Activity

LR was an independent risk factor for predicting RFS in T1-2N0M0 lung adenocarcinoma patients after curative surgical resection. BLR can be used as a biomarker for evaluating the risk of lung cancer recurrence.

BLR was an independent risk factor for predicting RFS in T1-2N0M0 lung adenocarcinoma patients after curative surgical resection. BLR can be used as a biomarker for evaluating the risk of lung cancer recurrence.

The prognostic implications of left ventricular (LV) mass and geometry have been confirmed in populations with different cardiac diseases. However, the prognostic value of LV geometry in coronary artery bypass grafting (CABG) patients is unclear.

A total of 2,517 patients undergoing CABG between January 2012 and September 2016 in our cardiac surgery unit were included. Patients were divided into the following 4 groups according to left ventricular mass index (LVMi) and relative wall thickness (RWT) normal geometry, concentric remodeling, eccentric hypertrophy, and concentric hypertrophy.

The median follow-up period was 47.0 months (interquartile range was 32.5-61.3 months). Compared to the normal geometry group, the concentric remodeling group [hazard ratio (HR) 3.023; 95% confidence interval (CI) 1.134-8.060], the eccentric hypertrophy group (HR 3.422; 95% CI 1.395-8.398), and the concentric hypertrophy group (HR 5.399; 95% CI 2.289-12.735) have higher main adverse cardiovascular and cerebrovascular event (MACCE) risk. Moreover, increased MACCE risk was associated with higher LVMi (HR 1.015 per 1 g/m

increase in LVMi; 95% CI 1.005-1.026) and RWT (HR 1.991 per 0.1-U increase in RWT; 95% CI 1.343-2.952). We observed similar results concerning mortality. Adding LV geometry to the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II significantly improved the area under the curve (AUC) for MACCE (from 0.621 to 0.703; P=0.042). The addition of LV geometry showed significant integrated discrimination improvement (IDI) and net reclassification improvement (NRI) for MACCE (IDI 0.043, P<0.001; NRI 0.200, P<0.001) and death (IDI 0.018, P=0.020; NRI 0.308, P=0.002), as was the addition of LVMi and RWT.

LV geometry is an independent and incremental prognostic factor for MACCE and death in CABG patients.

LV geometry is an independent and incremental prognostic factor for MACCE and death in CABG patients.

Magnetic resonance imaging (MRI) has the limitation of low imaging speed. Acceleration methods using under-sampled k-space data have been widely exploited to improve data acquisition without reducing the image quality. Sensitivity encoding (SENSE) is the most commonly used method for multi-channel imaging. GSK269962A purchase However, SENSE has the drawback of severe g-factor artifacts when the under-sampling factor is high. This paper applies generative adversarial networks (GAN) to remove g-factor artifacts from SENSE reconstructions.

Our method was evaluated on a public knee database containing 20 healthy participants. We compared our method with conventional GAN using zero-filled (ZF) images as input. Structural similarity (SSIM), peak signal to noise ratio (PSNR), and normalized mean square error (NMSE) were calculated for the assessment of image quality. A paired student’s t-test was conducted to compare the image quality metrics between the different methods. Statistical significance was considered at P<0.01.

The proposed method outperformed SENSE, variational network (VN), and ZF + GAN methods in terms of SSIM (SENSE + GAN 0.81±0.06, SENSE 0.40±0.07, VN 0.79±0.06, ZF + GAN 0.77±0.06), PSNR (SENSE + GAN 31.90±1.66, SENSE 22.70±1.99, VN 31.35±2.01, ZF + GAN 29.95±1.59), and NMSE (×10

) (SENSE + GAN 0.95±0.34, SENSE 4.81±1.33, VN 0.97±0.30, ZF + GAN 1.60±0.84) with an under-sampling factor of up to 6-fold.

This study demonstrated the feasibility of using GAN to improve the performance of SENSE reconstruction. The improvement of reconstruction is more obvious for higher under-sampling rates, which shows great potential for many clinical applications.

This study demonstrated the feasibility of using GAN to improve the performance of SENSE reconstruction. The improvement of reconstruction is more obvious for higher under-sampling rates, which shows great potential for many clinical applications.

Recently, there has been a range of studies about smartphone-based interventions and monitoring for reducing symptoms of bipolar disorder (BD). However, their efficacy for BD remains unclear.

To compare the effect of smartphone-based interventions and monitoring with control methods in treating patients with BD.

A systematic literature search was performed on PubMed, Embase, Clinical trials, psycINFO, Web of Science, and Cochrane Library. Randomized clinical trials (RCTs) or single-group trials in which smartphone-based interventions and monitoring were compared with control methods or baseline in patients with symptoms of BD were included. Data were synthesized using a random-effects or a fixed-effects model to analyze the effects of psychological interventions and monitoring delivered

smartphone on psychiatric symptoms in patients with BD. The primary outcome measures were set for mania and depression symptoms. Subgroups were created to explore which aspects of smartphone interventions are relevanthe controlled conditions, while the interventions without psychoeducation did not (

> 0.05). The contacts between therapists and patients that contributed to the implementation of psychological therapy reduced depression symptoms (g = -0.47, 95%CI -0.75 to -0.18,

= 0.01).

Smartphone-based interventions and monitoring have a significant positive impact on depressive and manic symptoms of BD patients in between-group and within-group analysis.

Smartphone-based interventions and monitoring have a significant positive impact on depressive and manic symptoms of BD patients in between-group and within-group analysis.

Nonadherence is a major problem in the treatment of psychotic disorders. It has been hypothesized that nonadherent patients with schizophrenia are not a homogeneous population and subtypes of nonadherence might exist, but this hypothesis has not been specifically tested.

To test the hypothesis of subtypes of nonadherence in schizophrenia and schizoaffective disorder.

This prospective study included 110 consecutively admitted patients diagnosed with schizophrenia or schizoaffective disorder. Assessments were performed at baseline and at 6 mo follow-up after discharge. Sociodemographic, clinical, psychopathological and treatment-related variables were evaluated. Adherence was defined as the concurrence of adherence to antipsychotic treatment and outpatient follow-up during the six-month period. Adherence to antipsychotic treatment was defined as the concurrence of objective and subjective adherence. Sixty-four patients (58%) fulfilled nonadherence criteria at the end of the follow-up period and were categorized according to their subtype of nonadherence.