Evaluation of hematological parameters and platelet yield in voluntary blood donors by plateletpheresis: a one-year study at the blood centre in a teaching hospital

Background: The present study was planned to compare of pre and post donation hematological parameters in healthy donors by plateletpheresis. Also to assess the platelet yield following plateletpheresis procedure with its correlation to pre donation platelet count. Methods: This is a retrospective cross-sectional study carried out in the Blood Centre of a tertiary care hospital in Haryana, India between January to December 2022. Plateletpheresis was done on Trima Accel Automated Collection System with ACD‐A as an anticoagulant. The data was collected from the hospital for hematological parameters (Hb, hematocrit, Total WBC count, total platelet count) pre and post donation. Categorical data is presented as frequency, percentage, mean±SD range. Correlation was established between the pre donation platelet count and the platelet yield. Results: A total of 125 donors were included in the study with majority of the donors 69 (55.2%) in the age group 21-30 years. Mean age of the donors included in the study was 31.58±7.5 years. The levels of hemoglobin dropped from 14.16±0.95 to 13.92±1.002 gm/dl, hematocrit dropped from 41.19±1.33 to 40.91±2.89%, total WBC count reduced from 7.64±1.38 to 7.61±1.36 103/ l and platelet count dropped from 279.5±62.96 to 259.9±58.38 lac/ l. There was a significant drop in the levels of platelet post donation by 7.01% compared to pre donation levels. majority of the donors (44%) had a mean platelet yield 2.49±0.33 with a platelet count between 1.5-2.5x1011/l. The maximum platelet yield was 4.93±0.34 in 6% donors with pre-donation platelet count of >4.5 5x1011/l. A linear significant relationship was established between the platelet count and the platelet yield (r=0.99). Conclusions: There were significant changes in the pre donation and post donation hematological parameters among the donors. It was concluded that donors with a high pre-donation platelet count can be considered for better platelet yield. Background: The present study was planned to compare of pre and post donation hematological parameters in healthy donors by plateletpheresis. Also to assess the platelet yield following plateletpheresis procedure with its correlation to pre donation platelet count. Methods: This is a retrospective cross-sectional study carried out in the Blood Centre of a tertiary care hospital in Haryana, India between January to December 2022. Plateletpheresis was done on Trima Accel Automated Collection System with ACD‐A as an anticoagulant. The data was collected from the hospital for hematological parameters (Hb, hematocrit, Total WBC count, total platelet count) pre and post donation. Categorical data is presented as frequency, percentage, mean±SD range. Correlation was established between the pre donation platelet count and the platelet yield. Results: A total of 125 donors were included in the study with majority of the donors 69 (55.2%) in the age group 21-30 years. Mean age of the donors included in the study was 31.58±7.5 years. The levels of hemoglobin dropped from 14.16±0.95 to 13.92±1.002 gm/dl, hematocrit dropped from 41.19±1.33 to 40.91±2.89%, total WBC count reduced from 7.64±1.38 to 7.61±1.36 103/ l and platelet count dropped from 279.5±62.96 to 259.9±58.38 lac/ l. There was a significant drop in the levels of platelet post donation by 7.01% compared to pre donation levels. majority of the donors (44%) had a mean platelet yield 2.49±0.33 with a platelet count between 1.5-2.5x1011/l. The maximum platelet yield was 4.93±0.34 in 6% donors with pre-donation platelet count of >4.5 5x1011/l. A linear significant relationship was established between the platelet count and the platelet yield (r=0.99). Conclusions: There were significant changes in the pre donation and post donation hematological parameters among the donors. It was concluded that donors with a high pre-donation platelet count can be considered for better platelet yield.

Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification

© 2020, Springer Nature Switzerland AG. Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or unseen categories. The state-of-the-art relies on Generative Adversarial Networks that synthesize unseen class features by leveraging class-specific semantic embeddings. During training, they generate semantically consistent features, but discard this constraint during feature synthesis and classification. We propose to enforce semantic consistency at all stages of (generalized) zero-shot learning: training, feature synthesis and classification. We first introduce a feedback loop, from a semantic embedding decoder, that iteratively refines the generated features during both the training and feature synthesis stages. The synthesized features together with their corresponding latent embeddings from the decoder are then transformed into discriminative features and utilized during classification to reduce ambiguities among categories. Experiments on (generalized) zero-shot object and action classification reveal the benefit of semantic consistency and iterative feedback, outperforming existing methods on six zero-shot learning benchmarks. Source code at https://github.com/akshitac8/tfvaegan

Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification

Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or unseen categories. The state-of-the-art relies on Generative Adversarial Networks that synthesize unseen class features by leveraging class-specific semantic embeddings. During training, they generate semantically consistent features, but discard this constraint during feature synthesis and classification. We propose to enforce semantic consistency at all stages of (generalized) zero-shot learning: training, feature synthesis and classification. We first introduce a feedback loop, from a semantic embedding decoder, that iteratively refines the generated features during both the training and feature synthesis stages. The synthesized features together with their corresponding latent embeddings from the decoder are then transformed into discriminative features and utilized during classification to reduce ambiguities among categories. Experiments on (generalized) zero-shot object and action classification reveal the benefit of semantic consistency and iterative feedback, outperforming existing methods on six zero-shot learning benchmarks. Source code at https://github.com/akshitac8/tfvaegan.Comment: Accepted for publication at ECCV 202

ECCV (22) - Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification

Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or unseen categories. The state-of-the-art relies on Generative Adversarial Networks that synthesize unseen class features by leveraging class-specific semantic embeddings. During training, they generate semantically consistent features, but discard this constraint during feature synthesis and classification. We propose to enforce semantic consistency at all stages of (generalized) zero-shot learning: training, feature synthesis and classification. We first introduce a feedback loop, from a semantic embedding decoder, that iteratively refines the generated features during both the training and feature synthesis stages. The synthesized features together with their corresponding latent embeddings from the decoder are then transformed into discriminative features and utilized during classification to reduce ambiguities among categories. Experiments on (generalized) zero-shot object and action classification reveal the benefit of semantic consistency and iterative feedback, outperforming existing methods on six zero-shot learning benchmarks. Source code at https://github.com/akshitac8/tfvaegan.Comment: Accepted for publication at ECCV 202

Physicochemical properties of free and calcium alginate immobilized alkaline pectin lyase from Bacillus cereus

305-314Purified pectin lyase from Bacillus cereus was successfully immobilized in alginate beads with a high binding efficiency of 84.55%. The optimal immobilization was achieved using 2.5% (w/v) alginate concentration. Both free and immobilized enzyme showed optimum pH of 10.0 and temperatures of 40 and 45°C respectively. Pectin lyase gave maximum activity at a substrate concentration of 0.5% w/v for free and 0.75% w/v for the immobilized enzyme and relatively similar Vmax values were obtained for both free (3.3 µmol/min) and immobilized pectin lyase (3.6 µmol/min). The Km for the immobilized pectin lyase (0.19 mg/ml) was slightly higher than that of the free (0.16 mg/ml) enzyme. The maximum inhibition of 50.2% was observed in the presence of Hg2+ ion for free pectin lyase and immobilized enzyme showed maximum inhibition of 67.32% in the presence of Na+ ion with statistically significant p-value (p th cycle. Furthermore, during storage at 4°C, immobilized pectin lyase retained relative activity of 79.77% and free enzyme retained 63.63% relative activity upto 35 days of storage, this indicated that the immobilization improved stability of the enzyme

Generative Multi-Label Zero-Shot Learning

Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embedding. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Comprehensive experiments are performed on three zero-shot image classification benchmarks: NUS-WIDE, Open Images and MS COCO. Our cross-level fusion-based generative approach outperforms the state-of-the-art on all three datasets. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods. The source code is available at https://github.com/akshitac8/Generative_MLZSL.Comment: 10 pages, source code is available at https://github.com/akshitac8/Generative_MLZS