Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Assessing Patterns of Human-Wildlife Conflicts and Compensation around a Central Indian Protected Area

<div><p>Mitigating crop and livestock loss to wildlife and improving compensation distribution are important for conservation efforts in landscapes where people and wildlife co-occur outside protected areas. The lack of rigorously collected spatial data poses a challenge to management efforts to minimize loss and mitigate conflicts. We surveyed 735 households from 347 villages in a 5154 km² area surrounding Kanha Tiger Reserve in India. We modeled self-reported household crop and livestock loss as a function of agricultural, demographic and environmental factors, and mitigation measures. We also modeled self-reported compensation received by households as a function of demographic factors, conflict type, reporting to authorities, and wildlife species involved. Seventy-three percent of households reported crop loss and 33% livestock loss in the previous year, but less than 8% reported human injury or death. Crop loss was associated with greater number of cropping months per year and proximity to the park. Livestock loss was associated with grazing animals inside the park and proximity to the park. Among mitigation measures only use of protective physical structures were associated with reduced livestock loss. Compensation distribution was more likely for tiger related incidents, and households reporting loss and located in the buffer. Average estimated probability of crop loss was 0.93 and livestock loss was 0.60 for surveyed households. Estimated crop and livestock loss and compensation distribution were higher for households located inside the buffer. Our approach modeled conflict data to aid managers in identifying potential conflict hotspots, influential factors, and spatially maps risk probability of crop and livestock loss. This approach could help focus allocation of conservation efforts and funds directed at conflict prevention and mitigation where high densities of people and wildlife co-occur.</p> </div

Sampling design and households surveyed around Kanha National Park.

<p>Sampling design and households surveyed around Kanha National Park.</p

Top models (cumulative weight >0.95) and beta coefficients for predicting household livestock loss around Kanha National Park.

¶<p>Note: Standard errors in brackets and top-ranked models are shown, wi is the AIC model weight <b>Δ</b> AICc is the difference in values between lowest AIC model and each model.</p

Household characteristics of 735 surveyed households around Kanha.

<p>Household characteristics of 735 surveyed households around Kanha.</p

Details on variables collected from surveys and used in models.

<p>Details on variables collected from surveys and used in models.</p

Predicted livestock predation loss within 15 km around Kanha National Park (dark green is the administrative buffer).

<p>Kriging generates probabilities for livestock loss in the landscape with blue areas depicting low risk and red areas depicting high risk.</p

Comparison loss and compensation reported by households surveyed inside and outside administrative buffer around Kanha National Park.

<p>Comparison loss and compensation reported by households surveyed inside and outside administrative buffer around Kanha National Park.</p

Predicted crop loss within 20 km around Kanha National Park (dark green is the administrative buffer).

<p>Kriging generates probabilities for crop loss in the landscape with blue areas depicting low risk and red areas depicting high risk.</p

Mitigation measures reported by surveyed households around Kanha National Park.

<p>Mitigation measures reported by surveyed households around Kanha National Park.</p