Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.

BACKGROUND: Parental myopia is an important risk factor for myopia in Chinese children and adolescents. This study aimed to assess the influence of parental myopia and the severity of myopia on offspring and to evaluate whether adopting healthy lifestyles can mitigate the effects of parental myopia on offspring. METHODS: This cross-sectional study analyzed data from children and adolescents aged 6-17 years with complete vision assessments and parental history of myopia from six provinces in China during 2013-2014. Parental demographic information, children's outdoor activity time, sleep time, and sedentary time were collected via questionnaire. Parental myopia was classified as no myopia, paternal myopia, maternal myopia, and both. The offspring were categorized into 10 groups based on parental myopia prescription. Associations of the above factors with myopia in children and adolescents were evaluated by logistic regression analysis. RESULTS: Among 40,864 children and adolescents (50.3% boys and 49.7% girls), 22,537 (55.2%) were diagnosed with myopia. In comparison to offspring devoid of parental myopia, children with one parent affected by myopia exhibited odds ratio (OR) of 1.75 (95% confidence interval [CI]: 1.62-1.88) for myopia, while those with both parents affected showed OR of 2.27 (95% CI: 2.02-2.55) after adjusted for lifestyle factors. The likelihood of myopia in offspring increased with increasing severity of parental myopia, with a 3.08-fold increase in risk observed when both parents presented high myopia. Nonetheless, children adhering to two or more healthy lifestyle factors demonstrated a diminished risk of myopia compared with those with fewer than two lifestyle factors, especially among offspring of non-myopic or mildly myopic parents. CONCLUSIONS Parental myopia has a dose-dependent association with their offspring. Healthy lifestyles may reduce the impact of parental factors on myopia in offspring. The observed associations suggest that although the genetic burden of parental myopia cannot be ignored, healthy lifestyles and nurturing are also very important.
Humans ; Myopia/epidemiology* ; Child ; Male ; Female ; Adolescent ; Cross-Sectional Studies ; China/epidemiology* ; Parents ; Surveys and Questionnaires ; Risk Factors

Osteoarthritis (OA) causes chronic pain that significantly impairs quality of life, with current treatments often proving insufficient and accompanied by adverse effects. Recent research has identified the dorsal root ganglion (DRG) and its resident macrophages as crucial mediators of chronic OA pain through neuroinflammation driven by macrophage polarization. We present a novel injectable thermo-sensitive hydrogel system, KAF@PLEL, designed to deliver an anti-inflammatory peptide (KAF) specifically to the DRG. This biodegradable hydrogel enables sustained KAF release, promoting the reprogramming of DRG macrophages from pro-inflammatory to anti-inflammatory phenotypes. Through comprehensive in vitro and in vivo studies, we evaluated the hydrogel's biocompatibility, effects on macrophage polarization, and therapeutic efficacy in chronic OA pain management. The system demonstrated significant capabilities in preserving macrophage mitochondrial function, suppressing neuroinflammation, alleviating chronic OA pain, reducing cartilage degradation, and improving motor function in OA rat models. The sustained-release properties of KAF@PLEL enabled prolonged therapeutic effects while minimizing systemic exposure and side effects. These findings suggest that KAF@PLEL represents a promising therapeutic approach for improving outcomes in OA patients through targeted, sustained treatment.

Objective: To prepare hollow mesoporous silicon nanoparticles ( HMSNs) loaded with allicin—diallyl trisulfide (DATS) , and to study their feasibility as a therapeutic agent for ischemic injury of lower limbs . Methods: HMSNs were synthesized by selective etching , and their microstructure was observed by scanning and transmis- sion electron microscopy. Their physical and chemical properties were analyzed by X-ray diffraction and dynamic light scattering (DLS) . Their biological safety was tested by erythrocyte hemolysis and cytotoxicity experiments . DATS was loaded into HMSNs by adsorption to obtain DATS sustained release nanoparticles (DATS-HMSNs) , and the cumulative release curve of DATS was calculated and produced by ultraviolet spectrophotometry. C57BL/6 mice were randomly divided into four groups (sham operation group , normal saline group , DATS group , and DATS-HM- SNs group) . Lower limb ischemia models were made by femoral artery ligation and resection . The exercise ability and the contents of tumor necrosis factor alpha (TNF-α ) , interleukin-6 (IL-6) , monocyte chemoattractant protein- 1 (MCP-1) , reactive oxygen species (ROS) , platelet-endothelial cell adhesion molecule (CD31) , alpha smooth muscle actin ( α-SMA) , basic fibroblast growth factor (bFGF) and vascular endothelial growth factor (VEGF) in muscles of mice in each group before and after limb ischemia were tested . Results : Scanning and transmission e- lectron microscope observation showed that the prepared HMSNs were hollow , spherical and uniform in particle size . DLS results showed that the particle size was (226. 5 ± 11 . 8) nm. The results of red blood cell hemolysis test and cytotoxicity test showed that HMSNs had good biocompatibility. The maximum drug loading rate of HMSNs on DATS was 27. 89% , the cumulative release rate of DATS in 7 days was about 80. 12% , and could reach 97. 27% in 21 days . Compared with the control group , after DATS-HMSNs were applied to mice with lower limb ischemia , immunohistochemical staining showed that the levels of CD31 , α-SMA , bFGF and VEGF increased ( P < 0. 05) . Elisa test showed that the levels of TNF-α , IL-6 , MCP-1 and ROS decreased (P < 0. 05) , and the exercise ability of mice recovered satisfactorily after ischemia. Conclusion DATS-HMSNs can release DATS slowly and continu- ously , providing protection against ischemic injury of lower limbs .

In view of the education and teaching problems in the course of Pediatrics about the construction of students' knowledge system and the cultivation of high-order abilities, on the basis of integrated organ and system courses, with the learner-centered and goal-oriented teaching concept seeking continuous improvement, we have developed a clinical problem-centered integrated teaching model in line with the learning law of medical students, innovatively integrating theoretical learning and clinical practice, and featuring the normalization of hybrid teaching and diversified teaching activities and evaluations. The curriculum innovation efforts substantially improve student satisfaction with teaching, and achieve good teaching effects. It has been actively promoted in the western pediatrics alliance of China, receiving extensive attention and positive feedback.

Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.

Objective:To explore the radiological damage to cells induced by the delayed particles of 9C-ions for heavy ion therapy, as well as the microdosimetric distribution and biological effects of these particles on a single model of V79 Chinese hamster lung cells. Methods:The Monte Carlo program was employed to simulate the endonuclear absorbed doses of α particles with various energies (3-10 MeV) transported in cells (cell radius RC = 10 μm, nucleus radius RN = 5 μm). Then, the result were compared with the S values ( SN←N, SN←Cy, and SN←CS) derived using the medical internal radiation dose (MIRD) method to demonstrate the feasibility of Monte Carlo simulations. Finally, the energy deposition of the delayed particles of 9C-ions generated at three sites (i.e., on the surface and in the cytoplasm and nucleus of the V79 cell model) during their transport in targets was simulated, and the result ing cell surviving fraction was analyzed. Results:Monte Carlo and MIRD method yielded differences in S values of 1.91%-4.95% for SN←N (nucleus to nucleus), 1.48%-5.11% for SN←Cy (cytoplasm to nucleus), and -1.99% to 0.80% for SN←CS(surface to nucleus), indicating highly consistent S values derived using both method(differences < 6%). When a 9C-ion decayed on the surface of the V79 cell model and the produced secondary particles entered the cell, the average endonuclear absorbed dose was 10 -2 Gy orders of magnitude, with a cell surviving fraction of about 88%. In the case where decay occurred in the cytoplasm, the cell surviving fraction was about 80%. However, when the 9C ion decayed in the nucleus, α particles had short ranges and deposited most of their energy in the cell (mean endonuclear absorbed dose: 0.1 Gy). In this case, severe cell damage was induced, with the cell surviving fraction reducing to about 53%. Conclusions:9C-ions emit secondary charged particles due to decay, among which α particles cause great damage to cells when entering the nucleus and trigger evident biological effects.

Background Formaldehyde is a toxic gas. Automatic iodine analyzer is a high-throughput automatic instrument for measuring water and urine iodine, but at present, there is a lack of its application in the determination for air formaldehyde. Objective To develop a high-throughput method for air formaldehyde determination with an automated iodine analyzer. Methods The formaldehyde in the air was collected with an atmospheric sampler, and 1.0 mL of working curve solution, standard solution, and sample solution were loaded to an automated iodine analyzer. Solution resting time and instrumental parameters, including detection wavelength, sample needle depth, and regent₂ (R₂) reaction time were optimized. Finally, the method was validated by precision, detection and quantification limits, and spiked recovery. Four different concentrations of standard substance solutions and 10 air samples were collected for comparison between manual measurement method and high-throughput fully automated detection method. Results The final optimized conditions in this study were: 2 h and above resting time for prepared solution, and the optimal parameters of the instrument work were: detection wavelength at 660 nm, sample needle depth at 2800, and R₂ reaction time at 900 s. Under the optimized conditions the linear equation of the method was optical density(D) =0.9787 × formaldehyde concentration (C) − 0.0364 and the correlation coefficient was 1.0000. The limits of detection (LOD) and quantification (LOQ) were 0.015 μg·mL⁻¹ and 0.052 μg·mL⁻¹ , respectively. The relative standard deviations (RSDs) of the intra-day precision experiments were 1.35% and 1.18% for the two different concentrations of standard sample solutions. The RSDs of the inter-day precision experiments were 1.82% and 1.50%, respectively. The recoveries ranged from 96.8% to 107.4%. The results produced by spectrophotometry and automated detection method in the comparison study were identical, and the measurements of selected four concentrations of standard solutions were within the uncertainty range. Conclusion The method established in this study features small solvent dosage, fully automated high throughput, comparable sensitivity to the national standard method, high accuracy, and inter-day and intra-day precision with a recovery rate above 96%. It can provide strong technical support for the determination of air formaldehyde.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.