J Multidiscip Healthc. 2025 Feb 7;18:675-695. doi: 10.2147/JMDH.S493873. eCollection 2025.

ABSTRACT

PURPOSE: Breast cancer is the most common major public health problems of women in the world. Until now, analyzing mammogram images is still the main method used by doctors to diagnose and detect breast cancers. However, this process usually depends on the experience of radiologists and is always very time consuming.

PATIENTS AND METHODS: We propose to introduce deep learning technology into the process for the facilitation of computer-aided diagnosis (CAD), and address the challenges of class imbalance, enhance the detection of small masses and multiple targets, and reduce false positives and negatives in mammogram analysis. Therefore, we adopted and enhanced RetinaNet to detect masses in mammogram images. Specifically, we introduced a novel modification to the network structure, where the feature map M5 is processed by the ReLU function prior to the original convolution kernel. This strategic adjustment was designed to prevent the loss of resolution for small mass features. Additionally, we introduced transfer learning techniques into training process through leveraging pre-trained weights from other RetinaNet applications, and fine-tuned our improved model using the INbreast dataset.

RESULTS: The aforementioned innovations facilitate superior performance of the enhanced RetiaNet model on the public dataset INbreast, as evidenced by a mAP (mean average precision) of 1.0000 and TPR (true positive rate) of 1.00 at 0.00 FPPI (false positive per image) on the INbreast dataset.

CONCLUSION: The experimental results demonstrate that our enhanced RetinaNet model defeats the existing models by having more generalization performance than other published studies, and it can also be applied to other types of patients to assist doctors in making a proper diagnosis.

PMID:39935433 | PMC:PMC11812562 | DOI:10.2147/JMDH.S493873

Front Genet. 2025 Jan 22;15:1464471. doi: 10.3389/fgene.2024.1464471. eCollection 2024.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.

METHODS: In this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in multiple pathways, while immune function analysis demonstrated a significant correlation between IPF onset and immune cell infiltration. Finally, the roles of PODNL1 and PIGA as biomarkers were validated through in vivo and in vitro experiments using qRT-PCR, Western blotting, and immunohistochemistry.

RESULTS: These findings suggest that PODNL1 and PIGA may serve as critical biomarkers for IPF onset and contribute to its pathogenesis.

DISCUSSION: This study highlights their potential for early biomarker discovery and risk prediction in IPF, offering insights into disease mechanisms and diagnostic strategies.

PMID:39935693 | PMC:PMC11811625 | DOI:10.3389/fgene.2024.1464471

J Inflamm Res. 2025 Feb 7;18:1899-1910. doi: 10.2147/JIR.S499029. eCollection 2025.

ABSTRACT

While TRIM proteins are extensively studied in the context of lung tumors, their roles in non-tumor chronic lung diseases remain underexplored. This review delves into the emerging significance of TRIM family proteins in the pathogenesis of idiopathic pulmonary fibrosis (IPF), asthma, chronic obstructive pulmonary disease (COPD), and pulmonary hypertension (PH). TRIM proteins modulate key pathological processes, including inflammation, fibrosis, and cellular remodeling, contributing to disease progression. We highlight their potential as biomarkers and therapeutic targets, offering promising avenues for drug development in these debilitating respiratory disorders. However, the translation of these findings into clinical applications faces significant challenges. These include the dual functional nature of TRIM proteins, their context-dependent roles, the complexity of their downstream signaling networks, and the limitations of current therapeutic strategies in achieving tissue-specific targeting with minimal off-target effects. Addressing these challenges will require innovative approaches and interdisciplinary efforts to unlock the therapeutic potential of TRIM proteins in non-tumor chronic lung diseases.

PMID:39935527 | PMC:PMC11812559 | DOI:10.2147/JIR.S499029

Front Plant Sci. 2025 Jan 28;16:1555255. doi: 10.3389/fpls.2025.1555255. eCollection 2025.

NO ABSTRACT

PMID:39935951 | PMC:PMC11810884 | DOI:10.3389/fpls.2025.1555255

Front Plant Sci. 2025 Jan 28;16:1533442. doi: 10.3389/fpls.2025.1533442. eCollection 2025.

ABSTRACT

INTRODUCTION: The genetic wealth present in pigmented rice varieties offer abundant variation in different sources of antioxidants to meet nutritional security targets among rice-consuming communities. There is limited knowledge of the dynamic changes in the lipidome of rice during germination and the corresponding genes associated with the antioxidant and anti-cancerous properties of lipophilic fractions of pigmented rice sprouts (PRS).

METHODS: In this study, we profiled the lipidome of diverse pigmented rice collections of germinated sprouts. Further, we employed Genome-wide association studies (GWAS), gene-set analysis, and targeted association analysis to identify the candidate genes linked to these lipids.

RESULTS: The genetic analyses revealed 72 candidate genes involved in the regulation of these accumulating lipids in PRS. Marker trait associations (MTA) analysis shown that the combination GGTAAC/ACAAGCTGGGCCC was associated with increased levels of unsaturated lipids and carotenoids, which likely underlie these beneficial effects. This superior MTA combination exhibited potent inhibitory activity against HCT116 and A549 cell lines, with average 1/IC50 values of 0.03 and 0.02 (mL/μg), respectively, compared to the inferior MTAs.

DISCUSSION: Collectively, our findings demonstrate that MTAs linked to selected GDSL esterase/lipase (GELP) genes, OsACP1, and lecithin-cholesterol acyltransferase significantly enhance antioxidant and anti-cancer properties, potentially through the mobilization of unsaturated lipids and carotenoids during germination. This study offers valuable insights into the health-promoting potential of germinated rice sprouts as a rich dietary source of antioxidants beneficial to human health.

PMID:39935946 | PMC:PMC11810972 | DOI:10.3389/fpls.2025.1533442

Front Genet. 2025 Jan 28;16:1547943. doi: 10.3389/fgene.2025.1547943. eCollection 2025.

NO ABSTRACT

PMID:39935834 | PMC:PMC11810898 | DOI:10.3389/fgene.2025.1547943

Front Plant Sci. 2025 Jan 28;15:1520474. doi: 10.3389/fpls.2024.1520474. eCollection 2024.

ABSTRACT

Seagrasses are a paraphyletic group of marine angiosperms and retain certain adaptations from the ancestors of all embryophytes in the transition to terrestrial environments. Among these adaptations is the production of flavonoids, versatile phenylpropanoid secondary metabolites that participate in a variety of stress responses. Certain features, such as catalytic promiscuity and metabolon interactions, allow flavonoid metabolism to expand to produce novel compounds and respond to a variety of stimuli. As marine environments expose seagrasses to a unique set of stresses, these plants display interesting flavonoid profiles, the functions of which are often not completely clear. Flavonoids will likely prove to be effective and versatile agents in combating the new host of stress conditions introduced to marine environments by anthropogenic climate change, which affects marine environments differently from terrestrial ones. These new stresses include increased sulfate levels, changes in salt concentration, changes in herbivore distributions, and ocean acidification, which all involve flavonoids as stress response mechanisms, though the role of flavonoids in combatting these climate change stresses is seldom discussed directly in the literature. Flavonoids can also be used to assess the health of seagrass meadows through an interplay between flavonoid and simple phenolic levels, which may prove to be useful in monitoring the response of seagrasses to climate change. Studies focusing on the genetics of flavonoid metabolism are limited for this group, but the large chalcone synthase gene families in some species may provide an interesting topic of research. Anthocyanins are typically studied separately from other flavonoids. The phenomenon of reddening in certain seagrass species typically focuses on the importance of anthocyanins as a UV-screening mechanism, while the role of anthocyanins in cold stress is discussed less often. Both of these stress response functions would be useful for adaptation to climate change-induced deviations in tidal patterns and emersion. However, ocean warming will likely lead to a decrease in anthocyanin content, which may impact the performance of intertidal seagrasses. This review highlights the importance of flavonoids in angiosperm stress response and adaptation, examines research on flavonoids in seagrasses, and hypothesizes on the importance of flavonoids in these organisms under climate change.

PMID:39935685 | PMC:PMC11810914 | DOI:10.3389/fpls.2024.1520474

Front Microbiol. 2025 Jan 28;16:1535114. doi: 10.3389/fmicb.2025.1535114. eCollection 2025.

ABSTRACT

Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) is a plant pathogenic bacterium that possesses complicated motility regulation pathways including a typical chemotaxis system. A significant portion of our understanding about the genes functioning in Pst DC3000 motility is based on comparison to other bacteria. This leaves uncertainty about whether gene functions are conserved, especially since specific regulatory modules can have opposite functions in sets of Pseudomonas. In this study, we used a competitive selection to enrich for mutants with altered swimming motility and used random barcode transposon-site sequencing (RB-TnSeq) to identify genes with significant roles in swimming motility. Besides many of the known or predicted chemotaxis and motility genes, our method identified PSPTO_0406 (dipA), PSPTO_1042 (chrR) and PSPTO_4229 (hypothetical protein) as novel motility regulators. PSPTO_0406 is a homolog of dipA, a known cyclic di-GMP degrading enzyme in P. aeruginosa. PSPTO_1042 is part of an extracytoplasmic sensing system that controls gene expression in response to reactive oxygen species, suggesting that PSPTO_1042 may function as part of a mechanism that enables Pst DC3000 to alter motility when encountering oxidative stressors. PSPTO_4229 encodes a protein containing an HD-related output domain (HDOD), but with no previously identified functions. We found that deletion and overexpression of PSPTO_4229 both reduce swimming motility, suggesting that its function is sensitive to expression level. We used the overexpression phenotype to screen for nonsense and missense mutants of PSPTO_4229 that no longer reduce swimming motility and found a pair of conserved arginine residues that are necessary for motility suppression. Together these results provide a global perspective on regulatory and structural genes controlling flagellar motility in Pst DC3000.

PMID:39935648 | PMC:PMC11813219 | DOI:10.3389/fmicb.2025.1535114

Front Microbiol. 2025 Jan 28;16:1534826. doi: 10.3389/fmicb.2025.1534826. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fmicb.2024.1481702.].

PMID:39935631 | PMC:PMC11813217 | DOI:10.3389/fmicb.2025.1534826

Alzheimers Dement (N Y). 2025 Feb 11;11(1):e70051. doi: 10.1002/trc2.70051. eCollection 2025 Jan-Mar.

ABSTRACT

INTRODUCTION: Brain steady-state gamma oscillations evoked using a non-invasive medical device (Spectris) have shown potential clinical benefits in patients with mild-moderate Alzheimer's disease (AD), including reduced functional and cognitive decline, reduced brain volume and myelin loss, and increased brain functional connectivity. We analyzed changes in cerebrospinal fluid (CSF) proteins after Spectris treatment in mild cognitive impairment (MCI) and their relationship to established biological pathways implicated in AD.

METHODS: Unbiased proteomic analysis of CSF samples from participants with amyloid-positive MCI (n = 10) was conducted from the FLICKER (NCT03543878) clinical trial. Participants used the Cognito Therapeutics medical device (Spectris), confirmed to evoke steady-state gamma oscillations. Participants were instructed to use the device daily for 1 hour each day during the trial. CSF was collected prior to the start of stimulation and after 4 and 8 weeks of treatment. The proteome was analyzed using tandem mass tag mass spectrometry.

RESULTS: Differential expression analysis of proteins at baseline and after 8 weeks of treatment (N = 5) revealed that 110 out of 2951 proteins met the significance threshold (analysis of variance, P < 0.05, no false discovery rate). Sixty proteins were upregulated, and 50 proteins were downregulated after treatment. Changes in protein expression were mapped to the consensus human AD protein network, representing co-expressed and functionally linked modules linked to cell type and biochemical pathways. Treatment altered CSF proteins linked to AD-related brain proteome modules, including those involved in myelination (proteolipid protein 1, ecotropic viral integration site 2A), synaptic and neuroimmune functions, and regulation of cellular lipid transportation. Biological pathway analysis revealed that most impacted pathways were associated with lipoproteins, cholesterol, phospholipids processing, and phosphatidylcholine biosynthesis.

DISCUSSION: The CSF proteomic changes observed in this study suggest pleiotropic effects on multiple pathways involved in AD, including myelination, synaptic and neuroimmune function, and lipid transport. These findings are also consistent with observations of white matter and myelin preservation after Spectris treatment of AD.

HIGHLIGHTS: We analyzed changes in cerebrospinal fluid (CSF) proteins in response to sensory-evoked gamma oscillations in individuals with mild cognitive impairment.Sensory evoked steady-state gamma oscillations were evoked by Spectris medical device.Changes in CSF proteins were observed after 8 weeks of daily 1 hour treatment.Affected proteins were related to myelination, synaptic and neuroimmune functions, and regulation of cellular lipid transportation.Proteomic changes support clinical outcomes and myelin preservation of Spectris treatment.

PMID:39935616 | PMC:PMC11812123 | DOI:10.1002/trc2.70051

F1000Res. 2024 Jul 9;13:1. doi: 10.12688/f1000research.145146.2. eCollection 2024.

ABSTRACT

Protein phosphatase 2A is a serine/threonine phosphatase with activity dependent on an associated regulatory subunit, serine/threonine-protein phosphatase 2A 56 kDa regulatory subunit delta (δ) isoform (PPP2R5D). PPP2R5D is the δ isoform in the B56 family of regulatory subunits. Abundantly expressed in the brain and involved in a broad range of cellular processes, PPP2R5D plays an essential role in modulating key neuronal pathways and signalling. Pathogenic mutations in the PPP2R5D gene are linked to clinical symptoms characterized by neurodevelopmental delay, intellectual disability, and autism spectrum disorders. The etiology of these genetic disorders remains unknown, which can partly be due to the lack of independently characterized antibodies. Here we have characterized six PPP2R5D commercial antibodies for Western Blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

PMID:39935523 | PMC:PMC11811605 | DOI:10.12688/f1000research.145146.2

F1000Res. 2024 Jul 23;11:1388. doi: 10.12688/f1000research.125815.2. eCollection 2022.

ABSTRACT

Background Tuberculosis (TB) continues to pose a serious threat to the public health system in India. Although the National Tuberculosis Elimination Program (NTEP) is providing a wide range of interventions from early diagnosis to complete treatment to reduce morbidity and mortality from TB, adverse drug reactions (ADR) remain a challenge in treatment adherence and completion. Methods An observational cross-sectional study was conducted in selected districts of Gujarat state. A total of 593 reported TB patients were recruited with an adjusted unified distribution based on the type of cases, site of diseases, and service facility through a simple random sampling method. A semi-structured questionnaire tool was used to collect socio-demographic, clinical, and ADR-related data from the TB patients. Data was analyzed for the frequency, percentage, chi-squared, and adjusted odds ratio to find the association between the variables. Results The majority of the study participants were male (87.2%), aged 15 to 60 (57.8%), daily laborers (22.4%), and married (64.2%). Over 75% of individuals had pulmonary TB, with 87% having experienced their first episode, 83% being new cases, and 44.7% having a history of addiction. ADR with mild symptoms was reported by more than a quarter (29%) of TB patients during the intensive phase (77%). The association between ADR experience and drug susceptibility was significant (p<0.005) and drug-resistant TB patients experience two times more ADRs than drug-sensitive TB patients (OR 2.04). Binomial logistic regression was carried out to describe the association between various variables and occurrence of ADRs. Conclusion The study highlighted a need to enhance health care providers' capacity and program structure for managing ADRs among TB patients. In order to completely eliminate TB across the country, it also emphasized the attention for a holistic and all-encompassing strategy for managing TB patients at the field level.

PMID:39935535 | PMC:PMC11811607 | DOI:10.12688/f1000research.125815.2

Orphanet J Rare Dis. 2025 Feb 11;20(1):68. doi: 10.1186/s13023-024-03436-y.

ABSTRACT

BACKGROUND: CD55 deficiency with hyper-activation of complement, angiopathic thrombosis, and protein-losing enteropathy (CHAPLE) disease is a newly identified condition with an estimated worldwide prevalence of < 100 patients. Patient interviews can ensure that what is important to patients is assessed in a clinical trial program. Due to the rare and potentially fatal nature of CHAPLE disease, interviews were conducted as part of the pozelimab clinical trial, rather than in a separate study before the trial. The aim of the interviews was to identify the key disease-related signs, symptoms, and health-related quality-of-life (HRQoL) impacts that are important and relevant to patients with CHAPLE disease.

METHODS: Interviews were conducted with patients and/or caregivers at two timepoints (screening and Week 24) during the pozelimab trial to document the signs/symptoms and HRQoL impacts of CHAPLE disease, and document the most bothersome sign/symptom at screening. At Week 24, interviews gathered additional information on the patient experience from caregivers and patients (note: the impact of pozelimab treatment was also collected, though these results are presented elsewhere).

RESULTS: Ten patients, aged 3-19 years, were enrolled in the trial; caregivers contributed to nine interviews. Thirty-one signs‌/symptoms and 65 HRQoL impacts were reported during the interviews. Abdominal pain, diarrhea, facial and peripheral edema/‌swelling, nausea, and vomiting emerged as the core signs/‌symptoms of CHAPLE disease (i.e., experienced by ≥ 90% of patients prior to treatment). The remaining 25 signs/symptoms were experienced by four or fewer (n ≤ 4, ≤ 40.0%) patients, and 15 were only reported by one patient each. Abdominal pain and facial edema were reported as the most bothersome signs/‌symptoms (n = 9, 90.0% and n = 1, 10.0%, respectively). The most frequently reported (i.e., ≥ 80% of interviews) HRQoL impacts were restricted diet (n = 10, 100.0%), sleep disruptions (n = 10, 100.0%), missing school (n = 9, 90.0%), ability to get dressed independently (n = 8, 80.0%), and difficulty engaging in play activities (n = 8, 80.0%).

CONCLUSIONS: The main finding from these patient interviews is the identification of six core signs/symptoms of CHAPLE disease: abdominal pain, diarrhea, facial edema/swelling, peripheral edema/swelling, nausea, and vomiting. The severity of the core signs/symptoms leads to substantial impacts on patients' lives.

TRIAL REGISTRATION: ClinicalTrials.gov, NCT04209634. Registered 20 December 2019 https://classic.

CLINICALTRIALS: gov/ct2/show/NCT04209634 .

PMID:39934837 | DOI:10.1186/s13023-024-03436-y

Sci Rep. 2025 Feb 11;15(1):5093. doi: 10.1038/s41598-025-89096-9.

ABSTRACT

Medical image segmentation plays a crucial role in addressing emerging healthcare challenges. Although several impressive deep learning architectures based on convolutional neural networks (CNNs) and Transformers have recently demonstrated remarkable performance, there is still potential for further performance improvement due to their inherent limitations in capturing feature correlations of input data. To address this issue, this paper proposes a novel encoder-decoder architecture called MedFuseNet that aims to fuse local and global deep feature representations with hybrid attention mechanisms for medical image segmentation. More specifically, the proposed approach contains two branches for feature learning in parallel: one leverages CNNs to learn local correlations of input data, and the other utilizes Swin-Transformer to capture global contextual correlations of input data. For feature fusion and enhancement, the designed hybrid attention mechanisms combine four different attention modules: (1) an atrous spatial pyramid pooling (ASPP) module for the CNN branch, (2) a cross attention module in the encoder for fusing local and global features, (3) an adaptive cross attention (ACA) module in skip connections for further performing fusion, and (4) a squeeze-and-excitation attention (SE-attention) module in the decoder for highlighting informative features. We evaluate our proposed approach on the public ACDC and Synapse datasets, and achieves the average DSC of 89.73% and 78.40%, respectively. Experimental results on these two datasets demonstrate the effectiveness of our proposed approach on medical image segmentation tasks, outperforming other used state-of-the-art approaches.

PMID:39934248 | DOI:10.1038/s41598-025-89096-9

Radiologie (Heidelb). 2025 Feb 11. doi: 10.1007/s00117-025-01422-4. Online ahead of print.

ABSTRACT

BACKGROUND: The rapid development of large language models (LLMs) opens up new possibilities for the automated processing of medical texts. Transforming unstructured radiology reports into structured data is crucial for efficient use in clinical decision support systems, research, and improving patient care.

OBJECTIVES: What are the challenges of transforming natural language radiology reports into structured data using LLMs? Which methods and architectures are promising? How can the quality and reliability of the extracted data be ensured?

MATERIALS AND METHODS: This article examines current research on the application of LLMs in radiological information processing. Various approaches such as rule-based systems, machine learning, and deep learning models, particularly neural network architectures, are analyzed and compared. The focus is on extracting information such as diagnoses, anatomical locations, findings, and measurements.

RESULTS AND CONCLUSION: LLMs show great potential in transforming reports into structured data. In particular, deep learning models trained on large datasets achieve high accuracies. However, challenges remain, such as dealing with ambiguities, abbreviations, and the variability of linguistic expressions. Combining LLMs with domain-specific knowledge, for example, in the form of ontologies, can further improve the performance of the systems. Integrating contextual information and developing robust evaluation metrics are also important research directions.

PMID:39934245 | DOI:10.1007/s00117-025-01422-4

Sci Rep. 2025 Feb 12;15(1):5157. doi: 10.1038/s41598-024-84922-y.

ABSTRACT

Early detection of ocular diseases is vital to preventing severe complications, yet it remains challenging due to the need for skilled specialists, complex imaging processes, and limited resources. Automated solutions are essential to enhance diagnostic precision and support clinical workflows. This study presents a deep learning-based system for automated classification of ocular diseases using the Ocular Disease Intelligent Recognition (ODIR) dataset. The dataset includes 5,000 patient fundus images labeled into eight categories of ocular diseases. Initial experiments utilized transfer learning models such as DenseNet201, EfficientNetB3, and InceptionResNetV2. To optimize computational efficiency, a novel two-level feature selection framework combining Linear Discriminant Analysis (LDA) and advanced neural network classifiers-Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM)-was introduced. Among the tested approaches, the "Combined Data" strategy utilizing features from all three models achieved the best results, with the BiLSTM classifier attaining 100% accuracy, precision, and recall on the training set, and over 98% performance on the validation set. The LDA-based framework significantly reduced computational complexity while enhancing classification accuracy. The proposed system demonstrates a scalable, efficient solution for ocular disease detection, offering robust support for clinical decision-making. By bridging the gap between clinical demands and technological capabilities, it has the potential to alleviate the workload of ophthalmologists, particularly in resource-constrained settings, and improve patient outcomes globally.

PMID:39934192 | DOI:10.1038/s41598-024-84922-y

Acad Radiol. 2025 Feb 10:S1076-6332(25)00087-X. doi: 10.1016/j.acra.2025.01.032. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develop a deep learning framework for quantification of aortic phenotypes from magnetic resonance imaging (MRI) and to investigate the association between aortic features and impaired glucose metabolism beyond traditional cardiovascular (CV) risk factors.

MATERIALS AND METHODS: This study used data from the prospective Cooperative Health Research in the Region of Augsburg (KORA) study to develop a deep learning framework for automatic quantification of aortic features (maximum aortic diameter, total volume, length, and width of the aortic arch) derived from MRI. Aortic features were compared between different states of glucose metabolism and tested for associations with impaired glucose metabolism adjusted for traditional CV risk factors (age, sex, height, weight, hypertension, smoking, and lipid panel).

RESULTS: The deep learning framework yielded a high performance for aortic feature quantification with a Dice coefficient of 91.1±0.02. Of 381 participants (58% male, mean age 56 years), 231 (60.6%) had normal blood glucose, 97 (25.5%) had prediabetes, and 53 (13.9%) had diabetes. All aortic features showed a significant increase between different groups of glucose metabolism (p≤0.04). Total aortic length and total aortic volume were associated with impaired glucose metabolism (OR 0.85, 95%CI 0.74-0.96; p=0.01, and OR 0.99, 95%CI 0.98-0.99; p=0.02) independent of CV risk factors.

CONCLUSION: Aortic features showed a glucose level dependent increase from normoglycemic individuals to those with prediabetes and diabetes. Total aortic length and volume were independently and inversely associated with impaired glucose metabolism beyond traditional CV risk factors.

PMID:39934079 | DOI:10.1016/j.acra.2025.01.032

J Prosthet Dent. 2025 Feb 10:S0022-3913(25)00050-2. doi: 10.1016/j.prosdent.2025.01.020. Online ahead of print.

NO ABSTRACT

PMID:39934030 | DOI:10.1016/j.prosdent.2025.01.020

Cancer Metastasis Rev. 2025 Feb 11;44(1):31. doi: 10.1007/s10555-025-10249-3.

ABSTRACT

Cellular plasticity and the ability to avoid terminal differentiation are hallmarks of cancer. Here, we review the evidence that tumor cells themselves can take on properties of neurons of the central nervous system, which can regulate tumor growth and metastasis. We discuss recent evidence that axon guidance molecules and regulators of electrical activity and synaptic transmission, such as ion channels and neurotransmitters, can drive the oncogenic and invasive properties of tumor cells from a range of cancers. We also review how FDA-approved treatments for neurological disorders are being tested in pre-clinical models and clinical trials for repurposing as anti-cancer agents, offering the potential for new therapies for cancer patients that can be accessed more quickly.

PMID:39934425 | DOI:10.1007/s10555-025-10249-3

Eur Arch Psychiatry Clin Neurosci. 2025 Feb 12. doi: 10.1007/s00406-025-01970-9. Online ahead of print.

ABSTRACT

This study investigates treatment-resistant schizophrenia (TRS) by analysing genetic markers in dopamine and serotonin receptors. Conducted on a cohort of 221 patients with treatment-resistant mental disorders, the research focused on DRD2 and HTR2A gene variants-specifically, rs1801028, rs6314, rs7997012, and rs6311. The findings suggest specific associations between certain genetic variants and TRS. Notably, the HTR2A rs6314 A|G genotype and rs7997012 G|G genotype were significantly more prevalent in TRS patients compared to healthy controls (HCs). Haplotype analyses revealed associations between specific haplotypes-such as A|G (rs6314-rs7997012)-and TRS, indicating their potential predictive value for TRS versus HCs. The study underscores the involvement of the serotonergic system in TRS. These findings offer valuable insights into the genetic factors contributing to TRS, paving the way for future research and the development of personalised prevention and treatment strategies in psychiatry.

PMID:39934320 | DOI:10.1007/s00406-025-01970-9