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Previous studies reported microplastics (MPs), antibiotics, and antibiotic resistance genes (ARGs) in wastewater treatment plants (WWTPs). There is still a lack of research progress on the origin, fate, impact and hazards of MPs and ARGs in WWTPs. This paper fills a gap in this regard. In our search, we used “microplastics”, “antibiotic resistance genes”, and “wastewater treatment plant” as topic terms in Web of Science, checking the returned results for relevance by examining paper titles and abstracts. This study mainly explores the following points: (1) the origins and fate of MPs, antibiotics and ARGs in WWTPs; (2) the mechanisms of action of MPs, antibiotics and ARGs in sludge biochemical pools; (3) the impacts of MPs in WWTPs and the spread of ARGs; (4) and the harm inflicted by MPs and ARGs on the environment and human body. Contaminants in sewage sludge such as MPs, ARGs, and antibiotic-resistant bacteria enter the soil and water. Contaminants can travel through the food chain and thus reach humans, leading to increased illness, hospitalization, and even mortality. This study will enhance our understanding of the mechanisms of action among MPs, antibiotics, ARGs, and the harm they inflict on the human body.

Three-dimensional (3D) imaging is a crucial information acquisition technology for light detection, autonomous vehicles, gesture recognition, machine vision, and other applications. Metasurface, as a subwavelength scale two-dimensional array, offers flexible control of optical wavefront owing to abundant design freedom. Metasurfaces are promising for use as optical devices because they have large field of view and powerful functionality. In this study, we propose a flat optical device based on a single-layer metasurface to project a coded point cloud in the Fourier space and explore a sophisticated matching algorithm to achieve 3D reconstruction, offering a complete technical roadmap for single-shot detection. We experimentally demonstrate that the depth accuracy of our system is smaller than 0.24 mm at a measurement distance of 300 mm, indicating the feasibility of the submillimetre measurement platform. Our method can pave the way for practical applications such as surface shape detection, gesture recognition, and personal authentication. The authors present a single-shot 3D imaging approach utilizing carefully designed point clouds projection based on a metasurface device. They show submillimeter depth accuracy and demonstrate the potential for hand gesture detection.

Soil salt crust can change the structure of aeolian soil and improve its resistance to wind erosion. Four ions (Na + , Ca 2+ , Cl − , and SO 4 2− ) with high contents in aeolian soil were selected for a salt crust experiment. The experiment set a variety of gradients of soil salt contents and salt mixing ratios of Na 2 SO 4 and CaCl 2 . The physical properties of the salt crust were tested, and the wind erosion resistance of the salt crust was discussed. The results showed that the soil salt contents and salt mixing ratio influenced the resistance of the salt crust, especially in terms of its compressive strength and toughness. The former affected the compressive strength of the salt crust by changing the amount of cemented soil salt. The latter affected the kinds of crystals by changing the ion ratio, thus changing the structure of the salt crust and affecting its wind erosion resistance. The wind erosion resistance of the salt crust is complicated by the interaction between the soil salt conten t and salt mixing ratio. A multilayer crust can be formed in mixed salt, which has a strong wind erosion resistance. This result provides new findings on flowing sand soil and a new method for the treatment of flowing sand soil.

Chemotherapy is one of the conventional treatments for cancer in clinical practice. However, poor delivery efficiency, systemic toxicity, and the lack of pharmacokinetic monitoring during treatment are the critical limitations of current chemotherapy. Herein, we reported a brand-new antitumor drug delivery strategy that harnesses an optical fiber endoscopically therapeutic probe. The fiber probe carries photosensitizers in the fiber core and antitumor agents on the fiber surface mediated by a temperature-responsive hydrogel film, giving rise to an activable photothermal-chemotherapy that orchestrates the localized hyperthermia and thermal-stimuli drug release to the tumor lesion. Furthermore, the dynamical drug release and in-situ temperature can be real-time supervised through the built-in fiber sensors, including the reflective Mach–Zehnder interferometer and fiber Bragg grating, to visualize the therapy process and thus improve the safety of treatment. Compared with conventional methods, the fiber-optic drug delivery can adequately take advantage of the chemotherapeutics through collaboratively recruiting the photoheating-mediated enhanced permeability and the hydrogel particle-assisted high drug retention, shedding new light on a “central-to-peripheral” drug pervasion and retention mechanism to destroy tumors completely. The fiber-optic chemotherapy strategy incorporates precise drug delivery, accurate controllability of drug release, high drug permeability and retention in tumor, low off-target rate, and real-time drug release and temperature feedback, performing a straightforward and precise photothermal-chemotherapy pathway. More than that, the proposed strategy holds tremendous promise to provide a revolutionized on-demand drug delivery platform for the highly efficient evaluation and screening of antitumor pharmaceuticals.Analogous to a jet-fighter, the drug-loaded fiber-optic therapeutics can attack tumor synergistically combing direct PTT and controlled-released chemo-drugs regardless the tissue barrier, and monitor therapeutic dosage at the lesion.

Objective To develop and validate a risk scoring scale model (RSSM) for stratifying prognostic risk after intra-arterial therapies (IATs) for hepatocellular carcinoma (HCC). Methods Between February 2014 and October 2022, 2338 patients with HCC who underwent initial IATs were consecutively enrolled. These patients were divided into training datasets (TD, n  = 1700), internal validation datasets (ITD, n  = 428), and external validation datasets (ETD, n  = 200). Five-years death was used to predict outcome. Thirty-four clinical information were input and five supervised machine learning (ML) algorithms, including eXtreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBT), and Random Forest (RF), were compared using the areas under the receiver operating characteristic (AUC) with DeLong test. The variables with top important ML scores were used to build the RSSM by stepwise Cox regression. Results The CatBoost model achieved the best discrimination when 12 top variables were input, with the AUC of 0.851 (95% confidence intervals (CI), 0.833–0.868) for TD, 0.817 (95%CI, 0.759–0.857) for ITD, and 0.791 (95%CI, 0.748–0.834) for ETD. The RSSM was developed based on the immune checkpoint inhibitors (ICI) (hazard ratios (HR), 0.678; 95%CI 0.549, 0.837), tyrosine kinase inhibitors (TKI) (HR, 0.702; 95%CI 0.605, 0.814), local therapy (HR, 0.104; 95%CI 0.014, 0.747), response to the first IAT (HR, 4.221; 95%CI 2.229, 7.994), tumor size (HR, 1.054; 95%CI 1.038, 1.070), and BCLC grade (HR, 2.375; 95%CI 1.950, 2.894). Kaplan–Meier analysis confirmed the role of RSSM in risk stratification ( p  < 0.001). Conclusions The RSSM can stratify accurately prognostic risk for HCC patients received IAT. On the basis, an online calculator permits easy implementation of this model. Clinical relevance statement The risk scoring scale model could be easily implemented for physicians to stratify risk and predict prognosis quickly and accurately, thereby serving as a more favorable tool to strengthen individualized intra-arterial therapies and management in patients with unresectable hepatocellular carcinoma. Key Points • The Categorical Gradient Boosting (CatBoost) algorithm achieved the optimal and robust predictive ability (AUC, 0.851 (95%CI, 0.833–0.868) in training datasets, 0.817 (95%CI, 0.759–0.857) in internal validation datasets, and 0.791 (95%CI, 0.748–0.834) in external validation datasets) for prediction of 5-years death of hepatocellular carcinoma (HCC) after intra-arterial therapies (IATs) among five machine learning models. • We used the SHapley Additive exPlanations algorithms to explain the CatBoost model so as to resolve the black boxes of machine learning principles. • A simpler restricted variable, risk scoring scale model (RSSM), derived by stepwise Cox regression for risk stratification after intra-arterial therapies for hepatocellular carcinoma , provides the potential forewarning to adopt combination strategies for high-risk patients.

Ferroptosis requires not only the accumulation of iron ions, but also changes in many ferroptosis-related regulators, including a decrease in GPX4 and inhibition of SLC7A11 for classical ferroptosis, a deletion of FSP1 or GCH1. Surprisingly, adipose tissue (AT) in the obesity conditions is also accompanied by iron buildup, decreased GSH, and increased ROS. On the neurological side, the pro-inflammatory factor released by AT may have first caused ferroptosis in the vagus nerve by inhibiting of the NRF2-GPX4 pathway, resulting in disorders of the autonomic nervous system. On the immune side, obesity may cause M2 macrophages ferroptosis due to damage to iron-rich ATMs (MFe hi ) and antioxidant ATMs (Mox), and lead to Treg cells ferroptosis through reductions in NRF2, GPX4, and GCH1 levels. At the same time, the reduction in GPX4 may also trigger the ferroptosis of B1 cells. In addition, some studies have also found the role of GPX4 in neutrophil autophagy, which is also worth pondering whether there is a connection with ferroptosis. In conclusion, this review summarizes the associations between neuroimmune regulation associated with obesity and ferroptosis, and on the basis of this, highlights their potential molecular mechanisms, proposing that ferroptosis in one or more cells in a multicellular tissue changes the fate of that tissue.

Background An abdominal aortic aneurysm is a life-threatening enlargement in the major vessel at the abdomen level. This study investigated the associations between different levels of red blood cell distribution width and all-cause mortality among patients with abdominal aortic aneurysm rupture. It developed predictive models for all-cause mortality risk. Methods This was a retrospective cohort study using 2001 to 2012 MIMIC-III dataset. The study sample included 392 U.S. adults with abdominal aortic aneurysms who were admitted to ICU after the aneurysm rupture. Then we used two single-factor and four multivariable logistic regression models to examine the associations between different levels of red blood cell distribution and all-cause mortality (30 days and 90 days), controlling for demographics, comorbidities, vital signs, and other laboratory measurements. The receiver operator characteristic curves were calculated, and the areas under the curves were recorded. Results There were 140 (35.7%) patients with an abdominal aortic aneurysm in the red blood cell distribution width range between 11.7 and 13.8%, 117 (29.8%) patients in the range between 13.9 and 14.9%, and 135 (34.5%) patients in the range between 15.0 and 21.6%. Patients with higher red blood cell distribution width level (> 13.8%) tended to have a higher mortality rate (both 30 days and 90 days), congestive heart failure, renal failure, coagulation disorders, lower hemoglobin, hematocrit, MCV, red blood cell count, higher levels of chloride, creatinine, sodium, and BUN (All P < 0.05). Results of multivariate logistic regression models indicated that patients with higher red blood cell distribution width levels (> 13.8%) had the highest statistically significant odd ratios of 30 days and 90 days of all-cause mortality than lower red blood cell distribution width levels. The area under the RDW curve was lower ( P  = 0.0009) than that of SAPSII scores. Conclusions Our study found that patients with abdominal aortic aneurysm rupture with a higher blood cell distribution had the highest risk of all-cause mortality. Using the blood cell distribution width level in patients with abdominal aortic aneurysm rupture to predict mortality should be considered in future clinical practice.

Refractory multi-principal element alloys (RMPEAs) draw great interest with their superior mechanical properties and extremely high melting points, yet the strengthening mechanism remains unclear. Here, we calculate the critical resolved shear stress (CRSS) for a single dislocation to move in RMPEAs consisting of 4 or 5 elements with or without short-range order (SRO) to represent strength by a machine learning-based interatomic potential. The increased CRSS is then attributed to high lattice distortion, elastic mismatch, and SRO strengthening, all of which originate from the solid solution strengthening theory. After detailed research of the CRSS across many RMPEAs systems with different composition ratios, we construct an XGBoost model to predict the CRSS from a few parameters and rank their importance. We find that lattice distortion strongly influences both dislocation types and reduces the screw-to-edge ratio in CRSS, while the elastic mismatch has a more significant impact on the screw dislocation than the edge one.

Marine Aspergillus fungi, adapted to extreme marine environments (e.g., sediments, corals, mangroves), are prolific producers of structurally diverse secondary metabolites with significant bioactivities. This review comprehensively analyzes 340 novel natural products reported from 81 marine-derived Aspergillus strains over the past three years, classifying them into six major categories: alkaloids (31.2%), polyketides (29.4%), terpenoids, lignans, cyclopeptides, and others. Bioactivity assessments reveal broad therapeutic potential, including antitumor, antimicrobial, anti-inflammatory, and antiviral effects. Notably, marine sediments constitute the primary source (25.9% of strains), followed by sponges and corals. The predominance of alkaloids and polyketides underscores their pharmacological relevance. These findings highlight marine Aspergillus as a critical resource for drug discovery, offering promising scaffolds for developing treatments against human diseases and agricultural pathogens.

Alpha-fetoprotein producing gastric carcinoma (AFPGC) is a rare and aggressive subtype of gastric cancer. However, little is known about the genomic features of this disease. We perform whole-exome sequencing analysis of AFPGC, and identify 34 significantly mutated genes. Somatic copy number alterations analysis reveals several significant focal amplifications (e.g. 19q12, 17q12) and focal deletions (e.g. 1p36.11, 9p21.3), and some of these negatively affect the patient prognosis. Comparative analyses reveal that AFPGC has distinct genomic features from gastric cancer of The Cancer Genome Atlas as well as four molecular subtypes. Several frequently altered genes with potential as therapeutic targets are identified in AFPGC. Further analysis reveals that AFPGC with amplification of CCNE1 at 19q12 and/or ERBB2 at 17q12 show poorer survival and more aggressive. Subsequently, based on our established patient-derived xenograft models for AFPGC, translational research is performed and the therapeutic value of targeting CCNE1 and ERBB2 is validated. In this work, we provide an understanding of genomic characteristics of AFPGC and propose a platform to explore and validate the genome-guided personalized treatment for this disease. Alpha-fetoprotein producing gastric carcinomas (AFPGC) are rare and aggressive. Here, the authors profile AFPGC tumours using whole exome sequencing, and find amplifications in CCNE1 and ERBB2 that are associated with poor outcomes but are potential therapeutic targets, as shown in patient-derived xenografts.