Literature Watch

PBCS-ConvNeXt: Convolutional Network-Based Automatic Diagnosis of Non-alcoholic Fatty Liver in Abdominal Ultrasound Images

Deep learning - Wed, 2025-01-22 06:00

J Imaging Inform Med. 2025 Jan 22. doi: 10.1007/s10278-025-01394-w. Online ahead of print.

ABSTRACT

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent chronic liver condition characterized by excessive hepatic fat accumulation. Early diagnosis is crucial as NAFLD can progress to more severe conditions like steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma without timely intervention. While liver biopsy remains the gold standard for NAFLD assessment, abdominal ultrasound (US) imaging has emerged as a widely adopted non-invasive modality due to convenience and low cost. However, the subjective interpretation of US images is challenging and unpredictable. This study proposes a deep learning-based computer-aided diagnosis (CAD) model, termed potent boosts channel-aware separable intent - ConvNeXt (PBCS-ConvNeXt), for automated NAFLD classification using B-mode US images. The model architecture comprises three key components: The potent stem cell, an advanced trainable preprocessing module for robust feature extraction; Enhanced ConvNeXt Blocks that amplify channel-wise features to refine processing; and the boosting block that integrates multi-stage features for effective information extraction from US data. Utilizing fatty liver gradings from attenuation imaging (ATI) as the ground truth, the PBCS-ConvNeXt model was evaluated using 5-fold cross-validation, achieving an accuracy of 82%, sensitivity of 81% and specificity of 83% for identifying fatty liver on abdominal US. The proposed CAD system demonstrates high diagnostic performance in NAFLD classification from US images, enabling early detection and informing timely clinical management to prevent disease progression.

PMID:39841370 | DOI:10.1007/s10278-025-01394-w

Categories: Literature Watch

CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model

Deep learning - Wed, 2025-01-22 06:00

Neuroinformatics. 2025 Jan 22;23(2):12. doi: 10.1007/s12021-024-09701-6.

ABSTRACT

Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost. To address these challenges, a novel U-Net model for tumour segmentation in magnetic resonance images (MRI) is proposed. Initially, images are claimed from the dataset and pre-processed with the Probabilistic Hybrid Wiener filter (PHWF) to remove unwanted noise and improve image quality. To reduce model complexity, the pre-processed images are submitted to a feature extraction procedure known as 3D Convolutional Vision Transformer (3D-VT). To perform the segmentation approach using chaotic optimization assisted Dilated Channel Gate attention U-Net (CDCG-UNet) model to segment brain tumour regions effectively. The proposed approach segments tumour portions as whole tumour (WT), tumour Core (TC), and Enhancing Tumour (ET) positions. The optimization loss function can be performed using the Chaotic Harris Shrinking Spiral optimization algorithm (CHSOA). The proposed CDCG-UNet model is evaluated with three datasets: BRATS 2021, BRATS 2020, and BRATS 2023. For the BRATS 2021 dataset, the proposed CDCG-UNet model obtained a dice score of 0.972 for ET, 0.987 for CT, and 0.98 for WT. For the BRATS 2020 dataset, the proposed CDCG-UNet model produced a dice score of 98.87% for ET, 98.67% for CT, and 99.1% for WT. The CDCG-UNet model is further evaluated using the BRATS 2023 dataset, which yields 98.42% for ET, 98.08% for CT, and 99.3% for WT.

PMID:39841321 | DOI:10.1007/s12021-024-09701-6

Categories: Literature Watch

CTCNet: a fine-grained classification network for fluorescence images of circulating tumor cells

Deep learning - Wed, 2025-01-22 06:00

Med Biol Eng Comput. 2025 Jan 22. doi: 10.1007/s11517-025-03297-y. Online ahead of print.

ABSTRACT

The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are imperative for advancing cancer diagnostics and prognostics. The intricacy of various CTCs subtypes, coupled with the difficulty in developing exhaustive datasets, has impeded progress in this specialized domain. To date, no methods have been dedicated exclusively to overcoming the classification challenges of CTCs. To address this deficit, we have developed CTCDet, a large-scale dataset meticulously annotated based on the distinctive pathological characteristics of CTCs, aimed at advancing the application of deep learning techniques in oncological research. Furthermore, we introduce CTCNet, an innovative hybrid architecture that merges the capabilities of CNNs and Transformers to achieve precise classification of CTCs. This architecture features the Parallel Token mixer, which integrates local window self-attention with large-kernel depthwise convolution, enhancing the network's ability to model intricate channel and spatial relationships. Additionally, the Deformable Large Kernel Attention (DLKAttention) module leverages deformable convolution and large-kernel operations to adeptly delineate the nuanced features of CTCs, substantially boosting classification efficacy. Comprehensive evaluations on the CTCDet dataset validate the superior performance of CTCNet, confirming its ability to outperform other general methods in accurate cell classification. Moreover, the generalizability of CTCNet has been established across various datasets, establishing its robustness and applicability. What is more, our proposed method can lead to clinical applications and provide some help in assisting cancer diagnosis and treatment. Code and Data are available at https://github.com/JasonWu404/CTCs_Classification .

PMID:39841310 | DOI:10.1007/s11517-025-03297-y

Categories: Literature Watch

Enhanced accuracy and stability in automated intra-pancreatic fat deposition monitoring of type 2 diabetes mellitus using Dixon MRI and deep learning

Deep learning - Wed, 2025-01-22 06:00

Abdom Radiol (NY). 2025 Jan 22. doi: 10.1007/s00261-025-04804-3. Online ahead of print.

ABSTRACT

PURPOSE: Intra-pancreatic fat deposition (IPFD) is closely associated with the onset and progression of type 2 diabetes mellitus (T2DM). We aimed to develop an accurate and automated method for assessing IPFD on multi-echo Dixon MRI.

MATERIALS AND METHODS: In this retrospective study, 534 patients from two centers who underwent upper abdomen MRI and completed multi-echo and double-echo Dixon MRI were included. A pancreatic segmentation model was trained on double-echo Dixon water images using nnU-Net. Predicted masks were registered to the proton density fat fraction (PDFF) maps of the multi-echo Dixon sequence. Deep semantic segmentation feature-based radiomics (DSFR) and radiomics features were separately extracted on the PDFF maps and modeled using the support vector machine method with 5-fold cross-validation. The first deep learning radiomics (DLR) model was constructed to distinguish T2DM from non-diabetes and pre-diabetes by averaging the output scores of the DSFR and radiomics models. The second DLR model was then developed to distinguish pre-diabetes from non-diabetes. Two radiologist models were constructed based on the mean PDFF of three pancreatic regions of interest.

RESULTS: The mean Dice similarity coefficient for pancreas segmentation was 0.958 in the total test cohort. The AUCs of the DLR and two radiologist models in distinguishing T2DM from non-diabetes and pre-diabetes were 0.868, 0.760, and 0.782 in the training cohort, and 0.741, 0.724, and 0.653 in the external test cohort, respectively. For distinguishing pre-diabetes from non-diabetes, the AUCs were 0.881, 0.688, and 0.688 in the training cohort, which included data combined from both centers. Testing was not conducted due to limited pre-diabetic patients. Intraclass correlation coefficients between radiologists' pancreatic PDFF measurements were 0.800 and 0.699 at two centers, suggesting good and moderate reproducibility, respectively.

CONCLUSION: The DLR model demonstrated superior performance over radiologists, providing a more efficient, accurate and stable method for monitoring IPFD and predicting the risk of T2DM and pre-diabetes. This enables IPFD assessment to potentially serve as an early biomarker for T2DM, providing richer clinical information for disease progression and management.

PMID:39841227 | DOI:10.1007/s00261-025-04804-3

Categories: Literature Watch

Impact of Scanner Manufacturer, Endorectal Coil Use, and Clinical Variables on Deep Learning-assisted Prostate Cancer Classification Using Multiparametric MRI

Deep learning - Wed, 2025-01-22 06:00

Radiol Artif Intell. 2025 Jan 22:e230555. doi: 10.1148/ryai.230555. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To assess the impact of scanner manufacturer and scan protocol on the performance of deep learning models to classify prostate cancer (PCa) aggressiveness on biparametric MRI (bpMRI). Materials and Methods In this retrospective study, 5,478 cases from ProstateNet, a PCa bpMRI dataset with examinations from 13 centers, were used to develop five deep learning (DL) models to predict PCa aggressiveness with minimal lesion information and test how using data from different subgroups-scanner manufacturers and endorectal coil (ERC) use (Siemens, Philips, GE with and without ERC and the full dataset)-impacts model performance. Performance was assessed using the area under the receiver operating characteristic curve (AUC). The impact of clinical features (age, prostate-specific antigen level, Prostate Imaging Reporting and Data System [PI-RADS] score) on model performance was also evaluated. Results DL models were trained on 4,328 bpMRI cases, and the best model achieved AUC = 0.73 when trained and tested using data from all manufacturers. Hold-out test set performance was higher when models trained with data from a manufacturer were tested on the same manufacturer (within-and between-manufacturer AUC differences of 0.05 on average, P < .001). The addition of clinical features did not improve performance (P = .24). Learning curve analyses showed that performance remained stable as training data increased. Analysis of DL features showed that scanner manufacturer and scan protocol heavily influenced feature distributions. Conclusion In automated classification of PCa aggressiveness using bpMRI data, scanner manufacturer and endorectal coil use had a major impact on DL model performance and features. Published under a CC BY 4.0 license.

PMID:39841063 | DOI:10.1148/ryai.230555

Categories: Literature Watch

Gait patterns in unstable older patients related with vestibular hypofunction. Preliminary results in assessment with time-frequency analysis

Deep learning - Wed, 2025-01-22 06:00

Acta Otolaryngol. 2025 Jan 22:1-6. doi: 10.1080/00016489.2025.2450221. Online ahead of print.

ABSTRACT

BACKGROUND: Gait instability and falls significantly impact life quality and morbi-mortality in elderly populations. Early diagnosis of gait disorders is one of the most effective approaches to minimize severe injuries.

OBJECTIVE: To find a gait instability pattern in older adults through an image representation of data collected by a single sensor.

METHODS: A sample of 13 older adults (71-85 years old) with instability by vestibular hypofunction is compared to a sample of 19 adults (21-75 years old) without instability and normal vestibular function. Image representations of the gait signals acquired on a specific walk path were generated using a continuous wavelet transform and analyzed as a texture using grey level co-occurrence matrix metrics as features. A support vector machine (SVM) algorithm was used to discriminate subjects.

RESULTS: First results show a good classification performance. According to analysis of extracted features, most information relevant to instability is concentrated in the medio-lateral acceleration (X axis) and the frontal plane angular rotation (Z axis gyroscope). Performing a ten-fold cross-validation through the first ten seconds of the sample dataset, the algorithm achieves a 92,3 F1 score corresponding to 12 true-positives, 1 false positive and 1 false negative.

DISCUSSION: This preliminary report suggests that the method has potential use in assessing gait disorders in controlled and non-controlled environments. It suggests that deep learning methods could be explored given the availability of a larger population and data samples.

PMID:39840938 | DOI:10.1080/00016489.2025.2450221

Categories: Literature Watch

Gait Video-Based Prediction of Severity of Cerebellar Ataxia Using Deep Neural Networks

Deep learning - Wed, 2025-01-22 06:00

Mov Disord. 2025 Jan 22. doi: 10.1002/mds.30113. Online ahead of print.

ABSTRACT

BACKGROUND: Pose estimation algorithms applied to two-dimensional videos evaluate gait disturbances; however, a few studies have used this method to evaluate ataxic gait.

OBJECTIVE: The aim was to assess whether a pose estimation algorithm can predict the severity of cerebellar ataxia by applying it to gait videos.

METHODS: We video-recorded 66 patients with degenerative cerebellar diseases performing the timed up-and-go test. Key points from the gait videos extracted by a pose estimation algorithm were input into a deep learning model to predict the Scale for the Assessment and Rating of Ataxia (SARA) score. We also evaluated video segments that the model focused on to predict ataxia severity.

RESULTS: The model achieved a root-mean-square error of 2.30 and a coefficient of determination of 0.79 in predicting the SARA score. It primarily focused on standing, turning, and body sway to assess severity.

CONCLUSIONS: This study demonstrated that the model may capture gait characteristics from key-point data and has the potential to predict SARA scores. © 2025 International Parkinson and Movement Disorder Society.

PMID:39840857 | DOI:10.1002/mds.30113

Categories: Literature Watch

AggNet: Advancing protein aggregation analysis through deep learning and protein language model

Deep learning - Wed, 2025-01-22 06:00

Protein Sci. 2025 Feb;34(2):e70031. doi: 10.1002/pro.70031.

ABSTRACT

Protein aggregation is critical to various biological and pathological processes. Besides, it is also an important property in biotherapeutic development. However, experimental methods to profile protein aggregation are costly and labor-intensive, driving the need for more efficient computational alternatives. In this study, we introduce "AggNet," a novel deep learning framework based on the protein language model ESM2 and AlphaFold2, which utilizes physicochemical, evolutionary, and structural information to discriminate amyloid and non-amyloid peptides and identify aggregation-prone regions (APRs) in diverse proteins. Benchmark comparisons show that AggNet outperforms existing methods and achieves state-of-the-art performance on protein aggregation prediction. Also, the predictive ability of AggNet is stable across proteins with different secondary structures. Feature analysis and visualizations prove that the model effectively captures peptides' physicochemical properties effectively, thereby offering enhanced interpretability. Further validation through a case study on MEDI1912 confirms AggNet's practical utility in analyzing protein aggregation and guiding mutation for aggregation mitigation. This study enhances computational tools for predicting protein aggregation and highlights the potential of AggNet in protein engineering. Finally, to improve the accessibility of AggNet, the source code can be accessed at: https://github.com/Hill-Wenka/AggNet.

PMID:39840791 | DOI:10.1002/pro.70031

Categories: Literature Watch

Mfgnn: Multi-Scale Feature-Attentive Graph Neural Networks for Molecular Property Prediction

Deep learning - Wed, 2025-01-22 06:00

J Comput Chem. 2025 Jan 30;46(3):e70011. doi: 10.1002/jcc.70011.

ABSTRACT

In the realm of artificial intelligence-driven drug discovery (AIDD), accurately predicting the influence of molecular structures on their properties is a critical research focus. While deep learning models based on graph neural networks (GNNs) have made significant advancements in this area, prior studies have primarily concentrated on molecule-level representations, often neglecting the impact of functional group structures and the potential relationships between fragments on molecular property predictions. To address this gap, we introduce the multi-scale feature attention graph neural network (MfGNN), which enhances traditional atom-based molecular graph representations by incorporating fragment-level representations derived from chemically synthesizable BRICS fragments. MfGNN not only effectively captures both the structural information of molecules and the features of functional groups but also pays special attention to the potential relationships between fragments, exploring how they collectively influence molecular properties. This model integrates two core mechanisms: a graph attention mechanism that captures embeddings of molecules and functional groups, and a feature extraction module that systematically processes BRICS fragment-level features to uncover relationships among the fragments. Our comprehensive experiments demonstrate that MfGNN outperforms leading machine learning and deep learning models, achieving state-of-the-art performance in 8 out of 11 learning tasks across various domains, including physical chemistry, biophysics, physiology, and toxicology. Furthermore, ablation studies reveal that the integration of multi-scale feature information and the feature extraction module enhances the richness of molecular features, thereby improving the model's predictive capabilities.

PMID:39840745 | DOI:10.1002/jcc.70011

Categories: Literature Watch

Utilizing deep learning for automatic segmentation of the cochleae in temporal bone computed tomography

Deep learning - Wed, 2025-01-22 06:00

Acta Radiol. 2025 Jan 22:2841851241307333. doi: 10.1177/02841851241307333. Online ahead of print.

ABSTRACT

BACKGROUND: Segmentation of the cochlea in temporal bone computed tomography (CT) is the basis for image-guided otologic surgery. Manual segmentation is time-consuming and laborious.

PURPOSE: To assess the utility of deep learning analysis in automatic segmentation of the cochleae in temporal bone CT to differentiate abnormal images from normal images.

MATERIAL AND METHODS: Three models (3D U-Net, UNETR, and SegResNet) were trained to segment the cochlea on two CT datasets (two CT types: GE 64 and GE 256). One dataset included 77 normal samples, and the other included 154 samples (77 normal and 77 abnormal). A total of 20 samples that contained normal and abnormal cochleae in three CT types (GE 64, GE 256, and SE-DS) were tested on the three models. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess the models.

RESULTS: The segmentation performances of the three models improved after adding abnormal cochlear images for training. SegResNet achieved the best performance. The average DSC on the test set was 0.94, and the HD was 0.16 mm; the performance was higher than those obtained by the 3D U-Net and UNETR models. The DSCs obtained using the GE 256 CT, SE-DS CT, and GE 64 CT models were 0.95, 0.94, and 0.93, respectively, and the HDs were 0.15, 0.18, and 0.12 mm, respectively.

CONCLUSION: The SegResNet model is feasible and accurate for automated cochlear segmentation of temporal bone CT images.

PMID:39840644 | DOI:10.1177/02841851241307333

Categories: Literature Watch

Performance of the FORD Versus Other Available Models for the Noninvasive Prediction of Pulmonary Hypertension in Patients with Interstitial Lung Disease

Idiopathic Pulmonary Fibrosis - Wed, 2025-01-22 06:00

Lung. 2025 Jan 22;203(1):27. doi: 10.1007/s00408-024-00783-2.

ABSTRACT

PURPOSE: Pulmonary hypertension (PH) is associated with morbidity and mortality in patients with interstitial lung disease (ILD). Several prediction models have been proposed to predict PH in ILD patients. We sought to discern how previously described prediction models perform in predicting PH in patients with ILD.

METHODS: Patients with ILD who completed a baseline right heart catheterization, from Inova Fairfax Hospital, Northwestern Memorial Hospital, and Asan Medical Center in Korea were enrolled. The performance of various prediction models (FORD model, the FORD calculator, the PH-ILD Detection tool, and the mean pulmonary artery pressure prediction model) were assessed using receiver operating characteristic (ROC) curves and area under the receiver operating characteristic curve (AUROC). There were four definitions of pulmonary hypertension against which the models were evaluated.

RESULTS: There were a total of 192 patients with ILD, of whom 32.8% (n = 63/192) met the modified 5th world symposium on PH definition of precapillary PH. Among the models assessed, the FORD calculator had an AUROC (0.733) that was marginally highest. Subgroup analysis revealed that the FORD index had the highest AUROC (0.817) in patients with idiopathic pulmonary fibrosis, while the FORD calculator had the highest AUROC (0.751) in patients with non-IPF ILD.

CONCLUSION: The FORD model can be used to predict group 3 PH in both IPF patients and non-IPF ILD patients. It could serve as a tool for ILD patient selection for right heart catheterization as well as an enrichment tool for clinical trials targeting the pulmonary vasculature.

PMID:39841267 | DOI:10.1007/s00408-024-00783-2

Categories: Literature Watch

Multisensory integration operates on correlated input from unimodal transient channels

Systems Biology - Wed, 2025-01-22 06:00

Elife. 2025 Jan 22;12:RP90841. doi: 10.7554/eLife.90841.

ABSTRACT

Audiovisual information reaches the brain via both sustained and transient input channels, representing signals' intensity over time or changes thereof, respectively. To date, it is unclear to what extent transient and sustained input channels contribute to the combined percept obtained through multisensory integration. Based on the results of two novel psychophysical experiments, here we demonstrate the importance of the transient (instead of the sustained) channel for the integration of audiovisual signals. To account for the present results, we developed a biologically inspired, general-purpose model for multisensory integration, the multisensory correlation detectors, which combines correlated input from unimodal transient channels. Besides accounting for the results of our psychophysical experiments, this model could quantitatively replicate several recent findings in multisensory research, as tested against a large collection of published datasets. In particular, the model could simultaneously account for the perceived timing of audiovisual events, multisensory facilitation in detection tasks, causality judgments, and optimal integration. This study demonstrates that several phenomena in multisensory research that were previously considered unrelated, all stem from the integration of correlated input from unimodal transient channels.

PMID:39841060 | DOI:10.7554/eLife.90841

Categories: Literature Watch

Murine Models and Human Cell Line Models to Study Altered Dynamics of Ovarian Follicles in Polycystic Ovary Syndrome

Systems Biology - Wed, 2025-01-22 06:00

Adv Biol (Weinh). 2025 Jan 22:e2400713. doi: 10.1002/adbi.202400713. Online ahead of print.

ABSTRACT

Polycystic ovary syndrome is one of the most common endocrine disorders in women of reproductive age, characterized by functional and structural alterations of the female reproductive organs. Due to the unknown underlying molecular mechanisms, in vivo murine models and in vitro human cellular models are developed to study the syndrome. These models are used to analyze various aspects of the pathology by replicating the conditions of the syndrome. Even though the complexity of polycystic ovary syndrome and the challenge of reproducing all its features leave several questions unanswered, studies conducted to date have elucidated some of the alterations in ovarian follicle molecular and cellular mechanisms involved in the syndrome, and do not require the employment of complex and invasive techniques on human patients. This review examines ovarian functions and their alterations in polycystic ovary syndrome, explores preclinical in vivo and in vitro models, and highlights emerging research and medical perspectives. It targets researchers, healthcare professionals, and academics, including endocrinologists, cell biologists, and reproductive medicine specialists, studying the molecular and cellular mechanisms of the syndrome.

PMID:39840999 | DOI:10.1002/adbi.202400713

Categories: Literature Watch

LEA_4 motifs function alone and in conjunction with synergistic cosolutes to protect a labile enzyme during desiccation

Systems Biology - Wed, 2025-01-22 06:00

Protein Sci. 2025 Feb;34(2):e70028. doi: 10.1002/pro.70028.

ABSTRACT

Organisms from all kingdoms of life depend on Late Embryogenesis Abundant (LEA) proteins to survive desiccation. LEA proteins are divided into broad families distinguished by the presence of family-specific motif sequences. The LEA_4 family, characterized by 11-residue motifs, plays a crucial role in the desiccation tolerance of numerous species. However, the role of these motifs in the function of LEA_4 proteins is unclear, with some studies finding that they recapitulate the function of full-length LEA_4 proteins in vivo, and other studies finding the opposite result. In this study, we characterize the ability of LEA_4 motifs to protect a desiccation-sensitive enzyme, citrate synthase (CS), from loss of function during desiccation. We show here that LEA_4 motifs not only prevent the loss of function of CS during desiccation but also that they can do so more robustly via synergistically interactions with cosolutes. Our analysis further suggests that cosolutes induce synergy with LEA_4 motifs in a manner that correlates with transfer free energy. This research advances our understanding of LEA_4 proteins by demonstrating that during desiccation their motifs can protect specific clients to varying degrees and that their protective capacity is modulated by their chemical environment. Our findings extend beyond the realm of desiccation tolerance, offering insights into the interplay between IDPs and cosolutes. By investigating the function of LEA_4 motifs, we highlight broader strategies for understanding protein stability and function.

PMID:39840786 | DOI:10.1002/pro.70028

Categories: Literature Watch

On the importance of data curation for knowledge mining in antiviral research

Drug Repositioning - Wed, 2025-01-22 06:00

Sci Prog. 2025 Jan-Mar;108(1):368504241301535. doi: 10.1177/00368504241301535.

ABSTRACT

The recent severe acute respiratory syndrome coronavirus 2 pandemic has clearly exemplified the need for broad-spectrum antiviral (BSA) medications. However, previous outbreaks show that about one year after an outbreak, interest in antiviral research diminishes and the work toward an effective medication is left unfinished. Martin et al. endeavored to support the early stages of focused BSA development by creating the Small Molecule Antiviral Compound Collection (SMACC), which is publicly available online at https://smacc.mml.unc.edu. SMACC is a highly curated database with over 32,500 entries of chemical compounds tested in both phenotypic and target-based assays across 13 viruses from the NIAID's list of emerging infectious diseases/pathogens. The authors advise judicious use of knowledge collections such as SMACC and recommend users critically evaluate retrieved data and resulting hypotheses prior to experimental testing. When used correctly, SMACC-like databases may serve as a reference for medicinal chemists and virologists working to develop novel BSA medications. To summarize, we emphasize the importance of data curation for both novel outbreak prediction and development of BSAs against these outbreaks.

PMID:39840476 | DOI:10.1177/00368504241301535

Categories: Literature Watch

Data-driven discovery of associations between prescribed drugs and dementia risk: A systematic review

Drug Repositioning - Wed, 2025-01-22 06:00

Alzheimers Dement (N Y). 2025 Jan 21;11(1):e70037. doi: 10.1002/trc2.70037. eCollection 2025 Jan-Mar.

ABSTRACT

ABSTRACT: Recent clinical trials on slowing dementia progression have led to renewed focus on finding safer, more effective treatments. One approach to identify plausible candidates is to assess whether existing medications for other conditions may affect dementia risk. We conducted a systematic review to identify studies adopting a data-driven approach to investigate the association between a wide range of prescribed medications and dementia risk. We included 14 studies using administrative or medical records data for more than 130 million individuals and 1 million dementia cases. Despite inconsistencies in identifying specific drugs that may modify Alzheimer's or dementia risk, some themes emerged for drug classes with biological plausibility. Antimicrobials, vaccinations, and anti-inflammatories were associated with reduced risk, while diabetes drugs, vitamins and supplements, and antipsychotics were associated with increased risk. We found conflicting evidence for antihypertensives and antidepressants. Drug repurposing for use in dementia is an urgent priority. Our findings offer a basis for prioritizing candidates and exploring underlying mechanisms.

HIGHLIGHTS: ·We present a systematic review of studies reporting association between drugs prescribed for other conditions and risk of dementia including 139 million people and 1 million cases of dementia.·Our work supports some previously reported associations, for example, showing decreased risk of dementia with drugs to treat inflammatory disease and increased risk with antipsychotic treatment.·Antimicrobial treatment was perhaps more surprisingly associated with decreased risk, supportive of recent increased interest in this potential therapeutic avenue.·Our work should help prioritize drugs for entry into adaptive platform trials in Alzheimer's disease and will serve as a useful resource for those investigating drugs or classes of drugs and risk of dementia.

PMID:39839078 | PMC:PMC11747987 | DOI:10.1002/trc2.70037

Categories: Literature Watch

Pharmacogenetic Testing in Treatment-resistant Panic Disorder: a Preliminary Analysis

Pharmacogenomics - Wed, 2025-01-22 06:00

Clin Pract Epidemiol Ment Health. 2024 Dec 3;20:e17450179337258. doi: 10.2174/0117450179337258241031035148. eCollection 2024.

ABSTRACT

BACKGROUND: Many pharmacological treatments are considered effective in the treatment of panic disorder (PD), however, about 20 to 40% of the patients have treatment-resistant PD. Pharmacogenetics could explain why some patients are treatment-resistant.

OBJECTIVE: Our objective was to gather preliminary data on the clinical usefulness of pharmacogenetic testing in this disorder.

METHODS: Twenty patients with treatment-resistant PD were included in this observational study and submitted to commercial pharmacogenetic testing. Testing panel included gene polymorphisms related to CYP, genes EPHX1, UGT1A4, UGT2B15, ABCB1, ADRA2A, ANKK1, COMT, DRD2, FKBP5, GRIK4, GSK3B, HTR1A, HTR2A, HTR2C, MC4R, OPRM1, SCN1A, SLC6A4 and MTHFR. Participants received treatment-as-usual for PD before being enrolled in this study, including first-line and second-line medications for PD.

RESULTS: In 30% of the patients, the tests indicated reduced chance of response to the prescribed drug, while they indicated very low serum levels of the prescribed drug in 20% of the subjects. The pharmacogenetic tests predicted reduction of MTHFR enzyme activity in 74% of the patients. ABCB1 gene alleles associated to drug resistance were found in 90% of the samples.

CONCLUSION: Commercial pharmacogenetic testing failed to predict negative treatment outcome in most patients with PD. The association between treatment-resistance in PD and the genes CYP2C19, MTHFR and ABCB1 deserves further study.

PMID:39839219 | PMC:PMC11748058 | DOI:10.2174/0117450179337258241031035148

Categories: Literature Watch

Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol

Pharmacogenomics - Wed, 2025-01-22 06:00

Front Psychiatry. 2025 Jan 7;15:1497565. doi: 10.3389/fpsyt.2024.1497565. eCollection 2024.

ABSTRACT

The application of personalized medicine in patients with first-episode psychosis (FEP) requires tools for classifying patients according to their response to treatment, considering both treatment efficacy and toxicity. However, several limitations have hindered its translation into clinical practice. Here, we describe the rationale, aims and methodology of Applied Pharmacogenetics to Predict Response to Treatment of First Psychotic Episode (the FarmaPRED-PEP project), which aims to develop and validate predictive algorithms to classify FEP patients according to their response to antipsychotics, thereby allowing the most appropriate treatment strategy to be selected. These predictors will integrate, through machine learning techniques, pharmacogenetic (measured as polygenic risk scores) and epigenetic data together with clinical, sociodemographic, environmental, and neuroanatomical data. To do this, the FarmaPRED-PEP project will use data from two already recruited cohorts: the PEPS cohort from the "Genotype-Phenotype Interaction and Environment. Application to a Predictive Model in First Psychotic Episodes" study (the PEPs study from the Spanish abbreviation) (N=335) and the PAFIP cohort from "Clinical Program on Early Phases of Psychosis" (PAFIP from the Spanish abbreviation) (N = 350). These cohorts will be used to create the predictor, which will then be validated in a new cohort, the FarmaPRED cohort (N = 300). The FarmaPRED-PEP project has been designed to overcome several of the limitations identified in pharmacogenetic studies in psychiatry: (1) the sample size; (2) the phenotype heterogeneity and its definition; (3) the complexity of the phenotype and (4) the gender perspective. The global reach of the FarmaPRED-PEP project is to facilitate the effective deployment of precision medicine in national health systems.

PMID:39839139 | PMC:PMC11747510 | DOI:10.3389/fpsyt.2024.1497565

Categories: Literature Watch

Cross-Section of Hypertensive Molecular Signaling Pathways: Understanding Pathogenesis and Identifying Improved Drug Targets

Pharmacogenomics - Wed, 2025-01-22 06:00

Curr Hypertens Rev. 2025 Jan 20. doi: 10.2174/0115734021342501250107052350. Online ahead of print.

ABSTRACT

INTRODUCTION: Hypertension is a chronic medical state and a major determining factor for cardiovascular and renal diseases. Both genetic and non-genetic factors contribute to hypertensive conditions among individuals. The renin-angiotensin-aldosterone system (RAAS) is a major genetic target for the anti-hypertension approach.

PURPOSE OF THE STUDY: The majority of classical antihypertensive drugs were mainly focused on the RAAS signaling pathways. Though these antihypertensive drugs control blood pressure (BP), they have mild to severe life-threatening effects. Unrevealing effective hypertensive targets for BP management is essential. The effective targets could emerge either from RAAS-dependent or RAAS-independent pathways and/or through the cross-talks among them.

RESULTS: Analyzing the physiopathological mechanisms of hypertension has the benefit of understanding the interactions between these systems which helps in better understanding of drug targets and the importance of emergence of novel therapeutics.

CONCLUSION: This review is about the signaling pathways involved in hypertension pathogenesis and their cross-talks and it contributes to a better understanding of the etiology of hypertension.

PMID:39838689 | DOI:10.2174/0115734021342501250107052350

Categories: Literature Watch

Pharmacological Approaches and Innovative Strategies for Individualized Patient Care

Pharmacogenomics - Wed, 2025-01-22 06:00

Recent Pat Biotechnol. 2025 Jan 20. doi: 10.2174/0118722083359334250116063638. Online ahead of print.

ABSTRACT

Personalized medicine is an evolving paradigm that aims to tailor therapeutic interventions to individual patient characteristics. With a growing understanding of the genetic, epigenetic, and molecular mechanisms underlying diseases, tailored therapies are becoming more feasible and effective. This review highlights the significant advancements in personalized medicine, focusing specifically on pharmacological strategies. The article explores the integration of genomics, transcriptomics, proteomics, and metabolomics in drug development and therapy optimization. Pharmacogenomics, the customization of drug therapy based on an individual's genetic makeup, receives particular emphasis. This leads to the identification of specific biomarkers that can predict therapeutic response, drug toxicity, and susceptibility to various diseases. Together with computational tools and artificial intelligence, these advancements contribute to tailored treatment plans for patients with conditions such as cancer, cardiovascular diseases, and neurological disorders. We also highlight the challenges and ethical considerations in implementing personalized medicine, such as data privacy, cost-effectiveness, and accessibility. We outline future prospects and ongoing research in this field, highlighting the importance of collaborative efforts between researchers, clinicians, pharmacists, and regulatory authorities.

PMID:39838664 | DOI:10.2174/0118722083359334250116063638

Categories: Literature Watch

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