Acceptance of birth spacing methods and it’s determinants among postnatal women in a tertiary care setting from Kerala

Background: According to VISION FP2020 healthy birth spacing is defined as delaying the first birth by two years and maintaining the birth interval of at least three years between the two children with the help of various contraceptives.Methods: This was a hospital based cross sectional study conducted among primiparous postnatal women in the department of obstetrics and gynecology SAT hospital, Trivandrum for one year duration. 134 women who had their first delivery were included in this study. A semi structured questionnaire was used to study the sociodemographic variables, reproductive history, contraceptive knowledge, attitude towards contraception and intention to use birth spacing methods by interview technique.Results: The acceptance of birth spacing methods among the study population was 56.8%. 59.1% opted to use family planning methods 6 weeks after delivery while 40.9% planned to use after 6 months. Determinants which were significantly associated with acceptance of family planning methods were education of wife (p<0.01), husbands’ education, socioeconomic status, religion and contraceptive awareness (p<0.01). 47.1% of women preferred DMPA injections, 39.1% opted for intrauterine devices. 13.8% of women opted for condom. Convenience, long term protection, and ease of use were the important reasons cited. The reasons for non-acceptance included fear of side effects, spouse not staying together, opposition from husbands, lack of awareness and religious restrictions.Conclusions: Female literacy, accessibility, cafeteria approach, and counselling contributed to acceptance. If the availability and awareness of injectable contraceptives is made at all the points of health delivery system, the acceptance of birth spacing can further be increased and unplanned pregnancies avoided

CO2 fixation by seaweeds and their role in De-acidifying Ocean - An experimental approach

CO2 fixation by seaweeds and their role in De-acidifying Ocean - An experimental approac

Large chorangiomas: a seven years study in a tertiary care obstetrics and gynaecology hospital

Background: Chorangioma is a benign vascular placental tumour. It is composed of fetal capillary proliferation within the chorionic villi supported by a variable stroma. Smaller lesions are incidental, are often missed and carry no clinical significance. Larger lesions are associated with feto-maternal complications and are infrequently sent for histopathological examination.Methods: The study was conducted at the department of pathology, at a tertiary care obstetrics and gynaecology hospital. The study was a retrospective study which covered 7 years. Paraffin embedded blocks of placental specimens containing mass were taken up for the study. Sections were stained with haematoxylin and eosin (H and E). The results are compared and correlated with clinicopathologic factors. The statistical data are analysed manually.Results: A total of seven cases were included in the study, 4 cases were primi gravida, 1 case each in second, third and fourth gravida. Pregnancy outcome was intrauterine death in 1 case, dead born in 1 case, alive healthy children in 5 cases, birth weight was normal in 4 children, low birth weight in 2 children and 1 was extremely low birth weight. Of the total of 7 placental specimens 3 showed extraplacental mass and rest 4 showed intraplacental mass. All cases showed solitary lesions and measured > 5cm (large). Histopathological examination of all 7 specimens showed features of chorangioma.Conclusions: Careful inspection of the placenta is necessary following all deliveries. Any suspicious lesions should be documented and evaluated by histopathological examination there by predicting feto maternal complications and help the clinicians in better management of the mother and child accordingly. Meagre documentation of such cases prompted us to present this series of 7 cases of large chorangiomas with a mixed fetal outcome

Diagnostic circulating biomarkers to detect vision-threatening diabetic retinopathy: Potential screening tool of the future?

With the increasing prevalence of diabetes in developing and developed countries, the socio-economic burden of diabetic retinopathy (DR), the leading complication of diabetes, is growing. Diabetic retinopathy (DR) is currently one of the leading causes of blindness in working-age adults worldwide. Robust methodologies exist to detect and monitor DR; however, these rely on specialist imaging techniques and qualified practitioners. This makes detecting and monitoring DR expensive and time-consuming, which is particularly problematic in developing countries where many patients will be remote and have little contact with specialist medical centres. Diabetic retinopathy (DR) is largely asymptomatic until late in the pathology. Therefore, early identification and stratification of vision-threatening DR (VTDR) is highly desirable and will ameliorate the global impact of this disease. A simple, reliable and more cost-effective test would greatly assist in decreasing the burden of DR around the world. Here, we evaluate and review data on circulating protein biomarkers, which have been verified in the context of DR. We also discuss the challenges and developments necessary to translate these promising data into clinically useful assays, to detect VTDR, and their potential integration into simple point-of-care testing devices

Deep Learning

Deep Learning was developed as a Machine learning approach to influence advanced input-output mappings. It had been for learning concerning multiple levels of illustration and abstraction to create sense of the information such as images, text and sound. Deep learning excels at distinguish patterns in unstructured information, that most of the people grasp as media like images, sound, video and text

Neurosymbolic AI approach to Attribution in Large Language Models

Attribution in large language models (LLMs) remains a significant challenge, particularly in ensuring the factual accuracy and reliability of the generated outputs. Current methods for citation or attribution, such as those employed by tools like Perplexity.ai and Bing Search-integrated LLMs, attempt to ground responses by providing real-time search results and citations. However, so far, these approaches suffer from issues such as hallucinations, biases, surface-level relevance matching, and the complexity of managing vast, unfiltered knowledge sources. While tools like Perplexity.ai dynamically integrate web-based information and citations, they often rely on inconsistent sources such as blog posts or unreliable sources, which limits their overall reliability. We present that these challenges can be mitigated by integrating Neurosymbolic AI (NesyAI), which combines the strengths of neural networks with structured symbolic reasoning. NesyAI offers transparent, interpretable, and dynamic reasoning processes, addressing the limitations of current attribution methods by incorporating structured symbolic knowledge with flexible, neural-based learning. This paper explores how NesyAI frameworks can enhance existing attribution models, offering more reliable, interpretable, and adaptable systems for LLMs

Role of seaweeds in neutralizing the impact of seawater acidification- A laboratory study with beached shells of certain bivalves and spines of a sea urchin

Ocean acidification is one of the major impacts of climate change in sea which is manifested by the decrease in hydrogen ion concentration (pH) of seawater mainly due to increased uptake of CO2 and reduction in carbonate ions. This is a report on the dissolution rate of dead shells of four marine bivalves and spines of a sea urchin when treated with different levels of CO2 dissolved in seawater for 48 hours which was measured gravimetrically. Dissolution of dead shells expressed as reduction in shell weight was directly proportional to the concentration of dissolved CO2. Live thallus of green seaweed Chaetomorpha antennina did reduce the magnitude of dissolution rates (P<0.05) of all the shells and spines considerably as well as the change in pH of ambient seawater due to the addition of CO2. The remedial property of seaweeds was more effective at lower concentrations of dissolved CO2. The induced change in pH was restored by green seaweed only at concentrations above 250 ppm. Although we noticed strong impact of dissolved CO2 on the dead shells of Mactrinula plicataria even at 100 ppm level, the remedial action by the green seaweed was maximum in Siliqua radiata followed by Perna viridis. Results of this laboratory study shows the positive role of seaweeds in neutralizing the acidification impacts