Characterization of Pancreatic Fistula after Post-pancreatectomy Acute Pancreatitis.

OBJECTIVE: This study aimed to investigate the clinical significance and risk factors of postoperative pancreatic fistula (POPF) after post-pancreatectomy acute pancreatitis (PPAP) in patients who underwent pancreaticoduodenectomy (PD). SUMMARY BACKGROUND DATA: PPAP has been recognized as a critical factor in the pathophysiology of POPF after PD. METHODS: A total of 817 consecutive patients who underwent elective PD between January 2020 and June 2022 were included. PPAP and POPF were defined in accordance with the International Study Group for Pancreatic Surgery (ISGPS) definitions. Multivariate logistic analyses were performed to investigate the risk factors for POPF. Comparisons between PPAP-associated POPF and non-PPAP-associated POPF were made to further characterize this intriguing complication. RESULTS: Overall, 159 (19.5%) patients developed POPF after PD, of which 73 (45.9%) occurred following PPAP, and the remaining 86 (54.1%) had non-PPAP-associated POPF. Patients with PPAP-associated POPF experienced significantly higher morbidity than patients without POPF. Multivariate analyses revealed distinct risk factors for each POPF type. For PPAP-associated POPF, independent risk factors included estimated blood loss >200 mL (OR 1.93), MPD ≤3 cm (OR 2.88), and soft pancreatic texture (OR 2.01), largely overlapping with FRS (Fistula Risk Score) elements. On the other hand, non-PPAP-associated POPF was associated with age >65 years (OR 1.95), male (OR 2.10), and MPD ≤3 cm (OR 2.57). Notably, among patients with PPAP, the incidence of POPF consistently hovered around 50% regardless of the FRS stratification. CONCLUSIONS: PPAP-associated POPF presents as a distinct pathophysiology in the development of POPF after PD, potentially opening doors for future prevention strategies targeting the early postoperative period.

Constructing Matching Cathode-Anode Interphases with Improved Chemo-mechanical Stability for High-Energy Batteries.

Coupling Ni-rich layered oxide cathodes with Si-based anodes is one of the most promising strategies to realize high-energy-density Li-ion batteries. However, unstable interfaces on both cathode and anode sides cause continuous parasitic reactions, resulting in structural degradation and capacity fading of full cells. Herein, lithium tetrafluoro(oxalato) phosphate is synthesized and applied as a multifunctional electrolyte additive to mitigate irreversible volume swing of the SiOx anode and suppress undesirable interfacial evolution of the LiNi0.83Co0.12Mn0.05O2 (NCM) cathode simultaneously, resulting in improved cycle life. Benefiting from its desirable redox thermodynamics and kinetics, the molecularly tailored additive facilitates matching interphases consisting of LiF, Li3PO4, and P-containing macromolecular polymer on both the NCM cathode and SiOx anode, respectively, modulating interfacial chemo-mechanical stability as well as charge transfer kinetics. More encouragingly, the proposed strategy enables 4.4 V 21700 cylindrical batteries (5 Ah) with excellent cycling stability (92.9% capacity retention after 300 cycles) under practical conditions. The key finding points out a fresh perspective on interfacial optimization for high-energy-density battery systems.

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

Robotic versus open pancreatoduodenectomy in patients with pancreatic duct adenocarcinoma after the learning curve: a propensity score-matched analysis.

BACKGROUND: Studies have demonstrated that the learning curve plays an important role in robotic pancreatoduodenectomy (RPD). Although improved short-term outcomes of RPD after the learning curve have been reported compared to open pancreatoduodenectomy (OPD), there is a lack of long-term survival analyses. METHODS: Patients who underwent curative intended RPD and OPD for pancreatic duct adenocarcinoma (PDAC) between January 2017 and June 2020 were retrospectively reviewed. A 1:2 propensity score matching (PSM) analysis was performed to balance the baseline characteristics between the RPD and OPD groups. RESULTS: Of the 548 patients (108 RPD and 440 OPD), 103 RPD patients were matched with 206 OPD patients after PSM. There were 194 (62.8%) men and 115 (37.2%) women, with a median age of 64 (58-69) years. The median overall survival (OS) in the RPD group was 33.2 months compared with 25.7 months in the OPD group (p = 0.058, log-rank). The median disease-free survival (DFS) following RPD was longer than the OPD (18.5 vs. 14.0 months, p = 0.011, log-rank). The RPD group has a lower incidence of local recurrence compared the OPD group (36.9% vs. 51.2%, p = 0.071). Multivariate Cox analysis demonstrated that RPD was independently associated with improved OS (HR 0.70, 95% CI 0.52-0.94, p = 0.019) and DFS (HR 0.66, 95% CI 0.50-0.88, p = 0.005). CONCLUSION: After the learning curve, RPD had improved oncologic outcomes in PDAC patients compared to OPD. Future prospective randomized clinical trials will be required to validate these findings.

Prediction of Lumbar Pedicle Screw Loosening Using Hounsfield Units in Computed Tomography

INTRODUCTION: One of the most common issues following pedicle screw fixation is pedicle screw loosening. There are, however, few trustworthy methods for predicting screw loosening. The goal of the current study was to identify an efficient technique for using preoperative CT scanning to predict screw loosening in older patients and to offer recommendations for preoperative surgical planning. METHODS: The current analysis included retrospectively all patients who underwent lumbar pedicle screw fixation for degenerative lumbar diseases in our department between January 2015 and January 2022. Hounsfield units were used to assess each vertebra's attenuation in a CT scan (HU). Postoperative X-ray testing identified screw loosening. Using IBMSPSS 24.00 software, one-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis were performed. RESULTS: Over a mean follow-up period of 28.4±11.5 (range 12-44 months) months, screw loosening was noted in 53 of 242 patients (136 male and 106 female, average age 58.7±7.3 years). Gender, BMI, smoking habits, and whether or not a patient had diabetes or spondylolisthesis were not shown to be significantly different among the patients (P>0.05). The difference between the average lumbar vertebral HU values in the screw-loosening group and the control group was significant (P<0.01) at 120.3±31.5HU and 138.6±37.6HU, respectively. The average HU value of L1-L4 exhibited an area under the curve (AUC) of 0.691 (95% CI: 0.614-0.784), according to ROC curve analysis. A HU cut-off value of 122 HU is a likely cut-off point to predict screw loosening with a sensitivity of 70% and a specificity of 58%. CONCLUSION: The use of screw augmentation techniques can be decided using a prospective CT scan HU value-based prediction. An independent risk factor for screw loosening in an instrumented lumbar vertebra is a cutoff L1-L5 average HU value of 122 HU.

TP53 missense mutation reveals gain-of-function properties in small-sized KRAS transformed pancreatic ductal adenocarcinoma.

BACKGROUND: Although the molecular features of pancreatic ductal adenocarcinoma (PDAC) have been well described, the impact of detailed gene mutation subtypes on disease progression remained unclear. This study aimed to evaluate the impact of different TP53 mutation subtypes on clinical characteristics and outcomes of patients with PDAC. METHODS: We included 639 patients treated with PDAC in Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine between Jan 2019 and Jun 2021. The genomic alterations of PDAC were analyzed, and the association of TP53 mutation subtypes and other core gene pathway alterations with patients' clinical characteristics were evaluated by Chi-squared test, Kaplan-Meier method and Cox regression model. RESULTS: TP53 missense mutation was significantly associated with poor differentiation in KRASmut PDAC (50.7% vs. 36.1%, P = 0.001). In small-sized (≤ 2 cm) KRASmut tumors, significantly higher LNs involvement (54.8% vs. 23.5%, P = 0.010) and distal metastic rate (20.5% vs. 2.9%, P = 0.030) were observed in those with TP53 missense mutation instead of truncating mutation. Compared with TP53 truncating mutation, missense mutation was significantly associated with reduced DFS (6.6 [5.6-7.6] vs. 9.2 [5.2-13.3] months, HR 0.368 [0.200-0.677], P = 0.005) and OS (9.6 [8.0-11.1] vs. 18.3 [6.7-30.0] months, HR 0.457 [0.248-0.842], P = 0.012) in patients who failed to receive chemotherapy, while higher OS (24.2 [20.8-27.7] vs. 23.8 [19.0-28.5] months, HR 1.461 [1.005-2.124], P = 0.047) was observed in TP53missense cases after chemotherapy. CONCLUSIONS: TP53 missense mutation was associated with poor tumor differentiation, and revealed gain-of-function properties in small-sized KRAS transformed PDAC. Nonetheless, it was not associated with insensitivity to chemotherapy, highlighting the neoadjuvant therapy before surgery as the potential optimized strategy for the treatment of a subset of patients.

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.

BACKGROUND: To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. METHODS: A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. RESULTS: Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. CONCLUSION: Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

Drain fluid volume combined with amylase level predicts clinically relevant postoperative pancreatic fistula after pancreaticoduodenectomy: A retrospective clinical study.

BACKGROUND AND AIM: Several indicators are recognized in the development of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy (PD). However, drain fluid volume (DFV) remains poorly studied. We aimed to discover the predictive effects of DFV and guide clinical management. METHODS: We retrospectively reviewed the clinical data of patients that received PD between January 2015 and December 2019 in a high-volume center. DFV was analyzed as a potential risk factor and postoperative short-term outcomes as well as drain removal time were compared stratified by different DFV levels. Receiver operating characteristic curves and area under curves (AUC) were compared for DFV alone and DFV combined with drain fluid amylase (DFA). Subgroup analysis of DFV stratified by DFA evaluated the predictability of CR-POPF. RESULTS: CR-POPF occurred in 19.7% of 841 patients. Hypertension, postoperative day 3 (POD3) DFA ≥ 300 U/L, and POD3 DFV ≥ 30 mL were independent risk factors, while pancreatic main duct diameter ≥ 3 mm was a protective factor. POD3 DFV ≥ 30 mL increased the overall occurrences of CR-POPF and major complications (P = 0.017; P = 0.029). POD3 DFV alone presented a low predictive value (AUC 0.602), while POD3 DFV combined with DFA had a high predictive value (AUC 0.759) for CR-POPF. Subgroup analysis showed that the combination of POD3 DFV ≥ 30 mL and DFA ≥ 300 U/L led to higher incidences of CR-POPF (P = 0.003). CONCLUSION: CR-POPF is common after PD, and high DFV combined with DFA may predict its occurrence and facilitate appropriate management.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

A microprotein N1DARP encoded by LINC00261 promotes Notch1 intracellular domain (N1ICD) degradation via disrupting USP10-N1ICD interaction to inhibit chemoresistance in Notch1-hyperactivated pancreatic cancer.

The extensively activated Notch signaling pathway in pancreatic cancer cells is important in carcinogenesis, chemoresistance, and recurrence. Targeting this pathway is a promising therapeutic strategy for pancreatic cancer; however, few successful approaches have been reported, and currently used molecular inhibitors of this pathway exhibit limited clinical benefits. In this study, we identified a previously uncharacterized microprotein, Notch1 degradation-associated regulatory polypeptide (N1DARP), encoded by LINC00261. N1DARP knockout accelerated tumor progression and enhanced stem cell properties in pancreatic cancer organoids and LSL-Kras, LSL-Trp53, and Pdx1-Cre (KPC) mice. Mechanistically, N1DARP suppressed canonical and non-canonical Notch1 pathways by competitively disrupting the interaction between N1ICD and ubiquitin-specific peptidase 10 (USP10), thereby promoting K11- and K48-linked polyubiquitination of N1ICD. To evaluate the therapeutic potential of N1DARP, we designed a cell-penetrating stapled peptide, SAH-mAH2-5, with a helical structure similar to that of N1DARP that confers remarkable physicochemical stability. SAH-mAH2-5 interacted with and promoted the proteasome-mediated degradation of N1ICD. SAH-mAH2-5 injection provided substantial therapeutic benefits with limited off-target and systemic adverse effects in Notch1-activated pancreatic cancer models. Taken together, these findings confirm that N1DARP acts as a tumor suppressor and chemosensitizer by regulating USP10-Notch1 oncogenic signaling, and suggest a promising therapeutic strategy targeting the N1DARP-N1ICD interaction in Notch1-activated pancreatic cancer.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

Health-related quality of life and survival outcomes for patients with major depressive disorder and anxiety: A longitudinal study in pancreatic ductal adenocarcinoma.

BACKGROUND: Major depressive disorder (MDD) and anxiety were recognized in treating pancreatic ductal adenocarcinoma (PDAC). This longitudinal study identified risk factors for MDD and anxiety and established associations with patients' quality of life (QoL) and survival outcomes. MATERIALS AND METHODS: We used PHQ-9 and GAD-7 questionnaires to diagnose MDD and anxiety in PDAC patients between October 2021 and March 2022 at a Chinese center. Characteristics and clinical data were analyzed for risk factors and EORTC QLQ-C30 questionnaire was administered for QoL before the first chemotherapy. Furthermore, chemotherapy compliance and 1-year survival were compared during follow-up. RESULTS: MDD and anxiety occurred in 51.8% and 44.7% of 114 patients over the half-year period. Employment at work (odds ratio [OR]: 5.514, p = 0.001; OR: 3.420, p = 0.011) was an independent risk factor, while radical surgery (OR: 0.342, p = 0.034; OR: 0.238, p = 0.004) was a protective factor. Several aspects of decreased QoL were discovered after their onsets. Higher incidences of physical disorders (p = 0.004; p < 0.001), mental disorders (p = 0.001; p < 0.001), anti-therapy emotions (p = 0.002; 0.001), and chemotherapy suspensions (p = 0.001; p = 0.043) were observed. Furthermore, the 1-year mortalities for all patients and those receiving radical surgeries were correlated with MDD (p = 0.007; 0.036) and anxiety (p = 0.010; 0.031). CONCLUSIONS: MDD and anxiety are common in PDAC patients and correlated with poor QoL and survivals. Therefore, appropriate mental management is required in future.

CRISPR-Cas9 identifies growth-related subtypes of glioblastoma with therapeutical significance through cell line knockdown.

BACKGROUND: Glioblastoma (GBM) is a type of highly malignant brain tumor that is known for its significant intratumoral heterogeneity, meaning that there can be a high degree of variability within the tumor tissue. Despite the identification of several subtypes of GBM in recent years, there remains to explore a classification based on genes related to proliferation and growth. METHODS: The growth-related genes of GBM were identified by CRISPR-Cas9 and univariate Cox regression analysis. The expression of these genes in the Cancer Genome Atlas cohort (TCGA) was used to construct growth-related genes subtypes (GGSs) via consensus clustering. Validation of this subtyping was performed using the nearest template prediction (NTP) algorithm in two independent Gene Expression Omnibus (GEO) cohorts and the ZZ cohort. Additionally, copy number variations, biological functions, and potential drugs were analyzed for each of the different subtypes separately. RESULTS: Our research established multicenter-validated GGSs. GGS1 exhibits the poorest prognosis, with the highest frequency of chr 7 gain & chr 10 loss, and the lowest frequency of chr 19 & 20 co-gain. Additionally, GGS1 displays the highest expression of EGFR. Furthermore, it is significantly enriched in metabolic, stemness, proliferation, and signaling pathways. Besides we showed that Foretinib may be a potential therapeutic agent for GGS1, the worst prognostic subtype, through data screening and in vitro experiments. GGS2 has a moderate prognosis, with a slightly higher proportion of chr 7 gain & chr 10 loss, and the highest proportion of chr 19 & 20 co-gain. The prognosis of GGS3 is the best, with the least chr 7 gain & 10 loss and EGFR expression. CONCLUSIONS: These results enhance our understanding of the heterogeneity of GBM and offer insights for stratified management and precise treatment of GBM patients.

Revealing the Role of Active Fillers in Li-ion Conduction of Composite Solid Electrolytes.

Composite solid electrolytes (CSEs) consisting of polyethylene oxide (PEO) matrix and active inorganic fillers have shown great potential for practical applications. However, mechanisms of how different active fillers enhance ion transport in CSEs still remain inconclusive. In this work, the component dependencies of ionic conductivity of PEO-based CSEs are investigated by comparing two widely investigated active fillers: NASICON-type (LATP) and garnet-type (LLZTO). In terms of ionic conductivity, the optimum ratios are strikingly different for LLZTO (10 wt%) and LATP (50 wt%). Through experimental and computational studies, it is demonstrated that the high affinity between LATP and PEO facilitates unhindered interfacial Li+ transfer so that LATP functions as a bulk-active filler to provide additional inorganic ion pathways. By contrast, Li+ transfer between LLZTO and PEO is found to be sluggish. Instead, LLZTO mainly improves ionic conductivity by dissociating lithium salt, making it a surface-active filler. Through categorizing active fillers based on their Li+ conductive mechanisms, this work provides new understanding and guidelines for componential design and optimization of solid composite electrolytes.

Diffusion tensor imaging-based machine learning for IDH wild-type glioblastoma stratification to reveal the biological underpinning of radiomic features.

INTRODUCTION: This study addresses the lack of systematic investigation into the prognostic value of hand-crafted radiomic features derived from diffusion tensor imaging (DTI) in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM), as well as the limited understanding of the biological interpretation of individual DTI radiomic features and metrics. AIMS: To develop and validate a DTI-based radiomic model for predicting prognosis in patients with IDH wild-type GBM and reveal the biological underpinning of individual DTI radiomic features and metrics. RESULTS: The DTI-based radiomic signature was an independent prognostic factor (p < 0.001). Incorporating the radiomic signature into a clinical model resulted in a radiomic-clinical nomogram that predicted survival better than either the radiomic model or clinical model alone, with a better calibration and classification accuracy. Four categories of pathways (synapse, proliferation, DNA damage response, and complex cellular functions) were significantly correlated with the DTI-based radiomic features and DTI metrics. CONCLUSION: The prognostic radiomic features derived from DTI are driven by distinct pathways involved in synapse, proliferation, DNA damage response, and complex cellular functions of GBM.

Controlling the Zero Hall Plateau in a Quantum Anomalous Hall Insulator by In-Plane Magnetic Field.

We investigate the quantum anomalous Hall plateau transition in the presence of independent out-of-plane and in-plane magnetic fields. The perpendicular coercive field, zero Hall plateau width, and peak resistance value can all be systematically controlled by the in-plane magnetic field. The traces taken at various fields almost collapse into a single curve when the field vector is renormalized to an angle as a geometric parameter. These results can be explained consistently by the competition between magnetic anisotropy and in-plane Zeeman field, and the close relationship between quantum transport and magnetic domain structure. The accurate control of zero Hall plateau facilitates the search for chiral Majorana modes based on the quantum anomalous Hall system in proximity to a superconductor.

A novel pyroptosis-associated lncRNA LINC01133 promotes pancreatic adenocarcinoma development via miR-30b-5p/SIRT1 axis.

PURPOSE: Pancreatic adenocarcinoma (PAAD) remains a highly aggressive gastrointestinal malignancy with a dismal prognosis. Pyroptosis has a key role in tumor development. Long noncoding RNAs (lncRNAs) are involved in tumorigenesis and pyroptosis regulation. However, the prognostic potential and function of pyroptosis-related lncRNAs (PRLs) in PAAD remain unclear. We aimed to identify PRLs with promising predictive value for PAAD prognosis and investigate the mechanism by which PRLs affect pyroptosis and PAAD development. METHODS: Key genes that regulate pyroptosis were determined from previous studies, and PRLs were identified from lncRNAs shown to be co-expressed in The Cancer Genome Atlas. Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model was used to establish a prognostic PRL signature. The clinical significance and functional mechanisms of LINC01133 were explored in vitro and in vivo. RESULTS: A seven-lncRNA signature was established and the high-risk subgroup exhibited a shorter survival time. With lower immune infiltration abundance, poor immune function, and higher tumor mutational burden (TMB), the high-risk subgroup reflected a more immunosuppressive status with a greater scope for benefiting from immunotherapy. After LINC01133 knockdown, PAAD cells showed lower viability and higher pyroptosis-related gene expression. LINC01133 functioned as a competing endogenous RNA to sequester miR-30b-5p from sponging SIRT1 mRNA to inhibit PAAD pyroptosis. CONCLUSION: With significant prognostic value, our PRL signature are involved in the biological processes of PAAD cells and associated with the immune environment. LINC01133 suppresses pyroptosis to promote PAAD development and could serve as a potential target for PAAD treatment.

Exploiting Cation Intercalating Chemistry to Catalyze Conversion-Type Reactions in Batteries.

Effective harvest of electrochemical energy from insulating compounds serves as the key to unlocking the potential capacity from many materials that otherwise could not be exploited for energy storage. Herein, an effective strategy is proposed by employing LiCoO2, a widely commercialized positive electrode material in Li-ion batteries, as an efficient redox mediator to catalyze the decomposition of Na2CO3 via an intercalating mechanism. Differing from traditional redox mediation processes where reactions occur on the limited surface sites of catalysts, the electrochemically delithiated Li1-xCoO2 forms NayLi1-xCoO2 crystals, which act as a cation intercalating catalyzer that directs Na+ insertion-extraction and activates the reaction of Na2CO3 with carbon. Through altering the route of the mass transport process, such redox centers are delocalized throughout the bulk of LiCoO2, which ensures maximum active reaction sites. The decomposition of Na2CO3 thus accelerated significantly reduces the charging overpotential in Na-CO2 batteries; meanwhile, Na compensation can also be achieved for various Na-deficient cathode materials. Such a surface-induced catalyzing mechanism for conversion-type reactions, realized via cation intercalation chemistry, expands the boundary for material discovery and makes those conventionally unfeasible a rich source to explore for efficient utilization of chemical energy.