MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer.

Synthetic lethality (SL) is widely used to discover the anti-cancer drug targets. However, the identification of SL interactions through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for SL interactions prediction is of great significance. In this study, we propose MPASL, a multi-perspective learning knowledge graph attention network to enhance synthetic lethality prediction. MPASL utilizes knowledge graph hierarchy propagation to explore multi-source neighbor nodes related to genes. The knowledge graph ripple propagation expands gene representations through existing gene SL preference sets. MPASL can learn the gene representations from both gene-entity perspective and entity-entity perspective. Specifically, based on the aggregation method, we learn to obtain gene-oriented entity embeddings. Then, the gene representations are refined by comparing the various layer-wise neighborhood features of entities using the discrepancy contrastive technique. Finally, the learned gene representation is applied in SL prediction. Experimental results demonstrated that MPASL outperforms several state-of-the-art methods. Additionally, case studies have validated the effectiveness of MPASL in identifying SL interactions between genes.

Manipulating calcium homeostasis with nanoplatforms for enhanced cancer therapy.

Calcium ions (Ca2+) are indispensable and versatile metal ions that play a pivotal role in regulating cell metabolism, encompassing cell survival, proliferation, migration, and gene expression. Aberrant Ca2+ levels are frequently linked to cell dysfunction and a variety of pathological conditions. Therefore, it is essential to maintain Ca2+ homeostasis to coordinate body function. Disrupting the balance of Ca2+ levels has emerged as a potential therapeutic strategy for various diseases, and there has been extensive research on integrating this approach into nanoplatforms. In this review, the current nanoplatforms that regulate Ca2+ homeostasis for cancer therapy are first discussed, including both direct and indirect approaches to manage Ca2+ overload or inhibit Ca2+ signalling. Then, the applications of these nanoplatforms in targeting different cells to regulate their Ca2+ homeostasis for achieving therapeutic effects in cancer treatment are systematically introduced, including tumour cells and immune cells. Finally, perspectives on the further development of nanoplatforms for regulating Ca2+ homeostasis, identifying scientific limitations and future directions for exploitation are offered.

Analysis of prognostic factors and construction of prognostic models for invasive lobular carcinoma of the breast.

Invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) account for most cases of breast cancer. However, there is ongoing debate about any potential variations in overall survival (OS) between ILC and IDC. This study aimed to compare survival between IDC and ILC, identify prognostic factors for ILC patients, and construct a nomogram for predicting OS rates. This retrospective cohort analysis utilized data from the Surveillance, Epidemiology, and End Results (SEER) Cancer Database. Patients diagnosed with ILC and IDC between 2000 and 2019 were enrolled. To minimize baseline differences in clinicopathological characteristics and survival outcomes, a propensity score matching (PSM) method was used. Data from the multivariate Cox regression analyses were used to construct a predictive nomogram for OS at 1, 3, and 5 years, incorporating all independent prognostic factors. Following the PSM procedure, patients with ILC exhibited a better prognosis compared to those with IDC. TNM stage, age >70, radiotherapy, surgery, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HR-/HER2+) subtype were identified as independent factors for OS in ILC patients. Surgery and radiotherapy effectively reduced the risk of death, while chemotherapy did not demonstrate the same benefit. This model could support clinicians in evaluating the prognosis of ILC for decision-making and patient counseling.

Screening and Mechanism Study of Three Antagonistic Drugs, Oxysophoridine, Rutin, and Phellodendrine, against Zearalenone-Induced Reproductive Toxicity in Ovine Oocytes.

Zearalenone (ZEN) is a common fungal toxin with reproductive toxicity in various grains. It poses a serious threat to ovine and other animal husbandry industries, as well as human reproductive health. Therefore, investigating the mechanism of toxicity and screening antagonistic drugs are of great importance. In this study, based on the natural compound library and previous Smart-seq2 results, antioxidant and anti-apoptotic drugs were selected for screening as potential antagonistic drugs. Three natural plant compounds (oxysophoridine, rutin, and phellodendrine) were screened for their ability to counteract the reproductive toxicity of ZEN on ovine oocytes in vitro using quantitative polymerase chain reaction (qPCR) and reactive oxygen species detection. The compounds exhibited varying pharmacological effects, notably impacting the expression of antioxidant (GPX, SOD1, and SOD2), autophagic (ATG3, ULK2, and LC3), and apoptotic (CAS3, CAS8, and CAS9) genes. Oxysophoridine promoted GPX, SOD1, ULK2, and LC3 expression, while inhibiting CAS3 and CAS8 expression. Rutin promoted SOD2 and ATG3 expression, and inhibited CAS3 and CAS9 expression. Phellodendrine promoted SOD2 and ATG3 expression, and inhibited CAS9 expression. However, all compounds promoted the expression of genes related to cell cycle, spindle checkpoint, oocyte maturation, and cumulus expansion factors. Although the three drugs had different regulatory mechanisms in enhancing antioxidant capacity, enhancing autophagy, and inhibiting cell apoptosis, they all maintained a stable intracellular environment and a normal cell cycle, promoted oocyte maturation and release of cumulus expansion factors, and, ultimately, counteracted ZEN reproductive toxicity to promote the in vitro maturation of ovine oocytes. This study identified three drugs that antagonize the reproductive toxicity of ZEN on ovine oocytes, and compared their mechanisms of action, providing data support and a theoretical basis for their subsequent application in the ovine breeding industry, reducing losses in the breeding industry, screening of ZEN reproductive toxicity antagonists and various toxin antagonists, improving the study of ZEN reproductive toxicity mechanisms, and even protection of human reproductive health.

Analytical Quality by Design as applied to the development of a SEC-HPLC platform procedure for the determination of monoclonal antibody purity without mobile phase additives.

This work presents the application of AQbD principles to the development of a size exclusion chromatography (SEC) HPLC procedure for the determination of monoclonal antibody (mAb) product purity using state-of-the-art column technology available via the Waters™ XBridge Premier Protein SEC column. Analytical Quality by Design (AQbD) emphasizes a systematic, risk-based lifecycle approach to analytical procedure development based on sound statistical methodologies. It has recently become increasingly recommended by regulatory agencies as a response to the need for greater efficiency, improved reliability, and increased robustness among modern analytical procedures in the pharmaceutical industry. Use of an Analytical Target Profile (ATP) and formal risk assessments informed the application of Design of Experiments (DoE) to optimize this analytical procedure, as well as assess its robustness and ruggedness. Importantly, our ruggedness results demonstrated the transferability of this procedure between two laboratories within the Catalent Biologics Global Network. Application of this analytical procedure as a platform approach for evaluating mAb purity is expected to support expedited, first-in-human timelines of mAb molecules by enabling great quantitative performance with simple mobile phase buffer compositions. Taken together, this case study demonstrates the utility of adopting AQbD principles in analytical procedure development.

Chemical short-range disorder in lithium oxide cathodes.

Ordered layered structures serve as essential components in lithium (Li)-ion cathodes1-3. However, on charging, the inherently delicate Li-deficient frameworks become vulnerable to lattice strain and structural and/or chemo-mechanical degradation, resulting in rapid capacity deterioration and thus short battery life2,4. Here we report an approach that addresses these issues using the integration of chemical short-range disorder (CSRD) into oxide cathodes, which involves the localized distribution of elements in a crystalline lattice over spatial dimensions, spanning a few nearest-neighbour spacings. This is guided by fundamental principles of structural chemistry and achieved through an improved ceramic synthesis process. To demonstrate its viability, we showcase how the introduction of CSRD substantially affects the crystal structure of layered Li cobalt oxide cathodes. This is manifested in the transition metal environment and its interactions with oxygen, effectively preventing detrimental sliding of crystal slabs and structural deterioration during Li removal. Meanwhile, it affects the electronic structure, leading to improved electronic conductivity. These attributes are highly beneficial for Li-ion storage capabilities, markedly improving cycle life and rate capability. Moreover, we find that CSRD can be introduced in additional layered oxide materials through improved chemical co-doping, further illustrating its potential to enhance structural and electrochemical stability. These findings open up new avenues for the design of oxide cathodes, offering insights into the effects of CSRD on the crystal and electronic structure of advanced functional materials.

Predominant P3-Type Solid-Solution Phase Transition Enables High-Stability O3-Type Na-Ion Cathodes.

Layered O3-type oxides are one of the most promising cathode materials for Na-ion batteries owing to their high capacity and straightforward synthesis. However, these materials often experience irreversible structure transitions at elevated cutoff voltages, resulting in compromised cycling stability and rate performance. To address such issues, understanding the interplay of the composition, structure, and properties is crucial. Here, we successfully introduced a P-type characteristic into the O3-type layered structure, achieving a P3-dominated solid-solution phase transition upon cycling. This modification facilitated a reversible transformation of the O3-P3-P3' structure with minimal and gradual volume changes. Consequently, the Na0.75Ni0.25Cu0.10Fe0.05Mn0.15Ti0.45O2 cathode exhibited a specific capacity of approximately 113 mAh/g, coupled with exceptional cycling performance (maintaining over 70% capacity retention after 900 cycles). These findings shed light on the composition-structure-property relationships of Na-ion layered oxides, offering valuable insights for the advancement of Na-ion batteries.

Tunable Magnetism in Atomically Thin Itinerant Antiferromagnet with Room-Temperature Ferromagnetic Order.

Addressing the need for modulated spin configurations is crucial, as they serve as the foundational building blocks for next-generation spintronics, particularly in atomically thin structures and at room temperature. In this work, we realize intrinsic ferromagnetism in monolayer flakes and tunable ferro-/antiferromagnetism in (Fe0.56Co0.44)5GeTe2 antiferromagnets. Remarkably, the ferromagnetic ordering (≥1 L) and antiferromagnetic ordering (≥4 L) remain discernible up to room temperature. The TC (∼310 K) of the monolayer flakes sets a record high for known exfoliated monolayer van der Waals magnets. Within the framework of A-type antiferromagnetism, a notable odd-even layer-number effect at elevated temperatures (T = 150 K) is observed. Of particular interest is the strong ferromagnetic order in even-layer flakes at low temperatures. The intricate interplay among magnetic field strength, layer number, and temperature gives rise to a diverse array of phenomena, holding promise not only for new physics but also for practical applications.

The role of tumor-associated macrophages in the radioresistance of esophageal cancer cells via regulation of the VEGF-mediated angiogenic pathway.

Tumor-associated macrophages (TAMs) are known to promote tumor growth, invasion, metastasis, and protumor angiogenesis, but the role of TAMs in evading radiotherapy in esophagus cancer remains unclear. In this study, we first induced TAMs from human monocytes (THP-1) and identified using immunofluorescence and Western blotting assays. We then co-cultured them with human esophageal cancer cell lines. CCK-8, colony formation, Transwell, scratch test, and TUNEL assays showed that TAMs could promote proliferation, survival rate, invasion, migration, and radioresistance and could inhibit apoptosis of the esophageal squamous carcinoma cell lines KYSE-150 and TE-1 before and after radiotherapy both in vivo and in vitro. Using LV-VEGFA-RNAi lentiviral vectors, we also found that TAMs could increase the expression of VEGFA and that inhibition of VEGFA could inhibit the biological function caused by TAMs. Finally, a Western blotting assay was used to evaluate the expression of various factors underlying the mechanism of TAMs. VEGFA, MAPK, P-MAPK, BCL-2, and Snail proteins were found to be overexpressed in co-cultured groups, whereas after VEGFA inhibition, MAPK, P-MAPK, BCL-2, and Snail proteins were found to be significantly downregulated in the radiotherapy group. These study results offer important information regarding the mechanism of radioresistance in esophageal cancer.

Nucleolin lactylation contributes to intrahepatic cholangiocarcinoma pathogenesis via RNA splicing regulation of MADD.

BACKGROUND & AIMS: Intrahepatic cholangiocarcinoma (iCCA) is a fatal malignancy of the biliary system. The lack of a detailed understanding of oncogenic signaling or global gene expression alterations has impeded clinical iCCA diagnosis and therapy. The role of protein lactylation, a newly unraveled post-translational modification that orchestrates gene expression, remains largely elusive in the pathogenesis of iCCA. METHODS: Proteomics analysis of clinical iCCA specimens and adjacent tissues was performed to screen for proteins aberrantly lactylated in iCCA. Mass spectrometry, macromolecule interaction and cell behavioral studies were employed to identify the specific lactylation sites on the candidate protein(s) and to decipher the downstream mechanisms responsible for iCCA development, which were subsequently validated using a xenograft tumor model and clinical samples. RESULTS: Nucleolin (NCL), the most abundant RNA-binding protein in the nucleolus, was identified as a functional lactylation target that correlates with iCCA occurrence and progression. NCL was lactylated predominantly at lysine 477 by the acyltransferase P300 in response to a hyperactivity of glycolysis, and promoted the proliferation and invasion of iCCA cells. Mechanistically, lactylated NCL bound to the primary transcript of MAP kinase-activating death domain protein (MADD) and led to efficient translation of MADD by circumventing alternative splicing that generates a premature termination codon. NCL lactylation, MADD translation and subsequent ERK activation promoted xenograft tumor growth and were associated with overall survival in patients with iCCA. CONCLUSION: NCL is lactylated to upregulate MADD through an RNA splicing-dependent mechanism, which potentiates iCCA pathogenesis via the MAPK pathway. Our findings reveal a novel link between metabolic reprogramming and canonical tumor-initiating events, and uncover biomarkers that can potentially be used for prognostic evaluation or targeted treatment of iCCA. IMPACT AND IMPLICATIONS: Intrahepatic cholangiocarcinoma (iCCA) is a highly aggressive liver malignancy with largely uncharacterized pathogenetic mechanisms. Herein, we demonstrated that glycolysis promotes P300-catalyzed lactylation of nucleolin, which upregulates MAP kinase-activating death domain protein (MADD) through precise mRNA splicing and activates ERK signaling to drive iCCA development. These findings unravel a novel link between metabolic rewiring and canonical oncogenic pathways, and reveal new biomarkers for prognostic assessment and targeting of clinical iCCA.

Identification of m6A/m5C-related lncRNA signature for prediction of prognosis and immunotherapy efficacy in esophageal squamous cell carcinoma.

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have garnered significant attention in the field of epigenetic research due to their close association with human cancers. This study we focus on elucidating the expression patterns of m6A/m5C-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) and assessing their prognostic significance and therapeutic potential. Transcriptomic profiles of ESCC were derived from public resources. m6A/m5C-related lncRNAs were obtained from TCGA using Spearman's correlations analysis. The m6A/m5C-lncRNAs prognostic signature was selected to construct a RiskScore model for survival prediction, and their correlation with the immune microenvironment and immunotherapy response was analyzed. A total of 606 m6A/m5C-lncRNAs were screened, and ESCC cases in the TCGA cohort were stratified into three clusters, which showed significantly distinct in various clinical features and immune landscapes. A RiskScore model comprising ten m6A/m5C-lncRNAs prognostic signature were constructed and displayed good independent prediction ability in validation datasets. Patients in the low-RiskScore group had a better prognosis, a higher abundance of immune cells (CD4 + T cell, CD4 + naive T cell, class-switched memory B cell, and Treg), and enhanced expression of most immune checkpoint genes. Importantly, patients with low-RiskScore were more cline benefit from immune checkpoint inhibitor treatment (P < 0.05). Our findings underscore the potential of RiskScore system comprising ten m6A/m5C-related lncRNAs as effective biomarkers for predicting survival outcomes, characterizing the immune landscape, and assessing response to immunotherapy in ESCC.

Effect of cyclophosphamide combined with glucocorticoid therapy on idiopathic membranous nephropathy: A multicenter open-label randomized controlled trial.

OBJECTIVE: We aimed to evaluate the effect of cyclophosphamide combined with glucocorticoid therapy on idiopathic membranous nephropathy through a multicenter open-label randomized controlled trial. MATERIALS AND METHODS: 92 patients with idiopathic membranous nephropathy admitted from March 2020 to September 2022 were included and assigned to a control group (n = 46) and a research group (n = 46) using a random number table. The control group was given glucocorticoid, and the research group was given cyclophosphamide combined with glucocorticoid. Clinical efficacy, renal function-related indicators (serum creatinine, blood urea nitrogen and albumin, and 24-hour urine protein quantification), inflammatory factors (interleukin (IL)-6, IL-18, transforming growth factor-ß, and tumor necrosis factor-α), immune function-related indicators (anti-phospholipase A2 receptor antibody, and T-lymphocyte subsets), oxidative stress-related indicators (heme oxygenase-1, superoxide dismutase, malondialdehyde, and nitric oxide), blood lipid-related indicators (total cholesterol, triacylglycerol, and low-density lipoprotein), and adverse reactions were compared. RESULTS: The overall remission rate of the research group was higher than that of the control group (93.48 vs. 78.26%, p < 0.05). After treatment, the research group had lower levels of 24-hour urine protein quantification, serum creatinine, blood urea nitrogen, IL-6, IL-18, transforming growth factor-ß, tumor necrosis factor-α, heme oxygenase-1, malondialdehyde, anti-phospholipase A2 receptor antibody, CD8+, total cholesterol, triacylglycerol and low-density lipoprotein, higher levels of albumin, superoxide dismutase, nitric oxide, and CD4+ and a higher CD4+/CD8+ ratio than the control group (p < 0.05). CONCLUSION: Cyclophosphamide combined with glucocorticoid therapy is effective for improving the overall remission rate and can suppress inflammatory responses and oxidative stress in patients with idiopathic membranous nephropathy.

Rainfall's impact on agricultural production and government poverty reduction efficiency in China.

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China's 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Developmental regulation of zinc homeostasis in differentiating oligodendrocytes.

Oligodendrocytes develop through sequential stages and understanding pathways regulating their differentiation remains an important area of investigation. Zinc is required for the function of enzymes, proteins and transcription factors, including those important in myelination and mitosis. Our previous studies using the ratiometric zinc sensor chromis-1 demonstrated a reduction in intracellular free zinc concentrations in mature MBP+ oligodendrocytes compared with earlier stages (Bourassa et al., 2018). We performed a more detailed developmental study to better understand the temporal course of zinc homeostasis across the oligodendrocyte lineage. Using chromis-1, we found a transient increase in free zinc after O4+,O1- pre-oligodendrocytes were switched from proliferation medium into terminal differentiation medium. To gather other evidence for dynamic regulation of free zinc during oligodendrocyte development, qPCR was used to evaluate mRNA expression of major zinc storage proteins metallothioneins (MTs) and metal regulatory transcription factor 1 (MTF1), which controls expression of MTs. MT1, MT2 and MTF1 mRNAs were increased several fold in mature oligodendrocytes compared to oligodendrocytes in proliferation medium. To assess the depth of the zinc buffer, we assayed zinc release from intracellular stores using the oxidizing thiol reagent 2,2'-dithiodipyridine (DTDP). Exposure to DTDP resulted in âˆ¼ 100% increase in free zinc in pre-oligodendrocytes but, paradoxically more modest âˆ¼ 60% increase in mature oligodendrocytes despite increased expression of MTs. These results suggest that zinc homeostasis is regulated during oligodendrocyte development, that oligodendrocytes are a useful model for studying zinc homeostasis in the central nervous system, and that regulation of zinc homeostasis may be important in oligodendrocyte differentiation.

Drug-drug interactions prediction based on deep learning and knowledge graph: A review.

Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public databases related to drugs are firstly summarized in this review. Then, we discuss the existing drug-drug interactions prediction methods which have utilized deep learning and knowledge graph techniques and group them into three main classes: deep learning-based methods, knowledge graph-based methods, and methods that combine deep learning with knowledge graph. We comprehensively analyze the commonly used drug related data and various DDI prediction methods, and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI prediction.

RNF20 contributes to epigenetic immunosuppression through CDK9-dependent LSD1 stabilization.

Cyclin-dependent kinase 9 (CDK9) plays a critical role in transcription initiation and is essential for maintaining gene silencing at heterochromatic loci. Inhibition of CDK9 increases sensitivity to immunotherapy, but the underlying mechanism remains unclear. We now report that RNF20 stabilizes LSD1 via K29-mediated ubiquitination, which is dependent on CDK9-mediated phosphorylation. This CDK9- and RNF20-dependent LSD1 stabilization is necessary for the demethylation of histone H3K4, then subsequent repression of endogenous retrovirus, and an interferon response, leading to epigenetic immunosuppression. Moreover, we found that loss of RNF20 sensitizes cancer cells to the immune checkpoint inhibitor anti-PD-1 in vivo and that this effect can be rescued by the expression of ectopic LSD1. Our findings are supported by the observation that RNF20 levels correlate with LSD1 levels in human breast cancer specimens. This study sheds light on the role of RNF20 in CDK9-dependent LSD1 stabilization, which is crucial for epigenetic silencing and immunosuppression. Our findings explore the potential importance of targeting the CDK9-RNF20-LSD1 axis in the development of new cancer therapies.

Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network.

Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug-disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug-disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. Based on the learned latent vector representation, the novel drug-disease associations can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.

Designing lithium halide solid electrolytes.

All-solid-state lithium batteries have attracted widespread attention for next-generation energy storage, potentially providing enhanced safety and cycling stability. The performance of such batteries relies on solid electrolyte materials; hence many structures/phases are being investigated with increasing compositional complexity. Among the various solid electrolytes, lithium halides show promising ionic conductivity and cathode compatibility, however, there are no effective guidelines when moving toward complex compositions that go beyond ab-initio modeling. Here, we show that ionic potential, the ratio of charge number and ion radius, can effectively capture the key interactions within halide materials, making it possible to guide the design of the representative crystal structures. This is demonstrated by the preparation of a family of complex layered halides that combine an enhanced conductivity with a favorable isometric morphology, induced by the high configurational entropy. This work provides insights into the characteristics of complex halide phases and presents a methodology for designing solid materials.

Enhanced ferromagnetic properties achieved by F-doping in BaFe1-xMnxO3-δ.

Tailoring the crystal structure, spin, and charge state of perovskite oxides through fluorine ion doping is an attractive and effective strategy, which could significantly modify the physical and chemical properties of base oxides. Here, BaFe1-xMnxO3-δ (x = 0, 0.1, 0.2, 0.3) and BaFe1-xMnxO2.9-δF0.1 (x = 0.1, 0.2, 0.3), belonging to 6H-type BaFeO3-δ, are prepared and investigated to evaluate the impact of F- doping. The distortion of crystal structure and the reduced average valence of Mn and Fe confirm the preference for F- substitution in the hexagonal layer, which are found as the key factors for the improved magnetic properties, including ferromagnetic ordering temperature, coercive force, and remanent magnetization. Moreover, the valence reduction of B-site ions and the increased resistance distinctly indicate the expense of electron hole via fluorine doping. This work describes the adjustment of crystal structure, electronic configuration, and ferromagnetic performance by simple F- doping, which provides a prospect for practical magnetic materials.