A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

Glioblastoma (GBM) is one of the most aggressive cancers, comprising 60–70% of all gliomas. The large G-protein-coupled receptor family includes cannabinoid receptors CB1, CB2, GPR55, and non-specific ion receptor protein transporters TRPs. First, we found up-regulated CNR1, GPR55, and TRPV1 expression in glioma patient-derived tissue samples and cell lines compared with non-malignant brain samples. CNR1 and GPR55 did not correlate with glioma grade, whereas TRPV1 negatively correlated with grade and positively correlated with longer overall survival. This suggests a tumour-suppressor role of TRPV1. With respect to markers of GBM stem cells, preferred targets of therapy, TRPV1 and GPR55, but not CNR1, strongly correlated with different sets of stemness gene markers: NOTCH, OLIG2, CD9, TRIM28, and TUFM and CD15, SOX2, OCT4, and ID1, respectively. This is in line with the higher expression of TRPV1 and GPR55 genes in GSCs compared with differentiated GBM cells. Second, in a panel of patient-derived GSCs, we found that CBG and CBD exhibited the highest cytotoxicity at a molar ratio of 3:1. We suggest that this mixture should be tested in experimental animals and clinical studies, in which currently used Δ9-tetrahydrocannabinol (THC) is replaced with efficient and non-psychoactive CBG in adjuvant standard-of-care therapy.

Data-driven intelligent tutoring systems learn to provide feedback based on past student behavior, reducing the effort required for their development. A major obstacle to applying data-driven methods in the programming domain is the lack of meaningful observable actions for describing the students problem-solving process. We propose rewrite rules as a language-independent formalization of programming actions in terms of code edits. We describe a method for automatically extracting rewrite rules from students program-writing traces, and a method for debugging new programs using these rules. We used these methods to automatically provide hints in a web application for learning programming. In-class evaluation showed that students receiving automatic feedback solved problems faster and submitted fewer incorrect programs. We believe that rewrite rules provide a good basis for further research into how humans write and debug programs.

Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations. More specifically, the aim was to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication). This work combined spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The study involved 65 advanced PD patients and over 30,000 spiral-drawing measurements over the course of three years. Machine learning methods were used to learn to predict the "cause" (bradykinesia or dyskinesia) of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. The classification model was also tested for comprehensibility. For this purpose a visualisation technique was used to present visual clues to clinicians as to which parts of the spiral drawing (or its animation) are important for the given classification. Using the machine learning methods with feature descriptions and pre-processing from the Slovenian application resulted in 86% classification accuracy and over 0.90 AUC. The clinicians also rated the computer's visual explanations of its classifications as at least meaningful if not necessarily helpful in over 90% of the cases. The relatively high classification accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting.

Background. The severity of both the COVID-19 clinical picture and confinement measures in Slovenia was higher during the initial phase of the pandemic in 2020 than during the Omicron wave in 2022. This could lead us to expect a higher level of distress during the initial phase. On the other hand, prolonged stress can have a detrimental effect on mental health. This study aimed to explore how the prolonged stress of the COVID-19 pandemic and the accompanying changes affected the mental health of young adults in Slovenia. We analyzed and compared the levels of depression, anxiety, stress, and suicidal ideation in young adults during the initial phase of the pandemic and the Omicron wave, as well as between the COVID-19-infected and non-infected individuals. Methods. An online survey was used to survey 587 young adults in the first wave (July–December 2020) and 511 in the Omicron wave (January–February 2022). Levels of depression, anxiety, stress, and suicidal ideation were compared using Mann–Whitney U test. Results. Results show that the Omicron wave significantly worsened depression, anxiety, stress, and suicidal ideation. Young adults who had tested positive for COVID-19 reported no worse or only slightly worse mental health than those who never tested positive. Conclusions. The current study provides new evidence about the mental health of young adults during the Omicron wave. Our results show that two years into the pandemic, they expressed more negative emotions and suicidal thoughts than at the beginning.

Background: Hypoxia correlates with poor prognosis in several cancer types, including lung cancer. Prolyl hydroxylase domain proteins (PHDs) play a role in cell oxygen sensing, negatively regulating the hypoxia-inducible factor (HIF) pathway. Our study aim was to evaluate PHD1, PHD2 and PHD3 mRNA expression levels in primary tumours and normal lungs of non-small-cell lung cancer (NSCLC) patients and to correlate it with selected regulators of HIF signalling, with clinicopathological characteristics and overall survival (OS). Methods: Tumour tissue samples were obtained from 60 patients with surgically resected NSCLC who were treated with radical surgery. In 22 out of 60 cases, matching morphologically normal lung tissue was obtained. PHD1, PHD2 and PHD3 mRNA expressions were measured using RT-qPCR. Results: The PHD1 and PHD2 mRNA levels in primary tumours were significantly decreased compared to those in normal lungs (both p < 0.0001). PHD1 and PHD2 expression in tumours was positively correlated (r_s = 0.82; p < 0.0001) and correlated well with HIF pathway downstream genes HIF1A, PKM2 and PDK1. Decreased PHD1 and PHD2 were associated with larger tumour size, higher tumour stage (PHD1 only) and squamous cell carcinoma. Patients with low PHD1 and patients with low PHD2 expression had shorter OS than patients with high PHD1 (p = 0.02) and PHD2 expression (p = 0.01). PHD1 showed borderline independent prognostic values in multivariate analysis (p = 0.06). In contrast, we found no associations between PHD3 expression and any of the observed parameters. Conclusions: Our results show that reduced expression of PHD1 and PHD2 is associated with the development and progression of NSCLC. PHD1 could be further assessed as a prognostic marker in NSCLC.