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.

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.

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.

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.

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.

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.

Background Long-term exposure to ambient fine particulate matter (PM_2.5) may increase the risk of diabetes, and a healthy diet can effectively control fasting blood glucose levels. However, it is unclear whether dietary factors have a moderating effect on the risk of diabetes associated with atmospheric PM_2.5 exposure. Objective To investigate the association between long-term exposure to PM_2.5 and diabetes in rural areas of Ningxia, and potential interaction of long-term exposure to atmospheric PM_2.5 and diet on diabetes. Methods The study subjects were selected from the baseline survey data of the China Northwest Cohort-Ningxia (CNC-NX) , a natural population cohort. A total of 13917 subjects were included, excluding participants with missing covariate information. We utilized the annual average ambient PM_2.5 concentration from 2014 to 2018 as the long-term exposure level. Logistic regression and multiple linear regression were employed to analyze the associations of long-term atmospheric PM_2.5 exposure with diabetes and fasting blood glucose levels. Stratification by frequency of vegetable consumption, frequency of fruit consumption, and salty taste was used to examine moderating effects on the diabetes risk associated with atmospheric PM_2.5 exposure. Results The mean age of the 13917 subjects was (56.8±10.0) years, and the prevalence of diabetes was 9.8%. Between 2014 and 2018, the average annual concentration of PM_2.5 was (38.10±4.67) μg·m⁻³. The risk (OR) of diabetes was 1.018 (95%CI: 1.005, 1.032) and the fasting blood glucose was increased by 0.011 (95%CI: 0.004, 0.017) mmol·L⁻¹ for each 1 μg·m⁻³ increase in PM_2.5 concentration. Compared to those who consumed vegetables < 1 time per week, individuals who consume vegetables 1-3 times per week and ≥4 times per week had a reduced risk of developing diabetes by 27.1% (OR=0.729, 95%CI: 0.594, 0.893) and 16.8% (OR=0.832, 95%CI: 0.715, 0.971) respectively. Similarly, when compared to those who consumed fruits <1 time per week, individuals who consumed fruits 1-3 times per week and ≥4 times per week exhibited a reduced risk of diabetes by 16.4% (OR=0.836, 95%CI: 0.702, 0.998) and 18.2% (OR=0.818, 95%CI: 0.700, 0.959) respectively. Fasting blood glucose decreased by 0.202 (95%CI: -0.304, -0.101) mmol·L⁻¹ in participants who ate vegetables 1-3 times per week. The effect of salty taste on diabetes and fasting blood glucose was not significant. The results of stratified analysis by dietary factors and PM_2.5 concentration showed that the risks of diabetes were increased in the low PM_2.5 pollution-low vegetable intake frequency group and the high PM_2.5 pollution-low vegetable intake frequency group compared with the low PM_2.5 pollution-high vegetable intake frequency group, with OR values of 3.987 (95%CI: 2.943, 5.371) and 1.433 (95%CI: 1.143, 1.796) respectively. The risk of diabetes was 50.1% higher in participants with high PM_2.5 pollution and low fruit intake frequency than in participants with low PM_2.5 pollution and high fruit intake frequency (OR=1.501, 95%CI: 1.171, 1.926). No interaction was found between salty taste and PM_2.5 on diabetes. Conclusion Long-term exposure to ambient PM_2.5 is associated with an increased fasting blood glucose and an elevated risk of diabetes in rural Ningxia population. Increasing the frequency of weekly consumption of vegetables or fruits may have a certain protective effect against diabetes occurrence, as well as a moderating effect on diabetes and fasting blood glucose levels associated with long-term exposure to atmospheric PM_2.5.

Objective To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database,to explore research hotspots and developmen-tal trends.Methods A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the litera-ture measuring tool CiteSpace.The authors,institution,country(region),title,journal,keywords,cited references and other information of relevant literatures were analyzed.Results A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries(regions)were identi-fied,with the number of articles published showing an increasing trend year by year.Among them,the United States had the highest number of publications and China ranked the second.Academy of Forensic Science had the highest number of publications among the institutions.Forensic Science Inter-national,Journal of Forensic Sciences,International Journal of Legal Medicine ranked high in publica-tion and citation frequency.Through the analysis of keywords,it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technol-ogy for sex and age estimation,cause of death analysis,postmortem interval estimation,individual identification and so on.Conclusion It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research.Exploring the combination of advanced ar-tificial intelligence technologies with forensic research will be a hotspot and direction for future re-search.

Objective To explore the role of pulmonary surfactant(PS)combined with budesonide in improving oxygenation and clinical outcomes of neonatal acute respiratory distress syndrome(ARDS).Methods The present study is a historically controlled trial.Infants with ARDS requiring mechanical ventilation and PS replacement therapy were collected from the neonatal unit of Southwest Medical University.Those from January 2022 to November 2022 were set as intervention group(PS+ budesonid,n=35),treated with intratracheal instillation of a mixed suspension of budesonide(0.25 mg/kg)and PS(200 mg/kg),and continuous budesonide nebulization(0.25 mg/kg,twice per day)until withdrawal,then compared with a historical cohort,who just received intratracheal instillation of PS(200 mg/kg)(January 2020-December 2021,PS group,n=35).Baseline data such as gender,mode of delivery,1 min and 5 min Apgar score,birth weight,gestational age,time of onset,and cause of onset were recorded in both groups.The oxygenation and clinical outcomes of infants were compared between the two groups,including:(1)Arterial blood gas analysis indicators,such as partial pressure of oxygen(PaO2)and oxygenation index(OI)before treatment and at 6,12 and 24 hours of treatment;(2)Clinical observation and evaluation indicators,such as the time to withdrawal,duration of oxygen supplementation,length of stay,improvement of the radiological images of the lungs at 72 h of treatment,and repeated PS use;(3)Blood chemistry indicators,such as white blood cell(WBC),neutrocyte(NEU),procalcitonin(PCT)before treatment and at 3 and 7 days of treatment;and(4)Observation indicators of complications,weight growth,and mortality outcomes,such as the incidences of intracranial hemorrhage,gastrointestinal hemorrhage,neonatal necrotizing enterocolitis(NEC),and hyperglycemia,weight growth,and fatality rate.Results The differences in baseline data between the two groups were not statistically different(P＞0.05).The levels of PaO2 of the two groups were increased after treatment for different time periods,while the levels of OI were decreased(P＜0.001),and the levels of above indexes changed more significantly in PS+budesonide group than those in PS group(P＜0.05).The time to withdrawal,duration of oxygen supplementation,and length of stay in PS+budesonide group were shorter than those in PS group;the radiological images of the lungs showed that the pulmonary inflammation absorption was significantly better in PS+ budesonide group than that in PS group,while no significant difference between the two groups of infants with repeated PS use.The NEU was significantly higher in PS+budesonide group than in PS group at 3 d and 7 d of treatment(P＜0.001);and at 3 days of treatment,the PCT levels were significantly lower in PS+budesonide group than that in PS group(P＜0.05).The incidences of intracranial hemorrhage,gastrointestinal hemorrhage,NEC,hyperglycemia,weight growth,and fatality rate were not significantly different between the two groups(P＞0.05).Conclusion The use of budesonide in addition to surfactant may improve the oxygenation of neonates with ARDS,improve the inflammatory infiltrates in lungs,shorten the duration of mechanical ventilation and oxygen supplementation,and without short-term complications associated with budesonide use.

Objective·To analyze the clinicopathologic characteristics,gene mutation profile,and prognostic factors of thyroid diffuse large B-cell lymphoma(DLBCL).Methods·From November 2003 to December 2021,a total of 66 patients with thyroid DLBCL[23 cases(34.8%)with primary thyroid DLBCL,and 43 cases(65.2%)with secondary thyroid DLBCL]admitted to Ruijin Hospital,Shanghai Jiao Tong University School of Medicine were retrospectively analyzed for their clinicopathological data,survival and prognostic factors.Gene mutation profiles were evaluated by targeted sequencing(55 lymphoma-related genes)in 40 patients.Results·Compared to primary thyroid DLBCL,secondary thyroid DLBCL had advanced ratio of Ann Arbor stage Ⅲ?Ⅳ(P=0.000),elevated serum lactate dehydrogenase(LDH)(P=0.043),number of affected extranodal involvement≥2(P=0.000),non-germinal center B cell(non-GCB)(P=0.030),BCL-2/MYC double expression(DE)(P=0.026),and international prognostic index(IPI)3?5-scores(P=0.000).The proportion of patients who underwent thyroid surgery(P=0.012)was lower than that of patients with primary thyroid DLBCL.The complete remission(CR)rate in primary thyroid DLBCL patients was higher than that in secondary thyroid DLBCL patients(P=0.039).Fifty-five patients(83%)received rituximab combined with cyclophosphamide,doxorubicin,vincristine,and prednisone(R-CHOP)-based first-line regimen.The estimated 5-year progression free survival(PFS)rate of primary thyroid DLBCL patients was 95.0%,higher than the 49.7%of the secondary patients(P=0.010).Univariate analysis showed that Ann Arbor Ⅲ?Ⅳ(HR=4.411,95%CI 1.373?14.170),elevated LDH(HR=5.500,95%CI 1.519?19.911),non-GCB(HR= 5.291,95%CI 1.667?16.788),and DE(HR=6.178,95%CI 1.813?21.058)were adverse prognostic factors of PFS in patients with thyroid DLBCL.Ann Arbor Ⅲ?Ⅳ(HR=7.088,95%CI 0.827?60.717),elevated LDH(HR=6.982,95%CI 0.809?60.266),and DE(HR=18.079,95%CI 1.837?177.923)were adverse prognostic factors of overall survival(OS).Multivariate analysis showed that Ann Arbor Ⅲ?Ⅳ(HR=4.693,95%CI 1.218?18.081)and elevated LDH(HR=5.058,95%CI 1.166?21.941)were independent adverse prognostic factors of PFS in patients with thyroid DLBCL.Targeted sequencing data showed mutation frequency>20%in TET2(n=14,35%),KMT2D(n=13,32%),TP53(n=11,28%),GNA13(n=10,25%),KMT2C(n=9,22%),and TP53 were adverse prognostic factors of PFS in patients with thyroid DLBCL(P=0.000).Conclusion·Patients with primary thyroid DLBCL have better PFS and OS than those with secondary thyroid DLBCL.Ann Arbor Ⅲ?Ⅳ,elevated LDH,non-GCB,and DE(MYC and BCL2)are adverse prognostic factors in thyroid DLBCL.TET2,KMT2D,TP53,GNA13,and KMT2C are commonly highly mutated genes in thyroid DLBCL,and the prognosis of patients with TP53 mutations is poor.