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Oral cancer is increasing in prevalence and its treatment is associated with high degree of morbidity and mortality. Thus, prevention of oral cancer is of utmost importance. Chemoprevention is the use of natural, synthetic, or biologic compounds to halt, reverse, or prevent the initial phases of carcinogenesis or the progression of neoplastic cells to cancer. This modality has been extensively researched in the last two decades for the prevention of oral cancer with the emergence of new information. Retinoids were the first chemopreventive agents to be tested in clinical settings. Since then, a number of new agents such as COX2 inhibitors, EGFR inhibitors, p53 targeted agents, thiazolidinediones and several natural agents have shown promise in oral cancer prevention. Chemopreventive trials in oral cancer tend to be long term studies and are thus challenging. This review article looks into the clinical evidence for the application of chemopreventive agents in clinical settings and also highlights the recent trends in oral cancer chemopreventive trials.

Introduction: Maternal health is important for the development of any country in terms of increasing equity and reducing poverty and is important in their own right and is central to solving economic, social and developmental challenges. Hence, maternal mortality is the key indicator of health and socio economic status and development of a community and the whole national system. Aim: To study the factors responsible for maternal mortality and to analyse the causes and trends in maternal mortality. Materials and Methods: A retrospective study on maternal deaths that occurred in the maternity ward of Government General Hospital, Siddhartha Medical College, Vijayawada during the period of five years from January 2017 to December 2021. Covid related deaths excluded. Results: Total 137 maternal deaths occurred during the study period. 121 maternal deaths (88.32%) were unbooked cases, 16 (11.67%) were booked cases. 58 women (42.33%) were primiparous, 76 (55.47%) were multiparous and 3 (2.18%) were grand multiparous. 76 women (55.47%) were of 20-24 years age, 38 women (27.73%)were of 25-29 years age. Maternal deaths due to direct causes were 89 (65.92%) and due to indirect causes were 47 (34.07%). Hypertensive disorders complicating pregnancy was the leading cause of death followed by hemorrhage in pregnancy. 105 (76.64%) maternal deaths occurred after delivery, 14 deaths (10.21%) during 3rd trimester of gestation. Type 1 delay (20.74%), type 2 delay (57.7%) type 3 (21.48%) delays contributed to the maternal deaths. Conclusion: Health education regarding adolescent health, pregnancy, abortion and contraception. Regular antenatal checkup, early identification of risk factors, awareness, timely intervention and referral to higher institute, proper intra natal and post natal care, multispecial team management in high risk cases are needed to reduce the maternal mortality.

Cement is the primary raw material in any construction works, being an indispensable binding material. The production associate degreed consumption of cement is an index of its industrial development. However, excess production of cement pollutes the setting by emotional CO2 gas. So, it is necessary to exchange cement with alternative binding materials. In this study, one such trial is attempted using mineral admixtures like Fly ash, GGBS which are industrial waste byproducts. The concrete specimens are cast using, Fly ash, GGBS, and Metakaolin, with Alkaline liquid like Sodium silicate, and Sodium Hydroxide solution with 10 M, in the ratio of 1:1.5 and 1:2. Fibers such as steel fibers and polypropylene fibers are added with a 0–1.5% percentage to enhance the concrete properties. The moulds are cured under sunlight. The mechanical properties such as flexural strength, compressive strength, and split-tensile strength are performed along with durability tests to evaluate the optimized ratio of using mineral admixtures and fibers. The test results indicate that the combination of Metakaolin and GGBS can be used to develop environmentally friendly geopolymer concrete.

Internet of things (IoT) and Machine to Machine (M2M) technology has become indispensable in everyday life, finding real-time applications in smart tracking/monitoring services, wearable technologies, telemetry, autonomous vehicles, agriculture, healthcare, transportation, etc. It employs millions of sensors and devices connected to automate the tasks to be accomplished by collecting data, activating sensors and communicating between the devices either through the internet or point-to-point connections. Consequently, the smart device usage and the connected devices in the IoT network is increasing exponentially with limitless potential. However, with the increasing connectivity comes the increasing attack surface with attack entry points from different sources in the network. Hence security related risk and network anomalies need to be detected earlier in-order to prevent the devices in the IoT network from bringing down the whole network. In our proposed system, two-layers IoT Stacked Ensemble Learning Network (IoT_SLINK) is designed for attack detection in the IoT network at the source with feature selection using machine learning algorithms. This work focuses on detecting attacks originating from OSI layer 1 to layer 6based on the Random Forest (RF), Decision Tree (DT), Navie Bayes (NB) and K-Nearest Neighbour (KNN)machine learning algorithms. The feature selection algorithms are used to improve the model’s performance in terms of accuracy, reduce overfitting and reduce the training time. The prominent features from the datasets are selected by using Particle Swarm Optimization (PSO) algorithm and Improved Salp Swarm Optimization Algorithm (ISSA) feature selection technique. The performance of the proposed system is evaluated with three IoT benchmark datasets IoT23, BoT-IoT and Distributed Smart Space Orientation System (DS2OS).The proposed system outperforms state-of-art techniques with more than 99% of accuracy with very low false positive rate.

The hydrogels are three-dimensional, water-swollen structures composed mainly of hydrophilic homo-polymers or copolymers utilized in various fields. This review explains in detail about the various methods used in the synthesis of hydrogel, their loading mechanism, their swelling index, their chemical and physical properties, and also comparative analysis between chemo-drug and drug-entrapped hydrogel. In this, we will also discuss various applications of hydrogel, which aid in treating diseases, like Alzheimer’s disease, Parkinson disease, etc., using modified drug delivery mechanism. Though these diseases have various methods of treatment, they have a common ground and that is the need for definite amounts and a prolonged release of drugs which will be possible with the aid of hydrogels. The use of hydrogel is believed to be an upgrade in the medical field, and its involvement in treatments is considered to be sincerely encouraged.

Introduction: Metamemory awareness refers to our feelings or experiences of our own memory. For example, if a person feels certain that he or she will remember later something just learned now, that person is having a metamemory experience. Metamemory is a subarea of metacognitions. Aim: The aim is to assess metamemory in primigravid individuals. Materials and methods: A study was conducted among 120 pregnant female of south Indian population. The participants were asked to complete a self-reported questionnaire on metamemory. It includes multifactorial memory questionnaire (MMQ), developed to assess separate dimensions of memory ratings that are applicable to clinical assessment and intervention, includes scales of Contentment, Ability, and Strategy. Results: Association between corresponding subscale scores and gestational period indicates a weak correlation for satisfaction (r = -.09), ability (r = -.12) and strategy (r = -.17). In addition, the MMQ subscales and the total score showed good internal consistency (αs = 0.81-0.89). Conclusion: The results would conclude metamemory components were found to be generally poorer in pregnancy particularly less in third trimester. These impact in pregnant female on quality of life and everyday functioning requires further investigation.

Human health are to be monitored for glucose level, blood pressure level and intimated at the right time for taking the necessary pills or food. Defense forces, Diabetes victim and victim with heart attack must be given alert on their BP level and accordingly they can react for taking the appropriate pill or food. A victim may go low in his /her BP level while sleeping more vulnerable to this kind of situation which requires salient tracking and alerting system. Smart polymers combined with Biomimetic Elastomers forms the Injectable Polymer hydrogels. Moreover, many IoT devices have been discovered. A study throws light on a fact that most of continuous tracking observations, human skin suffers a bone disorder called Osteoporosis which ultimately leads to deformation of bones. But hydration loss must be reducible for a bi tissue to be discovered; thereby bypassing the drawbacks caused due to standalone IoT sensors. Injectable Polymer hydrogels is already used for plants and animals for avoiding damage to them during research experiments. The polymers must be molded to the required consistency for synthesizing wearable sensors eliminating health hazards. The conventional IoT system is substituted with the biotechnological solution to enhance continuous monitoring and alert system for victim and elderly aged people. Injectable Polymer hydrogels an artificial skin protein constitutes the composition of nutrients and proteins available in the form of powder can be used for manufacturing the wearable devices to maintain the conductivity range in such a way that it does not affect the human skin. It is proposed to synthesize the artificial skin gel in a relative state that it produces accurate results with sensor data transfer also. The solution proposed here provides the safe usage of irremovable monitoring and tracking of health status.

Natural and human anthropogenic activities increase the concentration of the toxic pollutant in the water environment; they could cause harmful effects even in their lower concentration. In humans, toxic pollutants damage the structural and functional properties of essential organs including the heart, liver, kidneys, reproductive systems and pancreas. To avoid the toxicity of the pollutant, they should be removed from the water environment. Since various conventional water/wastewater treatment technologies including precipitation, ion exchange, flocculation, filtration, electrodialysis and membrane separation are employed to reduce the concentration of the pollutant, they have various difficulties in implementation, efficiency and ecological perspective. Therefore, several researchers are now focusing on alternative and eco-friendly approach called biosorption to remove toxic contaminants from the water environment. The biosorption innovation is one of the acclaimed systems for water treatment. The noteworthy endeavours have been made throughout the years to grow profoundly particular and effective biosorbent materials that are more effective, abundantly available, and cost-effective. Biosorption is effectively executed by utilizing both living and dead biomasses of bacteria, fungi and algae. Moreover, agro-waste materials are also utilized as biosorbents due to their excellent surface properties, abundant availability and cost-effectiveness. A variety of physical and chemical treatments enhances the biosorption capabilities of biosorbents via modifying their surface properties. In this review, biosorption mechanism, influencing parameters and application of biosorbent materials towards the removal of toxic pollutants are discussed. The future research opportunities for sustainable wastewater treatment were also explained.

Smart IOT-based devices are used to extract the emotions and sentiment metrics expressed by the course members for calculating the utility function value for each e-learning content. This is mainly used for rating the E-learning content available on online sources. ELMs are used to combine the output of classifier models implemented by CNN and RNN. Text, image, and acoustic data with respect to time are extracted by CNN, and the extracted output is processed by both CNN and RNN for sentiment analysis. The hidden layers of CNN are trained with ensemble-based algorithms, namely EN-ELM and progressive EN-ELM. The output of CNN and RNN is fed to the two consecutive ELM further whose output is fed to the SVM classifier. Unlike other CNN algorithms, ensemble algorithms are used in the hidden neurons of CNN to carry the time series information to the RNN with the help of stumps and identity activation function. The accuracy of progressive convolution neural network is compared to the accuracy of CNN with RNN. Arousal in the accuracy level is detected with 93–97%.

Multimodal sentiment analysis framework is tremendously applied in the medical and healthcare sectors. Identifying the depression, differently disabled person speech recognition, Alzheimer, low pressure or heart problems and those kinds of impairments are widely addressed. Video data is processed for mining feature polarity from acoustic, linguistic and visual data upon extraction from the same. The feature data set extracted from the YouTube videos contains comments, likes, views and shares for expressing the polarity of information conveyed through the streaming videos. Static information from a video file is extracted in the form of linguistic representation. Musical data extracted and transformed in the linguistic form is used for polarity classification using ensemble-based random forest algorithm which has encountered with the error rate of 4.76%. Short feature vectors are expressed in the visualizing musical data and trending YouTube videos data set for utilizing the transformed and SF vectors from video and musical data. Accuracy of the ensemble-based learning is obtained as 91.6% which is tougher than any other algorithms to achieve using the same set of machine learning algorithms. Proper filter wrapping of batch data is used for the split ratio of 5 percentage split window. When SVM is used in discrimination with ensemble random forest algorithm, the predicted results contain an error rate of 2.64% which improves the accuracy of the classifier along the soft margin with an accuracy of 96.53% accuracy.

Internet of Everything (IoE) and Internet of Nano Technology (IoNT) are the most prominently distributed knowledge over reviews on current trends. Nanoparticles communicating in the perceiving and propagating end cause AI and NLP usage in a better manner. IoNT is used as the medical smart watch calorimeter and pulse rate instrumentation. A sensor of IoE category is used in the mobile devices to accurately provide the alert on calorimeter results and pulse sensed. Voice-based results are gained with the help of AI and NLP. The fuzzy expert system is used to decide on the perceptions received by IoNT devices. Human intelligence is incorporated to further query on installation of food diet control mobile applications in the registry of the programmable devices. There is a strong interest to use social networking to enhance the communications among different IoT things. A new paradigm, named Social Internet of Things (SIoT), was recently proposed. Health-problem-specific solutions are extracted from the social networking blogs for solutions in the recent era. Solutions referred and improvement are recorded for recommendations as a social network post with the concerned proprietary owner permission and rights.

Emulgel is one of the recent technologies in Novel Drug Delivery system (NDDS) used for dual controlled release of emulsion and gel for topical use. The stability of emulsion is increased, when it is incorporated into gel. Clotrimazole is a broad spectrum anti - fungal agent, also a hydrophobic drug. It is difficult to formulate as topical delivery. So emulgel is one of the best approaches to incorporate hydrophobic drugs. The purpose of the present study was to develop and optimized the Clotrimazole emulgel using polymers like Carbopol 934 and HPMC K4M at different concentrations. The Preformulation studies were conducted by using Fourier Transform Infrared (FTIR) studies, the prepared emulgels were evaluated in terms of physical appearance, measurement of pH, viscosity, spreadability, drug content and in vitro diffusion studies. In vitrodrug release studies were performed up to 8hrs. Among all formulations F6 shows better releasing property. So F6 was proved as the best formulation among all. F6 containing HPMC K4M polymer in low concentration showed better releasing property when compared to formulations containing Carbopol 934. So it was suggested that the Clotrimazole emulgel have potential advantages when compared to other topical drug delivery systems for incorporating hydrophobic drugs and posses better activity and stability.

The aim of the project is to develop a new simple, accurate, precise, rapid, selective and reproducible high performance thin layer chromatographic method for quantitative analysis of caffeine in chocolate products has been established and validated. Chocolate is a preparation of roasted andground cocoa seeds, which contains caffeine. Daily limit of caffeine 300-400mga day. More than 400mg of caffeine consumption leads to disrupted sleep, nervousness, dizziness, alsoincreases release ofacid. Inthis theextraction ofcaffeine fromchocolates had done and obtained product was quantified by using HPTLC. High performance thin layer chromatography (HPTLC) on aluminium – baked silica gel 60 F254 plates with butanol- chloroform-ammonia-acetone 4:3:2:1% (v/v/v/v) as mobile phase was followed by densitometry measurement at 254nm. This system was found to give compact bands for caffeine. Calibration plots were linear with correlation coefficient. The developed method was validated and proved to meet the requirements delineated by ICH guidelines with respect to linearity, accuracy, precision, and robustness and can be used for analysis of marketed products.