A clinical case of concurrent mycoplasma pneumonia and dengue hemorrhagic fever in a tourist from Thailand

The article describes a clinical case of concurrent infectious diseases - dengue hemorrhagic fever and mycoplasma pneumonia in a patient who came back from the area endemic for dengue fever. Further, the patient was diagnosed with clostridial colitis which was the complication. Clinical, laboratory, and radiological parameters used in the diagnosis of diseases were evaluated.

Medllecta. Hematological Preventive Method (HPM). Part 2

A complete dynamic model of the protein and, in particular, the the enzymatic process of synthesis and degradation could significantly improve the quality of diagnosis of diseases of various etiologies at the earliest stages of their development. In this article, we describe our initial attempt to create the above model based on a radically new mathematical approach, Sense Logic [1] in terms of enzymatic kinetics.

N-Terminal Pro-C-Type Natriuretic Peptide: The Novel Marker in Selected Disease Units

Background: Amino-terminal pro C type natriuretic peptide (NT proCNP) is the N terminal fragment of the CNP precursor. NT proCNP occurs in an equimolar concentration with CNP in human plasma and is considered to be a marker of the extent of CNP biosynthesis. A recent study has shown associations between plasma NT proCNP and blood pressure; it is also an independent predictor of death and cardiac readmission in people with unstable angina. Beyond that, recent studies have focused on the applicability of assessing NT proCNP peptide levels in the diagnosis of diseases with different etiologies but the same denominator, i.e., inflammation. Methods: This study reviewed recent results on the usability of NT proCNP peptide levels in the diagnosis of diseases accompanied by statistical analysis of previously reported results. Results: The data obtained confirmed the applicability of the assessment of NT proCNP levels in biological fluids in diseases, such as Parkinson's disease, sepsis, meningitis, and asthenozoospermia. Conclusion: The reported results demonstrated that NT-proCNP is helpful in a variety of diseases. Furthermore, changes in serum or CSF levels of NT-proCNP reflect only inflammatory states related to general inflammation. Local inflammation does not trigger an increase in NT proCNP level.

GOVERNING THE TEACHING HOSPITALS IN MALAYSIA: LEGAL ISSUES AND CHALLENGES

Hospital, is an institution that is built, staffed, and equipped for the diagnosis of diseases; for the treatment, both medical and surgical, of the sick and the injured; and for their housing during this process. Generally, hospitals in Malaysia are being categorized into two categories which are: public and private. Another type of hospital that serves as a centre for investigation and teaching is known as a teaching hospital. It provides clinical education and training for future and current doctors, nurses, and other health professionals, in addition to delivering medical care to patients. Some teaching hospitals also have a commitment to research and are centres for experimental, innovative and technically sophisticated services. Despite its important roles, there is no specific legislation to govern the teaching hospital in Malaysia. This situation leads to uncertainties in some crucial areas of the governance. Therefore, this study aims to explore the legal issues and challenges with regard to the teaching hospital’s governance in Malaysia. A thorough doctrinal analysis is being conducted to examine the loopholes of the current legal position. The findings will be used to propose some recommendations for the improvement of the governance of the teaching hospitals in Malaysia.

Morphological features of the skeleton of a chinchilla and a rat in a comparative aspect

Exotic species of animals have recently become more and more popular and widespread; the approach to their diagnosis of diseases and treatment has its own nuances. Despite the similarity of species, there are differences that need to be taken into account. One of the fundamental problems of comparative anatomy is the disclosure of general patterns and specific features of the skeleton of animals. Despite the available detailed work in this direction, many questions concerning this problem still remain open and require further study.

A literary study of purvarupa (prodromal features ) w. s. r. to prameha vyadhi purvarupa.

In Ayurvedic science, diagnosis of the disease is done with help of Nidan Panchakaaa. In five-fold examination, Purvarupa is one of the component parts of Nidan Panchaka. Purvarupa means Prodromal features of diseases. Any disease does not appear suddenly. Time taken between etiological factors intake to the appearance of specific disease is of Purvarupa. They are premonitory features that manifest before the actual emergence of disease. These symptoms could be doshik or adoshik. Purvarupa are of two types, general and specific. Careful understanding of Purvarupa helps in the differential diagnosis of diseases. Purvarupa also helps to determine the prognosis of any disease. The appearance of all Purvarupa of any disease is considered a bad prognosis because it means the disease is incurable. At Purvarupa stage, treatment modalities for any disease are simpler or easier. So the knowledge of Purvarupa is essential. In samhitas the Purvarupa (Prodromal features )of Prameha vyadhi is described very well. As the Prameha vyadhi is asadhya in its chronic stage as well as it may cause many serious complications if it is not treated and controlled in the early stage, it is very important to know the disease in very primary stage.This is possible by detecting the prameha Purvarupas in patient earlier. So that we can diagnose this stage and can prevent developing it further in vyaktavastha. So in this review study, an attempt is made to elaborate on the importance of Purvarupas and to compile the Purvarupas of Prameha from all ayurvedic samhitas.

MACHINE LEARNING ALGORITHMS IMPLEMENTATION IN THE HEALTHCARE SYSTEM AS A PROSPECTIVE AREA FOR SCIENCE, HEALTHCARE, AND BUSINESS

Relevance. The current state of medicine is imperfect as in every other field. Some main discrete problems may be separated in diagnostics and disease management. Biomedical data operation difficulties are a serious limiting factor in solving crucial healthcare problems, represented in the statistically significant groups of diseases. Accumulation of life science data creates as possibilities as challenges to effectively utilize it in clinical practice. Machine learning-based tools are necessary for the generation of new insights and the discovery of new hidden patterns especially on big datasets. AI-based decisions may be successfully utilized for diagnosis of diseases, monitoring of general health, prediction of risks, treatment solutions, and biomedical knowledge generation. Objective. To analyze the potential of machine learning algorithms in healthcare on exact existing problems and make a forecast of their development in near future. Method. An analytical review of the literature on keywords from the scientometric databases Scopus, PubMed, Wiley. Search depth 7 years from 2013 to 2020. Results. Analyzing the current general state of the healthcare system we separated the most relevant problems linked to diagnostics, treatment, and systemic management: diagnostics errors, delayed diagnostics (including during emergencies), overdiagnosis, bureaucracy, communication issues, and "handoff" difficulties. We examined details of the convenient decision-making process in the clinical environment in order to define exact points which may be significantly improved by AI-based decisions, among them: diagnosis of diseases, monitoring of general health, prediction of risks, treatment solutions, and biomedical knowledge generation. We defined machine learning algorithms as a prospective tool for disease diagnostics and management, as well as for new utilizable insights generation and big data processing. Conclusion. Machine learning is a group of technologies that can become a cornerstone for dealing with various medical problems. But still, we have some problems to solve before the intense implementation of such tools in the healthcare system.

Adoption of machine learning for medical diagnosis

The healthcare industry has historically been an early adopter of technologyadvancements and has reaped significant benefits. Machine learning (an artificialintelligence subset) is being used in a variety of health-related fields, including theinvention of new medical treatments, the management of patient data and records, andthe treatment of chronic diseases. One of the most important uses of machine learningin healthcare is the detection and diagnosis of diseases and conditions that areotherwise difficult to identify. This can range from tumors that are difficult to detect intheir early stages to other hereditary illnesses. This research identifies and discussesthe various usages of machine learning in medical diagnosis.