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.

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.

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.

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.

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.

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 To explore the clinical characteristics and treatment scheme of patients with spinal infection caused by Prevotella intermedia(P.intermedia).Methods Clinical diagnosis and treatment processes of a patient with spinal infection caused by P.intermedia admitted to the spinal surgery department of a hospital were summa-rized,and relevant literature was retrieved from database for reviewing.Results The patient,a 50 year old male,was admitted to the hospital due to"lumbago pain complicated with pain in double lower extremities for 2 months".The lesion tissue was taken for metagenomic next-generation sequencing(mNGS)detection,which detected P.in-termedia,and the patient was diagnosed with P.intermedia spondylitis.After treatments with open lesion clea-rance,tube rinsing+autologous bone transplantation fusion internal fixation,intravenous drip of ceftriaxone sodium and metronidazole,as well as metronidazole rinsing,infection was under control.A total of 16 available papers were retrieved,together with this case,a total of 17 patients were included,with 7 males and 10 females.The main risk factors were diabetes and history of corticosteroid use(35.3％).The most common invasion sites were lumbar ver-tebra(n=12)and thoracic vertebra(n=6).13 cases were positive for pathogen culture,3 cases were positive for molecular detection,and 1 case was positive for staining microscopy.17 patients received anti-anaerobic bacteria treatment,with 14 cases receiving combined surgical treatment.One case died,with a mortality of 5.9％;5 cases had partial neurological impairment,with a disability rate of 29.4％.The survival rate of patients who received treatment of anti-anaerobic bacteria combined with surgery was 92.8％,3 patients only with anti-anaerobic bacteria treatment but without surgery were all cured.Conclusion P.intermedia is an opportunistic pathogeanic bacteria which often causes infection in immunocomprised individuals and is prone to be misdiagnosed.It is recommended to perform mNGS detection to identify the pathogen as early as possible and seize the opportunity for treatment to reduce mortality.

Objective To investigate the distribution patterns of traditional Chinese medicine(TCM)constitution in 959 patients with endometriosis(EMs).Methods From January 2019 to November 2019,959 EMs patients were selected from Guang'anmen Hospital of China Academy of Chinese Medical Sciences,Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University,Beijing Hospital,Dongfang Hospital of Beijing University of Chinese Medicine,Beijing Friendship Hospital Affiliated to Capital Medical University,and Fuxing Hospital Affiliated to Capital Medical University.The general clinical information of the patients was recorded and then the TCM constitution was identified.After that,the correlation of TCM constitution distribution with concurrent constitution and the relationship of TCM constitution distribution with age and the complication of dysmenorrhea were analyzed.Results(1)The constitution types of EMs patients listed in descending order of the proportion were yang deficiency constitution(65.1％,624/959),qi stagnation constitution(58.4％,560/959),qi deficiency constitution(52.8％,506/959),blood stasis constitution(44.2％,424/959),phlegm-damp constitution(42.5％,408/959),damp-heat constitution(41.9％,402/959),yin deficiency constitution(39.6％,380/959),balanced constitution(26.8％,257/959),and inherited special constitution(16.6％,159/959).Among the patients,there were fewer patients with single constitution,accounting for 20.2％(194/959),and most of them had concurrent constitution types,accounting for 79.8％(765/959).(2)The association rule mining based on Apriori algorithm obtained 33 related rules.The concurrent constitution types of qi deficiency-yang deficiency,blood stasis-yang deficiency,and blood stasis-qi stagnation were the association rules with high confidence.(3)Compared with patients aged 35 years and below,the patients over 35 years old were predominated by high proportion of blood stasis constitution(P＜0.05).Compared with patients without dysmenorrhea,the patients with dysmenorrhea had the increased proportion of biased constitutions and the decreased proportion of balanced constitution(P＜0.05 or P＜0.01).Conclusion Yang deficiency constitution,qi stagnation constitution,qi deficiency constitution and blood stasis constitution are the high-risk constitution types of EMs patients.The concurrent constitution types are commonly seen in EMs patients,which are more common than single biased constitution.Management of EMs patients with the methods of warming yang,relieving stagnation,benefiting qi and activating blood will be helpful for correcting the biased constitutions in time and preventing disease progression,which will achieve the preventive treatment efficacy through TCM constitution correction.

Objective:To explore the genotypes and frequency distribution of thalassemia in Lingui District,Guilin City,and provide reference for the prevention and control of thalassemia in this area. Methods:The results of genetic testing for thalassemia in 1501 suspected cases at the Second Affiliated Hospital of Guilin Medical University were analyzed retrospectively. The deletional mutations of α-thalassemia were detected by gap-PCR,the non-deletional mutations of α-thalassemia and β-thalassemia mutations were detected by PCR-reverse dot blot (PCR-RDB). Results:In 1501 samples,a total of 678 cases of thalassemia carriers were detected,with a detection rate of 45.17％. Among them,379 cases were α-thalassemia (including deletional α-thalassemia and non-deletional α-thalassemia),with a detection rate of 25.25％,the most common genotype was--SEA/αα (227 cases,15.12％),followed by-α3.7/αα (53 cases,3.53％). 270 cases of β-thalassemia were detected,with a detction rate of 17.99％,and βCD41-42/βN (144 cases,9.59％) was the main genotypes,followed by βCD17/βN (66 cases,4.40％) . In addition,there were 29 cases of αβ compound thalassemia,accounting for 1.93％,and the most common genotype was--SEA/αα complex βCD41-42/βN (5 cases,0.33％). Conclusion:Lingui District in Guilin City is a high-incidence area of thalassemia,and the genotypes of carriers are complex and diverse,with genetic heterogeneity. The results of this study provide a scientific basis for genetic counseling and prenatal diagnosis in this area.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.