Tag: pubmed

Acta Neuropathol Commun. 2025 Sep 24;13(1):200. doi: 10.1186/s40478-025-02104-x.

ABSTRACT

Pituitary neuroendocrine tumors (PitNETs) represent the most prevalent category of neuroendocrine neoplasms. Contemporary classification paradigms emphasize transcription factor immunohistochemistry (IHC) as a cornerstone for molecular subtyping and risk stratification. However, the clinical adoption of this approach is hindered by the lack of standardized interpretative thresholds for antibody staining and limited global availability of specialized reagents, particularly in resource-limited settings. To address these challenges, we developed a semi-automated computational framework that predicts PitNET lineages directly from hematoxylin and eosin (H&E)-stained histology slides. The pipeline employs a dynamic confidence threshold: samples below this threshold undergo confirmatory IHC staining and manual pathological review, while those surpassing it are classified automatically. In prospective validation, this approach achieved a 68.9% reduction in diagnostic workload while maintaining 95.9% overall accuracy. Similar efficacy was observed in functional (74.4% workload reduction, 99.0% accuracy) and external (39.3% reduction, 95.1% accuracy) cohorts. Statistical analysis confirmed non-inferiority between semi-automated predictions and fully manual IHC-based evaluations in all the cohorts. Furthermore, we implemented a deep learning-based virtual IHC staining module, generating synthetic transcription factor images demonstrating high morphological concordance with ground-truth IHC slides. Notably, our computational analysis revealed distinct histomorphological correlates of lineages: SF1-lineage tumors exhibited homogeneous cellular architecture characterized by densely packed, compact cells with reduced cytoplasmic volume, whereas PIT1-lineage neoplasms displayed larger cells with expanded intercellular spacing and disorganized spatial arrangements.

PMID:41063205 | DOI:10.1186/s40478-025-02104-x

Trials. 2025 Oct 8;26(1):392. doi: 10.1186/s13063-025-09128-7.

ABSTRACT

BACKGROUND: After a severe acquired brain injury (sABI), individuals might experience mild-to-severe cognitive impairments. In these patients, cognitive rehabilitation is provided as early as possible, to take advantage of and guide brain plasticity. According to a restorative approach, traditional cognitive training (TCT) usually involves repeated paper-and-pencil exercises of increasing difficulty and targeting specific cognitive domains. Recently, some evidence supported the use of virtual reality (VR) for ABI rehabilitation, particularly focusing on stroke cases. Nonetheless, there is limited evidence in sABI and in different etiologies. The present multicenter randomized controlled trial aims at exploring the effectiveness, in terms of clinical-functional, neurophysiological, and biomarker changes, of a cognitive rehabilitation focused on executive functions performed by a non-immersive VR device, compared to TCT, in a cohort of patients with sABI.

METHODS: According to an a priori power analysis, 28 adult patients with sABI will be enrolled by 5 Italian neurorehabilitation units within the Fit for Medical Robotics ( https://www.fit4medrob.it/ ) Consortium. Patients will be randomly assigned to receive either 30 min of VR or TCT sessions each day throughout the trial. At study entry (T0), patients will undergo clinical-functional evaluation, neurophysiological assessments, and serum blood sampling. Thereafter, VR or TCT will be provided daily, five times per week, for 5 weeks (25 total treatment sessions). Clinical-functional and neurophysiological assessments will be repeated at the end of the treatment (T1). A follow-up evaluation will be performed after 1 month from T1 (T2). Statistical analyses will be conducted blindly, according to the intention-to-treat principle.

DISCUSSION: VR has recently been gaining popularity as a cognitive rehabilitation tool. Notwithstanding substantial evidence supporting the use of VR for ABI rehabilitation, there is limited evidence in sABI from traumatic or anoxic etiology. This multicenter pragmatic trial will provide new insights into the effectiveness of VR in sABI cognitive rehabilitation.

TRIAL REGISTRATION: ClinicalTrials.gov NCT06474871. Registered on December 2, 2024.

PMID:41063192 | DOI:10.1186/s13063-025-09128-7

BMC Med. 2025 Oct 8;23(1):542. doi: 10.1186/s12916-025-04285-7.

ABSTRACT

BACKGROUND: A robust insomnia screening and measuring tool is essential for accurately assessing and diagnosing insomnia in research and clinical settings. The Sleep Condition Indicator (SCI) is an initial screening tool designed to assess insomnia complaints according to the DSM-5 criteria. This study aims to systematically evaluate item content, psychometric performance, diagnostic performance, and overall application of the SCI through a methodological quality assessment of original validation studies. These findings offer valuable information for optimizing insomnia diagnosis, assessment, and monitoring.

METHODS: A comprehensive search was conducted for finding studies published from 2012 to 2024, in PubMed, EMBASE, CINAHL, and MEDLINE electronic databases, and citation searching in PubMed, SCOPUS, Web of Science, and Google Scholar. Full-text articles focusing on the translation, validation, and application of the SCI were included. The psychometric studies were assessed regarding their measurement properties and methodological quality, using the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines. The diagnostic studies were assessed using the Standard for Reporting of Diagnostic Accuracy (STARD) guidelines. Finally, studies in which the SCI was used for assessment or screening purposes provided general information on the application of the scale.

RESULTS: We identified 285 studies with over 720,000 participants that used the SCI, and 13 language versions of the SCI were employed across at least 31 regions. The most commonly assessed measurement properties of the SCI within 19 studies were structural validity, internal consistency, criterion validity, and reliability, with findings supporting a stable two-factor structure and credible overall psychometric properties. The SCI demonstrated adequate sensitivity and specificity in 14 studies evaluating its diagnostic performance, and a cut-off value of 16 was recommended for screening insomnia. Finally, the studies showed that the SCI is widely used across clinical and non-clinical settings and provides valuable information for assessing insomnia risks.

CONCLUSIONS: The SCI includes items that align with the most current diagnostic criteria for insomnia disorder from the DSM-5. This tool demonstrates excellent psychometric performance and strong diagnostic performance. Overall, the SCI provides useful information for screening, diagnosing, and monitoring insomnia, making it a valuable tool in both research and clinical settings.

PMID:41063190 | DOI:10.1186/s12916-025-04285-7

BMC Health Serv Res. 2025 Oct 8;25(1):1324. doi: 10.1186/s12913-025-13119-4.

ABSTRACT

BACKGROUND: Efficiency in healthcare delivery is crucial for maximizing resource utilization and achieving broader health system objectives, particularly in resource-constrained settings like hard-to-reach (HTR) areas in Bangladesh. Despite people facing barriers in these areas in accessing health services, health facilities have not undergone comparative efficiency evaluations with facilities in non-hard-to-reach (non-HTR) areas.

OBJECTIVES: This study aims to comparatively assess the technical efficiency of secondary-level hospitals, as well as identify the factors contributing to inefficiencies in both HTR and non-HTR regions.

METHODS: Data for 62 secondary level hospitals (30 located in HTR and 32 located in non-HTR areas) for the year 2022 were collected from the Local Health Bulletin of the Directorate General of Health Services in Bangladesh. Geographic locations with specific characteristics, such as wetland, coastal, river island, and hilly areas, were categorized as HTR areas, using the Water and Sanitation Program report published by the World Bank in 2012. Initially, data envelopment analysis was employed with output orientation to calculate the efficiency scores using inputs (e.g., number of physicians, nurses and hospital beds) and health service outputs (e.g., maternal care, outpatient visits and inpatient admission) under both constant and variable returns to scale assumptions. Then multiple Tobit regression models were conducted to determine the associations between hospital inefficiency and HTR area characteristics.

RESULTS: The findings revealed that, on average, secondary level hospitals were 73% and 80% efficient in terms of constant returns to scale (CRS) and variable returns to scale (VRS) assumptions, respectively. Hospitals located in HTR regions were relatively less technically efficient (efficiency score = 0.76) than those in non-HTR areas (efficiency score = 0.84), and the differences were statistically significant (p-value < 0.10) when the VRS assumption was used. Similar significant differences in efficiency scores (efficiency score was 0.68 in HTR and 0.77 in non-HTR areas) were observed between these two groups of hospitals when CRS assumption was used (p-value < 0.10). According to multiple Tobit regression, the positive coefficient (Coeff = 0.258, p-value < 0.05) of the association between inefficiency scores and HTR areas implies that hospitals in HTR regions had lower efficiency scores than hospitals in non-HTR regions. Comparable patterns were identified in two additional models using characteristics of geographical locations of the hospitals. Findings revealed that hospitals located in wetland (Coeff = 0.295; p < 0.05), river island (Coeff = 0.372; p < 0.05), and hilly areas (Coeff = 0.422; p < 0.05) exhibited lower technical efficiency compared to those in plain land areas under the CRS assumption. Furthermore, the negative coefficients under CRS (Coeff = -0.004) and VRS (Coeff = -0.003) reflecting a negative association between the bed occupancy ratio (BOR) and inefficiency scores across all models. This suggests that higher BOR levels were associated with greater efficiency (p < 0.01). Results from sensitivity analysis observed no substantial differences in coefficients with the relationship between HTR characteristics and efficiency scores of the hospitals.

CONCLUSION: Hospitals in HTR regions are found to be less technically efficient than non-HTR regions in Bangladesh. Policymakers should address the underlying causes of inefficiencies, implement targeted interventions such as improving healthcare access for vulnerable populations residing in HTR regions, optimize bed occupancy rates and reduce length of stays with proper referral systems and discharge planning in these particular areas. This indicates that further research on health systems in climate-vulnerable areas in Bangladesh should be prioritized to effectively address these challenges.

PMID:41063170 | DOI:10.1186/s12913-025-13119-4

BMC Oral Health. 2025 Oct 8;25(1):1566. doi: 10.1186/s12903-025-06874-7.

ABSTRACT

BACKGROUND: Effective removal of intracanal drugs such as calcium hydroxide is important for the success of endodontic treatment and to prevent possible complications. This study aimed to evaluate the effect of modified salt solution (RISA), a new irrigation solution, in removing calcium hydroxide from artificial grooves in straight root canals.

METHODS: The root canals of 60 human maxillary central incisors were prepared using Reciproc R40 files (VDW) up to size 40/0.04, rinsed with 2 mL of 2.5% NaOCl after each pecking motion, and then the teeth were split longitudinally. A lateral groove in the apical part was prepared in each root half and filled with calcium hydroxide, the root halves were reassembled and samples were placed in Eppendorf tubes. Three groups were established according to the irrigation solutions: group 1, Etilendiamintetraacetic acid (EDTA); group 2, RISA; group 3, Citric acid. After the root canals were irrigated with the irrigation solutions in each group, irrigation activation was performed with the Ultra X ultrasonic device (1 min). The samples were removed from the Eppendorf tubes, divided into two halves, and examined under a 24× magnification microscope by the two evaluators. Statistical analysis was performed using IBM SPSS version 23. Fisher Freeman Halton Test was used to analyze categorical data (p < 0.05).

RESULTS: There is no statistically significant difference between the score values ​​of the groups (p = 0.632).

CONCLUSIONS: RISA solution was similarly effective in removing calcium hydroxide compared to EDTA and citric acid solutions in the apical region.

PMID:41063168 | DOI:10.1186/s12903-025-06874-7

Mol Cancer. 2025 Oct 8;24(1):248. doi: 10.1186/s12943-025-02409-4.

NO ABSTRACT

PMID:41063167 | DOI:10.1186/s12943-025-02409-4

JMIR Hum Factors. 2025 Oct 8;12:e67081. doi: 10.2196/67081.

ABSTRACT

BACKGROUND: The collection of dietary behavior data is crucial in childbearing populations. In addition to observed inequities in perinatal dietary intake and quality, burdensome assessment methods (eg, 24-h dietary recall) may limit research participation for some groups. Ecological momentary assessment (EMA) is associated with reduced recall bias and participant convenience, but there is a dearth of studies with diverse cohorts.

OBJECTIVE: Our aim is to describe participant completion of food intake items in EMA surveys, overall and across individual characteristics (eg, prepregnancy BMI).

METHODS: Using secondary EMA data from participants in a longitudinal study, we report average completion rates of survey items regarding dietary behavior (eg, number of meals eaten in a day) across individual demographic variables (eg, age) and combined strata (eg, race+age) during late pregnancy and throughout 12 months post partum.

RESULTS: In our analytic sample (N=310), the average completion rate was 52.4% (SD 27.8%) during pregnancy, rising to 59.1% (SD 22.0%) after giving birth. Participants who were older (>30 y), overweight before pregnancy, self-identified as White, working, or earning higher annual income (>US $50,000) had higher average completion rates than their counterparts. Examining combined strata, we found some variation in survey completion within racial groups. Black participants using a study phone had higher average completion rates during pregnancy and post partum, but this relationship was reversed for White participants.

CONCLUSIONS: Our secondary analysis showed relatively stable engagement with EMA surveys in a childbearing cohort across 15 months. Increased completion rates among privileged groups (eg, White, higher income) may demonstrate the impact of socioeconomic advantages on individual health behaviors. Investigators should consider how intersections between race and other factors (eg, employment) may impact participation and data collection.

PMID:41061268 | DOI:10.2196/67081

J Speech Lang Hear Res. 2025 Oct 8:1-12. doi: 10.1044/2025_JSLHR-24-00677. Online ahead of print.

ABSTRACT

PURPOSE: The aim of this study was to investigate the acoustic patterns of six emotions and a neutral state in Cantonese speech by focusing on the prosodic modulations that convey emotional content in this tonal language, which has six lexical tones.

METHOD: We employed the extended Geneva minimalistic acoustic parameter set to systematically analyze the acoustic features of 3,474 recordings from the Cantonese Audio-Visual Emotional Speech Database. Linear mixed-effects models were fitted to examine variations in acoustic parameters across emotional states. Decision tree models were used to assess the relative contributions of 22 acoustic parameters in classifying emotions.

RESULTS: By fitting linear mixed-effects models, our results revealed statistically significant variations in most of the acoustic parameters across diverse emotional states. The decision tree models showed the relative contributions of 22 acoustic parameters in the classification of emotions, with spectral parameters accounting for 65.45% of the significance in distinguishing all seven emotional states, significantly exceeding other groups of features.

CONCLUSIONS: Our findings highlight the unique characteristics of emotional expression in Cantonese, in which spectral parameters play a more significant role compared to the frequency-related parameters that are often emphasized in nontonal languages. Our results contribute significantly to understanding vocal emotion expression in tonal languages and are particularly useful for designing emotion-recognition systems and hearing aids that are tailored to tonal language environments. Furthermore, these insights have potential implications for enhancing emotional communication and cognitive training interventions for Cantonese-speaking individuals who use hearing aids or have cochlear implants, are on the autism spectrum, or have Alzheimer’s disease.

PMID:41061266 | DOI:10.1044/2025_JSLHR-24-00677

JMIR Res Protoc. 2025 Oct 8;14:e76536. doi: 10.2196/76536.

ABSTRACT

BACKGROUND: Menopause symptoms are common but often inadequately addressed by primary care clinicians due to limited time for discussions and resources. Mobile health apps can play a crucial role in symptom identification and management; yet, many existing menopause-focused apps lack evidence-based content and medical expertise.

OBJECTIVE: The aim of this study is to describe the protocol study design and methodology of a randomized controlled trial to evaluate the effectiveness of the emmii mobile app for improving menopause-related knowledge and shared decision-making compared to a traditional menopause education pamphlet.

METHODS: This randomized controlled trial will recruit women aged 45-55 years with upcoming primary care appointments at Mayo Clinic within 3 weeks of the date of initial outreach. Eligible patients must be English-speaking, able to provide informed consent, and report a Menopause Rating Scale score ≥5, which indicates that they are experiencing significant menopause-related symptoms. Patients will be randomized to have access to either the emmii app (intervention, n=200) or an evidence-based menopause education pamphlet (control, n=200). The emmii app is developed with direct input from primary care clinicians certified by The Menopause Society and offers symptom tracking, personalized treatment recommendations based on a protocol, and a discussion guide to support communication between patients and their primary care clinicians. Outcomes will include a postappointment survey sent to the patients and their primary care clinicians within 1-3 weeks of the appointment, and assessment of patient knowledge, clinical treatment plans, and both the patient and clinician experience. The study will also compare prescribing rates of hormonal and nonhormonal therapies for menopause symptoms between the emmii intervention and control groups to assess for influence on treatment patterns. Data will be analyzed using descriptive statistics, including chi-square tests, Wilcoxon rank sum tests, and multivariable modeling.

RESULTS: Data collection is scheduled to begin in April 2025.

CONCLUSIONS: This protocol outlines the design and methodology of a randomized controlled trial that aims to assess the impact of the emmii app in facilitating menopause care through primary care clinician-patient communication and shared decision-making.

TRIAL REGISTRATION: ClinicalTrials.gov NCT06919887; https://clinicaltrials.gov/ct2/show/NCT06919887.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/76536.

PMID:41061260 | DOI:10.2196/76536

J Med Internet Res. 2025 Oct 8;27:e71034. doi: 10.2196/71034.

ABSTRACT

BACKGROUND: With 28%-35% of individuals aged 65 years and older experiencing incidents of falling, falls are the second leading cause of unintentional injury-related deaths globally. Limited availability of clinical staff often impedes the timely detection and prevention of potential falls. Advances in artificial intelligence (AI) could complement existing fall risk assessment and help better allocate nursing care resources. Yet, many studies are based on small datasets from a single institution, which can restrict the generalizability of the model, and do not investigate important aspects in AI model development, such as fairness across demographic groups.

OBJECTIVE: This study aimed to provide a comprehensive empirical evaluation of the potential of AI in nursing care, focusing on the case of fall risk prediction. To account for demographic and contextual differences in fall incidences, we analyze data from a university and a geriatric hospital in Germany. To the best of our knowledge, these are the largest fall risk prediction datasets to date with heterogeneous data distributions. We focus on 3 key objectives. First, does AI help in improving fall risk prediction? Second, how can AI models be trained safely across different hospitals? Finally, are these models fair?

METHODS: This study used 2 datasets for fall risk prediction: one from a university hospital with 931,726 participants, 10,442 of whom experienced falls, and another from a geriatric hospital with 12,773 participants, 1728 of whom have fallen. State-of-the-art AI models were trained with 3 approaches, including 2 decentralized learning paradigms. First, separate models were trained on data from each hospital; second, models were retrained on the respective other dataset; and federated learning (FL) was applied to both datasets. The performance of these models was compared with the rule-based systems as implemented in clinical practice for fall risk prediction. Additional analyses were conducted to test for model fairness.

RESULTS: Our findings demonstrate that AI models consistently outperform rule-based systems across all experimental setups, with the area under the receiver operating characteristic curve of 0.735 (90% CI 0.727-0.744) for the geriatric hospital, and 0.926 (90% CI 0.924-0.928) for the university hospital. FL did not improve the fall risk prediction in this setting. Our fairness analysis ruled out disparities in model performance between different sex groups, but we found fairness infringements across age groups.

CONCLUSIONS: This study demonstrates that AI models consistently outperform traditional rule-based systems across heterogeneous datasets in predicting fall risk. However, it also reveals the challenges related to demographic shifts and label distribution imbalances, which limited the FL models’ ability to generalize. While the fairness analysis indicated fair results across sex subgroups, age-related disparities emerged. Addressing data imbalances and ensuring broader representation across demographic groups will be crucial for developing more fair and generalizable models.

PMID:41061259 | DOI:10.2196/71034