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.

In view of the few studies on the influence of Armillaria spp. infection on the content of the chemical components in different parts of Polyporus umbellatus sclerotia, this study determined the biomass of P. umbellatus sclerotia and the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in the separated cavity wall of the sclerotia and the uninfected part of the sclerotia in different harvesting years under the conditions of A. gallica and A. mellea infection respectively. According to the difference of content and dynamic changes of the polysaccharide and the steroid substances, the superior Armillaria sp. was screened to obtain the best harvest years of P. umbellatus. Using HPLC and UV-VIS spectrophotometry methods, the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in P. umbellatus sclerotia infected by the two Armillaria spp. in different years were determined. In addition, the differentially expressed genes related to P. umbellatus polysaccharide synthesis were screened according to the transcriptomic data of different parts of P. umbellatus after A. mellea infection. With the increase of years, the biomass of sclerotia infected by different Armillaria spp. had significant differences, and there were significant differences in the four components of sclerotia. The four components of the separated cavity wall of the sclerotia were significantly higher than those of the uninfected part. The best harvest time was the third year after cultivation. Transcriptomic analysis showed that the infection of Armillaria spp. could significantly promote polysaccharide synthesis, which provided a basis for polysaccharide content determination at the molecular level. The study clarified the influence of different Armillaria spp. infection on the accumulation of chemical components of P. umbellatus sclerotia, laying a foundation for exploring the symbiosis mechanism and provided a scientific clue for screening superior Armillaria sp. and guiding the artificial cultivation of P. umbellatus sclerotia.

Background: Breast cancer is a common malignant tumor that has a high tendency to metastasis to the bone, leading to cancer-induced bone pain (CIBP). Ghrelin can not only stimulate appetite and regulate energy balance, but also alleviate CIBP by inducing NPY expression. Octanoic acid (OA), a type of medium chain fatty acids, provides an energy substrate and promotes acylation of ghrelin. However, it remains to be elucidated whether an OA-rich diet can alleviate CIBP by activating the acyl-ghrelin/NPY pathway. Methods: First, thirty-six Sprague–Dawley rats were randomly divided into the sham, CIBP, CIBP + OA (20), CIBP + OA (40), CIBP + OA (60) and CIBP + OA (80) groups to investigate the effects of diets with different ratios of OA on CIBP and the acyl-ghrelin/NPY pathway. Next, a ghrelin O-acyltransferase (GOAT) inhibitor was exogenously administered to investigate whether an OA-rich diet alleviated CIBP through increasing the level of acyl-ghrelin and activating the acyl-ghrelin/NPY pathway. Results: An OA-rich diet significantly alleviated nociceptive behaviors and increased the levels of acyl-ghrelin and NPY in a dose-dependent manner in cancer-bearing rats. With the exogenous administration of the GOAT inhibitor, the beneficial effects of an OA-rich diet on the acyl-ghrelin/NPY pathway and its pain-relieving effects were attenuated. Conclusions An OA-rich diet could alleviate CIBP through increasing the level of acyl-ghrelin and activating the acylghrelin/NPY pathway.

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: Breast cancer is a common malignant tumor that has a high tendency to metastasis to the bone, leading to cancer-induced bone pain (CIBP). Ghrelin can not only stimulate appetite and regulate energy balance, but also alleviate CIBP by inducing NPY expression. Octanoic acid (OA), a type of medium chain fatty acids, provides an energy substrate and promotes acylation of ghrelin. However, it remains to be elucidated whether an OA-rich diet can alleviate CIBP by activating the acyl-ghrelin/NPY pathway. Methods: First, thirty-six Sprague–Dawley rats were randomly divided into the sham, CIBP, CIBP + OA (20), CIBP + OA (40), CIBP + OA (60) and CIBP + OA (80) groups to investigate the effects of diets with different ratios of OA on CIBP and the acyl-ghrelin/NPY pathway. Next, a ghrelin O-acyltransferase (GOAT) inhibitor was exogenously administered to investigate whether an OA-rich diet alleviated CIBP through increasing the level of acyl-ghrelin and activating the acyl-ghrelin/NPY pathway. Results: An OA-rich diet significantly alleviated nociceptive behaviors and increased the levels of acyl-ghrelin and NPY in a dose-dependent manner in cancer-bearing rats. With the exogenous administration of the GOAT inhibitor, the beneficial effects of an OA-rich diet on the acyl-ghrelin/NPY pathway and its pain-relieving effects were attenuated. Conclusions An OA-rich diet could alleviate CIBP through increasing the level of acyl-ghrelin and activating the acylghrelin/NPY pathway.

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: Breast cancer is a common malignant tumor that has a high tendency to metastasis to the bone, leading to cancer-induced bone pain (CIBP). Ghrelin can not only stimulate appetite and regulate energy balance, but also alleviate CIBP by inducing NPY expression. Octanoic acid (OA), a type of medium chain fatty acids, provides an energy substrate and promotes acylation of ghrelin. However, it remains to be elucidated whether an OA-rich diet can alleviate CIBP by activating the acyl-ghrelin/NPY pathway. Methods: First, thirty-six Sprague–Dawley rats were randomly divided into the sham, CIBP, CIBP + OA (20), CIBP + OA (40), CIBP + OA (60) and CIBP + OA (80) groups to investigate the effects of diets with different ratios of OA on CIBP and the acyl-ghrelin/NPY pathway. Next, a ghrelin O-acyltransferase (GOAT) inhibitor was exogenously administered to investigate whether an OA-rich diet alleviated CIBP through increasing the level of acyl-ghrelin and activating the acyl-ghrelin/NPY pathway. Results: An OA-rich diet significantly alleviated nociceptive behaviors and increased the levels of acyl-ghrelin and NPY in a dose-dependent manner in cancer-bearing rats. With the exogenous administration of the GOAT inhibitor, the beneficial effects of an OA-rich diet on the acyl-ghrelin/NPY pathway and its pain-relieving effects were attenuated. Conclusions An OA-rich diet could alleviate CIBP through increasing the level of acyl-ghrelin and activating the acylghrelin/NPY pathway.

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: Breast cancer is a common malignant tumor that has a high tendency to metastasis to the bone, leading to cancer-induced bone pain (CIBP). Ghrelin can not only stimulate appetite and regulate energy balance, but also alleviate CIBP by inducing NPY expression. Octanoic acid (OA), a type of medium chain fatty acids, provides an energy substrate and promotes acylation of ghrelin. However, it remains to be elucidated whether an OA-rich diet can alleviate CIBP by activating the acyl-ghrelin/NPY pathway. Methods: First, thirty-six Sprague–Dawley rats were randomly divided into the sham, CIBP, CIBP + OA (20), CIBP + OA (40), CIBP + OA (60) and CIBP + OA (80) groups to investigate the effects of diets with different ratios of OA on CIBP and the acyl-ghrelin/NPY pathway. Next, a ghrelin O-acyltransferase (GOAT) inhibitor was exogenously administered to investigate whether an OA-rich diet alleviated CIBP through increasing the level of acyl-ghrelin and activating the acyl-ghrelin/NPY pathway. Results: An OA-rich diet significantly alleviated nociceptive behaviors and increased the levels of acyl-ghrelin and NPY in a dose-dependent manner in cancer-bearing rats. With the exogenous administration of the GOAT inhibitor, the beneficial effects of an OA-rich diet on the acyl-ghrelin/NPY pathway and its pain-relieving effects were attenuated. Conclusions An OA-rich diet could alleviate CIBP through increasing the level of acyl-ghrelin and activating the acylghrelin/NPY pathway.

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.