RAD-FS - Inherent and Embedded SCA-Security in Ultra-Low Power IoTs

High-performance and energy-efficient encryption engines have become crucial components in modern System-On-Chip (SoC) architectures across multiple platforms, including servers, desktops, mobile devices, and IoT edge devices. Alas, the secure operation of cryptographic engines faces a significant obstacle caused by information leakage through various side-channels. Adversaries can exploit statistical analysis techniques on measured (e.g.,) power and timing signatures generated during (e.g.,) encryption process to extract secret material. Countermeasures against such side-channel attacks often impose substantial power, area, and performance overheads. Consequently, designing side-channel secure encryption engines becomes a critical challenge when ensuring high-performance and energy-efficient operations. In this paper we will suggest a novel technique for low cost, high impact, easily scalable protection based on Adaptive Dynamic Voltage and Frequency Scaling (A-DVFS) capabilities in ultra-low-power (ULP) sub-threshold chips. We review the improvement of using integrated voltage regulators and DVFS, normally used for efficient power management, towards increasing side-channel resistance of encryption engines; Pushing known prior-art in the topic to ULP-regime. The hardware measurements were performed on PLS15 test-chip fabricated in ULP 40nm process going down from nominal voltage to 580 mV power-supply. Various results and detailed analysis is presented to demonstrate the impact of power management circuits on side-channel security, performance-impact and comparison to prior-art. Importantly, we highlight security sensitivities DVFS embeds in terms of software side-channels such as timing, and their mitigation with our proposed technique, successfully masking the time signature introduced by DVFS

Artificial intelligence based prediction model of in-hospital mortality among females with acute coronary syndrome: for the Jerusalem Platelets Thrombosis and Intervention in Cardiology (JUPITER-12) Study Group

IntroductionDespite ongoing efforts to minimize sex bias in diagnosis and treatment of acute coronary syndrome (ACS), data still shows outcomes differences between sexes including higher risk of all-cause mortality rate among females. Hence, the aim of the current study was to examine sex differences in ACS in-hospital mortality, and to implement artificial intelligence (AI) models for prediction of in-hospital mortality among females with ACS.MethodsAll ACS patients admitted to a tertiary care center intensive cardiac care unit (ICCU) between July 2019 and July 2023 were prospectively enrolled. The primary outcome was in-hospital mortality. Three prediction algorithms, including gradient boosting classifier (GBC) random forest classifier (RFC), and logistic regression (LR) were used to develop and validate prediction models for in-hospital mortality among females with ACS, using only available features at presentation.ResultsA total of 2,346 ACS patients with a median age of 64 (IQR: 56–74) were included. Of them, 453 (19.3%) were female. Female patients had higher prevalence of NSTEMI (49.2% vs. 39.8%, p < 0.001), less urgent PCI (<2 h) rates (40.2% vs. 50.6%, p < 0.001), and more complications during admission (17.7% vs. 12.3%, p = 0.01). In-hospital mortality occurred in 58 (2.5%) patients [21/453 (5%) females vs. 37/1,893 (2%) males, HR = 2.28, 95% CI: 1.33–3.91, p = 0.003]. GBC algorithm outscored the RFC and LR models, with area under receiver operating characteristic curve (AUROC) of 0.91 with proposed working point of 83.3% sensitivity and 82.4% specificity, and area under precision recall curve (AUPRC) of 0.92. Analysis of feature importance indicated that older age, STEMI, and inflammatory markers were the most important contributing variables.ConclusionsMortality and complications rates among females with ACS are significantly higher than in males. Machine learning algorithms for prediction of ACS outcomes among females can be used to help mitigate sex bias

The Effect of War on STEMI Incidence: Insights from Intensive Cardiovascular Care Unit Admissions

(1) Background: The impact of armed conflicts on public health is undeniable, with psychological stress emerging as a significant risk factor for cardiovascular disease (CVD). Nevertheless, contemporary data regarding the influence of war on CVD, and especially on acute coronary syndrome (ACS), are scarce. Hence, the aim of the current study was to assess the repercussions of war on the admission and prognosis of patients admitted to a tertiary care center intensive cardiovascular care unit (ICCU). (2) Methods: All patients admitted to the ICCU during the first three months of the Israel–Hamas war (2023) were included and compared with all patients admitted during the same period in 2022. The primary outcome was in-hospital mortality. (3) Results: A total of 556 patients (184 females [33.1%]) with a median age of 70 (IQR 59–80) were included. Of them, 295 (53%) were admitted to the ICCU during the first three months of the war. Fewer Arab patients and more patients with ST-segment elevation myocardial infraction (STEMI) were admitted during the war period (21.8% vs. 13.2%, p p = 0.04, respectively), whereas non-STEMI (NSTEMI) patients were admitted more frequently in the pre-war year (19.3% vs. 25.7%, p = 0.09). In-hospital mortality was similar in both groups (4.4% vs. 3.4%, p = 0.71; HR 1.42; 95% CI 0.6–3.32, p = 0.4). (4) Conclusions: During the first three months of the war, fewer Arab patients and more STEMI patients were admitted to the ICCU. Nevertheless, in-hospital mortality was similar in both groups

D-Dimer as a Prognostic Factor in a Tertiary Center Intensive Coronary Care Unit

Introduction D-dimer is a small protein fragment produced during fibrinolysis. High D-dimer levels were shown to have prognostic impact in critically ill patients. Nevertheless, data regarding D-dimer's prognostic impact among tertiary care intensive coronary care unit (ICCU) patients is scarce. Material and method All patients admitted to the ICCU between 1-12/2020 were prospectively included. Based on admission D-dimer level, patients were categorized into low and high D-dimer groups (50 years old). Results and discussion A total of 959 consecutive patients were included, including 296 (27.4%) and 663 (61.3%) patients with low and high D-Dimer levels, respectively. Patients with high D-dimer level were older compared with patients with low D-dimer level (age 70.4 ± 15 and 59 ± 13 years, p = 0.004) and had more comorbidities. The most common primary diagnosis on admission among the low D-dimer group was acute coronary syndrome (ACS) (74.3%), while in the high D-dimer group it was a combination of ACS (33.6%), cardiac structural interventions (26.7%) and various arrhythmias (21.1%). High D-dimer levels were associated with increased mortality rate, even after adjustment for age, gender, comorbidities and left ventricular ejection fraction (LVEF). High D-dimer levels were independently associated with increased overall 1-year mortality rate (HR = 5.8; 95% CI; 1.7-19.1; p = 0.004). Conclusion Elevated D-dimer levels on admission in ICCU patients is an independently poor prognostic factor for in-hospital morbidity and 1-year overall mortality rate following hospitalization

Mean Platelet Volume as a Predictor of Coronary Artery Disease Severity and its Association With Coronary Artery Calcification

Coronary calcium score (CCS) is a highly sensitive marker for estimating coronary artery calcification (CAC) and detecting coronary artery disease (CAD). Mean platelet volume (MPV (is a platelet indicator that represent platelet stimulation and production. The aim of the current study was to examine the association between MPV values and CAC. We examined 290 patients who underwent coronary computerized tomography (CT) exam between the years 2017 and 2020 in a tertiary care medical center. Only patients evaluated for chest pain were included. The Multi-Ethnic Study of Atherosclerosis (MESA) CAC calculator was used to categorize patients CCS by age, gender, and ethnicity to CAC severity percentiles (<50, 50-74, 75-89, ≥90). Thereafter, the association between CAC percentile and MPV on admission was evaluated. Out of 290 patients, 251 (87%) met the inclusion and exclusion criteria. There was a strong association between higher MPV and higher CAC percentile ( P  = .009). The 90th CAC percentile was associated with the highest prevalence of diabetes mellitus (DM), hypertension, dyslipidemia, and statin therapy ( P  = .002, .003, .001, and .001, respectively). In a multivariate analysis (including age, gender, DM, hypertension, statin therapy, and low-density lipoprotein level) MPV was found to be an independent predictor of CAC percentile (OR 1.55-2.65, P  < .001). Higher MPV was found to be an independent predictor for CAC severity. These findings could further help clinicians detect patients at risk for CAD using a simple and routine blood test