Nocturnal paroxysmal dystonia – Case report

Nocturnal paroxysmal dystonia describes a syndrome consisting of recurrent motor episodes of dystonic–dyskinetic features arising from non-rapid eye movement (NREM) sleep. In the article, the authors present female case of nocturnal paroxysmal dystonia. The patient has had attacks since her childhood and was eventually diagnosed at the age of 48. Therapy with carbamazepine considerably reduced the frequency and entent of seizures. The present case evidences that nocturnal paroxysmal dystonia still is a diagnostic challenge for clinicians. Especially, we emphasize the importance of polysomnography in the verification of the diagnosis

Circulating Visfatin in Hypothyroidism Is Associated with Free Thyroid Hormones and Antithyroperoxidase Antibodies

We hypothesized that regulation of visfatin in hypothyroidism might be altered by coexisting chronic autoimmune thyroiditis. This is a prospective case-control study of 118 subjects. The autoimmune study group (AIT) consisted of 39 patients newly diagnosed with hypothyroidism in a course of chronic autoimmune thyroiditis. The nonautoimmune study group (TT) consisted of 40 patients thyroidectomized due to the differentiated thyroid cancer staged pT1. The control group comprised 39 healthy volunteers adjusted for age, sex, and BMI with normal thyroid function and negative thyroid antibodies. Exclusion criteria consisted of other autoimmune diseases, active neoplastic disease, diabetes mellitus, and infection, which were reported to alter visfatin level. Fasting blood samples were taken for visfatin, TSH, free thyroxine (FT4), free triiodothyronine (FT3), antithyroperoxidase antibodies (TPOAb), antithyroglobulin antibodies (TgAb), glucose, and insulin levels. The highest visfatin serum concentration was in AIT group, and healthy controls had visfatin level higher than TT (p=0.0001). Simple linear regression analysis revealed that visfatin serum concentration was significantly associated with autoimmunity (β=0.1014; p=0.003), FT4 (β=0.05412; p=0.048), FT3 (β=0.05242; p=0.038), and TPOAb (β=0.0002; p=0.0025), and the relationships were further confirmed in the multivariate regression analysis

Czynność osi GH/IGF-I, stężenie hormonów kalciotropowych we krwi oraz gęstość mineralna kości u młodych osób z przewlekłym wirusowym zapaleniem wątroby

Introduction: Chronic liver disease caused by HBV and HCV infections, due to its great prevalence and serious medical consequences, is at the present time a significant clinical problem. An impaired liver function can provoke severe disturbances in calcium and phosphorus homeostasis, and consequently in the bone metabolism resulting in hepatic osteodystrophy. The aim of this study was to determine whether there are significant differences in bone mineral density (BMD) and/or circadian levels of hormones connected with bone metabolism and bone turnover markers in patients with chronic viral hepatitis. Material and methods: Circadian levels (AUC, area under the curve) of GH, IGF-I, IGFBP-3, osteocalcin (BGLAP), C-terminal telopeptide of type I collagen (ICTP), PTH, 25(OH)D, total calcium and total phosporus were measured in the blood of members of the study group (n = 80). BMD was assessed using the dual-energy X-ray absorptiometry method of the L2-L4 lumbar spine. Data was compared to that of healthy individuals (n = 40). Results: BMD (1.05 g/cm3 vs. 1.20 g/cm3), total calcium concentration (2.20 mmol/L vs. 2.45 mmol/L), total phosphorus concentration (1.06 mmol/L vs. 1.33 mmol/L), IGF-I (AUC 3,982.32 ng/mL vs. 5,167.61 ng/mL), IGFBP-3 (AUC 725.09 ng/L vs. 944.35 ng/L), 25(OH)D (AUC 356.35 ng/mL vs. 767.53 ng/mL) and BGLAP (AUC 161.39 ng/L vs. 298 ng/L) were lower in the study group. GH (AUC 88.3 ng/mL vs. 48.04 ng/mL), iPTH (AUC 1,201.94 pg/mL vs. 711.73 pg/mL) and ICTP (AUC 104.30 μg/L vs. 54.49 μg/L) were higher in patients with hepatitis. Positive correlations were noted between bone mineral density and IGF-I, IGFBP-3, and BGLAP levels. Conclusions: Chronic viral hepatitis causes a decrease in bone mineral density. Impaired liver function disrupts homeostasis of the calcium– vitamin D–parathyroid hormone axis and provokes secondary hyperparathyroidism. Chronic viral hepatitis induces a decrease in the synthesis of IGF-I and IGFBP-3 and an increase in GH secretion. Hepatic osteodystrophy is probably caused by both changes in calciotropic hormones as well as in the somatotropin hormone axis.Wstęp: Przewlekłe zakażenia HBV i HCV są obecnie znaczącym problemem klinicznym. W wyniku zaburzeń czynności wątroby może dochodzić do zaburzeń w homeostazie wapnia i fosforu oraz w metabolizmie kostnym prowadzących do osteodystrofii wątrobowej. Celem badania była ocena gęstości mineralnej kości (BMD), okołodobowych stężeń hormonów związanych z metabolizmem kości oraz markerów obrotu kostnego u chorych na przewlekłe wirusowe zapalenie wątroby. Materiał i metody: W grupie badanej (n = 80) oznaczano we krwi okołodobowe stężenia (AUC, area under the curve [pole pod krzywą]) GH, IGF-I, IGFBP-3, osteokalcyny (BGLAP), C-terminalnego telopeptydu kolagenu typu I (ICTP), PTH, 25(OH)D, całkowitego wapnia oraz fosforu. BMD (L2-L4) oceniono z użyciem DEXA. Dane porównano ze zdrową grupą kontrolną (n = 40). Wyniki: BMD (1,05 g/cm3 vs. 1,20 g/cm3), stężenia wapnia (2,20 mmol/l vs. 2,45 mmol/l) i fosforu (1,06 mmol/l vs. 1,33 mmol/l), IGF-I (AUC 3982,32 ng/ml vs. 5167,61 ng/ml), IGFBP-3 (AUC 725,09 ng/l vs. 944,35 ng/l), 25(OH)D (AUC 356,35 ng/ml vs. 767,53 ng/ml), BGLAP (AUC 161,39 ng/l vs. 298 ng/l) okazały się niższe w grupie badanej niż w grupie kontrolnej, zaś stężenia GH (AUC 88,3 ng/ml vs. 48,04 ng/ml), PTH (AUC 1201,94 pg/ml vs. 711,73 pg/ml) i ICTP (AUC 104,30 μg/l vs. 54,49 μg/l) były większe u osób z zapaleniem wątroby. Stwierdzono dodatnią korelację między BMD a stężeniami IGF-I, IGFBP-3 oraz BGLAP. Wnioski: Przewlekłe wirusowe zapalenie wątroby prowadzi do zmniejszenia gęstości mineralnej kości. Upośledzona funkcja wątroby zakłóca homeostazę wapnia, witaminy D, PTH, prowadzi do wtórnej nadczynności przytarczyc. Dochodzi do zmniejszenia syntezy IGF-I i IGFBP-3 oraz do zwiększenia wydzielania GH. Osteodystrofia wątrobowa jest prawdopodobnie spowodowana zarówno poprzez zmiany stężenia hormonów kalciotropowych, jak i zaburzenia funkcjonowania osi somatotropinowej

Systemic treatment of patients with advanced pancreatic cancer — is there still a place for gemcitabine in the first-line setting? Experience of Polish oncology centers

Introduction. Despite some progress in the treatment of patients with pancreatic cancer, it is still a malignancy with a poor prognosis, which results from its rapid local growth with a tendency to infiltrate surrounding tissues and metastasize, and late diagnosis at the advanced stage. The use of multi-drug regimens and modern targeted therapies did not completely eliminate the use of gemcitabine in monotherapy, which is a therapeutic option mainly in patients with poor performance status, ineligible for more advanced therapies. This study aimed to evaluate the results of treatment with single-agent gemcitabine in everyday clinical practice in Poland and to attempt to identify the predictors of obtaining long-term responses resulting from this treatment.  Material and methods. A retrospective analysis of 167 patients with advanced pancreatic cancer treated with single-agent gemcitabine in five oncology centers in Poland in the years 2017–2022 was conducted. Gemcitabine was used as monotherapy at an initial dose of 1000 mg/m2 of body surface area (BSA) weekly, 7 times in an 8-week cycle, then 3 times in a 4-week cycle.  Results. Median overall survival (OS) in the entire group of patients was 6.1 months (range — 0.2–32.3 months), and median progression-free survival (PFS) was 4.2 months (range — 0.2–31.3 months). A group of 60 patients was identified as “long responders” (LR), with a response of at least 6 months and a group of 107 as “short responders” (SR). Median PFS in the LR group was 9.15 months (range — 6.0–31.3 months) and in the SR group, it was 3.2 months (range — 0.2–5.8 months). Median OS was 11.6 months (range — 5.9–30.8) and 3.8 months (range — 0.2–32.3 months), respectively. In multivariate analysis, the likelihood of achieving at least a 6-month response (LR) was assessed using a logistic regression model. The model takes into account four variables: the neutrophil/lymphocyte (NLR) ratio, liver metastases, sex, and Hb level. Conclusions. The obtained results confirm that gemcitabine monotherapy is still useful in the first-line treatment of patients with advanced and metastatic pancreatic adenocarcinoma. An appropriate selection of patients for this treatment may improve the results while maintaining lower toxicity compared to combined treatment.

Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune-inflammation index as clinical predictive and prognostic markers in patients with advanced pancreatic cancer receiving gemcitabine monotherapy

Introduction. Pancreatic cancer is characterized by an increasing incidence and still poor prognosis despite the availability of various therapeutic options, currently including single- and multi-drug chemotherapy as well as molecularly targeted therapy. Therefore, appropriate qualification for particular therapies, based mainly on clinical and histological factors, is extremely important. Inflammatory status, associated with cancer development, justifies the search for prognostic markers related to the immune system, which could be additional factors facilitating selection of appropriate therapy. This study aimed at assessing the prognostic value of the neutrophil-to-lymphocyte ratio (NLR), plateletto- lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) in patients with advanced pancreatic cancer undergoing gemcitabine monotherapy.  Material andmethods. A retrospective analysis of blood morphological parameters was performed in 167 patients with advanced pancreatic cancer treated with gemcitabine monotherapy in the first line in five oncology centers in Poland in the years 2017–2022. The NLR, PLR, and SII were calculated, and cut-off points between high and low values were defined. Clinical parameters and their distribution were assessed depending on the overall survival (OS) value equal to or greater than or less than median OS. The distribution of patients within OS intervals in relation to the categories of inflammatory markers was assessed.  Results. The median age of patients was 71 years, the majority were women (58%), with clinical stage IV (57%), and with dominant location of metastases in the liver (42.5%). The median NLR was 2.69 (range 0.5–36.65), PLR 146.54 (range 18.53–1118.57), and SII 784.75 (range 79.86–10622.67). The cut-off points were defined as 4.5625 for the NLR [125 patients (75.8%) with a value less than and 40 patients (24.3%) with a value equal to or greater], 150 for the PLR [87 (52.7%)/ 78 (47.3%)], and 897.619 for the SII [96 (58.2%)/69 (41.8%)]. Comparing the groups with OS longer than or equal to the median and OS shorter than the median, statistically significant differences were found in relation to body mass index (BMI) (p = 0.02), baseline stage (p < 0.001), and location of metastases (p < 0.001). There were statistically significantly more NLR and SII values below the cut-off points in patients with survival at least equal to median OS. Concerning the PLR, no statistically significant differences were found between groups determined by OS value.  Conclusions. We demonstrated the relationship between indicators calculated on the basis of blood count parameters and treatment results. It may indicate the predictive and prognostic importance of indices reflecting immune system status, which can be a valuable addition to the clinical criteria included in prognostic models.

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.

BACKGROUND AND OBJECTIVES Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed and the question arises whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see if data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. RESULTS We included 1078 unmedicated adolescents and adults. Seven clusters were identified, of which four clusters included predominantly individuals with cataplexy. The two most distinct clusters consisted of 158 and 157 patients respectively, were dominated by those without cataplexy and, amongst other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening and weekend-week sleep length difference. Patients formally diagnosed as narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these two clusters. DISCUSSION Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset rapid eye moment periods (SOREMPs) in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features

Automatic Human Sleep Stage Scoring Using Deep Neural Networks

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.</p