Fatty liver in familial hypobetalipoproteinemia: Triglyceride assembly into VLDL particles is affected by the extent of hepatic steatosis

Familial hypobetalipoproteinemia (FHBL) subjects may develop fatty liver. Liver fat was assessed in 21 FHBL with six different apolipoprotein B (apoB) truncations (apoB-4 to apoB-89) and 14 controls by magnetic resonance spectroscopy (MRS). Liver fat percentages were 16.7 ± 11.5 and 3.3 ± 2.9 (mean ± SD) (P = 0.001). Liver fat percentage was positively correlated with body mass index, waist circumference, and areas under the insulin curves of 2 h glucose tolerance tests, suggesting that obesity may affect the severity of liver fat accumulation in both groups. Despite 5-fold differences in liver fat percentage, mean values for obesity and insulin indexes were similar. Thus, for similar degrees of obesity, FHBL subjects have more hepatic fat. VLDL-triglyceride (TG)-fatty acids arise from plasma and nonplasma sources (liver and splanchnic tissues). To assess the relative contributions of each, [2H2] palmitate was infused over 12 h in 13 FHBL subjects and 11 controls. Isotopic enrichment of plasma free palmitate and VLDL-TG-palmitate was determined by mass spectrometry. Nonplasma sources contributed 51 ± 15% in FHBL and 37 ± 13% in controls (P = 0.02). Correlations of liver fat percentage and percent VLDL-TG-palmitate from liver were r = 0.89 (P = 0.0001) for FHBL subjects and r = 0.69 (P = 0.01) for controls. Thus, apoB truncation-producing mutations result in fatty liver and in altered assembly of VLDL-TG

Synthesis, characterization, molecular modeling and anti-algal activities of a Schiff base and its m+2 complexes

In present work Four new complexes of 2-hydroxy-5-methylbenzaldehyde -N-(2-oxo-1,2-dihydro-3H-indol-3-ylidene)hydrazine have been synthesized with some transition metals, i.e. Ni+2, Cu+2 , Co+2 and Zn+2 in non-aqueous medium. Complexes were characterized by magnetic moment, conductance, scanning electron microscopy (SEM) and spectroscopic investigations including infrared, ultraviolet-visible and atomic absorption spectroscopy. To support experimental characterization, quantum mechanical and molecular mechanical (QM/MM) calculations were performed. Experimental results with the support of QM and MM computations highlighted the proposition about the ligand to be bound to the metal ions in a tridentate manner through its phenolic oxygen, azomethine nitrogen and carbonyl group (C=O). On the basis of experimental and computational results, tetrahedral geometry is proposed for Cu+2 complex and distorted tetrahedral geometry is proposed for Zn+2 complex while octahedral geometries are proposed for Co+2 and Ni+2 complexes. For all compounds, anti-cyanobacterial (algicidal) activity was evaluated against three marine cyanobacteria i.e. Pseudoanabaena lonchoides, Lyngbya contorta, and Spirulina major. It was found that the metal complexes are more potent anti-cyanobacterial agents than the ligand

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome

Hepatic steatosis does not cause insulin resistance in people with familial hypobetalipoproteinaemia

Item does not contain fulltextAIMS/HYPOTHESIS: Hepatic steatosis is strongly associated with hepatic and whole-body insulin resistance. It has proved difficult to determine whether hepatic steatosis itself is a direct cause of insulin resistance. In patients with familial hypobetalipoproteinaemia (FHBL), hepatic steatosis is a direct consequence of impaired hepatic VLDL excretion, independently of metabolic derangements. Thus, patients with FHBL provide a unique opportunity to investigate the relation between increased liver fat and insulin sensitivity. METHODS: We included seven male participants with FHBL and seven healthy matched controls. Intrahepatic triacylglycerol content and intramyocellular lipid content were measured using localised proton magnetic resonance spectroscopy ((1)H-MRS). A two-step hyperinsulinaemic-euglycaemic clamp, using stable isotopes, was assessed to determine hepatic and peripheral insulin sensitivity. RESULTS: (1)H-MRS showed moderate to severe hepatic steatosis in patients with FHBL. Basal endogenous glucose production (EGP) and glucose levels did not differ between the two groups, whereas insulin levels tended to be higher in patients compared with controls. Insulin-mediated suppression of EGP during lower dose insulin infusion and insulin-mediated peripheral glucose uptake during higher dose insulin infusion were comparable between FHBL participants and controls. Baseline fatty acids and lipolysis (glycerol turnover) at baseline and during the clamp did not differ between groups. CONCLUSIONS/INTERPRETATION: In spite of moderate to severe hepatic steatosis, people with FHBL do not display a reduction in hepatic or peripheral insulin sensitivity compared with healthy matched controls. These results indicate that hepatic steatosis per se is not a causal factor leading to insulin resistance. TRIAL REGISTRATION: ISRCTN35161775

Seizure episodes detection via smart medical sensing system

Cyber-physical systems (CPS) consist of seamless network of sensors and actuators integrated with physical processes related to human activities. The CPS exploits sensors and actuators to monitor and control different physical process that can affect the computations of the devices. This paper presents the monitoring of physical activities exploiting wireless devices as sensors used in medical cyber-physical systems. Patients undergoing epileptic seizures experience involuntary body movements such as jerking, muscle twitching, falling, and convulsions. The proposed method exploits S-Band sensing used in medical CPS that leverage wireless devices such as omni-directional antenna at the transmitter side, four-beam patch antenna at the receiver side, RF signal generator and vector signal analyzer that perform signal conditioning by providing amplitude and raw phase data. The method uses wireless monitoring and recording system for measurement and classification of a clinical condition (epileptic seizures) versus normal daily routine activities. The data acquired that are perturbations of the radio signal is analyzed as amplitude, phase information, and statistical models. Extracting the statistical features, we leverage various machine learning algorithms such as support vector machine, random forest, and K-nearest neighbor that classify the data to differentiate patient’s various activities such as press-ups, walking, sitting, squatting, and seizure episodes. The performance parameters used in three machine learning algorithms are accuracy, precision, recall, Cohen’s Kappa coefficient, and F-measure. The values obtained using five performance parameters provide the accuracy of more than 90%

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Synthesis, Characterization, In-Vitro Antimicrobial and Antioxidant Activities of Co+2, Ni+2, Cu+2 and Zn+2 Complexes of 3-(2-(2-hydroxy- 3-methoxybenzylidene)hydrazono)indolin-2-one

Four novel complexes of “3-(2-(2-hydroxy-3-methoxybenzylidene)hydrazono)indolin-2-one” have been synthesized with Co+2, Ni+2, Cu+2 and Zn+2. Physical and analytical techniques including CHN, IR, UV-Vis, AAS, molar conductivity values and magnetic susceptibility data were used to characterize all complexes. The bis Schiff base ligand i.e. 3-(2-(2-hydroxy-3-methoxybenzylidene)hydrazono)indolin-2-one, acted as a tridentate ligand and coordinated through phenolic oxygen, azomethine nitrogen and carbonyl group. Low values of molar conductance suggested the non-electrolyte nature of all complexes. Elemental analysis of complexes indicated the 1:1 metal to ligand mole ratios for [Cu(Inh)(OAc)] and [Zn(Inh)(OAc)] metal complexes and 1:2 metal to ligand mole ratios for [Co(Inh)2] and [Ni(Inh)2] metal complexes. Square planner geometry is proposed for [Cu(Inh)(OAc)] and distorted tetrahedral geometry is proposed for [Zn(Inh)(OAc)] while octahedral geometries are proposed for [Co(Inh)2] and [Ni(Inh)2] metal complexes. Antimicrobial and antioxidant studies were performed for all compounds and it was discovered that the complexes are more potent antibacterial and antifungal agents while the ligand exhibited comparatively more DPPH (1,1-diphenyl-2-picryl-hydrazil) radical scavenging activity than the complexes