Streamlining collection of training samples for object detection and classification in video

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Assessment of the relationship between the molecular properties of calcium channel blockers and plasma protein binding data

In this study we investigated the relationship between the calcium channel blockers (CCBs), amlodipine, felodipine, isradipine, nicardipine, nifedipine, nimodipine, nisoldipine, verapamil and diltiazem, and their calculated molecular descriptors: polar surface area (PSA), molecular weight (Mw), volume value (Vol), aqueous solubility data (logS), lipophilicity (logP), acidity (pKa values) and plasma protein binding (PPB) data, obtained from relevant literature. The relationships between the computed molecular properties of selected CCBs and their PPB data were investigated by simple linear regression analysis that revealed very low correlations (R2 lt 0.35). When multiple linear regression (MLR) analysis was applied to investigate reliable correlations between the CCBs' calculated molecular descriptors and PPB data, the best correlations were found for the relationships between CCBs, and PPB data and lipophilicity, and with application of the molecular descriptor (Mw, Vol, or pKa) data as additional independent variables (R2=0.623; R2=0.741; R2=0.657, respectively), with an acceptable probability value (P lt 0.05), confirming that lipophilicity, together with other molecular properties, are essential for the drugs' PPB. We conclude that this could be considered as an additional in vitro approach for modeling CCBs

Relationship between the bioavailability and molecular properties of angiotensin II receptor antagonists

In the present study, we investigated the relationships between several molecular properties and bioavailability data for seven of the most commonly prescribed angiotensin II receptor antagonists (also known as angiotensin II receptor blockers (ARBs) or sartans), candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan and valsartan. The molecular descriptors of ARBs are:, aqueous solubility (logS values), polar surface area (PSA), molecular weight (Mw), volume value (Vol), lipophilicity (logP values) and the acidity descriptor (pK(a1)). The respective descriptors were calculated using four different software packages. The relevant bioavailability data were obtained from literature. Among calculated molecular descriptors, simple linear regression analysis showed the best correlation between bioavailability data and the lipophilicity descriptor, logP (R-2 = 0.568). Multiple linear regression established good correlations between bioavailability and the lipophilicity descriptor, logP, using the molecular weight, Mw, or the acidity descriptor, pK(a1), as an additional, independent variable (with R-2 = 0.661 and 0.682, respectively). Finally, excluding candesartan from the calculations resulted in a very good correlation (R-2 = 0.852) between the remaining ARB bioavailability and molecular descriptors MlogP and Mw as independent variables, determined by multiple linear regression

Evaluation of ACE inhibitors lipophilicity using in silico and chromatographically obtained hydrophobicity parameters

The aim of this study was to compare different calculation methods to determine lipophilicity, expressed as logP value, of seven ACE inhibitors (enalapril, quinapril, fosinopril, lisinopril, cilazapril, ramipril, and benazapril) with significantly different structure. Experimentally determined n-octanol/water partition coefficients, logPO/W values, were obtained from relevant literature. The correlations between all collected logP values were studied and the best agreements between calculated logP and experimentally determined logPO/W values, were observed for KOWWINlogP or MilogP values (r = 0.999 or r = 0.974, respectively). The correlations between all collected logP values and chromatographically (reversed-phase thin-layer chromatography) obtained hydrophobicity parameters, RM0 and C0, were established. The good correlations (r > 0.90) were obtained in the majority of relationships. The KOWWINlogP was established as the most suitable hydrophobicity parameter of investigated group of ACE inhibitors with r = 0.981 for correlation with RM0 and r = 0.977 for correlation with C0 parameters (water-methanol mobile phase). Using multiple linear regressions, it was established that application of two selected logP, calculated by different mathematical approaches, led to very good correlation due to the benefits of both calculation methods. The good relationships indicate that the computed logP, with careful selection of method calculation, can be useful in ACE inhibitors lipophilicity evaluation, as high-throughput screening technique

Employing machine learning to assess the accuracy of near-infrared spectroscopy of spent dialysate fluid in monitoring the blood concentrations of uremic toxins

Hemodialysis (HD) removes nitrogenous waste products from patients’ blood through a semipermeable mem- brane along a concentration gradient. Near-infrared spectroscopy (NIRS) is an underexplored method of monitoring the concentrations of several molecules that reflect the efficacy of the HD process in dialysate samples. In this study, we aimed to evaluate NIRS as a technique for the non-invasive detection of uremic solutes by assessing the correlations between the spectrum of the spent dialysate and the serum levels of urea, creatinine, and uric acid. Blood and dialysate samples were taken from 35 patients on maintenance HD. The absorption spectrum of each dialysate sample was measured three times in the wavelength range of 700-1700 nm, resulting in a dataset with 315 spectra. The artificial neural network (ANN) learn- ing technique was used to assess the correlations between the recorded NIR-absorbance spectra of the spent dialysate and serum levels of selected uremic toxins. Very good correlations between the NIR-absorbance spectra of the spent dialysate fluid with serum urea (R=0.91) and uric acid (R=0.91) and an excellent correlation with serum creatinine (R=0.97) were obtained. These results support the application of NIRS as a non-invasive, safe, accurate, and repetitive technique for online monitoring of uremic toxins to assist clinicians in assessing HD efficiency and individualization of HD treatments

Influence of Enalapril on the progression of chronic renal failure in diabetic nephropathy and nephropathies of and other aethiology: A two-year study

Chronic renal failure (CRF) is almost always associated with high arterial blood pressure. Adequate control of hypertension slows down the progression of the disease, Inhibitors of angiotenzin-converting enzyme (ACE inhibitors) have proved to be very efficacious in decreasing high blood pressure. The aim of this study was to assess the influence of ACE inhibitor enalapril on the progression of CRF in patients with diabetic nephropathy and nephropathies of other origin. During 1998 and 1999 thirty patients (20 males and 10 females, aged 525+1.3) have been followed-up at the Department of Nephrology, Clinical Centre of Serbia. On regular monthly controls serum creatinine, urea, calcium and protein levels, creatinine clearance, and blood pressure, were measured. All patients were suggested a low protein diet. Progression of the disease was expressed by the slope of the regression line showing reciprocal serum creatinine values. Proteinaemia was significantly higher in diabetic patients after 12 months (p<0.35) but in the next 12 months the difference between groups disappeared. The same patients had significantly lower serum urea (p<0.05) after 24 months and creatinine values (p<0.05) dur ing the whole study. Other variables changed in the same manner and with similar progression in both groups. The direction of slope lines suggested recovery of kidney function in both examined groups. However, a smaller slope in patients with diabetic nephropathy together with other results showed that enalapril had better influence on slowing down the progression of CRF in this group of patients

Assessment of the relationship between the molecular properties of calcium channel blockers and plasma protein binding data

In this study we investigated the relationship between the calcium channel blockers (CCBs), amlodipine, felodipine, isradipine, nicardipine, nifedipine, nimodipine, nisoldipine, verapamil and diltiazem, and their calculated molecular descriptors: polar surface area (PSA), molecular weight (Mw), volume value (Vol), aqueous solubility data (logS), lipophilicity (logP), acidity (pKa values) and plasma protein binding (PPB) data, obtained from relevant literature. The relationships between the computed molecular properties of selected CCBs and their PPB data were investigated by simple linear regression analysis that revealed very low correlations (R2<0.35). When multiple linear regression (MLR) analysis was applied to investigate reliable correlations between the CCBs’ calculated molecular descriptors and PPB data, the best correlations were found for the relationships between CCBs, and PPB data and lipophilicity, and with application of the molecular descriptor (Mw, Vol, or pKa) data as additional independent variables (R2=0.623; R2=0.741; R2=0.657, respectively), with an acceptable probability value (P<0.05), confirming that lipophilicity, together with other molecular properties, are essential for the drugs’ PPB. We conclude that this could be considered as an additional in vitro approach for modeling CCBs. [Projekat Ministarstva nauke Republike Srbije, br. TR34031

Relationship between the bioavailability and molecular properties of angiotensin II receptor antagonists

In the present study, we investigated the relationships between several molecular properties and bioavailability data for seven of the most commonly prescribed angiotensin II receptor antagonists (also known as angiotensin II receptor blockers (ARBs) or sartans), candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan and valsartan. The molecular descriptors of ARBs are:, aqueous solubility (logS values), polar surface area (PSA), molecular weight (Mw), volume value (Vol), lipophilicity (logP values) and the acidity descriptor (pKa1). The respective descriptors were calculated using four different software packages. The relevant bioavailability data were obtained from literature. Among calculated molecular descriptors, simple linear regression analysis showed the best correlation between bioavailability data and the lipophilicity descriptor, logP (R2 = 0.568). Multiple linear regression established good correlations between bioavailability and the lipophilicity descriptor, logP, using the molecular weight, Mw, or the acidity descriptor, pKa1, as an additional, independent variable (with R2 0.661 and 0.682, respectively). Finally, excluding candesartan from the calculations resulted in a very good correlation (R2 = 0.852) between the remaining ARB bioavailability and molecular descriptors MlogP and Mw as independent variables, determined by multiple linear regression. [Projekat Ministarstva nauke Republike Srbije, br. TR34031