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Background Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient’s responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features. Results We propose a novel deep neural network model that integrates multi-omics data available as gene expressions, copy number variations, gene mutations, reverse phase protein array expressions, and metabolomics expressions, in order to predict cellular responses to known anti-cancer drugs. We employ a novel graph embedding layer that incorporates interactome data as prior information for prediction. Moreover, we propose a novel attention layer that effectively combines different omics features, taking their interactions into account. The network outperformed feedforward neural networks and reported 0.90 for R 2 values for prediction of drug responses from cancer cell lines data available in CCLE and GDSC. Conclusion The outstanding results of our experiments demonstrate that the proposed method is capable of capturing the interactions of genes and proteins, and integrating multi-omics features effectively. Furthermore, both the results of ablation studies and the investigations of the attention layer imply that gene mutation has a greater influence on the prediction of drug responses than other omics data types. Therefore, we conclude that our approach can not only predict the anti-cancer drug response precisely but also provides insights into reaction mechanisms of cancer cell lines and drugs as well.

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R 2 , Q 2 , and IIC for the validation set of splits 1 to 3 are in the range of 0.7180–0.7755, 0.6891–0.7561, and 0.4431–0.8611 respectively. The numerical result of CR p 2 > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC 50 are recognized and used for the mechanistic interpretation of structural attributes.

Multi-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis. In this paper, we evaluate two network-based approaches for integration of multi-omics data in an application of clinical outcome prediction of neuroblastoma. We derive Patient Similarity Networks (PSN) as the first step for individual omics data by computing distances among patients from omics features. The fusion of different omics can be investigated in two ways: the network-level fusion is achieved using Similarity Network Fusion algorithm for fusing the PSNs derived for individual omics types; and the feature-level fusion is achieved by fusing the network features obtained from individual PSNs. We demonstrate our methods on two high-risk neuroblastoma datasets from SEQC project and TARGET project. We propose Deep Neural Network and Machine Learning methods with Recursive Feature Elimination as the predictor of survival status of neuroblastoma patients. Our results indicate that network-level fusion outperformed feature-level fusion for integration of different omics data whereas feature-level fusion is more suitable incorporating different feature types derived from same omics type. We conclude that the network-based methods are capable of handling heterogeneity and high dimensionality well in the integration of multi-omics.

Malaria, a nocuous disease, which has become a major challenge for the health resulting in deaths of millions of people around the globe. Malaria is a parasitic disease propagated by mosquitoes and infects the human beings. Several species of Plasmodium are responsible for this life-threatening disease and Plasmodium falciparum being the most virulent. In order to eradicate the malarial parasite, the researchers are making consistent efforts in synthesizing new antimalarial drug candidates by paying attention to the various drug targets. In this manuscript, the main focus is on the antimalarial activity of numerous heterocyclic compounds reported by the researchers since 2010 against the different strains of Plasmodium . Antimalarial activities of the two and three nitrogen-containing heterocycles along with their structure–activity relationship are described.

Extensive dispersal of the pesticides to shield the different types of vegetation from pests has increased the production but at the same it has resulted in an increase in environmental pollution. Consequently, it is necessary to eliminate these undesired pollutants from the environment. The current investigation offers the synthesis of humic acid-coated magnetite nanoparticles towards effective removal of the most common insecticide, imidacloprid, from aqueous solution using a batch adsorption method. These synthesized nanoparticles were characterized with the help of several analytical and spectroscopic techniques. To acquire the maximum conceivable adsorption, effects of different influencing parameters like pH of the solution, time of contact, concentration of pesticide solution, amount of adsorbent and temperature were also examined. Moreover, the kinetic studies were found to be in good agreement with a pseudo-second-order kinetic model supporting the occurrence of chemisorption phenomenon. Additionally, isotherm modeling proved that the adsorption process was in accordance with the Langmuir model of isotherm. Thermodynamic parameters depicted the endothermic and spontaneous behavior of the adsorption process. Desorption studies were also carried out to examine the reusability of these nano-adsorbents. These verdicts confirmed that the surface modified magnetite nanoparticles may be treated as proficient material for exclusion of imidacloprid from the aqueous solution.

Introduction Hypertensive disorders of pregnancy (HDP) or prepregnancy overweight/obesity are independently associated with the risk for certain neurodevelopmental and psychiatric disorders in offspring. These two conditions often co‐exist but the risk from combined exposure is unknown. We investigated whether specific subtypes of maternal HDP, along with prepregnancy overweight/obesity, were associated with the distinct risk of neurodevelopmental and psychiatric disorders in offspring during childhood. Material and Methods This prospective, population‐based cohort study used data from 652 732 singleton children born alive in Finland between 2004 and 2014 and followed until 2018. The Cox proportional hazards model was used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI). Results Children exposed to both chronic hypertension and obesity exhibited a 2.4–3.5‐fold higher risk for mood disorders, specific developmental disorder, autism spectrum disorders, and attention‐deficit/hyperactivity disorders. Similarly, exposure to both gestational hypertension and overweight increased the risk for anxiety disorders and attention‐deficit hyperactivity disorders by 2.4‐fold. Meanwhile, combined exposure to preeclampsia and overweight increased the risk of mood and anxiety disorders, specific developmental disorders, and other behavioral disorders, by 1.8–2.2‐fold. The effect size of combined exposure to HDP and overweight/obesity was greater than that of the individual exposure to HDP subtypes or overweight/obesity. Furthermore, overweight/obesity synergistically modified these associations between the HDP subtype exposure and offspring mental disorders, except for specific developmental disorders. Conclusions Our findings suggest that combined exposure to different subtypes of HDP and higher prepregnancy BMI have distinct impacts on the mental health of offspring. Notably, a more pronounced effect was observed in cases where chronic hypertension and obesity coexisted. Future research should focus on exploring dose‐related relationships rather than amalgamating maternal HDP for investigating the offspring outcomes. Combined exposure to maternal prepregnancy with high BMI and hypertension was associated with a higher risk of certain mental disorders than that of individual exposure alone. High BMI status modified the association between maternal hypertension and these offspring mental disorders synergistically.

BackgroundAcyl hydrazones are an important class of heterocyclic compounds promising pharmacological characteristics. Malaria is a life-threatening mosquito-borne blood disease caused by a plasmodium parasite. In some places, malaria can be treated and controlled with early diagnosis. However, some countries lack the resources to do this effectively.ResultsThe present work involves the design and synthesis of some novel acyl hydrazone based molecular hybrids of 1,4-dihydropyridine and pyrazole (5a–g). These molecular hybrids were synthesised by condensation of 1,4-dihydropyridin-4-yl-phenoxyacetohydrazides with differently substituted pyrazole carbaldehyde. The final compound (5) showed two conformations (the major, E, s-cis and the minor, E, s-trans) as revealed by NMR spectral data and further supported by the energy calculations (MOPAC2016 using PM7 method). All the synthesised compounds were screened for their in vitro antimalarial activities against chloroquine-sensitive malaria parasite Plasmodium falciparum (3D7) and antimicrobial activity against Gram positive bacteria i.e. Bacillus cereus, Gram negative bacteria i.e. Escherichia coli and antifungal activity against one fungus i.e. Aspergillus niger. All these compounds were found more potent than chloroquine and clotrimazole, the standard drugs.ConclusionsIn vitro antiplasmodial IC50 value of the most potent compound 5d was found to be 4.40 nM which is even less than all the three reference drugs chloroquine (18.7 nM), pyrimethamine (11 nM) and artimisinin (6 nM). In silico binding study of compound 5d with plasmodial cysteine protease falcipain-2 indicated the inhibition of falcipain-2 as the probable reason for the antimalarial potency of compound 5d. All the compounds had shown good to excellent antimicrobial and antifungal activities.

The QSAR models are employed to predict the anti-proliferative activity of 81 derivatives of flavonol against prostate cancer using the Monte Carlo algorithm based on the index of ideality of correlation (IIC) criterion. CORAL software is employed to design the QSAR models. The molecular structures of flavonols are demonstrated using the simplified molecular input line entry system (SMILES) notation. The models are developed with the hybrid optimal descriptors i.e. using both SMILES and hydrogen-suppressed molecular graph (HSG). The QSAR model developed for split 3 is selected as a prominent model (RValidation2= 0.727, IICvalidation= 0.628, QValidation2= 0.642, and r¯m2=0.615). The model is interpreted mechanistically by identifying the characteristics responsible for the promoter of the increase or decrease. The structural attributes as promoters of increase of pIC50 were aliphatic carbon atom connected to double-bound (C…=…, aliphatic oxygen atom connected to aliphatic carbon (O…C…), branching on aromatic ring (c…(…), and aliphatic nitrogen (N…). The pIC50 of eight natural flavonols with pIC50 more than 4.0, were predicted by the best model. The molecular docking is also performed for natural flavonols on the PC-3 cell line using the protein (PDB: 3RUK).

In the current study, tannic acid-functionalized iron oxide nanoparticles have been synthesized using a cost-effective co-precipitation method and subsequently characterized using various instrumentation techniques such as Fourier transform infrared spectroscopy, X-ray diffractometer, field emission scanning electron microscopy, and thermal gravimetric analysis. Further, these surface-modified magnetite nanoparticles have been used for the adsorption of toluidine dye from an aqueous solution. The adsorption process was accompanied using batch procedure, and influences of several factors such as adsorbent dose, contact time, pH, temperature, and initial concentration of adsorbate were inspected concurrently. The maximum adsorption capacity of tannic acid-functionalized magnetite nanoparticles was found to be 50.68 mg/g. The adsorption process was observed to follow the Temkin isotherm model, whereas the kinetic study was well described by pseudo-second order. The thermodynamic study revealed the adsorption process to be endothermic and spontaneous in nature with a high degree of freedom between adsorbent and adsorbate. Therefore, the study indicated that the tannic acid-functionalized magnetite nanoparticles have promising adsorption capability and can be used as an excellent adsorbent for the removal of toluidine blue O dye from the aqueous solution.

Mobility disability (MD) refers to substantial limitations in life activities that arise because of movement impairments. Although MD is most prevalent in older individuals, it can also affect younger adults. Increasing evidence suggests that inflammation can drive the development of MD and may need to be targeted for MD prevention. Physical exercise has anti-inflammatory properties and has been associated with MD prevention. However, no studies to date have examined whether exercise interventions affect the peripheral inflammatory status in younger adults with MD. To this end, we used blood samples from young and middle-aged adults with MD (N = 38; median age = 34 years) who participated in a 12-week intervention that included aerobic and resistance exercise training. A pre-post assessment of inflammatory biomarkers was conducted in plasma from two timepoints, i.e., before the exercise trial and at follow-up (3–7 days after the last exercise session). We successfully measured 15 inflammatory biomarkers and found that exercise was associated with a significant reduction in levels of soluble fractalkine, transforming growth factor beta 1 (TGF-β1), eotaxin-1 and interleukin (IL) 6 (corrected α = 0.004). We also found significant male-specific effects of exercise on (i) increasing IL-16 and (ii) decreasing vascular endothelial growth factor-A (VEGF-A). In line with our results, previous studies have also found that exercise can reduce levels of TGF-β1, eotaxin-1 and IL-6. However, our finding that exercise reduces plasma levels of fractalkine in younger adults with MD, as well as the sex-dependent findings, have not been previously reported and warrant replication in larger cohorts. Given the suggested role of inflammation in promoting MD development, our study provides additional support for the use of physical exercise as a treatment modality for MD.