-
Journal of Orthopaedic Surgery and... Jun 2024Tuberculosis (TB) is one of the top ten causes of death worldwide, with approximately 10 million cases annually. Focus has been on pulmonary TB, while extrapulmonary TB... (Comparative Study)
Comparative Study
Comparing gene expression profiles of adults with isolated spinal tuberculosis to disseminated spinal tuberculosis identified by FDG-PET/CT at time of diagnosis, 6- and 12-months follow-up: classifying clinical stages of spinal tuberculosis and monitoring treatment response (Spinal TB X cohort...
BACKGROUND
Tuberculosis (TB) is one of the top ten causes of death worldwide, with approximately 10 million cases annually. Focus has been on pulmonary TB, while extrapulmonary TB (EPTB) has received little attention. Diagnosis of EPTB remains challenging due to the invasive procedures required for sample collection. Spinal TB (STB) accounts for 10% of EPTB and often leads to lifelong debilitating disease due to devastating spinal deformation and compression of neural structures. Little is known about the extent of disease, although both isolated STB and a disseminated form of STB have been described. In our Spinal TB X cohort study, we aim to describe the clinical phenotype of STB using whole-body FDG-PET/CT, identify a specific gene expression profile for different stages of dissemination and compare findings to previously described gene expression signatures for latent and active pulmonary TB.
METHODS
A single-centre, prospective cohort study will be established to describe the distributional pattern of STB detected by whole-body FDG-PET/CT and gene expression profile of patients with suspected STB on magnetic resonance imaging (MRI) at point of diagnosis, six months, and 12 months. Blood biobanking will be performed at these time points. Specimens for microbiology will be obtained from sputum/urine, from easily accessible sites of disease (e.g., lymph nodes, abscess) identified in the first FDG-PET/CT, from CT-guided biopsy and/or surgery. Clinical parameters and functional scores will be collected at every physical visit. Data will be entered into RedCap® database; data cleaning, validation and analysis will be performed by the study team. The University of Cape Town Ethics Committee approved the protocol (243/2022).
DISCUSSION
The Spinal TB X cohort study is the first prospective cohort study using whole-body 18FDG-PET/CT scans in patients with microbiologically confirmed spinal tuberculosis. Dual imaging techniques of the spine using FDG-PET/CT and magnetic resonance imaging as well as tissue diagnosis (microbiology and histopathology) will allow us to develop a virtual biopsy model. If successful, a distinct gene-expression profile will aid in blood-based diagnosis (point of care testing) as well as treatment monitoring and would lead to earlier diagnosis of this devastating disease.
TRIAL REGISTRATION
The study has been registered on ClinicalTrials.gov (NCT05610098).
Topics: Humans; Tuberculosis, Spinal; Positron Emission Tomography Computed Tomography; Fluorodeoxyglucose F18; Prospective Studies; Adult; Follow-Up Studies; Cohort Studies; Transcriptome; Time Factors; Treatment Outcome; Male; Radiopharmaceuticals; Gene Expression Profiling; Female
PubMed: 38918806
DOI: 10.1186/s13018-024-04840-7 -
Scientific Reports Jun 2024Cerebrovascular resistance (CVR) regulates blood flow in the brain, but little is known about the vascular resistances of the individual cerebral territories. We present...
Cerebrovascular resistance (CVR) regulates blood flow in the brain, but little is known about the vascular resistances of the individual cerebral territories. We present a method to calculate these resistances and investigate how CVR varies in the hemodynamically disturbed brain. We included 48 patients with stroke/TIA (29 with symptomatic carotid stenosis). By combining flow rate (4D flow MRI) and structural computed tomography angiography (CTA) data with computational fluid dynamics (CFD) we computed the perfusion pressures out from the circle of Willis, with which CVR of the MCA, ACA, and PCA territories was estimated. 56 controls were included for comparison of total CVR (tCVR). CVR were 33.8 ± 10.5, 59.0 ± 30.6, and 77.8 ± 21.3 mmHg s/ml for the MCA, ACA, and PCA territories. We found no differences in tCVR between patients, 9.3 ± 1.9 mmHg s/ml, and controls, 9.3 ± 2.0 mmHg s/ml (p = 0.88), nor in territorial CVR in the carotid stenosis patients between ipsilateral and contralateral hemispheres. Territorial resistance associated inversely to territorial brain volume (p < 0.001). These resistances may work as reference values when modelling blood flow in the circle of Willis, and the method can be used when there is need for subject-specific analysis.
Topics: Humans; Male; Female; Cerebrovascular Circulation; Hydrodynamics; Vascular Resistance; Middle Aged; Aged; Magnetic Resonance Imaging; Stroke; Carotid Stenosis; Hemodynamics; Computed Tomography Angiography; Circle of Willis; Blood Flow Velocity; Brain
PubMed: 38918589
DOI: 10.1038/s41598-024-65431-4 -
Cureus Jun 2024Sinus venosus atrial septal defects (SVASD) associated with partial anomalous pulmonary venous return (PAPVR) can be overlooked as a source of dyspnea in adult patients...
Sinus venosus atrial septal defects (SVASD) associated with partial anomalous pulmonary venous return (PAPVR) can be overlooked as a source of dyspnea in adult patients with pulmonary hypertension. We present the case of a 61-year-old male with exertional dyspnea initially attributed to pulmonary hypertension, who was subsequently diagnosed with SVASD and right superior PAPVR. This case underscores the critical importance of maintaining high clinical awareness and utilizing multimodal imaging techniques in cardiology to accurately diagnose and manage pulmonary hypertension secondary to congenital heart disease. Timely surgical correction can significantly improve morbidity and mortality outcomes.
PubMed: 38915839
DOI: 10.7759/cureus.62935 -
BioRxiv : the Preprint Server For... Jun 2024We implemented a multimodal set of functional imaging techniques optimized for deep-tissue imaging to investigate how cancer cells invade surrounding tissues and how...
We implemented a multimodal set of functional imaging techniques optimized for deep-tissue imaging to investigate how cancer cells invade surrounding tissues and how their physiological properties change in the process. As a model for cancer invasion of the extracellular matrix, we created 3D spheroids from triple-negative breast cancer cells (MDA-MB-231) and non-tumorigenic breast epithelial cells (MCF-10A). We analyzed multiple hallmarks of cancer within the same spheroid by combining a number of imaging techniques, such as metabolic imaging of NADH by Fluorescence Lifetime Imaging Microscopy (NADH-FLIM), hyperspectral imaging of a solvatochromic lipophilic dye (Nile Red) and extracellular matrix imaging by Second Harmonic Generation (SHG). We included phasor-based bioimage analysis of spheroids at three different time points, tracking both morphological and biological properties, including cellular metabolism, fatty acids storage, and collagen organization. Employing this multimodal deep-imaging framework, we observed and quantified cancer cell plasticity in response to changes in the environment composition.
PubMed: 38915530
DOI: 10.1101/2024.06.10.598307 -
Frontiers in Immunology 2024Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA... (Review)
Review
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.
Topics: Arthritis, Rheumatoid; Humans; Precision Medicine; Machine Learning; Rheumatology; Disease Management
PubMed: 38915408
DOI: 10.3389/fimmu.2024.1409555 -
Arthritis Research & Therapy Jun 2024Treatments for rheumatoid arthritis (RA) are associated with complex changes in lipids and lipoproteins that may impact cardiovascular (CV) risk. The objective of this... (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
Treatments for rheumatoid arthritis (RA) are associated with complex changes in lipids and lipoproteins that may impact cardiovascular (CV) risk. The objective of this study was to examine lipid and lipoprotein changes associated with two common RA treatment strategies, triple therapy or tumor necrosis factor inhibitor (TNFi), and association with CV risk.
METHODS
In this secondary data analysis of the TARGET trial, methotrexate (MTX) inadequate responders with RA were randomized to either add sulfasalazine and hydroxychloroquine (triple therapy), or TNFi for 24-weeks. The primary trial outcome was the change in arterial inflammation measured in the carotid arteries or aorta by FDG-PET/CT at baseline and 24-weeks; this change was described as the target-to-background ratio (TBR) in the most diseased segment (MDS). Routine lipids and advanced lipoproteins were measured at baseline and 24-weeks; subjects on statin therapy at baseline were excluded. Comparisons between baseline and follow-up lipid measurements were performed within and across treatment arms, as well as change in lipids and change in MDS-TBR.
RESULTS
We studied 122 participants, 61 in each treatment arm, with median age 57 years, 76% female, and 1.5 year median RA disease duration. When comparing treatment arms, triple therapy had on average a larger reduction in triglycerides (15.9 mg/dL, p = 0.01), total cholesterol to HDL-C ratio (0.29, p-value = 0.01), and LDL particle number (111.2, p = 0.02) compared to TNFi. TNFi had on average a larger increase in HDL particle number (1.6umol/L, p = 0.006). We observed no correlation between change in lipid measurements and change in MDS-TBR within and across treatment arms.
CONCLUSIONS
Both treatment strategies were associated with improved lipid profiles via changes in different lipids and lipoproteins. These effects had no correlation with change in CV risk as measured by vascular inflammation by FDG-PET/CT.
TRIAL REGISTRATION
ClinicalTrials.gov ID NCT02374021.
Topics: Humans; Arthritis, Rheumatoid; Female; Middle Aged; Male; Antirheumatic Agents; Hydroxychloroquine; Lipids; Drug Therapy, Combination; Methotrexate; Aged; Sulfasalazine; Adult; Tumor Necrosis Factor Inhibitors; Treatment Outcome; Positron Emission Tomography Computed Tomography; Vasculitis
PubMed: 38915065
DOI: 10.1186/s13075-024-03352-3 -
BMC Complementary Medicine and Therapies Jun 2024The clinical symptoms of Lumbar Disc Herniation (LDH) can be effectively ameliorated through Lever Positioning Manipulation (LPM), which is closely linked to the brain's...
INTRODUCTION
The clinical symptoms of Lumbar Disc Herniation (LDH) can be effectively ameliorated through Lever Positioning Manipulation (LPM), which is closely linked to the brain's pain-regulating mechanisms. Magnetic Resonance Imaging (MRI) offers an objective and visual means to study how the brain orchestrates the characteristics of analgesic effects. From the perspective of multimodal MRI, we applied functional MRI (fMRI) and Magnetic Resonance Spectrum (MRS) techniques to comprehensively evaluate the characteristics of the effects of LPM on the brain region of LDH from the aspects of brain structure, brain function and brain metabolism. This multimodal MRI technique provides a biological basis for the clinical application of LPM in LDH.
METHODS AND ANALYSIS
A total of 60 LDH patients and 30 healthy controls, matched by gender, age, and years of education, will be enrolled in this study. The LDH patients will be divided into two groups (Group 1, n = 30; Group 2, n = 30) using a random number table method. Group 1 will receive LPM treatment once every two days, for a total of 12 times over 4 weeks. Group 2 will receive sham LPM treatment during the same period as Group 1. All 30 healthy controls will be divided into Group 3. Multimodal MRI will be performed on Group 1 and Group 2 at three time points (TPs): before LPM (TP1), after one LPM session (TP2), and after a full course of LPM treatment. The healthy controls (Group 3) will not undergo LPM and will be subject to only a single multimodal MRI scan. Participants in both Group 1 and Group 2 will be required to complete clinical questionnaires. These assessments will focus on pain intensity and functional disorders, using the Visual Analog Scale (VAS) and the Japanese Orthopaedic Association (JOA) scoring systems, respectively.
DISCUSSION
The purpose of this study is to investigate the multimodal brain response characteristics of LDH patients after treatment with LPM, with the goal of providing a biological basis for clinical applications.
TRIAL REGISTRATION NUMBER
https://clinicaltrials.gov/ct2/show/NCT05613179 , identifier: NCT05613179.
Topics: Humans; Magnetic Resonance Imaging; Intervertebral Disc Displacement; Adult; Male; Female; Brain; Middle Aged; Multimodal Imaging; Young Adult; Intervertebral Disc Degeneration
PubMed: 38915038
DOI: 10.1186/s12906-024-04549-4 -
Precision Clinical Medicine Jun 2024The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to...
BACKGROUND
The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS).
METHODS
We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort ( = 741), internal validation cohort ( = 184), and external testing cohort ( = 95).
RESULT
Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041-0.334, < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017-0.218, < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively.
CONCLUSION
This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.
PubMed: 38912415
DOI: 10.1093/pcmedi/pbae012 -
Frontiers in Cardiovascular Medicine 2024Little is known about left ventricular (LV) sequences of contraction and electrical activation in hypertrophic cardiomyopathy (HCM). A better understanding of the...
BACKGROUND
Little is known about left ventricular (LV) sequences of contraction and electrical activation in hypertrophic cardiomyopathy (HCM). A better understanding of the underlying relation between mechanical and electrical activation may allow the identification of predictive response criteria to right ventricular DDD pacing in obstructive patients.
OBJECTIVE
To describe LV mechanical and electrical activation sequences in HCM patients compared to controls.
MATERIALS AND METHODS
We prospectively studied, in 40 HCM patients (20 obstructive and 20 non-obstructive) and 20 healthy controls: (1) mechanical activation using echocardiography at rest and cardiac magnetic resonance imaging, (2) electrical activation using 3-dimensional electrocardiographic mapping (ECM).
RESULTS
In echocardiography, healthy controls had a physiological apex-to-base delay (ABD) during contraction (23.8 ± 16.2 ms). Among the 40 HCM patients, 18 HCM patients presented a loss of this ABD (<10 ms, defining hypersynchrony) more frequently than controls (45% vs. 5%, = 0.017). These patients had a lower LV end-diastolic volume (71.4 ± 9.7 ml/m vs. 82.4 ± 14.8 ml/m, = 0.01), lower native T1 values (988 ± 32 ms vs. 1,028 ± 39 ms, = 0.001) and tended to have lower LV mass (80.7 ± 23.7 g/m vs. 94.5 ± 25.3 g/m, = 0.08) compared with HCM patients that had a physiological contraction sequence. There was no significant relation between ABD and LV outflow tract obstruction. While HCM patients with a physiological contraction sequence presented an ECM close to those encountered in controls, patients with a loss of ABD presented a particular pattern of ECM with the first potential more frequently occurring in the postero-basal region.
CONCLUSION
The LV contraction sequence can be modified in HCM patients, with a loss of the physiological ABD, and is associated with smaller LV dimensions and a particular pattern of ECM. Further research is needed to determine whether this pattern is related to an electrical substrate or is the consequence of the hypertrophied heart's specific geometry.
CLINICAL TRIAL REGISTRATION
ClinicalTrial.gov: NCT02559726.
PubMed: 38911519
DOI: 10.3389/fcvm.2024.1359657 -
Cureus May 2024Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these... (Review)
Review
Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these tumors is crucial for appropriate management and improved patient outcomes. In recent years, exciting advancements in artificial intelligence (AI) technologies have been revolutionizing medical diagnostics, particularly in the realm of detecting and characterizing pulmonary NETs, offering promising avenues for improved patient care. This article aims to provide a comprehensive overview of the role of AI in diagnosing lung NETs. We discuss the current challenges associated with conventional diagnostic approaches, including histopathological examination and imaging modalities. Despite advancements in these techniques, accurate diagnosis remains challenging due to the overlapping features with other pulmonary lesions and the subjective interpretation of imaging findings. AI-based approaches, including machine learning and deep learning algorithms, have demonstrated remarkable potential in addressing these challenges. By leveraging large datasets of radiological images, histopathological samples, and clinical data, AI models can extract complex patterns and features that may not be readily discernible to human observers. Moreover, AI algorithms can continuously learn and improve from new data, leading to enhanced diagnostic accuracy and efficiency over time. Specific AI applications in the diagnosis of lung NETs include computer-aided detection and classification of pulmonary nodules on CT scans, quantitative analysis of PET imaging for tumor characterization, and integration of multi-modal data for comprehensive diagnostic assessments. These AI-driven tools hold promise for facilitating early detection, risk stratification, and personalized treatment planning in patients with lung NETs.
PubMed: 38910787
DOI: 10.7759/cureus.61012