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.

Objective To observe the therapeutic effect and mechanism of external application with Shilian Powder combined with oral administration of Shumai Capsules for the treatment of rats with diabetic foot ulcer(DFU).Methods Eight male rats with successfully modeled foot ulcer(DF)were used as the control group.While 24 male rats with successfully modeled DFU were randomly divided into DFU group,Shumai Capsules group and Shilian Powder combined with Shumai Capsules group,with eight rats in each group.After the corresponding interventions,we determined the wound healing rate,histopathological changes of wound,levels of inflammatory factors such as interleukin(IL)-6,IL-1β and vascular endothelial growth factor(VEGF)in serum,levels of Apelin and Relaxin,and protein expressions of phosphatidylinositol 3-kinase(PI3K),protein kinase B(AKT)and VEGF in wound tissue,as well as mRNA expressions of PI3K,AKT,Relaxin and Apelin.Results Compared with the control group,the DFU group showed a significant decrease in wound healing rate,VEGF level in serum and wound,wound Relaxin level,protein and mRNA levels of wound AKT(P<0.05),and a significant increase in serum IL-6 and IL-1β levels,wound Apelin level,wound PI3K protein and mRNA levels(P<0.05),and the reduced wound granulation tissue and formation of new capillaries and increased inflammatory cell infiltration were seen under the microscope.Compared with the DFU group,the wound healing rate,VEGF level in serum and wound,wound Relaxin and Apelin levels,protein and mRNA levels of wound PI3K and AKT in the Shumai Capsules group and Shilian Powder combined with Shumai Capsules group were significantly increased(P<0.05),and serum IL-6 and IL-1β levels were significantly decreased(P<0.05),and the increased wound granulation tissue and formation of new capillaries and reduced inflammatory cell infiltration were seen under the microscope.Compared with the Shumai Capsules group,the wound healing rate,wound VEGF level,wound Relaxin and Apelin levels,protein and mRNA levels of wound PI3K and AKT in Shilian Powder combined with Shumai Capsules group were significantly increased(P<0.05),and the serum IL-6 and IL-1β levels were significantly decreased(P<0.05),and the increased wound granulation tissue and formation of new capillaries and reduced inflammatory cell infiltration were seen under the microscope.Conclusion External application with Shilian Powder combined with oral administration of Shumai Capsules can promote the wound healing in rats with DFU,its mechanism is related to the activation of PI3K/AKT/Relaxin/Apelin signaling pathway.

Objective:To investigate the adjunctive diagnostic value of retinal imaging structural parameters combined with apolipoprotein E (ApoE) gene polymorphisms for Alzheimer′s disease (AD).Methods:It was a case-control study, 71 confirmed AD patients who attended the Department of Neurology in Sichuan Provincial People′s Hospital from May 2023 to June 2024 and 156 healthy medical checkups who participated in medical checkups in the Health Management Center were continuously with convenience sampling method; the subjects were included as the AD case group and healthy control group, respectively. Optical coherence tomography (OCT) was used to measure the structural parameters of retinal imaging such as the thickness of the retinal nerve fiber layer (RNFL) and the retinal nerve fiber layer-inner plexiform layer (RNFL-IPL) in the study subjects. Information on demographic characteristics and disease history of the study participants were collected through a questionnaire, and venous blood was collected to test for ApoE gene polymorphisms. The retinal imaging structural parameters, ApoE gene polymorphisms and other related indicators were included in a multifactorial logistic regression model to analyze the main factors affecting the risk of AD. Based on the results of the multifactorial analysis, the receiver operating characteristic (ROC) curves were plotted and the areas under the curve (AUC) were calculated to evaluate the efficacy of different models in the adjunctive diagnosis of AD.Results:Of the 227 study subjects included in the analysis, 153 were females and 74 were males; there were 71 cases in the AD case group with a mean age of (66.73±8.83) years, and there were 156 subjects in the healthy control group with an average age of (61.95±8.21) years. Educational attainment of elementary school and below ( OR=4.683, 95% CI: 2.133-10.282), living visual acuity<0.5 ( OR=2.716, 95% CI: 1.12-6.583), and carrying ≥1 ApoE ε4 genes ( OR=5.331, 95% CI: 2.309-11.891) were positively correlated with the risk of AD. RNFL thickening ( OR=0.923, 95% CI: 0.854-0.998) was negatively associated with the risk of AD (all P<0.05). The AD risk assessment model (Model 4), which included fundus imaging features and ApoE gene polymorphisms, had the highest predictive efficacy (AUC=0.857, P<0.001). Conclusion:Retinal imaging structural parameters differ significantly between AD patients and healthy examinees, and a risk assessment model combining retinal imaging structural parameters and ApoE gene polymorphisms has high predictive value and is expected to serve as an auxiliary diagnostic tool for AD.

Objective:To investigate the adjunctive diagnostic value of retinal imaging structural parameters combined with apolipoprotein E (ApoE) gene polymorphisms for Alzheimer′s disease (AD).Methods:It was a case-control study, 71 confirmed AD patients who attended the Department of Neurology in Sichuan Provincial People′s Hospital from May 2023 to June 2024 and 156 healthy medical checkups who participated in medical checkups in the Health Management Center were continuously with convenience sampling method; the subjects were included as the AD case group and healthy control group, respectively. Optical coherence tomography (OCT) was used to measure the structural parameters of retinal imaging such as the thickness of the retinal nerve fiber layer (RNFL) and the retinal nerve fiber layer-inner plexiform layer (RNFL-IPL) in the study subjects. Information on demographic characteristics and disease history of the study participants were collected through a questionnaire, and venous blood was collected to test for ApoE gene polymorphisms. The retinal imaging structural parameters, ApoE gene polymorphisms and other related indicators were included in a multifactorial logistic regression model to analyze the main factors affecting the risk of AD. Based on the results of the multifactorial analysis, the receiver operating characteristic (ROC) curves were plotted and the areas under the curve (AUC) were calculated to evaluate the efficacy of different models in the adjunctive diagnosis of AD.Results:Of the 227 study subjects included in the analysis, 153 were females and 74 were males; there were 71 cases in the AD case group with a mean age of (66.73±8.83) years, and there were 156 subjects in the healthy control group with an average age of (61.95±8.21) years. Educational attainment of elementary school and below ( OR=4.683, 95% CI: 2.133-10.282), living visual acuity<0.5 ( OR=2.716, 95% CI: 1.12-6.583), and carrying ≥1 ApoE ε4 genes ( OR=5.331, 95% CI: 2.309-11.891) were positively correlated with the risk of AD. RNFL thickening ( OR=0.923, 95% CI: 0.854-0.998) was negatively associated with the risk of AD (all P<0.05). The AD risk assessment model (Model 4), which included fundus imaging features and ApoE gene polymorphisms, had the highest predictive efficacy (AUC=0.857, P<0.001). Conclusion:Retinal imaging structural parameters differ significantly between AD patients and healthy examinees, and a risk assessment model combining retinal imaging structural parameters and ApoE gene polymorphisms has high predictive value and is expected to serve as an auxiliary diagnostic tool for AD.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Leonurine(SCM-198)was discovered as one of the active constituents of the Herba Leonuri(HL).Now it can be artificially synthesized.Several recent researches has proven that it exhibits anti-inflammatory effect in several systems in animal models and cell culture in vitro.The key mechanism involves downgrading the activity of nuclear transcription factor-κB(NF-κB),thereby inhibiting the phosphorylation of several signal pathways such as PI3K/Akt,MAPK,ERK,and JNK,or upregulating the activity of Nrf2 related pathways,resulting in downregulated expression of inflammatory cytokines such as tumor necrosis factor-α(TNF-α),IL-1β,IL-2,IL-6,IL-8,inducible nitric oxide synthase(iNOS),cyclooxygenase-2(COX-2),chemokines,adhesion molecules,etc.Owing to the advantages of high safety and efficiency,the ease of administration,as well as its effectiveness in many organs and systems,leonurine has a widely prospect for future research and clinical applications.This article reviews the progress in the fundamental research of leonurine in multiple inflammation-related disease,and it could be expect to offer new possibilities for the treatment of these disease.

ObjectiveTo explore the practical value of environment-friendly sample release agent combined with ultrasound in the preparation of pathological tissue sections. MethodsFrom February 2013 to December 2022， 2 518 pathological specimens submitted by Foshan Municipal Hospital of Traditional Chinese Medicine were selected as the study objects. Two samples of the same specimen were randomly divided into two groups： the environment-friendly fast group， in which the pathological tissue sections were made by using the environment-friendly sample release agent combined with ultrasound； and the traditional group， in which formaldehyde， ethanol and xylene were used to make slices in the conventional way. The differences of hematoxylin （HE） staining effect， immunohistochemistry （IHC） staining effect and MDM2 gene detection result of atypical lipomatous tumor/highly differentiated liposarcoma （ALT/WDL） tissue sections between the two groups were compared. Results① The wax of the two groups' pathological tissues was dehydrated well and the tissue hardness was moderate. After HE staining， the sections of the two groups were intact， without cracks and tremor marks， and the contrast between nucleus and cytoplasm was appropriate， with good transparency， uniform staining， and no tissue loss. The excellent rate and score of HE staining in the environmental fast group were higher than those in the traditional group， but the difference was not statistically significant （χ² = 3.125，P₁ = 0.070；t = 0.965，P₂ = 0.334）. ②After IHC staining of the two groups of sections， the positive location of the cells was accurate， the staining was specific and uniform， the staining intensity was moderate， the staining sensitivity was good， and there was no tissue loss. The excellent rate of IHC staining and the positive rate of IHC staining in the environmental fast group were lower than those in the traditional group， but the difference was not statistically significant （χ₁² = 2.769，P₁ = 0.092；χ₂² = 0.800，P₂ = 0.375）. ③The background and outline of the two groups of WDL tissue sections were clear， the staining was uniform， the cells were clear and visible， the nuclear boundary was clear， the hybridization signal was clear and bright under the background fluorescence， and there was no miscellaneous signal. The two groups of sections were hybridized successfully， and MDM2 showed positive amplification. The number of cells successfully hybridized in the environment-friendly fast group was lower than that in the traditional group， but the difference was not statistically significant （t = 1.414，P = 0.230）. ConclusionsThe tissue treatment method of using environment-friendly sample release agent combined with ultrasound can ensure the detection effect of HE staining， IHC staining and MDM2 gene detection of pathological tissue sections， and is more efficient and environment-friendly， suitable for promotion and use in hospitals at all levels.

In the outbreak of COVID-19,triage procedures based on epidemiology were implemented in a local hospital in Changsha to control the transmission of SARS-CoV-2 and avoid healthcare-associated infection.This re-trospective study analyzed the data collected during the triage period and found that COVID-19 patients were en-riched 7 folds into the Section A designated for patients with obvious epidemiological history.On the other side,nearly triple amounts of visits were received at the Section B for patients without obvious epidemiological history.8 COVID-19 cases were spotted out of 247 suspected patients.More than 50％of the suspected patients were submi-tted to multiple rounds of nucleic acid analysis for SARS-CoV-2 infection.Of the 239 patients who were diagnosed as negative of the virus infection,188 were successfully revisited and none was reported as COVID-19 case.Of the 8 COVID-19 patients,3 were confirmed only after multiple rounds of nucleic acid analysis.Besides comorbidities,delayed sharing of epidemiological history added complexity to the diagnosis in practice.The triaging experience and strategy will be helpful for the control of infectious diseases in the future.

Objective To analyze the genetic subtypes of human immunodeficiency virus (HIV) and the prevalence of pretreatment drug resistance in the newly reported HIV-infected men in Guangxi. Methods The stratified random sampling method was employed to select the newly reported HIV-infected men aged≥50 years old in 14 cities of Guangxi from January to June in 2020.The pol gene of HIV-1 was amplified by nested reverse transcription polymerase chain reaction and then sequenced.The mutation sites associated with drug resistance and the degree of drug resistance were then analyzed. Results A total of 615 HIV-infected men were included in the study.The genetic subtypes of CRF01_AE,CRF07_BC,and CRF08_BC accounted for 57.4% (353/615),17.1% (105/615),and 22.4% (138/615),respectively.The mutations associated with the resistance to nucleoside reverse transcriptase inhibitors (NRTI),non-nucleoside reverse transcriptase inhibitors (NNRTI),and protease inhibitors occurred in 8 (1.3%),18 (2.9%),and 0 patients,respectively.M184V (0.7%) and K103N (1.8%) were the mutations with the highest occurrence rates for the resistance to NRTIs and NNRTIs,respectively.Twenty-two (3.6%) patients were resistant to at least one type of inhibitors.Specifically,4 (0.7%),14 (2.3%),4 (0.7%),and 0 patients were resistant to NRTIs,NNRTIs,both NRTIs and NNRTIs,and protease inhibitors,respectively.The pretreatment resistance to NNRTIs had much higher frequency than that to NRTIs (2.9% vs.1.3%;χ²=3.929,P=0.047).The prevalence of pretreatment resistance to lamivudine,zidovudine,tenofovir,abacavir,rilpivirine,efavirenz,nevirapine,and lopinavir/ritonavir was 0.8%, 0.3%, 0.7%, 1.0%, 1.3%, 2.8%, 2.9%, and 0, respectively. Conclusions CRF01_AE,CRF07_BC,and CRF08_BC are the three major strains of HIV-infected men≥50 years old newly reported in Guangxi,2020,and the pretreatment drug resistance demonstrates low prevalence.
Male ; Humans ; Middle Aged ; Reverse Transcriptase Inhibitors/therapeutic use* ; HIV Infections/drug therapy* ; Drug Resistance, Viral/genetics* ; China/epidemiology* ; Mutation ; HIV-1/genetics* ; Protease Inhibitors/therapeutic use* ; Genotype

Objective To establish a ultra performance liquid chromatography(UPLC)fingerprint of extract of Cuscutae Semen,and analyze the relationship between the UPLC fingerprint and antioxidant activity.Methods The fingerprint of 11 batches of extract of Cuscutae Semen were determined by UPLC method,the antioxidant activity of Cuscutae Semen in vitro was determined by 1,1-diphenyl-2-picrylhydrazine radical,2,2-diazo-bis(3-ethylbenzothiazole-6-sulfonic acid)diamine salt,and the correlation between the fingerprints and antioxidant activity was analyzed by orthogonal partial least squares(OPLS)and gray correlation method.The key substances that contributed greatly to the antioxidant activity were selected.Results The extract of Cuscutae Semen contains 21 common peaks,all of which exhibited a similarity of more than 0.97.By comparing with the reference sample,10 peaks were identified,of which peak 5 was neochlorogenic acid,peak 8 was chlorogenic acid,peak 9 was cryptochlorogenic acid,peak 10 was caffeic acid,peak 12 was coumaric acid,peak 15 was hyperin,peak 16 was isoquercitrin,peak 17 was astragaloside,peak 20 was quercetin,and peak 21 was kaempferol.According to the grey correlation degree and OPLS results,the peaks 8,15,16 and 18 were positively correlated with the antioxidant activity,and were thus considered to be main effective components.Conclusion The antioxidant activity of Cuscutae Semen is the result of the combined effect of multiple components.The fingerprint and antioxidant spectrum analysis can provide evidential reference for further research of Cuscutae Semen.