Jiegengtang is a basic formula for treating sore throat and cough. By means of bibliometrics， this study conducted a textual research and analysis on the key information such as formula origin， decocting methods， and clinical application of Jiegengtang. After the research， it can be seen that Jiegengtang is firstly contained in Treatise on Febrile and Miscellaneous Disease， which is also known as Ganjietang， and it has been inherited and innovated by medical practitioners of various dynasties in later times. The origins of Chinese medicines in this formula is basically clear， Jiegeng is the dried roots of Platycodon grandiflorum， Gancao is the dried roots and rhizomes of Glycyrrhiza uralensis， the two medicines are selected raw products. The dosage is 27.60 g of Glycyrrhizae Radix et Rhizoma and 13.80 g of Platycodonis Radix， decocted with 600 mL of water to 200 mL， taken warmly after meals， twice a day， 100 mL for each time. In ancient times， Jiegengtang was mainly used for treating Shaoyin-heat invasion syndrome， with cough and sore throat as its core symptoms. In modern clinical practice， Jiegengtang is mainly used for respiratory diseases such as pharyngitis， esophagitis， tonsillitis and lung abscess， especially for pharyngitis and lung abscess with remarkable efficacy. This paper can provide literature reference basis for the modern clinical application and new drug development of Jiegengtang.

Jiegengtang is a basic formula for treating sore throat and cough. By means of bibliometrics， this study conducted a textual research and analysis on the key information such as formula origin， decocting methods， and clinical application of Jiegengtang. After the research， it can be seen that Jiegengtang is firstly contained in Treatise on Febrile and Miscellaneous Disease， which is also known as Ganjietang， and it has been inherited and innovated by medical practitioners of various dynasties in later times. The origins of Chinese medicines in this formula is basically clear， Jiegeng is the dried roots of Platycodon grandiflorum， Gancao is the dried roots and rhizomes of Glycyrrhiza uralensis， the two medicines are selected raw products. The dosage is 27.60 g of Glycyrrhizae Radix et Rhizoma and 13.80 g of Platycodonis Radix， decocted with 600 mL of water to 200 mL， taken warmly after meals， twice a day， 100 mL for each time. In ancient times， Jiegengtang was mainly used for treating Shaoyin-heat invasion syndrome， with cough and sore throat as its core symptoms. In modern clinical practice， Jiegengtang is mainly used for respiratory diseases such as pharyngitis， esophagitis， tonsillitis and lung abscess， especially for pharyngitis and lung abscess with remarkable efficacy. This paper can provide literature reference basis for the modern clinical application and new drug development of Jiegengtang.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

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 explore the CT imaging features and independent risk factors for cystic pulmonary nodules and establish a malignant probability prediction model. Methods The patients with cystic pulmonary nodules admitted to the Department of Thoracic Surgery of the First People's Hospital of Neijiang from January 2017 to February 2022 were retrospectively enrolled. They were divided into a malignant group and a benign group according to the pathological results. The clinical data and preoperative chest CT imaging features of the two groups were collected, and the independent risk factors for malignant cystic pulmonary nodules were screened out by logistic regression analysis, so as to establish a prediction model for benign and malignant cystic pulmonary nodules. Results A total of 107 patients were enrolled. There were 76 patients in the malignant group, including 36 males and 40 females, with an average age of 59.65±11.74 years. There were 31 patients in the benign group, including 16 males and 15 females, with an average age of 58.96±13.91 years. Multivariate logistic analysis showed that the special CT imaging features such as cystic wall nodules [OR=3.538, 95%CI (1.231, 10.164), P=0.019], short burrs [OR=4.106, 95%CI (1.454, 11.598), P=0.008], cystic wall morphology [OR=6.978, 95%CI (2.374, 20.505), P<0.001], and the number of cysts [OR=4.179, 95%CI (1.438, 12.146), P=0.009] were independent risk factors for cystic lung cancer. A prediction model was established: P=ex/(1+ex), X=–2.453+1.264×cystic wall nodules+1.412×short burrs+1.943×cystic wall morphology+1.430×the number of cysts. The area under the receiver operating charateristic curve was 0.830, the sensitivity was 82.9%, and the specificity was 74.2%. Conclusion Cystic wall nodules, short burrs, cystic wall morphology, and the number of cysts are the independent risk factors for cystic lung cancer, and the established prediction model can be used as a screening method for cystic pulmonary nodules.

Objective To investigate the morphological typing and clinical significance of the distal tibiofibular syndesmosis fibular notch based on CT images. Methods According to the inclusion and exclusion ceiteria‚ the imaging data of patients undergoing ankle joint CT examination were analyzed‚ and the inferior tibiofibular joint fibula notch was classified according to the morphological characteristics. The measurements included 8 distances. There were 123 males and 102 females‚ all of whom were Han nationality‚ aged 18-60 years old. Results Retrospectively analyzed the result of 225 patients from December 2013 to December 2022. The distal tibiofibular syndesmosis fibular notch was divided into four types according to morphological characteristics‚ C-shaped (50. 67%)‚ V-shaped (26. 67%)‚ flat-shaped (15. 11%) and L-shaped (7. 56%). The angle between the anterior and posterior facets of the flat shape (145. 56 ± 9. 25)° was the largest and the angle between the anterior and posterior facets of the L shape (125. 07 ± 13. 54)° was the smallest(P< 0. 05); the depth of the notch in the flat shape (3. 11 ± 0. 83) mm was the smallest and in the L shape (4. 47±1. 11) mm was the largest(P<0. 05);The posterior facet length (13. 06 ± 3. 56) mm and anterior tibiofibular gap (3. 83±1. 49) mm on left were larger than on the right side (P<0. 05); The posterior facet length (13. 36 ± 3. 46) mm‚ fibular notch depth (3. 93 ± 1. 10) mm and vertical distance of tibiofibular overlap (9. 10 ± 2. 55) mm larger in men than in women (P<0. 05). Conclusion In this study‚ the data related to the inferior tibiofibular syndesmosis notch were measured and divided into four types according to the shape. The flat inferior tibiofibular syndesmosis notch is more likely to have chronic ankle instability‚ and the fibula is more likely to move forward during anatomical reduction. The inferior tibiofibular syndesmosis of L-shaped and C-shaped notches is more prone to posterior displacement of fibula or poor rotation reduction during anatomical reduction.

Acute hepatic porphyria （AHP） is a rare disease with abnormal heme metabolism， and breakthroughs have been made in the treatment of this disease in recent years. In addition to conventional treatment methods， this article reviews new therapies for AHP that are in the stage of initial clinical application or are still in the research stage， including RNAi therapy， enzyme replacement therapy， genetic supplementation of DNA or mRNA， drug molecular chaperones， and glycine transporter inhibitors for reducing heme synthesis. Moreover， this article also reviews the treatment of AHP-related comorbidities and complications， such as hyponatremia and posterior reversible encephalopathy syndrome. High glucose infusion is the main treatment method for AHP in China， and the improvement in diagnosis and increased attention to rare diseases in China has promoted the development of the diagnosis and treatment of AHP， and it is expected to explore more suitable treatment methods for AHP in the Chinese population in the future.

Objective    To predict the probability of lymph node metastasis after thoracoscopic surgery in patients with lung adenocarcinoma based on nomogram. Methods    We analyzed the clinical data of the patients with lung adenocarcinoma treated in the department of thoracic surgery of our hospital from June 2018 to May 2021. The patients were randomly divided into a training group and a validation group. The variables that may affect the lymph node metastasis of lung adenocarcinoma were screened out by univariate logistic regression, and then the clinical prediction model was constructed by multivariate logistic regression. The nomogram was used to show the model visually, the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve to evaluate the calibration degree and practicability of the model. Results    Finally 249 patients were collected, including 117 males aged 53.15±13.95 years and 132 females aged 47.36±13.10 years. There were 180 patients in the training group, and 69 patients in the validation group. There was a significant correlation between the 6 clinicopathological characteristics and lymph node metastasis of lung adenocarcinoma in the univariate logistic regression. The area under the ROC curve in the training group was 0.863, suggesting the ability to distinguish lymph node metastasis, which was confirmed in the validation group (area under the ROC curve was 0.847). The nomogram and clinical decision curve also performed well in the follow-up analysis, which proved its potential clinical value. Conclusion    This study provides a nomogram combined with clinicopathological characteristics, which can be used to predict the risk of lymph node metastasis in patients with lung adenocarcinoma with a diameter≤3 cm.

Objective To explore the role of pulmonary surfactant(PS)combined with budesonide in improving oxygenation and clinical outcomes of neonatal acute respiratory distress syndrome(ARDS).Methods The present study is a historically controlled trial.Infants with ARDS requiring mechanical ventilation and PS replacement therapy were collected from the neonatal unit of Southwest Medical University.Those from January 2022 to November 2022 were set as intervention group(PS+ budesonid,n=35),treated with intratracheal instillation of a mixed suspension of budesonide(0.25 mg/kg)and PS(200 mg/kg),and continuous budesonide nebulization(0.25 mg/kg,twice per day)until withdrawal,then compared with a historical cohort,who just received intratracheal instillation of PS(200 mg/kg)(January 2020-December 2021,PS group,n=35).Baseline data such as gender,mode of delivery,1 min and 5 min Apgar score,birth weight,gestational age,time of onset,and cause of onset were recorded in both groups.The oxygenation and clinical outcomes of infants were compared between the two groups,including:(1)Arterial blood gas analysis indicators,such as partial pressure of oxygen(PaO2)and oxygenation index(OI)before treatment and at 6,12 and 24 hours of treatment;(2)Clinical observation and evaluation indicators,such as the time to withdrawal,duration of oxygen supplementation,length of stay,improvement of the radiological images of the lungs at 72 h of treatment,and repeated PS use;(3)Blood chemistry indicators,such as white blood cell(WBC),neutrocyte(NEU),procalcitonin(PCT)before treatment and at 3 and 7 days of treatment;and(4)Observation indicators of complications,weight growth,and mortality outcomes,such as the incidences of intracranial hemorrhage,gastrointestinal hemorrhage,neonatal necrotizing enterocolitis(NEC),and hyperglycemia,weight growth,and fatality rate.Results The differences in baseline data between the two groups were not statistically different(P＞0.05).The levels of PaO2 of the two groups were increased after treatment for different time periods,while the levels of OI were decreased(P＜0.001),and the levels of above indexes changed more significantly in PS+budesonide group than those in PS group(P＜0.05).The time to withdrawal,duration of oxygen supplementation,and length of stay in PS+budesonide group were shorter than those in PS group;the radiological images of the lungs showed that the pulmonary inflammation absorption was significantly better in PS+ budesonide group than that in PS group,while no significant difference between the two groups of infants with repeated PS use.The NEU was significantly higher in PS+budesonide group than in PS group at 3 d and 7 d of treatment(P＜0.001);and at 3 days of treatment,the PCT levels were significantly lower in PS+budesonide group than that in PS group(P＜0.05).The incidences of intracranial hemorrhage,gastrointestinal hemorrhage,NEC,hyperglycemia,weight growth,and fatality rate were not significantly different between the two groups(P＞0.05).Conclusion The use of budesonide in addition to surfactant may improve the oxygenation of neonates with ARDS,improve the inflammatory infiltrates in lungs,shorten the duration of mechanical ventilation and oxygen supplementation,and without short-term complications associated with budesonide use.

Objective:To investigate the effect of the virtual simulation system-based teaching method for the experimental course of oral local nerve block anesthesia in improving the effect of traditional teaching methods.Methods:One hundred and eighteen undergraduate dental students were randomly divided into two groups, the experimental group was taught using a virtual simulation-based system, and the control group was taught using traditional teaching. The results of the teaching were comprehensively evaluated through course feedback questionnaires, analysis of theoretical test scores, evaluations of the trainees administering and receiving anesthesia on the current anesthesia, and faculty evaluations of the success of the anesthesia, and t-tests and chi-square tests were performed using SPSS 23.0.Results:There was no significant difference in baseline level between the two groups. The students in the experimental group thought that the learning was more vivid ( t=4.24, P=0.005) and had more self-confidence in local anesthesia ( t=4.99, P<0.001). The students in the experimental group felt less needle tip jitter during injection ( t=2.22, P=0.048) and better contact with the medial surface of the mandible ( t=2.22, P=0.020). The students who received anesthesia reported less pain during injection ( t=1.99, P=0.029) and better anesthesia of the inferior alveolar nerve ( t=3.36, P=0.039) in the experimental group. Teacher assessment revealed that the experimental group had a significantly lower failure rate of inferior alveolar nerve block than the control group ( χ2=4.40, P=0.036). Conclusions:The virtual simulation system can optimize the experimental teaching of oral local nerve block anesthesia and can achieve a satisfactory teaching effect.