Objective: To construct prediction models of female stress urinary incontinence (SUI), and evaluate the efficacy of each model, so as to provide reference for the early diagnosis of SUI. Methods: Female SUI patients treated in our hospital during Oct. 2019 and Oct. 2023 and healthy women undergoing physical examination during the same period were involved. Women 42 days after delivery were included in the postpartum group (n=611), and perimenopausal and postmenopausal women were included in the non-postpartum group (n=409). The number of random seeds was set and the participants were divided into the training and verification sets in a ratio of 7∶3. Relevant clinical data were collected, and meaningful variables were screened using single factor and Lasso regression, which were then incorporated into the K-nearest neighbor method (KNN), support vector machine (SVM)，decision tree (DT) and random forest (RF) algorithms. The sensitivity, specificity, accuracy and area under the receiver operating characteristic curve (AUC) of the models were calculated to screen out the optimal model. Results: There were 352 SUI patients (57.6%) in the postpartum group. According to single factor and Lasso regression, significant variables included age, body mass index (BMI), maximum rapid muscle stage, parity, bladder neck mobility (BND), urethral rotation angle (URA), lateral perineal incision, past incontinence, and constipation. In the verification set, the AUC of KNN，SVM，DT and RF models were 0.881，0.878，0.750 and 0.905,respectively; the AUC, accuracy, F1 index and Kappa value of RF model were the largest. In the non-postpartum group, there were 260 SUI patients, accounting for 63.6%. The significant variables were age，BMI, maximum value and recovery time of fast muscle stage, mean value of slow muscle stage, post-resting stage variability, vaginal delivery, past incontinence, and constipation. In the verification set, the AUC of KNN,SVM，DT and RF models were 0.819,0.805，0.603 and 0.830, respectively; the AUC, accuracy, Kappa value of the RF model were the largest. Conclusion: This study successfully established 4 prediction models for the incidence of SUI in women at 42 days postpartum, perimenopausal and postmenopausal women based on machine learning. Among them, the model adopting the RF algorithm had the best prediction efficiency.

This paper aimed to explore the effects of Duzhong Decoction(DZD)-containing serum on the proliferation and osteoblast differentiation of MC3T3-E1 cells through L-type voltage-gated calcium channels(L-VGCCs). L-VGCCs inhibitors, nifedipine and verapamil, were used to block L-VGCCs in osteoblasts. MC3T3-E1 cells were divided into a control group, a low-dose DZD-containing serum(L-DZD) group, a medium-dose DZD-containing serum(M-DZD) group, a high-dose DZD-containing serum(H-DZD) group, a nifedipine group, a H-DZD + nifedipine group, verapamil group, and a H-DZD + verapamil group. The CCK-8 method was used for cell proliferation analysis, alkaline phosphatase(ALP) assay kits for intracellular ALP activity measurement, Western blot for protein expression level in cells, real-time fluorescence quantitative PCR technology for intracellular mRNA expression level determination, fluorescence spectrophotometer for free Ca~(2+) concentration determination in osteoblasts, and alizarin red staining(ARS) for mineralized nodule formation in osteoblasts. The experimental results show that compared to the control group, DZD groups can promote MC3T3-E1 cell proliferation, ALP activity, and mineralized nodule formation, increase intracellular Ca~(2+) concentrations, and upregulate the protein expression of bone morphogenetic protein 2(BMP2), collagen Ⅰ(COL1), α2 subunit protein of L-VGCCs(L-VGCCα2), and the mRNA expression of Runt-related transcription factor 2(RUNX2), and BMP2. After blocking L-VGCCs with nifedipine and verapamil, the intervention effects of DZD-containing serum were inhibited to varying degrees. Both nifedipine and verapamil could inhibit ALP activity, reduce mineralized nodule areas, and downregulate the expression of bone formation-related proteins. Moreover, the effects of DZD-containing serum on increasing MC3T3-E1 cell proliferation, osteoblast differentiation, and Ca~(2+) concentrations, upregulating the mRNA expression of osteoprotegerin(OPG) and protein expression of phosphorylated protein kinase B(p-Akt) and phosphorylated forkhead box protein O1(p-FOXO1), and upregulating phosphatase and tensin homolog(PTEN) expression were reversed by nifedipine. The results indicate that DZD-containing serum can increase the Ca~(2+) concentration in MC3T3-E1 cells to promote bone formation, which may be mediated by L-VGCCs and the PTEN/Akt/FoxO1 signaling pathway, providing a new perspective on the mechanism of DZD in treating osteoporosis.
Animals ; Osteoblasts/metabolism* ; Cell Proliferation/drug effects* ; Cell Differentiation/drug effects* ; Mice ; Drugs, Chinese Herbal/pharmacology* ; Calcium Channels, L-Type/genetics* ; Alkaline Phosphatase/genetics* ; Serum/chemistry* ; Cell Line ; Osteogenesis/drug effects* ; Bone Morphogenetic Protein 2/genetics*

This study aims to explore the scientific connotation of sinking Rehmanniae Radix has the best quality and compare the quality between floating and sinking fresh Rehmanniae Radix samples. Ultra-performance liquid chromatography tandem quadrupole electrostatic field Orbitrap high-resolution mass spectrometry(UHPLC-Q-Orbitrap HRMS) was employed to detect the chemical components in floating and sinking fresh Rehmanniae Radix samples. The fingerprint of fresh Rehmanniae Radix was established by high performance liquid chromatography(HPLC), and four index components were determined simultaneously. The cluster analysis, principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) were conducted to compare the quality of floating and sinking fresh Rehmanniae Radix samples. An evaporative light-scattering detector was used to compare the content of five sugars. The extract yield and drying rate were determined, and the quality connotation of sinking Rehmanniae Radix has the best quality was explained by multiple indicators. A total of 41 components were preliminarily identified from fresh Rehmanniae Radix by UHPLC-Q-Orbitrap HRMS, including 7 iridoid glycosides, 9 phenylethanol glycosides, 6 amino acids, 4 sugars, 3 phenolic acids, 5 nucleosides, 3 organic acids, 1 ionone, 1 furan, 1 coumarin, and 1 phenylpropanoid. The results showed that the main chemical components were consistent between floating and sinking fresh Rehmanniae Radix. Nine common peaks were identified in the fingerprints of 15 batches of floating and sinking fresh Rehmanniae Radix samples, and the similarity of fingerprints was greater than 0.9. The cluster analysis, PCA, and OPLS-DA classified floating and sinking fresh Rehmanniae Radix sasmples into two categories, indicating differences in the quality between them. The total content of catalpol, rehmannioside D, ajugol, and verbascoside in sinking fresh Rehmanniae Radix samples was higher than that in floating samples of the same batch and specification, and the main differential component was catalpol. The total content of fructose, glucose, sucrose, raffinose, and stachyose in sinking fresh Rehmanniae Radix samples was higher than that in floating samples of the same batch and specification, and the main differential component was stachyose. The extract yield and drying rate of the sinking samples were higher than those of floating samples. This study preliminarily showed that floating and sinking fresh Rehmanniae Radix samples had the same components but great differences in the content of medicinal substance basis. The total content of four glycosides and five sugars, extract yield, and drying rate of sinking fresh Rehmanniae Radix samples is higher than that of floating samples of the same batch and specification. These findings, to a certain extent, explains the scientificity of sinking Rehmanniae Radix has the best quality recorded in ancient books and provide a reference for the quality control and clinical application of fresh Rehmanniae Radix.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Rehmannia/chemistry* ; Chemometrics ; Mass Spectrometry/methods* ; Quality Control ; Principal Component Analysis ; Plant Extracts

Microbial detection and control of traditional Chinese medicine(TCM) decoction pieces are crucial for the quality control of TCM preparations. It is also a key area of research in the measurement technology and equipment development for TCM manufacturing. Guided by TCM manufacturing measurement methodologies, this study presented a design of a novel portable microbial detection device, using Atractylodis Macrocephalae Rhizoma decoction pieces as a demonstration. Immunomagnetic separation technology was employed for specific isolation and labeling of target microorganisms. Enzymatic signal amplification was utilized to convert weak biological signals into colorimetric signals, constructing an optical biosensor. A self-developed smartphone APP was further applied to analyze the colorimetric signals and quantify target concentrations. A portable and automated detection system based on Arduino microcontroller was developed to automatically perform target microbial separation/extraction, as well as mimetic enzyme labeling and catalytic reactions. The developed equipment specifically focuses on the rapid and quantitative microbial analysis of TCM active pharmaceutical ingredients, intermediates in TCM manufacturing, and final TCM products. Experimental results demonstrate that the equipment could detect Salmonella in samples within 2 h, with a detection limit as low as 5.1 × 10~3 CFU·mL~(-1). The equipment enables the rapid detection of microorganisms in TCM decoction pieces, providing a potential technical solution for on-site rapid screening of microbial contamination indicators in TCM. It has broad application prospects in measurement technology for TCM manufacturing and offers strong technical support for the modernization, industrialization, and intelligent development of TCM.
Drugs, Chinese Herbal/analysis* ; Atractylodes/microbiology* ; Rhizome/microbiology* ; Biosensing Techniques/methods* ; Medicine, Chinese Traditional ; Colorimetry/instrumentation* ; Quality Control

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

ObjectiveTo evaluate the efficacy of Shuanghua drink in treating primary dysmenorrhea in the rat model and explore its mechanism of action. MethodsAn oxytocin-induced writhing mouse model was established to evaluate the analgesic effect of Shuanghua drink. Forty-eight non-pregnant female institute of cancer research （ICR） mice were randomly divided into six groups， including a blank group， a model group， an ibuprofen group （85.00 mg·kg^-1）， a low-dose group of Shuanghua drink （7.14 mL·kg^-1）， a medium-dose group of Shuanghua drink （14.28 mL·kg^-1）， and a high-dose group of Shuanghua drink （28.57 mL·kg^-1）. Each group consisted of eight mice. All treatment groups received daily intragastric administration at corresponding doses for 10 consecutive days. One hour after the final administration， 2 U of oxytocin was intraperitoneally injected per mouse. The writhing latency and number of writhing within 20 minutes were recorded. A primary dysmenorrhea rat model was established by using estradiol benzoate and oxytocin to evaluate the inhibitory effect of Shuanghua drink on the contraction of uterine smooth muscle. Forty-eight non-pregnant female Sprague-Dawley （SD） rats were divided into six groups， including a blank group， a model group， an ibuprofen group （51.00 mg·kg^-1）， a low-dose group of Shuanghua drink （4.28 mL·kg^-1）， a medium-dose group of Shuanghua drink （8.57 mL·kg^-1）， and a high-dose group of Shuanghua drink （17.10 mL·kg^-1）. Each group consisted of eight rats. Rats received subcutaneous injections of estradiol benzoate for 10 consecutive days to enhance uterine sensitivity. On the eleventh day， oxytocin （2 U/rat） was intraperitoneally administered to induce abnormal uterine contractions for establishing the primary dysmenorrhea model. All treatment groups received daily intragastric administration from the second day of modeling for 10 days. The effects of Shuanghua drink were evaluated by using parameters including uterine motility and the variation rate of uterine motility. The mechanism of action was investigated in rats with primary dysmenorrhea. The content of prostaglandin F_2α （PGF_2α）， prostaglandin E₂ （PGE₂）， thromboxane B₂ （TXB₂）， prostacyclin metabolite （6-keto-PGF_1α）， and β-endorphin （β-EP） in uterine tissue of rats was detected by using enzyme-linked immunosorbent assay （ELISA）. The changes in the content of nitric oxide （NO） and inducible nitric oxide synthase （iNOS） were analyzed via colorimetric assay. Western blot was performed to determine the content of phosphorylated inhibitor of kappa B kinase beta （p-IKKβ）/IKKβ， phosphorylated inhibitor of kappa B alpha （p-IκBα）， IκBα， phosphorylated p65 （p-p65）， p65， and cyclooxygenase-2 （COX-2） proteins in uterine tissue of rats. ResultsIn the oxytocin-induced writhing mouse model， the model group exhibited significantly shortened writhing latency and increased writhing frequency compared to the control group （P<0.01）. Both the ibuprofen group and the high-dose group of Shuanghua drink displayed prolonged writhing latency （P<0.05）， while the ibuprofen group and the low-dose， medium-dose， and high-dose groups of Shuanghua drink exhibited reduced writhing frequency （P<0.01）. In the primary dysmenorrhea rat model， the uterine motility and its variation rate in the model group were significantly higher than those in the blank group （P<0.01）. These parameters were markedly suppressed by ibuprofen and Shuanghua drink at all tested doses （P<0.01）. For the mechanism of action， the model group showed significantly increased PGF_2α/PGE₂， TXB₂/6-keto-PGF_1α， NO， and iNOS in uterine tissue （P<0.05， P<0.01） and significantly decreased β-EP （P<0.01）. These parameters were significantly attenuated in the ibuprofen group and the low-dose， medium-dose， and high-dose groups of Shuanghua drink. The PGF_2α/PGE₂ （P<0.01）， TXB₂/6-keto-PGF_1α （P<0.01）， NO （medium-dose group P<0.05）， and iNOS （P<0.01） were reduced， and the β-EP （medium-dose group P<0.05） was up-regulated. Compared to the model group， the ibuprofen group and medium-dose group of Shuanghua drink showed significantly increased content of β-EP in the serum of rats （P<0.05）. Compared to the blank group， the model group showed significantly elevated expressions of COX-2， p-IKKβ/IKKβ， p-IκBα/IκBα， and p-p65/p65 proteins （P<0.01） and significantly reduced anti-inflammatory protein IκBα （P<0.05）. Compared to the model group， the ibuprofen group and the low-dose， medium-dose， and high-dose groups of Shuanghua drink showed significantly reduced expressions of COX-2 （P<0.01）， p-IKKβ/IKKβ （P<0.01）， p-IκBα/IκBα （P<0.05， P<0.01）， and p-p65/p65（P<0.01） and up-regulated expression of IκBα protein （P<0.05， P<0.01）. ConclusionShuanghua drink effectively alleviates primary dysmenorrhea through analgesia and suppression of abnormal contractions of uterine smooth muscle. Its mechanism may be mediated by reduced levels of PGF_2α/PGE₂， TXB₂/6-keto-PGF_1α， iNOS， and NO， elevated β-EP level， and inhibited COX-2/NF-κB signaling pathway.

ObjectiveTo evaluate the efficacy of Shuanghua drink in treating primary dysmenorrhea in the rat model and explore its mechanism of action. MethodsAn oxytocin-induced writhing mouse model was established to evaluate the analgesic effect of Shuanghua drink. Forty-eight non-pregnant female institute of cancer research （ICR） mice were randomly divided into six groups， including a blank group， a model group， an ibuprofen group （85.00 mg·kg^-1）， a low-dose group of Shuanghua drink （7.14 mL·kg^-1）， a medium-dose group of Shuanghua drink （14.28 mL·kg^-1）， and a high-dose group of Shuanghua drink （28.57 mL·kg^-1）. Each group consisted of eight mice. All treatment groups received daily intragastric administration at corresponding doses for 10 consecutive days. One hour after the final administration， 2 U of oxytocin was intraperitoneally injected per mouse. The writhing latency and number of writhing within 20 minutes were recorded. A primary dysmenorrhea rat model was established by using estradiol benzoate and oxytocin to evaluate the inhibitory effect of Shuanghua drink on the contraction of uterine smooth muscle. Forty-eight non-pregnant female Sprague-Dawley （SD） rats were divided into six groups， including a blank group， a model group， an ibuprofen group （51.00 mg·kg^-1）， a low-dose group of Shuanghua drink （4.28 mL·kg^-1）， a medium-dose group of Shuanghua drink （8.57 mL·kg^-1）， and a high-dose group of Shuanghua drink （17.10 mL·kg^-1）. Each group consisted of eight rats. Rats received subcutaneous injections of estradiol benzoate for 10 consecutive days to enhance uterine sensitivity. On the eleventh day， oxytocin （2 U/rat） was intraperitoneally administered to induce abnormal uterine contractions for establishing the primary dysmenorrhea model. All treatment groups received daily intragastric administration from the second day of modeling for 10 days. The effects of Shuanghua drink were evaluated by using parameters including uterine motility and the variation rate of uterine motility. The mechanism of action was investigated in rats with primary dysmenorrhea. The content of prostaglandin F_2α （PGF_2α）， prostaglandin E₂ （PGE₂）， thromboxane B₂ （TXB₂）， prostacyclin metabolite （6-keto-PGF_1α）， and β-endorphin （β-EP） in uterine tissue of rats was detected by using enzyme-linked immunosorbent assay （ELISA）. The changes in the content of nitric oxide （NO） and inducible nitric oxide synthase （iNOS） were analyzed via colorimetric assay. Western blot was performed to determine the content of phosphorylated inhibitor of kappa B kinase beta （p-IKKβ）/IKKβ， phosphorylated inhibitor of kappa B alpha （p-IκBα）， IκBα， phosphorylated p65 （p-p65）， p65， and cyclooxygenase-2 （COX-2） proteins in uterine tissue of rats. ResultsIn the oxytocin-induced writhing mouse model， the model group exhibited significantly shortened writhing latency and increased writhing frequency compared to the control group （P<0.01）. Both the ibuprofen group and the high-dose group of Shuanghua drink displayed prolonged writhing latency （P<0.05）， while the ibuprofen group and the low-dose， medium-dose， and high-dose groups of Shuanghua drink exhibited reduced writhing frequency （P<0.01）. In the primary dysmenorrhea rat model， the uterine motility and its variation rate in the model group were significantly higher than those in the blank group （P<0.01）. These parameters were markedly suppressed by ibuprofen and Shuanghua drink at all tested doses （P<0.01）. For the mechanism of action， the model group showed significantly increased PGF_2α/PGE₂， TXB₂/6-keto-PGF_1α， NO， and iNOS in uterine tissue （P<0.05， P<0.01） and significantly decreased β-EP （P<0.01）. These parameters were significantly attenuated in the ibuprofen group and the low-dose， medium-dose， and high-dose groups of Shuanghua drink. The PGF_2α/PGE₂ （P<0.01）， TXB₂/6-keto-PGF_1α （P<0.01）， NO （medium-dose group P<0.05）， and iNOS （P<0.01） were reduced， and the β-EP （medium-dose group P<0.05） was up-regulated. Compared to the model group， the ibuprofen group and medium-dose group of Shuanghua drink showed significantly increased content of β-EP in the serum of rats （P<0.05）. Compared to the blank group， the model group showed significantly elevated expressions of COX-2， p-IKKβ/IKKβ， p-IκBα/IκBα， and p-p65/p65 proteins （P<0.01） and significantly reduced anti-inflammatory protein IκBα （P<0.05）. Compared to the model group， the ibuprofen group and the low-dose， medium-dose， and high-dose groups of Shuanghua drink showed significantly reduced expressions of COX-2 （P<0.01）， p-IKKβ/IKKβ （P<0.01）， p-IκBα/IκBα （P<0.05， P<0.01）， and p-p65/p65（P<0.01） and up-regulated expression of IκBα protein （P<0.05， P<0.01）. ConclusionShuanghua drink effectively alleviates primary dysmenorrhea through analgesia and suppression of abnormal contractions of uterine smooth muscle. Its mechanism may be mediated by reduced levels of PGF_2α/PGE₂， TXB₂/6-keto-PGF_1α， iNOS， and NO， elevated β-EP level， and inhibited COX-2/NF-κB signaling pathway.

T7 RNA polymerase (T7 RNAP) is one of the simplest known RNA polymerases. Its unique structural features make it a critical model for studying the mechanisms of RNA synthesis. This review systematically examines the static crystal structure of T7 RNAP, beginning with an in-depth examination of its characteristic “thumb”, “palm”, and “finger” domains, which form the classic “right-hand-like” architecture. By detailing these structural elements, this review establishes a foundation for understanding the overall organization of T7 RNAP. This review systematically maps the functional roles of secondary structural elements and their subdomains in transcriptional catalysis, progressively elucidating the fundamental relationships between structure and function. Further, the intrinsic flexibility of T7 RNAP and its applications in research are also discussed. Additionally, the review presents the structural diagrams of the enzyme at different stages of the transcription process, and through these diagrams, it provides a detailed description of the complete transcription process of T7 RNAP. By integrating structural dynamics and kinetics analyses, the review constructs a comprehensive framework that bridges static structure to dynamic processes. Despite its advantages, T7 RNAP has a notable limitation: it generates double-stranded RNA (dsRNA) as a byproduct. The presence of dsRNA not only compromises the purity of mRNA products but also elicits nonspecific immune responses, which pose significant challenges for biotechnological and therapeutic applications. The review provides a detailed exploration of the mechanisms underlying dsRNA formation during T7 RNAP catalysis, reviews current strategies to mitigate this issue, and highlights recent progress in the field. A key focus is the semi-rational design of T7 RNAP mutants engineered to minimize dsRNA generation and enhance catalytic performance. Beyond its role in transcription, T7 RNAP exhibits rapid development and extensive application in fields, including gene editing, biosensing, and mRNA vaccines. This review systematically examines the structure-function relationships of T7 RNAP, elucidates the mechanisms of dsRNA formation, and discusses engineering strategies to optimize its performance. It further explores the engineering optimization and functional expansion of T7 RNAP. Furthermore, this review also addresses the pressing issues that currently need resolution, discusses the major challenges in the practical application of T7 RNAP, and provides an outlook on potential future research directions. In summary, this review provides a comprehensive analysis of T7 RNAP, ranging from its structural architecture to cutting-edge applications. We systematically examine: (1) the characteristic right-hand domains (thumb, palm, fingers) that define its minimalistic structure; (2) the structure-function relationships underlying transcriptional catalysis; and (3) the dynamic transitions during the complete transcription cycle. While highlighting T7 RNAP’s versatility in gene editing, biosensing, and mRNA vaccine production, we critically address its major limitation—dsRNA byproduct formation—and evaluate engineering solutions including semi-rationally designed mutants. By synthesizing current knowledge and identifying key challenges, this work aims to provide novel insights for the development and application of T7 RNAP and to foster further thought and progress in related fields.

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.