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In this study, we investigated a novel anti-cancer drug design approach by revisiting diclofenac-based carborane-substituted prodrugs. The redesigned compounds combine the robust carborane scaffold with the oxindole framework, resulting in four carborane-derivatized oxindoles and a unique zwitterionic amidine featuring a nido -cluster. We tested the anti-cancer potential of these prodrugs against murine colon adenocarcinoma (MC38), human colorectal carcinoma (HCT116), and human colorectal adenocarcinoma (HT29). The tests showed that diclofenac and the carborane-substituted oxindoles exhibited no cytotoxicity, the dichlorophenyl-substituted oxindole had moderate anti-cancer activity, while with the amidine this effect was strongly potentiated with activity mapping within low micromolar range. Compound 3 abolished the viability of selected colon cancer cell line MC38 preferentially through strong inhibition of cell division and moderate apoptosis accompanied by ROS/RNS depletion. Our findings suggest that carborane-based prodrugs could be a promising direction for new anti-cancer therapies. Inhibition assays for COX-1 and COX-2 revealed that while diclofenac had strong COX inhibition, the re-engineered carborane compounds demonstrated a varied range of anti-cancer effects, probably owing to both, COX inhibition and COX-independent pathways.

Owing to the involvement of cyclooxygenase-2 (COX-2) in carcinogenesis, COX-2-selective inhibitors are increasingly studied for their potential cytotoxic properties. Moreover, the incorporation of carboranes in structures of established anti-inflammatory drugs can improve the potency and metabolic stability of the inhibitors. Herein, we report the synthesis of carborane-containing derivatives of rofecoxib that display remarkable cytotoxic or cytostatic activity in the micromolar range with excellent selectivity for melanoma and colon cancer cell lines over normal cells. Furthermore, it was shown that the carborane-modified derivatives of rofecoxib showed different modes of action that were dependent on the cell type.

As camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp&See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp&See community scientists. We considered 29 classification categories, including 17 species and 12 higher‐level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4%, which increased to 98.2% when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring. Résumé Alors que le piégeage photographique gagne en popularité et en application, certaines limites analytiques persistent, notamment le temps de traitement et la précision de l'annotation des données. Les images statiques sont le plus souvent enregistrées par les pièges photographiques, mais la collecte de vidéos est de plus en plus répandue, même si l'annotation des données prend beaucoup plus de temps. Pour surmonter les limitations liées à l’annotation des images, les études sur les pièges photographiques sont de plus en plus souvent associées aux plateformes de sciences communautaires (SC). Ici, nous étendons les travaux précédents sur les annotations d'images par la SC aux vidéos de pièges photographiques provenant d'un environnement difficile : une forêt tropicale dense avec une faible visibilité et une forte obstruction visuelle en raison de l'épaisse couverture de la canopée et des sous‐bois buissonneux au niveau de la caméra. En utilisant la plateforme de SC Chimp&See, établie pour la classification de 599 956 clips vidéo d'Afrique tropicale, nous évaluons la précision et l'exactitude des annotations en comparant la classification de 13 531 clips vidéo d'une durée d'une minute par un écologiste professionnel avec les résultats de 1 744 scientifiques communautaires enregistrés sur Chimp&See, en plus de ceux non‐enregistrés. Nous avons pris en compte 29 catégories de classification, comprenant 17 espèces et 12 catégories de niveau supérieur, dans lesquelles les espèces phénotypiquement similaires ont été regroupées. Dans l'ensemble, la précision des annotations était de 95,4 %, et de 98,2 % lorsque les groupes d'espèces similaires étaient regroupés. Nos résultats démontrent la compétence des scientifiques communautaires travaillant avec des vidéos issues de pièges photographiques, même dans des environnements difficiles, et sont très prometteurs pour les études futures sur le comportement des animaux, la dynamique des interactions entre les espèces et le suivi des populations. Even though they require more time for annotation compared to still images, camera trap videos provide valuable acoustic and visual information on species’ behaviour and can improve identification of individuals. Here we use Community Science (CS) to annotate camera trap videos from a challenging environment, a dense tropical forest with low visibility and high occlusion. Using the CS platform Chimp&See, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist with 1744 Chimp&See community scientists. Overall, annotation precision was between 95.4 and 98.1%. Our findings demonstrate the competence of community scientists with challenging videos and hold great promise for future studies on animal behaviour and population monitoring.