简介：摘要BACKGROUND AND OBJECTIVESTranscranial magnetic stimulation (TMS) is a well-established tool in probing cortical plasticity in vivo. Changes in corticomotor excitability can be induced using paired associative stimulation (PAS) protocol, in which TMS over the primary motor cortex is conditioned with an electrical peripheral nerve stimulation of the contralateral hand. PAS with an inter-stimulus interval of 25 ms induces long-term potentiation (LTP)-like effects in cortical excitability. However, the response to a PAS protocol tends to vary substantially across individuals. In this study, we used univariate and multivariate data-driven methods to investigate various previously proposed determinants of inter-individual variability in PAS efficacy, such as demographic, cognitive, clinical, neurophysiological, and neuroimaging measures.METHODSForty-one right-handed participants, comprising 22 patients with amnestic mild cognitive impairment (MCI) and 19 healthy controls (HC), underwent the PAS protocol. Prior to stimulation, demographic, genetic, clinical, as well as structural and resting-state functional MRI data were acquired.RESULTSThe two groups did not differ in any of the variables, except by global cognitive status. Univariate analysis showed that only 61% of all participants were classified as PAS responders, irrespective of group membership. Higher PAS response was associated with lower TMS intensity and with higher resting-state connectivity within the sensorimotor network, but only in responders, as opposed to non-responders. We also found an overall positive correlation between PAS response and structural connectivity within the corticospinal tract, which did not differ between groups. A multivariate random forest (RF) model identified age, gender, education, IQ, global cognitive status, sleep quality, alertness, TMS intensity, genetic factors, and neuroimaging measures (functional and structural connectivity, gray matter (GM) volume, and cortical thickness as poor predictors of PAS response. The model resulted in low accuracy of the RF classifier (58%; 95% CI: 42 - 74%), with a higher relative importance of brain connectivity measures compared to the other variables.CONCLUSIONS We conclude that PAS variability in our sample was not well explained by factors known to influence PAS efficacy, emphasizing the need for future replication studies.

简介：AIMToevaluatewhetherindividualswithgastriccancer（GC）arediagnosedearlieriftheyhavefirstdegreerelativeswithGC.METHODS：Atotalof4282patientsdiagnosedwithGCatNationalCancerCenterHospitalfrom2002to2012wereenrolledinthisretrospectivestudy.Weclassifiedthepatientsaccordingtopresenceorabsenceoffirst-degreefamilyhistoryofGCandcomparedageatdiagnosisandclinicopathologiccharacteristics.Inaddition,wefurtherclassifiedpatientsaccordingtospecificfamilymemberwithGC（father,mother,sibling,oroffspring）andcomparedageatGCdiagnosisamongthesepatientgroups.Baselinecharacteristicswereobtainedfromaprospectivelycollecteddatabase.Informationaboutthefamilymember＇sageatGCdiagnosiswasobtainedbyquestionnaire.RESULTS：Atotalof924patients（21.6%）hadafirstdegreefamilyhistoryofGC.ThemeanageatGCdiagnosisinpatientshavingpaternalhistoryofGCwas54.4±10.4yearsandwassignificantlyyoungerthaninthosewithoutafirst-degreefamilyhistory（58.1±12.0years,P〈0.001）.However,thisfindingwasnotobservedinpatientswhohadanaffectedmother（57.2±10.0years）orsibling（62.2±9.8years）.Amongpatientswithfamilymemberhavingearly-onsetGC（〈50yearsold）,meanageatdiagnosiswas47.7±10.3yearsforthosewithanaffectedfather,48.6±10.4yearsforthosewithanaffectedmother,and57.4±11.5yearsforthosewithanaffectedsibling.Thus,patientswithaparentdiagnosedbefore50yearsofagedevelopedGC10.4or9.5yearsearlierthanindividualswithoutafamilyhistoryofGC（bothP〈0.001）CONCLUSION：Early-onsetGCbeforeageof50wasassociatedwithparentalhistoryofearly-onsetofGC.Individualhavingsuchfamilyhistoryneedtostartscreeningearlier.

简介：AbstractBackground:The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm.Methods:The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People’s Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models.Results:Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients.Conclusions:The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.

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