Abstract The prevention and control of myopia in Chinese children and adolescents has become a major public health issue. While maintaining increased outdoor activity as a cornerstone intervention, there is an urgent need to explore new complementary approaches that can be effectively implemented in both indoor and outdoor settings. In recent years, environmental spatial frequency has gained increasing attention as one of the key environmental factors influencing the development and progression of myopia. Both animal studies and human research have confirmed that indoor environments lacking mid to high spatial frequency components, often characterized as "visually impoverished", can promote axial elongation and myopia through mechanisms such as disruption of retinal neural signaling, impaired accommodative function, and altered expression of related molecules. Based on the scientific consensus, it is recommended that "enriching of environmental spatial frequency" should be integrated into the myopia prevention and control framework. Following the principles of schoolled organization, family cooperation, community involvement, and student participation, specific measures are put forward in three areas：optimizing school visual settings, improving home spatial environments, and promoting healthy visual behavior. The aim is to create "visually friendly" indoor environments as an important supplement to outdoor activity, thereby providing a novel perspective and strategy for comprehensively advancing myopia prevention and control among children and adolescents.

AIM:To analyze the influencing factors of visual outcome after vitrectomy for polypoidal choroidal vasculopathy(PCV)combined with vitreous hemorrhage and establish a predictive model.METHODS: A retrospective analysis was conducted on the clinical data of 129 cases(129 eyes)of patients who underwent vitrectomy for PCV combined with vitreous hemorrhage from June 2021 to January 2024 in our hospital. They were divided into elevated group(71 eyes)and non-elevated group(58 eyes)according to visual outcome at early posoperative stage(within 24 mo). Another 30 cases(30 eyes)of PCV with vitreous hemorrhage undergoing vitrectomy were selected as external validation data. The predictive value of the model for the postoperative visual outcomes of both internal and external populations was evaluated.RESULTS: The non-elevated group had a higher proportion of patients aged ≥60 years, diabetes, continuous abnormalities of the ellipsoid zone(EZ)during surgery, bleeding involving the macular fovea, and postoperative retinal scar formation than the elevated group were independent factors affecting postoperative visual acuity(all P<0.05). The AUC of the predictive model for predicting the postoperative visual outcomes of internal and external populations was 0.824(95%CI: 0.750-0.898)and 0.809(95%CI: 0.723-0.865), respectively.CONCLUSION:Patients aged ≥60 years, diabetes, intraoperative continuous abnormalities of EZ, bleeding involving the macular fovea, and postoperative retinal scar formation are influencing factors for visual outcome after vitrectomy in patients with PCV combined with vitreous hemorrhage. A predictive model based on those factors has been established, which has a certain predictive value for postoperative visual outcome.

Abstract Myopia is a complex disease influenced by multiple factors, and its prevention and control require a thorough identification and monitoring of its risk factors. The paper examines the defocus characteristics of the visual environment and their association with myopia among children and adolescents, and further describes their role in the prevention and control of myopia, as well as analyses the limitations of the current study and proposes directions for further research. Finally, the paper calls attention to the etiological significance of the defocus characteristics in the visual environment during the development of myopia among children and adolescents, which could provide a scientific basis for optimizing the learning environment and designing other measures to promote myopia prevention and control among children and adolescents.

Objective: To explore the association between sleep quality and dry eye symptoms in adolescents,so as to provide the evidence for reducing the prevalence of dry eye symptoms. Methods: The study population was adolescents aged 12-24 years from the Psychology and Behavior Investigation of Chinese Residents (PBICR) survey, which was conducted from 20 June to 31 August 2022. A stratified random sampling and quota sampling method was used to select 6 456 adolescents within mainland China. The Ocular Surface Disease Index (OSDI) and Brief version of the Pittsburgh Sleep Quality Index (B-PSQI) were used to assess dry eye symptoms and sleep quality. Multiple Logistic regression model was used to explore the relationship between sleep quality and dry eye symptoms in adolescents. The influence of gender on the association was explored by using interaction terms. Results: A total of 2 815 adolescents reported having dry eye symptoms, with a prevalence of 43.6%. Logistic regression analysis results showed an increased risk of exacerbation of dry eye symptoms in adolescents with poor sleep quality. The OR (95% CI ) for mild, moderate, and severe dry eye symptoms groups were 1.39(1.16-1.67), 1.52(1.28-1.81), and 2.35(2.02-2.72), respectively, compared with the ocularly normal group ( P <0.05). There was a significant interaction between sleep quality and gender on dry eye symptoms in adolescents ( P <0.01). Conclusions Sleep quality is associated with dry eye symptoms in adolescents, and those with poor sleep quality have a higher risk of dry eye symptoms. The effect of sleep quality on dry eye symptoms is greater in boys.

Tumors exhibit abnormal glucose metabolism, consuming excessive glucose and excreting lactate, which constructs a tumor microenvironment that facilitates cancer progression and disrupts immunotherapeutic efficacy. Currently, tumor glucose metabolic dysregulation to reshape the immunosuppressive microenvironment and enhance immunotherapy efficacy is emerging as an innovative therapeutic strategy. However, glucose metabolism modulators lack specificity and still face significant challenges in overcoming tumor delivery barriers, microenvironmental complexity, and metabolic heterogeneity, resulting in poor clinical benefit. Nanomedicines, with their ability to selectively target tumors or immune cells, respond to the tumor microenvironment, co-deliver multiple drugs, and facilitate combinatorial therapies, hold significant promise for enhancing immunotherapy through tumor glucose metabolic reprogramming. This review explores the complex interactions between tumor glucose metabolism-specifically metabolite transport, glycolysis processes, and lactate-and the immune microenvironment. We summarize how nanomedicine-mediated reprogramming of tumor glucose metabolism can enhance immunotherapy efficacy and outline the prospects and challenges in this field.

To summarize the nursing experience of 5 patients with severe ARDS complicated with hypercapnia who underwent extracorporeal carbon dioxide removal(ECCO2R).Key points of nursing care included:establishing a rapid emergency response nursing team for ECCO2R;PETCO2 real-time monitoring to grasp the timing of the machine;ensuring continuity of treatment and improving the removal efficiency;respiratory-related monitoring;prevention of complications of blood coagulation and hypothermia;and weaning from extracorporeal carbon dioxide removal.After careful treatment and care,all the 5 patients were successfully removed from ECCO2R treatment.

Atherosclerosis is a chronic inflammatory disease characterized by autoimmune component,ser-ving as a primary factor in cardiovascular diseases.The atherosclerotic plaques involve a variety of immune cells which mediated complex immune and inflammatory responses at each stage of the disease's progression.Notably,within atherosclerotic plaques,T-cells,as the predominant immune cells,can be differentiated into various subsets with distinct functions.These T-cell subsets modulate the progression of atherosclerosis by se-creting either pro-inflammatory or anti-inflammatory cytokines.Understanding the impact of these immune cells on the disease's development is imperative for formulating novel therapeutic strategies.This article aims to review the roles of T-cell subsets and their secreted cytokines in atherosclerosis,aiming to offer new in-sights for the scientific research and clinical management of this condition.

Epilepsy with eyelid myoclonia (EEM), also known as Jeavons syndrome, is a type of genetic generalized epilepsy officially recognized by the International League Against Epilepsy. The clinical characteristics of EEM include eyelid myoclonia, eye closure sensitivity, and photosensitivity, typically manifesting in childhood with a higher prevalence in female patients. The prognosis of EEM is poor, and seizures are difficult to fully control even with standardized drug treatment. The electroencephalogram of EEM often shows fast interictal multiple spike-wave complexes, which can be induced by eye closure or photic stimulation. Functional magnetic resonance imaging and other studies have found that EEM is associated with brain network abnormalities, particularly the key role of the central medial thalamic nucleus in the regulation of cortical excitability. Genetic research has found that EEM may be related to variations in genes such as CHD2, SYNGAP1, NEXMIF, RORB, and GABRA1.

Drug adulteration and contamination are serious threats to human health therefore,their accurate monitoring is very important.Allopurinol(Alp)and theophylline(Thp)are commonly used drugs for the treatment of gout and bronchitis,while their isomers hypoxanthine(Hyt)and theobromine(Thm)have no effect and affect the efficacy of the drug.In this work,the drug isomers of Alp/Hyt and Thp/Thm are simply mixed with α-,β-,y-cyclodextrin(CD)and metal ions and separated using trapped ion mobility spectrometry-mass spectrometry(TIMS-MS).TIMS-MS results showed that Alp/Hyt and Thp/Thm iso-mers could interact with CD and metal ions and form corresponding binary or ternary complexes to achieve their TIMS separation.Different metal ions and CDs showed different separation effect for the isomers,among which Alp and Hyt could be successfully distinguished from the complexes of[Alp/Hyt+y-CD+Cu-H]+with separation resolution(Rp-p)of 1.51;whereas Thp and Thm could be baseline separated by[Thp/Thm+y-CD+Ca-H]+with Rp-p of 1.96.Besides,chemical calculations revealed that the complexes were in the inclusion forms,and microscopic interactions were somewhat different,making their mobility separation.Moreover,relative and absolute quantification was investigated with an internal standard to determine the precise isomers content,and good linearity(R2＞0.99)was ob-tained.Finally,the method was applied for the adulteration detection where different drugs and urine were analyzed.In addition,due to the advantages of fast speed,simple operation,high sensitivity,and no chromatographic separation required,the proposed method provides an effective strategy for the drug adulteration detection of isomers.

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.