Sepsis is one of the common severe diseases caused by the dysregulated host response to infection， which seriously threatens the life and health of human beings all over the world. The incidence and mortality of the disease are extremely high， and it has always been an urgent problem to be solved in the field of acute and critical diseases. At present， anti-infection， fluid resuscitation， mechanical ventilation and other programs are most used in clinic to treat sepsis， but their poor prognosis and high cost and other issues remain to be resolved. Therefore， it is necessary to explore a new， efficient， safe and inexpensive drug and treatment model at this stage. The treatment of traditional Chinese medicine （TCM） is based on syndrome differentiation and holistic concept. It can effectively regulate the progression of sepsis， maintain the homeostasis of the body， and has fewer adverse reactions. It has achieved good clinical results. In recent years， a large number of studies have shown that TCM can reduce the inflammatory response by regulating the Toll-like receptor 4（TLR4） signaling pathway， thereby reducing the severity and mortality of sepsis patients. However， there is still a lack of systematic exposition of TCM regulating TLR4 signaling pathway in the treatment of sepsis. Therefore， this article summarizes the relationship between TLR4 signaling pathway and sepsis and the mechanism of TCM in the disease by searching and consulting relevant literature in recent years. It is found that some Chinese medicine monomers and active ingredients， Chinese medicine compounds and Chinese medicine preparations can effectively reduce systemic inflammatory response， repair organ damage and improve the prognosis of sepsis by inhibiting the activation of TLR4 signaling pathway. However， due to various limitations， some studies have directly focused on the differential expression and function of TLR4， ignoring the downstream molecular expression and phenotypic effects of TLR4. The alternative mechanism， relationship and specific molecular mechanism of the pathway are still unclear. There are problems such as unclear pharmacokinetics and unclear mechanism in the pro- and anti-inflammatory balance， which need to be further studied and explored in order to provide new ideas for the potential treatment and drug development for sepsis.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

BACKGROUND: LY01005 (Goserelin acetate sustained-release microsphere injection) is a modified gonadotropin-releasing hormone (GnRH) agonist injected monthly. This phase III trial study aimed to evaluated the efficacy and safety of LY01005 in Chinese patients with prostate cancer. METHODS: We conducted a randomized controlled, open-label, non-inferiority trial across 49 sites in China. This study included 290 patients with prostate cancer who received either LY01005 or goserelin implants every 28 days for three injections. The primary efficacy endpoints were the percentage of patients with testosterone suppression ≤50 ng/dL at day 29 and the cumulative probability of testosterone ≤50 ng/dL from day 29 to 85. Non-inferiority was prespecified at a margin of -10%. Secondary endpoints included significant castration (≤20 ng/dL), testosterone surge within 72 h following repeated dosing, and changes in luteinizing hormone, follicle-stimulating hormone, and prostate specific antigen levels. RESULTS: On day 29, in the LY01005 and goserelin implant groups, testosterone concentrations fell below medical-castration levels in 99.3% (142/143) and 100% (140/140) of patients, respectively, with a difference of -0.7% (95% confidence interval [CI], -3.9% to 2.0%) between the two groups. The cumulative probabilities of maintaining castration from days 29 to 85 were 99.3% and 97.8%, respectively, with a between-group difference of 1.5% (95% CI, -1.3% to 4.4%). Both results met the criterion for non-inferiority. Secondary endpoints were similar between groups. Both treatments were well-tolerated. LY01005 was associated with fewer injection-site reactions than the goserelin implant (0% vs . 1.4% [2/145]). CONCLUSION: LY01005 is as effective as goserelin implants in reducing testosterone to castration levels, with a similar safety profile. TRIAL REGISTRATION ClinicalTrials.gov, NCT04563936.
Humans ; Male ; Antineoplastic Agents, Hormonal/therapeutic use* ; East Asian People ; Gonadotropin-Releasing Hormone/agonists* ; Goserelin/therapeutic use* ; Prostate-Specific Antigen ; Prostatic Neoplasms/drug therapy* ; Testosterone

The treatment of patients with diabetes mellitus, which is characterized by defective insulin secretion and/or the inability of tissues to respond to insulin, has been studied for decades. Many studies have focused on the use of incretin-based hypoglycemic agents in treating type 2 diabetes mellitus (T2DM). These drugs are classified as GLP-1 receptor agonists, which mimic the function of GLP-1, and DPP-4 inhibitors, which avoid GLP-1 degradation. Many incretin-based hypoglycemic agents have been approved and are widely used, and their physiological disposition and structural characteristics are crucial in the discovery of more effective drugs and provide guidance for clinical treatment of T2DM. Here, we summarize the functional mechanisms and other information of the drugs that are currently approved or under research for T2DM treatment. In addition, their physiological disposition, including metabolism, excretion, and potential drug-drug interactions, is thoroughly reviewed. We also discuss similarities and differences in metabolism and excretion between GLP-1 receptor agonists and DPP-4 inhibitors. This review may facilitate clinical decision making based on patients' physical conditions and the avoidance of drug-drug interactions. Moreover, the identification and development of novel drugs with appropriate physiological dispositions might be inspired.

Objective: Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear. Methods: A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio ( Results: Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks. Conclusion Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.
Adult ; Aged ; COVID-19/virology* ; China/epidemiology* ; Comorbidity ; Female ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Severity of Illness Index ; Treatment Outcome

Objective To evaluate the clinical application of a three-dimensional (3D) printing personalized guide for medial open wedge high tibial osteotomy(MOWHTO) in the treatment of knee varus osteoarthritis.Methods A retrospective study was conducted of the 16 patients with knee varus osteoarthritis who had been treated at Department of Orthopaedics,Zhengzhou Central Hospital of Zhengzhou University from January 2016 to January 2017.They were 6 men and 10 women,aged from 49 to 65 years (mean,55.8 years).Bilateral knees were involved in 2 cases and a unilateral knee was involved in 14 cases.Their disease duration ranged from one to 12 years (mean,5.3 years).A personalized guide for MOWHTO was designed and manufactured by 3D printing for every patient preoperatively.All the patients underwent knee arthroscopy before osteotomy which was assisted by the personalized guide.The femorotibial angle (FTA),medial proximal tibial angle (MPTA),weight bearing line (WBL),posterior tibial slope (PTSA) and the patellofemoral height Insall-Salvati index (IS index) were measured on their X-ray radiographs preoperatively and 6 months postoperatively.The Hospital for Special Surgery (HSS) score and visual analogue scale (VAS) were used to evaluate the outcomes at the final follow-ups.Results All surgeries were successful.The 16 patients were followed up for 6 to 12 months (mean,9.1 months).The FTA,MPTA,WBL and IS index at postoperative 6 months were significantly improved than the preoperative values (P ＜ 0.05).There was no significant difference between postoperative PTSA and preoperative PTSA (P =0.990).The mean VAS scores for the 16 patients at the final follow-ups were 0.8 ± 0.7 peints,significantly better than the preoperative ones (4.2 ± 0.9 points) (P ＜ 0.05);their mean postoperative HSS scores (89.3 ± 6.7 points) were also significantly improved than the preoperative ones (61.9 ± 10.5 points) (P ＜ 0.05).According to the HSS scores at the final follow-ups,the surgical outcomes were excellent in 14 knees,good in 3 and fair in one.Conclusion A 3D printed osteotomy guide can be used to perform precise osteotomy in MOWHTO for knee varus osteoarthritis,leading to effective correction of the alignment of the lower limb and good short-term surgical outcomes.

Objective To investigate the effects of exogenous hydrogen sulfide (H2S) on injury of rat hippocampus neurons induced by oxygen-glucose deprivation and restoration (OGD/R) and explore its mechanism.Methods Hippocampus neurons were isolated from embryonic day 16-18 (E16-18) rat embryos.Hippocampus was immediately removed and digested with 0.25％ trypsin.The neurons were isolated and cultured at 37 ℃ for 7 days and neuron-specific enolase (NSE) was detected by immunohistochemical staining method to identify neurons.At 8th day,the neurons were placed in deoxygenated glucose-free medium and exposed to 95％ N2-5％ CO2 in an air tight chamber for 1 hour,and then replaced the glucose-free medium with original medium,and returned the cultures to a standard incubator in 5％ CO2 at 37 ℃ and incubated for another 24 h.The neurons were divided into 3 groups:group Ⅰ control; group Ⅱ OGD/R,and group ⅢOGD + NaHS,the latter was further divided into 5 subgroups:groups Ⅲ1-5 with 25,50,100,200,400 μmol/L NaHS added,respectively.Then cell viability was quantified by MTT method,the level of lactate dehydrogenase (LDH) were detected,apoptosis was measured by Annexin V FITC/PI Apoptosis Kits,and RT-PCR was used to assay HIF-1 α mRNA in neurons in all groups.Results Compared with control group,the LDH level,apoptosis and expression of HIF-1α mRNA in group Ⅱ were significantly increased,the cell viability was significantly decreased (P ＜ 0.01).There were no significant differences in the LDH level,apoptosis and expression of HIF-1 α mRNA and the cell viability between group Ⅱ and group Ⅲ1 (P ＞ 0.05).Compared with group Ⅱ,the LDH level,apoptosis and expression of HIF-1α mRNA in group Ⅲ2-4 were significantly increased,the cell viability was significantly increased (P ＜ 0.01).Compared with group Ⅱ,the LDH level,apoptosis and expression of HIF-1 α mRNA in the group Ⅲ 5 were significantly decreased,the cell viability was significantly decreased (P ＜ 0.01).Conclusions H2S of low concentration has no significant effects on injury of rat hippocampus neurons induced by OGD/R.H2S of moderate doses can protect rat hippocampus neurons from OGD/R injury and H2S of high concentration can aggravate injury.The expression of HIF-1α mRNA in rat hippocampus neurons was increased after OGD/R,and the protective role of H2S is associated with increase in the expression of HIF-1α mRNA.