Femoral Nerve Blocks versus Standard Pain Control for Hip Fractures: A Retrospective Comparative Analysis.

Introduction: Pain control for hip fractures is often achieved via intravenous opioids. However, opioids can have dangerous adverse effects, including respiratory depression and delirium. Peripheral nerve blockade is an alternative option for pain control, which reduces the need for opioid analgesia. The purpose of this study was to compare the use of femoral nerve blocks versus standard pain control for patients with hip fractures. Methods: This retrospective study included adult patients presenting to the emergency department (ED) with isolated hip fractures between April 2021 and September 2022. The intervention group included all patients who received a femoral nerve block during this time. An equivalent number of patients who received standard pain control during that period were randomly selected to represent the control group. The primary outcome was pre-operative opioid requirement, assessed by morphine milligram equivalents (MME). Results: During the study period, 90 patients were identified in each treatment group. Mean pre-operative MME was 10.3 (95% confidence interval [CI]: 7.4-13.2 MME) for the intervention group and 14.0 (95% CI: 10.2-17.8) for the control group (P=0.13). Patients who received a femoral nerve block also had shorter time from ED triage to hospital discharge (7.2 days, 95% CI: 6.2-8.0 days) than patients who received standard care (8.6 days, 95% CI: 7.2-10.0 days). Still, this difference was not statistically significant (P=0.09). Conclusions: Femoral nerve blockade is a safe and effective alternative to opioids for pain control in patients with hip fractures.

An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment.

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at https://github.com/nadeemlab/DeepLIIF.

Analgesic Trends in the Management of Pain Following Total Knee Arthroplasty: A Comparison of Peri-Articular Infiltration, Adductor Canal Block, and Adjuvant Treatment for Posterior Knee Pain.

The rising number of total knee arthroplasties (TKA's) in the United States increases demand for perioperative pain modalities, which can promote early mobilization and discharge. Over the decades, a focus has shifted from opioid-dominant regimens to motor-sparing multimodal protocols, which have not only improved pain scores and reduced opioid consumption but also improved overall patient outcomes. In this article, we briefly review the evolution of post-operative pain management in patients undergoing TKA and summarize the literature on the most popular modalities currently used including periarticular injections, adductor canal blocks, distal selective nerve blocks, as well as liposomal bupivacaine as part of a multimodal approach.

Ki67 proliferation index in medullary thyroid carcinoma: a comparative study of multiple counting methods and validation of image analysis and deep learning platforms.

AIMS: The International Medullary Thyroid Carcinoma Grading System, introduced in 2022, mandates evaluation of the Ki67 proliferation index to assign a histological grade for medullary thyroid carcinoma. However, manual counting remains a tedious and time-consuming task. METHODS AND RESULTS: We aimed to evaluate the performance of three other counting techniques for the Ki67 index, eyeballing by a trained experienced investigator, a machine learning-based deep learning algorithm (DeepLIIF) and an image analysis software with internal thresholding compared to the gold standard manual counting in a large cohort of 260 primarily resected medullary thyroid carcinoma. The Ki67 proliferation index generated by all three methods correlate near-perfectly with the manual Ki67 index, with kappa values ranging from 0.884 to 0.979 and interclass correlation coefficients ranging from 0.969 to 0.983. Discrepant Ki67 results were only observed in cases with borderline manual Ki67 readings, ranging from 3 to 7%. Medullary thyroid carcinomas with a high Ki67 index (≥ 5%) determined using any of the four methods were associated with significantly decreased disease-specific survival and distant metastasis-free survival. CONCLUSIONS: We herein validate a machine learning-based deep-learning platform and an image analysis software with internal thresholding to generate accurate automatic Ki67 proliferation indices in medullary thyroid carcinoma. Manual Ki67 count remains useful when facing a tumour with a borderline Ki67 proliferation index of 3-7%. In daily practice, validation of alternative evaluation methods for the Ki67 index in MTC is required prior to implementation.

An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment.

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at \url{https://github.com/nadeemlab/DeepLIIF}.

Insights From Generative Modeling for Neural Video Compression.

While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images. In a similar spirit, we view recently proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling. We present these codecs as instances of a generalized stochastic temporal autoregressive transform, and propose new avenues for further improvements inspired by normalizing flows and structured priors. We propose several architectures that yield state-of-the-art video compression performance on high-resolution video and discuss their tradeoffs and ablations. In particular, we propose (i) improved temporal autoregressive transforms, (ii) improved entropy models with structured and temporal dependencies, and (iii) variable bitrate versions of our algorithms. Since our improvements are compatible with a large class of existing models, we provide further evidence that the generative modeling viewpoint can advance the neural video coding field.

Geometry-Aware Planar Embedding of Treelike Structures.

The growing complexity of spatial and structural information in 3D data makes data inspection and visualization a challenging task. We describe a method to create a planar embedding of 3D treelike structures using their skeleton representations. Our method maintains the original geometry, without overlaps, to the best extent possible, allowing exploration of the topology within a single view. We present a novel camera view generation method which maximizes the visible geometric attributes (segment shape and relative placement between segments). Camera views are created for individual segments and are used to determine local bending angles at each node by projecting them to 2D. The final embedding is generated by minimizing an energy function (the weights of which are user adjustable) based on branch length and the 2D angles, while avoiding intersections. The user can also interactively modify segment placement within the 2D embedding, and the overall embedding will update accordingly. A global to local interactive exploration is provided using hierarchical camera views that are created for subtrees within the structure. We evaluate our method both qualitatively and quantitatively and demonstrate our results by constructing planar visualizations of line data (traced neurons) and volume data (CT vascular and bronchial data).

DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides.

In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell-level quantification, e.g. in IHC protein expression scoring, can be extremely inefficient and subjective. We present DeepLIIF (https://deepliif.org), a first free online platform for efficient and reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art approaches (relying on manual error-prone annotations) by virtually restaining clinical IHC slides with more informative multiplex immunofluorescence staining. Our DeepLIIF cloud-native platform supports (1) more than 150 proprietary/non-proprietary input formats via the Bio-Formats standard, (2) interactive adjustment, visualization, and downloading of the IHC quantification results and the accompanying restained images, (3) consumption of an exposed workflow API programmatically or through interactive plugins for open source whole slide image viewers such as QuPath/ImageJ, and (4) auto scaling to efficiently scale GPU resources based on user demand.

3D Virtual Pancreatography.

We present 3D virtual pancreatography (VP), a novel visualization procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes: A machine learning based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, a machine learning based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists.

Low-Dose Metformin Treatment Reduces In Vitro Growth of the LL/2 Non-small Cell Lung Cancer Cell Line.

Lung cancer maintains a relatively small survival rate (~19%) over a 5-year period and up to 80-85% of all lung cancer diagnoses are Non-Small Cell Lung Cancer (NSCLC). To determine whether metformin reduces non-small cell lung cancer (NSCLC) LL/2 cell growth, cells were grown in vitro and treated with metformin for 48 h. qPCR was used to assess genes related to cell cycle regulation and pro-apoptotic markers, namely Cyclin D, CDK4, p27, p21, and HES1. Treatment with 10 mM metformin significantly reduced HES1 expression (p = 0.011). Furthermore, 10 mM metformin treatment significantly decreased REDD1 (p = 0.0082) and increased p-mTOR Ser2448 (p = 0.003) protein expression. Control cells showed significant reductions in phosphorylated p53 protein expression (p = 0.0367), whereas metformin treated cells exhibited reduced total p53 protein expression (p = 0.0078). There were no significant reductions in AMPK, PKB/AKT, or STAT3. In addition, NSCLC cells were treated for 48 h. with 10 mM metformin, 4 µM gamma-secretase inhibitor (GSI), or the combination of metformin (10 mM) and GSI (4 µM) to determine the contribution of respective signaling pathways. Metformin treatment significantly reduced total nucleus expression of the proliferation maker Ki-67 with an above 65% reduction in Ki-67 expression between control and metformin-treated cells (p = 0.0021). GSI (4 µM) treatment significantly reduced Ki-67 expression by ~20% over 48 h (p = 0.0028). Combination treatment (10 mM metformin and 4 µM GSI) significantly reduced Ki-67 expression by more than 50% over 48 h (p = 0.0245). As such, direct administration of metformin (10 mM for 48 h) proved to be an effective pharmaceutical agent in reducing the proliferation of cultured non-small cell cancer cells. These intriguing in vitro results, therefore, support the further study of metformin in appropriate in vivo models as an anti-oncogenic agent and/or an adjunctive therapy.

Current Thoughts of Notch's Role in Myoblast Regulation and Muscle-Associated Disease.

The evolutionarily conserved signaling pathway Notch is unequivocally essential for embryogenesis. Notch's contribution to the muscle repair process in adult tissue is complex and obscure but necessary. Notch integrates with other signals in a functional antagonist manner to direct myoblast activity and ultimately complete muscle repair. There is profound recent evidence describing plausible mechanisms of Notch in muscle repair. However, the story is not definitive as evidence is slowly emerging that negates Notch's importance in myoblast proliferation. The purpose of this review article is to examine the prominent evidence and associated mechanisms of Notch's contribution to the myogenic repair phases. In addition, we discuss the emerging roles of Notch in diseases associated with muscle atrophy. Understanding the mechanisms of Notch's orchestration is useful for developing therapeutic targets for disease.

Low-Dose Metformin as a Monotherapy Does Not Reduce Non-Small-Cell Lung Cancer Tumor Burden in Mice.

Non-small-cell lung cancer (NSCLC) makes up 80-85% of lung cancer diagnoses. Lung cancer patients undergo surgical procedures, chemotherapy, and/or radiation. Chemotherapy and radiation can induce deleterious systemic side effects, particularly within skeletal muscle. To determine whether metformin reduces NSCLC tumor burden while maintaining skeletal muscle health, C57BL/6J mice were injected with Lewis lung cancer (LL/2), containing a bioluminescent reporter for in vivo tracking, into the left lung. Control and metformin (250 mg/kg) groups received treatments twice weekly. Skeletal muscle was analyzed for changes in genes and proteins related to inflammation, muscle mass, and metabolism. The LL/2 model effectively mimics lung cancer growth and tumor burden. The in vivo data indicate that metformin as administered was not associated with significant improvement in tumor burden in this immunocompetent NSCLC model. Additionally, metformin was not associated with significant changes in key tumor cell division and inflammation markers, or improved skeletal muscle health. Metformin treatment, while exhibiting anti-neoplastic characteristics in many cancers, appears not to be an appropriate monotherapy for NSCLC tumor growth in vivo. Future studies should pursue co-treatment modalities, with metformin as a potentially supportive drug rather than a monotherapy to mitigate cancer progression.

Predictive Coding, Variational Autoencoders, and Biological Connections.

We present a review of predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and mathematical framework underlying both areas. As each area is prominent within its respective field, more firmly connecting these areas could prove useful in the dialogue between neuroscience and machine learning. After reviewing each area, we discuss two possible correspondences implied by this perspective: cortical pyramidal dendrites as analogous to (nonlinear) deep networks and lateral inhibition as analogous to normalizing flows. These connections may provide new directions for further investigations in each field.

GSI Treatment Preserves Protein Synthesis in C2C12 Myotubes.

It has been demonstrated that inhibiting Notch signaling through γ-secretase inhibitor (GSI) treatment increases myogenesis, AKT/mTOR signaling, and muscle protein synthesis (MPS) in C2C12 myotubes. The purpose of this study was to determine if GSI-mediated effects on myogenesis and MPS are dependent on AKT/mTOR signaling. C2C12 cells were assessed for indices of myotube formation, anabolic signaling, and MPS following GSI treatment in combination with rapamycin and API-1, inhibitors of mTOR and AKT, respectively. GSI treatment increased several indices of myotube fusion and MPS in C2C12 myotubes. GSI-mediated effects on myotube formation and fusion were completely negated by treatment with rapamycin and API-1. Meanwhile, GSI treatment was able to rescue MPS in C2C12 myotubes exposed to rapamycin or rapamycin combined with API-1. Examination of protein expression revealed that GSI treatment was able to rescue pGSK3ß Ser9 despite AKT inhibition by API-1. These findings demonstrate that GSI treatment is able to rescue MPS independent of AKT/mTOR signaling, possibly via GSK3ß modulation.

Discovery of soluble epoxide hydrolase inhibitors through DNA-encoded library technology (ELT).

Inhibition of soluble epoxide hydrolase (sEH) has recently emerged as a new approach to treat cardiovascular disease and respiratory disease. Inhibitors based on 1,3,5-triazine chemotype were discovered through affinity selection against two triazine-based DNA-encoded libraries. The structure and activity relationship study led to the expansion of the original 1,4-cycloalkyl series to related aniline, piperidine, quinoline, aryl-ether and benzylic series. The 1,3-cycloalkyl chemotype led to the discovery of a clinical candidate (GSK2256294) for COPD.

CMed: Crowd Analytics for Medical Imaging Data.

We present a visual analytics framework, CMed, for exploring medical image data annotations acquired from crowdsourcing. CMed can be used to visualize, classify, and filter crowdsourced clinical data based on a number of different metrics such as detection rate, logged events, and clustering of the annotations. CMed provides several interactive linked visualization components to analyze the crowd annotation results for a particular video and the associated workers. Additionally, all results of an individual worker can be inspected using multiple linked views in our CMed framework. We allow a crowdsourcing application analyst to observe patterns and gather insights into the crowdsourced medical data, helping him/her design future crowdsourcing applications for optimal output from the workers. We demonstrate the efficacy of our framework with two medical crowdsourcing studies: polyp detection in virtual colonoscopy videos and lung nodule detection in CT thin-slab maximum intensity projection videos. We also provide experts' feedback to show the effectiveness of our framework. Lastly, we share the lessons we learned from our framework with suggestions for integrating our framework into a clinical workflow.

Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions.

Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, leading to a limited understanding of the internal decision process, impossibility to gain insights, and ultimately to skepticism from clinicians. We present a visual analytics approach to uncover the decision-making process of a CAD system for classifying pancreatic cystic lesions. This CAD algorithm consists of two distinct components: random forest (RF), which classifies a set of predefined features, including demographic features, and a convolutional neural network (CNN), which analyzes radiological (imaging) features of the lesions. We study the class probabilities generated by the RF and the semantical meaning of the features learned by the CNN. We also use an eye tracker to better understand which radiological features are particularly useful for a radiologist to make a diagnosis and to quantitatively compare with the features that lead the CNN to its final classification decision. Additionally, we evaluate the effects and benefits of supplying the CAD system with a case-based visual aid in a second-reader setting.