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Developing non-invasive brain stimulation interventions to improve attentional control is extremely relevant to a variety of neurological and psychiatric populations, yet few studies have identified reliable biomarkers that can be readily modified to improve attentional control. One potential biomarker of attention is functional connectivity in the core cortical network supporting attention - the dorsal attention network (DAN). We used a network-targeted cerebellar transcranial magnetic stimulation (TMS) procedure, intended to enhance cortical functional connectivity in the DAN. Specifically, in healthy young adults we administered intermittent theta burst TMS (iTBS) to the midline cerebellar node of the DAN and, as a control, the right cerebellar node of the default mode network (DMN). These cerebellar targets were localized using individual resting-state fMRI scans. Participants completed assessments of both sustained (gradual onset continuous performance task, gradCPT) and transient attentional control (attentional blink) immediately before and after stimulation, in two sessions (cerebellar DAN and DMN). Following cerebellar DAN stimulation, participants had significantly fewer attentional lapses (lower commission error rates) on the gradCPT. In contrast, stimulation to the cerebellar DMN did not affect gradCPT performance. Further, in the DAN condition, individuals with worse baseline gradCPT performance showed the greatest enhancement in gradCPT performance. These results suggest that temporarily increasing functional connectivity in the DAN via network-targeted cerebellar stimulation can enhance sustained attention, particularly in those with poor baseline performance. With regard to transient attention, TMS stimulation improved attentional blink performance across both stimulation sites, suggesting increasing functional connectivity in both networks can enhance this aspect of attention. These findings have important implications for intervention applications of TMS and theoretical models of functional connectivity. •Dorsal attention network cerebellar-TMS enhances sustained attention.•TMS enhancements of sustained attention are greatest in those worse at baseline.•Cerebellar TMS of dorsal attention and default network enhances transient attention.

BackgroundCognitive deficits typically precede motor manifestation in premanifest Huntington’s disease (preHD) and can therefore serve as early biomarkers of disease progression. Subtle cognitive impairments in HD have been detected on verbal learning paradigms, however optimal task design has not yet been established.AimsWe assessed cognitive impairment in preHD by comparing performance on two verbal learning tasks: California Verbal Learning Test II (CVLT-II) and the Loewenstein-Acevedo Scales of Semantic Interference and Learning (LASSI-L). Each task is sensitive to different potential early deficits: CVLT-II measures learning strategies including semantic clustering and serial order while the LASSI-L includes robust measures of interference.MethodsWe administered LASSI-L and CVLT-II to 8 preHD participants and 8 healthy controls (HC), matched for age and education. The tasks were administered 6 months apart to mitigate confounding effects.ResultsThree sections on the LASSI-L captured significant differences between groups: proactive semantic interference (PSI) (P = 0.043), delayed recall (P = 0.008), and B1 cued recall intrusions (P = 0.032). Although the preHD cohort appeared relatively impaired across several sections of CVLT-II, the results were not significantly different.ConclusionsSeveral sections of the LASSI-L showed significant differences between our preHD and HC cohorts. Despite the small sample size, LASSI-L appeared to capture subtle deficits that were not readily apparent on CVLT-II. These findings underline the importance of using verbal learning paradigms that emphasize measures of interference in preHD. Further validation of the LASSI-L using larger preHD cohorts remains important.

INTRODUCTION: Huntington’s disease (HD) is a progressive neurodegenerative disorder characterized by motor, cognitive, and psychiatric decline. The Unified Huntington’s Disease Rating Scale Total Motor Score (UHDRS-TMS) is standard for staging manifest disease, but is relatively insensitive to subtle premanifest changes. Speech abnormalities are emerging as candidate digital biomarkers; however, reliably separating premanifest HD (preHD) from healthy controls remains challenging. Here, we assess the feasibility of a speech-only approach by training and comparing multiple classifiers across diverse feature sets and structured tasks to determine whether speech alone can discriminate preHD from controls. METHODS: Speech samples were collected from 94 individuals with HD (38 premanifest, 56 manifest) and 36 controls using a standardized six-task protocol administered via tablet. From these recordings, 188 lexical and prosodic features were automatically extracted. We trained 4 machine learning classifiers: random forest, support vector machine, XGBoost, and deep neural networks (DNN), within 10-fold cross-validation using three feature configurations: (1) all tasks (188 features), (2) the top 30 ANOVA-ranked features, and (3) 22 features from the Caterpillar Passage alone. RESULTS: Traditional classifiers showed limited accuracy. A DNN using only the Caterpillar task achieved 81% unweighted accuracy for classifying premanifest HD versus controls. Accuracy increased to 83% for prodromal HD and 87% when all HD participants were compared to controls. Adding features from additional tasks did not improve performance. DISCUSSION: A brief, structured speech task combined with deep learning enabled accurate classification of premanifest HD. These findings support speech analysis as a scalable, objective tool for early disease detection and monitoring.

NK-cell resistance to transduction is a major technical hurdle for developing NK-cell immunotherapy. By using Baboon envelope pseudotyped lentiviral vectors (BaEV-LVs) encoding eGFP, we obtained a transduction rate of 23.0 ± 6.6% (mean ± SD) in freshly-isolated human NK-cells (FI-NK) and 83.4 ± 10.1% (mean ± SD) in NK-cells obtained from the NK-cell Activation and Expansion System (NKAES), with a sustained transgene expression for at least 21 days. BaEV-LVs outperformed Vesicular Stomatitis Virus type-G (VSV-G)-, RD114- and Measles Virus (MV)- pseudotyped LVs ( < 0.0001). mRNA expression of both BaEV receptors, ASCT1 and ASCT2, was detected in FI-NK and NKAES, with higher expression in NKAES. Transduction with BaEV-LVs encoding for CAR-CD22 resulted in robust CAR-expression on 38.3 ± 23.8% (mean ± SD) of NKAES cells, leading to specific killing of NK-resistant pre-B-ALL-RS4;11 cell line. Using a larger vector encoding a dual CD19/CD22-CAR, we were able to transduce and re-expand dual-CAR-expressing NKAES, even with lower viral titer. These dual-CAR-NK efficiently killed both CD19 - and CD22 -RS4;11 cells. Our results suggest that BaEV-LVs may efficiently enable NK-cell biological studies and translation of NK-cell-based immunotherapy to the clinic.

NK-cell resistance to transduction is a major technical hurdle for developing NK-cell immunotherapy. By using Baboon envelope pseudotyped lentiviral vectors (BaEV-LVs) encoding eGFP, we obtained a transduction rate of 23.0+/-6.6% in freshly-isolated NK-cells (FI-NK) and 83.4+/-10.1% in NK-cells obtained from the NK-cell Activation and Expansion System (NKAES), even at low MOI, with a sustained transgene expression for at least 21 days. BaEV-LVs outperformed Vesicular Stomatitis Virus type-G (VSV-G)-, RD114- and Measles Virus (MV)- pseudotyped LVs (p<0.001). mRNA expression of both BaEV receptors, ASCT1 and ASCT2, was detected in FI-NK and NKAES, with much higher expression in NKAES. Transduction with BaEV-LVs encoding for CAR-CD22 resulted in robust CAR-expression on 44.2%+/-14.2% of NKAES cells, which allowed the specific killing of the NK-resistant pre-B-ALL-RS4;11 cell line. Using a larger vector, encoding a dual CD19/CD22-CAR separated by T2A, we were able to transduce and re-expand dual-CAR-expressing NKAES, even with low viral titer. These dual-CAR-NK efficiently and specifically killed both CD19KO- and CD22KO-RS4;11 cells, which may overcome antigen-loss escape in the clinical setting. Our results suggest that BaEV-LVs may efficiently enable NK-cell biological studies and translation of NK-cell-based immunotherapy to the clinic.