 slides from 24 patients (12 class 1 tumors; 12 class 2 tumors; 1 slide per patient). Our DL system for GEP prediction achieved an area under the receiver operating characteristic curve of 0.944, an accuracy of 91.7%, a sensitivity of 91.7%, and a specificity of 91.7% on a slide-level analysis. The incorporation of slide-level feature aggregation and data augmentation produced a more predictive DL model (P = 0.0031). Conclusions: Our current work established a complete pipeline for GEP prediction in UM tumors: from automatic ROI extraction from digital cytopathology whole-slide images to slide-level predictions. Our DL system demonstrated robust performance and, if validated prospectively, could serve as an image-based alternative to GEP testing.