Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations.

Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.

Promising 8-Aminoquinoline-Based Metal Complexes in the Modulation of SIRT1/3-FOXO3a Axis against Oxidative Damage-Induced Preclinical Neurons.

The discovery of novel bioactive molecules as potential multifunctional neuroprotective agents has clinically drawn continual interest due to devastating oxidative damage in the pathogenesis and progression of neurodegenerative diseases. Synthetic 8-aminoquinoline antimalarial drug is an attractive pharmacophore in drug development and chemical modification owing to its wide range of biological activities, yet the underlying molecular mechanisms are not fully elucidated in preclinical models for oxidative damage. Herein, the neuroprotective effects of two 8-aminoquinoline-uracil copper complexes were investigated on the hydrogen peroxide-induced human neuroblastoma SH-SY5Y cells. Both metal complexes markedly restored cell survival, alleviated apoptotic cascades, maintained antioxidant defense, and prevented mitochondrial function by upregulating the sirtuin 1 (SIRT1)/3-FOXO3a signaling pathway. Intriguingly, in silico molecular docking and pharmacokinetic prediction suggested that these synthetic compounds acted as SIRT1 activators with potential drug-like properties, wherein the uracil ligands (5-iodoracil and 5-nitrouracil) were essential for effective binding interactions with the target protein SIRT1. Taken together, the synthetic 8-aminoquinoline-based metal complexes are promising brain-targeting drugs for attenuating neurodegenerative diseases.

MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction.

Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.

VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.

Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.

Synthesis of acetamidosulfonamide derivatives with antioxidative and QSAR studies.

A series of sixteen acetamidosulfonamide derivatives (1-16) have been synthesized and investigated for their antioxidant (radical scavenging and superoxide dismutase (SOD)) and antimicrobial activities. Most compounds exhibited antioxidant activities in which compound 15 displayed the most potent radical scavenging and SOD activities. Quantitative structure-activity relationship (QSAR) has been studied using multiple linear regression. The constructed QSAR models displayed high correlation coefficient (Q 2 LOO-CV = 0.9708 and 0.8753 for RSA and SOD activities, respectively), but low root mean square error (RMSE LOO-CV = 0.5105 and 1.3571 for RSA and SOD activities, respectively). The structure-activity relationship showed that an ethylene group connected to pyridine ring provided significant antioxidant activities. The QSAR models give insight into the rational designed of eighty new sulfonamides with various electron donating and withdrawing groups. The top five new designed sulfonamides with nitro group are potential antioxidants to be further developed for medicinal applications.

Identification of the ubiquitin-proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network.

The major mechanism of proteolysis in the cytosol and nucleus is the ubiquitin-proteasome pathway (UPP). The highly controlled UPP has an effect on a wide range of cellular processes and substrates, and flaws in the system can lead to the pathogenesis of a number of serious human diseases. Knowledge about UPPs provide useful hints to understand the cellular process and drug discovery. The exponential growth in next-generation sequencing wet lab approaches have accelerated the accumulation of unannotated data in online databases, making the UPP characterization/analysis task more challenging. Thus, computational methods are used as an alternative for fast and accurate identification of UPPs. Aiming this, we develop a novel deep learning-based predictor named "2DCNN-UPP" for identifying UPPs with low error rate. In the proposed method, we used proposed algorithm with a two-dimensional convolutional neural network with dipeptide deviation features. To avoid the over fitting problem, genetic algorithm is employed to select the optimal features. Finally, the optimized attribute set are fed as input to the 2D-CNN learning engine for building the model. Empirical evidence or outcomes demonstrates that the proposed predictor achieved an overall accuracy and AUC (ROC) value using 10-fold cross validation test. Superior performance compared to other state-of-the art methods for discrimination the relations UPPs classification. Both on and independent test respectively was trained on 10-fold cross validation method and then evaluated through independent test. In the case where experimentally validated ubiquitination sites emerged, we must devise a proteomics-based predictor of ubiquitination. Meanwhile, we also evaluated the generalization power of our trained modal via independent test, and obtained remarkable performance in term of 0.862 accuracy, 0.921 sensitivity, 0.803 specificity 0.803, and 0.730 Matthews correlation coefficient (MCC) respectively. Four approaches were used in the sequences, and the physical properties were calculated combined. When used a 10-fold cross-validation, 2D-CNN-UPP obtained an AUC (ROC) value of 0.862 predicted score. We analyzed the relationship between UPP protein and non-UPP protein predicted score. Last but not least, this research could effectively analyze the large scale relationship between UPP proteins and non-UPP proteins in particular and other protein problems in general and our research work might improve computational biological research. Therefore, we could utilize the latest features in our model framework and Dipeptide Deviation from Expected Mean (DDE) -based protein structure features for the prediction of protein structure, functions, and different molecules, such as DNA and RNA.

Anticancer activity and QSAR study of sulfur-containing thiourea and sulfonamide derivatives.

Sulfur-containing compounds are considered as attractive pharmacophores for discovery of new drugs regarding their versatile properties to interact with various biological targets. Quantitative structure-activity relationship (QSAR) modeling is one of well-recognized in silico tools for successful drug discovery. In this work, a set of 38 sulfur-containing derivatives (Types I-VI) were evaluated for their in vitro anticancer activities against 6 cancer cell lines. In vitro findings indicated that compound 13 was the most potent cytotoxic agent toward HuCCA-1 cell line (IC50 = 14.47 µM). Compound 14 exhibited the most potent activities against 3 investigated cell lines (i.e., HepG2, A549, and MDA-MB-231: IC50 range = 1.50-16.67 µM). Compound 10 showed the best activity for MOLT-3 (IC50 = 1.20 µM) whereas compound 22 was noted for T47D (IC50 = 7.10 µM). Subsequently, six QSAR models were built using multiple linear regression (MLR) algorithm. All constructed QSAR models provided reliable predictive performance (training sets: Rtr range = 0.8301-0.9636 and RMSEtr = 0.0666-0.2680; leave-one-out cross validation sets: RCV range = 0.7628-0.9290 and RMSECV = 0.0926-0.3188). From QSAR modeling, chemical properties such as mass, polarizability, electronegativity, van der Waals volume, octanol-water partition coefficient, as well as frequency/presence of C-N, F-F, and N-N bonds in the molecule are essential key predictors for anticancer activities of the compounds. In summary, a series of promising fluoro-thiourea derivatives (10, 13, 14, 22) were suggested as potential molecules for future development as anticancer agents. Key structure-activity knowledge obtained from the QSAR modeling was suggested to be advantageous for suggesting the effective rational design of the related sulfur-containing anticancer compounds with improved bioactivities and properties.

Interpretable SMILES-based QSAR Model of Inhibitory Activity of Sirtuins 1 and 2.

BACKGROUND: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+-dependent histone deacetylases which play important functional roles in the removal of the acetyl group of acetyllysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. OBJECTIVE: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. METHODS: Simplified molecular-input line-entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The dataset was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration, and external sets. RESULTS: Statistical indices for the evaluation of QSAR models suggested the good statistical quality of models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e., promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved in Sirt1 and Sirt2 inhibitors. CONCLUSION: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

Rational design of novel coumarins: A potential trend for antioxidants in cosmetics.

Coumarins are well-known for their antioxidant effect and aromatic property, thus, they are one of ingredients commonly added in cosmetics and personal care products. Quantitative structure-activity relationships (QSAR) modeling is an in silico method widely used to facilitate rational design and structural optimization of novel drugs. Herein, QSAR modeling was used to elucidate key properties governing antioxidant activity of a series of the reported coumarin-based antioxidant agents (1-28). Several types of descriptors (calculated from 4 softwares i.e., Gaussian 09, Dragon, PaDEL and Mold2 softwares) were used to generate three multiple linear regression (MLR) models with preferable predictive performance (Q 2 LOO-CV = 0.813-0.908; RMSE LOO-CV = 0.150-0.210; Q 2 Ext = 0.875-0.952; RMSE Ext = 0.104-0.166). QSAR analysis indicated that number of secondary amines (nArNHR), polarizability (G2p), electronegativity (D467, D580, SpMin2_Bhe, and MATS8e), van der Waals volume (D491 and D461), and H-bond potential (SHBint4) are important properties governing antioxidant activity. The constructed models were also applied to guide in silico rational design of an additional set of 69 structurally modified coumarins with improved antioxidant activity. Finally, a set of 9 promising newly design compounds were highlighted for further development. Structure-activity analysis also revealed key features required for potent activity which would be useful for guiding the future rational design. In overview, our findings demonstrated that QSAR modeling could possibly be a facilitating tool to enhance successful development of bioactive compounds for health and cosmetic applications.

Discovery of novel halogenated 8-hydroxyquinoline-based anti-MRSA agents: In vitro and QSAR studies.

Methicillin-resistant Staphylococcus aureus (MRSA) infection has been considered to be one of global health problems due to limited classes of effective antimicrobial drugs. Herein, 8-hydroxyquinoline (8HQ) and its derivatives (1-7) were investigated for their anti-MRSA and antioxidant activities. Cloxyquin (2), a halogenated 8HQ, exerted the highest antimicrobial activity (MIC50 ≤ 5.57 µM) with high safety index, whereas an amino-derivative 7 showed the strongest antioxidant activity. Additionally, quantitative structure-activity relationship (QSAR) study demonstrated that mass, polarizability, topological charge, and van der Waals volume are essential properties governing the anti-MRSA activity. Taken together, cloxyquin was highlighted as a promising compound for further development as a novel anti-MRSA agent. QSAR findings would also benefit for further rational design of novel 8HQ-based compounds to combat the MRSA resistance.

Synthesis, molecular docking, and QSAR study of bis-sulfonamide derivatives as potential aromatase inhibitors.

A library of bis-sulfonamides (9-26) were synthesized and tested for their aromatase inhibitory activities. Interestingly, all bis-sulfonamide derivatives inhibited the aromatase with IC50 range of 0.05-11.6 µM except for compound 23. The analogs 15 and 16 bearing hydrophobic chloro and bromo groups exhibited the potent aromatase inhibitory activity in sub-micromolar IC50 values (i.e., 50 and 60 nM, respectively) with high safety index. Molecular docking revealed that the chloro and bromo benzenesulfonamides (15 and 16) may play role in the hydrophobic interaction with Leu477 of the aromatase to mimic steroidal backbone of the natural substrate, androstenedione. QSAR study also revealed that the most potent activity of compounds was governed by van der Waals volume (GATS6v) and mass (Mor03m) descriptors. Finally, the two compounds (15 and 16) were highlighted as promising compounds to be further developed as novel aromatase inhibitors.

Rational Design of Colchicine Derivatives as anti-HIV Agents via QSAR and Molecular Docking.

BACKGROUND: Human immunodeficiency virus (HIV) is an infective agent that causes an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for preventing the progression of the disease is required. OBJECTIVE: This study aims to construct quantitative structure-activity relationship (QSAR) models, molecular docking and newly rational design of colchicine and derivatives with anti-HIV activity. METHODS: A data set of 24 colchicine and derivatives with anti-HIV activity were employed to develop the QSAR models using machine learning methods (e.g. multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular docking. RESULTS: The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed MLR and SVM methods that displayed LOO-CV 2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV set, and Ext 2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design of colchicine derivatives using informative QSAR and molecular docking was proposed. CONCLUSION: These findings serve as a guideline for the rational drug design as well as potential development of novel anti-HIV agents.

Data mining for the identification of metabolic syndrome status.

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

Quantitative Structure-activity Relationship Study of Betulinic Acid Derivatives Against HIV using SMILES-based Descriptors.

BACKGROUND: Human Immunodeficiency Virus (HIV) is the causative agent of Acquired Immunodeficiency Syndrome (AIDS) that imposes a global health burden. Therefore, HIV therapeutic agents have been discovery and development. OBJECTIVE: To construct Quantitative-structure Activity Relationship (QSAR) models of betulinic acid derivatives with anti-HIV activity using Simplified Molecular-Input Line-Entry System (SMILES)- based descriptors. METHODS: A data set of 107 betulinic acid derivatives and their anti-HIV activity was used to develop QSAR models. The SMILES format of the compounds was employed as descriptors for model construction using the CORAL software by means of the Monte Carlo method. RESULTS: Constructed QSAR models provided good correlation coefficients (R2) and root mean square error (RMSE) with values in the range of 0.5660-0.5890 and 0.963-1.020, respectively, for the training set, R2 value of 0.7206-0.7837 and RMSE as 0.609-1.250, respectively, for the calibration set, and R2 value of 0.6257-0.7748 and RMSE as 0.837-0.995, respectively, for the validation set. The best QSAR model displayed statistical parameters for training set: R2 = 0.5660 and RMSE = 0.963; calibration set: R2 = 0.7273 and RMSE = 0.609, and validation set: R2 = 0.7748 and RMSE = 0.972. In addition, features of the molecular structure that are promoters of the endpoint increase and decrease were defined and discussed. These are the basis for the mechanistic interpretation of the suggested models. CONCLUSION: These findings provide useful knowledge for guiding the design of novel compounds with promising anti-HIV activity.

Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study.

Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predisposing factor for the growth of breast cancer cells. As such, ERα represents a lucrative therapeutic target for breast cancer that has attracted wide interest in the search for inhibitory agents. However, the conventional laboratory processes are cost- and time-consuming. Thus, it is highly desirable to develop alternative methods such as quantitative structure-activity relationship (QSAR) models for predicting ER-mediated endocrine agitation as to simplify their prioritization for future screening. In this study, we compiled and curated a large, non-redundant data set of 1231 compounds with ERα inhibitory activity (pIC50). Using comprehensive validation tests, it was clearly observed that the model utilizing the substructure count as descriptors, performed well considering two objectives: using less descriptors for model development and achieving high predictive performance (R Tr 2 = 0.94, Q CV 2 = 0.73, and Q Ext 2 = 0.73). It is anticipated that our proposed QSAR model may become a useful high-throughput tool for identifying novel inhibitors against ERα.

Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study.

A series of 2-amino(chloro)-3-chloro-1,4-naphthoquinone derivatives (1-11) were investigated for their aromatase inhibitory activities. 1,4-Naphthoquinones 1 and 4 were found to be the most potent compounds affording IC50 values 5.2 times lower than the reference drug, ketoconazole. A quantitative structure-activity relationship (QSAR) model provided good predictive performance (R2CV = 0.9783 and RMSECV = 0.0748) and indicated mass (Mor04m and H8m), electronegativity (Mor08e), van der Waals volume (G1v) and structural information content index (SIC2) descriptors as key descriptors governing the activity. To investigate the effects of structural modifications on aromatase inhibitory activity, the model was employed to predict the activities of an additional set of 39 structurally modified compounds constructed in silico. The prediction suggested that the 2,3-disubstitution of 1,4-naphthoquinone ring with halogen atoms (i.e., Br, I and F) is the most effective modification for potent activity (1a, 1b and 1c). Importantly, compound 1b was predicted to be more potent than its parent compound 1 (11.90-fold) and the reference drug, letrozole (1.03-fold). The study suggests the 1,4-naphthoquinone derivatives as promising compounds to be further developed as a novel class of aromatase inhibitors.

Roles of Pyridine and Pyrimidine Derivatives as Privileged Scaffolds in Anticancer Agents.

BACKGROUND: Cancer has been considered to be a global health concern due to the impact of disease on the quality of life. The continual increase of cancer cases as well as the resistance of cancer cells to the existing drugs have driven the search for novel anticancer drugs with better potency and selectivity, improved pharmacokinetic profiles, and minimum toxicities. Pyridine and pyrimidine are presented in natural products and genetic materials. These pyridine/pyrimidine core structures have been noted for their roles in many biological processes as well as in cancer pathogenesis, which make such compounds become attractive scaffolds for discovery of novel drugs. RESULTS & CONCLUSION: In the recent years, pyridine- and pyrimidine-based anticancer drugs have been developed based on structural modification of these core structures (i.e., substitution with moieties and rings, conjugation with other compounds, and coordination with metal ions). Detailed discussion is provided in this review to highlight the potential of these small molecules as privileged scaffolds with attractive properties and biological activities for the search of novel anticancer agents.

Coincidence of obesity associated with cardio-renal abnormalities in Thais obese population.

AIM: To investigate the patterns and relationship among the metabolic syndrome (MS), coronary heart disease (CHD) and kidney function. METHODS: A cross-sectional secondary data set of 9359 individuals, age 30-74 years, receiving annual health check-up in 2012 were used in this studied. Identification of MS and CHD development was determined by International Diabetes Federation criteria and Framingham risk score, respectively, while kidney function was assessed by using the estimate glomerulus filtration rate (eGFR) and chronic kidney disease epidemiology (CKD-EPI) formula. RESULTS: The prevalence of MS was 16.1%. The majority pattern of MS in male displayed abnormalities of body mass index (BMI) plus triglyceride and blood pressure (BP). Most of them had high risk of CHD, and kidney function in stage 1 and 2. Furthermore, abnormalities of BMI plus BP and blood glucose were the main components related to high risk of CHD, and stage 1 of kidney function in female. CONCLUSION: This finding showed the cleared pattern of the sequential abnormality factors which potentially use for setting the activity and empowerment team to prevention, promotion, and treatment strategy in MS patients. Particularly, BMI is the first assessment and then follow by blood pressure and blood sugar which could be used as the guideline for reducing MS associated with CHD and kidney disorder in Thai population.

Determining a new formula for calculating low-density lipoprotein cholesterol: data mining approach.

Low-density lipoprotein cholesterol (LDL-C) is a risk factor of coronary heart diseases. The estimation of LDL-C (LDL-Cal) level was performed using Friedewald's equation for triglyceride (TG) level less than 400 mg/dL. Therefore, the aim of this study is to generate a new formula for LDL-Cal and validate the correlation coefficient between LDL-Cal and LDL-C directly measured (LDL-Direct). A data set of 1786 individuals receiving annual medical check-ups from the Faculty of Medical Technology, Mahidol University, Thailand in 2008 was used in this study. Lipid profiles including total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C) and LDL-C were determined using Roche/Hitachi modular system analyzer. The estimated LDL-C was obtained using Friedewald's equation and the homogenous enzymatic method. The level of TG was divided into 6 groups (TG<200, <300, <400, <500, <600 and < 1000 mg/dL) for constructing the LDL-Cal formula. The pace regression model was used to construct the candidate formula for the LDL-Cal and determine the correlation coefficient (r) with the LDL-Direct. The candidate LDL-Cal formula was generated for 6 groups of TG levels that displayed well correlation between LDL-Cal and LDL-Direct. Interestingly, The TG level was less than 1000 mg/dL, the regression model was able to generate the equation as shown as strong r of 0.9769 with LDL-Direct. Furthermore, external data set (n = 666) with TG measurement (36-1480 mg/dL) was used to validate new formula which displayed high r of 0.971 between LDL-Cal and LDL-direct. This study explored a new formula for LDL-Cal which exhibited higher r of 0.9769 and far beyond the limitation of TG more than 1000 mg/dL and potential used for estimating LDL-C in routine clinical laboratories.

Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification.

Considerable attention has been given on the search for novel anticancer drugs with respect to the disease sequelae on human health and well-being. Triazole is considered to be an attractive scaffold possessing diverse biological activities. Structural modification on the privileged structures is noted as an effective strategy towards successful design and development of novel drugs. The quantitative structure-activity relationships (QSAR) is well-known as a powerful computational tool to facilitate the discovery of potential compounds. In this study, a series of thirty-two 1,2,3-triazole derivatives (1-32) together with their experimentally measured cytotoxic activities against four cancer cell lines i.e., HuCCA-1, HepG2, A549 and MOLT-3 were used for QSAR analysis. Four QSAR models were successfully constructed with acceptable predictive performance affording R CV ranging from 0.5958 to 0.8957 and RMSECV ranging from 0.2070 to 0.4526. An additional set of 64 structurally modified triazole compounds (1A-1R, 2A-2R, 7A-7R and 8A-8R) were constructed in silico and their predicted cytotoxic activities were obtained using the constructed QSAR models. The study suggested crucial moieties and certain properties essential for potent anticancer activity and highlighted a series of promising compounds (21, 28, 32, 1P, 8G, 8N and 8Q) for further development as novel triazole-based anticancer agents.