Audit of pre-hysterectomy medical treatment

Background: Variations in rates of hysterectomy have been observed and have been associated with women demographic characteristics such as race, socio-economic status, educational status and geographic location. However, with the advent of novel medical and conservative measures, there are doubts on justification of hysterectomy. Aims and objectives were to study prior treatment taken with respect to clinical profile in women undergoing hysterectomy in a tertiary care centre. Methods: An observational study was conducted by collecting data from medical records of 72 consenting subjects undergoing hysterectomy for benign gynaecological reasons in a tertiary care centre over a period of 12 months after ethics approval. Results: Out of 72 subjects, only 34 subjects (47.22%) had taken a prior medical treatment before undergoing hysterectomy. 100% of the subjects with endometriosis, chronic PID and DUB had taken some form of prior treatment whereas 27.5% of subjects with fibroid and 68.75% of subjects with adenomyosis had taken prior treatment. Conclusions: Our study indicates that prior medical management is not being adequately discussed and trialled among women undergoing hysterectomy. Hence, improving the counselling and understanding the women’s perspective for resistance towards medical management is an important area for research in improving quality of health care.

Fetomaternal outcome of women with thrombocytopenia in labour

Background: This study was conducted to study fetomaternal outcome in pregnancy with severity of thrombocytopenia. Methods: It was a prospective observational study of fetomaternal outcome of women with thrombocytopenia admitted in labour room of a tertiary health care center of south Gujarat for 1-year period (April 2021-March 2022) after official approval from ethical committee. 100 consecutive consenting women with thrombocytopenia admitted in labour room of New Civil Hospital Surat were enrolled in this study. Results: Mild thrombocytopenia was noted in 68% of the total cases, moderate thrombocytopenia in 27% and severe thrombocytopenia in 5% of cases. 59 subjects belong to age group of 20-25 years, 56 subjects were multipara, 70 subjects were registered antenatally, 68 subjects were delivered beyond 37 weeks of gestation. 48 subjects underwent LSCS and rest were delivered vaginally. 29 subjects had maternal complication. 95 subjects delivered alive baby of which 14 had NICU admission and 5 subjects had preterm still birth baby. Conclusions: Thrombocytopenia is the second most common haematological finding in pregnancy next to anaemia. Majority of cases generally present at gestational age beyond thirty-seven weeks and belong to category of mild thrombocytopenia. Efforts should be made on improving antenatal registration, screening of maternal thrombocytopenia, early diagnosis and treatment

Characterizing and Analyzing Diffusion Tensor Images by Learning their Underlying Manifold Structure

The growing importance of diffusion tensor imaging (DTI) in studying the white matter architecture in normal and pathologic states necessitates the development of tools for comprehensive analysis of diffusion tensor data. Operations such as multivariate statistical analysis and hypothesis testing, interpolation and filtering, must now be performed on tensor data, and must overcome challenges introduced by the non-linearity and high dimensionality of the tensors. In this paper, we present a novel approach to performing these computations by modeling the underlying manifold structure of the tensors, using a combination of two manifold learning techniques, isometric mapping (ISOMAP) and local tangent space alignment (LTSA). While ISOMAP identifies the dimensionality of the manifold of the tensors and embeds the tensors into a linear space, facilitating statistical computations therein, operations like interpolation and filtering, integral to the process of normalization, require the reconstruction of the tensor in the tensor domain. To obtain this reverse mapping from the linear space to the tensor domain, i.e. to the domain of the original tensor data, we use LTSA. The modeling of the underlying manifold structure renders our approach better applicable to tensor data than existing methods that may not always be able to capture the non-linearity present in the tensors under consideration. In various simulations with known ground truth, we demonstrate the effectiveness of our framework based on ISOMAP and LTSA in performing a comprehensive analysis of DTI data

Class-Level Spectral Features for Emotion Recognition

The most common approaches to automatic emotion recognition rely on utterance-level prosodic features. Recent studies have shown that utterance-level statistics of segmental spectral features also contain rich information about expressivity and emotion. In our work we introduce a more fine-grained yet robust set of spectral features: statistics of Mel-Frequency Cepstral Coefficients computed over three phoneme type classes of interest – stressed vowels, unstressed vowels and consonants in the utterance. We investigate performance of our features in the task of speaker-independent emotion recognition using two publicly available datasets. Our experimental results clearly indicate that indeed both the richer set of spectral features and the differentiation between phoneme type classes are beneficial for the task. Classification accuracies are consistently higher for our features compared to prosodic or utterance-level spectral features. Combination of our phoneme class features with prosodic features leads to even further improvement. Given the large number of class-level spectral features, we expected feature selection will improve results even further, but none of several selection methods led to clear gains. Further analyses reveal that spectral features computed from consonant regions of the utterance contain more information about emotion than either stressed or unstressed vowel features. We also explore how emotion recognition accuracy depends on utterance length. We show that, while there is no significant dependence for utterance-level prosodic features, accuracy of emotion recognition using class-level spectral features increases with the utterance length

A Joint Transformation and Residual Image Descriptor for Morphometric Image Analysis using an Equivalence Class Formulation

Existing computational anatomy methodologies for morphometric analysis of medical images are often based solely on the shape transformation, typically being a diffeomorphism, that warps these images to a common template or vice versa. However, anatomical differences as well as changes induced by pathology, prevent the warping transformation from producing an exact correspondence. The residual image captures information that is not reflected by the diffeomorphism, and therefore allows us to maintain the entire morphological profile for analysis. In this paper we present a morphological descriptor which combines the warping transformation with the residual image in an equivalence class formulation, to characterize morphology of anatomical structures. Equivalence classes are formed by pairs of transformation and residual, for different levels of smoothness of the warping transformation. These pairs belong to the same equivalence class, since they jointly reconstruct the exact same morphology. Moreover, pattern classification methods are trained on the entire equivalence class, instead of a single pair, in order to become more robust to a variety of factors that affect the warping transformation, including the anatomy being measured. This joint descriptor is evaluated by statistical testing and estimation of class separation by classification, initially for 2-D synthetic images with simulated atrophy and subsequently for a volumetric dataset consisting of schizophrenia patients and healthy controls. Results of class separation indicate that this joint descriptor produces generally better and more robust class separation than using each of the components separately

Mother’s experience in alternate birth positions during second stage of labour

Background: The main objective of our research was to study mother’s experience in alternate birth positions during second stage of labour. Evidently, providing freedom to choose birthing positions positively impacts the women’s comfort level and intrapartum birthing experience. Methods: In the present study we counselled our antenatal and intranatal patients about the benefits and risks of alternate birthing positions. We enrolled 50 consecutive consenting parturients with prior vaginal delivery, who opted to adopt alternate birth position during second stage of labour. Maternal experience of birthing in alternate positions was compared to her experience in previous childbirth in dorsal position by using a standard pre-validated questionnaire using Likert scale with close ended and open-ended questions. Results: In our study, 49/50 (98%) mothers found alternate positions helpful, 46/50 (92%) mothers reported bearing down was easier, 47/50 (94%) mothers felt they would recommend alternate birth positions to others, for 49/50 (98%) parturients progression of labour was satisfactory, 48/50 (96%) had a good birthing experience, 44/50 (88%) felt duration of labour was less than what they had expected and 46/50 (92%) parturients reported that birthing experience in alternate birth position was more comfortable as compared to their previous delivery. Conclusions: Overall experience of delivering in alternate birth position was positive. Thus, it is clear that when women are informed and educated about their options, they are in a better position to make decisions from the available options and gain a sense of control over their healthcare with improved satisfaction and experience

Suicidal Human Poisoning with Fungicide Pencycuron; a Rare Case Report from Rural India with Brief Review of Literature

Background: Pencycuron is frequently used worldwide as fungicide.  It is considered to be very effective and safe for humans and its acute toxicity is not yet known. Case Presentation: Here, we describe a case of severe acute Pencycuron poisoning in a young female with suicidal intent. She was immediately hospitalized in ICU and could be saved with meticulous management. Conclusion: Such acute and severe poisoning with Pencycuron has not been reported earlier and to the best of our knowledge this is the first case reported so far

Comparison of transvaginal sonography and saline infusion sonohysterography for the diagnosis of causes of abnormal uterine bleeding: a diagnostic accuracy study

Background: Abnormal uterine bleeding (AUB) is one of the frequently observed gynecological problems in outpatient settings. Diagnosis of the cause of AUB is important and hysteroscopy with biopsy is considered is best method for diagnosis of the same. Recent studies suggest the role of transvaginal sonography (TVS) and saline infusion sonohysterography (SIS) for the diagnosis of AUB though data about accuracy and comparison of these techniques with gold standard is not available. The study was designed with the aim of comparison of TVS and SIS for the diagnosis of abnormal uterine bleeding in reference to microscopical examination after hysterectomy.Methods: 100 consecutive patients of AUB were included in the study on the basis of inclusion and exclusion criteria. TVS and SIS were performed on each patient before the surgery for hysterectomy. The findings of TVS and SIS were compared with microscopical examination of the specimen after the hysterectomy. Sensitivity, specificity, positive predictive and negative predictive values were measured.Results: For sub mucosal myoma sensitivity , specificity, positive predictive value, negative predictive value and kappa statistics of SIS were 100%, 100%, 100%, 100%, 1 respectively while for TVS It were 18.1%, 98.8%, 66.6%, 90.7% and 0.25 respectively.Conclusions: SIS has superior diagnostic accuracy and compared to TVS. These findings need to be confirmed by randomized studies with more sample size

Probabilistic Segmentation of Brain Tumors Based on Multi-Modality Magnetic Resonance Images

In this paper, multi-modal Magnetic Resonance (MR) images are integrated into a tissue profile that aims at differentiating tumor components, edema and normal tissue. This is achieved by a tissue classification technique that learns the appearance models of different tissue types based on training samples identified by an expert and assigns tissue labels to each voxel. These tissue classifiers produce probabilistic tissue maps reflecting imaging characteristics of tumors and surrounding tissues that may be employed to aid in diagnosis, tumor boundary delineation, surgery and treatment planning. The main contributions of this work are: 1) conventional structural MR modalities are combined with diffusion tensor imaging data to create an integrated multimodality profile for brain tumors, and 2) in addition to the tumor components of enhancing and non-enhancing tumor types, edema is also characterized as a separate class in our framework. Classification performance is tested on 22 diverse tumor cases using cross-validation