Recent clinical findings in patients with chronic myeloid leukemia (CML) suggest that the risk of molecular recurrence after stopping tyrosine kinase inhibitor (TKI) treatment substantially depends on an individual's leukemia-specific immune response. However, it is still not possible to prospectively identify patients that will remain in treatment-free remission (TFR). Here, we used an ordinary differential equation model for CML, which explicitly includes an antileukemic immunologic effect, and applied it to 21 patients with CML for whom BCR-ABL1/ABL1 time courses had been quantified before and after TKI cessation. Immunologic control was conceptually necessary to explain TFR as observed in about half of the patients. Fitting the model simulations to data, we identified patientspecific parameters and classified patients into three different groups according to their predicted immune system configuration ("immunologic landscapes"). While one class of patients required complete CML eradication to achieve TFR, other patients were able to control residual leukemia levels after treatment cessation. Among them were a third class of patients that maintained TFR only if an optimal balance between leukemia abundance and immunologic activation was achieved before treatment cessation. Model simulations further suggested that changes in the BCR-ABL1 dynamics resulting from TKI dose reduction convey information about the patient-specific immune system and allow prediction of outcome after treatment cessation. This inference of individual immunologic configurations based on treatment alterations can also be applied to other cancer types in which the endogenous immune system supports maintenance therapy, long-term disease control, or even cure.Significance: This mathematical modeling approach provides strong evidence that different immunologic configurations in patients with CML determine their response to therapy cessation and that dose reductions can help to prospectively infer different risk groups.See related commentary by Triche Jr, p. 2083

Background In Parkinson's disease, postural instability and falls are of particular socioeconomic relevance. Although effective fall prevention and the prophylaxis of fall‐related injuries depend on low‐threshold symptom monitoring, validated instruments are lacking. Objectives To develop a self‐report questionnaire for the assessment of falls, near falls, fear of falling, fall‐related injuries, and causes of falls for patients with Parkinson's disease (PwPD). Methods A pool of potential items was generated from a literature review and by discussion in an expert panel. The first version of the Dresden Fall Questionnaire (DREFAQ) was tested in a group of German‐speaking movement disorder specialists as well as PwPD. The resulting 5‐item questionnaire was assessed in a validation cohort of 36 PwPD who documented fall events and near‐fall events in a calendar for 3 months and completed the DREFAQ at the end of the study. The questionnaire was subsequently used in a separate cohort of 46 PwPD to determine test–retest reliability and confirm the factor structure. Results The DREFAQ showed good internal consistency (Cronbach's α = 0.84) and good test–retest reliability (intraclass correlation coefficient, 0.76; 95% confidence interval, 0.60–0.86). The total DREFAQ score showed good concurrent validity with fall events (Spearman's ρ = 0.82) and near‐fall events (Spearman's ρ = 0.78) as determined by fall and near‐fall diaries. Factor analysis revealed a 2‐factor structure composed of near falls with fear of falling and severe falls with injuries. Conclusions The DREFAQ is a reliable and valid 5‐item questionnaire for determining the incidence of falls, near falls, fear of falling, fall‐related injuries, and causes of falls in PwPD.

BACKGROUND Phonematic and Semantic Verbal Fluency Tasks (VFT) are widely used to capture cognitive deficits in people with neurodegenerative diseases. Counting the total number of words produced within a given time frame constitutes the most commonly used analysis for VFTs. The analysis of semantic and phonematic word clusters can provide additional information about frontal and temporal cognitive functions. Traditionally, clusters in the semantic VFT are identified by using fixed word lists, which need to be created manually, lack standardization and are language-specific. Furthermore, it is not possible to identify semantic clusters in the phonematic VFT by this technique. OBJECTIVE The objective of this paper was to develop an automated analysis of semantically related word clusters for the semantic and phonematic VFT. Furthermore, we aimed to explore the cognitive domains captured by this analysis for people with Parkinson’s Disease (PwPD). METHODS PwPD performed tablet-based semantic (n=51) and phonematic (n=69) VFT. For both tasks, semantic word clusters were determined using a semantic relatedness model based on a neural network trained on the Wikipedia text corpus. Cluster characteristics were compared to traditional evaluation methods of VFTs and a set of neuropsychological parameters. RESULTS For the semantic VFT, clustering characteristics obtained by automated analyses showed good correlations with the cluster characteristics obtained from the traditional method. Cluster characteristics from automated analyses of phonematic and semantic VFT correlated with the Montreal Cognitive Assessment reporting overall cognitive function, executive functioning reported by Frontal Assessment Battery and Trail Making Test and language function reported by Boston Naming Test. CONCLUSIONS Our study demonstrates the feasibility of standardized automated cluster analyses of VFTs by using semantic relatedness models. These models do not require manually creating and updating categorized word lists and therefore can be easily and objectively implemented in different languages, and potentially allow comparison of results across different languages. Furthermore, this method provides information about semantic clusters in phonematic VFTs that cannot be obtained from traditional methods. Hence, this method could provide easily accessible digital biomarkers for executive and language function in PwPD.

Background Phonematic and semantic verbal fluency tasks (VFTs) are widely used to capture cognitive deficits in people with neurodegenerative diseases. Counting the total number of words produced within a given time frame constitutes the most commonly used analysis for VFTs. The analysis of semantic and phonematic word clusters can provide additional information about frontal and temporal cognitive functions. Traditionally, clusters in the semantic VFT are identified using fixed word lists, which need to be created manually, lack standardization, and are language specific. Furthermore, it is not possible to identify semantic clusters in the phonematic VFT using this technique. Objective The objective of this study was to develop a method for the automated analysis of semantically related word clusters for semantic and phonematic VFTs. Furthermore, we aimed to explore the cognitive domains captured by this analysis for people with Parkinson disease (PD). Methods People with PD performed tablet-based semantic (51/85, 60%) and phonematic (69/85, 81%) VFTs. For both tasks, semantic word clusters were determined using a semantic relatedness model based on a neural network trained on the Wikipedia (Wikimedia Foundation) text corpus. The cluster characteristics derived from this model were compared with those derived from traditional evaluation methods of VFTs and a set of neuropsychological parameters. Results For the semantic VFT, the cluster characteristics obtained through automated analyses showed good correlations with the cluster characteristics obtained through the traditional method. Cluster characteristics from automated analyses of phonematic and semantic VFTs correlated with the overall cognitive function reported by the Montreal Cognitive Assessment, executive function reported by the Frontal Assessment Battery and the Trail Making Test, and language function reported by the Boston Naming Test. Conclusions Our study demonstrated the feasibility of standardized automated cluster analyses of VFTs using semantic relatedness models. These models do not require manually creating and updating categorized word lists and, therefore, can be easily and objectively implemented in different languages, potentially allowing comparison of results across different languages. Furthermore, this method provides information about semantic clusters in phonematic VFTs, which cannot be obtained from traditional methods. Hence, this method could provide easily accessible digital biomarkers for executive and language functions in people with PD.