[Clinical analysis of 28 cases of calculous pyonephrosis undergoing B-ultrasound-guided renal puncture and drainage followed by secondary percutaneous nephrolithotomy].

Objective: To investigate the therapeutic effects of first phase renal puncture and drainage guided by B ultrasound and second phase percutaneous nephrolithotomy(PCNL) in the treatment of urinary calculi complicated with pyonephrosis. Methods: From January 2014 to April 2018, 28 patients with upper ureteral segment and kidney calculi complicated with pyonephrosis were collected. All patients received the pyonephrosis puncture under B ultrasound. After the inflammation was controlled and the clinical situation improved, the second phase was treated by PCNL. During the operation, routine in dwelling ureteral stent drainage and renal fistula wereperformed. The outcomes of the operation were observed. Results: A total of 28 cases were successfully punctured, the obstruction was relieved and the inflammation was controlled. Additionally, the second phase of PCNL surgery was successful, and there were no significant stone residues after PCNL. There were no complications such as severe systemic inflammatory response syndrome and severe hemorrhage. After 3 to 12 months of follow-up, renal function was restored to varying degrees, and there were no renal failure patients who needednephrectomy. Conclusions: Early diagnosis of urinary calculi complicated with pyonephrosis is the key to successful treatment. Active and effective B ultrasound-guided renal puncture and drainage, drainage of pus, and removal of urinary obstruction can improve the safety of the second phase of PCNL, and thus it attaches great importance to the treatment of pyonephrosis.

[Relationship between intake volume of carbohydrates solution and gastric emptying time in termed parturients].

Objective: To investigate the relationship between intake volume of carbohydrates solution (CHO) and gastric emptying time in termed parturient, thus to optimize fasting time before anesthesia. Methods: This study was a prospective study. From May to July in 2016, a total of 100 termed parturients in Huangyan Hospital of Wenzhou Medical University, were divided into four groups equally by means of random number table: D1 (200 ml), D2 (300 ml), D3 (400 ml) and D4 (500 ml), based on the different intake volume of 12.5% CHO. All the parturients had been fasted preoperatively before the treatment of oral CHO. The cross-sectional area of gastric antrum (CSA) was measured with an ultrasound machine at baseline (T(0)), 3 min (T(1)), 30 min (T(2)), 60 min (T(3)), 90 min (T(4)), 120 min (T(5)) and 150 min (T(6)) after oral CHO, respectively. Gastric emptying in this study is defined as CSA at any time point is equal to or less than that at T(0). Results: Measurement was failed in eight parturients because of poor imaging quality, two each in D1 and D2 group and four in D4 group. The cubic curve was the best model for gastric emptying among several candidate models estimated with SPSS software, with R(2)>0.9 in all groups. Both the cubic curve and Kaplan-Meier curve indicated that gastric emptying time was prolonged as intake volume increased. Gastric emptying times in D1, D2, D3 and D4 were (76.96±17.69), (96.52±20.14), (109.20±14.70) and (122.86±16.17) min respectively, the difference was significant (F=50.471, P<0.001). All parturients in D1, D2 and D3 group had been in the status of gastric emptying 120 min after oral CHO, and all parturients in D4 group had been in the status of gastric emptying either 150 min after oral CHO. Conclusion: Gastric emptying time will be within two hours when 400 ml or less oral CHO is taken by termed parturient, and it will be prolonged as intake volume increases.

Prediction of plasma protein binding of cephalosporins using an artificial neural network.

An artificial neural network model is developed to predict the fraction of cephalosporins bound to plasma proteins (f(b)) from their molecular structural parameters. These molecular structural parameters are the molecular weight (MW), the surface area occupied by oxygen and nitrogen atoms (S(O),N), and the surface area occupied by hydrogen atoms attached to oxygen or nitrogen atoms (S(H)). For a training set of 20 cephalosporins and a test set of 3 cephalosporins, root mean squared errors (RMSE) between experimental fb values and calculated/predicted fb values are 0.036 and 0.045, respectively.

Anti-apoptotic effect and the mechanism of orientin on ischaemic/reperfused myocardium.

We investigated the anti-apoptotic effect of orientin, from bamboo leaves (Phyllostachys nigra), on rat heart after treatment with ischemia/reperfusion (I/R), and on rat cardiomyocytes injured by hypoxia/reoxygenation (H/R). I/R injury was induced by occluding the left anterior descending coronary artery for 45 min and restoring perfusion for 240 min. Orientin (0.5, 1.0 and 2.0 mg kg(-1)) or its vehicle was injected i.v. 10 min prior to ischemia. Cultured cardiomyocytes were subjected to hypoxia for 120 min, then reoxygenated for 60 min to induce H/R. Vehicle or orientin (3, 10, 30 micromol l(-1) was added 10 min before hypoxia and reoxygenated. TUNEL assay and DNA fragmentation assay demonstrated that myocardium apoptosis was attenuated by pretreatment with orientin (0.5, 1.0 and 2.0 mg kg(-1). Flow cytometric analysis also showed that apoptosis of cardiomyocytes was reduced by pretreatment with orientin (3, 10 and 30 micromol l(-1)). In addition, results of immunohistochemistry and Western blot analysis showed that orientin increased the expression of bcl-2 and reduced Bax expression, resulting in up-regulation of the bcl-2/Bax ratio. Cytochrome c (Cyt-c) and caspase-3 expression was also reduced in myocardium and cardiomyocytes injured by I/R and H/R. These observations indicate that orientin exerts a potent cardioprotective effect on I/R- and H/R-treated myocardium and cardiomyocytes, and inhibits apoptosis by preventing activation of the mitochondrial apoptotic pathway (cytochrome c-caspase-3).

Branched-chain alkanols as skin permeation enhancers: quantitative structure-activity relationships.

One long-standing approach for improving transdermal drug delivery is using penetration enhancers which reversibly decrease the skin barrier resistance. Though the skin permeation enhancement effect of chemical penetration enhancers has been studied extensively, their quantitative structure-activity relationships have not been adequately investigated. In this paper, we established the correlation equations between enhancement potencies and the physico-chemical parameters relevant to lipophilicity and position of hydroxyl group for 16 alkanols using the stepwise multiple linear regression analysis. These equations reveal that the enhancement potencies of alkanols are excellently correlated with their lipophilicity and position of the hydroxyl group. The enhancement potency of an alkanol will increase when it has greater lipophilicity but will decrease as the hydroxyl group moves from the end of the alkyl chain towards the center.

Prediction of human intestinal absorption using an artificial neural network.

An artificial neural network model is developed to predict percent human intestinal absorption (%FA) of compounds from their molecular structural parameters. These parameters are the polar molecular surface area (PSA), the fraction of polar molecular surface area (FPSA, polar molecular surface area/ molecular surface area), the sum of the net atomic charges of oxygen atoms (Q(O)), the sum of the net atomic charges of nitrogen atoms with net negative atomic charges (Q(N)), the sum of the net atomic charges of hydrogen atoms attached to oxygen or nitrogen atoms (Q(H)), and the number of carboxyls (nCOOH). For a training set of 85 compounds anda test set of 10 compounds, root mean squared errors (RMSE) between experimental %FA valuesand calculated/predicted %FA values are 8.86% and 14.1%, respectively.

A simple predictive model for blood-brain barrier penetration.

A simple two-descriptor model to predict blood-brain barrier penetration is derived from a training set of 79 compounds: log BB = - 13.31V2 + 9.601V - 2.231PSA - 0.5290 (n = 79, r2 = 0.83) where log BB is the logarithm of the ratio of the steady-state concentration of the compound in the brain to in the blood, V (nm3) is the molecular volume, PSA (nm2) is the polar surface area which is defined as the sum of the van der Waals surface areas of oxygen atoms, nitrogen atoms, and attached hydrogen atoms in a molecule, n is the number of compounds, and r is the correlation coefficient. The model is validated by a leave-one-out procedure and an external test set (25 compounds). The results indicate that the model developed is statistically sound and is sufficiently reliable and robust for predictive use. The descriptors in the model can be easily computed and it is suitable for the rapid prediction of the blood-brain barrier penetration for a wide range of drug candidates.

A mathematical model to predict the release of water-soluble drugs from HPMC matrices.

A mathematical model to predict the fraction of water-soluble drug released as a function of release time (t, h), HPMC concentration (CH, w/w), and volume of drug molecule (V, nm3) was derived with ranitidine hydrochloride, diltiazem hydrochloride, and ribavirin as model drugs. The model is log (M(t)/M(infinity)) = 0.5 log t-0.3322CH-0.2222V-0.2988 (n = 140, r = 0.9848), where M(t) is the amount of drug released at time t, M(infinity) is the amount of drug released over a very long time, which corresponds in principle to the initial loading, n is the number of samples, and r is the correlation coefficient. The model was validated using isoniazid and satisfactory results were obtained. The model can be used to predict the release fraction of various soluble drugs from HPMC matrices having different polymer levels.

A predictive model for the release of slightly water-soluble drugs from HPMC matrices.

A model to predict the fraction of slightly water-soluble drug released as a function of release time (t, h), HPMC concentration (C(H), w/w), drug solubility in distilled water at 37 degrees C (C(s), g/100 mL), and volume of drug molecule (V, nm3) was derived when theophyline, tinidazole, and propylthiouracil were selected as model drugs. The model is log (M(t)/M(infinity)) = 0.8683 logt-0.1930C(s) logt + 0.5406V logt-1.227C(H) + 0.1594C(s) + 0.4423C(H)C(s) - 0.8655 (n = 130, r = 0.9969), where Mt is the amount of drug released at time t, Minfinity is the amount of drug released over a very long time, which corresponds in principle to the initial loading, n is the number of samples, and r is the correlation coefficient. The model was validated using sulfamethoxazole and satisfactory results were obtained. The model can be used to predict the release fraction of variousslightly water-soluble drugs from HPMC matrices having different polymer levels.

Limitation of Potts and Guy's model and a predictive algorithm for skin permeability including the effects of hydrogen-bond on diffusivity.

The Potts and Guy's model for skin permeability, log P = alpha log K - beta MV + delta where P is the permeability coefficient of a compound from aqueous solution through human skin in vitro, K and MV are octanol-water partition coefficient and molecular volume of the compound respectively, and alpha, beta, delta are constants, is examined for a data set of 53 miscellaneous compounds. The model will result in over-estimation for penetrants having higher hydrogen-bond donor activity and underestimation for penetrants having no hydrogen-bond donor. A predictive algorithm for skin permeability including the effects of hydrogen-bond on diffusivity is proposed: log P = alpha log K - beta MV - gamma Hb + delta where Hb is the descriptor of hydrogen-bonding capacity of penetrants and gamma is a constant. The calculated log P values from the latter model are in good accordance with respective experimental ones for the data set.

Predicting blood-brain barrier penetration of drugs using an artificial neural network.

An artificial neural network model is developed to predict the ratios of the steady-state concentrations of drugs in the brain to those in the blood (log BB) from their molecular structural parameters. These molecular structural parameters are the molecular volume (V), the sum of the absolute values of the net atomic charges of oxygen and nitrogen atoms which are hydrogen-bond acceptors (Q(O, N)), and the sum of the net atomic charges of hydrogen atoms attached to oxygen or nitrogen atoms (Q(H)). For a training set of 56 compounds and a test set of 5 compounds, root mean squared errors (RMSE) between experimental log BB values and calculated/predicted log BB values were 0.236 and 0.258, respectively. These molecular structural parameters can be obtained easily from quantum chemical calculations. The model is suitable for the rapid prediction of the blood-brain barrier penetration of drugs.

Prediction of drug release from HPMC matrices: effect of physicochemical properties of drug and polymer concentration.

A working equation to predict drug release from hydroxypropyl methylcellulose (HPMC) matrices was derived using a training set of HPMC matrices having different HPMC concentration (w/w, 16.5-55%) and different drugs (solubilities of 1.126-125.5 g/100 ml in water and molecular volumes of 0.1569-0.4996 nm(3)). The equation was log(M(t)/M( infinity ))=-0.6747+1.027 log t -0.1759 (log C(s)) log t +0.4027 (log V) log t -1.041C(H) +0.3213 (log C(s)) C(H) -0.4101 (log V) C(H) -0.3521 (log V) log C(s) (n=263, r=0.9831), where M(t) is the amount of drug released at time t, M( infinity ) the amount of drug released over a very long time, which corresponds in principle to the initial loading, t the release time (h), C(s) the drug solubility in water (g/100 ml), V the volume of drug molecule (nm(3)), and C(H) is HPMC concentration (w/w). The benefit of the novel model is to predict M(t)/M( infinity ) values of a drug from formulation and its physicochemical properties, so applicable to the HPMC matrices of different polymer levels and different drugs including soluble drugs and slightly soluble drugs.

Predicting blood-brain barrier penetration of drugs by polar molecular surface area and molecular volume.

AIM: To predict the blood-brain barrier penetration by polar molecular surface area and molecular volume. METHODS: Polar molecular surface area and molecular volume are calculated by Monte Carlo method from the lowest energy conformation obtained using the semiempirical self-consistent field molecular orbital calculation AM1 method. The stepwise multiple regression analysis is used to derive the correlation equations between the ratios of the steady-state concentrations of the training compounds in the brain to in the blood (logBB)and their structural parameters. RESULTS: For a training set of 56 compounds, logBB values are well correlated with the sums of surface areas of oxygen and nitrogen atoms (SO,N, A2, excluding the nitrogen atoms in nitrogen molecule or in nitro) and molecular volumes (V, A3). The regression equation is logBB = -1.331 x 10(-5)V2 + 9.228 x 10(-3)V -0.02439 SO,N -0.4318 (n = 56, r = 0.9043). The calculated logBB values of a test set of 10 compounds from the model agree well with their experimental logBB values. CONCLUSION: The model is simple and effective. It can be used to predict the logBB values of candidate molecule in drug design.

[Predicting skin permeability of drugs with theoretical parameters].

AIM: To predict skin permeability of drugs with theoretical parameters. METHODS: The semiempirical self-consistent field molecular calculation AM1 method is utilized to obtain the structural parameters of drug molecules. Stepwise multiple regression analysis or BP network is then utilized to establish the correlation between skin permeability of drugs and their structural parameters. RESULTS: The calculated human skin permeability coefficients (kp) of 22 model drugs in vitro or the R values (R = absorbed/unabsorbed) of 17 drugs in vivo are in good agreement with their observed values. CONCLUSION: Theoretical parameters can be used to predict skin permeability of drugs.