Fungi-derived agriculturally important nanoparticles and their application in crop stress management - Prospects and environmental risks.

Nanotechnology has a wide range of agricultural applications, with emphasize on the development of novel nano-agrochemicals such as, nano-fertilizer and nano-pesticides. It has a significant impact on sustainable agriculture by increasing agricultural productivity, while reducing the use of inorganic fertilizers, pesticides, and herbicides. Nano-coating delivery methods for agrochemicals have improved agrochemical effectiveness, safety, and consistency. Biosynthesis of nanoparticles (NPs) has recently been recognized as an effective tool, contrary to chemically derived NPs, for plant abiotic and biotic stress control, and crop improvement. In this regard, fungi have tremendous scope and importance for producing biogenic NPs of various sizes, shapes, and characteristics. Fungi are potential candidates for synthesis of biogenic NPs due to their enhanced bioavailability, biological activity, and higher metal tolerance. However, their biomimetic properties and high capacity for dispersion in soil, water environments, and foods may have negative environmental consequences. Furthermore, their bioaccumulation raises significant concerns about the novel properties of nanomaterials potentially causing adverse biological effects, including toxicity. This review provides a concise outline of the growing role of fungal-mediated metal NPs synthesis, its potential applications in crop field, and associated issues of nano-pollution in soil and its future implications.

Leveraging Providers' Preferences to Customize Instructional Content in Information and Communications Technology-Based Training Interventions: Retrospective Analysis of a Mobile Phone-Based Intervention in India.

BACKGROUND: Many public health programs and interventions across the world increasingly rely on using information and communications technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, and patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness of these programs is the low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so with traditional training. OBJECTIVE: This study aimed to discover preferences among providers on the form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. METHODS: We used data from a mobile phone-based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. K-means clustering was used to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Subsequently, we compared the accuracy of the cluster-based prediction model with one based on demographic variables of providers (eg, specialty and geographic location). RESULTS: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). CONCLUSIONS: Providers participating in a mobile phone-based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement as compared to targeting providers based on demographic variables.