Background: Surgeons face challenges in selecting cost-effective and biologically active agents for rotator cuff healing, given the numerous commercial products available, such as polydeoxyribonucleotide (PDRN) and atelocollagen (ATC). However, the precise efficacy of PDRN and ATC in rotator cuff healing remains debatable, and there is currently a lack of studies directly comparing the effects of the 2 agents on repaired cuff tendons. Therefore, the purpose of this study was to compare the efficacy of PDRN and ATC on bone-to-tendon interface (BTI) healing using a chronic rotator cuff tear (RCT) model in rabbits. Methods: Forty-eight rabbits were randomly divided into 3 groups. To create chronic RCT models, transected tendons were left untreated for 6 weeks, and then were repaired in a transosseous manner with PDRN and ATC injection into the repair site according to group allocation (group A: saline, group B: PDRN, group C: ATC; n = 16 per group). Genetic and immunofluorescence analyses were performed at 4 weeks after surgery. Furthermore, genetic, histologic, and biomechanical analyses were performed at 12 weeks after surgery. Results: At 4 weeks after surgery, ATC-injected shoulders showed the highest mRNA expression levels of collagen type I alpha 1 and aggrecan compared to the other 2 groups (p < 0.001 and p = 0.002, respectively). Meanwhile, there was more preliminary fibrocartilaginous matrix formation in the ATC-injected group. At 12 weeks after surgery, ATC-injected shoulders demonstrated better collagen fiber continuity and orientation, denser collagen fibers, a more mature bone-to-tendon junction, and greater fibrocartilage layer formation compared to the other 2 groups (all p < 0.001). Furthermore, ATC-injected shoulders also demonstrated a significantly higher load-to-failure value (40.4 ± 4.5 N/kg) than the remaining groups (group A, 26.7 ± 3.0 N/kg; group B, 32.8 ± 4.2 N/kg; p < 0.001). Conclusions ATC demonstrated superior efficacy in promoting BTI healing following surgical repair in a chronic RCT model of rabbits.

ObjectiveOxygen partial pressure (pO₂) is a critical indicator of the tumor microenvironment and plays an essential role in regulating tumor progression and therapeutic response, particularly for oxygen-dependent treatments such as photodynamic therapy (PDT). Although photoacoustic imaging (PAI) has shown great potential for functional and molecular imaging, conventional oxygenation imaging approaches based on hemoglobin absorption mainly provide relative oxygen saturation information and cannot directly quantify tissue oxygen partial pressure. Therefore, this study aimed to develop a fast laser sequence-based pump-probe photoacoustic tomography (PP-PAT) system for quantitative, non-invasive, and dynamic monitoring of oxygen partial pressure in tumor tissues. MethodsThe proposed PP-PAT system was developed based on the oxygen-sensitive triplet-state lifetime characteristics of methylene blue (MB). Upon optical excitation, MB molecules undergo transitions from the ground state to an excited state and subsequently form metastable triplet states through intersystem crossing. The lifetime of the triplet state is strongly affected by surrounding oxygen concentration due to dynamic oxygen quenching, providing a direct correlation between triplet-state kinetics and local oxygen partial pressure. A dual-wavelength pump-probe excitation scheme was implemented to selectively interrogate MB triplet-state dynamics. The pump pulse was used to initiate the triplet-state population, while the delayed probe pulse was applied to detect the transient triplet-state response. By optimizing the temporal interval between the pump and probe pulses, transient triplet-state differential (TTD) signals were extracted to enhance the specificity of oxygen-dependent molecular information and suppress background interference from conventional photoacoustic signals. The performance of the PP-PAT system was systematically evaluated through phantom and in vivo experiments. First, a single-tube phantom containing MB solution was used to investigate signal stability, reproducibility, and the influence of signal averaging on imaging quality. Subsequently, a dual-tube phantom model was established to simulate oxygen consumption during PDT, where one MB-containing tube was exposed to laser irradiation and the other served as a control. Finally, in vivo experiments were performed using a subcutaneous tumor model in nude mice. MB was locally injected into the tumor region before imaging to evaluate the feasibility of tumor oxygen partial pressure mapping using the proposed PP-PAT system. ResultsPhantom experiments demonstrated that the extracted TTD signals exhibited high stability and effectively reflected MB triplet-state dynamics while reducing non-specific background contributions. Signal averaging significantly improved the signal-to-noise ratio, and 50 repeated acquisitions provided an optimal compromise between image quality and acquisition efficiency. In the dual-tube phantom experiments, continuous PDT irradiation induced progressive oxygen depletion in the treated sample. The corresponding TTD decay rate gradually decreased, accompanied by an increase in the calculated triplet-state lifetime, indicating reduced oxygen quenching and decreased oxygen partial pressure. In contrast, the control sample showed relatively stable TTD kinetics and oxygenation levels during the same period. These results demonstrated that PP-PAT could quantitatively characterize oxygen consumption dynamics associated with photochemical reactions. In vivo experiments further demonstrated the capability of PP-PAT for spatially resolved oxygen partial pressure imaging in MB-injected tumor tissues. The reconstructed pO₂ maps revealed heterogeneous oxygen distributions within tumors, showing relatively hypoxic regions in the tumor core and higher oxygenation levels near the tumor boundary. This spatial oxygen distribution pattern was consistent with the typical physiological characteristics of solid tumors, where abnormal vascular structures and limited oxygen diffusion result in intratumoral oxygen gradients. ConclusionThis study presents a fast laser sequence-based PP-PAT system capable of quantitative and non-invasive imaging of tissue oxygen partial pressure. By utilizing the oxygen-dependent triplet-state lifetime of MB and extracting TTD signals, the proposed method provides molecularly specific information related to oxygen dynamics beyond conventional photoacoustic oxygenation imaging. The system enables dynamic monitoring of oxygen consumption during PDT and reveals spatial oxygen heterogeneity within tumors. This technique provides a promising imaging approach for tumor hypoxia characterization, evaluation of oxygen-dependent therapeutic responses, and precision-guided cancer treatment.

Through consulting herbal medicine， medical books， and local chronicles from past dynasties to modern times， this paper systematically researched Spatholobi Caulis from name， origin， producing areas， harvesting， processing， usage， quality evaluation， functions and indications， providing a reference for the development and utilization of famous classical formulas containing Spatholobi Caulis. According to the research， Spatholobi Caulis was first recorded in the Annals of Shunning Prefecture from the Qing dynasty. It was originally a medicinal herb commonly used in Shunning， Yunnan， and was named from the red juice resembling chicken blood that flowed out after the vein was cut off. The mainstream original plants of each dynasty were Kadsura heteroclita and Spatholobus suberectus. Among them， K. heteroclita mainly focused on dispersing blood stasis and unblocking meridians， mainly treating rheumatic pain and injuries caused by falls or blows， and it is mostly used as the raw material of Jixueteng ointments. S. suberectus was commonly used as decoction pieces in decoction， which had the functions of promoting blood circulation and replenishing blood， activating meridians and collaterals， and mainly used for treating anemia， irregular menstruation， and rheumatic bone pain. The production area of Spatholobi Caulis recorded in the Qing dynasty was Yunnan. Currently， the main production area of S. suberectus is Guangxi， while the main production area of K. interior is Yunnan. In the Qing dynasty， the usage of Spatholobi Caulis was an individual prescription with other herbs before making ointments， which was usually composed of the juice of it， safflower， angelica， and glutinous rice. But in modern times， Spatholobi Caulis is mostly sliced and dried for use. The quality of Spatholobi Caulis is often determined by the number of reddish-brown concentric circles on the cut surface， with a higher number indicating better quality. Additionally， the presence of resinous secretions is also considered desirable. Based on the research findings， it is suggested that when developing famous classical formulas containing Spatholobi Caulis， the choice of the primary source should be S. suberectus or K. heteroclita， taking into consideration the therapeutic effects of the formula. It is also recommended that the latest plant classification be referenced in the next edition of Chinese Pharmacopoeia， adjusting the primary source of Kadsurae Caulis to K. heteroclita to avoid confusion caused by inconsistent original names， and the functions adjust to promote Qi circulation and relieve pain， disperse blood stasis and unblock collaterals， treating injuries caused by falls and bruises.

Aim To elucidate the underlying mecha-nism by which total triterpenoids extracted from Hove-nia dulcis(H-TP)enhance the sensitivity of A549/DDP cells to cisplatin.Methods The ARE-Nrf2 lu-ciferase reporter assay was applied to investigate the impact of H-TP on Nrf2 expression.Western blot was used to detect the protein levels of Keap-1/Nrf2/HO-1,Nrf2-GPX4 signaling pathway,apoptosis-related proteins of Bcl-2 and Bax.Further validation of its effects on Nrf2 was conducted by using Nrf2 activator/inhibitor.Results H-TP could enhance the sensitivi-ty of A549/DDP cells to cisplatin by modulating the expression of apoptosis-related proteins Bax and Bcl-2,inhibiting the Keap-1/Nrf2/HO-1/GPX4 signating pathway in A549/DDP cells,and inducing ferroptosis.Conclusion H-TP enhances the sensitivity of A549/DDP cells to cisplatin by inducing the Nrf2-mediated ferroptosis pathway.

Objective To investigate the value of CT perfusion imaging (CTP) combined with serum eicosapentaenoic acid (EPA)/arachidonic acid (AA) in predicting hemorrhage transformation and short-term prognosis after thrombolysis in acute ischemic stroke (AIS) patients with leukoaraiosis. Methods Ninety-eight AIS patients with leukoaraiosis admitted to the department of neurology of our hospital from January 2021 to December 2022 were selected and divided into the hemorrhage transformation group and non-hemorrhage transformation group according to whether hemorrhage transformation occurred after thrombolysis. The Fazekas scale was used to evaluate the leukoaraiosis of the patients. CTP parameters and EPA/AA were compared between patients in the two groups and patients with different degrees of leukoaraiosis in the hemorrhage transformation group. The predictive value of CTP parameters and EPA/AA on the occurrence of hemorrhage transformation was evaluated by receiver operating characteristic (ROC) curve. The prognosis was assessed according to the modified Rankin scale (mRS) score 1 month after thrombolysis;the linear and linear combinations are used to evaluate the linear relationship between variables;the ROC curve was used to evaluate the predictive value of CTP parameters and EPA/AA in the short-term prognosis of patients. Results The reactive cerebral blood flow (rCBF),reactive cerebral blood volume (rCBV),CTP integration index and EPA/AA in the hemorrhage transformation group were significantly lower than those in the non-hemorrhage transformation group (P<0.05),while the relative time to peak (rTTP) was significantly longer than that in the non-hemorrhage transformation group (P<0.05). The incidence of hemorrhage transformation increased with the increase of leukoaraiosis degree (P<0.05). In the hemorrhage transformation group,rCBF,rCBV,CTP integration index and EPA/AA of patients with mild leukoaraiosis were higher than those of patients with moderate-severe leukoaraiosis (P<0.05). In patients with mild leukoaraiosis,the area under the curve (AUC) of rCBF and EPA/AA in predicting hemorrhage transforma-tion were 0.712 and 0.720,respectively (P<0.05);in patients with moderate-severe leukoaraiosis,the AUC of rCBF,rCBV,rTTP,CTP integration index and EPA/AA in predicting hemorrhage transformation were 0.738,0.714,0.717,0.739 and 0.742,respectively (P<0.05). Among the 98 patients received thrombolysis,35 patients had a poor prognosis. The AUC of rCBF,rCBV,CTP integration index and EPA/AA in predicting short-term prognosis were 0.742,0.732,0.704 and 0.738,respectively,and the AUC of the four combined prediction was 0.968. Conclusion CTP parameters and EPA/AA have a certain predictive value for the occurrence of hemorrhage transformation after thrombolysis in AIS patients with leukoaraiosis,and rCBV,rCBF,CTP integration index and EPA/AA are important influencing factors of the short-term prognosis for these patients.

The complex rhythmic patterns of central pattern generator (CPG) and its response to electromagnetic induction are explored. The response of an individual neuron's discharge activity to electromagnetic induction is analyzed when the slow variable parameter changes,and it is found that the smaller the slow variable parameter,the stronger the neuron's response to electromagnetic induction. The simulation reveals that the discharge rhythm of CPG that are sequentially inhibited (neuron 1 inhibits 2,neuron 2 inhibits 3,and neuron 3 inhibits 1) has a stronger response to electromagnetic induction in synchronous states and is more robust in typical discharge states (3 neurons firing in sequence). In addition,the response of the discharge rhythm of CPG with mutual inhibition coupling structure to electromagnetic induction is discussed,and it is found that small changes in electromagnetic induction will fundamentally transform the discharge rhythm pattern of CPG.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Objective:This study aimed to investigate the regulatory role and mechanism of myosin heavy chain 9 (MYH9) in the invasion of Zika virus (ZIKV) into human glioma cells (U251).Methods:Utilizing CRISPR/Cas9 technology, MYH9-knockout U251 cells (U251-MYH9 KD) were constructed. Following ZIKV infection, the protein expression levels, RNA load, and viral titer of ZIKV were detected through western blot (WB), Real-time fluorescence quantitative polymerase chain reaction (qPCR), and plaque formation assays, respectively. The infection efficiency of ZIKV in U251 cells treated with the MYH9 inhibitor blebbistatin was assessed. The binding and internalization efficiency of ZIKV were measured in U251-MYH9 KD cells. The interaction between MYH9 and the ZIKV envelope protein (E) was studied using co-immunoprecipitation (Co-IP). The effects of soluble MYH9 recombinant protein and anti-human MYH9 antibodies on ZIKV infection were evaluated by qPCR and plaque formation assays. Results:It was found that knockout or inhibition of MYH9 significantly suppressed ZIKV infection in U251 cells. MYH9 knockout notably inhibited the binding and internalization of ZIKV in U251 cells. MYH9 interacted with the ZIKV E protein, and both MYH9 recombinant protein and anti-human MYH9 antibodies, by blocking the binding of ZIKV E protein to cell surface MYH9, inhibited ZIKV infection in U251 cells in a dose-dependent manner.Conclusions:MYH9 facilitates ZIKV invasion into U251 cells through interaction with the ZIKV E protein.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.