Predicting Memory Score Using Paralinguistic Features.

An automated method of assessing short term memory can act as a dementia risk predictor, as poor short-term memory is strongly linked to early signs of dementia. While previous works show the feasibility of using speech to predict healthy and diagnosed dementia participants, there are still gaps in predicting 'dementia risk' and clear difficulties distinguishing early dementia with regular ageing. We extracted paralinguistic features from audio of individuals completing an over the phone episodic memory test, LOGOS. These paralinguistic features were used to discriminate between those with strong and poor short term memory performance. This work also explored various feature selection methods and tested this method across multiple datasets. Our best result was achieved using a Support Vector Machine (SVM) classifier, obtaining accuracy of 84% per audio recording.Clinical relevance- This work establishes the efficacy of using speech from older participants completing the LOGOS episodic memory test to estimate risk of dementia.

Domain and Patient Adversarial Multi-Task Learning for Arrhythmia Classification.

Manual screening of electrocardiograms (ECGs) for heart arrhythmias by clinicians is time-consuming and labor-intensive. A machine learning model for the automated diagnosis of heart arrhythmia from ECG signals can facilitate improved diagnosis, greater accessibility and earlier intervention for patients. The potential of such models is limited however by the small size of clinical datasets available for training. Methods that can be trained with multiple datasets to classify heart arrhythmia are needed to overcome this problem.In this paper, we propose using adversarial multi-task learning (AMTL) to extract domain and patient invariant features from two electrocardiogram databases. We further investigated the influence of beat segmentation location and beat normalization on domain invariance. Our proposed methods were tested on the MIT-BIH Arrhythmia and the St Petersburg INCART 12-lead Arrhythmia Databases. The domain adversarial models achieved a higher accuracy and average F1 score than their counterparts without domain adversarial learning. In particular, the patient and domain adversarial model improved the F1 scores on the two tested databases from 70% and 74% to 77% each.Clinical Relevance-This establishes that adversarial multitask learning with multiple datasets and multiple adversarial tasks can improve the F1 score of arrhythmia classification.

Response of Diverse Peanut Cultivars to Nano and Conventional Calcium Forms under Alkaline Sandy Soil.

Calcium is one of the most limiting factors for the growth and reproduction of peanut, which ultimately affects pod and seed yields. A two-year field experiment was carried out to assess the impact of five calcium applications, including nano-calcium and conventional forms, on growth, leaf nutrient content, yield traits, and quality parameters of three diverse peanut cultivars (Ismailia-1, Giza-5, and Giza-6). The applied calcium applications were calcium sulfate, which is recommended for commercial peanut cultivation and commonly referred to as gypsum (coded as Ca-1), calcium nitrate (Ca-2), nano-calcium nitrate (Ca-3), 50% calcium nitrate + 50% nano-calcium (Ca-4), and 50% calcium sulfate + 50% nano-calcium (Ca-5). Calcium sulfate (gypsum, Ca-1) was soil-supplied during the seedbed preparation as recommended, while the other calcium applications (Ca-2, Ca-3, Ca-4, and Ca-5) were exogenously sprayed three times at 30, 45, and 60 days after sowing. The soil of the experimental site was alkaline, with a high pH of 8.6. The results revealed significant differences among cultivars, calcium applications, and their interactions. The soil-supplied gypsum Ca-1 displayed lower agronomic performance on all recorded growth, leaf nutrient content, yield traits, and quality parameters. On the other hand, the foliar-supplied calcium, particularly Ca-4 and Ca-5, displayed superior effects compared to the other simple calcium forms. Ca-4 and Ca-5 produced significantly higher seed yield (3.58 and 3.38 t/ha) than the simple recommended form (Ca-1, 2.34 t/ha). This could be due to the difficulty of calcium uptake from soil-supplied calcium under high soil pH compared to the exogenously sprayed nano-calcium form. Moreover, the superior performance of Ca-4 and Ca-5 could be caused by the mixture of fertilizers from the synergistic effect of calcium and nitrate or sulfate. Furthermore, the effect of nitrate was applied in nano form in the Ca4 and Ca-5 treatments, which contributed to improving nutrient uptake efficiency and plant growth compared to the other treatments. The peanut cultivar Giza-6 showed superiority for most measured traits over the other two cultivars. The interaction effect between the assessed cultivars and calcium applications was significant for various traits. The cultivar Giza-6 showed a significant advantage for most measured traits with the mixture of 50% calcium nitrate + 50% nano-calcium (Ca-4). Conclusively, the results pointed out the advantage of the exogenously sprayed nano-calcium form combined with calcium nitrate or calcium sulfate for promoting growth, leaf nutrient content, yield, and quality traits of peanut, particularly with high-yielding cultivars under sandy soil with high pH.

Influence of Channel Selection and Subject's Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms.

The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants' age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1-52 weeks).

An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech.

Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural network (DNN)-based classification tool are evaluated. Sixteen children with typical development (TD) and 26 with CAS produced 50 polysyllabic words. Words with strong-weak (SW, e.g., dinosaur) or WS (e.g., banana) stress were fed to the classification tool, and the accuracy measured (a) against expert judgment, (b) for speaker group, and (c) with/without prior knowledge of phonemic errors in the sample. The influence of segmental features and participant factors on tool accuracy was analysed. Linear mixed modelling showed significant interaction between group and stress type, surviving adjustment for age and CAS severity. For TD, agreement for SW and WS words was >80%, but CAS speech was higher for SW (>80%) than WS (~60%). Prior knowledge of segmental errors conferred no clear advantage. Automatic lexical stress classification shows promise for identifying errors in children's speech at diagnosis or with treatment-related change, but accuracy for WS words in apraxic speech needs improvement. Further training of algorithms using larger sets of labelled data containing impaired speech and WS words may increase accuracy.

Roles of Exogenous α-Lipoic Acid and Cysteine in Mitigation of Drought Stress and Restoration of Grain Quality in Wheat.

Cysteine (Cys) and α-lipoic acid (ALA) are naturally occurring antioxidants (sulfur-containing compounds) that can protect plants against a wide spectrum of environmental stresses. However, up to now, there are no conclusive data on their integrative roles in mitigation of drought stress in wheat plants. Here, we studied the influence of ALA at 0.02 mM (grain dipping pre-cultivation treatment) and Cys (25 and 50 ppm as a foliar application) under well watered and deficit irrigation (100% and 70% of recommended dose). The results showed that deficit irrigation markedly caused obvious cellular oxidative damage as indicated by elevating the malondialdehyde (MDA) and hydrogen peroxide content (H2O2). Moreover, water stressed plants exhibited multiple changes in physiological metabolism, which affected the quantitative and qualitative variables of grain yield. The enzymatic antioxidants, including superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT) and peroxidase (POX) were improved by Cys application. SOD and APX had the same response when treated with ALA, but CAT and POX did not. Moreover, both studied molecules stimulated chlorophyll (Chl) and osmolytes' biosynthesis. In contrast, the Chl a/b ratio was decreased, while flavonoids were not affected by either of the examined molecules. Interestingly, all above-mentioned changes were associated with an improvement in the scavenging capacity of reactive oxygen species (ROS), leaf relative water content (RWC), grain number, total grain yield, weight of 1000 kernels, gluten index, falling number, and alveographic parameters (P, W, and P/L values). Furthermore, heatmap plot analysis revealed several significant correlations between different studied parameters, which may explore the importance of applied Cys and ALA as effective compounds in wheat cultivation under water deficit conditions.

Adversarial Multi-Task Learning for Robust End-to-End ECG-based Heartbeat Classification.

In clinical practice, heart arrhythmias are manually diagnosed by a doctor, which is a time-consuming process. Furthermore, this process is error-prone due to noise from the recording equipment and biological non-idealities of patients. Thus, an automated arrhythmia classifier would be time and cost-effective as well as offer better generalization across patients. In this paper, we propose an adversarial multitask learning method to improve the generalization of heartbeat arrythmia classification. We built an end-to-end deep neural network (DNN) system consisting of three sub-networks; a generator, a heartbeat-type discriminator, and a subject (or patient) discriminator. Each of these two discriminators had its own loss function to control its impact. The generator was "friendly" to the heartbeat-type discrimination task by minimizing its loss function and "hostile" to the subject discrimination task by maximizing its loss function. The network was trained using raw ECG signals to discriminate between five types of heartbeats - normal heartbeats, right bundle branch blocks (RBBB), premature ventricular contractions (PVC), paced beats (PB) and fusion of ventricular and normal beats (FVN). The method was tested with the MIT-BIH arrhythmia dataset and achieved a 17% reduction in classification error compared to a baseline using a fully-connected DNN classifier.Clinical Relevance-This work validates that it is possible to develop a subject-independent automated heart arrhythmia detection system to assist clinicians in the diagnosis process.

A Two Stage Approach for the Automatic Detection of Insomnia.

Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exist that can assist clinicians in the timely detection of insomnia. In this paper, we propose a two stage approach to automatically detect insomnia from an overnight EEG recording. In the first stage we trained a sleep stage scoring model and an epoch-level insomnia detection model. Both models are deep neural network (DNN)- based which are fed by a set of temporal and spectral features derived from 2 EEG channels. In the second stage we computed two subject-level feature sets. One is computed using the output of the sleep stage scoring model and consists of the sleep stage ratios, the stage pair ratios and the stage transition ratios. The second feature set is derived from the output of the epoch-level insomnia detection model and represents the ratio of detected insomniac epochs in each stage and their average posterior probability. These features are then used to train a final binary classifier to classify each subject as control, i.e., with no sleep complaints, or insomniac. We compared 5 different binary classifiers, namely the linear discriminant analysis (LDA), the classification and regression trees (CART) and the support vector machine (SVM) with linear, Gaussian and sigmoid kernels. The system was evaluated against data collected from 115 participants, 61 control and 54 with insomnia, and achieved $F1$ score, sensitivity and specificity of 0.88, 84% and 91% respectively.

Comparing two insomnia detection models of clinical diagnosis techniques.

Sleep disorders are becoming increasingly prevalent in society. However most of the burgeoning research on automated sleep analysis has been in the realm of sleep stage classification with limited focus on accurately diagnosing these disorders. In this paper, we explore two different models to discriminate between control and insomnia patients using support vector machine (SVM) classifiers. We validated the models using data collected from 124 participants, 70 control and 54 with insomnia. The first model uses 57 features derived from two channels of EEG data and achieved an accuracy of 81%. The second model uses 15 features from each participant's hypnogram and achieved an accuracy of 74%. The impetus behind using these two models is to follow the clinician's diagnostic decision-making process where both the EEG signals and the hypnograms are used. These results demonstrate that there is potential for further experimentation and improvement of the predictive capability of the models to help in diagnosing sleep disorders like insomnia.

Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis.

Effective sleep analysis is hampered by the lack of automated tools catering to disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channels to accurately differentiate between patients with insomnia or controls with no sleep complaints. We investigated two different approaches to achieve this. The first approach used EEG data from the whole sleep recording irrespective of the sleep stage (stage-independent classification), while the second used only EEG data from insomnia-impacted specific sleep stages (stage-dependent classification). We trained and tested our system using both healthy and disordered sleep collected from 41 controls and 42 primary insomnia patients. When compared with manual assessments, an NREM + REM based classifier had an overall discrimination accuracy of 92% and 86% between two groups using both two and one EEG channels, respectively. These results demonstrate that deep learning can be used to assist in the diagnosis of sleep disorders such as insomnia.

Cleft palate reconstruction using collagen and nanofiber scaffold incorporating bone morphogenetic protein in rats.

BACKGROUND: Absorbable collagen sponge (ACS) loaded with bone morphogenetic protein-2 (BMP-2) is approved for selected clinical applications; however, burst release limits its widespread use. Therefore, nanofiber (NF)-based scaffold with ACS backbone was developed to sustain release of loaded BMP-2 to improve the outcomes of bone grafting in a rodent model of cleft palate. METHODS: BMP-2 was loaded on ACS scaffold and then NF hydrogel with different densities (1-2%) was added to sustain the BMP-2 release. The release profiles of BMP-2 from constructs with different NF densities were evaluated in vitro to explore the optimum NF density that could recapitulate physiological bone healing process. Subsequently, scaffold with the appropriate NF density was implanted into a rodent model of cleft palate. Wistar rats, with surgically induced maxillary cleft defects, were then assigned to one of the following groups (n=6/group): no scaffold (control), ACS, ACS+BMP-2, NF+ACS, and NF+ACS+BMP-2. Micro-computed tomography (µCT) was utilized to evaluate percent bone filling (%BF) at defect site as well as changes in anteroposterior and transverse dimensions of the maxilla at weeks 0, 4, and 8. Histological assessment of bone healing was performed at week 8. RESULTS: In vitro release experiments showed that scaffolds containing 2% NF exhibited a release profile conducive to the natural stages of bone healing and, hence, it was utilized for subsequent in vivo studies. Bone healing occurred at the defect margins leaving a central bone void in the control, ACS, and NF+ACS groups over the 8-week study period. BMP-2-treated groups demonstrated higher %BF as compared with other groups at week 8 (p<0.05). Whereas the NF+ACS+BMP-2 group showed bone bridging of the defect as early as 4 weeks, which was not evident in ACS+BMP-2 group. In all groups, bone grafts did not disrupt anteroposterior and transverse growth of maxilla. Based on histological evaluations together with µCT data, NF+ACS+BMP-2 treatment resulted in clinically significant and consistent bone healing throughout the implanted scaffold when compared with the ACS+BMP-2 group. CONCLUSION: NF+ACS+BMP-2 constructs exhibited osteoinductive properties together with preparation simplicity, which makes it a novel approach for BMP-2 delivery for cleft palate reconstruction.

Polymeric micelles for pH-responsive delivery of cisplatin.

Methoxy poly(ethylene oxide)-block-poly-(α-carboxylate-ε-caprolactone) (PEO-b-PCCL) was used to develop pH-responsive polymeric micelles for the delivery of cisplatin (CDDP). Micelles were prepared through complexation of CDDP with the pendant carboxyl groups on the poly(ε-caprolactone) core, perhaps through coordinate bonding. The obtained micelles were characterized using dynamic light scattering (DLS) measurement for size and stability. The in vitro release of CDDP at different pHs (7.4, 6.0 and 5.0) was evaluated. The in vitro cell uptake as well as cytotoxicity of developed micelles against two breast cancer cell lines, i.e. MDA-MB-435 and MDA-MB-231, were also assessed and compared to free CDDP as control. DLS results showed PEO-b-PCCL to form stable micelles with an average diameter of <50 nm upon complexation with CDDP. Developed polymeric micelles were capable of slowly releasing CDDP in physiological pH. However, CDDP release from polymeric micelles was triggered upon exposure to electrolytes and/or acidic pHs mimicking that of extracellular tumor microenvironment or intracellular organelles. Consistent with the slow release of CDDP from its polymeric micellar formulation, polymeric micellar CDDP exhibited lower cytotoxicity and CDDP intracellular uptake compared to free drug. The results indicate a great potential for the developed formulation in platinum therapy of breast cancer.

Peptide modified polymeric micelles specific for breast cancer cells.

The specific targeting ability of novel breast cancer targeting peptides as ligands coupled to polymeric micelles was evaluated in the present study. In this context, engineered breast cancer cell targeting peptides, denoted as peptide 11 (RGDPAYQGRFL) and peptide 18 (WXEAAYQRFL), were compared with the lead 12-mer p160 peptide and cyclic RGDfK peptide. All four peptides were conjugated individually to poly(ethylene oxide)-b-poly(caprolactone) (PEO-b-PCL) diblock polymeric micelles to obtain targeted carrier systems PM11, PM18, PM 160, and PM c-RGD. Physical blending of the peptides 11 and 18 with PEO-b-PCL was also done to yield combination micelles, comPM11 and comPM18. The structural confirmation of polymer was carried out using (1)H NMR and MALDI-TOF, and the size distribution and zeta potential of the micelles were determined using dynamic light scattering. Lipophilic cyanine fluorescent probe DiI was physically incorporated in the polymeric micelles to imitate the hydrophobic drug loaded in the micellar core. The cellular uptake of DiI-loaded peptide-containing polymeric micelles by MDA-MB-435, MDA-MB-231, and MCF7 breast cancer cell lines, as well as HUVEC and MCF10A noncancerous cells, were analyzed using flow cytometry and confocal microscopy techniques. Modification of polymeric micelles with peptide 11 or 18 led to an increase in micellar uptake specifically in breast cancer cells compared to p160, c-RGD modified, or naked micelles. The peptide-micelle combinations (comPM11 and comPM18) displayed better uptake by the cells compared to the covalently conjugated PM11 and PM18 micelles; however, the combinations were less selective toward cancer cells. The results point to a potential for peptides 11- and 18-micelle conjugates as attractive platforms for improved performance of a wide range of chemotherapeutic drugs and/or imaging agents in cancer therapy and diagnosis.

Engineered breast tumor targeting peptide ligand modified liposomal doxorubicin and the effect of peptide density on anticancer activity.

This study aimed to develop actively targeted liposomal formulations of doxorubicin (DOX) using an engineered breast tumor targeting peptide ligand, p18-4 (WxEAAYQrFL). Towards this goal, stealth liposomes bearing different molar ratios of p18-4 peptide (1.5 and 0.3 peptide/total lipid mol %), namely HD and LD liposomes, were successfully prepared. The effect of p18-4 peptide modification and density on breast cancer cell uptake, selective cytotoxicity as well as inhibition of tumor growth and the tissue disposition of encapsulated DOX in breast tumor xenograft models in mice were assessed. The results showed a 2.4 and 5 folds decrease in the IC50 of HD liposomes in MDA-MB-435 and MCF-7 breast tumor cells, respectively. Although LD liposomes showed less (1.6 and 2.2 folds) decrease in the IC50 of DOX in the same breast cancer cell lines, they were more selective in their cytotoxic effect and uptake towards breast cancer over normal breast epithelial cells, MCF10A. Evaluation of the anticancer activity in NOD-SCID mice bearing MDA-MB-435 xenografts after receiving six i.v. injections of 2.5mg/kg/week DOX equivalent showed a superior anticancer activity for LD liposomal DOX compared to HD and unmodified liposomal formulations. Mice treated with LD liposomal DOX illustrated 4.8 folds reduction in the mean relative tumor volume compared to non-targeted DOX liposomes. This was despite similar tumor accumulation of DOX as part of LD liposomes compared to that for unmodified liposomes 24h following the last injection. In contrast, HD liposomes showed decreased DOX accumulation in the tumor and preferential uptake by liver and spleen. Treatment with unmodified and LD liposomes did not have any adverse impact on the activity level and mean body weight of live animals during the study period. In conclusion, surface modification of liposomal DOX with engineered p18-4 peptide at an optimum density can improve the antitumor efficacy and selectivity of liposomal DOX.

Engineered peptides for the development of actively tumor targeted liposomal carriers of doxorubicin.

Chemotherapy is still the treatment of choice for many types of cancer; but its effectiveness is hampered by dose limiting toxicity. Properly designed delivery systems can overcome this shortcoming by reducing the non-specific distribution and toxicity of chemotherapeutics in healthy organs and at the same time increasing drug concentrations at tumor tissue. In this study, we developed stealth liposomal formulations of doxorubicin (DOX) having a novel stable engineered peptide ligand, namely p18-4, that binds specifically to breast cancer cell line MDA-MB-435 on its surface. The coupling of p18-4 to liposomes was carried out through conventional, post insertion and post conjugation techniques and prepared liposomes were characterized for their size and level of peptide modification. The p18-4 decorated liposomal DOX formulations were then evaluated for their cellular uptake as well as cytotoxicity against the human breast cancer MDA-MB-435 cells. In this context, the effect of coupling technique on the uptake and cytotoxicity of p18-4 liposomal DOX in MDA-MB-435 cells was evaluated. The conventional and post conjugation methods of peptide incorporation were found to be more reliable for the preparation of p18-4 decorated liposomes for active DOX targeting to MDA-MB-435 cells. p18-4 decoration of liposomes by these methods did not have a notable effect on the size of prepared liposomes and DOX release, but increased the uptake and cytotoxicity of encapsulated DOX in MDA-MB-435 cells. The results show a potential for p18-4 decorated liposomes prepared by conventional and post conjugation method for tumor targeted delivery of DOX in breast tumor models.

Decoration of polymeric micelles with cancer-specific peptide ligands for active targeting of paclitaxel.

Polymeric micelles based on poly(ethylene oxide)-b-poly(ε-caprolactone) PEO-b-PCL or poly(ethylene oxide)-b-poly(α-benzyl carboxylate-ε-caprolactone) PEO-b-PBCL block copolymers were prepared and decorated with either c(RGDfK) or p160, a cancer cell-specific peptide ligand, on their surface. The cellular uptake of p160-decorated PEO-b-PBCL micelles containing DiI fluorescent label by MDA-MB-435 cancer cells was assessed and compared to that for c(RGDfK)-decorated micelles. The hydrophobic anticancer drug paclitaxel (PTX) was physically encapsulated into PEO-b-PCL or PEO-b-PBCL micelles (with and without peptide ligands) using a dialysis technique. The effect of the micellar formulation on the specificity of encapsulated PTX against cancer cells was assessed by investigating the in vitro cytotoxicity of free and encapsulated PTX against MDA-MB-435 cancer cell line versus two normal cells, Human Umbilical Vein Endothelial Cells (HUVEC) and MCF10A cells, using the MTT assay. Our results showed both peptide ligands to facilitate the association of micelles with MDA-MB-435 cells. The p160-micelles, however showed better binding and internalizing in MDA-MB-435 cells than c(RGDfK)-micelles. In general, peptide decoration enhanced the selective cytotoxicity of encapsulated PTX against MDA-MB-435 cells over normal HUVEC and MCF10A cells. The extent of this increase in cancer cell specificity for encapsulated PTX was more for p160-decorated micelles than c(RGDfK)-decorated ones.

Novel jojoba oil-based emulsion gel formulations for clotrimazole delivery.

Jojoba oil-based emulgel formulations were prepared using different concentrations of various gelling agents, such as hydroxypropyl methylcellulose (HPMC) and Carbopol 934 P and combination of both. The prepared emulgels were physically evaluated for their stability after temperature cycle test, centrifugation and long-term shelf storage for 1 year at room temperature. The in vitro release at 37 °C was studied to define the effect of the concentration and type of the gelling agent. A comparison between the formulated emulgels and two commercially available products, Candistan® and Canesten® creams, was carried out to judge their efficacy and stability. The prepared emulgels exhibited non-Newtonian shear thinning behavior with little or no thixotropy. Four emulgels showed excellent stability as they demonstrated consistent rheological model under different treatment conditions. The in vitro release test showed variation in the extent of percent drug released. The drug release from the commercial preparation was lower than some of the prepared emulgel formulae. One formula containing combination of the two gelling agents (HPMC and Carbopol 934 P), showed excellent stability and high extent of clotrimazole release was microbiologically evaluated against Candida albicans using cylinder and plate method. The selected formula showed superior antimycotic activity compared to the commercially available formulation. Further in vivo animal studies for the obtained stable formula is recommended.

Optimized formulation for topical administration of clotrimazole using Pemulen polymeric emulsifier.

BACKGROUND: Emulgel topical formulation is a vehicle of potential for topical delivery of antifungal drugs. METHODS: The imidazole derivative antifungal drug, clotrimazole (CZ), was formulated into emulgels using two grades of hydrophobically modified co-polymers of acrylic acid, namely Pemulen TR1 and TR2. The prepared emulgels were evaluated for their rheological properties, short- and long-term stability, in vitro release at 37°C. Microbiological evaluation of the formula showed that optimum stability and release was carried out to measure its antifungal activity. RESULTS: All formulae showed non-Newtonian shear thinning behavior with little thixotropy or antithixotropy. Five of the prepared formulae showed good physical stability under different treatment conditions. Isopropyl myristate (IPM) emulgels exhibited higher rate of CZ release than either jojoba oil (JB) or liquid paraffin-based emulgels. A selected formula containing JB together with a combination of Pemulen TR1 and TR2 showed excellent stability as well as high rate of CZ release. Microbiological evaluation of the selected formula containing similar amount of CZ revealed 1.2-folds increase in the antifungal activity compared to commercially available formulation. CONCLUSION: Emulgel dosage form based on Pemulen polymeric emulsifier and JB is a promising vehicle for topical delivery of CZ and further in vivo animal studies are recommended.

Novel self-associating poly(ethylene oxide)-b-poly(epsilon-caprolactone) based drug conjugates and nano-containers for paclitaxel delivery.

Poly(ethylene oxide)-block-poly(epsilon-caprolactone) (PEO-b-PCL) copolymers bearing paclitaxel (PTX) side groups on PCL (PEO-b-P(CL-PTX) were synthesized and assembled to particles of 123 nm average diameter. At 20% (w/w) PTX to polymer conjugation, PEO-b-P(CL-PTX) demonstrated only 5.0 and 6.7% PTX release after 72 h incubation at pH 7.4 and 5.0, respectively, but revealed signs of chain cleavage at pH 5.0. The cytotoxicity of PEO-b-P(CL-PTX) against MDA-MB-435 cancer cells increased as incubation time was raised from 72 to 96 h (IC(50) of 680 and 475 ng/mL, respectively), but it was still significantly lower than the cytotoxicity of free PTX (IC(50) of 3.5 ng/mL at 72 h). In further studies, micelles of PEO-b-PCL and those bearing benzyl or PTX on PCL were used for physical encapsulation of PTX, where maximum level of loading was achieved by PEO-b-P(CL-PTX) (2.22%, w/w). The release of PTX from this carrier was rapid; however. The in vitro cytotoxicity of physically loaded PTX was independent of carrier and similar to that of free PTX. This was attributed to the low concentration of polymers which fell below their critical micellar concentration in the cytotoxicity study. The results point to the potential of chemically tailored PEO-b-PCL for optimum PTX solubilization and delivery.

Disposition of drugs in block copolymer micelle delivery systems: from discovery to recovery.

Since their discovery in the early 1980s, polymeric micelles have been the subject of several studies as delivery systems that can potentially improve the therapeutic performance and modify the toxicity profile of encapsulated drugs by changing their pharmacokinetic characteristics. The efforts in this area have led in recent years to the advancement of several polymeric micellar formulations to clinical trials, some of which have shown promise in changing the biodistribution of the incorporated drug after intravenous administration as a means of tumour-targeted drug delivery. Recently, the possible benefit of polymeric micellar delivery in enhancing the absorption and bioavailability of incorporated drugs from alternative routes of drug administration has attracted interest. This article provides an overview of the effect of polymeric micellar delivery on absorption, distribution, metabolism and excretion of incorporated therapeutic agents. It also aims to assess the current information on the performance of polymeric micellar delivery systems in modifying the pharmacokinetics/pharmacodynamics of the incorporated drugs in clinical trials, and to re-examine the important structural factors required for successful design of polymeric micellar delivery systems capable of inducing favourable changes in the pharmacokinetics of the encapsulated drug.