Spontaneous Haemoperitoneum in Pregnancy with Placenta Percreta in Bicornuate Uterus with Successful Pregnancy Outcome: A case study.

Intraperitoneal haemorrhage during pregnancy or postpartum without any history of trauma (spontaneous haemoperitoneum in pregnancy [SHiP]) is a rare condition, causing significant morbidity and mortality for the mother and the neonate. We report a 27-year-old female patient with SHiP at 31 weeks of gestation who was referred to a tertiary care hospital in Muscat, Oman, in 2019, with right iliac fossa pain, pallor and tachycardia. Radiological investigations revealed intraperitoneal bleeding and a right adnexal haematoma. She was managed conservatively with blood transfusion and supportive care. At 36 weeks of gestation, lower segment caesarean section was done and a live baby with good Apgar score was delivered. Pre-operatively, she was found to have a bicornuate uterus, placenta percreta at the junction of the horns and a right adnexal haematoma. This case highlights the significance of thorough evaluation of acute abdominal pain in pregnancy in avoiding unnecessary surgical intervention and iatrogenic prematurity.

Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features.

This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.

Implications of Early Pregnancy Obesity on Maternal, Fetal and Neonatal Health: Retrospective cohort study from Oman.

OBJECTIVES: This study aimed to determine the prevalence of early pregnancy obesity among Omani women and to review maternal antenatal complications, intrapartum and postpartum events and neonatal complications among such women in comparison to women of normal weight. METHODS: This retrospective cohort study included 2,652 pregnant Omani women who delivered at the Royal Hospital, Muscat, Oman, between November 2011 and April 2012. The patients' electronic medical records were reviewed for antenatal, intrapartum and postpartum data. Body mass index was measured during the first trimester (≤12 gestational weeks) and classified according to the World Health Organization categories. Maternal and neonatal complications were compared between obese women and those of normal weight. Obstetric outcomes in uncomplicated pregnancies were also compared. RESULTS: In the study cohort, there were 901 (34%) obese women and 912 (34.4%) women of normal weight; of these, 440 (48.8%) and 672 (73.7%) had uncomplicated pregnancies, respectively. Obese women had a significantly increased incidence of gestational diabetes (relative risk [RR]: 2.23; 95% confidence interval [CI]: 1.70-2.92; P <0.01), gestational hypertension (RR: 3.04; 95% CI: 1.63-5.65; P <0.01), Caesarean delivery (RR: 1.48; 95% CI: 1.08-2.03; P <0.01), postpartum haemorrhage (RR: 2.10; 95% CI: 1.11-4.10; P = 0.01) and fetal macrosomia (RR: 2.71; 95% CI: 1.21-6.09; P <0.01). CONCLUSION: Approximately one-third of the studied Omani women were obese. These women had a significantly increased risk of various maternal antenatal complications, intrapartum and postpartum events and neonatal complications.

Study of target and non-target interplay in spatial attention task.

Selective visual attention is the ability to selectively pay attention to the targets while inhibiting the distractors. This paper aims to study the targets and non-targets interplay in spatial attention task while subject attends to the target object present in one visual hemifield and ignores the distractor present in another visual hemifield. This paper performs the averaged evoked response potential (ERP) analysis and time-frequency analysis. ERP analysis agrees to the left hemisphere superiority over late potentials for the targets present in right visual hemifield. Time-frequency analysis performed suggests two parameters i.e. event-related spectral perturbation (ERSP) and inter-trial coherence (ITC). These parameters show the same properties for the target present in either of the visual hemifields but show the difference while comparing the activity corresponding to the targets and non-targets. In this way, this study helps to visualise the difference between targets present in the left and right visual hemifields and, also the targets and non-targets present in the left and right visual hemifields. These results could be utilised to monitor subjects' performance in brain-computer interface (BCI) and neurorehabilitation.

The Evaluation of Physical Stillness with Wearable Chest and Arm Accelerometer during Chan Ding Practice.

Chan Ding training is beneficial to health and emotional wellbeing. More and more people have taken up this practice over the past few years. A major training method of Chan Ding is to focus on the ten Mailuns, i.e., energy points, and to maintain physical stillness. In this article, wireless wearable accelerometers were used to detect physical stillness, and the created physical stillness index (PSI) was also shown. Ninety college students participated in this study. Primarily, accelerometers used on the arms and chest were examined. The results showed that the PSI values on the arms were higher than that of the chest, when participants moved their bodies in three different ways, left-right, anterior-posterior, and hand, movements with natural breathing. Then, they were divided into three groups to practice Chan Ding for approximately thirty minutes. Participants without any Chan Ding experience were in Group I. Participants with one year of Chan Ding experience were in Group II, and participants with over three year of experience were in Group III. The Chinese Happiness Inventory (CHI) was also conducted. Results showed that the PSI of the three groups measured during 20-30 min were 0.123 ± 0.155, 0.012 ± 0.013, and 0.001 ± 0.0003, respectively (p < 0.001 ***). The averaged CHI scores of the three groups were 10.13, 17.17, and 25.53, respectively (p < 0.001 ***). Correlation coefficients between PSI and CHI of the three groups were -0.440, -0.369, and -0.537, respectively (p < 0.01 **). PSI value and the wearable accelerometer that are presently available on the market could be used to evaluate the quality of the physical stillness of the participants during Chan Ding practice.

A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals.

Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. The electroencephalogram (EEG) signals are highly complex, nonlinear, and nonstationary in nature. The characteristics of the signal vary with the age and mental state of the subject. The signs of abnormality may be invisible to the naked eyes. Even when they are visible, deciphering the minute changes indicating abnormality is tedious and time consuming for the clinicians. This paper presents a novel method for automated EEG-based diagnosis of depression using nonlinear methods: fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis. A novel Depression Diagnosis Index (DDI) is presented through judicious combination of the nonlinear features. The DDI calculated automatically based on the EEG recordings can be used to diagnose depression objectively using just one numeric value. Also, these features extracted from nonlinear methods are ranked using the t value and fed to the support vector machine (SVM) classifier. The SVM classifier yielded the highest classification performance with an average accuracy of about 98%, sensitivity of about 97%, and specificity of about 98.5%.

Computer-Aided Diagnosis of Depression Using EEG Signals.

The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.

Role of morphological structure, doping, and coating of different materials in the sensing characteristics of humidity sensors.

The humidity sensing characteristics of different sensing materials are important properties in order to monitor different products or events in a wide range of industrial sectors, research and development laboratories as well as daily life. The primary aim of this study is to compare the sensing characteristics, including impedance or resistance, capacitance, hysteresis, recovery and response times, and stability with respect to relative humidity, frequency, and temperature, of different materials. Various materials, including ceramics, semiconductors, and polymers, used for sensing relative humidity have been reviewed. Correlations of the different electrical characteristics of different doped sensor materials as the most unique feature of a material have been noted. The electrical properties of different sensor materials are found to change significantly with the morphological changes, doping concentration of different materials and film thickness of the substrate. Various applications and scopes are pointed out in the review article. We extensively reviewed almost all main kinds of relative humidity sensors and how their electrical characteristics vary with different doping concentrations, film thickness and basic sensing materials. Based on statistical tests, the zinc oxide-based sensing material is best for humidity sensor design since it shows extremely low hysteresis loss, minimum response and recovery times and excellent stability.