Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor.

Low-grade gliomas almost invariably progress into secondary glioblastoma (sGBM) with limited therapeutic option and poorly understood mechanism. By studying the mutational landscape of 188 sGBMs, we find significant enrichment of TP53 mutations, somatic hypermutation, MET-exon-14-skipping (METex14), PTPRZ1-MET (ZM) fusions, and MET amplification. Strikingly, METex14 frequently co-occurs with ZM fusion and is present in ∼14% of cases with significantly worse prognosis. Subsequent studies show that METex14 promotes glioma progression by prolonging MET activity. Furthermore, we describe a MET kinase inhibitor, PLB-1001, that demonstrates remarkable potency in selectively inhibiting MET-altered tumor cells in preclinical models. Importantly, this compound also shows blood-brain barrier permeability and is subsequently applied in a phase I clinical trial that enrolls MET-altered chemo-resistant glioma patients. Encouragingly, PLB-1001 achieves partial response in at least two advanced sGBM patients with rarely significant side effects, underscoring the clinical potential for precisely treating gliomas using this therapy.

Aerial additive manufacturing with multiple autonomous robots.

Additive manufacturing methods1-4 using static and mobile robots are being developed for both on-site construction5-8 and off-site prefabrication9,10. Here we introduce a method of additive manufacturing, referred to as aerial additive manufacturing (Aerial-AM), that utilizes a team of aerial robots inspired by natural builders11 such as wasps who use collective building methods12,13. We present a scalable multi-robot three-dimensional (3D) printing and path-planning framework that enables robot tasks and population size to be adapted to variations in print geometry throughout a building mission. The multi-robot manufacturing framework allows for autonomous three-dimensional printing under human supervision, real-time assessment of printed geometry and robot behavioural adaptation. To validate autonomous Aerial-AM based on the framework, we develop BuilDrones for depositing materials during flight and ScanDrones for measuring the print quality, and integrate a generic real-time model-predictive-control scheme with the Aerial-AM robots. In addition, we integrate a dynamically self-aligning delta manipulator with the BuilDrone to further improve the manufacturing accuracy to five millimetres for printing geometry with precise trajectory requirements, and develop four cementitious-polymeric composite mixtures suitable for continuous material deposition. We demonstrate proof-of-concept prints including a cylinder 2.05 metres high consisting of 72 layers of a rapid-curing insulation foam material and a cylinder 0.18 metres high consisting of 28 layers of structural pseudoplastic cementitious material, a light-trail virtual print of a dome-like geometry, and multi-robot simulations. Aerial-AM allows manufacturing in-flight and offers future possibilities for building in unbounded, at-height or hard-to-access locations.

Selective binding and periodic arrangement of magic boron clusters on monolayer borophene.

The synthesis and characterization of small boron clusters with unique size and regular arrangement are crucial for boron chemistry and two-dimensional borophene materials. In this study, together with theoretical calculations, the joint molecular beam epitaxy and scanning tunneling microscopy experiments achieve the formation of unique B5 clusters on monolayer borophene (MLB) on a Cu(111) surface. The B5 clusters tend to selectively bind to specific sites of MLB with covalent boron-boron bonds in the periodic arrangement, which can be ascribed to the charge distribution and electron delocalization character of MLB and also prohibits nearby co-adsorption of B5 clusters. Furthermore, the close-packed adsorption of B5 clusters would facilitate the synthesis of bilayer borophene, exhibiting domino effect-like growth mode. The successful growth and characterization of uniform boron clusters on a surface enrich the boron-based nanomaterials and reveal the essential role of small clusters during the growth of borophene.

Efficient prediction of peptide self-assembly through sequential and graphical encoding.

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.

Heterozygous variants in USP25 cause genetic generalized epilepsy.

USP25 encodes ubiquitin-specific proteases 25, a key member of deubiquitinating enzyme family and is involved in neural fate determination. Although abnormal expression in Down's syndrome was reported previously, the specific role of USP25 in human diseases has not been defined. In this study, we performed trio-based whole exome sequencing in a cohort of 319 cases (families) with generalized epilepsy of unknown etiology. Five heterozygous USP25 variants including two de novo and three co-segregated variants were determined in eight individuals affected by generalized seizures and/or febrile seizures from five unrelated families. The frequency of USP25 variants showed a significantly high aggregation in this cohort compared to the East Asian population and all populations in the gnomAD database. The mean onset ages of febrile and afebrile seizures were 10 months (infancy) and 11.8 years (juvenile), respectively. The patients achieved seizure freedom except one had occasional nocturnal seizures at the last follow-up. Two patients exhibited intellectual disability. Usp25 was ubiquitously expressed in mouse brain with two peaks on embryonic days (E14‒E16) and postnatal day 21, respectively. Similarly, USP25 expressed in fetus/early childhood stage with a second peak at approximately 12‒20 years old in human brain, consistent with the seizure onset age at infancy and juvenile in the patients. To investigate the functional impact of USP25 deficiency in vivo, we established Usp25 knock-out mice, which showed increased seizure susceptibility compared to wild-type mice in pentylenetetrazol-induced seizure test. To explore the impact of USP25 variants, we employed multiple functional detections. In HEK293T cells, the severe phenotype associated variant (p.Gln889Ter) led to a significant reduction of mRNA and protein expressions but formed a stable truncated dimers with increment of deubiquitinating enzyme activities and abnormal cellular aggregations, indicating a gain-of-function effect. The p.Gln889Ter and p.Leu1045del increased neuronal excitability in mice brain, with a higher firing ability in p.Gln889Ter. These functional impairments align with the severity of the observed phenotypes, suggesting a genotype-phenotype correlation. Hence, a moderate association between USP25 and epilepsy was noted, indicating USP25 is potentially a predisposing gene for epilepsy. Our results from Usp25 null mice and the patient-derived variants indicated that USP25 would play epileptogenic role via loss-of-function or gain-of-function effects. The truncated variant p.Gln889Ter would have profoundly different effect on epilepsy. Together, our results underscore the significance of USP25 heterozygous variants in epilepsy, thereby highlighting the critical role of USP25 in the brain.

Differential cortical gray matter changes in early- and late-onset patients with amyotrophic lateral sclerosis.

Age at onset may be an important feature associated with distinct subtypes of amyotrophic lateral sclerosis (ALS). Little is known about the neuropathological mechanism of early-onset ALS (EO-ALS) and late-onset ALS (LO-ALS). Ninety ALS patients were divided into EO-ALS and LO-ALS group, and 128 healthy controls were matched into young controls(YCs) and old controls (OCs). A voxel-based morphometry approach was employed to investigate differences in gray matter volume (GMV). Significant age at onset-by-diagnosis interactions were found in the left parietal operculum, left precentral gyrus, bilateral postcentral gyrus, right occipital gyrus, and right orbitofrontal cortex. Post hoc analysis revealed a significant decrease in GMV in all affected regions of EO-ALS patients compared with YCs, with increased GMV in 5 of the 6 brain regions, except for the right orbitofrontal cortex, in LO-ALS patients compared with OCs. LO-ALS patients had a significantly increased GMV than EO-ALS patients after removing the aging effect. Correspondingly, GMV of the left postcentral gyrus correlated with disease severity in the 2 ALS groups. Our findings suggested that the pathological mechanisms in ALS patients with different ages at onset might differ. These findings provide unique insight into the clinical and biological heterogeneity of the 2 ALS subtypes.

Seawater electrolysis for fuels and chemicals production: fundamentals, achievements, and perspectives.

Seawater electrolysis for the production of fuels and chemicals involved in onshore and offshore plants powered by renewable energies offers a promising avenue and unique advantages for energy and environmental sustainability. Nevertheless, seawater electrolysis presents long-term challenges and issues, such as complex composition, potential side reactions, deposition of and poisoning by microorganisms and metal ions, as well as corrosion, thus hindering the rapid development of seawater electrolysis technology. This review focuses on the production of value-added fuels (hydrogen and beyond) and fine chemicals through seawater electrolysis, as a promising step towards sustainable energy development and carbon neutrality. The principle of seawater electrolysis and related challenges are first introduced, and the redox reaction mechanisms of fuels and chemicals are summarized. Strategies for operating anodes and cathodes including the development and application of chloride- and impurity-resistant electrocatalysts/membranes are reviewed. We comprehensively summarize the production of fuels and chemicals (hydrogen, carbon monoxide, sulfur, ammonia, etc.) at the cathode and anode via seawater electrolysis, and propose other potential strategies for co-producing fine chemicals, even sophisticated and electronic chemicals. Seawater electrolysis can drive the oxidation and upgrading of industrial pollutants or natural organics into value-added chemicals or degrade them into harmless substances, which would be meaningful for environmental protection. Finally, the perspective and prospects are outlined to address the challenges and expand the application of seawater electrolysis.

A protein network refinement method based on module discovery and biological information.

BACKGROUND: The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs. METHODS: Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules. RESULTS: To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

SMYD4 monomethylates PRMT5 and forms a positive feedback loop to promote hepatocellular carcinoma progression.

Both lysine and arginine methyltransferases are thought to be promising therapeutic targets for malignant tumors, yet how these methyltransferases function in malignant tumors, especially hepatocellular carcinoma (HCC), has not been fully elucidated. Here, we reported that SMYD4, a lysine methyltransferase, acts as an oncogene in HCC. SMYD4 was highly upregulated in HCC and promoted HCC cell proliferation and metastasis. Mechanistically, PRMT5, a well-known arginine methyltransferase, was identified as a SMYD4-binding protein. SMYD4 monomethylated PRMT5 and enhanced the interaction between PRMT5 and MEP50, thereby promoting the symmetrical dimethylation of H3R2 and H4R3 on the PRMT5 target gene promoter and subsequently activating DVL3 expression and inhibiting expression of E-cadherin, RBL2, and miR-29b-1-5p. Moreover, miR-29b-1-5p was found to inversely regulate SMYD4 expression in HCC cells, thus forming a positive feedback loop. Furthermore, we found that the oncogenic effect of SMYD4 could be effectively suppressed by PRMT5 inhibitor in vitro and in vivo. Clinically, high coexpression of SMYD4 and PRMT5 was associated with poor prognosis of HCC patients. In summary, our study provides a model of crosstalk between lysine and arginine methyltransferases in HCC and highlights the SMYD4-PRMT5 axis as a potential therapeutic target for the treatment of HCC.

Title High Solar-Thermal Conversion Aerogel for Efficient Atmospheric Water Harvesting.

The shortage of freshwater is a global problem, however, the gel that can be used for atmospheric water harvesting (AWH) in recent years studying, suffer from salt leakage, agglomeration, and slow water evaporation efficiency. Herein, a solar-driven atmospheric water harvesting (SAWH) aerogel is prepared by UV polymerization and freeze-drying technique, using poly(N-isopropylacrylamide) (PNIPAm), hydroxypropyl cellulose (HPC), ethanolamine-decorate LiCl (E-LiCl) and polyaniline (PANI) as raw materials. The PNIPAm and HPC formed aerogel networks makes the E-LiCl stably and efficiently loaded, improving the water adsorption-desorption kinetics, and PANI achieves rapid water vapor evaporation. The aerogel has low density ≈0.12-0.15 g cm-3, but can sustain a weight of 1000 times of its own weight. The synergist of elements and structure gives the aerogel has 0.46-2.95 g g-1 water uptake capability at 30-90% relative humidity, and evaporation rate reaches 1.98 kg m-2 h-1 under 1 sun illumination. In outdoor experiments, 88% of the water is harvesting under natural light irradiation, and an average water harvesting rate of 0.80 gwater gsorbent -1 day-1. Therefore, the aerogel can be used in arid and semi-arid areas to collect water for plants and animals.

Interlayer Entropy Engineering Inducing the Symmetry-Broken Layered Oxide Cathodes to Activate Reversible High-Voltage Redox Reaction.

The as-reported doping entropy engineering of electrode materials that are usually realized by the sharing of multiple metal elements with the metal element from the lattice body, potentially has three shortages of stringent synthesis conditions, large active element loss, and serious lattice distortion. Herein, an interlayer entropy engineering of layered oxide cathodes is proposed, where the multiple metal ions are simultaneously intercalated into the same interlayer sites, thus avoiding the three shortages. Concretely, a novel interlayer medium-entropy V2O5 ((MnCoNiMgZn)0.26V2O5∙0.84H2O) is successfully constructed by a one-step hydrothermal method. The interlayer medium-entropy effect is revealed to be that five metal ions pre-intercalation induces the local symmetry-broken [VO6] octahedra in bilayer V2O5, thus activating the reversible high-voltage redox reaction, inhibiting the layer slip and following phase transformation by its pinning effect, and enhancing the charge transfer kinetics. As a result, the medium-entropy cathode realizes the trade-off between specific capacity and structural stability with a discharge capacity of 152 mAh g-1 at 0.1 A g-1 after 100 cycles, and a capacity retention rate of 98.7% at 0.5 A g-1 after 150 cycles for Li+ storage. This engineering provides a new guideline for the rational design of high-performance layered oxide cathodes.

Enabling Quasi-Zero-Strain Behavior of Layered Oxide Cathodes via Multiple-cations Induced Order-to-Disorder Transition.

The chemically pre-intercalated lattice engineering is widely applied to elevate the electronic conductivity, expand the interlayer spacing, and improve the structural stability of layered oxide cathodes. However, the mainstream unitary metal ion pre-intercalation generally produces the cation/vacancy ordered superstructure, which astricts the further improvement of lattice respiration and charge-carrier ion storage and diffusion. Herein, a multiple metal ions pre-intercalation lattice engineering is proposed to break the cation/vacancy ordered superstructure. Taking the bilayer V2O5 as an example, Ni, Co, and Zn ternary ions are simultaneously pre-intercalated into its interlayer space (NiCoZnVO). It is revealed that the Ni─Co neighboring characteristic caused by Ni(3d)-O(2p)-Co(3d) orbital coupling and the Co-Zn/Ni-Zn repulsion effect due to chemical bond incompatibility, endow the NiCoZnVO sample with the cation/vacancy disordered structure. This not only reduces the Li+ diffusion barrier, but also increases the diffusion dimension of Li+ (from one-dimension to two-dimension). Particularly, Ni, Co, and Zn ions co-pre-intercalation causes a prestress, which realizes a quasi-zero-strain structure at high-voltage window upon charging/discharging process. The functions of Ni ion stabilizing the lattice structure and Co or Zn ions activating more Li+ reversible storage reaction of V5+/V4+ are further revealed. The cation/vacancy disordered structure significantly enhances Li+ storage properties of NiCoZnVO cathode.

Tuning Redox Behavior of Sulfur Cathodes Via Ternary-Coordinated Single Fe Atom in Lithium-Sulfur Batteries.

Modulating the coordination configuration of single Fe atom has been an efficient strategy to strengthen the redox dynamics for lithium-sulfur batteries (LSBs) but remains challenging. Herein, the single Fe atom is functioned with nitrogen and carbon atoms in the first shell, and simultaneously, oxidized sulfur (─SOx) in the second shell, which presents a lower antibonding state and well address the redox activity of sulfur cathodes. In the ternary-coordinated single Fe atom catalyst (FeN2C2-SOx-NC), the binary structure of FeN2C2 provides a lower Fe-S bonding strength and d-p orbital hybridization, which obviously optimizes the adsorption and desorption behavior of sulfur species during the reduction and oxidation reaction processes. Simultaneously, the ─SOx redistributes the electron density of the coordinating nitrogen atoms, which possesses high electron-withdrawing ability and develops electrocatalytic activity. As a result, the sulfur cathodes with FeN2C2-SOx-NC present an excellent high-rate cyclic performance, accompanied by a capacity decay rate of 0.08% per cycle for 500 cycles at 4.0 C. This study provides new insights for optimizing the redox dynamics of sulfur cathodes in LSBs at the atomic level.

Unveiling divergent treatment prognoses in IDHwt-GBM subtypes through multiomics clustering: a swift dual MRI-mRNA model for precise subtype prediction.

BACKGROUND: IDH1-wildtype glioblastoma multiforme (IDHwt-GBM) is a highly heterogeneous and aggressive brain tumour characterised by a dismal prognosis and significant challenges in accurately predicting patient outcomes. To address these issues and personalise treatment approaches, we aimed to develop and validate robust multiomics molecular subtypes of IDHwt-GBM. Through this, we sought to uncover the distinct molecular signatures underlying these subtypes, paving the way for improved diagnosis and targeted therapy for this challenging disease. METHODS: To identify stable molecular subtypes among 184 IDHwt-GBM patients from TCGA, we used the consensus clustering method to consolidate the results from ten advanced multiomics clustering approaches based on mRNA, lncRNA, and mutation data. We developed subtype prediction models using the PAM and machine learning algorithms based on mRNA and MRI data for enhanced clinical utility. These models were validated in five independent datasets, and an online interactive system was created. We conducted a comprehensive assessment of the clinical impact, drug treatment response, and molecular associations of the IDHwt-GBM subtypes. RESULTS: In the TCGA cohort, two molecular subtypes, class 1 and class 2, were identified through multiomics clustering of IDHwt-GBM patients. There was a significant difference in survival between Class 1 and Class 2 patients, with a hazard ratio (HR) of 1.68 [1.15-2.47]. This difference was validated in other datasets (CGGA: HR = 1.75[1.04, 2.94]; CPTAC: HR = 1.79[1.09-2.91]; GALSS: HR = 1.66[1.09-2.54]; UCSF: HR = 1.33[1.00-1.77]; UPENN HR = 1.29[1.04-1.58]). Additionally, class 2 was more sensitive to treatment with radiotherapy combined with temozolomide, and this sensitivity was validated in the GLASS cohort. Correspondingly, class 2 and class 1 exhibited significant differences in mutation patterns, enriched pathways, programmed cell death (PCD), and the tumour immune microenvironment. Class 2 had more mutation signatures associated with defective DNA mismatch repair (P = 0.0021). Enriched pathways of differentially expressed genes in class 1 and class 2 (P-adjust < 0.05) were mainly related to ferroptosis, the PD-1 checkpoint pathway, the JAK-STAT signalling pathway, and other programmed cell death and immune-related pathways. The different cell death modes and immune microenvironments were validated across multiple datasets. Finally, our developed survival prediction model, which integrates molecular subtypes, age, and sex, demonstrated clinical benefits based on the decision curve in the test set. We deployed the molecular subtyping prediction model and survival prediction model online, allowing interactive use and facilitating user convenience. CONCLUSIONS: Molecular subtypes were identified and verified through multiomics clustering in IDHwt-GBM patients. These subtypes are linked to specific mutation patterns, the immune microenvironment, prognoses, and treatment responses.

Metal-Organic Framework Based Sensors for Benzene Vapor.

Sensing of benzene vapor is a hot spot due to the volatile drastic carcinogen even at trace concentration. However, achieving convenient and rapid detection is still a challenge. As a sort of functional porous material, metal-organic frameworks (MOFs) have been developed as detection sensors by adsorbing benzene vapor and converting it into other signals (fluorescence intensity/wavelength, chemiresistive, weight or color, etc.). Supramolecular interaction between benzene molecules and the host framework, aperture size/shape and structural flexibility are influential factors in the performance of MOF-based sensors. Therefore, enhancing the host-guest interactions between the host framework and benzene molecules, or regulating the diffusion rate of benzene molecules by changing the aperture size/shape and flexibility of the host framework to enhance the detection signal are effective strategies for constructing MOF-based sensors. This concept highlights several types of MOF-based sensors for the detection of benzene vapor.

Transformation of A1/A2 Astrocytes Participates in Brain Ischemic Tolerance Induced by Cerebral Ischemic Preconditioning via Inhibiting NDRG2.

Cerebral ischemic preconditioning (CIP) has been shown to improve brain ischemic tolerance against subsequent lethal ischemia. Reactive astrocytes play important roles in cerebral ischemia-reperfusion. Recent studies have shown that reactive astrocytes can be polarized into neurotoxic A1 phenotype (C3d) and neuroprotective A2 phenotype (S100A10). However, their role in CIP remains unclear. Here, we focused on the role of N-myc downstream-regulated gene 2 (NDRG2) in regulating the transformation of A1/A2 astrocytes and promoting to brain ischemic tolerance induced by CIP. A Sprague Dawley rat model of middle cerebral artery occlusion/reperfusion (MCAO/R) was used. Rats were divided into the following six groups: (1) sham group; (2) CIP group: left middle cerebral artery was blocked for 10 min; (3) MCAO/R group: left middle cerebral artery was blocked for 90 min; (4) CIP + MCAO/R group: CIP was performed 72 h before MCAO/R; (5) AAV-NDRG2 + CIP + MCAO/R group: adeno-associated virus (AAV) carrying NDRG2 was administered 14 days before CIP + MCAO/R; (6) AAV-Ctrl + CIP + MCAO/R group: empty control group. The rats were subjected to neurological evaluation 24 h after the above treatments, and then were sacrificed for 2, 3, 5-triphenyltetraolium chloride staining, thionin staining, immunofluorescence and western blot analysis. In CIP + MCAO/R group, the neurological deficit scores decreased, infarct volume reduced, and neuronal density increased compared with MCAO/R group. Notably, CIP significantly increased S100A10 expression and the number of S100A10+/GFAP+ cells, and also increased NDRG2 expression. MCAO/R significantly decreased S100A10 expression and the number of S100A10+/GFAP+ cells yet increased C3d expression and the number of C3d+/GFAP+ cells and NDRG2 expression, and these trends were reversed by CIP + MCAO/R. Furthermore, over-expression of NDRG2 before CIP + MCAO/R, the C3d expression and the number of C3d+/GFAP+ cells increased, while S100A10 expression and the number of S100A10+/GFAP+ cells decreased. Meanwhile, over-expression of NDRG2 blocked the CIP-induced brain ischemic tolerance. Taken together, these results suggest that CIP exerts neuroprotective effects against ischemic injury by suppressing A1 astrocyte polarization and promoting A2 astrocyte polarization via inhibiting NDRG2 expression.

Fe-Doped SrCoOx FET Sensors for Extreme Alkaline pH Sensing.

The accurate measurement of pH in highly alkaline environments is critical for various industrial applications but remains a complex task. This paper discusses the development of novel Fe-doped SrCoOx-based FET sensors for the detection of extreme alkaline pH levels. Through a comprehensive investigation of the effects of Fe doping on the structure, electrical properties, and sensing performance of SrCoOx, we have identified the optimal doping level that significantly enhances the sensor's performance in highly alkaline conditions. With a Fe doping level of 5 mol %, the sensitivity of the sensor improves to 0.86 lg(Ω)/pH while maintaining the response rate. Further increasing the Fe doping to 10 mol % results in a sensor that demonstrates favorable response time, a suitable pH range, and a linear correlation between lg(R) and pH. The combination of X-ray photoelectron spectroscopy and X-ray diffraction analysis provides insight into the regulation mechanisms of Fe doping on the crystal structure, electronic structure, and oxygen vacancy concentration of SrCoOx. Our findings indicate that Fe doping leads to an increase in oxygen vacancy concentration and a decrease in the energy barrier for oxygen ion migration, which contributes to the improved sensing performance of the Fe-doped SrCoOx sensors. Additionally, the study highlights the influence of oxygen vacancy concentration on the electrical properties of SrCoOx. Precise control over the concentration of oxygen vacancies is crucial for optimizing the sensitivity and response speed of SrCoOx FET sensors under extreme alkalinity conditions.

Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study.

BACKGROUND: Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD. METHODS: Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics. RESULTS: High-risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal , p = .022) and clustering coefficient (Cp , p = .029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal : p < .001; Cp : p = .001) in the high-risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p = .004; betweenness: p = .005; age-by-group interaction, p = .038) and right hippocampus (degree: p = .003; betweenness: p = .003). The case-control classifier achieved a cross-validation accuracy of 78.4%. CONCLUSIONS: Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at-risk youth.

Prognostic factors analysis of diffuse midline glioma.

PURPOSE: This study retrospectively analyzes cases of diffuse midline glioma treated with radiotherapy, with the aim of investigating the prognosis of the tumor and its influencing factors. METHODS: From January 2018 to November 2022, we treated 64 patients who were pathologically diagnosed with diffuse midline glioma. Among them, 41 underwent surgical resection, and 23 underwent biopsy procedures. All patients received postoperative radiotherapy. We followed up with the patients to determine the overall survival rate and conducted univariate and multivariate analyses on relevant indicators. RESULTS: The median survival time for the entire patient group was 33.3 months, with overall survival rates of 92.9%, 75.4%, and 45.0% at 1 year, 2 years, and 3 years, respectively. Univariate and multivariate analyses indicated that older patients had a better prognosis. CONCLUSION: Patient age is an independent prognostic factor for patients with diffuse midline glioma undergoing radiation therapy.