Spontaneous mid-trimester rupture of a non-scarred gravid uterus

Gravid uterine rupture is a life threatening condition for both mother and her fetus. Spontaneous rupture usually occurs in scarred uterus in late pregnancy without any myometrial contraction. But, rupture of a non-scarred gravid uterus in early pregnancy is a rare complication. Clinical signs and symptoms of rupture in early pregnancy are non-specific and should be differentiated from other causes of acute abdomen so that proper action should be taken in time to reduce maternal morbidity and mortality. Here we report a case of spontaneous mid-trimester rupture of an unscarred uterus in which prior evacuation caused weakening of myometrium that might have given way in this pregnancy

Prevalence of vitamin D deficiency in postmenopausal women and its association with fasting blood sugar

Background: Vitamin D is indispensable for human body as it caters for both skeletal as well as extra-skeletal needs, especially in postmenopausal women. Aim of this study was to know the prevalence of vitamin D deficiency among apparent healthy postmenopausal women and to find its association with fasting blood sugar.Methods: This was a cross-sectional observational study of postmenopausal women attending Gynae OPD of Sri Ram Murti Smarak Institute of Medical Sciences, Bareilly, over a period of six months. Apparently healthy postmenopausal women were selected after satisfying inclusion-exclusion criteria and were subjected to fasting blood sugar and serum 25-hydroxy-vitamin D level. They were categorized as vitamin D deficient, insufficient or sufficient according to corresponding levels of 30ng/ml respectively. Upper reference level for fasting blood sugar was taken as 110mg/dl. Statistical analysis was done to see the association between vitamin D deficiency and fasting blood sugar.Results: Mean age of study group was 56.9 years and the average age of attainment of menopause was 50.3 years. Prevalence of vitamin D deficiency (57.4%) including insufficiency (13%) among postmenopausal women was high 70.4%, but there was no association between hypovitaminosis D and fasting blood sugar (p=0.949).Conclusions: Despite high prevalence of vitamin D deficiency among Indian postmenopausal women, there is no correlation between it and fasting blood sugar

Scalable Microfabrication Procedures for Adhesive-Integrated Flexible and Stretchable Electronic Sensors.

New classes of ultrathin flexible and stretchable devices have changed the way modern electronics are designed to interact with their target systems. Though more and more novel technologies surface and steer the way we think about future electronics, there exists an unmet need in regards to optimizing the fabrication procedures for these devices so that large-scale industrial translation is realistic. This article presents an unconventional approach for facile microfabrication and processing of adhesive-peeled (AP) flexible sensors. By assembling AP sensors on a weakly-adhering substrate in an inverted fashion, we demonstrate a procedure with 50% reduced end-to-end processing time that achieves greater levels of fabrication yield. The methodology is used to demonstrate the fabrication of electrical and mechanical flexible and stretchable AP sensors that are peeled-off their carrier substrates by consumer adhesives. In using this approach, we outline the manner by which adhesion is maintained and buckling is reduced for gold film processing on polydimethylsiloxane substrates. In addition, we demonstrate the compatibility of our methodology with large-scale post-processing using a roll-to-roll approach

Induction of labour and its feto-maternal outcome

Background: Induction of labour is an iatrogenic deliberate attempt to terminate the pregnancy in order to achieve vaginal delivery in cases of valid indication. It should be carefully supervised as it is a challenge to the clinician, mother and the fetus. Aim of this study was to find out common indications for IOL in a tertiary care teaching centre and its feto-maternal outcome.Methods: An institutional based retrospective observational study was conducted to describe the prevalence of labour induction and factors associated with its outcome, during the time-period of one year from January 2018 to December 2018, at SRMS IMS, Bareilly. Logistic regression analysis was employed to assess the relative effect of determinants and statistical tests were used to see the associations.Results: Most of the patients were primigravidas of younger age-group. Idiopathic oligohydramnios and postdatism were the commonest indications for induction of labour and Misoprost was the commonest drug used for it. Though majority had vaginal delivery, as the method was changed to combined method it was significantly associated with increased likelihood of LSCS. Similarly there was increased association with maternal cervico-vaginal tear / lacerations as the method was changed to combined type. However there were no association between post-partum hemorrhage, meconium stained liquor or fetal distress.Conclusions: Common indications for induction of labour were oligohydramnios and postdatism. Misoprost can be safely used for induction of labour without any increased risk for LSCS or any fetal / neonatal risks

Severe acute maternal morbidity: study of epidemiology and risk factors

Background: For last decade, severe acute maternal morbidity (SAMM) has emerged as a promising alternative to maternal mortality reviews. Maternal mortality may represent the tip of iceberg whose large base is formed by SAMM. The objective was to study the cases of critically ill obstetric i.e. pregnant and puerperal patients requiring intensive care due to severe acute maternal morbidity (near miss cases) and to analyze the common preventable risk factors associated with them.             Methods: This is a retrospective cohort study of women admitted during pregnancy or within 6 weeks postpartum, to multidisciplinary intensive care unit (ICU) of a tertiary-care teaching hospital, Sri Ram Murti Smarak Institute of Medical Sciences, Bareilly, UP, India, from April 2013 to April 2016. The data was collected pertaining to demographics, obstetric history, any pre-existing medical illness, reasons for admission to ICU, treatment given and their outcome in terms of maternal morbidity and mortality.Results: The study found the prevalence of SAMM to be 1.87%. The average age of admission was 26.6 years and most were primigravidas (42%), unbooked (97%) coming from rural areas (75%). The most common obstetric cause of ICU admission was hypertensive disorders of pregnancy and the most common non-obstetric cause was sepsis. Maternal mortality index was 25.8% and fetal mortality rate was 257 per 1000 births among SAMM cases.Conclusions: Study of risk factors associated with SAMM can provide important contributions to improve quality of available health care system in order to achieve reduction in maternal mortality

Improving Clinical Practices in the Neonatal Intensive Care Unit: A Bioengineering Approach

The greatest risk of childhood deaths occurs in the first few weeks of life. Two of the major concerns, birth asphyxia, and sepsis are responsible for the loss of over 2.5 million infants in this vulnerable period. Worse, the survivors are at a high risk for long-term morbidity. This doctoral research work focuses on developing screening tools to influence timely clinical decision-making for targeted treatment for such high risk-infants. First, we developed a web-based decision support tool to encourage timely initiation of therapeutic hypothermia, the only available therapy for infants at risk for brain injury due to birth asphyxia. This tool provides access to widely accepted clinical guidelines and strategies in a simplified way which can be easy to follow and access in clinical settings. Such a clinical decision support tool can obviate some of the time and effort needed for rigorous training and refresher sessions for providers at low acuity, low volume birthing centers. Second, we developed a platform for rapid, reliable and automated identification of bloodborne pathogens responsible for neonatal sepsis using DNA melting analysis directly after digital PCR amplification. Specifically, we designed a high resolution digital melt platform with precise thermal control to accomplish reliable, high-throughput heat ramping of microfluidic chip digital PCR reactions. We characterized the sources of variability to minimize run to run variations with the system using synthetic DNA oligos. We also demonstrate the use of novel rate-dependent melt signatures for enhancing automated melt genotyping. Further, we developed software for analysis to classify melt curves and identify novel pathogens. Our hope is that in future, this platform can translate into a near-point of care, cost-effective technology for screening for sepsis

Improving Clinical Practices in the Neonatal Intensive Care Unit: A Bioengineering Approach

The greatest risk of childhood deaths occurs in the first few weeks of life. Two of the major concerns, birth asphyxia, and sepsis are responsible for the loss of over 2.5 million infants in this vulnerable period. Worse, the survivors are at a high risk for long-term morbidity. This doctoral research work focuses on developing screening tools to influence timely clinical decision-making for targeted treatment for such high risk-infants. First, we developed a web-based decision support tool to encourage timely initiation of therapeutic hypothermia, the only available therapy for infants at risk for brain injury due to birth asphyxia. This tool provides access to widely accepted clinical guidelines and strategies in a simplified way which can be easy to follow and access in clinical settings. Such a clinical decision support tool can obviate some of the time and effort needed for rigorous training and refresher sessions for providers at low acuity, low volume birthing centers. Second, we developed a platform for rapid, reliable and automated identification of bloodborne pathogens responsible for neonatal sepsis using DNA melting analysis directly after digital PCR amplification. Specifically, we designed a high resolution digital melt platform with precise thermal control to accomplish reliable, high-throughput heat ramping of microfluidic chip digital PCR reactions. We characterized the sources of variability to minimize run to run variations with the system using synthetic DNA oligos. We also demonstrate the use of novel rate-dependent melt signatures for enhancing automated melt genotyping. Further, we developed software for analysis to classify melt curves and identify novel pathogens. Our hope is that in future, this platform can translate into a near-point of care, cost-effective technology for screening for sepsis

Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power.

MOTIVATION: The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tasks likely to arise for a given application and test the performance of benchmark classifiers. RESULTS: This toolbox enables the in silico design and testing of broad-based dHRM screening assays and the selection of optimal classifiers. For an example application of screening common human bacterial pathogens, we show that human pathogens having the most similar sequences and melt curves are still reliably identifiable in the presence of experimental noise. Further, we find that ensemble methods outperform whole series classifiers for this task and are in some cases able to resolve melt curves with single-nucleotide resolution. AVAILABILITY AND IMPLEMENTATION: Data and code available on https://github.com/lenlan/dHRM-noise-modeling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Emerging Technologies for Molecular Diagnosis of Sepsis

Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making