Fully Automated Versions of Clinically Validated Nephrometry Scores Demonstrate Superior Predictive Utility versus Human Scores.

OBJECTIVE: To automate the generation of three validated nephrometry scoring systems on preoperative computerised tomography (CT) scans by developing artificial intelligence (AI)-based image processing methods. Subsequently, we aimed to evaluate the ability of these scores to predict meaningful pathological and perioperative outcomes. PATIENTS AND METHODS: A total of 300 patients with preoperative CT with early arterial contrast phase were identified from a cohort of 544 consecutive patients undergoing surgical extirpation for suspected renal cancer. A deep neural network approach was used to automatically segment kidneys and tumours, and then geometric algorithms were used to measure the components of the concordance index (C-Index), Preoperative Aspects and Dimensions Used for an Anatomical classification of renal tumours (PADUA), and tumour contact surface area (CSA) nephrometry scores. Human scores were independently calculated by medical personnel blinded to the AI scores. AI and human score agreement was assessed using linear regression and predictive abilities for meaningful outcomes were assessed using logistic regression and receiver operating characteristic curve analyses. RESULTS: The median (interquartile range) age was 60 (51-68) years, and 40% were female. The median tumour size was 4.2 cm and 91.3% had malignant tumours. In all, 27% of the tumours were high stage, 37% high grade, and 63% of the patients underwent partial nephrectomy. There was significant agreement between human and AI scores on linear regression analyses (R ranged from 0.574 to 0.828, all P < 0.001). The AI-generated scores were equivalent or superior to human-generated scores for all examined outcomes including high-grade histology, high-stage tumour, indolent tumour, pathological tumour necrosis, and radical nephrectomy (vs partial nephrectomy) surgical approach. CONCLUSIONS: Fully automated AI-generated C-Index, PADUA, and tumour CSA nephrometry scores are similar to human-generated scores and predict a wide variety of meaningful outcomes. Once validated, our results suggest that AI-generated nephrometry scores could be delivered automatically from a preoperative CT scan to a clinician and patient at the point of care to aid in decision making.

Identification of "Structural Pin" Interactions and their Significance for the Conformational Control of Macrocyclic Scaffolds.

While peptide macrocycles with rigidified conformations have proven to be useful in the design of chemical probes of protein targets, conformational flexibility and rapid interconversion can be equally vital for biological activity and favorable physicochemical properties. This study introduces the concept of "structural pin", which describes a hydrogen bond that is largely responsible for stabilizing the entire macrocycle backbone conformation. Structural analysis of macrocycles using nuclear magnetic resonance (NMR), molecular modelling and X-ray diffraction indicates that disruption of the structural pin can drastically influence the conformation of the entire ring, resulting in novel states with increased flexibility. This finding provides a new tool to interrogate dynamic behaviour of macrocycles. Identification of structural pins offers a useful conceptual framework to understand positions that can either be modified to give flexible structures or retained to maintain the rigidity of the scaffold.

Teraryl Braces in Macrocycles: Synthesis and Conformational Landscape Remodeling of Peptides.

The three-dimensional structure of medium-sized cyclic peptides accounts for their biological activity and other important physiochemical properties. Despite significant advances in the past few decades, chemists' ability to fine-tune the structure, in particular, the backbone conformation, of short peptides made of canonical amino acids is still quite limited. Nature has shown that cross-linking the aromatic side chains of linear peptide precursors via enzyme catalysis can generate cyclophane-braced products with unusual structures and diverse activities. However, the biosynthetic path to these natural products is challenging to replicate in the synthetic laboratory using practical chemical modifications of peptides. Herein, we report a broadly applicable strategy to remodel the structure of homodetic peptides by cross-linking the aromatic side chains of Trp, His, and Tyr residues with various aryl linkers. The aryl linkers can be easily installed via copper-catalyzed double heteroatom-arylation reactions of peptides with aryl diiodides. These aromatic side chains and aryl linkers can be combined to form a large variety of assemblies of heteroatom-linked multi-aryl units. The assemblies can serve as tension-bearable multijoint braces to modulate the backbone conformation of peptides as an entry to previously inaccessible conformational space.

Using electron energy-loss spectroscopy to measure nanoscale electronic and vibrational dynamics in a TEM.

Electron energy-loss spectroscopy (EELS) can measure similar information to x-ray, UV-Vis, and IR spectroscopies but with atomic resolution and increased scattering cross-sections. Recent advances in electron monochromators have expanded EELS capabilities from chemical identification to the realms of synchrotron-level core-loss measurements and to low-loss, 10-100 meV excitations, such as phonons, excitons, and valence structures. EELS measurements are easily correlated with electron diffraction and atomic-scale real-space imaging in a transmission electron microscope (TEM) to provide detailed local pictures of quasiparticle and bonding states. This perspective provides an overview of existing high-resolution EELS (HR-EELS) capabilities while also motivating the powerful next step in the field-ultrafast EELS in a TEM. Ultrafast EELS aims to combine atomic-level, element-specific, and correlated temporal measurements to better understand spatially specific excited-state phenomena. Ultrafast EELS measurements also add to the abilities of steady-state HR-EELS by being able to image the electromagnetic field and use electrons to excite photon-forbidden and momentum-specific transitions. We discuss the technical challenges ultrafast HR-EELS currently faces, as well as how integration with in situ and cryo measurements could expand the technique to new systems of interest, especially molecular and biological samples.

Virtual Reality Warm-up Before Robot-assisted Surgery: A Randomized Controlled Trial.

TRIAL DESIGN: This was a randomized controlled trial. BACKGROUND: Intraoperative errors correlate with surgeon skill and skill declines with intervals of inactivity. The goals of this research were to identify the optimal virtual reality (VR) warm-up curriculum to prime a surgeon's technical skill and validate benefit in the operating room. MATERIALS AND METHODS: Surgeons were randomized to receive six trial sessions of a designated set of VR modules on the da Vinci Skills Simulator to identify optimal VR warm-up curricula to prime technical skill. After performing their curricula, warm-up effect was assessed based on performance on a criterion task. The optimal warm-up curriculum was chosen from the group with the best task time and video review-based technical skill. Robot-assisted surgery-experienced surgeons were then recruited to either receive or not receive warm-up before surgery. Skill in the first 15 min of surgery was assessed by blinded surgeon and crowdworker review as well as tool motion metrics. The intervention was performing VR warm-up before human robot-assisted surgery. Warm-up effect was measured using objective performance metrics and video review using the Global Evaluative Assessment of Robotic Skills tool. Linear mixed effects models with a random intercept for each surgeon and nonparametric modified Friedman tests were used for analysis. RESULTS: The group performing only a Running Suture task on the simulator was on average 31.3 s faster than groups performing other simulation tasks and had the highest Global Evaluative Assessment of Robotic Skills scores from 41 surgeons who participated. This was chosen as the optimal curriculum. Thereafter, 34 surgeons completed 347 surgeries with corresponding video and tool motion data. No statistically significant differences in skill were observed with the warm-up intervention. CONCLUSIONS: We conclude that a robotic VR warm-up before performing the early stages of surgery does not impact the technical skill of the surgeon.

Using Artificial Intelligence to Predict Surgical Shunts in Men with Ischemic Priapism.

PURPOSE: Ischemic priapism is a urological emergency that requires prompt intervention to preserve erectile function. Characteristics that influence escalation to surgical intervention remain unclear. We identified factors and developed machine learning models to predict which men presenting with ischemic priapism will require shunting. MATERIALS AND METHODS: We identified men with ischemic priapism admitted to the emergency department of our large county hospital between January 2010 and June 2019. We collected patient demographics, etiology, duration of priapism prior to intervention, interventions attempted and escalation to shunting. Machine learning models were trained and tested using R to predict which patients require surgical shunting. RESULTS: A total of 334 encounters of ischemic priapism were identified. The majority resolved with intracavernosal phenylephrine injection and/or cavernous aspiration (78%). Shunting was required in 10% of men. Median duration of priapism before intervention was longer for men requiring shunting than for men who did not (48 vs 7 hours, p=0.030). Patients with sickle cell disease as the etiology were less likely to require shunting compared to all other etiologies (2.2% vs 15.2%, p=0.035). CONCLUSIONS: Men with longer duration of priapism before treatment more often underwent shunting. However, phenylephrine injection and aspiration remained effective for priapism lasting more than 36 hours. Having sickle cell disease as the etiology of priapism was protective against requiring shunting. We developed artificial intelligence models that performed with 87.2% accuracy and created an online probability calculator to determine which patients with ischemic priapism may require shunting.

Demonstration of Convolutional Neural Networks to Determine Patch Test Reactivity.

Background: Convolutional neural networks (CNNs) have the potential to assist allergists and dermatologists in the analysis of patch tests. Such models can help reduce interprovider variability and improve consistency of patch test interpretations. Objective: Our aim is to evaluate the performance of a CNN model as a proof of concept in discriminating between patch tests with reactions and patch tests without reactions. Methods: We performed a retrospective analysis of patch test images from March 2020 to March 2021. The CNN model was trained as a binary classifier to discriminate between reaction and nonreaction patches. Performance of the model was determined using summary statistics and receiver operator characteristics (ROC) curves. Results: In total, 13,622 images from 125 patients were recorded for analysis. The majority of patients in the cohort were female (81.6%) with Fitzpatrick skin types I-II (88.0%). The area under curve was 0.940, indicating a high discriminative performance of the model for this data set. This resulted in a total accuracy of 90.1%, sensitivity of 86.0%, and specificity of 90.2%. Conclusions: CNNs have the capacity to determine the presence of delayed-type reactions in patch tests. Future prospective studies are required to assess the generalizability of such models.

Nitrilium ion trapping as a strategy to access structurally diverse heterobiaryl-containing peptide macrocycles.

Biaryl and heterobiaryl-containing cyclic peptides represent promising scaffolds for the development of bioactive molecules. The incorporation of heterobiaryl motifs continues to pose synthetic challenges, which is partially due to the difficulties in effecting late-stage metal-catalyzed cross-couplings. We report a new strategy to form heterobiaryls that is based on trapping nitrilium ions. The sequence is exemplified using oxadiazole- and oxazole-containing biaryl linkages. NMR analysis and molecular dynamics simulations reveal structural control elements common to each member of the heterobiaryl containing peptide family in this study. Strategic substitutions on the C-terminal aminobenzoic acid moiety paired with installation of oxadiazole or oxazole heterobiaryl backbone linkages allow for the modulation of peptide backbone conformation, which should assist efforts to optimize the biophysical properties of peptide macrocycles.

AI-generated R.E.N.A.L.+ Score Surpasses Human-generated Score in Predicting Renal Oncologic Outcomes.

OBJECTIVE: To determine whether we can surpass the traditional R.E.N.A.L. nephrometry score (H-score) prediction ability of pathologic outcomes by creating artificial intelligence (AI)-generated R.E.N.A.L.+ score (AI+ score) with continuous rather than ordinal components. We also assessed the AI+ score components' relative importance with respect to outcome odds. METHODS: This is a retrospective study of 300 consecutive patients with preoperative computed tomography scans showing suspected renal cancer at a single institution from 2010 to 2018. H-score was tabulated by three trained medical personnel. Deep neural network approach automatically generated kidney segmentation masks of parenchyma and tumor. Geometric algorithms were used to automatically estimate score components as ordinal and continuous variables. Multivariate logistic regression of continuous R.E.N.A.L. components was used to generate AI+ score. Predictive utility was compared between AI+, AI, and H-scores for variables of interest, and AI+ score components' relative importance was assessed. RESULTS: Median age was 60years (interquartile range 51-68), and 40% were female. Median tumor size was 4.2 cm (2.6-6.12), and 92% were malignant, including 27%, 37%, and 23% with high-stage, high-grade, and necrosis, respectively. AI+ score demonstrated superior predictive ability over AI and H-scores for predicting malignancy (area under the curve [AUC] 0.69 vs 0.67 vs 0.64, respectively), high stage (AUC 0.82 vs 0.65 vs 0.71, respectively), high grade (AUC 0.78 vs 0.65 vs 0.65, respectively), pathologic tumor necrosis (AUC 0.81 vs 0.72 vs 0.74, respectively), and partial nephrectomy approach (AUC 0.88 vs 0.74 vs 0.79, respectively). Of AI+ score components, the maximal tumor diameter ("R") was the most important outcomes predictor. CONCLUSION: AI+ score was superior to AI-score and H-score in predicting oncologic outcomes. Time-efficient AI+ score can be used at the point of care, surpassing validated clinical scoring systems.

"The Algorithm Will See You Now": The Role of Artificial (and Real) Intelligence in the Future of Urology.

Modern AI systems have achieved impressive performance and are poised to have a substantial impact on urology. It's important for clinicians to get actively involved in the development and validation of these systems to ensure that their impact is positive.

Automatic Segmentation of Kidneys and Kidney Tumors: The KiTS19 International Challenge.

Purpose: Clinicians rely on imaging features to calculate complexity of renal masses based on validated scoring systems. These scoring methods are labor-intensive and are subjected to interobserver variability. Artificial intelligence has been increasingly utilized by the medical community to solve such issues. However, developing reliable algorithms is usually time-consuming and costly. We created an international community-driven competition (KiTS19) to develop and identify the best system for automatic segmentation of kidneys and kidney tumors in contrast CT and report the results. Methods: A training and test set of CT scans that was manually annotated by trained individuals were generated from consecutive patients undergoing renal surgery for whom demographic, clinical and outcome data were available. The KiTS19 Challenge was a machine learning competition hosted on grand-challenge.org in conjunction with an international conference. Teams were given 3 months to develop their algorithm using a full-annotated training set of images and an unannotated test set was released for 2 weeks from which average Sørensen-Dice coefficient between kidney and tumor regions were calculated across all 90 test cases. Results: There were 100 valid submissions that were based on deep neural networks but there were differences in pre-processing strategies, architectural details, and training procedures. The winning team scored a 0.974 kidney Dice and a 0.851 tumor Dice resulting in 0.912 composite score. Automatic segmentation of the kidney by the participating teams performed comparably to expert manual segmentation but was less reliable when segmenting the tumor. Conclusion: Rapid advancement in automated semantic segmentation of kidney lesions is possible with relatively high accuracy when the data is released publicly, and participation is incentivized. We hope that our findings will encourage further research that would enable the potential of adopting AI into the medical field.

The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge.

There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-quality 3D segmentations of these structures. Recently, methods based on deep learning have shown excellent results in automatic 3D segmentation, but they require large datasets for training, and there remains little consensus on which methods perform best. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem. A training set of 210 cross sectional CT images with kidney tumors was publicly released with corresponding semantic segmentation masks. 106 teams from five continents used this data to develop automated systems to predict the true segmentation masks on a test set of 90 CT images for which the corresponding ground truth segmentations were kept private. These predictions were scored and ranked according to their average Sørensen-Dice coefficient between the kidney and tumor across all 90 cases. The winning team achieved a Dice of 0.974 for kidney and 0.851 for tumor, approaching the inter-annotator performance on kidney (0.983) but falling short on tumor (0.923). This challenge has now entered an "open leaderboard" phase where it serves as a challenging benchmark in 3D semantic segmentation.

Temporal variability of surgical technical skill perception in real robotic surgery.

PURPOSE: Summary score metrics, either from crowds of non-experts, faculty surgeons or from automated performance metrics, have been trusted as the prevailing method of reporting surgeon technical skill. The aim of this paper is to learn whether there exist significant fluctuations in the technical skill assessments of a surgeon throughout long durations of surgical footage. METHODS: A set of 12 videos of robotic surgery cases from common human patient robotic surgeries were used to evaluate the perceived technical skill at each individual minute of the surgical videos, which were originally 12-15 min in length. A linear mixed-effects model for each video was used to compare the ratings of each minute to those from every other minute in order to learn whether a change in scores over time can be detected and reliably measured apart from inter- and intrarater variation. RESULTS: Modeling the change over time of the global evaluative assessment of robotic skills scores significantly contributed to the prediction models for 11 of the 12 surgeons. This demonstrates that measurable changes in technical skill occur over time during robotic surgery. CONCLUSION: The findings from this research raise questions about the optimal duration of footage needed to be evaluated to arrive at an accurate rating of surgical technical skill for longer procedures. This may imply non-negligible label noise for supervised machine learning approaches. In the future, it may be necessary to report a surgeon's skill variability in addition to their mean score to have proper knowledge of a surgeon's overall skill level.

A Very Oil Yellow1 Modifier of the Oil Yellow1-N1989 Allele Uncovers a Cryptic Phenotypic Impact of Cis-regulatory Variation in Maize.

Forward genetics determines the function of genes underlying trait variation by identifying the change in DNA responsible for changes in phenotype. Detecting phenotypically-relevant variation outside protein coding sequences and distinguishing this from neutral variants is not trivial; partly because the mechanisms by which DNA polymorphisms in the intergenic regions affect gene regulation are poorly understood. Here we utilized a dominant genetic reporter to investigate the effect of cis and trans-acting regulatory variation. We performed a forward genetic screen for natural variation that suppressed or enhanced the semi-dominant mutant allele Oy1-N1989, encoding the magnesium chelatase subunit I of maize. This mutant permits rapid phenotyping of leaf color as a reporter for chlorophyll accumulation, and mapping of natural variation in maize affecting chlorophyll metabolism. We identified a single modifier locus segregating between B73 and Mo17 that was linked to the reporter gene itself, which we call very oil yellow1 (vey1). Based on the variation in OY1 transcript abundance and genome-wide association data, vey1 is predicted to consist of multiple cis-acting regulatory sequence polymorphisms encoded at the wild-type oy1 alleles. The vey1 locus appears to be a common polymorphism in the maize germplasm that alters the expression level of a key gene in chlorophyll biosynthesis. These vey1 alleles have no discernable impact on leaf chlorophyll in the absence of the Oy1-N1989 reporter. Thus, the use of a mutant as a reporter for magnesium chelatase activity resulted in the detection of expression-level polymorphisms not readily visible in the laboratory.

Novel Methods of Producing Low-Reflectance Coatings Utilizing Synergistic Effects of Polymer Phase Separation.

Novel methods were developed to generate and characterize surface structures formed from polymer segregation within a powder coating system. A blend of unique acrylic polyol resins and low concentrations of matting agent afforded a durable coating exhibiting consistent low reflectance. An enhanced synergistic effect was observed from the phase separation and domain formation of the two polymeric resins with varying pendent hydroxyl group functionality and the incorporated matting agents. Together the domains and incorporated matting agents produced a significantly lower reflectance coating than the matting agent in combination with either polymeric resin alone. The rigorous thermal, optical, and spectroscopic analysis of the pigmented coating and control coatings culminated in the complete characterization of polymeric phases within the resulting coatings. Raman analysis of the control coatings via a distinct spectroscopic handle allowed for positive identification of the segregated polymer resins within the coating structure. Domains observed by optical microscopy within the control coating structure were chemically identified via Raman analysis as the high-hydroxyl content resin. Subsequent Raman mapping of the peak intensity over an entire cross-section provided consistent evidence for positive identification of the polymeric composition within the domain.