Isoliquiritigenin (ISL) is a chalcone compound isolated from licorice, known for its anti-diabetic, anti-cancer, and antioxidant properties. Our previous study has demonstrated that ISL effectively lowers blood glucose levels in type 2 diabetes mellitus (T2DM) mice and improves disturbances in glucolipid and energy metabolism induced by T2DM. This study aims to further investigate the effects of ISL on alleviating abnormal endoplasmic reticulum stress (ERS) caused by T2DM and to elucidate its molecular mechanisms. In vivo experiments were conducted using 8-week-old SPF male C57BL/6J mice. The T2DM animal model was established by high-fat and high-sugar diet combined with intraperitoneal injections of streptozotocin (STZ), in compliance with the ethical guidelines set by the Animal Welfare Committee of Beijing University of Chinese Medicine (approval number: BUCM-2022021503-1134). In vitro experiments employed human liver cancer HepG2 cells, which were induced with tunicamycin (TM) to establish the ERS cell model. Transcriptomic sequencing was used to analyze changes in gene expression in the liver samples of T2DM mice following ISL treatment. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess the regulatory effects of ISL on key ERS genes. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), and immunofluorescence techniques were used to evaluate ISL's effects on ERS-related proteins. Results indicate that ISL significantly downregulates the expression of ERS-related genes, reduces the level of glucose-regulated protein 78 (GRP78), and inhibits the phosphorylation of protein kinase RNA-like endoplasmic reticulum kinase (PERK), thereby alleviating abnormal ERS induced by T2DM. Additionally, ISL increases the protein levels of insulin receptor substrate (IRS) 1 and IRS2 and enhances the phosphorylation of protein kinase B (Akt), thereby improving insulin sensitivity. In conclusion, ISL is able to alleviate T2DM associated symptoms by improving abnormal ERS and enhancing insulin sensitivity.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

ObjectiveTo investigate the effects of different concentrations of Shaoyaotang-containing serum on lipopolysaccharide （LPS）-induced inflammation of human colorectal adenocarcinoma （Caco-2） cells by inhibiting apoptosis via activating the tumor necrosis factor （TNF） receptor superfamily member 6 （Fas）/Fas ligand （FasL） pathway. MethodsCaco-2 cells were allocated into blank， model （LPS， 10 mg·L^-1）， Shaoyaotang-containing serum （5%， 10%， 15%， 20%）， and Fas inhibitor （KR-33493， 20 mmol·L^-1） groups. Except the blank group， the other groups were stimulated with 10 mg·L^-1 LPS for 24 h for the modeling of inflammation. After successful modeling， the blank， Fas inhibitor， and model groups were treated with blank serum， and the Shaoyaotang-containing serum groups were treated with the serum samples at corresponding concentrations for 24 h. The Fas inhibitor group was subjected to KR-33493 pretreatment for 1 h. Cell proliferation and viability were examined by the cell-counting kit-8 （CCK-8） method. The levels of interleukin （IL）-6， IL-1β， and TNF-α were measured by enzyme-linked immunosorbent assay. Apoptosis was detected by flow cytometry. The protein and mRNA levels of Fas， FasL， cysteinyl aspartate-specific proteinase （Caspase）-3， Caspase-9， B-cell lymphoma 2 （Bcl-2）， and Bcl-2-associated X protein （Bax） were determined by Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. ResultsCompared with the blank group， the model group presented a decrease in cell survival rate （P<0.01）. Compared with that in the model group， the cell survival rate showed no significant change in the 5% Shaoyaotang-containing serum group but increased in the 10%， 15%， and 20% Shaoyaotang-containing serum groups （P<0.01）. Since there was no statistical difference between the 5% Shaoyaotang-containing serum group and the model group， 10%， 15%， and 20% Shaoyaotang-containing sera were selected for the follow-up study. Compared with the blank group， the model group showed risen levels of IL-6， IL-1β， and TNF-α （P<0.01）， an increased apoptosis rate （P<0.01）， up-regulated protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.01）， and down-regulated protein and mRNA levels of Bcl-2 （P<0.01）. Compared with the model group， the Fas inhibitor group and the 10%， 15%， and 20% Shaoyaotang-containing serum groups showed declined levels of IL-6， IL-1β， and TNF-α （P<0.01）， decreased apoptosis rates （P<0.01）， down-regulated protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.05， P<0.01）， and up-regulated protein and mRNA levels of Bcl-2 （P<0.05， P<0.01）. In addition， the 15% and 20% Shaoyaotang-containing serum groups had lower levels of IL-6， IL-1β， and TNF-α （P<0.05， P<0.01）， lower apoptosis rates （P<0.05， P<0.01）， lower protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.05， P<0.01）， and higher protein and mRNA levels of Bcl-2 （P<0.05， P<0.01） than the 10% Shaoyaotang-containing serum group. ConclusionThe Shaoyaotang-containing serum can reduce the content of inflammatory factors in Caco-2 cells， down-regulate the protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax， and up-regulate the protein and mRNA levels of Bcl-2 under the intervention of LPS by regulating the Fas/FasL pathway and inhibiting the apoptosis of intestinal epithelial cells in ulcerative colitis.

ObjectiveTo investigate the effects of different concentrations of Shaoyaotang-containing serum on lipopolysaccharide （LPS）-induced inflammation of human colorectal adenocarcinoma （Caco-2） cells by inhibiting apoptosis via activating the tumor necrosis factor （TNF） receptor superfamily member 6 （Fas）/Fas ligand （FasL） pathway. MethodsCaco-2 cells were allocated into blank， model （LPS， 10 mg·L^-1）， Shaoyaotang-containing serum （5%， 10%， 15%， 20%）， and Fas inhibitor （KR-33493， 20 mmol·L^-1） groups. Except the blank group， the other groups were stimulated with 10 mg·L^-1 LPS for 24 h for the modeling of inflammation. After successful modeling， the blank， Fas inhibitor， and model groups were treated with blank serum， and the Shaoyaotang-containing serum groups were treated with the serum samples at corresponding concentrations for 24 h. The Fas inhibitor group was subjected to KR-33493 pretreatment for 1 h. Cell proliferation and viability were examined by the cell-counting kit-8 （CCK-8） method. The levels of interleukin （IL）-6， IL-1β， and TNF-α were measured by enzyme-linked immunosorbent assay. Apoptosis was detected by flow cytometry. The protein and mRNA levels of Fas， FasL， cysteinyl aspartate-specific proteinase （Caspase）-3， Caspase-9， B-cell lymphoma 2 （Bcl-2）， and Bcl-2-associated X protein （Bax） were determined by Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. ResultsCompared with the blank group， the model group presented a decrease in cell survival rate （P<0.01）. Compared with that in the model group， the cell survival rate showed no significant change in the 5% Shaoyaotang-containing serum group but increased in the 10%， 15%， and 20% Shaoyaotang-containing serum groups （P<0.01）. Since there was no statistical difference between the 5% Shaoyaotang-containing serum group and the model group， 10%， 15%， and 20% Shaoyaotang-containing sera were selected for the follow-up study. Compared with the blank group， the model group showed risen levels of IL-6， IL-1β， and TNF-α （P<0.01）， an increased apoptosis rate （P<0.01）， up-regulated protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.01）， and down-regulated protein and mRNA levels of Bcl-2 （P<0.01）. Compared with the model group， the Fas inhibitor group and the 10%， 15%， and 20% Shaoyaotang-containing serum groups showed declined levels of IL-6， IL-1β， and TNF-α （P<0.01）， decreased apoptosis rates （P<0.01）， down-regulated protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.05， P<0.01）， and up-regulated protein and mRNA levels of Bcl-2 （P<0.05， P<0.01）. In addition， the 15% and 20% Shaoyaotang-containing serum groups had lower levels of IL-6， IL-1β， and TNF-α （P<0.05， P<0.01）， lower apoptosis rates （P<0.05， P<0.01）， lower protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax （P<0.05， P<0.01）， and higher protein and mRNA levels of Bcl-2 （P<0.05， P<0.01） than the 10% Shaoyaotang-containing serum group. ConclusionThe Shaoyaotang-containing serum can reduce the content of inflammatory factors in Caco-2 cells， down-regulate the protein and mRNA levels of Fas， FasL， Caspase-3， Caspase-9， and Bax， and up-regulate the protein and mRNA levels of Bcl-2 under the intervention of LPS by regulating the Fas/FasL pathway and inhibiting the apoptosis of intestinal epithelial cells in ulcerative colitis.

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.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

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.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

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.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.