Drug administration via hollow microneedles (HMN) have the advantages of painlessness, avoidance of first-pass effect, capability of sustained infusion, and no need for professional personnel operation. In addition, HMN can also be applied in the fields of body fluid extraction and biosensors, showing broad application prospects. However, traditional manufacturing technologies cannot meet the demand for low-cost mass production of HMN, limiting its widespread application. This paper reviews the main manufacturing technologies used for HMN in recent years, which include photolithography and etching, laser etching, sputtering and electroplating, micro-molding, three-dimensional (3D) printing and drawing lithography. It further analyzes the characteristics and limitations of existing manufacturing technologies and points out that the combination of various manufacturing technologies can improve production efficiency to a certain extent. In addition, this paper looks forward to the future trends of HMN manufacturing technology and proposes possible directions for its development. In conclusion, it is expected that this review can provide new ideas and references for follow-up research.
Printing, Three-Dimensional ; Needles ; Humans ; Drug Delivery Systems/methods* ; Equipment Design ; Microinjections/methods*

Probiotics play a crucial role in colon cancer treatment by metabolizing prebiotics to generate short-chain fatty acids (SCFAs). Colon cancer patients are frequently propositioned to supplement with probiotics to enhance the conversion and utilization of prebiotics. Nevertheless, the delivery and colonization of probiotics is hindered by the harsh conditions of gastrointestinal tract (GIT). Here, we devised a straightforward yet potent modified prebiotic-based "shield" (Gelatin-Inulin, GI), employing dietary inulin and natural polymer gelatin crosslinked via hydrogen bonding for enveloping Lactobacillus reuteri (Lr) to formulate synbiotic hydrogel capsules (Lr@Gl). The GI "shield" serves as a dynamic barrier, augmenting the resistance of Lr to gastric acid and facilitating its bioactivity and adherence in the GIT, synergizing with Lr to elicit an anti-tumor effect. Simultaneously, Lr@GI demonstrates anti-tumor effects by depleting glutathione to release reactive oxygen species, accompanied by the activation of NLRP3 (NOD-like receptor family pyrin domain containing 3), and the induction M1 macrophage polarization. Furthermore, Lr@GI can not only promote the recovery of intestinal barrier but also regulate intestinal flora, promoting the production of SCFAs and further exerting anti-tumor effect. Crucially, Lr@GI also potentiates the anti-tumor effect of 5-Fluorouracil. The construction and synergistic anti-tumor mechanism of synbiotic hydrogel capsules system provide valuable insights for gut microbial tumor therapy.

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

The peeled stems of Syringa pinnatifolia(SP) is a representative Mongolian folk medicine with the effects of anti-depression, heat clearance, pain relief, and respiration improvement. It has been clinically used for the treatment of coronary heart disease, insomnia, asthma, and other cardiopulmonary diseases. As part of the systematic study on pharmacological substances of SP, 11 new sesquiterpenoids were isolated from the terpene-containing fractions of the ethanol extract of SP by liquid chromatography-mass spectrometry(LC-MS) and proton nuclear magnetic resonance(~1H-NMR) guided isolation methods. The planar structures of the sesquiterpenoids were identified by MS, 1D NMR, and 2D NMR data analysis, and were named pinnatanoids C and D(1 and 2), and alashanoids T-ZI(3-11), respectively. The structure types of the sesquiterpenoids included pinnatane, humulane, seco-humulane, guaiane, carryophyllane, seco-erimolphane, isodaucane, and other types. However, limited to the low content of compounds, the existence of multiple chiral centers, the flexibility of the structure, or lack of ultraviolet absorption, the stereoscopic configuration remained unresolved. The discovery of various sesquiterpenoids enriches the understanding of the chemical composition of the genus and species and provides references for further analysis of pharmacological substances of SP.
Syringa ; Sesquiterpenes ; Terpenes ; Asthma ; Chromatography, Liquid

Objective To investigate the influencing factors for prognosis in patients with ST-segment elevation myocardial infarction(STEMI)with cardiogenic shock(CS)treated with extracorporeal membrane oxygenation(ECMO)combined with percutaneous coronary intervention(PCI).Methods The clinical data of patients with STEMI and CS who received ECMO combined with PCI treatment in the cardiology department of our hospital from May 2019 to July 2023 were retrospectively analyzed.According to the clinical outcome,the patients were divided into death group and survival group.The clinical data of the two groups was compared.Results The study analyzed a total of 37 patients,including 34 males with an average age of(52.4±11.7)years.There were 15 survivors and 22 deaths,with a survival rate of 40.5％.Compared with the death group,the survival group had higher systolic blood pressure[(100.6±17.7)mmHg vs.(84.6±22.0)mmHg,P=0.025]and diastolic blood pressure[(64.5±11.8)mmHg vs.(54.3±16.0)mmHg,P=0.043]at admission,and longer time from shock to ECMO support[4.0(3.0,10.0)h vs.2.8(1.9,5.1)h,P=0.048]and shorter time from ECMO support to passage of guide wire[1.5(0.5,3.0)h vs.3.8(2.3,7.0)h,P=0.008].The proportion of thrombolysis in myocardial infarction(TIMI)blood flow classification reaching level Ⅲ in the first frame is higher[9(60.0％)vs.5(22.7％),P=0.038].The level of serum alanine aminotransferase[261.8(100.1,944.9)U/L vs.106.6(27.4,193.3)U/L,P=0.033]and shorter time from aspartate aminotransferase[753.6(432.7,1533.0)U/L vs.244.7(113.7,594.3)U/L,P=0.009]in the death group are significantly higher than that in the survival group.Conclusions This study suggests that the time from ECMO support and ECMO support to passage of guide wire,and the first frame TIMI blood flow grading are important factors affecting the prognosis of STEMI patients with CS treated with ECMO combined with PCI.

Purpose: We aimed to analyze the optimal timing of enteral nutrition (EN) in the treatment of sepsis and its effect on sepsis-associated acute kidney injury (SA-AKI.) Materials and Methods: The MIMIC-III database was employed to identify patients with sepsis who had received EN. With AKI as the primary outcome variable, receiver operating characteristic (ROC) curves were utilized to calculate the optimal cut-off time of early EN (EEN). Propensity score matching (PSM) was employed to control confounding effects. Logistic regressions and propensity score-based inverse probability of treatment weighting were utilized to assess the robustness of our findings. Comparisons within the EEN group were performed. Results: 2364 patients were included in our study. With 53 hours after intensive care units (ICU) admission as the cut-off time of EEN according to the ROC curve, 1212 patients were assigned to the EEN group and the other 1152 to the delayed EN group. The risk of SA-AKI was reduced in the EEN group (odds ratio 0.319, 95% confidence interval 0.245–0.413, p<0.001). The EEN patients received fewer volumes (mL) of intravenous fluid (IVF) during their ICU stay (3750 mL vs. 5513.23 mL, p<0.001). The mediating effect of IVF was significant (p<0.001 for the average causal mediation effect). No significant differences were found within the EEN group (0–48 hours vs. 48–53 hours), except that patients initiating EN within 48 hours spent fewer days in ICU and hospital. Conclusion EEN is associated with decreased risk of SA-AKI, and this beneficial effect may be proportionally mediated by IVF volume.

Objective:To evaluate artificial intelligence constructed by deep convolutional neural network (DCNN) for the site identification in upper gastrointestinal endoscopy.Methods:A total of 21 310 images of esophagogastroduodenoscopy from the Cancer Hospital of Chinese Academy of Medical Sciences from January 2019 to June 2021 were collected. A total of 19 191 images of them were used to construct site identification model, and the remaining 2 119 images were used for verification. The performance differences of two models constructed by DCCN in the identification of 30 sites of the upper digestive tract were compared. One model was the traditional ResNetV2 model constructed by Inception-ResNetV2 (ResNetV2), the other was a hybrid neural network RESENet model constructed by Inception-ResNetV2 and Squeeze-Excitation Networks (RESENet). The main indices were the accuracy, the sensitivity, the specificity, positive predictive value (PPV) and negative predictive value (NPV).Results:The accuracy, the sensitivity, the specificity, PPV and NPV of ResNetV2 model in the identification of 30 sites of the upper digestive tract were 94.62%-99.10%, 30.61%-100.00%, 96.07%-99.56%, 42.26%-86.44% and 97.13%-99.75%, respectively. The corresponding values of RESENet model were 98.08%-99.95%, 92.86%-100.00%, 98.51%-100.00%, 74.51%-100.00% and 98.85%-100.00%, respectively. The mean accuracy, mean sensitivity, mean specificity, mean PPV and mean NPV of ResNetV2 model were 97.60%, 75.58%, 98.75%, 63.44% and 98.76%, respectively. The corresponding values of RESENet model were 99.34% ( P<0.001), 99.57% ( P<0.001), 99.66% ( P<0.001), 90.20% ( P<0.001) and 99.66% ( P<0.001). Conclusion:Compared with the traditional ResNetV2 model, the artificial intelligence-assisted site identification model constructed by RESENNet, a hybrid neural network, shows significantly improved performance. This model can be used to monitor the integrity of the esophagogastroduodenoscopic procedures and is expected to become an important assistant for standardizing and improving quality of the procedures, as well as an significant tool for quality control of esophagogastroduodenoscopy.

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

Objective: To construct the diagnostic model of superficial esophageal squamous cell carcinoma (ESCC) and precancerous lesions in endoscopic images based on the YOLOv5l model by using deep learning method of artificial intelligence to improve the diagnosis of early ESCC and precancerous lesions under endoscopy. Methods: 13, 009 endoscopic esophageal images of white light imaging (WLI), narrow band imaging (NBI) and lugol chromoendoscopy (LCE) were collected from June 2019 to July 2021 from 1, 126 patients at the Cancer Hospital, Chinese Academy of Medical Sciences, including low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, ESCC limited to the mucosal layer, benign esophageal lesions and normal esophagus. By computerized random function method, the images were divided into a training set (11, 547 images from 1, 025 patients) and a validation set (1, 462 images from 101 patients). The YOLOv5l model was trained and constructed with the training set, and the model was validated with the validation set, while the validation set was diagnosed by two senior and two junior endoscopists, respectively, to compare the diagnostic results of YOLOv5l model and those of the endoscopists. Results: In the validation set, the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the YOLOv5l model in diagnosing early ESCC and precancerous lesions in the WLI, NBI and LCE modes were 96.9%, 87.9%, 98.3%, 88.8%, 98.1%, and 98.6%, 89.3%, 99.5%, 94.4%, 98.2%, and 93.0%, 77.5%, 98.0%, 92.6%, 93.1%, respectively. The accuracy in the NBI model was higher than that in the WLI model (P<0.05) and lower than that in the LCE model (P<0.05). The diagnostic accuracies of YOLOv5l model in the WLI, NBI and LCE modes for the early ESCC and precancerous lesions were similar to those of the 2 senior endoscopists (96.9%, 98.8%, 94.3%, and 97.5%, 99.6%, 91.9%, respectively; P>0.05), but significantly higher than those of the 2 junior endoscopists (84.7%, 92.9%, 81.6% and 88.3%, 91.9%, 81.2%, respectively; P<0.05). Conclusion: The constructed YOLOv5l model has high accuracy in diagnosing early ESCC and precancerous lesions in endoscopic WLI, NBI and LCE modes, which can assist junior endoscopists to improve diagnosis and reduce missed diagnoses.
Artificial Intelligence ; Endoscopy/methods* ; Esophageal Neoplasms/pathology* ; Esophageal Squamous Cell Carcinoma/diagnostic imaging* ; Humans ; Narrow Band Imaging ; Precancerous Conditions/diagnostic imaging* ; Sensitivity and Specificity