A step-scheme-based Cs3Bi2Br9 perovskite quantum dots@mesoporous Nb2O5 photocatalyst with boosted charge separation and CO2 reduction.

Solar-energy-powered CO2 reduction into valuable chemical fuels represents a highly promising strategy to address the currently energy and environmental issues. Owing to the nontoxicity and robust reduction capability, lead-free Cs3Bi2Br9 perovskite quantum dots (PQDs) are regarded as an attractive material for CO2 photoreduction. Nevertheless, the potential of their applications in this field has been restricted by the severe charge recombination, resulting in unsatisfactory photocatalytic performance. Herein, a step-scheme-based Cs3Bi2Br9@Nb2O5 (CBB@Nb2O5) nanocomposite was fabricated by embedding the CBB PQDs into mesoporous Nb2O5. Experimental studies, along with theoretical calculations, revealed that the charge migration route in the CBB@Nb2O5 nanocomposite conformed to the step-scheme (S-scheme) mode, enabling effective charge separation and strong redox ability preservation. Profiting from the promoted charge separation, as well as the improved CO2 adsorption contributed by mesoporous Nb2O5, the CBB@Nb2O5 nanocomposite unveiled superior CO2 photoreduction performance, with CO evolution rate reaching 143.63 µmol g-1h-1. The present study provides a potential strategy to manufacture highly-efficient perovskite-based photocatalysts for achieving carbon neutrality.

A review on fermented vegetables: Microbial community and potential upgrading strategy via inoculated fermentation.

Fermentation is a traditional method utilized for vegetable preservation, with microorganisms playing a crucial role in the process. Nowadays, traditional spontaneous fermentation methods are widely employed, which excessively depend on the microorganisms attached to the surface of raw materials, resulting in great difficulties in ideal control over the fermentation process. To achieve standardized production and improve product quality, it is essential to promote inoculated fermentation. In this way, starter cultures can dominate the fermentation processes successfully. Unfortunately, inoculated fermentation has not been thoroughly studied and applied. Therefore, this paper provides a systematic review of the potential upgrading strategy of vegetable fermentation technology. First, we disclose the microbial community structures and succession rules in some typical spontaneously fermented vegetables to comprehend the microbial fermentation processes well. Then, internal and external factors affecting microorganisms are explored to provide references for the selection of fermented materials and conditions. Besides, we widely summarize the potential starter candidates with various characteristics isolated from spontaneously fermented products. Subsequently, we exhibited the inoculated fermentation strategies with those isolations. To optimize the product quality, not only lactic acid bacteria that lead the fermentation, but also yeasts that contribute to aroma formation should be combined for inoculation. The inoculation order of the starter cultures also affects the microbial fermentation. It is equally important to choose a proper processing method to guarantee the activity and convenience of starter cultures. Only in this way can we achieve the transition from traditional spontaneous fermentation to modern inoculated fermentation.

The Use of Real-World Evidence for Regulatory Decisions in China.

There is a growing demand for the use of high-quality real-world evidence (RWE) to support regulatory decision-making worldwide and in China, which highlights the need for conducting literature reviews to evaluate the available data and evidence. This study aims to review the use of RWE in Chinese regulatory decisions and to summarize relevant regulatory and methodological considerations to inform the future use of RWE in China. We identified policy documents, technical guidance documents, and cases on official Chinese government websites and extracted their contents separately. We consulted experts from the National Medical Products Administration (NMPA) and academic institutes and searched case-related articles for enrichment. We also searched and included articles related to the use of RWE/Real-world data in Chinese regulatory decisions. Six trial versions of technical guidance documents, 7 case studies, and 40 articles related to the Chinese regulatory decisions were included in this study. Based on the technical guidance, data quality, and appropriate study design and statistical analysis are the main concerns for RWE generation. The cases and articles related to regulatory decisions revealed 9 main concerns, including data sources and applicability, data quality, strength of existing evidence, appropriate study design and statistical analysis, regulated and transparent process for analysis and evidence generation, product safety and efficacy, product characteristics and clinical needs, ethical considerations and data security, and communicate adequately with regulatory authorities. Among these concerns, data issues are central. Preliminary attempts have been made by the NMPA to promote the use of RWE, but substantial challenges still remain.

Na3K6(CO3)3(NO3)2X·6H2O (X = NO3, Cl, Br): Exploring High-Performance UV Birefringent Crystals Induced by Coplanar π-Conjugated CO3 and NO3 Groups.

Planar π-conjugated groups, like CO3, NO3, and BO3 triangles, are ideal functional units for designing birefringent materials due to their large optical anisotropy and wide band gap. The key point for designing birefringent crystals is to select appropriate functional building blocks (FBBs) and the proper arrangement mode. It is well known that the substitution strategy has proven to be a promising and accessible approach. In this work, alkali metals were chosen to regulate and control two different π-conjugated groups, CO3 and NO3, to build new compounds with large birefringence. Subsequently, three new compounds, Na3K6(CO3)3(NO3)2X·6H2O (X = NO3, Cl, Br), were successfully synthesized using the hydrothermal method. The aliovalent substitution between the [NO3]- anionic group and halogen anions [Cl]-/[Br]- has been achieved in these compounds. Na3K6(CO3)3(NO3)2X·6H2O feature the well-coplanar CO3 and NO3 groups in their crystal structure. This coplanar arrangement mode may effectively enhance the anisotropic polarizability of Na3K6(CO3)3(NO3)2X·6H2O. And their experimental birefringence can reach 0.094-0.131 at 546 nm. Diffuse reflectance spectra demonstrate that these compounds exhibit short ultraviolet (UV) absorption edges of ∼235 nm. Meanwhile, Na3K6(CO3)3(NO3)2X·6H2O also have an easily grown capacity under facile conditions. This work not only reports three new potential UV birefringent crystals but also provides a strategy to make the π-conjugated MO3 group coplanar.

A Three-in-One Integrated Cs3Bi2Br9@Co3O4 Heterostructure with Photoinduced Self-Heating Effect for Synergistically Enhancing the Photothermal CO2 Reduction.

Photothermal catalysis, which applies solar energy to produce photogenerated e-/h+ pairs as well as provide heat input, is recognized as a promising technology for high conversion efficiency of CO2 to value-added solar fuels. In this work, a "shooting three birds with one stone" approach is demonstrated to significantly enhance the photothermal CO2 reduction over the Cs3Bi2Br9@Co3O4 (CBB@Co3O4) heterostructure. Initially, Co3O4 with photoinduced self-heating effect serves as a photothermal material to elevate the temperature of the photocatalyst, which kinetically accelerates the catalytic reaction. Meanwhile, a p-n heterojunction is constructed between the p-type Co3O4 and n-type Cs3Bi2Br9 semiconductors, which has an intrinsic built-in electric field (BEF) to facilitate the separation of photogenerated e-/h+ pairs. Furthermore, the mesoporous Co3O4 matrix can afford abundant active sites for promoting adsorption/activation of CO2 molecules. Benefiting from these synergistic effects, the as-developed CBB@Co3O4 heterostructure achieves an impressive CO2-to-CO conversion rate of 168.56 µmol g-1 h-1 with no extra heat input. This work provides an insightful guidance for the construction of effective photothermal catalysts for CO2 reduction with high solar-to-fuel conversion efficiency.

Handling time-varying treatments in observational studies: A scoping review and recommendations.

OBJECTIVE: Time-varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time-varying treatments. This study aimed to examine the current methods for dealing with time-varying treatments in observational studies and developed practical recommendations. METHODS: We searched PubMed from 2000 to 2021 for methodological articles about time-varying treatments, and qualitatively summarized the current methods for handling time-varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts. RESULTS: Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time-varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time-varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time-independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention-to-treat, per-protocol, or as-treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings. CONCLUSIONS: There is no consensus process for assessing treatment effects in observational studies with time-varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice.

Paeoniflorin ameliorates oxaliplatin-induced peripheral neuropathy via inhibiting neuroinflammation through influence on gut microbiota.

Oxaliplatin (OXA)-induced peripheral neuropathy (OIPN) is a severe side effect that greatly limits OXA clinical use and threatens patients' life and health. Paeoniflorin exhibits extensive anti-inflammatory and neuroprotective effects, but whether it can protect against OIPN and the underlying mechanisms remain unclear. This study aimed to investigate the effects of paeoniflorin on OIPN and probe into the underlying mechanisms. The OIPN model was established through oxaliplatin injection in rats. The ameliorative effects of paeoniflorin on OIPN was assessed by nociceptive hypersensitivities through pain behavioral methods. Neuroinflammation were examined by measuring the levels of inflammatory cytokines and immune cells infiltration. The signaling pathway of TLR4/MyD88/NF-κB was evaluated by Western blotting. Gut microbial changes were detected by 16S rDNA sequencing technology. In addition, antibiotics-induced microbiota eradication and fecal microbial transplantation (FMT) were applied for exploring the function of gut microbiota in the protective effects of paeoniflorin. The results revealed that paeoniflorin significantly alleviated mechanical and cold hypersensitivity, mitigated neuroinflammation and influenced gut microbial composition in OIPN rats. Fecal microbiota transplantation further verified that gut microbiota was required for paeoniflorin ameliorating OIPN and that the underlying mechanism involved downregulation of TLR4/MyD88/NF-κB signaling. Specifically, Akkermansia, Dubosiella and Corynebacterium might serve as crucial genera regulated by paeoniflorin in the treatment of OIPN. In summary, our investigations delineate paeoniflorin's ameliorative effects on OIPN by alleviating neuroinflammation through regulations of gut microbiota. This suggests that paeoniflorin may serve as a new potential strategy for treatment of OIPN in clinical practice.

Electroencephalography oscillations can predict the cortical response following theta burst stimulation.

BACKGROUND: Continuous theta burst stimulation and intermittent theta burst stimulation are clinically popular models of repetitive transcranial magnetic stimulation. However, they are limited by high variability between individuals in cortical excitability changes following stimulation. Although electroencephalography oscillations have been reported to modulate the cortical response to transcranial magnetic stimulation, their association remains unclear. This study aims to explore whether machine learning models based on EEG oscillation features can predict the cortical response to transcranial magnetic stimulation. METHOD: Twenty-three young, healthy adults attended two randomly assigned sessions for continuous and intermittent theta burst stimulation. In each session, ten minutes of resting-state electroencephalography were recorded before delivering brain stimulation. Participants were classified as responders or non-responders based on changes in resting motor thresholds. Support vector machines and multi-layer perceptrons were used to establish predictive models of individual responses to transcranial magnetic stimulation. RESULT: Among the evaluated algorithms, support vector machines achieved the best performance in discriminating responders from non-responders for intermittent theta burst stimulation (accuracy: 91.30%) and continuous theta burst stimulation (accuracy: 95.66%). The global clustering coefficient and global characteristic path length in the beta band had the greatest impact on model output. CONCLUSION: These findings suggest that EEG features can serve as markers of cortical response to transcranial magnetic stimulation. They offer insights into the association between neural oscillations and variability in individuals' responses to transcranial magnetic stimulation, aiding in the optimization of individualized protocols.

Sample size calculations for randomized controlled trials with repeatedly measured continuous variables as primary outcomes need improvements: a cross-sectional study.

OBJECTIVES: Randomized controlled trials (RCTs) with repeatedly measured continuous variables as primary outcomes are common. Although statistical methodologies for calculating sample sizes in such trials have been extensively investigated, their practical application remains unclear. This study aims to provide an overview of sample size calculation methods for different research questions (e.g., key time point treatment effect, treatment effect change over time) and evaluate the adequacy of current practices in trial design. STUDY DESIGN AND SETTING: We conducted a comprehensive search of PubMed to identify RCTs published in core journals in 2019 that utilized repeatedly measured continuous variables as their primary outcomes. Data were extracted using a predefined questionnaire including general study characteristics, primary outcomes, detailed sample size calculation methods, and methods for analyzing the primary outcome. We re-estimated the sample size for trials that provided all relevant parameters. RESULTS: A total of 168 RCTs were included, with a median of four repeated measurements (interquartile range 3-6) per outcome. In 48 (28.6%) trials, the primary outcome used for sample size calculation differed from the one used in defining the primary outcomes. There were 90 (53.6%) trials exhibited inconsistencies between the hypotheses specified for sample size calculation and those specified for primary analysis. The statistical methods used for sample size calculation in 158 (94.0%) trials did not align with those used for primary analysis. Additionally, only 6 (3.6%) trials accounted for the number of repeated measurements, and 7 (4.2%) trials considered the correlation among these measurements when calculating the sample size. Furthermore, of the 128 (76.2%) trials that considered loss to follow-up, 33 (25.8%) used an incorrect formula (i.e., N∗(1+lose rate) for sample size adjustment. In 53 (49.5%) out of 107 trials, the re-estimated sample size was larger than the reported sample size. CONCLUSION: The practice of sample size calculation for RCTs with repeatedly measured continuous variables as primary outcomes displayed significant deficiencies, with a notable proportion of trials failed to report essential parameters about repeated measurement required for sample size calculation. Our findings highlight the urgent need to use optimal sample size methods that align with the research hypothesis, primary analysis method, and the form of the primary outcome.

REDH: A database of RNA editome in hematopoietic differentiation and malignancy.

BACKGROUND: The conversion of adenosine (A) to inosine (I) through deamination is the prevailing form of RNA editing, impacting numerous nuclear and cytoplasmic transcripts across various eukaryotic species. Millions of high-confidence RNA editing sites have been identified and integrated into various RNA databases, providing a convenient platform for the rapid identification of key drivers of cancer and potential therapeutic targets. However, the available database for integration of RNA editing in hematopoietic cells and hematopoietic malignancies is still lacking. METHODS: We downloaded RNA sequencing (RNA-seq) data of 29 leukemia patients and 19 healthy donors from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, and RNA-seq data of 12 mouse hematopoietic cell populations obtained from our previous research were also used. We performed sequence alignment, identified RNA editing sites, and obtained characteristic editing sites related to normal hematopoietic development and abnormal editing sites associated with hematologic diseases. RESULTS: We established a new database, "REDH", represents RNA editome in hematopoietic differentiation and malignancy. REDH is a curated database of associations between RNA editome and hematopoiesis. REDH integrates 30,796 editing sites from 12 murine adult hematopoietic cell populations and systematically characterizes more than 400,000 edited events in malignant hematopoietic samples from 48 cohorts (human). Through the Differentiation, Disease, Enrichment, and knowledge modules, each A-to-I editing site is systematically integrated, including its distribution throughout the genome, its clinical information (human sample), and functional editing sites under physiological and pathological conditions. Furthermore, REDH compares the similarities and differences of editing sites between different hematologic malignancies and healthy control. CONCLUSIONS: REDH is accessible at http://www.redhdatabase.com/ . This user-friendly database would aid in understanding the mechanisms of RNA editing in hematopoietic differentiation and malignancies. It provides a set of data related to the maintenance of hematopoietic homeostasis and identifying potential therapeutic targets in malignancies.

Regulating Oxygen Vacancies and Fermi Level of Mesoporous CeO2-x for Intensified Built-In Electric Field and Boosted Charge Separation of Cs3 Bi2 Br9 /CeO2-x S-Scheme Heterojunction.

Regulating the built-in electric field (BEF) in the heterojunction is is a great challenge in developing high-efficiency photocatalysts. Herein, by tailoring the content of oxygen vacancies in the constituent reduction semiconductor (mesoporous CeO2-x ), a precise Fermi level (EF ) regulation of CeO2-x is realized, yielding an amplified EF gap and intensified BEF in the Cs3 Bi2 Br9 perovskite quantum dots/CeO2-x S-scheme heterojunction. Such an enhanced BEF offers a strong driving force for directional electron transfer, boosting charge separation in the S-scheme heterojunction. As a result, the optimized Cs3 Bi2 Br9 /CeO2-x heterojunction delivers a remarkable CO2 conversion efficiency, with an impressive CO production rate of 80.26 µmol g-1  h-1 and a high selectivity of 97.6%. The S-scheme charge transfer mode is corroborated comprehensively by density functional theory (DFT) calculations, in situ X-ray photoelectron spectroscopy (XPS), and photo-irradiated Kelvin probe force microscopy (KPFM). Moreover, diffuse reflectance infrared Fourier transform spectra (DRIFTS) and theoretical calculations are conducted cooperatively to reveal the CO2 photoreduction pathway.

Validation and impact of algorithms for identifying variables in observational studies of routinely collected data.

BACKGROUND AND OBJECTIVES: Among observational studies of routinely collected health data (RCD) for exploring treatment effects, algorithms are used to identify study variables. However, the extent to which algorithms are reliable and impact the credibility of effect estimates is far from clear. This study aimed to investigate the validation of algorithms for identifying study variables from RCD, and examine the impact of alternative algorithms on treatment effects. METHODS: We searched PubMed for observational studies published in 2018 that used RCD to explore drug treatment effects. Information regarding the reporting, validation, and interpretation of algorithms was extracted. We summarized the reporting and methodological characteristics of algorithms and validation. We also assessed the divergence in effect estimates given alternative algorithms by calculating the ratio of estimates of the primary vs. alternative analyses. RESULTS: A total of 222 studies were included, of which 93 (41.9%) provided a complete list of algorithms for identifying participants, 36 (16.2%) for exposure, and 132 (59.5%) for outcomes, and 15 (6.8%) for all study variables including population, exposure, and outcomes. Fifty-nine (26.6%) studies stated that the algorithms were validated, and 54 (24.3%) studies reported methodological characteristics of 66 validations, among which 61 validations in 49 studies were from the cross-referenced validation studies. Of those 66 validations, 22 (33.3%) reported sensitivity and 16 (24.2%) reported specificity. A total of 63.6% of studies reporting sensitivity and 56.3% reporting specificity used test-result-based sampling, an approach that potentially biases effect estimates. Twenty-eight (12.6%) studies used alternative algorithms to identify study variables, and 24 reported the effects estimated by primary analyses and sensitivity analyses. Of these, 20% had differential effect estimates when using alternative algorithms for identifying population, 18.2% for identifying exposure, and 45.5% for classifying outcomes. Only 32 (14.4%) studies discussed how the algorithms may affect treatment estimates. CONCLUSION: In observational studies of RCD, the algorithms for variable identification were not regularly validated, and-even if validated-the methodological approach and performance of the validation were often poor. More seriously, different algorithms may yield differential treatment effects, but their impact is often ignored by researchers. Strong efforts, including recommendations, are warranted to improve good practice.

Reporting, handling, and interpretation of time-varying drug treatments in observational studies using routinely collected healthcare data.

BACKGROUND: Time-varying drug treatments are common in studies using routinely collected health data (RCD) for assessing treatment effects. This study aimed to examine how these studies reported, handled, and interpreted time-varying drug treatments. METHODS: A systematic search was conducted on PubMed from 2018 to 2020. Eligible studies were those used RCD to explore drug treatment effects. We summarized the reporting characteristics and methods employed for handling time-varying treatments. Logistic regressions were performed to investigate the association between study characteristics and the reporting of time-varying treatments. RESULTS: Two hundred and fifty-six studies were included, and 225 (87.9%) studies involved time-varying treatments. Of these, 24 (10.7%) reported the proportion of time-varying treatments and 105 (46.7%) reported methods used to handle time-varying treatments. Multivariable logistic regression showed that medical studies, prespecified protocol, and involvement of methodologists were associated with a higher likelihood of reporting the methods applied to handle time-varying treatments. Among the 105 studies that reported methods, as-treated analyses were the most commonly used analysis sets, which were employed in 73.9%, 75.3% and 88.2% of studies that reported approaches for treatment discontinuation, treatment switching and treatment add-on. Among the 225 studies involved time-varying treatments, 27 (12.0%) acknowledged the potential bias introduced by treatment change, of which 14 (51.9%) suggested that potential biases may impact acceptance or rejection of the null hypothesis. CONCLUSIONS: Among observational studies using RCD, the underreporting about the presence and methods for handling time-varying treatments was largely common. The potential biases due to time-varying treatments have frequently been disregarded. Collaborative endeavors are strongly needed to enhance the prevailing practices.

Pediatric intensive care unit treatment alters the diversity and composition of the gut microbiota and antimicrobial resistance gene expression in critically ill children.

Introduction: Common critical illnesses are a growing economic burden on healthcare worldwide. However, therapies targeting the gut microbiota for critical illnesses have not been developed on a large scale. This study aimed to investigate the changes in the characteristics of the gut microbiota in critically ill children after short-term pediatric intensive care unit (PICU) treatments. Methods: Anal swab samples were prospectively collected from March 2021 to March 2022 from children admitted to the PICU of Xinhua Hospital who received broad-spectrum antibiotics on days 1 (the D1 group) and 7 (the D7 group) of the PICU treatment. The structural and functional characteristics of the gut microbiota of critically ill children were explored using metagenomic next-generation sequencing (mNGS) technology, and a comparative analysis of samples from D1 and D7 was conducted. Results: After 7 days of PICU admission, a significant decrease was noted in the richness of the gut microbiota in critically ill children, while the bacterial diversity and the community structure between groups remained stable to some extent. The relative abundance of Bacilli and Lactobacillales was significantly higher, and that of Campylobacter hominis was significantly lower in the D7 group than in the D1 group. The random forest model revealed that Prevotella coporis and Enterobacter cloacae were bacterial biomarkers between groups. LEfSe revealed that two Gene Ontology entries, GO:0071555 (cell wall organization) and GO:005508 (transmembrane transport), changed significantly after the short-term treatment in the PICU. In addition, 30 KEGG pathways were mainly related to the activity of enzymes and proteins during the processes of metabolism, DNA catabolism and repair, and substance transport. Finally, 31 antimicrobial resistance genes had significantly different levels between the D7 and D1 groups. The top 10 up-regulated genes were Erm(A), ErmX, LptD, eptB, SAT-4, tetO, adeJ, adeF, APH(3')-IIIa, and tetM. Conclusion: The composition, gene function, and resistance genes of gut microbiota of critically ill children can change significantly after short PICU treatments. Our findings provide a substantial basis for a better understanding of the structure and function of gut microbiota and their role in critical illnesses.

E2LNet: An Efficient and Effective Lightweight Network for Panoramic Depth Estimation.

Monocular panoramic depth estimation has various applications in robotics and autonomous driving due to its ability to perceive the entire field of view. However, panoramic depth estimation faces two significant challenges: global context capturing and distortion awareness. In this paper, we propose a new framework for panoramic depth estimation that can simultaneously address panoramic distortion and extract global context information, thereby improving the performance of panoramic depth estimation. Specifically, we introduce an attention mechanism into the multi-scale dilated convolution and adaptively adjust the receptive field size between different spatial positions, designing the adaptive attention dilated convolution module, which effectively perceives distortion. At the same time, we design the global scene understanding module to integrate global context information into the feature maps generated using the feature extractor. Finally, we trained and evaluated our model on three benchmark datasets which contains the virtual and real-world RGB-D panorama datasets. The experimental results show that the proposed method achieves competitive performance, comparable to existing techniques in both quantitative and qualitative evaluations. Furthermore, our method has fewer parameters and more flexibility, making it a scalable solution in mobile AR.

Causal Association of Cytokines and Growth Factors with Stroke and Its Subtypes: a Mendelian Randomization Study.

Cytokines and growth factors contribute to nerve growth and angiogenesis and are associated with the development of vascular disease. This Mendelian randomization (MR) study was designed to examine the causal relationship between factors associated with stem cell paracrine mechanisms and with stroke and its subtypes. We used pooled statistics on cytokine levels from three studies (INTERIAL, Olink Proseek CVD array, and KORA) encompassing 7795 participants in Europe. Data for stroke and its subtypes were pooled from these European populations (40,585 cases and 406,111 controls) in a multiprogenitor genome-wide association study (GWAS). MR was performed using established analytical methods, including inverse variance weighting (IVW), weighted median (WM), and MR-Egger. Genetically determined high IGF-1 levels were found to associate negatively with risk of stroke, ischemic stroke (large-artery atherosclerosis), and ischemic stroke (cardiogenic embolism). Meanwhile, high IL-13 levels had a positive causal relationship with ischemic stroke (large-artery atherosclerosis). An additional 27 cytokines were found to have a causal association with stroke or its subtypes. However, these results should be interpreted with caution given that the power efficacy was <80%. This MR study supports the concept of a causal relationship of 29 cytokines with stroke or its subtypes. Our genetic analysis provides new insights into stroke prevention and treatment by demonstrating an association of stem cell paracrine-related cytokines with stroke risk.

Deciphering the Functional Long Non-Coding RNAs Derived from MicroRNA Loci.

Albeit the majority of eukaryotic genomes can be pervasively transcribed to a diverse population of lncRNAs and various subtypes of lncRNA are discovered. However, the genome-wide study of miRNA-derived lncRNAs is still lacking. Here, it is reported that over 800 miRNA gene-originated lncRNAs (molncRNAs) are generated from miRNA loci. One of them, molnc-301b from miR-301b and miR-130b, functions as an "RNA decoy" to facilitate dissociation of the chromatin remodeling protein SMARCA5 from chromatin and thereby sequester transcription and mRNA translation. Specifically, molnc-301b attenuates erythropoiesis by mitigating the transcription of erythropoietic and translation-associated genes, such as GATA1 and FOS. In addition, a useful and powerful CRISPR screen platform to characterize the biological functions of molncRNAs at large-scale and single-cell levels is established and 29 functional molncRNAs in hematopoietic cells are identified. Collectively, the focus is on miRNA-derived lncRNAs, deciphering their landscape during normal hematopoiesis, and comprehensively evaluating their potential roles.

Exploration of Chick Embryo and Chorioallantoic Membrane on Imaging Navigated Platforms for Anticancer Pharmaceutical Evaluations.

Conforming to the current replace-reduce-refine 3Rs' guidelines in animal experiments, a series of explorative efforts have been made to set up operable biomedical imaging-guided platforms for qualitative and quantitative evaluations on pharmacological effects of tumor vascular-disrupting agents (VDAs), based on the chick embryos (CEs) with its chorioallantoic membrane (CAM), in this overview. The techniques and platforms have been hierarchically elaborated, from macroscopic to microscopic and from overall to specific aspects. A protocol of LED lamplight associated with a new deep-learning algorithm was consolidated to screen out weak CEs by using the CAM vasculature imaging. 3D magnetic resonance imaging (MRI) and laser speckle contrast imaging (LSCI) to monitor the evolution of CE and vascular changes in CAM are introduced. A LSCI-CAM platform for studying the effects of VDAs on normal and cancerous vasculature of CAM and possible molecular mechanisms has been demonstrated. Finally, practical challenges and future perspectives are highlighted. The aim of this article is to help peers in biomedical research to familiarize with the CAM platform and to optimize imaging protocols for the evaluation of vasoactive pharmaceuticals, especially anticancer vascular targeted therapy.

Comparative transcriptome analysis of eyes reveals the adaptive mechanism of mantis shrimp (oratosquilla oratoria) induced by a dark environment.

The light-dark cycle significantly impacts the growth and development of animals. Mantis shrimps (Oratosquilla oratoria) receive light through their complex photoreceptors. To reveal the adaptive expression mechanism of the mantis shrimp induced in a dark environment, we performed comparative transcriptome analysis with O. oratoria cultured in a light environment (Oo-L) as the control group and O. oratoria cultured in a dark environment (Oo-D) as the experimental group. In the screening of differentially expressed genes (DEGs) between the Oo-L and Oo-D groups, a total of 88 DEGs with |log2FC| > 1 and FDR < 0.05 were identified, of which 78 were upregulated and 10 were downregulated. Then, FBP1 and Pepck were downregulated in the gluconeogenesis pathway, and MKNK2 was upregulated in the MAPK classical pathway, which promoted cell proliferation and differentiation, indicating that the activity of mantis shrimp was slowed and the metabolic rate decreases in the dark environment. As a result, the energy was saved for its growth and development. At the same time, we performed gene set enrichment analysis (GSEA) on all DEGs. In the KEGG pathway analysis, each metabolic pathway in the dark environment showed a slowing trend. GO was enriched in biological processes such as eye development, sensory perception and sensory organ development. The study showed that mantis shrimp slowed down metabolism in the dark, while the role of sensory organs prominent. It provides important information for further understanding the energy metabolism and has great significance to study the physiology of mantis shrimp in dark environment.

Multi-supervised bidirectional fusion network for road-surface condition recognition.

Rapid developments in automatic driving technology have given rise to new experiences for passengers. Safety is a main priority in automatic driving. A strong familiarity with road-surface conditions during the day and night is essential to ensuring driving safety. Existing models used for recognizing road-surface conditions lack the required robustness and generalization abilities. Most studies only validated the performance of these models on daylight images. To address this problem, we propose a novel multi-supervised bidirectional fusion network (MBFN) model to detect weather-induced road-surface conditions on the path of automatic vehicles at both daytime and nighttime. We employed ConvNeXt to extract the basic features, which were further processed using a new bidirectional fusion module to create a fused feature. Then, the basic and fused features were concatenated to generate a refined feature with greater discriminative and generalization abilities. Finally, we designed a multi-supervised loss function to train the MBFN model based on the extracted features. Experiments were conducted using two public datasets. The results clearly demonstrated that the MBFN model could classify diverse road-surface conditions, such as dry, wet, and snowy conditions, with a satisfactory accuracy and outperform state-of-the-art baseline models. Notably, the proposed model has multiple variants that could also achieve competitive performances under different road conditions. The code for the MBFN model is shared at https://zenodo.org/badge/latestdoi/607014079.