The modern era of regenerative medicine has given us Artificial Intelligence (AI) with its vast cumulative knowledge, analytical depth of thinking and accurate diagnostics. Its application in diagnostic imaging data collection, image superimposition, de-pixelation and real time clinical corelation is far superior than the most experienced human mind. Clinical decision support systems are actively under development which use algorithms to analyse medical imaging, capable of interpreting data ranging from conventional X rays to complex MRIs [1]. Now combine this thought with state-of-the-art real time navigational robotic surgery and you get the best surgeon from any part of the world operating in a hospital near you [2].

This seminar paper aims to explore the intricate relationship between vitamin D status and brain development, shedding light on the molecular and biological role of this crucial nutrient. Vitamin D plays a pivotal role in a wide range of physiological processes, including brain development, making it a vital component for optimal neural functioning. To provide a comprehensive understanding, the paper begins by presenting an overview of vitamin D, including its sources. It then delves into the absorption, metabolism, and functions of vitamin D within the body.

Moving forward, the paper focuses on the impact of vitamin D on brain development during both prenatal and postnatal stages. During prenatal development, vitamin D influences critical processes such as neuronal proliferation, differentiation, and maturation. The paper elucidates the intricate relationship between maternal vitamin D levels and fetal brain development, highlighting the importance of adequate prenatal vitamin D exposure.

To provide insights into the underlying mechanisms, the paper investigates potential pathways through which vitamin D exerts its influence on brain development. These include gene regulation, the modulation of neurotrophic factors, and the synthesis of neurotransmitters, all of which play crucial roles in shaping neural circuits and supporting cognitive functions.

In conclusion, this seminar paper highlights the profound impact of vitamin D on brain development, emphasizing its role during both prenatal and postnatal stages. It underscores the need for further research in this field to enhance our understanding of the molecular mechanisms and the potential therapeutic applications of vitamin D in promoting optimal brain development and preventing neurodevelopmental disorders.

Keywords: Vitamin D’ Pregnancy; Brain; Neural defect

Convolutional Neural Networks (CNNs) have become an effective tool for medical picture identification, and one of its most important applications is the detection of pneumonia. The study delves into the significance of trustworthy diagnostic techniques, examines several convolutional neural network (CNN) designs, and tackles obstacles such as data imbalance, noise, and uncertainty in medical imaging datasets. Techniques like Grad-CAM and LIME are also covered, as are assessment measures like accuracy, sensitivity, specificity, and AUC-ROC. Additionally, methods for studying decision-making processes are covered. The paper lays up a plan for further study into the use of CNNs for pneumonia diagnosis and emphasizes their potential. To make sure that CNN-based systems can be safely and effectively used in clinical practice, there has to be constant work to fix problems with data quality, model interpretability, and generalization.

Keywords: CNN; Pneumonia; Medical Imaging Analysis

It is known that obesity is traceable to gut dysbiosis. Changes in the microbial ecosystem in the gut have major implications for the content of microbial signaling molecules in the host organism. The representation of tryptophan (Trp) signaling molecules in the gut will have a significant impact on intestinal microbiota diversity. Using metagenomic analysis, we investigated the taxonomic representation of the intestinal microbiota in healthy individuals and obese patients, which allowed us to reconstruct its potential metabolic activity depending on the level of Trp metabolites using PICRUSt software. We found that the key microbial Trp catabolite (MICT) are indole-3-lactate. Using the data of metagenomic sequencing analysis and bioinformatic tools we have analyzed a potential metabolic activity of the microbial communities or so-called «enzymatic landscape». We have found a twofold increase in the correlation between the content of Trp metabolites in feces and the «enzymatic landscape» of microbiome. The maximum number of statistically significant correlations between Trp metabolites and the potential metabolic activity of the microbial communities were established for indole-3-lactate. We have shown statistically significant relationships for indole-3-lactate and the abundance of genes for the metabolism of monosaccharides, nucleotides, amino acids, polyamines, and sulfosaccharides. It has been established that in obese patients there is a threefold increase in indole-3-lactate-producing microbiota. The phenotype of the microbiotic population is represented by completely different genera and species of microorganisms in obese individuals. The content of indole-3-lactate in the feces of obese patients mainly depends on the taxonomic representation of Escherichia-Shigella.

Keywords: tryptophan metabolites; indole-3-lactate; microbiome; obesity; enzymatic landscape

Sickle cell disease (SCD) presents a complex medical challenge for both patients and healthcare providers. One of the most agonizing aspects of this condition is the recurrent episodes of severe pain known as sickle cell crises. In the management of this excruciating pain, physicians often face a dilemma when it comes to prescribing medications like morphine. While morphine is highly effective in alleviating pain, concerns about addiction and misuse loom large, prompting careful consideration and a nuanced approach.

Sickle cell crises are characterized by sudden and intense pain caused by the obstruction of blood flow due to the sickle-shaped red blood cells. The pain can be debilitating, often requiring hospitalization and aggressive pain management. Morphine, a potent opioid analgesic, is commonly used to manage pain during these crises [3]. Its effectiveness in providing relief is undisputed, but physicians must weigh this against the risks associated with opioid use, particularly in a population that may require frequent medical interventions.

Clinical information systems record large amounts of data from multiple sources. This data could be used to model the process and learn how a decision-making algorithm (CDSS) was actually executed.

We have developed an open source interface that uses data from different patient repositories for the integration of large data mining in a Fast Healthcare Interoperability Resources (FHIR) format. An interoperable solution that has emerged as a multi-layer architecture to manage critical medicine data. The layered architecture focuses on sharing processed information and making it easily available in a control panel for users of the Intensive Care Unit, improving the security and quality of medical care, meeting cybersecurity requirements and under standards. very strict ethics. The introduction of these techniques will allow health care in a more predictive way, through new algorithms.

The results demonstrate that the data mining obtained is a constant flow of highly dynamic and unstructured information; The above goes against the dogma that only EHRs are useful when making decisions. Only 4% of the data is used for this purpose and between 0.002% comes from the EHR, while even new algorithm designs will require structured data to exceed a greater percentage of all data.

We conclude that any clinical process requires a volume of structured data, robust on the basis of data mining and is not feasible with the use of background information only from the EHR, in a highly demanding unit.

Keywords: Data sources; Intensive Care Units; data mining; Artificial Intelligence; Interoperability; Electronic Health Records