Vitamin D Receptor Regulates Liver Regeneration After Partial Hepatectomy in Male Mice.

Vitamin D signals through the vitamin D receptor (VDR) to induce its end-organ effects. Hepatic stellate cells control development of liver fibrosis in response to stressors and vitamin D signaling decreases fibrogenesis. VDR expression in hepatocytes, however, is low in healthy liver, and the role of VDR in hepatocyte proliferation is unclear. Hepatocyte-VDR null mice (hVDR) were used to assess the role of VDR and vitamin D signaling in hepatic regeneration. hVDR mice have impaired liver regeneration and impaired hepatocyte proliferation associated with significant differential changes in bile salts. Notably, mice lacking hepatocyte VDR had significant increases in expression of conjugated bile acids after partial hepatectomy, consistent with failure to normalize hepatic function by the 14-day time point tested. Real-time PCR of hVDR and control livers showed significant changes in expression of cell cycle genes including cyclins D1 and E1 and cyclin-dependent kinase 2. Gene expression profiling of hepatocytes treated with vitamin D or control showed regulation of groups of genes involved in liver proliferation, hepatitis, liver hyperplasia/hyperproliferation and liver necrosis/cell death. Together these studies demonstrate an important functional role for VDR in hepatocytes during liver regeneration. Combined with the known profibrotic effects of impaired VDR signaling in stellate cells, the studies provide a mechanism whereby vitamin D deficiency would both reduce hepatocyte proliferation and permit fibrosis, leading to significant liver compromise.

HIV-1 subtype A1, D, and recombinant proviral genome landscapes during long-term suppressive therapy.

The primary obstacle to curing HIV-1 is a reservoir of CD4+ cells that contain stably integrated provirus. Previous studies characterizing the proviral landscape, which have been predominantly conducted in males in the United States and Europe living with HIV-1 subtype B, have revealed that most proviruses that persist during antiretroviral therapy (ART) are defective. In contrast, less is known about proviral landscapes in females with non-B subtypes, which represents the largest group of individuals living with HIV-1. Here, we analyze genomic DNA from resting CD4+ T-cells from 16 female and seven male Ugandans with HIV-1 receiving suppressive ART (n = 23). We perform near-full-length proviral sequencing at limiting dilution to examine the proviral genetic landscape, yielding 607 HIV-1 subtype A1, D, and recombinant proviral sequences (mean 26/person). We observe that intact genomes are relatively rare and clonal expansion occurs in both intact and defective genomes. Our modification of the primers and probes of the Intact Proviral DNA Assay (IPDA), developed for subtype B, rescues intact provirus detection in Ugandan samples for which the original IPDA fails. This work will facilitate research on HIV-1 persistence and cure strategies in Africa, where the burden of HIV-1 is heaviest.

X chromosome dosage drives statin-induced dysglycemia and mitochondrial dysfunction.

Statin drugs lower blood cholesterol levels for cardiovascular disease prevention. Women are more likely than men to experience adverse statin effects, particularly new-onset diabetes (NOD) and muscle weakness. Here we find that impaired glucose homeostasis and muscle weakness in statin-treated female mice are associated with reduced levels of the omega-3 fatty acid, docosahexaenoic acid (DHA), impaired redox tone, and reduced mitochondrial respiration. Statin adverse effects are prevented in females by administering fish oil as a source of DHA, by reducing dosage of the X chromosome or the Kdm5c gene, which escapes X chromosome inactivation and is normally expressed at higher levels in females than males. As seen in female mice, we find that women experience more severe reductions than men in DHA levels after statin administration, and that DHA levels are inversely correlated with glucose levels. Furthermore, induced pluripotent stem cells from women who developed NOD exhibit impaired mitochondrial function when treated with statin, whereas cells from men do not. These studies identify X chromosome dosage as a genetic risk factor for statin adverse effects and suggest DHA supplementation as a preventive co-therapy.

Leveraging altered lipid metabolism in treating B cell malignancies.

B cell malignancies, comprising over 80 heterogeneous blood cancers, pose significant prognostic challenges due to intricate oncogenic signaling. Emerging evidence emphasizes the pivotal role of disrupted lipid metabolism in the development of these malignancies. Variations in lipid species, such as phospholipids, cholesterol, sphingolipids, and fatty acids, are widespread across B cell malignancies, contributing to uncontrolled cell proliferation and survival. Phospholipids play a crucial role in initial signaling cascades leading to B cell activation and malignant transformation through constitutive B cell receptor (BCR) signaling. Dysregulated cholesterol and sphingolipid homeostasis support lipid raft integrity, crucial for propagating oncogenic signals. Sphingolipids impact malignant B cell stemness, proliferation, and survival, while glycosphingolipids in lipid rafts modulate BCR activation. Additionally, cancer cells enhance fatty acid-related processes to meet heightened metabolic demands. In obese individuals, the obesity-derived lipids and adipokines surrounding adipocytes rewire lipid metabolism in malignant B cells, evading cytotoxic therapies. Genetic drivers such as MYC translocations also intrinsically alter lipid metabolism in malignant B cells. In summary, intrinsic and extrinsic factors converge to reprogram lipid metabolism, fostering aggressive phenotypes in B cell malignancies. Therefore, targeting altered lipid metabolism has translational potential for improving risk stratification and clinical management of diverse B cell malignancy subtypes.

Weakly-Supervised Detection of Bone Lesions in CT.

The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. CT is routinely used to measure the size of lesions in the bones. However, they can be difficult to spot due to the wide variations in their sizes, shapes, and appearances. Precise localization of such lesions would enable reliable tracking of interval changes (growth, shrinkage, or unchanged status). To that end, an automated technique to detect bone lesions is highly desirable. In this pilot work, we developed a pipeline to detect bone lesions (lytic, blastic, and mixed) in CT volumes via a proxy segmentation task. First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks. Then, we trained a 3D full-resolution nnUNet model using these weak 3D annotations to segment the lesions and thereby detected them. Our automated method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data. To the best of our knowledge, we are the first to attempt the direct detection of bone lesions in CT via a proxy segmentation task.

A Comparison of CT-Based Pancreatic Segmentation Deep Learning Models.

RATIONALE AND OBJECTIVES: Pancreas segmentation accuracy at CT is critical for the identification of pancreatic pathologies and is essential for the development of imaging biomarkers. Our objective was to benchmark the performance of five high-performing pancreas segmentation models across multiple metrics stratified by scan and patient/pancreatic characteristics that may affect segmentation performance. MATERIALS AND METHODS: In this retrospective study, PubMed and ArXiv searches were conducted to identify pancreas segmentation models which were then evaluated on a set of annotated imaging datasets. Results (Dice score, Hausdorff distance [HD], average surface distance [ASD]) were stratified by contrast status and quartiles of peri-pancreatic attenuation (5 mm region around pancreas). Multivariate regression was performed to identify imaging characteristics and biomarkers (n = 9) that were significantly associated with Dice score. RESULTS: Five pancreas segmentation models were identified: Abdomen Atlas [AAUNet, AASwin, trained on 8448 scans], TotalSegmentator [TS, 1204 scans], nnUNetv1 [MSD-nnUNet, 282 scans], and a U-Net based model for predicting diabetes [DM-UNet, 427 scans]. These were evaluated on 352 CT scans (30 females, 25 males, 297 sex unknown; age 58 ± 7 years [ ± 1 SD], 327 age unknown) from 2000-2023. Overall, TS, AAUNet, and AASwin were the best performers, Dice= 80 ± 11%, 79 ± 16%, and 77 ± 18%, respectively (pairwise Sidak test not-significantly different). AASwin and MSD-nnUNet performed worse (for all metrics) on non-contrast scans (vs contrast, P < .001). The worst performer was DM-UNet (Dice=67 ± 16%). All algorithms except TS showed lower Dice scores with increasing peri-pancreatic attenuation (P < .01). Multivariate regression showed non-contrast scans, (P < .001; MSD-nnUNet), smaller pancreatic length (P = .005, MSD-nnUNet), and height (P = .003, DM-UNet) were associated with lower Dice scores. CONCLUSION: The convolutional neural network-based models trained on a diverse set of scans performed best (TS, AAUnet, and AASwin). TS performed equivalently to AAUnet and AASwin with only 13% of the training set size (8488 vs 1204 scans). Though trained on the same dataset, a transformer network (AASwin) had poorer performance on non-contrast scans whereas its convolutional network counterpart (AAUNet) did not. This study highlights how aggregate assessment metrics of pancreatic segmentation algorithms seen in other literature are not enough to capture differential performance across common patient and scanning characteristics in clinical populations.

Engineering a long acting, non-biased relaxin agonist using Protein-in-Protein technology.

The peptide hormone relaxin plays a critical role in tissue remodeling in a variety of tissues through activation of its cognate receptor, RXFP1. Relaxin's ability to modify extracellular matrices has provided a strong rationale for treating fibrosis in a variety of tissues. Treatment with recombinant relaxin peptides in clinical studies of heart failure has not yet proven useful, likely due to the short half-life of infused peptide. To circumvent this particular pharmacokinetic pitfall we have used a Protein-in-Protein (PiP) antibody technology described previously, to insert a single-chain human relaxin construct into the complementarity-determining region (CDR) of an immunoglobulin G (IgG) backbone, creating a relaxin molecule with a half-life of ∼4-5 days in mice. Relaxin-PiP biologics displaced Europium-labeled human relaxin in RXFP1-expressing cells and demonstrated full agonist activity on both human and mouse RXFP1 receptors. Relaxin-PiPs did not show signal transduction bias, as they activated cAMP in THP-1 cells, and cGMP and pERK signaling in primary human cardiac fibroblasts. In an induced carbon tetrachloride mouse model of liver fibrosis one relaxin-PiP, R2-PiP, caused reduction of liver lesions, ameliorated collagen accumulation in the liver with the corresponding reduction of Collagen1a1 gene expression, and increased cell proliferation in hepatic parenchyma. These relaxin biologics represent a novel approach to the design of a long-acting RXFP1 agonist to probe the clinical utility of relaxin/RXFP1 signaling to treat a variety of human fibrotic diseases.

Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports.

In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical Efficacy (CE), often fall short in capturing the semantic intricacies of clinical contexts or overemphasize clinical details, undermining report clarity. To overcome these issues, our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs), like GPT-3.5 and GPT-4. Utilizing In-Context Instruction Learning (ICIL) and Chain of Thought (CoT) reasoning, our approach aligns LLM evaluations with radiologist standards, enabling detailed comparisons between human and AI-generated reports. This is further enhanced by a Regression model that aggregates sentence evaluation scores. Experimental results show that our "Detailed GPT-4 (5-shot)" model achieves a 0.48 score, outperforming the METEOR metric by 0.19, while our "Regressed GPT-4" model shows even greater alignment with expert evaluations, exceeding the best existing metric by a 0.35 margin. Moreover, the robustness of our explanations has been validated through a thorough iterative strategy. We plan to publicly release annotations from radiology experts, setting a new standard for accuracy in future assessments. This underscores the potential of our approach in enhancing the quality assessment of AI-driven medical reports.

AI-based opportunistic quantitative image analysis of lung cancer screening CTs to reduce disparities in osteoporosis screening.

Osteoporosis is underdiagnosed, especially in ethnic and racial minorities who are thought to be protected against bone loss, but often have worse outcomes after an osteoporotic fracture. We aimed to determine the prevalence of osteoporosis by opportunistic CT in patients who underwent lung cancer screening (LCS) using non-contrast CT in the Northeastern United States. Demographics including race and ethnicity were retrieved. We assessed trabecular bone and body composition using a fully-automated artificial intelligence algorithm. ROIs were placed at T12 vertebral body for attenuation measurements in Hounsfield Units (HU). Two validated thresholds were used to diagnose osteoporosis: high-sensitivity threshold (115-165 HU) and high specificity threshold (<115 HU). We performed descriptive statistics and ANOVA to compare differences across sex, race, ethnicity, and income class according to neighborhoods' mean household incomes. Forward stepwise regression modeling was used to determine body composition predictors of trabecular attenuation. We included 3708 patients (mean age 64 ± 7 years, 54 % males) who underwent LCS, had available demographic information and an evaluable CT for trabecular attenuation analysis. Using the high sensitivity threshold, osteoporosis was more prevalent in females (74 % vs. 65 % in males, p < 0.0001) and Whites (72 % vs 49 % non-Whites, p < 0.0001). However, osteoporosis was present across all races (38 % Black, 55 % Asian, 56 % Hispanic) and affected all income classes (69 %, 69 %, and 91 % in low, medium, and high-income class, respectively). High visceral/subcutaneous fat-ratio, aortic calcification, and hepatic steatosis were associated with low trabecular attenuation (p < 0.01), whereas muscle mass was positively associated with trabecular attenuation (p < 0.01). In conclusion, osteoporosis is prevalent across all races, income classes and both sexes in patients undergoing LCS. Opportunistic CT using a fully-automated algorithm and uniform imaging protocol is able to detect osteoporosis and body composition without additional testing or radiation. Early identification of patients traditionally thought to be at low risk for bone loss will allow for initiating appropriate treatment to prevent future fragility fractures. CLINICALTRIALS.GOV IDENTIFIER: N/A.

Incidence and Types of Cardiac Arrhythmias in the Peri-Ictal Period in Patients Having a Generalized Convulsive Seizure.

BACKGROUND AND OBJECTIVES: Generalized convulsive seizures (GCSs) are the main risk factor of sudden unexpected death in epilepsy (SUDEP), which is likely due to peri-ictal cardiorespiratory dysfunction. The incidence of GCS-induced cardiac arrhythmias, their relationship to seizure severity markers, and their role in SUDEP physiopathology are unknown. The aim of this study was to analyze the incidence of seizure-induced cardiac arrhythmias, their association with electroclinical features and seizure severity biomarkers, as well as their specific occurrences in SUDEP cases. METHODS: This is an observational, prospective, multicenter study of patients with epilepsy aged 18 years and older with recorded GCS during inpatient video-EEG monitoring for epilepsy evaluation. Exclusion criteria were status epilepticus and an obscured video recording. We analyzed semiologic and cardiorespiratory features through video-EEG (VEEG), electrocardiogram, thoracoabdominal bands, and pulse oximetry. We investigated the presence of bradycardia, asystole, supraventricular tachyarrhythmias (SVTs), premature atrial beats, premature ventricular beats, nonsustained ventricular tachycardia (NSVT), atrial fibrillation (Afib), ventricular fibrillation (VF), atrioventricular block (AVB), exaggerated sinus arrhythmia (ESA), and exaggerated sinus arrhythmia with bradycardia (ESAWB). A board-certified cardiac electrophysiologist diagnosed and classified the arrhythmia types. Bradycardia, asystole, SVT, NSVT, Afib, VF, AVB, and ESAWB were classified as arrhythmias of interest because these were of SUDEP pathophysiology value. The main outcome was the occurrence of seizure-induced arrhythmias of interest during inpatient VEEG monitoring. Moreover, yearly follow-up was conducted to identify SUDEP cases. Binary logistic generalized estimating equations were used to determine clinical-demographic and peri-ictal variables that were predictive of the presence of seizure-induced arrhythmias of interest. The z-score test for 2 population proportions was used to test whether the proportion of seizures and patients with postconvulsive ESAWB or bradycardia differed between SUDEP cases and survivors. RESULTS: This study includes data from 249 patients (mean age 37.2 ± 23.5 years, 55% female) who had 455 seizures. The most common arrhythmia was ESA, with an incidence of 137 of 382 seizures (35.9%) (106/224 patients [47.3%]). There were 50 of 352 seizure-induced arrhythmias of interest (14.2%) in 41 of 204 patients (20.1%). ESAWB was the commonest in 22 of 394 seizures (5.6%) (18/225 patients [8%]), followed by SVT in 18 of 397 seizures (4.5%) (17/228 patients [7.5%]). During follow-up (48.36 ± 31.34 months), 8 SUDEPs occurred. Seizure-induced bradycardia (3.8% vs 12.5%, z = -16.66, p < 0.01) and ESAWB (6.6% vs 25%; z = -3.03, p < 0.01) were over-represented in patients who later died of SUDEP. There was no association between arrhythmias of interest and seizure severity biomarkers (p > 0.05). DISCUSSION: Markers of seizure severity are not related to seizure-induced arrhythmias of interest, suggesting that other factors such as occult cardiac abnormalities may be relevant for their occurrence. Seizure-induced ESAWB and bradycardia were more frequent in SUDEP cases, although this observation was based on a very limited number of SUDEP patients. Further case-control studies are needed to evaluate the yield of arrhythmias of interest along with respiratory changes as potential SUDEP biomarkers.

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

Impact of planning horizon length on traits in an economic breeding goal and ranking of selection candidates in beef cattle.

In beef production herds, unique situations such as breeding system, economic parameters, and current phenotypic performance can affect the emphasis of traits in the breeding goal and consequently the weighting of traits within a selection index. An often overlooked component of breeding goals is the planning horizon, or the time span to consider the economic impact of a selection decision, that varies between enterprises. A platform for constructing economic selection indexes (iGENDEC) was used to determine the impact of planning horizon length, breeding system, and sale endpoint on the relative emphasis of traits in the breeding goal and the re-ranking of selection candidates. As part of this investigation, the adjustment of phenotypic means for hot carcass weight and planning horizons were used to determine the impact of the relative emphasis on hot carcass weight as its mean approached a predetermined discount threshold. General-purpose indexes were created for animals sold at weaning and slaughter for three breeding systems with six different planning horizons (2, 5, 10, 20, 30, and 50 yr). As planning horizon increased, the relative emphasis on weaning weight or hot carcass weight, which affected revenue, decreased while the relative emphasis on stayability and mature weight increased. As the phenotypic mean for hot carcass weight approached and surpassed a predetermined discount threshold, the relative emphasis decreased before increasing again, once the mean weight surpassed the threshold. Rank correlations between indexes with different sale endpoints was 0.71 ±â€…0.1. Within a slaughter endpoint, re-ranking occurred between short and long planning horizons (r = 0.78 ±â€…0.09) while that of a weaning endpoint was less substantial (r = 0.85 ±â€…0.10). Jacard index scores between indexes with different planning horizons ranged from 39.7% to 87.9% and from 47.9% to 78.7% for weaning and carcass endpoints, respectively, for the top 5% of selection candidates. These results illustrate that the determination of a planning horizon can impact the rank of selection candidates and increases in net profit.

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age, 60 years ± 11 [SD]; 143 female), was tested on two internal (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the Dice coefficient, standard deviations, and 95% confidence intervals, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved Dice scores of 85.5% ± 6.1% (CI: 83.1%-87.8%) and 82.6% ± 15.3% (CI: 76.4%-88.7%), with median volume estimation errors of 19.6% (IQR: 13.2%-29.0%) and 5.3% (IQR: 2.4%- 9.7%), respectively. On UofW-LC (124 patients; mean age, 46 years ± 12; 73 female), the model had a Dice score of 83.0% ± 10.7% (CI: 79.8%-86.3%) and median volume estimation error of 9.7% (IQR: 4.5%-15.1%). The model showed strong agreement with expert assessments, with r2 values of 0.79, 0.98, and 0.97 across the test sets. Conclusion The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in concordance with expert radiologist assessments. ©RSNA, 2024.

AI and machine learning in medical imaging: key points from development to translation.

Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

Post-contrast CT liver attenuation alone is superior to the liver-spleen difference for identifying moderate hepatic steatosis.

OBJECTIVE: To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values. MATERIALS AND METHODS: A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis. RESULTS: The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001). CONCLUSION: Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less. CLINICAL RELEVANCE STATEMENT: Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection. KEY POINTS: The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone.

Evaluating Patient-reported Outcomes after Bilateral Reduction Mammoplasty: A Comparison of Reduction Techniques at a University Hospital.

Background: Macromastia, defined as the abnormal enlargement of breasts, burdens individuals physically and psychologically, impacting their daily lives beyond aesthetics. Reduction mammoplasty offers relief by restoring proportional breast volume and appropriate contour. Surgical success relies on choosing a suitable individualized operative technique tailored to the patient's presentation and postoperative goals. This study examines postoperative, patient-reported outcomes across different reduction techniques to gauge the impact of reduction technique on overall patient perspective of aesthetic and functional satisfaction. Methods: A retrospective review identified reduction mammoplasty patients by a single surgeon between 2018 and 2022. Exclusion criteria included augmentation-related or cancer reconstructive procedures. Phone interviews were conducted using a survey adapted from BREAST-Q to assess postoperative outcomes in patients. Data analysis included Pearson chi-square test in STATA 16.1. Results: Among 155 patients identified, 64 completed the survey. Average postsurgical interval was 24 months postoperative. After stratifying patients by operative technique, there was no significant difference in postoperative satisfaction among the cohorts with regard to nipple and breast appearance, sensation, symmetry, or shape. Conclusions: This study highlights no significant disparity in perceived aesthetic or functional outcomes among different reduction mammoplasty techniques. Personalized considerations, such as patient factors, surgical expertise, and anatomical specifics, should guide technique selection, emphasizing individualized approaches over presumed superior methods for optimal results.

Associations Between Stroke Type, Ischemic Stroke Subtypes, and Post-Stroke Cognitive Trajectories.

Background: It is unclear how post-stroke cognitive trajectories differ by stroke type and ischemic stroke subtype. We studied associations between stroke types (ischemic, hemorrhagic), ischemic stroke subtypes (cardioembolic, large artery atherosclerotic, lacunar/small vessel, cryptogenic/other determined etiology), and post-stroke cognitive decline. Methods: This pooled cohort analysis from four US cohort studies (1971-2019) identified 1,143 dementia-free individuals with acute stroke during follow-up: 1,061 (92.8%) ischemic, 82 (7.2%) hemorrhagic, 49.9% female, 30.8% Black. Median age at stroke was 74.1 (IQR, 68.6, 79.3) years. Outcomes were change in global cognition (primary) and changes in executive function and memory (secondary). Outcomes were standardized as T-scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1-SD difference in cognition. Median follow-up for the primary outcome was 6.0 (IQR, 3.2, 9.2) years. Linear mixed-effects models estimated changes in cognition after stroke. Results: On average, the initial post-stroke global cognition score was 50.78 points (95% CI, 49.52, 52.03) in ischemic stroke survivors and did not differ in hemorrhagic stroke survivors (difference, -0.17 points [95% CI, -1.64, 1.30]; P=0.82) after adjusting for demographics and pre-stroke cognition. On average, ischemic stroke survivors showed declines in global cognition, executive function, and memory. Post-stroke declines in global cognition, executive function, and memory did not differ between hemorrhagic and ischemic stroke survivors. 955 ischemic strokes had subtypes: 200 (20.9%) cardioembolic, 77 (8.1%) large artery atherosclerotic, 207 (21.7%) lacunar/small vessel, 471 (49.3%) cryptogenic/other determined etiology. On average, small vessel stroke survivors showed declines in global cognition and memory, but not executive function. Initial post-stroke cognitive scores and cognitive declines did not differ between small vessel survivors and survivors of other ischemic stroke subtypes. Post-stroke vascular risk factor levels did not attenuate associations. Conclusion: Stroke survivors had cognitive decline in multiple domains. Declines did not differ by stroke type or ischemic stroke subtype.

Autoinflammatory Diseases - A Review.

Autoinflammatory Diseases (AIDs) are a vast spectrum of disorders characterized by recurrent attacks of sterile inflammation. Since the first cloning of the Familial Mediterranean Fever gene in 1997, there has been a rapid rate of discovery of new AIDs. As of 2022, there have been 485 inborn errors of immunity documented by the International Union of Immunological Societies, for which many display aspects of autoinflammation. The pathophysiology of AIDs is complex. While many are caused by rare mutations in genes that govern innate immunity, others are polygenic where disease expression is thought to be triggered by environmental factors in genetically predisposed hosts.AIDs range in prevalence from common entities like gout, to ultra rare monogenic diseases. While AIDs were initially studied in pediatric populations, it is now apparent that they can present in adulthood and even in the elderly. AIDs can be clinically challenging given their rarity, as well as the heterogeneity in presentation and underlying etiology. While the care of AIDs can span medical disciplines, the rheumatologist often plays a central role given the inflammatory nature of these illnesses.In this review, we explore the current understanding of pathophysiology of these complex conditions and describe a classification system for AIDs. We place an emphasis on AIDs that present to the adult rheumatologist and discuss important AIDs that can mimic more classic rheumatologic diseases such as systemic lupus and inflammatory arthritis. Finally, we offer an approach to clinical assessment, diagnosis and management of AIDs.

Acute internal medicine physicians' clinical intuition based on acute care telephone referral: A prospective study.

INTRODUCTION: In the Netherlands, most emergency department (ED) patients are referred by a general practitioner (GP) or a hospital specialist. Early risk stratification during telephone referral could allow the physician to assess the severity of the patients' illness in the prehospital setting. We aim to assess the discriminatory value of the acute internal medicine (AIM) physicians' clinical intuition based on telephone referral of ED patients to predict short-term adverse outcomes, and to investigate on which information their predictions are based. METHODS: In this prospective study, we included adult ED patients who were referred for internal medicine by a GP or a hospital specialist. Primary outcomes were hospital admission and triage category according to the Manchester Triage System (MTS). Secondary outcome was 31-day mortality. The discriminatory performance of the clinical intuition was assessed using an area under the receiver operating characteristics curve (AUC). To identify which information is important to predict adverse outcomes, we performed univariate regression analysis. Agreement between predicted and observed MTS triage category was assessed using intraclass and Spearman's correlation. RESULTS: We included 333 patients, of whom 172 (51.7%) were referred by a GP, 146 (43.8%) by a hospital specialist, and 12 (3.6%) by another health professional. The AIM physician's clinical intuition showed good discriminatory performance regarding hospital admission (AUC 0.72, 95% CI: 0.66-0.78) and 31-day mortality (AUC 0.73, 95% CI: 0.64-0.81). Univariate regression analysis showed that age ≥65 years and a sense of alarm were significant predictors. The predicted and observed triage category were similar in 45.2%, but in 92.5% the prediction did not deviate by more than one category. Intraclass and Spearman's correlation showed fair agreement between predicted and observed triage category (ICC 0.48, Spearman's 0.29). CONCLUSION: Clinical intuition based on relevant information during a telephone referral can be used to accurately predict short-term outcomes, allowing for early risk stratification in the prehospital setting and managing ED patient flow more effectively.

Prognostic value of total tumor volume in patients with colorectal liver metastases: A secondary analysis of the randomized CAIRO5 trial with external cohort validation.

BACKGROUND: This study aimed to assess the prognostic value of total tumor volume (TTV) for early recurrence (within 6 months) and overall survival (OS) in patients with colorectal liver metastases (CRLM), treated with induction systemic therapy followed by complete local treatment. METHODS: Patients with initially unresectable CRLM from the multicenter randomized phase 3 CAIRO5 trial (NCT02162563) who received induction systemic therapy followed by local treatment were included. Baseline TTV and change in TTV as response to systemic therapy were calculated using the CT scan before and the first after systemic treatment, and were assessed for their added prognostic value. The findings were validated in an external cohort of patients treated at a tertiary center. RESULTS: In total, 215 CAIRO5 patients were included. Baseline TTV and absolute change in TTV were significantly associated with early recurrence (P = 0.005 and P = 0.040, respectively) and OS in multivariable analyses (P = 0.024 and P = 0.006, respectively), whereas RECIST1.1 was not prognostic for early recurrence (P = 0.88) and OS (P = 0.35). In the validation cohort (n = 85), baseline TTV and absolute change in TTV remained prognostic for early recurrence (P = 0.041 and P = 0.021, respectively) and OS in multivariable analyses (P < 0.0001 and P = 0.012, respectively), and showed added prognostic value over conventional clinicopathological variables (increase C-statistic, 0.06; 95 % CI, 0.02 to 0.14; P = 0.008). CONCLUSION: Total tumor volume is strongly prognostic for early recurrence and OS in patients who underwent complete local treatment of initially unresectable CRLM, both in the CAIRO5 trial and the validation cohort. In contrast, RECIST1.1 did not show prognostic value for neither early recurrence nor OS.