Tea infusions are the most consumed beverages in the world after water, their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories guaranteeing high sample throughputs even without the human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LV-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy acts as an AI smelling and taste machine predicting teas sensory features without the human panel intervention.

We lack a unified theory of aging that spans multiple scientific disciplines, including life history theory, evolution, cellular and molecular biology, and medicine. This gap has hindered both our ability to effectively target and treat age-related diseases in humans and our understanding of the vast variation in aging rates across species. This paper builds on the Blueprint Theory of Aging, which suggests that biological pathways can be ‘dis’-activated outside their intended context, leading to pathological pathways (patho-pathways) that drive cellular and tissue ‘dys’-function and age-related ‘dis’-eases, ultimately limiting both healthspan and lifespan. We propose that evolution operates through complex trade-offs within interconnected networks (network constraints), optimizing beneficial functions while minimizing the costs of misactivated patho-pathways. These optimizations are shaped by a species' environment and are driven by a central evolutionary dilemma: prioritize immediate reproduction or reduce current fecundity in favour of future reproductive success. Evolution adjusts network constraints along a continuum, balancing early-life reproduction with lifetime reproductive output. When environmental pressures result in lower juvenile survival than adult survival, the optimal strategy shifts toward maximizing total progeny over a lifetime. This strategy, in turn, determines the final trade-offs selected, the structure of network constraints, and ultimately, the rate of aging for that species. Understanding these evolutionary calculations not only explains species-specific aging patterns but also sheds light on the wide variability in aging rates across nature. Finally, we reveal hidden evolutionary connections between mechanisms such as Antagonistic Pleiotropy and Mutational Accumulation, deepening our insight into the evolutionary dynamics underlying aging.

Age-related diseases place an intense and growing stress on healthcare systems. Numerous theories of ageing exist. Yet prediction, prevention and treatment limitations of age-related disease persist because we lack a complete aetiological understanding of their origins. Here we propose a new theory that aims to provide a blueprint-type understanding of ageing and disease aetiology, working across multiple scientific disciplines. Supported by specific examples, we demonstrate that the primary driver of much of age-related diseases is the futile triggering of specific biological pathways in a pathological manner, which we term pathophysiological pathways or patho-pathways. Patho-pathways induce pathological cell, tissue and system changes in the form of dysactivity (out of context activity that is at the wrong time and/or place), which is characteristic of age related disease. As part of patho-pathways, all wild-type (normal) genes have the potential to contribute to age-related diseases (though specific risk varies and can be formularised). In a domino-style effect, one patho-pathway typically triggers others, resulting in complex, but traceable, cascades. Utility, in terms of changing clinical and scientific practice, is derived from mapping these cascades to create Blueprint Maps that allow identification of biomarkers and therapeutic targets far upstream in the causal chain of events. Finally, our theory provides testable hypotheses concerning predictions of links between otherwise seemingly disparate diseases as well as new interventions to reduce ageing.