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The unique physicochemical characteristics of nanoparticles have recently gained increasing attention in a diverse set of applications, particularly in the biomedical field. However, concerns about the potential toxicological effects of nanoparticles remain, as they have a higher tendency to generate excessive amounts of reactive oxygen species (ROS). Due to the strong oxidation potential, the excess ROS induced by nanoparticles can result in the damage of biomolecules and organelle structures and lead to protein oxidative carbonylation, lipid peroxidation, DNA/RNA breakage, and membrane structure destruction, which further cause necrosis, apoptosis, or even mutagenesis. This review aims to give a summary of the mechanisms and responsible for ROS generation by nanoparticles at the cellular level and provide insights into the mechanics of ROS-mediated biotoxicity. We summarize the literature on nanoparticle toxicity and suggest strategies to optimize nanoparticles for biomedical applications.

Cheirotonus jansoni (Jordan, 1898), a beetle species of ecological and ornamental significance, is predominantly found in southern China. With limited dispersal ability, it is classified as a Class 2 protected species in China. In this study, the widely employed maximum entropy (MaxEnt) model and the ensemble Biomod2 model were applied to simulate C. jansoni habitat suitability in China under current environmental conditions based on available distribution data and multiple environmental variables. The optimized MaxEnt model demonstrated improved accuracy and robust predictive capabilities, making it the preferred choice for simulating dynamic changes in potentially suitable habitats for C. jansoni under future climate scenarios. Protection gaps were further identified through analyses of the overlap between nature reserves and highly suitable areas for C. jansoni. The established models indicated that this species primarily resides in southeastern mountainous regions of China below 2000 m, with a preferred altitude of 1000–2000 m. Future climate scenarios suggest a reduction in the overall suitable habitat for C. jansoni with an increase in temperature, underscoring the urgent need for enhanced conservation efforts for this beetle species.

The tiger nut is the tuber of Cyperus esculentus L., which is a high-quality wholesome crop that contains lipids, protein, starch, fiber, vitamins, minerals and bioactive factors. This article systematically reviewed the nutritional composition of tiger nuts; the processing methods for extracting oil, starch and other edible components; the physiochemical and functional characteristics; as well as their applications in food industry. Different extraction methods can affect functional and nutritional properties to a certain extent. At present, mechanical compression, alkaline methods and alkali extraction–acid precipitation are the most suitable methods for the production of its oil, starch and protein in the food industry, respectively. Based on traditional extraction methods, combination of innovative techniques aimed at yield and physiochemical characteristics is essential for the comprehensive utilization of nutrients. In addition, tiger nut has the radical scavenging ability, in vitro inhibition of lipid peroxidation, anti-inflammatory and anti-apoptotic effects and displays medical properties. It has been made to milk, snacks, beverages and gluten-free bread. Despite their ancient use for food and feed and the many years of intense research, tiger nuts and their components still deserve further exploitation on the functional properties, modifications and intensive processing to make them suitable for industrial production.

Olive pomace was used as raw material and then activated by potassium hydroxide to obtain olive pomace activated carbon (OP-AC). The effects of different dosage, pH and adsorption time of OP-AC on the removal of seven organic dyes (methylene blue MB, methyl orange MO, Congo red CV, neutral red CR, malachite green MG, crystal violet BL and rhodamine B RHB) in water were investigated. The adsorption behavior of OP-AC on seven organic dyes was studied through adsorption experiments, and the feasibility of treating mixed printing and dyeing water by OP-AC was also discussed. The results show that the removal rate of seven organic dyes is better when the dosage of OP-AC is 0.6 g and the adsorption time is 24 h. The removal efficiency of dyes is different under different pH conditions, among which the removal rate of MO, CR and BL is better in acidic environment (pH = 4), while it is beneficial to the removal of MB, RHB, MG and CV in alkaline environment (pH = 12). The removal efficiency of dyes under better conditions is CV > MB > RHB > Mo > BL > Mg > Cr. The adsorption process of olive pomace activated carbon for seven dyes is more in line with Langmuir isothermal adsorption model, and the correlation coefficients are all greater than 0.98, indicating that the adsorption process of seven dyes is of single layer adsorption; The adsorption kinetics is more in line with the quasi-second-order kinetic model, and chemical adsorption is dominant in the adsorption process, with correlation coefficients greater than 0.97. Under the conditions of OP-AC dosage of 4.0 g, adsorption time of 24 h and pH equaling 10.9 (unadjusted), the removal efficiency of RHB is the highest (99.6%) and that of CR is the lowest (59.6%), and the removal efficiency of mixed organic dyes is the highest. The removal efficiency of seven organic dyes is: RHB > MG > MB > CV > BL > MO > CR.Kindly check and confirm the corresponding affiliation has been correctly processed.The author's affiliation is checked and correct.Please confirm the inserted city name for the affiliation 4 is correct and amend if necessary.affiliation 4 is correct.

Olive pomace (OP) is a widely used agricultural by-product with the potential to promote a circular low-carbon bioeconomy. In this work, an environmentally friendly and low-cost olive pomace adsorbent (OPA) was obtained from olive pomace by drying, crushing, sieving and sterilizing, and confirmed to remove anionic and cationic dyes from wastewater. The present study investigated the impact of adsorbent dosage, dye concentration, contact time, temperature, and pH on the adsorption mechanism, providing a more comprehensive understanding of the adsorption mechanism. The adsorption isotherm and kinetic studies revealed that the adsorption behavior of all 12 dyes followed the Langmuir isotherm model. Among them, the adsorption kinetics of 8 dyes were well described by the pseudo-second-order model, while the remaining 4 dyes fitted better with the pseudo-first-order model. The presence of electrostatic interactions and pore filling in the adsorption process contributed to the excellent adsorption performance of OPA for the 12 dyes. OPA also maintained a high removal rate for dyes after five cycles of regeneration. In addition, the water quality evaluation results show that the dye wastewater quality after OPA adsorption is equivalent to that of drinking water. These results highlight the potential of olive pomace as an eco-friendly and efficient adsorbent for the removal of cationic/anionic dyes from water, contributing to sustainable waste management and environmental remediation.

The present work aimed to establish a new model to accurately estimate overall survival (OS) as well as cancer-specific survival (CSS) of osteosarcoma. Osteosarcoma cases were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017 and randomized as training or validation sets. Then, the OS- and CSS-related variables were discovered through multivariate Cox regression analysis to develop new nomograms to predict the 1-, 3- and 5-year OS and CSS. Besides, consistency index (C-index), decision curve analysis (DCA), along with calibration curve were adopted for assessing the predicting ability of our constructed nomograms after calibrating for 1-, 3- and 5-year OS and CSS. Altogether, 1727 osteosarcoma cases were enrolled in the present study and randomly divided as training (n = 1149, 70%) or validation (n = 576, 30%) set. As shown by univariate as well as multivariate Cox regression analyses, age, grade, T stage, M stage, surgery, chemotherapy, and histological type were identified to be the adverse factors to independently predict OS and CSS among the osteosarcoma cases. Besides, based on results of multivariate Cox regression analysis, we constructed the OS and CSS prediction nomograms. The C-index in training set was 0.806 (95% CI 0.769–0.836) for OS nomogram and 0.807 (95% CI 0.769–0.836) for CSS nomogram. In the meantime, C-index value in validation set was 0.818 (95% CI 0.789–0.847) for OS nomogram, while 0.804 (95% CI 0.773–0.835) for CSS nomogram. Besides, those calibration curves regarding the 3- and 5-year CSS of our constructed nomogram were highly consistent between the predicted values and the measurements in the training set as well as the external validation set. Our constructed nomogram outperformed the TNM stage in prediction. Our constructed nomogram is facile, creditable, and feasible; it efficiently predicts OS and CSS for osteosarcoma cases and can assist clinicians in assessing the prognosis for individuals and making decisions.

A new stag beetle fossil, Prostreptocerus burmiticus Yu & Cai gen. et sp. nov., is described based on a single male specimen. This is the first representative of the subfamily Lampriminae (Coleoptera: Scarabaeoidea: Lucanidae) from mid-Cretaceous Burmese amber. The new species is distinctive among Lucanidae due to its well-developed, right-angled mandible, frons featuring a pair of large protuberances, a coarse and sparsely punctate elytral disc, and large tubercles on the humeri. Prostreptocerus Yu & Cai is placed within Lampriminae based on several key characteristics. Morphologically, it is most similar to the extant Streptocerus Fairmaire, 1850. The current distribution of Streptocerus and Lampriminae is primarily restricted to the Southern Hemisphere, suggesting that this lineage is ancient and existed on Gondwanaland, which has significant geographical implications. This discovery extends the fossil record of Lampriminae and provides additional evidence for the existence of sexual dimorphism and potential combat behavior in Mesozoic lucanids. Additionally, Electraesalopsis Bai, Zhang & Qiu, 2017, previously placed as Lucanidae incertae sedis, shares many characteristics with Prostreptocerus Yu & Cai and is also assigned to Lampriminae based on a suite of traits.

Background An increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers. Methods We retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building. Results Among LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99). Conclusions These results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.

Background Chronic mucocutaneous candidiasis (CMC) is the most common clinical symptom of singer transducer and signal transducer and activator of transcription 1 (STAT1) gain-of-function (GOF) mutations. Bronchiectasis is a chronic lung disease that is characterized by permanent bronchiectasis, causing cough, expectoration, and even haemoptysis. The underlying pathogeny is not yet clear. Immunoglobulin (Ig) A is derived from memory B cells and correlates with immune-related diseases. STAT1 is closely associated with signal transmission and immune regulation. Case presentation We report a 17-year-old male patient carrying a GOF mutation in STAT1. The variant led to CMC, bronchiectasis, and elevated serum IgA levels, as well as stunting. Whole-exome sequencing (WES) revealed a c.986C>G (p.P329R) heterozygous mutation in the STAT1 gene. Conclusion Further Sanger sequencing analysis of STAT1 in the patient and his parents showed that the patient harboured a de novo mutation.