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There is emerging evidence suggesting that vitamin D and fibrinogen play contrasting roles in ACS pathophysiology and their combined impact, expressed as the vitamin D/fibrinogen ratio, can be a potential biomarker for ACS severity. This study aimed to investigate the relationship between vitamin D, fibrinogen, and their ratio with ACS types, and assess their potential as risk stratification biomarkers. This multicenter observational study was conducted in tertiary care hospitals in Afghanistan, Egypt, and Pakistan, including 300 ACS patients. Serum vitamin D and fibrinogen levels were measured using electrochemiluminescence immunoassay and the Clauss method, respectively. Statistical analyses included ANOVA, Kruskal-Wallis, post-hoc Games-Howell tests, Spearman's correlation, Fisher's Z-test, and multivariable logistic regression. Vitamin D levels were significantly lower (p < 0.001) and fibrinogen levels significantly higher (p < 0.001) in STEMI patients compared to NSTEMI and UA. The vitamin D/fibrinogen ratio showed a stronger correlation with ACS severity (Spearman's rho = -0.45, p = 0.01) than vitamin D alone (-0.41, p = 0.01), but this difference was not statistically significant (Fisher Z = 0.34, p = 0.73). Logistic regression revealed that a 1 nmol/L increase in vitamin D reduced ACS severity by 7.1% (p = 0.043), while a unit increase in the vitamin D/fibrinogen ratio reduced severity by 6.2% (p = 0.048). The contrasting effects of vitamin D and fibrinogen can prove useful biomarkers and modifiable risk factors for ACS. The superiority of the vitamin D/fibrinogen ratio over vitamin D only, however, needs further validation in larger studies.

Coronary artery disease (CAD), clinically manifested as coronary syndrome (CS), is the leading cause of death and a significant contributor to morbidity worldwide. Elevated serum homocysteine levels have been associated with an increased risk of cardiovascular diseases, including CAD. Despite extensive research, the relationship between serum homocysteine and coronary syndromes with related short-term mortality is still under-studied. The main objective of this study is to evaluate the correlation between serum homocysteine levels and various types of CS, as well as in-hospital mortality in these patients. This multicenter study included 381 CS patients from Afghanistan, Egypt, and Pakistan tertiary care hospitals. The relation of serum homocysteine levels with different types of CS as well as with in-hospital mortality was measured and analyzed using inferential statistics (ANOVA, Kruskal-Wallis test, Tukey's post-hoc, Pearson correlation, etc.) and regression analysis (Binary regression). Among 381 patients from both genders, 160 were from Pakistan, 130 from Egypt, and 91 from Afghanistan. There was no significant difference in baseline characteristics, like age, gender, homocysteine level, CS type, and mortality, among the three countries ( > 0.05). The one-way ANOVA, the Kruskal Wallis Test, and Tukey's post hoc test showed a significant difference among different CS groups based on serum homocysteine levels, and Pearson correlation showed a strong correlation between serum homocysteine and CS (r = 0.4). Binary regression analysis showed a 10.5% increase in in-hospital mortality for each 1 µmol/L increase in homocysteine levels. Serum homocysteine could serve as a valuable biomarker and mortality predictor in CS patients.

Purpose: Coronary artery disease (CAD), clinically manifested as coronary syndrome (CS), is the leading cause of death and a significant contributor to morbidity worldwide. Elevated serum homocysteine levels have been associated with an increased risk of cardiovascular diseases, including CAD. Despite extensive research, the relationship between serum homocysteine and coronary syndromes with related short-term mortality is still under-studied. The main objective of this study is to evaluate the correlation between serum homocysteine levels and various types of CS, as well as in-hospital mortality in these patients. Patients and Methods: This multicenter study included 381 CS patients from Afghanistan, Egypt, and Pakistan tertiary care hospitals. The relation of serum homocysteine levels with different types of CS as well as with in-hospital mortality was measured and analyzed using inferential statistics (ANOVA, Kruskal--Wallis test, Tukey's post-hoc, Pearson correlation, etc.) and regression analysis (Binary regression). Results: Among 381 patients from both genders, 160 were from Pakistan, 130 from Egypt, and 91 from Afghanistan. There was no significant difference in baseline characteristics, like age, gender, homocysteine level, CS type, and mortality, among the three countries (p > 0.05). The one-way ANOVA, the Kruskal Wallis Test, and Tukey's post hoc test showed a significant difference among different CS groups based on serum homocysteine levels, and Pearson correlation showed a strong correlation between serum homocysteine and CS (r = 0.4). Binary regression analysis showed a 10.5% increase in in-hospital mortality for each 1 [micro]mol/L increase in homocysteine levels. Conclusion: Serum homocysteine could serve as a valuable biomarker and mortality predictor in CS patients. Keywords: serum homocysteine, coronary artery disease (CAD), coronary syndromes (CS), cardiac biomarkers, risk factors of CAD

Globally, systems used for energy management are framework for users to manage, optimize, monitor and control utilization and access to the generated energy. It helps to identify leakages and procedure to adopt in proper management and utilization. It is achieved by real-time analyses of loads, sources of energy and predictive control of data. Intelligent control and management provides convenience and affordability. Various classes of loads are positioned in AFIT library. There may be desire to monitor utilization of the solar generated power to prevent unnecessary access that may drain the storage completely. Managing the output power of the and monitoring how it feed the appliances enhances performance and enable long-term utilization. Internet of Things (IoT) gives an attractive and scientific platform that link both the energy source and the loads. In this paper, the use of IoT-based intelligent control mechanism that manage, control and monitor power generation and utilization by the DC loads present in the library that are connected to SIMO DC-DC converter is presented. The work aim to significantly decrease power leakages and improve performance of the loads in the library. The work has achieved 98% performance efficiency.

Breast cancer is the most common cancer in females and ranked second after skin cancer. The use of natural compounds is a good alternative for the treatment of breast cancer with less toxicity than synthetic drugs. The aim of the present study is to develop and characterize hybrid Apigenin (AN) Nanoparticles (NPs) for oral delivery (AN-NPs). The hybrid AN-NPs were prepared by the self-assembly method using lecithin, chitosan and TPGS. Further, the NPs were optimized by Box-Behnken design (3-factor, 3-level). The hybrid NPs were evaluated for particle size (PS), entrapment efficiency (EE), zeta potential (ZP), and drug release. The optimized hybrid NPs (ON2), were further evaluated for solid state characterization, permeation, antioxidant, cytotoxicity and antimicrobial study. The formulation (ON2) exhibited small PS of 192.6 ± 4.2 nm, high EE 69.35 ± 1.1%, zeta potential of +36.54 mV, and sustained drug release (61.5 ± 2.5% in 24 h), as well as significantly (p < 0.05) enhanced drug permeation and antioxidant activity. The IC50 of pure AN was found to be significantly (p < 0.05) lower than the formulation (ON2). It also showed significantly greater (p < 0.05) antibacterial activity than pure AN against Bacillus subtilis and Salmonella typhimurium. From these findings, it revealed that a hybrid AN polymeric nanoparticle is a good carrier for the treatment of breast cancer.

The greening of analytical methods has gained interest in the quantitative analysis field to reduce environmental impact and improve safety health conditions for analysts. Nirmatrelvir plus ritonavir is a new FDA approved co-packaged medication developed for the treatment of COVID-19. The aim of this research was to develop green fitted HPLC method using pre experimental computational testing of different stationary phases as well as selecting mobile phase regarding to green analytical chemistry principles . Computational study was designed to test the physical interaction between nirmatrelvir and ritonavir and different columns (C8, C18, Cyano column). The study showed that the C18 column was better for simultaneous HPLC analysis of the cited drugs. Regarding to green point of view, mobile phase consisted of ethanol: water (80:20, v/v) provided an efficient chromatographic separation of nirmatrelvir and ritonavir within a short analytical run time, reasonable resolution and excellent sensitivity. Isocratic elution was performed on a selected C18 column and a green adjusted mobile phase at flow rate of 1 mL/min and UV detection at 215 nm. The chromatographic system allowed complete baseline separation with retention times of 4.9 min for nirmatrelvir and 6.8 min for ritonavir. The method succeeded to determine nirmatrelvir and ritonavir over the concentration range of 1.0–20.0 μg/mL in the pure form and in pharmaceutical dosage form. Greenness profiles of the applied HPLC method was assessed using analytical eco-scale, the green analytical procedure index and the AGREE evaluation method. The results revealed adherence of the described method to the green analytical chemistry principles. The authors hope to provide a promising challenge for achieving green goals through integrating computational tools and applying them with green assessment metrics.

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

Total hip arthroplasty (THA) is most likely one of the most successful surgical procedures in medicine. It is estimated that three in four patients live beyond the first post-operative year, so appropriate surgery is needed to alleviate an otherwise long-standing suboptimal functional level. However, research has shown that during a complete THA procedure, a solid hip implant inserted in the femur can damage the main arterial supply of the cortex and damage the medullary space, leading to cortical bone resorption. Therefore, this study aimed to design a porous hip implant with a focus on providing more space for better osteointegration, improving the medullary revascularisation and blood circulation of patients. Based on a review of the literature, a lightweight implant design was developed by applying topology optimisation and changing the materials of the implant. Gyroid and Voronoi lattice structures and a solid hip implant (as a control) were designed. In total, three designs of hip implants were constructed by using SolidWorks and nTopology software version 2.31. Point loads were applied at the x, y and z-axis to imitate the stance phase condition. The forces represented were x = 320 N, y = −170 N, and z = −2850 N. The materials that were used in this study were titanium alloys. All of the designs were then simulated by using Marc Mentat software version 2020 (MSC Software Corporation, Munich, Germany) via a finite element method. Analysis of the study on topology optimisation demonstrated that the Voronoi lattice structure yielded the lowest von Mises stress and displacement values, at 313.96 MPa and 1.50 mm, respectively, with titanium alloys as the materials. The results also indicate that porous hip implants have the potential to be implemented for hip implant replacement, whereby the mechanical integrity is still preserved. This result will not only help orthopaedic surgeons to justify the design choices, but could also provide new insights for future studies in biomechanics.

This research work focuses on the potential application of an organic compound, santalol, obtained from santalum album, in the inhibition of the enzyme tyrosinase, which is actively involved in the biosynthesis of melanin pigment. Over-production of melanin causes undesirable pigmentation in humans as well as other organisms and significantly downgrades their aesthetic value. The study is designed to explain the purification of tyrosinase from the mushroom Agaricus bisporus, followed by activity assays and enzyme kinetics to give insight into the santalol-modulated tyrosinase inhibition in a dose-dependent manner. The multi-spectroscopic techniques such as UV-vis, fluorescence, and isothermal calorimetry are employed to deduce the efficiency of santalol as a potential candidate against tyrosinase enzyme activity. Experimental results are further verified by molecular docking. Santalol, derived from the essential oils of santalum album, has been widely used as a remedy for skin disorders and a potion for a fair complexion since ancient times. Based on enzyme kinetics and biophysical characterization, this is the first scientific evidence where santalol inhibits tyrosinase, and santalol may be employed in the agriculture, food, and cosmetic industries to prevent excess melanin formation or browning.