Explore the vast range of titles available.

This research aimed to design, optimize, and evaluate berberine-laden nanostructured lipid carriers overlaid with chitosan (BER-CTS-NLCs) for efficient brain delivery via the intranasal route. The nanostructured lipid carriers containing berberine (BER-NLCs) were formulated via hot homogenization and ultrasonication strategy and optimized for the influence of a variety of causal variables, including the amount of glycerol monostearate (solid lipid), poloxamer 407 (surfactant) concentration, and oleic acid (liquid lipid) amount, on size of the particles, entrapment, and the total drug release after 24 h. The optimal BER-NLCs formulation was then coated with chitosan. Their diameter, in vitro release, surface charge, morphology, ex vivo permeability, pH, histological, and in vivo (pharmacokinetics and brain uptake) parameters were estimated. BER-CTS-NLCs had a size of 180.9 ± 4.3 nm, sustained-release properties, positive surface charge of 36.8 mV, and augmented ex-vivo permeation via nasal mucosa. The histopathological assessment revealed that the BER-CTS-NLCs system is safe for nasal delivery. Pharmacokinetic and brain accumulation experiments showed that animals treated intranasally with BER-CTS-NLCs had substantially greater drug levels in the brain. The ratios of BER brain/blood levels at 30 min, AUCbrain/AUCblood, drug transport percentage, and drug targeting efficiency for BER-CTS-NLCs (IN) were higher compared to BER solution (IN), suggesting enhanced brain targeting. The optimized nanoparticulate system is speculated to be a successful approach for boosting the effect of BER in treating CNS diseases, such as Alzheimer’s disease, through intranasal therapy.

Aim. This study is aimed at evaluating the use of curcumin-loaded polylactic-co-glycolic acid nanoparticles (CUR-loaded PLGA NPs) as a treatment against monosodium iodoacetate- (MIA-) induced knee OA. Materials and Methods. Eighteen rats were assigned to three groups (n=6), namely, normal control group that received intra-articular injections (IAIs) of saline, an OA control group that received an IAIs of MIA (2 mg/50 μL), and a treatment group (MIA+CUR-loaded PLGA NPs) that received IAIs of CUR-loaded PLGA NPs (200 mg/kg b.wt). Results. The CUR NP treatment against knee OA alleviated radiographic alternations and histopathological changes and inhibited the upregulation in the serum levels of interleukin-1β, tumor necrosis factor-α, interleukin-6, and transforming growth factor-beta and the downregulation in interleukin-10. CUR NP-treated joints also decreased the mRNA expression of nuclear factor-kappa B and inducible nitric oxide synthase and the protein expression of matrix metalloproteinase-13 and caspase-3. Finally, CUR-loaded PLGA NP treatment mitigated the loss of type II collagen, which resulted in a significant reduction in malondialdehyde level and increased the glutathione content and superoxide dismutase activity compared with that of the OA group. Conclusion. This study demonstrated that the administration of CUR NPs could provide effective protection against MIA-induced OA and knee joint histological deteriorated changes due to its anti-inflammatory, antioxidant, and antiapoptotic properties.

Industrial-based application of supercritical CO2 (SCCO2) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C15H18O3) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO2, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10−4, 7.814 × 10−5, and 1.664 × 10−4 for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10−4, 6.197 × 10−5, and 8.777 × 10−5errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x1 = 40.0, x2 = 338.0, Y = 1.27 × 10−3) vector.

Naftazone is a quinone-semi carbazone drug that possesses a strong orange color, and hence it was usually analyzed colorimetrically or by HPLC-UV. However, these methods are not sensitive enough to determine naftazone in biological samples. Naftazone lacks intrinsic fluorescence and does not possess easily derivatizable functional groups. In this contribution, we introduced the first spectrofluorimetric method for naftazone assay through reduction-elicited fluorogenic derivatization through the reduction of its quinone-semicarbazone moiety to the corresponding quinol-semicarbazide derivative by potassium borohydride as a reduction probe. The solvent-dependent fluorescence of the reaction product was studied in various protic and aprotic solvents. Eventually, the fluorescence of the reduced naftazone was measured in 2-propanol at λemission of 350 nm after excitation at λecxitation of 295 nm. The relative fluorescence intensity was linearly correlated to the drug concentration (r = 0.9995) from 10.0 to 500 ng/mL with high sensitivity, where the lower detection limit was 2.9 ng/mL. Hence, the method was effectively applied for naftazone tablets quality control with a mean %recovery of 100.3 ± 1.5, and the results agreed with those of the comparison HPLC-UV method. Furthermore, a new salting-out assisted liquid-liquid extraction (SALLE) method was established for naftazone extraction from human serum, followed by its determination using the developed reduction-based fluorogenic method. The developed SALLE method showed excellent recovery for naftazone from human serum (92.3–106.5%) with good precision (RSD ≤ 6.8%). Additionally, the reaction of naftazone with potassium borohydride was kinetically monitored, and it was found to follow pseudo-first-order kinetics with an activation energy of 43.8 kcal/mol. The developed method’s greenness was approved using three green analytical chemistry metrics.

In the original publication [...]

Novel manipulations of the well-established multivariate calibration models namely; partial least square regression (PLSR) and support vector regression (SVR) are introduced in the presented comparative study. Two preprocessing methods comprising first derivatization and orthogonal projection to latent structures (OPLS) are implemented prior to modeling with PLSR and SVR. Quantitative determination of pyridostigmine bromide (PR) in existence of its two associated substances; impurity a (IMP A) and impurity b (IMP B); was utilized as a case study for achieving comparison. A series consisting of 16 mixtures with numerous percentages of the studied compounds was applied for implementation of a 3 factor 4 level experimental design. Additionally, a series consisting of 9 mixtures was employed in an independent test to verify the predictive power of the suggested models. Significant improvement of predictive abilities of the two studied chemometric models was attained via implementation of OPLS processing method. The root mean square error of prediction RMSEP for the test set mixtures was employed as a key comparison tool. About PLSR model, RMSEP was found 0.5283 without preprocessing method, 1.1750 when first derivative data was used and 0.2890 when OPLS preprocessing method was applied. With regard to SVR model, RMSEP was found 0.2173 without preprocessing method, 0.3516 when first derivative data was used and 0.1819 when OPLS preprocessing method was applied.

These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO 2 ). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO 2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E−09, 9.71E−09, and 3.78E−11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

Multidrug resistance (MDR) pathogens are usually associated with higher morbidity and mortality rates. Flavonoids are good candidates for the development of new potential antimicrobials. This research investigated whether luteolin 4′-neohesperidoside (L4N) has antibacterial and synergistic activities against four antibiotic-resistant pathogens: methicillin-resistant Staphylococcus aureus (MRSA), Klebsiella pneumoniae, fosA-positive shiga toxin producing the Escherichia coli serogroup O111 (STEC O111), and Bacillus cereus. In vitro antimicrobial susceptibility testing revealed highly potent anti-MRSA (MIC of 106.66 ± 6.95 µg/mL), anti-K. pneumoniae (MIC of 53.33 ± 8.47 µg/mL) and anti-STEC O111 (MIC of 26.66 ± 5.23 µg/mL) activities. Significant synergistic combination was clearly noted in the case of gentamycin (GEN) against Gram-negative bacteria. In the case of B. cereus, the combination of vancomycin (VAN) with L4N could efficiently inhibit bacterial growth, despite the pathogen being VAN-resistant (MIC of 213.33 ± 7.9 µg/mL). In vivo evaluation of L4N showed significant decreases in K. pneumoniae and STEC shedding and colonization. Treatment could significantly diminish the levels of pro-inflammatory markers, tumor necrosis factor-alpha (TNF-α), and immunoglobulin (IgM). Additionally, the renal and pulmonary lesions were remarkably enhanced, with a significant decrease in the bacterial loads in the tissues. Finally, this study presents L4N as a potent substitute for traditional antibiotics with anti-STEC O111 and anti-K. pneumoniae potential, a finding which is reported here for the first time.

Sixteen compounds (TR1–TR16) were synthesized and evaluated for their inhibitory activities against monoamine oxidase A and B (MAOs). Most of the derivatives showed potent and highly selective MAO-B inhibition. Compound TR16 was the most potent inhibitor against MAO-B with an IC50 value of 0.17 μM, followed by TR2 (IC50 = 0.27 μM). TR2 and TR16 selectivity index (SI) values for MAO-B versus MAO-A were 84.96 and higher than 235.29, respectively. Compared to the basic structures, the para-chloro substituent in TR2 and TR16 increased the inhibitory activity of MAO-B. TR2 and TR16 were reversible MAO-B inhibitors that were competitive, with Ki values of 0.230 ± 0.004 and 0.149 ± 0.016 µM, respectively. The PAMPA method indicated that compounds TR2 and TR16 had the tendency to traverse the blood–brain barrier. Docking investigations revealed that lead compounds were beneficial for MAO-B inhibition via association with key as well as selective E84 or Y326 residues, but not for MAO-A inhibition via interaction primarily driven by hydrophobic contacts. In conclusion, TR2 and TR16 are therapeutic prospects for the management of multiple neurodegenerative diseases.

Interest in plant-based diets has been on the rise in recent years owing to the potential health benefits of their individual components and the notion that plant-based diets might reduce the incidence of several diseases. Egyptian dukkah and Syrian za’atar are two of the most historic and famous Middle Eastern herbal blends used for their anti-inflammatory, hypolipidemic, and antidiabetic effects. Headspace SPME-GCMS and HPLC-DAD were adopted for characterizing the aroma profile and phenolic compounds of both herbal blends, respectively. Further, vapor-phase minimum inhibitory concentration was employed for assessing each blend’s antibacterial potential, while their antioxidant potential was estimated via in vitro antioxidant assays. SPME headspace analysis indicated the abundance of ethers and monoterpene hydrocarbons, while HPLC revealed the presence of several phenolics including rosmarinic acid, ferulic acid, and rutin. Biological investigations affirmed that vapor-phase of the tested blends exhibited antibacterial activities against Gram-positive and Gram-negative pathogens, while the antioxidant potential of the blends was investigated and expressed as Trolox (125.15 ± 5.92 to 337.26 ± 13.84 μM T eq/mg) and EDTA (18.08 ± 1.62 to 51.69 41 ± 5.33 μM EDTA eq/mg) equivalent. The presented study offers the first insight into the chemical profile and biological activities of both dukkah and za’atar.