Lessons from autopsy: Topographical variability of atherosclerosis plaques.

Atherosclerosis is a complex disease characterized by the accumulation of plaques in arterial walls. Understanding its pathogenesis remains incomplete, with factors like inflammation, oxidative stress, and hypertension playing critical roles. The disease exhibits preferential localization of plaques, with variability observed even within the same individual. Genetic, environmental, and lifestyle factors contribute to its heterogeneity. Histological plaque phenotypes vary widely, prompting classification schemes focusing on systemic and local factors deteriorating fibrous caps. Recent research highlights differences in plaque histology among arterial systems, suggesting unique pathophysiological mechanisms. This study reports on multiple atherosclerotic plaques detected at autopsy in various vascular sites of a single subject, emphasizing their histological diversity and underscoring the systemic nature of atherosclerosis.

Prediction of Response to Anti-Angiogenic Treatment for Advanced Colorectal Cancer Patients: From Biological Factors to Functional Imaging.

Colorectal cancer (CRC) is a leading tumor worldwide. In CRC, the angiogenic pathway plays a crucial role in cancer development and the process of metastasis. Thus, anti-angiogenic drugs represent a milestone for metastatic CRC (mCRC) treatment and lead to significant improvement of clinical outcomes. Nevertheless, not all patients respond to treatment and some develop resistance. Therefore, the identification of predictive factors able to predict response to angiogenesis pathway blockade is required in order to identify the best candidates to receive these agents. Unfortunately, no predictive biomarkers have been prospectively validated to date. Over the years, research has focused on biologic factors such as genetic polymorphisms, circulating biomarkers, circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and microRNA. Moreover, research efforts have evaluated the potential correlation of molecular biomarkers with imaging techniques used for tumor assessment as well as the application of imaging tools in clinical practice. In addition to functional imaging, radiomics, a relatively newer technique, shows real promise in the setting of correlating molecular medicine to radiological phenotypes.

CD44: A New Prognostic Marker in Colorectal Cancer?

Cluster of differentiation 44 (CD44) is a non-kinase cell surface glycoprotein. It is overexpressed in several cell types, including cancer stem cells (CSCs). Cells overexpressing CD44 exhibit several CSC traits, such as self-renewal, epithelial-mesenchymal transition (EMT) capability, and resistance to chemo- and radiotherapy. The role of CD44 in maintaining stemness and the CSC function in tumor progression is accomplished by binding to its main ligand, hyaluronan (HA). The HA-CD44 complex activates several signaling pathways that lead to cell proliferation, adhesion, migration, and invasion. The CD44 gene regularly undergoes alternative splicing, resulting in the standard (CD44s) and variant (CD44v) isoforms. The different functional roles of CD44s and specific CD44v isoforms still need to be fully understood. The clinicopathological impact of CD44 and its isoforms in promoting tumorigenesis suggests that CD44 could be a molecular target for cancer therapy. Furthermore, the recent association observed between CD44 and KRAS-dependent carcinomas and the potential correlations between CD44 and tumor mutational burden (TMB) and microsatellite instability (MSI) open new research scenarios for developing new strategies in cancer treatment. This review summarises current research regarding the different CD44 isoform structures, their roles, and functions in supporting tumorigenesis and discusses its therapeutic implications.

The fascinating theory of fetal programming of adult diseases: A review of the fundamentals of the Barker hypothesis.

The theory of fetal programming of adult diseases was first proposed by David J.P. Barker in the eighties of the previous century, to explain the higher susceptibility of some people toward the development of ischemic heart disease. According to his hypothesis, poor maternal living conditions during gestation represent an important risk factor for the onset of atherosclerotic heart disease later in life. The analysis of the early phases of fetal development is a fundamental tool for the risk stratification of children and adults, allowing the identification of susceptible or resistant subjects to multiple diseases later in life. Here, we provide a narrative summary of the most relevant evidence supporting the Barker hypothesis in multiple fields of medicine, including neuropsychiatric disorders, such as Parkinson disease and Alzheimer disease, kidney failure, atherosclerosis, coronary heart disease, stroke, diabetes, cancer onset and progression, metabolic syndrome, and infectious diseases including COVID-19. Given the consensus on the role of body weight at birth as a practical indicator of the fetal nutritional status during gestation, every subject with a low birth weight should be considered an "at risk" subject for the development of multiple diseases later in life. The hypothesis of the "physiological regenerative medicine," able to improve fetal organs' development in the perinatal period is discussed, in the light of recent experimental data indicating Thymosin Beta-4 as a powerful growth promoter when administered to pregnant mothers before birth.

Coronary Artery Calcification: Current Concepts and Clinical Implications.

Coronary artery calcification (CAC) accompanies the development of advanced atherosclerosis. Its role in atherosclerosis holds great interest because the presence and burden of coronary calcification provide direct evidence of the presence and extent of coronary artery disease; furthermore, CAC predicts future events independently of concomitant conventional cardiovascular risk factors and to a greater extent than any other noninvasive biomarker of this disease. Nevertheless, the relationship between CAC and the susceptibility of a plaque to provoke a thrombotic event remains incompletely understood. This review summarizes the current understanding and literature on CAC. It outlines the pathophysiology of CAC and reviews laboratory, histopathological, and genetic studies, as well as imaging findings, to characterize different types of calcification and to elucidate their implications. Some patterns of calcification such as microcalcification portend increased risk of rupture and cardiovascular events and may improve prognosis assessment noninvasively. However, contemporary computed tomography cannot assess early microcalcification. Limited spatial resolution and blooming artifacts may hinder estimation of degree of coronary artery stenosis. Technical advances such as photon counting detectors and combination with nuclear approaches (eg, NaF imaging) promise to improve the performance of cardiac computed tomography. These innovations may speed achieving the ultimate goal of providing noninvasively specific and clinically actionable information.

Carotid Plaque-RADS: A Novel Stroke Risk Classification System.

BACKGROUND: Carotid artery atherosclerosis is highly prevalent in the general population and is a well-established risk factor for acute ischemic stroke. Although the morphological characteristics of vulnerable plaques are well recognized, there is a lack of consensus in reporting and interpreting carotid plaque features. OBJECTIVES: The aim of this paper is to establish a consistent and comprehensive approach for imaging and reporting carotid plaque by introducing the Plaque-RADS (Reporting and Data System) score. METHODS: A panel of experts recognized the necessity to develop a classification system for carotid plaque and its defining characteristics. Using a multimodality analysis approach, the Plaque-RADS categories were established through consensus, drawing on existing published reports. RESULTS: The authors present a universal classification that is applicable to both researchers and clinicians. The Plaque-RADS score offers a morphological assessment in addition to the prevailing quantitative parameter of "stenosis." The Plaque-RADS score spans from grade 1 (indicating complete absence of plaque) to grade 4 (representing complicated plaque). Accompanying visual examples are included to facilitate a clear understanding of the Plaque-RADS categories. CONCLUSIONS: Plaque-RADS is a standardized and reliable system of reporting carotid plaque composition and morphology via different imaging modalities, such as ultrasound, computed tomography, and magnetic resonance imaging. This scoring system has the potential to help in the precise identification of patients who may benefit from exclusive medical intervention and those who require alternative treatments, thereby enhancing patient care. A standardized lexicon and structured reporting promise to enhance communication between radiologists, referring clinicians, and scientists.

DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images.

Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models' performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions.

Mismatch Repair system protein deficiency as a resistance factor for locally advanced rectal adenocarcinoma patients receiving neoadjuvant chemo-radiotherapy.

BACKGROUND: Available data on Mismatch Repair system (MMR) deficiency are conflicting and derived from small studies. Our study aimed to evaluate the therapeutic implications of MMR status in patients with locally advanced rectal cancer (LARC). METHODS: We retrospectively collected data from 318 patients affected by LARC treated in Italy at the Medical Oncology Units of the University Hospital of Cagliari, Istituto Nazionale dei Tumori Milan, and AOU Ospedali Riuniti Ancona. All patients underwent neoadjuvant chemoradiotherapy. The primary objective was major TRG while secondary objectives were pathological complete response, disease-free survival (DFS) and overall survival (OS). RESULTS: One hundred sixty patients (148 pMMR and 12 dMMR) were included in the exploratory cohort and 158 (146 pMMR and 12 dMMR) were included in the validation cohort. A major TRG has been shown in 42.6% and 43.1% patients with pMMR in exploratory and validation cohort, respectively; while no major TRG have been shown in dMMR patients in both cohorts. Exploratory and validation cohorts showed a statistically significant higher mDFS in pMMR patients compared to dMMR: NR vs. 14 months and NR vs. 17 months, respectively. CONCLUSION: Our results indicated an association between dMMR and poor response to preoperative chemoradiotherapy and they represent a hypothesis-generating data for new neoadjuvant strategies.

The Post-Translational Modifications of Human Salivary Peptides and Proteins Evidenced by Top-Down Platforms.

In this review, we extensively describe the main post-translational modifications that give rise to the multiple proteoforms characterized to date in the human salivary proteome and their potential role. Most of the data reported were obtained by our group in over twenty-five years of research carried out on human saliva mainly by applying a top-down strategy. In the beginning, we describe the products generated by proteolytic cleavages, which can occur before and after secretion. In this section, the most relevant families of salivary proteins are also described. Next, we report the current information concerning the human salivary phospho-proteome and the limited news available on sulfo-proteomes. Three sections are dedicated to the description of glycation and enzymatic glycosylation. Citrullination and N- and C-terminal post-translational modifications (PTMs) and miscellaneous other modifications are described in the last two sections. Results highlighting the variation in the level of some proteoforms in local or systemic pathologies are also reviewed throughout the sections of the manuscript to underline the impact and relevance of this information for the development of new diagnostic biomarkers useful in clinical practice.

Combined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification.

Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases that target the liver and have a wide spectrum of presentation. A global overview of quantitative variations on the salivary proteome in presence of these two pathologies is investigated in this study. The acid-insoluble salivary fraction of AIH and PBC patients, and healthy controls (HCs), was analyzed using a gel-based bottom-up proteomic approach combined with a robust machine learning statistical analysis of the dataset. The abundance of Arginase, Junction plakoglobin, Desmoplakin, Hexokinase-3 and Desmocollin-1 decreased, while that of BPI fold-containing family A member 2 increased in AIHp compared to HCs; the abundance of Gelsolin, CD14, Tumor-associated calcium signal transducer 2, Clusterin, Heterogeneous nuclear ribonucleoproteins A2/B1, Cofilin-1 and BPI fold-containing family B member 2 increased in PBCp compared to HCs. The abundance of Hornerin decreased in both AIHp and PBCp with respect to HCs and provided an area under the ROC curve of 0.939. Machine learning analysis confirmed the feasibility of the salivary proteome to discriminate groups of subjects based on AIH or PBC occurrence as previously suggested by our group. The topology-based functional enrichment analysis performed on these potential salivary biomarkers highlights an enrichment of terms mostly related to the immune system, but also with a strong involvement in liver fibrosis process and with antimicrobial activity.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Cardiovascular disease/stroke risk stratification in deep learning framework: a review.

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

Recurrent nasal basal cell carcinoma treated with standard surgery excision: evaluation of volume ratio.

BACKGROUND: Basal cell carcinoma (BCC) is the most common malignant skin tumor. Although it rarely evolves into a metastatic disease, BCC can lead to a significant morbidity due to local invasion. The risk of lesion recurrence depends on clinical and histopathological factors as described by the Nation Comprehensive Cancer Network (NCCN). The surgical excision margins have a well-known role: there is a close relationship between high recurrence rate of BCC and the tumor proximity to the surgical margins. Aim of our study was to assess whether there is a significative correlation between recurring BCC and volume ratio (VRb/t), defined as ratio between excisional biopsy volume and tumor volume, and if VRb/t is a useful parameter to assess the risk of recurrence of BCC. METHODS: Retrospective case-control study in 80 patients with history of recurrent basal cell carcinoma of the nose (cases), and 43 patients with history of basal cell carcinoma of the nose with no evidence of relapse (controls) in the following 8 years. RESULTS: Surgical excision margins, histological subtype, ulceration, depth of invasion and volume ratio (VRb/t) were evaluated in case and controls. The evaluation of VRb/t evidenced a significant difference between recurrent BCC and non-recurrent BCC. The mean values of VRb/t were 6.17 for cases and 11.94 for controls. The Binomial Logistic Regression has displayed, for values of VRb/t around 7, a probability of 75% to identify BCCs belonging to the recurrent group. CONCLUSIONS: Our data show a significant correlation between recurrent BCCs and VRb/t. VRb/t can help in the assessment of recurrence risk, used together with others prognostic factor. For values of VRb/t close to 7 it should be recommended a close follow-up to promptly identify a possible recurrence.

Non-invasive coronary imaging in elderly population.

Age is a non-modifiable cardiovascular risk factor, which leads to development and progression of chronic conditions, such as coronary artery disease, by promoting atherosclerosis. Aging is responsible for morphological structure changes of the coronary arteries and specific atherosclerotic plaque features, which can be studied with non-invasive coronary imaging techniques, particularly coronary CT angiography. The aim of this review is to evaluate current knowledge on this technique applied to the elderly population, and to describe CAD manifestation and plaque features of coronary atherosclerosis in this particular set of patients. We also discuss the clinical implication of frailty assessment and customization of diagnostic strategies in order to shift the approach from disease-centered to patient-centered care.

CDX-2 expression correlates with clinical outcomes in MSI-H metastatic colorectal cancer patients receiving immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) showed efficacy in metastatic colorectal cancer (mCRC) with mismatch-repair deficiency or high microsatellite instability (dMMR-MSI-H). Unfortunately, a patient's subgroup did not benefit from immunotherapy. Caudal-related homeobox transcription factor 2 (CDX-2) would seem to influence immunotherapy's sensitivity, promoting the chemokine (C-X-C motif) ligand 14 (CXCL14) expression. Therefore, we investigated CDX-2 role as a prognostic-predictive marker in patients with mCRC MSI-H. We retrospectively collected data from 14 MSI-H mCRC patients treated with ICIs between 2019 and 2021. The primary endpoint was the 12-month progression-free-survival (PFS) rate. The secondary endpoints were overall survival (OS), PFS, objective response rate (ORR), and disease control rate (DCR). The PFS rate at 12 months was 81% in CDX-2 positive patients vs 0% in CDX-2 negative patients (p = 0.0011). The median PFS was not reached (NR) in the CDX-2 positive group versus 2.07 months (95%CI 2.07-10.8) in CDX-2 negative patients (p = 0.0011). Median OS was NR in CDX-2-positive patients versus 2.17 months (95% Confidence Interval [CI] 2.17-18.7) in CDX2-negative patients (p = 0.026). All CDX-2-positive patients achieved a disease response, one of them a complete response. Among CDX-2-negative patients, one achieved stable disease, while the other progressed rapidly (ORR: 100% vs 0%, p = 0.0005; DCR: 100% vs 50%, p = 0.02). Twelve patients received 1st-line pembrolizumab (11 CDX-2 positive and 1 CDX-2 negative) not reaching median PFS, while two patients (1 CDX-2 positive and 1 CDX-2 negative) received 3rd-line pembrolizumab reaching a median PFS of 10.8 months (95% CI, 10.8-12.1; p = 0.036). Although our study reports results on a small population, the prognostic role of CDX-2 in CRC seems confirmed and could drive a promising predictive role in defining the population more sensitive to immunotherapy treatment. Modulating the CDX-2/CXCL14 axis in CDX-2-negative patients could help overcome primary resistance to immunotherapy.

New Horizons in Metastatic Colorectal Cancer: Prognostic Role of CD44 Expression.

BACKGROUND: The transmembrane glycoprotein CD44, the major hyaluronan (HA) receptor, has been proven to regulate cell growth, survival, differentiation, and migration. It is therefore widely considered to be involved in carcinogenesis. Its role as a new therapeutic target in solid tumors is under evaluation in clinical trials. The prognostic value remains controversial. Here, we aimed to investigate the correlation between CD44 expression and the clinicopathological features and survival in metastatic colorectal cancer (mCRC) patients. METHODS: Data from 65 mCRC patients of the Medical Oncology Unit, University Hospital and University of Cagliari were retrospectively collected from 2008 to 2021. Immunohistochemical analysis was performed at the Pathology Division, University Hospital of Cagliari on 3 µm thick sections obtained from paraffin blocks. The intensity of immunohistochemical staining was subclassified into four groups: score 0 if negative or weak membrane staining in less than 10% of tumor cells; score 1+ if weak membrane staining in at least 10% of tumor cells or moderate membrane staining in less than 10% of tumor cells; score 2+ if moderate membrane staining in at least 10% of tumor cells or intensive membrane staining in less than 10% of tumor cells; score 3+ if intense membrane staining in at least 10% of tumor cells. Based on this score, we distinguished patients into low CD44 expression (score 0, 1+, 2+) and high CD44 expression (score 3+). Statistical analysis was performed with MedCalc (survival distribution: Kaplan-Meier; survival comparison: log-rank test; association between categorical variables: Fisher's exact test). RESULTS: Patients' median age was 66 years (range 49-85). Regarding CD44 expression, score was 0 in 18 patients, 1+ in 15 patients, 2+ in 18 patients, and 3+ in 14 patients. Median overall survival (mOS) was 28.1 months (95%CI: 21.3-101). CD44 overexpression (3+) was correlated with poor prognosis (p = 0.0011; HR = 0.2), with a mOS of 14.5 months (95%CI 11.7 to 35.9) versus 30.7 months (95%CI 27.8 to 101) in lower CD44 expression. Higher CD44 expression was associated with clinically poor prognostic features: age ≥ 70 years (p = 0.0166); inoperable disease (p = 0.0008); stage IV at diagnosis (p = 0.0241); BRAF mutated (p = 0.0111), high-grade tumor (p = 0.0084). CONCLUSIONS: CD44 markedly correlated with aggressive tumor behavior and contributed to the earlier progression of disease, thus suggesting its role as a novel prognostic marker and potential therapeutic target for mCRC patients.

Exploring cell surface markers and cell-cell interactions of human breast milk stem cells.

Background: Breakthrough studies have shown that pluripotent stem cells are present in human breast milk. The expression of pluripotency markers by breast milk cells is heterogeneous, relating to cellular hierarchy, from early-stage multi-lineage stem cells to fully differentiated mammary epithelial cells, as well as weeks of gestation and days of lactation. Design and methods: Here, we qualitatively analyze cell marker expression in freshly isolated human breast milk cells, without any manipulation that could influence protein expression. Moreover, we use electron microscopy to investigate cell-cell networks in breast milk for the first time, providing evidence of active intercellular communication between cells expressing different cellular markers. Results: The immunocytochemistry results of human breast milk cells showed positive staining in all samples for CD44, CD45, CD133, and Ki67 markers. Variable positivity was present with P63, Tß4 and CK14 markers. No immunostaining was detected for Wt1, nestin, Nanog, OCT4, SOX2, CK5, and CD34 markers. Cells isolated from human breast milk form intercellular connections, which together create a cell-to-cell communication network. Conclusions: Cells freshly isolated form human breast milk, without particular manipulations, show heterogeneous expression of stemness markers. The studied milk staminal cells show "pluripotency" at different stages of differentiation, and are present as single cells or grouped cells. The adjacent cell interactions are evidenced by electron microscopy, which showed the formation of intercellular connections, numerous contact regions, and thin pseudopods.

Cell starvation increases uptake of extracellular Thymosin ß4 and its complexes with calcium.

Cell metastasis is the main cause of cancer mortality. Inhibiting early events during cell metastasis and invasion could significantly improve cancer prognosis, but the initial mechanisms of cell transition and migration are barely known. Calcium regulates cell migration, whilst Thymosin ß4 is a G-actin and iron binding peptide associated with tumor metastasis and ferroptosis. Under normal cell growth conditions, intracellular free calcium ions and Thymosin ß4 concentrations are strictly regulated, and are not influenced by extracellular supplementation. However, cell starvation decreases intracellular Thymosin ß4 and increases extracellular peptide uptake above the normal range. Unexpectedly, cell starvation significantly increases internalization of extracellular Ca2+/Thymosin ß4 complexes. Elucidating the role of Ca2+/Thymosin ß4 in the early events of metastasis will likely be important in the future to develop therapies targeting metastasis.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.