Objective To investigate the role and regulatory mechanism of LINC00657 in the progression of cervical cancer. Methods Bioinformatics analysis predicted potential binding sites between LINC00657 and miR-30a-5p and between miR-30a-5p and Skp2. These sites were verified by using RNA immunoprecipitation and dual-luciferase reporter experiments. LINC00657, miR-30a-5p, and Skp2 mRNA expression levels in cervical cancer tissues and cell lines were assessed by utilizing RT-qPCR. Western blot analysis was employed to examine the protein levels of Skp2 in cells and subcutaneous xenograft tumor models in nude mice. Immunohistochemistry was applied to analyze Skp2 expression in animal tissues. The cellular processes of cervical cancer cell lines were evaluated through CCK-8, scratch, and Transwell assays. Results LINC00657 and Skp2 presented binding sites for miR-30a-5p. In cervical cancer, LINC00657 and Skp2 showed high expression levels (P<0.05), whereas miR-30a-5p displayed low expression (P<0.05). Functional experiments demonstrated that linc00657 upregulates Skp2 expression, a process that is dependent on its sequestration of miR-30a-5p. Conclusion LINC00657 promoted the malignant progression of cervical cancer by upregulating Skp2 expression through specifically sequestering miR-30a-5p, thereby relieving its inhibitory effect on the target gene Skp2.

ObjectivePancreatic ductal adenocarcinoma (PDAC) exhibits a limited response to current treatments due to its dense fibrotic stroma and highly immunosuppressive tumor microenvironment. In recent years, advancements in cellular immunotherapy, particularly chimeric antigen receptor macrophage (CAR-M) therapy, have offered new hope for pancreatic cancer treatment. Although CAR-M therapy demonstrates dual potential in directly killing tumor cells and remodeling the immune microenvironment, it still faces challenges such as complex in vitro preparation processes and low in vivo targeting and delivery efficiency. Therefore, developing strategies for efficient and targeted in vivo delivery of CAR genes has become crucial for overcoming current therapeutic limitations. This study aims to develop an orally administrable nano-gene delivery system for the targeted delivery of CAR genes to pancreatic tumor sites. MethodsCore nano-gene particles (PNP/pCAR) were constructed by loading plasmid DNA encoding CAR (pCAR) with cationic polypeptides (PNP). Subsequently, PNP/pCAR was surface-modified with β-glucan to prepare the targeted nanoparticles (βGlus-PNP/pCAR). The loading efficiency of PNP for pCAR was quantitatively assessed by gel retardation assay. The particle size, Zeta potential, morphology, and storage stability of PNP/pCAR were characterized using a Malvern particle size analyzer and transmission electron microscopy. At the cellular level, RAW 264.7 macrophages were selected. The cytotoxicity of PNP/pCAR was evaluated using the CCK-8 assay. The cellular uptake efficiency and lysosomal escape ability of the nanoparticles were assessed via flow cytometry and confocal microscopy. Transfection efficiency was quantitatively evaluated by detecting the expression of the reporter gene GFP using flow cytometry. At the in vivo level, an orthotopic pancreatic cancer mouse model was established. Cy7-labeled βGlus-PNP/pCAR nanoparticles were administered orally, and the fluorescence distribution in mice was dynamically monitored at 1, 2, 4, 8, and 16 h post-administration using a small animal in vivo imaging system. Forty-eight hours after oral gavage, the mice were euthanized, and pancreatic tumor tissues were collected for further analysis of intratumoral fluorescence signals using the imaging system. Additionally, βGlus-PNP/pCAR-GFP nanoparticles loaded with the reporter gene (GFP) were administered orally. Forty-eight hours post-administration, pancreatic tumor tissues were harvested to prepare frozen sections, and GFP expression was observed and analyzed under a fluorescence microscope. ResultsThe PNP carrier exhibited a high loading capacity for pCAR. The successfully prepared PNP/pCAR nanoparticles were regular spheres with a hydrodynamic diameter of approximately (120±10) nm and a Zeta potential of about +(6±1) mV. They maintained good structural stability after incubation in PBS buffer for 7 d. Cell experiments demonstrated that PNP/pCAR exhibited no significant cytotoxicity in RAW 264.7 cells while being efficiently internalized and effectively escaping lysosomal degradation. The transfection positive rate of PNP/pCAR-GFP in RAW 264.7 cells reached (25±3)%, surpassing that of Lipofectamine 2000-loaded pCAR-GFP (Lipo/pCAR-GFP), which was (20±1)%.In vivo experiments revealed that, compared to unmodified PNP/pCAR, βGlus-PNP/pCAR exhibited strongerin situ pancreatic tumor targeting ability after oral administration. Furthermore, oral administration of βGlus-PNP/pCAR-GFP resulted in significant GFP protein expression detectable within pancreatic tumor tissues. ConclusionThis study successfully constructed and validated an orally administrable, pancreatic cancer-targeting polypeptide-based nano-gene delivery system. It provides an important technological foundation in delivery systems and experimental basis for the subsequent development of in situ CAR-M-based therapeutic strategies for pancreatic cancer.

ObjectivePancreatic ductal adenocarcinoma (PDAC) exhibits a limited response to current treatments due to its dense fibrotic stroma and highly immunosuppressive tumor microenvironment. In recent years, advancements in cellular immunotherapy, particularly chimeric antigen receptor macrophage (CAR-M) therapy, have offered new hope for pancreatic cancer treatment. Although CAR-M therapy demonstrates dual potential in directly killing tumor cells and remodeling the immune microenvironment, it still faces challenges such as complex in vitro preparation processes and low in vivo targeting and delivery efficiency. Therefore, developing strategies for efficient and targeted in vivo delivery of CAR genes has become crucial for overcoming current therapeutic limitations. This study aims to develop an orally administrable nano-gene delivery system for the targeted delivery of CAR genes to pancreatic tumor sites. MethodsCore nano-gene particles (PNP/pCAR) were constructed by loading plasmid DNA encoding CAR (pCAR) with cationic polypeptides (PNP). Subsequently, PNP/pCAR was surface-modified with β-glucan to prepare the targeted nanoparticles (βGlus-PNP/pCAR). The loading efficiency of PNP for pCAR was quantitatively assessed by gel retardation assay. The particle size, Zeta potential, morphology, and storage stability of PNP/pCAR were characterized using a Malvern particle size analyzer and transmission electron microscopy. At the cellular level, RAW 264.7 macrophages were selected. The cytotoxicity of PNP/pCAR was evaluated using the CCK-8 assay. The cellular uptake efficiency and lysosomal escape ability of the nanoparticles were assessed via flow cytometry and confocal microscopy. Transfection efficiency was quantitatively evaluated by detecting the expression of the reporter gene GFP using flow cytometry. At the in vivo level, an orthotopic pancreatic cancer mouse model was established. Cy7-labeled βGlus-PNP/pCAR nanoparticles were administered orally, and the fluorescence distribution in mice was dynamically monitored at 1, 2, 4, 8, and 16 h post-administration using a small animal in vivo imaging system. Forty-eight hours after oral gavage, the mice were euthanized, and pancreatic tumor tissues were collected for further analysis of intratumoral fluorescence signals using the imaging system. Additionally, βGlus-PNP/pCAR-GFP nanoparticles loaded with the reporter gene (GFP) were administered orally. Forty-eight hours post-administration, pancreatic tumor tissues were harvested to prepare frozen sections, and GFP expression was observed and analyzed under a fluorescence microscope. ResultsThe PNP carrier exhibited a high loading capacity for pCAR. The successfully prepared PNP/pCAR nanoparticles were regular spheres with a hydrodynamic diameter of approximately (120±10) nm and a Zeta potential of about +(6±1) mV. They maintained good structural stability after incubation in PBS buffer for 7 d. Cell experiments demonstrated that PNP/pCAR exhibited no significant cytotoxicity in RAW 264.7 cells while being efficiently internalized and effectively escaping lysosomal degradation. The transfection positive rate of PNP/pCAR-GFP in RAW 264.7 cells reached (25±3)%, surpassing that of Lipofectamine 2000-loaded pCAR-GFP (Lipo/pCAR-GFP), which was (20±1)%.In vivo experiments revealed that, compared to unmodified PNP/pCAR, βGlus-PNP/pCAR exhibited strongerin situ pancreatic tumor targeting ability after oral administration. Furthermore, oral administration of βGlus-PNP/pCAR-GFP resulted in significant GFP protein expression detectable within pancreatic tumor tissues. ConclusionThis study successfully constructed and validated an orally administrable, pancreatic cancer-targeting polypeptide-based nano-gene delivery system. It provides an important technological foundation in delivery systems and experimental basis for the subsequent development of in situ CAR-M-based therapeutic strategies for pancreatic cancer.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE To investigate the mechanism of isochlorogenic acid A against hepatocellular carcinoma based on the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin （PI3K/Akt/mTOR） signaling pathway and multi-omics technology. METHODS The invasion rate and migration rate of human hepatocellular carcinoma HepG2 cells after 48 h of intervention with 0 （control group）， 0.25 and 0.5 mg/mL isochlorogenic acid A were examined； mRNA expression of DEP domain-containing mTOR-interacting protein （DEPTOR）， the protein expressions of mTOR， PI3K and phosphatase and tensin homologue deleted on chromosome ten （PTEN）， as well as the phosphorylation level of Akt protein were determined in the cells. Metabolomics analysis was performed using liquid chromatography-tandem mass spectrometry， and differential metabolites were screened and subjected to Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis； transcriptomics monitoring was conducted by RNA sequencing， and differentially expressed genes were screened and subjected to gene ontology （GO） and KEGG pathway enrichment analyses. RESULTS Compared with the control group， intervention with 0.25 and 0.5 mg/mL isochlorogenic acid A for 48 h significantly inhibited the invasion rate and migration rate of HepG2 cells， significantly up-regulated the mRNA expression of DEPTOR and the protein expression of PTEN， and significantly down-regulated the protein expression of PI3K and the phosphorylation level of Akt protein （except for 0.25 mg/mL isochlorogenic acid A） （ P ＜0.05）. A total of 304 differential metabolites and 212 differentially expressed genes were screened by multi-omics analysis. KEGG pathway enrichment analysis suggested that isochlorogenic acid A regulated key signaling of HepG2 cell growth mainly by inhibiting the PI3K/Akt signaling pathway， synergizing with metabolic reprogramming such as mTOR signaling pathway， ferroptosis， pentose phosphate pathway and purine/pyrimidine metabo lism. CONCLUSIONS The anti-hepatocellular carcinoma effect of isochlorogenic acid A is associated with the blockade of abnormal activation of the PI3K/Akt/mTOR signaling pathway. In addition， it may also be related to the inhibition of the pentose phosphate pathway and purine/pyrimidine metabolism， as well as the induction of ferroptosis，etc.

As a new generation of time-of-flight mass spectrometry,multiple-reflection time-of-flight mass spectrometry(MR-TOF-MS)has been increasingly applied in the fields such as nuclear physics,chemistry,and biology due to its ultra-high resolution and rapid analysis capabilities.However,the analytical performance of MR-TOF-MS largely depends on the ion bunch state entering the mass analyzer.In this study,a rectangular ion trap(RIT)was developed,designed and processed using printed circuit board technology,as an ion accumulating and focusing device for MR-TOF mass analyzer.Compared to traditional ion traps composed of two sets of planar electrodes,this RIT had higher voltage utilization efficiency,resulting in more efficient ion collection and focusing.The ions were cooled to a sufficiently small bunch for precise mass measurement with MR-TOF-MS mass spectrometry in only 1 ms of cooling time in the RIT,then orthogonally ejected to the MR-TOF mass spectrometer for mass analysis.Experimental results indicated that the working cycle,ion flux,and ion focusing state of the RIT fully met the requirements of the MR-TOF mass analyzer.When coupled with the MR-TOF mass analyzer,the RIT enabled MR-TOF-MS to achieve a mass resolution of 1.5×105.

Objective To investigate the incidence of functional bowel disease(FBD)and its relationship with depression,anxiety and sleep in navy submarine forces.Methods A questionnaire survey on the incidence of FBD was conducted on 364 naval soldiers who were enrolled according to Rome Ⅳ classification and diagnostic criteria.The risk factors of FBD were analyzed.The incidence of specific diseases of FBD was compared among soldiers with different jobs.The depression,anxiety,and sleep quality were investigated in FBD patients.Results In the 364 participants,132(36.3%)were diagnosed with FBD according to Roman Ⅳcriteria.Military rank and marital status might be risk factors for FBD.There was no significant difference in the overall incidence of FBD among the participants with various jobs(P>0.05).The incidence of functional constipation in the submariners was significantly higher than that in the land soldiers,while the incidence of functional diarrhea in the land soldiers was higher than that in the submariners(all P<0.05).The incidences of depression,anxiety and sleep disorder in the participants with FBD were higher than those in the participants without FBD.Conclusion There is a high incidence of FBD in submariners,which may be related to their psychological states.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.