Plastron effect enhanced electrochemical CO2 reduction activity over hydrophobic three-dimensional nanoporous silver.

The electrochemical reduction of CO2 on catalyst surfaces is hindered by the inefficient mass transfer of CO2 in aqueous solutions. In this study, we employed an electrochemical reduction approach to fabricate a hydrophobic three-dimensional nanoporous silver catalyst with a plastron effect, aiming to enhance the CO2 diffusion. The resulting catalyst exhibited an exceptional performance with the FECO peaking at 95% at -0.65 V (vs. RHE) and demonstrated remarkable stability during continuous electrolysis for 48 hours. Control experiments, together with Tafel analysis, EIS measurements, and contact angle results, confirmed that the notable enhancement of performance was attributed to the hydrophobic porous structure that facilitated efficient storage and rapid mass transfer of low-solubility CO2 gas reactants.

Influencing Factors of Delirium during Recovery in Urological Postoperative Patients Undergoing Sevoflurane Anaesthesia.

OBJECTIVE: To analyse the incidence and influencing factors of delirium during recovery in urological postoperative patients undergoing sevoflurane anaesthesia. METHODS: The clinical data of patients undergoing sevoflurane anaesthesia in the urology surgery department in our hospital from January 2022 to December 2022 were retrospectively analysed. The incidence of delirium during the recovery period was recorded by using the Chinese version of the Confusion Assessment Method (CAM) for Severity of Delirium after surgery, and the patients were divided into occurrence and non-occurrence groups. Whether delirium occurred during recovery was determined through univariate analysis. In binary logistic regression analysis, the occurrence of emergence delirium was the dependent variable, and the variables with statistical differences in the univariate analysis were the independent variables. The influencing factors of emergence delirium in post-urological surgery patients who underwent sevoflurane anaesthesia were determined. RESULTS: Delirium during recovery occurred in 10 of 100 patients (10.00%). Odds ratio (OR) of age (OR = 1.445, p = 0.022), history of diabetes (OR = 1.798, p = 0.010), operation time (OR = 1.670, p = 0.008), American Society of Anesthesiologists (ASA) classification (OR = 1.740, p = 0.006) and sevoflurane inhalation concentration (OR = 1.890, p = 0.001) are the influencing factors of postoperative delirium in urologic patients undergoing sevoflurane anaesthesia. CONCLUSIONS: Age, history of diabetes, operation time, ASA classification and sevoflurane inhalation concentration are the influencing factors.

A fine-scale Arabidopsis chromatin landscape reveals chromatin conformation-associated transcriptional dynamics.

Plants, as sessile organisms, deploy transcriptional dynamics for adapting to extreme growth conditions such as cold stress. Emerging evidence suggests that chromatin architecture contributes to transcriptional regulation. However, the relationship between chromatin architectural dynamics and transcriptional reprogramming in response to cold stress remains unclear. Here, we apply a chemical-crosslinking assisted proximity capture (CAP-C) method to elucidate the fine-scale chromatin landscape, revealing chromatin interactions within gene bodies closely associated with RNA polymerase II (Pol II) densities across initiation, pausing, and termination sites. We observe dynamic changes in chromatin interactions alongside Pol II activity alterations during cold stress, suggesting local chromatin dynamics may regulate Pol II activity. Notably, cold stress does not affect large-scale chromatin conformations. We further identify a comprehensive promoter-promoter interaction (PPI) network across the genome, potentially facilitating co-regulation of gene expression in response to cold stress. Our study deepens the understanding of chromatin conformation-associated gene regulation in plant response to cold.

Mechanically treated Mn2O3 triggers peracetic acid activation for superior non-radical oxidation of micropollutants: Identification of reactive complexes.

This study used a simple mechanical ball milling strategy to significantly improve the ability of Mn2O3 to activate peracetic acid (PAA) for sustainable and efficient degradation of organic micropollutant (like bisphenol A, BPA). BPA was successfully removed and detoxified via PAA activation by the bm-Mn2O3 within 30 min under neutral environment, with the BPA degradation kinetic rate improved by 3.4 times. Satisfactory BPA removal efficiency can still be achieved over a wide pH range, in actual water and after reuse of bm-Mn2O3 for four cycles. The change in hydrophilicity of Mn2O3 after ball milling evidently elevated the affinity of Mn2O3 for binding to PAA, while the reduction in particle size exposed more active sites contributing partially to catalytic oxidation. Further analysis revealed that BPA oxidation in the ball mill-treated Mn2O3 (bm-Mn2O3)/PAA process mainly depends on the bm-Mn2O3-PAA complex (i.e., Mn(III)-OO(O)CCH3) mediated non-radical pathway rather than R-O• and Mn(IV). Especially, the existence of the Mn(III)-PAA complex was definitely verified by in situ Raman spectroscopy and in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Simultaneously, density functional theory calculations determined that PAA adsorbs readily on manganese sites thereby favoring the formation of Mn(III)-OO(O)CCH3 complexes. This study advances an in-depth understanding of the underlying mechanisms involved in the manganese oxide-catalyzed activation of PAA for superior non-radical oxidation of micropollutants.

USP8-governed GPX4 homeostasis orchestrates ferroptosis and cancer immunotherapy.

Ferroptosis is an iron-dependent type of regulated cell death resulting from extensive lipid peroxidation and plays a critical role in various physiological and pathological processes. However, the regulatory mechanisms for ferroptosis sensitivity remain incompletely understood. Here, we report that homozygous deletion of Usp8 (ubiquitin-specific protease 8) in intestinal epithelial cells (IECs) leads to architectural changes in the colonic epithelium and shortens mouse lifespan accompanied by increased IEC death and signs of lipid peroxidation. However, mice with heterozygous deletion of Usp8 in IECs display normal phenotype and become resistant to azoxymethane/dextran sodium sulfate-induced colorectal tumorigenesis. Mechanistically, USP8 interacts with and deubiquitinates glutathione peroxidase 4 (GPX4), leading to GPX4 stabilization. Thus, USP8 inhibition destabilizes GPX4 and sensitizes cancer cells to ferroptosis in vitro. Notably, USP8 inhibition in combination with ferroptosis inducers retards tumor growth and enhances CD8+ T cell infiltration, which potentiates tumor response to anti-PD-1 immunotherapy in vivo. These findings uncover that USP8 counteracts ferroptosis by stabilizing GPX4 and highlight targeting USP8 as a potential therapeutic strategy to boost ferroptosis for enhancing cancer immunotherapy.

Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy.

BACKGROUND: Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC. METHODS: This retrospective study analyzed 337 ESCC resection specimens from 2020-2021 at the Pudong-Branch (Cohort 1) and 114 from 2021-2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations. RESULTS: The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment. CONCLUSION: This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption (Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhance the efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.

Heavy-Quark Pair Production at Lepton Colliders at NNNLO in QCD.

We compute the total cross section and invariant mass distribution for heavy-quark pair production in e^{+}e^{-} annihilation at the next-to-next-to-next-to-leading order in QCD. The obtained results are expressed as piecewise functions defined by several deeply expanded power series, facilitating a rapid numerical evaluation. Utilizing top-pair production at a collision energy of 500 GeV as a benchmark, we observe a correction of approximately 0.1% for the total cross section and around 10% for the majority of the invariant mass distribution range. These results play a crucial role in significantly reducing theoretical uncertainty: the scale dependence has been diminished to 0.06% for the total cross section and to 5% for the invariant mass distribution. This reduction of uncertainty meets the stringent requirements of future lepton colliders.

Identification of polymorphic markers for germplasm conservation of three precious Chinese palace goldfish using whole-genome sequencing.

China was the first country in the world to breed goldfish and has generated many unique goldfish varieties, including the most aristocratic Chinese palace goldfish. Due to the lack of scientific research on Chinese palace goldfish, their selection and breeding are mainly carried out through traditional hybridization, leading to serious inbreeding and the degradation of germplasm resources. To this end, whole-genome resequencing was performed to understand the genetic variation among three different varieties (eggpompons, goosehead, and tigerhead) from nine core conserved populations in China. A total of 15 polymorphic SSRs were developed for population genetics, and all tested populations were considered moderately polymorphic with an average polymorphism information content value of 0.4943. Genetic diversity in different varieties showed that all conserved populations were well protected with the potential for continued exploitation. This study provides reliable molecular tools and a basis for designing conservation and management programs in Chinese palace goldfish.

Safety and efficacy of dexmedetomidine vs. midazolam in complex gastrointestinal endoscopy: A systematic review and meta-analysis.

OBJECTIVE: This study aims to perform a meta-analysis to evaluate the safety and efficacy of dexmedetomidine versus midazolam for complex digestive endoscopy procedures, with the goal of offering comprehensive clinical evidence. METHODS: Following predefined inclusion criteria, five databases were systematically searched, with a focus on identifying randomized controlled trials (RCTs) that compared the administration of dexmedetomidine and midazolam during complex digestive endoscopy procedures. The statistical software Stata 15.1 was employed for meticulous data analysis. RESULTS: Sixteen RCTs were encompassed, involving a total of 1218 patients. In comparison to the midazolam group, dexmedetomidine administration was associated with a reduced risk of respiratory depression (RR=0.25, 95 %CI: 0.11-0.56) and hypoxemia (RR=0.22, 95 %CI: 0.12-0.39). Additionally, the dexmedetomidine group exhibited lower incidence rates of choking (RR=0.27, 95 %CI: 0.16-0.47), physical movement (RR=0.16, 95 %CI: 0.09-0.27), and postoperative nausea and vomiting (RR=0.56,95 %CI: 0.34-0.92). Patients and endoscopists in the dexmedetomidine group reported higher levels of satisfaction (patient satisfaction: SMD=0.73, 95 %CI: 0.26-1.21; endoscopist satisfaction: SMD=0.84, 95 %CI: 0.24-1.44). The incidence of hypotension and anesthesia recovery time did not significantly differ between the two groups (hypotension: RR=1.73,95 %CI:0.94-3.20; anesthesia recovery time: SMD=0.02, 95 %Cl: 0.44-0.49). It is noteworthy that the administration of dexmedetomidine was associated with a significant increase in the incidence of bradycardia in patients. CONCLUSION: Compared to midazolam, dexmedetomidine exhibits a favorable safety profile for use in complex gastrointestinal endoscopy by significantly reducing the risk of respiratory depression and hypoxemia. Despite this, dexmedetomidine is associated with a higher incidence of bradycardia. These findings underscore the need for further research through larger, multi-center studies to thoroughly investigate dexmedetomidine's safety and efficacy.

X-type silyl ligands for transition-metal catalysis.

Given the critical importance of novel ligand development for transition-metal (TM) catalysis, as well as the resurgence of the field of organosilicon chemistry and silyl ligands, to summarize the topic of X-type silyl ligands for TM catalysis is highly attractive and timely. This review particularly emphasizes the unique σ-donating characteristics and trans-effects of silyl ligands, highlighting their crucial roles in enhancing the reactivity and selectivity of various catalytic reactions, including small molecule activation, Kumada cross-coupling, hydrofunctionalization, C-H functionalization, and dehydrogenative Si-O coupling reactions. Additionally, future developments in this field are also provided, which would inspire new insights and applications in catalytic synthetic chemistry.

Fe-N co-doped biochar derived from biomass waste triggers peracetic acid activation for efficient water decontamination.

In this study, the porous carbon material (FeN-BC) with ultra-high catalytic activity was obtained from waste biomass through Fe-N co-doping. The prominent degradation rate (> 96.8%) of naproxen (NAP) was achieved over a wide pH range (pH 3.0-9.0) in FeN-BC/PAA system. Unlike previously reported iron-based peracetic acid (PAA) systems with •OH or RO• as the dominated reactive species, the degradation of contaminants was attributed to singlet oxygen (1O2) produced by organic radicals (RO•) decomposition, which was proved to be thermodynamically feasible and favorable by theoretical calculations. Combining the theoretical calculations, characteristic and experimental analysis, the synergistic effects of Fe and N were proposed and summarized as follows: i) promoted the formation of extensive defects and Fe0 species that facilitated electron transfer between FeN-BC and PAA and continuous Fe(II) generation; ii) modified the specific surface area (SSA) and the isoelectric point of FeN-BC in favor of PAA adsorption on the catalyst surface. This study provides a strategy for waste biomass reuse to construct a heterogeneous catalyst/PAA system for efficient water purification and reveals the synergistic effects of typical metal-heteroatom for PAA activation.

m6A RNA methylation regulates mitochondrial function.

RNA methylation of N6-methyladenosine (m6A) is emerging as a fundamental regulator of every aspect of RNA biology. RNA methylation directly impacts protein production to achieve quick modulation of dynamic biological processes. However, whether RNA methylation regulates mitochondrial function is not known, especially in neuronal cells which require a high energy supply and quick reactive responses. Here we show that m6A RNA methylation regulates mitochondrial function through promoting nuclear-encoded mitochondrial complex subunit RNA translation. Conditional genetic knockout of m6A RNA methyltransferase Mettl14 (Methyltransferase like 14) by Nestin-Cre together with metabolomic analysis reveals that Mettl14 knockout-induced m6A depletion significantly downregulates metabolites related to energy metabolism. Furthermore, transcriptome-wide RNA methylation profiling of wild type and Mettl14 knockout mouse brains by m6A-Seq shows enrichment of methylation on mitochondria-related RNA. Importantly, loss of m6A leads to a significant reduction in mitochondrial respiratory capacity and membrane potential. These functional defects are paralleled by the reduced expression of mitochondrial electron transport chain complexes, as well as decreased mitochondrial super-complex assembly and activity. Mechanistically, m6A depletion decreases the translational efficiency of methylated RNA encoding mitochondrial complex subunits through reducing their association with polysomes, while not affecting RNA stability. Together, these findings reveal a novel role for RNA methylation in regulating mitochondrial function. Given that mitochondrial dysfunction and RNA methylation have been increasingly implicate in neurodegenerative disorders, our findings not only provide insights into fundamental mechanisms regulating mitochondrial function, but also open up new avenues for understanding the pathogenesis of neurological diseases.

Chemical manipulation of m1A mediates its detection in human tRNA.

N 1-methyl adenosine (m1A) is a widespread RNA modification present in tRNA, rRNA, and mRNA. m1A modification sites in tRNAs are evolutionarily conserved and its formation on tRNA is catalyzed by methyltransferase TRMT61A and TRMT6 complex. m1A promotes translation initiation and elongation. Due to its positive charge under physiological conditions, m1A can notably modulate RNA structure. It also blocks Watson-Crick-Franklin base-pairing and causes mutation and truncation during reverse transcription. Several misincorporation-based high-throughput sequencing methods have been developed to sequence m1A. In this study, we introduce a reduction-based m1A sequencing (red-m1A-seq). We report that NaBH4 reduction of m1A can improve the mutation and readthrough rates using commercially available RT enzymes to give a better positive signature, while alkaline-catalyzed Dimroth rearrangement can efficiently convert m1A to m6A to provide good controls, allowing the detection of m1A with higher sensitivity and accuracy. We applied red-m1A-seq to sequence human small RNA, and we not only detected all the previously reported tRNA m1A sites, but also new m1A sites in mt-tRNAAsn-GTT and 5.8S rRNA.

Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images.

Background: Radiomics and artificial intelligence approaches have been developed to predict chronic obstructive pulmonary disease (COPD), but it is still unclear which approach has the best performance. Therefore, we established five prediction models that employed deep-learning (DL) and radiomics-based machine-learning (ML) approaches to identify COPD on low-dose computed tomography (LDCT) images and compared the relative performance of the different models to find the best model for identifying COPD. Methods: This retrospective analysis included 1,024 subjects (169 COPD patients and 855 control subjects) who underwent LDCT scans from August 2018 to July 2021. Five prediction models, including models that employed computed tomography (CT)-based radiomics features, chest CT images, quantitative lung density parameters, and demographic and clinical characteristics, were established to identify COPD by DL or ML approaches. Model 1 used CT-based radiomics features by ML method. Model 2 used a combination of CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by ML method. Model 3 used CT images only by DL method. Model 4 used a combination of CT images, lung density parameters, and demographic and clinical characteristics by DL method. Model 5 used a combination of CT images, CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by DL method. The accuracy, sensitivity, specificity, highest negative predictive values (NPVs), positive predictive values, and areas under the receiver operating characteristic (AUC) curve of the five prediction models were compared to examine their performance. The DeLong test was used to compare the AUCs of the different models. Results: In total, 107 radiomics features were extracted from each subject's CT images, 17 lung density parameters were acquired by quantitative measurement, and 18 selected demographic and clinical characteristics were recorded in this study. Model 2 had the highest AUC [0.73, 95% confidence interval (CI): 0.64-0.82], while model 3 had the lowest AUC (0.65, 95% CI: 0.55-0.75) in the test set. Model 2 also had the highest sensitivity (0.84), the highest accuracy (0.81), and the highest NPV (0.36). In the test set, based on the AUC results, Model 2 significantly outperformed Model 1 (P=0.03). Conclusions: The results showed that the identification ability of models that employ CT-based radiomics features combined with lung density parameters, and demographic and clinical characteristics using ML methods performed better than the chest CT image-based DL methods. ML methods are more suitable and beneficial for COPD identification.

Influencing Factors of Delirium during Recovery in Urological Postoperative Patients Undergoing Sevoflurane Anaesthesia

Objective: To analyse the incidence and influencing factors of delirium during recovery in urological postoperative patients undergoing sevoflurane anaesthesia. Methods: The clinical data of patients undergoing sevoflurane anaesthesia in the urology surgery department in our hospital from January 2022 to December 2022 were retrospectively analysed. The incidence of delirium during the recovery period was recorded by using the Chinese version of the Confusion Assessment Method (CAM) for Severity of Delirium after surgery, and the patients were divided into occurrence and non-occurrence groups. Whether delirium occurred during recovery was determined through univariate analysis. In binary logistic regression analysis, the occurrence of emergence delirium was the dependent variable, and the variables with statistical differences in the univariate analysis were the independent variables. The influencing factors of emergence delirium in post-urological surgery patients who underwent sevoflurane anaesthesia were determined. Results: Delirium during recovery occurred in 10 of 100 patients (10.00%). Odds ratio (OR) of age (OR = 1.445, p = 0.022), history of diabetes (OR = 1.798, p = 0.010), operation time (OR = 1.670, p = 0.008), American Society of Anesthesiologists (ASA) classification (OR = 1.740, p = 0.006) and sevoflurane inhalation concentration (OR = 1.890, p = 0.001) are the influencing factors of postoperative delirium in urologic patients undergoing sevoflurane anaesthesia. Conclusions: Age, history of diabetes, operation time, ASA classification and sevoflurane inhalation concentration are the influencing factors. (AU)

Evaluation and cultivation method of high-tech value patents for mechanical products.

Evaluation of high value patents is essential for the enterprise's technical layout and innovative product design. The existing research on the patent value needs the support of a large number of professional statistical information and is difficult to directly reflect the technical value. Since technological innovation is the fundamental means to enhance the sustainable competitiveness of enterprises. Therefore, a high-tech value patent evaluation and cultivation method for engineering designers need to be proposed. Firstly, the patent samples based on design methodology are retrieved and the indicators for evaluating technical value are summarized and the rationality of the evaluation indicators is verifier through empirical study based on improved evidence theory. Secondly, based on principal component analysis and factor analysis, a high-tech value patent evaluation and cultivation method is proposed. Finally, the proposed method is applied to identify the high-tech value patents in the cutting machine industry, and structural improvement is made based on this patent to demonstrate the cultivation process of high-tech value patents. The proposed method provides a clear guiding direction for the cultivation of high novelty patents and enterprise innovative product design. The method can effectively assist the product R&D activities of engineering designers and enhance the sustainable competitiveness of enterprises from a technological perspective.

Machine learning identifies cell-free DNA 5-hydroxymethylation biomarkers that detect occult colorectal cancer in PLCO Screening Trial subjects.

Background: Colorectal cancer (CRC) is a leading cause of cancer-related mortality, and CRC detection through screening improves survival rates. A promising avenue to improve patient screening compliance is the development of minimally-invasive liquid biopsy assays that target CRC biomarkers on circulating cell-free DNA (cfDNA) in peripheral plasma. In this report, we identify cfDNA biomarker candidate genes bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that diagnose occult CRC up to 36 months prior to clinical diagnosis using the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial samples. Methods: Archived PLCO Trial plasma samples containing cfDNA were obtained from the National Cancer Institute (NCI) biorepositories. Study subjects included those who were diagnosed with CRC within 36 months of blood collection (i.e., case, n = 201) and those who were not diagnosed with any cancer during an average of 16.3 years of follow-up (i.e., controls, n = 402). Following the extraction of 3 - 8 ng cfDNA from less than 300 microliters plasma, we employed the sensitive 5hmC-Seal chemical labeling approach, followed by next-generation sequencing (NGS). We then conducted association studies and machine-learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio. Results: Despite the technical challenges associated with the PLCO samples (e.g., limited plasma volumes, low cfDNA amounts, and long archival times), robust genome-wide 5hmC profiles were successfully obtained from these samples. Association analyses using the Cox proportional hazards models suggested several epigenetic pathways relevant to CRC development distinguishing cases from controls. A weighted Cox model, comprised of 32-associated gene bodies, showed predictive detection value for CRC as early as 24-36 months prior to overt tumor presentation, and a trend for increased predictive power was observed for blood samples collected closer to CRC diagnosis. Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and self-reported race/ethnicity, and significantly outperformed risk factors such as age and obesity according to BMI (body mass index). Additionally, further improvement of predictive performance was achieved by combining the 5hmC-based model and risk factors for CRC. Conclusions: An assay of 5hmC epigenetic signals on cfDNA revealed candidate biomarkers with the potential to predict CRC occurrence despite the absence of clinical symptoms or the availability of effective predictors. Developing a minimally-invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient survival through higher compliance screening and earlier intervention. Future investigation to expand this strategy to prospectively collected samples is warranted.

Dynamics of RNA localization to nuclear speckles are connected to splicing efficiency.

Nuclear speckles, a type of membraneless nuclear organelle in higher eukaryotic cells, play a vital role in gene expression regulation. Using the reverse transcription-based RNA-binding protein binding sites sequencing (ARTR-seq) method, we study human transcripts associated with nuclear speckles. We identify three gene groups whose transcripts demonstrate different speckle localization properties and dynamics: stably enriched in nuclear speckles, transiently enriched in speckles at the pre-mRNA stage, and not enriched in speckles. Specifically, we find that stably-enriched transcripts contain inefficiently spliced introns. We show that nuclear speckles specifically facilitate splicing of speckle-enriched transcripts. We further reveal RNA sequence features contributing to transcript speckle localization, underscoring a tight interplay between genome organization, RNA cis-elements, and transcript speckle enrichment, and connecting transcript speckle localization with splicing efficiency. Finally, we show that speckles can act as hubs for the regulated retention of introns during cellular stress. Collectively, our data highlight a role of nuclear speckles in both co- and post-transcriptional splicing regulation.