Casopis lekaru ceskych最新文献

Diabetic foot attack is an acute manifestation of diabetic foot syndrome that requires rapid diagnosis and early initiation of complex therapy based on multidisciplinary team cooperation to save the affected limb. The term "attack" evokes the need for a rapid reaction as for "heart attack". Patients with diabetes mellitus and acute manifestations of diabetic foot syndrome have high risk for amputation of the lower limb, which is associated with higher morbidity and mortality. Complex therapy includes systemic antibiotic therapy, surgical debridement of ulceration, revascularization in the case of proven lower limb ischemia and an individualized offloading. Without the subsequent outpatient podiatric follow-up, the risk of recurrence increases significantly.

From anthropometrics point of view, the Roma have retained a distinct individuality and thus differ from the majority population. They are also distinguished by cultural differences, which are reflected in the concept of health and health care consumption. The available data show socially and culturally determined health inequalities and disadvantages of the Roma compared to the majority population, which do not change in the long term. Among others, the low level of cultural competence and sensitivity of health professionals to the needs of minorities and specifically to the Roma ethnicity also plays a role. The article describes health-relevant cultural differences in the context of Roma culture and way of life of the Czech Roma and outlines some of the barriers faced by Roma in healthcare.

The incidence of monoclonal gammopathy (MG) increases with age. In individuals over 80 years of age, we can diagnose the presence of monoclonal immunoglobulin (MIg) in up to 10 % of cases. Not only malignant diseases such as multiple myeloma (MM), but also benign forms such as MGUS (monoclonal gammopathy of undetermined significance) can lead to renal involvement. The light chains of immunoglobulins (LC) are the most damaging to the kidneys, as they are freely filtered into the urine due to their molecular weight. Detection of MIg relies mainly on a combination of immunofixation electrophoresis of serum (IELFO) and urine and determination of free light chains (FLC) of kappa and lambda and their ratio (κ/λ) in serum. The combination of these tests will detect the presence of MIg with 99 % sensitivity. Renal damage in MG may be caused by direct deposition of MIg in the glomeruli (e.g. AL amyloidosis, LC deposition disease) or tubules (in the distal tubule as a myeloma kidney or in the proximal tubule as Fanconi syndrome or proximal tubulopathy). Typical urinary findings in these diseases are moderate or severe proteinuria or nephrotic syndrome. Acute kidney injury (AKI) can be expected especially when serum FLC is >500 mg/l. Renal biopsy is crucial to establish an accurate diagnosis and thus initiate the correct treatment. Treatment of these types of renal damage involves the same treatment regimens used in the treatment of MM, including proteasome inhibitors or daratumumab.

The aim of the article to present the development of artificial intelligence (AI) methods and their applications in medicine and health care. Current technological development contributes to generation of large volumes of data that cannot be evaluated only manually. We describe the process of patient care and its individual parts that can be supported by technology and data analysis methods. There are many successful applications that help in the decision support process, in processing complex multidimensional heterogeneous and/or long-term data. On the other side, failures appear in AI methods applications. In recent years, deep learning became very popular and to a certain extend it delivered promising results. However, it has certain flaws that might lead to misclassification. The correct methodological steps in design and implementation of selected methods to data processing are briefly presented.

SARS-CoV-2 is a virus which infects the respiratory tract and may cause severe, occasionally life-threatening disease COVID-19. In more than 5% of symptomatic patients the infection is associated with post-acute symptoms. The initial contact of the virus with the immune system of the nasopharynx and oropharynx induces a mucosal immune response manifested by the production of secretory IgA (sIgA) antibodies which may contribute to the restriction of the infection to the upper respiratory tract and an asymptomatic or clinically mild disease. The current systemically administered vaccines protected against the severe COVID-19 infection and its post-acute sequelae. However, they do not induce antibodies in mucosal secretions in SARS-CoV-2-naive individuals. In contrast, in those who previously experienced mucosal infection, systemically administered vaccines may stimulate sIgA production. The clinical benefit of systemic vaccination convincingly documented in tens of millions of individuals overshadows the rare, sometimes controversial reports of complications encountered after vaccination. The inability of current SARS-CoV-2 vaccines to induce mucosal immune responses and to prevent the spreading of the virus by external secretions demonstrates the mutual independence of mucosal and systemic compartments of the immune system, and thus emphasizes need for the development of vaccines inducing protective immune responses in both compartments.

Both obesity and malnutrition in the elderly population are raising concerns. As majority of pharmacokinetic data are obtained from subjects with a healthy weight range in productive age, administration of correct dosing of drugs to obese elderly patients is quite challenging. All phases of the pharmacokinetics of drugs (absorption, distribution, metabolism and elimination) are potentially affected by the effect of aging but also by obesity or malnutrition. These changes are often potentiated in the case of a combination of obesity and older age. The problem is also that not all drugs are affected in the same way, therefore it is necessary to know the influence of age and obesity on the kinetics of the particular substance being assessed. 50% of seniors suffer from pain at home and up to 80% during hospitalization. In addition to general knowledge about the effect of age but also obesity and malnutrition on the kinetics of drugs, this overview presents the pharmacokinetic changes of analgesics caused by both obesity and malnutrition.

Polypharmacy is currently a serious problem that causes decrease in adherence and increased number of hospitalizations and mortality. WHO addresses polypharmacy in the Medication Without Harm campaign. Other initiatives that deal with polypharmacy are the International Group for Reducing Inappropriate Medication Use & Polypharmacy (IGRIMUP) and Choosing Wisely campaign. The correct approach to address polypharmacy consists of its prevention, i.e. not prescribing inappropriate or unnecessary medication and providing clear timeframe for medication that should not be continued life-long. Further on we should actively seek patients suffering from polypharmacy and intervene it by deprescription. Correctly provided deprescription can be done by means of various tools beginning from simple lists of inappropriate drugs (Beers criteria, STOPP/START) to more comprehensive approaches that evaluate the importance of each particular drug in patient's medication list and help to identify the least important ones that are candidates for deprescription (Medication Appropriateness Index, Good Palliative Geriatric Practice Algorithm and others). When evaluating the appropriateness of pharmacotherapy, we always check if the treatment aim is achieved, if the indication persists, appropriateness of dosing and if the patient understands the pharmacotherapeutical regimen. By this approach we try to eliminate the pharmacotherapy with very low or no benefit for particular patient. Clinical pharmacologist or pharmacist can significantly help with this time-consuming process.

The advent of large language models (LLMs) based on neural networks marks a significant shift in academic writing, particularly in medical sciences. These models, including OpenAI's GPT-4, Google's Bard, and Anthropic's Claude, enable more efficient text processing through transformer architecture and attention mechanisms. LLMs can generate coherent texts that are indistinguishable from human-written content. In medicine, they can contribute to the automation of literature reviews, data extraction, and hypothesis formulation. However, ethical concerns arise regarding the quality and integrity of scientific publications and the risk of generating misleading content. This article provides an overview of how LLMs are changing medical writing, the ethical dilemmas they bring, and the possibilities for detecting AI-generated text. It concludes with a focus on the potential future of LLMs in academic publishing and their impact on the medical community.

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI's role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions. We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists. In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.

Postoperative pneumonia is the most common complication in patients after lung resection for non-small cell lung cancer (NSCLC). The tolerable incidence of this complication ranges from 5 to 8 %. The aim of this study was to evaluate the influence of initial risk factors on the incidence of postoperative pneumonia in patients undergoing lung resection for NSCLC. A retrospective cohort study was conducted at the University Hospital Ostrava between January 1, 2016, and December 31, 2022. All adult patients who underwent pulmonary lobectomy for primary NSCLC during the study period were included in the study. A total of 350 patients were included in the study. The incidence of postoperative pneumonia was 10.9%. Analysis of baseline risk factors did not show a statistically significant association with the incidence of this complication. The only statistically significant finding was a longer hospital stay in patients with postoperative pneumonia. The risk of postoperative pneumonia in patients undergoing lung resection for non-small cell lung cancer cannot be clearly explained by the initial risk factors examined alone. The complex nature of this risk also requires a comprehensive approach to prevention, including both patient-centred measures and improved postoperative care.