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Ferroptosis, which has been widely associated with many diseases, is an iron-dependent regulated cell death characterized by intracellular lipid peroxide accumulation. It exhibits morphological, biochemical, and genetic characteristics that are unique in comparison to other types of cell death. The course of ferroptosis can be accurately regulated by the metabolism of iron, lipids, amino acids, and various signal pathways. In this review, we summarize the basic characteristics of ferroptosis, its regulation, as well as the relationship between ferroptosis and chronic diseases such as cancer, nervous system diseases, metabolic diseases, and inflammatory bowel diseases. Finally, we describe the regulatory effects of food-borne active ingredients on ferroptosis.

Gastrodia elata (G. elata) Blume is widely used as a health product with significant economic, medicinal, and ecological values. Due to variations in the geographical origin, soil pH, and content of organic matter, the levels of physiologically active ingredient contents in G. elata from different origins may vary. Therefore, rapid methods for predicting the geographical origin and the contents of these ingredients are important for the market. This paper proposes a visible–near-infrared (Vis-NIR) spectroscopy technology combined with machine learning. A variety of machine learning models were benchmarked against a one-dimensional convolutional neural network (1D-CNN) in terms of accuracy. In the origin identification models, the 1D-CNN demonstrated excellent performance, with the F1 score being 1.0000, correctly identifying the 11 origins. In the quantitative models, the 1D-CNN outperformed the other three algorithms. For the prediction set of eight physiologically active ingredients, namely, GA, HA, PE, PB, PC, PA, GA + HA, and total, the RMSEP values were 0.2881, 0.0871, 0.3387, 0.2485, 0.0761, 0.7027, 0.3664, and 1.2965, respectively. The Rp2 values were 0.9278, 0.9321, 0.9433, 0.9094, 0.9454, 0.9282, 0.9173, and 0.9323, respectively. This study demonstrated that the 1D-CNN showed highly accurate non-linear descriptive capability. The proposed combinations of Vis-NIR spectroscopy with 1D-CNN models have significant potential in the quality evaluation of G. elata.

Background Studies evaluating the attrition, physical and insecticidal durability of dual active ingredient (AI) insecticide-treated nets (ITNs) are essential for making programmatic decisions regarding their deployment. We performed a prospective study embedded in a cluster randomised controlled trial (cRCT) to evaluate the attrition, fabric integrity and insecticidal durability of Interceptor® G2 (alpha-cypermethrin-chlorfenapyr) and Royal Guard® (alpha-cypermethrin–pyriproxyfen), compared to Interceptor® (alpha-cypermethrin) in Benin. Methods A total of 2428 study nets in 1093 randomly selected households in five clusters per arm of the cRCT were monitored for ITN attrition and fabric integrity every 6–12 months post-distribution. Householders were further surveyed to investigate non-study net use and their preference for ITN fabric types used in the study nets. A second cohort of 120 nets per ITN type were withdrawn every 12 months and assessed for chemical content and insecticidal activity in laboratory bioassays. Alpha-cypermethrin bioefficacy was investigated using the susceptible Anopheles gambiae Kisumu strain, and chlorfenapyr and pyriproxyfen bioefficacy were investigated using the pyrethroid-resistant Anopheles coluzzii Akron strain. Net pieces were tested in WHO cone bioassays and tunnel tests for alpha-cypermethrin and in tunnel tests for chlorfenapyr; pyriproxyfen activity was assessed in cone bioassays as the reduction in fertility of blood-fed survivors using ovary dissection. Bioefficacy was expressed as the proportion of ITNs passing predetermined WHO criteria, namely knock-down ≥ 95% or 24/72 h mortality ≥ 80% or reduction in fertility ≥ 50%. Results Overall ITN survivorship was 52% at 24 months and fell to 15% at 36 months. Median ITN survival time was lower with Royal Guard® relative to Interceptor® [1.6 vs 2.3 years; hazard ratio (HR) 1.49, 95% confidence interval (CI) 1.36–1.66; p  < 0.001] and Interceptor® G2 (1.6 vs 2.1 years; HR 1.33, 95% CI 1.20–1.47; p  < 0.001). Householders overwhelmingly preferred polyester nets over polyethylene nets (96%), and more Royal Guard® nets were replaced with spare polyester nets from previous campaigns. All Royal Guard® nets passed efficacy criteria for alpha-cypermethrin at all time points (100%) while ITN pass rates after 24 months had fallen to < 40% for pyriproxyfen and chlorfenapyr. The chemical content analysis showed a higher loss rate of the non-pyrethroid insecticides relative to the pyrethroids in each dual ingredient AI ITN; 74% vs 47% for Royal Guard® and 85% vs 63% for Interceptor® G2 at 36 months. Conclusions The median ITN survival time for Interceptor® G2 (2.1 years) and Royal Guard® (1.6 years) in Benin is substantially lower than 3 years. Royal Guard® nets were discarded more quickly by householders, partly due to their low preference for polyethylene nets. The insecticidal activity of the non-pyrethroid insecticides in both dual AI ITNs was short-lived compared to alpha-cypermethrin. The results corroborate the findings from the cRCT conducted in Benin. Graphical Abstract

In the current study, a new hybrid machine learning (ML)-based model was developed by integrating a convolution neural network (CNN) with a random forest (RF) to forecast pesticide use on golf courses in Québec, Canada. Three main groups of independent variables were used to estimate pesticide use on golf courses, expressed as actual active ingredient rate (AAIR): (i) coordinates (i.e., longitude and latitude of the golf course), (ii) characteristics of the golf courses (i.e., pesticide type and the number of holes), and (iii) meteorological variables (i.e., total precipitation, P, and average temperature, T). The meteorological variables were collected from the Google Earth Engine by developing a JavaScript-based Code. On the basis of the different periods of total precipitation and average temperature, four different scenarios were defined. A data bank with more than 40,000 samples was used to calibrate and validate the developed model such that 70% of all samples were randomly selected to calibrate the model, while the remainder of the samples (i.e., 30%) that did not have any role in calibration were employed to validate the model’s generalizability. A comparison of different scenarios indicated that the model that considered the longitude and latitude of the golf course, pesticide type, and the number of holes in golf courses as well as total precipitation and average temperature from May to November as inputs (R = 0.997; NSE = 0.997; RMSE = 0.046; MAE = 0.026; NRMSE = 0.454; and PBIAS (%) = −0.443) outperformed the other models. Moreover, the sensitivity analysis result indicated that the total precipitation was the most critical variable in AAIR forecasting, while the average temperature, pesticide types, and the number of holes were ranked second to fourth, respectively.

Osteoporosis (OP) is a prevalent metabolic bone disease globally. Currently, the development of Traditional Chinese Medicine (TCM) resources to unblock joints, strengthen bones, and enhance muscle function to regulate anti‐osteogenic and anabolic metabolism and thus reshape intraosseous homeostasis was an effective way to alleviate OP. The F–E–D formula, comprising Fructus Psoraleae, Eucommia, and Drynariae Rhizoma, has shown efficacy in treating OP. However, its complex natural components necessitate the screening and simplification of bioactive compounds to further elucidate their therapeutic mechanisms and enhance therapeutic efficacy. In this study, we first used drug–target binding to produce different effects, which in turn exhibited different retention characteristics on the stationary phase. Using osteoblasts and osteoclasts as stationary phases, a chromatographic system (Solid‐phase Bio‐cell Chromatography, SBC) had been constructed to mimic the drug–target interaction, and the separation, analysis, and bioactivity screening of the chemical components of F–E–D had been performed. Then, the above collected eluates were analyzed by fine metabolomics, and 95 effective metabolites were initially screened and combined with database screening to finally select betaine, L‐fucose, and itaconic acid as potentially active candidate compound monomers for the interaction with osteoblast–osteoclast in F–E–D. In terms of cell validation experiments, we found that the screened active monomers significantly inhibited the formation of osteoclasts, and the itaconic acid–treated group played a significant inhibitory effect on the expression of inflammatory factors TNF‐α and IL‐6. The above experimental data showed that the monomeric active ingredients in TCM could be effectively screened by solid‐phase bio‐chromatography and HPLC–MS, and the in vitro cellular experiments verified that the active monomers of TCM slowed down the progression of OP by inhibiting osteoclast production and alleviating the expression of inflammation. In this study, we employed solid‐phase bio‐chromatography and HPLC–MS to screen the monomeric active ingredients in the three minor formulas (Fructus Psoraleae—Eucommia—Drynariae Rhizoma, F–E–D). Then, we investigated that the active monomers slowed down the progression of OP by inhibiting osteoclast production and alleviating the expression of inflammation by in vitro cellular experiments.

Fungus-growing termites (Macrotermitinae) are important pests in tropical countries. They are difficult to control with existing baiting methods, as chitin synthesis inhibitors are not effectual as active ingredients. We tested two neurotoxins, fipronil and imidacloprid, as potential bait active ingredients against Macrotermes gilvus (Hagen) in Singapore. In laboratory bioassays, M. gilvus showed no preference for doses of 0–64 ppm fipronil, or for doses of 0–250 ppm imidacloprid, indicating no repellence. We tested each insecticide in toilet paper as a bait matrix in a field experiment. After 28 days, termites had eaten 5–13% of the fipronil treated toilet paper, abandoned bait and monitoring stations, contacted no new stations, and repaired poorly their experimentally damaged mounds. Termites ate no imidacloprid treated toilet paper, abandoned bait stations although contacted new stations, and repaired fully their damaged mounds. Termites ate 60–70% of the control toilet paper, remained in bait stations, and fully repaired damaged mounds. After 56 days, all five fipronil colonies were eliminated, whereas all of the imidacloprid and control colonies were healthy. The results suggest that fipronil could be an effective active ingredient in bait systems for fungus-growing termites in tropical countries.

Biocatalysis holds immense potential for pharmaceutical development as it enables synthetic routes to various chiral building blocks with unparalleled selectivity. Therein, solvent and water use account for a large contribution to the environmental impact of the reactions. In the spirit of Green Chemistry, a transition from traditional highly diluted aqueous systems to intensified non-aqueous media to overcome limitations (e.g., water shortages, recalcitrant wastewater treatments, and low substrate loadings) has been observed. Benefiting from the spectacular advances in various enzyme stabilization techniques, a plethora of biotransformations in non-conventional media have been established. Deep eutectic solvents (DESs) emerge as a sort of (potentially) greener non-aqueous medium with increasing use in biocatalysis. This review discusses the state-of-the-art of biotransformations in DESs with a focus on biocatalytic pathways for the synthesis of active pharmaceutical ingredients (APIs). Representative examples of different enzyme classes are discussed, together with a critical vision of the limitations and discussing prospects of using DESs for biocatalysis.

Golf course maintenance requires the use of several inputs, such as pesticides and fertilizers, that can be harmful to human health or the environment. Understanding the factors associated with pesticide use on golf courses may help golf-course managers reduce their reliance on these products. In this study, we used a database of about 14,000 pesticide applications in the province of Québec, Canada, to develop a novel hybrid machine learning approach to predict pesticide use on golf courses. We created this proposed model, called RF-SVM-GOA, by coupling a support vector machine (SVM) with random forest (RF) and the grasshopper optimization algorithm (GOA). We applied RF to handle the wide range of datasets and GOA to find the optimal SVM settings. We considered five different dependent variables—region, golf course ID, number of holes, year, and treated area—as input variables. The experimental results confirmed that the developed hybrid RF-SVM-GOA approach was able to estimate the active ingredient total (AIT) with a high level of accuracy (R = 0.99; MAE = 0.84; RMSE = 0.84; NRMSE = 0.04). We compared the results produced by the developed RF-SVM-GOA model with those of four tree-based techniques including M5P, random tree, reduced error pruning tree (REP tree), and RF, as well as with those of two non-tree-based techniques including the generalized structure of group method of data handling (GSGMDH) and evolutionary polynomial regression (EPR). The computational results showed that the accuracy of the proposed RF-SVM-GOA approach was higher, outperforming the other methods. We analyzed sensitivity to find the most effective variables in AIT forecasting. The results indicated that the treated area is the most effective variable in AIT forecasting. The results of the current study provide a method for increasing the sustainability of golf course management.

Alpinia oxyphylla, a traditional herb, is widely used for its neuroprotective, antioxidant and memory-improving effects. However, the neuroprotective mechanisms of action of its active ingredients are unclear. In this study, we investigated the neuroprotective effects of various organic extracts of Alpinia oxyphylla on PC12 cells exposed to hydrogen peroxide-induced oxidative injury in vitro. Alpinia oxyphylla was extracted three times with 95% ethanol (representing extracts 1-3). The third 95% ethanol extract was dried and resuspended in water, and then extracted successively with petroleum ether, ethyl acetate and n-butanol (representing extracts 4-6). The cell counting kit-8 assay and microscopy were used to evaluate cell viability and observe the morphology of PC12 cells. The protective effect of the three ethanol extracts (at tested concentrations of 50, 100 and 200 µg/mL) against cytotoxicity to PC12 cells increased in a concentration-dependent manner. The ethyl acetate, petroleum ether and n-butanol extracts (each tested at 100, 150 and 200 μg/mL) had neuroprotective effects as well. The optimum effective concentration ranged from 50-200 μg/mL, and the protective effect of the ethyl acetate extract was comparatively robust. These results demonstrate that organic extracts of Alpinia oxyphylla protect PC12 cells against apoptosis induced by hydrogen peroxide. Our findings should help identify the bioactive neuroprotective components in Alpinia oxyphylla.

Mosquitoes may develop resistance to insecticide active ingredients (AIs) found in formulated products (FPs) due to environmental exposure from insecticides in mosquito control and/or unrelated to mosquito control, e.g., agricultural, household pest control. Mosquito control programs should implement resistance management strategies by assessing resistance in targeted populations, rotating different classes of insecticides based on resistance testing, and/or increasing insecticide concentration (i.e., saturation, using maximum labeled rate) to overcome emerging resistance. Resistance testing is often done solely on AIs, but should, in some cases, include both AIs and FPs at the concentrations mosquitoes may encounter in the field. The resistance/susceptibility status was determined for adulticides used in mosquito control. Centers for Disease Control and Prevention (CDC) bottle bioassays were used to assess resistance/susceptibility status for eight AIs (i.e., bifenthrin, permethrin, sumethrin/ prallethrin, deltamethrin, tau-fluvalinate, chlorpyrifos, malathion, and naled) and eight FPs (TalStar, Biomist 3 + 15, Duet, Suspend Polyzone, Mavrik, MosquitoMist, Fyfanon, and Dibrom) that respectively contain the AIs. Current CDC guidelines were utilized: susceptible (97–100% mortality at diagnostic time [DT]), developing resistance (90–96% mortality at DT), or resistant (<90% mortality at DT). Significant differences were observed in mosquito susceptibility/resistance among and between AIs and FPs.