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An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This study aimed at developing and validating externally patient-wise prognostic models of the global functional outcome at discharge from intensive inpatient post-acute rehabilitation after stroke, based on a standardized comprehensive multidimensional assessment performed at admission to rehabilitation. Patients addressing intensive inpatient rehabilitation pathways within 30 days from stroke were prospectively enrolled in two consecutive multisite studies. Demographics, description of the event, clinical/functional, and psycho-social data were collected. The outcome of interest was disability in basic daily living activities at discharge, measured by the modified Barthel Index (mBI). Machine learning-based prognostic models were developed, internally cross-validated, and externally validated. Interpretability techniques were applied for the analysis of predictors. 385 patients were considered, 220 (165) for training (external test) sets. A 50.9% (55.8%) of women, 79.5% (80.0%) of ischemic, and a median [interquartile range- IQR] age of 80.0[15.0] (79.0[17.0]) were registered. The Support Vector Machine obtained the best validation performances and a median absolute error [IQR] on discharge mBI estimation of 11.5[15.0] and 9.2[13.0] points on the internal and external testing, respectively. The baseline variables providing the main contributions to the predictions were mBI, motor upper-limb score, age, and cognitive screening score. We achieved a solution to support the formulation of a functional prognosis at intensive rehabilitation admission. The interpretability analysis confirms the relevance of easily collected motor and cognitive dataat admission and of the patient’s age. Trial registration : Prospectively registered on ClinicalTrials.gov (registration numbers RIPS NCT03866057, STRATEGY NCT05389878).

Background Rehabilitation treatments and services are essential for the recovery of post-stroke patients’ functions; however, the increasing number of available therapies and the lack of consensus among outcome measures compromises the possibility to determine an appropriate level of evidence. Machine learning techniques for prognostic applications offer accurate and interpretable predictions, supporting the clinical decision for personalised treatment. The aim of this study is to develop and cross-validate predictive models for the functional prognosis of patients, highlighting the contributions of each predictor. Methods A dataset of 278 post-stroke patients was used for the prediction of the class transition, obtained from the modified Barthel Index. Four classification algorithms were cross-validated and compared. On the best performing model on the validation set, an analysis of predictors contribution was conducted. Results The Random Forest obtained the best overall results on the accuracy (76.2%), balanced accuracy (74.3%), sensitivity (0.80), and specificity (0.68). The combination of all the classification results on the test set, by weighted voting, reached 80.2% accuracy. The predictors analysis applied on the Support Vector Machine, showed that a good trunk control and communication level, and the absence of bedsores retain the major contribution in the prediction of a good functional outcome. Conclusions Despite a more comprehensive assessment of the patients is needed, this work paves the way for the implementation of solutions for clinical decision support in the rehabilitation of post-stroke patients. Indeed, offering good prognostic accuracies for class transition and patient-wise view of the predictors contributions, it might help in a personalised optimisation of the patients’ rehabilitation path.

The Coma Recovery Scale-Revised (CRS-R) is the recommended tool to assess consciousness in patients with prolonged Disorders of Consciousness (pDoC). However, the time needed to administer it may limit its use. A shorter tool has been validated: the Simplified Evaluation of CONsciousness Disorders (SECONDs). This multicentre study aimed to develop and validate a cross-cultural adaptation of the SECONDs into Italian. An interdisciplinary expert team, from both Fondazione Don Carlo Gnocchi and Istituto Neurologico Carlo Besta, led the translation processes. Independent certified translators were also involved in a blinded modality. Patients diagnosed with Unresponsive Wakefulness Syndrome (UWS) or Minimally Conscious State (MCS) admitted to 3 Italian rehabilitation units were enrolled. The CRS-R and SECONDs were administered in 5 sessions over two weeks by 3 blinded examiners at each center (3 times, with 2 sessions conducted by the same examiner). Weighted Fleiss' kappa and Spearman correlation coefficients were used to assess intrarater and interrater reliability and concurrent validity. Sixty adults with pDoC were assessed: 23 women; median age: 64 years; 14 trauma, median post-onset time: 2 months. Intrarater and interrater reliability showed almost perfect agreement (kappa coefficients 0.968 and 0.935, respectively; p<0.001). The comparison of CRS-R vs. SECONDs on the same day or the best out of 5 SECONDs/CRS-R led to a substantial to almost perfect agreement both for the total score of the CRS-R and the SECONDs' Additional Index (ρ = 0.772-1.000; p<0.001) and for the consciousness diagnosis (k = 0.784-0.935; p<0.001). The disagreement rate between the overall best diagnosis of the SECONDs and the best CRS-R diagnosis was 6.7%. The Italian version of the SECONDs has been cross-culturally adapted to serve as a shorter assessment tool for the diagnosis of pDoC. Our study shows its excellent reliability and concurrent validity when compared to the CRS-R.

The objective of this study was to investigate the genetic diversity of the Garfagnina (GRF) goat, a breed that currently risks extinction. For this purpose, 48 goats were genotyped with the Illumina CaprineSNP50 BeadChip and analyzed together with 214 goats belonging to 9 other Italian breeds (~25 goats/breed), whose genotypes were available from the AdaptMap project [Argentata (ARG), Bionda dell’Adamello (BIO), Ciociara Grigia (CCG), Di Teramo (DIT), Garganica (GAR), Girgentana (GGT), Orobica (ORO), Valdostana (VAL) and Valpassiria (VSS)]. Comparative analyses were conducted on i) runs of homozygosity (ROH), ii) admixture ancestries and iii) the accuracy of breed traceability via discriminant analysis on principal components (DAPC) based on cross-validation. ROH analyses was used to assess the genetic diversity of GRF, while admixture and DAPC to evaluate its relationship to the other breeds. For GRF, common ROH (more than 45% in GRF samples) was detected on CHR 12 at, roughly 50.25–50.94Mbp (ARS1 assembly), which spans the CENPJ (centromere protein) and IL17D (interleukin 17D) genes. The same area of common ROH was also present in DIT, while a broader region (~49.25–51.94Mbp) was shared among the ARG, CCG, and GGT. Admixture analysis revealed a small region of common ancestry from GRF shared by BIO, VSS, ARG and CCG breeds. The DAPC model yielded 100% assignment success for GRF. Overall, our results support the identification of GRF as a distinct native Italian goat breed. This work can contribute to planning conservation programmes to save GRF from extinction and will improve the understanding of the socio-agro-economic factors related with the farming of GRF.

Gait and balance impairments are common and disabling in People with Multiple Sclerosis (PwMS), significantly affecting mobility and quality of life. Although several clinical and instrumental tools exist to assess these functions, there is no consensus on the most appropriate measures for detecting changes at the clinical, movement quality, and neural levels. This study aims to establish expert consensus on the most appropriate tools for assessing gait and balance in PwMS to support an individualized approach to rehabilitation. The process will begin with a focus group to gather initial feedback from a few experts, followed by a Delphi study consisting of iterative rounds of anonymous surveys with international experts to reach consensus on the most appropriate tools. The Delphi process will be conducted using an electronic platform to ensure anonymity and international participation. Experts will evaluate the proposed tools over several rounds until consensus is reached. The consensus threshold will be predefined, and statistical measures of agreement will guide the analysis. By identifying a core set of outcome measures covering clinical, movement quality, and neural aspects, this study aims to address the current fragmentation in clinical practice and research in PwMS. This comprehensive approach will improve the assessment of gait and balance and facilitate the design of tailored rehabilitation interventions that meet the specific needs and recovery potential of each patient. In addition, the study will establish a consensus-based framework for gait and balance assessment in MS rehabilitation, promoting consistency across clinical and research settings. The results are expected to inform future studies on patient stratification, treatment effectiveness, and precision rehabilitation strategies, ultimately leading to improved functional outcomes and quality of care.

Comparison studies on recovery outcomes in ischemic (IS) and hemorrhagic strokes (HS) have yielded mixed results. In this retrospective observational study of consecutive IS and HS patients, we aimed at evaluating functional outcomes at discharge from an intensive rehabilitation hospital, comparing IS vs. HS, analyzing possible predictors. Modified Rankin Scale (mRS) at discharge was the main outcome. Out of the 229 patients included (mean age 72.9 ± 13.9 years, 48% males), 81 had HS (35%). Compared with IS (n = 148), HS patients were significantly younger (75 ± 12.5 vs. 68.8 ± 15.4 years, p = 0.002), required longer hospitalizations both in acute (23.9 ± 36.7 vs. 35.2 ± 29.9 days, p = 0.019) and rehabilitation hospitals (41.5 ± 31.8 vs. 77.2 ± 51.6 days, p = 0.001), and had more severe initial clinical deficit (mean number of neurological impairments: 2.0 ± 1.1 vs. 2.6 ± 1.4, p = 0.001) and mRS scores at admission (p = 0.046). At discharge, functional status change, expressed as mRS, was not significantly different between IS and HS (F = 0.01, p = 0.902), nor was the discharge destination (p = 0.428). Age and clinical severity were predictors of functional outcome in both stroke types. On admission in an intensive rehabilitation hospital, HS patients presented a worse functional and clinical status compared to IS. Despite this initial gap, the two stroke types showed an overlapped trajectory of functional recovery, with age and initial stroke severity as the main prognostic factors.

Good data quality is vital for personalising plans in rehabilitation. Machine learning (ML) improves prognostics but integrating it with Multiple Imputation (MImp) for dealing missingness is an unexplored field. This work aims to provide post-stroke ambulation prognosis, integrating MImp with ML, and identify the prognostic influential factors. Stroke survivors in intensive rehabilitation were enrolled. Data on demographics, events, clinical, physiotherapy, and psycho-social assessment were collected. An independent ambulation at discharge, using the Functional Ambulation Category scale, was the outcome. After handling missingness using MImp, ML models were optimised, cross-validated, and tested. Interpretability techniques analysed predictor contributions. Pre-MImp, the dataset included 54.1% women, 79.2% ischaemic patients, median age 80.0 (interquartile range: 15.0). Post-MImp, 368 non-ambulatory patients on 10 imputed datasets were used for training, 80 for testing. The random forest (the validation best-performing algorithm) obtained 75.5% aggregated balanced accuracy on the test set. The main predictors included modified Barthel index, Fugl-Meyer assessment/motricity index, short physical performance battery, age, Charlson comorbidity index/cumulative illness rating scale, and trunk control test. This is among the first studies applying ML, together with MImp, to predict ambulation recovery in post-stroke rehabilitation. This pipeline reliably exploits the potential of incomplete datasets for healthcare prognosis, identifying relevant predictors.

CareToy system is an innovative tele-rehabilitative tool, useful in providing intensive, individualized, home-based, family-centred Early Intervention (EI) in infants. Our aim was to evaluate, through a Randomized Clinical Trial (RCT) study, the effects of CareToy intervention on early motor and visual development in preterm infants. 41 preterm infants (range age: 3.0-5.9 months of corrected age) were enrolled and randomized into two groups, CareToy and Standard Care. 19 infants randomized in CareToy group performed a 4-week CareToy program, while 22 allocated to control group completed 4 weeks of Standard Care. Infant Motor Profile (IMP) was primary outcome measure, Alberta Infant Motor Scale (AIMS) and Teller Acuity Cards were secondary ones. Assessments were carried out at baseline (T0) and at the end of CareToy training or Standard Care period (T1). T1 was the primary endpoint. After RCT phase, 17 infants from control group carried out a 4-week CareToy program, while 18 infants from the CareToy group continued with Standard Care. At the end of this phase, infants were re-assessed at T2. In RCT phase, delta IMP total score and variation and performance sub-domains were significantly higher (P<0.050) in CareToy group if compared to Standard Care group. Similar results were found for Teller Acuity Cards, while no differences between groups were found for AIMS. No differences were found in any outcome measure results (T2-T0), between infants who started CareToy training before or after one month of standard care. This RCT study confirms the results of a previous pilot study, indicating that CareToy system can provide effective home-based EI. This trial has been registered at www.clinicaltrials.gov (Identifier NCT01990183).

Diagnosing patients with a Prolonged Disorders of Consciousness (pDoC) is challenging due to the inherent limitations of the behavioral assessments recommended by international rehabilitation guidelines. To address this, we applied temporal graph theory to resting-state EEG recordings from 217 patients with severe Acquired Brain Injury (sABI), with and without pDoC. Higher temporal degree centrality in the and bands, as well as a higher burstiness coefficient, were found in the pDoC group. In contrast, lower temporal correlation coefficient was observed in the band in the pDoC group compared to the eMCS group. Furthermore, the analysis of separate frequency bands revealed different behavior of the average topological overlap, with higher values in the eMCS group in the band and higher values in the pDoC group in the band. Thus, repeatability of -connectivity between t-graphlets, the capability of sustaining more periodic-like changes in all-bands connectivity, and the higher integration of temporal network were found to be correlated with consciousness, providing valuable insights on the time-varying neural architecture of pDoC patients.