Stanislaw Szlufik

Small intestine dysfunction in Parkinson’s disease

The aim of this study was to assess the small bowel transit time in patients with Parkinson’s disease (PD). Ten patients with PD with no gastrointestinal complaints and ten healthy control subjects were investigated using single photon emission computed tomography fused with computed tomography after swallowing of a specially prepared capsule containing technetium 99m, which allowed visualization of the passage in the intestines. Preliminary results show that the small intestine passage in PD patients was prolonged compared to controls.

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients.

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease’s symptoms, with the help of various therapies. In the case of Parkinson’s disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist’s diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient ‘well-being’ scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naïve Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD.

Data Mining and Machine Learning on the Basis from Reflexive Eye Movements Can Predict Symptom Development in Individual Parkinson’s Patients

We are still not in a position to understand most of the brain’s deeper computational properties. As a consequence, we also do not know how brain processes are affected by nerve cell deaths in neurodegenerative diseases (ND). We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. Fortunately, with early diagnosis there are often many years of disease progression with symptoms that, when they are precisely monitored, may result in improved therapies. In the case of Parkinson’s disease, measurements of eye movements can be diagnostic. In order to better understand their relationship to the underlying disease process, we have performed measurements of reflexive eye movements in Parkinson’s disease (PD) patients. We have compared our measurements and algorithmic diagnoses with experts’ diagnoses. The purpose of our work was to find universal rules, using rough set theory, to classify how condition attributes predict the neurologist’s diagnosis. Prediction of individual UPDRS values only from reflexive saccade (RS) latencies was not possible. But for n = 10 patients, the patient’s age, latency, amplitude, and duration of RS gave a global accuracy in individual patients’ UPRDS predictions of about 80%, based on cross-validation. This demonstrates that broadening the spectrum of physical measurements and applying data mining and machine learning (ML) can lead to a powerful biomarker for symptom progression in Parkinson’s.

Ropinirole treatment in Parkinson's disease associated with higher serum level of inflammatory biomarker NT-proCNP.

There is rapidly growing evidence for the influence of inflammation on the development and progression of Parkinsons disease (PD). N-terminal pro C-type Natriuretic Peptide (NT-proCNP) is a novel potential inflammatory biomarker and has been recently correlated in PD with pro- and anti-inflammatory cytokines, especially TNF-α and IL-10. The study aims to explore serum level of NT-proCNP in group consisted of 132 patients with idiopathic Parkinsons disease (age 59.6±15.l years) and 46 healthy controls (age 58.5±11.5 years). Serum level of NT-proCNP was significantly higher in PD patients than in the control group (p<0.05; PD vs control: mean 3.65±5.5 vs 1.49±0.73, median 1.81 vs 1.46). The serum level of NT-proCNP was directly correlated with the treatment with dopamine agonist (ropinirole) (R=0.38; p<0.05). The higher serum level of NT-proCNP in PD patients being treated with ropinirole suggests a potential proinflammatory characteristic of dopamine agonists.

The prognostic value of concomitant assessment of NT-proCNP, C-reactive protein, procalcitonin and inflammatory cytokines in septic patients

TNF-α (pg/mL) 4.5 (1.6 to 6.2) 4.7 (1.6 to 6.0) Findings The mortality of patients with sepsis in ICUs has been decreasing since 1991 [1]. Nevertheless, there are still conflicting data regarding potential biomarkers of mortality rate among critically ill patients and sepsis development. N-terminal pro C-type natriuretic peptide (NT-proCNP) is one of the markers whose usefulness is still uncertain. Some authors have shown the possible role of NT-proCNP in the prognosis of critically patients with sepsis [2] and in polytrauma patients without brain injury who developed sepsis [3], but Gouel-Cheron and colleagues [4] showed no correlation between the serum level of NT-proCNP and development of sepsis. Our study aimed to explore the serum level of NTproCNP and its potential triggers (TNF-α, IL-6, IL-8, IL-10, IL-12) and to correlate them with serum levels of procalcitonin (PCT) and C-reactive protein (CRP) as reviewed by Uzzan and colleagues [5]. Samples were obtained from 90 critically ill adult patients during the first day in the ICU. The patients were divided in two groups: patients who died of sepsis in the ICU (43 patients), and ICU survivors (47 individuals). All patients were assessed with the use of Acute Physiology and Chronic Health Evaluation II score. ELISA kits were used for quantitative determination of human NT-proCNP. Cytokines were measured with the Fluorokine® MAP cytokine multiplex kit and the Luminex® 100 Platform. Statistical evaluation included Mann–Whitney U test and Spearman correlation coefficient. The median serum level of NT-proCNP was significantly higher in non-survivors than in survivors: 7.1 (interquartile range (IQR) 3.7 to 18.1) pmol/L versus 4.5 (IQR 2.3 to 7.2) pmol/L, P < 0.05. Likewise, serum CRP levels were higher in non-survivors than in survivors:

Data mining using SPECT can predict neurological symptom development in Parkinson's patients

We have compared in Parkinsons diseases patients neurological data with the local cerebral blood flow measured by the Single-Photon Emission Computed Tomography. Most of our patients underwent Deep Brain Stimulation surgery or were qualified for one in relation to the advanced disease progression. Local cerebral blood flow in different areas has correlated to the Unified Parkinsons Disease Rating Scale (UPDRS). We have used two different data mining methods: WEKA and Rough Set Exploration System to explore these correlations. We have demonstrated that cerebral blood flow changes gave good predictions for the UPDRS IV (84 %) that suggest that a general state of Parkinson Disease are stronger related to the cerebral blood flow than to only motor symptoms.

Rules Determine Therapy-Dependent Relationship in Symptoms Development of Parkinson’s Disease Patients

We still do not have cure for neurodegenerative disorders (ND) such as Parkinson’s disease (PD). Recent findings demonstrated that neurodegenerative processes related to ND have long periods without symptoms that effects lack in effective therapy when ND is diagnosed. Neurologists estimate PD progression on the basis of their tests: Hoehn and Yahr (H&Y) and Unified Parkinson’s Disease Rating (UPDRS) scales, but results of these tests are partly school-dependent. We have previously proposed that eye movement tests can give objective and precise measure of the PD progression.

Serum amino acid profile in patients with Parkinson’s disease

Amino acids play numerous roles in the central nervous system, serving as neurotransmitters, neuromodulators and regulators of energy metabolism. The free amino acid profile in serum of Parkinson’s disease (PD) patients may be influenced by neurodegeneration, mitochondrial dysfunction, malabsorption in the gastroenteric tract and received treatment. The aim of our study was the evaluation of the profile of amino acid concentrations against disease progression. We assessed the amino acid profile in the serum of 73 patients divided into groups with early PD, late PD with dyskinesia and late PD without dyskinesia. Serum amino acid analysis was performed by high-pressure liquid chromatography with fluorescence detection. We observed some significant differences amongst the groups with respect to concentrations of alanine, arginine, phenylalanine and threonine, although no significant differences were observed between patients with advanced PD with and without dyskinesia. We conclude that this specific amino acid profile could serve as biochemical marker of PD progression.

Higher level of NT-proCNP in cerebrospinal fluid of patients with meningitis

Aminoterminal pro-C type natriuretic peptide (NT-proCNP) as an active form of CNP, has been recently proven to be a potential marker of sepsis and to be linked to inflammatory diseases. So far, there are no studies describing the level of NT-proCNP in meningitis. The purpose of this study was to evaluate the diagnostic value of NT-proCNP in cerebrospinal fluid (CSF) in patients with meningitis and to compare it with the serum level of CRP and procalcitonin (PCT) in this group of patients. The results were compared to serum levels of CRP, PCT and CSF levels of cytosis, protein and lactate. NT-proCNP levels were statistically significant between the control group and the meningitis groups (p=0.02; R=0.3). We also noted a correlation between the level of NT-proCNP in the CSF of all of the study groups (controls and meningitis patients) and the CSF levels of cytosis (p<0.5; R=0.43), protein (p<0.05; R=0.39) and lactate (p<0.05; R=0.34), and also the serum level of CRP (p<0.05; R=0.30), but not serum PCT (p>0.05; R=0.11). These results suggest that NT-proCNP could be a potential marker of meningitis, but it cannot be used to distinguish between the types of meningitis.

Multimodal Learning Determines Rules of Disease Development in Longitudinal Course with Parkinson’s Patients

Parkinson’s disease (PD) is neurodegenerative disease (ND) related to the lost of dopaminergic neurons that elevates first by motor and later also by non-motor (dementia, depression) disabilities. Actually, there is no cure for ND as we are not able to revive death cells. Our purpose was to find, with help of data mining and machine learning (ML), rules that describe and predict disease progression in two groups of PD patients: 23 BMT patients that are taking only medication; 24 DBS patients that are on medication and on DBS (deep brain stimulation) therapies. In the longitudinal course of PD there were three visits approximately every 6 months with the first visit for DBS patients before electrode implantation. We have estimated disease progression as UPDRS (unified Parkinson’s disease rating scale) changes on the basis of patient’s disease duration, saccadic eye movement parameters, and neuropsychological tests: PDQ39, and Epworth tests. By means of ML and rough set theory we found rules on the basis of the first visit of BMT patients and used them to predict UPDRS changes in next two visits (global accuracy was 70% for both visits). The same rules were used to predict UPDRS in the first visit of DBS patients (global accuracy 71%) and the second (78%) and third (74%) visit of DBS patients during stimulation-ON. These rules could not predict UPDRS in DBS patients during stimulation-OFF visits. In summary, relationships between condition and decision attributes were changed as result of the surgery but restored by electric brain stimulation.