A preterm neonate with fetal anemia and immune hydrops fetalis requiring intrauterine transfusion and postnatal exchange transfusion: a case report

Hydrops fetalis is a presenting illness with various immune and non-immune etiologies. It involves fluid accumulation in body cavities, and symptoms specific to its underlying cause. In this case, we report on a preterm neonate with a history of bad obstetrics who presented with hydrops fetalis due to fetal anemia related to RH incompatibility. The patient received an intrauterine transfusion for severe fetal anemia and subsequently required NICU admission. Routine preterm care was provided, along with specific management for jaundice resulting from isoimmune hemolytic anemia.

Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning

Traditional methods like Graph Convolutional Networks (GCNs) face challenges with limited data and class imbalance, leading to suboptimal performance in graph classification tasks during toxicity prediction of molecules as a whole. To address these issues, we harness the power of Graph Isomorphic Networks, Multi Headed Attention and Free Large-scale Adversarial Augmentation separately on Graphs for precisely capturing the structural data of molecules and their toxicological properties. Additionally, we incorporate Few-Shot Learning to improve the model's generalization with limited annotated samples. Extensive experiments on a diverse toxicology dataset demonstrate that our method achieves an impressive state-of-art AUC-ROC value of 0.816, surpassing the baseline GCN model by 11.4%. This highlights the significance of our proposed methodology and Few Shot Learning in advancing Toxic Molecular Classification, with the potential to enhance drug discovery and environmental risk assessment processes

Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study

This survey paper presents a brief overview of recent research on graph data augmentation and few-shot learning. It covers various techniques for graph data augmentation, including node and edge perturbation, graph coarsening, and graph generation, as well as the latest developments in few-shot learning, such as meta-learning and model-agnostic meta-learning. The paper explores these areas in depth and delves into further sub classifications. Rule based approaches and learning based approaches are surveyed under graph augmentation techniques. Few-Shot Learning on graphs is also studied in terms of metric learning techniques and optimization-based techniques. In all, this paper provides an extensive array of techniques that can be employed in solving graph processing problems faced in low-data scenarios

Characterization of cytoskeletal and junctional proteins expressed by cells cultured from human arachnoid granulation tissue

BACKGROUND: The arachnoid granulations (AGs) are projections of the arachnoid membrane into the dural venous sinuses. They function, along with the extracranial lymphatics, to circulate the cerebrospinal fluid (CSF) to the systemic venous circulation. Disruption of normal CSF dynamics may result in increased intracranial pressures causing many problems including headaches and visual loss, as in idiopathic intracranial hypertension and hydrocephalus. To study the role of AGs in CSF egress, we have grown cells from human AG tissue in vitro and have characterized their expression of those cytoskeletal and junctional proteins that may function in the regulation of CSF outflow. METHODS: Human AG tissue was obtained at autopsy, and explanted to cell culture dishes coated with fibronectin. Typically, cells migrated from the explanted tissue after 7–10 days in vitro. Second or third passage cells were seeded onto fibronectin-coated coverslips at confluent densities and grown to confluency for 7–10 days. Arachnoidal cells were tested using immunocytochemical methods for the expression of several common cytoskeletal and junctional proteins. Second and third passage cultures were also labeled with the common endothelial markers CD-31 or VE-cadherin (CD144) and their expression was quantified using flow cytometry analysis. RESULTS: Confluent cultures of arachnoidal cells expressed the intermediate filament protein vimentin. Cytokeratin intermediate filaments were expressed variably in a subpopulation of cells. The cultures also expressed the junctional proteins connexin43, desmoplakin 1 and 2, E-cadherin, and zonula occludens-1. Flow cytometry analysis indicated that second and third passage cultures failed to express the endothelial cell markers CD31 or VE-cadherin in significant quantities, thereby showing that these cultures did not consist of endothelial cells from the venous sinus wall. CONCLUSION: To our knowledge, this is the first report of the in vitro culture of arachnoidal cells grown from human AG tissue. We demonstrated that these cells in vitro continue to express some of the cytoskeletal and junctional proteins characterized previously in human AG tissue, such as proteins involved in the formation of gap junctions, desmosomes, epithelial specific adherens junctions, as well as tight junctions. These junctional proteins in particular may be important in allowing these arachnoidal cells to regulate CSF outflow

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Distinguishing between Dirac and Majorana neutrinos using temporal correlations

In the context of two flavour neutrino oscillations, it is understood that the $2\times 2$ mixing matrix is parameterized by one angle and a Majorana phase. However, this phase does not impact the oscillation probabilities in vacuum or in matter with constant density. Interestingly, the Majorana phase becomes relevant when we describe neutrino oscillations along with neutrino decay. This is due to the fact that effective Hamiltonian has Hermitian and anti-Hermitian components which cannot be simultaneously diagonalized (resulting in decay eigenstates being different from the mass eigenstates). We consider the $\cal PT$ symmetric non-Hermitian Hamiltonian describing two flavour neutrino case and study the violation of Leggett-Garg Inequalities (LGI) in this context for the first time. We demonstrate that temporal correlations in the form of LGI allow us to probe whether neutrinos are Dirac or Majorana. We elucidate the role played by the mixing and decay parameters on the extent of violation of LGI. We emphasize that for optimized choice of parameters, the difference in $K_4$ ($K_3$) for Dirac and Majorana case is $\sim 15\%$ ($\sim 10\%$).Comment: 17 pages and 8 figures. Comments welcom

METHOD DEVELOPMENT AND VALIDATION FOR SIMULTANEOUS ESTIMATION OF LAMIVUDINE AND ZIDOVUDINE IN TABLET BY REVERSE-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY

Objective: The objective of the study was to develop and validate reverse-phase high-performance liquid chromatography (RP-HPLC) method and apply method to tablet dosage form. Methods: A simple, rapid, economical, precise, and accurate RP-HPLC method for simultaneous estimation of lamivudine and zidovudine in their combined dosage form has been developed. Results: A RP-HPLC method was developed for the simultaneous estimation of lamivudine and zidovudine. In their combined dosage form has been developed. The separation was achieved by LC-C18 column (150 mm ×4.6 mm, 5 μm) and water: methanol (65:35v/v) as mobile phase, at a flow rate of 0.8 ml/min. Detection was carried out at 272 nm. Retention time of lamivudine and zidovudine was found to be 3.007 min and 4.647, respectively. The method has been validated for linearity, accuracy, and precision. The assay method was found to be linear from 50% to 150% for lamivudine and zidovudine. Conclusion: Developed method was found to be accurate, precise, and rapid for simultaneous estimation of lamivudine and zidovudine in their combined dosage form