BACKGROUND:The regeneration and remodeling of Achilles tendon rupture after surgery are difficulties in clinical treatment.Tissue engineering hydrogels afford the possibility on the healing of postoperative Achilles tendon. OBJECTIVE:To investigate the effect of tannic acid modified interpenetrating network hydrogel on tissue regeneration and remodeling of ruptured Achilles tendon in rats. METHODS:(1)The interpenetrating network hydrogel was prepared under the blue light and the immersion of CaSO4 solution.The micromorphology,mechanical properties,adhesion properties,in vitro drug release properties,and biocompatibility of hydrogels were characterized.(2)Thirty Sprague-Dawley rats were randomly divided into sham operation group,operation group,and hydrogel group,with 10 rats in each group.The animal model of Achilles tendon rupture was established in the latter two groups.In the operation group,the ruptured Achilles tendon was sutured using the modified Kessler method.In the hydrogel group,the ruptured Achilles tendon was repaired by the same method,and the tannic acid modified interpenetrating network hydrogel patch was completely wrapped around the joint of the broken end.Four weeks after the operation,imaging examination,histological evaluation,biomechanical test,and the level test of inflammatory factors were performed. RESULTS AND CONCLUSION:(1)Scanning electron microscope showed that tannic acid modified interpenetrating network hydrogels had porous microstructure with pore size of 3-10 μm,and the hydrogels had good in vitro drug release properties,adhesion strength and tensile strength.CCK-8 assay and live/dead staining showed that the hydrogel had no significant effect on the proliferation activity of rat bone marrow mesenchymal stem cells,and had good biocompatibility.(2)MRI imaging showed that compared with the operation group,the Achilles tendon in the hydrogel group showed uniform low signal,the thickness of the anteroposterior diameter of the Achilles tendon was reduced,and the boundary between the Achilles tendon and the surrounding tissue was more clear,and the performance was more similar to that of the sham operation group.Hematoxylin-eosin staining and Masson staining showed that the tendon fibers in the operation group were arranged in a loose and chaotic manner,with increased cell density and disordered arrangement,accompanied by obvious inflammatory cell infiltration,and intratendinous ossification appeared in some areas.In the hydrogel group,the tendon fibers were arranged in an orderly manner;the cell density was reduced and arranged orderly;the inflammatory cell infiltration was significantly reduced.The tensile strength of Achilles tendon in the operation group was lower than that in the sham operation group(P<0.05).The tensile strength of Achilles tendon in the hydrogel group was higher than that in the operation group(P<0.05).Compared with sham operation group,the mass concentration and mRNA expression of interleukin-1β,interleukin-6,and tumor necrosis factor α in Achilles tendon of rats were increased in the operation group(P<0.05).Compared with the operation group,the level and mRNA expression of three inflammatory factors were decreased in the hydrogel group.(3)It is concluded that tannic acid modified interpenetrating network hydrogel can inhibit the local inflammatory response and promote the tendon remodeling.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Mutational dysphonia, a condition in which a pre-adolescent voice persists into adulthood, can significantly impact personal and professional life but is treatable with voice therapy. A patient with mutational dysphonia usually has a voice that is weak, breathy, or diplophonic, often classified as a “falsetto” voice. In this case report, we present a 20-year-old male who had a typical voice of a boy before adolescence, making it difficult to diagnose as mutational dysphonia. After voice therapy, he successfully gained his post-adolescent voice, highlighting the importance of thorough diagnosis and personalized treatment for mutational dysphonia.

We describe the updated Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of cervical cancer, version 5.1. The KSGO announced the fifth version of its clinical practice guidelines for the management of cervical cancer in March 2024. The selection of the key questions and the systematic reviews were based on data available up to December 2022. Between 2023 and 2024, substantial findings from large-scale clinical trials and new advancements in cervical cancer research remarkably emerged. Therefore, based on the existing version 5.0, we updated the guidelines with newly accumulated clinical data and added 4 new key questions reflecting the latest insights in the field of cervical cancer. For each question, recommendation was formulated with corresponding level of evidence and grade of recommendation, all established through expert consensus.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

With the completion of the "Human Genome Project" and the smooth progress of the "Herbal Genome Project", the research wave of RNAomics is gradually advancing, opening the research gateway for the modernization of traditional Chinese medicine (TCM) and initiating the post-genome era of medicinal plant RNA research. Therefore, this article proposes for the first time the concept of HerbRNomes, which involves constructing databases of medicinal plant, medicinal fungus, and medicinal animal RNA at different stages, from different origins, and in different organs. This research aims to explore the role of HerbRNA in self-genetic information transmission, functional regulation, as well as cross-species regulation functional mechanisms and key technologies. It also investigates application scenarios, providing a theoretical basis and research ideas for the resistance of TCM or medicinal plants to adversity and stress, molecular assistant breeding, and the development of small nucleic acid drugs. This article reviews recent research progress in elucidating the molecular mechanisms of the transmission and expression of genetic information, self-regulation and cross-species regulation of herbs at the RNA level, along with key technologies. It proposes a development strategy for small nucleic acid drugs based on HerbRNomes, providing theoretical support and guidance for the modernization of TCM based on HerbRNomes research.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

AIM: To investigate the correlation of serum Silent mating-type information regulation 2 homolog 1(SIRT1)and Vasostatin-2 content with pathological changes in diabetic retinopathy(DR)patients.METHODS: A total of 104 DR patients(104 eyes)admitted to our hospital from April 2021 to April 2024 were included as the DR group. According to different disease stages, they were assigned into a non-proliferative DR(NPDR)group of 44 cases(44 eyes)and a proliferative DR(PDR)group of 60 cases(60 eyes). Meantime, 104 patients(104 eyes)with simple diabetes were treated as non-DR group. ELISA was applied to detect the levels of SIRT1 and Vasostatin-2 in serum. The diagnostic value of serum SIRT1 and Vasostatin 2 in DR was analyzed by ROC curve. Multivariate Logistic regression was applied to analyze the factors that affected the occurrence of DR. Pearson correlation was applied to analyze the relationship between the levels of SIRT1 and Vasostatin-2 in the serum of DR patients and angiogenesis indicators(VEGF, Ang-2).RESULTS: Compared with the non-DR group, the levels of SIRT1 and Vasostatin-2 in the serum of the DR group were significantly decreased(P<0.05). Compared with the NPDR group, the levels of SIRT1 and Vasostatin-2 in the serum of the PDR group were significantly decreased(P<0.05). Compared with the non-DR group, the levels of VEGF and Ang-2 in the serum of the DR group were obviously higher(P<0.05). Compared with the single detection of serum SIRT1 and Vasostatin-2 levels, combined detection significantly increased the AUC in the diagnosis of DR(Z=4.180, 5.128, all P<0.05). Multivariate Logistic regression analysis showed that HOMA-IR(OR=3.455), fasting blood glucose(OR=1.467), SIRT1(OR=0.836), Vasostatin-2(OR=0.767), VEGF(OR=2.564), and Ang-2(OR=1.834)levels were the influencing factors on the occurrence of DR(all P<0.05). Pearson correlation analysis showed that the levels of SIRT1 and Vasostatin-2 in the serum of DR patients were negatively correlated with VEGF and Ang-2(rSIRT1 vs VEGF=-0.395, rSIRT1 vs Ang-2=-0.474, rVasostatin-2 vs VEGF=-0.323, rVasostatin-2 vs Ang-2=-0.583, all P<0.001).CONCLUSION: The abnormal decrease of serum SIRT1 and Vasostatin 2 levels in DR patients is closely related to the stage of DR lesions and angiogenesis.

Objective: To evaluate the safety and efficacy of coil embolization of venous segments in patients with Type IIa pelvic arteriovenous malformations (AVMs). Materials and Methods: A retrospective study was performed on 13 patients (median age, 43 years, range 20–62 years, 7 males) who underwent transvenous coil embolization for Type IIa pelvic AVM (characterized by multiple arterioles shunting to focal venous segments of a single draining vein) without the use of additional liquid embolic agents from March 2017 to February 2023. Treatment outcomes were analyzed based on clinical evaluations, post angiography findings, and follow-up CT. Results: Fourteen procedures were performed on 13 patients. Except in one patient, all treatments were completed in a single session. Transvenous access was employed in 10 procedures, whereas direct puncture was used in four sessions. The embolization procedures used an average of 55.7 ± 58.5 coils (range, 7–238) and lasted an average of 127.3 ± 39.5 minutes.The technical success rate was 92.9% (of 13/14). All patients reported symptom improvement. Follow-up CT scans showed complete occlusion of the AVM without recurrence in ten of the 13 patients. There was one minor adverse event: a small retroperitoneal hemorrhage, likely related to direct puncture, which resolved spontaneously. No other adverse events were observed. Conclusion Coil embolization of the draining vein segment, without the use of additional liquid embolic agents is a safe and effective method for managing Type IIa pelvic AVM.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.