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Background: Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing. Methods: This investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques—permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)—were applied for explaining the black-box ML models. Results: Fifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90–0.92) and 0.92 (0.91–0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model's prediction. LIME and SHAP showed similar local feature attribution explanations. Conclusion: In the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient’s tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.

A major obstacle for the effective treatment of pancreatic ductal adenocarcinoma (PDAC) is its molecular heterogeneity, reflected by the diverse clinical outcomes and responses to therapies that occur. The tumors of patients with PDAC must therefore be closely examined and classified before treatment initiation in order to predict the natural evolution of the disease and the response to therapy. To stratify patients, it is absolutely necessary to identify biological markers that are highly specific and reproducible, and easily measurable by inexpensive sensitive techniques. Several promising strategies to find biomarkers are already available or under development, such as the use of liquid biopsies to detect circulating tumor cells, circulating free DNA, methylated DNA, circulating RNA, and exosomes and extracellular vesicles, as well as immunological markers and molecular markers. Such biomarkers are capable of classifying patients with PDAC and predicting their therapeutic sensitivity. Interestingly, developing chemograms using primary cell lines or organoids and analyzing the resulting high-throughput data via artificial intelligence would be highly beneficial to patients. How can exploiting these biomarkers benefit patients with resectable, borderline resectable, locally advanced, and metastatic PDAC? In fact, the utility of these biomarkers depends on the patient's clinical situation. At the early stages of the disease, the clinician's priority lies in rapid diagnosis, so that the patient receives surgery without delay; at advanced disease stages, where therapeutic possibilities are severely limited, the priority is to determine the PDAC tumor subtype so as to estimate the clinical outcome and select a suitable effective treatment.

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is an incurable neurodegenerative condition, characterized by the loss of upper and lower motor neurons. It affects 1–1.8/100,000 individuals worldwide, and the number of cases is projected to increase as the population ages. Thus, there is an urgent need to identify both therapeutic targets and disease-specific biomarkers–biomarkers that would be useful to diagnose and stratify patients into different sub-groups for therapeutic strategies, as well as biomarkers to follow the efficacy of any treatment tested during clinical trials. There is a lack of knowledge about pathogenesis and many hypotheses. Numerous “omics” studies have been conducted on ALS in the past decade to identify a disease-signature in tissues and circulating biomarkers. The first goal of the present review was to group the molecular pathways that have been implicated in monogenic forms of ALS, to enable the description of patient strata corresponding to each pathway grouping. This strategy allowed us to suggest 14 strata, each potentially targetable by different pharmacological strategies. The second goal of this review was to identify diagnostic/prognostic biomarker candidates consistently observed across the literature. For this purpose, we explore previous biomarker-relevant “omics” studies of ALS and summarize their findings, focusing on potential circulating biomarker candidates. We systematically review 118 papers on biomarkers published during the last decade. Several candidate markers were consistently shared across the results of different studies in either cerebrospinal fluid (CSF) or blood (leukocyte or serum/plasma). Although these candidates still need to be validated in a systematic manner, we suggest the use of combinations of biomarkers that would likely reflect the “health status” of different tissues, including motor neuron health (e.g., pNFH and NF-L, cystatin C, Transthyretin), inflammation status (e.g., MCP-1, miR451), muscle health (miR-338-3p, miR-206) and metabolism (homocysteine, glutamate, cholesterol). In light of these studies and because ALS is increasingly perceived as a multi-system disease, the identification of a panel of biomarkers that accurately reflect features of pathology is a priority, not only for diagnostic purposes but also for prognostic or predictive applications.

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is an incurable neurodegenerative condition, characterized by the loss of upper and lower motor neurons. It affects 1–1.8/100,000 individuals worldwide, and the number of cases is projected to increase as the population ages. Thus, there is an urgent need to identify both therapeutic targets and disease-specific biomarkers–biomarkers that would be useful to diagnose and stratify patients into different sub-groups for therapeutic strategies, as well as biomarkers to follow the efficacy of any treatment tested during clinical trials. There is a lack of knowledge about pathogenesis and many hypotheses. Numerous "omics" studies have been conducted on ALS in the past decade to identify a disease-signature in tissues and circulating biomarkers. The first goal of the present review was to group the molecular pathways that have been implicated in monogenic forms of ALS, to enable the description of patient strata corresponding to each pathway grouping. This strategy allowed us to suggest 14 strata, each potentially targetable by different pharmacological strategies. The second goal of this review was to identify diagnostic/prognostic biomarker candidates consistently observed across the literature. For this purpose, we explore previous biomarker-relevant "omics" studies of ALS and summarize their findings, focusing on potential circulating biomarker candidates. We systematically review 118 papers on biomarkers published during the last decade. Several candidate markers were consistently shared across the results of different studies in either cerebrospinal fluid (CSF) or blood (leukocyte or serum/plasma). Although these candidates still need to be validated in a systematic manner, we suggest the use of combinations of biomarkers that would likely reflect the "health status" of different tissues, including motor neuron health (e.g., pNFH and NF-L, cystatin C, Transthyretin), inflammation status (e.g., MCP-1, miR451), muscle health (miR-338-3p, miR-206) and metabolism (homocysteine, glutamate, cholesterol). In light of these studies and because ALS is increasingly perceived as a multi-system disease, the identification of a panel of biomarkers that accurately reflect features of pathology is a priority, not only for diagnostic purposes but also for prognostic or predictive applications. [ABSTRACT FROM AUTHOR]

Cancer patients show heterogeneous phenotypes and very different outcomes and responses even to common treatments, such as standard chemotherapy. This state-of-affairs has motivated the need for the comprehensive characterization of cancer phenotypes and fueled the generation of large omics datasets, comprising multiple omics data reported for the same patients, which might now allow us to start deciphering cancer heterogeneity and implement personalized therapeutic strategies. In this work, we performed the analysis of four cancer types obtained from the latest efforts by The Cancer Genome Atlas, for which seven distinct omics data were available for each patient, in addition to curated clinical outcomes. We performed a uniform pipeline for raw data preprocessing and adopted the Cancer Integration via MultIkernel LeaRning (CIMLR) integrative clustering method to extract cancer subtypes. We then systematically review the discovered clusters for the considered cancer types, highlighting novel associations between the different omics and prognosis., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

A major obstacle for the effective treatment of pancreatic ductal adenocarcinoma (PDAC) is its molecular heterogeneity, reflected by the diverse clinical outcomes and responses to therapies that occur. The tumors of patients with PDAC must therefore be closely examined and classified before treatment initiation in order to predict the natural evolution of the disease and the response to therapy. To stratify patients, it is absolutely necessary to identify biological markers that are highly specific and reproducible, and easily measurable by inexpensive sensitive techniques. Several promising strategies to find biomarkers are already available or under development, such as the use of liquid biopsies to detect circulating tumor cells, circulating free DNA, methylated DNA, circulating RNA, and exosomes and extracellular vesicles, as well as immunological markers and molecular markers. Such biomarkers are capable of classifying patients with PDAC and predicting their therapeutic sensitivity. Interestingly, developing chemograms using primary cell lines or organoids and analyzing the resulting high-throughput data via artificial intelligence would be highly beneficial to patients. How can exploiting these biomarkers benefit patients with resectable, borderline resectable, locally advanced, and metastatic PDAC? In fact, the utility of these biomarkers depends on the patient's clinical situation. At the early stages of the disease, the clinician's priority lies in rapid diagnosis, so that the patient receives surgery without delay; at advanced disease stages, where therapeutic possibilities are severely limited, the priority is to determine the PDAC tumor subtype so as to estimate the clinical outcome and select a suitable effective treatment., Competing Interests: Declaration of Competing Interest The author has no competing or financial interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Parkinson's disease (PD) is a clinically heterogeneous disorder with a multi-factorial pathology. Various molecular mechanisms are involved in the pathogenesis of PD, converging to oxidative stress and proteinopathy. The accumulation of reactive aldehydes (i.e., the dopamine metabolite DOPAL, lipid-peroxidation products, and advanced glycation end-products) has been reported in PD patients' brains. Aldehydes easily react with primary amines such as lysine residues, which are involved in several regulatory processes in cells. Therefore, aldehyde adducts lead to severe consequences, including neuronal proteostasis, mitochondrial dysfunction, and cell death. In this review, we analyzed the scavenging role of amines toward toxic aldehydes in the brain. Interestingly, small molecules like metformin, rasagiline, hydralazine are already clinically available and used in the therapy for PD and other diseases. Hence, we propose to reevaluate this class of drugs as a disease-modifiers for PD, and we suggest that improved analysis of their pharmacology and bioavailability in the brain, together with a more precise patients stratification, should be considered before planning future clinical trials., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Sophistication in DNA and RNA sequencing technology is unraveling the tremendous genetic and molecular complexity of human cancer. However, the rate at which this knowledge is being translated into patient care is too slow. To this end, we have designed and implemented a new translational platform, 'The Co-Clinical Trial Project', where data obtained in genetically engineered mouse models (GEMMs) of human cancer treated with protocols identical to those of ongoing clinical trials or with therapies already established in patients serve to rapidly: (i) stratify patients in terms of response and resistance on the basis of genetic and molecular criteria; (ii) identify mechanisms responsible for tumor resistance; and (iii) evaluate the effectiveness of drug combinations to overcome such resistance based on mechanistic understanding., (Copyright © 2014 Elsevier Ltd. All rights reserved.)