Routine Ankle-Brachial Index (ABI) measurement: a window into atherosclerosis and early left ventricular dysfunction in patients diagnosed with cancer.

BACKGROUND: Cancer therapy is considered to cause accelerated ischemia. Ankle-Brachial Index (ABI) measurement is an inexpensive, simple, available test for the early diagnosis of peripheral artery disease (PAD); however, it is not performed routinely. We aimed to evaluate the role of routine ABI measurement for the diagnosis of PAD among patients diagnosed with cancer and whether it correlates with left ventricular (LV) dysfunction. METHODS: A retrospective, single-center study including patients diagnosed with cancer at Tel Aviv Sourasky Medical Center. The cohort included patients performing routine ABI and LV global longitudinal strain (GLS) echocardiography. The primary endpoint was the prevalence of PAD and whether it correlates with LV dysfunction, defined by LV GLS absolute value < 19%. The secondary composite endpoint evaluated the association between reduced ABI to LV dysfunction and all-cause mortality. RESULTS: Among 226 patients, PAD was diagnosed in 14 patients (6%). We revealed a positive correlation between ABI and LV GLS (r = 0.22, p < 0.01) with a reduced mean ABI score among patients with reduced LV GLS. A reduced mean ABI was observed among the positive composite endpoint group; however, it was not statistically significant (p = 0.35). CONCLUSIONS: We report, for the first time to our knowledge, the routine use of ABI testing among patients diagnosed with cancer. ABI showed a significant correlation to LV GLS, implying a potential tool in the early diagnosis of atherosclerosis and cardiotoxicity. Considering its low cost and availability, future prospective trials are needed to integrate its role in routine assessment.

Incorporating artificial intelligence in portable infrared thermal imaging for the diagnosis and staging of nonalcoholic fatty liver disease.

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is one of the most prevalent chronic liver diseases worldwide. Thermal imaging combined with advanced image-processing and machine learning analysis accurately classified disease status in a study on mice; this study aimed to develop this tool for humans. This prospective study included 46 patients who underwent liver biopsy. Liver thermal imaging was performed on the same day as liver biopsy. We developed an image-processing algorithm that measured the relative spatial thermal variation across the skin covering the liver. The texture parameters obtained from the thermal images were input into the machine learning algorithm. Patients were diagnosed with MASLD and stratified according to nonalcoholic fatty liver disease activity score (NAS) and fibrosis stage using the METAVIR score. Twenty-one of 46 patients were diagnosed with MASLD. Using thermal imaging followed by processing, detection accuracy for patients with NAS >4 was 0.72.

An Exploratory Analysis of Routine Ferritin Measurement Upon Admission and the Prognostic Implications of Low-Grade Ferritinemia During Inflammation.

BACKGROUND: Serum ferritin is usually measured in the presence of anemia or in suspected iron overload syndromes. Ferritin is also an acute-phase protein that is elevated during systemic inflammation. However, the prognostic value of routinely measuring ferritin upon admission to a medical facility is not clear. Therefore, we examined the association between ferritin concentrations measured at the time of hospital admission with 30-day and long-term mortality. METHODS: We obtained routine ferritin measurements taken within 24 hours of admission in 2859 patients hospitalized in an internal medicine department. Multiple clinical and laboratory parameters were used to assess the association between ferritin and overall mortality during a median follow-up of 15 months (interquartile range [IQR] 8-22). RESULTS: Ferritin levels were associated with increased 30-day mortality rates (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.03-1.06) for each 100 ng/mL increase. Patients with intermediate (78-220 ng/mL) and high (>221 ng/mL) ferritin concentrations (2nd and 3rd tertiles) had higher 30-day mortality rates even after adjustment for age, sex, and existing comorbidities (OR 2.05, 95% CI 1.70-2.5). Long-term overall mortality rates demonstrated a similar pattern across ferritin tertiles (hazard ratio [HR] 1.54, 95% CI 1.39-1.71). CONCLUSIONS: Routine admission ferritin concentrations are linearly and independently correlated with excess mortality risk in hospitalized patients, even those with apparently "normal" ferritin concentrations (<300 mg/mL). Thus, low-grade ferritinemia might not be an innocent finding in the context of the inflammatory response. Its potential biological and therapeutic implications warrant future research.

The CRP troponin test (CTT) stratifies mortality risk in patients with non-ST elevation myocardial infarction (NSTEMI).

INTRODUCTION: The C-reactive protein (CRP)-troponin-test (CTT) comprises simultaneous serial measurements of CRP and cardiac troponin and might reflect the systemic inflammatory response in patients with acute coronary syndrome. We sought to test its ability to stratify the short- and long-term mortality risk in patients with non-ST elevation myocardial infarction (NSTEMI). METHODS: We examined 1,675 patients diagnosed with NSTEMI on discharge who had at least two successive measurements of combined CRP and cardiac troponin within 48 h of admission. A tree classifier model determined which measurements and cutoffs could be used to best predict mortality during a median follow-up of 3 years [IQR 1.8-4.3]. RESULTS: Patients with high CRP levels ( > 90th percentile, >54 mg/L) had a higher 30-day mortality rate regardless of their troponin test findings (16.7% vs. 2.9%, p < 0.01). However, among patients with "normal" CRP levels ( < 54 mg/L), those who had high troponin levels ( > 80th percentile, 4,918 ng/L) had a higher 30-day mortality rate than patients with normal CRP and troponin concentrations (7% vs. 2%, p < 0.01). The CTT test result was an independent predictor for overall mortality even after adjusting for age, sex, and comorbidities (HR = 2.28 [95% CI 1.56-3.37], p < 0.01 for patients with high troponin and high CRP levels). CONCLUSIONS: Early serial CTT results may stratify mortality risk in patients with NSTEMI, especially those with "normal" CRP levels. The CTT could potentially assess the impact of inflammation during myocardial necrosis on the outcomes of patients with NSTEMI and identify patients who could benefit from novel anti-inflammatory therapies.

Sodium-glucose co-transporter-2 inhibitors in patients treated with immune checkpoint inhibitors.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have revolutionized the prognosis of cancer. Diabetes mellitus (DM) has been shown to have a negative effect on patients treated with ICIs. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are effective antidiabetic therapies associated with reduced all-cause mortality and cardiovascular (CV) outcomes. OBJECTIVE: To evaluate the prognostic value of SGLT2i on all-cause mortality and cardiotoxicity among patients treated with ICIs. METHODS: We performed a retrospective analysis of patients diagnosed with cancer and type 2 DM (DM2) and treated with ICIs at our center. Patients were divided into two groups according to baseline treatment with or without SGLT2i. The primary endpoint was all-cause mortality and the secondary endpoint was MACE, including myocarditis, acute coronary syndrome, heart failure, and arrhythmia. RESULTS: The cohort included 119 patients, with 24 (20%) patients assigned to the SGLT2i group. Both groups exhibited a comparable prevalence of cardiac risk factors, although the SGLT2i group displayed a higher incidence of ischemic heart disease. Over a median follow-up of 28 months, 61 (51%) patients died, with a significantly lower all-cause mortality rate in the SGLT2i group (21% vs. 59%, p = 0.002). While there were no significant differences in MACE, we observed zero cases of myocarditis and atrial fibrillation in the SGLT2i, compared to 2 and 6 cases in the non-SGLT2i group. CONCLUSIONS: SGLT2i therapy was associated with a lower all-cause mortality rate in patients diagnosed with cancer and DM2 and treated with ICIs. Further studies are needed to understand the mechanism and evaluate its benefit on cardiotoxicity.

Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm.

COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.

Transitioning from having no metabolic abnormality nor obesity to metabolic impairment in a cohort of apparently healthy adults.

INTRODUCTION: The global prevalence of metabolic syndrome and its association with increased morbidity and mortality has been rigorously studied. However, the true prevalence of "metabolic health", i.e. individuals without any metabolic abnormalities is not clear. Here, we sought to determine the prevalence of "metabolically healthy" individuals and characterize the "transition phase" from metabolic health to development of dysfunction over a follow-up period of 5 years. METHODS: We included 20,507 individuals from the Tel Aviv Sourasky Medical Center Inflammation Survey (TAMCIS) which comprises apparently healthy individuals attending their annual health survey. A second follow-up visit was documented after 4.8 (± 0.6) years. We defined a group of metabolically healthy participants without metabolic abnormalities nor obesity and compared their characteristics and change in biomarkers over time to participants who developed metabolic impairment on their follow-up visit. The intersections of all metabolic syndrome components and elevated high sensitivity C-reactive protein (hs-CRP) were also analyzed. RESULTS: A quarter of the cohort (5379 individuals, (26.2%) did not fulfill any metabolic syndrome criteria during their baseline visit. A total of 985 individuals (12.7% of returning participants) developed metabolic criteria over time with hypertension being the most prevalent component to develop among these participants. Individuals that became metabolically impaired over time demonstrated increased overlap between metabolic syndrome criteria and elevated hs-CRP levels. The group that became metabolically impaired over time also presented higher delta values of WBC, RBC, liver biomarkers, and uric acid compared with participants who were consistently metabolically impaired. LDL-C (low-density lipoprotein cholesterol) delta levels were similar. CONCLUSIONS: Roughly one-quarter of apparently healthy adults are defined as "metabolically healthy" according to current definitions. The transition from health to metabolic dysfunction is accompanied with active inflammation and several non-metabolic syndrome biomarkers. Aggressive screening for these biomarkers, blood pressure and hs-CRP might help identify apparently healthy individuals at increased risk of developing metabolic syndrome over time.

CRISPR-Cas9 editing of TLR4 to improve the outcome of cardiac cell therapy.

Inflammation and fibrosis limit the reparative properties of human mesenchymal stromal cells (hMSCs). We hypothesized that disrupting the toll-like receptor 4 (TLR4) gene would switch hMSCs toward a reparative phenotype and improve the outcome of cell therapy for infarct repair. We developed and optimized an improved electroporation protocol for CRISPR-Cas9 gene editing. This protocol achieved a 68% success rate when applied to isolated hMSCs from the heart and epicardial fat of patients with ischemic heart disease. While cell editing lowered TLR4 expression in hMSCs, it did not affect classical markers of hMSCs, proliferation, and migration rate. Protein mass spectrometry analysis revealed that edited cells secreted fewer proteins involved in inflammation. Analysis of biological processes revealed that TLR4 editing reduced processes linked to inflammation and extracellular organization. Furthermore, edited cells expressed less NF-ƙB and secreted lower amounts of extracellular vesicles and pro-inflammatory and pro-fibrotic cytokines than unedited hMSCs. Cell therapy with both edited and unedited hMSCs improved survival, left ventricular remodeling, and cardiac function after myocardial infarction (MI) in mice. Postmortem histologic analysis revealed clusters of edited cells that survived in the scar tissue 28 days after MI. Morphometric analysis showed that implantation of edited cells increased the area of myocardial islands in the scar tissue, reduced the occurrence of transmural scar, increased scar thickness, and decreased expansion index. We show, for the first time, that CRISPR-Cas9-based disruption of the TLR4-gene reduces pro-inflammatory polarization of hMSCs and improves infarct healing and remodeling in mice. Our results provide a new approach to improving the outcomes of cell therapy for cardiovascular diseases.

Automatized Detection of Crohn's Disease in Intestinal Ultrasound Using Convolutional Neural Network.

INTRODUCTION: The use of intestinal ultrasound (IUS) for the diagnosis and follow-up of inflammatory bowel disease is steadily growing. Although access to educational platforms of IUS is feasible, novice ultrasound operators lack experience in performing and interpreting IUS. An artificial intelligence (AI)-based operator supporting system that automatically detects bowel wall inflammation may simplify the use of IUS by less experienced operators. Our aim was to develop and validate an artificial intelligence module that can distinguish bowel wall thickening (a surrogate of bowel inflammation) from normal bowel images of IUS. METHODS: We used a self-collected image data set to develop and validate a convolutional neural network module that can distinguish bowel wall thickening >3 mm (a surrogate of bowel inflammation) from normal bowel images of IUS. RESULTS: The data set consisted of 1008 images, distributed uniformly (50% normal images, 50% abnormal images). Execution of the training phase and the classification phase was performed using 805 and 203 images, respectively. The overall accuracy, sensitivity, and specificity for detection of bowel wall thickening were 90.1%, 86.4%, and 94%, respectively. The network exhibited an average area under the ROC curve of 0.9777 for this task. CONCLUSIONS: We developed a machine-learning module based on a pretrained convolutional neural network that is highly accurate in the recognition of bowel wall thickening on intestinal ultrasound images in Crohn's disease. Incorporation of convolutional neural network to IUS may facilitate the use of IUS by inexperienced operators and allow automatized detection of bowel inflammation and standardization of IUS imaging interpretation.

We developed a machine-learning module based on a pretrained convolutional neural network that is highly accurate in the recognition of bowel wall thickening on intestinal ultrasound images in Crohn's disease.

Automated thermal imaging monitors the local response to cervical cancer brachytherapy.

Malignant tumors have high metabolic and perfusion rates, which result in a unique temperature distribution as compared to healthy tissues. Here, we sought to characterize the thermal response of the cervix following brachytherapy in women with advanced cervical carcinoma. Six patients underwent imaging with a thermal camera before a brachytherapy treatment session and after a 7-day follow-up period. A designated algorithm was used to calculate and store the texture parameters of the examined tissues across all time points. We used supervised machine learning classification methods (K Nearest Neighbors and Support Vector Machine) and unsupervised machine learning classification (K-means). Our algorithms demonstrated a 100% detection rate for physiological changes in cervical tumors before and after brachytherapy. Thus, we showed that thermal imaging combined with advanced feature extraction could potentially be used to detect tissue-specific changes in the cervix in response to local brachytherapy for cervical cancer.