Is it time to move up? Feasibility of medical abortion between 9-13 weeks of gestation

Background: Medical termination of pregnancy act helps to reduce incidence of illegal abortion its dreaded complication like maternal mortality to analyze the safety, efficacy and acceptability of medical regimen in 9-13 weeks of pregnancy.Methods: A prospective study was carried out for a period of two and half years. All patients between 9-13 weeks of pregnancy seeking medical termination of pregnancy were given either medical regimen or surgical abortion depending on patients’ preference. Medical regimen consisted of 200 mg of mifepristone followed by 600 mcg of misoprostol after 48 hours. If required 2nd and 3rd dose of misoprostol was repeated. Surgical abortion was done under sedation after cervical priming with misoprostol.Results: Out of 353 cases of medical termination of pregnancy, 92 cases (26.1%) were between 9-13 weeks of pregnancy. Two cases were excluded as surgical abortion was indicated in them. Out of 90 cases, only 30 cases (33.3%) were willing to participate in randomized controlled trial if needed. Out of 90 cases, 50 (55.6%) preferred surgical abortion, while 40 (44.4%) cases preferred medical abortion. Out of 40 cases of medical abortion, 5% cases required surgical curettage, while 3.8% cases required repeat curettage in surgical group. Minor complication rate was comparable in both groups except for prolonged bleeding, which was significantly higher in medical abortion group. Major complication in the surgical group was uterine perforation (1.9%). After completion of procedure, both group satisfied with same procedure, 92% in medical abortion group and 89% in surgical abortion group.Conclusions: Medical abortion is a safe and effective alternative to surgical abortion between 9-13 weeks of gestation.  It should be included routinely at these gestations, thus increasing women's choice. However randomized controlled trial for medical versus surgical abortion between 9-13 weeks will be difficult to initiate

Evaluation der Hochdosischemotherapie mit autologer Stammzelltransplantation in der Behandlung maligner Lymphome an der Medizinischen Klinik und Poliklinik I der Universität Bonn

Hintergrund: Die Hochdosistherapie (HDT) mit autologer Stammzelltransplantation (SZT) spielt in der Behandlung aggressiver Non-Hodgkin-Lymphome (NHL) eine große Rolle. Wir präsentieren die Ergebnisse einer retrospektiven Analyse sämtlicher Patienten mit diffus großzelligem Non-Hodgkin-Lymphom (DLBCL), die im Zeitraum zwischen 1996 und 2004 an der Universitätsklinik Bonn mittels HDT und nachfolgender autologer SZT behandelt worden sind. Methoden: Insgesamt wurden 25 Patienten mit bioptisch gesichertem DLBCL transplantiert. Bei acht Patienten erfolgte die HDT als geplanter Bestandteil der Erstlinientherapie („up-front“), vier Patienten wurden wegen inkompletten Ansprechens und sechs wegen primärer Refraktärität auf die konventionelle Chemotherapie transplantiert. Sieben Patienten erhielten die HDT wegen eines Rezidivs des DLBCL. Ergebnisse: Eine komplette Remission wurde bei 14 der 25 Patienten (56%) erreicht. Die geschätzten 3-Jahres-Überlebensraten von Patienten, die die HDT als Teil der Erstlinientherapie erhielten, lag bei 87,5%; für chemosensitive Patienten mit inkomplettem Ansprechen auf die Erstlinientherapie und Patienten mit chemosensitiver rezidivierter Erkrankung lagen die entsprechenden Werte bei 50% bzw. 60%. Demgegenüber lebte in der Gruppe der primär refraktären bzw. rezidivierten Patienten ohne erhaltene Chemosensitivität kein Patient länger als 8 Monate. In der multivariaten Analyse prognostisch relevanter Faktoren für das Gesamtüberleben erwies sich Chemosensitivität als einzig signifikanter Parameter. Schlussfolgerung: Unsere Ergebnisse zeigen, dass die HDT mit nachfolgender SZT in der Behandlung von Patienten mit DLBCL eine hocheffektive Therapie darstellt, die bei einem Großteil der Patienten ein Langzeitüberleben ermöglicht. Patienten, die die HDT in Hochrisikosituationen als Teil der Erstlinientherapie erhalten oder wegen inkompletten Ansprechens auf konventionelle Chemotherapie bzw. im chemosensiblen Rezidiv transplantiert werden, haben eine gute Prognose. Im Gegensatz dazu profitieren Patienten mit primär refraktärer Erkrankung und Patienten mit chemoresistentem Rezidiv nicht von einer HDT

HSAS: Hindi Subjectivity Analysis System

With the development of Web 2.0, we are abundant with the documents expressing user's opinions, attitudes and sentiments in the textual form. This user generated textual content is an important source of information to make sound decisions by the organizations and the government. The textual information can be categorized into two types: facts and opinions. Subjectivity analysis is the automatic extraction of subjective information from the opinions posted by users and divides the content into subjective and objective sentences. Most of the works in subjectivity analysis exists for English language data but with the introduction of unicode standards UTF-8, Hindi language content on the web is growing very rapidly. In this paper, Hindi Subjectivity Analysis System (HSAS) is proposed. It explores two different methods of generating subjectivity lexicon using the available resources in English language and their comparative evaluation in performing the task of subjectivity analysis at the sentence level. The first method uses English language OpinionFinder subjectivity lexicon. The second method uses a small seed word list of Hindi language and expands it to generate subjectivity lexicon. Different evaluation strategies are used to validate the lexicon. We achieved 71.4% agreement with human annotators and ~80% accuracy in classification on a parallel data set in English and Hindi. Extensive simulations conducted on the test dataset confirm the validity of the suggested method

HSRA: Hindi stopword removal algorithm

In the last few years, electronic documents have been the main source of data in many research areas like Web Mining, Information Retrieval, Artificial Intelligence, Natural Language Processing etc. Text Processing plays a vital role for processing structured or unstructured data from the web. Preprocessing is the main step in any text processing systems. One significant preprocessing technique is the elimination of functional words, also known as stopwords, which affects the performance of text processing tasks. An efficient stopword removal technique is required in all text processing tasks. In this paper, we are proposing a stopword removal algorithm for Hindi Language which is using the concept of a Deterministic Finite Automata (DFA). A large number of available works on stopword removal techniques are based on dictionary containing stopword lists. Then pattern matching technique is applied and the matched patterns, which is a stopword, is removed from the document. It is a time consuming task as searching process takes a long time. This makes the method inefficient and very expensive. In comparison of that, our algorithm has been tested on 200 documents and achieved 99% accuracy and also time efficient

HMDSAD: Hindi multi-domain sentiment aware dictionary

Sentiment Analysis is a fast growing sub area of Natural Language Processing which extracts user's opinion and classify it according to its polarity into positive, negative or neutral classes. This task of classification is required for many purposes like opinion mining, opinion summarization, contextual advertising and market analysis but it is domain dependent. The words used to convey sentiments in one domain is different from the words used to express sentiments in other domain and it is a costly task to annotate the corpora in every possible domain of interest before training the classifier for the classification. We are making an attempt to solve this problem by creating a sentiment aware dictionary using multiple domain data. The source domain data is labeled into positive and negative classes at the document level and the target domain data is unlabeled. The dictionary is created using both source and target domain data. The words used to express positive or negative sentiments in labeled data has relatedness weights assigned to it which signifies its co-occurrence frequency with the words expressing the similar sentiments in target domain. This work is carried out in Hindi, the official language of India. The web pages in Hindi language is booming very quickly after the introduction of UTF-8 encoding style. The dictionary can be used to classify the unlabeled data in the target domain by training a classifier

Annotating Humanities Research Data - Episteme, Metaphors and the Torah

Annotieren ist eine der ältesten Kulturtechniken der Menschheit. Während in den vergangenen Jahrhunderten Stift und Papier die Mittel der Wahl waren um Anmerkungen zu einer Quelle hinzuzufügen, verschiebt sich diese Aktivität in den letzten Jahren zunehmend in die digitale Welt. Mit der W3C-Empfehlung ‘Web Annotation Data Model’ steht seit 2017 ein mächtiges Werkzeug zur Verfügung, um Annotationen in verschiedensten Disziplinen zu modellieren und eine disziplin- sowie plattformübergreifende Auswertung zu ermöglichen. In diesem Poster möchten wir einen Einblick in unsere Annotationsinfrastruktur geben, die in drei geisteswissenschaftlichen Forschungsprojekten im Einsatz ist. Dabei stehen sowohl ein eigens entwickelter Annotationsserver mit RDF-Backend (vollständig kompatibel zum ‘Web Annotation Protocol’) als auch unsere Annotationsoberflächen im Fokus. Es sollen das Zusammenspiel dieser Komponenten untereinander aber auch mit weiteren Infrastrukturkomponenten, wie beispielsweise einem Forschungsdatenrepositorium oder einem Vokabulareditor, sowie die tägliche Arbeit der Forschenden mit diesen Komponenten illustriert werden. Besonderesr Augenmerk soll auf die Modellierung der Annotationen in unseren unterschiedlichen Anwendungsfällen gelegt werden. Die Beispiele reichen dabei von Auszeichnungen logischer Diagramme in mittelalterlichen Aristoteles-Handschriften, über die Analyse von Metaphern im Prozessin religiöser Sinnbildung bis hin zur Erfassung besonderer hebräischern Buchstabenvariationen in Torarollen. Die Diskussion von Gemeinsamkeiten und Unterschieden in diesen Anwendungsfällen birgt dabei ein großes Potential für die Übertragbarkeit und Fruchtbarmachung in weiteren wissenschaftlichen Disziplinen

Metaphors of Religion

The CRC studies the role of metaphor in religious meaning-making. In metaphors, meaning is transferred from one semantic domain to another. By adopting conceptual metaphor theory, the CRC seeks to more thoroughly understand this process and to research its semantic forms empirically and comparatively. Through its multidisciplinary subprojects the CRC contributes to the historiography and the comparative study of religions. It covers various religious traditions from across the globe, working with texts from multiple languages and diverse genres, dating from 3,000 BCE to the present. To enable comparability and interoperability between its extremely heterogeneous subprojects, the CRC deliberately puts emphasis on a shared digital infrastructure (data repository, annotation-tool, conceptual thesaurus), provided by the information infrastructure (INF) project. Utilizing this infrastructure, the subprojects annotate religious texts to not only mark the presence of metaphors, but to include complex analysis of the structural functionings of the metaphor and the resulting domain mappings. A contribution to the 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture

Development of Facial Expression Classifier using Neural Networks

A person's emotional and mental well being, together with the age, sex, race, can be easily depicted by one's face. A crucial role is played by facial expressions in day-today social interactions. An individual's emotional level as well as behavioral manners can be interpreted by these expressions. Facial expression classifier is a evolving, demanding and curious problem in computer vision. It has its potential applications in the field of robotics, behavioral science, human computer interaction, video games etc.. It assists in building more intelligent systems which have better ability to interpret human emotions. In this paper, a facial expression classifier is proposed based on Convolution Neural Networks (CNN). CNNs are biologically-inspired variants of multi-layer preceptor (MLP) networks. They use an architecture which is particularly well suitable to classify images. Detection of facial expression can be enhanced by

Sentiment analysis in a resource scarce language: Hindi

A common human behavior is to take other’s opinion before taking any decision. With the tremendous availability of documents which express opinions on different issues, the challenge arises to analyze it and produce useful knowledge from it. Many works in the area of Sentiment Analysis is available for English language. From last few years, opinion-rich resources are booming in other languages and hence there is a need to perform Sentiment Analysis in those languages. In this paper, a Sentiment Analysis in Hindi Languag