Samuel Genzor

Latent Toxoplasma gondii infection has been associated with various mental health conditions, but its potential role in the pathophysiology of borderline personality disorder (BPD) remains unclear. This pilot study investigated associations of anti-Toxoplasma IgG seropositivity with the severity of BPD symptoms among adult females with BPD. The sample consisted of 77 female patients diagnosed with BPD who were admitted for psychotherapeutic inpatient care for the personality disorder. T. gondii antibodies (IgG, IgA, and IgM) were measured using ELISA. Psychiatric and psychological assessments included Clinical Global Impression for BPD, Borderline Evaluation of Severity over Time, Buss-Perry Aggression Questionnaire, Suicide Behaviors Questionnaire-Revised, Beck Anxiety Inventory, and Beck Depression Inventory-II. Data were analyzed using descriptive statistics, Fisher’s exact test, χ² test, t-tests, and Mann–Whitney U-tests. Only nine patients (11.7%) were seropositive for T. gondii IgG antibodies. Although none of the hypothesized differences reached statistical significance, several medium-sized effects indicated potentially clinically relevant differences between the seropositive and seronegative groups. Specifically, the seropositive group reported more suicidal ideations during the past year (d = 0.67) and exhibited less severe symptoms of abandonment (d = 0.67) and depression (d = 0.57). The remaining symptoms showed small (suicidality, aggression, anger, and anxiety) or marginal (overall BPD severity, impulsivity) differences between the two groups. This exploratory pilot study observed lower anti-Toxoplasma IgG seropositivity among patients with BPD than previously expected. Although the findings were not statistically significant, several trends indicated that seropositive patients might differ from the seronegative group. These preliminary results should be viewed as hypothesis-generating and validated in larger samples.

Introduction: Electrical impedance tomography (EIT) is a noninvasive, radiation-free imaging modality that provides real-time information on regional lung ventilation from wearable sensors. Chronic obstructive pulmonary disease (COPD) is a highly prevalent respiratory disease causing persistent and progressive airway obstruction. The diagnosis is based primarily on spirometry, in which a Tiffeneau index (forced expiratory volume in 1 s/forced vital capacity ratio) below 0.7 is a key feature of the definitive diagnosis. Despite many advances in medicine, there is a lack of widely available methods for estimating airway obstruction using noninvasive bedside measurements, which could facilitate assessment in patients who are unable to perform standard spirometry, e.g., patients after laryngectomy and those with chronic tracheostomies. Moreover, the prevalence of COPD in this group of patients may be substantial. Methods: The study examined the relationship between EIT-derived signal features and the Tiffeneau index through a comprehensive statistical and machine learning analysis of patient data collected using the Dräger EIT system. Data from 15 adult patients who successfully completed conventional spirometry and EIT measurements were analyzed. Patients conducted the examination a couple of times; consequently, 30 measurements were collected. A total of 755 time-series features, complemented by physiological measurements, were extracted, analyzed, and evaluated using correlation metrics, P -value testing, categorical associations, and Shapley additive explanations explainability. Results: Frequency-domain features (fast Fourier transform angle coefficients), entropy measures, and autocorrelation-based descriptors have the strongest associations with the Tiffeneau index. Feature-selected machine learning models demonstrated that EIT-derived time–frequency features, combined with anthropometric variables (weight and body mass index), could approximate Tiffeneau index values in this small cohort (best model: R 2 = 0.72 on the testing set). Conclusion: These findings support the exploratory feasibility of EIT-based, noninvasive approaches for pulmonary function estimation in a general clinical cohort and motivate future validation studies in patients for whom spirometry cannot be performed.

Astma bronchiale je vysoce prevalentn? respira?n? onemocn?n?, kter? p?i nedostate?n? ?i neefektivn? l??b? sni?uje kvalitu ?ivota pacient?. Z?kladem terapie jsou inhala?n? glukokortikoidy, optim?ln? ve fixn? kombinaci s dlouhodob? p?sob?c?mi be... Astma bronchiale je vysoce prevalentn? respira?n? onemocn?n?, kter? p?i nedostate?n? ?i neefektivn? l??b? sni?uje kvalitu ?ivota pacient?. Z?kladem terapie jsou inhala?n? glukokortikoidy, optim?ln? ve fixn? kombinaci s dlouhodob? p?sob?c?mi be...

Purpose Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity. Methods Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity. Results 25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement. Conclusion Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations.

Rationale: Persistent respiratory symptoms following Coronavirus Disease 2019 (COVID-19) are associated with residual radiological changes in lung parenchyma, with a risk of development into lung fibrosis, and with impaired pulmonary function. Previous studies hinted at the possible efficacy of corticosteroids (CS) in facilitating the resolution of post-COVID residual changes in the lungs, but the available data is limited. Aim: To evaluate the effects of CS treatment in post-COVID respiratory syndrome patients. Patients and methods: Post-COVID patients were recruited into a prospective single-center observational study and scheduled for an initial (V1) and follow-up visit (V2) at the Department of Respiratory Medicine and Tuberculosis, University Hospital Olomouc, comprising of pulmonary function testing, chest x-ray, and complex clinical examination. The decision to administer CS or maintain watchful waiting (WW) was in line with Czech national guidelines. Results: The study involved 2729 COVID-19 survivors (45.7% male; mean age: 54.6). From 2026 patients with complete V1 data, 131 patients were indicated for CS therapy. These patients showed significantly worse radiological and functional impairment at V1. Mean initial dose was 27.6 mg (SD ± 10,64), and the mean duration of CS therapy was 13.3 weeks (SD ± 10,06). Following therapy, significantly better improvement of static lung volumes and transfer factor for carbon monoxide (DLCO), and significantly better rates of good or complete radiological and subjective improvement were observed in the CS group compared to controls with available follow-up data (n = 894). Conclusion: Better improvement of pulmonary function, radiological findings and subjective symptoms were observed in patients CS compared to watchful waiting. Our findings suggest that glucocorticoid therapy could benefit selected patients with persistent dyspnea, significant radiological changes, and decreased DLCO.

Background and Objective: Severe courses of COVID-19 disease can lead to long-term complications. The post-acute phase of COVID-19 refers to the persistent or new symptoms. This problem is becoming more relevant with the increasing number of patients who have contracted COVID-19 and the emergence of new virus variants. In this case, preventive treatment with corticosteroids can be applied. However, not everyone benefits from the treatment, moreover, it can have severe side effects. Currently, no study would analyze who benefits from the treatment. Methods: This work introduces a novel approach to the recommendation of Corticosteroid (CS) treatment for patients in the post-acute phase. We have used a novel combination of clinical data, including blood tests, spirometry, and X-ray images from 273 patients. These are very challenging to collect, especially from patients in the post-acute phase of COVID-19. To our knowledge, no similar dataset exists in the literature. Moreover, we have proposed a unique methodology that combines machine learning and deep learning models based on Vision Transformer (ViT) and InceptionNet, preprocessing techniques, and pretraining strategies to deal with the specific characteristics of our data. Results: The experiments have proved that combining clinical data with CXR images achieves 8% higher accuracy than independent analysis of CXR images. The proposed method reached 80.0% accuracy (78.7% balanced accuracy) and a ROC-AUC of 0.89. Conclusions: The introduced system for CS treatment prediction using our neural network and learning algorithm is unique in this field of research. Here, we have shown the efficiency of using mixed data and proved it on real-world data. The paper also introduces the factors that could be used to predict long-term complications. Additionally, this system was deployed to the hospital environment as a recommendation tool, which admits the clinical application of the proposed methodology.

Rationale: Persistent respiratory symptoms following Coronavirus Disease 2019 (COVID-19) are associated with residual radiological changes in lung parenchyma, with a risk of development into lung fibrosis; and with impaired pulmonary function. Previous studies hinted the possible efficacy of corticosteroids (CS) in facilitating the resolution of post-COVID residual changes in the lungs, but the available data is limited. Aim: To evaluate the effects of CS treatment in post-COVID respiratory syndrome patients. Patients and methods: Post-COVID patients were recruited into a prospective single-center observational study and scheduled for an initial (V1) and follow-up visit (V2) at the Department of Respiratory Medicine and Tuberculosis, University Hospital Olomouc, comprising of pulmonary function testing, chest x-ray, and complex clinical examination. The decision to administer CS or maintain watchful waiting (WW) was in line with Czech national guidelines. Results: The study involved 2729 COVID-19 survivors (45.7% male; mean age: 54.6). From 2090 patients with complete data, the largest proportion of patients with persistent lung abnormalities on imaging studies was seen in moderate (14.5%) and severe (31.4%) COVID-19 groups. The patients selected for CS therapy showed significantly worse radiological and functional impairment at V1. At V2, the rates of subjective and radiological improvement, and dynamic lung volumes, were similar in CS and WW groups. However, static lung volumes and lung diffusion capacity for carbon monoxide (DLCO) improved significantly more in the CS group, with the vital capacity (VC) increasing by a mean of 493.72 ml (13.01 %predicted) and DLCO by a mean of 9.19 %predicted (p Conclusion: While the rates of subjective and radiological improvement were similar between the study groups, the CS group saw significantly better rates of improvements of static lung volumes and DLCO. Our findings suggest that glucocorticoid therapy could benefit selected patients with persistent dyspnea, significant radiological changes, and decreased DLCO.

Objective: The study worked with depressive symptoms, anxiety score and cognitive functions in obstructive sleep apnea (OSA) patients treated with CPAP. Methods: Eighty-one subjects with OSA and without psychiatric comorbidity were treated with CPAP for one year and completed the following scales and cognitive tests: Trail Making Test, Verbal Fluency Test, d2 Test, Beck Depression Inventory-II and Beck Anxiety Inventory. MINI ruled out psychiatric disorder. At the two months check-up, subjects were re-evaluated for depressive and anxiety symptoms, and after one year of CPAP treatment, subjects repeated cognitive tests and scales. Data about therapy adherence and effectiveness were obtained from the patient’s CPAP machines. Results: The study was completed by 59 CPAP adherent patients and eight non-adherent patients. CPAP therapy effectiveness was verified in all patients by decreasing the apnea-hypopnoea index below 5 and/or 10% of baseline values. The adherent patients significantly improved depressive and anxiety symptoms. There was also an improvement in overall performance in the attention test; however, performance in many individual items did not change. The adherent patients also improved verbal fluency and in the Part B of the Trail making test. The non-adherent group significantly increased the number of mistakes made in the d2 test; other results were non-significant. Conclusion: According to our results, OSA patients’ mood, anxiety and certain cognitive domains improved during the one-year therapy with CPAP. Trial Registration Number: NCT03866161.

Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.

Introduction: Severe respiratory failure is one of the most serious complications of coronavirus disease 2019 (COVID-19). In a small proportion of patients, mechanical ventilation fails to provide adequate oxygenation and extracorporeal membrane oxygenation (ECMO) is needed. The surviving individuals need long-term follow-up as it is not clear what their prognosis is. Aim: To provide a complex clinical picture of patients during follow-up exceeding one year after the ECMO therapy due to severe COVID-19. Methods: All subjects involved in the study required ECMO in the acute stage of COVID-19. The survivors were followed-up for over one year at a specialized respiratory medical center. Results: Of the 41 patients indicated for ECMO, 17 patients (64.7% males) survived. The average age of survivors was 47.8 years, and the average BMI was 34.7 kg·m⁻². The duration of ECMO support was 9.4 days. A mild decrease in vital capacity (VC) and transfer factor (DLCO) was observed on the initial follow-up visit (82.1% and 60%, respectively). VC improved by 6.2% and by an additional 7.5% after 6 months and 1 year, respectively. DLCO improved by 21.1% after 6 months and remained stable after 1 year. Post-intensive care consequences included psychological problems and neurological impairment in 29% of patients; 64.7% of the survivors got vaccinated against SARS-CoV-2 within 12 months of hospitalization and 17.6% experienced reinfection with a mild course. Conclusion: The COVID-19 pandemic has significantly increased the need for ECMO. Patients’ quality of life after ECMO is temporarily significantly reduced but most patients do not experience permanent disability.