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Automated speech and language analysis (ASLA) is gaining momentum as a noninvasive, affordable, and scalable approach for the early detection of Alzheimer disease (AD). Nevertheless, the literature presents 2 notable limitations. First, many studies use computationally derived features that lack clinical interpretability. Second, a significant proportion of ASLA studies have been conducted exclusively in English speakers. These shortcomings reduce the utility and generalizability of existing findings. To address these gaps, we investigated whether interpretable linguistic features can reliably identify AD both within and across language boundaries, focusing on English- and Spanish-speaking patients and healthy controls (HCs). We analyzed speech recordings from 211 participants, encompassing 117 English speakers (58 patients with AD and 59 HCs) and 94 Spanish speakers (47 patients with AD and 47 HCs). Participants completed a validated picture description task from the Boston Diagnostic Aphasia Examination, eliciting natural speech under controlled conditions. Recordings were preprocessed and transcribed before extracting (1) speech timing features (eg, pause duration, speech segment ratios, and voice rate) and (2) lexico-semantic features (lexical category ratios, semantic granularity, and semantic variability). Machine learning classifiers were trained with data from English-speaking patients and HCs, and then tested (1) in a within-language setting (with English-speaking patients and HCs) and (2) in a between-language setting (with Spanish-speaking patients and HCs). Additionally, the features were used to predict cognitive functioning as measured by the Mini-Mental State Examination (MMSE). In the within-language condition, combined speech timing and lexico-semantic features yielded maximal classification (area under the receiver operating characteristic curve [AUC]=0.88), outperforming single-feature models (AUC=0.79 for timing features; AUC=0.80 for lexico-semantic features). Timing features showed the strongest MMSE prediction (R=0.43, P<.001). In the between-language condition, speech timing features generalized well to Spanish speakers (AUC=0.75) and predicted Spanish-speaking patients' MMSE scores (R=0.39, P<.001). Lexico-semantic features showed lower performance (AUC=0.64) and no significant MMSE prediction (R=-0.31, P=.05). The combined model did not improve results (AUC=0.65; R=0.04, P=.79). These results suggest that while both timing and lexico-semantic features are informative within the same language, only speech timing features demonstrate consistent performance across languages. By focusing on clinically interpretable features, this approach supports the development of clinically usable ASLA tools.

INTRODUCTION Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease‐specific semantic memory patterns. Here, we leveraged automated word‐property analysis to capture neurocognitive markers of AD vis‐à‐vis behavioral variant frontotemporal dementia (bvFTD). METHODS Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group‐level discrimination, patient‐level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns. RESULTS Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group‐ and subject‐level discrimination only in AD, also predicting executive outcomes. Disease‐specific cortical thickness patterns were predicted by frequency in both disorders. Default‐mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD. DISCUSSION Word‐property analysis of fluency can boost AD characterization and diagnosis. Highlights We report novel word‐property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word‐property analysis of fluency can boost AD characterization and diagnosis.

INTRODUCTION Digital speech biomarkers (DSBs) support the detection and monitoring of Alzheimer's disease (AD) in Latinos. However, they have not been benchmarked against standard cognitive and neuroimaging measures, missing a critical validation milestone. METHODS Thirty‐three AD patients and 33 healthy controls completed verbal fluency tasks, episodic memory and executive tests, and magnetic resonance imaging (MRI) (volume) and functional MRI (fMRI) (connectivity) scans. Between‐group machine learning classification was compared among fluency‐derived DSBs, episodic and executive test scores, MRI, and fMRI measures. RESULTS The fluency classifier's performance (area under the curve [AUC] = 0.84) was comparable (p > 0.14) to the episodic (AUC = 0.90), executive (AUC = 0.79), and structural (AUC = 0.90) classifiers and superior to the functional classifier (AUC = 0.65, p = 0.002). Top discriminating features were word length and frequency, both associated with right (pre)frontal volume upon adjusting for sociodemographic factors. DISCUSSION DSBs appear non‐inferior to standard cognitive and imaging measures, supporting scalable AD assessments in Latinos. Highlights We examined digital speech biomarkers (DSBs) for detecting AD in Latinos. DSBs were benchmarked against cognitive and neuroimaging features. DSB‐based classifiers matched or outperformed cognitive and brain classifiers. Top DSBs included word length, phonological neighborhood, and frequency. Word length and frequency correlated with right (pre)frontal brain volume.

INTRODUCTION Verbal fluency tasks are widely used in primary progressive aphasia (PPA), but most studies rely only on total correct responses, overlooking qualitative features of the words produced. We applied a scalable computational framework to extract item‐level features from fluency responses in semantic variant (svPPA) and logopenic variant PPA (lvPPA) to test their value for differential diagnosis. METHODS We analyzed animal fluency responses from 113 participants (40 svPPA, 40 lvPPA, 33 controls) using an automated pipeline extracting nine psycholinguistic features. Group differences were examined with (co)variance models, classification with logistic regression, and brain–behavior associations via structural magnetic resonance imaging. RESULTS All features except semantic variability distinguished svPPA from lvPPA. Models including features outperformed (area under the curve [AUC] = 0.86) those using only total correct or clinical variables (AUC = 0.60–0.68). Features related mainly to temporal lobe atrophy, whereas total correct also related to the angular gyrus. DISCUSSION Automated item‐level analysis offers a sensitive, scalable method for supporting PPA diagnosis and monitoring. Highlights Automated item‐level features from verbal fluency aid semantic variant primary progressive aphasia (PPA) versus logopenic variant PPA diagnosis. Item‐level fluency features outperform total correct responses for classification. Item‐level fluency features map onto syndrome‐relevant temporal lobe atrophy. A scalable, fully automated pipeline enables integration into clinical practice. There is potential to support low‐burden, objective monitoring of disease progression in PPA.

Background: Glioblastoma (GBM) is a highly aggressive tumor characterized by elevated plasticity and poor differentiation. Platelet-derived preparations such as Concentrated Growth Factors (CGF) are rich in bioactive molecules, but their effects on tumor biology remain underexplored. Methods: U87MG glioblastoma cells were cultured with a conditioned medium obtained from CGF over 14 days (CGF-CM). We analyzed cell viability, morphology, DNA integrity, migration, proliferation, and expression of astrocytic markers. Results: CGF-CM treatment induced early enhancement of cell viability, followed by decreased proliferation and reduced migration at later time points. Morphological analyses revealed astrocyte-like features. The expression of glial fibrillary acidic protein (GFAP), an astrocytic marker, and its α/δ isoform ratio increased over time, while GBM -GBM-associated markers, such as AQP-4 and S100B, were downregulated. Conclusions: Our findings demonstrate that CGF-CM modulates the phenotypic plasticity of U87MG cells and promotes differentiation toward an astroglial-like profile. These results provide a basis for future studies on the modulation of GBM aggressiveness using bioactive autologous derivatives.

The application of scaffolding materials together with stem cell technologies plays a key role in tissue regeneration. Therefore, in this study, CGF (concentrated growth factor), which represents an autologous and biocompatible blood-derived product rich in growth factors and multipotent stem cells, was used together with a hydroxyapatite and silicon (HA-Si) scaffold, which represents a very interesting material in the field of bone reconstructive surgery. The aim of this work was to evaluate the potential osteogenic differentiation of CGF primary cells induced by HA-Si scaffolds. The cellular viability of CGF primary cells cultured on HA-Si scaffolds and their structural characterization were performed by MTT assay and SEM analysis, respectively. Moreover, the matrix mineralization of CGF primary cells on the HA-Si scaffold was evaluated through Alizarin red staining. The expression of osteogenic differentiation markers was investigated through mRNA quantification by real-time PCR. We found that the HA-Si scaffold was not cytotoxic for CGF primary cells, allowing their growth and proliferation. Furthermore, the HA-Si scaffold was able to induce increased levels of osteogenic markers, decreased levels of stemness markers in these cells, and the formation of a mineralized matrix. In conclusion, our results suggest that HA-Si scaffolds can be used as a biomaterial support for CGF application in the field of tissue regeneration.

This study aimed to evaluate the combined use of the Concentrated Growth Factor (CGF) and the liquid phase of CGF (LPCGF) on dental implant surfaces, using a medical device to determine the migration of growth factors, from the implant surface to the recipient. The implants were permeated by autologous growth factors, using a specific centrifuge device. CGF adhesion on the implant surface was evaluated through a scanning electron microscope analysis. To assess the release of the vascular endothelial growth factor (VEGF) from CGF, LPCGF, and CGF- or LPCGF-permeated implant, an ELISA assay was carried out. The results showed that the concentration of the growth factor VEGF was greater in CGF than in LPCGF. Our innovative technique allowed the incorporation of autologous growth factors on the surface of the dental implants. Moreover, we reported the release of VEGF, over time, by CGF- or LPCGF-permeated implant. On this basis, it was possible to obtain a biologically active implant surface, essential to create intercellular communication and neo-angiogenesis, to facilitate wound healing and tissue regeneration.

Cognitive studies on Parkinson’s disease (PD) reveal abnormal semantic processing. Most research, however, fails to indicate which conceptual properties are most affected and capture patients’ neurocognitive profiles. Here, we asked persons with PD, healthy controls, and individuals with behavioral variant frontotemporal dementia (bvFTD, as a disease control group) to read concepts (e.g., ‘sun’) and list their features (e.g., hot ). Responses were analyzed in terms of ten word properties (including concreteness, imageability, and semantic variability), used for group-level comparisons, subject-level classification, and brain-behavior correlations. PD (but not bvFTD) patients produced more concrete and imageable words than controls, both patterns being associated with overall cognitive status. PD and bvFTD patients showed reduced semantic variability, an anomaly which predicted semantic inhibition outcomes. Word-property patterns robustly classified PD (but not bvFTD) patients and correlated with disease-specific hypoconnectivity along the sensorimotor and salience networks. Fine-grained semantic assessments, then, can reveal distinct neurocognitive signatures of PD.

Hypopituitarism is a disorder caused by a reduced level of trophic hormones that may be consequent on different destructive processes. The clinical manifestations depend on the type of hormone involved. A deficiency of growth hormone (GH) in children causes the lack of growth known as pituitary dwarfism. The case is reported of a patient with pituitary dwarfism, multiple dental anomalies, functional prosthetic problems, and a revision of the literature. She was subjected to prosthetic rehabilitation without surgical intervention, using zirconium substructures, thus eliminating the potential complications that may require trauma surgery. The therapeutic approach adopted led to excellent results and restored an aesthetic smile.