Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization.

Cancer is a manifestation of disorders caused by the changes in the body's cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

Breast Cancer Prediction Empowered with Fine-Tuning.

In the world, in the past recent five years, breast cancer is diagnosed about 7.8 million women's and making it the most widespread cancer, and it is the second major reason for women's death. So, early prevention and diagnosis systems of breast cancer could be more helpful and significant. Neural networks can extract multiple features automatically and perform predictions on breast cancer. There is a need for several labeled images to train neural networks which is a nonconventional method for some types of data images such as breast magnetic resonance imaging (MRI) images. So, there is only one significant solution for this query is to apply fine-tuning in the neural network. In this paper, we proposed a fine-tuning model using AlexNet in the neural network to extract features from breast cancer images for training purposes. So, in the proposed model, we updated the first and last three layers of AlexNet to detect the normal and abnormal regions of breast cancer. The proposed model is more efficient and significant because, during the training and testing process, the proposed model achieves higher accuracy 98.44% and 98.1% of training and testing, respectively. So, this study shows that the use of fine-tuning in the neural network can detect breast cancer using MRI images and train a neural network classifier by feature extraction using the proposed model is faster and more efficient.

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

New dibenzocyclooctadiene lignan from stems of Kadsura heteroclita.

Kadsura heteroclita Roxb. Craib. (Schisandraceae), is a vine plant mainly distributed in southwest part of China. A new dibenzocyclooctadiene lignan, kadsulignan W (1), along with eleven known lignans (2-12) were isolated from chloroform soluble fraction of stems of Kadsura heteroclita. The structure of new lignan was elucidated by extensive spectroscopic techniques, namely one- and two-dimensional NMR spectroscopy, and HRESI-MS analysis. The absolute configuration of the biphenyl ring in the new dibenzocyclooctadiene lignan was discerned by circular dichroism (CD) spectroscopy. Antioxidative effects of these compounds were evaluated on human isolated neutrophils, and compounds 5, 8, 9, and 10 were found to be strongly active with the IC50 of 36.68, 34.41, 35.97, and 33.65 µM, respectively. Furthermore, compound 8 was also found to be cytotoxic against human gastric cancer cells (BGC 823), and human cervical cancer cell lines (HeLa) with the IC50 values of 11.0, and 23.8 µM, respectively.

Dibenzocyclooctadiene lignans from Kadsura coccinea alleviate APAP-induced hepatotoxicity via oxidative stress inhibition and activating the Nrf2 pathway in vitro.

Phytochemical investigation on the roots of Kadsura coccinea led to the isolation five previously unknown dibenzocyclooctadiene lignans, named heilaohusuins A-E (1-5). Their structures determined by NMR spectroscopy, HR-ESI-MS, and ECD spectra. Hepatoprotection effects of a series of dibenzocyclooctadiene derivatives (1-68) were investigated against acetaminophen (APAP) induced HepG2 cells. Compounds 2, 10, 13, 21, 32, 41, 46, and 49 showed remarkable protective effects, increasing the viabilities to > 52.2% (bicyclol, 52.1 ± 1.3%) at 10 µM. The structure-activity relationships (SAR) for hepatoprotective activity were summarized, according to the activity results of dibenzocyclooctadiene derivatives. Furthermore, we found that one new dibenzocyclooctadiene lignan heilaohusuin B attenuates hepatotoxicity, the mechanism might be closely correlated with oxidative stress inhibition via activating the Nrf2 pathway.

Thymidine phosphorylase and prostrate cancer cell proliferation inhibitory activities of synthetic 4-hydroxybenzohydrazides: In vitro, kinetic, and in silico studies.

Over-expression of thymidine phosphorylase (TP) plays a key role in many pathological complications, including angiogenesis which leads to cancer cells proliferation. Thus in search of new anticancer agents, a series of 4-hydroxybenzohydrazides (1-29) was synthesized, and evaluated for in vitro thymidine phosphorylase inhibitory activity. Twenty compounds 1-3, 6-14, 16, 19, 22-24, and 27-29 showed potent to weak TP inhibitory activities with IC50 values in the range of 6.8 to 229.5 µM, in comparison to the standards i.e. tipiracil (IC50 = 0.014 ± 0.002 µM) and 7-deazaxanthine (IC50 = 41.0 ± 1.63 µM). Kinetic studies on selected inhibitors 3, 9, 14, 22, 27, and 29 revealed uncompetitive and non-competitive modes of inhibition. Molecular docking studies of these inhibitors indicated that they were able to interact with the amino acid residues present in allosteric site of TP, including Asp391, Arg388, and Leu389. Antiproliferative (cytotoxic) activities of active compounds were also evaluated against mouse fibroblast (3T3) and prostate cancer (PC3) cell lines. Compounds 1, 2, 19, and 22-24 exhibited anti-proliferative activities against PC3 cells with IC50 values between 6.5 to 10.5 µM, while they were largely non-cytotoxic to 3T3 (mouse fibroblast) cells proliferation. Present study thus identifies a new class of dual inhibitors of TP and cancer cell proliferation, which deserves to be further investigated for anti-cancer drug development.

Kadsura coccinea: A rich source of structurally diverse and biologically important compounds.

Kadsura coccinea belongs to medicinally important genus Kadsura from the Schisandraceae family. It has been used in traditional Chinese medicine (TCM) for the treatment of rheumatoid arthritis and gastroenteric disorders. The initial phytochemical work focused on the identification of some structurally novel and diverse natural products, which turned the attention of many researchers towards this plant. Thus far, 202 compounds have been reported in this plant. Lignans and terpenoids were found as the main chemical constituents of this plant. Some of the triterpenoids and sesquiterpenoids with novel structures are of particular interest for natural product researchers. The isolated compounds of this plant have shown different bioactivities including anti-tumor, anti-HIV, anti-inflammatory, nitric oxide (NO) production inhibitory and other pharmacological effects. This review systematically summarizes all the phytochemical and pharmacological work done so far on K. coccinea, and can be used as a reference for future research on this plant.

Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita.

In the present work, we take advantage of the characteristic NMR signal (δC-10 = 96.0-99.9) for guiding the isolation of schinortriterpenoids (SNTs) from n-butanol fraction of stems of Kadsura heteroclita which is a Tujia ethnomedicine with trivial name "Xuetong". This effort resulted in the identification of three unreported 3,4:9,10-disecocycloartane triterpenoids xuetongdilactones A-C and three undescribed SNTs xuetongdilactones D-F, along with two known SNTs, namely, wuweizidilactone B and micrandilactone B. The structures of the unreported compounds were established based on 1D, and 2D NMR, HRESIMS, and ECD spectroscopic data analysis. The absolute stereochemistry of xuetongdilactone A was determined by X-ray diffraction analysis along with ECD calculation. The antioxidant and cytotoxic activities were evaluated for all the isolated compounds.

Two new alkaloids isolated from traditional Chinese medicine Binglang the fruit of Areca catechu.

Binglang, the fruit of Areca catechu L, has a long history as an important Chinese herbal medicine. Two new alkaloids (1 and 2), along with forty-one known compounds (3-43) were isolated from the dried fruit of Areca catechu L. The structures were elucidated on basis of the IR, UV, MS and 1D, 2D NMR spectroscopic data. Compounds 26 and 33 showed weak cytotoxicity against human gastric cancer cell line (BGC-823) with IC50 of 15.91 µM and 20.13 µM, respectively.

Xuetonglactones A-F: Highly Oxidized Lanostane and Cycloartane Triterpenoids From Kadsura heteroclita Roxb. Craib.

Xuetonglactones A-F (1-6), six unreported highly oxidized lanostane- and cycloartane-type triterpenoids along with 22 known scaffolds (7-28) were isolated from the stems of Kadsura heteroclita (Roxb.) Craib. Compared with previous congeners, xuetonglactone A (1), possesses an unprecedented 20,21-α-epoxide, and xuetonglactone D (4) features an unusual 19-α-hydroperoxyl moiety. The structures and the absolute configurations of the compounds were established by extensive one- and two-dimensional NMR, and electronic circular dichroism (ECD) spectroscopic analysis, with those of 1 and 5 confirmed by single-crystal X-ray diffraction technique. Compounds 1 and 2 exhibited inhibition of iNOS activity in LPS-induced macrophages with IC50 values of 22.0, and 17.0 µg/mL, respectively. While compounds 6, 7, 8, and 24 showed potent cytotoxic activities against human cervical cancer cell lines (HeLa) with the IC50 values of 4.0, 5.8, 5.0, and 6.4 µM, and against human gastric cancer cells (BGC 823) with the IC50 values of 2.0, 5.0, 2.5, and 2.0 µM, respectively. Moreover, plausible biogenetic pathways of (1-6) were also proposed.

Screening of inhibitors of angiotensin-converting enzyme (ACE) employing high performance liquid chromatography-electrospray ionization triple quadrupole mass spectrometry (HPLC-ESI-QqQ-MS).

Angiotensin-converting enzyme (ACE) plays a key role in regulating blood pressure in the body by converting the angiotensin I (AI) into angiotensin II (AII). Angiotensin II is a potent vaso-active peptide that causes arterioles to constrict, resulting in increased blood pressure. A rapid and sensitive method for the identification of inhibitors of ACE was developed, and optimized employing HPLC-ESI-QqQ-MS. In this assay, angiotensin I substrate was converted into the product angiotensin II with the catalytic action of ACE. A calibration curve for depleting concentration of angiotensin I was developed and linearity of R2=0.999 with a remarkably low concentration of substrate range 20-200nM. The limit of detection and quantification of angiotensin I was found to be 1.93 and 5.84nM, respectively. The enzymatic reaction was optimized for incubation time, concentration, and volume of enzyme and substrate. All reactions were performed at 37°C at pH7.5 with standard incubation time of 20min. Two standard inhibitors, Captopril and Lisinopril, were checked through the newly developed method for their inhibitory potential, and their IC50 values were found to be 3.969 and 0.852µM, respectively. Reproducibility and precision analysis of different experiments showed <9.9% RSD. The developed method can be used for the identification of new ACE inhibitors.

Biotransformation of a potent anabolic steroid, mibolerone, with Cunninghamella blakesleeana, C. echinulata, and Macrophomina phaseolina, and biological activity evaluation of its metabolites.

Seven metabolites were obtained from the microbial transformation of anabolic-androgenic steroid mibolerone (1) with Cunninghamella blakesleeana, C. echinulata, and Macrophomina phaseolina. Their structures were determined as 10ß,17ß-dihydroxy-7α,17α-dimethylestr-4-en-3-one (2), 6ß,17ß-dihydroxy-7α,17α-dimethylestr-4-en-3-one (3), 6ß,10ß,17ß-trihydroxy-7α,17α-dimethylestr-4-en-3-one (4), 11ß,17ß-dihydroxy-(20-hydroxymethyl)-7α,17α-dimethylestr-4-en-3-one (5), 1α,17ß-dihydroxy-7α,17α-dimethylestr-4-en-3-one (6), 1α,11ß,17ß-trihydroxy-7α,17α-dimethylestr-4-en-3-one (7), and 11ß,17ß-dihydroxy-7α,17α-dimethylestr-4-en-3-one (8), on the basis of spectroscopic studies. All metabolites, except 8, were identified as new compounds. This study indicates that C. blakesleeana, and C. echinulata are able to catalyze hydroxylation at allylic positions, while M. phaseolina can catalyze hydroxylation of CH2 and CH3 groups of substrate 1. Mibolerone (1) was found to be a moderate inhibitor of ß-glucuronidase enzyme (IC50 = 42.98 ± 1.24 µM) during random biological screening, while its metabolites 2-4, and 8 were found to be inactive. Mibolerone (1) was also found to be significantly active against Leishmania major promastigotes (IC50 = 29.64 ± 0.88 µM). Its transformed products 3 (IC50 = 79.09 ± 0.06 µM), and 8 (IC50 = 70.09 ± 0.05 µM) showed a weak leishmanicidal activity, while 2 and 4 were found to be inactive. In addition, substrate 1 (IC50 = 35.7 ± 4.46 µM), and its metabolite 8 (IC50 = 34.16 ± 5.3 µM) exhibited potent cytotoxicity against HeLa cancer cell line (human cervical carcinoma). Metabolite 2 (IC50 = 46.5 ± 5.4 µM) also showed a significant cytotoxicity, while 3 (IC50 = 107.8 ± 4.0 µM) and 4 (IC50 = 152.5 ± 2.15 µM) showed weak cytotoxicity against HeLa cancer cell line. Compound 1 (IC50 = 46.3 ± 11.7 µM), and its transformed products 2 (IC50 = 43.3 ± 7.7 µM), 3 (IC50 = 65.6 ± 2.5 µM), and 4 (IC50 = 89.4 ± 2.7 µM) were also found to be moderately toxic to 3T3 cell line (mouse fibroblast). Interestingly, metabolite 8 showed no cytotoxicity against 3T3 cell line. Compounds 1-4, and 8 were also evaluated for inhibition of tyrosinase, carbonic anhydrase, and α-glucosidase enzymes, and all were found to be inactive.

Serum metabonomics of acute leukemia using nuclear magnetic resonance spectroscopy.

Acute leukemia is a critical neoplasm of white blood cells. In order to differentiate between the metabolic alterations associated with two subtypes of acute leukemia, acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), we investigated the serum of ALL and AML patients and compared with two controls (healthy and aplastic anemia) using (1)H NMR (nuclear magnetic resonance) spectroscopy. Thirty-seven putative metabolites were identified using Carr-Purcell-Meiboom-Gill (CPMG) sequence. The use of PLS-DA and OPLS-DA models gave results with 84.38% and 90.63% classification rate, respectively. The metabolites responsible for classification are mainly lipids, lactate and glucose. Compared with controls, ALL and AML patients showed serum metabonomic differences involving aberrant metabolism pathways including glycolysis, TCA cycle, lipoprotein changes, choline and fatty acid metabolisms.

Biotransformation of 6-dehydroprogesterone with Aspergillus niger and Gibberella fujikuroi.

Microbial transformation of 6-dehydroprogesterone (1) with Aspergillus niger yielded three new metabolites, including 6ß-chloro-7α,11α-dihydroxypregna-4-ene-3,20-dione (2), 7α-chloro-6ß,11α-dihydroxypregna-4-ene-3,20-dione (3), and 6α,7α-epoxy-11α-hydroxypregna-4-ene-3,20-dione (4), and two known metabolites; 6α,7α-epoxypregna-4-ene-3,20-dione (5), and 11α-hydroxypregna-4,6-diene-3,20-dione (6). Compounds 2, and 3 contain chlorohydrin moiety at C-6, and C-7, respectively. The biotransformation of 1 with Gibberella fujikuroi yielded a known compound, 11α,17ß-dihydroxyandrosta-4,6-dien-3-one (7).

New Anti-Inflammatory Metabolites by Microbial Transformation of Medrysone.

Microbial transformation of the anti-inflammatory steroid medrysone (1) was carried out for the first time with the filamentous fungi Cunninghamella blakesleeana (ATCC 8688a), Neurospora crassa (ATCC 18419), and Rhizopus stolonifer (TSY 0471). The objective was to evaluate the anti-inflammatory potential of the substrate (1) and its metabolites. This yielded seven new metabolites, 14α-hydroxy-6α-methylpregn-4-ene-3,11,20-trione (2), 6ß-hydroxy-6α-methylpregn-4-ene-3,11,20-trione (3), 15ß-hydroxy-6α-methylpregn-4-ene-3,11,20-trione (4), 6ß,17α-dihydroxy-6α-methylpregn-4-ene-3,11,20-trione (5), 6ß,20S-dihydroxy-6α-methylpregn-4-ene-3,11-dione (6), 11ß,16ß-dihydroxy-6α-methylpregn-4-ene-3,11-dione (7), and 15ß,20R-dihydroxy-6α-methylpregn-4-ene-3,11-dione (8). Single-crystal X-ray diffraction technique unambiguously established the structures of the metabolites 2, 4, 6, and 8. Fungal transformation of 1 yielded oxidation at the C-6ß, -11ß, -14α, -15ß, -16ß positions. Various cellular anti-inflammatory assays, including inhibition of phagocyte oxidative burst, T-cell proliferation, and cytokine were performed. Among all the tested compounds, metabolite 6 (IC50 = 30.3 µg/mL) moderately inhibited the reactive oxygen species (ROS) produced from zymosan-induced human whole blood cells. Compounds 1, 4, 5, 7, and 8 strongly inhibited the proliferation of T-cells with IC50 values between <0.2-10.4 µg/mL. Compound 7 was found to be the most potent inhibitor (IC50 < 0.2 µg/mL), whereas compounds 2, 3, and 6 showed moderate levels of inhibition (IC50 = 14.6-20.0 µg/mL). Compounds 1, and 7 also inhibited the production of pro-inflammatory cytokine TNF-α. All these compounds were found to be non-toxic to 3T3 cells (mouse fibroblast), and also showed no activity when tested against HeLa (human epithelial carcinoma), or against PC3 (prostate cancer) cancer cell lines.

Structural basis of binding and rationale for the potent urease inhibitory activity of biscoumarins.

Urease belongs to a family of highly conserved urea-hydrolyzing enzymes. A common feature of these enzymes is the presence of two Lewis acid nickel ions and reactive cysteine residue in the active sites. In the current study we examined a series of biscoumarins 1-10 for their mechanisms of inhibition with the nickel containing active sites of Jack bean and Bacillus pasteurii ureases. All these compounds competitively inhibited Jack bean urease through interaction with the nickel metallocentre, as deduced from Michaelis-Menten kinetics, UV-visible absorbance spectroscopic, and molecular docking simulation studies. Some of the compounds behaved differently in case of Bacillus pasteurii urease. We conducted the enzyme kinetics, UV-visible spectroscopy, and molecular docking results in terms of the known protein structure of the enzyme. We also evaluated possible molecular interpretations for the site of biscoumarins binding and found that phenyl ring is the major active pharmacophore. The excellent in vitro potency and selectivity profile of the several compounds described combined with their nontoxicity against the human cells and plants suggest that these compounds may represent a viable lead series for the treatment of urease associated problems.

Microbial transformation of anti-cancer steroid exemestane and cytotoxicity of its metabolites against cancer cell lines.

BACKGROUND: Microbial transformation of steroids has been extensively used for the synthesis of steroidal drugs, that often yield novel analogues, not easy to obtain by chemical synthesis. We report here fungal transformation of a synthetic steroidal drug, exemestane, used for the treatment of breast cancer and function through inhibition of aromatase enzyme. RESULTS: Microbial transformation of anti-cancer steroid, exemestane (1), was investigated by using two filamentous fungi. Incubation of 1 with fungi Macrophomina phaseolina, and Fusarium lini afforded three new, 11α-hydroxy-6-methylene-androsta-1, 4-diene-3,17-dione (2), 16ß, 17ß-dihydroxy-6-methylene-androsta-1, 4-diene-3-one (3), and 17ß-hydroxy-6-methylene-androsta-1, 4-diene-3, 16-dione (4), and one known metabolites, 17ß-hydroxy-6-methylene-androsta-1, 4-diene-3-one (5). Their structures were deduced spectroscopically. Compared to 1 (steroidal aromatase inactivator), the transformed metabolites were also evaluated for cytotoxic activity by using a cell viability assay against cancer cell lines (HeLa and PC3). Metabolite 2 was found to be moderately active against both the cell lines. CONCLUSIONS: Biotransformation of exemestane (1) provides an efficient method for the synthesis of new analogues of 1. The metabolites were obtained as a result of reduction of double bond and hydroxylation. The transformed product 2 exhibited a moderate activity against cancer cell lines (HeLa and PC3). These transformed products can be studied for their potential as drug candidates.

Suppression of c-Fos protein and mRNA expression in pentylenetetrazole-induced kindled mouse brain by isoxylitones.

An early immediate gene c-fos has been proposed as the gene responsible for turning on molecular events that might underlie the long-term neural changes occurring during kindling. We have evaluated the effects of novel anticonvulsant isomeric compounds isoxylitones [(E/Z)-2-propanone-1,3,5,5-trimethyl-2-cyclohexen-1-ylidine] on the c-Fos protein and mRNA expression in the brain samples of kindled mice and compared it with the normal and untreated kindled groups. Kindling was induced in male NMRI mice by repeated administration of sub-convulsive dose (50 mg/kg) of pentylenetetrazole (PTZ) until a seizure score of 4-5 was achieved. The c-Fos expression was quantified by combination of immunohistochemistry and RT-PCR protocols. Both the immunohistochemical and RT-PCR analysis revealed a marked increase in the expression of c-fos mRNA and protein in the brain regions tested in case of PTZ-kindled control group compared to normal control. In contrast, the isoxylitone (30 mg/kg)-treated group demonstrated significant reduction of c-Fos expression compared to PTZ-kindled control animals. However, low expression of c-fos mRNA was only detected in the thalamus of the isoxylitone-treated brain samples. Based on these observations, we suggest that isoxylitones may have the capacity to control the seizure pattern by mechanism such as the suppression of c-Fos protein and mRNA levels in different regions of the brain. Further investigations to explore the mechanism of action of these compounds are under process.