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Jainism is said to be existed from eternal period of time and Thirthankar’s are the promoters of Jain religion. Principles of Jain Darshan are extremely precious and based on scientific values and researches. In present time peoples are suffering from various mental health issues. According to WHO, A complete health status includes physical, mental and social well-being, so mental health stability is very much important and for these various principles are must include in our life. The principles of Jain Darshan teach us about self-control, concentration, positive emotions through 5 Anuvritta viz 1. Ahimsa (non-violence) 2. Aparigraha (abstinence) 3. Satya (truth) 4. Brahmacharya (celibacy) 5. Asteya (non-stealing) and 10 Dharma’s viz 1. Kshama (forgiveness) 2. Mardava (kindness) 3. Arjava (honesty) 4. Satya (truthiness) 5. Shauch (purity) 6. Sanyam (austerity) 7. Tapa (tenacity) 8. Tyaga (renunciation) 9. Akinchana (non-attachment) 10. Brahmacharya (celibacy). These principles of Jainism can contribute in parity of mental health.

In past few years many rituximab (RTX) biosimilars have been launched in India. Biosimilars are products that are similar in terms of quality, safety, and efficacy to its innovator product and are expected to offer improved affordability. The less clinical examination is a significant source of reduction in the cost of development of a biosimilar. However, this clinical relief is predicated on the assumption that there is analytical similarity between the biosimilar and the innovator product. Therefore, the role of National Control Laboratory become very important to ensure the quality of these drugs by carrying out analytical characterization at the point of drug product release level as when referred by National Regulatory Authority for quality evaluation. To assess the similarity between innovator and biosimilars, different physicochemical and biological quality attributes were assessed. A multitude of state-of-the-art analysis of

Intratumoral heterogeneity can exist along multiple axes: Cancer stem cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states, and a spectrum of epithelial–hybrid–mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple stable gene expression patterns (‘attractors’). These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

This report has been prepared as partial fulfilment of the requirement for the cooperative education, faculty of Business Administration at Chandigarh University. Having expressed my interest and desire to work in the field of Digital Marketing department. I work on this project for 8 weeks straight from March 01,2023 to May 01,2023. The report initially contains a brief introduction of Innovative Marketing Strategies. Afterwards, the significance of the study is mentioned along with the different marketing strategies. Then the effectiveness of digital marketing strategy is thoroughly discussed, from the digital marketing strategies work how social media marketing and digital marketing have been a dominating factor in the growth of Marketing. Some of the new digital marketing and social media marketing strategies is been discussed followed by digital presence of innovation on various social media platforms. The report has been integrated with lot of data tables that verify and analyse the digital innovation effectiveness. Based on these analysis, managerial implication was suggested which can improve the Marketing strategies in today’s world. However, the report has been made in a very short time and some of the data provided on the report may not be accurate by the time it is evaluated as we know digital analytics literally change every day. In spite of all that, I believe this report has enough reasons to analysis the SEO Ranking and social media presence of Consumers.

Epithelial–mesenchymal transition (EMT) and its reverse mesenchymal–epithelial transition (MET) are critical during embryonic development, wound healing and cancer metastasis. While phenotypic changes during short-term EMT induction are reversible, long-term EMT induction has been often associated with irreversibility. Here, we show that phenotypic changes seen in MCF10A cells upon long-term EMT induction by TGF β need not be irreversible, but have relatively longer time scales of reversibility than those seen in short-term induction. Next, using a phenomenological mathematical model to account for the chromatin-mediated epigenetic silencing of the miR-200 family by ZEB family, we highlight how the epigenetic memory gained during long-term EMT induction can slow the recovery to the epithelial state post-TGF β withdrawal. Our results suggest that epigenetic modifiers can govern the extent and time scale of EMT reversibility and advise caution against labelling phenotypic changes seen in long-term EMT induction as ‘irreversible’.

Epithelial-Mesenchymal Transition (EMT) and its reverse Mesenchymal-Epithelial Transition (MET) are critical during embryonic development, wound healing and cancer metastasis. While phenotypic changes during short-term EMT induction are reversible, long-term EMT induction has been often associated with irreversibility. Here, we show that phenotypic changes seen in MCF10A cells upon long-term EMT induction by TGFβ need not be irreversible, but have relatively longer timescales of reversibility than those seen in short-term induction. Next, using a phenomenological mathematical model incorporating the epigenetic silencing of miR-200 by ZEB, we highlight how the epigenetic memory gained during long-term EMT induction can slow the recovery to the epithelial state post-TGFβ withdrawal. Our results suggest that epigenetic modifiers can govern the extent and timescale of EMT reversibility, and advise caution against labelling phenotypic changes seen in long-term EMT induction as ‘irreversible’.

The rapid onset of the global COVID-19 pandemic has led to multiple challenges for accurately diagnosing the infection. One of the main bottlenecks for COVID-19 detection is reagent and material shortages for sample collection, preservation, and purification prior to testing. Currently, most authorized diagnostic tests require RNA extraction from patient samples and detection by reverse transcription polymerase chain reaction (RT-PCR). However, RNA purification is expensive, time consuming, and requires technical expertise to perform. Additionally, there have been reported shortages of the RNA purification kits needed for most tests. With these challenges in mind, we report on extraction-free amplification of SARS-CoV-2 RNA directly from patient samples. In addition, we have developed a multiplex RT-PCR using the CDC singleplex targets. This multiplex has a limit of detection of 2 copies/μL. We have demonstrated these improvements to the current diagnostic workflow, which reduce complexity and cost, minimize reagent usage, expedite time to results, and increase testing capacity.

Internationally, diabetes mellitus is recognised as a risk factor for severe COVID-19. The relationship between diabetes mellitus and severe COVID-19 has not been reported in the Australian population.The objective of this study was to determine the prevalence of and outcomes for patients with diabetes admitted to Australian intensive care units (ICUs) with COVID-19.This is a nested cohort study of four ICUs in Melbourne participating in the Short Period Incidence Study of Severe Acute Respiratory Infection (SPRINT-SARI) Australia project. All adult patients admitted to the ICU with COVID-19 from 20 February 2020 to 27 February 2021 were included. Blood glucose and glycated haemoglobin (HbA1c) data were retrospectively collected. Diabetes was diagnosed from medical history or an HbA1c ≥6.5% (48 mmol/mol). Hospital mortality was assessed using logistic regression.There were 136 patients with median age 58 years [48-68] and median Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 14 [11-19]. Fifty-eight patients had diabetes (43%), 46 patients had stress-induced hyperglycaemia (34%), and 32 patients had normoglycaemia (23%). Patients with diabetes were older, were with higher APACHE II scores, had greater glycaemic variability than patients with normoglycaemia, and had longer hospital length of stay. Overall hospital mortality was 16% (22/136), including nine patients with diabetes, nine patients with stress-induced hyperglycaemia, and two patients with normoglycaemia.Diabetes is prevalent in patients admitted to Australian ICUs with severe COVID-19, highlighting the need for prevention strategies in this vulnerable population.

This work addresses the automatic classification of arrhythmia beats into four generalized classes as described by the Association for the Advancement of Medical Instrumentation (AAMI) standard. We propose a method that includes time-series, statistical and frequency features of RR-interval, DWT, and EMD analysis of QRS morphology. Also, a data filtration technique using support vector selection and under-sampling is applied to find those features as well as data points having significant prediction capabilities. While testing the above combination on MIT-BIH arrhythmia database, adopting the inter-patient paradigm, we achieved 70%, 99.79%, 64.5%, and 80.55% Se and 61.76%, 94.64%, 83.22%, and 77.48% PPV for F, N, SVEB, and VEB classes respectively. Further, the proposed method reduced the classifier’s complexity through feature selection and computation time by data reduction while maintaining the generalization capability of the model. Another finding includes the significant contribution that RR-interval, 180–360 Hz and 0–45 Hz band power, and non-linear statistical characteristics have in distinguishing the arrhythmia classes. The feature and data selection criterion used is F -score and one-class classification by RBF-SVM respectively. The classifier used for building the final model is SVM with the cubic kernel.

Free radicals play a major role in development of many human diseases like cancer etc. In the present investigation Plumbago zeylanica (Pz) was used for anti-fungal activity, anti-oxidant activity as well as phytochemicals investigation. Anti-fungal activities were determined against four pathogenic fungal species Fusarium oxysporum, Rhizoctonia solanii, Alternaria sp. and Sclerotium rolfsii. Anti-oxidant activity and phytochemical investigation were determined by standard method using spectrophotometer, gas chromatography and mass spectroscopy. The excellent inhibitory activities were observed against Alternaria spp. (98%), F. oxysporum (68%), R. solanii (97.5%) followed by S. rolfsii (93.4%) at 62.5 μg/ml. The P. zeylanica showed free radicals scavenging activity by DPPH method (77.8% inhibition at 1000 µg/ml), CUPRAC assay (0.07940 TE mM at 1000 µg/ml), Anthocyanin content (2.404 CGE mg/ml), Flavonoid content (55,330 GAE/g), Phenols (14.75 µg/ml), Alkaloids (54 mg/g), Saponins (3.95%) and Tannin (7 µg/10 ml). So P. zeylanica extracts can be used as a source of bio-fungicide and scavenger of free radicals.

Wireless sensor networks (WSNs) are a widely studied area in the field of networked embedded computing. They are made up of several sensor nodes, which keep track of a variety of physical and environmental parameters, like temperature and humidity. The nodes are autonomous, self-configuring, and wireless. A significant problem in WSNs is that sensors in these networks consume a lot of energy. Energy consumption is a big issue when it comes to the deployment of sensor networks. The reason for this is the cost of operating a sensor node and the cost incurred due to energy consumption. Energy optimization is based on intelligent energy management. This paper presents a reinforcement learning-based and clustering-enhanced method. Reinforcement learning is a set of algorithms inspired by operant conditioning in animal behavior, and clustering-based methods have been extensively used for devising energy-efficient protocols. The proposed method is able to plan and schedule the nodes to ensure an extended network lifetime. In this work, we aim to assess and increase the efficiency of power consumption and reduce sensor node energy loss. The simulation results prove that the presented protocol effectively reduces the energy consumption of sensor nodes and ensures a prolonged lifetime of the sensor network.

Through the use of blockchain technology, sensitive information may be securely communicated without the need to replicate it, which can assist in decreasing medical record mistakes and saving time by eliminating the need to duplicate information. Furthermore, the information is timestamped, which further enhances the security of the data even further. The deployment of blockchain technology in a range of healthcare situations may enhance the security and efficiency of payment transactions. In this way, only those who have been allowed access to patient medical information can see or modify such information. It is proposed in this study that blockchain technology be used to provide an accessible data storage and retrieval mechanism for patients and healthcare professionals in a healthcare system that is both safe and efficient. As of 1970, a variety of traditional knowledge-based approaches such as Personal Identification Recognition Number (PIRN), passwords, and other similar methods have been made available; however, many token-based approaches such as drivers’ licenses, passports, credit cards, bank accounts, ID cards, and keys have also been made available; however, they have all failed to establish a secure and reliable transaction channel. Because they are easily misplaced, stolen, or lost, they are usually unable to protect secrecy or authenticate the identity of a legitimate claimant. Aside from that, personally identifiable information such as passwords and PINs is very prone to fraud since they are easily forgotten or guessed by an imposter. Biometric identification and authentication (commonly known as biometrics) are attracting a great deal of attention these days, particularly in the realm of information security systems, due to its inherent potential and advantages over other conventional ways for identifying and authenticating. As a result of the device’s unique biological characteristics, which include features such as fingerprints, facepalms, hand geometry (including the iris), and the device’s iris, it can be used in a variety of contexts, such as consumer banking kiosks, airport security systems, international ports of entry, universities, office buildings, and forensics, to name a few. It is also used in several other contexts, including forensics and law enforcement. Consequently, every layer of the system—sensed data, computation, and processing of data, as well as the storage and administration of data—is susceptible to a broad variety of threats and weaknesses (cloud). There does not seem to be any suitable methods for dealing with the large volumes of data created by the fog computing architecture when normal data storage and security technologies are used. Because of this, the major objective of this research is to design security countermeasures against medical data mining vulnerabilities that originate from the sensing layer and data storage in the Internet of Things’ cloud database, both of which are discussed in more depth further down. A key allows for the creation of a distributed ledger database and provides an immutable security solution, transaction transparency, and the prohibition of tampering with patient information. This mechanism is particularly useful in healthcare settings, where patient information must be kept confidential. When used in a hospital environment, this method is extremely beneficial. As a result of incorporating blockchain technology into the fog paradigm, it is possible to alleviate some of the current concerns associated with latency, centralization, and scalability.

ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.

Education provides access to subject matter experts, interaction with career role models. It provides interaction with students in other schools, increases access to information instructional resources, offers opportunities for staff development/in-service training, and increase school community linkages. The rapid growth of satellite networks has generated interest by many state officials in statewide satellite networks. Rural areas are showing great interest in interactive satellite instruction as a way to resolve teacher shortages and meet rigorous graduation requirements. Televised classes permitting live teacher-student interaction via satellite communication systems, and regular telephone lines provide equity increasing quality of educational opportunity.

A comparison of various analysis algorithms based for recommendation systems used in the market and businesses has their usage without considering the fact that if used with correct algorithm can increase its efficiency. Comparison is obtained in this paper after studying and researching various obtained algorithms present currently. This paper presents an introduction to the concept of recommendation systems which are recently working on different domain and then comparison is made, that too employed for scalability, reliability, faster process and efficiency.

Heme is both an essential cofactor and an abundant source of nutritional iron for the human pathogen Mycobacterium tuberculosis (Mtb). While heme is required for Mtb survival and virulence, it is also potentially cytotoxic. Since Mtb has the ability to both make and uptake heme, the de novo synthesis of heme and its acquisition from the host must be balanced in order to mitigate heme toxicity. However, the mechanisms employed by Mtb to regulate heme uptake, synthesis, and bioavailability are poorly understood. By integrating ratiometric heme sensors with mycobacterial genetics, cell biology, and biochemistry, we determined that the terminal heme biosynthetic enzyme, coproheme decarboxylase (ChdC), plays a role in regulating both heme bioavailability and uptake in Mtb. Moreover, we found that Mtb has a preference for scavenging reduced ferrous heme and exhibits a cell surface heme reductase activity that is regulated by ChdC. In Mtb, ChdC expression is down-regulated when iron is limiting, which in-turn increases both heme import and bioavailability. Such a mechanism may serve to protect cells from heme toxicity while trying to meet the nutritional demand for iron. Our results demonstrate that heme synthesis and uptake are tightly integrated in mycobacteria and represent the first example of a heme synthetic enzyme playing a role in controlling heme uptake. Significance Statement Heme is an essential but potentially cytotoxic cofactor and iron source for the pathogen, Mycobacterium tuberculosis (Mtb). To understand how Mtb coordinates heme uptake and synthesis to mitigate heme toxicity, we integrated heme sensors with mycobacterial genetics and biochemical approaches to probe the interplay between heme synthesis and scavenging. We discovered that the terminal heme synthetic enzyme, coproheme decarboxylase (ChdC), negatively regulates heme uptake and utilization in response to iron availability through a mechanism involving control of a ferric heme reductase. During iron limitation, ChdC is downregulated, thereby enhancing exogenous heme reduction, uptake and utilization while simultaneously suppressing heme synthesis, which allows Mtb to avoid heme toxicity. Our results highlight the close coordination between heme synthesis and uptake in mycobacteria. Classification Biological sciences : Biochemistry

Paper currency recognition (PCR) is one sort of insightful framework which is a significant need of the present computerization frameworks in the cutting edge universe of today. It has different potential applications including electronic banking, cash checking frameworks, cash trade machines, and so on. This paper proposes a programmed paper cash acknowledgment framework for paper currency. It utilizes InceptionV3 for extraction and neural network for classification and utilizes the instance of Indian paper currency as a model. The strategy is very sensible regarding exactness. The framework manages 3100 pictures. The pictures are dispersed in six classifications—10, 20, 50, 100, 200, and 500, and they are being utilized for examination and characterization. The proposed calculation is completely programmed and requires no human intercession. To approve the adequacy of system and appropriateness of neural network for cash picture arrangement, examinations have been finished with different classifiers like K-nearest neighbors (KNN) and support vector machine (SVM). The proposed procedure delivers very palatable outcomes as far as acknowledgment and effectiveness as it has accomplished an average accuracy of 97.2% over six categories.

To the Editor: The global coronavirus disease (COVID-19) pandemic has seen a significant increase in the use of preprint services to enable the widespread dissemination of research findings (1). However, whether such facilities influence practice change is currently unknown. Here, we describe the impact of the preprint release and eventual publication of the RECOVERY (Randomised Evaluation of COVID-19 Therapy) trial (2) on corticosteroid use in clinical practice in Australian ICUs. The RECOVERY trial tested the efficacy of dexamethasone (6 mg by mouth or intravenously for up to 10 d) in hospitalized patients with clinically suspected or confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and demonstrated a reduction in 28-day mortality, particularly in those receiving either mechanical ventilation or oxygen. Before these results, the Australian and New Zealand Intensive Care Society COVID-19 guidelines recommended against the routine use of corticosteroids (3).

Mitochondrial beta-oxidation is one of the most common modes of fatty acids' oxidation in most organisms, particularly mammals. Biochemistry undergraduate curriculum often contains the description of the process, with emphasis on ATP calculations for various types of fatty acids. During our decade long teaching experience in biochemistry, we observed the difficulty faced by students in calculating energetics of several fatty acids beyond palmitic acid. We developed a canonical formula by mathematical transformations and logical derivation to aid the calculation in a much simpler manner to ease both teaching and learning experience. ATP yield of even-numbered fatty acids may be calculated using a formula [(7C - 6 - 1.5 D) - 2(D-2)], andadenosine triphosphate (ATP) yield for odd-numbered fatty acids can be calculated using [(7C - 19 - 1.5 D) - 2(D-2)], where C is the number of carbon atoms in fatty acids, D is the number of double bonds. The unbold part of the formulae is limited to polyunsaturated fatty acids. Moreover, we integrated these formulae into an HTML based web-interface for handily calculations, which is likely to augment fatty acids oxidation learning-teaching processes easier. This tool has been recently tested in our classroom programs on biochemistry and received an excellent feedback from the learners. Also, the mathematical formula is ready for being incorporated into standard biochemistry textbooks. The webtool as an opensource biochemical calculator can be effectively used in classrooms by both instructors and students while solving comprehension based questions on lipid metabolism.