Citrinin as a potential anti-cancer therapy: A comprehensive review.

Citrinin (CIT) is a polyketide-derived mycotoxin, which is produced by many fungal strains belonging to the gerena Monascus, Aspergillus, and Penicillium. It has been postulated that mycotoxins have several toxic mechanisms and are potentially used as antineoplastic agents. Therefore, the present study carried out a systematic review, including articles from 1978 to 2022, by collecting evidence in experimental studies of CIT antiplorifactive activity in cancer. The Data indicate that CIT intervenes in important mediators and cell signaling pathways, including MAPKs, ERK1/2, JNK, Bcl-2, BAX, caspases 3,6,7 and 9, p53, p21, PARP cleavage, MDA, reactive oxygen species (ROS) and antioxidant defenses (SOD, CAT, GST and GPX). These factors demonstrate the potential antitumor drug CIT in inducing cell death, reducing DNA repair capacity and inducing cytotoxic and genotoxic effects in cancer cells.

Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review.

MOTIVATION: Emergency Departments' (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage. OBJECTIVES: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation. METHODS: We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers' title, abstract and key words for the topics "triage", "emergency department"/"emergency room" and concepts within the field of intelligent systems. RESULTS: From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients' age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient's prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage. CONCLUSIONS: In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals' decision-making thereby leading to better clinical management and patients' outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.

Association of Phytol with Toxic and Cytotoxic Activities in an Antitumoral Perspective: A Meta-Analysis and Systemic Review.

BACKGROUND: Phytol have various pharmacological activities such as antimicrobial, cytotoxic, antitumoral, antimutagenic, anti-atherogenic, antidiabetic, lipid-lowering, antispasmodic, antiepileptic, antinociceptive, antioxidant, anti-inflammatory, anxiolytic, antidepressant and immunoadjuvant. Several studies point to an association of phytol with implications for apoptosis and necrosis at cellular levels in cancer, yet no clear conclusions were drawn. METHOD: To clarify this, we conducted a meta-analysis of non-clinical studies of phytol and its associations with toxicity and cytotoxicity emphasizing the mechanisms of apoptosis and necrosis induction and its importance in tumor therapy. Relevant studies were systematically searched in PubMed and Web of Science. The association between phytol and cyto-/toxicity was assessed by odds ratio (ORs) and 95% confidence intervals (CI). Twentythree studies were finally included in the meta-analysis. A significant association between phytol and toxicity (OR: 1.47; 95% CI = 0.86-2.48) was found among in vivo studies and cytotoxicity (OR: 1.81; 95% CI = 1.12- 2.65, p<0.05) in in vitro and ex vivo studies. In in vitro studies, 24% of them indicate that phytol at high doses induces apoptosis by several mechanisms; while about 40% of ex vivo studies indicate that phytol induces reactive oxygen species generation. But, Phytol does not act as a direct oxidant, unlike its metabolite phytanic acid. The 24% of in vivo studies also highlighted the mechanisms for apoptosis-like including expression of Bcl2 protein or mutations in pro-apoptotic protein Bax. Of them, 8% studies show necrosis and hepatotoxicity. However, in 24% of the articles, the mechanisms of toxicity and cytotoxicity are still not well elucidated. CONCLUSION: This study confirms that the association between phytol and cyto-/toxicity depends on the dose/concentration used in the given experimental conditions. Thus, there are still great prospects for new research aimed at the use of phytol and its metabolite as anticancer agents.

Computational intelligence applied to discriminate bee pollen quality and botanical origin.

The aim of this work was to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), and support vector machines (SVM) to predict physicochemical composition of bee pollen mixture given their botanical origin. To obtain the predominant plant genus of pollen (was the output variable), based on physicochemical composition (were the input variables of the predictive model), prediction models were learned from data. For the inverse case study, input/output variables were swapped. The probabilistic NN prediction model obtained 98.4% of correct classification of the predominant plant genus of pollen. To obtain the secondary and tertiary plant genus of pollen, the results present a lower accuracy. To predict the physicochemical characteristic of a mixture of bee pollen, given their botanical origin, fuzzy models proven the best results with small prediction errors, and variability lower than 10%.

Assessment of chemotherapy on various biochemical markers in breast cancer patients.

Chemotherapy is a standard treatment method for the patients with locally advanced breast cancer. Lately, cyclophosphamide (CYP) and doxorubicin (DOX) are used as the major chemotherapeutic agents especially for the treatment of breast cancer. Till date, no serum biomarker has been able to provide an early diagnosis of breast cancer. This study aimed to assess inflammatory, cardiac, renal and hematological markers in 56 breast cancer patients (BCP) before, during and after termination of chemotherapy with CYP and DOX. Blood samples were collected from the patients at the each treatment stages mentioned above. These samples were assessed for interleukin 6 (IL-6), interleukin 10 (IL-10), lactate dehydrogenase (LDH), creatine kinase (CK), creatinine, hemoglobin (Hb), leukocyte, platelet and Na+ /K+ -ATPase levels either by ELISA or colorimetric methods. The results suggest a significant increase in IL-6 level at all the stages in BCP as compared to control group. On the other hand, IL-10, CK and Na+ /K+ -ATPase levels were found to be significantly declined during all the stages. Moreover, the majority of hematological parameters remained unchanged throughout the treatment period with the exception of creatinine and Hb which showed slight modulation in their level at different stages. Based on the results, we conclude that breast cancer and co-treatment with CYP and DOX, interfere arious biological markers, thereby, showing the physiological imbalance.

Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology.

Left ventricular ejection fraction (LVEF) constitutes an important physiological parameter for the assessment of cardiac function, particularly in the settings of coronary artery disease and heart failure. This study explores the use of routinely and easily acquired variables in the intensive care unit (ICU) to predict severely depressed LVEF following ICU admission. A retrospective study was conducted. We extracted clinical physiological variables derived from ICU monitoring and available within the MIMIC II database and developed a fuzzy model using sequential feature selection and compared it with the conventional logistic regression (LR) model. Maximum predictive performance was observed using easily acquired ICU variables within 6 hours after admission and satisfactory predictive performance was achieved using variables acquired as early as one hour after admission. The fuzzy model is able to predict LVEF ≤ 25% with an AUC of 0.71 ± 0.07, outperforming the LR model, with an AUC of 0.67 ± 0.07. To the best of the authors' knowledge, this is the first study predicting severely impaired LVEF using multivariate analysis of routinely collected data in the ICU. We recommend inclusion of these findings into triaged management plans that balance urgency with resources and clinical status, particularly for reducing the time of echocardiographic examination.

Missing data in medical databases: impute, delete or classify?

BACKGROUND: The multiplicity of information sources for data acquisition in modern intensive care units (ICUs) makes the resulting databases particularly susceptible to missing data. Missing data can significantly affect the performance of predictive risk modeling, an important technique for developing medical guidelines. The two most commonly used strategies for managing missing data are to impute or delete values, and the former can cause bias, while the later can cause both bias and loss of statistical power. OBJECTIVES: In this paper we present a new approach for managing missing data in ICU databases in order to improve overall modeling performance. METHODS: We use a statistical classifier followed by fuzzy modeling to more accurately determine which missing data should be imputed and which should not. We firstly develop a simulation test bed to evaluate performance, and then translate that knowledge using exactly the same database as previously published work by [13]. RESULTS: In this work, test beds resulted in datasets with missing data ranging 10-50%. Using this new approach to missing data we are able to significantly improve modeling performance parameters such as accuracy of classifications by an 11%, sensitivity by 13%, and specificity by 10%, including also area under the receiver-operator curve (AUC) improvement of up to 13%. CONCLUSIONS: In this work, we improve modeling performance in a simulated test bed, and then confirm improved performance replicating previously published work by using the proposed approach for missing data classification. We offer this new method to other researchers who wish to improve predictive risk modeling performance in the ICU through advanced missing data management.