Sulfur (Sâ ¥)-containing heterocyclic hybrids as antibacterial agents against methicillin-resistant Staphylococcus aureus (MRSA) and its SAR.

The discovery of new small molecule-based inhibitors is an attractive field in medicinal chemistry. Structurally diversified heterocyclic derivatives have been investigated to combat multi-drug resistant bacterial infections and they offers several mechanism of action. Methicillin-resistant Staphylococcus aureus (MRSA) is becoming more and more deadly to humans because of its simple method of transmission, quick development of antibiotic resistance, and ability to cause hard-to-treat skin and filmy diseases. The sulfur (SVI) particularly sulfonyl and sulfonamide based heterocyclic moieties, have found to be good anti-MRSA agents. The development of new nontoxic, economical and highly active sulfur (SVI) containing derivatives has become hot research topics in drug discovery research. Presently, more than 150 FDA approved Sulfur (SVI)-based drugs are available in the market, and they are widely used to treat various types of diseases with different therapeutic potential. The present collective data provides the latest advancements in Sulfur (SVI)-hybrid compounds as antibacterial agents against MRSA. It also examines the outcomes of in-vitro and in-vivo investigations, exploring potential mechanisms of action and offering alternative perspectives on the structure-activity relationship (SAR). Sulfur (SVI)-hybrids exhibits synergistic effects with existing drugs to provide antibacterial action against MRSA.

Recent developments of P-glycoprotein inhibitors and its structure-activity relationship (SAR) studies.

P-glycoprotein (P-gp) over-expression is a key factor in multi-drug resistance (MDR), which is a major factor in the failure of cancer treatment. P-gp inhibitors have been demonstrated to have powerful pharmacological properties and may be used as a therapeutic approach to overcome the MDR in cancer cells. Combining clinical investigations with biochemical and computational research may potentially lead to a clearer understanding of the pharmacological properties and the mechanisms of action of these P-gp inhibitors. The task of turning these discoveries into effective therapeutic candidates for a variety of malignancies, including resistant and metastatic kinds, falls on medicinal chemists. A variety of P-gp inhibitors with great potency, high selectivity, and minimal toxicity have been identified in recent years. The latest advances in drug design, characterization, structure-activity relationship (SAR) research, and modes of action of newly synthesized, powerful small molecules P-gp inhibitors over the previous ten years are highlighted in this review. P-gp transporter over-expression has been linked to MDR, therefore the development of P-gp inhibitors will expand our understanding of the processes and functions of P-gp-mediated drug efflux, which will be helpful for drug discovery and clinical cancer therapies.

Development of Pharmacovigilance System in India and Paradigm of Pharmacovigilance Research: An Overview.

The drugs are projected to cure, prevent and treat diseases; however, there are also chances of mild or severe adverse drug reactions (ADRs) in the patients. Pharmacovigilance (PV) enhances the approach to safe medicines and healthcare, but integration into public healthcare remains a challenge in many countries. The Pharmacovigilance Programme of India (PvPI) is a nationwide programme launched by the Ministry of Health and Family Welfare (MoHFW), Government of India, on 14 July 2010. It is currently run by the Indian Pharmacopoeia Commission (IPC). Presently, 567 ADR Monitoring Centres (AMC) are functioning under PvPI across India. The Central Drugs Standard Control Organization (CDSCO), under the Directorate General of Health Services (DGHS), and the Ministry of Health, Family & Welfare (MoHFW) Government of India is the National Regulatory Authority (NRA) of India. This review aims to trace the development of PV in India and its status among the top ADR reporting countries, with a focus on the current status of the studied ADR in different regions in India. Among the top 20 countries enthusiastic about sending ICSR per million population between 2015 and 2020, India shares 2% of the total ICSRs in VigiBase. India is now being recognized as a hub of global clinical trials and clinical research studies. Hence, it is the need of the hour in India to have a dynamic PV network system with an efficient and prudent operation method. Nevertheless, in India, PV programs are at an early stage on par with other countries, and only because of underreporting of ADRs. However, in the future, PvPI may appear as a big dashboard for ADR reporting culture in India as it continuously works hard to improve patients and drug safety.

Cross-covariance based affinity for graphs.

The accuracy of graph based learning techniques relies on the underlying topological structure and affinity between data points, which are assumed to lie on a smooth Riemannian manifold. However, the assumption of local linearity in a neighborhood does not always hold true. Hence, the Euclidean distance based affinity that determines the graph edges may fail to represent the true connectivity strength between data points. Moreover, the affinity between data points is influenced by the distribution of the data around them and must be considered in the affinity measure. In this paper, we propose two techniques, C C G A L and C C G A N that use cross-covariance based graph affinity (CCGA) to represent the relation between data points in a local region. C C G A L also explores the additional connectivity between data points which share a common local neighborhood. C C G A N considers the influence of respective neighborhoods of the two immediately connected data points, which further enhance the affinity measure. Experimental results of manifold learning on synthetic datasets show that CCGA is able to represent the affinity measure between data points more accurately. This results in better low dimensional representation. Manifold regularization experiments on standard image dataset further indicate that the proposed CCGA based affinity is able to accurately identify and include the influence of the data points and its common neighborhood that increase the classification accuracy. The proposed method outperforms the existing state-of-the-art manifold regularization methods by a significant margin.

A rotation based regularization method for semi-supervised learning.

In manifold learning, the intrinsic geometry of the manifold is explored and preserved by identifying the optimal local neighborhood around each observation. It is well known that when a Riemannian manifold is unfolded correctly, the observations lying spatially near to the manifold, should remain near on the lower dimension as well. Due to the nonlinear properties of manifold around each observation, finding such optimal neighborhood on the manifold is a challenge. Thus, a sub-optimal neighborhood may lead to erroneous representation and incorrect inferences. In this paper, we propose a rotation-based affinity metric for accurate graph Laplacian approximation. It exploits the property of aligned tangent spaces of observations in an optimal neighborhood to approximate correct affinity between them. Extensive experiments on both synthetic and real world datasets have been performed. It is observed that proposed method outperforms existing nonlinear dimensionality reduction techniques in low-dimensional representation for synthetic datasets. The results on real world datasets like COVID-19 prove that our approach increases the accuracy of classification by enhancing Laplacian regularization.

Catalytic pyrolysis of ulva lactuca macroalgae: Effects of mono and bimetallic catalysts and reaction parameters on bio-oil up-gradation.

Catalytic pyrolysis of ulva lactuca (UL) macroalgae was studied over a series of ZrO2 supported metal such as Co, Ni and Co-Ni metal catalysts at temperature range of 300-500 °C. Highest bio-oil yield (47.8 wt%) was found with Co-Ni/ZrO2 (10 wt%) catalyst while non-catalytic yielded 42.5 wt% bio-oil. Moreover with increases the metal amount to 15 wt%, the bio-oil yield slightly increased (49.2 wt%). The bio-oil quality significantly improved with using the catalysts compared to the non-catalytic pyrolysis. Catalytic pyrolysis also revealed that introducing Co-Ni into the ZrO2 could result in higher surface area and which increased active sites. Catalytic bio-oils were consisted of mainly long chain hydrocarbon in the range of C6-C16. Moreover, the catalytic bio-oils were showed the higher 'high heating value' (HHV) 38.1 MJ/kg as compare to non-catalytic bio-oils (29.4 MJ/kg). Catalysts have been showed excellent recyclability on bio-oil yield and compounds selectivity.

Anti-tuberculosis activity and its structure-activity relationship (SAR) studies of oxadiazole derivatives: A key review.

With the increasing number of cases of inactive and drug-resistance tuberculosis, there is an urgent need to develop new potent molecules set for fighting this brutal disease. Medicinal chemistry concerns the discovery, the development, the identification, and the interpretation of the mode of action of biologically active compounds at the molecular level. Molecules bearing oxadiazoles are one such class that could be considered to satisfy this need. Oxadiazole regioisomers have been investigated in drug discovery programs for their capacity to go about as powerful linkers and as pharmacophoric highlights. Oxadiazoles can go about as bioisosteric substitutions for the hydrazide moiety which can be found in first-line anti-TB drugs, and some have been likewise answered to cooperate with more current anti-TB targets. This present review summarizes the current innovations of oxadiazole-based derivatives with potential antituberculosis activity and bacteria discussing various aspects of structure-activity relationship (SAR).

Interpreting SVM for medical images using Quadtree.

In this paper, we propose a quadtree based approach to capture the spatial information of medical images for explaining nonlinear SVM prediction. In medical image classification, interpretability becomes important to understand why the adopted model works. Explaining an SVM prediction is difficult due to implicit mapping done in kernel classification is uninformative about the position of data points in the feature space and the nature of the separating hyperplane in the original space. The proposed method finds ROIs which contain the discriminative regions behind the prediction. Localization of the discriminative region in small boxes can help in interpreting the prediction by SVM. Quadtree decomposition is applied recursively before applying SVMs on sub images and model identified ROIs are highlighted. Pictorial results of experiments on various medical image datasets prove the effectiveness of this approach. We validate the correctness of our method by applying occlusion methods.

Role of semi-purified andrographolide from Andrographis paniculata extract as nano-phytovesicular carrier for enhancing oral absorption and hypoglycemic activity.

Objective: Andrographis paniculata is a well-known medicinal plant in Southeast Asia, India and China. The plant contains andrographolide (AN), a very important phytochemical used in various health problems. However, AN is low in oral absorption bioavailability of AN due to the rapid clearance and high protein binding capacity. Methods: The present study was aimed to develop a nano-phytovesicular formulation of semi-purified AN extracts from a naturally occurring phospholipid (soya phosphatidylcholine) in order to increase the oral absorption and antihyperglycemic activity in rats. Results: The nano-phyto vesicle of semi-purified AN extracts equivalent to 25 mg /kg AN significantly protected the hyperglycemic condition of rats. The in vitro and in vivo experiments results proved that the nano- phytovesicular system of plant extracts containing AN produced better oral absorption, bioavailability and improved antihyperglycemic activity compared with that of free AN at dose of 50 mg/kg. Conclusion: Hence, the prepared semi-purified extract nano-phytovesicular system is helpful in solving the problem of rapid clearance of AN.

Enhancement of site specific delivery of diloxanide furoate as an antiamoebic drug.

The basic aim of the present research work is to deliver the diloxanide furoate (DF) at specific area using pectin microspheres. The microspheres were prepared by spray drying method and cross-linked by zinc acetate. Different concentrations of polymer (pectin 0.5-3%) and cross-linking agent (0-3% w/v in a mixture of ethanol:water) are taken to optimize the entrapment efficiency, swelling behavior, size and first 6h in-vitro release in simulated gastric fluids. Optimized formulation was characterized in the terms of in-vitro release, in-vivo drug disposition in various organs and in the blood of Sprague-Dawley albino rats and in-vivo gastrointestinal tract transit behavior using X-ray imaging method on albino rabbits. Findings suggested that microspheres containing a concentration of polymer (2% w/v) have average size of 100-500 µm, entrapment efficiency 85.82 ± 0.5 with swelling index 18.77 ± 5.21. In-vitro results and in-vivo gastric transit behavior (using X-ray imaging) have shown no release in first 3-6h that proved the colon specific delivery of DF. The results also suggested that the above approach have not only site specific delivery, but it improves the conversion of active drug by increasing the enzyme mediated hydrolytic degradation of DF due to the presence of polysaccharide polymer:water gel complex.

Anti-tumour, anti-mutagenic and chemomodulatory potential of Chlorophytum borivilianum.

In the present investigation anti-tumour, anti-mutagenic and chemomodulatory potential of Chlorophytum borivilianum were evaluated. Chlorophytum borivilianum root extract had no toxic effect up to a dose of 800 mg/kg body weight/day. Significant increase (p<0.05 to p<0.001) in the activity of reduced glutathione (GSH), catalase (CAT) and superoxide dismutase (SOD) and a significant decrease in the hepatic MDA level were observed at 100, 400, and 800 mg/kg body weight of Chlorophytum borivilianum root extract when compared with the control value. Skin papillomagenesis studies demonstrated a significant (p<0.001) decrease in cumulative numbers of papilloma, tumour incidence, tumour burden, tumour size and tumour weight and significant (p<0.01) increase in average latent period when the animals received Chlorophytum borivilianum root extract at a dose level of 800 mg/kg body weight/day orally in double distilled water at pre, peri and post initiation stages of carcinogenesis. A significant reduction in the frequency of chromosomal aberration and micronuclei was observed in the treated animals as compared to carcinogen controls. The present investigation suggests that Chlorophytum borivilianum has anti-tumour, anti-mutagenic and chemomodulatory effects.

Feature selection using Haar wavelet power spectrum.

BACKGROUND: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. RESULTS: Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem 1. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special software or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. CONCLUSION: In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for classification based on Haar wavelet power spectrum. Application of this technique in this area is novel, simple, and faster than other methods, fit for a wide range of data types. The results are encouraging and throw light into the possibility of using this technique for problem domains like disease classification, gene network identification and personalized drug design.