Med J Aust. 2025 Apr 10. doi: 10.5694/mja2.52650. Online ahead of print.

ABSTRACT

Paediatric cancer is the leading cause of disease-related death in Australian children. Limited research focuses on cancer in Aboriginal and Torres Strait Islander children. Although there appears to be a lower incidence of cancer overall in Aboriginal and Torres Strait Islander children compared with non-Indigenous children, a high proportion of Aboriginal and Torres Strait Islander children are diagnosed with acute myeloid leukaemia. Five-year overall survival is lower for many cancer types in Aboriginal and Torres Strait Islander children. There is a need for Indigenous-specific research focused on molecular and genetic profiles, pharmacogenomics and survivorship, both within Australia and globally. Future research in this space should be co-designed and led by Aboriginal and Torres Strait Islander communities; alongside clinicians, researchers and services to ensure that the priorities of Aboriginal and Torres Strait Islander people are met.

PMID:40207417 | DOI:10.5694/mja2.52650

Mol Ther Oncol. 2025 Mar 8;33(2):200965. doi: 10.1016/j.omton.2025.200965. eCollection 2025 Jun 18.

ABSTRACT

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative breast cancer (TNBC) and human epidermal growth factor receptor 2 positive breast cancer, are confronted with a heightened risk of drug resistance, often leading to early recurrence; the biomarkers and biological processes associated with recurrence is still unclear. In this study, we analyzed bulk RNA sequencing (RNA-seq) data from 285 cancer and paracancerous samples from 155 TNBC patients, along with transcriptome data from 11 independent public cohorts comprising 7,449 breast cancer patients and 26 single-cell RNA-seq datasets. Our results revealed differential enrichment of nerve-related pathways between TNBC patients with and without 10-year recurrence-free survival. We developed an early recurrence index (ERI) using a machine learning model and constructed a nomogram that accurately predicts the 10-year survival of ERneg patients (area under the curve [AUC]Training = 0.79; AUCTest = 0.796). Further analysis linked ERI to enhanced neural function and immunosuppression. Additionally, we identified EN1, the most significant ERI gene, as a potential biomarker that may regulate the tumor microenvironment and sensitize patients to immunotherapy.

PMID:40207200 | PMC:PMC11981748 | DOI:10.1016/j.omton.2025.200965

Front Pharmacol. 2025 Mar 26;16:1558897. doi: 10.3389/fphar.2025.1558897. eCollection 2025.

ABSTRACT

BACKGROUND: Despite the emergence of numerous innovative targeted therapies for the management of pediatric inflammatory bowel disease (IBD), azathioprine continues to be a pivotal first-line therapeutic agent. Nonetheless, the considerable frequency of myelosuppression associated with its use warrants careful consideration and further investigation. This study aims to investigate the application of pharmacogenomics in Chinese pediatric IBD treated with azathioprine, and to elucidate its association with the occurrence of myelosuppression.

METHODS: We conducted a retrospective analysis to determine the prevalence of pharmacogenetic abnormalities and thiopurine-induced myelosuppression in Chinese pediatric patients with IBD.

RESULTS: Among the 227 patients underwent pharmacogenetic testing, abnormal genetypes occurred in 66 patients, among which 7 patients exhibited aberrant TPMT and 59 had aberrant NUDT15. Of the 58 patients who were treated with azathioprine, 23 cases experienced myelosuppression. All three children with heterozygous mutations in NUDT15 developed leukopenia following azathioprine treatment. Among patients with normal pharmacogenetic results, 20 cases (36.4%) developed myelosuppression, while 35 cases (63.6%) did not. The dose of azathioprine was below the recommended level in guidelines. The mean dose of azathioprine (mg/kg/day) in the myelosuppression group was 1.22 ± 0.32, compared to 1.42 ± 0.42 in the non-myelosuppression group, which represented a statistically significant difference (p < 0.05). Age, gender, and the use of concomitant biologics, mesalazine, or glucocorticoids did not show significant differences between the groups (p > 0.05).

CONCLUSION: NUDT15 C415T is prevalent in China and is associated with an increased risk of azathioprine-induced myelosuppression. A reduced dose of azathioprine should be considered for Chinese pediatric patients with IBD, even in those with normal pharmacogenetic profiles.

PMID:40206080 | PMC:PMC11979209 | DOI:10.3389/fphar.2025.1558897

Front Pharmacol. 2025 Mar 26;16:1590955. doi: 10.3389/fphar.2025.1590955. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fphar.2024.1526101.].

PMID:40206068 | PMC:PMC11979611 | DOI:10.3389/fphar.2025.1590955

Pharmacogenomics. 2025 Apr 9:1-6. doi: 10.1080/14622416.2025.2489920. Online ahead of print.

ABSTRACT

INTRODUCTION: Genotype-guided tacrolimus management is not routine in clinical practice despite the availability of Clinical Pharmacogenetics Implementation Consortium dosing guidelines. Prior surveys have evaluated patient and provider perspectives of pharmacogenetics (PGx) in transplant, but limited recent data exists on tacrolimus PGx implementation across United States transplant centers.

METHODS: An electronic survey was distributed to transplant pharmacists regarding utilization of tacrolimus PGx, methods of implementing PGx, and barriers to clinical implementation. A survey response was requested for each organ program within the transplant center.

RESULTS: A total of 90 programs from 69 transplant centers (28.1% of active U.S. transplant centers) responded to the survey. Tacrolimus PGx was utilized for patient care in 14 programs (15.6%). There was substantial variability in the implementation methods and application of tacrolimus PGx results among transplant programs. In programs that had not implemented tacrolimus PGx, common barriers for implementation included PGx testing cost and availability and lack of evidence for clinical utility.

CONCLUSION: Implementation of PGx guided tacrolimus in solid organ transplant centers remains limited with heterogeneity in the implementation approach. Additional research is needed to establish the clinical utility of PGx guided tacrolimus and education on reimbursement and testing resources may help to increase uptake.

PMID:40205800 | DOI:10.1080/14622416.2025.2489920

MethodsX. 2025 Mar 22;14:103267. doi: 10.1016/j.mex.2025.103267. eCollection 2025 Jun.

ABSTRACT

In this study, we present a detailed workflow for the isolation, quantitation, and evaluation of mucin proteins. These methods are applicable to a variety of biological, mucin-containing samples from the airways and other mucosal organ systems. While this report focuses on the salivary MUC5B protein from the respiratory system, the presented methodologies can be applied to other mucins, contributing to a broader application of these techniques. We used a simplified isopycnic centrifugation to purify and enrich MUC5B from human saliva. Isolated MUC5B was then subjected to a Bradford protein assay using a bovine submaxillary mucin (BSM) standard, which more accurately reflects the mucin concentration in our samples compared to a bovine serum albumin (BSA) standard. Additionally, we compare the mucin levels following quantitation using agarose polyacrylamide gel electrophoresis. Our findings show a near 2-fold increase in quantitation from the more representative, BSM standard, suggesting its importance for mucin studies. These methods support a wide range of experimental applications looking to assess mucins, thereby contributing to the broader field of mucin studies and advancing our understanding of the implications of mucins in health and disease.•A streamlined, one-step isopycnic ultracentrifugation to isolate MUC5B from human saliva•A Mucin Bradford assay that is modified from existing Bradford assay techniques to better quantitate mucin for mucin studies•An agarose-polyacrylamide gel electrophoresis method used to visualize and confirm the isolation and quantitation of mucin.

PMID:40207064 | PMC:PMC11981757 | DOI:10.1016/j.mex.2025.103267

Pharmazie. 2025 Mar 31;80(1):10-16. doi: 10.1691/ph.2025.4544.

ABSTRACT

Transmembrane 16A (TMEM16A) is highly expressed in interstitial cells of Cajal (ICC) and participates in ICC-mediated rhythmic contractile activity of intestinal smooth muscle. TMEM16A is also expressed in epithelium of intestine with a minor contributor to transepithelial fluid secretion, while other unidentified Ca2+ -activated Cl - channels (unCaCCs) are mainly responsible for this physiological process. TMEM16A/CaCCs dysfunction can lead to disorders of intestinal motility and transepithelial fluid secretion. TMEM16A/CaCCs regulators are important tools to identify unCaCCs and study the physiopathological functions related to TMEM16A/CaCCs. In the present study, coumarins were identified as TMEM16A inhibitors in a concentration- and time-dependent manner in TMEM16A-expressed Fischer rat thyroid (FRT) epithelial cells. Coumarins attenuated intestinal motility by inhibiting TMEM16A in vivo and ex vivo. Coumarins inhibited CaCCs-mediated Cl- currents induced by ATP in T84 and HT-29 cells or by carbachol (CCh) in mouse colonic mucosa with reduction of ATP-induced increase of cytoplasmic Ca2+ concentration in HT-29 cells. Coumarins inhibited basolateral Ca2+ -activated K+ channels without affecting Na + /K + -ATPase activity in mouse colonic mucosa. Coumarins did not show inhibition of cystic fibrosis transmembrane conductance regulator (CFTR), but mild activation of CFTR-mediated Cl - currents under the low concentration forskolin (FSK) in CFTR-expressed FRT cells, while coumarins did not activate CFTR-mediated Cl- currents in mouse colonic mucosa. This study was the first to demonstrate that coumarins attenuate intestinal motility by inhibiting TMEM16A, which may provide a strategy for clinical drug intervention aimed at reducing secretory diarrhea.

PMID:40205671 | DOI:10.1691/ph.2025.4544

Br J Nutr. 2025 Apr 10:1-13. doi: 10.1017/S0007114525000522. Online ahead of print.

ABSTRACT

One of the most significant challenges in research related to nutritional epidemiology is the achievement of high accuracy and validity of dietary data to establish an adequate link between dietary exposure and health outcomes. Recently, the emergence of artificial intelligence (AI) in various fields has filled this gap with advanced statistical models and techniques for nutrient and food analysis. We aimed to systematically review available evidence regarding the validity and accuracy of AI-based dietary intake assessment methods (AI-DIA). In accordance with PRISMA guidelines, an exhaustive search of the EMBASE, PubMed, Scopus and Web of Science databases was conducted to identify relevant publications from their inception to 1 December 2024. Thirteen studies that met the inclusion criteria were included in this analysis. Of the studies identified, 61·5 % were conducted in preclinical settings. Likewise, 46·2 % used AI techniques based on deep learning and 15·3 % on machine learning. Correlation coefficients of over 0·7 were reported in six articles concerning the estimation of calories between the AI and traditional assessment methods. Similarly, six studies obtained a correlation above 0·7 for macronutrients. In the case of micronutrients, four studies achieved the correlation mentioned above. A moderate risk of bias was observed in 61·5 % (n 8) of the articles analysed, with confounding bias being the most frequently observed. AI-DIA methods are promising, reliable and valid alternatives for nutrient and food estimations. However, more research comparing different populations is needed, as well as larger sample sizes, to ensure the validity of the experimental designs.

PMID:40207441 | DOI:10.1017/S0007114525000522

Front Comput Neurosci. 2025 Mar 26;19:1545971. doi: 10.3389/fncom.2025.1545971. eCollection 2025.

ABSTRACT

In recent years, the integration of machine vision and neuroscience has provided a new perspective for deeply understanding visual information. This paper proposes an innovative deep learning model, NeuroFusionNet, designed to enhance the understanding of visual information by integrating fMRI signals with image features. Specifically, images are processed by a visual model to extract region-of-interest (ROI) features and contextual information, which are then encoded through fully connected layers. The fMRI signals are passed through 1D convolutional layers to extract features, effectively preserving spatial information and improving computational efficiency. Subsequently, the fMRI features are embedded into a 3D voxel representation to capture the brain's activity patterns in both spatial and temporal dimensions. To accurately model the brain's response to visual stimuli, this paper introduces a Mutli-scale fMRI Timeformer module, which processes fMRI signals at different scales to extract both fine details and global responses. To further optimize the model's performance, we introduce a novel loss function called the fMRI-guided loss. Experimental results show that NeuroFusionNet effectively integrates image and brain activity information, providing more precise and richer visual representations for machine vision systems, with broad potential applications.

PMID:40207297 | PMC:PMC11978827 | DOI:10.3389/fncom.2025.1545971

Med Image Comput Comput Assist Interv. 2024 Oct;15011:37-47. doi: 10.1007/978-3-031-72120-5_4. Epub 2024 Oct 3.

ABSTRACT

The robustness of supervised deep learning-based medical image classification is significantly undermined by label noise in the training data. Although several methods have been proposed to enhance classification performance in the presence of noisy labels, they face some challenges: 1) a struggle with class-imbalanced datasets, leading to the frequent overlooking of minority classes as noisy samples; 2) a singular focus on maximizing performance using noisy datasets, without incorporating experts-in-the-loop for actively cleaning the noisy labels. To mitigate these challenges, we propose a two-phase approach that combines Learning with Noisy Labels (LNL) and active learning. This approach not only improves the robustness of medical image classification in the presence of noisy labels but also iteratively improves the quality of the dataset by relabeling the important incorrect labels, under a limited annotation budget. Furthermore, we introduce a novel Variance of Gradients approach in the LNL phase, which complements the loss-based sample selection by also sampling under-represented examples. Using two imbalanced noisy medical classification datasets, we demonstrate that our proposed technique is superior to its predecessors at handling class imbalance by not misidentifying clean samples from minority classes as mostly noisy samples. Code available at: https://github.com/Bidur-Khanal/imbalanced-medical-active-label-cleaning.git.

PMID:40207034 | PMC:PMC11981598 | DOI:10.1007/978-3-031-72120-5_4

Mayo Clin Proc Digit Health. 2024 Nov 27;3(1):100183. doi: 10.1016/j.mcpdig.2024.11.004. eCollection 2025 Mar.

ABSTRACT

OBJECTIVE: To develop a deep learning framework to predict lower-limb joint kinematics from inertial measurement unit (IMU) data across multiple gait tasks (walking, jogging, and running) and evaluate the impact of dynamic time warping (DTW) on reducing prediction errors.

PATIENTS AND METHODS: Data were collected from 18 participants fitted with IMUs and an optical motion capture system between May 25, 2023, and May 30, 2023. A long short-term memory autoencoder supervised regression model was developed. The model consisted of multiple long short-term memory and convolution layers. Acceleration and gyroscope data from the IMUs in 3 axes and their magnitude for the proximal and distal sensors of each joint (hip, knee, and ankle) were inputs to the model. Optical motion capture kinematics were considered ground truth and used as an output to train the prediction model.

RESULTS: The deep learning models achieved a root-mean-square error of less than 6° for hip, knee, and ankle joint sagittal plane angles, with the ankle showing the lowest error (5.1°). Task-specific models reported enhanced performance during certain gait phases, such as knee flexion during running. The application of DTW significantly reduced root-mean-square error across all tasks by at least 3° to 4°. External validation of independent data confirmed the model's generalizability.

CONCLUSION: Our findings underscore the potential of IMU-based deep learning models for joint kinematic predictions, offering a practical solution for remote and continuous biomechanical assessments in health care and sports science.

PMID:40207006 | PMC:PMC11975825 | DOI:10.1016/j.mcpdig.2024.11.004

Mayo Clin Proc Digit Health. 2025 Jan 10;3(1):100194. doi: 10.1016/j.mcpdig.2025.100194. eCollection 2025 Mar.

ABSTRACT

OBJECTIVE: To develop a fully end-to-end deep learning framework capable of estimating left ventricular ejection fraction (LVEF), estimating patient age, and classifying patient sex from echocardiographic videos, including videos collected using handheld cardiac ultrasound (HCU).

PATIENTS AND METHODS: Deep learning models were trained using retrospective transthoracic echocardiography (TTE) data collected in Mayo Clinic Rochester and surrounding Mayo Clinic Health System sites (training: 6432 studies and internal validation: 1369 studies). Models were then evaluated using retrospective TTE data from the 3 Mayo Clinic sites (Rochester, n=1970; Arizona, n=1367; Florida, n=1562) before being applied to a prospective dataset of handheld ultrasound and TTE videos collected from 625 patients. Study data were collected between January 1, 2018 and February 29, 2024.

RESULTS: Models showed strong performance on the retrospective TTE datasets (LVEF regression: root mean squared error (RMSE)=6.83%, 6.53%, and 6.95% for Rochester, Arizona, and Florida cohorts, respectively; classification of LVEF ≤40% versus LVEF > 40%: area under curve (AUC)=0.962, 0.967, and 0.980 for Rochester, Arizona, and Florida, respectively; age: RMSE=9.44% for Rochester; sex: AUC=0.882 for Rochester), and performed comparably for prospective HCU versus TTE data (LVEF regression: RMSE=6.37% for HCU vs 5.57% for TTE; LVEF classification: AUC=0.974 vs 0.981; age: RMSE=10.35% vs 9.32%; sex: AUC=0.896 vs 0.933).

CONCLUSION: Robust TTE datasets can be used to effectively power HCU deep learning models, which in turn demonstrates focused diagnostic images can be obtained with handheld devices.

PMID:40207004 | PMC:PMC11975991 | DOI:10.1016/j.mcpdig.2025.100194

Mayo Clin Proc Digit Health. 2025 Jan 21;3(1):100195. doi: 10.1016/j.mcpdig.2025.100195. eCollection 2025 Mar.

ABSTRACT

OBJECTIVE: To develop an efficient and automated method for selecting appropriate coronary angiography videos for training deep learning models, thereby improving the accuracy and efficiency of medical image analysis.

PATIENTS AND METHODS: We developed deep learning models using 232 coronary angiographic studies from the Mayo Clinic. We utilized 2 state-of-the-art convolutional neural networks (CNN: ResNet and X3D) to identify low-quality angiograms through binary classification (satisfactory/unsatisfactory). Ground truth for the quality of the input angiogram was determined by 2 experienced cardiologists. We validated the developed model in an independent dataset of 3208 procedures from 3 Mayo sites.

RESULTS: The 3D-CNN models outperformed their 2D counterparts, with the X3D-L model achieving superior performance across all metrics (AUC 0.98, accuracy 0.96, precision 0.87, and F1 score 0.92). Compared with 3D models, 2D architectures are smaller and less computationally complex. Despite having a 3D architecture, the X3D-L model had lower computational demand (19.34 Giga Multiply Accumulate Operation) and parameter count (5.34 M) than 2D models. When validating models on the independent dataset, slight decreases in all metrics were observed, but AUC and accuracy remained robust (0.95 and 0.92, respectively, for the X3D-L model).

CONCLUSION: We developed a rapid and effective method for automating the selection of coronary angiogram video clips using 3D-CNNs, potentially improving model accuracy and efficiency in clinical applications. The X3D-L model reports a balanced trade-off between computational efficiency and complexity, making it suitable for real-life clinical applications.

PMID:40206993 | PMC:PMC11975815 | DOI:10.1016/j.mcpdig.2025.100195

Comput Struct Biotechnol J. 2025 Mar 7;28:94-105. doi: 10.1016/j.csbj.2025.02.039. eCollection 2025.

ABSTRACT

Corneal diseases present a significant challenge to global health. Given the uneven distribution of ophthalmic resources, the development of a system to facilitate remote diagnosis of corneal diseases is particularly crucial. In this study, we developed an artificial intelligence system named Gancor, based on a large-scale clinical dataset comprising 9669 anterior segment (AS) images and corresponding corneal fluorescein staining (CFS) images from the Affiliated Eye Hospital of Nanchang University, as well as 967 pairs of AS-CFS images captured via smartphone from the Jiangxi Province Division of National Clinical Research Center for Ocular Diseases. The system utilizes Generative Adversarial Networks (GANs) to convert AS images into CFS images for the screening of 11 common corneal diseases. Objective assessments of the generated CFS images were conducted using Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), along with subjective evaluations by three experienced ophthalmologists, confirming the high quality and diagnostic relevance of the synthesized images. In terms of diagnostic performance for corneal diseases, the accuracy rate exceeded 75 %, and the Area Under the Curve (AUC) value reached above 0.90. This innovative approach not only provides images with greater diagnostic value for telemedicine but also enhances the efficiency of remote diagnosis, offering an effective tool for achieving the goal of comprehensive, equitable, and accessible eye care services.

PMID:40206787 | PMC:PMC11981786 | DOI:10.1016/j.csbj.2025.02.039

J Hepatocell Carcinoma. 2025 Apr 5;12:685-704. doi: 10.2147/JHC.S505752. eCollection 2025.

ABSTRACT

BACKGROUND: With the global epidemic of obesity and diabetes, non-alcoholic fatty liver disease (NAFLD) is becoming the most common chronic liver disease, and NASH is increasingly becoming a major risk factor for hepatocellular carcinoma. Therefore, it is essential to explore novel biomarkers in NASH-related HCC.

METHODS: Deep Learning (DL) methods are a promising and encouraging tool widely used in genomics by automatically applying neural networks (NNs). Therefore, DL, "limma package", weighted gene co-expression network analysis (WGCNA), and Protein-Protein Interaction Networks (PPI) were used to screen feature genes. Real-time quantitative PCR was used to validate the expression of feature genes in the NAFLD mice model. Enrichment and single-cell sequencing analyses of single genes were performed to investigate the role of feature genes in NASH-related HCC.

RESULTS: Combined core genes screened by DL in NAFLD with important genes in metabolic syndrome, six feature genes (FDFT1, TNFSF10, DNAJC16, RDH11, PGRMC1, and MYC) were obtained. ROC analysis demonstrates the model's superiority with the AUC was 0.983 (0.9241-0.98885). Animal experiments based on NAFLD mouse models have also shown that FDFT1, TNFSF10, DNAJC16, RDH11, and PGRMC1 have a higher expression in NAFLD livers. Among the feature genes, FDFT1 and PGRMC1 showed significant expression trends and outstanding diagnosis value in NASH-HCC.

CONCLUSION: In conclusion, FDFT1 and PGRMC1 are key enzymes in the cholesterol synthesis pathway, our study validates the important role of cholesterol metabolism in NAFLD from another perspective, implying they may be new prognostic and diagnostic markers for NASH-HCC.

PMID:40206734 | PMC:PMC11980943 | DOI:10.2147/JHC.S505752

Front Artif Intell. 2025 Mar 26;8:1426455. doi: 10.3389/frai.2025.1426455. eCollection 2025.

ABSTRACT

INTRODUCTION: Handwriting is a complex skill that requires coordination between human motor system, sensory perception, cognitive processing, memory retrieval, and linguistic proficiency. Various aspects of hand and stylus kinematics can affect the legibility of a handwritten text. Assessing handwriting legibility is challenging due to variations in experts' cultural and academic backgrounds, which introduce subjectivity biases in evaluations.

METHODS: In this paper, we utilize a deep-learning model to analyze kinematic features influencing the legibility of handwriting based on temporal convolutional networks (TCN). Fifty subjects are recruited to complete a 26-word paragraph handwriting task, designed to include all possible orthographic combinations of Arabic characters, during which the hand and stylus movements are recorded. A total of 117 different spatiotemporal features are recorded, and the data collected are used to train the model. Shapley values are used to determine the important hand and stylus kinematics features toward evaluating legibility. Three experts are recruited to label the produced text into different legibility scores. Statistical analysis of the top 6 features is conducted to investigate the differences between features associated with high and low legibility scores.

RESULTS: Although the model trained on stylus kinematics features demonstrates relatively high accuracy (around 76%), where the number of legibility classes can vary between 7 and 8 depending on the expert, the addition of hand kinematics features significantly increases the model accuracy by approximately 10%. Explainability analysis revealed that pressure variability, pen slant (altitude, azimuth), and hand speed components are the most prominent for evaluating legibility across the three experts.

DISCUSSION: The model learns meaningful stylus and hand kinematics features associated with the legibility of handwriting. The hand kinematics features are important for accurate assessment of handwriting legibility. The proposed approach can be used in handwriting learning tools for personalized handwriting skill acquisition as well as for pathology detection and rehabilitation.

PMID:40206709 | PMC:PMC11979204 | DOI:10.3389/frai.2025.1426455

Front Artif Intell. 2025 Mar 26;8:1544183. doi: 10.3389/frai.2025.1544183. eCollection 2025.

ABSTRACT

Optimizing building energy consumption holds significant untapped potential, particularly in a developing economy such as India. Existing solutions have yet to concentrate on a methodology that is cost-effective, small-scale, precise, and open source data-driven. In response, we have implemented an automated, DL-enabled approach to predict energy consumption with the goal to enable cost and schedule optimization. For two years from December 2021 to December 2023 the energy consumption and twenty seven associated energy parameters was monitored by developing an IoT enabled BEMS. The data collected was preprocessed, cleaned, transformed and used for training a machine learning model. Based on the previous literature, a hybrid DL model was developed using artificial neural networks and fuzzy logic by integrating fuzzy layers in the deep neural architecture. The collected electrical data was used for training, hyper-parameter tuning and testing the hybrid DL model. The proposed model when tested for out-of-sample dataset had comparable results on error and performance metrics as compared to other states of the art models. On deployment in the premises of a university, the BEMS achieved a reduction in the electricity bill of 20% highlighting its effectiveness and efficacy.

PMID:40206707 | PMC:PMC11979119 | DOI:10.3389/frai.2025.1544183

Pathologica. 2025 Feb;117(1):3-9. doi: 10.32074/1591-951X-1123.

NO ABSTRACT

PMID:40205925 | DOI:10.32074/1591-951X-1123

ACS Catal. 2025 Mar 13;15(7):5257-5272. doi: 10.1021/acscatal.5c00819. eCollection 2025 Apr 4.

ABSTRACT

Halohydrin dehalogenases (HHDHs) are powerful enzymes for the asymmetric diversification of oxyfunctionalized synthons. They feature two characteristic sequence motifs that distinguish them from homologous short-chain dehydrogenases and reductases. Sequence motif 1, carrying a conserved threonine, glycine, and a central aromatic residue, lines the nucleophile binding pocket of HHDHs. It could therefore impact nucleophile binding and presumably also the activity of the enzymes. However, experimental evidence supporting this theory is largely missing. Herein, we systematically studied the mutability of the three conserved motif 1 residues as well as their resulting impact on enzyme activity, stability, and selectivity in two model HHDHs: HheC from Agrobacterium radiobacter AD1 and HheG from Ilumatobacter coccineus. In both HheC and HheG, the conserved threonine and glycine tolerated mutations to only structurally similar amino acids. In contrast, the central aromatic (i.e., phenylalanine or tyrosine) residue of motif 1 demonstrated much higher variability in HheC. Remarkably, some of these variants featured drastically altered activity, stability, and selectivity characteristics. For instance, variant HheC F12Y displayed up to 5-fold increased specific activity in various epoxide ring opening and dehalogenation reactions as well as enhanced enantioselectivity (e.g., an E-value of 74 in the azidolysis of epichlorohydrin compared to E = 22 for HheC wild type) while also exhibiting a 10 K higher apparent melting temperature. QM and MD simulations support the experimentally observed activity increase of HheC F12Y and reveal alterations in the hydrogen bonding network within the active site. As such, our results demonstrate that multiple enzyme properties of HHDHs can be altered through the targeted mutagenesis of conserved motif 1 residues. In addition, this work illustrates that motif 1 plays vital roles beyond nucleophile binding by impacting the solubility and stability properties. These insights advance our understanding of HHDH active sites and will facilitate their future engineering.

PMID:40207069 | PMC:PMC11976700 | DOI:10.1021/acscatal.5c00819

Food Sci Nutr. 2025 Apr 9;13(4):e70077. doi: 10.1002/fsn3.70077. eCollection 2025 Apr.

ABSTRACT

Mitochondria are essential for maintaining health, and dysfunction of them leads to various diseases. Their role is not limited to energy production but serves multiple mechanisms varying from calcium hemostasis, reactive oxygen species production, and regulation of apoptotic cell death. In recent years, several strategies have been developed to preserve mitochondria. Ellagic acid (EA) is a polyphenol extracted from many plants. The intestinal microflora converts EA to urolithins with high bioavailability. EA and urolithins exhibit mitochondrial-protective effects by regulating mitochondrial complexes, sirtuins, mitophagy, and mitochondrial antioxidant enzymes. This review highlights the mito-protective effects of EA and urolithins on mitochondrial injuries induced by various drugs and toxic compounds.

PMID:40206693 | PMC:PMC11979624 | DOI:10.1002/fsn3.70077