Climate change and food safety: A review

The impact of temperature on non-typhoidal Salmonella and Campylobacter infections: an updated systematic review and meta-analysis of epidemiological evidence

BACKGROUND

As temperatures rise, the transmission and incidence of enteric infections such as those caused by Salmonella and Campylobacter increase. This study aimed to review and synthesise the available evidence on the effects of exposure to ambient temperatures on non-typhoidal Salmonella and Campylobacter infections.

METHODS

A systematic search was conducted for peer-reviewed epidemiological studies published between January 1990 and March 2024, in PubMed, Scopus, Embase, and Web of Science databases. Original observational studies using ecological time-series, case-crossover or case-series study designs reporting the association between ambient temperature and non-typhoidal Salmonella and Campylobacter infections in the general population were included. A random-effects meta-analysis was performed to pool the relative risks (RRs) per 1 °C temperature increase, and further meta regression, and subgroup analyses by climate zone, temperature metrics, temporal resolution, lag period, and continent were conducted. The Navigation Guide systematic review methodology framework was used to assess the quality and strength of evidence. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO).

FINDINGS

Out of 3472 results, 44 studies were included in this systematic review encompassing over one million cases each of Salmonella and Campylobacter infections. Geographically, the 44 studies covered 27 countries across five continents and most of the studies were from high income countries. The meta-analysis incorporated 23 Salmonella studies (65 effect estimates) and 15 Campylobacter studies (24 effect estimates). For each 1 °C rise in temperature, the risk of non-typhoidal Salmonella and Campylobacter infections increased by 5% (RR: 1.05, 95% CI: 1.04-1.06), and 5% (RR: 1.05, 95% CI: 1.04-1.07%), respectively, with varying risks across different climate zones. The overall evidence was evaluated as being of "high" quality, and the strength of the evidence was determined to be "sufficient" for both infections.

INTERPRETATION

These findings emphasise the relationship between temperature and the incidence of Salmonella and Campylobacter infections. It is crucial to exercise caution when generalising these findings, given the limited number of studies conducted in low and middle-income countries. Nevertheless, the results demonstrate the importance of implementing focused interventions and adaptive measures, such as the establishment of localised early warning systems and preventive strategies that account for climatic fluctuations. Furthermore, our research emphasises the ongoing need for surveillance and research efforts to monitor and understand the changing dynamics of temperature-related enteric infections in the context of climate change.

FUNDING

Australian Research Council Discovery Projects grant (ARC DP200102571) Program.

How heatwaves affect short-term emergency hospital admissions due to bacterial foodborne diseases

The coming decades are likely to see of extreme weather events becoming more intense and frequent across Europe as a whole and around the Mediterranean in particular. The reproduction rate of some microorganisms, including the bacteria that cause foodborne diseases, will also be affected by these events. The aim of this study was thus to ascertain whether there might be a statistically significant relationship between emergency hospital admissions due to the principal bacterial foodborne diseases (BFDs) and the various meteorological variables, including heatwaves. We conducted a time-series study, with daily observations of both the dependent variable (emergency hospital admissions due to BFDs) and the independent variables (meteorological variables and control variables of chemical air pollution) across the period 2013-2018 in the Madrid Region (Spain), using Generalised Linear Models with Poisson regression, in which control and lag variables were included for the purpose of fitting the models. We calculated the threshold value of the maximum daily temperature above which such admissions increased statistically significantly, analysed data for the whole year and for the summer months alone, and estimated the relative and attributable risks. The estimated attributable risk was 3.6 % for every one-degree rise in the maximum daily temperature above 12 °C throughout the year, and 12.21 % for every one degree rise in temperature above the threshold heatwave definition temperature (34 °C) in summer. Furthermore, different meteorological variables displayed a statistically significant association. Whereas hours of sunlight and mean wind speed proved significant in the analyses of both the whole year and summer, the variables "rain" and "relative humidity", only showed a significant relationship in the analysis for the whole year. High ambient temperature is a risk factor that favours the increase in emergency hospitalisations attributable to the principal BFDs, with a greater impact being observed on days coinciding with heatwave periods. The results yielded by this study could serve as a basis for implementing BFD prevention strategies, especially on heatwave days.

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Mitigation of Mycotoxins in Food-Is It Possible?

Among microorganisms found in food, fungi stand out because they are adaptable and competitive in a large range of water activities, temperatures, pHs, humidities and substrate types. Besides sporulating, some species are toxigenic and produce toxic metabolites, mycotoxins, under adverse biotic and abiotic variables. Microorganisms are inactivated along the food chain, but mycotoxins have stable structures and remain in ready-to-eat food. The most prevalent mycotoxins in food, which are aflatoxins, fumonisins, ochratoxin A, patulin, tenuazonic acid, trichothecenes and zearalenone, have maximum tolerable limits (MTLs) defined as ppb and ppt by official organizations. The chronic and acute toxicities of mycotoxins and their stability are different in a chemical family. This critical review aims to discuss promising scientific research that successfully mitigated levels of mycotoxins and focus the results of our research group on this issue. It highlights the application of natural antifungal compounds, combinations of management, processing parameters and emergent technologies, and their role in reducing the levels and bioaccessibility. Despite good crop management and processing practices, total decontamination is almost impossible. Experimental evidence has shown that exposure to mycotoxins may be mitigated. However, multidisciplinary efforts need to be made to improve the applicability of successful techniques in the food supply chain to avoid mycotoxins' impact on global food insecurity.

Nanomaterials based sensors for analysis of food safety

The freshnessof the food is a major issue because spoiled food lacks critical nutrients for growth and could be harmful to human health if consumed directly. Nanomaterials are captivating due to their unique properties like large surface area, high selectivity, small dimension, great biocompatibility and conductivity, real-time onsite analysis, etc. which give them an advantage over conventional evaluation techniques. Despite these advantages of nanomaterials used in food safety and their preservation, food products can still get affected by various environmental factors (like pH, temperature, etc.), making the use of time-temperature indicators more condescending. This review is a comprehensive study on food safety, its causes, the responsible analytes, their remedies by various nanomaterials, the development of various nanosensors, and the various challenges faced in maintaining food safety standards to reduce the risk of contaminants.

A Recent Advancement in Food Quality Assessment: Using MOF-Based Sensors: Challenges and Future Aspects

Monitoring food safety is crucial and significantly impacts the ecosystem and human health. To adequately address food safety problems, a collaborative effort needed from government, industry, and consumers. Modern sensing technologies with outstanding performance are needed to meet the growing demands for quick and accurate food safety monitoring. Recently, emerging sensors for regulating food safety have been extensively explored. Along with the development in sensing technology, the metal-organic frameworks (MOF)-based sensors gained more attention due to their excellent sensing, catalytic, and adsorption properties. This review summarizes the current advancements and applications of MOFs-based sensors, including colorimetric, electrochemical, luminescent, surface-enhanced Raman scattering, and electrochemiluminescent sensors. and also focused on the applications of MOF-based sensors for the monitoring of toxins such as heavy metals, pesticide residues, mycotoxins, pathogens, and illegal food additives from food samples. Future trends, as well as current developments in MOF-based materials.

Food Safety Aspects of Breeding Maize to Multi-Resistance against the Major (Fusarium graminearum, F. verticillioides, Aspergillus flavus) and Minor Toxigenic Fungi (Fusarium spp.) as Well as to Toxin Accumulation, Trends, and Solutions-A Review

Maize is the crop which is most commonly exposed to toxigenic fungi that produce many toxins that are harmful to humans and animals alike. Preharvest grain yield loss, preharvest toxin contamination (at harvest), and storage loss are estimated to be between 220 and 265 million metric tons. In the past ten years, the preharvest mycotoxin damage was stable or increased mainly in aflatoxin and fumonisins. The presence of multiple toxins is characteristic. The few breeding programs concentrate on one of the three main toxigenic fungi. About 90% of the experiments except AFB1 rarely test toxin contamination. As disease resistance and resistance to toxin contamination often differ in regard to F. graminearum, F. verticillioides, and A. flavus and their toxins, it is not possible to make a food safety evaluation according to symptom severity alone. The inheritance of the resistance is polygenic, often mixed with epistatic and additive effects, but only a minor part of their phenotypic variation can be explained. All tests are made by a single inoculum (pure isolate or mixture). Genotype ranking differs between isolates and according to aggressiveness level; therefore, the reliability of such resistance data is often problematic. Silk channel inoculation often causes lower ear rot severity than we find in kernel resistance tests. These explain the slow progress and raise skepticism towards resistance breeding. On the other hand, during genetic research, several effective putative resistance genes were identified, and some overlapped with known QTLs. QTLs were identified as securing specific or general resistance to different toxicogenic species. Hybrids were identified with good disease and toxin resistance to the three toxigenic species. Resistance and toxin differences were often tenfold or higher, allowing for the introduction of the resistance and resistance to toxin accumulation tests in the variety testing and the evaluation of the food safety risks of the hybrids within 2-3 years. Beyond this, resistance breeding programs and genetic investigations (QTL-analyses, GWAM tests, etc.) can be improved. All other research may use it with success, where artificial inoculation is necessary. The multi-toxin data reveal more toxins than we can treat now. Their control is not solved. As limits for nonregulated toxins can be introduced, or the existing regulations can be made to be stricter, the research should start. We should mention that a higher resistance to F. verticillioides and A. flavus can be very useful to balance the detrimental effect of hotter and dryer seasons on aflatoxin and fumonisin contamination. This is a new aspect to secure food and feed safety under otherwise damaging climatic conditions. The more resistant hybrids are to the three main agents, the more likely we are to reduce the toxin losses mentioned by about 50% or higher.

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

Complex adaptive systems-based framework for modeling the health impacts of climate change

Introduction

Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes.

Objectives

The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate.

Methods

Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework.

Results and discussion

The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change.

Conclusion

This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

Socio-economic sustainability with circular economy - An alternative approach

As the global population and living standards rise, it pushes the demand for basic amenities like food, health, and energy resources. Additionally, manufacturing automation has led to mass production and consumption, triggering waste production. The existing linear economy approach has led to increasing waste production and resource depletion, posing significant environmental and public health threats. To overcome these impediments, an alternative model called the circular economy concept has gained popularity in the global industry community. This closed-loop, restorative, waste-free concept has the potential to protect the environment and improve economics by reducing energy and resource consumption. Thus, major impetus should be given to strengthening the backbone of the economy where tools such as green technologies, decarbonization strategies, bio refinery processes, material flow analysis, life cycle assessment, ecological footprints (water, carbon, and material), substance flow analysis, circularity index, eco-designing, bioresource management, new business models, and policy play an essential role in the areas of socio-economic sustainability, ecological facts, and industrial aspects to enhance socio-economic growth in a sustainable manner. Sectoral awareness, collaborations, and partnerships among the Government, stakeholders, policymakers, and competent authorities are also essential to enabling circularity within the eco-systems.

Advancements in Research on Prevention and Control Strategies for Maize White Spot Disease

Maize white spot (MWS), caused by the bacterium Pantoea ananatis, is a serious disease that significantly impacts maize production and productivity. In recent years, outbreaks of white spot disease have resulted in substantial maize yield losses in southwest China. Researchers from various countries worldwide have conducted extensive research on this pathogen, including its isolation and identification, the localization of resistance genes, transmission pathways, as well as potential control measures. However, the information related to this disease remains fragmented, and standardized preventive and control strategies have not yet been established. In light of this, this review aims to comprehensively summarize the research findings on MWS, providing valuable insights into understanding its occurrence, prevention, and control measures in the southwestern and southern regions of China while also mitigating the detrimental impact and losses caused by MWS on maize production in China and across the world.

Current Trends in Mycotoxin Detection with Various Types of Biosensors

One of the most important tasks in food safety is to properly manage the investigation of mycotoxin contamination in agricultural products and foods made from them, as well as to prevent its occurrence. Monitoring requires a wide range of analytical methods, from expensive analytical procedures with high-tech instrumentation to significantly cheaper biosensor developments or even single-use assays suitable for on-site monitoring. This review provides a summary of the development directions over approximately a decade and a half, grouped according to the biologically sensitive components used. We provide an overview of the use of antibodies, molecularly imprinted polymers, and aptamers, as well as the diversity of biosensors and their applications within the food industry. We also mention the possibility of determining multiple toxins side by side, which would significantly reduce the time required for the analyses.

Projected climate extremes over agro-climatic zones of Ganga River Basin under 1.5, 2, and 3° global warming levels

Recurring floods, droughts, heatwaves, and other hydro-meteorological extreme events are likely to be increased under the climate change scenarios. The increased risk of these extreme events might have more exposure to the population; thus, it is important to discuss such extreme events and their projected behavior under a changing climate scenario. In the present study, we have computed the extreme precipitation and temperature indices over the 10 agro-climatic zones falling under the Ganga River Basin (GRB)utilizing a high-resolution daily gridded temperature and precipitation multi-model ensembled CMIP6 dataset (0.25° × 0.25°) under global warming levels of 1.5 °C, 2 °C, and 3 °C. We found that the annual daily minimum temperature (TNN) showed a higher rise of about 67% than the maximum temperature (TXX) of 48% in GRB. The basin also experiences a greater increase in the frequency of warm nights (TN90P) of about 67.71% compared to warm days (TX90P) of 29.1% for the 3 °C global warming level. Along with extreme indices, the population exposed due to the impact of the extreme maximum temperature has also been analyzed for progressive warming levels. Population exposure to extreme temperature event (TXX) has been analyzed with 20-year return period using GEV distribution method. The study concludes that the exposed population to extreme temperature event experienced an increase from 46.99 to 52.16% for the whole Ganga Basin. Consecutive dry days (CDD) and consecutive wet days (CWD) both show a significant increasing trend, but CWD has a significant increase in the majority of the zones, while CDD shows a significant decreasing trend for some of the zones for three warming levels periods. Extreme climate indices help to understand the frequency and intensity of extreme weather events such as heavy rainfall, droughts, and heatwaves to develop early warning systems and adaptation strategies to mitigate such events.

Microbial contamination in grocery stores from Portugal and Spain - The neglected indoor environment to be tackled in the scope of the One Health approach

Microbial contamination in grocery shops (GS) should be evaluated since food commodities are commonly handled by workers and customers increasing the risk of food contamination and disease transmission. The aim of this study was to evaluate the microbial contamination in Portuguese and Spanish GS with a multi-approach protocol using passive (electrostatic dust cloths and surface swabs) sampling methods. The molecular detection of Aspergillus sections, mycotoxin analysis, screening of azole resistance as well as cytotoxicity measurement were conducted to better estimate the potential health risks of exposure and to identify possible relations between the risk factors studied. Fruits/vegetables sampling location was the one identified has being the most contaminated (bacteria and fungi) area in GS from both countries. Aspergillus section Fumigati and Fusarium species were observed in samples from Portuguese groceries with reduced susceptibilities to azoles commonly used in the clinical treatment of fungal infections. Fumonisin B2 was detected in Portuguese GS possible unveiling this emergent threat concerning occupational exposure and food safety. Overall, the results obtained raise concerns regarding human health and food safety and must be surveilled applying a One Health approach.

The changing landscape of agriculture: role of precision breeding in developing smart crops

Food plants play a crucial role in human survival, providing them essential nutrients. However, traditional breeding methods have not been able to keep up with the demands of the growing population. The improvement of food plants aims to increase yield, quality, and resistance to biotic and abiotic stresses. With CRISPR/Cas9, researchers can identify and edit key genes conferring desirable qualities in agricultural plants, including increased yield, enhanced product quality attributes, and increased tolerance to biotic and abiotic challenges. These modifications have enabled the creation of "smart crops" that exhibit rapid climatic adaptation, resistance to extreme weather conditions and high yield and quality. The use of CRISPR/Cas9 combined with viral vectors or growth regulators has made it possible to produce more efficient modified plants with certain conventional breeding methods. However, ethical and regulatory aspects of this technology must be carefully considered. Proper regulation and application of genome editing technology can bring immense benefits to agriculture and food security. This article provides an overview of genetically modified genes and conventional as well as emerging tools, including CRISPR/Cas9, that have been utilized to enhance the quality of plants/fruits and their products. The review also discusses the challenges and prospects associated with these techniques.

Multiresidues Multiclass Analytical Methods for Determination of Antibiotics in Animal Origin Food: A Critical Analysis

Veterinary drugs are widely used to prevent and treat diseases. The European Union has forbidden the use of antibiotics as growth promoters since 2006. Its abusive use leads to the presence of antibiotic residues (AR) in foods of animal origin which is associated with antibiotic resistance. The monitoring of AR in food intended for human consumption is of utmost importance to assure Food Safety. A systematic bibliographic review was carried out on the analytical methodologies, published in 2013, for the determination of AR in foods of animal origin. The food processing effect in the AR detected in animal products is also addressed. However, there is a preference for multiresidues multiclass methods, i.e., methodologies that allow determining simultaneously different classes of antibiotics, which is still a challenge for researchers. The wide diversity of physico-chemical properties of these drugs is an obstacle to achieving excellent analytical performance for a vast number of molecules analyzed concurrently. New techniques in sample preparation continue to be developed in order to obtain a compromise between good recoveries and extracts without interferences (clean extracts). The most widely used analytical methodology for the determination of AR is liquid chromatography coupled with mass spectrometry. However, the current trend is focused on the use of powerful high-resolution MS detectors such as Time of Flight and Orbitrap with modern chromatographic systems. Cooking time and temperature control are the key processing conditions influencing the reduction of AR in foods.

Does climate change transform military medicine and defense medical support?

Background

Climate change has effects on multiple aspects of human life, such as access to food and water, expansion of endemic diseases as well as an increase of natural disasters and related diseases. The objective of this review is to summarize the current knowledge on climate change effects on military occupational health, military healthcare in a deployed setting, and defense medical logistics.

Methods

Online databases and registers were searched on August 22^nd, 2022 and 348 papers retrieved, published between 2000 and 2022, from which we selected 8 publications that described climate effects on military health. Papers were clustered according to a modified theoretical framework for climate change effects on health, and relevant items from each paper were summarized.

Results

During the last decades a growing body of climate change related publications was identified, which report that climate change has a significant impact on human physiology, mental health, water- and vector borne infectious diseases, as well as air pollution. However, regarding the specific climate effects on military health the level of evidence is low. The effects on defense medical logistics include vulnerabilities in the cold supply chain, in medical devices functioning, in need for air conditioning, and in fresh water supply.

Conclusions

Climate change may transform both the theoretical framework and practical implementations in military medicine and military healthcare systems. There are significant knowledge gaps on climate change effects on the health of military personnel in operations of both combat and non-combat nature, alerting the need for prevention and mitigation of climate-related health issues. Further research within the fields of disaster and military medicine is needed to explore this novel field. As climate effects on humans and the medical supply chain may degrade military capability, significant investments in military medical research and development are needed.

Climate Change and Nutrition: Implications for the Eastern Mediterranean Region

The Eastern Mediterranean Region (EMR) is considered among the world's most vulnerable to the dire impacts of climate change. This review paper aims at (1) characterizing climate change in countries of the EMR; (2) examining the potential effects of climate change on the nutritional and health status of the population; and (3) identifying the most vulnerable population groups. The paper explored several climate change indicators including daily temperatures, extreme temperature, daily precipitation, extreme precipitation (flooding, drought, storms, etc.), humidity, CO2 concentrations and sea surface temperature in EMR countries. Findings suggest that climate change will exert a significant adverse effect on water and food security and showed that the nutritional status of the population, which is already characterized by the triple burden of malnutrition, is likely to worsen via three main pathways mediated by climate change, namely, its impact on food security, care and health. Women, infants, children, those living in poor households and those experiencing displacement will be among the most vulnerable to the nutritional impacts of climate change. The paper concludes with a set of recommendations from the Initiative on Climate Action and Nutrition, which can support the region in tackling the critical nexus of climate change and nutrition.

The impact of climate change on eating disorders: An urgent call for research

Climate change affects many of the documented risk factors for eating disorders (EDs) through direct and indirect pathways, yet to date the research in this area is nonexistent. Our aim is to identify the specific mechanisms through which climate change might be associated with increased risk for EDs, an exacerbation in symptoms, or poor clinical outcomes; highlight limited empirical data addressing these issues; and propose directions for a research program in this important area. Pathways for the impact of climate change on eating disorders and related data were reviewed. Four main pathways for the effects of climate change on EDs were identified including (1) decreased food access and security; (2) changes in mean temperature; (3) concerns related to food safety and eco-anxiety; and (4) indirect pathways through trauma, adversity, and increased mental health concerns. Except for the relationship between increased food insecurity and EDs, these pathways remain largely uninvestigated. Numerous factors may be implicated in the relationship between climate change and EDs. Future work in this area is imperative and should be conducted through a social justice lens with particular attention paid to the global areas most impacted by climate change and related vulnerabilities. Climate change will likely have adverse impacts on individuals with eating disorders and increase the risk for eating disorders. This paper reviews the different ways in which climate change may have these effects and calls for researchers to pay attention to this important area.

Prevalence and Antimicrobial Resistance of Bacterial Foodborne Pathogens Isolated from Raw Bivalve Molluscs Subjected to Consumption in Poland during a Ten-Year Period

The aim of this study was to evaluate the microbiological contamination of raw bivalve molluscan shellfish (BMS) available on the Polish market and determinate the antimicrobial resistance of the obtained isolates. A total of 1000 mollusc samples were tested for the presence of Salmonella spp., L. monocytogenes, V. parahaemolyticus, and S. aureus using the ISO standard methods. Additionally, the bacterial isolates’ susceptibility to antimicrobials was determined using the minimum inhibitory concentration (MIC) method. The obtained results showed that Salmonella spp. was detected in 31 (3.1%) samples, and 51.6% of the bacterial isolates were classified as Salmonella Typhimurium. A total of 74.2% of the Salmonella isolates were sensitive to all antimicrobial agents, whereas three isolates were multiresistant. L. monocytogenes was isolated from 18 (1.8%) BMS, and the isolates belonged to serogroups IIa, IIb, and IVb. Most of them were resistant to ceftriaxone (77.8%) and oxacillin (55.6%). V. parahaemolyticus was present in 24.2% BMS. These isolates were mainly resistant to ampicillin (77.3%) and streptomycin (64.0%). Moreover, 15.2% of the bivalve molluscs were contaminated with S. aureus. Most isolates belonging to this species were resistant to penicillin (84.9%). A total of 60 (6.0%) bivalve molluscs were contaminated with more than one pathogen simultaneously. In addition, the tested bacteria were more likely to be identified during the warmer period (53.9%) compared to the samples analyzed in colder months (35.7%). The obtained results indicate that raw bivalve molluscs from the Polish market are frequently contaminated with bacterial foodborne pathogens, which may be resistant to antimicrobials.

Study on the Impact of Climate Change on China's Import Trade of Major Agricultural Products and Adaptation Strategies

With global warming, China's agricultural products are facing severe production conditions and a complex international trade situation. In order to clarify the relationship between climate change and China's agricultural trade, this paper uses the GTAP model to explore the impact of climate change on China's agricultural trade from the perspectives of agricultural production and supply, energy substitution and trade policy. The results show that: (1) From the overall effect, the production supply risk and energy substitution risk caused by climate change have a positive impact on China's import trade, among which the energy substitution risk has brought about an import trade growth of 38.050%, the production supply risk has brought about an import trade growth of 12.635%, and the trade policy risk has a negative impact, bringing about an import trade decline of 12.589%. (2) Under the impact of production and supply risks caused by climate change, the import volume of different industrial sectors has increased by varying degrees, including livestock products (16.521%) > food crops (14.162%) > cash crops (7.220%). The increase in import trade mainly comes from the United States (10.731%), Canada (10.650%) and Australia (9.455%). (3) Under the impact of energy substitution risk caused by climate change, the increase in import trade was concentrated in food crops (48.144%) and livestock products (42.834%), mainly from the United States (57.098%), the European Union (55.014%) and Canada (53.508%). (4) Under the impact of trade policy risks caused by climate change, the import trade of different industrial sectors showed a downward trend, with cash crops (13.039%) > livestock products (12.588%) > cash crops (12.140%). The countries and regions with significant decline in import trade were ASEAN (-46.131%) and the United States (-28.028%). The trade deficit shifted to surplus, and the terms of trade were improved. Therefore, this paper suggests that we should deal with the impact of climate change on agricultural trade by developing "climate smart" agriculture, actively responding to low-carbon trade measures, and establishing an agricultural trade promotion mechanism to address the risk of climate change.

Climatic conditions and household food security: Evidence from Tanzania

Food security and adequate nutrition are critical for achieving progress toward sustainable development. Two billion people worldwide experience moderate to severe food insecurity, and rates of hunger have increased over the past several years after declining steadily for decades. The FAO attributes this increase in large part to climate change, though empirical evidence on the relationship between climatic conditions and food security remains limited. We examine this question by linking nationally representative longitudinal data from four rounds of the Tanzania National Panel Survey to high-resolution gridded climate data. We then estimate a set of fixed effects regression models to understand the linkages between recent rainy season precipitation and temperature and two indicators of household food security: Food Consumption Score (FCS) and reduced Coping Strategies Index (rCSI). We find that low rainfall-particularly dry and cool conditions-is negatively associated with food security. Moving from a typical rainfall year to a particularly dry one increases the risk of being food insecure on both measures simultaneously by 13-percentage points. This suggests that a lack of rainfall impedes households' ability to access food, likely through reduced agricultural production and increased food prices, leading to lower dietary diversity and food shortages. Vulnerability is higher among households with fewer working age members, suggesting that households with a greater supply of labor can better withstand droughts. As climate change alters precipitation and temperature patterns over the coming decades, policies to increase resilience will be critical for improving food security, particularly among populations heavily reliant on agriculture.

Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism

Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with complex cropland feature types in remote sensing images that resulted in blurred boundaries and low accuracy in plot partitioning. The proposed network contains different modules, such as spatial gradient guidance and dilated semantic fusion. It employs the image gradient attention guidance module to fully extract cropland plot features. This causes the feature to be transferred from the encoding layer to the decoding layer, creating layers full of key features within the cropland and making the extracted cropland information more accurate. In addition, this study also solves the problem caused by a large amount of spatial feature information, which losses easily during the downsampling process of continuous convolution in the coding layer. Aiming to solve this issue, we put forward a model for consensus fusion of multiscale spatial features to fuse each-layer feature of the coding layer through dilated convolution with different dilated ratios. This approach was proposed to make the segmentation results more comprehensive and complete. The lab findings showed that the Precision, Recall, MIoU, and F1 score of the multiscale fusion segmentation SSGNet network based on the attention mechanism had achieved 93.46%, 90.91%, 85.54%, and 92.73%, respectively. Its segmentation effect on cropland was better than other semantic segmentation networks and can effectively promote cropland semantic extraction.

Evaluation of Tourism Food Safety and Quality with Neural Networks

Food safety issues are inextricably linked to people's lives and, in extreme cases, endanger public safety and social stability. People are becoming increasingly concerned about food safety issues in a modern society with high-quality economic development. People's incomes are increasing day by day as the economy continues to grow, and the tourism industry has grown by leaps and bounds. However, many problems arose, such as the issue of food safety in tourism. Tourism food safety issues affect not only the development of the food industry but also the development of tourism. Food safety oversight of tourist attractions has always been a relatively concerning issue in the country, and it is also something that the general public is concerned about. It can be said that food safety supervision of tourist attractions is the most important thing in food safety supervision. In this context, it becomes an important task to evaluate the safety of tourist food. This work proposes a multiscale convolutional neural network (AMCNN) combined with neural networks and attention layers to realize the safety and quality evaluation of tourist food. The algorithm uses the lightweight Xception network as a basic model and utilizes multiscale depth-separable convolution modules of different sizes for feature extraction and fusion to extract richer food safety feature information. Furthermore, the convolutional attention module (CBAM) is embedded on the basis of the multiscale convolutional neural network, which makes the network model focus more on discriminative features.

Advances in Crop Breeding Through Precision Genome Editing

The global climate change and unfavourable abiotic and biotic factors are limiting agricultural productivity and therefore intensifying the challenges for crop scientists to meet the rising demand for global food supply. The introduction of applied genetics to agriculture through plant breeding facilitated the development of hybrid varieties with improved crop productivity. However, the development of new varieties with the existing gene pools poses a challenge for crop breeders. Genetic engineering holds the potential to broaden genetic diversity by the introduction of new genes into crops. But the random insertion of foreign DNA into the plant's nuclear genome often leads to transgene silencing. Recent advances in the field of plant breeding include the development of a new breeding technique called genome editing. Genome editing technologies have emerged as powerful tools to precisely modify the crop genomes at specific sites in the genome, which has been the longstanding goal of plant breeders. The precise modification of the target genome, the absence of foreign DNA in the genome-edited plants, and the faster and cheaper method of genome modification are the remarkable features of the genome-editing technology that have resulted in its widespread application in crop breeding in less than a decade. This review focuses on the advances in crop breeding through precision genome editing. This review includes: an overview of the different breeding approaches for crop improvement; genome editing tools and their mechanism of action and application of the most widely used genome editing technology, CRISPR/Cas9, for crop improvement especially for agronomic traits such as disease resistance, abiotic stress tolerance, herbicide tolerance, yield and quality improvement, reduction of anti-nutrients, and improved shelf life; and an update on the regulatory approval of the genome-edited crops. This review also throws a light on development of high-yielding climate-resilient crops through precision genome editing.

Trends in climate, socioeconomic indices and food security in Nigeria: Current realities and challenges ahead

Food security in Nigeria is presently in dire strait owing to several factors, such as skyrocketing energy prices, climate change, and terrorism. This study is aimed at revealing the role of the aforementioned factors in shaping food affordability and availability in the country. The study used descriptive statistics and coefficients of variation and determination to ascertain the change in the trend in these factors and their correlates to food security over time. From the results of our research team, we inferred that temperature increases, political instability, rising food prices and erratic energy supply have had distressing consequences in the areas of affordability, availability and stability of food supplies. We conclude that a rapidly growing population such as Nigeria's would need crucial interventions in increasing food production, mitigating the impacts of climate change, and buffering energy supplies. Ultimately, Nigeria needs to overhaul the important components of her food systems and the respective linkages between these components in order to ensure food security for the entire population.

Uncovering the Research Gaps to Alleviate the Negative Impacts of Climate Change on Food Security: A Review

Climatic variability has been acquiring an extensive consideration due to its widespread ability to impact food production and livelihoods. Climate change has the potential to intersperse global approaches in alleviating hunger and undernutrition. It is hypothesized that climate shifts bring substantial negative impacts on food production systems, thereby intimidating food security. Vast developments have been made addressing the global climate change, undernourishment, and hunger for the last few decades, partly due to the increase in food productivity through augmented agricultural managements. However, the growing population has increased the demand for food, putting pressure on food systems. Moreover, the potential climate change impacts are still unclear more obviously at the regional scales. Climate change is expected to boost food insecurity challenges in areas already vulnerable to climate change. Human-induced climate change is expected to impact food quality, quantity, and potentiality to dispense it equitably. Global capabilities to ascertain the food security and nutritional reasonableness facing expeditious shifts in biophysical conditions are likely to be the main factors determining the level of global disease incidence. It can be apprehended that all food security components (mainly food access and utilization) likely be under indirect effect via pledged impacts on ménage, incomes, and damages to health. The corroboration supports the dire need for huge focused investments in mitigation and adaptation measures to have sustainable, climate-smart, eco-friendly, and climate stress resilient food production systems. In this paper, we discussed the foremost pathways of how climate change impacts our food production systems as well as the social, and economic factors that in the mastery of unbiased food distribution. Likewise, we analyze the research gaps and biases about climate change and food security. Climate change is often responsible for food insecurity issues, not focusing on the fact that food production systems have magnified the climate change process. Provided the critical threats to food security, the focus needs to be shifted to an implementation oriented-agenda to potentially cope with current challenges. Therefore, this review seeks to have a more unprejudiced view and thus interpret the fusion association between climate change and food security by imperatively scrutinizing all factors.

Associations between long-term drought and diarrhea among children under five in low- and middle-income countries

Climate change is projected to intensify drought conditions, which may increase the risk of diarrheal diseases in children. We constructed log-binomial generalized linear mixed models to examine the association between diarrhea risk, ascertained from global-scale nationally representative Demographic and Health Surveys, and drought, represented by the standardized precipitation evapotranspiration index, among children under five in 51 low- and middle-income countries (LMICs). Exposure to 6-month mild or severe drought was associated with an increased diarrhea risk of 5% (95% confidence interval 3-7%) or 8% (5-11%), respectively. The association was stronger among children living in a household that needed longer time to collect water or had no access to water or soap/detergent for handwashing. The association for 24-month drought was strong in dry zones but weak or null in tropical or temperate zones, whereas that for 6-month drought was only observed in tropical or temperate zones. In this work we quantify the associations between exposure to long-term drought and elevated diarrhea risk among children under five in LMICs and suggest that the risk could be reduced through improved water, sanitation, and hygiene practices, made more urgent by the likely increase in drought due to climate change.

Food Risk Entropy Model Based on Federated Learning

The safety of agricultural products is a guarantee of national security. The increasing variety of pesticides used on crops has led to an increasing abundance of pesticide residues in agricultural products, making pesticide residues an important factor in threatening health. Traditional indicators for evaluating the safety of agricultural products, such as pass rates and residue rates, can only qualitatively describe the level of pesticide residues. Isolated data leads to low data utilization, data is distributed between different terminals or departments and cannot be shared, while the security of private data needs to be ensured. Therefore, we propose a risk entropy model based on federated learning. The model is able to quantitatively describe the risk level of agricultural products and achieve data fusion without exposing private data in the federated learning model. In this paper, a total of 90,510 agricultural product data samples from 2015 to 2019 are collected, with each sample containing 58 indicators. The experimental results show that the developed food safety risk entropy model can quantitatively reflect the level of risk in the target region and time interval. In addition, we have developed a multidimensional data analysis tool based on federated learning, which can achieve data integration across multiple regions and departments.

Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China

Agricultural fields are essential in providing human beings with paramount food and other materials. Quick and accurate identification of agricultural fields from the remote sensing images is a crucial task in digital and precision agriculture. Deep learning methods have the advantages of fast and accurate image segmentation, especially for extracting the agricultural fields from remote sensing images. This paper proposed a deep neural network with a dual attention mechanism and a multi-scale feature fusion (Dual Attention and Scale Fusion Network, DASFNet) to extract the cropland from a GaoFen-2 (GF-2) image of 2017 in Alar, south Xinjiang, China. First, we constructed an agricultural field segmentation dataset from the GF-2 image. Next, seven evaluation indices were selected to assess the extraction accuracy, including the location shift, to reveal the spatial relationship and facilitate a better evaluation. Finally, we proposed DASFNet incorporating three ameliorated and novel deep learning modules with the dual attention mechanism and multi-scale feature fusion methods. The comparison of these modules indicated their effects and advantages. Compared with different segmentation convolutional neural networks, DASFNet achieved the best testing accuracy in extracting fields with an F1-score of 0.9017, an intersection over a union of 0.8932, a Kappa coefficient of 0.8869, and a location shift of 1.1752 pixels. Agricultural fields can be extracted automatedly and accurately using DASFNet, which reduces the manual record of the agricultural field information and is conducive to further farmland surveys, protection, and management.

Assessment of Fusarium-Damaged Kernels in Common Wheat in Romania in the Years 2015 and 2016 with Extreme Weather Events

This article assesses the occurrence of Fusarium-damaged kernels (FDKs) in common wheat (Triticum aestivum) under the influence of environmental factors and extreme weather events in Romania (exceptionally high air temperatures and extreme pedological drought produced by a dipole block in summer 2015, and extreme precipitation and floods produced by an omega block in spring 2016). Wheat samples (N = 272) were analyzed for FDKs via visual estimation and manual weighing according to ISO 7970 and are statistically evaluated using SPSS. The dipole block in 2015 reduced the effects of environmental factors to non-significant correlations with FDKs, while the omega block in 2016 was non-significantly to very significantly correlated with FDKs in the northwestern and western regions. The occurrence of FDKs was favored for wheat cultivation in acidic soils and inhibited in alkaline soils. Wheat samples with FDKs ≥ 1% were sampled from crops grown in river meadows with high and very high risks of flooding. Knowing the contaminants' geographical and spatial distributions under the influence of regular and extreme weather events is important for establishing measures to mitigate the effects of climate change and to ensure human and animal health.

Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data

Precise and timely information on crop spatial distribution over large areas is paramount to agricultural monitoring, food security, and policy development. Currently, automatically classifying crop types at a large scale is challenging due to the scarcity of ground data. Although previous studies have indicated that transductive transfer learning (TTL) is a promising method to address this problem, it performs poorly within regions where crop compositions and phenology differ largely. Here we transferred random forest classifiers trained in limited regions with diversified growing conditions and land covers to the rest of the study area where ground data are scarce, with more than 130,000 Sentinel-2 images processed using the Google Earth Engine (GEE) platform. We established the 10 m crop maps for four major crops (i.e., maize, rapeseed, winter, and spring Triticeae crops) across 10 European Union (EU) countries from 2018 to 2019. The final crop maps had a high accuracy with overall accuracy generally greater than 0.89, with user’s accuracy and producer’s accuracy ranging from 0.72 to 0.98. Moreover, the resulting maps were consistent with the NUTS-2 level official statistics, with R2 consistently greater than 0.9. We further analyzed the crop rotation patterns and found that the rotation intervals across these EU countries were generally at least one year. Maize was dominantly rotated with winter Triticeae crops or converted to other land covers in the following year. Rapeseed was generally grown in rotation with winter Triticeae crops, whereas the rotation patterns of winter and spring Triticeae crops were more diversified. Red Edge Position (REP) and Normalized Difference Yellow Index (NDYI) played significant roles in crop classification across the EU. This study highlights the potential of the developed TTL method for crop classification over large spatial extents where labeled data are limited and the differences in crop compositions and phenology are relatively large.

Natural dynamics and residues of pymetrozine for typical rice-growing areas of China

Pymetrozine has replaced toxic organophosphate pesticides previously used for controlling pests of rice crops in China. Existing data on its environmental behavior are usually related to studies on artificial plots that do not adequately address the natural dynamics and residues in actual field conditions. Therefore, studies under field conditions were carried out to investigate the natural dynamics and residues of pymetrozine in two typical rice-growing areas in China - Hunan and Guangxi provinces. Samples of paddy soil and water were collected in relation to spraying events in the study areas. The quick, easy, cheap, effective, rugged and safe (QuEChERS) method was used to extract pymetrozine residues from the samples by a Waters ACQUITY UPLC (Milford, MA, USA) system interfaced with a triple-quadrupole mass spectrometer (Xevo TQ-D, Waters Corp., USA). The initial deposition of pymetrozine in paddy soils was higher than in paddy waters in both areas. The decay of pymetrozine followed an exponential trend consistent with the first order kinetics. The half-life of pymetrozine in paddy water was determined to be 3.0 and 3.8 days, whereas the half-life in soil was 3.8 and 3.5 days in the Guangxi and Hunan samples, respectively. The decline rates of pymetrozine in paddy soil and paddy water in this field study were faster than those conducted under non-field conditions reported in previous studies. Compared to other pesticides used in China as reported in previous studies, the environmental persistence of pymetrozine in both paddy water and soils in Guangxi and Hunan provinces is very low. This has important implications for the use of pymetrozine in agricultural systems globally.

Projecting Temperature-Attributable Mortality and Hospital Admissions due to Enteric Infections in the Philippines

BACKGROUND

Enteric infections cause significant deaths, and global projection studies suggest that mortality from enteric infections will increase in the future with warmer climate. However, a major limitation of these projection studies is the use of risk estimates derived from nonmortality data to project excess enteric infection mortality associated with temperature because of the lack of studies that used actual deaths.

OBJECTIVE

We quantified the associations of daily temperature with both mortality and hospital admissions due to enteric infections in the Philippines. These associations were applied to projections under various climate and population change scenarios.

METHODS

We modeled nonlinear temperature associations of mortality and hospital admissions due to enteric infections in 17 administrative regions of the Philippines using a two-stage time-series approach. First, we quantified nonlinear temperature associations of enteric infections by fitting generalized linear models with distributed lag nonlinear models. Second, we combined regional estimates using a meta-regression model. We projected the excess future enteric infections due to nonoptimal temperatures using regional temperature-enteric infection associations under various combinations of climate change scenarios according to representative concentration pathways (RCPs) and population change scenarios according to shared socioeconomic pathways (SSPs) for 2010-2099.

RESULTS

Regional estimates for mortality and hospital admissions were significantly heterogeneous and had varying shapes in association with temperature. Generally, mortality risks were greater in high temperatures, whereas hospital admission risks were greater in low temperatures. Temperature-attributable excess deaths in 2090-2099 were projected to increase over 2010-2019 by as little as 1.3% [95% empirical confidence intervals (eCI): -3.1%, 6.5%] under a low greenhouse gas emission scenario (RCP 2.6) or as much as 25.5% (95% eCI: -3.5%, 48.2%) under a high greenhouse gas emission scenario (RCP 8.5). A moderate increase was projected for temperature-attributable excess hospital admissions, from 0.02% (95% eCI: -2.0%, 1.9%) under RCP 2.6 to 5.2% (95% eCI: -12.7%, 21.8%) under RCP 8.5 in the same period. High temperature-attributable deaths and hospital admissions due to enteric infections may occur under scenarios with high population growth in 2090-2099.

DISCUSSION

In the Philippines, futures with hotter temperatures and high population growth may lead to a greater increase in temperature-related excess deaths than hospital admissions due to enteric infections. Our results highlight the need to strengthen existing primary health care interventions for diarrhea and support health adaptation policies to help reduce future enteric infections. https://doi.org/10.1289/EHP9324.

Metal-Organic Frameworks-Based Sensors for Food Safety

Food contains a variety of poisonous and harmful substances that have an impact on human health. Therefore, food safety is a worldwide public concern. Food detection approaches must ensure the safety of food at every step of the food supply chain by monitoring and evaluating all hazards from every single step of food production. Therefore, early detection and determination of trace-level contaminants in food are one of the most crucial measures for ensuring food safety and safeguarding consumers' health. In recent years, various methods have been introduced for food safety analysis, including classical methods and biomolecules-based sensing methods. However, most of these methods are laboratory-dependent, time-consuming, costly, and require well-trained technicians. To overcome such problems, developing rapid, simple, accurate, low-cost, and portable food sensing techniques is essential. Metal-organic frameworks (MOFs), a type of porous materials that present high porosity, abundant functional groups, and tunable physical and chemical properties, demonstrates promise in large-number applications. In this regard, MOF-based sensing techniques provide a novel approach in rapid and efficient sensing of pathogenic bacteria, heavy metals, food illegal additives, toxins, persistent organic pollutants (POPs), veterinary drugs, and pesticide residues. This review focused on the rapid screening of MOF-based sensors for food safety analysis. Challenges and future perspectives of MOF-based sensors were discussed. MOF-based sensing techniques would be useful tools for food safety evaluation owing to their portability, affordability, reliability, sensibility, and stability. The present review focused on research published up to 7 years ago. We believe that this work will help readers understand the effects of food hazard exposure, the effects on humans, and the use of MOFs in the detection and sensing of food hazards.

Immuno-phenotyping of Canadian Beef Cattle: Adaptation of the High Immune Response Methodology for Utilization in Beef Cattle

The high immune response (HIR) methodology measures the genetic performance of the adaptive immune system to identify and breed animals with balanced and robust immunity. The HIR methodology has previously been used in dairy and swine to reduce disease but has not been fully investigated in beef cattle. The first objective of the current study was to examine whether the HIR methodology as standardized for use in dairy cattle was appropriate for use in beef cattle. The second objective was to determine the earliest age for immune response phenotyping of beef calves. In this study, beef calves (n = 295) of various ages, as well as mature beef cows (n = 170) of mixed breeds, were immunized using test antigens to assess their antibody- (AMIR) and cell-mediated immune responses (CMIR). Heritability for AMIR and CMIR was estimated at 0.43 and 0.18, respectively. The HIR methodology was appropriate for use in beef cattle; beef calves as young as 2–3 wk of age were capable of mounting AMIR responses comparable with those seen historically in mature Holstein dairy cows. Three-week-old beef calves mounted CMIR responses comparable with those of Holstein cows, but 9-mo-old calves and mature beef cows had significantly higher CMIR responses than Holsteins. The HIR methodology can be used to measure both AMIR and CMIR in beef calves as young as 3 wk of age.

Over half of known human pathogenic diseases can be aggravated by climate change

It is relatively well accepted that climate change can affect human pathogenic diseases; however, the full extent of this risk remains poorly quantified. Here we carried out a systematic search for empirical examples about the impacts of ten climatic hazards sensitive to greenhouse gas (GHG) emissions on each known human pathogenic disease. We found that 58% (that is, 218 out of 375) of infectious diseases confronted by humanity worldwide have been at some point aggravated by climatic hazards; 16% were at times diminished. Empirical cases revealed 1,006 unique pathways in which climatic hazards, via different transmission types, led to pathogenic diseases. The human pathogenic diseases and transmission pathways aggravated by climatic hazards are too numerous for comprehensive societal adaptations, highlighting the urgent need to work at the source of the problem: reducing GHG emissions.

Climate Change and Emerging Food Safety Issues: A Review

ABSTRACT

Throughout the past decades, climate change has been one of the most complex global issues. Characterized by worldwide alterations in weather patterns, along with a concomitant increase in the temperature of the Earth, climate change will undoubtedly have significant effects on food security and food safety. Climate change engenders climate variability: significant variations in weather variables and their frequency. Both climate variability and climate change are thought to threaten the safety of the food supply chain through different pathways. One such pathway is the ability to exacerbate foodborne diseases by influencing the occurrence, persistence, virulence and, in some cases, toxicity of certain groups of disease-causing microorganisms. Food safety can also be compromised by various chemical hazards, such as pesticides, mycotoxins, and heavy metals. With changes in weather patterns, such as lower rainfall, higher air temperature, and higher frequency of extreme weather events among others, this translates to emerging food safety concerns. These include the shortage of safe water for irrigation of agricultural produce, greater use of pesticides due to pest resistance, increased difficulty in achieving a well-controlled cold chain resulting in temperature abuse, or the occurrence of flash floods, which cause runoff of chemical contaminants in natural water courses. Together, these can result in foodborne infection, intoxication, antimicrobial resistance, and long-term bioaccumulation of chemicals and heavy metals in the human body. Furthermore, severe climate variability can result in extreme weather events and natural calamities, which directly or indirectly impair food safety. This review discusses the causes and impacts of climate change and variability on existing and emerging food safety risks and also considers mitigation and adaptation strategies to address the global warming and climate change problem.

HIGHLIGHTS

Mycotoxins survey in feed materials and feedingstuffs in years 2015-2020

Mycotoxins are secondary metabolites produced by several fungi, mainly from Penicillium, Fusarium, and Aspregillus genera. They can contaminate many raw materials both in the field and during storage under very different conditions. This study aimed to evaluate mycotoxin contamination levels of raw materials and products for animals in Poland in the years 2015-2020. Producers, farmers, and veterinary doctors delivered a total of 3980 samples (642 maize samples, 2027 feed samples, 990 small grain samples, 142 maize silage samples and 179 TMR samples). Samples were analyzed for the occurrence of several mycotoxins, including aflatoxins, fumonisins, ochratoxin A, deoxynivalenol, nivalenol, T-2 toxin, H-2 toxin, and zearalenone. Trichothecenes, zearalenone, and fumonisins were determined using the LC-MS/MS technique. Ochratoxin A and aflatoxins were determined using the HPLC-FLD method. Deoxynivalenol and zearalenone were the most common contamination in maize samples (97.3% and 98.4%, respectively) and feed samples (99.7% and 100% samples, respectively). They were also present in all maize silage and TMR samples. The highest concentration levels of deoxynivalenol and zearalenone were 16,889 μg/kg in the wheat sample and 1420 μg/kg in the maize sample. Additionally, in 51 samples, mycotoxins level (mainly deoxynivalenol and zearalenone levels) was above recommended levels set by the European Union. The present study showed that both feeding and raw materials are contaminated by mycotoxins, often by more than one.

Effect of spatio-temporal variables, host fish species and on-farm biosecurity measures on the prevalence of potentially pathogenic Aeromonas species in freshwater fish farms

AIMS

To determine the prevalence of Aeromonas species in freshwater fish farms, factors affecting their prevalence and virulence factors associated with each species.

METHODS AND RESULTS

In a cross-sectional study from 128 farms in four districts of Uttar Pradesh, India, 11 species of Aeromonas were identified by gyrB sequencing including the first report of Aeromonas crassostreae from fish. Four species of Aeromonas were more prevalent (MP) in fish farms, A. veronii bv. sobria (50.0%) was the highest, followed by A. caviae (18.8%), A. veronii bv. veronii (11.7%) and A. dhakensis (7.0%). The less prevalent (LP) species were A. hydrophila, A. media, A. jandaei, A. allosaccharophila, A. salmonicida, A. crassostreae and A. taiwanensis. Spatial variation in the prevalence of Aeromonas species was observed. Dominance of biovar sobria ranged from 33.3 to 68.6%, notably lesser in farms with on-farm biosecurity measures. The prevalence of biovar veronii was significantly associated with pangas fish, rainy season and farms with on-farm biosecurity measures. The prevalence of LP species was significantly higher in mrigal fish and winter season. Multiple virulence factors (>6) were detected in 70.2% of the Aeromonas species. Significant association of β-hemolysin, DNase, slime production, act, ahh1, aexT and lip was observed with different species of Aeromonas. Moreover, 75.8% of Aeromonas species possessed one or more enterotoxins genes (act/alt/ast).

CONCLUSION

Significant association of spatio-temporal variables, host fish species and on-farm biosecurity measures were observed on the prevalence of some of the Aeromonas species in freshwater fish farms. Most of the Aeromonas species harboured virulence factors indicating their potential for pathogenicity.

SIGNIFICANCE AND IMPACT OF THE STUDY

This is the first study that determined the prevalence and identified the factors that affect the prevalence of Aeromonas species in freshwater fish farms. This information will be useful in managing Aeromonas infection in fish and their risks to public health.

Organisation of Multi-Mycotoxin Proficiency Tests: Evaluation of the Performances of the Laboratories Using the Triple A Rating Approach

In accordance with the International Standard Organization ISO 17043, two proficiency tests (PTs) for the simultaneous determination of aflatoxins (AFB1, AFB2, AFG1, AFG2); deoxynivalenol; fumonisins FB1, FB2, and B3; ochratoxin A, the T-2 toxin; and the HT-2 toxin were conducted in 2019 and 2020 using cornflakes and rusk flours that were prepared in house. The homogeneity and the stability of these materials were verified according to the criteria laid down in ISO 13528 using randomly selected samples. Most of the targeted toxins were found to be homogenously distributed in both materials with no significant changes during the timescale of the PTs. Next, the materials were distributed to approximately 25 participating laboratories from Europe, Canada, and the United States. The obtained datasets were computed using robust statistics. The outliers were checked and removed, and the toxin concentrations were assigned as the consensus value of the results of the participants at Horwitz ratios <1.2. The z scores were generated for all mycotoxins, and the results were pooled to calculate the relative sum of squared z scores (SZ2) indexes and were clustered according to the triple A rating. Overall, at least 80% of the participating laboratories achieved good and acceptable performances. The most frequent categories assigned to good performances (SZ2 ≤ 2) were AAA (51%) and BAA (13%). Clusters of BBA + CBA (6%) included laboratories reporting acceptable z scores <90% of the total z scores for less than 90% or 50% of the mycotoxins targeted in the 2 matrices. The triple A rating seems to be appropriate in evaluating the performances of laboratories involved in multi-mycotoxin analyses. Accredited and non-accredited analytical methods achieved good and acceptable performances.

Interrelationship between climatic factors and incidence of FBD caused by Clostridioides difficile toxin B, Clostridium perfringens, Campylobacter spp., and Escherichia coli O157:H7

Foodborne diseases (FBDs) remain a global public health concern. Climatic factors such as wind-chill temperature, rainfall, and relative humidity affect the incidence of several FBDs. This study was performed to analyze how the various factors of the climate influence the incidence and severity of FBDs. This study retrospectively analyzed the results of multiplex polymerase chain reaction (mPCR) tests for diarrhea-causing bacteria performed on 2300 fecal samples obtained from patients at Dankook University Hospital, Cheonan, from June 2010 to December 2019. The Clostridioides difficile toxin B infection rate positively correlated with the intensity of sunshine, and the content of particulate matter. The Campylobacter spp. infection rate positively correlated with wind-chill temperature and the content of particulate matter. The Escherichia coli O157:H7 infection rate positively correlated with relative humidity. These findings may explain the dynamics and risks of Clostridioides difficile toxin B, Clostridium perfringens, Campylobacter spp., and Escherichia coli O157:H7 infection. They may help predict interrelationships among climatic factors and standardize national environmental health policies. However, in-depth research with large-scale data, molecular biology, and epidemiology would be required going forward. Future research would also require objective indicators of the changes in the prevalence of FBD-causing microbial pathogens for the effective prevention and management of these bacterial infections.