Category Archives: Online

Biological profiles of Q. cerris, Q. dalechampii, and Q. robur bark extracts: A characterization study

DOI: 10.2478/amma-2024-0003

Objective: The main objective of the present study was to characterize the extracts obtained from the bark of three oak species in order to assess their use in potential cosmetic products.
Methods: The extracts were obtained from the oak barks (periderm and rhytidome) using ultrasound-assisted extraction. The total polyphenolic content was assessed afterward, using the Folin–Ciocâlteu method, while the antioxidant capacity was determined using methods based on the neutralization of the 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) and 2,2-diphenyl-1-picrylhydrazyl radicals. To assess the tyrosinase inhibitory effect, a protocol using L–DOPA as the substrate of the enzyme was employed.
Results: The extracts presented high levels of polyphenolic compounds, with Q. cerris having the highest content. Because of the high concentration of the extracts in polyphenolic compounds, they revealed a great reducing capacity against both DPPH and ABTS radicals, but unfortunately the tyrosinase inhibitory activity of the tested extracts was very weak compared to the positive control.
Conclusions: The extracts may have beneficial effects when used in cosmetic products because of the antioxidant effects, but more studies must be conducted for the determination of the main phytochemical compounds comprised in the extracts and their correlation to the biological effects.

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Smart science: How artificial intelligence is revolutionizing pharmaceutical medicine

DOI: 10.2478/amma-2024-0002

Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of emulating human behavior, particularly regarding the astute examination and interpretation of data. AI operates through the utilization of specialized algorithms, and it includes techniques such as deep (DL), and machine learning (ML), and natural language processing (NLP). As a result, AI has found its application in the study of pharmaceutical chemistry and healthcare. The AI models employed encompass a spectrum of methodologies, including unsupervised clustering techniques applied to drugs or patients to discern potential drug compounds or appropriate patient cohorts. Additionally, supervised ML methodologies are utilized to enhance the efficacy of therapeutic drug monitoring. Further, AI-aided prediction of the clinical outcomes of clinical trials can improve efficiency by prioritizing therapeutic intervention that are likely to succeed, hence benefiting the patient. AI may also help create personalized treatments by locating potential intervention targets and assessing their efficacy. Hence, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.

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Maternal sepsis – challenges in diagnosis and management: A mini-summary of the literature

DOI: 10.2478/amma-2024-0001

Sepsis is still one of the leading causes of maternal mortality and morbidity, being the third most common cause of maternal death, after hemorrhage and hypertensive disorders. Maternal sepsis may appear due to obstetric causes such as: chorioamnionitis, endometritis, abortion-related uterine infections, and wound infections. For non-obstetric causes of maternal sepsis, the most common are urinary tract infections and respiratory tract infections. This mini summary presents the challenges in early diagnosis and prompt management, caused by pregnancy physiological changes. Physiological alterations during pregnancy, like an increase in white cell count, heart rate, and respiratory rate, associated with a decrease in blood pressure are also known signs of infection, making the diagnosis of sepsis during pregnancy more difficult. The three pillars of sepsis treatment are early antibiotics, vital organ support and fluid therapy, the last one being controversial. A more restrictive approach for fluid resuscitation could be more suitable for pregnant women, considering the risk of fluid overload and pulmonary edema. Criteria for early recognition and appropriate management customized for maternal sepsis are mandatory.

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