Publisher Correction: A practical guide to placental examination for forensic pathologists.

The Publisher would like to correct the introduced formatting errors caused by production on figures 16 and 23 of the original article. The errors are purely typesetting mistakes and the corrections made to the figures did not impact upon the veracity and content of the overall text of the article in any way.

A practical guide to placental examination for forensic pathologists.

The placenta is a complex interface organ that may hold clues to the reasons for fetal, neonatal or maternal demise. For this reason, placental examination should be a mandatory part of all perinatal or maternal autopsies. While published protocols for the examination of the placenta exist, they are often not adopted. The following review provides practical guidelines for placental examination, with discussion of specific medical conditions that can negatively impact upon the fetus, neonate or mother involving placental pathology to cause death. The review aims to discuss concepts, with illustrations, that forensic pathologists may not routinely focus on in death investigations that may either contribute or mask the cause of a fetal or neonatal death, or are associated with a recurrence risk. While it is recognized that many forensic facilities do not have formal guidelines for placental examination, involvement of local perinatal pathology services in cases is one way of obtaining additional specialist expertise.

Recurrent Chronic Intervillositis: The Diagnostic Challenge - A Case Report and Review of the Literature.

BACKGROUND: Chronic intervillositis (CI) is a rare placental condition involving diffuse infiltration of intervillous spaces by CD68- or CD45-positive maternal mononuclear inflammatory cells. Because no validated clinical or biochemical markers are specific to CI, the diagnosis is purely histopathological and is made postpartum. CASE: This report describes a case of recurrent CI associated with adverse complications in two successive pregnancies. Both pregnancies were complicated by intrauterine growth restriction. Coexistent massive perivillous fibrin deposition was present in the first placenta. This case highlights the importance of CI in explaining and predicting adverse perinatal outcomes. CONCLUSION: CI is associated with adverse pregnancy outcomes and a high risk of recurrence, and it can coexist with massive perivillous fibrin deposition. Pathologists must ensure that the significance of these diagnoses is adequately conveyed to clinicians, to optimize management of subsequent pregnancies.

Placenta Previa Increta in an Unscarred Uterus With Marked Thinning of Myometrium in the Entire Uterus in a Patient With Systemic Lupus Erythematosus.

A 36-yr-old woman, G5P2, who had a background history of systemic lupus erythematosus (SLE) was found to have placenta previa and placenta accreta on second trimester ultrasound scan. She had previous 3 spontaneous miscarriages but there was no history of gynecologic interventions. Apart from SLE, there was no other explanation for her recurrent miscarriage. The patient had ongoing thrombocytopenia in this pregnancy. The patient was taken for elective lower uterine segment cesarean section at 36 wk+5 d gestation. Balloon catheters were placed in the anterior branches of the internal iliac arteries before the operation. Despite this and aggressive medical management, she experienced significant bleeding requiring peripartum hysterectomy. Histologic examination showed placenta increta with marked thinning of the myometrium. The myometrium was <1 mm thick in most of the uterus except for lower uterine segment without any evidence of uterine rupture or perforation. This paper presents this unusual case of diffuse marked thinning of myometrium in the uterus as well as presence of placenta previa increta, without any prior history of gynecologic intervention in a patient with SLE.

AVNM: A Voting based Novel Mathematical Rule for Image Classification.

BACKGROUND AND OBJECTIVES: In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of a number of nearest neighbors i.e. "k" for computation. At present, it is hard to find the optimal value of "k" using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate. METHOD: Motivated by the prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. The proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. The proposed classifier also reduces the effect of outliers. RESULTS: To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard datasets taken from UCI database and one manually created dataset. The experimental result shows that the proposed AVNM rule outperforms the KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule. CONCLUSIONS: The proposed AVNM rule is based on sample space reduction mechanism for identification of an optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with the state-of-art algorithm, KNN, and its variants. The proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manually created dataset.

Sentiment analysis using common-sense and context information.

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.