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The application of semiconductor metal oxides in chemiresistive methane gas sensors has seen significant progress in recent years, driven by their promising sensitivity, miniaturization potential, and cost-effectiveness. This paper presents a comprehensive review of recent developments and future perspectives in this field. The main findings highlight the advancements in material science, sensor fabrication techniques, and integration methods that have led to enhanced methane-sensing capabilities. Notably, the incorporation of noble metal dopants, nanostructuring, and hybrid materials has significantly improved sensitivity and selectivity. Furthermore, innovative sensor fabrication techniques, such as thin-film deposition and screen printing, have enabled cost-effective and scalable production. The challenges and limitations facing metal oxide-based methane sensors were identified, including issues with sensitivity, selectivity, operating temperature, long-term stability, and response times. To address these challenges, advanced material science techniques were explored, leading to novel metal oxide materials with unique properties. Design improvements, such as integrated heating elements for precise temperature control, were investigated to enhance sensor stability. Additionally, data processing algorithms and machine learning methods were employed to improve selectivity and mitigate baseline drift. The recent developments in semiconductor metal oxide-based chemiresistive methane gas sensors show promising potential for practical applications. The improvements in sensitivity, selectivity, and stability achieved through material innovations and design modifications pave the way for real-world deployment. The integration of machine learning and data processing techniques further enhances the reliability and accuracy of methane detection. However, challenges remain, and future research should focus on overcoming the limitations to fully unlock the capabilities of these sensors. Green manufacturing practices should also be explored to align with increasing environmental consciousness. Overall, the advances in this field open up new opportunities for efficient methane monitoring, leak prevention, and environmental protection.

Non-alcoholic steatohepatitis (NASH)-associated hepatocellular carcinoma (HCC) has been emerging a predominant reason for liver transplantation (LT). The complexity of comorbidities in this population increases the possibility of poor transplant outcomes. The purpose of this study was to evaluate the differences in survival after transplantation among patients with NASH HCC and those with non-NASH HCC. We conducted systematic searches of the PubMed, Embase, Web of Science, and Cochrane Library databases. To analyze the data, both fixed and random-effects models were employed to aggregate hazard ratios (HRs) along with 95% confidence intervals (CIs) for recurrence-free survival (RFS) and overall survival (OS) outcomes. This study is registered with PROSPERO as CRD42024578441. A total of seven studies were included in this study. This study revealed that there was no significant difference in OS between liver transplant recipients with NASH HCC and those with non-NASH HCC. The RFS of NASH HCC patients were significantly longer. The HRs were 0.70 (95% CI: 0.51-0.97, P = 0.03) for RFS and 0.88 (95% CI: 0.72-1.07, P = 0.21) for OS, respectively. This study indicates that patients with NASH HCC who undergo LT have comparable OS as those with non-NASH HCC, while NASH HCC was associated with increased RFS. However, further research in randomized trials is necessary to verify these results and address potential selection biases.

Background Papillary thyroid cancer (PTC) is the most common malignancy of all thyroid cancers. LncRNA LINC00460 has been proved to play roles in the oncogenesis and progression of various tumors, including papillary thyroid cancer. However, the potential molecular mechanism of LINC00460 in PTC is poorly investigated. Results LINC00460 was upregulated in PTC tissues and cells. Raf1 was upregulated in PTC tissues, but miR-485-5p was down-regulated. High LINC00460 expression was associated with poor prognosis. LINC00460 knockdown suppressed proliferation, migration, invation and EMT of PTC cells. Bioinformatics prediction revealed that LINC00460 had binding sites with miR-485-5p, which was validated by luciferase reporter assay. In addition, miR-485-5p was confirmed to directly target Raf1 3′-UTR. Moreover, LINC00460 promoted PTC progression by sponging miR-485-5p to elevate the expression of Raf1. Knockdown of LINC00460 restrained tumor growth in vivo. Conclusion LINC00460 induced proliferation, migration, invation and EMT of PTC cells by regulating the LINC00460/miR-485-5p/Raf1 axis, which indicated that LINC00460 may be a potential biomarker and therapeutic target for PTC.

To determine the path of disease in different types of networks, a new method based on compressive sensing is proposed for identifying the disease propagation paths in two-layer networks. If a limited amount of data from network nodes is collected, according to the principle of compressive sensing, it is feasible to accurately identify the path of disease propagation in a multilayer network. Experimental results show that the method can be applied to various networks, such as scale-free networks, small-world networks, and random networks. The impact of network density on identification accuracy is explored. The method could be used to aid in the prevention of disease spread.

NUcleoside Diphosphate-linked to moiety X (NUDIX) hydrolases are ubiquitous enzymes that maintain metabolic homeostasis by hydrolyzing potentially toxic nucleoside diphosphates. In plants and other eukaryotes, inositol pyrophosphates (PP-InsPs) act as central signaling molecules, linking cellular phosphate status to gene expression via SPX-domain receptors. A recent study (McCombe et al., Science 387:955–962, 2025) showed that several plant pathogenic fungi secrete NUDIX effector proteins that hydrolyze PP-InsPs and manipulate host phosphate signaling. In the blast fungus Magnaporthe oryzae , a cytoplasmic NUDIX effector (MoNUDIX) hydrolyzes PP-InsPs, triggers a phosphate starvation response and suppresses immunity in rice, thereby facilitating disease progression. In contrast, the lentil anthracnose pathogen Colletotrichum lentis secretes CtNUDIX into the apoplast, where it disrupts PP-InsP-dependent endocytic machinery and elicits a hypersensitive cell death response. Collectively, these findings demonstrate how NUDIX effectors exemplify mechanistic diversification within a single effector family: manipulating phosphate signaling promotes biotrophic colonization, whereas disrupting host membrane integrity induces a switch to necrotrophy.

Viruses can hijack autophagosomes as the nonlytic release vehicles in cultured host cells. However, how autophagosome-mediated viral spread occurs in infected host tissues or organs in vivo remains poorly understood. Here, we report that an important rice reovirus, rice gall dwarf virus (RGDV) hijacks autophagosomes to traverse multiple insect membrane barriers in the midgut and salivary gland of leafhopper vector to enhance viral spread. Such virus-containing double-membraned autophagosomes are prevented from degradation, resulting in increased viral propagation. Mechanistically, viral nonstructural protein Pns11 induces autophagy and embeds itself in the autophagosome membranes. The autophagy-related protein 5 (ATG5)-ATG12 conjugation is essential for initial autophagosome membrane biogenesis. RGDV Pns11 specifically interacts with ATG5, both in vitro and in vivo . Silencing of ATG5 or Pns11 expression suppresses ATG8 lipidation, autophagosome formation, and efficient viral propagation. Thus, Pns11 could directly recruit ATG5-ATG12 conjugation to induce the formation of autophagosomes, facilitating viral spread within the insect bodies. Furthermore, Pns11 potentially blocks autophagosome degradation by directly targeting and mediating the reduced expression of N-glycosylated Lamp1 on lysosomal membranes. Taken together, these results highlight how RGDV remodels autophagosomes to benefit viral propagation in its insect vector.

β-Branched aromatic α-amino acids are valuable building blocks in natural products and pharmaceutically active compounds. However, their chemical or enzymatic synthesis is challenging due to the presence of two stereocenters. We design phenylalanine ammonia lyases (PAL) variants for the direct asymmetric synthesis of β-branched aromatic α-amino acids. Based on extensive computational analyses, we unravel the enigma behind PAL’s inability to accept β-methyl cinnamic acid (β-MeCA) as substrate and achieve the synthesis of the corresponding amino acids of β-MeCA and analogs using a double (PcPAL-L256V-I460V) and a triple mutant (PcPAL-F137V-L256V-I460V). The reactions are scaled-up using an optimized E. coli based whole-cell biotransformation system to produce ten β-branched phenylalanine analogs with high diastereoselectivity (dr > 20:1) and enantioselectivity (ee > 99.5%) in yields ranging from 41-71%. Moreover, we decipher the mechanism of PcPAL-L256V-I460V for the acceptance of β-MeCA and converting it with excellent stereoselectivity by computational simulations. Thus, this study offers an efficient method for synthesizing β-branched aromatic α-amino acids. β-Branched aromatic α-amino acids are valuable building blocks in natural products and pharmaceutically active compounds, but their synthesis is challenging due to the presence of two stereocenters. Here, the authors design phenylalanine ammonia lyases variants for the direct asymmetric synthesis of β-branched aromatic α-amino acids and reveal the reasons for enzyme’s inability to accept β-methyl cinnamic acid.

Maize leaf and sheath spot disease caused by Epicoccum sorghinum is an emerging disease of maize in China. To disentangle the molecular pathogenesis, we sequenced the genome and infection transcriptomes of the E. sorghinum strain NJC07. The genome was sequenced on Oxford Nanopore GridION and Illumina NovaSeq 6000, producing a near-complete gapless nuclear genome assembly of 32.69 Mb at 285.20-fold depth, comprising 23 contigs (including 12 full-length chromosomes) with an N 50 contig number/length of 6/1.66 Mb, and a complete mitochondrial genome assembly of 61.24 kb. The nuclear genome contains 11,779 protein-coding genes, including those predicted to encode potential virulence/pathogenicity factors, such as effectors and carbohydrate-active enzymes. Temporal RNA-Seq analysis revealed that 4,058 of the 11,779 genes were induced during maize infection, with a subset potentially implicated in fungal invasion and colonization of maize plants. Together, the genomic and transcriptomic data generated in the study provide a valuable foundation for the functional analysis of virulence and pathogenicity factors, offering critical insights into the molecular mechanisms driving E. sorghinum pathogenesis on maize.

Purpose To assess the efficacy and toxicity between high-dose radiotherapy (HDRT) and conventional-dose radiotherapy (CDRT) by collecting randomized controlled trials of long-term follow-ups. Methods Unrestricted by language, we searched Ovid MEDLINE, Ovid EMBASE, Cochrane Library, Science Citation Index (Web of Science) and ClinicalTrials.gov for the following end points: biochemical failure (BF), overall survival (OS), prostate cancer-specific survival (PCSS) and side effects. The meta-analysis was performed by using Review Manager 5.2 and Stata version 12.0 software packages. Results were expressed as the odds ratio (OR) with the corresponding 95 % confidence interval (CI). Results Six randomized controlled trials, with a total population of 2,822, were eligible. In terms of 10-year efficacy relative to CDRT, the HDRT was associated with almost an equivalent OS (73.4 vs. 74.3 %, OR 1.05, 95 % CI 0.86–1.28; p  = 0.64) and PCSS (90.7 vs. 91.6 %, OR 1.11, 95 % CI 0.83–1.49; p  = 0.47), but a significant decrease in the BF (34.0 vs. 24.7 %, OR 0.61, 95 % CI 0.51–0.74; p  < 0.00001). In terms of toxicity, HDRT significantly increased the late Grade 2 or higher ( G  ≥ 2) gastrointestinal toxicity (28.0 vs. 18.6 %, OR 1.72, 95 % CI 1.42–2.08; p  < 0.00001) and late G  ≥ 2 genitourinary (GU) toxicity (22.6 vs. 19.5 %, OR 1.24, 95 % CI 1.01–1.52; p  = 0.04). In the subgroup analysis, trials with or without androgen deprivation therapy both had a significant decrease in the BF at 10 years. With regard to quality of life, there was no significant difference between HDRT and CDRT ( p  > 0.05). Conclusion This was the first meta-analysis of trials with long-term follow-up to indicate that HDRT is superior to CDRT in terms of preventing BF in localized prostate cancer patients. However, this advantage did not translate into an improvement in OS and PCSS. This was also the first meta-analysis to suggest that the HDRT in three-dimensional conformal radiotherapy (3D-CRT) significantly increases the late G  ≥ 2 GU toxicity. Thus, the dose escalation in 3D-CRT should be discreetly used in the treatment of prostate cancer due to the increase in late toxicities.

Background Surface-guided radiation therapy can be used to continuously monitor a patient’s surface motions during radiotherapy by a non-irradiating, noninvasive optical surface imaging technique. In this study, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context. Methods Seven groups of interrelated external/internal respiratory liver motion samples lasting from 5 to 6 min collected simultaneously were used as a dataset, D v . Long short-term memory (LSTM) and support vector regression (SVR) networks were then used to establish external respiratory signal prediction models (LSTMpred/SVRpred) and external/internal respiratory motion correlation models (LSTMcorr/SVRcorr). These external prediction and external/internal correlation models were then combined into an integrated model. Finally, the LSTMcorr model was used to perform five groups of model updating experiments to confirm the necessity of continuously updating the external/internal correlation model. The root-mean-square error (RMSE), mean absolute error (MAE), and maximum absolute error (MAX_AE) were used to evaluate the performance of each model. Results The models established using the LSTM neural network performed better than those established using the SVR network in the tasks of predicting external respiratory signals for latency-compensation (RMSE < 0.5 mm at a latency of 450 ms) and predicting internal liver motion using external signals (RMSE < 0.6 mm). The prediction errors of the integrated model (RMSE ≤ 1.0 mm) were slightly higher than those of the external prediction and external/internal correlation models. The RMSE/MAE of the fifth model update was approximately ten times smaller than that of the first model update. Conclusions The LSTM networks outperform SVR networks at predicting external respiratory signals and internal liver motion because of LSTM’s strong ability to deal with time-dependencies. The LSTM-based integrated model performs well at predicting liver motion from external respiratory signals with system latencies of up to 450 ms. It is necessary to update the external/internal correlation model continuously.