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The discovery of monolayer MA2Z4 (M = transition metals; A = IVA elements; Z = VA elements) [Hong et al 2020 Science 369 670] family has led another advance for facilitating and harnessing magnetism in low-dimensional materials. However, only Cr and V based MA2N4 compounds exhibit intrinsic magnetism yet with unsatisfied magnetic ordering temperature. Herein, we identify a stable ferromagnetic number of this family, i.e., VGe2Z4 monolayer, by means of first-principles calculations. It is found that the magnetic configuration sustains under both compression and tensile uniaxial in-plane strain, and the former can act as a positive modulator to enhance magnetic ordering temperature (TC). Electronic structure calculations reveal a large band gap in the spin down channel while band-gapless in the spin up channel, an impressive near-half-metallic character, which is a favorable candidate for spintronic device.

The claudin18.2 (CLDN18.2) protein, an isoform of claudin18, a member of the tight junction protein family, is a highly selective biomarker with limited expression in normal tissues and often abnormal expression during the occurrence and development of various primary malignant tumors, such as gastric cancer/gastroesophageal junction (GC/GEJ) cancer, breast cancer, colon cancer, liver cancer, head and neck cancer, bronchial cancer and non-small-cell lung cancer. CLDN18.2 participates in the proliferation, differentiation and migration of tumor cells. Recent studies have identified CLDN18.2 expression as a potential specific marker for the diagnosis and treatment of these tumors. With its specific expression pattern, CLDN18.2 has become a unique molecule for targeted therapy in different cancers, especially in GC; for example, agents such as zolbetuximab (claudiximab, IMAB362), a monoclonal antibody (mAb) against CLDN18.2, have been developed. In this review, we outline recent advances in the development of immunotherapy strategies targeting CLDN18.2, including monoclonal antibodies (mAbs), bispecific antibodies (BsAbs), chimeric antigen receptor T (CAR-T) cells redirected to target CLDN18.2, and antibody–drug conjugates (ADCs).

Background Approximately 8–9% of the world’s population is affected by autoimmune diseases, and yet the mechanism of autoimmunity trigger is largely understudied. Two unique cell death modalities, ferroptosis and pyroptosis, provide a new perspective on the mechanisms leading to autoimmune diseases, and development of new treatment strategies. Methods Using scRNA-seq datasets, the aberrant trend of ferroptosis and pyroptosis-related genes were analyzed in several representative autoimmune diseases (psoriasis, atopic dermatitis, vitiligo, multiple sclerosis, systemic sclerosis-associated interstitial lung disease, Crohn’s disease, and experimental autoimmune orchitis). Cell line models were also assessed using bulk RNA-seq and qPCR. Results A substantial difference was observed between normal and autoimmune disease samples involving ferroptosis and pyroptosis. In the present study, ferroptosis and pyroptosis showed an imbalance in different keratinocyte lineages of psoriatic skinin addition to a unique pyroptosis-sensitive keratinocyte subset in atopic dermatitis (AD) skin. The results also revealed that pyroptosis and ferroptosis are involved in epidermal melanocyte destruction in vitiligo. Aberrant ferroptosis has been detected in multiple sclerosis, systemic sclerosis-associated interstitial lung disease, Crohn’s disease, and autoimmune orchitis. Cell line models adopted in the study also identified pro-inflammatory factors that can drive changes in ferroptosis and pyroptosis. Conclusion These results provide a unique perspective on the involvement of ferroptosis and pyroptosis in the pathological process of autoimmune diseases at the scRNA-seq level. IFN-γ is a critical inducer of pyroptosis sensitivity, and has been identified in two cell line models.

An effective fault detection strategy has always been the focus of smart grid system research. Fast and accurate fault detection is the basis for complex systems to maintain reliability and security. However, traditional fault detection methods often ignore the interpretability of the model while pursuing high detection accuracy. Complex models can usually provide higher detection accuracy, but often lack transparency and interpretability, making it difficult for operators to understand and trust the detection results of the model. Therefore, a new fault detection strategy based on an adaptive interpretable belief rule base (AI-BRB) is proposed. This method considers the adaptive updating of the search domain of the model accuracy to achieve the balance and optimization between the two conflicting objectives of the model interpretability and the detection accuracy. The fault detection model based on AI-BRB considers the interpretability of modeling, inference and optimization processes. In the optimization process, interpretability constraints are added to maintain the interpretability of the optimized model. In addition, in order to avoid falling into the local optimal solution in the optimization process, the search domain is updated adaptively according to the accuracy of the model, which improves the interpretability and robustness of the fault detection model. Finally, an example is given to prove that the proposed method can improve the accuracy of fault detection and the interpretability of the model compared with the existing methods.

Co-contamination of heavy metals from antimony (Sb) mining poses significant threats to soil quality and human health. Although many studies have examined Sb sources, few have provided quantitative source apportionment combined with population-specific risk assessment. This study examined a representative Sb mining area in southwestern China through an integrated approach. The methodology combined Positive Matrix Factorization ( PMF ), Monte Carlo Simulation ( MCS ), the geo-accumulation index ( I geo ), the enrichment factor ( EF ), the modified Nemerow pollution index ( I NI ), and health risk assessment models. Results indicated a mean Sb concentration of 125.61 mg·kg −1 , nearly 50 times the regional background value (2.50 mg·kg −1 ), with pronounced spatial variability ( CV  = 246.97%). PMF analysis revealed three major sources: regional mixed sources (36.8%), natural geological sources (30.1%), and industrial point sources (33.1%). MCS results suggested high ecological risks for Cd and Sb, with associated probabilities of 94.43% and 83.45%, respectively. The probability of non-carcinogenic risk ( HI  > 1) in children reached 85.61%. The total carcinogenic risk ( TCR ) exceeded acceptable thresholds for all individuals, with children showing higher susceptibility. Natural geological sources accounted for 57.9% of carcinogenic risk and 62.3% of non-carcinogenic risk in children. These findings underscore the urgent need for targeted pollution control and remediation strategies to mitigate both ecological and human health risks in Sb mining areas.

Toll-like receptors (TLRs) are a large family of proteins that are expressed in immune cells and various tumor cells. TLR7/8 are located in the intracellular endosomes, participate in tumor immune surveillance and play different roles in tumor growth. Activation of TLRs 7 and 8 triggers induction of a Th1 type innate immune response in the highly sophisticated process of innate immunity signaling with the recent research advances involving the small molecule activation of TLR 7 and 8. The wide range of expression and clinical significance of TLR7/TLR8 in different kinds of cancers have been extensively explored. TLR7/TLR8 can be used as novel diagnostic biomarkers, progression and prognostic indicators, and immunotherapeutic targets for various tumors. Although the mechanism of action of TLR7/8 in cancer immunotherapy is still incomplete, TLRs on T cells are involved in the regulation of T cell function and serve as co-stimulatory molecules and activate T cell immunity. TLR agonists can activate T cell-mediated antitumor responses with both innate and adaptive immune responses to improve tumor therapy. Recently, novel drugs of TLR7 or TLR8 agonists with different scaffolds have been developed. These agonists lead to the induction of certain cytokines and chemokines that can be applied to the treatment of some diseases and can be used as good adjutants for vaccines. Furthermore, TLR7/8 agonists as potential therapeutics for tumor-targeted immunotherapy have been developed. In this review, we summarize the recent advances in the development of immunotherapy strategies targeting TLR7/8 in patients with various cancers and chronic hepatitis B.

Accurate prediction of photovoltaic (PV) output power is of great significance for reasonable scheduling and development management of power grids. In PV power generation prediction system, there are two problems: the uncertainty of PV power generation and the inexplicability of the prediction result. The belief rule base (BRB) is a rule-based modeling method and can deal with uncertain information. Moreover, the modeling process of BRB has a certain degree of interpretability. However, rule explosion and the inexplicability of the optimized model limit the modeling ability of BRB in complex systems. Thus, a PV output power prediction model is proposed based on a deep belief rule base with interpretability (DBRB-I). In the DBRB-I model, the deep BRB structure is constructed to solve the rule explosion problem, and inefficient rules are simplified by a sensitivity analysis of the rules, which reduces the complexity of the model. Moreover, to ensure that the interpretability of the model is not destroyed, a new optimization method based on the projection covariance matrix adaptation evolution strategy (P-CMA-ES) algorithm is designed. Finally, a case study of the prediction of PV output power is conducted to illustrate the effectiveness of the proposed method.

Acute myeloid leukemia (AML) is a genetically heterogeneous clonal malignancy characterized by recurrent gene mutations. Genomic heterogeneity, patients’ individual variability, and recurrent gene mutations are the major obstacles among many factors that impact treatment efficacy of the AML patients. With the application of cost- and time-effective next-generation sequencing (NGS) technologies, an enormous diversity of genetic mutations has been identified. The recurrent gene mutations and their important roles in acute myeloid leukemia (AML) pathogenesis have been studied extensively. In this review, we summarize the recent development on the gene mutation in patients with AML.

The genesis of heavy metal contamination in arable soils is complex, and scientifically identifying risks and precisely analyzing contamination sources are essential for safely using contaminated arable land. In this study, we systematically evaluated the pollution characteristics of Cu, Zn, As, Hg, Cd, Pb, Ni, and Cr in soil, and then applied the APCS-MLR and PMF models to jointly analyze pollution sources and their contributions. The results showed that the concentrations of the eight heavy metals were significantly higher than the background values for soils in Yunnan Province, exhibiting clear spatial heterogeneity. The overall pollution level ranged from mild to severe, with Cd and Pb being the most critical contaminants. Four major pollution sources (industrial transportation, parent material, agriculture, and mining) were identified through the dual modeling approach. The results of both models corroborated each other, and the accuracy of the analysis was significantly improved compared to using a single method. This study not only provides a scientific basis for the safe utilization of contaminated arable land in western Yunnan, an area with a complex genesis of soil contamination, but also offers a generalized framework for source analysis in areas affected by geological-anthropogenic composite pollution.

Background Cognitive dysfunction is the most common non-motor symptom in Parkinson’s disease (PD), and timely detection of a slight cognitive decline is crucial for early treatment and prevention of dementia. This study aimed to build a machine learning model based on intra- and/or intervoxel metrics extracted from diffusion tensor imaging (DTI) to automatically classify PD patients without dementia into mild cognitive impairment (PD-MCI) and normal cognition (PD-NC) groups. Methods We enrolled PD patients without dementia (52 PD-NC and 68 PD-MCI subtypes) who were assigned to the training and test datasets in an 8:2 ratio. Four intravoxel metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), and two novel intervoxel metrics, local diffusion homogeneity (LDH) using Spearman’s rank correlation coefficient (LDHs) and Kendall’s coefficient concordance (LDHk), were extracted from the DTI data. Decision tree, random forest, and eXtreme gradient boosting (XGBoost) models based on individual and combined indices were built for classification, and model performance was assessed and compared via the area under the receiver operating characteristic curve (AUC). Finally, feature importance was evaluated using SHapley Additive exPlanation (SHAP) values. Results The XGBoost model based on a combination of the intra- and intervoxel indices achieved the best classification performance, with an accuracy of 91.67%, sensitivity of 92.86%, and AUC of 0.94 in the test dataset. SHAP analysis showed that the LDH of the brainstem and MD of the right cingulum (hippocampus) were important features. Conclusions More comprehensive information on white matter changes can be obtained by combining intra- and intervoxel DTI indices, improving classification accuracy. Furthermore, machine learning methods based on DTI indices can be used as alternatives for the automatic identification of PD-MCI at the individual level.