The caring competencies of nursing students: Comparing the four and two-year Bachelor of Nursing Programme.

The study was conducted to determine and compare the caring behaviours of nursing students in four-year BS Nursing (Bachelor of Science in nursing) and two-year Post- RN BSN (Registered nurse to Bachelor of Science in Nursing) programmes. A cross-sectional analytical study was conducted in the nursing institutes of KP from July to September 2022. The sample size was 383 calculated by using the Rao soft sample size calculator. through Caring Behaviour Inventory (CBI-24) having six point Likert scale. Descriptive and inferential statistics were calculated through SPSS 20.0. There were more males, i.e. 60.6%, as compared to female students (39.4%). The overall mean score of caring competencies of Post-RN BS Nursing (Registered nurse to Bachelor of Science in Nursing) was 27.17±2.17 which is higher than 23.19±4.1 for BSN students (Bachelor of Science in nursing). Caring behaviours of overall participants are correlated with gender (p=0.001), age (p=0.001), and semester (p=0.001). The caring competency of Post-RN BS nursing students' are higher than the BS Nursing programme students. That could be due to the clinical exposure of Post-Rn students after completing a three-year Diploma in General Nursing.

Characterizing directional dynamics of semantic prediction based on inter-regional temporal generalization.

The event-related potential/field component N400(m) has been widely used as a neural index for semantic prediction. It has long been hypothesized that feedback information from inferior frontal areas plays a critical role in generating the N400. However, due to limitations in causal connectivity estimation, direct testing of this hypothesis has remained difficult. Here, magnetoencephalography (MEG) data was obtained during a classic N400 paradigm where the semantic predictability of a fixed target noun was manipulated in simple German sentences. To estimate causality, we implemented a novel approach based on machine learning and temporal generalization to estimate the effect of inferior frontal gyrus (IFG) on temporal areas. In this method, a support vector machine (SVM) classifier is trained on each time point of the neural activity in IFG to classify less predicted (LP) and highly predicted (HP) nouns and then tested on all time points of superior/middle temporal sub-regions activity (and vice versa, to establish spatio-temporal evidence for or against causality). The decoding accuracy was significantly above chance level when the classifier was trained on IFG activity and tested on future activity in superior and middle temporal gyrus (STG/MTG). The results present new evidence for a model predictive speech comprehension where predictive IFG activity is fed back to shape subsequent activity in STG/MTG, implying a feedback mechanism in N400 generation. In combination with the also observed strong feedforward effect from left STG/MTG to IFG, our findings provide evidence of dynamic feedback and feedforward influences between IFG and temporal areas during N400 generation.

Delays in latencies of median-nerve evoked magnetic fields in patients with succinic semialdehyde dehydrogenase deficiency.

OBJECTIVE: Succinic semialdehyde dehydrogenase deficiency (SSADHD) is a genetic disorder resulting in abnormal regulation of γ-aminobutyric acid, lipid metabolism, and myelin biogenesis, leading to ataxia, seizures, and cognitive impairment. Since the myelin sheath is thinner in a murine model of SSADHD compared to a wild type, we hypothesized that this also holds for human brain. We tested whether the conduction velocity in the somatosensory pathway is accordingly delayed. METHODS: Somatosensory evoked magnetic fields (SEF) produced by transcutaneous electrical stimulation of the median nerve were measured in 13 SSADHD patients, 11 healthy and 14 disease controls with focal epilepsy. The peak latencies of the initial four components (M1, M2, M3 and M4) were measured. RESULTS: The SEF waveforms and scalp topographies were comparable across the groups. The latencies were statistically significantly longer in the SSADHD group compared to the two controls. We found these latencies for the SSADHD, healthy and disease controls respectively to be: M1: (21.9 ± 0.8 ms [mean ± standard error of the mean], 20.4 ± 0.6 ms, and 21.0 ± 0.4 ms) (p < 0.05); M2: (36.1 ± 1.0 ms, 33.1 ± 0.6 ms, and 32.1 ± 1.1 ms) (p < 0.005); M3: (62.5 ± 2.4 ms, 54.7 ± 2.0 ms, and 49.9 ± 1.8 ms) (p < 0.005); M4: (86.2 ± 2.3 ms, 78.8 ± 2.8 ms, and 73.5 ± 2.9 ms) (p < 0.005). CONCLUSIONS: The SEF latencies are delayed in patients with SSADHD compared with healthy controls and disease controls. SIGNIFICANCE: This is the first study that compares conduction velocities in the somatosensory pathway in SSADHD, an inherited disorder of GABA metabolism. The longer peak latency implying slower conduction velocity supports the hypothesis that myelin sheath thickness is decreased in SSADHD.

Using large language models for safety-related table summarization in clinical study reports.

Objectives: The generation of structured documents for clinical trials is a promising application of large language models (LLMs). We share opportunities, insights, and challenges from a competitive challenge that used LLMs for automating clinical trial documentation. Materials and Methods: As part of a challenge initiated by Pfizer (organizer), several teams (participant) created a pilot for generating summaries of safety tables for clinical study reports (CSRs). Our evaluation framework used automated metrics and expert reviews to assess the quality of AI-generated documents. Results: The comparative analysis revealed differences in performance across solutions, particularly in factual accuracy and lean writing. Most participants employed prompt engineering with generative pre-trained transformer (GPT) models. Discussion: We discuss areas for improvement, including better ingestion of tables, addition of context and fine-tuning. Conclusion: The challenge results demonstrate the potential of LLMs in automating table summarization in CSRs while also revealing the importance of human involvement and continued research to optimize this technology.

RNA methyltransferase SPOUT1/CENP-32 links mitotic spindle organization with the neurodevelopmental disorder SpADMiSS.

SPOUT1/CENP-32 encodes a putative SPOUT RNA methyltransferase previously identified as a mitotic chromosome associated protein. SPOUT1/CENP-32 depletion leads to centrosome detachment from the spindle poles and chromosome misalignment. Aided by gene matching platforms, we identified 24 individuals with neurodevelopmental delays from 18 families with bi-allelic variants in SPOUT1/CENP-32 detected by exome/genome sequencing. Zebrafish spout1/cenp-32 mutants showed reduction in larval head size with concomitant apoptosis likely associated with altered cell cycle progression. In vivo complementation assays in zebrafish indicated that SPOUT1/CENP-32 missense variants identified in humans are pathogenic. Crystal structure analysis of SPOUT1/CENP-32 revealed that most disease-associated missense variants mapped to the catalytic domain. Additionally, SPOUT1/CENP-32 recurrent missense variants had reduced methyltransferase activity in vitro and compromised centrosome tethering to the spindle poles in human cells. Thus, SPOUT1/CENP-32 pathogenic variants cause an autosomal recessive neurodevelopmental disorder: SpADMiSS ( SPOUT1 Associated Development delay Microcephaly Seizures Short stature) underpinned by mitotic spindle organization defects and consequent chromosome segregation errors.