Disease Stratification Research Articles

Common carotid artery diameter and the risk of cardiovascular disease mortality: a prospective cohort study in northeast China.

The association between the common carotid artery (CCA) diameter and cardiovascular disease (CVD) is recognized, but the precise nature of this link remains elusive. This study aimed to investigate the potential relationship between CCA diameter and the risk of CVD mortality in a large population in northeast China. The current study included 5668 participants (mean age 58.9 ± 10.1years) from a population-based study conducted in rural areas of northeast China between September 2017 and May 2018. Information on death was collected from baseline until July 31, 2022. The CCA inter-adventitial diameter was measured using ultrasound. Cox proportional-hazard models were employed to explore the relationship between the common carotid artery diameter and cardiovascular mortality. At baseline, the mean CCA diameter (mm) of subjects was 7.30 ± 0.99 and increased significantly with age, ranging from 6.65 ± 0.71 among people 40-49years to 7.99 ± 1.04 among people ≥ 80years. CCA diameter was significantly larger in males compared to females (7.51 ± 1.03 versus vs. 7.16 ± 0.94; P < 0.001). A total of 185 participants died of CVD during a median follow-up of 4.48years. CCA diameters were divided into quartiles, and the highest quartile of carotid diameter (≥ 8.06mm) had a 2.29 (95% confidence interval [CI]: 1.24, 4.22) times higher risk of CVD mortality than the lowest quartile (≤ 6.65mm) (P < 0.01) in the fully adjusted model. Each increase in the diameter of the common carotid artery (per SD) raised the risk of cardiovascular death by 36% (hazard ratio [HR]: 1.36; 95% CI: 1.18, 1.57). The subgroup analysis results demonstrated that a per SD increase was associated with a 42% increased risk of CVD mortality in participants aged ≥ 64years in the fully adjusted model (HR: 1.42; 95%CI: 1.21, 1.66). Our study indicates the possible incremental value of CCA diameter in optimizing the risk stratification of cardiovascular disease and provides essential insights into reducing the burden of cardiovascular disease.

A pragmatic approach to selective genetic testing in kidney transplant candidates.

Advances in the field of genetic testing have spurred its use in transplantation. Potential benefits of genetic testing in transplant nephrology include diagnosis, treatment, risk stratification of recurrent disease, and risk stratification in potential donors. However, it is unclear how to best apply genetic testing in this population to maximize its yield. We describe our transplant center's approach to selective genetic testing as part of kidney transplant candidate and donor evaluation. Transplant recipient candidates were tested if they had a history of ESRD at age <50, primary FSGS, complement-mediated or unknown etiology of kidney disease, or had a family history of kidney disease. Donors were tested if age <35, were related to their potential recipients with known genetic susceptibility or had a first-degree relative with a history of kidney disease of unknown etiology. A targeted NGS gene panel of 385 genes was used. Clinical implications and downstream effects were monitored. Over 30% of recipients tested within the established criteria were positive for a pathogenic variant. The most common pathogenic variants were APOL1 high-risk genotypes as well as collagen 4-alpha-3, -4 and -5. Donor testing done according to our inclusion criteria resulted in about 12% yield. Positive test results in recipients helped with stratification of the risk of recurrent disease. Positive test results in potential donors guided informed decisions on when not to move forward with a donation. Integrating targeted panel genetic testing into a kidney transplant clinic in conjunction with a selective criteria for testing donors and recipients ensured a reasonable diagnostic yield. The results had implications on clinical management, risk stratification and in some cases were instrumental in directing downstream changes including when to stop the evaluation process. Given the impact on management and transplant decisions, we advocate for the widespread use of genetic testing in selected individuals undergoing transplant evaluation and donation who meet pre-defined criteria.

Revealing the Urinary Microbiota in Prostate Cancer: A Comprehensive Review Unveiling Insights into Pathogenesis and Clinical Application

Prostate cancer is one of the most common malignancies and seriously affects public health. Recently, contemporary microbiome research has found hints that the urinary microflora may at least to some extent impact prostate cancer formation and treatment outcomes. A comprehensive review This article attempts to provide a brief summary and integration of the current knowledge about involvement in prostate cancer development by urinary microbiota, diagnostic capabilities, as well as therapeutic opportunities.It examines the different microbial spheres found inside residents in the genitourinary system and their relevance to tumorigenic microenvironments. The review looks at microbial signatures linked to prostate cancer, and tries to identify potential biological markers for diagnosis of the disease or patient stratification according to risk levels, as well as potential predictors of outcome. Secondly, the potential effect of urine microbiota upon treatment response and how to design novel therapeutic interventions also form key topics in this study.After a comprehensive review of existing literature and recent new studies, this article attempts to illuminate the intricate interrelationships between urinary microbiota and prostate cancer. It is hoped that this effort will provide some insight on future directions of research, clinical applications and even possibilities for precision-medicine paradigms in prostate cancer care.

Validation of the new nomenclature of steatotic liver disease in patients with a history of excessive alcohol intake: an analysis of data from a prospective cohort study

Steatotic liver disease is a new overarching term that includes metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-related steatotic liver disease (MetALD), and alcohol-related liver disease (ALD). We aimed to validate the prognostic importance of MASLD, MetALD, and ALD as steatotic liver disease subclasses. Between April 18, 2013, and Sept 17, 2018, we prospectively recruited patients aged 18-75 years with current or previous excessive alcohol intake (>24 g/day for women and >36 g/day for men) for at least a year and no previous hepatic decompensation from the Department of Gastroenterology and Hepatology at Odense University Hospital (Odense, Denmark). Participants were followed up until Sept 15, 2022. Here, we characterise these patients according to steatotic liver disease subclasses. We classified patients as having MASLD, MetALD, or ALD in accordance with the nomenclature definitions, on the basis of metabolic comorbidity and self-reported average alcohol intake in the 3 months leading up to inclusion. Histological scoring was done by a pathologist who was masked to the clinical data. We compared prognoses between classes using Cox regression analyses on hepatic decompensation and overall mortality as the two outcome measures. Patients not meeting the criteria for steatotic liver disease were classified as no steatotic liver disease and served as a reference group. We enrolled 446 patients with a history of excessive alcohol intake were included in this analysis (334 [75%] were male and 112 [25%] were female; median age 56 years [SD 10]). Cirrhosis was present in 58 (13%), and 435 (98%) had at least one cardiometabolic risk factor. 321 (72%) met steatotic liver disease criteria and 125 (28%) did not have steatotic liver disease, meaning no evident liver steatosis and no significant fibrosis (≥F2). Of the 321 patients with steatotic liver disease, six (2%) were identified as having ALD due to the absence of cardiometabolic risk factors. The remaining 315 (98%) patients presented with at least one cardiometabolic risk factor. Of these patients, 153 (49%) had MASLD, 76 (24%) had MetALD, and 86 (27%) had ALD. During follow-up, 67 (15%) of 446 patients decompensated and 97 (22%) died (median follow-up 70 months [IQR 53-94]). Patients with steatotic liver disease had a significantly higher risk of hepatic decompensation and overall mortality than those without steatotic liver disease, independent of age, sex, and liver stiffness. The risk of decompensation increased in a stepwise manner from MASLD (hazard ratio 4·73 [95% CI 1·03-21·6]), through MetALD (7·69 [1·66-35·6]), to ALD (10·2 [2·24-46·4]). Similarly, overall mortality increased from MASLD (HR 2·30 [95% CI 1·08-4·90]), through MetALD (2·94 [1·31-6·58]), to ALD (3·57 [1·64-7·80]), independent of age, sex, and liver stiffness. Steatotic liver disease and its subclasses portend distinct prognoses. There is a need to specify how historical alcohol intake should be integrated into the nomenclature and risk stratification of steatotic liver disease. EU Horizon 2020 Research and Innovation Program.

Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes.

Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.

Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.

To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD). Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index. In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively (p<0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively (p< 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9-97.7%) and 94.3% (89.5-97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%). FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events. Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events. • Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. • Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. • Full automation can facilitate the application of CCTA in early detection of coronary artery disease.

Early Detection of Molecular Residual Disease and Risk Stratification for Children with Acute Myeloid Leukemia via Circulating Tumor DNA.

Patient-tailored minimal residual disease (MRD) monitoring based on circulating tumor DNA (ctDNA) sequencing of leukemia-specific mutations enables early detection of relapse for pre-emptive treatment, but its utilization in pediatric acute myelogenous leukemia (AML) is scarce. Thus, we aim to examine the role of ctDNA as a prognostic biomarker in monitoring response to the treatment of pediatric AML. A prospective longitudinal study with 50 children with AML was launched, and sequential bone marrow (BM) and matched plasma samples were collected. The concordance of mutations by next-generation sequencing-based BM-DNA and ctDNA was evaluated. In addition, progression-free survival (PFS) and overall survival (OS) were estimated. In 195 sample pairs from 50 patients, the concordance of leukemia-specific mutations between ctDNA and BM-DNA was 92.8%. Patients with undetectable ctDNA were linked to improved OS and PFS versus detectable ctDNA in the last sampling (both P < 0.001). Patients who cleared their ctDNA post three cycles of treatment had similar PFS compared with persistently negative ctDNA (P = 0.728). In addition, patients with >3 log reduction but without clearance in ctDNA were associated with an improved PFS as were patients with ctDNA clearance (P = 0.564). Thus, ctDNA-based MRD monitoring appears to be a promising option to complement the overall assessment of pediatric patients with AML, wherein patients with continuous ctDNA negativity have the option for treatment de-escalation in subsequent therapy. Importantly, patients with >3 log reduction but without clearance in ctDNA may not require an aggressive treatment plan due to improved survival, but this needs further study to delineate.

Pathogenic mechanisms of disease in idiopathic inflammatory myopathies: autoantibodies as clues.

Idiopathic inflammatory myopathies (IIMs) encompass a spectrum of autoimmune diseases characterized by muscle inflammation and systemic involvement. This review aimed to synthesize current evidence on the clinical significance and pathogenic mechanisms underlying autoantibodies associated with IIMs. Autoantibodies targeting aminoacyl-tRNA synthetases (ARS) play a pivotal role in antisynthetase syndrome (ASS), highlighting associations with interstitial lung disease (ILD) and distinctive clinical features. Anti-Mi-2 antibodies in dermatomyositis (DM) are hallmarked by characteristic cutaneous manifestations and favorable prognostic outcomes. Conversely, anti-TIF1 antibodies are correlated with DM and a higher risk of malignancies, implicating CD8+ T cells in its pathogenesis. Anti-MDA5 antibodies signify clinically amyopathic DM (CADM) with severe ILD, linked to dysregulated neutrophil extracellular trap (NET) formation. In immune-mediated necrotizing myopathies (IMNMs), anti-SRP and anti-HMGCR antibodies induce complement-mediated myopathy, typically following statin exposure. Additionally, anti-TRIM72 antibodies emerge as potential diagnostic markers in IIMs. Anti-cN1A autoantibodies are linked to inclusion body myositis (IBM) and play a decisive role in muscle protein degradation. Meanwhile, anti-FHL1 autoantibodies are associated with severe disease manifestations and muscle damage, as established in experimental models. Anti-eIF3 autoantibodies, recently identified in polymyositis (PM) patients, are rarely detected (<1%) and associated with a favorable prognosis. Elucidating these autoantibodies is anticipated to not only assist in early diagnosis and disease stratification but also inform targeted therapeutic interventions, emphasizing the intricate interplay between autoimmunity, cellular dysfunction, and clinical outcomes in IIMs.

AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease using Novel Non-invasive Biomarkers.

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography. Novel clinical, chemical, and molecular cardiac biomarkers were used as input features from an especially collected dataset. Following a thorough evaluative search in the biomarker feature space, the optimum feature and classifier or regression technique (regressor) set were selected using K-fold cross-validation. Ten machine learning (ML) classifiers were employed in classification tasks to determine the number of affected cardiac vessels, the Gensini group, and the severity of CAD with 82.58%, 86.26%, and 90.91% accuracy, respectively. Eleven approaches were used in regression tasks to calculate stenosis percentage and Gensini score, with R-squared values of 0.58 and 0.56, respectively. Following a thorough evaluative search in the biomarkers feature space, the optimum feature and classifier or regressor set were selected using K-fold cross-validation. The biomarkers and classifier or regressor combinations serve as the foundation for the proposed risk stratification framework, incorporating clinical protocol. Finally, our proposed framework is compared to state-of-the-art studies, offering a robust, well-rounded, early detection capable, and novel 'biomarkers-ML combination' approach to risk stratification.

Prevalence, determinants, and fibrosis risk stratification of metabolic-associated fatty liver disease in a Turkish primary care setting: A retrospective study.

Metabolic-associated fatty liver disease (MAFLD) is a condition that frequently goes unnoticed as it typically remains asymptomatic until progressing to an advanced stage. As a result, it is essential to implement opportunistic screening initiatives within family medicine practices to accurately identify and refer selected at-risk patients to specialized care. This study aims to investigate the prevalence of MAFLD and advanced hepatic fibrosis among primary care patients in Turkiye by utilizing non-invasive tests. We performed a retrospective analysis of prospectively collected data from February 1, 2022, to April 14, 2023, at a Family Medicine Outpatient Clinic. The Hepatic Steatosis Index (HSI) was used to identify fatty liver cases, followed by established MAFLD criteria for diagnosis. Patients were then categorized based on advanced fibrosis risk using the fibrosis-4 (FIB-4) index. Among the 450 patients who sought primary care during the study period (286 women and 164 men; mean age: 48.2±13.7 years), 295 (65.6%) were diagnosed with MAFLD using HSI values and established criteria. Diabetes mellitus emerged as the sole independent predictor of MAFLD. FIB-4 values classified 242 (82%) and 53 (18%) patients with MAFLD at low and intermediate risk of advanced fibrosis, respectively, with none at high risk. MAFLD exhibits a notable prevalence among Turkish patients who presented at a Family Medicine Outpatient Clinic. Given the growing impact of metabolic diseases, primary care providers and non-liver specialists should actively participate in MAFLD screening programs.

Prognostic enrichment for early-stage Huntington’s disease: An explainable machine learning approach for clinical trial

BackgroundIn Huntington’s disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process. ObjectivesTo improve stratification of Huntington’s disease individuals for clinical trials. MethodsWe used data from 451 gene positive individuals with Huntington’s disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement. ResultsThe predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %). ConclusionsThis study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

Managing childhood acute lymphoblastic leukemia in a child with Emery-Dreifuss muscular dystrophy

Genetics is increasingly being integrated into clinical oncology practice. It is necessary for risk stratification of disease, for prognostication, for recognition of underlying predisposition, and for recognition of potential toxicity and predicting outcomes. The prevalence of genetic syndromes, their clinical significance, effect on cancer, and its treatment have been defined in recent times. In this present work, we describe the challenges we faced while managing a child with acute lymphoblastic leukemia with an underlying rare muscular dystrophy. The details were recorded from case files and prospectively collected data. Dose modification of certain chemotherapeutic drugs was planned to reduce toxicity in the child. The child responded to treatment and is currently in remission. The recognition of genetic entity and their potential interactions with treatment received for cancer is vital to plan the treatment.

Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.

Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder-decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent-Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81.93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model's effectiveness for our particular application.

Use of the Afirma Xpression Atlas for cytologically indeterminate, Afirma Genomic Sequencing Classifier suspicious thyroid nodules: Clinicopathologic analysis with postoperative molecular testing.

Afirma has recently introduced its Xpression Atlas (XA) as an adjunct to its Genomic Sequencing Classifier (GSC) for risk stratification of cytologically indeterminate thyroid nodules. We evaluated the performance of Afirma XA and associated pathologic findings for Afirma GSC suspicious nodules. Intradepartmental records of thyroid fine-needle aspirations (FNAs) from January 2021 to December 2022 were identified and reviewed for patient and nodule characteristics, FNA findings, molecular test results, and final surgical pathology, if available. Material for Afirma GSC testing was collected in 624 thyroid FNAs, and 148 (24%) were classified as cytologically indeterminate. Afirma GSC testing was successful in 132 (89%) of those cases, of which 35 (27%) were Afirma GSC suspicious. Afirma XA testing was positive in 11 cases (11/35 [31%]). Eight (73%) patients underwent surgery that revealed 7 patients with papillary thyroid carcinoma and 1 patient with noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) (risk of malignancy: 100% [8/8]). Among the 24 patients with negative Afirma XA results, 19 (79%) underwent surgery, revealing 5 patients with malignancy and 3 patients with NIFTP (risk of malignancy: 42% [8/19]). Overall, the risk of malignancy for Afirma GSC suspicious nodules was 59% (16/27). Afirma XA improved risk stratification of thyroid disease with a high risk of malignancy in Afirma GSC suspicious nodules. A negative Afirma XA result, however, should not be used as a rule-out test.

Cellular and molecular alterations in a human hepatocellular in vitro model of nonalcoholic fatty liver disease development and stratification

The rapidly increasing incidence of nonalcoholic fatty liver disease (NAFLD) is a growing health crisis worldwide. If not detected early, NAFLD progression can lead to irreversible pathological states, including liver fibrosis and cirrhosis. Using in vitro models to understand the molecular pathogenesis has been extremely beneficial; however, most studies have utilized only short-term exposures, highlighting a limitation in current research to model extended fat-induced liver injury. We treated Hep3B cells continuously with a low dose of oleic and palmitic free fatty acids (FFAs) for 7 or 28 days. Transcriptomic analysis identified dysregulated molecular pathways and differential expression of 984 and 917 genes after FFA treatment for 7 and 28 days respectively. DNA methylation analysis of altered DNA methylated regions (DMRs) found 7 DMRs in common. Pathway analysis of differentially expressed genes (DEGs) revealed transcriptomic changes primarily involved in lipid metabolism, small molecule biochemistry, and molecular transport. Western blot analysis revealed changes in PDK4 and CPT1A protein levels, indicative of mitochondrial stress. In line with this, there was mitochondrial morphological change demonstrating breakdown of the mitochondrial network. This in vitro model of human NAFL mimics results observed in human patients and may be used as a pre-clinical model for drug intervention.

Immunological and biochemical biomarker alterations among SARS-COV-2 patients with varying disease phenotypes in Uganda

Every novel infection requires an assessment of the host response coupled with identification of unique biomarkers for predicting disease pathogenesis, treatment targets and diagnostic utility. Studies have exposed dysregulated inflammatory response induced by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as significant predictor or cause of disease severity/prognosis and death. This study evaluated inflammatory biomarkers induced by SARS-CoV-2 in plasma of patients with varying disease phenotypes and healthy controls with prognostic or therapeutic potential. We stratified SARS-CoV-2 plasma samples based on disease status (asymptomatic, mild, severe, and healthy controls), as diagnosed by RT-PCR SARS-CoV-2. We used a solid phase sandwich and competitive Enzyme-Linked Immunosorbent Assay (ELISA) to measure levels of panels of immunological (IFN-γ, TNF-α, IL-6, and IL-10) and biochemical markers (Ferritin, Procalcitonin, C-Reactive Protein, Angiotensin II, Homocysteine, and D-dimer). Biomarker levels were compared across SARS-CoV-2 disease stratification. Plasma IFN-γ, TNF-α, IL-6, and IL-10 levels were significantly (P < 0.05) elevated in the severe SARS-CoV-2 patients as compared to mild, asymptomatic, and healthy controls. Ferritin, Homocysteine, and D-dimer plasma levels were significantly elevated in severe cases over asymptomatic and healthy controls. Plasma C-reactive protein and Angiotensin II levels were significantly (P < 0.05) higher in mild than severe cases and healthy controls. Plasma Procalcitonin levels were significantly higher in asymptomatic than in mild, severe cases and healthy controls. Our study demonstrates the role of host inflammatory biomarkers in modulating the pathogenesis of COVID-19. The study proposes a number of potential biomarkers that could be explored as SARS-CoV-2 treatment targets and possible prognostic predictors for a severe outcome. The comprehensive analysis of prognostic biomarkers may contribute to the evidence-based management of COVID-19 patients.

Per-patient Negative Predictive Value of the CT and MRI Liver Imaging Reporting and Data System Version 2018 Treatment Response Algorithm for Hepatocellular Carcinoma.

Background The Liver Imaging Reporting and Data System version 2018 (LI-RADS) treatment response algorithm (TRA) is a high-specificity, lower-sensitivity grading system to diagnose hepatocellular carcinoma (HCC) and recurrence after local-regional therapy. However, the emphasis on specificity can result in disease understaging, potentially leading to poorer posttransplant outcomes. Purpose To determine the negative predictive value (NPV) of pretransplant CT and MRI assessment for viable HCC on a per-patient basis using the LI-RADS TRA, considering explant pathology as the reference standard. Materials and Methods Patient records from 218 consecutive adult patients from a single institution with HCC who underwent liver transplant from January 2011 to November 2017 were retrospectively reviewed. Two readers blinded to the original report reviewed immediate (within 90 days) pretransplant imaging and characterized observations according to the LI-RADS TRA. Based on this, patients with LR-4, LR-5, or LR-TR (treatment response) viable tumors were designated as viable tumor; patients with solely LR-3 or LR-TR equivocal tumors were designated as equivocal; and patients with only LR-TR nonviable lesions were designated as no viable disease. Patients were designated as within or outside the Milan criteria. These per-patient designations were compared with the presence of viable disease at explant pathology. Fisher exact test was used to compare the differences between CT and MRI. Weighted κ values were used to calculate interreader reliability. Results Final study sample consisted of 206 patients (median age, 61 years [IQR, 57-65 years]; 157 male patients and 49 female patients). Per-patient LI-RADS TRA assessment of pretransplant imaging had an NPV of 32% (95% CI: 27, 38) and 26% (95% CI: 20, 33) (readers 1 and 2, respectively) for predicting viable disease. Seventy-five percent (reader 1) and 77% (reader 2) of patients deemed equivocal had residual tumors at explant pathology. Weighted interreader reliability was substantial (κ = 0.62). Conclusion Patient-based stratification of viable, equivocal, and nonviable disease at pretransplant CT or MRI, based on LI-RADS TRA, demonstrated low negative predictive value in excluding HCC at explant pathology. © RSNA, 2023 See also the editorial by Tamir and Tau in this issue.

Review and Updates on Systemic Mastocytosis and Related Entities

Mast cell disorders range from benign proliferations to systemic diseases that cause anaphylaxis and other diverse symptoms to mast cell neoplasms with varied clinical outcomes. Mastocytosis is the pathologic process of the accumulation of abnormal mast cells in different organs, mostly driven by KIT mutations, and can present as cutaneous mastocytosis, systemic mastocytosis (SM), and mast cell sarcoma. The WHO 5th edition classification divides systemic mastocytosis into bone marrow mastocytosis, indolent systemic mastocytosis, smoldering systemic mastocytosis, aggressive systemic mastocytosis, systemic mastocytosis with an associated hematologic neoplasm, and mast cell leukemia. The new ICC classifies SM slightly differently. The diagnosis of SM requires the integration of bone marrow morphologic, immunophenotypic, and molecular findings, as well as clinical signs and symptoms. Moreover, understanding the wide range of clinical presentations for patients with mast cell disorders is necessary for accurate and timely diagnosis. This review provides an updated overview of mast cell disorders, with a special emphasis on SM, including the latest approaches to diagnosis, prognostic stratification, and management of this rare disease.

Coronary CT Angiography in the Cath Lab: Leveraging Artificial Intelligence to Plan and Guide Percutaneous Coronary Intervention.

The role of coronary CT angiography for the diagnosis and risk stratification of coronary artery disease is well established. However, its potential beyond the diagnostic phase remains to be determined. The current review focuses on the insights that coronary CT angiography can provide when planning and performing percutaneous coronary interventions. We describe a novel approach incorporating anatomical and functional pre-procedural planning enhanced by artificial intelligence, computational physiology and online 3D CT guidance for percutaneous coronary interventions. This strategy allows the individualisation of patient selection, optimisation of the revascularisation strategy and effective use of resources.

Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms.

Accurate extraction of coronary arteries from invasive coronary angiography (ICA) images is essential for the diagnosis and risk stratification of coronary artery disease (CAD). In this study, a novel deep learning (DL) method is proposed for automatically extracting coronary arteries from ICA images. A convolutional neural network (CNN) was developed with full-scale skip connections and full-scale deep supervisions. The encoder architecture was based on the residual and inception modules to obtain multi-scale features from multiple convolutional layers with different window shapes. Transfer learning was utilized to improve both the initial performance and learning efficiency. A hybrid loss function was employed to further optimize the segmentation model. The model was tested on a data set of 616 ICAs obtained from 210 patients, composed of 437 images for training, 49 images for validation, and 130 images for testing. The segmentation model achieved a Dice score of 0.8942, a sensitivity of 0.8735, a specificity of 0.9954, and a Hausdorff distance of 6.0794 mm; it could predict arteries for a single ICA frame in 0.2114 seconds. The results showed that our model outperformed the state-of-the-art deep-learning models. Our new method has great potential for clinical use.