OBJECTIVE To analyze the impact of the diagnosis-related groups （DRG） payment reform on the length of stay and medical costs in patients with chronic obstructive pulmonary disease （COPD） in Kashgar region， aiming to provide localized empirical evidence for the optimization of regional medical insurance payment methods. METHODS Based on the inpatient settlement database of the Xinjiang Uygur Autonomous Region Healthcare Security Administration， settlement data of COPD inpatients from 17 medical institutions in Kashgar region between January 1， 2022， and December 31， 2024， were extracted. The overall changes in patients’ length of stay and costs were compared before and after the reform. Subsequently， interrupted time series analysis （ITSA） was employed to explore the impact of the DRG payment reform on these variables. RESULTS Following the reform， both the average length of stay and various cost decreased significantly compared to the pre-reform period （ P ＜0.001）. At the overall sample level， the average length of stay， average total cost， average drug cost， average medical service cost， and average examination cost per admission all demonstrated significant long-term downward trends after the reform （ P ＜0.05）. However， the decrease in average out-of-pocket costs and the increase in average consumable costs per admission were not statistically significant （ P ＞0.05）. In tertiary medical institutions， the average length of stay and all categories of costs （except average consumable costs per admission） exhibited significant long-term upward trends after the reform （ P ＜0.05）； conversely， in secondary and lower-level medical institutions， the average length of stay， average total cost， average drug cost， average medical service cost， and average examination cost per admission showed significant long-term downward trends （ P ＜0.05）. CONCLUSIONS The DRG payment reform has achieved an overall effect of reducing the length of stay and controlling costs in COPD patients from Kashgar region. However， the effects vary across different levels of medical institutions： secondary and lower-level institutions show a long-term downward trend in length of stay and costs， whereas tertiary institutions exhibit a long-term upward trend. Furthermore， patients’ out-of-pocket financial burden does not show significant improvement.

ObjectiveThe most prevalent mRNA modification, N6-methyladenosine (m⁶A) plays an important role in various RNA metabolism, including gene expression and translation. By recruiting different “reader” proteins and their cofactors, m⁶A modification can affect messenger RNA (mRNA) degradation, splicing, nuclear export and translation. However, the selective mechanism by which m⁶A sites regulate mRNA translation through m⁶A reader YTHDF1 binding remains poorly understood, due to a lack of computational methods for identifying context-specific m⁶A sites that regulate translation. To address this, we developed a novel computational framework named m⁶ATEpre, the first tool designed to predict cell-specific m⁶A sites that regulate translation efficiency. Methodsm⁶ATEpre integrates multi-omics data, introduces a novel feature representation strategy for m⁶A site sequences, and employs an autoencoder to effectively capture embedded feature representations. Specifically, m⁶ATEpre first integrated MeRIP-seq data and PAR-CLIP data through overlapping m⁶A sites with YTHDF1 binding sites and identified YTHDF1-mediated m⁶A sites. Then, m⁶ATEpre detected the translation gene by analyzing the Ribo-seq data under YTHDF1 knockdown vs control condition. Genes whose translation is mediated by YTHDF1 in an m⁶A-dependent manner were identified by a significant decrease in translation efficiency upon YTHDF1 knockdown. Next, we proposed a binary vector indicating the presence or absence of YTHDF1 binding motifs to characterize each m⁶A site sequence. This represents a novel feature representation strategy for m⁶A sites. m⁶ATEpre utilized the autoencoder to extract the potentially important feature representations and constructed a multilayer perceptron neural networks model to predict potential m⁶A sites that regulating translation efficiency. ResultsA comprehensive evaluation of m⁶ATEpre was conducted through a series of experiments. We compared its performance against that of a similar prediction task model, as well as other classifiers. The results indicate that m⁶ATEpre achieved the best prediction performance. In addition, we analyzed different feature representation strategies and performed ablation experiments to validate the rationality of the model design. The results demonstrate that our proposed feature representation strategy has a greater advantage in improving prediction performance. In the HeLa cell line, bioinformatic analysis of the metagene distribution and sequence minimum free energy of m⁶A sites regulating translation efficiency (m⁶A-reg-TE sites) revealed their specific properties in translation regulation. Functional enrichment analysis indicated that m⁶A-reg-TE genes are associated with specific biological processes and KEGG pathways. By integrating the binding sites of YTHDF1 co-factors with m⁶A-reg-TE sites, we revealed that YTHDF1-mediated and m⁶A-dependent translation efficiency regulation requires the cooperation of multiple translation-regulatory RNA-binding proteins among its co-factors in the HeLa cell line. Furthermore, we extended our predictions to the dataset of the HEK293T cell line. Similarly, bioinformatic analysis of the metagene distribution and functional enrichment revealed the cell-specific characteristic of these predicted m⁶A-reg-TE sites in HEK293T cells. Likewise, integrated analysis of multiple YTHDF1 co-factors and m⁶A-reg-TE sites predicted in the HEK293T cell line reveals their m⁶A-dependent cooperation in regulating translation efficiency. Conclusionm⁶ATEpre is a timely tool that will advance our understanding of the mechanisms of m⁶A regulation in translation efficiency. The source code and datasets used in this work can be downloaded from https://www.scidb.cn/s/bAZZFr.

Objective: To develop a RHCE genotyping assay based on kompetitive allele-specific PCR (KASP) and assess its clinical accuracy for RhCE blood group determination. Methods: KASP primers were designed to interrogate three RHCE loci: the 109 bp insertion/deletion in intron 2, c. 307T>C, and c. 676C>G. A total of 1 194 RhD-positive inpatients from Chengdu were typed by both KASP genotyping and manual tube serology. Discordant samples (n=10) were retested by both methods and further resolved by Sanger sequencing. An additional 377 cases were tested for the c. 48C>G locus to evaluate the predictive accuracy of individual loci and combined locus testing for RhC antigen. Results: Genotyping concordance with serology was 100.0% for both the c. 676C>G locus (RhE/Rhe) and the c. 307T>C locus (Rhc). For RhC prediction using the 109 bp insertion, overall accuracy was 99.7% (1 191/1 194); the 3 discordant cases were confirmed by Sanger sequencing to be false negatives attributable to 109 bp deletion in intron 2. Testing the c. 48C>G allele for RhC prediction yielded 7 false positives, with an accuracy of 98.1% (370/377). RhC antigen status was determined by combining the 109 bp insertion and the c. 48C allele. After excluding 10 samples with inconsistent results between the two loci, the accuracy reached 100% in the remaining 367 samples. When both loci were applied in combination, accuracy reached 100% in the 367 cases with concordant results. Among the 1 194 patients, CCee (45.8%) and CcEe (31.7%) were the most common RhCE phenotypes. The e antigen had the highest positivity rate (92.2%), and the Ce haplotype was the most frequent (66.9%). Conclusion: The KASP-based RHCE genotyping method achieves high accuracy for clinical RhCE typing. Combining the 109 bp insertion/deletion with the c. 48C allele significantly improves RhC antigen prediction compared with either locus alone. This method was applied to RhCE genotyping of 1 194 RhD-positive inpatients in Chengdu, providing local RhCE phenotype and haplotype distribution data to support RhCE-matched transfusion practice.

OBJECTIVE A clinical comprehensive evaluation of five biologic targeted drugs for the treatment of severe eosinophilic asthma （SEA） was conducted to provide objective evidence for the rational selection of these medications in medical institutions. METHODS Using the evaluation methods from the Chinese Medical Institution Drug Evaluation and Selection Rapid Guide （ Third Edition ）， the effectiveness， safety， pharmaceutical characteristics， cost-effectiveness， and other attributes of five biologics targeting SEA （omalizumab， mepolizumab， benralizumab， dupilumab， and tezepelumab） already marketed in China were quantitatively scored across five dimensions， and their recommendation levels were categorized based on the scores. RESULTS The comprehensive scoring results showed that mepolizumab scored 81.29， making it a highly recommended variety； benralizumab scored 78.05， dupilumab scored 77.84 and omalizumab scored 70.14，all of which are recommended varieties； tezepelumab scored 67.77， considered weakly recommended variety. CONCLUSIONS Mepolizumab shows excellent performance in eosinophil clearance， good accessibility， and is more cost-effective than the others， making it more selective in SEA treatment； omalizumab has more advantages in the treatment of SEA patients with elevated immunoglobulin E levels； dupilumab can be used as the preferred treatment for SEA patients with chronic obstructive pulmonary disease.

OBJECTIVE A clinical comprehensive evaluation of five biologic targeted drugs for the treatment of severe eosinophilic asthma （SEA） was conducted to provide objective evidence for the rational selection of these medications in medical institutions. METHODS Using the evaluation methods from the Chinese Medical Institution Drug Evaluation and Selection Rapid Guide （ Third Edition ）， the effectiveness， safety， pharmaceutical characteristics， cost-effectiveness， and other attributes of five biologics targeting SEA （omalizumab， mepolizumab， benralizumab， dupilumab， and tezepelumab） already marketed in China were quantitatively scored across five dimensions， and their recommendation levels were categorized based on the scores. RESULTS The comprehensive scoring results showed that mepolizumab scored 81.29， making it a highly recommended variety； benralizumab scored 78.05， dupilumab scored 77.84 and omalizumab scored 70.14，all of which are recommended varieties； tezepelumab scored 67.77， considered weakly recommended variety. CONCLUSIONS Mepolizumab shows excellent performance in eosinophil clearance， good accessibility， and is more cost-effective than the others， making it more selective in SEA treatment； omalizumab has more advantages in the treatment of SEA patients with elevated immunoglobulin E levels； dupilumab can be used as the preferred treatment for SEA patients with chronic obstructive pulmonary disease.

Objective To investigate the incidence of placental abruption in the third trimester of pregnancy in Hebei Province,identify its associated risk factors,and develop a corresponding nomogram prediction model.Methods Data from pregnant women at 22 monitored hospitals in Hebei Province,collected between 2013 and 2023,were analyzed to assess the incidence,trends,and associated risk factors of placental abruption.A prediction model was developed and visualized using R programming to generate the receiver operating characteristic(ROC)curve.The model's predictive performance was evaluated using the area under the curve(AUC)and calibration curve parameters.Results A total of 480 690 pregnant women were included in this study over the specified period.The incidence of placental abruption was 0.29%(1 395 out of 480 690),with an average annual percentage change of 2.73%,indicating a stable trend(P=0.34).Multivariate logistic regression analysis revealed that pregnancy-related anemia,preeclampsia,placenta previa,vaginal bleeding before 28 weeks'gestation,and between 28 and 31+6 weeks'gestation were significant risk factors for placental abruption(all P<0.05).In contrast,regular prenatal examinations(7～11 visits)and multiple pregnancies were identified as protective factors against placental abruption(both P<0.05).The area under the curve(AUC)for the nomogram model constructed based on independent risk factors for placental abruption was 0.79,and the calibration curve demonstrated that the predicted values closely aligned with the observed values.Conclusions Prenatal examinations should be prioritized,especially for women with preeclampsia,placenta previa,or a history of vaginal bleeding before 28 weeks of gesta-tion.Management of pregnancies between 28 and 31+6 weeks is also crucial to reduce the incidence of placental abruption and mitigate adverse maternal and neonatal outcomes.The nomogram model constructed based on these factors exhibits excellent predictive performance,providing a solid theoretical foundation for the prevention and clinical management of placental abruption.

Objective:To investigate the diagnostic value of metagenomic next-generation sequencing (mNGS) for pyogenic spinal infections.Methods:A total of 255 patients diagnosed with pyogenic spinal infections were enrolled between September 2022 and September 2024 at Qingdao University Affiliated Hospital, Fuzhou Second General Hospital, First Affiliated Hospital of Nanchang University, Shandong University Affiliated Public Health Clinical Center, and the Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Among them, 155 were male and 100 were female, with an average age of 62.5±14.2 years (ranging from 13 to 90 years). All patients had samples of infected tissue and/or pus collected for microbial culture and mNGS testing. The number, types, and positive rates of pathogens detected by microbial culture and mNGS were compared. Using culture results as the gold standard, receiver operating characteristic (ROC) curve analysis was performed for mNGS testing and the combined method of mNGS and microbial culture, calculating the area under the curve (AUC) and 95% CI. Results:All 255 cases were clinically diagnosed as pyogenic spinal infections, with 194 cases providing microbiological evidence. The most common Gram-positive bacterium was Staphylococcus aureus, while the most common Gram-negative bacterium was Escherichia coli. A total of 33 pathogenic microorganisms were detected by mNGS, while microbial culture detected 18 pathogenic microorganisms. The positive rate of mNGS was 72.2% (184 out of 255), which was significantly higher than that of 30.2% (77 out of 255) for microbial culture, showing a significant difference (χ 2=90.150, P<0.001); the positive rate of mNGS combined with microbial culture was 76.1% (194 out of 255) with significant difference compared to mNGS alone (χ 2=8.100, P<0.001). Among 178 culture-negative samples, the detection rate of mNGS was 65.7% (117 out of 178); among 77 culture-positive samples, the detection rate of mNGS was 87.0% (67 out of 77), and 97.0% (65 out of 67) of the detected pathogens matched the culture results at the species level. The AUCs of the ROC curves for mNGS testing and the combination of mNGS with microbial culture were 0.606 [95% CI (0.534, 0.678)] and 0.671 [95% CI (0.606, 0.736)], respectively, with significant differences compared to microbial culture ( P=0.007; P=0.007). Conclusions:mNGS demonstrates superior performance over conventional culture in identifying pathogens in pyogenic spinal infections. Moreover, combining mNGS with culture further improves diagnostic yield, supporting its integration into clinical practice.

The National Institute for Health and Care Excellence(NICE)first updated its 2013 Health Technology Evaluations Guidelines in 2022,followed by further minor adjustments in 2023.This round of updates primarily focuses on five key areas:mea-suring the value of health technologies,understanding and improving the evidence base,structured decision-making,addressing challenging diseases and technologies,and ensuring consistency in methods across evaluation and review projects.It outlines the up-date process and major changes,providing an analysis of these key points with the aim of offering valuable insights and methodologi-cal references for health technology assessment in China.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective:This study aimed to investigate the effects of glucagon-like peptide-1 receptor agonists(GLP-1RAs) on the emotional behaviors in wild-type mice and explore the underlying mechanisms.Methods:The study consisted of two parts.(1) Drug effect evaluation: C57BL/6 mice were randomly assigned to saline, semaglutide, or liraglutide groups. After 4 weeks of continuous subcutaneous administration, anxiety-like behaviors were evaluated by the open field tests and elevated plus maze, and depression-like behaviors were assessed by the forced swimming test and tail suspension test. Neurotransmitter levels, including NE, DA, 5-HT, GABA, and glutamate, were measured in target brain regions using LC-MS/MS. Hypothalamic activation subregions were identified by c-fos immunohistochemistry.(2) Time-course analysis: Mice received short-term administration(2 weeks), long-term administration(4 weeks), or drug withdrawal(assessed 4 weeks post-withdrawal). Behavioral performance, hypothalamic neurotransmitter levels, and plasma adrenocorticotropic hormone and corticosterone were measured.Results:Compared with the saline group, both GLP-1RAs significantly prolonged immobility time in the forced swimming and tail suspension tests. Multiple brain regions exhibited neurotransmitter abnormalities, including marked reductions in hypothalamic norepinephrine, dopamine, and γ-aminobutyric acid levels. Both GLP-1RAs significantly increased c-fos positive cell counts in the suprachiasmatic nucleus and arcuate nucleus of the hypothalamus. Time-course analysis revealed that short-term administration did not induce depressive phenotypes, whereas long-term administration led to depression-like behaviors, which recovered after drug withdrawal. Plasma adrenocorticotropic hormone and corticosterone levels were elevated even after short-term treatment and remained high after drug withdrawal, while hypothalamic neurotransmitter abnormalities normalized upon withdrawal.Conclusion:GLP-1RAs induce depression-like behaviors in a time-dependent manner, with neurotransmitter imbalance and persistent hyperactivation of the HPA axis likely contributing to the pathophysiological mechanism.