Development of a robust parallel and multi-composite machine learning model for improved diagnosis of Alzheimer's disease: correlation with dementia-associated drug usage and AT(N) protein biomarkers.

Introduction: Machine learning (ML) algorithms and statistical modeling offer a potential solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging multiple data sources and combining information on neuropsychological, genetic, and biomarker indicators. Among others, statistical models are a promising tool to enhance the clinical detection of early AD. In the present study, early AD was diagnosed by taking into account characteristics related to whether or not a patient was taking specific drugs and a significant protein as a predictor of Amyloid-Beta (Aß), tau, and ptau [AT(N)] levels among participants. Methods: In this study, the optimization of predictive models for the diagnosis of AD pathologies was carried out using a set of baseline features. The model performance was improved by incorporating additional variables associated with patient drugs and protein biomarkers into the model. The diagnostic group consisted of five categories (cognitively normal, significant subjective memory concern, early mildly cognitively impaired, late mildly cognitively impaired, and AD), resulting in a multinomial classification challenge. In particular, we examined the relationship between AD diagnosis and the use of various drugs (calcium and vitamin D supplements, blood-thinning drugs, cholesterol-lowering drugs, and cognitive drugs). We propose a hybrid-clinical model that runs multiple ML models in parallel and then takes the majority's votes, enhancing the accuracy. We also assessed the significance of three cerebrospinal fluid biomarkers, Aß, tau, and ptau in the diagnosis of AD. We proposed that a hybrid-clinical model be used to simulate the MRI-based data, with five diagnostic groups of individuals, with further refinement that includes preclinical characteristics of the disorder. The proposed design builds a Meta-Model for four different sets of criteria. The set criteria are as follows: to diagnose from baseline features, baseline and drug features, baseline and protein features, and baseline, drug and protein features. Results: We were able to attain a maximum accuracy of 97.60% for baseline and protein data. We observed that the constructed model functioned effectively when all five drugs were included and when any single drug was used to diagnose the response variable. Interestingly, the constructed Meta-Model worked well when all three protein biomarkers were included, as well as when a single protein biomarker was utilized to diagnose the response variable. Discussion: It is noteworthy that we aimed to construct a pipeline design that incorporates comprehensive methodologies to detect Alzheimer's over wide-ranging input values and variables in the current study. Thus, the model that we developed could be used by clinicians and medical experts to advance Alzheimer's diagnosis and as a starting point for future research into AD and other neurodegenerative syndromes.

A fine-tuned vision transformer based enhanced multi-class brain tumor classification using MRI scan imagery.

Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors. Detecting brain tumors in their initial phases is vital for diagnosis where MRI (magnetic resonance imaging) scans play an important role. Over the years, deep learning models have been extensively used for medical image processing. The current study primarily investigates the novel Fine-Tuned Vision Transformer models (FTVTs)-FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32-for brain tumor classification, while also comparing them with other established deep learning models such as ResNet50, MobileNet-V2, and EfficientNet - B0. A dataset with 7,023 images (MRI scans) categorized into four different classes, namely, glioma, meningioma, pituitary, and no tumor are used for classification. Further, the study presents a comparative analysis of these models including their accuracies and other evaluation metrics including recall, precision, and F1-score across each class. The deep learning models ResNet-50, EfficientNet-B0, and MobileNet-V2 obtained an accuracy of 96.5%, 95.1%, and 94.9%, respectively. Among all the FTVT models, FTVT-l16 model achieved a remarkable accuracy of 98.70% whereas other FTVT models FTVT-b16, FTVT-b32, and FTVT-132 achieved an accuracy of 98.09%, 96.87%, 98.62%, respectively, hence proving the efficacy and robustness of FTVT's in medical image processing.

Posaconazole-hemp seed oil loaded nanomicelles for invasive fungal disease.

Invasive fungal infections (IFI) pose a significant health burden, leading to high morbidity, mortality, and treatment costs. This study aims to develop and characterize nanomicelles for the codelivery of posaconazole and hemp seed oil for IFI via the oral route. The nanomicelles were prepared using a nanoprecipitation method and optimized through the Box Behnken design. The optimized nanomicelles resulted in satisfactory results for zeta potential, size, PDI, entrapment efficiency, TEM, and stability studies. FTIR and DSC results confirm the compatibility and amorphous state of the prepared nanomicelles. Confocal laser scanning microscopy showed that the optimized nanomicelles penetrated the tissue more deeply (44.9µm) than the suspension (25µm). The drug-loaded nanomicelles exhibited sustained cumulative drug release of 95.48 ± 3.27% for 24 h. The nanomicelles showed significant inhibition against Aspergillus niger and Candida albicans (22.4 ± 0.21 and 32.2 ± 0.46 mm, respectively). The pharmacokinetic study on Wistar rats exhibited a 1.8-fold increase in relative bioavailability for the nanomicelles compared to the suspension. These results confirm their therapeutic efficacy and lay the groundwork for future research and clinical applications, providing a promising synergistic antifungal nanomicelles approach for treating IFIs.

A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI.

Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository's datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model's trustworthiness in the SDLC.

E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

Optimising barrier placement for intrusion detection and prevention in WSNs.

This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method's effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.

Renormalization of the shell model of turbulence.

Renormalization enables a systematic scale-by-scale analysis of multiscale systems. In this paper, we employ renormalization group (RG) to the shell model of turbulence and show that the RG equation is satisfied by |u_{n}|^{2}=K_{Ko}ε^{2/3}k_{n}^{-2/3}, where Ko is the Kolmogorov constant and ν_{n}=ν_{*}sqrt[K_{Ko}]ε^{1/3}k_{n}^{-4/3}, where k_{n}andu_{n} are the wave number and velocity of shell n; ν_{*}andK_{Ko} are RG and Kolmogorov's constants; and ε is the energy dissipation rate. We find that ν_{*}≈0.5 and K_{Ko}≈1.7, consistent with earlier RG works on the Navier-Stokes equation. We verify the theoretical predictions using numerical simulations.

Reduced Order Machine Learning Models for Accurate Prediction of CO2 Capture in Physical Solvents.

CO2 sorption in physical solvents is one of the promising approaches for carbon capture from highly concentrated CO2 streams at high pressures. Identifying an efficient solvent and evaluating its solubility data at different operating conditions are highly essential for effective capture, which generally involves expensive and time-consuming experimental procedures. This work presents a machine learning based ultrafast alternative for accurate prediction of CO2 solubility in physical solvents using their physical, thermodynamic, and structural properties data. First, a database is established with which several linear, nonlinear, and ensemble models were trained through a systematic cross-validation and grid search method and found that kernel ridge regression (KRR) is the optimum model. Second, the descriptors are ranked based on their complete decomposition contributions derived using principal component analysis. Further, optimum key descriptors (KDs) are evaluated through an iterative sequential addition method with the objective of maximizing the prediction accuracy of the reduced order KRR (r-KRR) model. Finally, the study resulted in the r-KRR model with nine KDs exhibiting the highest prediction accuracy with a minimum root-mean-square error (0.0023), mean absolute error (0.0016), and maximum R2 (0.999). Also, the validity of the database created and ML models developed is ensured through detailed statistical analysis.

Bolgiano-Obukhov spectrum and mixing efficiency in stably stratified turbulence.

In this paper, using a shell model, we simulate highly turbulent stably stratified flow for weak to moderate stratification at unitary Prandtl number. We investigate the energy spectra and fluxes of velocity and density fields. We observe that for moderate stratification, in the inertial range, the kinetic energy spectrum E_{u}(k) and the potential energy spectrum E_{b}(k) show dual scaling-Bolgiano-Obukhov scaling [E_{u}(k)∼k^{-11/5} and E_{b}(k)∼k^{-7/5}] for kk_{B}. In addition, we find that the mixing efficiency η_{mix} varies as η_{mix}∼Ri for weak stratification, whereas η_{mix}∼Ri^{1/3} for moderate stratification, where Ri is the Richardson number.

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain.

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

Blood Pressure Reducing Potential and Renoprotective Action of Cilnidipine Among Hypertensive Patients Suffering From Chronic Kidney Disease: A Meta-Analysis.

Hypertension is a risk factor for cardiovascular diseases which also causes progressive kidney damage leading to chronic kidney disease (CKD), so the rate of progression of CKD can be controlled by reducing blood pressure (BP). Many anti-hypertensive drugs are available. Cilnidipine is a new-generation calcium channel blocker (CCB). This meta-analysis is aimed to generate pooled evidence about the effectiveness of cilnidipine as an anti-hypertensive and to explore its reno-protective actions. Pubmed, Scopus, Cochrane Library, and Google Scholar were searched from January 2000 to December 2022 to include the studies. The pooled mean difference, along with 95% CI, was computed using Revman 5.4.1 software (Revman International, Inc., New York City, New York). The Cochrane risk-of-bias assessment tool was used for bias assessment. This meta-analysis was registered in PROSPERO with Reg. no. CRD42023395224. This meta-analysis included seven studies with 289 participants in the intervention group and 269 in the comparator group, and were selected from Japan, India, and Korea. Systolic blood pressure (SBP) was significantly reduced in cilnidipine treated group among hypertensives with CKD subjects weighted mean difference (WMD) was 4.33, and the 95% confidence interval (CI) was 1.26 to 7.31 as compared to the other group. Cilnidipine also shows a significant reduction in proteinuria with WMD 0.61 and 95% CI 0.42 to 0.80. Both groups were similar in adverse drug reactions (ADR). Cilnidipine is a more effective anti-hypertensive as compared to Amlodipine or other CCBs, mainly in reducing SBP. Besides this, cilnidipine also shows better reno-protective action because it also significantly reduces proteinuria in such patients.

Antioxidant Silk Fibroin Composite Hydrogel for Rapid Healing of Diabetic Wound.

Wound healing is a complex process requiring multiple biological pathways and chemical responses to be activated and synchronized to recover tissue integrity. In normal physiological circumstances, the epidermal barrier restoration process through new tissue formation is highly efficient. However, increased production of reactive oxygen species (ROS), attack of pathogenic microorganisms, and high glucose level delay the normal healing process in diabetic patients. The successful treatment of diabetic wounds requires efficient strategies to control oxidative stress, promoting angiogenesis, re-epithelialization, and collagen deposition. In this study, a composite hydrogel for rapid wound healing in diabetic condition is developed by the amalgamation of hypolipidemic property of silk fibroin (SF), antioxidant property of melanin, and therapeutic effect of berberine. Studies have revealed that cross-linked mesoporous morphology of hydrogel matrix facilitates slow release of berberine to impart long-term therapeutic effects at wound site. The composite hydrogel formulation is biocompatible, stimulates effective migration of fibroblast cells, and control oxidative stress under in vitro conditions. The hydrogel served as scaffold for tissue re-epithelialization and promotes wound repair in diabetic type I Wistar rat model. This study demonstrates the ability of berberine- loaded SF-melanin composite hydrogel as a potential dressing formulation for wound healing in diabetic conditions.

Blockchain-Based Trust Management Framework for Cloud Computing-Based Internet of Medical Things (IoMT): A Systematic Review.

The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational power, the collected data can be processed and analyzed in the cloud by more computationally intensive tools. Cloud computing in IoMT is also used to store IoT data as part of a collaborative effort. Cloud computing has provided new avenues for providing services to users with better user experience, scalability, and proper resource utilization compared to traditional platforms. However, these cloud platforms are susceptible to several security breaches evident from recent and past incidents. Trust management is a crucial feature required for providing secure and reliable service to users. The traditional trust management protocols in the cloud computing situation are centralized and result in single-point failure. Blockchain has emerged as the possible use case for the domain that requires trust and reliability in several aspects. Different researchers have presented various blockchain-based trust management approaches. This study reviews the trust challenges in cloud computing and analyzes how blockchain technology addresses these challenges using blockchain-based trust management frameworks. There are ten (10) solutions under two broad categories of decentralization and security. These challenges are centralization, huge overhead, trust evidence, less adaptive, and inaccuracy. This systematic review has been performed in six stages: identifying the research question, research methods, screening the related articles, abstract and keyword examination, data retrieval, and mapping processing. Atlas.ti software is used to analyze the relevant articles based on keywords. A total of 70 codes and 262 quotations are compiled, and furthermore, these quotations are categorized using manual coding. Finally, 20 solutions under two main categories of decentralization and security were retrieved. Out of these ten (10) solutions, three (03) fell in the security category, and the rest seven (07) came under the decentralization category.

Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net.

Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions. These two methods are combined in which the response of a Cauchy matched filter is used to replace the noisy red channel of the fundus images. Consequently, a U-shaped fully connected convolutional neural network (U-net) is employed to train end-to-end segmentation of pixels into vessel and background classes. Each preprocessed image is divided into several patches to provide enough training images and speed up the training per each instance. The DRIVE public database has been analyzed to test the proposed method, and metrics such as Accuracy, Precision, Sensitivity and Specificity have been measured for evaluation. The evaluation indicates that the average extraction accuracy of the proposed model is 0.9640 on the employed dataset.

Immunity credentials using self-sovereign identity for combating COVID-19 pandemic.

The refugees and migrants are not recorded generally and deemed invisible by governments without providing them with identity and welfare services. The COVID-19 pandemic has badly impacted the economy, and the poor migrants and refugees have suffered most due to the closure of industries and informal sectors. Lack of legal identity made them more vulnerable and excluded them from getting benefits of even meagre government support and welfare schemes. Self-sovereign identity is a form of distributed digital identity that can provide immutable identity with full user control and interoperability features. Self-sovereign identities also ensure the privacy and security of personal information. SSI model can effectively provide migrants and refugees with an effective legal identity and include them in government welfare schemes and other schemes run by non-governmental agencies. Also, SSI can be used for uniquely identifying the people who have been already vaccinated or tested negative from COVID-19 within a stipulated time. This paper reviews the aspects of SSI application during the pandemic situation like COVID-19.

Injectable Silk Fibroin-Based Hydrogel for Sustained Insulin Delivery in Diabetic Rats.

Diabetes is a chronic disease affecting over 400 million people worldwide. Inadequate production of insulin due to loss of beta cells or insulin resistance within the body imbalances the glucose homeostasis, resulting in an abrupt increase of blood glucose level. The conventional and last resort of treatment involves repeated subcutaneous insulin injections to maintain the physiological glucose homeostasis. However, continuous and multiple subcutaneous injections are associated with poor patient compliance and local amyloidosis of insulin, which can be overcome with controlled and sustained insulin delivery. In this context, we have designed and formulated an injectable silk fibroin hydrogel (iSFH) to realize sustained insulin delivery over a prolonged period under diabetic conditions. The specific composition of glycol additives (ethylene glycol and triethylene glycol) allowed the silk fibroin protein to form an injectable hydrogel within 50 min. The detailed characterization of iSFH by a field-emission scanning electron microscope displayed the desired mesoporous structures, which are appropriate for drug (insulin) encapsulation in its active form. Interestingly, the subcutaneous injection of iSFH-encapsulated insulin (insulin-iSFH) in diabetic T1DM Wistar rats showed controlled release of insulin and restored physiological glucose homeostasis up to 4 days. The biocompatible and biodegradable nature of iSFH makes it a potential drug delivery system for active storage, and controlled and sustained delivery of insulin in diabetic conditions to maintain the physiological glucose level.

Natural Product-Based 1,2,3-Triazole/Sulfonate Analogues as Potential Chemotherapeutic Agents for Bacterial Infections.

Despite the vast availability of antibiotics, bacterial infections remain a leading cause of death worldwide. In an effort to enhance the armamentarium against resistant bacterial strains, 1,2,3-triazole (5a-x) and sulfonate (7a-j) analogues of natural bioactive precursors were designed and synthesized. Preliminary screening against two Gram-positive (Streptococcus pneumoniae and Enterococcus faecalis) and four Gram-negative bacterial strains (Pseudomonas aeruginosa, Salmonella enterica, Klebsiella pneumoniae, and Escherichia coli) was performed to assess the potency of these analogues as antibacterial agents. Among all triazole analogues, 5e (derived from carvacrol) and 5u (derived from 2-hydroxy 1,4-naphthoquinone) bearing carboxylic acid functionality emerged as potent antibacterial agents against S. pneumoniae (IC50: 62.53 and 39.33 µg/mL), E. faecalis (IC50: 36.66 and 61.09 µg/mL), and E. coli (IC50: 15.28 and 22.57 µg/mL). Furthermore, 5e and 5u also demonstrated moderate efficacy against multidrug-resistant E. coli strains and were therefore selected for further biological studies. Compound 5e in combination with ciprofloxacin displayed a synergistic effect on multidrug-resistant E. coli MRA11 and MRC17 strains, whereas compound 5u was selective against E. coli MRA11 strain. Growth kinetic studies on S. pneumoniae and E. coli treated with 5e and 5u showed an extended lag phase. 5e and 5u did not show significant cytotoxicity up to 100 µg/mL concentration on human embryonic kidney (HEK293) cells. Transmission electron microscopic (TEM) analysis of bacterial cells (S. pneumoniae and E. coli) exposed to 5e and 5u clearly showed morphological changes and damaged cell walls. Moreover, these compounds also significantly inhibited biofilm formation in S. pneumoniae and E. coli strains, which was visualized by scanning electron microscopic (SEM) analysis. Treatment of larvae of Galleria mellonella (an in vivo model for antimicrobial studies) with 5e and 5u did not cause an alteration in the hemocyte density, thereby indicating lack of an immune response, and were nontoxic up to a concentration of 2.5 mg/mL.

Effect of quinoline based 1,2,3-triazole and its structural analogues on growth and virulence attributes of Candida albicans.

Candida albicans, along with some other non-albicans Candida species, is a group of yeast, which causes serious infections in humans that can be both systemic and superficial. Despite the fact that extensive efforts have been put into the discovery of novel antifungal agents, the frequency of these fungal infections has increased drastically worldwide. In our quest for the discovery of novel antifungal compounds, we had previously synthesized and screened quinoline containing 1,2,3-triazole (3a) as a potent Candida spp inhibitor. In the present study, two structural analogues of 3a (3b and 3c) have been synthesized to determine the role of quinoline and their anti-Candida activities have been evaluated. Preliminary results helped us to determine 3a and 3b as lead inhibitors. The IC50 values of compound 3a for C. albicans ATCC 90028 (standard) and C. albicans (fluconazole resistant) strains were 0.044 and 2.3 µg/ml, respectively while compound 3b gave 25.4 and 32.8 µg/ml values for the same strains. Disk diffusion, growth and time kill curve assays showed significant inhibition of C. albicans in the presence of compounds 3a and 3b. Moreover, 3a showed fungicidal nature while 3b was fungistatic. Both the test compounds significantly lower the secretion of proteinases and phospholipases. While, 3a inhibited proteinase secretion in C. albicans (resistant strain) by 45%, 3b reduced phospholipase secretion by 68% in C. albicans ATCC90028 at their respective MIC values. Proton extrusion and intracellular pH measurement studies suggested that both compounds potentially inhibit the activity of H+ ATPase, a membrane protein that is crucial for various cell functions. Similarly, 95-97% reduction in ergosterol content was measured in the presence of the test compounds at MIC and MIC/2. The study led to identification of two quinoline based potent inhibitors of C. albicans for further structural optimization and pharmacological investigation.

Short peptide based nanotubes capable of effective curcumin delivery for treating drug resistant malaria.

BACKGROUND: Curcumin (Ccm) has shown immense potential as an antimalarial agent; however its low solubility and less bioavailability attenuate the in vivo efficacy of this potent compound. In order to increase Ccm's bioavailability, a number of organic/inorganic polymer based nanoparticles have been investigated. However, most of the present day nano based delivery systems pose a conundrum with respect to their complex synthesis procedures, poor in vivo stability and toxicity issues. Peptides due to their high biocompatibility could act as excellent materials for the synthesis of nanoparticulate drug delivery systems. Here, we have investigated dehydrophenylalanine (ΔPhe) di-peptide based self-assembled nanoparticles for the efficient delivery of Ccm as an antimalarial agent. The self-assembly and curcumin loading capacity of different ΔPhe dipeptides, phenylalanine-α,ß-dehydrophenylalanine (FΔF), arginine-α,ß-dehydrophenylalanine (RΔF), valine-α,ß-dehydrophenylalanine (VΔF) and methonine-α,ß-dehydrophenylalanine (MΔF) were investigated for achieving enhanced and effective delivery of the compound for potential anti-malarial therapy. RESULTS: FΔF, RΔF, VΔF and MΔF peptides formed different types of nanoparticles like nanotubes and nanovesicles under similar assembling conditions. Out of these, F∆F nanotubes showed maximum curcumin loading capacity of almost 68 % W/W. Ccm loaded F∆F nanotubes (Ccm-F∆F) showed comparatively higher (IC50, 3.0 µM) inhibition of Plasmodium falciparum (Indo strain) as compared to free Ccm (IC50, 13 µM). Ccm-F∆F nano formulation further demonstrated higher inhibition of parasite growth in malaria infected mice as compared to free Ccm. The dipeptide nanoparticles were highly biocompatible and didn't show any toxic effect on mammalian cell lines and normal blood cells. CONCLUSION: This work provides a proof of principle of using highly biocompatible short peptide based nanoparticles for entrapment and in vivo delivery of Ccm leading to an enhancement in its efficacy as an antimalarial agent.

Modified dipeptide-based nanoparticles: vehicles for targeted tumor drug delivery.

AIM: Different nanoparticles have been investigated to deliver chemotherapeutic agents, but complex synthesis procedures and biocompatibility issues raise concerns in developing them for safe human usage. The aim of this work is to develop α,ß-dehydrophenylalanine-containing, self-assembled, amphipathic dipeptide nanoparticles for tumor-targeted drug delivery and therapy. MATERIAL & METHODS: Solution-phase peptide synthesis was used to synthesize dipeptides. Nanoparticles were prepared by molecular self-assembly. A tumor distribution study was carried out using a radiolabeling method. Tumor regression studies were carried out in murine ascitic tumors in BALB/c mice and breast tumor xenografts in in nonobese diabetic/severe combined immunodeficiency mice. RESULTS: Arg-α,ß-dehydrophenylalanine formed self-assembled nanoparticles that could be easily derivatized with folic acid. Folic acid-derivatized nanoparticles showed enhanced cellular uptake and, when loaded with doxorubicin, showed enhanced tumor regression compared with underivatized nanoparticles or native drug, without any adverse side effects, both in vitro and in vivo.