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.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment. Oral squamous cell carcinoma (OSCC), a representative hypoxic tumor, has a heterogeneous internal metabolic environment. To clarify the relationship between different metabolic regions and the tumor immune microenvironment (TME) in OSCC, Single cell (SC) and spatial transcriptomics (ST) sequencing of OSCC tissues were performed. The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data. The metabolic activity of each spot was calculated using scMetabolism, and k-means clustering was used to classify all spots into hyper-, normal-, or hypometabolic regions. CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others. Through CellPhoneDB and NicheNet cell-cell communication analysis, it was found that in the hypermetabolic region, fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts (iCAFs), and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12. The secretion of CXCL12 recruits regulatory T cells (Tregs), leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment. This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC, ST and TCGA bulk data, and highlights potential targets for therapy.
Humans ; Carcinoma, Squamous Cell/metabolism* ; Squamous Cell Carcinoma of Head and Neck ; Mouth Neoplasms/metabolism* ; Immunosuppression Therapy ; Transforming Growth Factor beta ; Head and Neck Neoplasms ; Gene Expression Profiling ; Tumor Microenvironment

Humans ; Vascular Stiffness ; Coal Mining ; Occupational Diseases/epidemiology* ; Risk Factors ; Coal ; Miners

Objective:The aim of this study was to investigate the clinical characteristics and analyze the risk factors of patients with acute dichlorvos poisoning combined with shock.Methods:The clinical data of patients with acute dichlorvos poisoning admitted to the Peking University Third Hospital and the Fifth Medical Center of the PLA General Hospital between January 2019 and September 2020 were retrospectively analyzed, and demographic data, poisoning, clinical manifestations, laboratory tests, therapeutic measures and clinical outcomes were collected to establish a clinical database. The patients were divided into two groups: the shock group and the non-shock group, and the clinical data were compared between the two groups to analyze the clinical characteristics and prognosis of shock in acute dichlorvos poisoning, and the risk factors of shock in acute dichlorvos poisoning were analyzed by logistic regression.Results:A total of 134 patients who met the criteria for acute dichlorvos poisoning were included in this study; the incidence of shock within 24 hours of admission was 39.6% (53/134), and 11 patients (8.21%) died in hospital; the in-hospital morbidity and mortality rate of patients in the shock group was higher than that in the non-shock group (20.8% vs. 0.0%, P<0.001). Symptoms of sphincter relaxation, coma, hypothermia, and organ function damage were more common in the shock group than in the non-shock group; and shock patients had longer hospitalization, ICU stay, and invasive ventilator use. Binary logistic regression analysis showed that the presence of sphincter relaxation manifestations ( OR=10.888, 95% CI: 1.677-70.684, P=0.012) was an independent risk factor for comorbid shock in patients with acute dichlorvos poisoning, and the use of cholinesterase reanimators ( OR=0.246, 95% CI: 0.072-0.846, P=0.026) was a protective factor for combined shock in patients with acute dichlorvos poisoning. Conclusions:The incidence of shock in patients with acute dichlorvos poisoning is high and affects the clinical prognosis, and the presence of sphincter relaxation and the absence of cholinesterase reenergizers are independent risk factors for combined shock in patients with acute dichlorvos poisoning.

Objective:To explore the clinical efficacy of areola approach endoscopic thyroidectomy (AET) and gasless axillary approach endoscopic thyroidectomy (GAET) in the treatment of papillary thyroid carcinoma (PTC) patients.Methods:A total of 96 PTC patients from the Thyroid Surgery Department of Linyi People’s Hospital from May. 2019 to May. 2022 were selected and randomly divided into 48 patients using a random number table method. The areola group received AET, while the armpit group received GAET. The surgical situation, postoperative recovery, relevant biochemical indicators [white blood cell count (WBC), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), parathyroid hormone (PTH), blood calcium] before and after surgery, postoperative pain level, discomfort level, neck function, and complications were compared between the two groups.Results:The surgical time and extubation time of the armpit group were (125.71±15.73) minutes and (3.12±0.53) days, respectively, which were shorter than those of the areola group (137.94±20.02) minutes and (3.48±0.46) days. The intraoperative bleeding volume was (14.19±4.16) mL, which was less than that of the areola group (22.65±7.39) mL, and the number of lymph nodes cleaned was 5.06±1.02, which was more than that of the areola group (4.23±1.14) ( P<0.05) ; there was no significant difference in postoperative drainage volume and hospital stay between the two groups ( P>0.05) ; Peripheral blood WBC in the armpit group on the 1st and 3rd day after surgery [ (5.69±0.15) ×10 9/L, (5.52±0.14) ] ×10 9/L, ESR [ (8.21±0.55) mm/h, (7.64±0.60) mm/h], CRP [ (10.06±1.78) ng/L, (8.93±1.33) ng/L] were lower than those in the areola group [ (5.83±0.21) ×10 9/L, (5.70±0.23) ×10 9/L, (8.87±0.74) mm/h, (8.19±0.68) mm/h, (12.45±1.90) ng/L, (10.45±1.50) ng/L] ( P<0.05). There was no significant difference in the levels of the above biochemical indicators 5 days after surgery ( P>0.05). There was no significant difference in peripheral blood PTH and calcium levels between the two groups on the 1st, 3rd, and 5th postoperative days ( P>0.05). The pain level [ (3.25±0.32) scores, (2.53±0.27) scores, (1.82±0.22) scores] and discomfort level [ (6.85±0.71) scores, (5.24±0.66) scores, (3.51±0.57) scores] in the axillary group were lower than those in the areola group [ (3.78±0.40) scores, (2.89±0.34) scores, (2.06±0.26) scores, (7.46±0.84) scores, (6.09±0.73) scores, (4.16±0.60) scores] on the 1st, 3rd, and 5th postoperative days ( P<0.05). The neck flexion, lateral flexion, and extension range of motion in the axillary group on the 3rd day after surgery were (33.16±3.09) °, (27.63±2.57) °, and (30.44 2.73) °, respectively, which were greater than those in the areola group[ (30.08±2.76) °, (25.14±2.30) °, and (27.98±2.54) °], and the swallowing disorder index was (30.16±4.97) points lower than the (34.83±4.13) points in the areola group ( P<0.05). The incidence of complications in the axillary group was 4.17% (2/48), lower than the 16.67% (8/48) in the areola group. Conclusion:GAET treatment for PTC patients can improve the effect of lymph node dissection, reduce the degree of surgical trauma, postoperative pain and discomfort, accelerate early postoperative recovery of neck function, and reduce complications.

The COVID-19 epidemic has spread to the whole world for three years and has had a serious impact on human life, health and economic activities. China's epidemic prevention and control has gone through the following stages: emergency unconventional stage, emergency normalization stage, and the transitional stage from the emergency normalization to the "Category B infectious disease treated as Category B" normalization, and achieved a major and decisive victory. The designated hospitals for prevention and control of COVID-19 epidemic in Tianjin has successfully completed its tasks in all stages of epidemic prevention and control, and has accumulated valuable experience. This article summarizes the experience of constructing a hospital infection prevention and control system during the "Category B infectious disease treated as Category A" period in designated hospital. The experience is summarized as the "Cluster" hospital infection prevention and control system, namely "three rings" outside, middle and inside, "three districts" of green, orange and red, "three things" before, during and after the event, "two-day pre-purification" and "two-director system", and "one zone" management. In emergency situations, we adopt a simplified version of the cluster hospital infection prevention and control system. In emergency situations, a simplified version of the "Cluster" hospital infection prevention and control system can be adopted. This system has the following characteristics: firstly, the system emphasizes the characteristics of "cluster" and the overall management of key measures to avoid any shortcomings. The second, it emphasizes the transformation of infection control concepts to maximize the safety of medical services through infection control. The third, it emphasizes the optimization of the process. The prevention and control measures should be comprehensive and focused, while also preventing excessive use. The measures emphasize the use of the least resources to achieve the best infection control effect. The fourth, it emphasizes the quality control work of infection control, pays attention to the importance of the process, and advocates the concept of "system slimming, process fattening". Fifthly, it emphasizes that the future development depends on artificial intelligence, in order to improve the quality and efficiency of prevention and control to the greatest extent. Sixth, hospitals need to strengthen continuous training and retraining. We utilize diverse training methods, including artificial intelligence, to ensure that infection control policies and procedures are simple. We have established an evaluation and feedback mechanism to ensure that medical personnel are in an emergency state at all times.

Objective:To develop and validate a postoperative infection nomogram of hepatitis B-associated hepatocellular carcinoma (HCC) after hepatectomy.Methods:Clinical data of 229 patients with HCC undergoing hepatectomy at the Department of Hepatobiliary Surgery of Tianjin Third Central Hospital from January 2014 to December 2022 were retrospectively analyzed, including 174 males and 55 females, aged (58.2±11.4) years. LASSO regression analysis screened the factors associated with hepatitis B-associated HCC infection after hepatectomy, which were further incorporated into multivariate logistic regression analysis. A nomographic prediction model was established based on the results of multivariate logistic regression analysis. Concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve were used to evaluate the model, and decision curve analysis (DCA) was used to analyze the clinical applicability of the model. Internal validation of the model was performed using bootstrap method.Results:A total of nine variables were screened as factors associated with the postoperative infections using LASSO regression, including gender, smoking history, body mass index (BMI), serum level of alpha fetoprotein, resection fashion (anatomical or non-anatomical), intraoperative blood loss, surgical method (laparoscopy or open), serum level of creatinine, and postoperative biliary fistula. Multivariate logistic regression analysis showed that BMI, resection fashion, intraoperative blood loss >500 ml, and postoperative biliary fistula were risk factors for postoperative infection (all P<0.05). Based on the above risk factors, a postoperative infection nomogram of hepatitis B-associated HCC after hepatectomy was established. The C-index was 0.839 (95% CI: 0.768-0.910), and the area under ROC curve was 0.853 (95% CI: 0.795-0.912), indicating that the model had a good predictive ability. The calibration curve was basically consistent with the ideal curve. The DCA showed that the model had a good clinical applicability. Internal validation C-index was 0.829 (95% CI: 0.766-0.892). Conclusion:The nomogram based on BMI, surgical resection fashion, intraoperative blood loss >500 ml, and postoperative biliary fistula has a high predictive accuracy and can be used to predict postoperative infections after hepatectomy for HCC.

OBJECTIVE To establish the quality standards of medicinal materials in light of related methods in the general principles of part four of Chinese Pharmacopoeia(2020 Edition), and to conduct systematic research on the Tibetan medicine "Yajima"(Chrysosplenium axillare). METHODS The powder characteristics of medicinal materials were described by microscopic identification method. Silica gel GF254 thin-layer plate was employed to establish a TLC identification method with 5-O-demethylapulein and oxyayanin A as reference substances. Loss on drying, total ash, acid-insoluble ash and ethanol-soluble extractives of 10 batches of Chrysosplenium axillare were determined according to the general principles of part four of Chinese Pharmacopoeia(2020 Edition). HPLC was used to establish the characteristic chromatogram of Chrysosplenium axillare, and the content determination method was established with chrysosplenoside I(CI) and chrysosplenoside A(CA) as the quality control index components of Chrysosplenium axillare. RESULTS The water content, total ash, acid-insoluble ash, ethanol-soluble extractive and the content of CI and CA of all samples varied in the ranges of 9.17%−12.52%, 14.11%−16.74%, 1.50%−4.72%, 32.77%−40.30%, 0.30%−0.99% and 0.28%−0.88%, respectively. CONCLUSION The identification and content determination methods of Yajima(Chrysosplenium axillare) are established for the first time. The methods are easy to operate and exclusive, which is of great significance to accurately evaluate the internal quality of medicinal materials and ensure the quality of drug used.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.