Explore the vast range of titles available.

Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.

Rapid industrialization, urbanization, global warming, and climate change are compromising surface water quality across the globe. Consequently, water conservation is essential for both environmental sustainability and human survival. This study assesses the water quality of the Jamuna River in Bangladesh at five distinct sites during wet and dry seasons. It employs six global water quality indices (WQIs) and contrasts the results with Bangladesh's Environmental Quality Standard (EQS) and the Department of Environment (DoE) criteria. The WQI models used are the Weighted Arithmetic WQI (WAWQI), British Columbia WQI (BCWQI), Canadian Council of Ministers of the Environment WQI (CWQI), Assigned WQI (AWQI), Malaysian WQI (MWQI), and Oregon WQI (OWQI). Fifteen physicochemical parameters were analyzed according to each WQI model's guidelines. The findings reveal that most parameters surpass the standard permissible values. The WQI model results indicate that the average water quality across the five sites falls into the lowest category. A comparison of the WQI models suggests potential correlations between WAWQI and AWQI, as well as between MWQI and OWQI. The straightforward presentation of the WQI models indicates that while the river water requires treatment for household and drinking use, it remains suitable for irrigation. The decline in water quality is likely attributable to human activities, urbanization, municipal waste disposal, and industrial effluents. Authorities must prioritize regular monitoring and assessment of water quality to address the identified challenges. Restoring the water to an acceptable standard will become increasingly difficult without proactive measures.

Background Child mortality is a reliable and significant indicator of a nation’s health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge. A deeper understanding is crucial for significantly reducing child mortality rates. Accurate predictions using machine learning models can empower authorities to implement timely interventions and raise awareness. So, the study aimed to explore the factors related to child mortality and assess the efficacy of various machine-learning models in predicting child mortality in Bangladesh. Methods and materials About Forty-two thousand observations, except the missing observations, were extracted for this study from the Bangladesh Demographic and Health Survey (BDHS) data conducted in 2017-18. The survey utilized a two-stage stratified sampling method, selecting 675 enumeration areas-250 in urban settings and 425 in rural areas-resulting in effective data collection from 672 clusters and 20160 households. The Chi-square test and recursive feature elimination (RFE) are used to find the relevant risk factors of child mortality among the number of factors. Six ML-based algorithms were implemented for predicting child mortality, such as Naïve Bayes, Classification and Regression Trees, Random Forest, C5.0 Classification, Gradient Boosting Machine, and Logistic Regression. Model evaluation metrics like accuracy, specificity, sensitivity, negative predictive value, F 1 score, positive predictive value, k-fold cross-validation, and area under the curve (AUC) techniques were used to evaluate the performance of the models. Results and discussion The child mortality rate is 8.2%, according to the data. The bivariate analysis showed that the child mortality rate was higher among the children whose mothers were uneducated, impoverished, underweight, aged 35-49, and gave birth before age 20. Families’ water sources and religious connections had no statistically significant impact on child mortality. The prediction of child mortality using machine learning models is the main objective of this study. None of the machine learning models correctly classified dead occurrences. Therefore, this study conducted over-sampling and under-sampling analysis. Approximately 76727 and 6910 observations were sampled for over-sampling and under-sampling techniques, respectively. According to the findings of the over-sampling data, the Random Forest outperformed all the other models in terms of total performance based on training and testing sets, with an accuracy of seventy percent. The k-fold cross-validation approach demonstrated the Random Forest model’s superior performance, and achieved the highest AUC (0.701). On the other hand, the Gradient Boosting Machine has the highest assessment for predicting child mortality in under-sampling analysis. The k-fold cross-validation also illustrated the better performance of the Gradient Boosting Machine. Conclusion The Gradient Boosting Machine and Random Forest produce the best predictive power for classifying child mortality and may help to ameliorate policy decision-making in this regard.

The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy sources (RES)-known as home microgrids-have become a critical enabling technology for the smart grid. This article proposes a new model for the energy management system of a home microgrid integrated with a battery ESS (BESS). The proposed dynamic model integrates a deep learning (DL)-based predictive model, bidirectional long short-term memory (Bi-LSTM), with an optimization algorithm for optimal energy distribution and scheduling of a BESS-by determining the characteristics of distributed resources, BESS properties, and the user’s lifestyle. The aim is to minimize the per-day electricity cost charged by time-of-use (TOU) pricing while considering the day-basis peak demand penalty. The proposed system also considers the operational constraints of renewable resources, the BESS, and electrical appliances. The simulation results from realistic case studies demonstrate the validation and responsibility of the proposed system in reducing a household’s daily electricity cost.

Turmeric, or Curcuma longa as it is formally named, is a multifunctional plant with numerous names. It was dubbed “the golden spice” and “Indian saffron” not only for its magnificent yellow color, but also for its culinary use. Turmeric has been utilized in traditional medicine since the dawn of mankind. Curcumin, demethoxycurcumin, and bisdemethoxycurcumin, which are all curcuminoids, make up turmeric. Although there have been significant advancements in cancer treatment, cancer death and incidence rates remain high. As a result, there is an increasing interest in discovering more effective and less hazardous cancer treatments. Curcumin is being researched for its anti-inflammatory, anti-cancer, anti-metabolic syndrome, neuroprotective, and antibacterial properties. Turmeric has long been used as a home remedy for coughs, sore throats, and other respiratory problems. As a result, turmeric and its compounds have the potential to be used in modern medicine to cure a variety of diseases. In this current review, we highlighted therapeutic potential of curcumin and its multiple health benefits on various diseases. Graphical abstract

Natural products derived from plants, as well as their bioactive compounds, have been extensively studied in recent years for their therapeutic potential in a variety of neurodegenerative diseases (NDs), including Alzheimer’s (AD), Huntington’s (HD), and Parkinson’s (PD) disease. These diseases are characterized by progressive dysfunction and loss of neuronal structure and function. There has been little progress in designing efficient treatments, despite impressive breakthroughs in our understanding of NDs. In the prevention and therapy of NDs, the use of natural products may provide great potential opportunities; however, many clinical issues have emerged regarding their use, primarily based on the lack of scientific support or proof of their effectiveness and patient safety. Since neurodegeneration is associated with a myriad of pathological processes, targeting multi-mechanisms of action and neuroprotection approaches that include preventing cell death and restoring the function of damaged neurons should be employed. In the treatment of NDs, including AD and PD, natural products have emerged as potential neuroprotective agents. This current review will highlight the therapeutic potential of numerous natural products and their bioactive compounds thatexert neuroprotective effects on the pathologies of NDs.

Bioactive plant derived compounds are important for a wide range of therapeutic applications, and some display promising anticancer properties. Further evidence suggests that phytochemicals modulate autophagy and apoptosis, the two crucial cellular pathways involved in the underlying pathobiology of cancer development and regulation. Pharmacological targeting of autophagy and apoptosis signaling using phytochemicals therefore offers a promising strategy that is complementary to conventional cancer chemotherapy. In this review, we sought to highlight the molecular basis of the autophagic-apoptotic pathway to understand its implication in the pathobiology of cancer, and explore this fundamental cellular process as a druggable anticancer target. We also aimed to present recent advances and address the limitations faced in the therapeutic development of phytochemical-based anticancer drugs.

Curcumin is the primary polyphenol in turmeric’s curcuminoid class. It has a wide range of therapeutic applications, such as anti-inflammatory, antioxidant, antidiabetic, hepatoprotective, antibacterial, and anticancer effects against various cancers, but has poor solubility and low bioavailability. Objective: To improve curcumin’s bioavailability, plasma concentration, and cellular permeability processes. The nanocurcumin approach over curcumin has been proven appropriate for encapsulating or loading curcumin (nanocurcumin) to increase its therapeutic potential. Conclusion: Though incorporating curcumin into nanocurcumin form may be a viable method for overcoming its intrinsic limitations, and there are reasonable concerns regarding its toxicological safety once it enters biological pathways. This review article mainly highlights the therapeutic benefits of nanocurcumin over curcumin.

Curcuma longa is very well-known medicinal plant not only in the Asian hemisphere but also known across the globe for its therapeutic and medicinal benefits. The active moiety of Curcuma longa is curcumin and has gained importance in various treatments of various disorders such as antibacterial, antiprotozoal, cancer, obesity, diabetics and wound healing applications. Several techniques had been exploited as reported by researchers for increasing the therapeutic potential and its pharmacological activity. Here, the dictum is the new room for the development of physicochemical, as well as biological, studies for the efficacy in target specificity. Here, we discussed nanoformulation techniques, which lend support to upgrade the characters to the curcumin such as enhancing bioavailability, increasing solubility, modifying metabolisms, and target specificity, prolonged circulation, enhanced permeation. Our manuscript tried to seek the attention of the researcher by framing some solutions of some existing troubleshoots of this bioactive component for enhanced applications and making the formulations feasible at an industrial production scale. This manuscript focuses on recent inventions as well, which can further be implemented at the community level.

In 2020, the world gained dramatic experience of the development of the 2019 coronavirus disease pandemic (COVID-19) caused by severe acute respiratory syndrome 2 (SARS-CoV-2). Recent researches notice an increasing prevalence of anxiety and circadian rhythm disorders during COVID-19 pandemic. The aim of the study was describing clinical features of circadian rhythm disorders and the level of anxiety in persons who have had COVID-19. We have conducted a cohort retrospective study that included 278 patients who were divided into 2 study groups according to medical history: group 1 includes patients with a history of COVID-19; group 2 consists of patients who did not have clinically confirmed COVID-19 and are therefore considered not to have had this disease. To objectify circadian rhythm disorders, they were verified in accordance with the criteria of the International Classification of Sleep Disorders-3. The level of anxiety was assessed by the State-Trait Anxiety Inventory. The most common circadian rhythm disorders were sleep phase shifts. We found that COVID-19 in the anamnesis caused a greater predisposition of patients to the development of circadian rhythm disorders, in particular delayed sleep phase disorder. In addition, it was found that after COVID-19 patients have increased levels of both trait and state anxiety. In our study, it was the first time that relationships between post-COVID-19 anxiety and circadian rhythm disorders had been indicated. Circadian rhythm disorders are associated with increased trait and state anxiety, which may indicate additional ways to correct post-COVID mental disorders and their comorbidity with sleep disorders.