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Abstract Sinapic acid (SA) is renowned for its many pharmacological activities as a polyphenolic compound. The cause of polycystic ovary syndrome (PCOS), a commonly encountered array of metabolic and hormonal abnormalities in females, has yet to be determined. The present experiment was performed to evaluate the antifibrotic properties of SA in rats with letrozole‐induced PCOS‐related ovarian fibrosis. SA treatment successfully mitigated the changes induced by letrozole in body weight (BW) (p

As artificial intelligence (AI) technologies are increasingly pervasive in our daily lives, the need for students to understand the working mechanisms of AI technologies has become more urgent. Data modeling is an activity that has been proposed to engage students in reasoning about the working mechanism of AI technologies. While Computational thinking (CT) has been conceptualized as critical processes that students engage in during data modeling, much remains unexplored regarding how students created features from unstructured data to develop machine learning models. In this study, we examined high school students' patterns of iterative model development and themes of CT processes in iterative model development. Twenty-eight students from a journalism class engaged in refining machine learning models iteratively for classifying negative and positive reviews of ice cream stores. This study draws on a theoretical framework of CT processes to examine students' model development processes. The results showed that students (1) demonstrated three patterns of iterative model development, including incremental, filter-based, and radical feature creation; (2) engaged in complex reasoning about language use in diverse contexts in trial and error; and (3) leveraged multiple data representations when applying mathematical and computational techniques. The results provide implications for designing accessible AI learning experiences for students to understand the role and responsibility of modelers in creating AI technologies and studying AI learning experiences from the angle of CT processes.

Rheumatoid arthritis (RA) is a prevalent autoimmune disease that involves the overgrowth and inflammation of synovial tissue, leading to the degeneration and impairment of joints. In recent years, numerous studies have shown a close relationship between the hypoxic microenvironment in joints and the occurrence and progression of RA. The main cause of the pathological changes in RA is widely believed to be the abnormal expression of hypoxia-inducible factor-1 (HIF-1) in joints. This paper describes and illustrates the structure and primary functions of HIF-1 and explains the main regulatory methods of HIF-1, including the PHDs/HIF-1/pVHL pathway, factor-inhibiting HIF (FIH), regulation of inflammatory cytokines, and the NF-B pathway. Furthermore, this paper discusses the mechanism of HIF-1 and its impact on inflammation, angiogenesis, and cartilage destruction in greater detail. We summarize previous research findings on the mechanism of HIF-1 and propose new potential treatments for RA based on the pathogenesis of HIF-1 in RA.

Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task-based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as "clickbait" or "news." Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human-perception level and the computer-extraction level and conceptualizing connections between them.

It's critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students' data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students' processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies.

Abstract Fruit ripening (FR) is attributed to the selective expression of several genes precisely governed by various specific transcription factors (TFs). The NAC (NAM, ATAF, and CUC) TF, MaNAC029, positively regulated banana ripening by directly inducing ethylene biosynthesis and transcription of fruit quality-related genes. However, its upstream regulatory mechanism still needs to be clarified. Herein, yeast one-hybrid screening revealed that a SQUAMOSA promoter binding protein-like (SPL) TF, MaSPL16, was a potentially upstream regulator of Musa acuminata N A C (NAM, ATAF, CUC) 0 2 9 (MaNAC029). Furthermore, gel mobility shift assay revealed that MaSPL16 can directly bound with the “GTAC” element of the MaNAC029 promoter. The gene expression and promoter activity assays demonstrated that Musa acuminata SPL (SQUAMOSA promoter binding protein-like) 16 (MaSPL16) expression was inducible by ethylene and ripening. MaSPL16 was localized to the nucleus, displayed a potenial capacity for transcriptional activation of MaNAC029. More critically, the transient expression of MaSPL16 in bananas accelerated FR via the upregulation of MaNAC029 and its downstream genes. Collectively, the mechanistic basis of a regulatory cascade involving MaSPL16-MaNAC029 that governed ethylene biosynthesis and fruit quality throughout the entire process of banana fruit ripening was unveiled. These outcomes increase the understanding of the gene-transcriptional regulatory mechanisms in FR. They are envisaged to help devise molecular techniques to regulate maturation and improve future fruit quality.

Introduction Traditional Chinese medicine (TCM) may have special advantages in facilitating smoking cessation, but consensus on effectiveness is lacking. We aim to comprehensively review, update, and refine current evidence on TCM effectiveness and safety. Methods Nine databases were searched from their inception up to 28 February 2023. Systematic reviews (SRs) and meta-analysis of TCM for smoking cessation were identified and retrieved. Additional databases and hand searches of RCTs from included SRs were performed for data pooling. Cochrane ROB tools and AMSTAR-2 were used to evaluate the methodological quality of RCTs and SRs, respectively. RCT data are presented as relative risks (RR) or mean differences (MD) with 95% confidence intervals (CI) using RevMan 5.4. Results Thirteen SRs involving 265 studies with 33081 participants were included. Among these 265 studies, 157 were duplicates (58.36%) and 52 were non-RCTs (19.62%). Combined with the remaining 56 RCTs identified through hand searches, 88 RCTs involving 12434 participants were finally included for data synthesis. All the SRs focused on acupoint stimulation, and the majority were of low or very low quality. The methodological quality of RCTs was either unclear or high risk. For continuous abstinence rate, TCM external interventions were better than placebo in 6 months to 1 year (RR=1.60; 95% CI: 1.14–2.25; I2=27%; n=5533 participants). Compared with placebo, TCM external application was effective in reducing nicotine withdrawal symptoms, and the effect was gradually stable and obvious in the fourth week (MD= -4.46; 95% CI: -5.43 – -3.49; n=165 participants). Twelve RCTs reported adverse events as outcome indicators for safety evaluation, and no serious adverse events occurred. Conclusions Despite the methodological limitations of the original studies, our review suggests that TCM intervention shows potential effectiveness on the continuous abstinence rate. Extending the intervention time can enhance the effect of TCM on nicotine withdrawal symptoms. Referred to adverse events, more data for safety evaluation are required.

Research focusing on the integration of computational thinking (CT) into science, technology, engineering, and mathematics (STEM) education started to emerge. We conducted a semi-systematic literature review on 55 empirical studies on this topic. Our findings include: (a) the majority of the studies adopted domain-general definitions of CT and a few proposed domain-specific CT definitions in STEM education; (b) the most popular instructional model was problem-based instruction, and the most popular topic contexts included game design, robotics, and computational modelling; (c) while the assessments of student learning in "integrated" CT and STEM education targeted different objectives with different formats, about a third of them assessed integrated CT and STEM; (d) about a quarter of the studies reported differential learning processes and outcomes between groups, but very few of them investigated how pedagogical design could improve equity. Based on the findings, suggestions for future research and practice in this field are discussed in terms of operationalizing and assessing CT in STEM contexts, instructional strategies for integrating CT in STEM, and research for broadening participation in integrated CT and STEM education.

To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K-12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K-12 AI education by examining student learning of modelling real-world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in-depth understandings of how text classification models--a type of AI application--are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in-depth reasoning about model decision making.

This study proposes and demonstrates a novel label-free DNA biosensor using high quality optical microcavity functionalized by 3D DNA nanostructure probes. To achieve ultra-low limit DNA biosensing, optical sensing interface of the hollow-core, thin wall-thickness microcavity was functionalized by self-assembled DNA tetrahedral nanostructure (DTN) probes with size of 17 bp and length of ∼5.8 nm. High efficiency binding of the DTN probes with the optical sensor interface and the target ssDNA are achieved. Whispering gallery mode (WGM) spectra with high-Q factor of >107 are excited and traced for DNA detection inside the microfluidic channel of the microcavity, with a small sample volume of nL. Incorporation of nanoscale DTN probes onto surface of the optical microcavity makes it an effective way for increasing efficient probe density and eliminating entanglement between DNA probes, thus ∼1000 times lower detection limit is achieved as compared to using 1D ssDNA probes. Due to its desirable merits of label-free, ultra-low LoD, real time and compact size, the proposed DNA biosensor has broad application prospects in bioengineering and medical diagnosis.