Objectives: . This study aimed to investigate the regulatory functions of gamma-aminobutyric acid (GABA)ergic neural circuits from the inferior colliculus (IC) to the medial geniculate body (MGB) in salicylate-induced tinnitus. Methods: . Mice were treated with salicylate to induce tinnitus, and tinnitus-like behaviors were evaluated via gap prepulse inhibition of acoustic startle. Using combined viral tracing methodologies, we identified and mapped the pathways and connections from the IC to the MGB. Furthermore, we employed Gq-coupled human M3 designer receptors exclusively activated by designer drugs (DREADDs) and Gi-coupled human M4 DREADDs to achieve targeted excitation or suppression of GABAergic neurons in the IC and MGB. Following the administration of clozapine N-oxide, which binds to these receptors, we modulated these neural circuits to assess their impact on tinnitus severity in a mouse model. Results: . Our findings demonstrated that mice exposed to salicylate exhibited tinnitus-like behaviors. GABAergic neurons projecting retrogradely from the MGB to the IC were primarily concentrated in the external nucleus of the IC. After clozapine N-oxide administration, chemogenetic activation of IC-MGB GABAergic neurons aggravated salicylate-induced tinnitus. Additionally, activation of GABAergic neurons between the IC and MGB induced the perception of tinnitus even without salicylate. However, chemogenetic inhibition of the IC-MGB GABAergic circuit did not reverse salicylate-induced tinnitus. Conclusion . These findings suggest that activation of the IC-MGB GABAergic neural circuit may contribute to tinnitus generation through a mechanism distinct from that of salicylate-induced tinnitus. This study provides novel insights into the mechanisms underlying tinnitus.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

AIM To investigate the effects of Jisuishang Formula on neurological function and ferroptosis in a rat model of cervical spondylotic myelopathy(CSM).METHODS The CSM rat models were established and randomly assigned to the model group,the Fer-1 group(2 g/kg Ferrostatin-1 via intraperitoneal injection),the low-dose(9.7 g/kg,intragastrically),medium-dose(19.4 g/kg,intragastrically)and high-dose(38.8 g/kg,intragastrically)Jisuishang Formula groups,and the sham operation group,with 6 rats in each group.Following 4 weeks of treatment administration,BBB locomotor scores and oblique plate test result were recorded to assess their neurological function in rats.Histopathological evaluation utilized HE staining for spinal cord tissue pathology,Nissl staining for Nissl body visualization,and Prussian blue staining for iron ion deposition analysis.Protein expressions of Nrf2,SLC7A11,GPX4,HO-1,TFRC and Cox2 in spinal cord tissues was detected by immunofluorescence and Western blot,while mRNA expressions were quantified using RT-qPCR.RESULTS Compared to the sham group,the CSM model group exhibited significantly reduced BBB locomotor scores and inclined plane test performance at 1,2 and 4 weeks post-operation(P＜0.05);obvious tissue cavitation,cellular edema and Prussian blue positive iron deposition in spinal cord tissues;downregulated protein and mRNA expressions of Nrf2,SLC7A11,GPX4,HO-1(P＜0.05);and upregulated protein and mRNA expressions of TFRC and Cox2(P＜0.05).Compared to the model group,the Jisuishang Formula and Fer-1 intervention groups showed significantly improved BBB scores and inclined plane test result at 1,2 and 4 weeks post-operation(P＜0.05);reduced tissue cavitation,attenuated cellular edema and decreased Prussian blue positive iron deposition in spinal cord tissues;upregulated protein and mRNA expression of Nrf2,SLC7A11,GPX4 and HO-1 in spinal cord tissues(P＜0.05);and downregulated protein and mRNA expressions of TFRC and Cox2(P＜0.05).CONCLUSION Targeting the Nrf2/SLC7A11/GPX4 signaling pathway,Jisuishang Formula potentially suppresses ferroptosis and alleviates iron accumulation in spinal cord neurons,thereby improving neurological recovery in CSM rats.

To improve the quality evaluation system of Hibisci Mutabilis Folium, this study established high performance liquid chromatography(HPLC) fingerprints of Hibisci Mutabilis Folium and evaluated the quality differences of medicinal materials from different places of production by chemometrics. Furthermore, a content measurement method of differential components was established based on quantitative analysis of multi-components by single-marker(QAMS). The fingerprints of 17 batches of Hibisci Mutabilis Folium from different places of production were constructed, with a total of 19 common peaks marked and seven components confirmed. The similarity between the sample fingerprints and the reference fingerprints ranged from 0.890 to 0.974. By utilizing principal component analysis(PCA), hierarchical cluster analysis(HCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA), the chemical patterns of fingerprints were identified. Five components that could be used to evaluate the quality differences of Hibisci Mutabilis Folium were screened, namely peak 6(quercetin 3-O-β-robinobioside), peak 7(rutin), peak 9(kaempferol-3-O-β-robinobioside), peak 10(kaempferol-3-O-rutinoside), and peak 14(tiliroside). The relative correction factors of isoquercitrin, kaempferol-3-O-β-robinobioside, kaempferol-3-O-rutinoside, kaempferol-3-O-β-D-glucoside, and tiliroside were measured with rutin as the internal reference. The QAMS method was established for the content measurement of six flavonoids, and the results showed there was no significant difference compared to the results obtained by an external standard method. In summary, the HPLC fingerprints and QAMS method established in the study, demonstrating stability and accuracy, can provide a reference for the overall quality evaluation of Hibisci Mutabilis Folium.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Principal Component Analysis

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

On the basis of that the fluorescence wavelength of copper nanoclusters(CuNCs)could cover the entire visible region,multicolor fluorescent CuNCs/starch composites were prepared and applied in fingermark development.With L-glutathione as the reducing agent and protective ligand,blue emissive and orange emissive CuNCs solutions were obtained in alkaline solutions at 90℃and 25℃,respectively.With the aggregation-induced emission effect induced by ethanol as a poor solvent,the fluorescence of orange emissive CuNCs with a higher intensity was achieved in an ethanol-water solution.With ascorbic acid as the reducing agent and 3-mercaptopropionic acid as the protective agent,green emissive CuNCs solution was prepared in an acid solution.Particle morphologies,chemical compositions and optical properties of these three CuNCs above were investigated using physical characterization and spectroscopic analysis,indicating that well-dispersed CuNCs had excellent photoluminescent properties.These CuNCs solutions were combined with starch to form composite powders by simply drying.The influences of the type of CuNCs and the ratio of CuNCs to starch on the emission wavelength and fluorescence intensity of the products were studied.The obtained CuNCs/starch composites could emit blue,green and orange fluorescence under 365 nm ultraviolet light,respectively,which were suitable for fingermark development.Minutiae and partial level-3 features of latent fingermarks could be effectively developed.High-quality fluorescence fingermark images would be captured using appropriate optical filters to eliminate background interference of various substrates.

A deep learning-based method for recognizing the minutiae in fingerprint,as well as a Python programming-based evaluation system for quantifying the evidence value of fingerprint was proposed.Firstly,latent fingerprints,which were developed using a series of fluorescent nanomaterials synthesized by chemical methods,were used as unknown fingerprint(UKFP),while ink impressed fingerprints were used as known fingerprint(KFP).Then,the bifurcations and terminations in minutiae were recognized using the improved YOLOv8 deep learning model.After that,the similarity index(Sim.)of UKFP vs KFP were calculated by analyzing the angle similarity factor(α)and the curve similarity factor(β)between UKFP and KFP,meanwhile,the sensitivity index(Sen.)were calculated by analyzing the fineness factor(γ)between UKFP and KFP.The evidence value(EV)of fingerprint was thus obtained by the combination of Sim.and Sen..The calculation formulas for above evaluation factors(i.e.α,β and γ),evaluation indexes(i.e.Sim.and Sen.),and EV were also put forward.Finally,the evaluation system for quantifying the evidence value of fingerprint was established,the feasibility and reliability of this system were verified,and the external factors that impacted on Sim.,Sen.,and EV were investigated in detail.The Python-based evaluation system for quantifying the evidence value of fingerprint could achieve the goals objectively,comprehensively,accurately and efficiently,exhibiting easy operability,high efficiency,responsiveness and reliability.This research was expected to provide beneficial references for quantitatively evaluating and thoroughly developing the evidence value.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.