Arch Dermatol Res. 2025 Jan 13;317(1):231. doi: 10.1007/s00403-024-03763-x.

ABSTRACT

Pityriasis rosea (PR) is an acute exanthematous disease with an uncertain physiopathology, increasingly recognized as potentially drug induced. This study aims to investigate medication triggers associated with PR by analyzing cases reported in the FDA Adverse Event Reporting System (FAERS) database. A retrospective review of 343 PR cases reported in the FAERS database from January 1, 1998, to March 31, 2024, was conducted. Reporting odds ratios (ROR) were calculated to assess associations between PR and specific drug classes, including tumor necrosis factor (TNF) inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Logistic regression analysis evaluated the influence of factors such as sex, age group, and seriousness of outcomes on the occurrence of PR. Females represented 56.3% of cases and the 18-64 age group comprised 55.4% of cases. TNF inhibitors were significantly associated with PR (ROR = 4.1881 [3.1970-5.4865], P < 0.0001), particularly infliximab (ROR = 6.5284 [3.9523-10.7837], P < 0.0001), etanercept (ROR = 3.4921 [2.2873-5.3315], P < 0.0001), and adalimumab (ROR = 3.086 [2.0213-4.7115], P < 0.0001). ACE inhibitors were also associated with PR (ROR = 9.9808 [6.0423-16.4864], P < 0.0001), with higher odds in older patients (OR 14.08 [4.2-47.2], P < 0.0001) and those reporting serious outcomes (OR 9.53 [1.24-72.99], P = 0.03). Based on the FAERS, there has been a consistent rise in PR cases, with TNF inhibitors and ACE inhibitors being associated medication classes tied to PR. Given the limited literature on drug-related triggers and patient demographics, we aimed to highlight the characteristics of PR cases that could enhance awareness and inform better clinical outcomes for affected patients.

PMID:39804489 | DOI:10.1007/s00403-024-03763-x

Egypt J Immunol. 2025 Jan;32(1):27-41.

ABSTRACT

Cryptococcal meningitis is an alarming fungal infection that usually affects the meninges surrounding the brain and spinal cord. The causative organism is Cryptococcus neoformans. Although this infection can occur in normal individuals, it is more often seen in patients with human immunodeficiency virus/acquired immunodeficiency syndrome. Amphotericin B is an antifungal medication often used to treat severe fungal infections. It belongs to the class of polyene antifungal drugs, and it acts by binding to the cell membrane of the fungus. This causes some essential cellular components to leak out and ultimately the fungus dies. However, the administration of Amphotericin B is associated with toxicity. Therefore, lipid formulations are preferred to decrease the toxicity and increase the therapeutic index of the drug. It is widely used since it has a longer tissue half-life, the drug induced toxic effects are lower and it can penetrate the brain tissue efficaciously. This review collects and analyzes several research studies and literature reviews found in the electronic databases. The inclusion criteria prioritize studies focusing on the efficacy and drawbacks of using liposomal Amphotericin B as a treatment for fungal meningitis. In conclusion, liposomal Amphotericin B showed more effective treatment compared to other available antifungal drugs. Patients treated with a single dose of liposomal Amphotericin B coupled with fluconazole and flucytosine exhibited fewer adverse events and the mortality rate was also lower as compared to the control group.

PMID:39803853

Hum Vaccin Immunother. 2025 Dec;21(1):2450878. doi: 10.1080/21645515.2025.2450878. Epub 2025 Jan 13.

ABSTRACT

As infants suffer significant morbidity and mortality due to norovirus-related acute gastroenteritis (AGE), we assessed four formulations of the bivalent virus-like particle (VLP) vaccine candidate (HIL-214) in Panamanian and Colombian infants. 360 infants aged 6 weeks to 5 months were randomly allocated to 8 groups to receive three doses of HIL-214 or two doses of HIL-214 and one dose of placebo (Days 1, 56 and 112), where HIL-214 doses contained 15/15, 15/50, 50/50 or 50/150 μg of GI.1/GII.4c genotype VLPs and 0.5 mg Al(OH)3. Solicited injection-site and systemic adverse events (AE) were collected within 7 days after each dose, unsolicited AEs were collected within 28 days after each, and serious AEs throughout the study. Pan-Ig and histoblood group antigen-blocking antibodies (HBGA) were measured on Days 1, 56, 84, and 140. All formulations were well-tolerated causing mainly mild-to-moderate transient solicited AEs, most frequently local pain and irritability/fussiness, but no vaccine-related serious AEs. Two doses of each formulation induced high titers of high avidity Pan-Ig and also HBGA antibodies; a third dose increased titers against both antigens and the avidity of Pan-Ig antibodies against GII.4c but not against GI.1. Two and three doses of HIL-214 were well-tolerated and induced potent immune responses at 4-6 months of age supporting further clinical assessment to protect against norovirus-related AGE.

PMID:39803784 | DOI:10.1080/21645515.2025.2450878

Discov Oncol. 2025 Jan 13;16(1):39. doi: 10.1007/s12672-024-01616-7.

ABSTRACT

The occurrence and progression of breast cancer (BCa) are complex processes involving multiple factors and multiple steps. The tumor microenvironment (TME) plays an important role in this process, but the functions of immune components and stromal components in the TME require further elucidation. In this study, we obtained the RNA-seq data of 1086 patients from The Cancer Genome Atlas (TCGA) database. We calculated the proportions of tumor-infiltrating immune cells (TICs) and immune and stromal components using the CIBERSORT and ESTIMATE methods, and we screened differentially expressed genes (DEGs). Univariate Cox regression analysis of overall survival was performed on the DEGs, and a protein-protein interaction network of their protein products was generated. Finally, the hub gene CD5 was obtained. High CD5 expression was found to be associated with longer survival than low expression. Gene set enrichment analysis showed that DEGs upregulated in the high-CD5 expression group were mainly enriched in tumor- and immune-related pathways, while those upregulated in the low-expression group were enriched in protein export and lipid synthesis. TIC analysis showed that CD5 expression was positively correlated with the infiltration of CD8+ T cells, activated memory CD4+ T cells, gamma delta T cells, and M1 macrophages and negatively correlated with the infiltration of M2 macrophages. CD5 can increase anticancer immune cell infiltration and reduce M2 macrophage infiltration. These results suggest that CD5 is likely a potential prognostic biomarker and therapeutic target, providing novel insights into the treatment and prognostic assessment of BCa.

PMID:39804513 | DOI:10.1007/s12672-024-01616-7

Endocr Connect. 2025 Jan 1:EC-24-0617. doi: 10.1530/EC-24-0617. Online ahead of print.

ABSTRACT

BACKGROUND: Arterial hypertension and left ventricular hypertrophy and remodeling are independent cardiovascular risk factors in patients with Cushing's syndrome. Changes in the renin-angiotensin system and in the mineralocorticoid axis activity could be involved as potential mechanisms in their pathogenesis, in addition to cortisol excess.

METHODS: In this ancillary study of our previous study prospectively investigating patients with ACTH-dependent Cushing's syndrome by cardiac magnetic resonance imaging (NCT02202902), 11 patients without any interfering medication were cross-sectionally compared to 20 control subjects matched for age, sex and body mass index. Angiotensin metabolites and adrenal steroids were measured by liquid chromatography tandem mass spectrometry and their relation to blood pressure and cardiac structure was evaluated.

RESULTS: Concentrations of angiotensin I and angiotensin II were comparable, but the angiotensin-converting enzyme activity was significantly lower (2.19 (1.67;3.08) vs 4.07 (3.1;5.6); p<0.001) in patients compared to controls. Aldosterone concentrations were significantly lower (6.9 (6.9;124.1) vs 239.9 (181.4;321.9) pmol/l; p<0.001) in the group of patients, but adrenal aldosterone precursor metabolites were comparable between patients and controls. Inverse correlations were observed for 24h urinary free cortisol and aldosterone with the ratio of left ventricular mass to end-diastolic volume (r=0.470, p=0.012 and r= -0.367, p=0.046, respectively).

CONCLUSIONS: We describe a disease specific profile of angiotensin metabolites in patients with ACTH dependent Cushing's syndrome. Low levels of aldosterone in the presence of unchanged precursor metabolites indicate a direct inhibitory action of cortisol excess on the aldosterone synthase. Further, glucocorticoid excess per se drives cardiac muscle remodeling.

PMID:39804209 | DOI:10.1530/EC-24-0617

RSC Adv. 2025 Jan 9;15(2):748-753. doi: 10.1039/d4ra07449a. eCollection 2025 Jan 9.

ABSTRACT

Due to their considerable chemical diversity, metal compounds are attracting increasing and renewed attention from the scientific and medical communities as potential antimicrobial agents to combat the growing problem of antibiotic resistance. The development of metal compounds as antimicrobial agents typically follows classical drug discovery procedures and suffers from the same problems; indeed, these procedures can be very expensive and time-consuming, and carry an intrinsically high risk of failure. Here, we show how some established drug discovery approaches can be conveniently and successfully applied to antimicrobial metal compounds to provide some shortcuts for faster clinical translation of new treatments. Specifically, we refer to (i) drug repurposing, (ii) drug combination and (iii) drug targeting by bioconjugation; some relevant examples will be illustrated.

PMID:39802470 | PMC:PMC11712697 | DOI:10.1039/d4ra07449a

Mol Ther Oncol. 2024 Nov 23;32(4):200911. doi: 10.1016/j.omton.2024.200911. eCollection 2024 Dec 19.

ABSTRACT

Drug repurposing has potential to improve outcomes for high-grade serous ovarian cancer (HGSOC). Repurposing drugs with PARP family binding activity may produce cytotoxic effects through the multiple mechanisms of PARP including DNA repair, cell-cycle regulation, and apoptosis. The aim of this study was to determine existing drugs that have PARP family binding activity and can be repurposed for treatment of HGSOC. In silico ligand-based virtual screening (BLAZE) was used to identify drugs with potential PARP-binding activity. The list was refined by dosing, known cytotoxicity, lipophilicity, teratogenicity, and side effects. The highest ranked drug, efavirenz, progressed to in vitro testing. Molecularly characterized HGSOC cell lines, 3D hydrogel-encapsulated models, and patient-derived organoid models were used to determine the IC50 for efavirenz, cell death, apoptosis, PARP1 enzyme expression, and activity in intact cancer cells following efavirenz treatment. The IC50 for efavirenz was 26.43-45.85 μM for cells in two dimensions; 27.81 μM-54.98 μM in three dimensions, and 14.52 μM-42.27 μM in HGSOC patient-derived organoids. Efavirenz decreased cell viability via inhibition of PARP; increased CHK2 and phosphor-RB; increased cell-cycle arrest via decreased CDK2; increased γH2AX, DNA damage, and apoptosis. The results of this study suggest that efavirenz may be a viable treatment for HGSOC.

PMID:39802157 | PMC:PMC11719850 | DOI:10.1016/j.omton.2024.200911

Fundam Clin Pharmacol. 2025 Feb;39(1):e13052. doi: 10.1111/fcp.13052.

ABSTRACT

Drug repurposing of well-established drugs to be targeted against lung cancer has been a promising strategy. Bosentan is an endothelin 1 (ET-1) blocker widely used in pulmonary hypertension. The current experiment intends to inspect the anticancer and antiangiogenic mechanism of bosentan targeting epidermal growth factor receptor (EGFR) /extra-cellular Signal Regulated Kinase (ERK) /c-Jun/vascular endothelial growth factor (VEGF) carcinogenic pathway. BALB/c mice were randomized into four groups, the first received the vehicle, the second received 100 mg/kg oral bosentan alone, the third has non-small cell lung cancer (NSCLC) induced by two doses of 1.5 g/kg urethane i.p. and finally the fourth has NSCLC received bosentan. To determine the anti-proliferative impact of bosentan, cytokeratin 19 fragments (CYFRA 21-1) level was assessed, and Ki-67 positive cells were counted by immunohistochemical (IHC). Molecular expression of EGFR via IHC, relative expression of p-ERK1/2 and p-c-Jun via western blotting and caspase 3, Bcl-2 Associated X-protein (BAX)/B-cell lymphoma 2 (Bcl-2) ratio and VEGF via ELISA were quantified. Bosentan showed pronounced improvement in lung index and histopathological examinations. Bosentan exerted a noticeable arrest of lung cancer growth indicated by the attenuation of CYFRA 21-1 and Ki-67 positive cell counts besides the boost of BAX/Bcl-2 ratio and caspase 3. Bosentan induced a remarkable decline of EGFR, T-ERK1/2/p-ERK1/2, T-c-Jun/p-c-Jun, and VEGF. Bosentan induced cytotoxic and anti-angiogenic impact through regulation of EGFR/ERK/c-Jun/VEGF axis suggesting its potential therapeutic impact against lung cancer.

PMID:39801131 | DOI:10.1111/fcp.13052

Turk J Pharm Sci. 2025 Jan 10;21(6):566-576. doi: 10.4274/tjps.galenos.2024.49768.

ABSTRACT

INTRODUCTION: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), from the family Coronaviridae, is the seventh known coronavirus to infect humans and cause acute respiratory syndrome. Although vaccination efforts have been conducted against this virus, which emerged in Wuhan, China, in December 2019 and has spread rapidly around the world, the lack of an Food and Drug Administration-approved antiviral agent has made drug repurposing an important approach for emergency response during the COVID-19 pandemic. The aim of this study was to investigate the potential of H1-antihistamines as antiviral agents against SARS-CoV-2 RNA-dependent RNA polymerase enzyme.

MATERIALS AND METHODS: Using molecular docking techniques, we explored the interactions between H1-antihistamines and RNA-dependent RNA polymerase (RdRp), a key enzyme involved in viral replication. The three-dimensional structure of 37 H1-antihistamine molecules was drawn and their energies were minimized using Spartan 0.4. Subsequently, we conducted a docking study with Autodock Vina to assess the binding affinity of these molecules to the target site. The docking scores and conformations were then visualized using Discovery Studio.

RESULTS: The results examined showed that the docking scores of the H1-antihistamines were between 5.0 and 8.3 kcal/mol. These findings suggested that among all the analyzed drugs, bilastine, fexofenadine, montelukast, zafirlukast, mizolastine, and rupatadine might bind with the best binding energy (< -7.0 kcal/mol) and inhibit RdRp, potentially halting the replication of the virus.

CONCLUSION: This study highlights the potential of H1-antihistamines in combating COVID-19 and underscores the value of computational approaches in rapid drug discovery and repurposing efforts. Finally, experimental studies are required to measure the potency of H1-antihistamines before their clinical use against COVID-19 as RdRp inhibitors.

PMID:39801109 | DOI:10.4274/tjps.galenos.2024.49768

Anal Methods. 2025 Jan 13. doi: 10.1039/d4ay01970a. Online ahead of print.

ABSTRACT

Near-infrared (NIR) spectroscopy, with its advantages of non-destructive analysis, simple operation, and fast detection speed, has been widely applied in various fields. However, the effectiveness of current spectral analysis techniques still relies on complex preprocessing and feature selection of spectral data. While data-driven deep learning can automatically extract features from raw spectral data, it typically requires large amounts of labeled data for training, limiting its application in spectral analysis. To address this issue, we propose a self-supervised learning (SSL) framework based on convolutional neural networks (CNN) to enhance spectral analysis performance with small sample sizes. The method comprises two learning stages: pre-training and fine-tuning. In the pre-training stage, a large amount of pseudo-labeled data is used to learn intrinsic spectral features, followed by fine-tuning with a smaller set of labeled data to complete the final model training. Applied to our own collected dataset of three tea varieties, the proposed model achieved a classification accuracy of 99.12%. Additionally, experiments on three public datasets demonstrated that the SSL model significantly outperforms traditional machine learning methods, achieving accuracies of 97.83%, 98.14%, and 99.89%, respectively. Comparative experiments further confirmed the effectiveness of the pre-training stage, with the highest accuracy improvement, reaching 10.41%. These results highlight the potential of the proposed method for handling small sample spectral data, providing a viable solution for improved spectral analysis.

PMID:39803715 | DOI:10.1039/d4ay01970a

Am J Cancer Res. 2024 Dec 15;14(12):5885-5896. doi: 10.62347/TRNO3190. eCollection 2024.

ABSTRACT

The use of routine adjuvant radiotherapy (RT) after breast-conserving surgery (BCS) is controversial in elderly patients with early-stage breast cancer (EBC). This study aimed to evaluate the efficacy of adjuvant RT for elderly EBC patients using deep learning (DL) to personalize treatment plans. Five distinct DL models were developed to generate personalized treatment recommendations. Patients whose actual treatments aligned with the DL model suggestions were classified into the Consistent group, while those with divergent treatments were placed in the Inconsistent group. The efficacy of these models was assessed by comparing outcomes between the two groups. Multivariate logistic regression and Poisson regression analyses were used to visualize and quantify the influence of various features on adjuvant RT selection. In a cohort of 8,047 elderly EBC patients, treatment following the Deep Survival Regression with Mixture Effects (DSME) model's recommendations significantly improved survival, with inverse probability of treatment weighting (IPTW)-adjusted benefits, including a hazard ratio of 0.70 (95% CI, 0.58-0.86), a risk difference of 4.63% (95% CI, 1.59-7.66), and an extended mean survival time of 8.96 months (95% CI, 6.85-10.97), outperforming other models and the National Comprehensive Cancer Network (NCCN) guidelines. The DSME model identified elderly patients with larger tumors and more advanced disease stages as ideal candidates for adjuvant RT, though no benefit was seen in patients not recommended for it. This study introduces a novel DL-guided approach for selecting adjuvant RT in elderly EBC patients, enhancing treatment precision and potentially improving survival outcomes while minimizing unnecessary interventions.

PMID:39803647 | PMC:PMC11711541 | DOI:10.62347/TRNO3190

Heart Rhythm O2. 2024 Sep 19;5(12):862-872. doi: 10.1016/j.hroo.2024.09.007. eCollection 2024 Dec.

ABSTRACT

BACKGROUND: Detection of atrial tachyarrhythmias (ATA) on long-term electrocardiogram (ECG) recordings is a prerequisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep learning (DL) algorithms provide improved performances on resting ECG databases. However, results on long-term Holter recordings are scarce.

OBJECTIVE: We aimed to build and evaluate a DL modular software using ECG features well known to cardiologists with a user interface that allows cardiologists to adjudicate the results and drive a second DL analysis.

METHODS: Using a large (n = 187 recordings, 249,419 one-minute samples), beat-to-beat annotated, two-lead Holter database, we built a DL algorithm with a modular structure mimicking expert physician ECG interpretation to classify atrial rhythms. The DL network includes 3 modules (cardiac rhythm regularity, electrical atrial waveform, and raw voltage by time data) followed by a decision network and a long-term weighting factor. The algorithm was validated on an external database.

RESULTS: F1 scores of our classifier were 99% for ATA detection, 95% for atrial fibrillation, and 90% for atrial flutter. Using the external Massachusetts Institute of Technology database, the classifier obtains an F1-score of 97% for the normal sinus rhythm class and 96% for the ATA class. Residual errors could be corrected by manual deactivation of 1 module in 7 of 15 of the recordings, with an accuracy < 90%.

CONCLUSION: A DL modular software using ECG features well known to cardiologists provided an excellent overall performance. Clinically significant residual errors were most often related to the classification of the atrial arrhythmia type (fibrillation vs flutter). The modular structure of the algorithm helped to edit and correct the artificial intelligence-based first-pass analysis and will provide a basis for explainability.

PMID:39803625 | PMC:PMC11721725 | DOI:10.1016/j.hroo.2024.09.007

bioRxiv [Preprint]. 2025 Jan 3:2024.11.17.624050. doi: 10.1101/2024.11.17.624050.

ABSTRACT

Variant calling using long-read RNA sequencing (lrRNA-seq) can be applied to diverse tasks, such as capturing full-length isoforms and gene expression profiling. It poses challenges, however, due to higher error rates than DNA data, the complexities of transcript diversity, RNA editing events, etc. In this paper, we propose Clair3-RNA, the first deep learning-based variant caller tailored for lrRNA-seq data. Clair3-RNA leverages the strengths of the Clair series pipelines and incorporates several techniques optimized for lrRNA-seq data, such as uneven coverage normalization, refinement of training materials, editing site discovery, and the incorporation of phasing haplotype to enhance variant-calling performance. Clair3-RNA is available for various platforms, including PacBio and ONT complementary DNA sequencing (cDNA), and ONT direct RNA sequencing (dRNA). Our results demonstrated that Clair3-RNA achieved a ~91% SNP F1-score on the ONT platform using the latest ONT SQK-RNA004 kit (dRNA004) and a ~92% SNP F1-score in PacBio Iso-Seq and MAS-Seq for variants supported by at least four reads. The performance reached a ~95% and ~96% F1-score for ONT and PacBio, respectively, with at least ten supporting reads and disregarding the zygosity. With read phased, the performance reached ~97% for ONT and ~98% for PacBio. Extensive evaluation of various GIAB samples demonstrated that Clair3-RNA consistently outperformed existing callers and is capable of distinguishing RNA high-quality editing sites from variants accurately. Clair3-RNA is open-source and available at (https://github.com/HKU-BAL/Clair3-RNA).

PMID:39803537 | PMC:PMC11722298 | DOI:10.1101/2024.11.17.624050

bioRxiv [Preprint]. 2024 Oct 15:2024.10.11.617857. doi: 10.1101/2024.10.11.617857.

ABSTRACT

AlphaFold2 (AF2), a deep-learning based model that predicts protein structures from their amino acid sequences, has recently been used to predict multiple protein conformations. In some cases, AF2 has successfully predicted both dominant and alternative conformations of fold-switching proteins, which remodel their secondary and tertiary structures in response to cellular stimuli. Whether AF2 has learned enough protein folding principles to reliably predict alternative conformations outside of its training set is unclear. Here, we address this question by assessing whether CFold-an implementation of the AF2 network trained on a more limited subset of experimentally determined protein structures- predicts alternative conformations of eight fold switchers from six protein families. Previous work suggests that AF2 predicted these alternative conformations by memorizing them during training. Unlike AF2, CFold's training set contains only one of these alternative conformations. Despite sampling 1300-4400 structures/protein with various sequence sampling techniques, CFold predicted only one alternative structure outside of its training set accurately and with high confidence while also generating experimentally inconsistent structures with higher confidence. Though these results indicate that AF2's current success in predicting alternative conformations of fold switchers stems largely from its training data, results from a sequence pruning technique suggest developments that could lead to a more reliable generative model in the future.

PMID:39803493 | PMC:PMC11722258 | DOI:10.1101/2024.10.11.617857

bioRxiv [Preprint]. 2024 Dec 30:2024.12.30.630782. doi: 10.1101/2024.12.30.630782.

ABSTRACT

The use of microcomputed tomography (Micro-CT) for imaging biological samples has burgeoned in the past decade, due to increased access to scanning platforms, ease of operation, isotropic three-dimensional image information, and the ability to derive accurate quantitative data. However, manual data analysis of Micro-CT images can be laborious and time intensive. Deep learning offers the ability to streamline this process, but historically has included caveats-namely, the need for a large amount of training data, which is often limited in many Micro-CT studies. Here we show that accurate deep learning models can be trained using only 1-3 Micro-CT images of the adult Drosophila melanogaster brain using Dragonfly's pre-trained neural networks and minimal user knowledge. We further demonstrate the power of our model by showing that it can accurately segment the brain across different tissue contrast stains, scanner models, and genotypes. Finally, we show how the model can assist in identifying morphological similarities and differences between mutants based on volumetric quantification, facilitating a rapid assessment of novel phenotypes. Our models are freely available and can be refined based on individual user needs.

SUMMARY: Micro-CT data can be automatically segmented and quantified using a deep learning model trained on as few as 3 samples, facilitating rapid comparison of developmental phenotypes.

PMID:39803485 | PMC:PMC11722237 | DOI:10.1101/2024.12.30.630782

Kidney Med. 2024 Nov 12;7(1):100927. doi: 10.1016/j.xkme.2024.100927. eCollection 2025 Jan.

ABSTRACT

Artificial intelligence (AI) is increasingly used in many medical specialties. However, nephrology has lagged in adopting and incorporating machine learning techniques. Nephrology is well positioned to capitalize on the benefits of AI. The abundance of structured clinical data, combined with the mathematical nature of this specialty, makes it an attractive option for AI applications. AI can also play a significant role in addressing health inequities, especially in organ transplantation. It has also been used to detect rare diseases such as Fabry disease early. This review article aims to increase awareness on the basic concepts in machine learning and discuss AI applications in nephrology. It also addresses the challenges in integrating AI into clinical practice and the need for creating an AI-competent nephrology workforce. Even though AI will not replace nephrologists, those who are able to incorporate AI into their practice effectively will undoubtedly provide better care to their patients. The integration of AI technology is no longer just an option but a necessity for staying ahead in the field of nephrology. Finally, AI can contribute as a force multiplier in transitioning to a value-based care model.

PMID:39803417 | PMC:PMC11719832 | DOI:10.1016/j.xkme.2024.100927

Eur J Radiol Open. 2024 Dec 17;14:100624. doi: 10.1016/j.ejro.2024.100624. eCollection 2025 Jun.

ABSTRACT

OBJECTIVES: To develop and validate a deep learning model using multimodal PET/CT imaging for detecting and classifying focal liver lesions (FLL).

METHODS: This study included 185 patients who underwent 18F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serological data and imaging. Liver lesions were segmented on PET and CT, serving as the "reference standard". Deep learning models were trained using PET and CT images to generate predicted segmentations and classify lesion nature. Model performance was evaluated by comparing the predicted segmentations with the reference segmentations, using metrics such as Dice, Precision, Recall, F1-score, ROC, and AUC, and compared it with physician diagnoses.

RESULTS: This study finally included 150 patients, comprising 46 patients with benign liver nodules, 51 patients with malignant liver nodules, and 53 patients with no FLLs. Significant differences were observed among groups for age, AST, ALP, GGT, AFP, CA19-9and CEA. On the validation set, the Dice coefficient of the model was 0.740. For the normal group, the recall was 0.918, precision was 0.904, F1-score was 0.909, and AUC was 0.976. For the benign group, the recall was 0.869, precision was 0.862, F1-score was 0.863, and AUC was 0.928. For the malignant group, the recall was 0.858, precision was 0.914, F1-score was 0.883, and AUC was 0.979. The model's overall diagnostic performance was between that of junior and senior physician.

CONCLUSION: This deep learning model demonstrated high sensitivity in detecting FLLs and effectively differentiated between benign and malignant lesions.

PMID:39803389 | PMC:PMC11720101 | DOI:10.1016/j.ejro.2024.100624

Phys Imaging Radiat Oncol. 2024 Sep 30;32:100654. doi: 10.1016/j.phro.2024.100654. eCollection 2024 Oct.

ABSTRACT

BACKGROUND AND PURPOSE: Deep-learning-based automatic segmentation is widely used in radiation oncology to delineate organs-at-risk. Dual-energy CT (DECT) allows the reconstruction of enhanced contrast images that could help with manual and auto-delineation. This paper presents a performance evaluation of a commercial auto-segmentation software on image series generated by a DECT.

MATERIAL AND METHODS: Different types of DECT images from seventy four head-and-neck (HN) patients were retrieved, including polyenergetic images at different voltages [80 kV reconstructed with a kernel corresponding to the commercial algorithm DirectDensity™ (PEI80-DD), 80 kV (PEI80), 120 kV-mixed (PEI120)] and a virtual-monoenergetic image at 40 keV (VMI40). Delineations used for treatment planning were considered as ground truth (GT) and were compared with the auto-segmentations performed on the 4 DECT images. A blinded qualitative evaluation of 3 structures (thyroid, left parotid, left nodes level II) was carried out. Performance metrics were calculated for thirteen HN structures to evaluate the auto-contours including dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD) and mean surface distance (MSD).

RESULTS: We observed a high rate of low scores for PEI80-DD and VMI40 auto-segmentations on the thyroid and for GT and VMI40 contours on the nodes level II. All images received excellent scores for the parotid glands. The metrics comparison between GT and auto-segmented contours revealed that PEI80-DD had the highest DSC scores, significantly outperforming other reconstructed images for all organs (p < 0.05).

CONCLUSIONS: The results indicate that the auto-contouring system cannot generalize to images derived from DECT acquisition. It is therefore crucial to identify which organs benefit from these acquisitions to adapt the training datasets accordingly.

PMID:39803347 | PMC:PMC11718415 | DOI:10.1016/j.phro.2024.100654

Korean J Neurotrauma. 2024 Dec 16;20(4):303-304. doi: 10.13004/kjnt.2024.20.e40. eCollection 2024 Dec.

NO ABSTRACT

PMID:39803342 | PMC:PMC11711028 | DOI:10.13004/kjnt.2024.20.e40

Ann Gastroenterol. 2025 Jan-Feb;38(1):80-84. doi: 10.20524/aog.2024.0932. Epub 2024 Dec 12.

ABSTRACT

BACKGROUND: The risk of gastrointestinal (GI) cancer after lung transplantation (LTx) in sarcoidosis patients is not well defined. Given the cancer risks linked to sarcoidosis and organ transplantation, this study investigated the incidence of GI de novo malignancies (DNM), comparing LTx recipients with sarcoidosis or idiopathic pulmonary fibrosis (IPF).

METHODS: We analyzed data from the United Network for Organ Sharing registry, including adults with sarcoidosis or IPF who underwent LTx between May 2005 and December 2018. The primary outcome was the incidence of GI DNM by March 2023.

RESULTS: Of 7996 lung transplant recipients, 108 (1.35%) developed GI malignancies post-transplantation. Among these, 662 patients (9%) had sarcoidosis and 7334 (91%) had IPF. Sarcoidosis patients showed a non-significant trend toward a higher risk of GI malignancies compared to those with IPF (subhazard ratio 1.72, 95% confidence interval 0.90-3.29; P=0.099), with no observed difference in the risk of non-GI cancers.

CONCLUSIONS: The overall incidence of GI DNM following LTx is low, and sarcoidosis does not appear to increase the risk of GI cancers compared to IPF. This finding suggests that enhanced GI cancer screening beyond standard guidelines may not be warranted in this population, allowing for targeted surveillance of more prevalent malignancies in sarcoidosis patients post-LTx.

PMID:39802287 | PMC:PMC11724385 | DOI:10.20524/aog.2024.0932