Clinical Considerations and Outcomes of Robotic Urologic Surgery in Obese Patients.

Obesity is associated with many significant physiological changes. These considerations are important to surgery, especially in urological procedures. Obese patients often undergo surgical procedures and are at higher risk of complications. This investigation reviews physiological and anaesthesia considerations for obese and morbidly obese patients. In addition, urological surgeries and procedures should be considered for these higher risk patients. Clinical anaesthesiologists must use detailed assessment and, when appropriate, consultation in developing safe anaesthesia plans for these patients. Newer technologies have improved safety related to airway management, advanced airway devices, and regional anaesthesia with ultrasound-guided nerve blocks, which can reduce the need for opioids postoperatively. Recent developments in drug and monitoring technologies have also been developed and can be effective for obese and morbidly obese patients undergoing urological procedures and perioperative surgery, thus improving the likelihood of safety in this higher risk population.

Infigratinib for the Treatment of Metastatic or Locally Advanced Cholangiocarcinoma With Known FGFR2 Gene Fusions or Rearrangements.

Cholangiocarcinoma (CCA) is an aggressive and diverse malignancy with a poor prognosis. Related to a typical indolent course of progression, most cases of CCA are metastatic or locally advanced at the time of presentation. For patients with nonresectable tumors or metastatic disease, the mainstay of treatment is comprehensive with combination chemotherapy. The first-line chemotherapeutic combination for the treatment of CCA are cisplatin and gemcitabine-based chemotherapies. However, many locally advanced and progressive CCA cases are refractory to first-line management. Within the past few years, the increase in the incidence of metastatic CCA and its poor prognosis has brought to light the need for novel therapeutic approaches to treatment. With advancements in next-generation genome sequencing, multiple molecular pathways have been identified in the pathogenesis of CCA and have shown great potential as alternative treatments in cases of CCA refractory to surgical resection. FGFR2 fusions or rearrangements have been identified in 10-16% of all intrahepatic CCA and are thought to serve as a pathway of resistance for a number of nonresectable and refractory cases of cholangiocarcinoma. A novel therapeutic agent that has been discussed is infigratinib, a selective, ATP-competitive inhibitor of fibroblast growth factor receptor 2 (FGFR2). In a phase 1 trial, infigratinib showed a safe profile and showed remarkable clinical efficacy in advanced CCA with FGFR2 fusions or rearrangements in phase II trials. As of May 2021, the Food and Drug Administration (FDA) approved infigratinib for CCA largely based on tumor response and duration of response. As of 2021, infigratinib, futibatinib, and pemigatinib, similar novel selective FGFR inhibitors, have been approved by the FDA for the treatment of locally advanced or metastatic CCA harboring FGFR2 gene mutations. The present investigation reviews the development of infigratinib in particular and its clinical efficacy compared to other available treatment options for cholangiocarcinoma. While the side effect profile of infigratinib is minimal, particularly GI side effects, when compared with futibatinib and pemigatinib, the overall response rate (ORR) and median overall survival (mOS) for infigratinib (ORR=23.1%, mOS=3.8 months) was significantly lower than futibatinib (ORR=35.8%, mOS=21.1 months) and pemigatinib (ORR=35.5%, mOS=21.1 months). While there is ample promise for the use of infigratinib as molecular-directed therapy in the treatment of CCA harboring FGFR2 mutations, there is an appropriate concern for patient-acquired resistance. The heterogeneous nature of FGFR mutations and the emergence of different resistance mechanisms emphasize a need for more agents to inhibit FGFR rearrangements effectively.

Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.

PURPOSE: Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. MATERIALS AND METHODS: Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. RESULTS: The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. CONCLUSION: The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.

A Deep Learning Onion Peeling Approach to Measure Oral Epithelium Layer Number.

Head and neck squamous cell carcinoma (HNSCC), specifically in the oral cavity (oral squamous cell carcinoma, OSCC), is a common, complex cancer that significantly affects patients' quality of life. Early diagnosis typically improves prognoses yet relies on pathologist examination of histology images that exhibit high inter- and intra-observer variation. The advent of deep learning has automated this analysis, notably with object segmentation. However, techniques for automated oral dysplasia diagnosis have been limited to shape or cell stain information, without addressing the diagnostic potential in counting the number of cell layers in the oral epithelium. Our study attempts to address this gap by combining the existing U-Net and HD-Staining architectures for segmenting the oral epithelium and introducing a novel algorithm that we call Onion Peeling for counting the epithelium layer number. Experimental results show a close correlation between our algorithmic and expert manual layer counts, demonstrating the feasibility of automated layer counting. We also show the clinical relevance of oral epithelial layer number to grading oral dysplasia severity through survival analysis. Overall, our study shows that automated counting of oral epithelium layers can represent a potential addition to the digital pathology toolbox. Model generalizability and accuracy could be improved further with a larger training dataset.

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.

Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images.

Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in clinical settings due to high financial and time costs. To improve accessibility for clinical samples, we developed a computational algorithm to quantify hepatic ploidy using hematoxylin-eosin (H&E) histopathology images, which are commonly obtained during routine clinical practice. Our algorithm uses a deep learning model to first segment and classify different types of cell nuclei in H&E images. It then determines cellular ploidy based on the relative distance between identified hepatocyte nuclei and determines nuclear ploidy using a fitted Gaussian mixture model. The algorithm can establish the total number of hepatocytes and their detailed ploidy information in a region of interest (ROI) on H&E images. This is the first successful attempt to automate ploidy analysis on H&E images. Our algorithm is expected to serve as an important tool for studying the role of polyploidy in human liver disease.

Assessing Coronary Artery and Aortic Calcification in Patients with Prostate Cancer Using 18F-Sodium Fluoride PET/Computed Tomography.

The aim of this study was to assess coronary artery and aortic calcification in healthy controls, angina pectoris patients, and prostate cancer patients using 18F-sodium fluoride PET/computed tomography (NaF-PET/CT). A retrospective analysis compared 33 prostate cancer patients with 33 healthy subjects and 33 patients with angina pectoris. Increased target-to-background ratio (TBR) of the coronary arteries, ascending aorta, aortic arch, and descending aorta was observed in cancer patients compared to healthy controls but not compared to angina pectoris patients. These results demonstrate the feasibility of assessing vascular microcalcification with NaF-PET/CT, with significant differences in uptake according to comorbidities.

Air quality monitoring using mobile microscopy and machine learning.

Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 µm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency-approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.

Structure of LP2179, the first representative of Pfam family PF08866, suggests a new fold with a role in amino-acid metabolism.

The structure of LP2179, a member of the PF08866 (DUF1831) family, suggests a novel α+ß fold comprising two ß-sheets packed against a single helix. A remote structural similarity to two other uncharacterized protein families specific to the Bacillus genus (PF08868 and PF08968), as well as to prokaryotic S-adenosylmethionine decarboxylases, is consistent with a role in amino-acid metabolism. Genomic neighborhood analysis of LP2179 supports this functional assignment, which might also then be extended to PF08868 and PF08968.

Structure of a tryptophanyl-tRNA synthetase containing an iron-sulfur cluster.

A novel aminoacyl-tRNA synthetase that contains an iron-sulfur cluster in the tRNA anticodon-binding region and efficiently charges tRNA with tryptophan has been found in Thermotoga maritima. The crystal structure of TmTrpRS (tryptophanyl-tRNA synthetase; TrpRS; EC 6.1.1.2) reveals an iron-sulfur [4Fe-4S] cluster bound to the tRNA anticodon-binding (TAB) domain and an L-tryptophan ligand in the active site. None of the other T. maritima aminoacyl-tRNA synthetases (AARSs) contain this [4Fe-4S] cluster-binding motif (C-x22-C-x6-C-x2-C). It is speculated that the iron-sulfur cluster contributes to the stability of TmTrpRS and could play a role in the recognition of the anticodon.

Structure of the γ-D-glutamyl-L-diamino acid endopeptidase YkfC from Bacillus cereus in complex with L-Ala-γ-D-Glu: insights into substrate recognition by NlpC/P60 cysteine peptidases.

Dipeptidyl-peptidase VI from Bacillus sphaericus and YkfC from Bacillus subtilis have both previously been characterized as highly specific γ-D-glutamyl-L-diamino acid endopeptidases. The crystal structure of a YkfC ortholog from Bacillus cereus (BcYkfC) at 1.8 Šresolution revealed that it contains two N-terminal bacterial SH3 (SH3b) domains in addition to the C-terminal catalytic NlpC/P60 domain that is ubiquitous in the very large family of cell-wall-related cysteine peptidases. A bound reaction product (L-Ala-γ-D-Glu) enabled the identification of conserved sequence and structural signatures for recognition of L-Ala and γ-D-Glu and, therefore, provides a clear framework for understanding the substrate specificity observed in dipeptidyl-peptidase VI, YkfC and other NlpC/P60 domains in general. The first SH3b domain plays an important role in defining substrate specificity by contributing to the formation of the active site, such that only murein peptides with a free N-terminal alanine are allowed. A conserved tyrosine in the SH3b domain of the YkfC subfamily is correlated with the presence of a conserved acidic residue in the NlpC/P60 domain and both residues interact with the free amine group of the alanine. This structural feature allows the definition of a subfamily of NlpC/P60 enzymes with the same N-terminal substrate requirements, including a previously characterized cyanobacterial L-alanine-γ-D-glutamate endopeptidase that contains the two key components (an NlpC/P60 domain attached to an SH3b domain) for assembly of a YkfC-like active site.

Structural basis of murein peptide specificity of a gamma-D-glutamyl-l-diamino acid endopeptidase.

The crystal structures of two homologous endopeptidases from cyanobacteria Anabaena variabilis and Nostoc punctiforme were determined at 1.05 and 1.60 A resolution, respectively, and contain a bacterial SH3-like domain (SH3b) and a ubiquitous cell-wall-associated NlpC/P60 (or CHAP) cysteine peptidase domain. The NlpC/P60 domain is a primitive, papain-like peptidase in the CA clan of cysteine peptidases with a Cys126/His176/His188 catalytic triad and a conserved catalytic core. We deduced from structure and sequence analysis, and then experimentally, that these two proteins act as gamma-D-glutamyl-L-diamino acid endopeptidases (EC 3.4.22.-). The active site is located near the interface between the SH3b and NlpC/P60 domains, where the SH3b domain may help define substrate specificity, instead of functioning as a targeting domain, so that only muropeptides with an N-terminal L-alanine can bind to the active site.

A structural basis for the regulatory inactivation of DnaA.

Regulatory inactivation of DnaA is dependent on Hda (homologous to DnaA), a protein homologous to the AAA+ (ATPases associated with diverse cellular activities) ATPase region of the replication initiator DnaA. When bound to the sliding clamp loaded onto duplex DNA, Hda can stimulate the transformation of active DnaA-ATP into inactive DnaA-ADP. The crystal structure of Hda from Shewanella amazonensis SB2B at 1.75 A resolution reveals that Hda resembles typical AAA+ ATPases. The arrangement of the two subdomains in Hda (residues 1-174 and 175-241) differs dramatically from that of DnaA. A CDP molecule anchors the Hda domains in a conformation that promotes dimer formation. The Hda dimer adopts a novel oligomeric assembly for AAA+ proteins in which the arginine finger, crucial for ATP hydrolysis, is fully exposed and available to hydrolyze DnaA-ATP through a typical AAA+ type of mechanism. The sliding clamp binding motifs at the N-terminus of each Hda monomer are partially buried and combine to form an antiparallel beta-sheet at the dimer interface. The inaccessibility of the clamp binding motifs in the CDP-bound structure of Hda suggests that conformational changes are required for Hda to form a functional complex with the clamp. Thus, the CDP-bound Hda dimer likely represents an inactive form of Hda.