Role of laparoscopy in the diagnosis and management of benign adnexal masses

Background: Adnexal masses are frequent findings in women of all age groups. It consists of the ovaries, fallopian tubes and uterine ligaments. Women can present with various gynaecological complaints and adnexal masses could be detected while examining and investigating for these complaints. The aim was to study the role of laparoscopy in diagnosis and management of benign adnexal masses.Methods: The study was conducted on 48 women of reproductive age group. Per speculum examination was done and PAP smear was taken before bimanual examination was done. A complete per vaginum examination was done and the adnexal mass was assessed for its size, side, consistency, laterality and tenderness. Laparoscopy was done to confirm preoperative diagnosis and appropriate procedure done depending on diagnosis.Results: Pain in the lower abdomen was the commonest chief complaint seen in 87.5% cases. 41.67% cases were suspected to have endometriosis while on laparoscopy it was seen in 47.92%, 33.33% were suspected to have ovarian cyst which decreased to 25% on laparoscopy, ectopic pregnancy in 16.67% cases both pre-operative and on laparoscopic examination and tubo-ovarian mass in 8.33% cases pre-operatively and 2.08% on laparoscopy.Conclusions: This study has shown that if proper preoperative evaluation was done, author can select the appropriate patients for laparoscopic approach

Clinical profile of patients with pelvic adnexal masses

Background: Adnexal masses are frequent findings in women of all age groups. It consists of the ovaries, fallopian tubes and uterine ligaments. Women can present with various gynaecological complaints and adnexal masses could be detected while examining and investigating for these complaints. Aim was to study the clinical profile of women in reproductive age group presented with adnexal masses.Methods: The study comprised of 48 women of reproductive age group. Per speculum examination was done and PAP smear was taken before bimanual examination was done.A complete per vaginum examination was done and the adnexal mass was assessed for its size, side, consistency, laterality and tenderness.Results: Pain in the lower abdomen was the commonest chief complaint seen in 87.5% cases. Out of these, majority i.e.66.67% had chronic pelvic pain and 23% had pain of less than 1-month duration which were cases of ectopic pregnancy. 41.67% cases were suspected to have endometriosis,33.33% were suspected to have ovarian cyst, followed by ectopic pregnancy in 16.67% cases and tubo-ovarian mass in 8.33% cases.Conclusions: The present study shows that if proper preoperative evaluation is done, we can select the appropriate patients for laparoscopic approach

Neural population geometry and optimal coding of tasks with shared latent structure

Humans and animals can recognize latent structures in their environment and apply this information to efficiently navigate the world. However, it remains unclear what aspects of neural activity contribute to these computational capabilities. Here, we develop an analytical theory linking the geometry of a neural population's activity to the generalization performance of a linear readout on a set of tasks that depend on a common latent structure. We show that four geometric measures of the activity determine performance across tasks. Using this theory, we find that experimentally observed disentangled representations naturally emerge as an optimal solution to the multi-task learning problem. When data is scarce, these optimal neural codes compress less informative latent variables, and when data is abundant, they expand these variables in the state space. We validate our theory using macaque ventral stream recordings. Our results therefore tie population geometry to multi-task learning.Comment: 26 Pages and 7 figures in main text. 20 Pages and 7 figures in supplemental materia

Linear Classification of Neural Manifolds with Correlated Variability

Understanding how the statistical and geometric properties of neural activations relate to network performance is a key problem in theoretical neuroscience and deep learning. In this letter, we calculate how correlations between object representations affect the capacity, a measure of linear separability. We show that for spherical object manifolds, introducing correlations between centroids effectively pushes the spheres closer together, while introducing correlations between the spheres' axes effectively shrinks their radii, revealing a duality between neural correlations and geometry. We then show that our results can be used to accurately estimate the capacity with real neural data.Comment: 6 pages and 5 figures in main text. 11 pages and 1 figure in supplementary materia

A Spectral Theory of Neural Prediction and Alignment

The representations of neural networks are often compared to those of biological systems by performing regression between the neural network responses and those measured from biological systems. Many different state-of-the-art deep neural networks yield similar neural predictions, but it remains unclear how to differentiate among models that perform equally well at predicting neural responses. To gain insight into this, we use a recent theoretical framework that relates the generalization error from regression to the spectral bias of the model activations and the alignment of the neural responses onto the learnable subspace of the model. We extend this theory to the case of regression between model activations and neural responses, and define geometrical properties describing the error embedding geometry. We test a large number of deep neural networks that predict visual cortical activity and show that there are multiple types of geometries that result in low neural prediction error as measured via regression. The work demonstrates that carefully decomposing representational metrics can provide interpretability of how models are capturing neural activity and points the way towards improved models of neural activity.Comment: First two authors contributed equally. To appear at NeurIPS 202

A study of maternal outcome in first trimester bleeding

Background: The outcome of first trimester vaginal bleeding is a matter of debate. Vaginal bleeding is common and potentially alarming symptom in early pregnancy. First trimester bleeding is a common occurrence. It has been estimated to occur in 15-25% of all pregnant women. Objective of this study was to evaluate the various maternal outcomes in women with first trimester bleeding.Methods: This prospective observational study was conducted in the postgraduate department of obstetrics and gynecology, SMGS Hospital, Government Medical College, Jammu, Jammu and Kashmir, India. The study included 200 pregnant women presented with first trimester bleeding. All the women were followed prospectively till delivery and early postpartum period for various outcomes such as preterm delivery, PROM, PPROM, anemia, oligohydramnios, placental abruption, placenta previa and postpartum hemorrhage.Results: Out of 200 patients studied, 19% patients aborted. Ectopic and molar pregnancy was seen in 5% and 1.5% patients respectively. Out of 74.5% patients who continued pregnancy, maternal complications included anemia (52%), PROM (14.09%), oligohydramnios (6.71%), placenta previa (5.37%), PPH (4.03%), PPROM (2.68%), preeclampsia (2.01%), gestational hypertension (1.34%), abruption and post-datism (0.67% each).Conclusions: From the results of this study, it can be concluded that first trimester bleeding can be a predicting factor in terms of mother and infant consequences of pregnancy and it is necessary to increase the knowledge of pregnant women in this regard for closer care