Unit for Research and Development of Information Products

The present work is based on formulation & analytical method development of Paracetamol (PCM), Chlorpheniramine Maleate (CPM) & Phenylephrine Hydrochloride (PPH) syrup. A Fixed dose combination (FDC) of PCM, PPH & CPM are commonly used in clinical practice as antipyretic-analgesic drugs to treat pain & common cold conditions. The objective of the study is to develop a sugar free syrup formulation which have been smashing in market for pediatric use. The formulation of the product was done by using QbD based development, that has being significantly evolved by following the introduction of ICH guidelines such as Q8(R2), Q9, Q10, Q11 that offers comprehensive direction on implementation & breadth of QbD. The excipients were selected on the basis of the literature survey along with it preformulation studies was carried out to check compatibility on ingredients. The results showed that all active compounds are compatible with excipients. The method of EG-DEG content was developed & validated, in which precision and intermediate precisionwas checked by evaluating the %RSD of the verified method along with it specificity, accuracy was also performed that helps access unequivocally the analyte product & matrix components as well as to determine accuracy of the EG-DEG method, followed by linearity and robustness. The studies has also focused on development of precise, accurate & specific RP-HPLC stability demonstrating method for real-time estimation of PCM, PPH & CPM in formulation by implementing quality by design approach. The method parameters for HPLC, like flow rate and mobile phase composition, are identified as Critical Method Attributes (CMA). A design space was established using Design of Experiments (DoE) facilitated by design experts.The resolution amongst peaks were achieved on an YMC Triart C18 HPLC column (4.6 × 150 mm; 5 µm) at 220 nm, by upholding column oven temperature at 40°C. The optimized conditions included a gradient elution sodium phosphate buffer (pH 3.0) as a mobile phase A then composition of Acetonitrile: Methanol in the ratio of 70:30 as mobile phase B, maintaining flow 1.2 mL/min. Product was stable up to 6 months’ period with liner stability.

<bold>Background and Aims:</bold> Preserved Ratio Impaired Spirometry (PRISm) is an emerging spirometry index with important clinical implications. We aimed to study the burden of PRISm & its associated factors in community health screening camps in the urban slum & rural population in India. <bold>Materials and Methods:</bold> We organized community health camps for Pollution Awareness & Wellness Assessment (PAWAN) during which we administered a lung health questionnaire that captured demographics & risk factors for lung diseases. Spirometry was performed according to ATS/ERS standards. Obstructive airway disease (OAD) was defined as FEV1/FVC<0.70 & PRISm was identified as FEV1/FVC ≥0.70 and FEV1<80% predicted. <bold>Results:</bold> Among 3402 subjects, interim analysis is presented for 1682 subjects. 588 (35%) had presence of respiratory symptoms (cough, breathlessness, wheeze) or family history of asthma or allergies. Spirometry was performed in 495 subjects, among whom the prevalence of OAD was 11% and among the remaining 442 subjects, 29% had PRISm whose detailed characteristics are as per Table 1. <fig><object-id>erj;64/suppl_68/PA3062/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> <bold>Conclusion:</bold> Overall, 26% of subjects screened using spirometry in the community in India had PRISm which was associated with significantly increased respiratory symptoms and small airways obstruction. PRISm should be considered as an indicator of underlying respiratory morbidity that merits more research.

<bold>Background:</bold> Air pollution (AP) causes around 6.2 million premature deaths globally. In 2019, global burden of diseases report attributed 17.8% of deaths and 11.7% of disability adjusted life years (DALYs) in India, to AP. <bold>Aim:</bold> To understand the knowledge, attitude and practices about AP amongst urban slum and rural population in India. <bold>Methodology:</bold> Through our Pollution Awareness and Health Assessment Initiative (PAWAN) project we administered a questionnaire about AP during health screening camps in Pune. <bold>Results:</bold> Interim analysis for 1682 of the 3402 screened adults is presented (M:F=39:61). 23% were illiterate and 51% had studied ≤10th grade. 98% comprised low or very low-income group. 80% stayed within 200 meters of a main thoroughfare. 39% had no separate kitchen, 12% had no windows and 77% had no exhaust fans to the kitchen. 63.5% used unclean fuel. 72%, 30% and 56% burnt incense sticks, dhoop sticks and mosquito coils respectively either once or twice daily, while only 33%, 11% and 7% identified them as a source of household AP (HAP). Beliefs about sources and health effects of AP are presented in the figure. <bold>Conclusion:</bold> Awareness about AP and its impact on health is poor amongst people living in urban slums and rural areas. Ignorance and practices about HAP are a greater problem. Awareness drives to address these are essential. <fig><object-id>erj;64/suppl_68/PA1317/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>

Abstract Machine learning and computer vision are some of the booming areas of applied artificial intelligence (AI). Through machine learning, we give machines the ability to make decisions on their own. Computer vision is a subset of AI where we give these machines the ability to see and we give these machines the ability to make sense of images and understand crucial parts of the images. Often these images get corrupted because of noise and atmospheric disturbances, and these noises and disturbances are identified as haze or atmospheric haze. The presence of haze in the image hides the key elements/subjects in the image, so the AI algorithm finds it difficult to identify the subjects in the image. In this paper, we propose to use a deep learning-based haze removal system that will prove to be one of the most important preprocessing steps before giving the data to your AI system.KeywordsMachine learningDeep learningComputer visionArtificial intelligenceHaze removalImage processingGenerative adversarial networks