Alzheimers Res Ther. 2025 Feb 12;17(1):40. doi: 10.1186/s13195-025-01681-2.

ABSTRACT

BACKGROUND: The importance of natural killer (NK) cells of the innate immune system in neurodegenerative disease has largely been overlooked despite studies demonstrating their ability to reduce neuroinflammation (thought to be mediated by the elimination of activated T cells, degradation of protein aggregates and secretion of anti-inflammatory cytokines). SNK01 is an autologous non-genetically modified NK cell product showing increased activity in vitro. We hypothesized that SNK01 can be safely infused to reduce neuroinflammation in Alzheimer's Disease (AD) patients.

METHODS: SNK01 was produced and characterized for its ability to eliminate activated T cells, degrade protein aggregates and secrete anti-inflammatory cytokines. In this phase 1 study, SNK01 was administered intravenously every three weeks for a total of 4 treatments using a 3 + 3 dose escalation design (1, 2 and 4 × 109 cells) in subjects with either mild, moderate, or severe AD (median CDR-SB 10.0). Cognitive assessments and cerebrospinal fluid biomarkers associated with protein aggregation, neurodegeneration and neuroinflammation including amyloid-β42 and 42/40, α-synuclein, total Tau, pTau217 and pTau181, neurofilament light, GFAP and YKL-40 analyses were performed at baseline, at 1 and 12 weeks after the last dose. The primary endpoint was safety; secondary endpoints included changes in cognitive assessments and biomarker levels.

RESULTS: In preclinical in vitro studies, SNK01 were able to uptake and degrade the protein aggregates of amyloid-β and α-synuclein, produce anti-inflammatory cytokines and eliminate activated T cells. In the phase 1 clinical study, eleven subjects were enrolled (10 evaluable). No drug-related adverse events were observed. Despite 70% of subjects being treated at relatively low doses of SNK01 (1 and 2 × 109 cells), 50-70% of all enrolled subjects had stable/improved CDR-SB, ADAS-Cog and/or MMSE scores and 90% had stable/improved ADCOMS at one-week after the last dose. SNK01 also appeared to have beneficial effects on protein aggregate levels and neuroinflammatory biomarkers in the cerebrospinal fluid, with decreases in pTau181 and GFAP appearing to be dose-dependent.

CONCLUSIONS: SNK01 was well tolerated and appeared to have clinical activity in AD while also having beneficial effects on cerebrospinal fluid protein and neuroinflammatory biomarker levels. A larger trial with a higher dosing/duration has been initiated in the USA in 2023.

TRIAL REGISTRATION: www.

CLINICALTRIALS: gov NCT04678453, date of registration: 2020-12-22.

PMID:39939891 | DOI:10.1186/s13195-025-01681-2

PLoS One. 2025 Feb 12;20(2):e0318560. doi: 10.1371/journal.pone.0318560. eCollection 2025.

ABSTRACT

Variability of myoelectric activity during walking is the result of human capability to adapt to both intrinsic and extrinsic perturbations. The availability of sEMG signals lasting at least some minutes (instead of seconds) is needed to comprehensively analyze the variability of surface electromyographic (sEMG) signals. The current study introduces a dataset of long-lasting sEMG signals recorded during walking sessions of 31 healthy subjects, aged between 20 and 30 years, conducted at the Movement Analysis Lab of Università Politecnica delle Marche, Ancona, Italy. The sEMG signals were captured from ten distinct lower-limb muscles (five per leg), including gastrocnemius lateralis (GL), tibialis anterior (TA), rectus femoris (RF), hamstrings (Ham), and vastus lateralis (VL). Synchronized electrogoniometric and foot-floor-contact signals are also supplied to enable the spatial/temporal analysis of the sEMG signals. The experimental procedure involves subjects walking barefoot on level ground for approximately 5 minutes at their natural speed and pace, following an eight-shaped path featuring linear diagonal segments, curves, accelerations, and decelerations. An advanced analysis of the sEMG signals was performed to test the reliability and usability of the current dataset. The considerable duration of the signals makes this dataset particularly useful for studies where a significant volume of data is crucial, such as machine/deep learning approaches, investigations examining the variability of muscle recruitment during physiological walking, validations of the reliability of novel sEMG-based algorithms, and assembly of reference datasets for pathological condition characterization.

PMID:39937870 | DOI:10.1371/journal.pone.0318560

PLoS One. 2025 Feb 12;20(2):e0316253. doi: 10.1371/journal.pone.0316253. eCollection 2025.

ABSTRACT

Intrusion detection plays a significant role in the provision of information security. The most critical element is the ability to precisely identify different types of intrusions into the network. However, the detection of intrusions poses a important challenge, as many new types of intrusion are now generated by cyber-attackers every day. A robust system is still elusive, despite the various strategies that have been proposed in recent years. Hence, a novel deep-learning-based architecture for detecting intrusions into a computer network is proposed in this paper. The aim is to construct a hybrid system that enhances the efficiency and accuracy of intrusion detection. The main contribution of our work is a novel deep learning-based hybrid architecture in which PSO is used for hyperparameter optimisation and three well-known pre-trained network models are combined in an optimised way. The suggested method involves six key stages: data gathering, pre-processing, deep neural network (DNN) architecture design, optimisation of hyperparameters, training, and evaluation of the trained DNN. To verify the superiority of the suggested method over alternative state-of-the-art schemes, it was evaluated on the KDDCUP'99, NSL-KDD and UNSW-NB15 datasets. Our empirical findings show that the proposed model successfully and correctly classifies different types of attacks with 82.44%, 90.42% and 93.55% accuracy values obtained on UNSW-B15, NSL-KDD and KDDCUP'99 datasets, respectively, and outperforms alternative schemes in the literature.

PMID:39937819 | DOI:10.1371/journal.pone.0316253

Gigascience. 2025 Jan 6;14:giae123. doi: 10.1093/gigascience/giae123.

ABSTRACT

Deep learning can revolutionise high-throughput image-based phenotyping by automating the measurement of complex traits, a task that is often labour-intensive, time-consuming, and prone to human error. However, its precision and adaptability in accurately phenotyping organ-level traits, such as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effectiveness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using images from a multiparent advanced generation intercross (MAGIC) mapping family. We trained an instance segmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong performance with an average precision of 88.0% for detection and 55.9% for segmentation. Quantitative trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on automated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scalable and provides high-quality phenotype data, facilitating genetic analysis and gene discovery, as well as advancing crop breeding research.

PMID:39937596 | DOI:10.1093/gigascience/giae123

Ann Am Thorac Soc. 2025 Feb 12. doi: 10.1513/AnnalsATS.202501-130ED. Online ahead of print.

NO ABSTRACT

PMID:39938064 | DOI:10.1513/AnnalsATS.202501-130ED

Physiology (Bethesda). 2025 Feb 12. doi: 10.1152/physiol.00045.2024. Online ahead of print.

ABSTRACT

Over the past several decades, physiological research has undergone a progressive shift toward greater-and-greater reductionism, culminating in the rise of 'molecular physiology.' The introduction of Omic techniques, chiefly protein mass spectrometry and next-generation DNA sequencing (NGS), has further accelerated this trend, adding massive amounts of information about individual genes, mRNA transcripts, and proteins. However, the long-term goal of understanding physiological and pathophysiological processes at a whole-organism level has not been fully realized. This review summarizes the major protein mass spectrometry and NGS techniques relevant to physiology and explores the challenges of merging data from Omic methodologies with data from traditional hypothesis-driven research to broaden the understanding of physiological mechanisms. It summarizes recent progress in large-scale data integration through: 1) creation of online user-friendly Omic data resources with cross-indexing across data sets to democratize access to Omic data; 2) application of Bayesian methods to combine data from multiple Omic data sets with knowledge from hypothesis-driven studies in order to address specific physiological and pathophysiological questions; and 3) application of concepts from Natural Language Processing to probe the literature and to create user-friendly causal graphs representing physiological mechanisms. Progress in development of so-called "Large Language Models", e.g. ChatGPT, for knowledge integration is also described along with a discussion of the shortcomings of Large Language Models with regard to management and integration of physiological data.

PMID:39938090 | DOI:10.1152/physiol.00045.2024

Sci Adv. 2025 Feb 14;11(7):eads0760. doi: 10.1126/sciadv.ads0760. Epub 2025 Feb 12.

ABSTRACT

Defects in cell wall integrity (CWI) profoundly affect plant growth, although, underlying mechanisms are not well understood. We show that in Arabidopsis mur1 mutant, CWI defects from compromising dimerization of RG-II pectin, a key component of cell wall, attenuate the expression of auxin response factors ARF7-ARF19. As a result, polar auxin transport components are misexpressed, disrupting auxin response asymmetry, leading to defective apical hook development. Accordingly, mur1 hook defects are suppressed by enhancing ARF7 expression. In addition, expression of brassinosteroid biosynthesis genes is down-regulated in mur1 mutant, and supplementing brassinosteroid or enhancing brassinosteroid signaling suppresses mur1 hook defects. Intriguingly, brassinosteroid enhances RG-II dimerization, showing hormonal feedback to the cell wall. Our results thus reveal a previously unrecognized link between cell wall defects from reduced RG-II dimerization and growth regulation mediated via modulation of auxin-brassinosteroid pathways in early seedling development.

PMID:39937898 | DOI:10.1126/sciadv.ads0760

ACS Infect Dis. 2025 Feb 12. doi: 10.1021/acsinfecdis.4c00666. Online ahead of print.

ABSTRACT

Neglected tropical diseases such as Chagas disease, human African trypanosomiasis, leishmaniasis, and schistosomiasis have a significant global health impact in predominantly developing countries, although these diseases are spreading due to increased international travel and population migration. Drug repurposing with a focus on increasing antiparasitic potency and drug-like properties is a cost-effective and efficient route to the development of new therapies. Here we identify compounds that have potent activity against Trypanosoma cruzi and Leishmania donovani, and the latter were progressed into the murine model of infection. Despite the potent in vitro activity, there was no effect on parasitemia, necessitating further work to improve the pharmacokinetic properties of this series. Nonetheless, valuable insights have been obtained into the structure-activity and structure-property relationships of this compound series.

PMID:39936822 | DOI:10.1021/acsinfecdis.4c00666

Mem Inst Oswaldo Cruz. 2025 Feb 7;120:e240183. doi: 10.1590/0074-02760240183. eCollection 2025.

ABSTRACT

BACKGROUND: The neglected illness Chagas disease is treated with limited efficacy and adverse effects by old drugs. Due to the low interest of pharmaceutical industry in targeting economically depressed-patients, repurposing is a tool that should be applied because it can introduce new anti-Chagas entities into the clinic at reduced costs.

OBJECTIVES: To investigate the repurposing/combination of medicines strategies as anti-Chagas treatment.

METHODS: Epimastigotes, trypomastigotes and amastigotes of Trypanosoma cruzi were in vitro exposed to 28 Uruguayan-approved medicines not previously tested, 28 FDA-approved medicines previously evaluated, and three reference agents. Parasite inhibition was assessed and for the best drugs, in pairs-isobolographic studies, looking for synergism/additivity/antagonism, were done. Macrophages were used to study selectivity. For some relevant agents, we analysed whether medicines mammals´ action mechanisms are operative in epimastigotes-T. cruzi.

FINDINGS: From the anti-epimastigotes monotherapy-screening, we found that 18% of them showed better/comparable activities than references. Additionally, for the binary-combinations 8% were additive, 4% were synergic and the rest showed antagonism. Favourably, in macrophages-cytotoxicity four of the binary-combinations were antagonists. Naftazone and pinaverium bromide, not previously tested against T. cruzi, maintained their activity against trypomastigotes and amastigotes. The identified action mechanisms open the door to new strategies designing anti-T. cruzi drugs.

MAIN CONCLUSIONS: Using approved-medicines is a good strategy for new anti-Chagas treatments.

PMID:39936704 | DOI:10.1590/0074-02760240183

Curr Neuropharmacol. 2025 Feb 6. doi: 10.2174/011570159X327908241121062335. Online ahead of print.

ABSTRACT

Neurodegenerative diseases represent a prevalent category of age-associated diseases. As human lifespans extend and societies become increasingly aged, neurodegenerative diseases pose a growing threat to public health. The lack of effective therapeutic drugs for both common and rare neurodegenerative diseases amplifies the medical challenges they present. Current treatments for these diseases primarily offer symptomatic relief rather than a cure, underscoring the pressing need to develop efficacious therapeutic interventions. Drug repositioning, an innovative and data-driven approach to research and development, proposes the re-evaluation of existing drugs for potential application in new therapeutic areas. Fueled by rapid advancements in artificial intelligence and the burgeoning accumulation of medical data, drug repositioning has emerged as a promising pathway for drug discovery. This review comprehensively examines drug repositioning for neurodegenerative diseases through the lens of translational informatics, encompassing data sources, computational models, and clinical applications. Initially, we systematized drug repositioning-related databases and online platforms, focusing on data resource management and standardization. Subsequently, we classify computational models for drug repositioning from the perspectives of drug-drug, drug-target, and drug-disease interactions into categories such as machine learning, deep learning, and networkbased approaches. Lastly, we highlight computational models presently utilized in neurodegenerative disease research and identify databases that hold potential for future drug repositioning efforts. In the artificial intelligence era, drug repositioning, as a data-driven strategy, offers a promising avenue for developing treatments suited to the complex and multifaceted nature of neurodegenerative diseases. These advancements could furnish patients with more rapid, cost-effective therapeutic options.

PMID:39936420 | DOI:10.2174/011570159X327908241121062335

Elife. 2025 Feb 12;13:RP98523. doi: 10.7554/eLife.98523.

ABSTRACT

Endosomes have emerged as major signaling hubs where different internalized ligand-receptor complexes are integrated and the outcome of signaling pathways are organized to regulate the strength and specificity of signal transduction events. Ezrin, a major membrane-actin linker that assembles and coordinates macromolecular signaling complexes at membranes, has emerged recently as an important regulator of lysosomal function. Here, we report that endosomal-localized EGFR/Ezrin complex interacts with and triggers the inhibition of the Tuberous Sclerosis Complex (TSC complex) in response to EGF stimuli. This is regulated through activation of the AKT signaling pathway. Loss of Ezrin was not sufficient to repress TSC complex by EGF and culminated in translocation of TSC complex to lysosomes triggering suppression of mTORC1 signaling. Overexpression of constitutively active EZRINT567D is sufficient to relocalize TSC complex to the endosomes and reactivate mTORC1. Our findings identify EZRIN as a critical regulator of autophagy via TSC complex in response to EGF stimuli and establish the central role of early endosomal signaling in the regulation of mTORC1. Consistently, Medaka fish deficient for Ezrin exhibit defective endo-lysosomal pathway, attributable to the compromised EGFR/AKT signaling, ultimately leading to retinal degeneration. Our data identify a pivotal mechanism of endo-lysosomal signaling involving Ezrin and its associated EGFR/TSC complex, which are essential for retinal function.

PMID:39937579 | DOI:10.7554/eLife.98523

Cien Saude Colet. 2025 Feb;30(2):e07652023. doi: 10.1590/1413-81232025302.07652023. Epub 2023 Nov 6.

ABSTRACT

The scope of this study was to understand the experiences of patients with rare diseases based on the reconstruction of therapeutic itineraries, obtained between 2021 and 2022 using thematic analysis. Common experiences in coping with rare diseases were observed, similar to those referred to globally, perpetuating the vicious circle between specialties, obtaining a diagnosis, post-diagnostic therapies, lack of qualified information and dissemination of knowledge. Arrival at the rare disease reference service revealed a new meaning, based on trust in therapeutic relationships and diagnostic chances, however there was an absence and discontinuity in the provision of some specialties and multidisciplinary therapies. on a continuous basis, providing opportunities in an equal manner. In addition to the need for coordination of care, it was evident that the responsibility for coping with illness is primarily exercised by women, who assume responsibility for the daily therapeutic activities and care of their children. The role of primary care in timely referral and coordination of care in the care network was reaffirmed, promoting equal access and alleviating the burden of management borne by families, which goes beyond and overloads their journey, especially for women.

PMID:39936676 | DOI:10.1590/1413-81232025302.07652023

Adv Biol (Weinh). 2025 Feb 12:e2400329. doi: 10.1002/adbi.202400329. Online ahead of print.

ABSTRACT

Prior work suggests influenza A virus (IAV) crosses the airway mucus barrier in a sialic acid-dependent manner through the actions of the viral envelope proteins, hemagglutinin, and neuraminidase. However, host and viral factors that influence how efficiently mucus traps IAV remain poorly defined. In this work, how the physicochemical properties of mucus influence its ability to effectively capture IAV is assessed using fluorescence video microscopy and multiple particle tracking. Our studies suggest an airway mucus gel layer must be produced with virus-sized pores to physically constrain IAV. While sialic acid binding by IAV may improve mucus trapping efficiency, sialic acid binding preference is found to have little impact on IAV mobility and the fraction of viral particles expected to penetrate the mucus barrier. Further, synthetic polymeric hydrogels engineered with mucus-like architecture are similarly protective against IAV infection despite their lack of sialic acid decoy receptors. Together, this work provides new insights on mucus barrier function toward IAV with important implications on innate host defense and transmission of respiratory viruses.

PMID:39936480 | DOI:10.1002/adbi.202400329

J Antimicrob Chemother. 2025 Feb 12:dkaf042. doi: 10.1093/jac/dkaf042. Online ahead of print.

ABSTRACT

BACKGROUND: Dalbavancin exposures select for VAN and daptomycin cross-resistance in Staphylococcus aureus often by walK-related mutations. Oritavancin is another long-acting lipoglycopeptide, but its proclivity to select for cross-resistance is unknown. The objective of this study was to determine if post-distributional pharmacokinetic oritavancin exposures select for meaningful susceptibility changes in S. aureus.

METHODS: We simulated average post-distributional, free-drug exposures of oritavancin 1200 mg IV once (fCmax 11.2 µg/mL; β-elimination t1/2 13.4 h; γ-elimination t1/2 245 h) in an in vitro pharmacodynamic model for 28 days against five S. aureus including four MRSA. Samples were taken daily for colony enumeration and resistance screening. Susceptibility testing was repeated on isolates from resistance screening plates against oritavancin, vancomycin, daptomycin, dalbavancin and 6 beta-lactams with varying penicillin-binding protein affinities.

RESULTS: Tested oritavancin exposures were bactericidal against 5/5 strains for 2-17 days before regrowth of less-susceptible subpopulations occurred. Isolates with reduced susceptibility to oritavancin were detected as early as 5 days, but the MIC increased above the susceptibility breakpoint (>0.125 mg/L) in 4/5 strains eventually. Vancomycin and daptomycin MICs increased by 2- to 8-fold but did not exceed the susceptibility breakpoints in most isolates. β-lactam MICs were largely unchanged among the recovered isolates with reduced oritavancin susceptibility. Mutations were diverse but often involved purR with 13 unique variants identified among 4/5 strains.

CONCLUSIONS: Oritavancin-selected resistance was primarily associated with purR mutation and less frequently associated with cross-resistance and walK mutation than dalbavancin-selected resistance in similar strains and conditions. The reason for this is unclear but may stem from differences in the mechanism(s) and divergent mutational pathways.

PMID:39936452 | DOI:10.1093/jac/dkaf042

J Synchrotron Radiat. 2025 Mar 1. doi: 10.1107/S1600577525000323. Online ahead of print.

ABSTRACT

Full-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of X-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD) and structural similarity (SSIM) of the proposed framework on two independent X-ray data sets with those obtained from a baseline deep learning model, a Bayesian fusion framework and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With three subsequent images from the low-resolution (LR) sequence of a four times lower spatial resolution and another two images from the high-resolution (HR) sequence of a 20 times lower frame rate, the proposed approach achieved average PSNRs of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore the scientific value of UHS X-ray imaging experiments.

PMID:39937516 | DOI:10.1107/S1600577525000323

Forensic Sci Med Pathol. 2025 Feb 12. doi: 10.1007/s12024-025-00962-4. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to develop an improved method for forensic age estimation using deep learning models applied to orthopantomography (OPG) images, focusing on distinguishing individuals under 12 years old from those aged 12 and above.

METHODS: A dataset of 1941 pediatric patients aged between five and 15 years was collected from two radiology departments. The primary research question addressed the identification of the most effective deep learning model for this task. Various deep learning models including Xception, ResNet, ShuffleNet, InceptionV3, DarkNet, NasNet, DenseNet, EfficientNet, MobileNet, ResNet18, GoogleNet, SqueezeNet, and AlexNet were evaluated using traditional metrics like Classification Accuracy (CA), Sensitivity (SE), Specificity (SP), Kappa (K), Area Under the Curve (AUC), alongside a novel Polygon Area Metric (PAM) designed to handle imbalanced datasets common in forensic applications.

RESULTS: "Forensic Xception" model derived from Xception outperformed others, achieving a PAM score of 0.8828. This model demonstrated superior performance in accurately classifying individuals' age groups, with high CA, SE, SP, K, AUC, and F1 Score. Notably, the introduction of the PAM metric provided a comprehensive evaluation of classifier performance.

CONCLUSION: This study represents a significant advancement in forensic age estimation from OPG images, emphasizing the potential of deep learning models, particularly the "Forensic Xception" model, in accurately classifying individuals based on age, especially in legal contexts. This research suggests a promising avenue for further advancements in forensic dental age estimation, with future studies encouraged to explore additional datasets, refine models, and address ethical and legal considerations.

PMID:39937388 | DOI:10.1007/s12024-025-00962-4

Discov Oncol. 2025 Feb 12;16(1):165. doi: 10.1007/s12672-025-01942-4.

ABSTRACT

PURPOSE: In this work, we propose to develop a 3D U-Net-based deep learning model that accurately predicts the dose distribution for breast cancer radiotherapy.

METHODS: This study included 176 breast cancer patients, divided into training, validating and testing sets. A deep learning model based on the 3D U-Net architecture was developed to predict dose distribution, which employed a double encoder combination attention (DECA) module, a cross stage partial + Resnet + Attention (CRA) module, a difficulty perception and a critical regions loss. The performance and generalization ability of this model were evaluated by the voxel mean absolute error (MAE), several clinically relevant dosimetric indexes and 3D gamma passing rates.

RESULTS: Our model accurately predicted the 3D dose distributions with each dosage level mirroring the clinical reality in shape. The generated dose-volume histogram (DVH) matched with the ground truth curve. The total dose error of our model was below 1.16 Gy, complying with clinical usage standards. When compared to other exceptional models, our model optimally predicted eight out of nine regions, and the prediction errors for the first planning target volume (PTV1) and PTV2 were merely 1.03 Gy and 0.74 Gy. Moreover, the mean 3%/3 mm 3D gamma passing rates for PTV1, PTV2, Heart and Lung L achieved 91.8%, 96.4%, 91.5%, and 93.2%, respectively, surpassing the other models and meeting clinical standards.

CONCLUSIONS: This study developed a new deep learning model based on 3D U-Net that can accurately predict dose distributions for breast cancer radiotherapy, which can improve the quality and planning efficiency.

PMID:39937302 | DOI:10.1007/s12672-025-01942-4

Eur Radiol. 2025 Feb 12. doi: 10.1007/s00330-025-11401-x. Online ahead of print.

ABSTRACT

OBJECTIVE: To systematically evaluate glioma radiomics literature on differentiating between radiation-induced brain injury and tumor recurrence.

METHODS: Literature was searched on PubMed and Web of Science (end date: May 7, 2024). Quality of eligible papers was assessed using METhodological RadiomICs Score (METRICS) and Radiomics Quality Score (RQS). Reliability of quality scoring tools were analyzed. Meta-analysis, meta-regression, and subgroup analysis were performed.

RESULTS: Twenty-seven papers were included in the qualitative assessment. Mean average METRICS score and RQS percentage score across three readers was 57% (SD, 14%) and 16% (SD, 12%), respectively. Score-wise inter-rater agreement for METRICS ranged from poor to excellent, while RQS demonstrated moderate to excellent agreement. Item-wise agreement was moderate for both tools. Meta-analysis of 11 eligible studies yielded an estimated area under the receiver operating characteristic curve of 0.832 (95% CI, 0.757-0.908), with significant heterogeneity (I2 = 91%) and no statistical publication bias (p = 0.051). Meta-regression did not identify potential sources of heterogeneity. Subgroup analysis revealed high heterogeneity across all subgroups, with the lowest I2 at 68% in studies with proper validation and higher quality scores. Statistical publication bias was generally not significant, except in the subgroup with the lowest heterogeneity (p = 0.044). However, most studies in both qualitative analysis (26/27; 96%) and primary meta-analysis (10/11; 91%) reported positive effects of radiomics, indicating high non-statistical publication bias.

CONCLUSION: While a good performance was noted for radiomics, results should be interpreted cautiously due to heterogeneity, publication bias, and quality issues thoroughly examined in this study.

KEY POINTS: Question Radiomic literature on distinguishing radiation-induced brain injury from glioma recurrence lacks systematic reviews and meta-analyses that assess methodological quality using radiomics-specific tools. Findings While the results are encouraging, there was substantial heterogeneity, publication bias toward positive findings, and notable concerns regarding methodological quality. Clinical relevance Meta-analysis results need cautious interpretation due to significant problems detected during the analysis (e.g., suboptimal quality, heterogeneity, bias), which may help explain why radiomics has not yet been translated into clinical practice.

PMID:39937273 | DOI:10.1007/s00330-025-11401-x

Abdom Radiol (NY). 2025 Feb 12. doi: 10.1007/s00261-025-04834-x. Online ahead of print.

ABSTRACT

The purpose of this study was to explore the association between structural changes in abdominal organs and visceral fat obesity (VFO) using a fully automated three-dimensional (3D) volumetric computed tomography (CT) measurement method based on deep learning algorithm. A total of 610 patients (295 men and 315 women; mean age, 68.4 years old) were included. Fully automated 3D volumetric CT measurements of the abdominal organs were performed to determine the volume and average CT attenuation values of each organ. All patients were divided into 2 groups based on the measured visceral fat area: the VFO group (≥ 100 cm2) and non-VFO group (< 100 cm2), and the structural changes in abdominal organs were compared between these groups. The volumes of all organs were significantly higher in the VFO group than in the non-VFO group (all of p < 0.001). Conversely, the CT attenuation values of all organs in the VFO group were significantly lower than those in the non-VFO group (all of p < 0.001). Pancreatic CT values (r = - 0.701, p < 0.001) were most strongly associated with the visceral fat, followed by renal CT values (r = - 0.525, p < 0.001) and hepatic CT values (r = - 0.510, p < 0.001). Fully automated 3D volumetric CT measurement using a deep learning algorithm has the potential to detect the structural changes in the abdominal organs, especially the pancreas, such as an increase in the volumes and a decrease in CT attenuation values, probably due to increased ectopic fat accumulation in patients with VFO. This technique may provide valuable imaging support for the early detection and intervention of metabolic-related diseases.

PMID:39937214 | DOI:10.1007/s00261-025-04834-x

Biomacromolecules. 2025 Feb 12. doi: 10.1021/acs.biomac.4c01243. Online ahead of print.

ABSTRACT

Natural topological proteins exhibit unique properties including enhanced stability, controlled quaternary structures, and dynamic switching properties, highlighting topology as a unique dimension in protein engineering. Although artificial design and synthesis of topological proteins have achieved certain success, their diversity and complexity remain rather limited due to the scarcity of available entangling motifs essential for the construction of nontrivial protein topologies. In this work, we developed a deep-learning model to predict the entanglement features of a homodimer based solely on its amino acid sequence via the Gauss linking number matrices. The model achieved a search speed that was dozens of times faster than AlphaFold-Multimer, while maintaining comparable mean squared error. It was used to screen for entangling motifs from the genome of a hyperthermophilic archaeon. We demonstrated the effectiveness of our model by successful wet-lab synthesis of protein catenanes using two candidate entangling motifs. These findings show the great potential of our model for advancing the design and synthesis of novel topological proteins.

PMID:39937127 | DOI:10.1021/acs.biomac.4c01243