J Med Case Rep. 2025 Jan 23;19(1):32. doi: 10.1186/s13256-025-05031-6.

ABSTRACT

BACKGROUND: Dyskeratosis congenita is a rare genetic disease due to telomere biology disorder and characterized by heterogeneous clinical manifestations and severe complications. "Porto-sinusoidal vascular disease" has been recently proposed, according to new diagnostic criteria, to replace the term "idiopathic non-cirrhotic portal hypertension." TERT plays an important role in telomeric DNA repair and replication. A TERT c.2286 + 1G/A mutation in a splicing consensus site was identified in a patient with pulmonary fibrosis. Recently, a pathogenic de novo TERT c.280A > T variant was associated with diffuse lung disease in an infant.

CASE PRESENTATION: A 16-year-old Han male patient experienced unexplained black stool for 7 days, accompanied by dizziness and fatigue. On examination, there were mesh pigmentations on the exposed areas of the skin on both hands and feet. Laboratory testing revealed moderate hemorrhagic anemia and mild elevation of alanine aminotransferase. A computed tomography scan showed portal hypertension, esophageal and gastric varices, and splenomegaly. The liver stiffness measurement by FibroScan was 6.0 kPa. Liver biopsy revealed typical features of porto-sinusoidal vascular disease. Whole exome sequencing identified a heterozygous TERT c.2286 + 1G > A de novo mutation and quantitative polymerase chain reaction revealed very short telomeres (less than the first percentile for his age). The patient was diagnosed as TERT de novo mutation-related dyskeratosis congenita and porto-sinusoidal vascular disease. He underwent esophageal and gastric variceal ligation treatment and received a carvedilol tablet (12.5 mg) every morning. After 6 months, he has moderate iron deficiency anemia and has started receiving polysaccharide iron complex therapy.

CONCLUSION: When discovering reticular rash and unknown portal hypertension, it is necessary to perform whole exome sequencing and chromosome length testing to clarify the possibility of dyskeratosis congenita/telomere biology disorder with porto-sinusoidal vascular disease.

PMID:39849589 | DOI:10.1186/s13256-025-05031-6

Front Neural Circuits. 2025 Jan 9;18:1522421. doi: 10.3389/fncir.2024.1522421. eCollection 2024.

ABSTRACT

The substantia nigra pars compacta (SNc), one of the main dopaminergic nuclei of the brain, exerts a regulatory function on the basal ganglia circuitry via the nigro-striatal pathway but its possible dopaminergic innervation of the thalamus has been only investigated in non-human primates. The impossibility of tract-tracing studies in humans has boosted advanced MRI techniques and multi-shell high-angular resolution diffusion MRI (MS-HARDI) has promised to shed more light on the structural connectivity of subcortical structures. Here, we estimated the possible dopaminergic innervation of the human thalamus via an MS-HARDI tractography of the SNc in healthy human young adults. Two MRI data sets were serially acquired using MS-HARDI schemes from ADNI and HCP neuroimaging initiatives in a group of 10 healthy human subjects (5 males, age range: 25-30 years). High resolution 3D-T1 images were independently acquired to individually segment the thalamus and the SNc. Starting from whole-brain probabilistic tractography, all streamlines through the SNc reaching the thalamus were counted, separately for each hemisphere, after excluding streamlines through the substantia nigra pars reticulata and all those reaching the basal ganglia, the cerebellum and the cortex. We found a reproducible structural connectivity between the SNc and the thalamus, with an average of ~12% of the total number of streamlines encompassing the SNc and terminating in the thalamus, with no other major subcortical or cortical structures involved. The first principal component map of dopamine receptor density from a normative PET image data set suggested similar dopamine levels across SNc and thalamus. This is the first quantitative report from in-vivo measurements in humans supporting the presence of a direct nigro-thalamic dopaminergic projection. While histological validation and concurrent PET-MRI remains needed for ultimate proofing of existence, given the potential role of this pathway, the possibility to achieve a good reproducibility of these measurements in humans might enable the monitoring of dopaminergic-related disorders, towards targeted personalized therapies.

PMID:39850841 | PMC:PMC11754968 | DOI:10.3389/fncir.2024.1522421

Comput Struct Biotechnol J. 2024 Dec 16;27:149-159. doi: 10.1016/j.csbj.2024.12.005. eCollection 2025.

ABSTRACT

Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis. This approach may enable more accurate and rapid identification of multiple hrHPV types within a single sample. Compared to traditional methods, MTIOT has the potential to overcome their core limitations and offer a more efficient and cost-effective solution for cervical cancer screening. When tested on both simulated samples (to mimic real-world complexities) and clinical samples, MTIOT achieved F1 scores (the harmonic mean of sensitivity and specificity) of 98 % and 92 % respectively for identifying subtypes with a sample size ≥ 50, suggesting that it may significantly improve the precision of cervical cancer screening programs. This work with MTIOT represents a significant step forward in the molecular diagnosis of hrHPV and may suggest a promising avenue for enhancing early detection strategies and potentially reducing the incidence of cervical cancer. This study also underscores the importance of methodological innovation in tackling public health challenges and sets the stage for future clinical trials to validate MTIOT's efficacy in practice.

PMID:39850660 | PMC:PMC11755069 | DOI:10.1016/j.csbj.2024.12.005

Front Pharmacol. 2025 Jan 9;15:1397995. doi: 10.3389/fphar.2024.1397995. eCollection 2024.

ABSTRACT

INTRODUCTION: TNFα inhibitor (TNFi) immunogenicity in rheumatoid arthritis (RA) is a major obstacle to its therapeutic effectiveness. Although methotrexate (MTX) can mitigate TNFi immunogenicity, its adverse effects necessitate alternative strategies. Targeting nuclear factor of activated T cells (NFAT) transcription factors may protect against biologic immunogenicity. Therefore, developing a potent NFAT inhibitor to suppress this immunogenicity may offer an alternative to MTX.

METHODS: We performed a structure-based virtual screen of the NFATC2 crystal structure to identify potential small molecules that could interact with NFATC2. For validation, we investigated the effect of the identified compound on NFAT transcriptional activity, nuclear localization, and binding to the NFAT consensus sequence. In vivo studies assessed the ability of the compound to protect against TNFi immunogenicity, while ex vivo studies evaluated its effect on CD4+ T cell proliferation and B cell antibody secretion.

RESULTS: We identified duvelisib (DV) as a novel NFATC2 and NFATC1 inhibitor that attenuates NFAT transcriptional activity without inhibiting calcineurin or NFAT nuclear localization. Our results suggest that DV inhibits NFAT independently of PI3K by interfering with nuclear NFAT binding to the NFAT consensus promoter sequence. DV significantly protected mice from adalimumab immunogenicity and attenuated ex vivo CD4+ T cell proliferation and B cell antibody secretion.

DISCUSSION: DV is a promising NFAT inhibitor that can protect against TNFi immunogenicity without inhibiting calcineurin phosphatase activity. Our results suggest that the future development of DV analogs may be of interest as agents to attenuate unwanted immune responses.

PMID:39850568 | PMC:PMC11754251 | DOI:10.3389/fphar.2024.1397995

Front Plant Sci. 2025 Jan 9;15:1502649. doi: 10.3389/fpls.2024.1502649. eCollection 2024.

ABSTRACT

Sugar, the primary product of photosynthesis, is a vital requirement for cell activities. Allocation of sugar from source to sink tissues is facilitated by sugar transporters (ST). These STs belong to the Major Facilitator Superfamily (MFS), the largest family of STs in plants. In this study, we performed genome wide and gene expression data analysis to identify the putative ST genes in Erianthus rufipilus (E. rufipilus) and in Saccharum officinarum (S. officinarum). We identified 78 ST gene families in E. rufipilus and 86 ST gene families in S. officinarum. Phylogenetic analysis distributed the ST genes into eight distinct subfamilies (INT, MST, VGT, pGlcT, PLT, STP, SFP and SUT). Chromosomal distribution of ST genes clustered them on 10 respective chromosomes. Furthermore, synteny analysis with S. spontaneum and Sorghum bicolor (S. bicolor) revealed highly colinear regions. Synonymous and non-synonymous ratio (Ka/Ks) showed purifying selection in gene evolution. Promoter analysis identified several cis-regulatory elements, mainly associated with light responsiveness. We also examined the expression pattern of ST genes in different developing tissues (mature leaf, pre-mature stem, mature stem and seedling stem). Under sugar stress, we identified the significant ST genes showing differential expression patterns. Moreover, our yeast one-hybrid (Y1H) assays identified NAM, ATAF and CUC (NAC) and Lesion Simulating Disease (LSD) potential transcription factors (TFs) that may bind to the SUT1-T1 promoter in S. officinarum, showing negative correlation pattern with SUT1-T1. Our results deepen our understanding of ST gene evolution in Saccharum species and will facilitate the future investigation of functional analysis of the ST gene family.

PMID:39850208 | PMC:PMC11755103 | DOI:10.3389/fpls.2024.1502649

G3 (Bethesda). 2025 Jan 24:jkaf012. doi: 10.1093/g3journal/jkaf012. Online ahead of print.

ABSTRACT

Sex-determining region Y box 2 (Sox2) is a critical transcription factor for embryogenesis and neural stem and progenitor cell (NSPC) maintenance. While distal enhancers control Sox2 in embryonic stem cells (ESCs), enhancers closer to the gene are implicated in Sox2 transcriptional regulation in neural development. We hypothesize that a downstream enhancer cluster, termed Sox2 regulatory regions 2-18 (SRR2-18), regulates Sox2 transcription in neural stem cells and we investigate this in NSPCs derived from mouse ESCs. Using functional genomics and CRISPR-Cas9 mediated deletion analyses we investigate the role of SRR2-18 in Sox2 regulation during neural differentiation. Transcriptome analyses demonstrate that loss of even one copy of SRR2-18 disrupts the region-specific identity of NSPCs, reducing the expression of genes associated with more anterior regions of the embryonic nervous system. Homozygous deletion of this Sox2 neural enhancer cluster causes reduced SOX2 protein, less frequent interaction with transcriptional machinery, and leads to perturbed chromatin accessibility genome-wide further affecting the expression of neurodevelopmental and anterior-posterior regionalization genes. Furthermore, homozygous NSPC deletants exhibit self-renewal defects and impaired differentiation into cell types found in the brain. Altogether, our data define a cis-regulatory enhancer cluster controlling Sox2 transcription in NSPCs and highlight the sensitivity of neural differentiation processes to decreased Sox2 transcription, which causes differentiation into posterior neural fates, specifically the caudal neural tube. This study highlights the importance of precise Sox2 regulation by SRR2-18 in neural differentiation.

PMID:39849901 | DOI:10.1093/g3journal/jkaf012

J Am Chem Soc. 2025 Jan 23. doi: 10.1021/jacs.4c13464. Online ahead of print.

ABSTRACT

The technology of native chemical ligation and postligation desulfurization has greatly expanded the scope of modern chemical protein synthesis. Here, we report that ultrasonic energy can trigger robust and clean protein desulfurization, and we developed an ultrasound-induced desulfurization (USID) strategy that is simple to use and generally applicable to peptides and proteins. The USID strategy involves a simple ultrasonic cleaning bath and an easy-to-use and easy-to-remove sonosensitizer, titanium dioxide. It features mild and convenient reaction conditions and excellent functional group compatibility, e.g., with thiazolidine (Thz) and serotonin, which are sensitive to other desulfurization strategies. The USID strategy is robust: without reoptimizing the reaction conditions, the same USID procedure can be used for the clean desulfurization of a broad range of proteins with one or more sulfhydryl groups, even in multi-hundred-milligram scale reactions. The utility of USID was demonstrated by the one-pot synthesis of bioactive cyclopeptides such as Cycloleonuripeptide E and Segetalin F, as well as convergent chemical synthesis of functionally important proteins such as histone H3.5 using Thz as a temporary protecting group. A mechanistic investigation indicated that USID proceeds via a radical-based mechanism promoted by low-frequency and low-intensity ultrasonication. Overall, our work introduces a mechanically triggered approach with the potential to become a robust desulfurization method for general use in chemical protein synthesis by both academic and industrial laboratories.

PMID:39849831 | DOI:10.1021/jacs.4c13464

BMC Bioinformatics. 2025 Jan 23;26(1):27. doi: 10.1186/s12859-025-06049-9.

ABSTRACT

BACKGROUND: Currently, synthetic genomics is a rapidly developing field. Its main tasks, such as the design of synthetic sequences and the assembly of DNA sequences from synthetic oligonucleotides, require specialized software. In this article, we present a program with a graphical interface that allows non-bioinformatics to perform the tasks needed in synthetic genomics.

RESULTS: We developed BAC-browser v.2.1. It helps to design nucleotide sequences and features the following tools: generate nucleotide sequence from amino acid sequences using a codon frequency table for a specific organism, as well as visualization of restriction sites, GC composition, GC skew and secondary structure. To assemble DNA sequences, a fragmentation tool was created: regular breakdown into oligonucleotides of a certain length and breakdown into oligonucleotides with thermodynamic alignment. We demonstrate the possibility of DNA fragments assemblies designed in different modes of BAC-browser.

CONCLUSIONS: The BAC-browser has a large number of tools for working in the field of systemic genomics and is freely available at the link with a direct link https://sysbiomed.ru/upload/BAC-browser-2.1.zip .

PMID:39849360 | DOI:10.1186/s12859-025-06049-9

Acad Radiol. 2025 Jan 22:S1076-6332(24)00968-1. doi: 10.1016/j.acra.2024.12.020. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Preoperative assessment of axillary lymph node (ALN) status is essential for breast cancer management. This study explores the use of photoacoustic (PA) imaging combined with attention-guided deep learning (DL) for precise prediction of ALN status.

MATERIALS AND METHODS: This retrospective study included patients with histologically confirmed early-stage breast cancer from 2022 to 2024, randomly divided (8:2) into training and test cohorts. All patients underwent preoperative dual modal photoacoustic-ultrasound (PA-US) examination, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALN status. Attention-guided DL model was developed using PA-US images to predict ALN status. A clinical model, constructed via multivariate logistic regression, served as the baseline for comparison. Subsequently, a nomogram incorporating the DL model and independent clinical parameters was developed. The performance of the models was evaluated through discrimination, calibration, and clinical applicability.

RESULTS: A total of 324 patients (mean age ± standard deviation, 51.0 ± 10.9 years) were included in the study and were divided into a development cohort (n = 259 [79.9%]) and a test cohort (n = 65 [20.1%]). The clinical model incorporating three independent clinical parameters yielded an area under the curve (AUC) of 0.775 (95% confidence interval [CI], 0.711-0.829) in the training cohort and 0.783 (95% CI, 0.654-0.897) in the test cohort for predicting ALN status. In comparison, the nomogram showed superior predictive performance, with an AUC of 0.906 (95% CI, 0.867-0.940) in the training cohort and 0.868 (95% CI, 0.769-0.954) in the test cohort. Decision curve analysis further confirmed the nomogram's clinical applicability, demonstrating a better net benefit across relevant threshold probabilities.

CONCLUSION: This study highlights the effectiveness of attention-guided PA imaging in breast cancer patients, providing novel nomograms for individualized clinical decision-making in predicting ALN node status.

PMID:39848886 | DOI:10.1016/j.acra.2024.12.020

AJNR Am J Neuroradiol. 2025 Jan 23. doi: 10.3174/ajnr.A8493. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Whether differences in the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status of glioblastoma (GBM) are reflected in MRI markers remains largely unknown. In this work, we analyze the ADC in the perienhancing infiltration zone of GBM according to the corresponding MGMT status by using a novel distance-resolved 3D evaluation.

MATERIALS AND METHODS: One hundred one patients with IDH wild-type GBM were retrospectively analyzed. GBM was segmented in 3D with deep learning. Tissue with FLAIR hyperintensity around the contrast-enhanced tumor was divided into concentric distance-resolved subvolumes. Mean ADC was calculated for the 3D tumor core and for the distance-resolved volumes around the core. Differences in group mean ADC between patients with MGMT promoter methylated (mMGMT, n = 43) and MGMT promoter unmethylated (uMGMT, n = 58) GBM was analyzed with Wilcoxon signed rank test.

RESULTS: For both mMGMT and uMGMT GBM, mean ADC values around the tumor core significantly increased as a function of distance from the core toward the periphery of the perienhancing FLAIR hyperintensity (approximately 10% increase within 5 voxels with P < 001). While group mean ADC in the tumor core was not significantly different, the distance-resolved ADC profile around the core was approximately 10% higher in mMGMT than in uMGMT GBM (P < 10-8 at 5 voxel distance from the tumor core).

CONCLUSIONS: Distance-resolved volumetric ADC analysis around the tumor core reveals tissue signatures of GBM imperceptible to the human eye on conventional MRI. The different ADC profiles around the core suggest epigenetically influenced differences in perienhancing tissue characteristics between mMGMT and uMGMT GBM.

PMID:39848779 | DOI:10.3174/ajnr.A8493

J Hepatol. 2025 Jan 21:S0168-8278(25)00018-2. doi: 10.1016/j.jhep.2025.01.011. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Accurate multi-classification is the prerequisite for reasonable management of focal liver lesions (FLLs). Ultrasound is the common image examination, but lacks accuracy. Contrast enhanced ultrasound (CEUS) offers better performance, but highly relies on experience. Therefore, we aimed to develop a CEUS-based artificial intelligence (AI) model for FLL multi-classification and evaluate its performance in multicenter clinical tests.

METHODS: Since January 2017 to December 2023, CEUS videos, immunohistochemical biomarkers and clinical information of solid FLLs>1cm in adults were collected from 52 centers to build and test the model. It aimed to classify FLLs into six types: hepatocellular carcinoma, hepatic metastasis, intrahepatic cholangiocarcinoma, hepatic hemangioma, hepatic abscess and others. First, Module-Disease, Module-Biomarker and Module-Clinic were built in training set A and validation set. Then, three modules were aggregated as Model-DCB in training set B and internal test set. Model-DCB performance was compared with CEUS and MRI radiologists in three external test sets.

RESULTS: In total 3725 FLLs from 52 centers were divided into training set A (n=2088), validation set (n=592), training set B (n=234), internal test set (n=110), external test set A (n=113), B (n=276) and C (n=312). In external test sets A, B and C, Model-DCB all achieved significantly better performance (Accuracy from 0.85 to 0.86) than junior CEUS-radiologists (0.59-0.73), and comparable to senior CEUS-radiologists (0.79-0.85) and senior MRI-radiologists (0.82-0.86). In multiple subgroup analyses on demographic characteristics, tumor characteristics and ultrasound devices, its accuracy ranged from 0.79 to 0.92.

CONCLUSIONS: CEUS-based Model-DCB provides accurate multi-classification of FLLs. It holds promise to benefit a wide range of population, especially for patients in remote suburban areas who have difficulty accessing MRI.

IMPACT AND IMPLICATIONS: Ultrasound is the most common image examination for screening focal liver lesions (FLLs), but it lacks accuracy for multi-classification, which is the prerequisite for reasonable management. Contrast enhanced ultrasound (CEUS) offers better diagnostic performance, but highly relies on work experience of radiologists. We develop a CEUS-based model (Model-DCB) can assist junior CEUS radiologists to achieve comparable diagnostic ability to senior CEUS radiologists and senior MRI radiologists. The combination of ultrasound device, CEUS examination and Model-DCB enables even patients in remote areas to obtain excellent diagnostic performance through examination by junior radiologists.

CLINICAL TRIAL: NCT04682886.

PMID:39848548 | DOI:10.1016/j.jhep.2025.01.011

Drug Alcohol Depend. 2025 Jan 15;268:112554. doi: 10.1016/j.drugalcdep.2025.112554. Online ahead of print.

ABSTRACT

BACKGROUND: The hypothalamus is involved in stress regulation and reward processing, with its various nuclei exhibiting unique functions and connections. However, human neuroimaging studies on the hypothalamic subregions are limited in drug addiction. This study examined the volumes and functional connectivity of hypothalamic subregions in individuals with alcohol use disorder (AUD).

METHOD: The study included 24 male patients with AUD who had maintained abstinence and 24 healthy male controls, all of whom underwent brain structural and resting-state functional magnetic resonance imaging. The hypothalamus was segmented into five subunits using a deep learning-based algorithm, with comparisons of volumes and functional connectivity (FC) between the two groups. The relationships between these measures and alcohol-related characteristics were examined in the AUD group.

RESULTS: Findings indicated lower volumes in the anterior-superior (corrected-p < 0.001) and tuberal-superior subunits (corrected-p = 0.002) and altered FC of these and the anterior-inferior subunit among AUD patients (corrected-p < 0.05). Moreover, greater disease severity and a longer history of heavy drinking correlated with lower volumes in the anterior-superior (r = -0.42, p = 0.045) and tuberal-superior subregions (r = -0.61, p = 0.013), respectively. Conversely, a longer abstinence duration was associated with larger volumes in the anterior-superior (r = 0.56, p = 0.008) and tuberal-superior subunits (r = 0.40, p = 0.048) and with higher FC between the tuberal-superior hypothalamus and the thalamus, caudate, and anterior cingulate cortex (r = 0.55, p = 0.014).

CONCLUSIONS: Our results suggest that specific regional alterations within the hypothalamus, particularly the superior subregions, are associated with AUD, and more importantly, that these alterations may be reversible with prolonged abstinence.

PMID:39848134 | DOI:10.1016/j.drugalcdep.2025.112554

Comput Methods Programs Biomed. 2025 Jan 20;261:108606. doi: 10.1016/j.cmpb.2025.108606. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is a significant cause of life-threatening heart disease due to its potential to lead to stroke and heart failure. Although deep learning-assisted diagnosis of AF based on ECG holds significance in clinical settings, it remains unsatisfactory due to insufficient consideration of noise and redundant features. In this work, we propose a novel multiscale feature-enhanced gating network (MFEG Net) for AF diagnosis.

METHOD: The network integrates multiscale convolution, adaptive feature enhancement (FE), and dynamic temporal processing. The multiscale convolution helps capture global and local information. The FE module consists of a soft-threshold residual shrinkage component, a dilated convolution module, and a Squeeze-and-Excitation (SE) module, eliminating redundant features and emphasizing effective features. The design allows the network to focus on the most relevant AF features, thereby enhancing its robustness and accuracy in the presence of noise and irrelevant information. The dynamic temporal module helps the network learn and recognize the time dependence associated with AF. The novel design endows the model with excellent robustness to cope with random noise in real-world environments.

RESULT: Compared with the state-of-the-art methods, our model exhibits excellent classification performance with an accuracy of 0.930, an F1 score of 0.883, and remarkable resilience to noise interference on the PhysioNet Challenge 2017 dataset. Moreover, the model was trained on the CinC2017 database and validated on the CPSC2018 database and AFDB database, achieving accuracies of 0.908 and 0.938, respectively.

CONCLUSION: The excellent classification performance of MFEG Net, coupled with its robustness in processing noisy electrocardiogram signals, makes it a powerful method for automatic atrial fibrillation detection. This method has made significant progress over state-of-the-art methods and may alleviate the burden of manual diagnosis for clinical doctors.

PMID:39847993 | DOI:10.1016/j.cmpb.2025.108606

Int J Biol Macromol. 2025 Jan 21:140204. doi: 10.1016/j.ijbiomac.2025.140204. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) affects the population worldwide, occupying the first place in terms of death and incidence. Synthetic peptides (SPs) emerged as alternative molecules due to their activity and low toxicity. Proteomic analysis of PepGAT-treated HCT-116 cells revealed a decreased abundance of proteins involved in ROS metabolism and energetic metabolisms, cell cycle, DNA repair, migration, invasion, cancer aggressiveness, and proteins involved in resistance to 5-FU. PepGAT induced earlier ROS and apoptosis in HCT-116 cells, cell cycle arrest, and inhibited HCT-116 migration. PepGAT enhances the action of 5-FU against HCT-116 cells by dropping down 6-fold the 5-FU toward HCT-116 and reduces its toxicity for non-cancerous cells. These findings strongly suggest the multiple mechanisms of action displayed by PepGAT against CRC cells and its potential to either be studied alone or in combination with 5-FU to develop new studies against CRC and might develop new drugs against it.

PMID:39848367 | DOI:10.1016/j.ijbiomac.2025.140204

J Cyst Fibros. 2025 Jan 22:S1569-1993(25)00006-2. doi: 10.1016/j.jcf.2025.01.007. Online ahead of print.

ABSTRACT

BACKGROUND: Reproductive life planning is key, now that people with cystic fibrosis (pwCF) may live into their 60s. This study explores contraceptive use, pregnancy trends, and whether concomitant cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy reduces contraceptive effectiveness.

METHODS: Females with CF aged 18-45 years from 10 U.S. CF centers completed a self-administered web-based questionnaire. Pregnancy rates were calculated by linear-mixed models with a logit link detected associations with contraception and modulator use.

RESULTS: A total of 561 pwCF (median age of 29 years [IQR 24.9-35.8] years) completed the survey. Most participants (n = 499, 89%) used modulators, and almost all (n = 555, 99%) used contraception. Condoms (n = 448, 80%) and oral contraceptive pills (n = 363, 65%) were the most prevalent methods used. One-third (n = 189, 34%) reported ever being pregnant. Of those reporting pregnancies (n = 319), about half (n = 151, 48%) were unintended. Pregnancy was significantly associated with age (20-29 years or 30-39 years), partner cohabitation (aOR 21.5, 95% CI 5.1 to 91.1), and non-hormonal contraceptive use (aOR 5.1, 95% CI 1.1 to23.0). Among pwCF cohabitating with a partner, modulator use was positively associated with pregnancy (OR 1.8, 95% CI 1.3 to 2.6) (p = 0.0008).

CONCLUSIONS: Despite almost universal contraceptive use, unintended pregnancy among pwCF is common. Likelihood of pregnancy is increased among CFTR modulator users who are partnered, although CFTR modulators themselves do not appear to decrease hormonal contraceptive effectiveness. Patient education about contraception is an increasingly critical aspect of CF care.

PMID:39848844 | DOI:10.1016/j.jcf.2025.01.007

Int J Comput Assist Radiol Surg. 2025 Jan 23. doi: 10.1007/s11548-024-03315-8. Online ahead of print.

ABSTRACT

PURPOSE: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for segmentation, it falls short in landmark detection, a strength of shape-based approaches.

METHODS: In this work, we propose a dense image-to-shape representation that enables the joint learning of landmarks and semantic segmentation by employing a fully convolutional architecture. Our method intuitively allows the extraction of arbitrary landmarks due to its representation of anatomical correspondences. We benchmark our method against the state-of-the-art for semantic segmentation (nnUNet), a shape-based approach employing geometric deep learning and a convolutional neural network-based method for landmark detection.

RESULTS: We evaluate our method on two medical datasets: one common benchmark featuring the lungs, heart, and clavicle from thorax X-rays, and another with 17 different bones in the paediatric wrist. While our method is on par with the landmark detection baseline in the thorax setting (error in mm of 2.6 ± 0.9 vs. 2.7 ± 0.9 ), it substantially surpassed it in the more complex wrist setting ( 1.1 ± 0.6 vs. 1.9 ± 0.5 ).

CONCLUSION: We demonstrate that dense geometric shape representation is beneficial for challenging landmark detection tasks and outperforms previous state-of-the-art using heatmap regression. While it does not require explicit training on the landmarks themselves, allowing for the addition of new landmarks without necessitating retraining.

PMID:39849288 | DOI:10.1007/s11548-024-03315-8

Neurosurg Rev. 2025 Jan 24;48(1):78. doi: 10.1007/s10143-025-03236-3.

ABSTRACT

Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data. A systematic search was performed in PubMed/MEDLINE, Embase, and the Cochrane Library for studies published up to April 1, 2024, and reporting the performance metrics of the ML models in predicting of WHO meningioma grade using imaging studies. Pooled area under the receiver operating characteristics curve (AUROC), specificity, and sensitivity were estimated. Subgroup and meta-regression analyses were performed based on a number of potential influencing variables. A total of 32 studies with 15,365 patients were included in the present study. The overall pooled sensitivity, specificity, and AUROC of ML methods for prediction of tumor grade in meningioma were 85% (95% CI, 79-89%), 87% (95% CI, 81-91%), and 93% (95% CI, 90-95%), respectively. Both the type of validation and study cohort (training or test) were significantly associated with model performance. However, no significant association was found between the sample size or the type of ML method and model performance. The ML predictive models show a high overall performance in predicting the WHO meningioma grade using imaging data. Further studies on the performance of DL algorithms in larger datasets using external validation are needed.

PMID:39849257 | DOI:10.1007/s10143-025-03236-3

Med Biol Eng Comput. 2025 Jan 24. doi: 10.1007/s11517-024-03279-6. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a significant neurological disorder that imposes a substantial burden on society, characterized by its high recurrence rate and associated suicide risk. Clinical diagnosis, which relies on interviews with psychiatrists and questionnaires used as auxiliary diagnostic tools, lacks precision and objectivity in diagnosing MDD. To address these challenges, this study proposes an assessment method based on EEG. It involves calculating the phase lag index (PLI) in alpha and gamma bands to construct functional brain connectivity. This method aims to find biomarkers to assess the severity of MDD and suicidal ideation. The convolutional inception with shuffled attention network (CISANET) was introduced for this purpose. The study included 61 patients with MDD, who were classified into mild, moderate, and severe levels based on depression scales, and the presence of suicidal ideation was evaluated. Two paradigms were designed for the study, with EEG analysis focusing on 32 selected electrodes to extract alpha and gamma bands. In the gamma band, the classification accuracy reached 77.37% in the visual paradigm and 80.12% in the auditory paradigm. The average accuracy in classifying suicidal ideation was 93.60%. The findings suggest that gamma bands can be used as potential biomarkers differentiating illness severity and identifying suicidal ideation of MDD, and that objective assessment methods can effectively assess MDD The objective assessment method can effectively assess the severity of MDD and identify suicidal ideation of MDD patients, which provides a valuable theoretical basis for understanding the biological characteristics of MDD.

PMID:39849234 | DOI:10.1007/s11517-024-03279-6

J Imaging Inform Med. 2025 Jan 23. doi: 10.1007/s10278-025-01402-z. Online ahead of print.

ABSTRACT

Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures. Initially, we treated the 3D images as sequences of two-dimensional (2D) slices and fed them sequentially into 2D convolutional neural network (CNN) models pretrained on ImageNet, averaging the outputs to obtain the final predicted stage. We also applied 3D CNN models pretrained on Kinetics-400. Additionally, we incorporated an attention mechanism to account for the varying importance of different slices in the prediction process. To further enhance model efficacy and robustness, we simultaneously trained the two data sets using weight sharing, a technique known as cotraining. Our results demonstrated that 2D models pretrained on ImageNet outperformed 3D models pretrained on Kinetics-400, and models utilizing the attention mechanism outperformed both 2D and 3D models. The cotraining technique proved effective in improving model performance when the cotraining data sets were sufficiently large.

PMID:39849204 | DOI:10.1007/s10278-025-01402-z

J Imaging Inform Med. 2025 Jan 23. doi: 10.1007/s10278-025-01386-w. Online ahead of print.

ABSTRACT

Accurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image analysis. In this paper, we introduce a novel dual attention U-Net model designed for precise wound segmentation. Our proposed architecture integrates two widely used deep learning models, VGG16 and U-Net, incorporating dual attention mechanisms to focus on relevant regions within the wound area. Initially trained on diabetic foot ulcer images, we fine-tuned the model to acute and chronic wound images and conducted a comprehensive comparison with other state-of-the-art models. The results highlight the superior performance of our proposed dual attention model, achieving a Dice coefficient and IoU of 94.1% and 89.3%, respectively, on the test set. This underscores the robustness of our method and its capacity to generalize effectively to new data.

PMID:39849203 | DOI:10.1007/s10278-025-01386-w