Automated system for classifying uni-bicompartmental knee osteoarthritis by using redefined residual learning with convolutional neural network.

Knee Osteoarthritis (OA) is one of the most common joint diseases that may cause physical disability associated with a significant personal and socioeconomic burden. X-ray imaging is the cheapest and most common method to detect Knee (OA). Accurate classification of knee OA can help physicians manage treatment efficiently and slow knee OA progression. This study aims to classify knee OA X-ray images according to anatomical types, such as uni or bicompartmental. The study proposes a deep learning model for classifying uni or bicompartmental knee OA based on redefined residual learning with CNN. The proposed model was trained, validated, and tested on a dataset containing 733 knee X-ray images (331 normal Knee images, 205 unicompartmental, and 197 bicompartmental knee images). The results show 61.81 % and 68.33 % for accuracy and specificity, respectively. Then, the performance of the proposed model was compared with different pre-trained CNNs. The proposed model achieved better results than all pre-trained CNNs.

Low-temperature cluster spin glass transition in the single-domain NiCr2O4nanoparticles.

NiCr2O4nanoparticles with average particle size ∼15 nm, a single-domain size maintains the bulk canted antiferromagnetic ground state, were synthesized by a microwave combustion method. The magnetic behavior was carefully investigated by static and dynamic magnetic susceptibility measurements. In addition to a spin-glass-like behavior below paramagnetic-ferrimagnetic transition atTC, the NiCr2O4nanoparticles demonstrate a low-temperature cluster spin glass transition below the spin canting transitionTS, which manifests itself as a magnetic anomaly peak around ∼12 K (at 100 Oe) in the zero-field cooled magnetization with a relatively stronger field dependence in a 'de Almeida-Thouless' line for spin glasses. The AC susceptibility analyses in different approaches demonstrate a larger relative peak temperature variation per frequency decade and a longer characteristic relaxation time in the order of 0.04 and 10-7s, against 0.01 and 10-9s for the high-temperature blocking, indicating the slow spin dynamics for the low-temperature cluster glassy phase. A field-temperature magnetic phase diagram is proposed for the single-domain NiCr2O4nanoparticles.

Pattern of hereditary renal tubular disorders in Egyptian children.

BACKGROUND: Hereditary renal tubular disorders (HRTD) represent a group of genetic diseases characterized by disturbances in fluid, electrolyte, and acid-base homeostasis. There is a paucity of studies on pediatric HRTD in Egypt. In this study, we aimed to study the pattern, characteristics, and growth outcome of HRTD at an Egyptian medical center. METHODS: This study included children from one month to < 18-years of age with HRTD who were diagnosed and followed up at the Pediatric Nephrology Unit of Sohag University Hospital from January 2015 to December 2021. Data on patients` demographics, clinical features, growth profiles, and laboratory characteristics were collected. RESULTS: Fifty-eight children (57% males; 72% parental consanguinity; 60% positive family history) were diagnosed with seven HRTD types. The most commonly encountered disorders were distal renal tubular acidosis (distal renal tubular acidosis [RTA] 27 cases, 46.6%) and Bartter syndrome (16 cases 27.6%). Other identified disorders were Fanconi syndrome (6 cases with cystinosis), isolated proximal RTA (4 cases), nephrogenic diabetes insipidus (3 cases), and one case for each RTA type IV and Gitelman syndrome. The median age at diagnosis was 17 months with a variable diagnostic delay. The most common presenting features were failure to thrive (91.4%), developmental delay (79.3%), and dehydration episodes (72.4%). Most children showed marked improvement in growth parameters in response to appropriate management, except for cases with Fanconi syndrome. Last, only one case (with cystinosis) developed end-stage kidney disease. CONCLUSIONS: HRTD (most commonly distal RTA and Bartter syndrome) could be relatively common among Egyptian children, and the diagnosis seems challenging and often delayed.

Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map.

Cervical spine (CS) fractures or dislocations are medical emergencies that may lead to more serious consequences, such as significant functional disability, permanent paralysis, or even death. Therefore, diagnosing CS injuries should be conducted urgently without any delay. This paper proposes an accurate computer-aided-diagnosis system based on deep learning (AlexNet and GoogleNet) for classifying CS injuries as fractures or dislocations. The proposed system aims to support physicians in diagnosing CS injuries, especially in emergency services. We trained the model on a dataset containing 2009 X-ray images (530 CS dislocation, 772 CS fractures, and 707 normal images). The results show 99.56%, 99.33%, 99.67%, and 99.33% for accuracy, sensitivity, specificity, and precision, respectively. Finally, the saliency map has been used to measure the spatial support of a specific class inside an image. This work targets both research and clinical purposes. The designed software could be installed on the imaging devices where the CS images are captured. Then, the captured CS image is used as an input image where the designed code makes a clinical decision in emergencies.

Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning.

Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathologists and resulting in more accurate and efficient histopathological diagnoses. Breast cancer is one of the most diagnosed cancers in women worldwide. Breast cancer may be detected and diagnosed using imaging methods such as histopathological images. Since various tissues make up the breast, there is a wide range of textural intensity, making abnormality detection difficult. As a result, there is an urgent need to improve computer-assisted systems (CAD) that can serve as a second opinion for radiologists when they use medical images. A self-training learning method employing deep learning neural network with residual learning is proposed to overcome the issue of needing a large number of labeled images to train deep learning models in breast cancer histopathology image classification. The suggested model is built from scratch and trained.

Refined Residual Deep Convolutional Network for Skin Lesion Classification.

Skin cancer is the most common type of cancer that affects humans and is usually diagnosed by initial clinical screening, which is followed by dermoscopic analysis. Automated classification of skin lesions is still a challenging task because of the high visual similarity between melanoma and benign lesions. This paper proposes a new residual deep convolutional neural network (RDCNN) for skin lesions diagnosis. The proposed neural network is trained and tested using six well-known skin cancer datasets, PH2, DermIS and Quest, MED-NODE, ISIC2016, ISIC2017, and ISIC2018. Three different experiments are carried out to measure the performance of the proposed RDCNN. In the first experiment, the proposed RDCNN is trained and tested using the original dataset images without any pre-processing or segmentation. In the second experiment, the proposed RDCNN is tested using segmented images. Finally, the utilized trained model in the second experiment is saved and reused in the third experiment as a pre-trained model. Then, it is trained again using a different dataset. The proposed RDCNN shows significant high performance and outperforms the existing deep convolutional networks.

Effect of autologous fat transfer in acute burn wound management: A randomized controlled study.

OBJECTIVE: The use of fat grafting is being widely used for different indications one of which is wound healing. In this study we compare the use of autologous fat grafting (AFG) as a novel indication in acute burn wounds healing and burn scarring to the conventional methods of burn wound management both clinically and histologically. Several small observational studies demonstrated the effect of the AFG in healing of chronic wounds, different vascular ulcers or effect on scars yet no randomized controlled trial is available to compare its role with conventional methods. METHODS: The study was a prospective, open-label single center, randomized control clinical trial included 100 patients with superficial and deep dermal burns from March 2019 to March 2020 randomized to AFG protocol consisted of a single injection of autologous fat grafting then dressed with nano fat (Group A) or conventional methods of serial dressings with 1% silver sulphadiazine or other topical agents (Group B). Inclusion criteria included newly admitted burn patients with affected total body surface area (TBSA) (10%-25%) while exclusion criteria included burns patients with affected TBSA of< 10% or> 25%, or loss of subcutaneous fat, fascia, muscles and bones, inhalational burn, and burns in genitalia, perineum and peri-anal areas and co-morbidity(ies) that might affect wound healing or eligibility for anaesthesia and surgery. Also, results were confirmed by histological analysis for samples from both groups by light microscopic examination, and the nano-fat was subjected to flow cytometric analysis of the cluster of differentiation (CD) markers of mesenchymal stem cells markers CD 90, CD44, CD45, CD 73, and CD 34. (ClinicalTrials.gov Identifier: NCT03791710) RESULTS: We found a significant reduction in total hospital stay days (p = <0.001), less further skin grafting (p = 0.003), less contracture formation (p = <0.002) while scar texture improved (p = <0.001) in group A compared to group B. Flow cytometric analysis documented that the nano-fat was positive to CD 90, 73, 44, 45 and 34. CONCLUSION: In a comparison between AFG protocol to the conventional methods in the treatment of acute burn wounds, AFG protocol was associated with significant clinical improvement in the form of lower hospital stay time, lower incidence of scaring or contracture and lower skin grafting use which was confirmed by serial photographic and histological assessment.

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review.

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

Unconventional critical behaviors at the magnetic phase transition of Co3Sn2S2 kagomé ferromagnet.

Co3Sn2S2 has generated a growing interest as a rare example of the highly uniaxial anisotropic kagomé ferromagnet showing a combination of frustrated-lattice magnetism and topology. Recently, via precise measurements of the magnetization and AC susceptibility we have found a low-field anomalous magnetic phase (A-phase) with very slow spin dynamics that appears just below the Curie temperature (T C). The A-phase hosts high-density domain bubbles after cooling through T C as revealed in a previous in-situ Lorentz-TEM study. Here, we present further signatures of the anomalous magnetic transition (MT) at T C revealed by a study of the critical behaviors of the magnetization and magnetocaloric effect using a high-quality single crystal. Analyses of numerous magnetization isotherms around T C (≃177 K) using different approaches (the modified Arrot plot, Kouvel-Fisher method and magnetocaloric effect) result in consistent critical exponents that do not satisfy the theoretical predictions of standard second-order-MT models. Scaling analyses for the magnetization, magnetic entropy change and field-exponent of the magnetic entropy change, all consistently show low-field deviations below T C from the universal curves. Our results reveal that the MT of Co3Sn2S2 can not be explained as a conventional second-order type and suggest an anomalous magnetic state below T C.

Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet.

Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classification process. The proposed method utilized transfer learning with pre-trained AlexNet. The parameters of the original model used as initial values, where we randomly initialize the weights of the last three replaced layers. The proposed method was tested using the most recent public dataset, ISIC 2018. Based on the obtained results, we could say that the proposed method achieved a great success where it accurately classifies the skin lesions into seven classes. These classes are melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The achieved percentages are 98.70%, 95.60%, 99.27%, and 95.06% for accuracy, sensitivity, specificity, and precision, respectively.

Classification of skin lesions using transfer learning and augmentation with Alex-net.

Skin cancer is one of most deadly diseases in humans. According to the high similarity between melanoma and nevus lesions, physicians take much more time to investigate these lesions. The automated classification of skin lesions will save effort, time and human life. The purpose of this paper is to present an automatic skin lesions classification system with higher classification rate using the theory of transfer learning and the pre-trained deep neural network. The transfer learning has been applied to the Alex-net in different ways, including fine-tuning the weights of the architecture, replacing the classification layer with a softmax layer that works with two or three kinds of skin lesions, and augmenting dataset by fixed and random rotation angles. The new softmax layer has the ability to classify the segmented color image lesions into melanoma and nevus or into melanoma, seborrheic keratosis, and nevus. The three well-known datasets, MED-NODE, Derm (IS & Quest) and ISIC, are used in testing and verifying the proposed method. The proposed DCNN weights have been fine-tuned using the training and testing dataset from ISIC in addition to 10-fold cross validation for MED-NODE and DermIS-DermQuest. The accuracy, sensitivity, specificity, and precision measures are used to evaluate the performance of the proposed method and the existing methods. For the datasets, MED-NODE, Derm (IS & Quest) and ISIC, the proposed method has achieved accuracy percentages of 96.86%, 97.70%, and 95.91% respectively. The performance of the proposed method has outperformed the performance of the existing classification methods of skin cancer.

Enhancement of dissolution and oral bioavailability of lacidipine via pluronic P123/F127 mixed polymeric micelles: formulation, optimization using central composite design and in vivo bioavailability study.

This study aims at preparing and optimizing lacidipine (LCDP) polymeric micelles using thin film hydration technique in order to overcome LCDP solubility-limited oral bioavailability. A two-factor three-level central composite face-centered design (CCFD) was employed to optimize the formulation variables to obtain LCDP polymeric micelles of high entrapment efficiency and small and uniform particle size (PS). Formulation variables were: Pluronic to drug ratio (A) and Pluronic P123 percentage (B). LCDP polymeric micelles were assessed for entrapment efficiency (EE%), PS and polydispersity index (PDI). The formula with the highest desirability (0.959) was chosen as the optimized formula. The values of the formulation variables (A and B) in the optimized polymeric micelles formula were 45% and 80%, respectively. Optimum LCDP polymeric micelles had entrapment efficiency of 99.23%, PS of 21.08 nm and PDI of 0.11. Optimum LCDP polymeric micelles formula was physically characterized using transmission electron microscopy. LCDP polymeric micelles showed saturation solubility approximately 450 times that of raw LCDP in addition to significantly enhanced dissolution rate. Bioavailability study of optimum LCDP polymeric micelles formula in rabbits revealed a 6.85-fold increase in LCDP bioavailability compared to LCDP oral suspension.

Electrosteric stealth Rivastigmine loaded liposomes for brain targeting: preparation, characterization, ex vivo, bio-distribution and in vivo pharmacokinetic studies.

Being one of the highly effective drugs in treatment of Alzheimer's disease, Rivastigmine brain targeting is highly demandable, therefore liposomal dispersion of Rivastigmine was prepared containing 2 mol% PEG-DSPE added to Lecithin, Didecyldimethyl ammonium bromide (DDAB), Tween 80 in 1:0.02:0.25 molar ratio. A major challenge during the preparation of liposomes is maintaining a stable formulation, therefore the aim of our study was to increase liposomal stability by addition of DDAB to give an electrostatic stability and PEG-DSPE to increase stability by steric hindrance, yielding what we called an electrosteric stealth (ESS) liposomes. A medium nano-sized liposome (478 ± 4.94 nm) with a nearly neutral zeta potential (ZP, -8 ± 0.2 mV) and an entrapment efficiency percentage of 48 ± 6.22 was prepared. Stability studies showed no major alteration after three months storage period concerning particle size, polydispersity index, ZP, entrapment efficiency and in vitro release study confirming the successful formation of a stable liposomes. No histopathological alteration was recorded for ESS liposomes of the sheep nasal mucosa. While ESS liposomes showed higher % of drug permeating through the sheep nasal mucosa (48.6%) than the drug solution (28.7%). On completing the in vivo pharmacokinetic studies of 36 rabbits showed 424.2% relative bioavailability of the mean plasma levels of the formula ESS compared to that of RHT intranasal solution and 486% relative bioavailability of the mean brain levels.

Enhanced Solubility and Dissolution Rate of Lacidipine Nanosuspension: Formulation Via Antisolvent Sonoprecipitation Technique and Optimization Using Box-Behnken Design.

Lacidipine (LCDP) is a highly lipophilic calcium channel blocker of poor aqueous solubility leading to poor oral absorption. This study aims to prepare and optimize LCDP nanosuspensions using antisolvent sonoprecipitation technique to enhance the solubility and dissolution of LCDP. A three-factor, three-level Box-Behnken design was employed to optimize the formulation variables to obtain LCDP nanosuspension of small and uniform particle size. Formulation variables were as follows: stabilizer to drug ratio (A), sodium deoxycholate percentage (B), and sonication time (C). LCDP nanosuspensions were assessed for particle size, zeta potential, and polydispersity index. The formula with the highest desirability (0.969) was chosen as the optimized formula. The values of the formulation variables (A, B, and C) in the optimized nanosuspension were 1.5, 100%, and 8 min, respectively. Optimal LCDP nanosuspension had particle size (PS) of 273.21 nm, zeta potential (ZP) of -32.68 mV and polydispersity index (PDI) of 0.098. LCDP nanosuspension was characterized using x-ray powder diffraction, differential scanning calorimetry, and transmission electron microscopy. LCDP nanosuspension showed saturation solubility 70 times that of raw LCDP in addition to significantly enhanced dissolution rate due to particle size reduction and decreased crystallinity. These results suggest that the optimized LCDP nanosuspension could be promising to improve oral absorption of LCDP.

In-vivo evaluation of clindamycin release from glyceryl monooleate-alginate microspheres by NIR spectroscopy.

The purpose of this study was to use near-infrared (NIR) transmission spectroscopic technique to determine clindamycin plasma concentration after oral administration of clindamycin loaded GMO-alginate microspheres using rabbits as animal models. Lyophilized clindamycin-plasma standard samples at a concentration range of 0.001-10 µg/ml were prepared and analyzed by NIR and HPLC as a reference method. NIR calibration model was developed with partial least square (PLS) regression analysis. Then, a single dose in-vivo evaluation was carried out and clindamycin-plasma concentration was estimated by NIR. Over 24 h time period, the pharmacokinetic parameters of clindamycin were calculated for the clindamycin loaded GMO-alginate microspheres (F3) and alginate microspheres (F2), and compared with the plain drug (F1). PLS calibration model with 7-principal components (PC), and 8000-9200 cm(-1) spectral range shows a good correlation between HPLC and NIR values with root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP), and calibration coefficient (R(2)) values of 0.245, 1.164, and 0.9753, respectively, which suggests that NIR transmission technique can be used for drug-plasma analysis without any extraction procedure. F3 microspheres exhibited controlled and prolonged absorption Tmax of 4.0 vs. 1.0 and 0.5 h; Cmax of 2.37±0.3 vs. 3.81±0.8 and 5.43±0.7 µg/ml for F2 and F1, respectively. These results suggest that the combination of GMO and alginate (1:4 w/w) could be successfully employed for once daily clindamycin microspheres formulation which confirmed by low Cmax and high Tmax values.

Development and validation of LC-MS/MS assay for the determination of the prodrug Midodrine and its active metabolite Desglymidodrine in plasma of ascitic patients: Application to individualized therapy and comparative pharmacokinetics.

Midodrine (MD) is a prodrug that is converted after oral administration to Desglymidodrine (DMD). In this study, an LC-MS/MS assay was developed and validated for investigation of the pharmacokinetics of MD and DMD in non azotemic patients with liver cirrhosis and tense ascites. Results were compared to those noted with healthy volunteers following the adminstration of a single oral dose of MD. Sample preparation was performed by liquid-liquid extraction using t-butyl methyl ether. HPLC separation was carried out using RP C18 column (4.6mm×50mm, 5µm). Isocratic elution was performed using methanol:0.2% formic acid (70:30, v/v) as the mobile phase, at a flow rate of 0.7mL/min. Tandem mass spectrometric detection was employed at positive electrospray ionization in MRM mode for the determination of MD and DMD. Analysis was carried out within 1.0min over a concentration range of 0.50-40.00ng/mL for the prodrug and its active metabolite. The assay was validated according to FDA guidelines for bioanalytical method validation and satisfactory results were obtained. The applicability of the assay for the determination of the pharmacokinetic parameters of MD and DMD and personalized therapy was demonstrated in healthy volunteers and ascitic patients. Results revealed significant differences in pharmacokinetic parameters among the studied groups. Such differences were explained on the basis of the medical condition and co-adminstered medications exerting possible drug-drug interaction. Results confirmed the need for implementation of reliable analysis tools for therapeutic dose adjustment.

Lyophilized sustained release mucoadhesive chitosan sponges for buccal buspirone hydrochloride delivery: formulation and in vitro evaluation.

This work aims to prepare sustained release buccal mucoadhesive lyophilized chitosan sponges of buspirone hydrochloride (BH) to improve its systemic bioavailability. Chitosan sponges were prepared using simple casting/freeze-drying technique according to 3(2) factorial design where chitosan grade was set at three levels (low, medium, and high molecular weight), and concentration of chitosan solution at three levels (0.5, 1, and 2%). Mucoadhesion force, ex vivo mucoadhesion time, percent BH released after 8 h (Q8h), and time for release of 50% BH (T50%) were chosen as dependent variables. Additional BH cup and core buccal chitosan sponge were prepared to achieve uni-directional BH release toward the buccal mucosa. Sponges were evaluated in terms of drug content, surface pH, scanning electron microscopy, swelling index, mucoadhesion strength, ex vivo mucoadhesion time, and in vitro drug release. Cup and core sponge (HCH 0.5E) were able to adhere to the buccal mucosa for 8 h. It showed Q8h of 68.89% and exhibited a uni-directional drug release profile following Higuchi diffusion model.

Enhanced bioavailability of buspirone hydrochloride via cup and core buccal tablets: formulation and in vitro/in vivo evaluation.

This work aims to prepare sustained release buccal mucoadhesive tablets of buspirone hydrochloride (BH) to improve its systemic bioavailability. The tablets were prepared according to 5×3 factorial design where polymer type was set at five levels (carbopol, hydroxypropyl methylcellulose, sodium alginate, sodium carboxymethyl cellulose and guar gum), and polymer to drug ratio at three levels (1:1, 2:1 and 3:1). Mucoadhesion force, ex vivo mucoadhesion time, percent BH released after 8 h (Q8h) and time for release of 50% BH (T(50%)) were chosen as dependent variables. Additional BH cup and core buccal tablets were prepared to optimize BH release profile and make it uni-directional along with the tablets mucoadhesion. Tablets were evaluated in terms of content uniformity, weight variation, thickness, diameter, hardness, friability, swelling index, surface pH, mucoadhesion strength and time and in vitro release. Cup and core formula (CA10) was able to adhere to the buccal mucosa for 8h, showed the highest Q8h (97.91%) and exhibited a zero order drug release profile. Pharmacokinetic study of formula CA10 in human volunteers revealed a 5.6 fold increase in BH bioavailability compared to the oral commercial Buspar® tablets. Conducting level A in vitro/in vivo correlation showed good correlation (r²=0.9805) between fractions dissolved in vitro and fractions absorbed in vivo.

Hydroxychloroquine niosomes: a new trend in topical management of oral lichen planus.

The work aimed at studying a novel topical niosomal gel formulation of hydroxychloroquine for the management of oral lichen planus. Niosomes have been reported as conceivable vesicles to deliver drug molecules to the desired mucous membrane or skin layers. Hydroxychloroquine niosomes were designed using different methods of preparation. Tween 20 and cholesterol in molar ratio (1:0.5) were used. The prepared systems were characterized for entrapment efficiency, particle size and in vitro drug release. Different factors affecting the encapsulation of hydroxychloroquine in niosomes were studied vs. varying the type of surfactant, the cholesterol:surfactant molar ratio and the amount of the drug. The selected noisome formulation was dispersed in different gel formulations and evaluated according to the in vitro drug release and the physical stability. The results showed that the type of surfactant, cholesterol ratio and incorporated amount of drug altered the entrapment efficiency and the in vitro release of hydroxychloroquine from niosomes. The optimum formulation was prepared by reverse phase evaporation technique using Brij 98:cholesterol molar ratio (1:1.5) and containing 20mg of hydroxychloroquine and incorporated in 20% w/v Pluronic F-127 gel. A double-blind, controlled clinical study was performed using two groups of patients. Group A (n=11) who received hydroxychloroquine niosomal gel formulation, one application-a-day over 4 months showed 64.28% reduction in the size of lesions and the average score of pain was reduced from "4" to "1". Compared to placebo group B (n=5), who showed only 3.94% reduction in the lesion size and the average score of pain was remained "3". Our results suggest that these niosomal formulations could constitute a promising approach for the topical treatment of oral lichen planus in short time with less side effects and no recurrence after stopping the treatment.

Efficacy of topical griseofulvin in treatment of tinea corporis.

Tinea infections are among the most common dermatological conditions throughout the world. Griseofulvin is a classical oral fungistatic antibiotic, active against Epidermophyton floccosum, Trichophyton and Microsporum species, the causative fungi of tinea corporis. To evaluate the efficacy of topical griseofulvin in the treatment of tinea circinata using three different vehicles for drug delivery. Sixteen patients with tinea circinata were instructed to apply either griseofulvin gel form in group A or a similar placebo gel for control group; a niosomal gel formulation of griseofulvin for group B or; a liposomal gel formulation of griseofulvin for group C. Patients were evaluated both clinically and mycologically after 3 weeks. Marked improvement was seen for groups A, B and C both clinically and mycologically while no improvement was observed in the placebo group. Mild and transient irritation was reported in four patients. Our results show that topical griseofulvin preparations may be effective and safe in treating tinea circinata and that further large-scale studies may establish the high efficacy of the niosomal gel formulation.