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The universally accepted best practice protocol for monitoring patients who receive intravenous thrombolysis for acute ischaemic stroke was established in the 1990s. However, the protocol is burdensome for nurses, disrupts the sleep of patients, and is potentially less relevant in patients at low risk of symptomatic intracerebral haemorrhage. We aimed to assess whether implementing a low-intensity monitoring protocol would be as safe and effective as standard high-intensity monitoring for patients with acute ischaemic stroke at low risk. OPTIMISTmain was an international, pragmatic, multicentre, stepped-wedge, cluster-randomised, controlled, non-inferiority, blinded-endpoint trial conducted at hospitals (clusters) in eight countries. It was designed to test the non-inferiority of a low-intensity monitoring protocol to a standard protocol among consecutive adults with acute ischaemic stroke who were clinically stable with mild to moderate neurological impairment (score <10 on the National Institutes of Health Stroke Scale) within 2 h of initiation of intravenous thrombolysis according to local guidelines. Participating hospitals were randomly allocated to three sequences of implementation across four periods, stratified by country and projected numbers of participants, in which sites switched from standard monitoring (control) to low-intensity monitoring (intervention) in a stepped manner. The low-intensity monitoring protocol included assessments of neurological and vital signs every 15 min for 2 h, every 2 h for 8 h (vs every 30 min for 6 h for standard monitoring), and every 4 h (vs every 1 h for standard monitoring) until 24 h after thrombolysis. The primary outcome was the proportion of participants with an unfavourable functional outcome defined by a score from 2 (indicating some disability) to 6 (death) on the modified Rankin Scale at 90 days, measured by research staff masked to group allocation. The non-inferiority margin was set at 1·15 for the risk ratio (RR) in the intention-to-treat population. A generalised linear mixed model was used for analysis with adjustments for cluster (hospital site) and time (6-month periods from April, 2021), and imputation of missing outcome data. This trial is registered at Clinicaltrials.gov (NCT03734640) and the Australian New Zealand Clinical Trial Registry (ACTRN 12619001556134p) and is completed. Of 181 hospitals assessed for eligibility, 120 hospitals agreed to join the trial and were randomly allocated between April 28, 2021, and Sept 30, 2024; however, one hospital withdrew, one was not activated, and four did not enrol any patients. Overall, 4922 participants were enrolled at 114 hospitals, with 2789 participants assigned to the low-intensity monitoring group and 2133 to the standard monitoring group. 809 (31·7%) of 2552 participants in the low-intensity group and 606 (30·9%) of 1963 in the standard monitoring group had a modified Rankin Scale score of 2–6 at 90 days (RR 1·03 [95% CI 0·92–1·15], pnon-inferiority=0·057). Symptomatic intracerebral haemorrhage occurred in five (0·2%) of 2783 patients in the low-intensity group and eight (0·4%) of 2122 patients in the standard monitoring group. The numbers of participants with a serious adverse event were similar between the low-intensity monitoring group (309 [11·1%] of 2789) and the standard monitoring group (240 [11·3%] of 2133). OPTIMISTmain provides weak evidence that low-intensity monitoring is non-inferior to standard monitoring in patients with a mild or moderate level of neurological impairment who receive thrombolysis treatment for acute ischaemic stroke. Hospitals could consider incorporating this approach into stroke services according to local circumstances. National Health and Medical Research Council of Australia; New South Wales Health Investigator Development Grant; University of New South Wales Medicine Non Communicable Diseases Theme Early–Mid Career Research Seed Grant Scheme; Medical Research Future Fund (for conduct in Australia); and Genentech (for conduct in the USA).

Modem search engines record user interactions and use them to improve search quality. In particular, user click-through has been successfully used to improve click- through rate （CTR）, Web search ranking, and query rec- ommendations and suggestions. Although click-through logs can provide implicit feedback of users＇ click preferences, de- riving accurate absolute relevance judgments is difficult be- cause of the existence of click noises and behavior biases. Previous studies showed that user clicking behaviors are bi- ased toward many aspects such as ＂position＂ （user＇s attention decreases from top to bottom） and ＂trust＂ （Web site reputa- tions will affect user＇s judgment）. To address these problems, researchers have proposed several behavior models （usually referred to as click models） to describe users？ practical browsing behaviors and to obtain an unbiased estimation of result relevance. In this study, we review recent efforts to construct click models for better search ranking and propose a novel convolutional neural network architecture for build- ing click models. Compared to traditional click models, our model not only considers user behavior assumptions as input signals but also uses the content and context information of search engine result pages. In addition, our model uses pa- rameters from traditional click models to restrict the meaning of some outputs in our model＇s hidden layer. Experimental results show that the proposed model can achieve consider- able improvement over state-of-the-art click models based on the evaluation metric of click perplexity.

Exosomes are extracellular vesicles secreted by most eukaryotic cells and participate in intercellular communication. The components of exosomes, including proteins, DNA, mRNA, microRNA, long noncoding RNA, circular RNA, etc., which play a crucial role in regulating tumor growth, metastasis, and angiogenesis in the process of cancer development, and can be used as a prognostic marker and/or grading basis for tumor patients. Hereby, we mainly summarized as followed: the role of exosome contents in cancer, focusing on proteins and noncoding RNA; the interaction between exosomes and tumor microenvironment; the mechanisms that epithelial-mesenchymal transition, invasion and migration of tumor affected by exosomes; and tumor suppression strategies based on exosomes. Finally, the application potential of exosomes in clinical tumor diagnosis and therapy is prospected, which providing theoretical supports for using exosomes to serve precise tumor treatment in the clinic.

Atmospheric particulates can be produced by emissions or form de novo. New particle formation usually occurs in relatively clean air. This is because preexisting particles in the atmosphere will scavenge the precursors of new particles and suppress their formation. However, observations in some heavily polluted megacities have revealed substantial rates of new particle formation despite the heavy loads of ambient aerosols. Yao et al. investigated new particle formation in Shanghai and describe the conditions that make this process possible. The findings will help inform policy decisions about how to reduce air pollution in these types of environments. Science , this issue p. 278 Atmospheric new particle formation in heavily polluted cities can occur in certain chemical environments. Atmospheric new particle formation (NPF) is an important global phenomenon that is nevertheless sensitive to ambient conditions. According to both observation and theoretical arguments, NPF usually requires a relatively high sulfuric acid (H 2 SO 4 ) concentration to promote the formation of new particles and a low preexisting aerosol loading to minimize the sink of new particles. We investigated NPF in Shanghai and were able to observe both precursor vapors (H 2 SO 4 ) and initial clusters at a molecular level in a megacity. High NPF rates were observed to coincide with several familiar markers suggestive of H 2 SO 4 –dimethylamine (DMA)–water (H 2 O) nucleation, including sulfuric acid dimers and H 2 SO 4 -DMA clusters. In a cluster kinetics simulation, the observed concentration of sulfuric acid was high enough to explain the particle growth to ~3 nanometers under the very high condensation sink, whereas the subsequent higher growth rate beyond this size is believed to result from the added contribution of condensing organic species. These findings will help in understanding urban NPF and its air quality and climate effects, as well as in formulating policies to mitigate secondary particle formation in China.

We elected to analyze the correlation between the pre-treatment apparent diffusion coefficient (ADC) and the clinical, histological, and immunohistochemical status of rectal cancers. Forty-nine rectal cancer patients who received surgical resection without neoadjuvant therapy were selected that underwent primary MRI and diffusion-weighted imaging (DWI). Tumor ADC values were determined and analyzed to identify any correlations between these values and pre-treatment CEA or CA19-9 levels, and/or the histological and immunohistochemical properties of the tumor. Inter-observer agreement of confidence levels from two separate observers was suitable for ADC measurement (k  =  0.775). The pre-treatment ADC values of different T stage tumors were not equal (p  =  0.003). The overall trend was that higher T stage values correlated with lower ADC values. ADC values were also significantly lower for the following conditions: tumors with the presence of extranodal tumor deposits (p  =  0.006) and tumors with CA19-9 levels ≥ 35 g/ml (p  =  0.006). There was a negative correlation between Ki-67 LI and the ADC value (r  =  -0.318, p  =  0.026) and between the AgNOR count and the ADC value (r  =  -0.310, p  =  0.030). Significant correlations were found between the pre-treatment ADC values and T stage, extranodal tumor deposits, CA19-9 levels, Ki-67 LI, and AgNOR counts in our study. Lower ADC values were associated with more aggressive tumor behavior. Therefore, the ADC value may represent a useful biomarker for assessing the biological features and possible relationship to the status of identified rectal cancers.

Background Fragility fracture is common in the elderly. Osteoblast differentiation is essential for bone healing and regeneration. Expression pattern of long non-coding RNA MIAT during fracture healing was examined, and its role in osteoblast differentiation was investigated. Methods 90 women with simple osteoporosis and 90 women with fragility fractures were included. Another 90 age-matched women were set as the control group. mRNA levels were tested using RT-qPCR. Cell viability was detected via CCK-8, and osteoblastic biomarkers, including ALP, OCN, Collagen I, and RUNX2 were tested via ELISA. The downstream miRNAs and genes targeted by MIAT were predicted by bioinformatics analysis, whose functions and pathways were annotated via GO and KEGG analysis. Results Serum MIAT was upregulated in osteoporosis women with high accuracy of diagnostic efficacy. Serum MIAT was even elevated in the fragility fracture group, but decreased in a time manner after operation. MIAT knockdown promoted osteogenic proliferation and differentiation of MC3T3-E1, but the influences were reversed by miR-181a-5p inhibitor. A total of 137 overlapping target genes of miR-181a-5p were predicted based on the miRDB, TargetScan and microT datasets, which were mainly enriched for terms related to signaling pathways regulating pluripotency of stem cells, cellular senescence, and osteoclast differentiation. Conclusions LncRNA MIAT serves as a promising biomarker for osteoporosis, and promotes osteogenic differentiation via targeting miR-181a-5p.

The repression of transposons by the Piwi-interacting RNA (piRNA) pathway is essential to protect animal germ cells. In Drosophila , Panoramix enforces transcriptional silencing by binding to the target-engaged Piwi–piRNA complex, although the precise mechanisms by which this occurs remain elusive. Here, we show that Panoramix functions together with a germline-specific paralogue of a nuclear export factor, dNxf2, and its cofactor dNxt1 (p15), to suppress transposon expression. The transposon RNA-binding protein dNxf2 is required for animal fertility and Panoramix-mediated silencing. Transient tethering of dNxf2 to nascent transcripts leads to their nuclear retention. The NTF2 domain of dNxf2 competes dNxf1 (TAP) off nucleoporins, a process required for proper RNA export. Thus, dNxf2 functions in a Panoramix–dNxf2-dependent TAP/p15 silencing (Pandas) complex that counteracts the canonical RNA exporting machinery and restricts transposons to the nuclear peripheries. Our findings may have broader implications for understanding how RNA metabolism modulates heterochromatin formation. Zhao et al. identify an unexpected role of the nuclear export factor Nxf2 as a partner of Panoramix in mediating piRNA-guided silencing. Nxf2 counteracts Nxf1-centred nuclear RNA transport to prevent the export of transposon transcripts.

Periphyton is a complex community composed of diverse prokaryotes and eukaryotes; understanding the characteristics of microbial communities within periphyton becomes crucial for biogeochemical cycles and energy dynamics of aquatic ecosystems. To further elucidate the community characteristics of periphyton across varied aquatic habitats, including unpolluted ecologically restored lakes, aquaculture ponds, and areas adjacent to domestic and industrial wastewater treatment plant outfalls, we explored the composition and diversity of prokaryotic and eukaryotic communities in periphyton by employing Illumina MiSeq sequencing. Our findings indicated that the prokaryotic communities were predominantly composed of Proteobacteria (40.92%), Bacteroidota (21.01%), and Cyanobacteria (10.12%), whereas the eukaryotic communities were primarily characterized by the dominance of Bacillariophyta (24.09%), Chlorophyta (20.83%), and Annelida (15.31%). Notably, Flavobacterium emerged as a widely distributed genus among the prokaryotic community. Unclassified_Tobrilidae exhibited higher abundance in unpolluted ecologically restored lakes. Chaetogaster and Nais were enriched in aquaculture ponds and domestic wastewater treatment plant outfall area, respectively, while Surirella and Gomphonema dominated industrial sewage treatment plant outfall area. The alpha diversity of eukaryotes was higher in unpolluted ecologically restored lakes. pH and nitrogen content ( NO 2 - - N , NO 3 - - N , and TN) significantly explained the variations for prokaryotic and eukaryotic community structures, respectively. Eukaryotic communities exhibited a more pronounced response to habitat variations compared to prokaryotic communities. Moreover, the association networks revealed an intensive positive correlation between dominant Bacillariophyta and Bacteroidota. This study provided useful data for identifying keystone species and understanding their ecological functions.

Background: Cognitive impairment (CI) is recognized as a debilitating complication of Parkinson’s disease (PD). This study was designed to develop a diagnostic classification model by integrating substantia nigra hyperechogenicity (SNH) and inflammationassociated biomarkers to evaluate its diagnostic performance in distinguishing PD CI stages. Methods: Between January, 2023 and May, 2024, 184 patients with PD who underwent transcranial sonography were prospectively enrolled. Based on Montreal Cognitive Assessment (MoCA) scores, participants were categorized into three groups: cognitive impairment (PD-CI, MoCA <26), mild cognitive impairment (PD-MCI, MoCA 22–25), and dementia (PD-dementia, MoCA ≤21). Ultrasound features and inflammationassociated biomarkers were screened with univariate analyses. Multivariate logistic regression was used to identify independent diagnostic factors, and receiver operating characteristic (ROC) curve analysis was used to assess model discrimination. Results: Multivariate regression analysis indicated that age <50 years and more years of education were significantly associated factors for CI (OR = 0.170, p = 0.0350; OR = 0.8780, p = 0.0020, respectively), whereas Unified Parkinson’s Disease Rating Scale Part III (UPDRSIII) score (OR = 1.024, p = 0.0270), SNH (OR = 2.550, p = 0.0030), elevated C-reactive protein (CRP) (OR = 2.038, p = 0.0350), and elevated homocysteine (Hcy) (OR = 2.830, p = 0.0020) were independent risk factors. The area uinder the curves (AUCs) for the combined SNH+CRP+Hcy model in predicting PD-CI, PD-MCI, and PD-dementia were 0.783, 0.729, and 0.823, respectively; these values were significantly superior to those for single or dual marker combinations (p < 0.05), with the strongest performance for distinguishing PD-dementia. Conclusion: An SNH and inflammationassociated biomarkerbased model was developed for predicting the stage of cognitive impairment in PD. Clinical targets for individualized intervention can be provided, and clinical risk stratification and care pathways can be optimized. Furthermore, the model supports the iron deposition-neuroinflammation-CI pathway hypothesis, providing a mechanistic rationale for ultrasoundbased PD-CI diagnosis.

With rapid urbanization and industrialization in China, existing gas supply networks urgently require renewal and optimization. This paper proposes a Gray Wolf Optimizer (GWO)-based method for reducing calculation errors and a Zebra Optimization Algorithm (ZOA)-based approach for gas supply pressure distribution. For error correction, the pipe friction coefficient is adjusted to minimize the deviation between calculated and actual flows. The GWO reduces average relative error to 0.01% with satisfactory iteration speed and efficiency. For pressure distribution, supply-end pressures are optimized to reduce energy consumption and enhance system performance. The ZOA shows strong convergence and global search capabilities. These methods provide valuable theoretical and practical insights for optimizing gas supply networks, supporting green transformation and sustainable development.