Indo-American Journal of Life Sciences and Biotechnology
Editor- In-Chief: Dr. Mai S. Mabrouk Title proper: Indo-American Journal of Life Sciences and Biotechnology Subject: Life Sciences Publisher: Unicorn Publishers ISSN : 2347-2243 Dates of publication: 2012 Frequency: Quarterly Language: English Country: India Medium: Online Indexed by: CROSSREF Indexed by: ROAD Indexed by: THE KEEPERS Title DOI of the Journal: http://doi.org/10.62644/issn.2347-2243
Materials for Pharmaceutical Dosage Forms: Aspects of Controlled Release Drug Delivery and Molecular Pharmaceutics
Compared to immediate release distribution, controlled release delivery has several benefits (such as microparticles and nanoparticles) and is accessible for a variety of administration methods. These benefits include less frequent dosing, improved therapeutic control, and fewer adverse effects; as a result, patients are more receptive to these dose forms. Numerous commercialized controlled release products have been made possible by advancements in polymer material science, particle engineering design, manufacturing, and nanotechnology. Several more are now in pre-clinical and clinical research.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
A study of the benefits and mechanisms of naturally occurring small molecule chemicals in the prevention and treatment of colorectal cancer
Colorectal cancer (CRC) was the third most common disease worldwide in 2022 and the leading cause of cancer deaths that year, and it is projected to significantly increase that number by 2040. Diet, lifestyle, and genetic factors all have a role in the cancerous development of normally functioning colonic cells, which takes a long time (usually more than ten years) and is accompanied by complicated interactions among several cellular pathways. The fact that there are significant variations in CRC incidence and death rates between demographic groupings, geographical locations, and biological sexes further supports the idea that CRC is present. Thus, a great method to lessen the burden of CRC in the next decades might be to intervene or halt the progression of the disease in high-risk groups as soon as possible. Some of the many benefits of NDSMCs—small molecule compounds generated from natural sources—include structural diversity, distinct biological activity, little toxicity, and the ability to target many targets at once. New data reveals that NDSMCs have the ability to treat colorectal cancer (CRC) by regulating apoptosis and autophagy, as well as by influencing the gut microbiota and inducing ferroptosis. Nevertheless, there is a dearth of thorough evaluations in this domain. Therefore, this study aims to give significant insights for treatment methods, preventative measures, and innovative medication development by comprehensively summarizing the research accomplishments over the last 5 years regarding the mechanisms of NDSMCs in battling CRC. We also go over the limits and clinical development of certain NDSMCs in treating CRC.
natural derived small molecule compounds
Siddha-Based Comparative Analysis of Water Quality Using Terminalia chebula and Terminalia bellerica
The purpose of this descriptive observational research was to assess the clinic epidemiological profile and seroprevalence of chronic hepatitis (HBV) and hepatitis C (HCV) in patients at Government Medical College and Memorial Government Hospital in Haldwani, Uttarakhand. and 141 HCV-positive patients (1.97%) during a two-year period. 141 HCV-positive cases (1.97%) and thorough data were used to evaluate the patients. Laboratory testing, imaging investigations, and thorough clinical assessments were used to evaluate the patients; SPSS was used to analyze the data. While HBV patients had substantially higher serum bilirubin (p=0.005) and SGOT levels (p=0.0057), most HCV patients had advanced liver disease, as shown by higher rates of ascites (13.97%), jaundice (11.73%), and increased urea levels (p=0.03). Age, gender, and education did not significantly vary across groups, according to sociodemographic analysis; however, geographic patterns indicated that HBV was more common in Nainital (63.2%) and HCV was more common in Udham Singh Nagar (27.7%) and Bijnor (29.1%). These results highlight the critical need for focused interventions to lower the incidence of viral hepatitis in marginalized communities, such as awareness campaigns, preventative measures, and region-specific healthcare initiatives.
Exosomal Delivery of Bipactive Compounds from Ancient Chinese Botanicals in the Management of Neurological Disorders
A bioactive substance derived from Chinese herbal remedies with known biological activity and pharmacological effects is known as a traditional Chinese medicine (TCM) monomer. It has drawn a lot of interest for the treatment of neurological disorders. However, TCM monomers' weak solubility and poor blood-brain barrier (BBB) crossing capabilities restrict their use. With special benefits including low toxicity, low immunogenicity, high blood stability, and the capacity to pass the blood-brain barrier, exosomes—small extracellular vesicles (EVs)) with a diameter of 30 to 150 nm—can be used as drug delivery vehicles that directly target cells or tissues. In addition to comparing exosomes with other comparable drug delivery methods, this study covers the biogenesis, components, stability, surface modification, separation technique, benefits, and drawbacks of exosomes as drug carriers. Exosome-encapsulated TCM monomers also have neuroprotective effects, such as anti-oxidation, anti-apoptosis, anti-inflammation, and anti-mitophagy, in a variety of neuronal diseases, such as multiple sclerosis, Alzheimer's disease, Parkinson's disease, and cerebral ischemia and reperfusion (CI/R) injury. They also have anti-drug resistance, anti-tumorigenesis, and anti-angiogenesis properties, as well as promoting the development of an exosome-based delivery tool in targeted therapy for neuronal diseases.
Neuropharmacological Profiling of Acanthus ilicifolius: A Coastal Mangrove's Potential in CNS Depression and Analgesia"
An exceptionally abundant supply of significant chemicals with structurally unique and physiologically active metabolites may be found in the marine environment. Mangrove plants have long been utilized in traditional medicine to cure a variety of illnesses all around the globe. Very few efforts have been undertaken to test the validity of these claims in controlled trials, despite the fact that several mangroves and mangal companions are suggested in traditional medicine as being effective against a variety of ailments. Acanthus ilicifolius Linn, often known as sea holly (acanthaceae), is a salty plant that is typically found around the southeast coastlines of India. It is well-known for its wide range of secondary metabolites and its traditional uses. Using a Soxhlet apparatus, the leaves of Acanthus ilicifolius were gathered, shade-dried, ground into a coarse powder, defatted with petroleum ether, and then extracted with ethanol. Assessment of the effects of an ethanolic extract of Acanthus ilicifolius leaves as a central nervous system depressant. Mice were subjected to a number of behavioral tests, including the Tail Suspension Test (TST) and the Forced Swimming Test (FST). The immobility periods in the FST extract at dosages of 100, 250, and 500 mg/kg were considerably (P<0.0001) longer than those in the control group. When compared to a normal medication, TST considerably lengthens the immobility period when the dosage is increased. The drug's interaction with the GABA ergic system may be the cause of these depressed patterns.
Macrophage-Based Therapeutic Strategies: Phenotypic Transition Control and Drug Delivery System Development
Macrophages, being highly phenotypic changeable immune cells, have distinct functions in different clinical situations. Modifying and managing macrophage morphologies is seen as a unique possible therapeutic intervention. In the meanwhile, several transmembrane proteins that attach on the macrophage membrane's surface remain comparatively preserved, maintaining its functional characteristics including tumor targeting and inflammatory chemotaxis. As a result, a number of medication delivery mechanisms associated with certain macrophage membrane proteins are often used to treat chronic inflammatory disorders. The regulation of macrophage phenotypes and their polarization processes, the design and implementation of site-specific targeted drug delivery systems in vivo based on macrophages and their derived membrane receptors, and a summary of macrophage-based strategies for chronic diseases are all covered in this review. It seeks to advance knowledge of macrophages' role in immunoregulation and suggests tailored therapy strategies based on macrophages for chronic illnesses.
Effects of Phosphate Fertilizers on Soybean Performance
For the development and production of soybeans (Glycine max L.), phosphorus (P) is an essential macronutrient. The effects of different phosphorus fertilizer rates on soybean growth, development, and yield characteristics are examined in this research. Plant height, leaf area index, pod number, seed output, and phosphorus use efficiency (PUE) were all assessed in field studies. The results show that at ideal phosphorus application rates, growth and yield metrics were improved, but at excessive or inadequate rates, performance was hampered. The research offers helpful suggestions for sustainable phosphorus management and emphasizes the significance of phosphorus optimization in raising soybean output.
Economic Evaluation of FDA-Approved Small Molecule Protein Kinase Inhibitors in Oncology and Non-Oncology Indications in the United States
Protein kinases are the focus of several drug discovery initiatives across the globe because genetic changes such as mutations, overexpression, translocations, and dysregulation of these enzymes contribute to the pathophysiology of numerous diseases. Of the 80 small molecule protein kinase inhibitors that the FDA has authorized, 77 are oral bioavailable medications. According to the information, 69 of these medications are authorized to treat neoplasms, including solid tumors like lung and breast cancer as well as non-solid tumors like leukemia. The remaining 11 medications also target non-neoplastic conditions such ulcerative colitis, rheumatoid arthritis, and psoriasis. The FDA label was used to calculate the dose and quantity of pills needed daily, and the cost of the medications was retrieved from www.pharmacychecker.com. Any government or private insurance coverage, which would pay the whole cost or, more likely, a portion of the advertised price, is not included in this technique. Treatment of neoplastic illnesses averaged $17,900 a month, with futibatinib (used to treat cholangiocarcinomas with FGFR2 fusions) costing $44,000 and binimetinib (melanoma) costing at least $5100. With a high of $17,000 for belumosudil (graft vs. host disease) and a minimum of $200 for netarsudil eye drops (glaucoma), the average monthly cost for treating non-neoplastic disorders was $6800. The incidence of the targeted condition is negatively correlated with the cost of the medications. Since there are less than 200,000 possible patients in the US, several of these medications are or were categorized as orphan pharmaceuticals.
Stress tolerance and adaptation as criteria for evaluating top rice genotypes in both typical and water-limited settings
The results showed that compared to irrigated circumstances, crop height, effective panicles per m², and grain production were all 22% lower under moisture-restricted conditions. Among the strains tested, IR17L1387, IR16L1713, and IR16L1801 had the best stress tolerance index (STI), suggesting that they might thrive in environments with limited water availability. Sukhadhan 3 genotype showed the highest degree of stability in both settings, according to GGE biplot analyses (Which-Won-Where and Mean vs. Stability). Furthermore, under conditions where moisture was scarce, the winning genotype was IR16L1713, while in conditions where water was abundant, the winning genotype was IR17L1387.
The ultrastructure, control, and function of plasmodesmata in the transport of assimilates across plant cells
Direct symplastic transport of chemicals, including photoassimilates, is made possible by tiny channels called plasmodesmata, which link neighboring plant cells. Their function in regulating the transfer of assimilates from source tissues to sink tissues and in intercellular communication is critical. The efficiency of sucrose and other carbohydrate transport is affected by the form, frequency, and permeability of plasmodesmata, which in turn affects plant growth and production. Under both normal and stress settings, plasmodesmata play an important role in nutrient partitioning, and this review delves into their ultrastructure and the molecular processes that control their regulation. Recent developments in imaging methods, molecular gating, and strategies for loading and unloading phloem with plasmodesmal function are highlighted. By delving into these channels, we may get valuable insights into how to enhance crop production by precisely manipulating assimilate transport processes.
SYNTHESIS AND CHARACTERISATION OFCHIRAL MIXEDLIGANDCO(II) COMPLEXES:A STUDY OF BIOLOGICAL SIGNIFICANCE
Chiral Mixedligand complexes of Cobalt(II), containing two different ligands and one metal center, have been synthesised with the general molecular formula [Co(PClINAP)(aa)·2H₂O], where PClINAP represents the sodium salt of p-chloroisonitrosoacetophenone and "aa" denotes an L-amino acid. Chiral mixed ligand complexes have been characterized using various physicochemical techniques, including elemental analysis, molar conductance measurements, magnetic susceptibility, electronic absorption spectroscopy, infrared (IR) spectroscopy, and thermogravimetric, differential thermal analysis. Based on the obtained data, the bonding and structural features of the complexes have been thoroughly discussed. Additionally, the mixed-ligand metal complexes were evaluated for their biological significance against selected microbial strains.
Comparative Anti-inflammatory Activity of NSAIDs and Herbal Extracts in Carrageenan-Induced Paw Edema
To assess the anti-inflammatory properties of fresh Zingiber officinale rhizome aqueous and methanolic extracts: 1. To use the Carrageenan-induced paw edema technique to test the anti-inflammatory activity of all extracts in two dosages and the standard medication in wistar rats. 2. To contrast the two extracts' anti-inflammatory properties with those of a prescription medication. The research was carried out with consent from the animal ethics committee. There were six groups of animals. While test groups received aspirin (150 mg/kg), aqueous extract of fresh Z. officinale rhizome (100 and 200 mg/kg), and methanolic extract of fresh Z. officinale rhizome (100 and 200 mg/kg), the control group received 0.2 ml NS IP. Ginger's anti-inflammatory properties were examined using an acute model of inflammation that included rat paw edema caused by carrageenan. Aqueous and methanolic extracts of fresh rhizome had considerable anti-inflammatory effect (p < 0.05) in the acute model of inflammation when compared to control, and at all time intervals, their activity was equivalent to that of aspirin. Gingiber officinale extracts, both aqueous and methanolic, have strong anti-inflammatory effects.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
Simultaneous Estimation of Multicomponent Dosage Forms Using UV-Spectroscopy and Chemometric Approaches
These days, UV-visible spectroscopy is widely used as a quick, easy, accurate, and very accurate technique for quantitative estimation. The fundamental idea behind this method is that the analyte concentration is directly correlated with the quantity of light absorbed. While multi-component analysis may be used on any degree of spectral overlap as long as two or more spectra are not precisely the same, the simultaneous equation is only useful for estimating medications whose spectra overlap appropriately. Before a medicine is introduced to the market, quantitative assessment is required since a formulation with a higher concentration may create toxicity issues, while one with a lower concentration may not work as intended.
Streptococcus mutans Adhesion: A Controlled In vitro Investigation Using Bracket and Composite Material in Relation to Saliva pH
The risk of caries is increased when Streptococcus mutans adheres to orthodontic equipment, which leads to the production of biofilms. The purpose of this research was to determine the in vitro effects of bracket material, composite type, and saliva pH on S. mutans adhesion. The following procedures were used to evaluate six different combinations of brackets and composites: plastic, metal, ceramic brackets with light-cure and self-cure composites, and artificial saliva with a pH of 5 and a pH of 7, respectively. Six samples were produced for each of the twelve groupings. Following the development of the pellicle, the samples were exposed to 1.5 × 10¹ CFU/mL of S. mutans and the number of colony-forming units (CFUs) was measured. With a significance level of 0.05, the data were examined using generalized estimating equations (GEEs). The results showed that at a pH of 5, the average CFU/mL for metal brackets was 59.10 ± 5.23, 93.11 ± 83.25, and for ceramic brackets it was 87.77 ± 35.61. Two kinds of composites were produced: 72.18 ± 67.92 for light-cure and 308.93 ± 75.19 for self-cure. A significant main impact of bracket material (P = 0.001) and an interaction between brackets and composites (P = 0.001) were seen when the two variables were combined, however GEE did not find any significant effects of either bracket material (P = 0.183) or composite type (P = 0.966) when examined separately. The averages for ceramic, metal, and plastic brackets at pH 7 were 816.38 ± 50.72, 393.32 ± 67.50, and 671.41 ± 83.25 CFU/mL, respectively. Light-cure and self-cure composites demonstrated 84.33 ± 78.94 and 243.22 ± 67.43 CFU/mL, respectively. According to GEE, there was no interaction (P = 0.234), a composite effect (P = 0.001), and a bracket effect (P = 0.095) that was not statistically significant. The results show that the saliva pH controls the material impacts on the adherence of S. mutans. Adhesion differences are driven by bracket-composite interactions in acidic circumstances, whereas the composite type predominates at neutral pH. The oral pH environment and material matching should be considered while selecting an appliance.
Staphylococcus mutans, dental materials, orthodontic equipment, saliva, and bacterial adherence are all important concepts to consider.
Acute Respiratory Syndrome and Parasitic In Vitro Effects of Pouteria alnifolia Extracts and Fractions on Coronavirus Type 2 Activity
Background: Herbal medicine is popular all across the globe, including in tropical Africa, but there isn't a ton of research to back up claims about its safety and effectiveness. Pouteria alnifolia was the subject of this investigation because of its possible use as a substitute anti-infective. The methods used in this study included bioactivity-guided isolation of P. alnifolia extracts from various parts of the plant, and the determination of the fractions' and extracts' cytotoxicity, anti-SARS-CoV-2, antitrypanosomal, and antileishmanial properties in vitro using the alamarBlue assay. Microscopical analysis of cell counts after parasite exposure to varying concentrations of chosen extracts and fractions allowed researchers to deduce the growth kinetics of Trypanosoma brucei brucei. While no anthracene or anthraquinone derivatives were detected in any of the fractions that were analyzed, the most prevalent phytochemicals found in these fractions were cardiac glycosides and terpenoids. In contrast to the butanol (BuOH) portions of the fruit pulp, stem, and roots, the dichloromethane (DCM) extracts of the seeds and whole fruit (WF) also included alkaloids. Leishmania donovani had IC50 values ranging from 1.3 ± 0.1 to 27.8 ± 1.8 g/mL, while T. b. brucei had values between 07 ± 0.0 and 59.5 ± 0.3 g/mL. BuOH fractions from seeds and WFs were particularly potent against SARS-CoV-2, whereas DCM extracts from stem, seeds, and WFs reduced the development of T. b. brucei. Conclusion: P. alnifolia extracts and fractions show promise as chemotherapeutic agents for a range of tropical African illnesses; additional research is needed to confirm this.
Variation in the Genomic Composition of Plasmodium falciparum Merozoite Surface Proteins 1 and 2 in Nigerian Children Less Than 5 Years Old in the State of Delta
In context, Nigeria has the greatest malaria burden among African countries, and the continent itself is still the malaria hub. Pharmacogenomics, genetic reference panels, and malaria biomarkers all need an upgrade to meet the current worldwide issues of malaria resistance. Children under the age of five in certain regions of Delta State, southern Nigeria, were studied to determine the genetic diversity and structure of Plasmodium falciparum merozoite surface proteins (msps 1 and 2). Methods: Due to low parasite density, P. falciparum deoxyribonucleic acid was recovered from 86 samples that tested positive for malaria out of 690 people. The msp1 and msp2 allele genotyping was examined by electrophoresis, polymerase chain reaction, and an ultraviolet-trans illumination gel method. Findings: MAD20+RO33+K1 (67.4% of cases) and 3D7+FC27 (90% of cases) were the most common forms of polyclonal infection for msp1 and msp2, respectively. Ro33 (36.3% of the total) and FC27 (53.4% of the total) were the most common msp1 and msp2 alleles, respectively. Furthermore, the allelic makeup of the research population did not vary significantly between men and females. But msp1 had an infection multiplicity of 1.10 and msp2 had an infection multiplicity of 1.08, whereas the corresponding heterozygosity values were 0.67 and 0.50, respectively. Conclusion: In Delta State, Nigeria, a high transmission rate of P. falciparum infection was observed, suggesting a large degree of genetic middling heterozygous alleles. These results emphasize the need for targeted and locally sourced anti-malaria treatments to be developed.
Plasmodium falciparum, genetic diversity, heterozygosity, merozoite surface proteins, malaria, infection multiplicity, pharmacogenomics
An Academic Referral Hospital's Evaluation of Three Staining Methods for Liver Fibrosis in Biopsy Samples
Context: The accumulation of extracellular matrix components, especially elastic fibers and collagen, causes liver fibrosis to worsen over time, causing the organ to stop working as it should. In order to diagnose, predict, and treat fibrosis, accurate histological staging is required. Common fibrosis evaluation stains include hematoxylin and eosin (H and E), Masson's trichrome (MT), and orcein (OR), although there is a lack of clarity about the relative efficacy and connection of these stains in everyday practice. Approach: From 2021 to 2023, 76 liver biopsy samples were reviewed in this retrospective analysis. The patients were sent to Taleghani Hospital in Tehran, Iran. We stained all of the samples with H&E, MT, and OR. Skilled pathologists staged the fibrosis using the Ishak grading method. The agreement and correlation between staining procedures were evaluated using statistical analysis, which included Fisher's exact test. Significant results were defined as P values less than 0.05. The results show that with 76 patients (34.2% male, mean age 41.2 years), the finding of fibrosis was most common at Stage 2 for MT (35.5%) and Stage 3 for OR (23.7%). H and E and MT showed significant differences in fibrosis staging (P < 0.001), as with MT and OR (P = 0.006). At Stage 2, there was a significant difference between MT and OR (P = 0.003). Importantly, in 36.8% of instances, OR staining revealed a lower level than MT, but in 23.7% of cases, OR staining revealed a higher stage. When looking at individuals between the ages of 0 and 32 (P = 0.032) and males (P = 0.036), there were statistically significant differences between MT and OR. In summary: In order to evaluate liver fibrosis, our results show that MT and OR staining techniques provide complementing information. The noticeable differences, particularly in the intermediate phases, indicate that OR staining, when used in conjunction with MT, improves diagnostic precision. For a thorough assessment of fibrosis in liver biopsies, it is advised to routinely utilize both stains.
Liver fibrosis, Masson's trichrome, orcein, hematoxylin and eosin, histopathology
Data-Driven Decisions: Clinical Data's Impact on Patient Outcomes SRINIVAS MADDELA
The integration of data-driven decision-making into clinical practice has become an essential strategy for enhancing patient outcomes. With advancements in healthcare technology, an increasing volume of clinical data is being generated through electronic health records (EHRs), wearables, diagnostic devices, and patient monitoring systems. By leveraging this vast repository of clinical data, healthcare providers can make more informed, evidence-based decisions that improve the quality of care, reduce errors, and optimize treatment pathways. This research article examines the impact of clinical data on patient outcomes, the role of data analytics in enhancing clinical decisions, challenges in implementing data-driven models, and the future potential of data-driven healthcare. It provides insights into how clinical data can empower healthcare professionals to deliver more personalized, timely, and effective care.
Copyright (c) 2015 https://creativecommons.org/licenses/by-nc-nd/4.0
Quantum-Resistant Cyber Defence in Nation-State Warfare: Mitigating Threats with Post-Quantum Cryptography
Quantum computing, very likely going to be a threat against current cryptographic systems especially with respect to war between nations, where secure communication and secured data access are very important. Conventional encrypting schemes such as RSA or ECC depend on mathematical problems that can easily be solved by quantum algorithms, typically Shor's, which makes such schemes vulnerable to attacks in the future. This research investigates how PQC is implemented to combat the looming threats of quantum cyber warfare so that national security is assured. Among the most powerful PQC candidates are lattice based cryptographic techniques like NTRU Encrypt, Kyber and Dilithium, all representing quantum-resilient security. The research looks at the time taken for key generation relative to various security levels and quantum attack detection rates, analyzing in detail how these PQC algorithms stand in protecting military communications and critical infrastructure. The inference seems to be that though security levels increase, so does the computational overhead, calling for optimization strategies for realistic deployment. Also, a novel adaptive quantum attack detection system can be appended to render threat identification more efficient along the years. The joining of lattice-based encryption with quantum-resistant cloud storage and secure communication protocols will thus yield a scalable efficient model for post-quantum cyber defense. Above all, the research states the need for quickly changing cryptographic standards to those which afford resistance to quantum attacks to secure sensitive data.
Copyright (c) 2018 https://creativecommons.org/licenses/by-nc-nd/4.0
ENHANCING CLOUD COMPUTING WITH EBPF POWERED SDN FOR SECURE AND SCALABLE NETWORK VIRTUALIZATION
Since cloud computing provides scalable and on-demand resources, it has transformed modern IT infrastructure. Nevertheless, high latency, security vulnerabilities and scalability limitations are significant challenges that conventional SDN-based cloud infrastructures have to contend with. By integrating eBPF technology to perform packet processing efficiently dynamic threat control and scalable resource management, this paper proposes an eBPF-based SDN architecture to enhance cloud network virtualization. This proposed solution enhances network flow for effortless scalability with less resource overhead provides security through deep packet inspection and reduces network latency through fewer control plane interactions. The findings substantiate that the security and traffic management mechanisms of eBPF heavily enhance cloud infrastructure resilience and adaptability. Performance tests and large-scale simulations demonstrate that eBPF-SDN improves packet processing latency by 40 percent, threat detection accuracy by 35 percent and network throughput by 50 percent compared to conventional SDN architectures. This paper presents a new hybridized method ensuring high performance, security and scalability to next-generation cloud computing scenarios.
ENHANCING FINANCIAL PREDICTIONS USING LSTM AND CLOUD TECHNOLOGIES: A DATA-DRIVEN APPROACH
The increasing complexity of financial markets necessitates advanced predictive models for informed decision-making. This study explores the integration of Long Short-Term Memory (LSTM) networks and cloud technologies to enhance financial forecasting accuracy and computational efficiency. By leveraging LSTMs' ability to capture long-term dependencies in time-series data and cloud computing's scalable infrastructure, this approach improves model performance while addressing computational constraints. Our results demonstrate that cloud integration optimizes resource utilization, reduces training time, and enhances real-time predictive capabilities. However, challenges such as data noise, security concerns, and real-time prediction reliability remain. The findings suggest that combining advanced preprocessing, hybrid AI models, and secure cloud environments can further refine financial forecasting methodologies.
Social Engineering Attack Prevention Through Deep NLP and Context-Aware Modeling
Social engineering attacks are among the most insidious and psychologically driven types of cyberattacks, founded on targeting individuals' trust instead of the technical flaws they exploit. It is sometimes challenging for normal protection mechanisms since normal detection depends so heavily on static rules as well as surface word forms. This paper introduces an effective, smart social engineering attack detection system based on a hybrid deep learning model combining RoBERTa and LSTM networks with an additional context-aware module. The method employs RoBERTa to capture the deep semantic context of text and LSTM to identify sequential patterns in communication. The context-aware module provides value to the model with the addition of external metadata such as sender reputation, organizational identity, communication pattern, urgency indicator and behavior indicators—critical in psychological manipulation identification. It utilizes exhaustive feature engineering to produce linguistic characteristics such as n-grams, POS tags and sentiment along with contextual characteristics such as authority, persuasion strategies and interaction frequency. The model is trained and tested on a hand-gathered dataset of real-world social engineering situations. This study demonstrates the strength of deep NLP unification and contextual awareness for active cybersecurity via a scalable and adaptive approach to defeat advanced social engineering attacks.
AI-DRIVEN LIVER CANCER DIAGNOSIS AND TREATMENT USING CLOUD COMPUTING IN HEALTHCARE
Liver disease is a global health problem that needs to be diagnosed early and accurately in order to receive the right treatment. In this paper, an AI-solution for liver disease diagnosis and treatment on a cloud computing platform has been suggested with high accuracy and real-time testing. patients were kept in consideration keeping in mind the data set, and ML and DL algorithms are utilized in classification. Scalability, secure data processing, and real-time processing can be ensured by cloud computing, thus enhanced efficiency in medical decision-making. According to the research, AI diagnosis of liver disease can minimize diagnosis errors and enhance patient outcomes using more efficient and accurate disease prediction. It is the first to show the coming revolution in cloud computing and artificial intelligence in medicine on which a computerized and evidence-based platform has been developed for medical decision-making. Post-preprocessing of the data and feature engineering, AI model had 96.5% accuracy, 94.8% precision, 91.2% recall, and 92.5% F1-score, which is enhanced in comparison with conventional diagnostic apparatuses. Optimization in the future will consist of handling greater amounts of data, better algorithms in AI, and integration of imaging modalities to deliver greatest clinical utility. The findings confirm the ability of AI to redesign disease diagnosis process to enable early diagnosis, optimized treatment protocols, and optimal efficiency of care in treatment of liver disease.
Efficient Remote Patient Monitoring Using Multi-Parameter Devices and Cloud with Priority-Based Data Transmission Optimization
Increasingly, cloud computing is transforming how data is managed and analyzed financially, providing scalable and secure solutions for SMEs and healthcare applications. Nevertheless, the remote patient monitoring (RPM) systems that have been established thus far encounter problems such as high latency, a poor usage of available bandwidth, as well as vulnerabilities in data security, thus restricting healthcare decision-making in real-time. Traditional encryption and authentication methods do not hold enough value against new-age evolving cyber threats and thus risk patient data privacy, whereas established means of data transmission do not prioritize significant health parameters leading to bring about delayed emergency response. E-RPM revolves proposing a very cost effective and safe Remote Patient Monitoring system on an integrated multi-parameter wearable device, cloud computing, and priority-based optimization of data transmission process for real-time health monitoring. Threats are detected using AI on hybrid encrypted data and then authenticated through multiple factors so that the system provides integrity of data as well as patient privacy and secured communication. The crux of this includes noise reduction and normalization of health data preprocessing, compression of data for high efficiency in storage and transmission, then the last nugget would be Convolutional Neural Networks (CNN) for predictive health analytics. It decreases congestion and proposes a priority-based transmission model which utilize bandwidth for real-time delivery of critical health data and reduced latencies.
Implementing Generative Adversarial Networks and Cloud Services for Identifying Breast Cancer in Healthcare Systems
The study primarily aims at investigating the deployment of GAN and cloud services in the identification of breast cancer. Providing a method of deep learning models integrated into cloud computing offers processing power and storage facilities on scalable and on-demand bases; it is the most critical solution when considering the huge bulk of medical data accumulated in healthcare systems and IoT-wearables. The research emphasizes that preprocessing methods, such as data normalization and augmentation, will improve the GAN model's efficiency. The features are extracted by Autoencoders and then classified via GANs for breast-cancer-image classification. This model has features like easy accessibility, hosted on-cloud, and scalable for deployment. Accuracy, precision, recall, and F1 score are among the used performance metrics to evaluate the effectiveness of the model. The results indicate high classification accuracy and robustness of the model, suggesting potential of employing cloud-based GANs for the breast cancer detection clinical settings.
A Hybrid CNN-BiLSTM Model for Heart Disease Prediction on Cloud-Hosted Health Data
Heart disease remains a leading cause of mortality worldwide, necessitating advanced predictive models for early detection and prevention. Traditional machine learning techniques, though widely used, often struggle with feature dependencies and sequential patient data analysis. This study proposes a Hybrid Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-BiLSTM) model for heart disease prediction. The CNN component extracts high-level spatial features from clinical parameters, while the BiLSTM captures temporal dependencies in patient records, enhancing classification performance. The model is trained and evaluated on the Heart Disease Ensemble Classifier dataset, achieving an accuracy of 99.57%, precision of 99.48%, recall of 99.65%, and an F1-score of 99.56%. Furthermore, the AUC-ROC score of 0.9968 and average precision (AP) of 0.9970 demonstrate superior classification capability. Comparative analysis against traditional classifiers such as Support Vector Machines (SVM), Decision Trees, and Random Forest highlights the effectiveness of the proposed approach. The results indicate that the hybrid deep learning model significantly improves heart disease prediction by reducing false negatives and increasing overall reliability.
Enhancing AI-Cloud Computing in Healthcare with BERT for Clinical Text Understanding
Financial fraud detection has become increasingly complex due to the rising sophistication of fraudulent activities and the need to preserve user privacy. Traditional fraud detection systems rely on centralized data processing, posing risks to data security and regulatory compliance. This study proposes a BERT-based Federated Learning (FL) framework integrated with Graph Neural Networks (GNNs) for privacy-preserving and scalable fraud detection. The framework enables multiple financial institutions to collaboratively train fraud detection models without sharing raw data, ensuring compliance with privacy regulations such as GDPR and CCPA. Additionally, deep reinforcement learning (DRL) optimizes fraud detection strategies by dynamically adjusting classification thresholds to reduce false positives. The model is trained and evaluated on the Bank Account Fraud Dataset Suite (NeurIPS 2022), achieving an accuracy of 96.2%, precision of 94.1%, recall of 95.6%, and an AUC-ROC score of 0.982, significantly outperforming traditional machine learning approaches such as Random Forest (88.2%) and Centralized CNN models (91.3%). Moreover, the proposed FL framework reduces communication overhead by 37% compared to traditional centralized approaches, making it highly efficient for real-world cloud environments. The experimental results demonstrate that the proposed framework enhances fraud detection performance while maintaining data confidentiality, offering a robust, privacy-preserving AI solution for modern financial institutions.
Enhancing Healthcare Cloud Security with a Comprehensive Analysis for Authentication
This research presents a robust cloud-based healthcare security framework that integrates Multi-Factor Authentication (MFA) and Secure Multi-Party Computation (SMPC) to safeguard sensitive patient data. The framework ensures data privacy, secure access control, and encryption for electronic health records (EHRs) stored in the cloud. The proposed system is evaluated based on authentication time, encryption/decryption performance, and computational overhead. Experimental results indicate that encryption time ranges from 0.5ms to 5.8ms as data size increases from 10 KB to 1 MB, with decryption following a similar pattern. Additionally, computational overhead increases by approximately 18% with each additional authentication factor, reinforcing security without significantly compromising efficiency. The integration of MFA ensures that unauthorized access is effectively minimized, while SMPC enhances privacy by enabling secure data computations without exposing sensitive information. This approach demonstrates a significant improvement in balancing security and system performance, making it a viable solution for real-time healthcare applications. Future enhancements may focus on optimizing encryption techniques and reducing computational complexity to further improve efficiency. This research contributes to the ongoing development of secure and scalable cloud-based healthcare solutions, offering a practical approach to addressing security challenges in modern healthcare data management.
Cloud-Enabled Time-Series Forecasting for Hospital Readmissions Using Transformer Models and Attention Mechanisms
Hospital readmissions are one of the biggest issues in healthcare. They increase the cost and worsen patient outcomes, putting additional strain on already-over-stretched resources. Traditional models for prediction often fail to handle the complexity and high dimensionality of healthcare data. This paper outlines a solution: an approach to the predictive model for hospital readmission using transformer models with attention mechanisms deployed on cloud infrastructure. This approach allows scalability and real-time processing, providing a healthcare professional with more accurate, actionable insights that reduce unnecessary hospitalizations and help in enhancing patient outcomes. The methodology allows the processing of complex time-series data, like vital signs, lab results, and treatment history, thus making the model focus on key features and time periods for improving predictions. The cloud-based infrastructure lets the model scale and adapt dynamically to new data. The experimental results reveal that the proposed model outperforms traditional methods, achieving 88% accuracy, 85% precision, 83% recall, 84% F1-score, and 92% AUC. Future improvements may include integration of genomic data and multi-center deployment.
Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-nd/4.0
Scalable Healthcare Analytics in the Cloud: Applying Bayesian Networks, Genetic Algorithms, and LightGBM for Pediatric Readmission Forecasting
Pediatric readmissions present significant challenges in healthcare, leading to increased costs, poor health outcomes, and psychological stress on families. Traditional prediction models often struggle to handle the complexity and variability of healthcare data, which limits their predictive capabilities. In response, this study explores the potential of integrating AI and cloud computing to enhance prediction accuracy and scalability for pediatric readmissions. The objective is to compare three machine learning techniques—Bayesian Networks, Genetic Algorithms, and LightGBM—in a cloud-based environment to determine the most effective method for predicting pediatric readmissions at scale. Bayesian Networks are used to model complex interdependencies between patient characteristics, while Genetic Algorithms are applied for feature optimization and selection. LightGBM, known for its speed and accuracy in handling large datasets, provides fast predictions with high accuracy. The models are implemented on a cloud platform, enabling scalable, real-time predictions using diverse data sources, such as electronic health records and patient demographics. The results show that the AI-powered cloud computing model outperforms individual methods, achieving an accuracy of 88%, precision of 85%, recall of 83%, F1-score of 84%, and an AUC of 92%. This combination of machine learning techniques significantly improves prediction accuracy, scalability, and real-time performance in healthcare applications. The study concludes that integrating Bayesian Networks, Genetic Algorithms, and LightGBM within a cloud environment offers a robust solution for predicting pediatric readmissions, making it a valuable tool for reducing unnecessary hospitalizations, optimizing care delivery, and improving patient outcomes in pediatric healthcare systems.
Copyright (c) 2020 https://creativecommons.org/licenses/by-nc-nd/4.0