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Kidney diseases are common health problems worldwide. Various etiologies ultimately lead to the development of chronic kidney disease and end-stage renal disease. Natural compounds from herbs or medicinal plants are widely used for therapy and prevention of various ailments, among which is Inonotus obliquus. I. obliquus is rich in triterpenoids and the main active ingredients include betulinic acid, trametenolic acid, inotodiol, and ergosterol. New evidence suggests that I. obliquus triterpenes may be an effective drug for the treatment and protection of various kidney diseases. The aim of this review is to highlight the pharmacological activities and potential role of I. obliquus triterpenes in the kidney disease treatment and protection.

In gastric cancer, gastrokine 1 (GKN1) is a potential theragnostic marker while the related mechanisms remain elusive. Exosomes mediate intercellular communications via transferring various molecules, yet there are limited research studies on the specific cargos of gastric cancer exosomes and the associated mechanisms in this disease. In the present study, AGS and N87‐C cells were transfected with an overexpressed GKN1 plasmid, followed by extraction of exosomes. The study utilized gastric cancer cell lines and a xenograft mouse model to investigate the functional significance of exosomal GKN1. Cell proliferation, metastasis, and apoptosis were assessed through CCK‐8, Transwell, and flow cytometry assays, respectively. The study further explored the mechanism of exosomal GKN1 and its interaction with the PI3K/AKT/mTOR signaling pathways, including immunofluorescence and western blot analyses. Exosomal GKN1 was observed to suppress cell proliferation and invasion while enhancing apoptosis. This effect was attributed to the modulation of key proteins involved in cellular processes, including Ki‐67, MMP‐9, Bcl‐2, Bax, caspase‐3, and caspase‐9, ultimately impacting the PI3K/AKT/mTOR signaling pathway. The findings suggest that exosomal GKN1 exerts inhibitory effects on gastric cancer cell growth and invasion through the regulation of the PI3K/AKT/mTOR signaling cascade, both in experimental cell cultures and animal models. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effect of various frequencies of bolus use on the superficial dose of volumetric modulated arc therapy after modified radical mastectomy for breast cancer. Methods: Based on the computed tomography images of a female anthropomorphic breast phantom, a 0.5 cm silicone-based 3D-printed bolus was created. Nine points evenly distributed on the breast skin were selected for assessing the skin dose, and a volume of subcutaneous lymphatic drainage of the breast (noted as ROI2-3) was delineated for assessing the chest wall dose. The treatment plans with and without bolus (plan_wb and plan_nb) were separately designed using the prescription of 50 Gy in 25 fractions following the standard dose constraints of the adjacent organ at risk. To characterize the accuracy of treatment planning system (TPS) dose calculations, the doses of the nine points were measured five times by thermoluminescence dosimeters (TLDs) and then were compared with the TPS calculated dose. Results: Compared with Plan_nb (144.46 ± 10.32 cGy), the breast skin dose for plan_wb (208.75 ± 4.55 cGy) was significantly increased (t = −18.56, P < 0.001). The deviation of skin dose was smaller for Plan_wb, and the uniformity was significantly improved. The calculated value of TPS was in good agreement with the measured value of TLD, and the maximum deviation was within 5%. Skin and ROI2-3 doses were significantly increased with increasing frequencies of bolus applications. The mean dose of the breast skin and ROI2-3 for 15 and 23 times bolus applications were 45.33 Gy, 50.88 Gy and 50.36 Gy, 52.39 Gy, respectively. Conclusion: 3D printing bolus can improve the radiation dose and the accuracy of the planned dose. Setting Plan_wb to 15 times for T1-3N+ breast cancer patients and 23 times for T4N+ breast cancer patients can meet the clinical need. Quantitative analysis of the bolus application frequency for different tumor stages can provide a reference for clinical practice. [ABSTRACT FROM AUTHOR]

The fine dead fuel load on forest floors is the most critical classification feature in fuel description systems, affecting several parameters in the manifestation of wildland fires. An accurate determination of this fine dead fuel load contributes substantially to effective wildland fire prevention, monitoring, and suppression. This study investigated the viability of incorporating image Euler numbers into predictive models of fine dead fuel load via the taking photos method. Pinus massoniana needles and Quercus fabri broad leaves—typical fuel types in karst areas—served as the research subjects. Accurate field data were collected in the Tianhe Mountain forests, China, while artificial fine dead fuelbeds of differing loads were constructed in the laboratory. Images of the artificial fuelbeds were captured and uniformly digitized according to various conversion thresholds. Thereafter, the Euler numbers were extracted, their relationship with fuel load was analyzed, and this relationship was applied to generate three load-prediction models based on stepwise regression, nonlinear fitting, and random forest algorithms. The Euler number had a significant relationship with both P. massoniana and Q. fabri fuel loads. At low conversion thresholds, the Euler number was negatively correlated with fuel load, whereas a positive correlation was recorded when this threshold exceeded a certain value. The random forest model showed the best prediction performance, with mean relative errors of 9.35% and 14.54% for P. massoniana and Q. fabri, respectively. The nonlinear fitting model displayed the next best performance, while the stepwise regression model exhibited the largest error, which was significantly different from that of the random forest model. This study is the first to propose the use of image features to predict the fine fuel load on a surface. The results are more objective, accurate, and time-saving than current fuel load estimates, benefiting fuel load research and the scientific management of wildland fires. [ABSTRACT FROM AUTHOR]

As a cigarette butt falls onto the forest surface fuel, it first smolders the fuel, then ignites into flames, and spreads as forest fire under certain conditions. In this study, the needles under a typical stand of P. massoniana were used as the research object. Needle beds with different moisture content and packing ratios were constructed indoors. Cigarette butt-ignition experiments were conducted under different wind velocities, and 30 experiment cycles were conducted under different conditions. There was a total of 5 (packing ratio) × 4 (moisture content) × 6 (wind velocity) = 120 sets of conditions, and a total of 3600 ignition experiments were conducted. The results showed that (1) the total ignition probability of the cigarette butts was 2.36%, which only occurred when the fuelbed moisture content was <10% and the wind velocity was >1 m/s. The ignition time of cigarette butts ranged from 2.73 to 7.25 min. (2) The fuelbed moisture content and wind velocity significantly influenced the ignition probability and time. With an increase in moisture content, the ignition probability of cigarette butts decreased, while the time required for ignition showed an increasing trend. Wind velocity had a dual effect on ignition. The ignition effect was optimal at a wind velocity of 4 m/s. With an increase in wind velocity, the ignition probability first increased and then decreased, and the ignition time first decreased and then increased. (3) The packing ratio had no significant effect on the ignition probability; however, the ignition time significantly decreased as the packing ratio increased. (4) The logistic regression method (LRM), general linear method (GLM), and nonlinear regression method (NLM) were used to establish a prediction model of ignition probability. The prediction effect of GLM was the worst, followed by LRM, and the NLM had the best prediction effect. The GLM was selected to establish the ignition time model, and the error was also within the allowance range. This study elucidated the underlying mechanism of factors affecting cigarette butt-based fuel ignition. In addition, the established prediction model provides a reference for human-caused forest fires and is highly significant for forest fire prevention. [ABSTRACT FROM AUTHOR]