Response of plasmodium vivax malaria induced thrombocytopenia to antimalarial treatment.

BACKGROUND: Thrombocytopenia is often seen in the patients of malaria infected with Plasmodium vivax. We studied patients admitted in hospital having coexisting thrombocytopenia and malaria, and recorded the response to anti-malarial therapy. METHODS: In this cross-sectional descriptive study, a total of 120 patients admitted in medical ward with Plasmodium vivax malaria and co-existing thrombocytopenia were studied. RESULTS: Out of total 120 slide positive Malaria patients who had low platelet count (<150x10(9) /L), platelet count increased to ≥150x10(9) /L in 73 (60.8%) patients after five days of anti-malarial therapy while in 47 (39.2%) patients thrombocytopenia persisted. After ten days of anti-malarial therapy, platelet count in all the patients recovered to ≥ 150x10(9) L. None of the patients required platelet transfusion. CONCLUSION: In majority of the patients of Plasmodium vivax malaria having thrombocytopenia, platelet count returns to normal within five to ten days of start of anti-malarial treatment and nlatelet transfusion is not required.

ASD-GANNet: A Generative Adversarial Network-Inspired Deep Learning Approach for the Classification of Autism Brain Disorder.

The classification of a pre-processed fMRI dataset using functional connectivity (FC)-based features is considered a challenging task because of the set of high-dimensional FC features and the small dataset size. To tackle this specific set of FC high-dimensional features and a small-sized dataset, we propose here a conditional Generative Adversarial Network (cGAN)-based dataset augmenter to first train the cGAN on computed connectivity features of NYU dataset and use the trained cGAN to generate synthetic connectivity features per category. After obtaining a sufficient number of connectivity features per category, a Multi-Head attention mechanism is used as a head for the classification. We name our proposed approach "ASD-GANNet", which is end-to-end and does not require hand-crafted features, as the Multi-Head attention mechanism focuses on the features that are more relevant. Moreover, we compare our results with the six available state-of-the-art techniques from the literature. Our proposed approach results using the "NYU" site as a training set for generating a cGAN-based synthetic dataset are promising. We achieve an overall 10-fold cross-validation-based accuracy of 82%, sensitivity of 82%, and specificity of 81%, outperforming available state-of-the art approaches. A sitewise comparison of our proposed approach also outperforms the available state-of-the-art, as out of the 17 sites, our proposed approach has better results in the 10 sites.

A Novel Knowledge Distillation-Based Feature Selection for the Classification of ADHD.

Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder with characteristics such as lack of concentration, excessive fidgeting, outbursts of emotions, lack of patience, difficulty in organizing tasks, increased forgetfulness, and interrupting conversation, and it is affecting millions of people worldwide. There is, until now, not a gold standard test using which an ADHD expert can differentiate between an individual with ADHD and a healthy subject, making accurate diagnosis of ADHD a challenging task. We are proposing a Knowledge Distillation-based approach to search for discriminating features between the ADHD and healthy subjects. Learned embeddings from a large neural network, trained on the functional connectivity features, were fed to one hidden layer Autoencoder for reproduction of the embeddings using the same connectivity features. Finally, a forward feature selection algorithm was used to select a combination of most discriminating features between the ADHD and the Healthy Controls. We achieved promising classification results for each of the five individual sites. A combined accuracy of 81% in KKI, 60% Peking, 56% in NYU, 64% NI, and 56% OHSU and individual site wise accuracy of 72% in KKI, 60% Peking, 73% in NYU, 70% NI, and 71% OHSU were obtained using our extracted features. Our results also outperformed state-of-the-art methods in literature which validates the efficacy of our proposed approach.

A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder.

Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a "Teacher " was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a "Student" autoencoder was given the task to learn those trained "Teacher" embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons.

Should high creatine kinase discourage the initiation or continuance of statins for the treatment of hypercholesterolemia?

Patients with high low-density lipoprotein cholesterol (LDLC) and asymptomatic high creatine kinase (CK) (>or=250 but <2500 IU/L, 10x the laboratory upper normal limit [UNL]) are often not started on statins or have statins stopped because of concern about myositis-rhabdomyolysis. In the current report, we prospectively examined the hypothesis that asymptomatic patients with high CK (>or=250 but <2500 IU/L) tolerate statins well at doses reducing LDLC to target, less than 100 mg/dL, without development of myalgia-myositis. We assessed outcomes of 3 groups of patients referred to us because of asymptomatic high CK (>or=250 but <2500 IU/L)--1 group (n = 29) on statins at referral and continued on statins, 1 group (n = 20) not on statins and started on statins, and 1 group (n = 19) not on statins and not given statins--all restudied 1 month after entry and then every 3 months. Of the 68 patients, 59 (87%) had CK greater than 1 to 3 times the UNL, 7 (10%) had CK greater than 3 to 5 times the UNL, and 2 (3%) had CK greater than 5 to 10 times the UNL. After 1.2 months of follow-up in 29 statin-->statin patients, median CK fell from 353 to 301 (P = .0018) and was 287 (P = .015) after 4 months. After 1.3 months of follow-up in 20 no statin-->statin patients, median CK fell from 397 to 292 (P = .0094) and was 419 after 4.1 months. After 1.1 months of follow-up in 19 no statin-->no statin patients, median CK fell from 392 to 323 (P = .14) and was 271 (P = .029) after 4.2 months. By repeated-measures analysis, there were no differences in entry CK among the 3 treatment groups; CK fell (P = .04) in the no statin-->no statin patients. Despite high baseline CK (48 patients with CK 1-5x the UNL, 1 with CK 5-10x UNL), no patients during follow-up on statins developed CK greater than 10 times the UNL (2500 IU/L), none discontinued statins or reduced statin dose because of myalgia-myositis, and there was no rhabdomyolysis. High pretreatment CK, particularly 1 to 5 times the UNL, should not be an impediment to start or continue statins to lower LDLC.