Charotar University of Science and Technology
Recent publications
Cu₂SnS₃ (CTS) is potent substitute for conventional compounds due to adjustable bandgap, conductivity of p-type, adaptable morphology, easy to synthesize, and superior thermoelectric properties. In this work, the CTS nano-spheres are synthesized by hydrothermal technique. The synthesized CTS nano-spheres are employed for the antibacterial, antioxidant, and cytotoxic application. Structural analysis by X-ray diffraction confirms cubic unit cell structure of CTS nano-spheres. The energy-dispersive X-ray analysis showed CTS nano-spheres to be copper-rich and tin-deficient. Scanning electron microscopy images revealed nano-spheres with needle-like surface features. The CTS nano-spheres possess direct bandgap of 1.58 eV, confirmed by diffuse reflectance spectroscopy. The antibacterial activity shows 100% activity index with higher zone of inhibition in Listeria monocytogenes and Staphylococcus aureus. The antioxidant activity of CTS nano-spheres determined using the DPPH assay showed IC50 value of 61.60 µg/ml stating moderate antioxidant efficiency. The in vitro cytotoxic analysis is carried out by employing A549 lung cancer cell lines. The in vivo and vitro cytotoxic analysis provided the potent cytotoxicity of CTS nano-spheres, as reflected in LC50 value of 40.40 µg/ml and IC50 value of 57.75 ± 2.34 μg/ml. The mechanistic evolution of CTS nano-spheres for their antibacterial activity is proposed in this work. The leaching behaviour of CTS nano-spheres revealed higher leaching of Sn⁴⁺ ions than Cu⁺ ions, contributing to their strong antibacterial activity. The zeta potential of CTS nano-spheres is found to be − 30.70 mV, which showed less agglomeration of CTS nano-spheres, depicting an efficient antibacterial activity. The obtained results are rigorously analysed and supported with relevant data.
In recent years, heightened concern has emerged regarding the pervasive presence of microplastics in the environment, particularly in aquatic ecosystems. This concern has prompted extensive scientific inquiry into microplastics’ ecological and physiological implications, including threats to biodiversity. The robust adsorption capacity of microplastic surfaces facilitates their widespread distribution throughout aquatic ecosystems, acting also as carriers of organic pollutants. However, to comprehensively understand the broader implications of this pollution, a thorough examination of the origins, composition, and widespread distribution of microplastics within aquatic biotopes is imperative. Diatoms, unicellular photosynthetic organisms, play a pivotal role in aquatic ecosystems as primary producers, forming the base of the aquatic food web. Investigating the relationship between microplastics and diatoms, leveraging methodological advancements, holds promise in unraveling the intricate action mechanisms underlying their interactions. Such inquiry sheds light on the physiological responses elicited and provides crucial insights into the ecological dynamics within aquatic environments. This study explores the understanding of microplastic-diatom interactions, focusing on how microplastic types, sizes, and concentrations influence diatoms. Ultimately, the current study strongly advocates for transdisciplinary collaborations, such as partnerships between ecologists, materials scientists, and policymakers, as the complexity of microplastic pollution demands collective efforts to address this critical and alarming environmental issue.
Time Series Forecasting (TSF) is crucial in various real-world applications such as climate forecasting and electricity demand prediction. Unlike traditional datasets, time series data points are influenced by their past values, necessitating specialized techniques to model these sequential dependencies specifically addressing non-linear patterns, abrupt changes, and outliers. The latest advancements have significantly enhanced TSF using machine learning and other methods. However, forecasting extreme events remains challenging. Extreme values, although rare, have significant real-world impacts such as heavy rainfall, fluctuations in electricity demand, and traffic surges. This paper proposes a TXtreme framework that uses Long-Short memory network, feed-forward neural network, and transformer to improve time series forecasting under extreme values. The model also uses statistical methods to explain the distribution of time series values. Extensive experiments are conducted using datasets from different domains to show the robustness of the proposed methodology. Results, derived by testing TXtreme on five datasets of different domain, indicate that TXtreme significantly outperforms state-of-the-art methods in time series forecasting, with improvements of 5–25% in root means squared error or mean absolute error. The proposed framework enhances TSF capabilities and ensures better generalization ability in extreme event forecasting, potentially leading to improved decision-making in critical applications.
Blood cell detection provides a significant amount of information about a person's health, aiding in the diagnosis and monitoring of various medical conditions. Red blood cells (RBCs) carry oxygen, white blood cells (WBCs) play a role in immune defence, and platelets contribute to blood clotting. Changes in the composition of these cells can signal various physiological and pathological conditions, which makes accurate blood cell detection essential for effective medical diagnosis. In this study, we apply convolutional neural networks (CNNs), a subset of deep learning (DL) techniques, to automate blood cell detection. Specifically, we compare the performance of multiple variants of the You Only Look Once (YOLO) model, including YOLO v5, YOLO v7, YOLO v8 (in medium, small and nano configurations), YOLO v9c and YOLO v10 (in medium, small and nano configurations), for the task of detecting RBCs, WBCs and platelets. The results show that YOLO v5 achieved the highest mean average precision (mAP50) of 93.5%, with YOLO v10 variants also performing competitively. YOLO v10m achieved the highest precision for RBC detection at 85.1%, while YOLO v10n achieved 98.6% precision for WBC detection. YOLO v5 demonstrated the highest precision for platelets at 88.8%. Overall, YOLO models provided high accuracy and precision in detecting blood cells, making them suitable for medical image analysis. In conclusion, the study demonstrates that the YOLO model family, especially YOLO v5, holds significant potential for advancing automated blood cell detection. These findings can help improve diagnostic accuracy and contribute to more efficient clinical workflows.
The idea of grid‐scale hydrogen production by water electrolysis has been made possible by developing catalyst‐anchored three‐dimensional (3D) foam‐based electrodes. Catalytic performance in hydrogen and oxygen evolution reactions is improved by incorporating catalyst in 3D interlinked porous architecture, which enhances electrical conductivity and speeds up the discharge of gas bubbles. The detailed study on the role‐play of 3D frameworks in energy generation is explained in this article. The review also focuses on the recent development in utilizing these 3D substrates in the field of electrochemistry. Furthermore, it is imperative to enhance their compatibility with renewable energy systems and high‐temperature electrolysis for the sustainable production of hydrogen. Therefore, this review briefly explores the innovative design of self‐supported 3D framework electrodes using heterostructures and doping techniques to develop stable, durable, and efficient electrocatalysts. These catalysts aim to provide near‐zero overpotential, high selectivity, and long‐term stability for hydrogen production through water electrolysis, paving the way for commercial‐scale green energy production. 3D foam‐based electrodes can emerge as a key technology in the field of electrochemistry.
Cancer treatment has evolved significantly over the years, incorporating a range of modalities including surgery, radiation, chemotherapy, and immunotherapy. However, challenges such as drug resistance, systemic toxicity, and poor targeting necessitate innovative approaches. Peptides have gained attention in cancer therapy due to their specificity, potency, and ability to modulate various biological pathways. Peptide-based drugs can act as hormones, enzyme inhibitors, or targeting ligands, contributing to their versatile role in cancer treatment. However, peptides face several challenges, including instability, rapid degradation, and poor bioavailability. One promising strategy is the use of niosomal delivery systems for peptidebased therapies. Niosomes, which resemble liposomes in structure, are vesicles based on nonionic surfactants. They are composed of a bilayer created through the self-assembly of non-ionic surfactants in water, enabling them to encapsulate hydrophilic, lipophilic, and amphiphilic drugs. Their unique properties, such as biocompatibility, biodegradability, and ability to encapsulate diverse therapeutic agents, make them suitable for drug delivery applications. This review aims to explore how the niosomal preparation of peptides can revolutionize oncology drugs by overcoming critical challenges like drug resistance, systemic toxicity, poor targeting, instability, rapid degradation, and low bioavailability. This review aims to explore how niosomes can specifically address key limitations in cancer therapy, including targeting, bioavailability, and stability of peptide-based drugs. By consolidating recent advancements, the review sheds light on how niosomal encapsulation can overcome barriers in cancer treatment and improve therapeutic outcomes for patients.
In comparison to a classical PID controller, the optimization of a fractional-order PID controller poses a significant challenge on account of increased complexity due to the presence of five tuning parameters. This study proposes the optimization of fractional-order PID controller optimization by employing a cultural algorithm, which is an evolutionary optimization technique. Unlike traditional methods like particle swarm optimization (PSO) and bacteria foraging optimization (BFO), cultural algorithms (CAs) feature a dual inheritance system and structured belief space. This study uniquely examines the impact of different objective functions on optimization outcomes. Additionally, a comprehensive comparative analysis is conducted to analyze the control performance of conventionally tuned PID controller and CA-optimized PID controller with CA-optimized FOPID controller. The effectiveness of the proposed approach is evaluated on an integer-order system model of brushless direct current (BLDC) motor and a fractional-order model of thermal system. The results show that CAs effectively minimize the objective functions in order to determine controller parameters. The CA-optimized FOPID controllers outperform conventional tuned PID controller as well as CA-optimized PID controller.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi‐modal insights. VGG16, known for its depth and high performance, is utilized for this purpose. The study demonstrates the model's potential for precise and effective diagnosis by examining how well it can differentiate between areas of normal brain tissue and cancerous regions, leveraging both MRI and microscopy data. We describe in full the pre‐processing actions taken to improve the quality of input data and maximize model efficiency. A carefully selected dataset, incorporating diverse tumor sizes and types from both microscopy and MRI sources, is used during the training phase to ensure representativeness. The proposed modified VGG19 model achieved 98.81% validation accuracy. Despite good accuracy, interpretation of the result still questionable. The proposed methodology integrates explainable AI (XAI) for brain tumor detection to interpret system decisions. The proposed study uses a gradient explainer to interpret classification results. Comparative statistical analysis highlights the effectiveness of the proposed explainer model over other XAI techniques.
The main challenge for water electrolysis is that continuous and effective hydrogen evolution at high current densities is unattainable due to the quick degradation of performance that occurs with extended large‐current operation. In this work, sulfur‐doped nickel ferrite nanocomposites were prepared using simple hydrothermal method with the objective of improving electrocatalytic green hydrogen production at industrial current densities. X‐ray diffraction (XRD), scanning electron microscopy (SEM), and X‐ray photoelectron spectroscopy (XPS) were used to analyse the crystalline structure, morphology, and chemical composition of the synthesised nanocomposites. The prepared S−NiFe2O4/NF (NS‐85) catalyst exhibits excellent electrochemical water‐splitting activity, a low overpotential, a high current density, and extended stability lasting more than 12 hours. The NS‐85/NF electrode has a cathodic current density of 300 mA cm⁻² at −0.329 V overpotential and at the lowest overpotential of −0.264 V, the electrode has a current density of 100 mA cm⁻². Our work provides new approaches to the development of earth‐abundant, stable, scalable, and highly effective catalysts for industrial water electrolysis.
Lumbopelvic pain is a common concern among postnatal women, significantly impacting their quality of life and daily functioning. This evidence-based study aimed to determine the scientific effectiveness of core stability exercises in alleviating lumbopelvic pain in this population. The study involved a systematic review of literature sourced from databases such as Google Scholar, PubMed, Wiley Online Library, TRIP, and CINAHL. Out of 70 initially identified records, eight studies met the eligibility criteria and were included in the final analysis. The findings consistently demonstrated that core stability exercises are highly effective in reducing lumbopelvic pain and improving functional outcomes in postnatal women. These exercises target the deep core muscles, enhancing muscular support and stability in the lumbopelvic region. This review underscores the importance of incorporating core stability exercises into postnatal rehabilitation programs to manage lumbopelvic pain effectively. Further research is recommended to explore long-term benefits and refine exercise protocols for diverse postnatal populations. Key words: core stability exercise, low back pain, pelvic pain, postpartum lumbopelvic pain
Purpose: To explore the factors that influence physiotherapists' adherence to evidence-recommended interventions in the management of knee osteoarthritis. Materials and methods: Using a qualitative descriptive design, we conducted semi-structured interviews with 15 physiotherapists across India. Participants were selected using purposive sampling to ensure diversity in experience and setting. We used inductive thematic analysis to explore emerging themes from the data, which were subsequently related to physiotherapists' adherence to evidence-recommended interventions in knee OA management. Results: We identified three main themes influencing physiotherapists' adherence to evidence-based care: biomechanical bias in decision-making, the influence of patient perceptions and preferences, and systemic challenges specific to healthcare settings. A significant biomechanical bias led to an emphasis on prioritizing joint protection strategies. Patient perceptions and demands, often influenced by trust in medical professionals over physiotherapists, pose significant challenges in aligning treatment with evidence-based practices. Systemic issues, including workload and infrastructural limitations, further complicate the delivery of effective OA care. Conclusion: This study illustrates the multifaceted barriers to implementing evidence-based interventions for knee OA among Indian physiotherapists. Addressing these challenges through context-specific knowledge translation strategies is critical for advancing evidence-based, patient-centered OA knee management.
Background: Tavaborole (TAV), a benzoxaborole derivative, is an FDA-approved antifungal agent for treating onychomycosis, a common and persistent fungal infection of the toenails. Objective: This study aimed to develop a robust stability-indicating HPTLC method to determine TAV in nanostructured lipid carriers (NLC) using a comprehensive approach that includes risk assessment, and Analytical Quality by Design. Methods: The critical method parameters influencing the HPTLC results were screened using a Plackett-Burman screening design followed by its optimization using a central composite optimization design. The developed method was validated as per ICH recommendation. Results: Optimized method utilized pre-coated aluminum-backed HPTLC plates using 10 µL/band injection volume, and the plate was developed using an isocratic mobile phase consisting of toluene: ethyl acetate: formic acid (75:25:1%v/v/v) in twin trough chamber pre-saturated for 20 mins with vapors of 10 mL of mobile phase. The separated components were detected at a wavelength of 221 nm. The developed HPTLC method resulted in a retardation factor of 0.49 ± 0.04 for TAV. Validation results revealed the HPTLC method's specificity (peak purity ≥ 0.999), linearity over a concentration range of 2-10 μg/band, sensitivity (LOD 0.21 μg and LOQ 0.64 μg), accuracy (99.68 - 101.43%w/w), and precision (%RSD < 2.0). Conclusion: The developed robust stability-indicating HPTLC method was successfully implemented for the sustainable testing of the TAV in the NLC formulations and stability testing.
Background Polycystic ovarian syndrome (PCOS) is a chronic, diverse endocrine condition that is frequently identified in women of reproductive age. PCOS has long-term health risks associated. Measuring quality of life (QOL) can provide beneficiary insights into health and associated risk factors. Early recognition and knowledge about PCOS enhances affected women’s reproductive potential. The current study aimed to assess the QOL in women diagnosed with PCOS in the Kheda District using a simple questionnaire survey. Materials and Methods A cross-sectional, self-administered questionnaire survey was conducted among eligible women of age 18–45 years after obtaining ethical clearance from the institutional ethical committee. The data were gathered using a nonprobability sampling technique. Descriptive analysis using the Likert Scale was used to summarize the five domains (menstrual irregularity, emotions, body hair, weight, and infertility) of health-related QOL. Results One hundred and nineteen women were found eligible for the study. The mean age of women with PCOS was 24.5 ± 3.5 years. Many women with PCOS were found to have a body mass index within or near the normal range. About 50% of participants reported no concerns about visible body hair, while only 16.8% were concerned about infertility. Experiences in the emotional domain varied; 51.9% felt disturbed by their body weight, and 82.4% reported severe difficulties in managing menstrual irregularities. Conclusion The observation leads to the conclusion that PCOS prevalence is steadily rising, which highlights requirement of support and widespread knowledge regarding the effects of their condition on QOL, as each domain has equal importance for the better livings. Although medical practitioners, gynecologists, and clinicians play key roles in diagnosing PCOS, still there is a need to educate and aware women for the ongoing changes in their life and the necessity to have early and timely diagnosis.
Background Degenerative joint disease, such as osteoarthritis, is characterized by the breakdown of cartilage in the joints, resulting in a decreased range of motion and stiffness. The T2 mapping approach is a very useful tool for detecting early osteoarthritis. The main goal of the study was to assess the variations in the relaxation time and articular cartilage thickness of knee cartilage across different age groups among healthy adults using the T2 mapping technique in 3 Tesla magnetic resonance imaging. Methods A total of 42 measurements were taken from each individual's knee cartilage and were confirmed to have no cartilage damage. The relaxation time was calculated from T2 maps using 21 circular regions of interest and the articular cartilage thickness was measured using 21 linear measurements from a self-created fusion image. The thickness was measured in the same regions where the relaxation time was measured. Overall, 2,142 measurements (1,071 circular regions of interest from T2 maps and 1,071 linear measurements from fusion images) were taken from 51 participants. Results The majority of the variables of T2 relaxation time show a positive linear correlation with age groups. As age increases, cartilage relaxation time increases, which may be connected to an increase in cartilage degeneration. However, half of the variables associated with knee cartilage thickness show a negative linear correlation with age group. As age increases, the thickness of the cartilage starts to decrease. T2 relaxation times of the middle lateral tibial condyle, middle superior patella, and central inferior patella differ significantly between healthy males and females (p < 0.05). The cartilage thickness of the posterior lateral tibial condyle, middle medial patella, central superior patella, central middle patella, and lateral middle patella varied significantly between healthy males and females (p < 0.05). Conclusion Using an additional T2 articular cartilage mapping sequence to a routine sequence in the knee joint can reveal age-related changes in relaxation time and cartilage thickness in knee cartilage. The T2 mapping technique also can help detect early changes in osteoarthritis, track progression, and plan treatment.
Among the most potent analytical techniques accessible today is nuclear magnetic resonance (NMR) spectroscopy. It allows us to examine individual molecules and atoms in a variety of media, both in the solid and solution states, making it an essential tool in scientific research and discovery. Within the dynamically growing domain of metabolomics, one important analytical technique is nuclear magnetic resonance (NMR) spectroscopy. NMR has considerable advantages for the field of metabolomics, including its high quantitative and reproducibility, its non-selective nature and non-invasive, and its capacity to monitor the downstream products of isotope-labeled substrates ex vivo, in vitro, or in vivo, as well as identify unknown metabolites in complicated assortments. The NMR principle is based on the observation of nuclear magnetic energy level transitions associated with nuclear spins with varying orientations under a static magnetic field. By employing a magnetic field that is time-dependent and perpendicular to the static one that oscillates at the Larmor frequency of the nuclei, we can unlock a wealth of information about the sample we are studying.
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3,298 members
Prasad Andhare
  • P. D. Patel Institute of Applied Sciences
Palash Mandal
  • P. D. Patel Institute of Applied Sciences
Mehul M Patel
  • Ramanbhai Patel College of Pharmacy
Umangkumar Harishchandra Shah
  • Ramanbhai Patel College of Pharmacy
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Anand, India
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Dr. Pankaj S. Joshi