Diagnostic efficacy of Striae Gravidarum in predicting intraperitoneal adhesions and uterine scar thickness in women undergoing repeat caesarean section

Background: Striae gravidarum (SG) are stretch marks on the skin which develop during pregnancy. Cesarean section is one of the most common operation performed worldwide which carries a potential risk of complications due to the intra-peritoneal adhesions and thickness of previous uterine scar. The aim of this study was to find the diagnostic efficacy of striae gravidarum in predicting intra-peritoneal adhesions (IPA) and uterine scar thickness in women undergoing repeated caesarean delivery.Methods: It was a cross-sectional study done over a period of 18 months duration. One hundred women with previous cesarean section scheduled for elective LSCS were enrolled for the study. Striae gravidarum was calculated according to the Atwal numerical scoring system. Presence of intra-peritoneal adhesions were graded intraoperatively according to modified Nair scoring system. After extraction of baby, the thickness of lower uterine segment at midpoint of lower flap of uterine incision was measured by metallic screw gauge.Results: Significant positive correlation was seen between striae gravidarum and intra-peritoneal adhesions. A negative association was established between striae gravidarum severity and thickness of LUS. Owing to its moderately high sensitivity and low specificity in predicting IPA Grade 3 or 4 and LUS scar thickness<3.74mm, striae gravidarum cannot be used as a diagnostic indicator, but can be a useful mass screening method.Conclusions: Striae gravidarum is a zero-cost simple quick and easy technique which can be universally used with minimal intra-observer variation to predict perioperative complications in low resource settings

A Polynomial Kernel for Deletion to Ptolemaic Graphs

For a family of graphs F, given a graph G and an integer k, the F-Deletion problem asks whether we can delete at most k vertices from G to obtain a graph in the family F. The F-Deletion problems for all non-trivial families F that satisfy the hereditary property on induced subgraphs are known to be NP-hard by a result of Yannakakis (STOC\u2778). Ptolemaic graphs are the graphs that satisfy the Ptolemy inequality, and they are the intersection of chordal graphs and distance-hereditary graphs. Equivalently, they form the set of graphs that do not contain any chordless cycles or a gem as an induced subgraph. (A gem is the graph on 5 vertices, where four vertices form an induced path, and the fifth vertex is adjacent to all the vertices of this induced path.) The Ptolemaic Deletion problem is the F-Deletion problem, where F is the family of Ptolemaic graphs. In this paper we study Ptolemaic Deletion from the viewpoint of Kernelization Complexity, and obtain a kernel with ?(k?) vertices for the problem

Study of obstetric and fetal outcome of post caesarean pregnancy

Background: With the sky rocketing caesarean section rates an increasing number of women face the issue of mode of delivery in their current pregnancy. There are conflicting reports regarding the safety of a trial for Vaginal Birth After Caesarean delivery (VBAC) in terms of uterine rupture and concern about, maternal and perinatal morbidity. The purpose of this study was to evaluate the obstetric and fetal outcomes of patients presenting at term with a history of previous LSCS.Methods: A one year prospective observational study was conducted where in all patients who had a term pregnancy with a history of prior LSCS were included in the study after obtaining their consent for participation. The obstetric and fetal outcomes of these patients in the present pregnancy were noted and tabulated. A descriptive analysis of these outcomes was carried out.Results: 100 patients at term, with a history of previous LSCS were studied. Of these, trial for a VBAC was attempted by 50 patients of these 46% (23) had a successful VBAC. And remaining 54% went for emergency LSCS. 50% patients underwent an elective repeat caesarean deliver. Scar dehiscence was seen in 2.72% of the patients who opted for a trial for VBAC. Perinatal morbidity was higher in cases of repeat caesarean delivery than in those who had a successful VBAC (12.12% vs. 0 percent). Maternal complications were also higher in patients who had a repeat LSCS compared to those who had a successful VBAC.Conclusions: With an increase in the proportion of patients with a history of previous LSCS, it is essential for health care institutions to have proper antenatal counseling regarding VBAC and a well-defined management protocol in place in an effort to increase the number of VBACs and bring down the overall caesarean rates. Patients with a history of prior vaginal delivery have an increased likelihood for a successful VBAC. A successful VBAC is associated with a lower perinatal and maternal morbidity than repeat caesarean delivery, and this is relevant for counseling women about their choices after a caesarean delivery

Automated Questions Unique Arrangement (A.Q.U.A)

With the world digitizing and moving at a fast pace, framing questions for examinations or learning is a time-consuming process and requires a lot of critical thinking. Questions we solve in the exams, for instance, school and college level examinations, are similar to the last year papers and contain repeated questions with little or no paraphrasing or modifications. Educators spend a significant amount of time in preparing question papers to come up with creative brainstorming questions. Automation has become a vital aspect of life. New technologies are coming up every day to minimize manual work and make everything automated with just a click. Considering the present pandemic scenario, education is now internet based and exams are being conducted online. Most of the examinations are based on multiple choice questions and these questions are, in most cases, taken from popular quizzing websites. This practice makes it easier for students to find the correct answer without even studying the subject and increases malpractices. We propose an automatic solution to the issue of making questions that will save time and energy and also promote proper learning with our model “A.Q.U.A – Automated Questions Unique Arrangement. It is a machine learning model that uses transformers for natural language processing and generating meaningful and understandable questions from the given context. A.Q.U.A will be of great use in online assessments , school level and university level exams, as well as competitive exams. It’ll be also helpful for students and learners to take practise tests for a topic and evaluate their knowledge in it

Improving Automatic Melanoma Diagnosis using Deep Learning-Based Segmentation of Irregular Networks

Deep Learning Has Achieved Significant Success in Malignant Melanoma Diagnosis. These Diagnostic Models Are Undergoing a Transition into Clinical Use. However, with Melanoma Diagnostic Accuracy in the Range of Ninety Percent, a Significant Minority of Melanomas Are Missed by Deep Learning. Many of the Melanomas Missed Have Irregular Pigment Networks Visible using Dermoscopy. This Research Presents an Annotated Irregular Network Database and Develops a Classification Pipeline that Fuses Deep Learning Image-Level Results with Conventional Hand-Crafted Features from Irregular Pigment Networks. We Identified and Annotated 487 Unique Dermoscopic Melanoma Lesions from Images in the ISIC 2019 Dermoscopic Dataset to Create a Ground-Truth Irregular Pigment Network Dataset. We Trained Multiple Transfer Learned Segmentation Models to Detect Irregular Networks in This Training Set. a Separate, Mutually Exclusive Subset of the International Skin Imaging Collaboration (ISIC) 2019 Dataset with 500 Melanomas and 500 Benign Lesions Was Used for Training and Testing Deep Learning Models for the Binary Classification of Melanoma Versus Benign. the Best Segmentation Model, U-Net++, Generated Irregular Network Masks on the 1000-Image Dataset. Other Classical Color, Texture, and Shape Features Were Calculated for the Irregular Network Areas. We Achieved an Increase in the Recall of Melanoma Versus Benign of 11% and in Accuracy of 2% over DL-Only Models using Conventional Classifiers in a Sequential Pipeline based on the Cascade Generalization Framework, with the Highest Increase in Recall Accompanying the Use of the Random Forest Algorithm. the Proposed Approach Facilitates Leveraging the Strengths of Both Deep Learning and Conventional Image Processing Techniques to Improve the Accuracy of Melanoma Diagnosis. Further Research Combining Deep Learning with Conventional Image Processing on Automatically Detected Dermoscopic Features is Warranted

Bronchiectasis in India:results from the European Multicentre Bronchiectasis Audit and Research Collaboration (EMBARC) and Respiratory Research Network of India Registry

BACKGROUND: Bronchiectasis is a common but neglected chronic lung disease. Most epidemiological data are limited to cohorts from Europe and the USA, with few data from low-income and middle-income countries. We therefore aimed to describe the characteristics, severity of disease, microbiology, and treatment of patients with bronchiectasis in India. METHODS: The Indian bronchiectasis registry is a multicentre, prospective, observational cohort study. Adult patients ( 6518 years) with CT-confirmed bronchiectasis were enrolled from 31 centres across India. Patients with bronchiectasis due to cystic fibrosis or traction bronchiectasis associated with another respiratory disorder were excluded. Data were collected at baseline (recruitment) with follow-up visits taking place once per year. Comprehensive clinical data were collected through the European Multicentre Bronchiectasis Audit and Research Collaboration registry platform. Underlying aetiology of bronchiectasis, as well as treatment and risk factors for bronchiectasis were analysed in the Indian bronchiectasis registry. Comparisons of demographics were made with published European and US registries, and quality of care was benchmarked against the 2017 European Respiratory Society guidelines. FINDINGS: From June 1, 2015, to Sept 1, 2017, 2195 patients were enrolled. Marked differences were observed between India, Europe, and the USA. Patients in India were younger (median age 56 years [IQR 41-66] vs the European and US registries; p&lt;0\ub70001]) and more likely to be men (1249 [56\ub79%] of 2195). Previous tuberculosis (780 [35\ub75%] of 2195) was the most frequent underlying cause of bronchiectasis and Pseudomonas aeruginosa was the most common organism in sputum culture (301 [13\ub77%]) in India. Risk factors for exacerbations included being of the male sex (adjusted incidence rate ratio 1\ub717, 95% CI 1\ub703-1\ub732; p=0\ub7015), P aeruginosa infection (1\ub729, 1\ub710-1\ub750; p=0\ub7001), a history of pulmonary tuberculosis (1\ub720, 1\ub707-1\ub734; p=0\ub7002), modified Medical Research Council Dyspnoea score (1\ub732, 1\ub725-1\ub739; p&lt;0\ub70001), daily sputum production (1\ub716, 1\ub703-1\ub730; p=0\ub7013), and radiological severity of disease (1\ub703, 1\ub701-1\ub704; p&lt;0\ub70001). Low adherence to guideline-recommended care was observed; only 388 patients were tested for allergic bronchopulmonary aspergillosis and 82 patients had been tested for immunoglobulins. INTERPRETATION: Patients with bronchiectasis in India have more severe disease and have distinct characteristics from those reported in other countries. This study provides a benchmark to improve quality of care for patients with bronchiectasis in India. FUNDING: EU/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative inhaled Antibiotics in Bronchiectasis and Cystic Fibrosis Consortium, European Respiratory Society, and the British Lung Foundation

Testing Workplace Support’s Direct and Interactive Effects on Perceptions of Time Management and Work-Family Conflict: Using a Sample of Frontline Hospital Employees in New York City

Frontline hospital employees in emergency rooms (ER) are among the most stressful occupations due to the emotional nature of patient care and work demands. Long work hours, newer healthcare technologies with inter-professional quality of work, and conflicts. Previous research focuses on individual factors, and less emphasis is on the lack of workplace support resources for employees. In managing conflict, using the Conservation of resources theory with the self-regulation framework will enable individuals to establish proactive coping attitudes and behaviors. A survey of 260 clinicians in the ER reveals how perceptions of time management and workplace support resources are influencing work-family conflict. Results show that supervisor support moderates the relationship between time management and work-family conflict. Findings show how workplace support and policies could act as a resource for employees. The conclusion includes a discussion on theory, research, and practical implications

Diagnostic Efficacy of DNA Ploidy in Liquid Based Cervical Cytology using DNA Cytometry

Worldwide cervical cancer is the fourth most common cancer in women and high incidence is reported from India. Liquid Based Cytology (LBC) provides good morphology for detection of cellular abnormalities. We, therefore, reviewed diagnostic efficacy of conventional Pap staining, flow cytometry and Human Papilloma Virus (HPV) testing in cervical pre cancer and cancer. Narrative review of cervical pre cancer and cancer candidate biomarkers including Pap staining, HPV and flow cytometry from cervical cytology fluids, is based on a detailed review of the literature. Based on the so far conducted studies, a promising conclusion can be drawn, that cytometry when coupled with HPV DNA typing or the conventional cytology gives better results as compared to that of conventional cytology or DNA cytometry alone. Liquid cytology provides a good and stable source of cervical cells to carry out ploidy studies using DNA cytometry. The procedure should be used in conjunction with LBC and HPV detection