Explore the vast range of titles available.

Reducing failed labor and emergency cesarean section (CS) rates is an important goal. A childbirth simulation tool (PREDIBIRTH software and SIM37 platform) that evaluates a 5-min magnetic resonance imaging (MRI) assessment performed at 37 weeks of gestation was developed to enhance the consulting obstetrician’s ability to predict the optimal delivery mode. We aimed to determine the potential value of this childbirth simulation tool in facilitating the selection of an optimal delivery mode for both mother and infant. A retrospective cohort study was performed on all patients referred by their obstetricians to our level 2 maternity radiology department between December 15, 2015 and November 15, 2016, to undergo MRI pelvimetry at approximately 37 weeks of gestation. The childbirth simulation software was employed to predict the optimal delivery mode based on the assessment of cephalopelvic disproportion. The prediction was compared with the actual outcome for each case. Including childbirth simulations in the decision-making process had the potential to reduce emergency CSs, inappropriately scheduled CSs, and instrumental vaginal deliveries by up to 30.1%, 20.7%, and 20.0%, respectively. Although the use of the simulation tool might not have affected the overall CS rate, consideration of predicted birthing outcomes has the potential to improve the allocation between scheduled CS and trial of labor. The routine use of childbirth simulation software as a clinical support tool when choosing the optimal delivery mode for singleton pregnancies with a cephalic presentation could reduce the number of emergency CSs, insufficiently justified CSs, and instrumental deliveries.

Bed rest or activity restriction is a common obstetrical practice, despite a paucity of data to support its efficacy. The aim of this study was to determine whether physical activity, as assessed by a smart band activity tracker, is associated with preterm birth in pregnant women at high risk for preterm delivery. This was a pilot prospective cohort study including pregnant women at high risk for preterm delivery between 24 and 32 weeks-of-gestation. Physical activity level was assessed by smart band activity. Patients with sonographic short cervical length (≤ 20 mm) were asked to wear the smart band activity tracker continuously for at least one week, including one weekend. Both physicians and patients were blinded to the data stored in the smart band activity tracker. No specific recommendations were given to participants as to the level or intensity of physical activity. The primary outcome was the rate of preterm birth (< 37 weeks-of-gestation). Secondary outcomes included the rate of delivery before 34 weeks of gestation and neonatal outcome. Parametric and nonparametric statistics were used for analysis. Study population included 49 pregnant women: 37 women (75.7%) delivered preterm and 12 (24.5%) delivered at or after 37 weeks-of-gestation. The median steps per day was significantly lower in patients who delivered preterm (3576, IQR: 2478-4775 vs. 4554, IQR: 3632-6337, p = 0.02). Regression analysis revealed that the median number of steps per day was independently inversely associated with preterm birth, after adjustment for maternal age, body mass index, gestational age at recruitment, cervical length, cervical dilatation and plurality. This pilot study represents the first quantitative assessment of the association between physical activity and preterm birth. The results of this pilot study do not support the efficacy of decreased physical activity in the prevention of preterm birth in patients with sonographic short cervical length.

To assess the influence of protective gear on intubation performance. Prospective, controlled measurement of duration and quality of intubations performed on mannequins by medical personnel with and without protective gear in a crossover design. Eight teams each comprising an anesthesiologist and a nurse. Intubation duration with and without chemical warfare gear was 69.2 +/- 7 and 47.3 +/- 6 seconds (mean +/- SEM), respectively (p < 0.05). Moreover, rating of intubation quality as \"very good\" by the anesthesiologists declined significantly from 62.5% without chemical warfare protective gear to 6.25% with the garment and mask. Tube fixation was the rate-limiting step when performed with protective gear (p < 0.05); it was assessed by 81% of the anesthesiologists as the critical step. A learning curve was not observed during the study. Protective gear causes a significant prolongation of intubation duration; however, endotracheal intubation can be performed effectively. Technical improvements are warranted for tube fixation because it is the critical step.

Pyridostigmine, a carbamate acetylcholinesterase (AChE) inhibitor, is routinely employed in the treatment of the autoimmune disease myasthenia gravis 1 . Pyridostigmine is also recommended by most Western armies for use as pretreatment under threat of chemical warfare, because of its protective effect against organophosphate poisoning 2,3 . Because of this drug's quaternary ammonium group, which prevents its penetration through the blood–brain barrier, the symptoms associated with its routine use primarily reflect perturbations in peripheral nervous system functions 1,4 . Unexpectedly, under a similar regimen, pyridostigmine administration during the Persian Gulf War resulted in a greater than threefold increase in the frequency of reported central nervous system symptoms 5 . This increase was not due to enhanced absorption (or decreased elimination) of the drug, because the inhibition efficacy of serum butyrylcholinesterase was not modified 5 . Because previous animal studies have shown stress–induced disruption of the blood–brain barrier 6 , an alternative possibility was that the stress situation associated with war allowed pyridostigmine penetration into the brain. Here we report that after mice were subjected to a forced swim protocol (shown previously to simulate stress 7 ), an increase in blood–brain barrier permeability reduced the pyridostigmine dose required to inhibit mouse brain AChE activity by 50% to less than 1/100th of the usual dose. Under these conditions, peripherally administered pyridostigmine increased the brain levels of c– fos oncogene and AChE mRNAs. Moreover, in vitro exposure to pyridostigmine increased both electrical excitability and c– fos mRNA levels in brain slices, demonstrating that the observed changes could be directly induced by pyridostigmine. These findings suggest that peripherally acting drugs administered under stress may reach the brain and affect centrally controlled functions.

BACKGROUND: The possible role of heme oxygenase and its byproduct carbon monoxide (CO) in the regulation of blood pressure is under investigation. The aim of this study was to compare end tidal breath CO (ETCO) levels in women with gestational hypertension (GH) or pre-eclampsia to the levels in healthy pregnant and nonpregnant women. MATERIALS AND METHODS: We prospectively performed ETCO measurements corrected for ambient CO (ETCOc) in two medical centers (Stanford, CA and Cleveland, OH). A Natus ® CO-Stat ® End Tidal Breath Analyzer (Natus Medical Inc., San Carlos, CA) was used. The study group included a convenience sample of 31 women with GH/pre-eclampsia (PE). Control groups included 46 nonpregnant healthy women, 44 first-trimester and 48 third-trimester pregnant healthy women. RESULTS: Mean±SD ETCOc measurements were significantly lower in the GH/PE group compared to first-trimester ( p =0.004) and third-trimester ( p =0.001) normotensive pregnant and nonpregnant women ( p =0.002) (1.36±0.30 vs 1.76±0.47, 1.72±0.42 and 1.78±0.54 ppm, respectively). The ETCOc values were ≤1.6 ppm in 89% of GH/PE women compared with, respectively, only 45, 54, and 46% of nonpregnant, first- and third-trimester normotensive pregnant women (p<0.05). ETCO measurements were not influenced by maternal age, parity, ethnicity, body mass index, gestational age or presence of household smokers. In the two centers, the controls had a similar mean ETCOc and the differences found remained significant when results for each center were analyzed separately. CONCLUSIONS: ETCOc levels were found to be significantly lower in women with GH/PE. Further investigation is required to determine if the lower CO levels reflect a deficient compensatory response to the increase in blood pressure or whether these are primary changes of significance to our understanding of the pathogenesis of GH/PE.

OBJECTIVE: To compare the levels of end-tidal carbon monoxide (ETCOc) among women with and without uterine contractions in term and preterm pregnancies. STUDY DESIGN: In all, 55 nonsmoking healthy pregnant women were enrolled. ETCOc levels were compared among women with contractions (10 preterm and 13 term) and 32 women without contractions (34–41 weeks gestation). RESULTS: Maternal age, gravidity and parity were similar among study and control groups. ETCOc levels were significantly lower among women that had uterine contractions (0.99±0.38 parts per million (ppm) and 1.15±0.34 p.p.m. respectively), compared to women with no contractions (1.70±0.52 p.p.m., P <0.002). However, there was no significant difference in the ETCOc levels between women with preterm or term contractions ( P =0.48). CONCLUSIONS: Low levels of ETCOc are associated with preterm and term uterine contractions.

Contemporary symptom-based diagnosis of post-traumatic stress disorder (PTSD) largely overlooks related neurobehavioral mechanisms and relies entirely on subjective interpersonal reporting. Previous studies associating biomarkers with PTSD have mostly used symptom-based diagnosis as the main outcome measure, disregarding the wide variability and richness of PTSD phenotypical features. Here, we aimed to computationally derive potential biomarkers that could efficiently differentiate PTSD subtypes among recent trauma survivors. A three-staged semi-unsupervised method (“3C”) was used to firstly categorize individuals by current PTSD symptom severity, then derive clusters based on clinical features related to PTSD (e.g. anxiety and depression), and finally to classify participants’ cluster membership using objective multi-domain features. A total of 256 features were extracted from psychometrics, cognitive functioning, and both structural and functional MRI data, obtained from 101 adult civilians (age = 34.80 ± 11.95; 51 females) evaluated within 1 month of trauma exposure. The features that best differentiated cluster membership were assessed by importance analysis, classification tree, and ANOVA. Results revealed that entorhinal and rostral anterior cingulate cortices volumes (structural MRI domain), in-task amygdala’s functional connectivity with the insula and thalamus (functional MRI domain), executive function and cognitive flexibility (cognitive testing domain) best differentiated between two clusters associated with PTSD severity. Cross-validation established the results’ robustness and consistency within this sample. The neural and cognitive potential biomarkers revealed by the 3C analytics offer objective classifiers of post-traumatic morbidity shortly following trauma. They also map onto previously documented neurobehavioral mechanisms associated with PTSD and demonstrate the usefulness of standardized and objective measurements as differentiating clinical sub-classes shortly after trauma.

•We propose a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD one, six and 14-months after the exposure.•The method utilizes connectivity maps extracted from pairs of brain regions which are subsequently updated by applying the algorithmic technique of pairwise attention.•The proposed deep learning method predicts PTSD status, PTSD symptom clusters and survival analysis within the prospective design. We demonstrate a significant improvement in performance on all the datasets and experiments in comparison to other relevant analytical techniques.•Pairwise association analysis reveals several significant functional connectivity patterns, in line with previous PTSD neuroimaging literature. Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method’s performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.

Post-traumatic stress disorder (PTSD) is a protracted and debilitating consequence of traumatic events. Identifying early predictors of PTSD can inform the disorder’s risk stratification and prevention. We used advanced computational models to evaluate the contribution of early neurocognitive performance measures to the accuracy of predicting chronic PTSD from demographics and early clinical features. We consecutively enrolled adult trauma survivors seen in a general hospital emergency department (ED) to a 14-month long prospective panel study. Extreme Gradient Boosting algorithm evaluated the incremental contribution to 14 months PTSD risk of demographic variables, 1-month clinical variables, and concurrent neurocognitive performance. The main outcome variable was PTSD diagnosis, 14 months after ED admission, obtained by trained clinicians using the Clinician-Administered PTSD Scale (CAPS). N = 138 trauma survivors (mean age = 34.25 ± 11.73, range = 18–64; n = 73 [53%] women) were evaluated 1 month after ED admission and followed for 14 months, at which time n = 33 (24%) met PTSD diagnosis. Demographics and clinical variables yielded a discriminatory accuracy of AUC = 0.68 in classifying PTSD diagnostic status. Adding neurocognitive functioning improved the discriminatory accuracy (AUC = 0.88); the largest contribution emanating from poorer cognitive flexibility, processing speed, motor coordination, controlled and sustained attention, emotional bias, and higher response inhibition, and recall memory. Impaired cognitive functioning 1-month after trauma exposure is a significant and independent risk factor for PTSD. Evaluating cognitive performance could improve early screening and prevention.