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Carbon nanotubes (CNTs), members of the nanomaterial family, are increasingly being used in consumer products and extensively studied for various biomedical applications. Due to their benign elemental composition, large surface area, and chemical and biological activities, CNTs demonstrate great potential in cancer therapy, including drug delivery, imaging analysis, photothermal therapy, photodynamic therapy, and radiotherapy. However, there is still a major knowledge gap when it comes to transitioning from research to clinical applications. One of the important issues is that the biological toxicity of CNTs, especially in terms of carcinogenesis, and the underlying mechanisms are not fully understood. Therefore, a thorough evaluation of toxicity and the underlying mechanisms of carcinogenesis is essential to enable the wide application of CNTs. In this review, we summarize the recent progress of CNTs as multifunctional therapeutics in cancer therapy. Furthermore, a detailed discussion is provided on the carcinogenesis and potential mechanisms of CNTs. Finally, the review ends with further challenges and prospects for CNTs with the expectation of facilitating their broader utilization. [ABSTRACT FROM AUTHOR]

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost. [ABSTRACT FROM AUTHOR]

The impact of global warming on extreme precipitation over China is projected based on CMIP5 simulations under three representative concentration pathway scenarios. When global warming is 1.5 °C above pre‐industrial (1861–1890), precipitation intensity and frequency increase, which leads to an increase relative to the period 1986–2005 in total wet daytime precipitation in northeast China, north China, and the Qinghai–Tibet Plateau. However, south China and southwest China experience fewer precipitation days and less total precipitation despite increasing simple daily intensity (SDII). Under 2 °C of warming, the number of wet days (R1mm) increases north of 30°N and decreases to the south, whereas consecutive dry days (CDD) displays the opposite pattern. The other eight extreme precipitation events increase during the simulation period nationwide, with varying intensity. An increase in global warming from 1.5 to 2 °C is projected to lead to an increase in precipitation intensity over China, except for some scattered regions in the northwest and southwest of the country. More frequent extreme precipitation days are also expected, although decreases in R1mm are projected in north China and extend to northwest China. An overall small decrease in CDD is predicted for China. All annual regional‐mean precipitation events have an apparent linear relationship with global mean temperature, except for CDD. The rate of increase of extreme precipitation with temperature in the future on an annual scale is much faster than for a reference period (1986–2005), whereas no noticeable difference exists on a daily scale. The relationships between daily precipitation extremes and temperature for the present day and for the future show a quadratic polynomial structure, increasing up to 19 °C but decreasing at higher temperatures. There is a significant positive influence on extreme precipitation when warming is limited to 1.5 °C, compared with a limit of 2 °C. An increase in global warming from 1.5 to 2 °C is projected to lead to an increase in precipitation intensity over China, except for some scattered regions in the northwest and southwest of the country. All annual regional‐mean precipitation events have an apparent linear relationship with global mean temperature, except for CDD. The relationships between daily precipitation extremes and temperature for the present day and for the future show a quadratic polynomial structure, increasing up to 19 °C but decreasing at higher temperatures. In the figure, difference in annual PRCPTOT (a), SDII (b), RX1day (c), RX5day (d), R1mm (e), R10mm (f), R20mm (g), R95p (h), R99p (i), and CDD (j) between 1.5 and 2 °C warming. The dots represent regions where at least 66% of all the models agree on the sign of change in the ensemble mean. [ABSTRACT FROM AUTHOR]