Background: The hypotension prediction index (HPI) is an algorithm designed to predict hypotension. Some studies have reported that HPI-guided hemodynamic management strategies decrease intraoperative hypotension and complications; however, the effect of HPI on reducing perioperative complications is controversial. This meta-analysis aimed to assess the efficacy of the HPI in reducing major complications and intraoperative hypotension. Methods: We conducted this meta-analysis according to the PRISMA statement and Cochrane Handbook guidelines. A comprehensive literature review was conducted to identify studies focusing on the efficacy of HPI-guided management in reducing intraoperative hypotension and postoperative complications. The PubMed, Embase, Scopus, and Web of Science databases were searched, and the resulting data were combined to calculate the pooled mean differences or risk ratios (RRs) with 95% CIs of both randomized controlled trials (RCTs) and retrospective studies, as appropriate. Heterogeneity and potential publication bias were also assessed. Results: Nineteen articles (12 RCTs and 7 retrospective studies) with 2570 recruited patients were included in this meta-analysis. The critical evaluation of the study quality revealed a low risk of bias in the included RCTs. Among the non-randomized trials, one was rated 7, two were rated 8, and the remaining four were rated 9 on the Newcastle-Ottawa Scale, indicating high quality and a low risk of bias. HPI-guided management significantly reduced intraoperative hypotension and associated major complications (RR = 0.79, 95% CI [0.69–0.90], I2 = 0, P < 0.001). Blood loss and length of hospital stay were comparable between the groups. Conclusions HPI-guided management significantly reduced intraoperative hypotension and major complications.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

Objective Metabolomics combined with machine learning algorithms was used to systematically study the preoperative serum metabolites of patients with early postoperative cognitive dysfunction(POCD)after heart valve replacement,so as to screen biomarkers that may predict early POCD after heart valve replacement and explore the corresponding metabolic regulatory mechanisms.Methods A total of 60 patients underwent heart valve replacement under extracorporeal circulation were selected and divided into early-POCD group(group P)and non-POCD group(group N)according to whether POCD occurred or not.Metabolomic analysis was performed on preoperative serum samples of patients in group P and group N to screen the differential metabolites and metabolic pathways.The biomarkers related to early POCD were identified by random forest algorithm.Results A total of 532 differential metabolites were detected by metabonomics analysis,and 5 biomarkers were screened by random forest algorithm,namely quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol.Among them,the main metabolic pathways were phenylalanine metabolism,primary bile acid biosynthesis,ascorbic acid and aldonate metabolism,pentose and glucuronate interconversion,tryptophan metabolism,drug metabolism-cytochrome P450,porphyrin and chlorophyll metabolism.Conclusion Many metabolic pathways in patients with early POCD after heart valve replacement under extracorporeal circulation have changed before operation,which may lead to the occurrence of early POCD.Quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol may be biomarkers for predicting early POCD.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

Transoral endoscopic thyroidectomy vestibular approach(TOETVA)has become a research hotspot because of its cosmetic advantages of no scar on the body surface,but there is still a risk of unique complications,among which mandibular sensory dysfunction is the most common,mainly related to mental nerve injury.This review focuses on the key anatomical structures closely related to the establishment of TOETVA space and the occurrence of complications.A deep understanding of the related anatomical structures of maxillofacial region and neck,especially the location and variation of mental nerve,the course of mandibular marginal branches of facial nerve,the level of mandibular muscles and the adjacent relationship of blood vessels,is the key to safely implement TOETVA and reduce postoperative complications.Preoperative image evaluation,intraoperative precise anatomy,following the design of safe area and improving the operation method in the future will contribute to the further promotion of this operation.

Objective:To analyze the preferences of medical staff in TCM hospitals in Beijing regarding the achievements transformation of hospital traditional Chinese medicine preparations,and to provide a reference for formulating incentive policies.Methods:233 medical staff from five TCM hospitals in Beijing were taken as the research objects,and surveyed with a questionnaire designed based on the discrete choice experiment(DCE).Mixed logit models and latent class models were then used to analyze their transformation preferences.Results:The mixed Logit model revealed that seven key attributes significantly influenced medical staff's preferences for the achievements transformation of traditional Chinese medicine preparations(P<0.05).Latent class analysis identified three distinct preference groups among respondents:an organization-dependent group(27.0%),a pro-transformation group(61.4%),and a conservative group(11.6%).Conclusions:Medical Staff preferred transformation conditions that increased monthly income;utilized"human use+re-experimentation";involved the hospital's achievements transformation department as the entity;were funded by the hospital;offered a 70%profit share;enabled promotion three years earlier,and assigned patents to the hospital.The study recommends implementing diverse incentive measures and developing differentiated strategies tailored to the distinct Medical Staff categories to facilitate the transformation of hospital traditional Chinese medicine preparations into new drugs.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

Objective To investigate the effect and mechanism of the signal transducer and activator of transcription 3 (STAT3) inhibitor Stattic on the rejection of mouse heart allograft. Methods BALB/c mice (donors) were used to transplant skin onto C57BL/6 mice (recipients). Four weeks later, memory CD4⁺ T cells (CD4⁺Tm) were isolated from the recipient mice's spleens. Mixed lymphocyte reaction experiment was conducted with C57BL/6 mouse splenocytes and CD4⁺Tm, and the EdU method was used to detect the effect of Stattic on CD4⁺Tm cell proliferation. A C57BL/6 mouse heart transplant (HTx) model was constructed, and the experiment was divided into four groups: Non-HTx group, HTx group, Tm/HTx group, and Tm/HTx+Stattic group. The survival of heart allografts in mice was observed daily. Hematoxylin-eosin staining was used to observe the histopathology of the heart allografts. Real-time fluorescent quantitative polymerase chain reaction was used to detect the expression levels of interferon (IFN)-γ, interleukin (IL)-2, IL-10, and transforming growth factor-β1 (TGF-β1) messenger RNA (mRNA) in the heart allografts. Enzyme-linked immunosorbent assay was used to detect the levels of IFN-γ, IL-2, IL-10, and TGF-β1 in the serum. Flow cytometry was used to detect the levels of CD4⁺Tm (CD4⁺CD44⁺CD62L⁺) in splenic lymphocytes. And Western blotting was used to detect the expression levels of STAT3 and p-STAT3 proteins in the heart allografts. Results When the concentration of Stattic exceeded 2.5 μmol/L, it could inhibit the proliferation of CD4⁺Tm cells. Compared with the HTx group, the Tm/HTx group showed shorter survival time of heart grafts, more severe histopathological damage, increased serum IFN-γ and IL-2 levels, decreased IL-10 and TGF-β1 levels, increased relative expression of IFN-γ and IL-2 mRNA, decreased relative expression of IL-10 and TGF-β1 mRNA in the heart allografts, increased proportion of CD4⁺Tm in splenic lymphocytes, and increased p-STAT3/STAT3 ratio in the heart allografts (all P<0.05). Compared with the Tm/HTx group, the Tm/HTx+Stattic group showed longer survival time of heart grafts, less severe histopathological damage, decreased serum IFN-γ and IL-2 levels, increased IL-10 and TGF-β1 levels, decreased relative expression of IFN-γ and IL-2 mRNA, increased relative expression of IL-10 and TGF-β1 mRNA in the heart allografts, decreased proportion of CD4⁺Tm in splenic lymphocytes, and decreased p-STAT3/STAT3 ratio in the heart allografts (all P<0.05). Conclusions Stattic may prolong the survival time of mouse heart allografts, and its mechanism may be related to the inhibition of CD4⁺Tm- mediated acute rejection.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.