Assess patient understanding of, potential concerns with, and implementation preferences related to automated suicide risk identification using electronic health record data and machine learning.

Focus groups (n = 23 participants) informed a web-based survey sent to 11,486 Kaiser Permanente Northwest members in April 2020. Survey items assessed patient preferences using Likert and visual analog scales (means scored from -50 to 50). Descriptive statistics summarized findings.

1357 (12%) participants responded. Most (84%) found machine learning-derived suicide risk identification an acceptable use of electronic health record data; however, 67% objected to use of externally sourced data. Participants felt consent (or opt-out) should be required (mean = -14). The majority (69%) supported outreach to at-risk individuals by a trusted clinician through care messages (57%) or telephone calls (47-54%). Highest endorsements were for psychiatrists/therapists (99%) or a primary care clinician (75-96%); less than half (42%) supported outreach by any clinician and participants generally felt only trusted clinicians should have access to risk information (mean = -16).

Patients generally support use of EHR data (not externally sourced risk information) to inform automated suicide risk identification models but prefer to consent or opt-out; trusted clinicians should outreach by telephone or care message to at risk individuals.

Patients generally support use of EHR data (not externally sourced risk information) to inform automated suicide risk identification models but prefer to consent or opt-out; trusted clinicians should outreach by telephone or care message to at risk individuals.Emerging high-throughput proteomic technologies have recently been considered as a powerful means of identifying substrates involved in mood disorders. We performed proteomic profiling using liquid chromatography-tandem mass spectrometry to identify dysregulated proteins in plasma samples of 42 and 45 patients with major depressive disorder (MDD) and bipolar disorder (BD), respectively, in comparison to 51 healthy controls (HCs). Fourteen and six proteins in MDD and BD patients, respectively, were differentially expressed compared to HCs, among which coagulation factor XIII A chain (F13A1), platelet basic protein (PPBP), platelet facor 4 (PF4), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and thymosin beta-4 (TMSB4X) were altered in both disorders. For proteins dysregulated in both, except F13A1, higher fold changes were observed in MDD than in BD patients. These findings may help identify candidate biomarkers of mood disorders and elucidate their underlying pathophysiology and biochemical abnormalities.Cytomegalovirus (CMV) promoter drives various gene expression and yields sufficient protein for further functional investigation. Receptor binding domain (RBD) on spike protein of the SARS_CoV2 is the most critical portal for virus infection. Thus native conformational RBD protein may facilitate biochemical identification of RBD and provide valuable support of drug and vaccine design for curing COVID-19. We attempted to express RBD under CMV promoter in vitro, but failed. RBD-specific mRNA cannot be detected in cell transfected with recombinant plasmids, in which CMV promoter governs the RBD transcription. Additionally, the pCMV-Tag2B-SARS_CoV2_RBD trans-inactivates CMV promoter transcription activity. Alternatively, we identified that both Chicken β-actin promoter and Vaccinia virus-specific medium/late (M/L) promoter (pSYN) can highly precede SARS_CoV2 RBD expression. Our findings provided evidence that SARS_CoV2 RBD gene can be driven by Chicken β-actin promoter or Vaccinia virus-specific medium/late promoter instead of CMV promoter, thus providing valuable information for RBD feature exploration.

Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI sequence, the specialized magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) shows a higher brain tissue and lesion contrast in multiple sclerosis (MS) patients. The goal of this work is to retrospectively generate realistic-looking MP2RAGE uniform images (UNI) from already acquired MPRAGE images in order to improve the automatic lesion and tissue segmentation.

For this task we propose a generative adversarial network (GAN). Multi-contrast MRI data of 12 healthy controls and 44 patients diagnosed with MS was retrospectively analyzed. Imaging was acquired at 3T using a SIEMENS scanner with MPRAGE, MP2RAGE, FLAIR, and DIR sequences. We train the GAN with both healthy controls and MS patients to generate synthetic MP2RAGE UNI images. These images were then compared to the real MP2RAGE UNI (considered as ground truth) analyzing the output of automatic brain tissue and lesion segmentation tools. Reference-based metrics as well as the lesion-wise true and false positives, Dice coefficient, and volume difference were considered for the evaluation. Statistical differences were assessed with the Wilcoxon signed-rank test.

The synthetic MP2RAGE UNI significantly improves the lesion and tissue segmentation masks in terms of Dice coefficient and volume difference (p-values<0.001) compared to the MPRAGE. For the segmentation metrics analyzed no statistically significant differences are found between the synthetic and acquired MP2RAGE UNI.

Synthesized MP2RAGE UNI images are visually realistic and improve the output of automatic segmentation tools.

Synthesized MP2RAGE UNI images are visually realistic and improve the output of automatic segmentation tools.

Recent years have seen an increased interest in electrohysterogram (EHG) signals as a means to evaluate the synchronization of uterine contractions. Several studies have pointed out that the quality of signal processing – and hence the interpretation of measurement results – is affected significantly by the choice of measurement technique and the presence of non-stationary frequency content in EHG signals. To our knowledge, the effect of time variance on the quality of EHG signal processing has never been fully investigated. How best to process EHG signals with the goal of distinguishing labor-induced contractions from their harmless, pre-labor cousins, remains an open question.

Our methodology is based on three pillars. TAK-901 The first consists of a new method for EHG preprocessing in which we apply a second-order Butterworth filter to retain only the EHG fast-wave, low-frequency band (FWL), then use a bivariate piecewise stationary pre-segmentation (bPSP) algorithm to segment the EHG signal into stationary parts.