EIF4A3 modulated circ_000999 promotes epithelial-mesenchymal transition in cadmium-induced malignant transformation through the miR-205-5p/ZEB1 axis.

Cadmium (Cd) is an accumulative toxic metal which poses a serious threat to human health, even in trace amounts. One of the most important steps in the pathophysiology of lung cancer (LC) is the epithelial-mesenchymal transition (EMT). In this investigation, a cell malignant transformation model was established by exposing human bronchial epithelial cells (16HBE) to a low dose of Cd for 30 weeks, after which a highly expressed circular RNA (circ_000999) was identified. Cd-induced EMT was clearly observed in rat lungs and 16HBE cells, which was further enhanced following circ_000999-overexpression. Furthermore, upregulated EIF4A3 interacted with the parental gene AGTPBP1 to promote high expression of circ_000999. Subsequent experiments confirmed that circ_000999 could regulate the EMT process by competitively binding miR-205-5p and inhibiting its activity, consequently upregulating expression of zinc finger E-box binding protein 1 (ZEB1). Importantly, the circ_000999 expression level in LC tissues was significantly increased, exhibiting a strong correlation with EMT indicators. Overall, these findings provide a new objective and research direction for reversing lung EMT and subsequent treatment and prevention of LC.

Broadband Room-Temperature Photodetection via InBiTe3 Nanosheet.

Broadband room-temperature photodetection has become a pressing need as application requirements for communication, imaging, spectroscopy, and sensing have evolved. Topological insulators (TIs) have narrow bandgap structures with a wide absorption spectral response range, which should meet the requirements of broadband detection. However, owing to their high carrier concentration and low carrier mobility resulting in poor noise equivalent power (NEP), they are generally considered unsuitable for photodetection. Here, InBiTe3 alloy nanosheet formed by doping In2Te3 into Bi2Te3(≈ 1:1) is utilized, effectively improving carrier mobility by over ten times while maintaining a narrow bandgap structure, to fabricate a broadband photodetector covering a wide response range from visible to millimeter wave (MMW). Under the synergistic multi-mechanism of the photoelectric effect in the visible-infrared region and the electromagnetic-induced potential well (EIW) effect in Terahertz band, the performance of NEP = 75 pW Hz-1/2 and response time τ ≈100 µs in visible to infrared band and the performance of NEP = 6.7 × 10-3 pW Hz-1/2, τ ≈8 µs in Terahertz region are achieved. The results demonstrate the promising prospects of topological insulator alloy (like InBiTe3) nanosheet in optoelectronic detection applications and provide a direction for the research into high-performance broadband photoelectric detectors via TIs.

Nontarget Identification of Novel Organophosphorus Flame Retardants and Plasticizers in Indoor Air and Dust from Multiple Microenvironments in China.

The indoor environment is a typical source for organophosphorus flame retardants and plasticizers (OPFRs), yet the source characteristics of OPFRs in different microenvironments remain less clear. This study collected 109 indoor air samples and 34 paired indoor dust samples from 4 typical microenvironments within a university in Tianjin, China, including the dormitory, office, library, and information center. 29 target OPFRs were analyzed, and novel organophosphorus compounds (NOPs) were identified by fragment-based nontarget analysis. Target OPFRs exhibited the highest air and dust concentrations of 46.2-234 ng/m3 and 20.4-76.0 µg/g, respectively, in the information center, where chlorinated OPFRs were dominant. Triphenyl phosphate (TPHP) was the primary OPFR in office air, while tris(2-chloroethyl) phosphate dominated in the dust. TPHP was predominant in the library. Triethyl phosphate (TEP) was ubiquitous in the dormitory, and tris(2-butoxyethyl) phosphate was particularly high in the dust. 9 of 25 NOPs were identified for the first time, mainly from the information center and office, such as bis(chloropropyl) 2,3-dichloropropyl phosphate. Diphenyl phosphinic acid, two hydroxylated and methylated metabolites of tris(2,4-ditert-butylphenyl) phosphite (AO168), and a dimer phosphate were newly reported in the indoor environment. NOPs were widely associated with target OPFRs, and their human exposure risk and environmental behaviors warrant further study.

SRY-Box transcription factor 9 triggers YAP nuclear entry via direct interaction in tumors.

The translocation of YAP from the cytoplasm to the nucleus is critical for its activation and plays a key role in tumor progression. However, the precise molecular mechanisms governing the nuclear import of YAP are not fully understood. In this study, we have uncovered a crucial role of SOX9 in the activation of YAP. SOX9 promotes the nuclear translocation of YAP by direct interaction. Importantly, we have identified that the binding between Asp-125 of SOX9 and Arg-124 of YAP is essential for SOX9-YAP interaction and subsequent nuclear entry of YAP. Additionally, we have discovered a novel asymmetrical dimethylation of YAP at Arg-124 (YAP-R124me2a) catalyzed by PRMT1. YAP-R124me2a enhances the interaction between YAP and SOX9 and is associated with poor prognosis in multiple cancers. Furthermore, we disrupted the interaction between SOX9 and YAP using a competitive peptide, S-A1, which mimics an α-helix of SOX9 containing Asp-125. S-A1 significantly inhibits YAP nuclear translocation and effectively suppresses tumor growth. This study provides the first evidence of SOX9 as a pivotal regulator driving YAP nuclear translocation and presents a potential therapeutic strategy for YAP-driven human cancers by targeting SOX9-YAP interaction.

Expert consensus on Prospective Precision Diagnosis and Treatment Strategies for Osteoporotic Fractures.

Osteoporotic fractures are the most severe complications of osteoporosis, characterized by poor bone quality, difficult realignment and fixation, slow fracture healing, and a high risk of recurrence. Clinically managing these fractures is relatively challenging, and in the context of rapid aging, they pose significant social hazards. The rapid advancement of disciplines such as biophysics and biochemistry brings new opportunities for future medical diagnosis and treatment. However, there has been limited attention to precision diagnosis and treatment strategies for osteoporotic fractures both domestically and internationally. In response to this, the Chinese Medical Association Orthopaedic Branch Youth Osteoporosis Group, Chinese Geriatrics Society Geriatric Orthopaedics Committee, Chinese Medical Doctor Association Orthopaedic Physicians Branch Youth Committee Osteoporosis Group, and Shanghai Association of Integrated Traditional Chinese and Western Medicine Osteoporosis Professional Committee have collaborated to develop this consensus. It aims to elucidate emerging technologies that may play a pivotal role in both diagnosis and treatment, advocating for clinicians to embrace interdisciplinary approaches and incorporate these new technologies into their practice. Ultimately, the goal is to improve the prognosis and quality of life for elderly patients with osteoporotic fractures.

Bruceantinol targeting STAT3 exerts promising antitumor effects in in vitro and in vivo osteosarcoma models.

Bruceantinol (BOL) is a quassinoid compound found in the fruits of Brucea javanica. Previous research has highlighted the manifold physiological and pharmacological activities of BOL. Notably, BOL has demonstrated antitumor cytotoxic and antibacterial effects, lending support to its potential as a promising therapeutic agent for various diseases. Despite being recognized as a potent antitumor inhibitor in multiple cancer types, its efficacy against osteosarcoma (OS) has not been elucidated. In this work, we investigated the antitumor properties of BOL against OS. Our findings showed that BOL significantly decreased the proliferation and migration of OS cells, induced apoptosis, and caused cell death without affecting the cell cycle. We further confirmed that BOL potently suppressed tumor growth in vivo. Mechanismly, we discovered that BOL directly bound to STAT3, and prevent the activation of STAT3 signaling at low nanomolar concentrations. Overall, our study demonstrated that BOL potently inhibited the growth and metastasis of OS, and efficiently suppressed STAT3 signaling pathway. These results suggest that BOL could be a promising therapeutic candidate for OS.

An analysis of reported cases shoulder injury related to vaccine administration of after COVID-19 vaccination.

To prevent COVID-19, the COVID-19 vaccine has been widely administered worldwide, but various complications accompany this vaccine. The aim of this study was to investigate the demographic patterns, clinical features, diagnostic findings, and treatment outcomes associated with shoulder injury related to vaccine administration (SIRVA). This study examined 22 patients with SIRVA following COVID-19 vaccination from the Web of Science (WOS) and PubMed databases. The patients were categorized based on sex, age, type of COVID-19 vaccine received, dose administered, latency of symptom onset, and the presence of specific clinical manifestations. Patients, evenly distributed by sex (12 females, 10 males), and aged 21 to 84 years (mean age 46.6), were analyzed. SIRVA cases were reported across all age groups. The Pfizer - BioNTech COVID-19 vaccine had the highest incidence (n = 8), followed by the Oxford/AstraZeneca COVID-19 vaccine (n = 4). Symptoms, primarily shoulder pain (n = 22) and shoulder mobility disorders (n = 18), occurred within three days post-vaccination. Some patients also reported shoulder swelling (n = 5) and fever (n = 2). Imaging revealed nonspecific X-ray findings, supraspinatus tendon calcification (n = 2), and shoulder edema and inflammation on MRI (n = 12). This study provides insights into the clinical aspects of SIRVA related to COVID-19 vaccination. Recognition and appropriate management of these complications are crucial for optimal patient outcomes.

Discovery of a Covalent Inhibitor Selectively Targeting the Autophosphorylation Site of c-Src Kinase.

Nonreceptor tyrosine kinase c-Src plays a crucial role in cell signaling and contributes to tumor progression. However, the development of selective c-Src inhibitors turns out to be challenging. In our previous study, we performed posttranslational modification-inspired drug design (PTMI-DD) to provide a plausible way for designing selective kinase inhibitors. In this study, after identifying a unique pocket comprising a less conserved cysteine and an autophosphorylation site in c-Src as well as a promiscuous covalent inhibitor, chemical optimization was performed to obtain (R)-LW-Srci-8 with nearly 75-fold improved potency (IC50 = 35.83 ± 7.21 nM). Crystallographic studies revealed the critical C-F···C═O interactions that may contribute to tight binding. The kinact and Ki values validated the improved binding affinity and decreased warhead reactivity of (R)-LW-Srci-8 for c-Src. Notably, in vitro tyrosine kinase profiling and cellular activity-based protein profiling (ABPP) cooperatively indicated a specific inhibition of c-Src by (R)-LW-Srci-8. Intriguingly, (R)-LW-Srci-8 preferentially binds to inactive c-Src with unphosphorylated Y419 both in vitro and in cells, subsequently disrupting the autophosphorylation. Collectively, our study demonstrated the feasibility of developing selective kinase inhibitors by cotargeting a nucleophilic residue and a posttranslational modification site and providing a chemical probe for c-Src functional studies.

Advances in machine-learning approaches to RNA-targeted drug design.

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Single-Atom Cu Nanozyme-Loaded Bone Scaffolds for Ferroptosis-Synergized Mild Photothermal Therapy in Osteosarcoma Treatment.

The rapid multiplication of residual tumor cells and poor reconstruction quality of new bone are considered the major challenges in the postoperative treatment of osteosarcoma. It is a promising candidate for composite bone scaffold which combines photothermal therapy (PTT) and bone regeneration induction for the local treatment of osteosarcoma. However, it is inevitable to damage the normal tissues around the tumor due to the hyperthermia of PTT, while mild heat therapy shows a limited effect on antitumor treatment as the damage can be easily repaired by stress-induced heat shock proteins (HSP). This study reports a new type of single-atom Cu nanozyme-loaded bone scaffolds, which exhibit exceptional photothermal conversion properties as well as peroxidase and glutathione oxidase mimicking activities in vitro experiments. This leads to lipid peroxidation (LPO) and reactive oxygen species (ROS) upregulation, ultimately causing ferroptosis. The accumulation of LPO and ROS also contributes to HSP70 inactivation, maximizing PTT efficiency against tumors at an appropriate therapeutic temperature and minimizing the damage to surrounding normal tissues. Further, the bone scaffold promotes bone regeneration via a continuous release of bioactive ions (Ca2+ , P5+ , Si4+ , and Cu2+ ). The results of in vivo experiments reveal that scaffolds inhibit tumor growth and promote bone repair.

Propagation of a rapid cell-to-cell H2O2 signal over long distances in a monolayer of cardiomyocyte cells.

Cell-to-cell communication plays a cardinal role in the biology of multicellular organisms. H2O2 is an important cell-to-cell signaling molecule involved in the response of mammalian cells to wounding and other stimuli. We previously identified a signaling pathway that transmits wound-induced cell-to-cell H2O2 signals within minutes over long distances, measured in centimeters, in a monolayer of cardiomyocytes. Here we report that this long-distance H2O2 signaling pathway is accompanied by enhanced accumulation of cytosolic H2O2 and altered redox state in cells along its path. We further show that it requires the production of superoxide, as well as the function of gap junctions, and that it is accompanied by changes in the abundance of hundreds of proteins in cells along its path. Our findings highlight the existence of a unique and rapid long-distance H2O2 signaling pathway that could play an important role in different inflammatory responses, wound responses/healing, cardiovascular disease, and/or other conditions.

Machine learning in RNA structure prediction: Advances and challenges.

RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.

The development and training effect of innovative undergraduate dental talents training project: A 6-year retrospective study.

INTRODUCTION: To summarize the development of Innovative Undergraduate Dental Talents Training Project (IUDTTP) and investigate the training effect of this extracurricular dental basic research education activity from 2015 to 2020 to obtain educational implications. MATERIALS AND METHODS: The Guanghua School of Stomatology established the IUDTTP in 2015. The authors recorded the development process and analysed the participation situation, training effect, academic performance and overall satisfaction during 2015-2020 through documental analysis, questionnaire and quiz. The t-test, chi-square test and ANOVA were used to test the difference. RESULTS: The educational goal, education module and assessment system of IUDTTP evolved and developed every year. A total of 336 students and 79 mentors attended the IUDTTP from 2015 to 2020, with the participation rate increasing from 45.1% to 73.5%. The participants exhibited favourable basic research abilities, manifesting as the increase of funded projects and published papers and satisfying quiz scores. Almost all students (94.94%) admitted their satisfaction with the IUDTTP. Moreover, the attended students surpassed the non-participants in terms of GPA, the number of acquired scholarships and outstanding graduates (p < .05). Likewise, the enrolment rate of postgraduate participants was significantly higher than non-participants. CONCLUSIONS: To date, the training effect indicated that the IUDTTP has fulfilled the education aim. It brought positive effects on promoting research interest, cultivating research capacities and enhancing academic performance. The potential deficiencies of extracurricular educational activities, including inflexibility in schedule and insufficiency in systematisms, may be remedied by more systematic educational settings in the future.

Development and validation of a nomogram for identifying venous thromboembolism following oral and maxillofacial oncological surgery with simultaneous reconstruction.

OBJECTIVES: This study aimed to establish and validate a novel predictive model for venous thromboembolism (VTE) in patients undergoing oral and maxillofacial oncological surgery with simultaneous reconstruction. MATERIAL AND METHODS: A total of 372 patients were selected, and their demographic data, comorbidities, medical history, laboratory variables, postoperative Caprini scores, perioperative indicators, and procedures were recorded and analyzed to build the model. The predictive model is displayed as a nomogram. RESULTS: The incidence of VTE was 20.7% (77/372). Several factors were found to be significantly associated with VTE, including age (67 vs. 56 years, P < 0.001), preoperative level of D-dimer (0.56 vs. 0.36 mg/L, P < 0.001), proportion of female patients (46.8% vs. 33.6%, P = 0.032), hypertension (33.8% vs. 21%, P = 0.019). The predictive model was composed of age, gender, and preoperative D-dimer level, with good discriminative ability, as reflected by an area under the curve (AUC) of 0.756, the 95% confidence interval (CI) was 0.696-0.816. Moreover, it showed favorable diagnostic performance compared with both the 2005 (AUC 0.646, 95% CI = 0.578-0.714) and 2010 (AUC 0.627, 95% CI = 0.559-0.694) versions of the Caprini risk assessment model. For patients with malignant tumor, neoadjuvant chemotherapy was also an independent risk factor. CONCLUSIONS: This novel predictive model consists of three readily available clinical variables that show good diagnostic performance in predicting postoperative VTE.

Discovery of GPX4 inhibitors through FP-based high-throughput screening.

Ferroptosis is a form of non-apoptotic cell death, regulated by phospholipid hydroperoxide glutathione peroxidase 4 (GPX4), a selenoprotein with a selenocysteine residue (sec) in the active site. GPX4 is a promising target for cancer cells in therapy-resistant conditions via ferroptosis, which can reduce the level of lipid reactive oxygen species (ROS). So far, all existing GPX4 inhibitors covalently bind to GPX4 via a reactive alkyl chloride moiety or masked nitrile-oxide electrophiles with poor selectivity and pharmacokinetic properties and most were obtained by cell phenotype-based screening. Lacking of effective high-throughput screening methods for GPX4 protein limits the discovery of GPX4 inhibitors. Here, we report a fluorescence polarization (FP)-based high throughput screening (HTS) assay for GPX4-U46C-C10A-C66A in vitro, and found Metamizole sodium from our in-house compound library inhibits GPX4-U46C-C10A-C66A enzyme activity. Structure-activity relationships (SAR) demonstrated the importance of sulfonyl group on interaction between Metamizole sodium and GPX4-U46C-C10A-C66A. Our FP assay could be an effective tool for discovery of GPX4 inhibitors and Metamizole sodium was a potential inhibitor for GPX4 in vitro.

Effect of intravenous low-dose norepinephrine on blood loss in non-tourniquet total knee arthroplasty under general anesthesia: a randomized, double-blind, controlled, single-center trial.

OBJECTIVE: This prospective trial aimed to evaluate the effects of low-dose intravenous norepinephrine (NE) on intraoperative blood loss and bleeding from osteotomy sites during non-tourniquet total knee arthroplasty (TKA) under general anesthesia. METHODS: A total of 120 patients who underwent TKA between December 2020 and May 2022 were enrolled and randomly assigned to the intravenous low-dose NE Group (NE Group) or the control group (C Group). During surgery, NE Group received 0.05-0.1 µg/(kg min) of NE intravenously to raise and maintain the patient's mean arterial pressure (MAP). C Group received the same dose of saline as placebo. Intraoperative blood loss, bleeding score at osteotomy sites, Δlactate levels (Lac), postoperative complications, and transfusion rate during hospitalization were compared between groups. RESULTS: Intraoperative and osteotomy blood loss was significantly lower in the NE Group than in the C Group (P < 0.001). No significant difference was observed in ΔLac between groups (P > 0.05). There was no significant difference in complications between the groups 3 days after surgery (P > 0.05). In addition, there was no significant difference in blood transfusion rates between the two groups during hospitalization (P > 0.05). CONCLUSION: In non-tourniquet TKA under general anesthesia, low-dose intravenous NE safely and effectively reduced intraoperative blood loss and provided a satisfactory osteotomy site while maintaining a higher MAP.

Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.

Background: The thickness of the buccal bone of the anterior maxilla is an important aesthetic-determining factor for dental implant, which is divided into the thick (≥1 mm) and thin type (<1 mm). However, as a micro-scale structure that is evaluated through low-resolution cone-beam computed tomography (CBCT), its thickness measurement is error-prone under the circumstance of enormous patients and relatively inexperienced primary dentists. Further, the challenges of deep learning-based analysis of the binary thickness of buccal bone include the substantial real-world variance caused by pixel error, the extraction of fine-grained features, and burdensome annotations. Methods: This study built bilinear convolutional neural network (BCNN) with 2 convolutional neural network (CNN) backbones and a bilinear pooling module to predict the binary thickness of buccal bone (thick or thin) of the anterior maxilla in an end-to-end manner. The methods of 5-fold cross-validation and model ensemble were adopted at the training and testing stages. The visualization methods of Gradient Weighted Class Activation Mapping (Grad-CAM), Guided Grad-CAM, and layer-wise relevance propagation (LRP) were used for revealing the important features on which the model focused. The performance metrics and efficacy were compared between BCNN, dentists of different clinical experience (i.e., dental student, junior dentist, and senior dentist), and the fusion of BCNN and dentists to investigate the clinical feasibility of BCNN. Results: Based on the dataset of 4,000 CBCT images from 1,000 patients (aged 36.15±13.09 years), the BCNN with visual geometry group (VGG)16 backbone achieved an accuracy of 0.870 [95% confidence interval (CI): 0.838-0.902] and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.924 (95% CI: 0.896-0.948). Compared with the conventional CNNs, BCNN precisely located the buccal bone wall over irrelevant regions. The BCNN generally outperformed the expert-level dentists. The clinical diagnostic performance of the dentists was improved with the assistance of BCNN. Conclusions: The application of BCNN to the quantitative analysis of binary buccal bone thickness validated the model's excellent ability of subtle feature extraction and achieved expert-level performance. This work signals the potential of fine-grained image recognition networks to the precise quantitative analysis of micro-scale structures.

FMRP phosphorylation modulates neuronal translation through YTHDF1.

RNA-binding proteins (RBPs) control messenger RNA fate in neurons. Here, we report a mechanism that the stimuli-induced neuronal translation is mediated by phosphorylation of a YTHDF1-binding protein FMRP. Mechanistically, YTHDF1 can condense with ribosomal proteins to promote the translation of its mRNA targets. FMRP regulates this process by sequestering YTHDF1 away from the ribosome; upon neuronal stimulation, FMRP becomes phosphorylated and releases YTHDF1 for translation upregulation. We show that a new small molecule inhibitor of YTHDF1 can reverse fragile X syndrome (FXS) developmental defects associated with FMRP deficiency in an organoid model. Our study thus reveals that FMRP and its phosphorylation are important regulators of activity-dependent translation during neuronal development and stimulation and identifies YTHDF1 as a potential therapeutic target for FXS in which developmental defects caused by FMRP depletion could be reversed through YTHDF1 inhibition.

Impacts investigation of gas barrier on effective thermal conductivity and service life of vacuum insulation panel.

Vacuum Insulation Panels (VIPs) are highly efficient thermal insulation materials with extremely low thermal conductivity based on the vacuum principle. With the sealing properties of the gas barrier envelopes, a long service life of the VIP is obtained. The mechanism and influence factors of gas and water vapor permeability were mathematically analyzed to explore the influence of gas barrier envelopes on the thermal performance of VIPs. Three typical gas barriers were studied, and the selection of the gas barrier and other aspects of optimization were involved. The relationships among temperature, humidity, solubility coefficient, diffusion coefficient, and permeability were concluded, which shows that temperature has a much greater effect on the permeability of the gas barrier relative to humidity. The numerical analysis and influencing factors of VIPs' service life were also exemplified with three different types of gas barrier envelopes. The experimental results show that depending on the environment, the temperature has a major impact on the effective thermal conductivity and service life of VIP. The research was significant in the selection of gas barriers, the optimization of the performance, and the development of vacuum insulation material.

Establishment and validation of a risk assessment model for myopia among Chinese primary school students during the COVID-19 pandemic: A lasso regression approach.

Purpose: To construct a risk assessment model for forecasting the likelihood of myopia in elementary school students. Design: A cross-sectional study. Methods: This study utilized convenient sampling and questionnaire survey to collect data from eligible elementary students and their parents during the coronavirus disease 2019 (COVID-19) pandemic period from March to December 2020. The data were divided into training and testing sets in a 7:3 ratio. Lasso regression was employed to screen variables for inclusion in the model to establish a generalized linear model, with a nomogram model as the final result. Results: The study included 1139 elementary students, comprising 54.5 % male and 45.5 % female participants. A total of 37 variables were obtained, which were analyzed using lasso regression. Cross-validation revealed that the best lambda value was 0.04201788. Five variables affecting myopia were identified: three risk and two protective factors. The three risk factors were student age (OR = 1.32), family location (urban vs. rural, OR = 2.33), and parents' occupation (compared with farmer: worker, OR = 2.03; teacher, OR = 1.62; medical worker, OR = 5.64; self-employed, OR = 1.78; civil servant, OR = 1.65; company employee, OR = 1.45; service industries, OR = 3.38; and others, OR = 3.20). The two protective factors were eye distance score (OR = 0.83) and eye health exercise score (OR = 0.95). The model was verified and showed good accuracy with an AUC of 0.778 and Brier score of 0.122 in addition to satisfactory clinical effects. Conclusions: The model effectively predicted the risk of myopia in elementary school students during the COVID-19 pandemic. Using this model, high-risk groups can be identified to provide a foundation for early intervention and follow-up, thereby reducing the incidence of myopia in this population.