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Currently, metal nanoparticles have varied uses for different medical, pharmaceutical, and agricultural applications. Nanobiotechnology, combined with green chemistry, has great potential for the development of novel and necessary products that benefit human health, environment, and industries. Green chemistry has an important role due to its contribution to unconventional synthesis methods of gold and silver nanoparticles from plant extracts, which have exhibited antimicrobial potential, among other outstanding properties. Biodiversity-rich countries need to collect and convert knowledge from biological resources into processes, compounds, methods, and tools, which need to be achieved along with sustainable use and exploitation of biological diversity. Therefore, this paper describes the relevant reported green synthesis of gold and silver nanoparticles from plant extracts and their capacity as antimicrobial agents within the agricultural field for fighting against bacterial and fungal pathogens that can cause plant, waterborne, and foodborne diseases. Moreover, this work makes a brief review of nanoparticles’ contribution to water treatment and the development of “environmentally-friendly” nanofertilizers, nanopesticides, and nanoherbicides, as well as presenting the harmful effects of nanoparticles accumulation in plants and soils.

Background Cervical cancer remains a leading cause of cancer death for women worldwide. Screen-and-treat programs are a key strategy to reduce disease burden in low and middle-income countries (LMICs). Thermal ablation (TA) has emerged as a portable alternative to cryotherapy, the treatment typically used in screen-and-treat initiatives. Interest in TA is growing, but there is limited research on its implementation in public health settings. Here we present results from a preliminary evaluation of the barriers and facilitators of TA in El Salvador, one of the first countries to adopt a national HPV screen-and-treat program. Methods This mixed-methods study took place between August 2022 to February 2023 across five clinics. The Practical, Robust Implementation and Sustainability Model (PRISM) was utilized to map findings to contextual factors that impact implementation and sustainability. Participants were frontline providers and women who presented for treatment after a positive HPV test. Providers took part in semi-structured interviews while women completed questionnaires. Additional data were collected from clinic records. Quantitative data were analyzed using inferential statistics and a rapid qualitative analysis approach was used for interviews. Results Providers perceived TA as easier to use and more comfortable for patients, but cryotherapy was utilized 20% more frequently during the study period (cryotherapy treatments = 133 vs. TA treatments = 100). Although the two treatments have the same contraindications, a greater proportion of women were deemed eligible for treatment with TA vs. cryotherapy (95% vs. 79%, p  < .001). There were discrepancies in provider and women’s perceptions of pain and side-effects. While > 80% of women received counseling during the screen-and-treat process, misconceptions regarding screenings results and treatment remained. Conclusions The new treatment (TA) was highly acceptable to participants. However, there may be a need for additional provider training to support TA adoption and fidelity to program guidelines, while patients would benefit from more effective counseling. As LMICs strive to meet cervical cancer elimination targets set by the World Health Organization, it is expected that innovations will be quickly introduced to clinical practice. Thus, it is critical to understand the factors that impact their implementation and sustainability in these settings.

The research was carried out with the objective of analyzing the social and economic factors that influence port informality in the scientific literature, for which it was essential to apply the PRISMA method, which allowed determining 32 articles as the object of review, which were found in the databases of Scopus, Science Direct, SciELO, Web of Science corresponding to the period 2015-2024. In a first instance, 2844 articles were identified and, after applying the corresponding inclusion and exclusion criteria, a convenient amount was determined to review and analyze the content to develop each of the objectives set. The results have shown that the port environment in different regions, due to its complexity and contribution to development, faces a series of problems involving environmental, economic and social aspects, among others. In spite of the authorities' interest in facing the problem of informality through the implementation of public policies and strategic initiatives, these have not proved to be totally efficient because these problems are still perceived. This is why this research has highlighted the relevance of the competent actors to act in a timely manner in order to ensure the competitiveness and good performance of port environments.

Objective. A bibliometric review of the scientific production in ecotourism in Latin America was carried out, with a focus on the contribution of Peru. Design/Methodology/Approach. Bibliometric techniques were combined with a content analysis approach to collect and analyze the literature on ecotourism in Latin America. Articles from 1992 to 2024 were collected using the Scopus database, and VOSviewer software was used to perform a bibliometric analysis. Results/Discussion. A total of 414 articles on ecotourism in Latin America were identified, with a diversity of thematic areas, including ecotourism and sustainable development; tourism, diseases, and dissemination; conservation and ecology; protected areas and marketing; and ecology and spatiotemporal analysis. The analysis revealed low collaboration between authors and the need to diversify the topics investigated in Peru. In addition, there has been an increase in the number of publications in recent years, indicating a growing interest in ecotourism research in the region. Conclusions. Scientific production in ecotourism in Latin America shows growth but with low collaboration and a need for diversification. In Peru, opportunities for improvement were identified, such as increasing research and promoting international collaboration to increase its visibility and impact.

IntroductionFrom 2012 to 2017, the Cervical Cancer Prevention in El Salvador (CAPE) piloted and scaled up a human papillomavirus (HPV) screen-and-treat intervention. Findings resulted in El Salvador’s adoption of the strategy as part of the national programme, but long-term clinical outcomes are unknown. Here, we compare the detection of high-grade cervical intraepithelial neoplasia grade 2 or higher (CIN2+) and HPV infection after recommended screening intervals between two groups: women who participated in CAPE and a comparable group screened via cytology.MethodsCAPE participants who had undergone screening at least 5 years previously (screen-and-treat group) and women in the same age range with conventional cytology screening 2 to 3 years previously (cytology group) were recruited for repeat screening with primary HPV testing. Women with positive HPV results were referred for colposcopy and cervical biopsy to determine further management. Women with negative HPV results received recommendations for routine future screening according to national guidelines.ResultsA total of 6631 women were enrolled (screen-and-treat = 4087; cytology=2544). Significantly less CIN2+ was detected in the screen-and-treat group at 0.7% (29/4087) than in the cytology group at 2.1% (54/2544) (p<0.001) with a risk ratio of 0.41 (95% CI 0.26 to 0.61). HPV positivity was also lower in the screen-and-treat group at 9.5% (388/4077) compared with the cytology group at 11.5% (293/2445) (p=0.008).ConclusionAt the first round of repeat screening after the implementation of CAPE, women who underwent HPV testing in a screen-and-treat strategy had significantly less CIN2+ and HPV positivity compared with those who underwent cytology. These outcomes occurred despite a longer screening interval for HPV testing than cytology. Findings provide reassurance for women and health systems that primary HPV screen-and-treat programmes with extended screening intervals, like the one in El Salvador, are achievable and effective in low- and middle-income settings.

A number of challenges hinder artificial intelligence (AI) models from effective clinical translation. Foremost among these challenges is the lack of generalizability, which is defined as the ability of a model to perform well on datasets that have different characteristics from the training data. We recently investigated the development of an AI pipeline on digital images of the cervix, utilizing a multi-heterogeneous dataset of 9,462 women (17,013 images) and a multi-stage model selection and optimization approach, to generate a diagnostic classifier able to classify images of the cervix into “normal”, “indeterminate” and “precancer/cancer” (denoted as “precancer+”) categories. In this work, we investigate the performance of this multiclass classifier on external data not utilized in training and internal validation, to assess the generalizability of the classifier when moving to new settings. We assessed both the classification performance and repeatability of our classifier model across the two axes of heterogeneity present in our dataset: image capture device and geography, utilizing both out-of-the-box inference and retraining with external data. Our results demonstrate that device-level heterogeneity affects our model performance more than geography-level heterogeneity. Classification performance of our model is strong on images from a new geography without retraining, while incremental retraining with inclusion of images from a new device progressively improves classification performance on that device up to a point of saturation. Repeatability of our model is relatively unaffected by data heterogeneity and remains strong throughout. Our work supports the need for optimized retraining approaches that address data heterogeneity (e.g., when moving to a new device) to facilitate effective use of AI models in new settings.

Cervical cancer is a disease of inequality. The majority of cervical cancer cases can be prevented through vaccination against the human papillomavirus (HPV) (primary prevention) and screening and early treatment of precancerous lesions caused by HPV infections (secondary prevention), and it can be controlled if treated in early stages (tertiary prevention). However, significant gaps in access to care have shifted the burden of disease to resource-poor countries in Africa, Asia and Latin America. The recent World Health Organization’s Call to Action to eliminate cervical cancer is a unique opportunity to galvanize change and remove barriers to prevention and care.Significant gaps in access to care have shifted the burden of cervical cancer disease to resource-poor countries in Africa, Asia and Latin America. The recent World Health Organization’s Call to Action to eliminate cervical cancer is a unique opportunity to galvanize change and remove barriers to prevention and care.

Background Cervical cancer is the third most commonly occurring cancer among women and the fourth leading cause of cancer-related deaths in women worldwide, with more than 85 % of these cases occurring in developing countries. These global disparities reflect the differences in cervical cancer screening rates between high-income and medium- and low-income countries. At 19 %, El Salvador has the lowest reported screening coverage of all Latin American countries. The purpose of this study is to identify factors affecting public sector HPV DNA-based cervical cancer screening participation in El Salvador. Methods This study was nested within a public sector screening program where health promoters used door-to-door outreach to recruit women aged 30–49 years to attend educational sessions about HPV screening. A subgroup of these participants was chosen randomly and questioned about demographic factors, healthcare utilization, previous cervical cancer screening, and HPV knowledge. Women then scheduled screening appointments at their public health clinics. Screening participants were adherent if they attended their scheduled appointment or rescheduled and were screened within 6 months. The association between non-adherence and demographic variables, medical history, history of cancer, sexual history, birth control methods, and screening barriers was assessed using Chi-square tests of significance and logistic regression. Results All women ( n  = 409) enrolled in the study scheduled HPV screening appointments, and 88 % attended. Non-adherence was associated with a higher number of lifetime partners and being under-screened—defined as not having participated in cervical cancer screening within the previous 3 years ( p  = 0.03 and p  = 0.04, respectively); 22.8 % of participants in this study were under-screened. Conclusions Adherence to cervical cancer screening after educational sessions was higher than expected, in part due to interactions with the community-based health promoters as well as the educational session itself. More effective recruitment methods targeted toward under-screened women are required.

ABSTRACT Objective.. Design/Methodology/Approach.. Results/Discussion.. Conclusions.

Successful translation of artificial intelligence (AI) models into clinical practice, across clinical domains, is frequently hindered by the lack of image quality control. Diagnostic models are often trained on images with no denotation of image quality in the training data; this, in turn, can lead to misclassifications by these models when implemented in the clinical setting. In the case of cervical images, quality classification is a crucial task to ensure accurate detection of precancerous lesions or cancer; this is true for both gynecologic-oncologists’ (manual) and diagnostic AI models’ (automated) predictions. Factors that impact the quality of a cervical image include but are not limited to blur, poor focus, poor light, noise, obscured view of the cervix due to mucus and/or blood, improper position, and over- and/or under-exposure. Utilizing a multi-level image quality ground truth denoted by providers, we generated an image quality classifier following a multi-stage model selection process that investigated several key design choices on a multi-heterogenous “SEED” dataset of 40,534 images. We subsequently validated the best model on an external dataset (“EXT”), comprising 1,340 images captured using a different device and acquired in different geographies from “SEED”. We assessed the relative impact of various axes of data heterogeneity, including device, geography, and ground-truth rater on model performance. Our best performing model achieved an area under the receiver operating characteristics curve (AUROC) of 0.92 (low quality, LQ vs. rest) and 0.93 (high quality, HQ vs. rest), and a minimal total %extreme misclassification (%EM) of 2.8% on the internal validation set. Our model also generalized well externally, achieving corresponding AUROCs of 0.83 and 0.82, and %EM of 3.9% when tested out-of-the-box on the external validation (“EXT”) set. Additionally, our model was geography agnostic with no meaningful difference in performance across geographies, did not exhibit catastrophic forgetting upon retraining with new data, and mimicked the overall/average ground truth rater behavior well. Our work represents one of the first efforts at generating and externally validating an image quality classifier across multiple axes of data heterogeneity to aid in visual diagnosis of cervical precancer and cancer. We hope that this will motivate the accompaniment of adequate guardrails for AI-based pipelines to account for image quality and generalizability concerns.