OBJECTIVE To explore the optimal preprocessing protocols for colonoscopes based on the orthogonal experimental design combined with cost analysis.METHODS An orthogonal design experiment was conducted in Nanjing Drum Tower Hospital,The Affiliated Hospital of Nanjing University Medical Hospital.Totally 4 influen-cing factors for preprocessing and 3 corresponding levels were formed to 9 types of preprocessing protocols on the bases of orthogonal design.The colonoscopes that were used for diagnosis and treatment of 180 patients were ran-domly divided into 9 groups,each group was treated with one preprocessing protocol.The adenosine triphos-phatase(ATP)test and microbial culture were carried out after the preprocessing,cleaning and disinfection.The costs of the 9 preprocessing protocols were analyzed.RESULTS There were significant differences in the ATP val-ue and bacterial colony counts between the factor A(the type of preprocessing solution)and the factor C(prepro-cessing suction time)after the preprocessing(all P＜0.05);A3 and C2 were the optimal levels.There were no significant differences in the ATP value and bacterial colony counts among the factor A(the type of preprocessing solution),the factor B(the change frequency of preprocessing solution),the factor C(the preprocessing suction time)and the factor D(the interval between preprocessing and the cleaning).The qualified rates of cleaning of all the 9 groups reached up to 100.00％,and the qualified rates of disinfection were no less than 90.00％.The result of rank sum test showed that there were no significant differences in the ATP value(H=5.449,P=0.709)and the bacterial colony counts(H=1.770,P=0.987)among the 9 groups after the disinfection.The cost analysis showed that the cost was lowest when water was used as the preprocessing solution.CONCLUSION The optimal protocol for preprocessing of colonoscopes is that the alkaline solution is used as the preprocessing solution,with the change frequency changing every 4 hours,the suction time 20 seconds;the delay time should be determined based on the specific clinical condition,and the colonoscopes should be cleaned as early as possible.

Hepatocellular carcinoma(HCC)expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming.Aldolase A(ALDOA)plays a prominent role in glycolysis;however,little is known about its role in HCC development.In the present study,we aim to explore how ALDOA is involved in HCC proliferation.HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout,which is consistent with ALDOA overexpression encouraging HCC prolifera-tion.Mechanistically,ALDOA knockout partially limits the glycolytic flux in HCC cells.Meanwhile,ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase;ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function.A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun,and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells.In HCC patients,the expression level of ALDOA was correlated with the phosphorylation level of c-Jun(Thr93)and poor prognosis.Remarkably,hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models,and the knockdown of Aldoa strikingly decreased HCC development in vivo.Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription,opening additional avenues for anti-cancer therapies.

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 To analyze risk factors for poor postoperative wound healing in spinal tuberculo-sis patients and construct prediction models along with a risk scoring table using machine learning algorithms,providing references for early prevention and management.Methods Clinical data from 420 spinal tuberculo-sis patients treated at four tertiary hospitals in Guizhou Province between January 2017 and February 2024 were retrospectively analyzed.Risk factors were identified through univariate and multivariate analyses.Logis-tic regression,random forest,and support vector machine prediction models were constructed.Model perform-ance was evaluated using receiver operating characteristic(ROC)curves,precision,recall,accuracy,and F1-score.A risk prediction scoring table for poor postoperative wound healing was subsequently developed.Re-sults Among the 420 patients with spinal tuberculosis,132 experienced poor postoperative wound healing,with an incidence rate of 31.43%.Logistic regression analysis showed that BMI≤18.5 kg/m2,preoperative albumin≤30 g/L,combined surgical procedure,intraoperative blood loss>1 000 mL,direct bilirubin>8 μmol/L within 3 days after surgery,neutrophil count≤75×109/L within 3 days after surgery,and postopera-tive drainage volume>500 mL were risk factors for poor postoperative wound healing(P<0.05).In the comparison among the three risk prediction models constructed based on machine learning algorithms,the ran-dom forest model demonstrated the best predictive performance.A risk prediction scoring table was construc-ted based on the partial regression coefficients from the multivariate analysis,with a total score range from 0 to 11 points.A score>6 points indicated an increased risk of poor postoperative wound healing.The area un-der the ROC curve(AUC)for this risk prediction scoring table was 0.846(95%CI:0.769 to 0.923),indica-ting good predictive performance.Conclusion The random forest model based on machine learning algorithms and the risk prediction scoring table have certain predictive value for assessing the risk of poor postoperative wound healing in patients with spinal tuberculosis;they can assist healthcare professionals in early identifica-tion of high-risk patients.

The telomere structure in the cell nucleus is crucial for maintaining the stability and functions of chromosomes.Telomerase is a ribonucleoprotein reverse transcriptase,which catalyzes the elongation of telomeres using its own RNA as a template,thereby counteracting the shortening of telomeres caused by chromosome replication and cell division.Due to its overexpression in over 85％of malignant tumor cells,telomerase has emerged as a highly promising biomarker and a novel target for cancer therapy.In recent years,given the importance of precise quantification of telomerase activity in guiding medical diagnosis and treatment strategies,researchers have developed various high-performance telomerase detection techniques.Among these,electrochemical biosensing technique has cause much attention due to its high sensitivity,operational convenience,rapid response,and ease of miniaturization.This paper focused on the latest advances in electrochemical sensing technique for detection of telomerase activity,aiming to provide inspiration for designing novel telomerase activity detection strategies by elucidating three unique properties of telomerase primer extension products.

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at the single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations. Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest novel candidate targets for splicing-based therapeutic strategies.
Humans ; Colorectal Neoplasms/metabolism* ; RNA Isoforms/metabolism* ; Single-Cell Analysis ; RNA Splicing ; Gene Expression Regulation, Neoplastic ; RNA, Neoplasm/metabolism* ; Transcriptome

Hepatocellular carcinoma (HCC) expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming. Aldolase A (ALDOA) plays a prominent role in glycolysis; however, little is known about its role in HCC development. In the present study, we aim to explore how ALDOA is involved in HCC proliferation. HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout, which is consistent with ALDOA overexpression encouraging HCC proliferation. Mechanistically, ALDOA knockout partially limits the glycolytic flux in HCC cells. Meanwhile, ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase; ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function. A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun, and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells. In HCC patients, the expression level of ALDOA was correlated with the phosphorylation level of c-Jun (Thr93) and poor prognosis. Remarkably, hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models, and the knockdown of A ldoa strikingly decreased HCC development in vivo. Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription, opening additional avenues for anti-cancer therapies.