Evaluation of thallium-201 scanning for detection of latent coronary artery disease

The use of thallium imaging as a noninvasive method to accurately screen shuttle passengers for latent coronary artery disease was investigated. All radionuclide procedures were performed using an Anger type camera with a high resolution collimator. A minimum of 200,000 counts were collected for each image using a 20% window centered on the 69-83 keV X-rays. For the images obtained following injection with the patient at rest, the testing was begun 10 minutes after injection. Injections of TT during exercise were made at a point near the termination of the treadmill procedure as determined by either the appearance of ST segment changes on the electrocardiogram consistant with subendocardial ischemia, the appearance of angina-like chest pain in the patient or fatigue in the patient which required cessation of the test. The severity of heart disease was based on the medical history, physical exam, exercise electrocardiograms, chest X-rays and the coronary arteriogram

In vivo nuclear magnetic resonance imaging

A number of physiological changes have been demonstrated in bone, muscle and blood after exposure of humans and animals to microgravity. Determining mechanisms and the development of effective countermeasures for long duration space missions is an important NASA goal. The advent of tomographic nuclear magnetic resonance imaging (NMR or MRI) gives NASA a way to greatly extend early studies of this phenomena in ways not previously possible; NMR is also noninvasive and safe. NMR provides both superb anatomical images for volume assessments of individual organs and quantification of chemical/physical changes induced in the examined tissues. The feasibility of NMR as a tool for human physiological research as it is affected by microgravity is demonstrated. The animal studies employed the rear limb suspended rat as a model of mucle atrophy that results from microgravity. And bedrest of normal male subjects was used to simulate the effects of microgravity on bone and muscle

Present status and future scope for fish production in cages and enclosures in India

The paper highlights the role of intensive fish husbandry system in cages and enclosures in the overall fisheries development of the country. This system of fish culture in widely dispersed aquatic ecosystems in India has yielded stimulating results, though there are some immediate constraints. The pressing problems of cage size, shape and material, diseases and parasites, and location of operational sites have been discussed. Such intensive culture systems have numerous advantages over the traditional pond culture. It 15 conduded that cage and enclosure culture of fifish and shellfish will ultimately carve its niche in the streams, rivers, canals, heels, lakes, reservoirs, estuaries, lagoons, bays and coastal areas of the country

Deep Learning-Based Dose Prediction To Improve the Plan Quality of Volumetric Modulated Arc Therapy for Gynecologic Cancers

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. Results: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D1% and D98% were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. Conclusions: Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model\u27s performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D CONCLUSIONS: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality

Genetic variation of wild and hatchery populations of the catla Indian major carp (Catla catla Hamilton 1822: Cypriniformes, Cyprinidae) revealed by RAPD markers

Genetic variation is a key component for improving a stock through selective breeding programs. Randomly amplified polymorphic DNA (RAPD) markers were used to assess genetic variation in three wild population of the catla carp (Catla catla Hamilton 1822) in the Halda, Jamuna and Padma rivers and one hatchery population in Bangladesh. Five decamer random primers were used to amplify RAPD markers from 30 fish from each population. Thirty of the 55 scorable bands were polymorphic, indicating some degree of genetic variation in all the populations. The proportion of polymorphic loci and gene diversity values reflected a relatively higher level of genetic variation in the Halda population. Sixteen of the 30 polymorphic loci showed a significant (p < 0.05, p < 0.01, p < 0.001) departure from homogeneity and the FST values in the different populations indicated some degree of genetic differentiation in the population pairs. Estimated genetic distances between populations were directly correlated with geographical distances. The unweighted pair group method with averages (UPGMA) dendrogram showed two clusters, the Halda population forming one cluster and the other populations the second cluster. Genetic variation of C. catla is a useful trait for developing a good management strategy for maintaining genetic quality of the species

Biology Open

Early phase diabetes is often accompanied by pain sensitization. In Drosophila, the insulin receptor (InR) regulates the persistence of injury-induced thermal nociceptive sensitization. Whether Drosophila InR also regulates the persistence of mechanical nociceptive sensitization remains unclear. Mice with a sensory neuron deletion of the insulin receptor (Insr) show normal nociceptive baselines; however, it is uncertain whether deletion of Insr in nociceptive sensory neurons leads to persistent nociceptive hypersensitivity. In this study, we used fly and mouse nociceptive sensitization models to address these questions. In flies, InR mutants and larvae with sensory neuron-specific expression of RNAi transgenes targeting InR exhibited persistent mechanical hypersensitivity. Mice with a specific deletion of the Insr gene in Nav1.8+ nociceptive sensory neurons showed nociceptive thermal and mechanical baselines similar to controls. In an inflammatory paradigm, however, these mutant mice showed persistent mechanical (but not thermal) hypersensitivity, particularly in female mice. Mice with the Nav1.8+ sensory neuron-specific deletion of Insr did not show metabolic abnormalities typical of a defect in systemic insulin signaling. Our results show that some aspects of the regulation of nociceptive hypersensitivity by the insulin receptor are shared between flies and mice and that this regulation is likely independent of metabolic effects

Metagenomes of Rectal Swabs in Larger, Advanced Stage Cervical Cancers Have Enhanced Mucus Degrading Functionalities and Distinct Taxonomic Structure

BACKGROUND: Gut microbiome community composition differs between cervical cancer (CC) patients and healthy controls, and increased gut diversity is associated with improved outcomes after treatment. We proposed that functions of specific microbial species adjoining the mucus layer may directly impact the biology of CC. METHOD: Metagenomes of rectal swabs in 41 CC patients were examined by whole-genome shotgun sequencing to link taxonomic structures, molecular functions, and metabolic pathway to patient\u27s clinical characteristics. RESULTS: Significant association of molecular functions encoded by the metagenomes was found with initial tumor size and stage. Profiling of the molecular function abundances and their distributions identified 2 microbial communities co-existing in each metagenome but having distinct metabolism and taxonomic structures. Community A (Clostridia and Proteobacteria predominant) was characterized by high activity of pathways involved in stress response, mucus glycan degradation and utilization of degradation byproducts. This community was prevalent in patients with larger, advanced stage tumors. Conversely, community B (Bacteroidia predominant) was characterized by fast growth, active oxidative phosphorylation, and production of vitamins. This community was prevalent in patients with smaller, early-stage tumors. CONCLUSIONS: In this study, enrichment of mucus degrading microbial communities in rectal metagenomes of CC patients was associated with larger, more advanced stage tumors

Experimental Induction of Paromomycin Resistance in Antimony-Resistant Strains of L. donovani: Outcome Dependent on In Vitro Selection Protocol

Paromomycin (PMM) has recently been introduced for treatment of visceral leishmaniasis in India. Although no clinical resistance has yet been reported, proactive vigilance should be warranted. The present in vitro study compared the outcome and stability of experimental PMM-resistance induction on promastigotes and intracellular amastigotes. Cloned antimony-resistant L. donovani field isolates from India and Nepal were exposed to stepwise increasing concentrations of PMM (up to 500 µM), either as promastigotes or intracellular amastigotes. One resulting resistant strain was cloned and checked for stability of resistance by drug-free in vitro passage as promastigotes for 20 weeks or a single in vivo passage in the golden hamster. Resistance selection in promastigotes took about 25 weeks to reach the maximal 97 µM inclusion level that did not affect normal growth. Comparison of the IC50 values between the parent and the selected strains revealed a 9 to 11-fold resistance for the Indian and 3 to 5-fold for the Nepalese strains whereby the resistant phenotype was also maintained at the level of the amastigote. Applying PMM pressure to intracellular amastigotes produced resistance after just two selection cycles (IC50 = 199 µM) compared to the parent strain (IC50 = 45 µM). In the amastigote-induced strains/clones, lower PMM susceptibilities were seen only in amastigotes and not at all in promastigotes. This resistance phenotype remained stable after serial in vitro passage as promastigote for 20 weeks and after a single in vivo passage in the hamster. This study clearly demonstrates that a different PMM-resistance phenotype is obtained whether drug selection is applied to promastigotes or intracellular amastigotes. These findings may have important relevance to resistance mechanism investigations and the likelihood of resistance development and detection in the field