In order to investigate whether the effect of Yuxuebi Tablets on the peripheral and central inflammation resolution of mice with inflammatory pain is related to their regulation of G protein-coupled receptor 37(GPR37), an inflammatory pain model was established by injecting complete Freund's adjuvant(CFA) into the paws of mice, with a sham-operated group receiving a similar volume of normal saline. The mice were assigned randomly to the sham-operated group, model group, ibuprofen group(91 mg·kg~(-1)), and low-, medium-, and high-dose groups of Yuxuebi Tablets(60, 120, and 240 mg·kg~(-1)). The drug was administered orally from days 1 to 19 after modeling. Von Frey method and the hot plate test were used to detect mechanical pain thresholds and heat hyperalgesia. The levels of interleukin-10(IL-10) and transforming growth factor-beta(TGF-β) in the spinal cord were quantified using enzyme-linked immunosorbent assay(ELISA), and the mRNA and protein expression of GPR37 in the spinal cord was measured by real-time quantitative reverse transcription PCR(qRT-PCR) and Western blot. Additionally, immunofluorescence was used to detect the expression of macrosialin antigen(CD68), mannose receptor(MRC1 or CD206), and GPR37 in dorsal root ganglia, as well as the expression of calcium-binding adapter molecule 1(IBA1), CD206, and GPR37 in the dorsal horn of the spinal cord. The results showed that compared with those of the sham-operated group, the mechanical pain thresholds and hot withdrawal latency of the model group significantly declined, and the expression of CD68 in the dorsal root ganglia and the expression of IBA1 in the dorsal horn of the spinal cord significantly increased. The expression of CD206 and GPR37 significantly decreased in the dorsal root ganglion and dorsal horn of the spinal cord, and IL-10 and TGF-β levels in the spinal cord were significantly decreased. Compared with those of the model group, the mechanical pain thresholds and hot withdrawal latency of the high-dose group of Yuxuebi Tablets significantly increased, and the expression of CD68 in the dorsal root ganglion and IBA1 in the dorsal horn of the spinal cord significantly decreased. The expression of CD206 and GPR37 in the dorsal root ganglion and dorsal horn of the spinal cord significantly increased, as well as IL-10 and TGF-β levels in the spinal cord. These findings indicated that Yuxuebi Tablets may reduce macrophage(microglial) infiltration and foster M2 macrophage polarization by enhancing GPR37 expression in the dorsal root ganglia and dorsal horn of the spinal cord of CFA-induced mice, so as to improve IL-10 and TGF-β levels, promote resolution of both peripheral and central inflammation, and play analgesic effects.
Inflammation/genetics* ; Pain/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Mice ; Freund's Adjuvant/pharmacology* ; Ibuprofen ; Pain Threshold/drug effects* ; Hyperalgesia/genetics* ; Ganglia, Spinal ; Interleukin-10/genetics* ; Transforming Growth Factor beta/genetics* ; Reverse Transcriptase Polymerase Chain Reaction ; Tablets ; Receptors, G-Protein-Coupled

To investigate the effects of Biyan Jiedu Capsules on the proliferation and apoptosis of nasopharyngeal carcinoma cells and their molecular mechanism, nasopharyngeal carcinoma cells CNE1 and CNE2 were used. They were divided into control group(30% blank serum medium), low-(10% drug-containing serum + 20% blank serum medium), medium-(20% drug-containing serum + 10% blank serum medium), and high-(30% drug-containing serum medium) concentration group of Biyan Jiedu Capsules according to in vitro experiment. After 24 h of intervention, the effects of Biyan Jiedu Capsules on the proliferation of CNE1 and CNE2 were detected by CCK-8 assay, clonal formation experiment, and EdU staining. The effect of Biyan Jiedu Capsules on apoptosis of CNE1 and CNE2 was detected by flow cytometry. Western blot was used to detect the effect of Biyan Jiedu Capsules on the expression of X-linked apoptosis inhibitor protein(XIAP), survivin, proliferating cell nuclear antigen(PCNA), and PI3K/Akt pathway-related proteins in CNE1 and CNE2. The results showed that compared with the control group, the survival rate of CNE1 and CNE2 in the medium and high concentration groups of Biyan Jiedu Capsules could be decreased in a concentration-dependent way(P<0.05, P<0.01). At the same time, EdU staining and clonal formation experiments showed that the proliferation of CNE1 and CNE2 was significantly inhibited in the medium and high concentration groups of Biyan Jiedu Capsules(P<0.05, P<0.01). Flow cytometry showed that the apoptosis rate of CNE1 and CNE2 was significantly increased in all concentration groups of Biyan Jiedu Capsules(P<0.01), and the apoptosis rate was concentration-dependent. Western blot showed that the expressions of XIAP, survivin, PCNA, p-PI3K, and p-Akt in all concentration groups of Biyan Jiedu Capsules were significantly down-regulated(P<0.05, P<0.01). In conclusion, Biyan Jiedu Capsules can inhibit the proliferation and induce apoptosis of nasopharyngeal carcinoma cells possibly by down-regulating the PI3K/Akt signaling pathway.
Humans ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Nasopharyngeal Carcinoma ; Nasopharyngeal Neoplasms/physiopathology* ; Proto-Oncogene Proteins c-akt/genetics* ; Cell Line, Tumor ; Drugs, Chinese Herbal/pharmacology* ; Phosphatidylinositol 3-Kinases/genetics* ; Signal Transduction/drug effects* ; Capsules ; Carcinoma/drug therapy*

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

OBJECTIVE: To investigate the characteristics of gut microbiota changes in patients with acute myeloid leukemia (AML) undergoing induction chemotherapy and to explore the relationship between infectious complications and gut microbiota. METHODS: Fecal samples were collected from 37 newly diagnosed AML patients at four time points: before induction chemotherapy, during chemotherapy, during the neutropenic phase, and during the recovery phase. Metagenomic sequencing was used to analyze the dynamic changes in gut microbiota. Correlation analyses were conducted to assess the relationship between changes in gut microbiota and the occurrence of infectious complications. RESULTS: During chemotherapy, the gut microbiota α-diversity (Shannon index) of AML patients exhibited significant fluctuations. Specifically, the diversity decreased significantly during induction chemotherapy, further declined during the neutropenic phase (P < 0.05, compared to baseline), and gradually recovered during the recovery phase, though not fully returning to baseline levels.The abundances of beneficial bacteria, such as Firmicutes and Bacteroidetes, gradually decreased during chemotherapy, whereas the abundances of opportunistic pathogens, including Enterococcus, Klebsiella, and Escherichia coli, progressively increased.Analysis of the dynamic changes in gut microbiota of seven patients with bloodstream infections revealed that the bloodstream infection pathogens could be detected in the gut microbiota of the corresponding patients, with their abundance gradually increasing during the course of infection. This finding suggests that bloodstream infections may be associated with opportunistic pathogens originating from the gut microbiota.Compared to non-infected patients, the baseline samples of infected patients showed a significantly lower relative abundance of Bacteroidetes (P < 0.05). Regression analysis indicated that Bacteroidetes abundance is an independent predictive factor for infectious complications (P < 0.05, OR =13.143). CONCLUSION During induction chemotherapy in AML patients, gut microbiota α-diversity fluctuates significantly, and the abundance of opportunistic pathogens increase, which may be associated with bloodstream infections. Patients with lower baseline Bacteroidetes abundance are more prone to infections, and its abundance can serve as an independent predictor of infectious complications.
Humans ; Gastrointestinal Microbiome ; Leukemia, Myeloid, Acute/microbiology* ; Induction Chemotherapy ; Feces/microbiology* ; Male ; Female ; Middle Aged

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

DNA origami is a powerful technique for generating nanostructures with dynamic properties and intelligent controllability. The precise geometric shapes, high programmability, and excellent biocompatibility make DNA origami nanostructures an emerging drug delivery vehicle. The shape, size of the carrier material, as well as the loading and release of drugs are important factors affecting the bioavailability of drugs. This paper focuses on the controllable design of DNA origami nanostructures, efficient drug loading, and intelligent drug release. It summarizes the cutting-edge applications of DNA origami technology in biomedicine, and discusses areas where researchers can contribute to further advancing the clinical application of DNA origami carriers.

Objective To investigate the influences of circular RNA(circRNA)DCUN1D4 on the proliferation,apoptosis and immune escape of lung cancer cells by regulating the microRNA(miR)-18a-5p/fructose-1,6-bisphosphatase 1(FBP1)axis.Methods The human lung cancer cell lines H1975,H1650,A549 and SPCA-1 and human normal lung epidermal cells HPL-1 were selected,qRT-PCR was used to detect the expression levels of circDCUN1D4,miR-18a-5p and FBP1 mRNA in various cells.A549 cells in logarithmic growth phase were selected and divided into the blank group,circDCUN1D4 overexpression plasmid(circDCUN1D4)group,overexpression plasmid negative control(NC)group,circDCUN1D4+miR-18a-5p mimics negative control(circDCUN1D4+mimics NC)group,and circDCUN1D4+miR-18a-5p mimics group.The cell viability of each group was detected by CCK-8 method,the cell apoptosis was detected by flow cytometry,the expression levels of circDCUN1D4,miR-18a-5p and FBP1 mRNA of cells in each group were detected by qRT-PCR,the expression levels of FBP1,caspase-3,Ki67,proliferating cell nuclear antigen(PCNA)and programmed death ligand-1(PD-L1)were detected by Western blot,the targeting relationships of miR-18a-5p with circDCUN1D4 and FBP1 were verified by dual luciferase assay.Results Compared with HPL-1 cells,the mRNA expressions of circDCUN1D4 and FBP1 were significantly decreased(P<0.05),and the expression of miR-18a-5p was significantly increased(P<0.05).miR-18a-5p had targeting relationships with circDCUN1D4 and FBP1,respectively.Compared with the blank group and NC group,the OD values at 24 hours and 48 hours,and the expressions of Ki67,PCNA,miR-18a-5p and PD-L1 of cells in the circDCUN1D4 group were significantly decreased(P<0.05),and the apoptosis rate,and the expressions of circDCUN1D4,FBP1 and caspase-3 were significantly increased(P<0.05).Overexpression of miR-18a-5p reversed the inhibitory effect of circDCUN1D4 on the malignant behavior of lung cancer cells(P<0.05).Conclusion Overexpression of circDCUN1D4 can promote lung cancer cell apoptosis,inhibit lung cancer cell proliferation and immune escape,and its mechanism may be related to the regulation of miR-18a-5p/FBP1 axis.

Purpose::In patients with heatstroke, disseminated intravascular coagulation (DIC) is associated with greater risk of in-hospital mortality. However, time-consuming assays or a complex diagnostic system may delay immediate treatment. Therefore, the present study proposes a new heatstroke-induced coagulopathy (HIC) score in patients with heat illness as an early warning indicator for DIC.Methods::This retrospective study enrolled patients with heat illness in 24 Chinese hospitals from March 2021 to May 2022. Patients under 18 years old, with a congenital clotting disorder or liver disease, or using anticoagulants were excluded. Data were collected on demographic characteristics, routine blood tests, conventional coagulation assays and biochemical indexes. The risk factors related to coagulation function in heatstroke were identified by regression analysis, and used to construct a scoring system for HIC. The data of patients who met the diagnostic criteria for HIC and International Society on Thrombosis and Haemostasis defined-DIC were analyzed. All statistical analyses were performed using SPSS 26.0.Results::The final analysis included 302 patients with heat illness, of whom 131 (43.4%) suffered from heatstroke, including 7 death (5.3%). Core temperature ( OR = 1.681, 95% CI 1.291 - 2.189, p < 0.001), prothrombin time ( OR = 1.427, 95% CI 1.175 - 1.733, p < 0.001) and D-dimer ( OR = 1.242, 95% CI 1.049 - 1.471, p = 0.012) were independent risk factors for heatstroke, and therefore used to construct an HIC scoring system because of their close relation with abnormal coagulation. A total score ≥ 3 indicated HIC, and HIC scores correlated with the score for International Society of Thrombosis and Hemostasis-DIC ( r = 0.8848, p < 0.001). The incidence of HIC (27.5%) was higher than that of DIC (11.2%) in all of 131 heatstroke patients. Meanwhile, the mortality rate of HIC (19.4%) was lower than that of DIC (46.7%). When HIC developed into DIC, parameters of coagulation dysfunction changed significantly: platelet count decreased, D-dimer level rose, and prothrombin time and activated partial thromboplastin time prolonged ( p < 0.05). Conclusions::The newly proposed HIC score may provide a valuable tool for early detection of HIC and prompt initiation of treatment.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.