Algae, both micro- and macroalgae, are recognized for their rich repository of bioactive compounds with potential therapeutic applications. These marine organisms produce a variety of secondary metabolites that exhibit significant anti-inflammatory, antioxidant, and antimicrobial properties, offering promising avenues for the development of new drugs and nutraceuticals. Algae-derived compounds, including polyphenols, carotenoids, lipids, and polysaccharides, have demonstrated efficacy in modulating key inflammatory pathways, reducing oxidative stress, and inhibiting microbial growth. At the molecular level, these compounds influence macrophage activity, suppress the production of pro-inflammatory cytokines, and regulate apoptotic processes. Studies have shown that algae extracts can inhibit inflammatory signaling pathways such as NF-κB and MAPK, reduce oxidative damage by activating Nrf2, and offer an alternative to traditional antibiotics by combatting bacterial infections. Furthermore, algae’s therapeutic potential extends to addressing diseases such as cardiovascular disorders, neurodegenerative conditions, and cancer, with ongoing research exploring their efficacy in preclinical animal models. The pig model, due to its physiological similarities to humans, is highlighted as particularly suitable for validating the bioactivities of algal compounds in vivo. This review underscores the need for further investigation into the specific mechanisms of action and clinical applications of algae-derived biomolecules.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.

Adverse drug reactions continue to be not only one of the most urgent problems in clinical medicine, but also a social problem. The aim of this study was a bibliometric analysis of the use of digital technologies to prevent adverse drug reactions and an overview of their main applications to improve the safety of pharmacotherapy. The search was conducted using the Web of Science database for the period 1991–2023. A positive trend in publications in the field of using digital technologies in the management of adverse drug reactions was revealed. A total of 72% of all relevant publications come from the following countries: the USA, China, England, India, and Germany. Among the organizations most active in the field of drug side effect management using digital technologies, American and Chinese universities dominate. Visualization of publication keywords using VOSviewer software 1.6.18 revealed four clusters: “preclinical studies”, “clinical trials”, “pharmacovigilance”, and “reduction of adverse drug reactions in order to improve the patient’s quality of life”. Molecular design technologies, virtual models for toxicity modeling, data integration, and drug repurposing are among the key digital tools used in the preclinical research phase. Integrating the application of machine learning algorithms for data analysis, monitoring of electronic databases of spontaneous messages, electronic medical records, scientific databases, social networks, and analysis of digital device data into clinical trials and pharmacovigilance systems, can significantly improve the efficiency and safety of drug development, implementation, and monitoring processes. The result of combining all these technologies is a huge synergistic provision of up-to-date and valuable information to healthcare professionals, patients, and health authorities.

Ginger (Zingiber officinale Roscoe) is broadly used as a traditional remedy and food ingredient, and numerous preclinical and clinical studies have demonstrated health benefits in a range of age-related disorders. Moreover, longevity-promoting effects have been demonstrated in several (preclinical) research models. With this work, we aimed to comprehensively review the reported effects of ginger and its bioactive constituents on the twelve established hallmarks of aging, with the ultimate goal of gaining a deeper understanding of the potential for future interventions in the area of longevity-extension and counteracting of aging-related diseases. The reviewed literature supports the favorable effects of ginger and some of its constituents on all twelve hallmarks of aging, with a particularly high number of animal research studies indicating counteraction of nutrient-sensing dysregulations, mitochondrial dysfunction, chronic inflammation, and dysbiosis. On this background, validation in human clinical trials is still insufficient or is entirely missing, with the exception of some studies indicating positive effects on deregulated nutrient-sensing, chronic inflammation, and dysbiosis. Thus, the existing body of literature clearly supports the potential of ginger to be further studied in clinical trials as a supplement for the promotion of both lifespan and health span.

Researchers are increasingly interested in discovering new pancreatic lipase inhibitors as anti-obesity ingredients. Medicine-and-food homology plants contain a diverse set of natural bioactive compounds with promising development potential. This study screened and identified potent pancreatic lipase inhibitors from 20 commonly consumed medicine-and-food homology plants using affinity ultrafiltration combined with spectroscopy and docking simulations. The results showed that turmeric exhibited the highest pancreatic lipase-inhibitory activity, and curcumin, demethoxycurcumin, and bisdemethoxycurcumin were discovered to be potent pancreatic lipase inhibitors within the turmeric extract, with IC₅₀ values of 0.52 ± 0.04, 1.12 ± 0.05, and 3.30 ± 0.08 mg/mL, respectively. In addition, the enzymatic kinetics analyses demonstrated that the inhibition type of the three curcuminoids was the reversible competitive model, and curcumin exhibited a higher binding affinity and greater impact on the secondary structure of pancreatic lipase than found with demethoxycurcumin or bisdemethoxycurcumin, as observed through fluorescence spectroscopy and circular dichroism. Furthermore, docking simulations supported the above experimental findings, and revealed that the three curcuminoids might interact with amino acid residues in the binding pocket of pancreatic lipase through non-covalent actions, such as hydrogen bonding and π-π stacking, thereby inhibiting the pancreatic lipase. Collectively, these findings suggest that the bioactive compounds of turmeric, in particular curcumin, can be promising dietary pancreatic lipase inhibitors for the prevention and management of obesity.

Background: Social media has been extensively used by the public to seek information and share views on health issues. Recently, the proper and off-label use of semaglutide drugs for weight loss has attracted huge media attention and led to temporary supply shortages. Objective: The aim of this study was to perform a content analysis on English YouTube (Google) videos related to semaglutide. Methods: YouTube was searched with the words semaglutide, Ozempic, Wegovy, and Rybelsus. The first 30 full-length videos (videos without a time limit) and 30 shorts (videos that are no longer than 1 minute) resulting from each search word were recorded. After discounting duplicates resulting from multiple searches, a total of 96 full-length videos and 93 shorts were analyzed. Video content was evaluated by 3 tools, that is, a custom checklist, a Global Quality Score (GQS), and Modified DISCERN. Readability and sentiment of the transcripts were also assessed. Results: There was no significant difference in the mean number of views between full-length videos and shorts (mean 288,563.1, SD 513,598.3 vs mean 188,465.2, SD 780,376.2, P=.30). The former had better content quality in terms of GQS, Modified DISCERN, and the number of mentioned points from the custom checklist (all P<.001). The transcript readability of both types of videos was at a fairly easy level and mainly had a neutral tone. Full-length videos from health sources had a higher content quality in terms of GQS and Modified DISCERN (both P<.001) than their counterparts. Conclusions: The analyzed videos lacked coverage of several important aspects, including the lack of long-term data, the persistence of side effects due to the long half-life of semaglutide, and the risk of counterfeit drugs. It is crucial for the public to be aware that videos cannot replace consultations with physicians.