Uterine artery pulsatility index less than 1.0 as an isolated marker in predicting low-risk subjects for preeclampsia

Background: Preeclampsia is one of the major obstetric vasculopathies. As the treatment of the cause of preeclampsia remains elusive, its prediction is much sought after. In this study the authors have tried to evaluate a relatively unstudied parameter of PI<1.0 in prediction of low-risk subjects for preeclampsia.Methods: Subjects enrolled for the study were prospectively and longitudinally studied clinically and through colour Doppler for changes in values I-trimester and II-trimester of pregnancy. The techniques used were as described by Clinical Standards Committee. First uterine artery scan was obtained between 11 to 14 weeks of pregnancy (I-Trimester scan). Second scan of the same woman was obtained between 20-22 weeks of pregnancy (II-Trimester scan). All such enrolled subjects were serially followed up to delivery and their obstetric outcome noted especially for development of preeclampsia. Data was carefully recorded and analysed using online statistical software.Results: It was found that those subjects who had PI<1 in II-trimester, have a significantly less chances of developing PIH (P value being<0.05). But when PI<1.0 in I-trimester was analysed; it was found that the difference between those who developed PIH and those who did not was not statistically significant. Nevertheless, PI<1.0 in I-trimester had an excellent specificity and positive predictive value.Conclusions: Pulsatility Index if<1.0 in II-trimester in any pregnant woman on colour Doppler indicates a low-risk subject for preeclampsia. In such subjects preventive medications like aspirin or aspirin + heparin combination can be safely stopped

Saving the unborn girl child: Are we doing enough?

In early part of this year film actor Amir Khan televised a hard-hitting and soul stirring series on Indian TV Channels titled “Satyameva Jayate”. It successfully showed a mirror to the society which many times didn’t like what it was seeing. As a result attempts were made to shatter the mirror rather than washing the dirty face. But the great thing about mirrors is that they always tell the truth even when shattered to pieces. One such episode covered the ills of prenatal sex determination and sex-selective abortions. It highlighted all relevant issues so well. One fact that clearly emerged was that prenatal sex determination is rampant inspite of the law and efforts to curb this evil are proving unproductive

Modular and Safe Event-Driven Programming

Asynchronous event-driven systems are ubiquitous across domains such as device drivers, distributed systems, and robotics. These systems are notoriously hard to get right as the programmer needs to reason about numerous control paths resulting from the complex interleaving of events (or messages) and failures. Unsurprisingly, it is easy to introduce subtle errors while attempting to fill in gaps between high-level system specifications and their concrete implementations.This dissertation proposes new methods for programming safe event-driven asynchronous systems.In the first part of the thesis, we present ModP, a modular programming framework for compositional programming and testing of event-driven asynchronous systems.The ModP module system supports a novel theory of compositional refinement for assume-guarantee reasoning of dynamic event-driven asynchronous systems. We build a complex distributed systems software stack using ModP.Our results demonstrate that compositional reasoning can help scale model-checking (both explicit and symbolic) to large distributed systems.ModP is transforming the way asynchronous software is built at Microsoft and Amazon Web Services (AWS). Microsoft uses ModP for implementing safe device drivers and other software in the Windows kernel.AWS uses ModP for compositional model checking of complex distributed systems. While ModP simplifies analysis of such systems, the state space of industrial-scale systems remains extremely large.In the second part of this thesis, we present scalable verification and systematic testing approaches to further mitigate this state-space explosion problem.First, we introduce the concept of a delaying explorer to perform prioritized exploration of the behaviors of an asynchronous reactive program. A delaying explorer stratifies the search space using a custom strategy (tailored towards finding bugs faster), and a delay operation that allows deviation from that strategy. We show that prioritized search with a delaying explorer performs significantly better than existing approaches for finding bugs in asynchronous programs.Next, we consider the challenge of verifying time-synchronized systems; these are almost-synchronous systems as they are neither completely asynchronous nor synchronous.We introduce approximate synchrony, a sound and tunable abstraction for verification of almost-synchronous systems. We show how approximate synchrony can be used for verification of both time-synchronization protocols and applications running on top of them.Moreover, we show how approximate synchrony also provides a useful strategy to guide state-space exploration during model-checking.Using approximate synchrony and implementing it as a delaying explorer, we were able to verify the correctness of the IEEE 1588 distributed time-synchronization protocol and, in the process, uncovered a bug in the protocol that was well appreciated by the standards committee.In the final part of this thesis, we consider the challenge of programming a special class of event-driven asynchronous systems -- safe autonomous robotics systems.Our approach towards achieving assured autonomy for robotics systems consists of two parts: (1) a high-level programming language for implementing and validating the reactive robotics software stack; and (2) an integrated runtime assurance system to ensure that the assumptions used during design-time validation of the high-level software hold at runtime.Combining high-level programming language and model-checking with runtime assurance helps us bridge the gap between design-time software validation that makes assumptions about the untrusted components (e.g., low-level controllers), and the physical world, and the actual execution of the software on a real robotic platform in the physical world. We implemented our approach as DRONA, a programming framework for building safe robotics systems.We used DRONA for building a distributed mobile robotics system and deployed it on real drone platforms. Our results demonstrate that DRONA (with the runtime-assurance capabilities) enables programmers to build an autonomous robotics software stack with formal safety guarantees.To summarize, this thesis contributes new theory and tools to the areas of programming languages, verification, systematic testing, and runtime assurance for programming safe asynchronous event-driven across the domains of fault-tolerant distributed systems and safe autonomous robotics systems

Healthcare Critical Diagnosis Accuracy: A Proposed Machine Learning Evaluation Metric for Critical Healthcare Analysis

Since at least a decade, Machine Learning has attracted the interest of researchers. Among the topics of discussion is the application of Machine Learning (ML) and Deep Learning (DL) to the healthcare industry. Several implementations are performed on the medical dataset to verify its precision. The four main players, True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), play a crucial role in determining the classifier\u27s performance. Various metrics are provided based on the main players. Selecting the appropriate performance metric is a crucial step. In addition to TP and TN, FN should be given greater weight when a healthcare dataset is evaluated for disease diagnosis or detection. Thus, a suitable performance metric must be considered. In this paper, a novel machine learning metric referred to as Healthcare-Critical-Diagnostic-Accuracy (HCDA) is proposed and compared to the well-known metrics accuracy and ROC_AUC score. The machine learning classifiers Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) are implemented on four distinct datasets. The obtained results indicate that the proposed HCDA metric is more sensitive to FN counts. The results show, that even if there is rise in %FN for dataset 1 to 10.31 % then too accuracy is 83% ad HCDA shows correlated drop to 72.70 %. Similarly, in dataset 2 if %FN rises to 14.80 for LR classifier, accuracy is 78.2 % and HCDA is 63.45 %. Similar kind of results are obtained for dataset 3 and 4 too. More FN counts result in a lower HCDA score, and vice versa. In common exiting metrics such as Accuracy and ROC_AUC score, even as the FN count increases, the score increases, which is misleading. As a result, it can be concluded that the proposed HCDA is a more robust and accurate metric for Critical Healthcare Analysis, as FN conditions for disease diagnosis and detection are taken into account more than TP and TN

Healthcare Critical Diagnosis Accuracy: A Proposed Machine Learning Evaluation Metric for Critical Healthcare Analysis

Since at least a decade, Machine Learning has attracted the interest of researchers. Among the topics of discussion is the application of Machine Learning (ML) and Deep Learning (DL) to the healthcare industry. Several implementations are performed on the medical dataset to verify its precision. The four main players, True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), play a crucial role in determining the classifier\u27s performance. Various metrics are provided based on the main players. Selecting the appropriate performance metric is a crucial step. In addition to TP and TN, FN should be given greater weight when a healthcare dataset is evaluated for disease diagnosis or detection. Thus, a suitable performance metric must be considered. In this paper, a novel machine learning metric referred to as Healthcare-Critical-Diagnostic-Accuracy (HCDA) is proposed and compared to the well-known metrics accuracy and ROC_AUC score. The machine learning classifiers Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) are implemented on four distinct datasets. The obtained results indicate that the proposed HCDA metric is more sensitive to FN counts. The results show, that even if there is rise in %FN for dataset 1 to 10.31 % then too accuracy is 83% ad HCDA shows correlated drop to 72.70 %. Similarly, in dataset 2 if %FN rises to 14.80 for LR classifier, accuracy is 78.2 % and HCDA is 63.45 %. Similar kind of results are obtained for dataset 3 and 4 too. More FN counts result in a lower HCDA score, and vice versa. In common exiting metrics such as Accuracy and ROC_AUC score, even as the FN count increases, the score increases, which is misleading. As a result, it can be concluded that the proposed HCDA is a more robust and accurate metric for Critical Healthcare Analysis, as FN conditions for disease diagnosis and detection are taken into account more than TP and TN

Anticancer Drugs Induced Chromosomal Rearrangements in Lymphocytes of Breast Cancer Patients

 Breast cancer is one of the most commonly diagnosed malignancies in women around the world. Chromosomal rearrangements are known to play important role in the pathogenesis of many diseases including cancer. In case of breast cancer, chromosomal changes are detectable at all stages of tumour development providing excellent opportunity for prognosis and therapy. Present work aimed to study the frequency of chromosomal aberrations and satellite associations in human peripheral blood lymphocyte culture of freshly diagnosed breast cancer patients after in vitro exposure to combination of anticancer drug treatment. The present study reveals that, combination of anticancer drugs significantly increases chromosomal aberrations without altering the frequency of satellite associations

IMPACT ON STROKE BECAUSE OF TYPE 2 DIABETES OCCURRENCE AND SUBTYPES: A COMPREHENSIVE CROSS-SECTIONAL STUDY

Substantial research indicates that Type 2 diabetes significantly increases the vulnerability to stroke. Despite the existing knowledge, there is still a gap in understanding the specific clinical attributes of strokes in diabetes patients. This study aims to contrast people with Type 2 diabetes with people without diabetes in terms of the frequency and patterns of strokes. Materials and Methodology: An 18-month cross-sectional observational study involved 160 patients diagnosed with either ischemic or haemorrhagic strokes. There were two groups of patients: one comprising individuals with diabetes (group 1) and the other without diabetes (group 2), based on specific criteria. The assessment process included a thorough examination of medical histories, physical assessments, and brain imaging. Various parameters, including blood pressure, cholesterol profiles, and types of strokes, were assessed through laboratory tests and statistical analyses. Results: Diabetes patients had considerably higher systolic and diastolic blood pressure readings than non-diabetics. The prevalence of ischemic strokes was significantly greater in the diabetes group as compared to the non-diabetic group (86.3%) (65%). Laboratory results revealed elevated levels of Diabetes patients' haemoglobin, random blood sugar, serum creatinine, LDL cholesterol, total cholesterol, and triglycerides. Conclusion: There is a link between type 2 diabetes and higher likelihood of hypertension and abnormal lipid profiles, and it considerably increases the risk of ischemic strokes. Identifying and managing these modifiable risk factors are essential in preventing various stroke types. Thorough assessments conducted by healthcare providers are vital in effectively managing complications for individuals with diabetes

Sickle cell disease and pregnancy outcomes: a study of the community-based hospital in a tribal block of Gujarat, India

Background: Sickle cell disease (SCD) is a hereditary blood disorder prevalent in tribal regions of India. SCD can increase complications during pregnancy and in turn negatively influence pregnancy outcomes. This study reports the analysis of tribal maternal admissions in the community-based hospital of SEWA Rural (Kasturba Maternity Hospital) in Jhagadia block, Gujarat. The objective of the study is to compare the pregnancy outcomes among SCD, sickle cell trait and non-SCD admissions. This study also estimated the risk of adverse pregnancy outcomes for SCD admissions. Methods: The data pertains to four and half years from March 2011 to September 2015. The total tribal maternal admissions were 14640, out of which 10519 admissions were deliveries. The admissions were classified as sickle cell disease, sickle cell trait and non-sickle cell disease. The selected pregnancy outcomes and maternal complications were abortion, stillbirth, Caesarean section, haemoglobin levels, blood transfusion, preterm pregnancy, newborn birth weight and other diagnosed morbidities (IUGR, PIH, eclampsia, preterm labour pain). The odds ratios for each risk factor were estimated for sickle cell patients. The odds ratios were adjusted for the respective years. Results: Overall, 1.2% (131 out of 10519) of tribal delivery admissions was sickle cell admissions. Another 15.6% (1645 out of 10519) of tribal delivery admissions have sickle cell trait. The percentage of stillbirth was 9.9% among sickle cell delivery admission compared to 4.2% among non-sickle cell deliveries admissions. Among sickle cell deliveries, 70.2% were low birth weight compared to 43.8% of non-sickle cell patient. Similarly, almost half of the sickle cell deliveries needed the blood transfusion. The 45.0% of sickle cell delivery admissions were pre-term births, compared to 17.3% in non-SCD deliveries. The odds ratio of severe anaemia, stillbirth, blood transfusion, Caesarean section, and low birth weight was significantly higher for sickle cell admissions compared to non-sickle cell admissions. Conclusions: The study exhibited that there is a high risk of adverse pregnancy outcomes for women with SCD. It may also be associated with the poor maternal and neonatal health in these tribal regions. Thus, the study advocates the need for better management of SCD in tribal Gujarat

Assessing Diagnostic Accuracy of Haemoglobin Colour Scale in Real-life Setting

The study was undertaken to determine diagnostic accuracy of Haemoglobin Colour Scale (HCS) in hands of village-based community health workers (CHWs) in real-life community setting in India. Participants (501 women) were randomly selected from 8 villages belonging to a project area of SEWA-Rural, a voluntary organization located in India. After receiving a brief training, CHWs and a research assistant obtained haemoglobin readings using HCS and HemoCueTM (reference) respectively. Sensitivity, specificity, positive and negative predictive-values, and likelihood ratios were calculated. Bland-Altman plot was constructed. Mean haemoglobin value, using HCS and HemoCueTM were 11.02 g/dL (CI 10.9-11.2) and 11.07 g/dL (CI 10.9-11.2) respectively. Mean difference between haemoglobin readings was 0.95 g/dL. Sensitivity of HCS was 0.74 (CI 0.65-0.81) and 0.84 (CI 0.8-0.87) whereas specificity was 0.84 (CI:0.51-0.98) and 0.99 (CI:0.97- 0.99) using haemoglobin cutoff limits of 10 g/dL and 7 g/dL respectively. CHWs can accurately diagnose severe and moderately-severe anaemia by using HCS in real-life field condition after a brief training