ACS Biomater Sci Eng. 2025 Jan 15. doi: 10.1021/acsbiomaterials.4c01503. Online ahead of print.

ABSTRACT

Mechanical properties of engineered connective tissues are critical for their success, yet modern sensors that measure physical qualities of tissues for quality control are invasive and destructive. The goal of this work was to develop a noncontact, nondestructive method to measure mechanical attributes of engineered skin substitutes during production without disturbing the sterile culture packaging. We optimized a digital holographic vibrometry (DHV) system to measure the mechanical behavior of Apligraf living cellular skin substitute through the clear packaging in multiple conditions: resting on solid agar as when the tissue is shipped, on liquid media in which it is grown, and freely suspended in air as occurs when the media is removed for feeding. We utilized full-field measurement to assess the complete surface deformation pattern to compare with vibration theory and found the patterns observed in air showed the closest behavior to theory. To simulate the effects of the actual culture dish geometry and the trilayer composition of the tissue on the porous membrane support, we employed finite element (FE) analysis. To simulate changes in thickness and stiffness that may occur with manufacturing process variations, we dried samples over time and observed measurable increases in the fundamental mode frequency which could be predicted by altering the thickness of the tissue layers in the FE model. However, quantitative estimates of the engineered tissue stiffness based on vibration theory are unrealistically high due to the signal being dominated by the stiff underlying membrane on which the tissue is cultured. Thus, although DHV is not able to specifically quantify the thickness or modulus or identify small spot defects, it has the potential to be used assess the overall properties of a tissue in-line and noninvasively for quality control.

PMID:39813060 | DOI:10.1021/acsbiomaterials.4c01503

J Speech Lang Hear Res. 2025 Jan 15:1-31. doi: 10.1044/2024_JSLHR-23-00740. Online ahead of print.

ABSTRACT

PURPOSE: In this review article, we present an extensive overview of recent developments in the area of dysarthric speech research. One of the key objectives of speech technology research is to improve the quality of life of its users, as evidenced by the focus of current research trends on creating inclusive conversational interfaces that cater to pathological speech, out of which dysarthric speech is an important example. Applications of speech technology research for dysarthric speech demand a clear understanding of the acoustics of dysarthric speech as well as of speech technologies, including machine learning and deep neural networks for speech processing.

METHOD: We review studies pertaining to speech technology and dysarthric speech. Specifically, we discuss dysarthric speech corpora, acoustic analysis, intelligibility assessment, and automatic speech recognition. We also delve into deep learning approaches for automatic assessment and recognition of dysarthric speech. Ethics committee or institutional review board did not apply to this study.

CONCLUSIONS: Overcoming the challenge of limited data and exploring new avenues in data collection, artificial intelligence-powered analysis and teletherapy hold immense potential for significant advancements in dysarthria research. To make longer and faster strides, researchers typically rely on existing research and data on a global scale. Therefore, it is imperative to consolidate the existing research and present it in a form that can serve as a basis for future work. In this review article, we have reviewed the contributions of speech technologists to the area of dysarthric speech with a focus on acoustic analysis, speech features, and techniques used. By focusing on the existing research and future directions, researchers can develop more effective tools and interventions to improve communication, quality of life, and overall well-being for people with dysarthria.

PMID:39813019 | DOI:10.1044/2024_JSLHR-23-00740

Clin Exp Nephrol. 2025 Jan 15. doi: 10.1007/s10157-024-02616-1. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinical practice. We aimed to investigate whether deep learning techniques, especially those suitable for processing missing values, can improve the accuracy of predicting future renal function compared to traditional statistical method, using the Japan Chronic Kidney Disease Database (J-CKD-DB), a nationwide multicenter CKD registry.

METHODS: From the J-CKD-DB-Ex, a prospective longitudinal study within the J-CKD-DB, we selected individuals who had at least two eGFR measurements recorded between 12 and 20 months apart (n = 22,929 CKD patients). We used the multiple linear regression model as a conventional statistical method, and the Feed Forward Neural Network (FFNN) and Gated Recurrent Unit (GRU)-D (decay) models as deep learning techniques. We compared the prediction accuracies of each model for future eGFR based on the existing data using the root mean square error (RMSE).

RESULTS: The RMSE values were 7.5 for multiple regression analysis, 7.9 for FFNN model, and 7.6 mL/min/1.73 m2 for GRU-D model. In the subgroup analysis according to CKD stages, lower RMSE values were observed in higher stages for all models.

CONCLUSION: Our result demonstrate the predictive accuracy of future eGFR based on the existing dataset in the J-CKD-DB-Ex. The accuracy was not improved by applying deep learning techniques compared to conventional statistical methods.

PMID:39813007 | DOI:10.1007/s10157-024-02616-1

Neuroradiology. 2025 Jan 15. doi: 10.1007/s00234-024-03536-3. Online ahead of print.

ABSTRACT

PURPOSE: We aimed to validate a clinically available artificial intelligence (AI) model to assist general radiologists in the detection of intracranial aneurysm (IA) in a multi-reader multi-case (MRMC) study, and to explore its performance in routine clinical settings.

METHODS: Two distinct cohorts of head CT angiography (CTA) data were assembled to validate an AI model. Cohort 1, comprising gold-standard consecutive CTA cases, was used in an MRMC study involving six board-certified general radiologists. Cohort 2, representing clinical CTA cases, was used to simulate a routine clinical setting. Following these evaluations, an algorithmic audit was conducted to identify any unusual or unexpected behaviors exhibited by the model.

RESULTS: Cohort 1 consisted of 131 CTA cases, while Cohort 2 included 515 CTA cases. In the MRMC study, the AI-assisted strategy demonstrated a significant improvement in aneurysm diagnostic performance, with the area under the receiver operating characteristic curve increasing from 0.815 (95%CI: 0.754-0.875) to 0.875 (95%CI: 0.831-0.921; p = 0.008). In the AI-based first-reader study, 60.4% of the CTA cases were identified as negative by the AI, with a high negative predictive value of 0.994 (95%CI: 0.977-0.999). The algorithmic audit highlighted two issues for improvement: the accurate detection of tiny aneurysms and the effective exclusion of false-positive lesions.

CONCLUSION: This study highlights the clinical utility of a high-performance AI model in detecting IAs, significantly improving general radiologists' diagnostic performance with the potential to reduce their workload in routine clinical practice. The algorithmic audit offers insights to guide the development and validation of future AI models.

PMID:39812775 | DOI:10.1007/s00234-024-03536-3

Neuroinformatics. 2025 Jan 15;23(1):8. doi: 10.1007/s12021-024-09697-z.

ABSTRACT

Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular system. ICAS causes a narrowing of the arterial lumen, thereby restricting blood flow, while aneurysms involve the ballooning of blood vessels. Both conditions can lead to severe outcomes, such as stroke or vessel rupture, which can be fatal. Early detection is crucial for effective intervention. In this study, we introduced a method that combines classical computer vision techniques with deep learning to detect intracranial aneurysms and ICAS in time-of-flight magnetic resonance angiography images. The process began with skull-stripping, followed by an affine transformation to align the images to a common atlas space. We then focused on the region of interest, including the circle of Willis, by cropping the relevant area. A segmentation algorithm was used to isolate the arteries, after which a patch-wise residual neural network was applied across the image. A voting mechanism was then employed to identify the presence of atrophies. Our method achieved accuracies of 76.5% for aneurysms and 82.4% for ICAS. Notably, when occlusions were not considered, the accuracy for ICAS detection improved to 85.7%. While the algorithm performed well for localized pathological findings, it was less effective at detecting occlusions, which involved long-range dependencies in the MRIs. This limitation was due to the architectural design of the patch-wise deep learning approach. Regardless, this can, in the future, be mitigated in a multi-scale patch-wise algorithm.

PMID:39812766 | DOI:10.1007/s12021-024-09697-z

Insights Imaging. 2025 Jan 15;16(1):20. doi: 10.1186/s13244-024-01865-8.

ABSTRACT

OBJECTIVE: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).

MATERIALS AND METHODS: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development. The study evaluated five prediction methods, including (1) A clinical-imaging model of clinical features and image features of suspected PCa lesions selected by AI algorithm, (2) the PIRADS category, (3) a conventional radiomics model, (4) a deep-learning bases radiomics model, and (5) biopsy pathology.

RESULTS: In the externally validated dataset, the deep learning-based radiomics model showed the highest area under the curve (AUC 0.700 to 0.791). It exceeded the clinical-imaging model (AUC 0.597 to 0.718), conventional radiomic model (AUC 0.566 to 0.632), PIRADS score (AUC 0.554 to 0.613), and biopsy pathology (AUC 0.537 to 0.578). The AUC predicted by the model did not show a statistically significant difference among the three externally verified hospitals (p > 0.05).

CONCLUSION: Deep-learning radiomics models utilizing AI-extracted image features from bpMRI images can potentially be used to predict PCa aggressiveness, demonstrating a generalized ability for external validation.

CRITICAL RELEVANCE STATEMENT: Predicting the aggressiveness of prostate cancer (PCa) is important for formulating the best treatment plan for patients. The radiomic model based on deep learning is expected to provide an objective and non-invasive method for evaluating the aggressiveness of PCa.

KEY POINTS: Predicting the aggressiveness of PCa is important for patients to obtain the best treatment options. The deep learning-based radiomics model can predict the aggressiveness of PCa with high accuracy. The model has good universality when tested on multiple external datasets.

PMID:39812752 | DOI:10.1186/s13244-024-01865-8

Neuroinformatics. 2025 Jan 15;23(1):7. doi: 10.1007/s12021-024-09712-3.

ABSTRACT

This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into the journal's evolution at the intersection of neuroscience and computational science. Using advanced tools such as VOS viewer and methodologies like co-citation analysis, bibliographic coupling, and keyword co-occurrence, we examine trends in publication, citation patterns, and the journal's influence. Our analysis reveals enduring research themes like neuroimaging, data sharing, machine learning, and functional connectivity, which form the core of Neuroinformatics. These themes highlight the journal's role in addressing key challenges in neuroscience through computational methods. Emerging topics like deep learning, neuron reconstruction, and reproducibility further showcase the journal's responsiveness to technological advances. We also track the journal's rising impact, marked by a substantial growth in publications and citations, especially over the last decade. This growth underscores the relevance of computational approaches in neuroscience and the high-quality research the journal attracts. Key bibliometric indicators, such as publication counts, citation analysis, and the h-index, spotlight contributions from leading authors, papers, and institutions worldwide, particularly from the USA, China, and Europe. These metrics provide a clear view of the scientific landscape and collaboration patterns driving progress. This analysis not only celebrates Neuroinformatics's rich history but also offers strategic insights for future research, ensuring the journal remains a leader in innovation and advances both neuroscience and computational science.

PMID:39812741 | DOI:10.1007/s12021-024-09712-3

Eur Radiol Exp. 2025 Jan 15;9(1):8. doi: 10.1186/s41747-024-00535-0.

ABSTRACT

BACKGROUND: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), including PH-1 (hematoma within infarcted tissue, occupying < 30%) and PH-2 (hematoma occupying ≥ 30% of the infarcted tissue)-in AIS patients following intravenous thrombolysis (IVT) based on noncontrast computed tomography (NCCT) and clinical data.

METHODS: In this six-center retrospective study, clinical and imaging data from 445 consecutive IVT-treated AIS patients were collected (01/2018-06/2023). The training cohort comprised 344 patients from five centers, and the test cohort included 101 patients from the sixth center. A clinical model was developed using eXtreme Gradient Boosting, an NCCT-based imaging model was created using deep learning, and an ensemble model integrated both models. Comparison with existing clinical scores (MSS, SEDAN, GRASPS) was performed using the DeLong test.

RESULTS: Of the 445 individuals, 202 (45.4%) had HT, 79 (17.8%) had hemorrhagic infarction, and 123 (27.6%) had PH. In the test cohort, the area under the receiver operating characteristic curve (AUROC) of the clinical, imaging, and ensemble model for HT prediction was 0.877, 0.920, and 0.937, respectively. The ensemble model for HT prediction outperformed MSS, SEDAN, and GRASPS scores (p ≤ 0.023). The ensemble model predicted PH and PH-2 with AUROC of 0.858 and 0.806, respectively.

CONCLUSION: Developing and validating an integrated model that can predict HT and its subtypes in AIS patients following IVT based on NCCT and clinical data is feasible.

RELEVANCE STATEMENT: The clinical, imaging, and ensemble models based on noncontrast CT and clinical data outperformed existing clinical scores in predicting hemorrhagic transformation of AIS and its subtypes with poor prognosis, facilitating personalized treatment decisions.

KEY POINTS: The models demonstrated the capability to predict hemorrhagic transformation of acute ischemic stroke quickly, accurately, and reliably. The proposed models outperformed existing clinical scores in predicting hemorrhagic transformation. The ensemble model provided risk assessment of parenchymal hemorrhage and parenchymal hemorrhage-2 outperforming existing clinical scores.

PMID:39812734 | DOI:10.1186/s41747-024-00535-0

Osteoporos Int. 2025 Jan 15. doi: 10.1007/s00198-024-07378-w. Online ahead of print.

ABSTRACT

This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) images from Huashan Hospital Medical Checkup Center. Results showed high accuracy and strong correlation with quantitative computed tomography (QCT) results. The proposed models offer potential for screening patients at a high risk of osteoporosis and reducing unnecessary radiation and costs.

PURPOSE: To explore the feasibility of using a hybrid deep learning framework (HDLF) to establish a model for BMD prediction and classification based on BPX images. This study aimed to establish an automated tool for screening patients at a high risk of osteoporosis.

METHODS: A total of 906 BPX scans from 453 subjects were included in this study, with QCT results serving as the reference standard. The training-validation set:independent test set ratio was 4:1. The L1-L3 vertebral bodies were manually annotated by experienced radiologists, and the HDLF was established to predict BMD and diagnose abnormality based on BPX images and clinical information. The performance metrics of the models were calculated and evaluated.

RESULTS: The R 2 values of the BMD prediction regression model in the independent test set based on BPX images and multimodal data (BPX images and clinical information) were 0.77 and 0.79, respectively. The Pearson correlation coefficients were 0.88 and 0.89, respectively, with P-values < 0.001. Bland-Altman analysis revealed no significant difference between the predictions of the models and QCT results. The classification model achieved the highest AUC of 0.97 based on multimodal data in the independent test set, with an accuracy of 0.93, sensitivity of 0.84, specificity of 0.96, and F1 score of 0.93.

CONCLUSION: This study demonstrates that deep learning neural networks applied to BPX images can accurately predict BMD and perform classification diagnoses, which can reduce the radiation risk, economic consumption, and time consumption associated with specialized BMD measurement.

PMID:39812675 | DOI:10.1007/s00198-024-07378-w

Elife. 2025 Jan 15;13:RP95842. doi: 10.7554/eLife.95842.

ABSTRACT

Collagen-I fibrillogenesis is crucial to health and development, where dysregulation is a hallmark of fibroproliferative diseases. Here, we show that collagen-I fibril assembly required a functional endocytic system that recycles collagen-I to assemble new fibrils. Endogenous collagen production was not required for fibrillogenesis if exogenous collagen was available, but the circadian-regulated vacuolar protein sorting (VPS) 33b and collagen-binding integrin α11 subunit were crucial to fibrillogenesis. Cells lacking VPS33B secrete soluble collagen-I protomers but were deficient in fibril formation, thus secretion and assembly are separately controlled. Overexpression of VPS33B led to loss of fibril rhythmicity and overabundance of fibrils, which was mediated through integrin α11β1. Endocytic recycling of collagen-I was enhanced in human fibroblasts isolated from idiopathic pulmonary fibrosis, where VPS33B and integrin α11 subunit were overexpressed at the fibrogenic front; this correlation between VPS33B, integrin α11 subunit, and abnormal collagen deposition was also observed in samples from patients with chronic skin wounds. In conclusion, our study showed that circadian-regulated endocytic recycling is central to homeostatic assembly of collagen fibrils and is disrupted in diseases.

PMID:39812558 | DOI:10.7554/eLife.95842

Methods Mol Biol. 2025;2891:1-14. doi: 10.1007/978-1-0716-4334-1_1.

ABSTRACT

Metabolic profiling continues to develop, and research is now conducted on this topic globally in hundreds of laboratories, from small groups up to national centers and core facilities. Here we briefly provide a perspective on the current status and challenges facing metabolic phenotyping (metabonomics/metabolomics) and consider future directions for this important area of biomarker and systems biology research.

PMID:39812974 | DOI:10.1007/978-1-0716-4334-1_1

Am J Primatol. 2025 Jan;87(1):e23723. doi: 10.1002/ajp.23723.

ABSTRACT

It is under debate whether intersubjectivity-the capacity to experience a sense of togetherness around an action-is unique to humans. In humans, heavy tickling-a repeated body probing play that causes an automatic response including uncontrollable laughter (gargalesis)-has been linked to the emergence of intersubjectivity as it is aimed at making others laugh (self-generated responses are inhibited), it is often asymmetrical (older to younger subjects), and it elicits agent-dependent responses (pleasant/unpleasant depending on social bond). Intraspecific tickling and the related gargalesis response have been reported in humans, chimpanzees, and anecdotally in other great apes, potentially setting the line between hominids and other anthropoids. Here we investigated this phenomenon in bonobos and predicted that in this species (sharing with humans and chimpanzees the last common ancestor) the presence of tickling would be modulated depending on the players' age, play session initiators, and familiarity. In April-June 2018, we collected videos on play sessions-including tickling-on a bonobo group housed at La Vallée des Singes (France). We showed that tickling received decreased while tickling performed increased with age, with tickling being mostly directed from older to younger individuals. Moreover, tickling was mostly performed by the individuals that started the play interaction and most of it occurred in strongly bonded dyads, particularly mother-infant ones. Bonobo tickling features, especially age profile and social modulation, mirror those of heavy tickling in humans thus suggesting a common evolutionary origin and shared patterns of basic intersubjectivity in hominins.

PMID:39812349 | DOI:10.1002/ajp.23723

J Med Virol. 2025 Jan;97(1):e70130. doi: 10.1002/jmv.70130.

ABSTRACT

We investigated whether antibody concentrations measured in plasma using the Roche Elecsys® Anti-SARS-CoV-2 S assay (targeting the receptor binding domain, RBD) could estimate levels of Wuhan-Hu-1 and Omicron XBB.1.5 spike-directed antibodies with neutralizing ability (NtAb) or those mediating NK-cell activity. We analyzed 135 plasma samples from 39 vaccinated elderly nursing home residents. A strong correlation was found for NtAb against both Wuhan-Hu-1 (Rho = 0.73, p < 0.001) and Omicron XBB.1.5 (sub)variants (Rho = 0.73, p < 0.001). Moderate positive correlations were observed for NK-cell activity, based on lysosome-associated membrane protein 1 (LAMP1)-producing NK cells stimulated with Wuhan-Hu-1 (Rho = 0.43, p < 0.001) and Omicron XBB.1.5 spike proteins (Rho = 0.50, p < 0.001). Similarly, interferon-gamma (IFN-γ)-producing NK-cell frequencies showed moderate correlations (Wuhan-Hu-1: Rho = 0.43, p < 0.001; Omicron XBB.1.5: Rho = 0.50, p < 0.001). Random Forest models accurately predicted NtAb levels against Wuhan-Hu-1 (R2 = 0.72), though models for Omicron XBB.1.5 were less robust. Anti-RBD antibody concentrations of 4.73 and 5.02 log10 BAU/mL predicted high NtAb levels for Wuhan-Hu-1 and Omicron XBB.1.5, respectively. Antibody thresholds for predicting functional NK cell-mediated responses were 4.73 log10 and 4.54 log10 BAU/mL for Wuhan-Hu-1 and Omicron XBB.1.5, respectively. For LAMP1-producing NK cells, the thresholds were 4.94 and 4.75 log10 BAU/mL for Wuhan-Hu-1 and Omicron XBB.1.5, respectively. In summary, total anti-RBD antibody levels measured by the Roche assay may allow inference of NtAb levels and, to a lesser extent, Fc-mediated NK-cell responses against Omicron XBB.1.5.

PMID:39812228 | DOI:10.1002/jmv.70130

Dermatol Ther (Heidelb). 2025 Jan 15. doi: 10.1007/s13555-024-01334-6. Online ahead of print.

ABSTRACT

INTRODUCTION: Results from randomized controlled trials of upadacitinib, a Janus kinase (JAK) inhibitor, have led to its approval for the treatment of moderate-to-severe atopic dermatitis (AD) in patients aged ≥ 12 years. The aim of this study was to report the effectiveness and safety of upadacitinib in real-world settings over a period of 96 weeks.

METHODS: This retrospective study included all patients treated with upadacitinib at our centre between April 2022 and September 2024. Clinical and patient-reported outcomes were recorded and assessed at each follow-up visit and included the eczema area severity index (EASI), investigator global assessment (IGA), scoring atopic dermatitis (SCORAD), dermatology life quality index (DLQI) and the worst pruritus numerical scale score (WP-NRS). All drug-related adverse events (AEs) were documented.

RESULTS: In total, 36 patients (44.4% female) were retrospectively included. After 4 weeks of treatment, the mean EASI was reduced from 29.97 to 3.72 with 83.3/52.8/19.4% achieving EASI75/90/100 respectively. Similar reductions were observed in the DLQI, which was reduced from 20.78 to 2.92, and in the WP-NRS, from 7.78 to 1.31. Further improvements were observed at week 16, with a mean EASI of 0.75 and 96.4% of the patients achieving EASI75 and EASI90. At week 48 of treatment, EASI75/90/100 were achieved by 100/93.8/81.3% along with a mean DLQI and pruritus NRS of 0.81. All nine patients that reached the 72- and 96-week timepoints had clear skin with no pruritus. Six (16.7%) patients experienced AEs with four of them discontinuing medication; no patient discontinued because of upadacitinib inefficacy.

CONCLUSION: This long-term real-world study of patients with moderate-to-severe AD receiving upadacitinib demonstrated that treatment success (EASI75/90/100) can be achieved in a high proportion of patients by week 16 and can be maintained for up to 96 weeks along with substantial improvements in pruritus and quality of life.

PMID:39812942 | DOI:10.1007/s13555-024-01334-6

Curr Pharm Des. 2025 Jan 13. doi: 10.2174/0113816128346655241112104045. Online ahead of print.

ABSTRACT

AIMS: This study aims to identify and evaluate promising therapeutic proteins and compounds for breast cancer treatment through a comprehensive database search and molecular docking analysis.

BACKGROUND: Breast cancer (BC), primarily originating from the terminal ductal-lobular unit of the breast, is the most prevalent form of cancer globally. In 2020, an estimated 2.3 million new cases were reported, resulting in approximately 685,000 deaths. Mutations in the BRCA1 and BRCA2 genes are well-established in hereditary breast cancer. The identification of effective therapeutic proteins for BC remains a complex and evolving area of research.

OBJECTIVE: This study aims to identify and evaluate promising therapeutic proteins and compounds specific to breast cancer through a comprehensive database search and molecular docking analysis.

METHODS: A rigorous search was conducted within the National Cancer Institute (NCI), NCI Metathesaurus, SIGnaling Network Open Resource (SIGNOR), Human Protein Atlas (HPA), and the Human Phenotype Ontology (HPO) to shortlist proteins linked to BC (CUI C0678222). Recent studies were reviewed to understand the administration of CDK4/6 inhibitors (palbociclib, ribociclib, abemaciclib) combined with endocrine therapy for HR-positive and HER2-negative breast cancer. Anticancer compound libraries available at ZINC and PubChem were analyzed. Compounds were evaluated based on their binding energies with CDK4 protein, a rationally selected druggable target.

RESULTS: Key proteins linked to breast cancer were identified through database searches. Proliferation, apoptosis, and G1/S transition pathways were frequently found dysregulated in breast cancer. ZINC13152284 exhibited the strongest binding energy at -10.9 Kcal/mol, followed by ZINC05492794 with a binding energy of -10.4 Kcal/mol. Preexisting drugs showed lower binding energies with the CDK4 protein.

CONCLUSION: The study highlights the importance of drug repurposing as a strategy for the safe and effective treatment of breast cancer. Synthetic inhibitors often cause severe side effects, emphasizing the need for novel targets and compounds with better therapeutic profiles. Molecular docking identified promising compounds from the ZINC database, suggesting potential new avenues for breast cancer therapy.

PMID:39812054 | DOI:10.2174/0113816128346655241112104045

Curr Drug Discov Technol. 2024 Dec 31. doi: 10.2174/0115701638329824241220055621. Online ahead of print.

ABSTRACT

Repurposing of drugs through nanocarriers (NCs) based platforms has been a recent trend in drug delivery research. Various routine drugs are now being repurposed to treat challenging neurodegenerative disorders including Alzheimer disease (AD). AD, at present is one of the challenging neurodegenerative disorders characterized by extracellular accumulation of amyloid-β and intracellular accumulations of neurofibrillary tangles. In spite of catchy progress in drug development, effective treatment outcome in AD patients is far-fetched dream. Out of several proposed hypothesis in the development and progression of AD, potential role of microorganisms causing dementia and AD cannot be ruled out. Several recent researches have been documented a clear correlation in between microbial infection and neuronal damage leading to progression of AD. Thus, antimicrobial drugs repurposing has been emerged as alternate, potential, cost-effective strategy to check progression of AD. Further, for efficient delivery of antimicrobial drugs to brain tissue, novel NCs based platforms are the preferred option to bypass blood-brain barrier. Several polymeric and lipid NCs have been extensively studied over the past years to improve antimicrobial drug delivery to brain. The present review encompasses various repurposing strategy of antimicrobial drugs delivered through various NCs to target AD. Evidence-based research outcome compiled from authentic database like Scopus, PubMed, Web of science have been pooled to provide an updated review. Side by side some light has been thrown on the practical problems faced by nanodrug carriers during technology transfer.

PMID:39810446 | DOI:10.2174/0115701638329824241220055621

MethodsX. 2024 Dec 16;14:103092. doi: 10.1016/j.mex.2024.103092. eCollection 2025 Jun.

ABSTRACT

This study explores the possibility of integrating and retrieving heterogenous data across platforms by using ontology graph databases to enhance educational insights and enabling advanced data-driven decision-making. Motivated by some of the well-known universities and other Higher Education Institutions ontology, this study improvises the existing entities and introduces new entities in order to tackle a new topic identified from the preliminary interview conducted in the study to cover the study objective. The paper also proposes an innovative ontology, referred to as Student Performance and Course, to enhance resource management and evaluation mechanisms on course, students, and MOOC performance by the faculty. The model solves the issues of data accumulation and their heterogeneity, including the problem of having data in different formats and various semantic similarities, and is suitable for processing large amounts of data in terms of scalability. Thus, it also offers a way to confirm the process of data retrieval that is based on performance assessment with the help of an evaluation matrix.

PMID:39811619 | PMC:PMC11731703 | DOI:10.1016/j.mex.2024.103092

Heliyon. 2024 Dec 18;11(1):e41108. doi: 10.1016/j.heliyon.2024.e41108. eCollection 2025 Jan 15.

ABSTRACT

INTRODUCTION: Severe cutaneous adverse reactions (SCARs) are life-threatening and often linked to antiepileptic drugs (AEDs). Common types of SCARs include Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and drug reaction with eosinophilia and systemic symptoms (DRESS). Immune-mediated mechanisms involving human leukocyte antigen (HLA) alleles have been implicated in the pathogenesis of this reaction. This study examines the association between specific HLA alleles (HLA-A, -B, and -DRB1) and AED-induced SCARs in the Iraqi population.

METHODOLOGY: A total of 50 patients diagnosed with SCARs and 90 tolerant controls were recruited from Dr. Saad Al-Wattari Hospital for Neurological Sciences and Baghdad Hospital - Medical City. HLA genotyping was performed using PCR-SSO method from peripheral blood samples. Statistical comparisons were made using the t-test or chi-square test, while univariate logistic regression with Bonferroni's correction (p < 0.05) were used to assess associations between HLA alleles and SCARs.

RESULTS: Among the patients, SJS was the most prevalent type of SCARs observed. Analysis of HLA allele frequencies revealed significant associations between specific alleles. HLA-A∗02:01 was found to be significantly associated with a lower risk of AED-induced SJS (OR = 0.36; 95 % CI: 0.13-0.97), while HLA-A∗24:02 and HLA-B∗15:02 were associated with an increased risk of AED-induced SJS (OR = 3.60; 95 % CI: 1.21-10.72 and OR = 4.41; 95 % CI: 1.18-16.47, respectively). For AED-induced TEN, HLA-A∗01:02, HLA-B∗15:02, and HLA-B∗52:01 showed significant associations (OR = 6.92; 95 % CI: 1.39-34.37 and OR = 6.55; 95 % CI: 1.62-26.52, respectively), with HLA-DRB1∗03:01 being highly significant (OR = 5.09; 95 % CI: 1.72-15.00). Additionally, HLA-B∗40:02 was strongly associated with AED-induced DRESS (OR = 29.33; 95 % CI: 3.50-245.32).

CONCLUSION: This study identifies key HLA alleles associated with AED-induced SCARs in the Iraqi population. These findings could facilitate personalized medicine approaches, aiding in better prediction and prevention of SCARs in AED therapy.

PMID:39811327 | PMC:PMC11732454 | DOI:10.1016/j.heliyon.2024.e41108

World Psychiatry. 2025 Feb;24(1):141-142. doi: 10.1002/wps.21289.

NO ABSTRACT

PMID:39810666 | DOI:10.1002/wps.21289

Anal Methods. 2025 Jan 15. doi: 10.1039/d4ay01941e. Online ahead of print.

ABSTRACT

Saponins are responsible for the clinical effects of Panax notoginseng leaves, which are traditionally produced as the single herb resource of 'Qiye Shenan Pian' in Chinese patent medicine. In this study, the metabolic characteristics of PNLSs were explored in rat feces. PNLSs as well as their metabolites were analyzed by ultra-performance liquid chromatography tandem/quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS). Subsequently, seventy-five metabolites were tentatively identified in the control group mainly due to the deglycosylation and dehydration biopathways, but only twenty low yields were determined in the pseudo-germ-free (GF) group. Ginsenoside compound K was the predominant metabolite in the control group. The data presented that gut microbiota played a pivotal role in the metabolic kinetics of PNLSs.

PMID:39810648 | DOI:10.1039/d4ay01941e