International Journal of Cell Science and Biotechnology
International Journal of Cell Science and Biotechnology (IJCSB-ISSN: 2320-7574) is a multidisciplinary and peer-reviewed open-access journal that provides a medium for rapid publication of innovative aspects of molecular cell biology and biotechnology. The journal invites contributions from researchers on topics including advances in pathogenesis at the molecular level, Molecular events in membrane trafficking, ER, Golgi, microtubules, motors, Integrins, cell adhesion, intermediate filaments, membrane-cytoskeletal interactions, protein trafficking, vesicles, autophagy, mitochondria, actin, dynamin, and membrane sorting, etc. It also covers the topics on events taking place at molecular level during embryo production, mammalian cloning, transgenesis and genetic engineering, stem cell technology, cryopreservation of germ cells, embryos and somatic cell banking, biotechnology of gut microbiome, nutrigenomics, fodder and feed biotechnology, biotechnology in aquaculture and marine ecosystem, immunogenetics, genomics. The journal publishes full-length articles, short communications, and current perspectives as well as appropriate review articles. For review articles, the authors are requested to first send the synopsis of the review for evaluation by the editorial board. Call for papers in the following fields: Embryo production, mammalian cloning, transgenesis, genetic engineering, cell and stem cell technology, cryopreservation of germplasm, embryos and somatic cell banking, conservation of animal genetic diversity, biotechnology of gut microbiome, nutrigenomics, fodder and feed biotechnology, biotechnology in aquaculture and marine ecosystem, immunogenetics, animal genomics and proteinomics and veterinary immunotechnology
Artificial Intelligence for Intelligent Stormwater Management in Environmental Engineering
Rapid urbanization and the intensification of extreme weather events driven by climate change have placed unprecedented stress on conventional stormwater management infrastructure worldwide. This paper presents a comprehensive review and synthesis of Artificial Intelligence (AI) methodologies applied to intelligent stormwater management across the full engineering lifecycle — from real-time flood prediction and drainage network optimization to adaptive control of green infrastructure and climate-resilient urban planning. Drawing on a corpus of over 120 peer-reviewed studies published between 2018 and 2025, we evaluate the performance of machine learning (ML), deep learning (DL), reinforcement learning (RL), and physics-informed neural networks (PINNs) against conventional hydrological simulation models. Our analysis demonstrates that AI-driven systems can achieve up to 40% improvement in flood prediction accuracy, reduce combined sewer overflow events by 30%, and cut operational energy costs by 22% compared to rule-based control strategies. We also identify critical challenges including data scarcity in developing regions, model interpretability gaps, and cross-cultural barriers in technology adoption. The study proposes a globally inclusive framework — the Intelligent Stormwater Management Architecture (ISMA) — that integrates AI with participatory design principles to ensure equitable and sustainable urban water governance. Findings underscore the transformative potential of AI as a core pillar of next-generation environmental engineering practice.
Stormwater management, Artificial intelligence, Machine learning, Flood prediction, Urban hydrology, Deep learning, Reinforcement learning, Green infrastructure, Climate resilience, Environmental engineering
Large Language Models as Cognitive Support Systems for Neurodivergent Individuals
As a cognitive extension of human reasoning, communication, and task management, the use of Large Language Models (LLMs) is being investigated more and more. This paper focuses on their possible role to assist neurodivergent people in three ways: by making cognitive function more accessible, making communication clearer, and by lessening executive functioning demands. Drawing from cognitive science and human–computer interaction, the research views LLMs as malleable instruments that can facilitate the planning, language, memorization, and social interaction of their users. The discussion focuses on how these systems can be incorporated into educational, occupational and assistive environments, to foster inclusion and cognitive equity. It also examines architectural strategies for personalisation, adaptive prompting and context-aware support and the potential dangers of relying too heavily, misunderstanding intent and accessibility imbalance. In summary, LLMs can be considered potential allies in neurodivergent support networks and are not just assistive tools, as they may help to increase autonomy and participation in more complex cognitive activities.
Large Language Models, Neurodiversity, Cognitive Support Systems, Assistive Artificial Intelligence, Human–Computer Interaction, Cognitive Accessibility, Adaptive Systems, Executive Function Support, Communication Assistance, Inclusive Technology
Scalable Enterprise Architecture for Biotechnology Operations: Challenges and Emerging Solutions
Scalable enterprise architecture has become a critical enabler for biotechnology operations, driven by increasing data intensity, cloud adoption, and the need for integrated digital infrastructures across research and industrial ecosystems. This study examines the structural, technological, and organizational challenges associated with scaling enterprise systems in biotechnology, with particular attention to interoperability, regulatory compliance, and computational complexity. Drawing on recent advancements in cloud computing, artificial intelligence, and decentralized systems, the paper explores emerging architectural solutions that enhance adaptability, efficiency, and system resilience in biotech environments. Findings indicate that while cloud-based and AI-driven infrastructures significantly improve scalability and operational performance, persistent challenges remain in governance alignment, data integration, and cross-organizational coordination. The study further highlights the role of data-driven enterprise architecture, digital twins, and blockchain-enabled systems in transforming biotechnology operations toward more autonomous and efficient models. Overall, the research contributes to a deeper understanding of how scalable enterprise architecture can support innovation and operational excellence in biotechnology industries.
enterprise architecture, biotechnology scalability, cloud computing, artificial intelligence, digital transformation, decentralized systems, data-driven architecture
Bioceramic-Mediated Pulp Preservation in Immature Permanent Teeth: A Regenerative Continuum Model
Preservation of pulp vitality in immature permanent teeth remains a critical goal in contemporary endodontic practice due to its importance in continued root development and long-term tooth survival. Bioceramic materials have emerged as bioactive agents capable of supporting pulp healing, dentin bridge formation, and regenerative responses through their biocompatibility, sealing ability, and stimulation of cellular activity. This abstract proposes a regenerative continuum model in which bioceramic-mediated pulp preservation is viewed not as a single therapeutic endpoint, but as a spectrum ranging from pulp protection and repair to partial and complete tissue regeneration. Within this framework, vital pulp therapy and regenerative endodontic procedures are interconnected biological processes influenced by material properties, pulp status, and host response. Understanding pulp preservation through this continuum model allows for more flexible clinical decision-making and highlights the role of bioceramics in promoting favorable biological outcomes in immature permanent teeth. This approach supports a paradigm shift toward biologically driven, minimally invasive strategies aimed at sustaining pulp vitality and enabling natural root maturation.
Bioceramics; Pulp preservation; Immature permanent teeth; Regenerative endodontics; Vital pulp therapy; Regenerative continuum model.
Predictive Artificial Intelligence Models for Long-Term Caries Risk Based on Early Childhood Oral Microbiome Patterns
Early childhood caries is a prevalent chronic disease influenced by complex interactions within the oral microbiome. Understanding microbiome patterns offers a promising avenue for predicting long-term caries risk. This study explores the development of predictive artificial intelligence (AI) models that leverage early childhood oral microbiome profiles to forecast future caries susceptibility. Longitudinal microbiome and clinical data from a pediatric cohort were analyzed using machine learning algorithms, including feature selection and model optimization techniques, to identify key microbial signatures associated with caries progression. Results demonstrate that AI-driven models can accurately stratify children by long-term caries risk, enabling early, personalized preventive interventions. These findings highlight the potential of integrating microbiome-based AI predictions into pediatric dental care to reduce the burden of dental caries.
Early childhood caries, oral microbiome, predictive modeling, artificial intelligence, machine learning, pediatric dentistry, long-term risk
AI-Based Early Detection of Enamel Hypomineralization Patterns in Primary Dentition Using Texture Analytics
Enamel hypomineralization in primary dentition is a prevalent condition that can lead to increased dental sensitivity, decay, and aesthetic concerns if not detected early. Traditional diagnostic methods rely on clinical examination, which may be subjective and prone to delayed detection. This study proposes an AI-based framework for early detection of enamel hypomineralization patterns using texture analytics on intraoral images. High-resolution images of primary teeth were processed to extract texture features such as Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), which were then analyzed using machine learning models to identify characteristic hypomineralization patterns. The proposed approach demonstrated high accuracy and sensitivity in distinguishing affected enamel from healthy tissue, highlighting the potential of AI-assisted diagnostics to support pediatric dental care. Early, non-invasive detection of enamel hypomineralization can improve preventive strategies and reduce the risk of long-term dental complications.
Enamel Hypomineralization, Primary Dentition, AI, Texture Analytics, Machine Learning, Early Detection, Pediatric Dentistry.
Cognitive Learning Models for Predicting Apical Microleakage Using Pre- and Post-Operative CBCT Data
Apical microleakage remains a major factor affecting the long-term success of endodontic treatment, often leading to treatment failure if undetected. Conventional diagnostic methods rely heavily on clinical expertise and qualitative image interpretation, which can be subjective and limited in sensitivity. This study proposes the application of cognitive learning models to predict apical microleakage by leveraging pre- and post-operative cone-beam computed tomography (CBCT) data. The framework integrates imaging-derived features from both treatment stages to capture structural and morphological changes associated with microleakage development. Advanced learning techniques are employed to model complex spatial patterns and variations within CBCT scans, enabling improved predictive accuracy compared to traditional assessment approaches. Experimental results demonstrate that the proposed cognitive learning-based approach enhances early detection and risk prediction of apical microleakage, supporting more informed clinical decision-making. The findings highlight the potential of intelligent imaging analysis to augment endodontic diagnostics and contribute to improved treatment outcomes.
Apical microleakage, Cognitive learning models, Cone-beam computed tomography (CBCT), Endodontics, Medical image analysis, Predictive modeling.
Big Data in Public Health: Real-Time Epidemiology Using Mobility and Environmental Data to Predict Outbreaks
The recent booming growth of Big Data analytics has revolutionized the contemporary surveillance of the health of human populations by providing real-time epidemiology that can spot outbreaks more quickly than conventional surveillance mechanisms. This paper examines how mobility data and environmental data can be integrated to better predict the outbreak of infectious diseases at hand. Based on massive data sets obtained through the movements of mobile phones, satellite derived environmental signals, and sensor generated weather forecasts, we have created a spatial-temporal predictive model based on the state-of-the-art machine learning models. The approach included the preprocessing of the heterogeneous data, the development of the outbreak risk models, and the assessment of the predictive performance in the form of the accuracy, sensitivity, and the space correlation measures. Findings indicate that changes in population mobility have a strong relationship with the dynamics of disease transmission, and the environmental factors especially temperature, humidity, and air quality are effective modulating factors of an outbreak. Together, such data sources enhance accuracy in prediction and contribute to the creation of real-time outbreak risk maps. These results demonstrate how Big Data, real-time epidemiology, and predictive analytics can enhance the quality of decisions in the field of public health, improve resource distribution, and contribute to proactive control. This paper finds that mobility and environmental data integration offer a powerful base upon which the next generation system of public health surveillance could be built.
Mobility Data, Environmental Data, Predictive Analytics, Outbreak Prediction,
Copyright (c) 2020 International Journal of Cell Science and Biotechnology
Effect of Deep Margin Elevation on Marginal Integrity and Microleakage of Indirect Restorations
Another conservative option has been suggested to replace surgical crown lengthening, Deep Margin Elevation (DME), which is to be used when indirect restorations necessitate subgingival margins. The technology is based on the movement of the cervicular margin towards a more supragingival position with the help of adhesive restorative materials, which would favour impressions-taking, adhesive bonding, and general clinical management. Recent data indicate that DME could maintain periodontal health, increase access to restoration, and enhance the marginal adaptation of the indirect restorations. Nevertheless, its influence on marginal integrity and microleakage is still an area of research, and the differences can be seen based on restorative materials, adhesive guidelines, and margin position. Laboratory and clinical research suggests that although DME is able to attain similar sealing capacity to conventional margins placement, some limitations including the inconsistency of the operator and adhesive system choice should be taken into consideration. This review examines the biological and clinical justification of DME and its impact on marginal integrity and the evidence on microleakage with an emphasis on clinical implications and future research.
margin elevation, Marginal integrity, Microleakage, Indirect restorations, Adhesive dentistry.
Clinical Decision Support Systems Using AI for Endodontic Retreatment vs. Extraction
Clinical decision-making in endodontics often hinges on whether to pursue nonsurgical retreatment or proceed with extraction and prosthetic replacement. Traditional approaches rely heavily on clinician experience, radiographic interpretation, and patient-specific factors such as periodontal status, coronal integrity, and systemic health. However, these processes can be subjective and prone to variability. The integration of artificial intelligence (AI) into Clinical Decision Support Systems (CDSS) offers a novel framework for improving the objectivity, consistency, and accuracy of treatment planning in complex endodontic cases. AI technologies including machine learning, deep learning, and radiomics are increasingly capable of analyzing cone-beam computed tomography (CBCT) scans, periapical radiographs, and electronic health records to predict treatment outcomes with high precision. Early studies demonstrate that AI-driven models can identify prognostic indicators for both retreatment success and implant survival, enabling clinicians to weigh therapeutic options more systematically. Despite these advances, challenges remain regarding data standardization, algorithm transparency, and medico-legal accountability. Furthermore, patient-centered considerations, including cost-effectiveness and individual preferences, must be integrated into AI-supported recommendations. Looking ahead, the convergence of multimodal data, validated predictive models, and user-friendly interfaces may foster collaborative human–AI decision-making, supporting clinicians while preserving the primacy of professional judgment. Ultimately, AI-enabled CDSS has the potential to enhance clinical outcomes, optimize resource allocation, and promote shared decision-making in the management of teeth requiring complex restorative or surgical interventions.
Clinical Decision Support Systems, Artificial Intelligence, Endodontic Retreatment, Tooth Extraction, Implant Dentistry, Prognosis, Decision-Making.
Impact of Irrigation Activation Techniques on Healing of Periapical Lesions: A Radiographic Evaluation
Periapical lesions are considered to be the typical outcome of pulpal infection, and can be treated mostly with the help of deep chemomechanical debridement of the root canal system. The efficiency of irrigation is vital in the removal of microbial biofilms, but traditional syringe irrigation is not always effective at canal anatomies that are considered complex. Sonic and ultrasonic agitation techniques have been added to provide irrigation activation methods that will promote canal penetration by the irrigant, as well as improve canal disinfection. In the study, the researcher intended to assess the effectiveness of various irrigation activation methods in the healing of periapical lesions using radiographic methods. The patients who came to the clinic with periapical pathosis were provided with the standardized endodontic treatment and divided into three irrigation groups including the use of a conventional syringe, sonic, or passive ultrasonic irrigation. Radiographic evaluation was done at baseline and follow-up periods by the use of the periapical index (PAI) scoring to measure the lesion regression and healing. Findings showed that the sonic as well as the ultrasonic activation method produced a superior percentage of complete or substantial healing in comparison with the conventional irrigation. The highest improvement in the outcome of periapical healing was observed in ultrasonic activation. These findings suggest that activation of irrigants plays a pivotal role in enhancing periapical tissue repair and supports the routine incorporation of activation techniques into endodontic practice.
Periapical lesion, irrigation activation, ultrasonic irrigation, sonic irrigation, root canal healing, radiographic evaluation.
Copyright (c) 2022 International Journal of Cell Science and Biotechnology
Assessment of Marginal Adaptation and Microleakage in Restorations Using Different Restorative Materials
Microleakage and marginal adaptation are very important factors that determine the survival and clinical success of dental restorations. Marginal non-adaptation may cause microleakage, which causes secondary caries, pulpal irritation, postoperative sensitivity and subsequent restoration failure. The paper is a review and comparison of the performance of various restorative materials such as composite resins, glass ionomer cements (GICs), resin-modified GICs and bulk-fill composites in terms of marginal sealing ability and resistance to microleakage. Recent evidence from 2021 research indicates that traditional composites have been shown to have better aesthetics and mechanical strength, but suffer due to polymerization shrinkage, which undermines marginal integrity. The use of bulk-fill composite possesses less shrinkage stress caused by altered resin matrices and filler technology, which enhances adaptation. GICs modified with resin and more recently high-viscosity GICs have greater fluoride release and chemical adhesion to tooth structure that leads to reduced microleakage, especially in dentin margins. But still, there is no material that offers an optimal seal in every clinical situation. The results indicate that the choice of restorative material must be based on the cavity design, location of the tooth and clinical needs. Additional research in vivo needs to be conducted over a longer period of time to confirm laboratory findings.
Marginal adaptation, Microleakage, Composite resin, Bulk-fill composites, Glass ionomer cement, Restorative materials, Dental restoration.
Copyright (c) 2025 International Journal of Cell Science and Biotechnology
Bioactive Root Canal Sealers: A Review of Material Science and Clinical Outcomes
Root canal sealers have evolved in transitioning away of inert obturation materials with active bioactive formulations that are actively involved in the healing process. Traditional sealers, though efficient in the provision of a seal, are sometimes not biologically integrated and stable enough as time goes by, thus coming the calcium silicate based and bioceramic sealers. These bioactive sealers demonstrate the ability to release calcium and hydroxyl ions, trigger the formation of hydroxyapatite and improve the formation of the biological seal at the dentin-sealer interface, which improves clinical outcomes. Their position is also complemented by the developments in material science and the endodontic adjuncts such as imaging and irrigation systems, which have refined diagnostic accuracy and treatment predictability. Evidence supports their favorable biological interactions, particularly in vital pulp therapy and regenerative procedures, where bioactive sealers foster tissue healing and hard tissue deposition. Comparative studies on root canal filling systems highlight their superior sealing ability, mechanical strength, and contribution to tooth survival, even under functional stresses. Besides, as artificial intelligence is becoming more and more part of endodontic diagnostics, the prospect of bioactive materials harmonizing with digital workflows has become an exciting prospect. Although the clinical outcomes are promising, it has difficulties in managing characteristics and cost. Long-term randomized trials are also required; however, bioactive root canal sealers represent a paradigm shift in endodontics, offering not only physical sealing but also biological activity that aligns with the principles of tissue preservation and regeneration.
Bioactive sealers, bioceramics, calcium silicate, endodontics, root canal therapy, clinical outcomes.
Copyright (c) 2024 International Journal of Cell Science and Biotechnology
Weed Management in Direct Seeded Rice under Different Row Spacing
A field study to evaluate the weed management in direct seeded rice under different row spacing was carried out at Agronomic Research Area, University of Agriculture, Faisalabad during summer 2018. Two herbicides Butachlor @ 118.61 g a.i. ha-1 (pre-emergence) was applied within 24 hours of sowing of direct seeded rice and combination of two herbicides Butachlor @ 118.61 g a.i. ha-1 (pre-emergence) within 24 hours after sowing sequentially followed by Bispyribac Sodium + Bensulfurin Methyl @ 26.7 g a.i. ha-1 + 17.8 g a.i. ha-1 (post-emergence) was applied 15 days after sowing. Bispyribac Sodium @ 26.7 g a.i. ha-1 was applied 15 days after sowing. A weedy check and weed free treatment were also run for comparison. A fine rice cultivar (Basmati 515) was sown during the last week of June in 22.5 cm and 11.25 cm spaced rows using a seed rate of 29 kg ha-1. To fulfill the requirement of nutrients 135 kg N, 88 kg P and 61 kg ha-1 K were applied. The experiment was replicated thrice in a randomized complete block design under split plot arrangement. The net plot size was 3 m × 2.25 m. Date regarding weed density (15, 30 and 45 DAS), weed biomass before harvesting and yield components of rice were recorded by following standard procedures and statistically analyzed by applying Fisher's analysis of variance technique. Least significant difference test (LSD) at 5% probability was used to compare treatments means. Results of the study showed that total weed density and weed dry weight were significantly influenced by different row spacing and weed control treatments. Significantly lower weed density and weed biomass were observed under narrow row spacing (11.25 cm) as compared to wider row spacing (22.5 cm). Among weed control treatments application of Butachlor @ 118.61 g a.i. ha-1 (pre-emergence) within 24 hrs after sowing of DSR and Bispyribac Sodium @ 26.7 g a.i. ha-1 (15 DAS) reduced the total weed density and weed dry weight. Similarly, paddy yield was also significantly influenced by different row spacing and weed control treatments as weed free at 11.25 cm row spacing gave maximum paddy yield (4.0 t ha-1).
Antioxidant and Antibacterial Activities of Sagina Procumbens
Sagina Procumbens, a traditional medicinal plant, was examined for its antioxidant activity with the 1, 1-diphenyl-2picrylhydrazyl (DPPH) assay, its total flavonoid levels with the Folin-Ciocalteu method, and its total phenolics with the Folin-Ciocalteu method. According to the findings of the study, the ethanolic extract of Sagina Procumbens leaf has a percentage of antioxidant activity (AA%) of 86.61, which was shown to be greater when compared to other extracts of buds and stem. In a similar vein, the methanolic extract of the stem has a high flavonoid concentration, whereas the phenolic level of the ethanolic extract of the bud is significantly higher. Antimicrobial activity exhibited by S. procumbens has a very significant zone of inhibition against gram-positive as well as gram-negative bacterial strains. The findings of this study indicated that plants are excellent sources of natural antioxidants, and that both ethanolic and methanolic extracts of plants contain a sizeable quantity of antioxidant chemicals.
Sagina Procumbens, extraction in different solvents, antioxidant activity
Total flavonoid contents, Antimicrobial activity
Bioactive Compounds in Tomatoes and Their Effects on Human Health -A Comprehensive Review
Bioactive compounds are the constituents of foods, especially functional foods that provide beneficial health properties. For example, these benefits include antioxidants, anti-inflammatory, antifungal, and various additional preventative properties, which illustrates how bioactive compounds are the real-life example of Hippocrates’ notion “let thy food be thy medicine”. Originally, many definitions of functional foods neglected to acknowledge the importance of bioactive compounds. Many of these compounds have antioxidant activities and are effective in protecting the human body against various oxidative stress-related diseases. Dietary tomatoes increase the body’s level of antioxidants, trapping reactive oxygen species and reducing oxidative damage to important biomolecules such as membrane lipids, enzymatic proteins and DNA, thereby ameliorating oxidative stress. We reviewed the nutritional and phytochemical compositions of tomatoes. In addition, the impacts of the constituents on human health, particularly in ameliorating some degenerative diseases, are also discussed. Moreover, tomato and its bioactive components hold potential to become effective modules in diet-based regimens; however, integrated research and meta-analysis are still required to enhance meticulousness.
Navigating Neurogenesis: Small Molecules as Key Players in Astrocyte Differentiation
Astrocytes play a critical role in maintaining the central nervous system (CNS) homeostasis, supporting neuronal function, and responding to injury. Recent advances in stem cell biology have highlighted the potential of small molecules to direct the differentiation of astrocytes from pluripotent stem cells. These small molecules offer a powerful tool for both basic research and therapeutic applications, allowing precise control over astrocyte development and function. This review explores the mechanisms by which small molecules influence astrocyte differentiation, focusing on key signaling pathways, transcription factors, and epigenetic modifications. Additionally, we discuss the therapeutic implications of small molecule-induced astrocyte differentiation in neurodegenerative diseases, CNS injuries, and other neurological conditions. The review also outlines future directions for research in this rapidly evolving field, emphasizing the need for further exploration of small molecules' role in astrocyte biology and their potential for clinical translation.
Astrocyte differentiation, small molecules, neurogenesis
neurodegenerative diseases, epigenetics, signaling pathways
Applications of stem cells in human degenerative diseases
Stem cells hold immense potential for the treatment of various human diseases due to their unique ability to differentiate into different cell types and their capacity for self-renewal. The applications of stem cells in regenerative medicine have opened new avenues for the treatment of degenerative diseases, such as Parkinson's disease, Alzheimer's disease, and spinal cord injuries, by replacing damaged or lost cells with healthy ones derived from stem cells. In cardiovascular diseases, stem cells are being explored for their potential to repair and regenerate damaged heart tissue, offering hope for patients with heart failure and myocardial infarction. In addition to regenerative medicine, stem cells are being utilized in the field of cancer research, where they play a crucial role in understanding tumor biology, developing new anticancer therapies, and potentially eradicating cancer stem cells to prevent tumor recurrence. Hematopoietic stem cell transplantation is already a well-established treatment for blood disorders like leukemia and lymphoma, demonstrating the life-saving potential of stem cell therapy. Furthermore, stem cells are being investigated for their use in treating diabetes by generating insulin-producing cells, and in genetic disorders through gene therapy approaches. Despite the promising advancements, challenges such as ethical concerns, immune rejection, and the risk of tumorigenesis must be addressed before stem cell therapies can be widely adopted. Nonetheless, the ongoing research and clinical trials continue to pave the way for innovative and personalized treatments for a variety of human diseases, underscoring the transformative potential of stem cells in modern medicine..
Stem cells, Human diseases, Pluripotency
Antimicrobial Activity of some Medicinal Plants against Human Pathogenic Bacteria
Medicinal plants are found to deliver a promising health benefits probably due to the secondary metabolites which are responsible for various activities of such plants. Antimicrobial activities of some important Indian medicinal plants are analyzed in present study using methanol, ethanol and acetone extracts of each plant. Methanol extract along with ethanol extract were found equally potent against human pathogenic bacteria by using agar well diffusion method and measuring the inhibition zone of activity ranging from 5mm – 20 mm diameter. Emblica officinalis showed maximum zone of inhibition (29 mm) against Pseudomonas aeruginosa, methanol extracts of Tabernaemontana undulata and Phyllanthous amarus have highest antimicrobial activity on B. subtilis (22 mm) and Pseudomonas aeruginosa (27 mm). ethanol extracts of Emblica officinalis on four different human pathogenic organisms using agar well diffusion method have showed maximum zone of inhibition (31 mm) against Pseudomonas aeruginosa.
Copyright (c) 2019 International Journal of Cell Science and Biotechnology
A Novel Technique for Fair and Accurate Reviewer Assignment in Peer Review
One of the normal issues looked in allocating recommendations submitted to the conferences, journal publications and so forth is the assignment of the proposition to the suitable reviewers. This is likewise named as the reviewer assignment issue. Here, where the skill level of a reviewer who is engaged with reviewing a proposal ought to be streamlined to ensure the choice of the best reasonable proposal. Picking a suitable reviewer includes the mastery as well as thinks about assorted variety and irreconcilable circumstances among them. Toward this path, various arrangements have been given by researcher previously, yet a portion of the issues continues in this field. The proposed system features a review on the current strategies proposed by the analysts to tackle the reviewer assignment issue and different keyholes in the current situation alongside the potential arrangements.
Copyright (c) 2018 International Journal of Cell Science and Biotechnology
Radiomodulatory Effects of different Phytochemicals on the Irradiated Mouse Model System
The present study was conducted to evaluate the radio-protective effect of three phytochemicals: emodin, acemannan and naringenin on the irradiated splenocytes, bone marrow and liver cells of mouse model system. γ- Radiation induces cellular and DNA damage which was studied by micronucleus assay and karyotypic study was done by metaphase arrest of chromosomes. The radio-protective efficacy of emodin was studied by cell viability assay, comet assay and plasmid relaxation assay. Acemannan showed to possess radio-protective potential when it elevated the levels of SOD and catalase anti-oxidant enzymes in the irradiated liver cells and improved cell proliferation in the irradiated splenocytes. The radio-protective effect of naringenin on the irradiated spleen cells and bone marrow cells was also studied via MTT assay and immunocytochemistry. All three studied phytochemicals were of natural origin and showed to exhibit good radio-protective properties, thereby providing an insight for improving defense mechanism of mouse model system against radiation exposure.
Targeting ceramidase and sphingosine kinase 1 of ceramide metabolic pathway induces apoptosis in human colon cancer cells
The present study investigates the apoptotic effect of ceramidase inhibitor, Ceranib 2 and Sphingosine kinase 1 inhibitor, Dimethyl sphingosine in human colon cancer cell line SW480. Cells were treated with different doses of Ceranib 2 and Dimethyl sphingosine for 24, 48 and 72 hours to determine the cell viability using 3-(4,5-dimethyl-2thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT assay). Morphological changes were examined in phase contrast microscope, nuclear changes were observed using DAPI staining. DNA gel electrophoresis was performed to visualize DNA changes, Acridine orange/Ethidium bromide staining was done to discriminate live, early apoptotic, late apoptotic, necrotic cells. Mitochondrial membrane potential was determined by JC-1 staining. MTT assay revealed the cytotoxic effect of Ceranib 2 and Dimethyl sphingosine in SW480 cell line in dose and time dependant manner. Morphological changes and chromatin condensation observed in DAPI staining are morphological hallmarks of apoptosis. DNA ladder pattern observed in drug treated groups with respect to control group is biochemical hallmark of apoptosis. Further, presence of early and late apoptotic cells in Acridine orange/Ethidium bromide staining, loss of mitochondrial membrane potential in drug treated groups confirmed the apoptotic effect of Ceranib 2 and Dimethyl sphingosine in SW480 cells.
Bacteria from Coral Ecosystem of Kiltan Island, Lakshadweep: Resource for Hydrolytic Enzymes
Gelatinase, Protease, Amylase and Lipase are the important enzymes which are widely used in Chemical, medical, food industries and basic biological science. Enzymes reported till date is produced mostly by microorganisms of terrestrial origin. However, only a few number of reports reported from marine producers, their capability have not been explored in details. More over an enzyme from the marine origin may be an inimitable protein molecule not found in any terrestrial organism or it may be a known enzyme from a terrestrial source but with novel properties. Coral reefs are highly productive natural ecosystem and provide an excellent habitat for a vast array of marine organisms due to their structure, efficient biological recycling and high retention of nutrients. Considering all these, the present study made an attempt to screen the potential gelatinase, protease, amylase and lipase producing bacteria from coral ecosystem of Kiltan Island of Lakshadweep. Depending on the diameter of zone production in the respective substrate containing medium, two bacterial strains was selected for further studies. The isolated bacterium has showed varying enzymatic activity in different temperature and pH. The biochemical analysis and 16S rRNA gene analysis revealed that, the isolated bacteria closely related to B. fluxes and B. cereus.
Asparagus racemosus: A Medicinal Plant of Immense Potential
Asparagus racemosus commonly known as satawar has also been regarded as ‘rasayana’. A. racemosus is a medicinal plant used in various ailments as anti-diarrheal, refrigerant, tonic, demulcent, diuretic, aphrodisiac & antispasmodic. A. racemosus comparises of Shatavarins I-IV as the major active constituents. Other active compounds such as quercetin, rutin and hyperoside are found in the flowers and fruits; while diosgenin and quercetin-3 glucuronide are present in the leaves. The review focuses on information regarding its medicinal uses, other species utilized as vegetable, its medicinal uses and on its proper conservation and propagation strategies.
Copyright (c) 2016 International Journal of Cell Science and Biotechnology
Legumes: The Natural Products for Industrial and Medicinal Importance-A Review
Legumes play a very important role in human diet in all over the world because of low cost and fat as well as richer sources of proteins and nutrient variety. The bioactive constituents of legumes like Alkaloids, Phytic acid, Genistein and Daidzein, Polyphenols, Phytoestrogens, Phenolic compounds, Phytic acid (myo-inositol hexaphosphate), Saponins, Lupin quinolizidine alkaloids, Isoflavones and lignans, Isoflavones; Formononetin, Biochanin, Flavanoids, Saponines, Lecitins A etc. were isolated and purified by scientists. These phytochemicals acts as an antioxidant, anti-diabetes, anticancerous, anti-inflammatory. The purpose of this review with scientific evidence is to encourage the people to consume legumes as natural medicine for treatment of nearly all health related diseases like coronary heart disease, cancer, tyrosinase and glucosidase inhibition activities, obesity, aging, heart related diseases, high blood pressure etc. It was also reviewed that various processing method like germination, sprouting, oil frying, steaming etc. increased the activity of various phytoconstituents. This review provides an attention to the lavish medicinal values of legumes that are being used in past decades as a better alternative to medicine without any side effects for curing of many diseases.
Anti-diabetes, Tyrosinase & Glucosidase inhibition,
Copyright (c) 2017 International Journal of Cell Science and Biotechnology
Investigating the Effect of Continuous Aerobic Exercises on the Enzymes in Women with Non-Alcoholic Fatty Liver
Background and goal: Fatty liver disease in obese, sedentary and people with diabetes and dyslipidemia was seen more. In 1970 liver disease associated with obesity in adults is detected, while increasing physical activity are associated with reduced liver fat. So, the aim of the present study was investigating the effect of continuous aerobic exercise on enzymes in women with nonalcoholic fatty liver. Materials and methods: In this quasi-experimental study, 12 women with non-alcoholic fatty liver disease were randomly assigned to a continuous exercise group. Each of the samples running aerobic exercise on a treadmill during three times a week for 12 weeks and 30 to 40 minutes per session with a mean intensity of 60% to 70% heart rate recorded that was gradually applied in the exercise program. Laboratory biosystems kit was made in Spain used to determine the level of liver enzymes; data were analyzed using t-test in SPPS software version 20. Findings: The results showed that p value of AST and ALT enzymes considering after 12 weeks of continuous exercise had a significant decline. Also, the variables CHOL, TG, LDL, and HDL were not significantly changed after 12 weeks of continuous exercise. Conclusion: According to the results, it can be concluded that blood lipids, due to the mean reduction in pre and posttest but any statistically significant change observed, it seems if the time of exercise more than 12 weeks has significant possibility of greater change.
Chemically induced low oxygen condition leads to significant induction of hypoxia-related genes in mammary epithelial cells of riverine buffaloes (Bubalus bubalis)
Hypoxic condition is known to influence the cell survivability by inhibiting its metabolism and growth. Till date, no information is available on how the mammary epithelial cells (MECs) of dairy species respond to chemically induced hypoxia. This study was therefore planned to assess the transcriptional responsiveness of mammary epithelial cells (MECs) of riverine buffaloes- the major dairy species of India to chemically induced hypoxic condition. Initially, the primary MECs culture from buffalo mammary gland tissue was established and thereafter different doses of CoCl2 (50 µM, 100 µM and 250 µM) were evaluated for inducing hypoxia in MECs under culture condition. The 250 µM of CoCl2 was selected and cells were grown for 48 h before assessing the expression of HIF-1α and its target genes in treated (hypoxic) as well as in control (normoxic) cells. The mRNA expression of all studied genes viz; HIF-1α, GLUT-1, GLUT8, HK2, JAK2 and STAT5 was found to be significantly induced in hypoxic condition in comparison to control condition. The present investigation suggests that CoCl2 induced hypoxia significantly alters the gene expression of hypoxia related genes in buffalo MECs. Such model can be utilized in future for studying the impact of hypoxia stress in mammary gland development and function of dairy species.
Copyright (c) 2013 International Journal of Cell Science and Biotechnology
Plant Growth Promoting Rhizobacteria: A Boon to Agriculture
Plant Growth Promoting Rhizobacteria (PGPR) are a group of bacteria that enhances plant growth and yield. PGPRs as biofertilizers, are the living micro-organisms which are applied to surface of plant, seed, or soil. They colonize the plant rhizosphere or inside the plant body (as endophyte) and promotes plant growth by providing fundamental nutrients to crop plants. The rapidly increasing population is exerting immense pressure on agricultural lands for higher crop yields, which results in increasing use of chemical fertilizers. But due to negative environmental impact of artificial fertilizers and their increasing costs, microorganisms have a vital role in agriculture as they promote the exchange of plant nutrients and reduce application of chemical fertilizers as much as possible. Substances produced by PGPRs protect the plants against various pathogens by direct antagonistic interactions between the biocontrol agent and the pathogen, as well as by induction of host resistance. Resistance-inducing and antagonistic rhizobacteria might be useful in formulating new inoculants with combinations of different mechanisms of action, leading to a more efficient use for biocontrol strategies to improve cropping systems.
Incidence of Urinary Tract Infections and determination of their susceptibility to antibiotics among Pregnant Women
Urinary tract infections (UTIs) are the second most common infections in community practise. Prevalence of UTI is an extremely in females than males, 40% to 50% of whom will suffer at least one clinical episode during their lifetime. This study therefore focuses on the detection and incidence of UTI among pregnant women. It also aimed to isolate and identify the organisms that causing UTI among pregnant women, and determined their susceptibility patterns to commonly used antimicrobial agents. A total of one hundred (100) midstream urine sample were collected from pregnant women at the aged ranges from 16 to 40 years attending to Sri Vijaya Hospital at Mehdipatnam , Hyderabad city India between June 2015 and December 2015. Early morning clean-catch midstream urine was collected from each pregnant woman into a wide-mouthed sterile screw capped container. With a calibrated microloop, 0.001 ml. of urine was cultured on to a Blood agar, MacConkey agar, EMB agar , Nutrient agar and Muller Hinton Agar plates. After overnight incubation at 37º C for 24 hours, A total of Thirty nine (39%) out of 100 patients had urinary tract infection . The bacterial isolates were identified based on colony morphology characteristics, Gram stain reaction and biochemical tests. the commonest isolated organism was S. aureus (43.53%), followed by E.coli (35.89 %), Klebsiella pneumonia (10.25%), Pseudomonas aeruginosa (7.69%) and lowest incidence of the isolates was Proteus spp (2.56%). More than 97% of the isolates were resistance to ampicillin (97.3%), followed by Netillin (84.2%), in other hand the most effective antibiotic was Amikacin which show (5.2%) of the isolates resistance, followed by the resistance of isolates to Cefotaxime and Gentamicin were each (21%).
Copyright (c) 2015 International Journal of Cell Science and Biotechnology
Forecasting & Sensitivity Analysis of the Decay of Viral Load in HCV Infection during Treatment by using Mathematical Tools and Simulation.
The present paper gives a brief description of study of decay of viral load in HCV infection during treatment of patients through various mathematical tools. In the study clinical data of few patients have been taken. The basic model of HCV infection adapted by Neumann et al has been taken in to account and explained to understand the decay or flow of viral load. For eight consecutive weeks data of viral load has been taken which is based on clinical data. Then, by using RStudio simulation (R-language) viral load is forecasted further for six more weeks. The forecasted viral load totally resembles with viral load through clinical data established by using Origin Lab. The sensitivity analysis of study of viral load of HCV during treatment for fourteen weeks shows specific pattern which will help in the treatment of Hepatitis C infected patients. In this study immunity of a human body is excluded.
Mathematical model, Graphical presentation (MATLAB), Simulation (RStudio)
Forecasting of decay of viral load, Sensitivity analysis (Origin Lab)
Clinical data (Data extracted from required references)