Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

ObjectiveTo investigate the ameliorative effect of Xingpi capsules on functional dyspepsia（FD） and the potential mechanism. MethodsSixty SPF-grade male SD neonatal rats（7 days old） were randomly divided into the normal group（n=12） and the modeling group（n=48）， and the FD model was prepared by iodoacetamide gavage in the modeling group. After the model was successfully prepared， the rats in the modeling group were randomly divided into the model group， the low-dose and high-dose groups of Xingpi capsules（0.135， 0.54 g·kg^-1） and the domperidone group（3 mg·kg^-1）， with 12 rats in each group. Rats in the normal and model groups were gavaged with distilled water， and rats in the rest of the groups were gavaged with the corresponding medicinal solution， once a day for 7 d. The general survival condition of the rats was observed， and the water intake and food intake of the rats were measured， the gastric emptying rate and the small intestinal propulsion rate were measured at the end of the treatment， the pathological damage of the rat duodenum was examined by hematoxylin-eosin（HE） staining， and the expressions of colonic tight junction protein（Occludin） and zonula occludens protein-1（ZO-1） were detected by immunofluorescence. The differentially expressed genes in the duodenal tissues of the model group and the normal group， and the high-dose group of Xingpi capsules and the model group were detected by transcriptome sequencing after the final administration， and Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analyses were carried out. The transcriptomic results were validated by Western blot， immunofluorescence， and real-time fluorescence quantitative polymerase chain reaction（Real-time PCR）， and the active ingredients of Xingpi capsules were screened for molecular docking with the key targets. ResultsCompared with the normal group， the general survival condition of rats in the model group was poorer， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly reduced（P<0.05）， inflammatory infiltration was seen in duodenal pathology， and the fluorescence intensities of Occludin and ZO-1 in the colon were significantly reduced（P<0.01）. Compared with the model group， the general survival condition of rats in the high-dose group of Xingpi capsules improved significantly， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly increased（P<0.05）， the duodenal pathology showed a decrease in inflammatory infiltration， and the fluorescence intensities of colonic Occludin and ZO-1 were significantly increased（P<0.01）. Transcriptomic results showed that Xingpi capsules might exert therapeutic effects by regulating the phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt） through the key genes such as Slc5a1， Abhd6. The validation results showed that compared with the normal group， the phosphorylation levels of PI3K and Akt proteins， the protein expression level of interleukin（IL）-1β， and the fluorescence intensities of IL-6 and IL-1β were significantly increased in the model group（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3， Slc5a9 and other key genes were significantly increased（P<0.01）. Compared with the model group， the phosphorylation levels of PI3K and Akt， the protein expression level of IL-1β and the fluorescence intensities of IL-6 and IL-1β in the high-dose group of Xingpi capsules were significantly reduced（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3 and Slc5a9 were significantly reduced（P<0.05）. Weighted gene co-expression network analysis and molecular docking results showed that E-nerolidol and Z-nerolidol in Xingpi capsules were well bound to ABDH6 protein， and linarionoside A， valerosidatum and senkirkine were well bound to Slc5a1 protein. ConclusionXingpi capsules can effectively improve the general survival and gastrointestinal motility of FD rats， its specific mechanism may be related to the inhibition of PI3K/Akt signaling pathway to alleviate the low-grade inflammation of duodenum， and E-nerolidol， Z-nerolidol， linarionoside A， valerosidatum and senkirkine may be its key active ingredients.

ObjectiveTo investigate the ameliorative effect of Xingpi capsules on functional dyspepsia（FD） and the potential mechanism. MethodsSixty SPF-grade male SD neonatal rats（7 days old） were randomly divided into the normal group（n=12） and the modeling group（n=48）， and the FD model was prepared by iodoacetamide gavage in the modeling group. After the model was successfully prepared， the rats in the modeling group were randomly divided into the model group， the low-dose and high-dose groups of Xingpi capsules（0.135， 0.54 g·kg^-1） and the domperidone group（3 mg·kg^-1）， with 12 rats in each group. Rats in the normal and model groups were gavaged with distilled water， and rats in the rest of the groups were gavaged with the corresponding medicinal solution， once a day for 7 d. The general survival condition of the rats was observed， and the water intake and food intake of the rats were measured， the gastric emptying rate and the small intestinal propulsion rate were measured at the end of the treatment， the pathological damage of the rat duodenum was examined by hematoxylin-eosin（HE） staining， and the expressions of colonic tight junction protein（Occludin） and zonula occludens protein-1（ZO-1） were detected by immunofluorescence. The differentially expressed genes in the duodenal tissues of the model group and the normal group， and the high-dose group of Xingpi capsules and the model group were detected by transcriptome sequencing after the final administration， and Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analyses were carried out. The transcriptomic results were validated by Western blot， immunofluorescence， and real-time fluorescence quantitative polymerase chain reaction（Real-time PCR）， and the active ingredients of Xingpi capsules were screened for molecular docking with the key targets. ResultsCompared with the normal group， the general survival condition of rats in the model group was poorer， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly reduced（P<0.05）， inflammatory infiltration was seen in duodenal pathology， and the fluorescence intensities of Occludin and ZO-1 in the colon were significantly reduced（P<0.01）. Compared with the model group， the general survival condition of rats in the high-dose group of Xingpi capsules improved significantly， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly increased（P<0.05）， the duodenal pathology showed a decrease in inflammatory infiltration， and the fluorescence intensities of colonic Occludin and ZO-1 were significantly increased（P<0.01）. Transcriptomic results showed that Xingpi capsules might exert therapeutic effects by regulating the phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt） through the key genes such as Slc5a1， Abhd6. The validation results showed that compared with the normal group， the phosphorylation levels of PI3K and Akt proteins， the protein expression level of interleukin（IL）-1β， and the fluorescence intensities of IL-6 and IL-1β were significantly increased in the model group（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3， Slc5a9 and other key genes were significantly increased（P<0.01）. Compared with the model group， the phosphorylation levels of PI3K and Akt， the protein expression level of IL-1β and the fluorescence intensities of IL-6 and IL-1β in the high-dose group of Xingpi capsules were significantly reduced（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3 and Slc5a9 were significantly reduced（P<0.05）. Weighted gene co-expression network analysis and molecular docking results showed that E-nerolidol and Z-nerolidol in Xingpi capsules were well bound to ABDH6 protein， and linarionoside A， valerosidatum and senkirkine were well bound to Slc5a1 protein. ConclusionXingpi capsules can effectively improve the general survival and gastrointestinal motility of FD rats， its specific mechanism may be related to the inhibition of PI3K/Akt signaling pathway to alleviate the low-grade inflammation of duodenum， and E-nerolidol， Z-nerolidol， linarionoside A， valerosidatum and senkirkine may be its key active ingredients.

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: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background/Aims: We investigated the clinical practice patterns of Korean endoscopists for the endoscopic resection of colorectal polyps. Methods: From September to November 2021, an online survey was conducted regarding the preferred resection methods for colorectal polyps, and responses were compared with the international guidelines. Results: Among 246 respondents, those with <4 years, 4–9 years, and ≥10 years of experiencein colonoscopy practices accounted for 25.6%, 34.1%, and 40.2% of endoscopists, respectively. The most preferred resection methods for non-pedunculated lesions were cold forceps polypectomy for ≤3 mm lesions (81.7%), cold snare polypectomy for 4–5 mm (61.0%) and 6–9 mm (43.5%) lesions, hot endoscopic mucosal resection (EMR) for 10–19 mm lesions (72.0%), precut EMR for 20–25 mm lesions (22.0%), and endoscopic submucosal dissection (ESD) for ≥26 mm lesions (29.3%). Hot EMR was favored for pedunculated lesions with a head size <20 mm and stalk size <10 mm (75.6%) and for those with a head size ≥20 mm or stalk size ≥10 mm (58.5%). For suspected superficial and deep submucosal lesions measuring 10–19 mm and ≥20 mm, ESD (26.0% and 38.6%) and surgery (36.6% and 46.3%) were preferred, respectively. The adherence rate to the guidelines ranged from 11.2% to 96.9%, depending on the size, shape, and histology of the lesions. Conclusions Adherence to the guidelines for endoscopic resection techniques varied depend-ing on the characteristics of colorectal polyps. Thus, an individualized approach is required to increase adherence to the guidelines.

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: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

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.