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A novel SPECT model with an elliptical orbit and multiple detectors is proposed for detecting the metabolism of the human heart. Under the structure of the proposed model, the distance between the ...

Background: Blood purification therapy has not been applied in the detoxification of germanium compounds. This report described a case of germanium poisoning with renal failure, liver dysfunction, ...

Background We sought to describe the tendency and extent of high-sensitivity cardiac troponin I (hs-cTnI) changes in patients with fulminant myocarditis (FM) after admission and to explore the relationship between the in-hospital mortality of FM and the absolute and relative changes in hs-cTnI within 24 h and 48 h after admission. Methods In the retrospective study, the object are patients diagnosed with FM in our single centre. The value of cardiac troponin was recorded after patients admitted to hospital in succession. The absolute and relative changes in hs-cTnI within 24 h and 48 h were described as range distributions. Receiver operating characteristic (ROC) curve and Cox analyses were performed to determine the relationship between in-hospital mortality of FM and hs-cTnI changes. Results A total of 83 FM patients admitted to our centre from January 1, 2010 to December 31, 2019 were included; 69 patients survived and 14 patients died. In the survival group, 78% of patients experienced a decline in hs-cTnI within 24 h, while 36% of the mortality group exhibited a declining tendency in hs-cTnI (P = 0.003). Nearly 60% of survival group had a 0–2000 ng/l reduction in troponin from baseline within 24 h of admission. However, troponin levels of 50% of patients in the mortality group were 0–10,000 ng/ L higher than baseline 24 h after admission. Multivariable logistic analysis revealed that the declining tendency of hs-cTnI within 24 h, in addition to time from onset to admittance to hospital, intravenous immunoglobulin treatment and the abnormal level of creatinine, were associated with the in-hospital mortality of FM (for the declining tendency of hs-cTnI within 24 h, OR = 0.10, 95% CI 0.02–0.68, P = 0.018). The ROC curve revealed optimal cut-off values of − 618 ng/l for absolute change within 24 h (AUC = 0.800, P P P P Conclusion Most FM patients who survived experienced a decline in hs-cTnI within 24 h. The absolute and relative changes in hs-cTnI within 24 h and 48 h were strong predictors of in-hospital mortality.

Pheochromocytoma multisystem crisis (PMC) is a potentially lethal emergency due to catecholamine secretion. The condition manifests as severe hypertension to intractable cardiogenic shock and has a high mortality rate. This study explored the efficacy and safety of applying chlorpromazine on PMC patients. The study included seven patients (median age, 42 years; range, 14–57 years) diagnosed with pheochromocytoma. Four consecutive PMC patients were admitted to our critical care unit between 2016 and 2020 due to abdominal or waist pain, nausea, and vomiting. Their blood pressure (BP) fluctuated between 200–330/120–200 and 40–70/30–50 mmHg. Chlorpromazine (25 or 50 mg) was injected intramuscularly, followed by continuous intravenous infusion (2–8 mg/h). The patients' BP decreased to 100–150/60–100 mmHg within 1–3 h and stabilized within 3–5 days. Two weeks later, surgical tumor resection was successfully performed in all four patients. Similar clinical outcomes were also obtained in three patients with sporadic PMC reported in the literature who received chlorpromazine treatment, which reduced their BP readings from >200/100 mmHg to 120/70 mmHg. Our observations, combined with sporadic reports, showed that chlorpromazine efficiently controlled PMC. Thus, future studies on the use of chlorpromazine are warranted.

Background: In current pandemic of COVID-19, approximately 15% to 30% of critically ill COVID-19 patients developed acute respiratory distress syndrome (ARDS) with a high mortality. Extracorporeal membrane of oxygenation (ECMO) provides direct support for both lung and heart in ARDS. However, the role of ECMO in COVID-19 related ARDS was still controversial. The aim of this study was to provide insights into the mortality, intensive care unit (ICU) management, risk factors for mortality, 180-day short term prognosis of the COVID-19 related severe ARDS patients receiving ECMO treatment. Methods: From Feb 2nd, 2020 to April 27th, 2020, we included adult COVID-19 related ARDS patients admitted to intensive care unit in Tongji Hospital. Totally, 53 patients were retrospectively analyzed. They were divided into ECMO (mechanical ventilation with ECMO, n=16) and non-ECMO group (mechanical ventilation, n=37). The primary outcome was all-cause 60-day mortality. The secondary outcomes were complications on ECMO, successful weaning from ECMO, and all-cause 180-day mortality. Results: The all-cause 60-day mortality was 37.5% (6/16) in ECMO group and 86.5% (32/37) in non-ECMO group (HR, 0.196; 95% CI, 0.053-0.721; p=0.014). 10 (62.5%) patients were successfully weaned from ECMO. The all-cause 180-day mortality was 56.3% (9/16) in ECMO group and 33 (89.2%, 33/37) in non-ECMO group (HR, 0.298; 95% CI, 0.130-0.680; p=0.004). All the patients in ECMO group suffered from at least one device-related complication with coagulopathy (81.3%) being most frequently seen. Up to 180-day follow up after disease onset, the ECMO-treated survivors maintained good quality of life without severe complications or disabilities. Hypercapnia, thrombopenia, myocardial injury and elevation of IL-8 and IL-10 during ECMO treatment were strongly associated with death.Conclusion: This study showed the COVID-19 patients significantly benefited from ECMO treatment during severe ARDS, which supported the application of ECMO as an indicated strategy in the management of COVID-19 related ARDS.

Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.

ImportanceCoronavirus disease 2019 (COVID-19) is a pandemic with no specific drugs and high mortality. The most urgent thing is to find effective treatments.ObjectiveTo determine whether hydroxychloroquine application may be associated with a decreased risk of death in critically ill COVID-19 patients and what is potential mechanism.Design, Setting and PatientsThis retrospective study included all 568 critically ill COVID-19 patients who were confirmed by pathogen laboratory tests despite antiviral treatment and had severe acute respiratory distress syndrome, PAO2/FIO2 MAIN OUTCOMES AND MEASURESIn-hospital death and hospital stay time (day) were obtained, level of inflammatory cytokine (IL-6) was measured and compared between HCQ and NHCQ treatments.RESULTSThe median age of 568 critically ill patients is 68 (57, 76) years old with 37.0% being female. Mortalities are 18.8% (9/48) in HCQ group and 45.8% (238/520) in NHCQ group (pCONCLUSIONS AND RELEVANCEHydroxychloroquine treatment is significantly associated with a decreased mortality in critically ill patients with COVID-19 through attenuation of inflammatory cytokine storm. Therefore, hydroxychloroquine should be prescribed for treatment of critically ill COVID-19 patients to save lives.Key PointsQuestionCould hydroxychloroquine administration be beneficial in the treatment of critically ill patients with COVID-19?FindingsIn this retrospective study, a total of 568 critically ill patients with COVID-19 all received basic therapy and additionally 48 of them received hydroxychloroquine for 7–10 days (200 mg twice per day). Hydroxychloroquine treatment is significantly associated with a decreased mortality in critically ill COVID-19 patients and attenuated inflammatory cytokine IL-6 level.MeaningOur data suggest that hydroxychloroquine could be used to treat critically ill patients with COVID-19 which may save a lot of lives.

Coronavirus disease 2019 (COVID-19) is a pandemic with no specific drugs and high fatality. The most urgent need is to find effective treatments. We sought to determine whether hydroxychloroquine (HCQ) application may reduce the death risk of critically ill COVID-19 patients. In this retrospective study, we included 550 critically ill COVID-19 patients who need mechanical ventilation in Tongji Hospital, Wuhan, from February 1, 2020 to April 4, 2020. All 550 patients received comparable basic treatments including antiviral drugs and antibiotics, and 48 of them were treated with oral HCQ treatment (200 mg twice a day for 7-10 days) in addition to the basic treatments. Primary endpoint is fatality of patients, and inflammatory cytokine levels were compared between HCQ and non-hydroxychloroquine (NHCQ) treatments. We found that fatalities are 18.8% (9/48) in HCQ group, which is significantly lower than 47.4% (238/502) in the NHCQ group (P

Coronavirus disease 2019 (COVID-19) is a highly contagious disease and a serious threat to human health. COVID-19 can cause multiple organ dysfunction, such as respiratory and circulatory failure, liver and kidney injury, disseminated intravascular coagulation, and thromboembolism, and even death. The World Health Organization reports that the mortality rate of severe-type COVID-19 is over 50%. Currently, the number of severe cases worldwide has increased rapidly, but the experience in the treatment of infected patients is still limited. Given the lack of specific antiviral drugs, multi-organ function support treatment is important for patients with COVID-19. To improve the cure rate and reduce the mortality of patients with severe- and critical-type COVID-19, this paper summarizes the experience of organ function support in patients with severe- and critical-type COVID-19 in Optical Valley Branch of Tongji Hospital, Wuhan, China. This paper systematically summarizes the procedures of functional support therapies for multiple organs and systems, including respiratory, circulatory, renal, hepatic, and hematological systems, among patients with severe- and critical-type COVID-19. This paper provides a clinical reference and a new strategy for the optimal treatment of COVID-19 worldwide.

A 360-area surface-based cortical parcellation is extended to study mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from healthy control (HC) using the joint human connectome project multi-modal parcellation (JHCPMMP) proposed by us. We propose a novel classification method named as JMMP-LRR to accurately identify different stages toward AD by integrating the JHCPMMP with the logistic regression-recursive feature elimination (LR-RFE). In three-group classification, the average accuracy is 89.0% for HC, MCI, and AD compared to previous studies using other cortical separation with the best classification accuracy of 81.5%. By counting the number of brain regions whose feature is in the feature subset selected with JMMP-LRR, we find that five brain areas often appear in the selected features. The five core brain areas are Fusiform Face Complex (L-FFC), Area 10d (L-10d), Orbital Frontal Complex (R-OFC), Perirhinal Ectorhinal (L-PeEc) and Area TG dorsal (L-TGd, R-TGd). The features corresponding to the five core brain areas are used to form a new feature subset for three classifications with the average accuracy of 80.0%. Results demonstrate the importance of the five core brain regions in identifying different stages toward AD. Experiment results show that the proposed method has better accuracy for the classification of HC, MCI, AD, and it also proves that the division of brain regions using JHCPMMP is more scientific and effective than other methods.

Background: Overall death cause of COVID-19 patients has been reported but attribution of myocardial injury remains undetermined br br Methods: We extracted d

Coronavirus disease 2019 (COVID-19) has been pandemic and is leading to many deaths due to refractory respiratory failure Here, we report our experience of tre

Functional brain networks were constructed from functional magnetic resonance imaging (fMRI) data originating from 96 healthy adults. These networks possessed a total of 360 nodes, derived from the latest multi-modal brain parcellation method. A novel group network (overlay network) analysis model is proposed to study common attributes as well as differences found in the human brain by analysis of the functional brain network. Currently, the mean network is generally used to represent the group network. But mean networks have a modularity problem making them distinct from real networks. The overlay network is constructed by calculating the connections between the whole brain network regions, and then filtering the connections by limiting the threshold value. We find that the overlay network is closer to the real network condition of the group in terms of network characteristics related to modularity. Multiple network features are applied to investigate the discrepancies between the new group network and the mean network. Individual divergences between brain regions of everyone are also explored. Results show that the brain network of different people has a high consistency in the global measures, while there exist great differences for local measures in brain regions. Some brain regions show variability over other brain regions on most measures. In addition, we explored the impact of different thresholds on the overlay network and find that different thresholds have a greater impact on the clustering coefficient, maximized modularity, strength, and global efficiency.

Evaluating the effect of stroke rehabilitation based on electroencephalogram (EEG) is still a challenging problem. This paper presents a novel nonlinear dynamic complexity method for the evaluation of stroke rehabilitation effect from EEG signal. Our method calculates the nonlinearly separable degree ( NLSD ) of EEG signal, and then employs an indicator, called mean nonlinearly separable complexity degree ( Mean_NLSD ), to efficiently and accurately evaluate therapy effect of stroke patients. This paper under twelve stimuli conditions on eleven patients and eleven control subjects indicates that in general Mean_NLSD is smaller at the lesion regions and that the Mean_NLSD of the control subjects is stochastic. Compared with conventional spectral methods, such as mean power spectral density ( PSD ), Mean_NLSD is more sensitive and robust. Overall Mean_NLSD may offer a promising approach to facilitate the evaluation of stroke rehabilitation effect.

This paper describes a novel algorithm for underdetermined speech separation problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The proposed algorithm consists of two steps. The unknown mixing matrix is firstly estimated from the speech mixtures in the transform domain by using K-means clustering algorithm. In the second step, the speech sources are recovered based on an autocalibration sparse Bayesian learning algorithm for speech signal. Numerical experiments including the comparison with other sparse representation approaches are provided to show the achieved performance improvement.

Mental rotation is generally analyzed based on event-related potential (ERP) in a time domain with several characteristic electrodes, but neglects the whole spatial-temporal brain pattern in the cognitive process which may reflect the underlying cognitive mechanism. In this paper, we mainly proposed an approach based on microstates to examine the encoding of mental rotation from the spatial-temporal changes of EEG signals. In particular, we collected EEG data from 11 healthy subjects in a mental rotation cognitive task using 12 different stimulus pictures representing left and right hands at various rotational angles. We applied the microstate method to investigate the microstates conveyed by the event-related potential extracted from EEG data during mental rotation, and obtained four microstate modes (referred to as modes A, B, C, D, respectively). Subsequently, we defined several measures, including microstate sequences, topographical map, hemispheric lateralization, and duration of microstate, to characterize the dynamics of microstates during mental rotation. We observed that (1) the microstates sequence had a specified progressing mode, i.e., A &rarr, B &rarr, A, (2) the activation of the right parietal occipital region was stronger than that of the left parietal occipital region according to the hemispheric lateralization of the microstates mode A, and (3) the duration of the second microstates mode A showed the shorter duration in the vertical stimuli, named &ldquo, angle effect&rdquo

With the continuous growth of WeChat users, many merchants use micromessaging platform as an important channel for marketing promotion. Based on TRA and TPB theory, this paper puts forward the relationship between perceived risk, product knowledge, interaction, information quality, Psychological impact on consumer search behavior and explore the impact of private relations on the two, build the model and put forward the relevant assumptions, through the questionnaire survey and empirical analysis to verify the model and assumptions, according to the results for the WeChat business to provide marketing recommendations to help WeChat marketing benign development.

Electroencephalography (EEG) and brain-computer interfaces (BCI) are receiving increasing attention and expanding application in stroke study. To identify stroke patients and normal controls during mental rotation task, common spatial pattern (CSP) algorithm is employed to extract features from binary-class EEG which will be further to form the dictionary for sparse representation. In the classification process, sparse representation-based classification (SRC) method is used; specifically, each test trial is sparsely represented over the formed dictionary and the sparse coefficient is obtained by solving a l1-norm regularized least squares minimization objective. A series of experiments demonstrated the effectiveness of features extracted by CSP of both classes and the SRC could obtain excellent results in classification. These result suggest that stroke patients have distinct EEG feature from normal controls. These EEG features may be potential biomarkers to monitor the rehabilitation process of stroke patients.

Personal identification plays an important role in the information society. However, the traditional methods of identification cannot fully guarantee security. As a new type of biometrics, brainprint has remarkable advantages of non-stealing and unforgeability. It is a more secure biometrics for personal identification. In this paper, we propose a new method for brainprint recognition based on brain networks of electroencephalogram (EEG) signals. Firstly, we construct the brain functional networks upon the phase synchronization of EEG channels. Then, the degree of brain networks is computed to form a novel feature vector. Lastly, we utilize linear discriminant analysis (LDA) to classify extracted features. Experiments are conducted on four data sets. The average recognition accuracy of each data set is over 0.937 and the best one reaches 0.993.

Background The evidence supporting the use of β‐blockers in patients with acute coronary syndrome after successful percutaneous coronary intervention has been inconsistent and scarce. Methods and Results Between March 1, 2009, and December 30, 2014, a total of 3180 eligible patients with acute coronary syndrome undergoing percutaneous coronary intervention were consecutively enrolled. The primary end point was all‐cause death and the secondary end point was a composite of all‐cause death, nonfatal myocardial infarction, heart failure readmission, and cardiogenic hospitalization. Patients were compared according to the use of β‐blockers at discharge. Compared with the no β‐blocker group, the risk of all‐cause death was significantly lower in the β‐blocker group (hazard ratio [ HR ], 0.33; 95% CI , 0.17–0.65 [ P =0.001]). A consistent result was obtained in multiple adjusted model and propensity score–matched analysis. The use of β‐blockers was also associated with decreased risk of composite of adverse cardiovascular events ( HR , 0.47; 95% CI , 0.28–0.81 [ P =0.006]), although statistical significance disappeared after multivariable adjustment and propensity score matching. Furthermore, we performed post hoc analysis for the subsets of patients and the results revealed that patients with non–ST‐segment elevation myocardial infarction benefited the most from β‐blocker therapy at discharge ( HR , 0.04; 95% CI , 0.00–0.27 [ P =0.001]), and the use of Conclusions The administration of relatively low β‐blocker dose is associated with improved clinical outcomes among patients with acute coronary syndrome after successful percutaneous coronary intervention, especially for patients with non‐ST‐segment elevation myocardial infarction.

Correction We deeply regret that we have inadvertently made a minor mistake in our publication [1] and we feel it necessary to correct this error. Following publication of our work, we reviewed all the Western blots of the experiments; all showed an identical trend in terms of scientific interpretation. Unfortunately, it was subsequently noticed that Figure one A (Figure 1 here) (CYP2J3 expression) was not the original unedited (un-cropped) blot, and we would like to correct that inadvertent error. The following Western blot (Figure 1) therefore replaces Figure one A in the original article. We recognize that this Western blot is not as clean, but it clearly presents the original without modifications (cropping) and thus can avoid confusion for the readers. We wish to emphasize that the experiments on CYP2J3 overexpression with Western blot analysis were repeated 4 times, with identical results obtained each time. The error had no effect on the scientific content or conclusions. We deeply apologize that this occurred, and take responsibility for the error and hereby present the correction.

This paper investigates the development, the breakdown process, and the discharge path selection of the lightning discharges while there exist two phase mixtures. 13 kinds of solid-gas mixtures and 3 kinds of liquid-gas mixtures are employed to study effect of two-phase mixtures (TPM) on the selection of the discharge path under lightning impulses. Grain size effects are shown upon these experimental results. When the diameter of solid or liquid grain is less than about 10 mum, the discharge path selects not TPM but air. And the discharge path selects TPM when the diameter is greater than about 100 mum. And the diameter is between about 10 mum and 100 mum, the discharge path selects TPM under negative lightning impulses, but it has a greater selection of air than TPM under positive lightning impulses. Volume fraction and permittivity of solid/liquid can also influence the selection of the discharge path.