Eurycoma longifolia Jack (known as tongkat ali), a popular traditional herbal medicine, is a flowering plant of the family Simaroubaceae, native to Indonesia, Malaysia, Vietnam and also Cambodia, Myanmar, Laos and Thailand. E. longifolia, is one of the well-known folk medicines for aphrodisiac effects as well as intermittent fever (malaria) in Asia. Decoctions of E. longifolia leaves are used for washing itches, while its fruits are used in curing dysentery. Its bark is mostly used as a vermifuge, while the taproots are used to treat high blood pressure, and the root bark is used for the treatment of diarrhea and fever. Mostly, the roots extract of E. longifolia are used as folk medicine for sexual dysfunction, aging, malaria, cancer, diabetes, anxiety, aches, constipation, exercise recovery, fever, increased energy, increased strength, leukemia, osteoporosis, stress, syphilis and glandular swelling. The roots are also used as an aphrodisiac, antibiotic, appetite stimulant and health supplement. The plant is reported to be rich in various classes of bioactive compounds such as quassinoids, canthin-6-one alkaloids, β-carboline alkaloids, triterpene tirucallane type, squalene derivatives and biphenyl neolignan, eurycolactone, laurycolactone, and eurycomalactone, and bioactive steroids. Among these phytoconstituents, quassinoids account for a major portion of the E. longifolia root phytochemicals. An acute toxicity study has found that the oral Lethal Dose 50 (LD50) of the alcoholic extract of E. longifolia in mice is between 1500-2000 mg/kg, while the oral LD50 of the aqueous extract form is more than 3000 mg/kg. Liver and renal function tests showed no adverse changes at normal daily dose and chronic use of E. longifolia. Based on established literature on health benefits of E. longifolia, it is important to focus attention on its more active constituents and the constituents' identification, determination, further development and most importantly, the standardization. Besides the available data, more evidence is required regarding its therapeutic efficacy and safety, so it can be considered a rich herbal source of new drug candidates. It is very important to conserve this valuable medicinal plant for the health benefit of future generations.
The use of technology in healthcare is one of the most critical application areas today. With the development of medical applications, people's quality of life has improved. However, it is impractical and unnecessary for medium-risk people to receive specialized daily hospital monitoring. Due to their health status, they will be exposed to a high risk of severe health damage or even life-threatening conditions without monitoring. Therefore, remote, real-time, low-cost, wearable, and effective monitoring is ideal for this problem. Many researchers mentioned that their studies could use electrocardiogram (ECG) detection to discover emergencies. However, how to respond to discovered emergencies in household life is still a research gap in this field.•This paper proposes a real-time monitoring of ECG signals and sending them to the cloud for Sudden Cardiac Death (SCD) prediction.•Unlike previous studies, the proposed system has an additional emergency response mechanism to alert nearby community healthcare workers when SCD is predicted to occur.
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.
The increase in global energy consumption and the related ecological problems have generated a constant demand for alternative energy sources superior to traditional ones. This is why unlimited photon-energy harnessing is important. A notable focus to address this concern is on advancing and producing cost-effective low-loss solar cells. For efficient light energy capture and conversion, we fabricated a ZnPC:PC70BM-based dye-sensitized solar cell (DSSC) and estimated its performance using a solar cell capacitance simulator (SCAPS-1D). We evaluated the output parameters of the ZnPC:PC70BM-based DSSC with different photoactive layer thicknesses, series and shunt resistances, and back-metal work function. Our analyses show that moderate thickness, minimum series resistance, high shunt resistance, and high metal-work function are favorable for better device performance due to low recombination losses, electrical losses, and better transport of charge carriers. In addition, in-depth research for clarifying the impact of factors, such as thickness variation, defect density, and doping density of charge transport layers, has been conducted. The best efficiency value found was 10.30% after tweaking the parameters. It also provides a realistic strategy for efficiently utilizing DSSC cells by altering features that are highly dependent on DSSC performance and output.