Cellular agriculture is an emerging branch of biotechnology that aims to address issues associated with the environmental impact, animal welfare and sustainability challenges of conventional animal farming for meat production. Cultured meat can be produced by applying current cell culture practices and biomanufacturing methods and utilizing mammalian cell lines and cell and gene therapy products to generate tissue or nutritional proteins for human consumption. However, significant improvements and modifications are needed for the process to be cost efficient and robust enough to be brought to production at scale for food supply. Here, we review the scientific and social challenges in transforming cultured meat into a viable commercial option, covering aspects from cell selection and medium optimization to biomaterials, tissue engineering, regulation and consumer acceptance. Producing meat without the drawbacks of conventional animal agriculture would greatly contribute to future food and nutrition security. This Review Article covers biological, technological, regulatory and consumer acceptance challenges in this developing field of biotechnology.
Current Large Language Model (LLM) approaches for information extraction (IE) in the healthy food policy domain are often hindered by various factors, including misinformation, specifically hallucinations, misclassifications, and omissions that result from the structural diversity and inconsistency of policy documents. To address these limitations, this study proposes a role-based LLM framework that automates the IE from unstructured policy data by assigning specialized roles: an LLM policy analyst for metadata and mechanism classification, an LLM legal strategy specialist for identifying complex legal approaches, and an LLM food system expert for categorizing food system stages. This framework mimics expert analysis workflows by incorporating structured domain knowledge, including explicit definitions of legal mechanisms and classification criteria, into role-specific prompts. We evaluate the framework using 608 healthy food policies from the Healthy Food Policy Project (HFPP) database, comparing its performance against zero-shot, few-shot, and chain-of-thought (CoT) baselines using Llama-3.3-70B. Our proposed framework demonstrates superior performance in complex reasoning tasks, offering a reliable and transparent methodology for automating IE from health policies.
Ferdi Riansyah, Ambia Nurdin, Ristiani Ristiani
et al.
Tuberculosis (TB) is an infectious disease that substantially affects the nutritional status of patients. Family support is believed to contribute to improved nutritional outcomes during the treatment. This study aimed to determine the relationship between family support and improvements in the nutritional status among TB patients with TB. A quantitative study with a cross-sectional design was conducted between May and June 2024, involving 44 patients with TB at the Langkahan Primary Health Center, North Aceh. Data were collected using a validated questionnaire that assessed four dimensions of family support and nutritional status. The Chi-square test was used for data analysis (p < 0.05). A total of 75% of the patients demonstrated improvements in their nutritional status. Overall family support was significantly associated with improved nutritional status (p = 0.013; OR = 7.875; 95% CI: 1.733–35.785), and all support dimensions showed significant relationships (p < 0.05). These findings indicate that family support plays an essential role in enhancing the nutritional status of patients with TB. Strengthening family based interventions is recommended to support nutritional recovery and successful treatment.
Nutrition. Foods and food supply, Food processing and manufacture
Ashley E. Mullan, P. D. Anh Nguyen, Sarah C. Lotspeich
Access to healthy food is key to maintaining a healthy lifestyle and can be quantified by the distance to the nearest grocery store. However, calculating this distance forces a trade-off between cost and correctness. Accurate route-based distances following passable roads are cost-prohibitive, while simple straight-line distances ignoring infrastructure and natural barriers are accessible yet error-prone. Categorizing low-access neighborhoods based on these straight-line distances induces misclassification and introduces bias into standard regression models estimating the relationship between disease prevalence and access. Yet, fully observing the more accurate, route-based food access measure is often impossible, which induces a missing data problem. We combat bias and address missingness with a new maximum likelihood estimator for Poisson regression with a binary, misclassified exposure (access to healthy food within some threshold), where the misclassification may depend on additional error-free covariates. In simulations, we show the consequence of ignoring the misclassification (bias) and how the proposed estimator corrects for bias while preserving more statistical efficiency than the complete case analysis (i.e., deleting observations with missing data). Finally, we apply our estimator to model the relationship between census tract diabetes prevalence and access to healthy food in northwestern North Carolina.
Emile Esmaili, Michael J. Puma, Francis Ludlow
et al.
The complex interplay between famine, warfare, and climate constitutes a multifaceted and context-dependent relationship that has profoundly influenced human history, particularly in early modern Europe. This study advances the literature on climate-economy interactions by leveraging multi-scale statistical techniques to quantify the compounded effects of climate variability and socio-political factors on food prices, offering novel model-based insights into the historical dynamics of climate and economic systems. Using Canonical Correlation Analysis (CCA), we investigate the influence of temperature fluctuations and drought severity on food prices across 14 European cities from 1565 to 1785. Our findings confirm a persistent negative relationship between temperature and food prices over the long term, while the relationship between drought severity and price dynamics appears positive yet inconsistent. Extending our analysis to higher-frequency patterns, we demonstrate that cold anomalies are strongly associated with food price that caused large-scale famines of the 1590s and 1690s. Likewise, we show that the severe and consecutive droughts of 1634 to 1636, coinciding with the Thirty Years' War, significantly amplified food price volatility, illustrating how climatic shocks can compound socio-economic and political crises. Furthermore, we identify years characterized by the simultaneous occurrence of extreme cold and drought as periods of heightened price instability, underscoring the compounded impact of concurrent climatic stressors on food prices during the early modern period.
The rapid proliferation of food delivery platforms has reshaped urban mobility but has also contributed significantly to environmental degradation through increased greenhouse gas emissions. Existing optimization mechanisms produce sub-optimal outcomes as they do not consider environmental sustainability their optimization objective. This study proposes a novel eco-friendly food delivery optimization framework that integrates demand prediction, delivery person routing, and order allocation to minimize environmental impact while maintaining service efficiency. Since recommending routes is NP-Hard, the proposed approach utilizes the submodular and monotone properties of the objective function and designs an efficient greedy optimization algorithm. Thereafter, it formulates order allocation problem as a network flow optimization model, which, to the best of our knowledge, has not been explored in the context of food delivery. A three-layered network architecture is designed to match orders with delivery personnel based on capacity constraints and spatial demand. Through this framework, the proposed approach reduces the vehicle count, and creates a sustainable food delivery ecosystem.
As technology increasingly integrates into farm settings, the food and agriculture sector has become vulnerable to cyberattacks. However, previous research has indicated that many farmers and food producers lack the cybersecurity education they require to identify and mitigate the growing number of threats and risks impacting the industry. This paper presents an ongoing research effort describing a cybersecurity initiative to educate various populations in the farming and agriculture community. The initiative proposes the development and delivery of a ten-module cybersecurity course, to create a more secure workforce, focusing on individuals who, in the past, have received minimal exposure to cybersecurity education initiatives.
Feeding a larger and wealthier global population without transgressing ecological limits is increasingly challenging, as rising food demand (especially for animal products) intensifies pressure on ecosystems, accelerates deforestation, and erodes biodiversity and soil health. We develop a stylized, spatially explicit global model that links exogenous food-demand trajectories to crop and livestock production, land conversion, and feedbacks from ecosystem integrity that, in turn, shape future yields and land needs. Calibrated to post-1960 trends in population, income, yields, input use, and land use, the model reproduces the joint rise of crop and meat demand and the associated expansion and intensification of agriculture. We use it to compare business-as-usual, supply-side, demand-side, and mixed-policy scenarios. Three results stand out. First, productivity-oriented supply-side measures (e.g. reduced chemical inputs, organic conversion, lower livestock density) often trigger compensatory land expansion that undermines ecological gains-so that supply-side action alone cannot halt deforestation or widespread degradation. Second, demand-side change, particularly reduced meat consumption, consistently relieves both intensification and expansion pressures; in our simulations, only substantial demand reductions (on the order of 40% of projected excess demand by 2100) deliver simultaneous increases in forest area and declines in degraded land. Third, integrated policy portfolios that jointly constrain land conversion, temper input intensification, and curb demand outperform any single lever. Together, these findings clarify the system-level trade-offs that frustrate piecemeal interventions and identify the policy combinations most likely to keep global food provision within ecological limits.
Phylogenetic trees represent certain species and their likely ancestors. In such a tree, present-day species are leaves and an edge from u to v indicates that u is an ancestor of v. Weights on these edges indicate the phylogenetic distance. The phylogenetic diversity (PD) of a set of species A is the total weight of edges that are on any path between the root of the phylogenetic tree and a species in A. Selecting a small set of species that maximizes phylogenetic diversity for a given phylogenetic tree is an essential task in preservation planning, where limited resources naturally prevent saving all species. An optimal solution can be found with a greedy algorithm [Steel, Systematic Biology, 2005; Pardi and Goldman, PLoS Genetics, 2005]. However, when a food web representing predator-prey relationships is given, finding a set of species that optimizes phylogenetic diversity subject to the condition that each saved species should be able to find food among the preserved species is NP-hard [Spillner et al., IEEE/ACM, 2008]. We present a generalization of this problem, where, inspired by biological considerations, the food web has weighted edges to represent the importance of predator-prey relationships. We show that this version is NP-hard even when both structures, the food web and the phylogenetic tree, are stars. To cope with this intractability, we proceed in two directions. Firstly, we study special cases where a species can only survive if a given fraction of its prey is preserved. Secondly, we analyze these problems through the lens of parameterized complexity. Our results include that finding a solution is fixed-parameter tractable with respect to the vertex cover number of the food web, assuming the phylogenetic tree is a star.
From the preparation of bread, cheese, beer, and condiments to vegetarian meat products, fungi play a leading role in the food fermentation industry. With the shortage of global protein resources and the decrease in cultivated land, fungal protein has received much attention for its sustainability. Fungi are high in protein, rich in amino acids, low in fat, and almost cholesterol-free. These properties mean they could be used as a promising supplement for animal and plant proteins. The selection of strains and the fermentation process dominate the flavor and quality of fungal-protein-based products. In terms of function, fungal proteins exhibit better digestive properties, can regulate blood lipid and cholesterol levels, improve immunity, and promote gut health. However, consumer acceptance of fungal proteins is low due to their flavor and safety. Thus, this review puts forward prospects in terms of these issues.
Julian Brummer, Christina Glasbrenner, Sieglinde Hechenbichler Figueroa
et al.
BackgroundAccurate dietary assessment remains a challenge, particularly in free-living settings. Continuous glucose monitoring (CGM) shows promise in optimizing the assessment and monitoring of ingestive activity (IA, i.e., consumption of calorie-containing foods/beverages), and it might enable administering dietary Just-In-Time Adaptive Interventions (JITAIs).ObjectiveIn a scoping review, we aimed to answer the following questions: (1) Which CGM approaches to automatically detect IA in (near-)real-time have been investigated? (2) How accurate are these approaches? (3) Can they be used in the context of JITAIs?MethodsWe systematically searched four databases until October 2023 and included publications in English or German that used CGM-based approaches for human (all ages) IA detection. Eligible publications included a ground-truth method as a comparator. We synthesized the evidence qualitatively and critically appraised publication quality.ResultsOf 1,561 potentially relevant publications identified, 19 publications (17 studies, total N = 311; for 2 studies, 2 publications each were relevant) were included. Most publications included individuals with diabetes, often using meal announcements and/or insulin boluses accompanying meals. Inpatient and free-living settings were used. CGM-only approaches and CGM combined with additional inputs were deployed. A broad range of algorithms was tested. Performance varied among the reviewed methods, ranging from unsatisfactory to excellent (e.g., 21% vs. 100% sensitivity). Detection times ranged from 9.0 to 45.0 min.ConclusionSeveral CGM-based approaches are promising for automatically detecting IA. However, response times need to be faster to enable JITAIs aimed at impacting acute IA. Methodological issues and overall heterogeneity among articles prevent recommending one single approach; specific cases will dictate the most suitable approach.
Significance With a third of all food production lost via leaky supply chains or spoilage, food loss is a key contributor to global food insecurity. Demand for resource-intensive animal-based food further limits food availability. In this paper, we show that plant-based replacements for each of the major animal categories in the United States (beef, pork, dairy, poultry, and eggs) can produce twofold to 20-fold more nutritionally similar food per unit cropland. Replacing all animal-based items with plant-based replacement diets can add enough food to feed 350 million additional people, more than the expected benefits of eliminating all supply chain food loss. Food loss is widely recognized as undermining food security and environmental sustainability. However, consumption of resource-intensive food items instead of more efficient, equally nutritious alternatives can also be considered as an effective food loss. Here we define and quantify these opportunity food losses as the food loss associated with consuming resource-intensive animal-based items instead of plant-based alternatives which are nutritionally comparable, e.g., in terms of protein content. We consider replacements that minimize cropland use for each of the main US animal-based food categories. We find that although the characteristic conventional retail-to-consumer food losses are ≈30% for plant and animal products, the opportunity food losses of beef, pork, dairy, poultry, and eggs are 96%, 90%, 75%, 50%, and 40%, respectively. This arises because plant-based replacement diets can produce 20-fold and twofold more nutritionally similar food per cropland than beef and eggs, the most and least resource-intensive animal categories, respectively. Although conventional and opportunity food losses are both targets for improvement, the high opportunity food losses highlight the large potential savings beyond conventionally defined food losses. Concurrently replacing all animal-based items in the US diet with plant-based alternatives will add enough food to feed, in full, 350 million additional people, well above the expected benefits of eliminating all supply chain food waste. These results highlight the importance of dietary shifts to improving food availability and security.
We study a regulation in Chile that mandates warning labels on products whose sugar or caloric concentration exceeds certain thresholds. We show that consumers substitute from labeled to unlabeled products—a pattern mostly driven by products that consumers mistakenly believe to be healthy. On the supply side, we find substantial reformulation of products and bunching at the thresholds. We develop and estimate an equilibrium model of demand for food and firms' pricing and nutritional choices. We find that food labels increase consumer welfare by 1.8% of total expenditure, and that these effects are enhanced by firms' responses. We then use the model to study alternative policy designs. Under optimal policy thresholds, food labels and sugar taxes generate similar gains in consumer welfare, but food labels benefit the poor relatively more.
Cadmiel Moldovan, Oleg Frumuzachi, Mihai Babotă
et al.
BackgroundShallot (Allium ascalonicum L.) is a traditional plant species used throughout the world both for culinary purposes and as a folk remedy. To date (i.e., April 2022), there is no report on the main pharmacological activities exerted by shallot preparations and/or extracts.Scope and ApproachThe aim of this study was to comprehensively review the pharmacological activities exerted by shallot, with rigorous inclusion and exclusion criteria based on the scientific rigor of studies. Prisma guidelines were followed to perform the literature search.Key Findings and ConclusionsThe literature search yielded 2,410 articles of which 116 passed the required rigorous criteria for inclusion in this review. The extracts exert a potent antioxidant activity both in vitro and in vivo, as well as a strong inhibitory capacity on various pathogens with relevant implications for public health. Moreover, shallot can be used as adjuvant therapy in cardiovascular diseases, diabetes, cancer prevention, and other non-communicable diseases associated with inflammatory and oxidative pathways. Future studies investigating the chemical composition of this species, as well as the molecular mechanisms involved in the empirically observed pharmacological actions are required.