Osteoporosis Treatment and Outcomes in Patients Undergoing Adult Spinal Deformity Surgery.

Background: This study aims to evaluate the prevalence and treatment of osteoporosis in patients undergoing long spinal fusion for adult spinal deformity (ASD) and compare the impact of osteoporosis treatment on surgical and radiographic outcomes.

Methods: We conducted a retrospective study of adult patients aged ≥40 years who underwent thoracolumbar ASD surgery at a single academic center between 2015 and 2021. We recorded demographic information, procedural details, and pharmacologic treatments.

Inhibition of HIV-1 release by ADAM metalloproteinase inhibitors.

HIV-1 gp120 glycan binding to C-type lectin adhesion receptor L-selectin/CD62L on CD4 T cells facilitates viral attachment and entry. Paradoxically, the adhesion receptor impedes HIV-1 budding from infected T cells and the viral release requires the shedding of CD62L. To systematically investigate CD62L-shedding mediated viral release and its potential inhibition, we screened compounds specific for serine-, cysteine-, aspartyl-, and Zn-dependent proteases for CD62L shedding inhibition and found that a subclass of Zn-metalloproteinase inhibitors, including BB-94, TAPI, prinomastat, GM6001, and GI25423X, suppressed CD62L shedding.

Detection of abdominopelvic lymph nodes in multi-parametric MRI.

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies.

How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for , datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being.

How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for , datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being.

Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity.

Radiologic reporting of MRI-proven thoracolumbar epidural metastases on body CT: 12-Year single-institution experience.

Rationale And Objectives: Metastatic epidural masses are an important radiological finding. The purpose of this study is to determine factors associated with non-reporting of thoracolumbar epidural metastases on body CT.

Materials And Methods: In a study population of 166 patients from a single institution over a 12-year period, 293 body CT examinations were identified which were performed within 30 days before or after a spine MRI diagnosis of epidural metastasis.

Improving segmentation and detection of lesions in CT scans using intensity distribution supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity.

Universal detection and segmentation of lymph nodes in multi-parametric MRI.

Purpose: Reliable measurement of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies of the body plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Previous approaches do not adequately exploit the complementary sequences in mpMRI to universally detect and segment lymph nodes, and they have shown fairly limited performance.

Methods: We propose a computer-aided detection and segmentation pipeline to leverage the T2 fat-suppressed (T2FS) and diffusion-weighted imaging (DWI) series from a mpMRI study.

Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.

Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation is to reduce the region of interest and inspect it at a higher sensitivity rather than performing it for the entire region. It is usually implemented as sequential or joint segmentation of organ and lesion, which requires additional supervision on organ segmentation.

Fully-automated detection of small bowel carcinoid tumors in CT scans using deep learning.

Background: Small bowel carcinoid tumor is a rare neoplasm and increasing in incidence. Patients with small bowel carcinoid tumors often experience long delays in diagnosis due to the vague symptoms, slow growth of tumors, and lack of clinician awareness. Computed tomography (CT) is the most common imaging study for diagnosis of small bowel carcinoid tumor.

Factors Associated With Total Discharge Opioid Prescription Morphine Milligram Equivalent Amounts Following Primary Anterior Cervical Spine Surgery.

Background: Opioid overuse is a substantial cause of morbidity and mortality in the United States, and orthopaedic surgeons are the third highest prescribers of opioids. Postoperative prescribing patterns vary widely, and there is a paucity of data evaluating patient and surgical factors associated with discharge opioid prescribing patterns after elective anterior cervical surgery (ACS). The purpose of this study was to evaluate the volume of postoperative opioids prescribed and factors associated with discharge opioid prescription volumes after elective ACS.

Universal lymph node detection in T2 MRI using neural networks.

Purpose: Identification of lymph nodes (LNs) that are suspicious for metastasis in T2 Magnetic Resonance Imaging (MRI) is critical for assessment of lymphadenopathy. Prior work on LN detection has been limited to specific anatomical regions of the body (pelvis, rectum). Therefore, an approach to universally detect both benign and metastatic nodes in T2 MRI studies of the body is highly desirable.

Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study.

Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual presentation of rare conditions by studying a few representative examples, teaching a machine to learn from such a "long-tailed" distribution is much more difficult, as standard methods would be easily biased toward the most frequent classes. In this paper, we present a comprehensive benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays.

A novel galactose electrochemical biosensor intended for point-of-care measurement of quantitative liver function using galactose single-point test.

Liver disease has emerged as a healthcare burden because of high hospitalization rates attributed both to steatohepatitis and to severe hepatic toxicity associated with changes of drug exposure. Early detection of hepatic insufficiency is critical to preventing long-term liver damage. The galactose single-point test is recommended by the US FDA as a sensitive means to quantify liver function, yet the conventional method used for quantitation of circulating galactose still relies on the standard colorimetric method, requiring time-consuming and labor-intensive processes, and is confined to the medical laboratory, thus limiting prevalence.

Gender, Racial, and Ethnic Differences in the Utilization of Cervical Disk Replacement for Cervical Radiculopathy.

Introduction: Cervical radiculopathy (CR) is commonly treated by spine surgeons, with surgical options including anterior cervical diskectomy and fusion (ACDF) and cervical disk replacement (CDR). CDR is a motion-sparing alternative to ACDF and was approved by the US FDA in 2007. CDR utilization has increased because evidence has emerged demonstrating its long-term efficacy.

Global-Local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images.

Accurate and reliable detection of abnormal lymph nodes in magnetic resonance (MR) images is very helpful for the diagnosis and treatment of numerous diseases. However, it is still a challenging task due to similar appearances between abnormal lymph nodes and other tissues. In this paper, we propose a novel network based on an improved Mask R-CNN framework for the detection of abnormal lymph nodes in MR images.

The Role of L-Selectin in HIV Infection.

HIV envelope glycoprotein is the most heavily glycosylated viral protein complex identified with over 20 glycans on its surface. This glycan canopy is thought to primarily shield the virus from host immune recognition as glycans are poor immunogens in general, however rare HIV neutralizing antibodies nevertheless potently recognize the glycan epitopes. While CD4 and chemokine receptors have been known as viral entry receptor and coreceptor, for many years the role of viral glycans in HIV entry was controversial.

Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training.

Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly for natural images.

Discriminative ensemble learning for few-shot chest x-ray diagnosis.

Few-shot learning is an almost unexplored area in the field of medical image analysis. We propose a method for few-shot diagnosis of diseases and conditions from chest x-rays using discriminative ensemble learning. Our design involves a CNN-based coarse-learner in the first step to learn the general characteristics of chest x-rays.

Omnidirectional beacon-localization using a catadioptric system.

We present a catadioptric beacon localization system that can provide mobile network nodes with omnidirectional situational awareness of neighboring nodes. In this system, a receiver composed of a hyperboloidal mirror and camera is used to estimate the azimuth, elevation, and range of an LED beacon. We provide a general framework for understanding the propagation of error in the angle-of-arrival estimation and then present an experimental realization of such a system.

Resonant interactions between a Mollow triplet sideband and a strongly coupled cavity.

We demonstrate resonant coupling of a Mollow triplet sideband to an optical cavity in the strong coupling regime. We show that, in this regime, the resonant sideband is strongly enhanced relative to the detuned sideband. Furthermore, the linewidth of the Mollow sidebands exhibits a highly nonlinear pump power dependence when tuned across the cavity resonance due to strong resonant interactions with the cavity mode.