Bipolar disorder is a highly heritable and functionally impairing disease.The recognition and intervention of BD especially that characterized by early onset remains challenging.Risk biomarkers for predicting BD trans...Bipolar disorder is a highly heritable and functionally impairing disease.The recognition and intervention of BD especially that characterized by early onset remains challenging.Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis.We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or(and)clinical risk of BD.Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses.Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores,epigenetic alterations,elevated immune parameters,front-limbic system deficits,and brain circuit dysfunction associated with emotion and reward processing.Future studies need to enhance machine learning integration,make clinical detection methods more objective,and improve the quality of cohort studies.展开更多
Parkinson’s disease(PD)is recognized as the second most common neurodegenerative disorder after Alzheimer disease.Although a fascinating 200-year journey of research has revealed the multifaceted nature of PD[1,2],it...Parkinson’s disease(PD)is recognized as the second most common neurodegenerative disorder after Alzheimer disease.Although a fascinating 200-year journey of research has revealed the multifaceted nature of PD[1,2],its fundamental features are the loss of dopaminergic neurons in the substantia nigra pars compacta(SNpc)and depletion of dopamine(DA)in the striatum.Iron accumulates in normal brains with aging.Such展开更多
Objective:To investigate the prevalence of diabetic at-risk foot and its associated factors.Methods:A total of 838 hospitalized patients with type 2 diabetes were screened for at-risk foot.Neural and vascular disorder...Objective:To investigate the prevalence of diabetic at-risk foot and its associated factors.Methods:A total of 838 hospitalized patients with type 2 diabetes were screened for at-risk foot.Neural and vascular disorders were evaluated by assessing vibration perception thresholds and ankle brachial indexes (ABIs).After excluding 12 patients with abnormally high ABIs,remaining individuals with neural and/or vascular disorder were identified as at-risk patients and further classified into three subtypes:isolated neural disorder,isolated vascular disorder and mixed disorder.Potential associated factors were examined using Logistic regression models.Results:In the final sample of 826 individuals,the prevalence of diabetic at-risk foot was 30.6％.Among all at-risk patients,isolated neural disorders (69.6％) were more common than mixed (16.2％) or isolated vascular disorders (14.2％).Isolated neural and vascular disorders shared specific risk factors,including age per 20-year increment (odds ratio [95％ CI],3.73 [2.59-5.37] and 4.01 [1.98-8.11]),diabetic duration ≥10 years (1.69 11.13-2.54] and 3.29 [1.49-7.24]) and systolic blood pressure ≥140 mmHg (1.96 [1.31-2.93] and 2.90 [1.38-6.10]) respectively.In addition,isolated neural disorders were associated with a heavy smoking history (95％CI 2.69 [1.15-6.31]),increased high-sensitivity C-reactive protein levels (95％CI 1.30 [1.04-1.62]) and mild obesity (95％CI 0.49 [0.20-1.241).Isolated vascular disorders were linked with decreased high density lipoprotein (HDL) cholesterol levels (95％CI 3.42 [1.31-8.96]) and increased triglycerides levels (95％CI 2.74 [1.26-5.97]).Conclusions:Diabetic at-risk foot is epidemic among hospitalized patients with type 2 diabetes.Aging,long-term diabetes,hypertension,smoking,inflammatory response and dyslipidemia may be associated with the prevalence of diabetic at-risk foot.展开更多
By using daily air temperature and precipitation data, and the weather phenomena data of daily snowfall from 98 meteorological stations over the Qinghai-Tibetan Plateau （QTP）, this paper performs an ＂at-risk＂ eval...By using daily air temperature and precipitation data, and the weather phenomena data of daily snowfall from 98 meteorological stations over the Qinghai-Tibetan Plateau （QTP）, this paper performs an ＂at-risk＂ evaluation on snowfall and accumulated snow over the QTP under current climate situation and future climate warming condition. When rainfall, snowfall, or accumulated snow weather phenomena occur, critical values are determined based on dally air temperature and precipitation for current climate conditions. Air temperature of 0 ℃ is defined as the critical value of temperature for rainfall or snowfall, while 0 ℃ air temperature and 4.0 mm （autumn） or 3.0 mm （spring） snowfall amounts are defined as the critical values for accumulated snowfall. Analyses based on the above critical values disclose that under current climate condition, stations with ＂at-risk＂ accumulated snow account for 33% and 36% of all stations, and the ＂at-risk＂ snowfall stations reach 78% and 81% in autumn and spring, respectively. Spatially, most stations with ＂at-risk＂ accumulated snow are located on the southern and eastern edge of the QTP, and stations with ＂at-risk＂ snowfall are also apparent at the northern edge. If the air temperature increases by 2.5 ℃ in 2050, only the snowfall at a few ＂at-risk＂ snowfall stations will transform into rainfall, while most ＂at-risk＂ accumulated snow stations will face the problem that snowfall is hardly accumulated. Additionally, most stations will become ＂at-risk＂ accumulated snow stations, indicating that both the snow depth and the snow cover duration will decline in most areas of the QTP, including a delay of the start date and an advancing of the end date of snow cover.展开更多
The emergence of new technologies such as GPS,cellphone,Bluetooth device,etc.offers opportunities for collecting high-fidelity temporal-spatial travel data in a cost-effective manner.With the vehicle trajectory data a...The emergence of new technologies such as GPS,cellphone,Bluetooth device,etc.offers opportunities for collecting high-fidelity temporal-spatial travel data in a cost-effective manner.With the vehicle trajectory data achieved from a smartphone app Metropia,this study targets on exploring the trajectory data and designing the measurements of the driving pattern.Metropia is a recently available mobile traffic app that uses prediction and coordinating technology combined with user rewards to incentivize drivers to cooperate,balance traffic load on the network,and reduce traffic congestion.Speed and celeration(acceleration and deceleration)are obtained from the Metropia platform directly and parameterized as individual and system measurements related to traffic,spatial and temporal conditions.A case study is provided in this paper to demonstrate the feasibility of this approach utilizing the trajectory data from the actual app usage.The driving behaviors at both individual and system levels are quantified from the microscopic speed and celeration records.The results from this study reveal distinct driving behavior pattern and shed lights for further opportunities to identify behavior characteristics beyond safety and environmental considerations.展开更多
基金supported by the Beijing Commission of Science and Technology (Z191100006619113)the National Natural Science Foundation of China (32070589 and 82171500).
文摘Bipolar disorder is a highly heritable and functionally impairing disease.The recognition and intervention of BD especially that characterized by early onset remains challenging.Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis.We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or(and)clinical risk of BD.Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses.Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores,epigenetic alterations,elevated immune parameters,front-limbic system deficits,and brain circuit dysfunction associated with emotion and reward processing.Future studies need to enhance machine learning integration,make clinical detection methods more objective,and improve the quality of cohort studies.
基金supported by grants from the National Natural Science Foundation of China(81430024,31771124,31571054,and 31371081)Excellent Innovative Team of Shandong Province and Taishan Scholars Construction Project
文摘Parkinson’s disease(PD)is recognized as the second most common neurodegenerative disorder after Alzheimer disease.Although a fascinating 200-year journey of research has revealed the multifaceted nature of PD[1,2],its fundamental features are the loss of dopaminergic neurons in the substantia nigra pars compacta(SNpc)and depletion of dopamine(DA)in the striatum.Iron accumulates in normal brains with aging.Such
文摘Objective:To investigate the prevalence of diabetic at-risk foot and its associated factors.Methods:A total of 838 hospitalized patients with type 2 diabetes were screened for at-risk foot.Neural and vascular disorders were evaluated by assessing vibration perception thresholds and ankle brachial indexes (ABIs).After excluding 12 patients with abnormally high ABIs,remaining individuals with neural and/or vascular disorder were identified as at-risk patients and further classified into three subtypes:isolated neural disorder,isolated vascular disorder and mixed disorder.Potential associated factors were examined using Logistic regression models.Results:In the final sample of 826 individuals,the prevalence of diabetic at-risk foot was 30.6％.Among all at-risk patients,isolated neural disorders (69.6％) were more common than mixed (16.2％) or isolated vascular disorders (14.2％).Isolated neural and vascular disorders shared specific risk factors,including age per 20-year increment (odds ratio [95％ CI],3.73 [2.59-5.37] and 4.01 [1.98-8.11]),diabetic duration ≥10 years (1.69 11.13-2.54] and 3.29 [1.49-7.24]) and systolic blood pressure ≥140 mmHg (1.96 [1.31-2.93] and 2.90 [1.38-6.10]) respectively.In addition,isolated neural disorders were associated with a heavy smoking history (95％CI 2.69 [1.15-6.31]),increased high-sensitivity C-reactive protein levels (95％CI 1.30 [1.04-1.62]) and mild obesity (95％CI 0.49 [0.20-1.241).Isolated vascular disorders were linked with decreased high density lipoprotein (HDL) cholesterol levels (95％CI 3.42 [1.31-8.96]) and increased triglycerides levels (95％CI 2.74 [1.26-5.97]).Conclusions:Diabetic at-risk foot is epidemic among hospitalized patients with type 2 diabetes.Aging,long-term diabetes,hypertension,smoking,inflammatory response and dyslipidemia may be associated with the prevalence of diabetic at-risk foot.
基金supported by the opening fund from the State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences(SKLCS 08-07)the National Postdoctoral Scientific Foundation (20080440342)
文摘By using daily air temperature and precipitation data, and the weather phenomena data of daily snowfall from 98 meteorological stations over the Qinghai-Tibetan Plateau （QTP）, this paper performs an ＂at-risk＂ evaluation on snowfall and accumulated snow over the QTP under current climate situation and future climate warming condition. When rainfall, snowfall, or accumulated snow weather phenomena occur, critical values are determined based on dally air temperature and precipitation for current climate conditions. Air temperature of 0 ℃ is defined as the critical value of temperature for rainfall or snowfall, while 0 ℃ air temperature and 4.0 mm （autumn） or 3.0 mm （spring） snowfall amounts are defined as the critical values for accumulated snowfall. Analyses based on the above critical values disclose that under current climate condition, stations with ＂at-risk＂ accumulated snow account for 33% and 36% of all stations, and the ＂at-risk＂ snowfall stations reach 78% and 81% in autumn and spring, respectively. Spatially, most stations with ＂at-risk＂ accumulated snow are located on the southern and eastern edge of the QTP, and stations with ＂at-risk＂ snowfall are also apparent at the northern edge. If the air temperature increases by 2.5 ℃ in 2050, only the snowfall at a few ＂at-risk＂ snowfall stations will transform into rainfall, while most ＂at-risk＂ accumulated snow stations will face the problem that snowfall is hardly accumulated. Additionally, most stations will become ＂at-risk＂ accumulated snow stations, indicating that both the snow depth and the snow cover duration will decline in most areas of the QTP, including a delay of the start date and an advancing of the end date of snow cover.
文摘The emergence of new technologies such as GPS,cellphone,Bluetooth device,etc.offers opportunities for collecting high-fidelity temporal-spatial travel data in a cost-effective manner.With the vehicle trajectory data achieved from a smartphone app Metropia,this study targets on exploring the trajectory data and designing the measurements of the driving pattern.Metropia is a recently available mobile traffic app that uses prediction and coordinating technology combined with user rewards to incentivize drivers to cooperate,balance traffic load on the network,and reduce traffic congestion.Speed and celeration(acceleration and deceleration)are obtained from the Metropia platform directly and parameterized as individual and system measurements related to traffic,spatial and temporal conditions.A case study is provided in this paper to demonstrate the feasibility of this approach utilizing the trajectory data from the actual app usage.The driving behaviors at both individual and system levels are quantified from the microscopic speed and celeration records.The results from this study reveal distinct driving behavior pattern and shed lights for further opportunities to identify behavior characteristics beyond safety and environmental considerations.
