Sri Venkateswara Medical College and Ruia Hospital

Cancer is a leading cause of death worldwide. Several classes of drugs are available to treat different types of cancer. Currently, researchers are paying significant attention to the development of drugs at the nanoscale level to increase their target specificity and to reduce their concentrations. Nanotechnology is a promising and growing field with multiple subdisciplines, such as nanostructures, nanomaterials, and nanoparticles. These materials have gained prominence in science due to their size, shape, and potential efficacy. Nanomedicine is an important field involving the use of various types of nanoparticles to treat cancer and cancerous cells. Synthesis of nanoparticles targeting biological pathways has become tremendously prominent due to the higher efficacy and fewer side effects of nanodrugs compared to other commercial cancer drugs. In this review, different medicinal plants and their active compounds, as well as green-synthesized metallic nanoparticles from medicinal plants, are discussed in relation to their anticancer activities.

Trauma with an accompanying fracture to the anterior teeth gives an agonizing experience for a young individual due to the physical disfigurement and the psychological impact that is imposed on them. This paper reports a case of complicated crown-root fracture in a young child that was treated by extra-oral fragment reattachment followed by the intentional reimplantation. The tooth was endodontically-treated followed by the placement of fiber-reinforced composite post. The fragments were reattached extra orally following an atraumatic extraction. The tooth was then reimplanted back into the socket followed by splinting. Clinical results were successful after 2 years. This case report demonstrates the importance of modifying a treatment protocol to maintain esthetics up to the completion of the developmental period.

With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An in-depth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

Background: Common mental disorders, such as mood, anxiety, and substance use disorders, are significant contributors to disability globally, including India. Available research is, however, limited by methodological issues and heterogeneities. Aim: The present paper focuses on the 12-month prevalence and 12-month treatment for anxiety, mood, and substance use disorders in India. Materials and Methods: As part of the World Health Organization World Mental Health (WMH) Survey Initiative, in India, the study was conducted at eleven sites. However, the current study focuses on the household sample of 24,371 adults (≥18 years) of eight districts of different states, covering rural and urban areas. Respondents were interviewed face-to-face using the WMH Composite International Diagnostic Interview after translation and country-specific adaptations. Diagnoses were generated as per the International Classification of Diseases, 10th edition, Diagnostic Criteria for Research. Results: Nearly 49.3% of the sample included males. The 12-month prevalence of common mental disorders was 5.52% - anxiety disorders (3.41%), mood disorders (1.44%), and substance use disorders (1.18%). Females had a relatively higher prevalence of anxiety and mood disorders, and lower prevalence of substance use disorders than males. The 12-month treatment for people with common mental disorders was 5.09% (range 1.66%–11.55% for individual disorders). The survey revealed a huge treatment gap of 95%, with only 5 out of 100 individuals with common mental disorders receiving any treatment over the past year. Conclusion: The survey provides valuable data to understand the mental health needs and treatment gaps in the Indian population. Despite the 12-month prevalence study being restricted to selected mental disorders, these estimates are likely to be conservative due to under-reporting or inadequate detection due to cultural factors.

Abstract In today's world, the advancement of telediagnostic equipment plays an essential role to monitor heart disease. The earlier diagnosis of heart disease proliferates the compatibility of treatment of patients and predominantly provides an expeditious diagnostic recommendation from clinical experts. However, the feature extraction is a major challenge for heart disease prediction where the high dimensional data increases the learning time for existing machine learning classifiers. In this article, a novel efficient Internet of Things‐based tuned adaptive neuro‐fuzzy inference system (TANFIS) classifier has been proposed for accurate prediction of heart disease. Here, the tuning parameters of the proposed TANFIS are optimized through Laplace Gaussian mutation‐based moth flame optimization and grasshopper optimization algorithm. The simulation scenario can be carried out using11 different datasets from the UCI repository. The proposed method obtains an accuracy of 99.76% for heart disease prediction and it has been improved upto 5.4% as compared with existing algorithms.

Efforts are continuously being made to understand the non-target effects of environmental pollutants toward microalgae and cyanobacteria because of their ubiquity in aquatic and terrestrial environments and their highly adaptive survival abilities under environmental and evolutionary pressure over geological time. Depending on the toxicity criteria employed for these ecologically beneficial organisms, the impact of low and high doses of pollutants can range from stimulation to total inhibition. All of the investigations carried out so far have been predominantly concerned with individual chemicals despite the occurrence of pollutants in mixtures. In addition, only individual isolates have been primarily used to gather scientific information on the toxicity of pollutants. The risk assessment of pollutants toward these organisms necessitates further investigations, combining innovative molecular ecological methods and those for in situ analysis at the community level. The present review highlights the toxic influences of organic and inorganic pollutants and the response in terms of detoxification and resistance by these organisms.

Artificial intelligence (AI) systems can assist in analyzing medical images and aiding in the early detection of diseases. AI can also ensure the quality of services by avoiding misdiagnosis caused by human errors. This study proposes a deep neural network (DNN) model with fine-tuned training and improved learning performance on dermoscopic images for skin cancer detection. A knowledge base for training the DL models is constructed by combining different dermoscopic datasets. Transfer learning and fine-tuning are implemented for faster training of the proposed model on a limited training dataset. The data augmentation techniques are applied to enhance the performance of the model. A total of 58,032 refined dermoscopic images were used in this study. The output of the layered architecture is aggregated to perform the binary classification for skin cancer. The performance of the trained models is investigated for multiclass and binary classification tasks. The performance metrics confirm that the DNN network with modified EfficientNetV2-M outperforms the state-of-the-art deep learning-based multiclass classification models.

To prevent re-infection and provide a hermetic seal of the root canal system, an endodontist must aim to produce a void-free obturation. This review aimed to compare the completeness of root canal obturation between the two most prevalent methods-cold lateral condensation and warm gutta-percha techniques-using micro-CT (PROSPERO reg no. 249815). MATERIALS AND METHODS: A search of Scopus, Embase, PubMed (Medline via PubMed), and Web of Science databases was done without any time restriction according to the PRISMA protocol. Articles that compared both techniques and were published in English were included. Data was extracted and the risk of bias was assessed using an adapted tool based on previous studies. RESULTS: A total of 141 studies were identified by the search. Following the screening and selection of articles, 9 studies were included for review. Data was extracted manually and tabulated. Most studies had a moderate risk of bias. None determined operator skill in both methods before comparison. The data extracted from the included studies suggests that both techniques produce voids in the obturation. The thermoplasticized gutta-percha techniques may result in fewer voids compared to cold lateral condensation. CONCLUSION: Considering the limitations of the included studies, it was concluded that neither technique could completely obturate the root canal. Thermoplasticized gutta-percha techniques showed better outcomes despite a possible learning bias in favor of cold lateral condensation. Establishing operator skills before comparison may help reduce this bias.

Abstract The foremost objective of the current article is to explore the impact of Brownian motion on magnetohydrodynamic Casson nanofluid flow toward a stretching sheet in the attendance of nonlinear thermal radiation. The combined heat and mass transfer characteristics are investigated. The influence of chemical reaction, nonuniform heat source/sink, Soret, and Dufour is deemed. The convective boundary condition is taken. The appropriate transformations are utilized to transform the flow regulating partial differential equations into dimensionless ordinary differential equations (coupled). The numerical outcomes of the converted nonlinear system are solved by the Runge‐Kutta based Shooting procedure. Results indicate that the temperature is an increasing function of both thermophoresis and Brownian motion parameters. The concentration of the fluid and the corresponding boundary layer thickness reduces with an enhancement in Lewis number.

Severe acute exacerbation of chronic obstructive pulmonary disease (AE-COPD) is a common reason for emergency room (ER) visit about which little has been documented from India. Prospective study of the clinical presentation and predictors of outcome in 116 patients presenting with severe AE-COPD requiring admission to the medical intensive care unit between January 2000 and December 2004. Their mean age was 62.1 ± 9.8 years. There were 102 males. Mean duration of COPD was 7.2 ± 5.8 years. All males were smokers (22.3 ± 11.2 pack years); 35.2% smoked cigarettes and 64.8% smoked bidis. All women were exposed to domestic fuel. Associated co-morbid illnesses were present in 81 patients (69.8%); 53(45.7%) had one co-morbid illness and the remaining 28 (54.3%) had two or more co-morbid illnesses. Evidence of past pulmonary tuberculosis (PTB) was present in 28.4% patients; 5 patients who also had type II diabetes mellitus had active PTB. Arterial blood gas analysis revealed respiratory failure in 40 (33.8%) patients (type I 17.5% and type II 82.5%). Invasive mechanical ventilation was required in 18 patients. Sixteen (13.7%) patients died. Stepwise multivariate logistic regression analysis revealed need for invasive ventilation (OR 45.809, 95%CI 607.46 to 3.009;p < 0.001); presence of co-morbid illness (OR 0.126, 95%CI 0.428 to 0.037;p < 0.01) and hypercapnia (OR 0.114, 95%CI 1.324 to 0.010;p < 0.05) were predictors of death. Co-morbid conditions and metabolic abnormalities render the diagnosis of AE-COPD difficult and also contribute to mortality. High prevalence of past PTB and active PTB in patients with AE-COPD suggests an intriguing relationship between smoking, PTB and COPD which merits further study.

For lifelong and reliable operation, advanced solar photovoltaic (PV) equipment is designed to minimize the faults. Irrespectively, the panel degradation makes the fault inevitable. Thus, the quick detection and classification of panel degradation is pivotal. Among various problems that promote panel degradation, hot spots and micro-cracks are the prominent reliability problems which affect the PV performance. When these types of faults occur in a solar cell, the panel gets heated up and it reduces the power generation hence its efficiency considerably. In this study, the effect of the hotspot is studied and a comparative fault detection method is proposed to detect different PV modules affected by micro-cracks and hotspots. The classification process is accomplished by utilizing Feed Forward Back Propagation Neural Network technique and Support Vector Machine (SVM) techniques. Six input parameters like percentage of power loss (PPL), Open-circuit voltage (VOC), Short circuit current (ISC), Irradiance (IRR), Panel temperature and Internal impedance (Z) are accounted to detect the faults. Experimental investigation and simulations using MATLAB are carried out to detect five categories of faulty and healthy panels. Both methods exhibited a promising result with an average accuracy of 87% for feed-forward back propagation neural network and 99% SVM technique which exposes the potential of this proposed technique.

We proposed an optimum de-noising filter using wavelet threshold for the removal of adaptive white Gaussian noise from degraded medical images/colour video sequence based on system noise calculation. The performance of the proposed Gaussian de-noising technique is compared with various de-noising techniques such as Bayesian least squares-Gaussian scale mixtures, bilateral filter, adaptive bilateral filter, multi-resolution bilateral filter, nonlocal-Means, wavelet threshold based filters, and kernel-based method. The proposed methods have comparable/superior performance and less computational time compared to other techniques.

The analysis is accomplished to discover the innovation of an incompressible magnetohydrodynamic Casson liquid flow driven by a stretchable sheet with convective boundary conditions. The variations of non-linear thermic heat, chemical response and unequal heat absorption/generation are reserved into account. The flow reckonings are mutated into a scheme of dimensionless non-linear ODEs with appropriate similarity variables. The elucidation of the problem is attained by captivating the help of the fourth-order RK-based shooting procedure. The bearings of influential parameters on velocity, thermal and solutal fields are revealed with the assistance of diagrams and also the physical measures skin friction, local solutal and thermic heat transport are revealed in a distinct chart. Escalating the non-linear thermal radiation parameter as well as the temperature ratio restriction escalates the fluid temperature. The rate of mass transport is extraordinary for the increase of chemical response parameter and Schmidt number.

The conventional methods of using autografts and allografts for repairing defects in bone, the osteochondral bone, and the cartilage tissue have many disadvantages, like donor site morbidity and shortage of donors. Moreover, only 30% of the implanted grafts are shown to be successful in treating the defects. Hence, exploring alternative techniques such as tissue engineering to treat bone tissue associated defects is promising as it eliminates the above-mentioned limitations. To enhance the mechanical and biological properties of the tissue engineered product, it is essential to fabricate the scaffold used in tissue engineering by the combination of various biomaterials. Three-dimensional (3D) printing, with its ability to print composite materials and with complex geometry seems to have a huge potential in scaffold fabrication technique for engineering bone associated tissues. This review summarizes the recent applications and future perspectives of 3D printing technologies in the fabrication of composite scaffolds used in bone, osteochondral, and cartilage tissue engineering. Key developments in the field of 3D printing technologies involves the incorporation of various biomaterials and cells in printing composite scaffolds mimicking physiologically relevant complex geometry and gradient porosity. Much recently, the emerging trend of printing smart scaffolds which can respond to external stimulus such as temperature, pH and magnetic field, known as 4D printing is gaining immense popularity and can be considered as the future of 3D printing applications in the field of tissue engineering.

OBJECTIVES: To qualitatively and quantitatively review the use of melatonin as a topical/systemic formulation for the management of periodontitis. MATERIALS AND METHODS: PubMed; Scopus; and Web of Science databases were searched using the MesH terms "melatonin" and "periodontitis". Title and abstracts were screened to eliminate irrelevant and duplicate articles. The full text data of the screened articles were assessed using the selection criteria. RESULTS: Of 176 identified articles (PubMed-66; Scopus-56; Web of Science-52; Cross-reference-2), only 12 studies qualified to be included in the systematic review. Four studies assessed the independent effect of 1% topical melatonin formulation while 8 articles assessed the adjunctive use of systemic melatonin formulation (1-10 mg) following scaling and root planing (SRP). All studies showed an improvement in periodontal parameters such as pocket depth, clinical attachment loss, periodontal disease index, community periodontal index, gingival bleeding scores, and prognostic marker levels in saliva and serum. A meta-analysis of data from 2 studies revealed that 1-2 mg (systemic) melatonin supplementation reduced pocket depth; although the difference was not statistically significant and hence cannot be interpreted or used for conclusive evidence. Risk of Bias Assessment tool (RoBANS) and Cochrane Collaboration RoB tool elicited a high risk of bias in the included studies. GRADE (recommendation assessment, development, and evaluation) inferred a weak recommendation for the use of melatonin in periodontitis management. CONCLUSIONS: Melatonin supplementation (topical and systemic) in periodontitis patients improved key periodontal parameters including pocket depth and clinical attachment loss. CLINICAL RELEVANCE: Melatonin could be a potential host modulatory agent for periodontitis management; although the data from the present review should be interpreted carefully due to the associated high risk of bias.

The objective of the present article was to qualitatively and quantitatively review the association between chronic mechanical irritation and oral squamous cell carcinoma (OSCC). PubMed, SCOPUS, and Web of Science databases were searched using the keyword combinations "chronic trauma and oral squamous cell carcinoma; chronic irritation and oral squamous cell carcinoma; chronic irritation and oral cancer; and chronic trauma and oral cancer." Duplicates and irrelevant articles were excluded after the title and abstract screening. The full texts of the remaining articles were assessed using selection criteria. A total of 375 (PubMed-126; SCOPUS-152; WOS-97) articles were screened, and 343 duplicates and irrelevant articles were excluded from the study. Only 9 of the remaining 32 articles met the selection criteria and were included in the qualitative analysis. Buccal mucosa and tongue, being highly prone to chronic irritation through the dental prosthesis, were the common sites for OSCC. Edentulous subjects with ill-fitting dentures were at a high risk of developing chronic irritation associated-OSCC. According to the Joanna Briggs Institute of risk assessment, eight of the nine included studies had a low risk of bias. The quantitative analysis showed a significant association (p < 0.00001) between the chronic oral mucosal irritation and OSCC with an overall risk ratio of 2.56 at a confidence interval of 1.96-3.35. Chronic oral mucosa irritation has a significant association with OSCC, and the nature of association could be that of a potential co-factor (dependent risk factor) rather than an independent risk factor.

Cryptography is the practice for secure communication in the presence of third parties. Steganography is technique of writing hidden messages such that except the sender and receiver, no one even suspects the existence of the message. In today's hi-tech age, threats from intruders are very great such that usage of either of the above techniques separately may not be able to provide the intended protection. In order to increase the level of protection, both the techniques may be used in a combined manner. Multimedia techniques can also be used to hide the data. In this paper, we propose an encrypting system which combines techniques of cryptography and steganography with data hiding. Instead of using a single level of data encryption, the message is encrypted twice. Traditional techniques have been used for this purpose. Then the cipher is hidden inside the image in encrypted format for further use. It uses a reference matrix for selection of passwords depending on the properties of the image. The image with the hidden data is used for further purposes.

Objective: To qualitative and quantitatively review published literature assessing the oxidative stress marker malondialdehyde (MDA) in oral squamous cell carcinoma (OSCC). Methodology: Pubmed (MeSH), Science Direct, Scopus, Web of Science, Willey Online Library, Cochrane, and Cross Reference were searched for studies assessing MDA levels in OSCC samples. Results: From the 1008 articles identified, 849 were excluded based on title and abstract screening due to duplication and irrelevance to the topic of interest. Full-text assessment of the remaining 159 articles led to the inclusion of only 46 articles that satisfied the selection criteria. Of these, only 26 studies had data compatible for quantitative analysis. The MDA levels in OSCC groups are significantly increased (p &lt; 0.00001) in plasma, serum, and saliva samples in the majority of the studies evaluated. In contrast, MDA levels in OSCC tissue samples are significantly attenuated (p &lt; 0.00001) compared to healthy controls, supported by fewer studies. Conclusions: The augmented MDA levels in plasma, serum, and saliva samples of the OSCC reflect the heightened oxidative stress level accurately. Further studies are required to understand the attenuated MDA levels in the tissue samples of OSCC. Correlation analysis between MDA levels with established clinicopathological prognostic markers could aid in formulating oxidative stress-based prognostication and treatment planning.

Experiments were carried out in rabbits to determine the effects of prolonged treatment of cadmium (8 mg/kg/day) for a period of 6 months on histopathological changes and biochemical alterations of lipid profiles in various tissues compared to normal rabbits. No ECG changes were observed before and at the end of cadmium treatment. Histopathological studies of the coronary artery revealed atherosclerotic changes. Total lipids, cholesterol, free fatty acids and phospholipids were significantly increased in heart and kidney, but decreased in serum and liver. Triglyceride content was increased significantly in heart and kidney with a significant depletion in liver and serum. It is postulated that atherosclerotic changes in rabbits probably occurred through toxic effects of cadmium but the exact mechanism needs to be elucidated.

In April 2016, an indigenous monovalent rotavirus vaccine (Rotavac) was introduced to the National Immunization Program in India. Hospital-based surveillance for acute gastroenteritis was conducted in five sentinel sites from 2012 to 2020 to monitor the vaccine impact on various genotypes and the reduction in rotavirus positivity at each site. Stool samples collected from children under 5 years of age hospitalized with diarrhea were tested for group A rotavirus using a commercial enzyme immunoassay, and rotavirus strains were characterized by RT-PCR. The proportion of diarrhea hospitalizations attributable to rotavirus at the five sites declined from a range of 56-29.4% in pre-vaccine years to 34-12% in post-vaccine years. G1P[8] was the predominant strain in the pre-vaccination period, and G3P[8] was the most common in the post-vaccination period. Circulating patterns varied throughout the study period, and increased proportions of mixed genotypes were detected in the post-vaccination phase. Continuous long-term surveillance is essential to understand the diversity and immuno-epidemiological effects of rotavirus vaccination.