Heart failure (HF) and atherosclerosis represent two major cardiovascular diseases that are intricately linked, both contributing significantly to global morbidity, mortality, and healthcare burden. Despite substantial progress in diagnostic methods and therapeutic strategies, the overall impact of these conditions remains considerable. This is largely due to their complex and overlapping pathophysiological mechanisms, persistent residual atherosclerotic risk, and the ongoing challenges associated with implementing guideline-directed medical therapy for HF in routine clinical practice. Recent advancements in the management of diverse HF phenotypes, lipid abnormalities, atherosclerotic cardiovascular disease (ASCVD), and obesity have facilitated the adoption of multidrug regimens. These include β-blockers, renin-angiotensin-aldosterone system inhibitors, sodium-glucose cotransporter 2 (SGLT2) inhibitors, and glucagon-like peptide-1 (GLP-1), which have collectively improved outcomes in HF populations. Lipid-lowering therapy, particularly statins, has demonstrated significant efficacy in reducing ASCVD events and slowing HF progression, as well as lowering the risk of HF-related hospitalizations. Elevated lipoprotein(a) [Lp(a)] has emerged as an independent risk factor for both ASCVD and HF, being associated with increased risk of incident HF, disease progression, hospitalization, and adverse outcomes. However, there remains a lack of conclusive evidence as to whether targeted reduction of Lp(a) leads to a decrease in major adverse cardiovascular events or improves HF incidence or outcomes. In parallel, contemporary therapeutic advances in coronary and peripheral artery revascularization, along with novel pharmacologic treatments for obesity such as GLP-1 receptor agonists including semaglutide and tirzepatide have shown beneficial effects in reducing cardiovascular mortality, HF progression, and body weight, irrespective of HF status. These converging therapeutic strategies underscore the close interrelationship between HF and atherosclerosis. This review aims to elucidate the shared pathophysiological mechanisms linking these conditions and to examine their clinical overlap with ischemic heart disease, cerebrovascular disease, peripheral arterial disease, dyslipidemia, and obesity. A comprehensive understanding of these interrelated cardiovascular entities may offer valuable insights to inform future research directions and optimize the clinical management of patients affected by both HF and atherosclerotic disease.

Pigmented skin lesions represent localized areas of increased melanin and can indicate serious conditions like melanoma, a major contributor to skin cancer mortality. The MedMNIST v2 dataset, inspired by MNIST, was recently introduced to advance research in biomedical imaging and includes DermaMNIST, a dataset for classifying pigmented lesions based on the HAM10000 dataset. This study assesses ResNet-50 and EfficientNetV2L models for multi-class classification using DermaMNIST, employing transfer learning and various layer configurations. One configuration achieves results that match or surpass existing methods. This study suggests that convolutional neural networks (CNNs) can drive progress in biomedical image analysis, significantly enhancing diagnostic accuracy.

Deep learning has significantly advanced the field of medical image classification, particularly with the adoption of Convolutional Neural Networks (CNNs). Various deep learning frameworks such as Keras, PyTorch and JAX offer unique advantages in model development and deployment. However, their comparative performance in medical imaging tasks remains underexplored. This study presents a comprehensive analysis of CNN implementations across these frameworks, using the PathMNIST dataset as a benchmark. We evaluate training efficiency, classification accuracy and inference speed to assess their suitability for real-world applications. Our findings highlight the trade-offs between computational speed and model accuracy, offering valuable insights for researchers and practitioners in medical image analysis.

The rapid increase in electricity demand and peak load consumption has led to rising energy costs and grid instability. This paper proposes an adaptive control strategy for peak shaving, integrating energy storage (ES) and electric vehicles (EV) with real-time power supply-demand monitoring. The proposed system dynamically estimates household demand, including EVs as a load, and adjusts ES and EV charging and discharging schedules based on energy availability, load conditions, and time-of-use (TOU) pricing. By leveraging photovoltaic (PV) generation, surplus energy is stored in the ES and EV during low-demand periods and discharged during peak-demand hours, thereby reducing grid dependency and electricity costs. A real-time simulation model is developed to validate the effectiveness of the proposed strategy. The results demonstrate significant improvements in load balancing, cost reduction, and peak-shaving efficiency, ensuring optimal utilization of renewable energy sources and storage assets.

Abstract Background Being able to estimate the risk of metachronous disease in a patient with colorectal cancer (CRC) could enable risk-appropriate surveillance. The aim of this study was to develop a risk-prediction model to estimate individual 10-year risk of metachronous disease following a CRC diagnosis. Methods A population-based cohort of patients with CRC was recruited soon after diagnosis between 1997 and 2012 from the United States, Canada, and Australia. Cox regression with the least absolute shrinkage and selection operator penalization was used to identify factors that predicted the risk of a new primary CRC diagnosed at least 1 year after the initial CRC diagnosis. Potential predictors included demography, anthropometry, lifestyle factors, comorbidities, personal and family cancer history, medication use, age at diagnosis, and pathological features of the first CRC. Internal validation through bootstrapping was used to evaluate the discrimination and calibration. Results We included 6085 CRC cases; 138 (2.3%) of these cases were diagnosed with metachronous disease over a median of 12 years (IQR = 5-17 years). Metachronous CRC risk was predicted by body mass index; smoking status; level of physical activity; family history of cancer and synchronous CRC; stage, grade, histological type, and DNA mismatch repair status; and age at diagnosis of the first CRC. The model was valid with a C statistic of 0.65 (95% CI = 0.63 to 0.68) and a calibration slope of 0.873 (SD = 0.087). Conclusions Metachronous CRC can be predicted with reasonable accuracy using a prediction model that consists of clinical variables collected as part of routine practice.

The aim of the study was to examine the effects of eccentric- and concentric-based plyometric programmes on the strength, speed, and tensiomyography (TMG) parameters of female athletes. The study included twenty junior female participants from three different sports equally divided into two experimental groups of n = 10. Two plyometric programmes with contrasting designs were conducted for a period of six weeks, with sessions held twice per week. The first plyometric programme (ECC-CON-G) was based on exercises with eccentric, and the second (CONC-CON-G) with concentric contractions. TMG was used to evaluate neuromuscular performances of six muscles of both legs – vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), semitendinosus (SM), gastrocnemius lateralis (GL), and gastrocnemius medialis (GM)) – and two strength and speed tests: countermovement jump (CMJ), and sprint test at 10 m and 20 m. The results show that both groups had significant effects between pre-post measurements in CMJ (Diff, ECC-CON-G = 9.02%, and CONC-CON-G = 5.59%, p < 0.05), at 10 m (Diff, ECC-CON-G = 9.23%, and CONC-CON-G = 9.35%, p < 0.001), and 20 m (Diff, ECC-CON-G = 6.16%, and CONC-CON-G = 5.35%, p < 0.001), and TMG parameters in ECC-CON-G (all 6 left leg muscles, and right leg-VL, BF, GL, GM, p < 0.05), in CONC-CON-G (left leg-BF, SM, GL, GM, and right leg-VL, BF, GL, GM, p < 0.05). There were significantly better effects in ECC-CON-G compared to CONC-CON-G for CMJ height and time, for only time in 20 m sprint, and TMG parameters for left leg VL and VM, and right leg BF and GM. A plyometric programme based on exercises with eccentric contractions proved more beneficial for strength, speed, and TMG parameters in young female athletes compared to a programme based on concentric contractions.

Parkes-Weber Syndrome is a rare congenital vascular abnormality characterized by aneurismatic illness of blood arteries in the afflicted limb, as well as hypertrophy, ulceration, ischemia, and high-output heart failure. Imaging investigations are required to provide a diagnosis, with contrast arteriography being the gold standard. The majority of treatment options are endovascular, with surgical excision for arteriovenous malformations and limb amputation as alternatives. We describe a 73-year-old male patient with mainly asymptomatic PWS, coronary disease, and borderline EF (45%) who had CABG surgery. In individuals with established CAD and other cardiac disorders, it is critical to identify additional diseases or syndromes that might have a compounding effect on the heart, such as PWS and high-output heart failure.

This paper proposes a mobile robot motion control and planning system for trajectory tracking and obstacle avoidance in a prior unknown robot environment. The proposed system has two-level control and planning architecture: the higher is used to generate a path, while the lower provides the control actions that drive the robot. The planning level represents a reactive planer which determines on-line way-points during the robot’s movement towards the target and allowing the robot to move autonomously through an environment without colliding with obstacles. The main objective of this algorithm is to reduce the number of obstacles that are taken into consideration when determining the intermediate target point (way-points) in the movement towards the target location. This proposed algorithm is based on the concept of calculating the intersection of the variable target circle and the robot perception circle (VTPC), as well as attractive and repulsive forces. The lower level includes a fuzzy logic controller that drives the robot along generated online trajectory. It compares the current position of the mobile robot with the desired position, generating the appropriate linear speeds for the robot’s wheels to reach the target point in the shortest possible time. A series of simulations demonstrate its effectiveness in generating and executing the paths in various unknown robot environments.

This paper presents a method for extending decision-making policies in active perception tasks for multi-agent systems within partially observable environments. Multiple agents obtain their policies by training in an environment of a certain size. Those policies are then used in the environments larger in size, that are divided into sub-environments of size similar (or smaller) to one that the agents were trained in. Learned policies are adapted accordingly by proposed Action-Space Reduced Policy (ASRP). By leveraging Multi-Agent Reinforcement Learning (MARL) within a POMDP framework, agents can use their learned policies across environments of differing complexity without requiring retraining. The consensus mechanism allows agents to maintain a common belief state, supporting collaborative decision-making based on observations from all agents involved. Validation of the method is conducted on the scenario of multi-agent exploration missions, demonstrating the use of extended policies and enhanced perception accuracy. Simulation results indicate expected success rates and decision-making times, regardless of the environment’s dimensionality. Potential applications for scalable, multi-agent perception systems are discussed, along with directions for future research.

Highlights • MBM tumors show significant intertumor and intratumor heterogeneity in cellular composition, gene mutations, and pathway enrichment.• Therapy-treated tumors (P2, P4) exhibited immune activation, while untreated tumors (P1, P3) showed cold tumor signatures.• P1 and P4 tumors were enriched in CAFs, correlating with epithelial-mesenchymal transition and angiogenesis pathways.• Proteomic analysis revealed activation of oncogenic pathways like JAK-STAT, NF-κB, MAPK, and EMT, driving tumor progression.

In patients with heart failure, alterations in electrical fields generated within the myocardium have been associated with myocardial oedema which can act as a substrate for left ventricular dysfunction. Safety and efficacy of a direct microcurrent therapy using an implanted generator (C‐MIC) remain uncertain.

Highlights • MBM tumors show significant intertumor and intratumor heterogeneity in cellular composition, gene mutations, and pathway enrichment.• Therapy-treated tumors (P2, P4) exhibited immune activation, while untreated tumors (P1, P3) showed cold tumor signatures.• P1 and P4 tumors were enriched in CAFs, correlating with epithelial-mesenchymal transition and angiogenesis pathways.• Proteomic analysis revealed activation of oncogenic pathways like JAK-STAT, NF-κB, MAPK, and EMT, driving tumor progression.

The development of technology has influenced agricultural production and the establishment of the Agriculture 4.0 system in practice. This research is focused on the selection of equipment and machinery suppliers for the needs of the MAMEX Company. When selecting suppliers, an approach based on the application of an intuitionistic fuzzy set for decision-making was used. This approach allows the uncertainty present in decision-making to be incorporated, considered, and, hopefully, reduced in order to make a final decision on which of the observed suppliers is the most suitable for this company. Ten criteria were used that enable the application of sustainability in the supply chain. Eight local suppliers of equipment and machinery were observed with these criteria. The results obtained by applying the SWARA (Step-wise Weight Assessment Ratio Analysis) method showed that the most important criterion for selecting suppliers is the reliability and quality of equipment and machinery, while the results of the CORASO (COmpromise Ranking from Alternative Solutions) method showed that the SUP2 supplier is the best choice for establishing partnership relations with the MAMEX company. This supplier should help the MAMEX company improve its business and achieve better results in the market. The contribution of this research is to improve the application of intuitionistic fuzzy sets in decision-making, and to emphasize the importance of equipment and machinery in agricultural production in the Agriculture 4.0 system.

Orthography and pronunciation in the French language are two categories in complete disparity. This discrepancy is reflected in the fact that when pronouncing French, far fewer phonemes are produced than are represented by graphemes in the written form. The goal of this paper is to present the concept of silent morphology, or inaudible morphemes in French, using examples of agreement in gender and number. For this study, we have built a written corpus of allophone French learners L2 called Didacquis, consisting of 30 written productions (narrative and argumentative texts on topics such as studies, travel, and friendships) by 12 learners at the post-initial (PI) stage based on grammatical profiles that show the developmental pathways of acquisition routes (Bartning & Schlyter, 2004). In this context allophone learners refers to people whose first language is not French. The results are interpreted through the Pienemann’s Processability Theory (1998). The conclusion is that understanding these letters in French is exclusively contextual due to the presence of silent morpheme endings and the phenomenon of homophony.