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Introduction Acetabular fractures are intra-articular fractures involving the lower extremity's weight-bearing dome. These fractures require an anatomical reduction of the fracture fragments. This aim can be accomplished by the selection of an appropriate surgical approach. This study aimed to analyze the clinical and radiological outcomes of patients with fractures in the anterior part of the acetabulum who were treated by the modified Stoppa approach. Methods This prospective observational study was conducted from April 2022 to September 2023. The inclusion criteria were: (i) age between 18 and 70 years, (ii) displaced acetabular fracture (displacement > 3 mm), (iii) within three weeks of trauma (iv) acetabular fractures with involvement of anterior column. Exclusion criteria included: (i) patients with visceral injuries requiring colostomy, (ii) pathological fracture, (iii) open fractures of the acetabulum, and (iv) neglected fracture (more than three weeks). Intraoperative data regarding surgical time, amount of blood loss, and incidence of intraoperative complications were recorded. In the postoperative period, anteroposterior X-ray and Judet views of the pelvis X-ray were obtained. Matta criteria were used to judge the quality of Fracture reduction and fixation. All the patients to be included in this study had undergone a minimum follow-up duration of six months. At the last follow-up, an assessment of the functional outcome of the affected hip by Merle d'Aubigné Hip Score and Harris Hip Score was done. Results Twenty-four patients were included in the study. The mean patient age was 36.08±11.65 years. Eighteen patients were male (75%) and six patients were female in this study. All acetabular fractures were due to high-energy trauma: road traffic accidents in 22 cases (91%) and fall from height in two cases (9%). According to Judet & Letournel's classification, there were 13 T-type fractures, five transverse fractures, and six associated both column fractures. The mean duration of surgery was 152.08 ±29.19 minutes, and the mean intraoperative blood loss was 277.08±85.95 ml. Intraoperatively one unit of blood transfusion was done in most cases. There were intraoperative complications of rent in the external iliac vein in two patients. Postoperative X-rays showed anatomical reduction in 17 cases, imperfect reduction in five cases, and poor reduction in two cases. Functional outcome of the hip by Merle d'Aubigné Hip Score was very good in 15, good in four, fair in three, and poor in two patients. Similar functional outcomes were obtained with the Harris Hip Score. Conclusion The results of the current study demonstrated that the modified Stoppa approach allows good visualization of the pelvic brim, quadrilateral surface, and posterior column. Lesser experienced orthopedic surgeons should utilize this approach to get good radiological and functional outcomes.
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Purpose: To develop a method for accurate automated real-time identification of instruments in cataract surgery videos. Methods: Cataract surgery videos were collected at University of Michigan's Kellogg Eye Center between 2020 and 2021. Videos were annotated for the presence of instruments to aid in the development, validation, and testing of machine learning (ML) models for multiclass, multilabel instrument identification. Results: A new cataract surgery database, BigCat, was assembled, containing 190 videos with over 3.9 million annotated frames, the largest reported cataract surgery annotation database to date. Using a dense convolutional neural network (CNN) and a recursive averaging method, we were able to achieve a test F1 score of 0.9528 and test area under the receiver operator characteristic curve of 0.9985 for surgical instrument identification. These prove to be state-of-the-art results compared to previous works, while also only using a fraction of the model parameters of the previous architectures. Conclusions: Accurate automated surgical instrument identification is possible with lightweight CNNs and large datasets. Increasingly complex model architecture is not necessary to retain a well-performing model. Recurrent neural network architectures add additional complexity to a model and are unnecessary to attain state-of-the-art performance. Translational Relevance: Instrument identification in the operative field can be used for further applications such as evaluating surgical trainee skill level and developing early warning detection systems for use during surgery.
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Extração de Catarata , Catarata , Oftalmologia , Catarata/diagnóstico , Humanos , Aprendizado de Máquina , Redes Neurais de ComputaçãoRESUMO
BACKGROUNDImmune cell profiling of primary and metastatic CNS tumors has been focused on the tumor, not the tumor microenvironment (TME), or has been analyzed via biopsies.METHODSEn bloc resections of gliomas (n = 10) and lung metastases (n = 10) were analyzed via tissue segmentation and high-dimension Opal 7-color multiplex imaging. Single-cell RNA analyses were used to infer immune cell functionality.RESULTSWithin gliomas, T cells were localized in the infiltrating edge and perivascular space of tumors, while residing mostly in the stroma of metastatic tumors. CD163+ macrophages were evident throughout the TME of metastatic tumors, whereas in gliomas, CD68+, CD11c+CD68+, and CD11c+CD68+CD163+ cell subtypes were commonly observed. In lung metastases, T cells interacted with CD163+ macrophages as dyads and clusters at the brain-tumor interface and within the tumor itself and as clusters within the necrotic core. In contrast, gliomas typically lacked dyad and cluster interactions, except for T cell CD68+ cell dyads within the tumor. Analysis of transcriptomic data in glioblastomas revealed that innate immune cells expressed both proinflammatory and immunosuppressive gene signatures.CONCLUSIONOur results show that immunosuppressive macrophages are abundant within the TME and that the immune cell interactome between cancer lineages is distinct. Further, these data provide information for evaluating the role of different immune cell populations in brain tumor growth and therapeutic responses.FUNDINGThis study was supported by the NIH (NS120547), a Developmental research project award (P50CA221747), ReMission Alliance, institutional funding from Northwestern University and the Lurie Comprehensive Cancer Center, and gifts from the Mosky family and Perry McKay. Performed in the Flow Cytometry & Cellular Imaging Core Facility at MD Anderson Cancer Center, this study received support in part from the NIH (CA016672) and the National Cancer Institute (NCI) Research Specialist award 1 (R50 CA243707). Additional support was provided by CCSG Bioinformatics Shared Resource 5 (P30 CA046592), a gift from Agilent Technologies, a Research Scholar Grant from the American Cancer Society (RSG-16-005-01), a Precision Health Investigator Award from University of Michigan (U-M) Precision Health, the NCI (R37-CA214955), startup institutional research funds from U-M, and a Biomedical Informatics & Data Science Training Grant (T32GM141746).
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Neoplasias Encefálicas , Glioblastoma , Neoplasias Pulmonares , Neoplasias Encefálicas/patologia , Sistema Nervoso Central/metabolismo , Glioblastoma/patologia , Humanos , Neoplasias Pulmonares/patologia , Macrófagos/metabolismo , Fator de Transcrição STAT3/metabolismo , Microambiente Tumoral , Estados UnidosRESUMO
High-grade gliomas with arginine or valine substitutions of the histone H3.3 glycine-34 residue (H3.3G34R/V) carry a dismal prognosis, and current treatments, including radiotherapy and chemotherapy, are not curative. Because H3.3G34R/V mutations reprogram epigenetic modifications, we undertook a comprehensive epigenetic approach using ChIP sequencing and ChromHMM computational analysis to define therapeutic dependencies in H3.3G34R/V gliomas. Our analyses revealed a convergence of epigenetic alterations, including (i) activating epigenetic modifications on histone H3 lysine (K) residues such as H3K36 trimethylation (H3K36me3), H3K27 acetylation (H3K27ac), and H3K4 trimethylation (H3K4me3); (ii) DNA promoter hypomethylation; and (iii) redistribution of repressive histone H3K27 trimethylation (H3K27me3) to intergenic regions at the leukemia inhibitory factor (LIF) locus to drive increased LIF abundance and secretion by H3.3G34R/V cells. LIF activated signal transducer and activator of transcription 3 (STAT3) signaling in an autocrine/paracrine manner to promote survival of H3.3G34R/V glioma cells. Moreover, immunohistochemistry and single-cell RNA sequencing from H3.3G34R/V patient tumors revealed high STAT3 protein and RNA expression, respectively, in tumor cells with both inter- and intratumor heterogeneity. We targeted STAT3 using a blood-brain barrierpenetrable small-molecule inhibitor, WP1066, currently in clinical trials for adult gliomas. WP1066 treatment resulted in H3.3G34R/V tumor cell toxicity in vitro and tumor suppression in preclinical mouse models established with KNS42 cells, SJ-HGGx42-c cells, or in utero electroporation techniques. Our studies identify the LIF/STAT3 pathway as a key epigenetically driven and druggable vulnerability in H3.3G34R/V gliomas. This finding could inform development of targeted, combination therapies for these lethal brain tumors.
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Neoplasias Encefálicas , Glioma , Animais , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/terapia , Epigênese Genética , Glioma/genética , Glicina , Histonas/metabolismo , Humanos , CamundongosRESUMO
The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures (namely VGG16, Inception-V4, and ResNet-34) with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as TIL-Maps-23, for 7983 WSIs spanning 23 types of cancer with complex and diverse visual appearances, which will be publicly available along with the code to evaluate performance. Code Available at: https://github.com/ShahiraAbousamra/til_classification.