Front Cell Infect Microbiol. 2025 Jan 20;14:1502339. doi: 10.3389/fcimb.2024.1502339. eCollection 2024.

ABSTRACT

Conserved histone methyltransferases of the DOT1 family are involved in replication regulation, cell cycle progression, stage differentiation, and gene regulation in trypanosomatids. However, the specific functions of these enzymes depend on the host evasion strategies of the parasites. In this study, we investigated the role of DOT1B in Leishmania mexicana, focusing on life cycle progression and infectivity. In contrast to Trypanosoma brucei, in which DOT1B is essential for the differentiation of mammal-infective bloodstream forms to insect procyclic forms, L. mexicana DOT1B (LmxDOT1B) is not critical for the differentiation of promastigotes to amastigotes in vitro. Additionally, there are no significant differences in the ability to infect or differentiate in macrophages or sand fly vectors between the LmxDOT1B-depleted and control strains. These findings highlight the divergence of the function of DOT1B in these related parasites, suggesting genus-specific adaptations in the use of histone modifications for life cycle progression and host adaptation processes.

PMID:39902184 | PMC:PMC11788152 | DOI:10.3389/fcimb.2024.1502339

Front Immunol. 2025 Jan 20;15:1439434. doi: 10.3389/fimmu.2024.1439434. eCollection 2024.

ABSTRACT

Multi-cellular biological systems, including the immune system, are highly complex, dynamic, and adaptable. Systems biologists aim to understand such complexity at a quantitative level. However, these ambitious efforts are often limited by access to a variety of high-density intra-, extra- and multi-cellular measurements resolved in time and space and across a variety of perturbations. The advent of automation, OMICs and single-cell technologies now allows high dimensional multi-modal data acquisition from the same biological samples multiplexed at scale (multi-OMICs). As a result, systems biologists -theoretically- have access to more data than ever. However, the mathematical frameworks and computational tools needed to analyze and interpret such data are often still nascent, limiting the biological insights that can be obtained without years of computational method development and validation. More pressingly, much of the data sits in silos in formats that are incomprehensible to other scientists or machines limiting its value to the vaster scientific community, especially the computational biologists tasked with analyzing these vast amounts of data in more nuanced ways. With the rapid development and increasing interest in using artificial intelligence (AI) for the life sciences, improving how biologic data is organized and shared is more pressing than ever for scientific progress. Here, we outline a practical approach to multi-modal data management and FAIR sharing, which are in line with the latest US and EU funders' data sharing policies. This framework can help extend the longevity and utility of data by allowing facile use and reuse, accelerating scientific discovery in the biomedical sciences.

PMID:39902035 | PMC:PMC11788310 | DOI:10.3389/fimmu.2024.1439434

Cancer Sci. 2025 Feb 3. doi: 10.1111/cas.16412. Online ahead of print.

ABSTRACT

BRCA1 is one of the causative genes for hereditary breast and ovarian cancer syndrome with a high risk of early-onset breast cancer. Whereas olaparib (OLA), an inhibitor of poly-ADP-ribose polymerase, has been applied as adjuvant therapy to those cancer patients, its effect on ovarian reproductive function remains unelucidated. Recently, a rat model (MUT; Brca1(L63X/+) mutation) mimicking a human BRCA1 pathogenic variant has been established. Using this model, we evaluated the effects of OLA on ovarian reproductive function in comparison with the wild-type (WT) rats. MUT showed a significantly reduced number of primordial follicles and subfertility in accordance with aging. Oxidative stress was significantly elevated in the young MUT granulosa cells (GCs) accompanied by increased mTOR but decreased PTEN signals. OLA administration in MUT further decreased primordial follicles, with gene set enrichment analysis, indicating upregulated DNA repair pathways. Furthermore, a combination of OLA and cyclophosphamide (CPA) induced empty primordial follicles, recognized as CPA-induced severe ovarian toxicity. Whereas OLA + CPA caused greater reduction in primordial follicles both in MUT and WT in comparison with CPA alone, MUT ovaries were more susceptible to oxidative stress, potentially depleting primordial follicles via activation of GCs and inducing oocyte death due to accumulated DNA damage by OLA treatment. Our findings in this preclinical model underscore the importance of evaluating ovarian reserve prior to chemotherapy by performing reproductive consultation with female patients with BRCA1 pathogenic variants.

PMID:39901592 | DOI:10.1111/cas.16412

Thorac Cancer. 2025 Feb;16(3):e70006. doi: 10.1111/1759-7714.70006.

ABSTRACT

BACKGROUND: Recently, the treatment needs of elderly lung cancer patients have become comparable with those of younger patients. This study evaluated the efficacy and safety of first-line immune checkpoint inhibitors (ICI) in elderly patients with nonsmall-cell lung cancer (NSCLC), stratified by immune-related adverse events (irAEs) severity, and identified key prognostic factors.

METHODS: This retrospective study targeted patients with advanced or recurrent NSCLC who received ICI therapy as first-line treatment between April 2017 and March 2023.

RESULT: Of the 138 patients enrolled in this study, 81 and 57 patients were classified into the elderly (aged 70 and above) and nonelderly (under 70 years old) groups, respectively. Severe irAEs were significantly associated with shorter overall survival (OS) in the elderly group (severe irAEs vs. others, 9.9 vs. 24.7 months; p = 0.043) and favorable OS in nonelderly group (severe irAEs vs. others, NR [not reached] vs. 21.0 months, p = 0.026). The OS of patients with severe irAEs was significantly worse in the elderly group than in the nonelderly group (elderly group vs. nonelderly group, 9.9 vs. NR months, p = 0.001). In the multivariate analysis, mild irAEs were associated with a favorable prognosis in elderly patients (hazard ratio, 0.446; p = 0.032).

CONCLUSION: Severe irAEs demonstrated different outcomes in elderly and nonelderly patients. Contrastingly, mild irAEs were associated with a favorable prognosis in elderly patients, emphasizing the need for appropriate patient selection, early intervention for irAEs and new tools to accurately predict irAE severity in elderly patients.

PMID:39901355 | DOI:10.1111/1759-7714.70006

HNO. 2025 Mar;73(3):203-206. doi: 10.1007/s00106-025-01551-1. Epub 2025 Feb 3.

ABSTRACT

We describe an extremely rare case of a clival mucocele and complement the case report with a literature review. A 30-year-old woman presented to the emergency department with unclear neurologic symptoms after bouldering and left-sided hyposensitivity in her face and radial surface of the forearm. Magnetic resonance imaging was suspicious of a clival mucocele, which was confirmed in computed tomography. Endoscopic drainage of the mucocele was undertaken, after which the patient recovered fully.

PMID:39900815 | DOI:10.1007/s00106-025-01551-1

Sci Rep. 2025 Feb 3;15(1):4125. doi: 10.1038/s41598-025-85372-w.

ABSTRACT

In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency or excess state, not only influencing cattle performance and beef quality, but also causing health problems. Researchers have been exploring eye photography monitoring methods for cattle blood vitamin A levels based on the relation between vitamin A and retina colour changes. But previous endeavours cannot realise real-time monitoring and their prediction accuracy still need improvement in a practical sense. This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. More importantly, a new method was exemplified to utilise visualisation heatmap for colour-related DNNs tasks, and it was found that chromatic features extracted from LRP heatmap highlighted-ROI could account for 70% accuracy for the prediction of vitamin A deficiency. This system can assist farmers in blood vitamin A level monitoring and related disease prevention, contributing to precision livestock management and animal well-being in wagyu industry.

PMID:39900776 | DOI:10.1038/s41598-025-85372-w

NPJ Precis Oncol. 2025 Feb 3;9(1):35. doi: 10.1038/s41698-025-00804-0.

ABSTRACT

The rapid development of deep learning has revolutionized medical image processing, including analyzing whole slide images (WSIs). Despite the demonstrated potential for characterizing gene mutations directly from WSIs in certain cancers, challenges remain due to image resolution and reliance on manual annotations for acute myeloid leukemia (AML). We, therefore, propose a deep learning model based on multiple instance learning (MIL) with ensemble techniques to predict gene mutations from AML WSIs. Our model predicts NPM1 mutations and FLT3-ITD without requiring patch-level or cell-level annotations. Using a dataset of 572 WSIs, the largest database with both WSI and genetic mutation information, our model achieved an AUC of 0.90 ± 0.08 for NPM1 and 0.80 ± 0.10 for FLT3-ITD in the testing cohort. Additionally, we found that blasts are pivotal indicators for gene mutation predictions, with their proportions varying between mutated and standard WSIs, highlighting the clinical potential of AML WSI analysis.

PMID:39900774 | DOI:10.1038/s41698-025-00804-0

Nat Commun. 2025 Feb 3;16(1):1284. doi: 10.1038/s41467-025-56475-9.

ABSTRACT

Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory mechanisms, limiting the decoding of regulations behind key cellular events, such as cell state transitions. Here, we present regX, a deep neural network incorporating both gene-level regulation and gene-gene interaction mechanisms, which enables prioritizing potential driver regulators of cell state transitions and providing mechanistic interpretations. Applied to single-cell multi-omics data on type 2 diabetes and hair follicle development, regX reliably prioritizes key transcription factors and candidate cis-regulatory elements that drive cell state transitions. Some regulators reveal potential new therapeutic targets, drug repurposing possibilities, and putative causal single nucleotide polymorphisms. This method to analyze single-cell multi-omics data demonstrates how the interpretable design of neural networks can better decode biological systems.

PMID:39900922 | DOI:10.1038/s41467-025-56475-9

Medicina (B Aires). 2025;85(1):152-164.

ABSTRACT

Rare encephalopathies are here described in order to summarize practical tools that should be considered in the anamnesis, as well as in the physical examination. The way in which the clinical picture was established was the primary point for structuring the review; subsequently, the encephalopathies were subclassified etiologically. Focal symptoms, headaches, abdominal pain, fever or extrapyramidalism, added to the findings in the magnetic resonance imaging, especially if damage to the gray or white matter is observed, and if the lesions are bilateral or not, can be helpful when hypothesizing the etiology of the encephalopathy.

PMID:39900060

Commun Med (Lond). 2025 Feb 3;5(1):34. doi: 10.1038/s43856-025-00745-6.

ABSTRACT

BACKGROUND: Although COVID-19 initially caused great concern about respiratory symptoms, mounting evidence shows that also the pancreas is productively infected by SARS-CoV-2. However, the severity of pancreatic SARS-CoV-2 infection and its pathophysiology is still under debate. Here, we investigate the consequences of SARS-CoV-2 pancreatic infection and the role of the host factor Placenta-associated protein (PLAC8).

METHODS: We analyze plasma levels of pancreatic enzymes and inflammatory markers in a retrospective cohort study of 120 COVID-19 patients distributed in 3 severity-stratified groups. We study the expression of SARS-CoV-2 and PLAC8 in the pancreas of deceased COVID-19 patients as well as in non-infected donors. We perform pseudovirus infection experiments in PLAC8 knock-out PDAC and human beta cell-derived cell lines and validate results with SARS-CoV-2 virus.

RESULTS: We find that analysis of circulating pancreatic enzymes aid the stratification of patients according to COVID-19 severity and predicts outcomes. Interestingly, we find an association between PLAC8 expression and SARS-CoV-2 infection in postmortem analysis of COVID-19 patients both in the pancreas and in other bonafide SARS-CoV-2 target tissues. Functional experiments demonstrate the requirement of PLAC8 in SARS-CoV-2 pancreatic productive infection by pseudovirus and full SARS-CoV-2 infectious virus inoculum from Wuhan-1 and BA.1 strains. Finally, we observe an overlap between PLAC8 and SARS-CoV-2 immunoreactivities in the pancreas of deceased patients.

CONCLUSIONS: Our data indicate the human pancreas as a SARS-CoV-2 target with plausible signs of injury and demonstrate that the host factor PLAC8 is required for SARS-CoV-2 pancreatic infection, thus defining new target opportunities for COVID-19-associated pancreatic pathogenesis.

PMID:39900678 | DOI:10.1038/s43856-025-00745-6

Health Qual Life Outcomes. 2025 Feb 4;23(1):10. doi: 10.1186/s12955-025-02338-2.

ABSTRACT

BACKGROUND: Measuring the quality of life in patients with cystic fibrosis is important, both in terms of assessing the implementation of new therapies and monitoring their effects, as well as the ongoing evaluation of patients' condition. The objective of this study is to present tools for measuring the quality of life in adult patients with cystic fibrosis, along with their characteristics and measurement properties.

METHODS: The systematic review was performed according to the PRISMA guidelines based on a previously prepared research protocol (PROSPERO: CRD42023491030). Searches were performed in Medline (via PubMed), Embase (via OVID), and Cochrane Library databases. In addition, manual searches of bibliographies from the studies included in the analysis and grey literature were performed. Quality assessment of the included studies was performed according to the guidelines of COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN).

RESULTS: The systematic search identified 3,359 studies, of which 26 met the inclusion criteria for the analysis. Two publications were additionally included as a result of the manual search. A total of 16 tools for measuring the quality of life in adults with cystic fibrosis were identified, the measurement properties of which were presented in the included studies. Among these tools, the Cystic Fibrosis Questionnaire-Revised (CFQ-R) and the Cystic Fibrosis Quality of Life Questionnaire (CFQoL) were most frequently analyzed. There were also other new, promising tools.

CONCLUSION: Most studies reported acceptable measurement properties of tools used to measure quality of life in adult patients with cystic fibrosis. In many cases, however, significant limitations were observed related to the lack of comprehensive analysis of the factor structure and other aspects related to validation and responsiveness. There have also been problems with the reliability of some tool scales (including the CFQ-R 14+). The small number of studies makes it difficult to present clear conclusions regarding the usefulness of existing tools. In turn, new tools that may be used in economic analyses (CFQ-R-8 dimensions) or in individualized assessment of quality of life using a mobile application (Q-Life) seem promising. However, further research on large patient populations is necessary to analyze the measurement properties of all tools.

PMID:39901267 | DOI:10.1186/s12955-025-02338-2

BMC Nurs. 2025 Feb 3;24(1):127. doi: 10.1186/s12912-025-02774-x.

ABSTRACT

BACKGROUND: Cystic Fibrosis (CF) significantly affects the respiratory system, often requiring lung transplantation in advanced stages. This life-saving procedure presents substantial challenges and uncertainties. While there is existing research on support and information needs post-lung transplant from various perspectives, this study aims to specifically address the unique experiences and challenges faced by individuals with CF during both the pre-transplant and post-transplant periods.

METHODS: Twenty-three lung-transplanted individuals with CF participated in this exploratory qualitative study. Data was collected through individual semi-structured interviews and analyzed using inductive content analysis.

RESULTS: Participants faced physical and mental challenges, including fatigue, depression, and anxiety. The waiting period involved isolation, dependence on family, and guilt. Post-transplant, they dealt with relief but also severe pain and adjusted to a new identity. Participants highlighted the importance of taking immunosuppressive medications as prescribed, even though the regimen was complicated and these medications had side effects. Participants stressed the need for earlier and more open dialogue with healthcare professionals and better emotional preparation for the transplant process, including preparedness for pain and previously inadequately addressed concerns such as depression and anxiety.

CONCLUSIONS: This study underscores the significant physical and emotional challenges individuals with CF face during lung transplantation, highlighting the need for comprehensive, person-centered care. Psychological support, effective post-transplant pain management, and early palliative care may be beneficial approaches to improve the patient experience. Nurses can play a pivotal role in this process by ensuring clear communication, managing pain, educating patients on immunosuppressive regimens, and advocating for holistic care.

PMID:39901222 | DOI:10.1186/s12912-025-02774-x

Semin Respir Crit Care Med. 2025 Feb 3. doi: 10.1055/a-2531-1018. Online ahead of print.

ABSTRACT

Sleep disorders that involve circadian rhythm disruption and sleep-disordered breathing (SDB) such as obstructive sleep apnea (OSA) are closely linked to respiratory infections. SDB leads to a proinflammatory state due to intermittent hypoxia, sleep fragmentation, increased oxidative stress, and elevation of inflammatory mediators such as tumor necrosis factor (TNF), interleukin-6 (IL-6), and C-reactive protein (CRP). Furthermore, inflammatory mediator levels correlate with SDB severity, especially in people with OSA. Nocturnal microaspiration, gastroesophageal reflux, and associated comorbidities (e.g. obesity) increase the risk of community-acquired pneumonia, viral infections such as SARS-CoV-2, respiratory complications, and death. OSA has been associated with post-COVID syndrome. It also increases the risk of postoperative complications in both adults and children. Circadian rhythm disorders such as insomnia predispose to immune disorders and increase the risk of infection. Chronic conditions such as bronchiectasis, with or without concomitant cystic fibrosis, can lead to structural sleep changes and increase the risk of OSA due to chronic cough, arousals, aspirations, hypoxia, upper airway edema, and overexpression of proinflammatory cytokines. The protective effect of treatment for sleep disorders against respiratory infection is currently unknown. However, in people presenting with respiratory infection, it is important to test for SDB to prevent complications.

PMID:39900109 | DOI:10.1055/a-2531-1018

BMC Womens Health. 2025 Feb 3;25(1):47. doi: 10.1186/s12905-025-03569-7.

ABSTRACT

BACKGROUND: Timely diagnosis is a crucial factor in decreasing the death rate of patients with breast cancer. BI-RADS categories IVc and V indicate a strong suspicion of cancer. The categorisation of each group is determined by the characteristics of the lesion. Certain benign breast lesions might have radiological features indicative of malignancy; thus, biopsy is mandatory. This study aimed to identify the histopathological diagnosis of benign breast masses classified into BIRADS IVc and V subgroups, investigate the radiological characteristics of these masses, and identify ultrasound features that could lead to false positive results (benign lesions that mimic malignancy on imaging).

METHODS: This was a retrospective cross-sectional study at a single facility. Breast lesions reported as BIRADS IVc and V that underwent needle core/stereotactic vacuum-assisted biopsy were reviewed. Patients with benign pathologic diagnoses were analysed, delineating pathological diagnoses. Radiological descriptors were compared to those of a matched control of 50 malignant cases with BIRADS IVc.

RESULTS: A total of 828 breast lesions classified as BIRADS IVc or V were detected during the period spanning from 2015 to 2022. Forty-four lesions (44/828, 5.3%) were benign at initial biopsy, while 784 lesions (784/828, 94.7%) were malignant. After histopathological testing and repeat biopsy, 26/828 (3.14%) patients had discordant benign diagnosis. Half of the repeated biopsies (10/20, 50%) showed malignant pathology. Compared to that in the control group, the presence of an oval shape of the mass was significantly more common in patients with benign pathology (p = 0.035). Conversely, the presence of posterior shadowing was significantly less common (p = 0.050) in benign lesions. No significant differences were observed for the other radiological characteristics. The most common histopathological diagnosis was fibrocystic change.

CONCLUSION: This study highlights key findings regarding the sonographic imaging descriptors and histopathological diagnoses of benign breast lesions categorised as BIRADS IVc/V. The study recommends a correlation between clinical and radiological findings and encourages multidisciplinary decision-making among radiologists, pathologists, and clinicians to determine if a repeat biopsy is warranted. There is a need for continuous research to improve the diagnosis and treatment of breast lesions and reduce false-positive rates by incorporating other methodologies such as sonoelastography and incorporating deep learning and artificial intelligence in the decision-making to eliminate unnecessary procedures.

PMID:39901102 | DOI:10.1186/s12905-025-03569-7

BMC Med Imaging. 2025 Feb 3;25(1):37. doi: 10.1186/s12880-025-01578-4.

ABSTRACT

Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomography (CT) images. This study compares two frameworks, Deep Convolutional Neural Network (DCNN) and Hierarchical Fusion Convolutional Neural Networks (HFCNN), to assess their effectiveness in liver cancer segmentation. The contribution includes enhancing the edges and textures of CT images through filtering to achieve precise liver segmentation. Additionally, an existing DL framework was employed for liver cancer detection and segmentation. The strengths of this paper include a clear emphasis on the criticality of liver cancer detection in biomedical imaging and diagnostics. It also highlights the challenges associated with CT image detection and segmentation and provides a comprehensive summary of recent literature. However, certain difficulties arise during the detection process in CT images due to overlapping structures, such as bile ducts, blood vessels, image noise, textural changes, size and location variations, and inherent heterogeneity. These factors may lead to segmentation errors and subsequently different analyses. This research analysis compares two advanced methodologies, DCNN and HFCNN, for liver cancer detection. The evaluation of DCNN and HFCNN in liver cancer detection is conducted using multiple performance metrics, including precision, F1-score, recall, and accuracy. This comprehensive assessment provides a detailed evaluation of these models' effectiveness compared to other state-of-the-art methods in identifying liver cancer.

PMID:39901085 | DOI:10.1186/s12880-025-01578-4

Sci Rep. 2025 Feb 3;15(1):4080. doi: 10.1038/s41598-025-88492-5.

ABSTRACT

Epilepsy is a common neurological disorder in which abnormal brain waves propagate rapidly in the brain in the form of a graph network during seizures, and seizures are extremely sudden. So, designing accurate and reliable prediction methods can provide early warning for patients, which is crucial for improving their lives. In recent years, a large number of studies have been conducted using deep learning models on epileptic open electroencephalogram (EEG) datasets with good results, but due to individual differences there are still some subjects whose seizure features cannot be accurately captured and are more difficult to differentiate, with poor prediction results. Important time-varying information may be overlooked if only graph space features during seizures are considered. To address these issues, we propose a synchronization-based graph spatio-temporal attention network (SGSTAN). This model effectively leverages the intricate information embedded within EEG recordings through spatio-temporal correlations. Experimental results on public datasets demonstrate the efficacy of our approach. On the CHB-MIT dataset, our method achieves accuracy, specificity, and sensitivity scores of 98.2%, 98.07%, and 97.85%, respectively. In the case of challenging subjects that are difficult to classify, we achieved an outstanding average classification accuracy of 97.59%, surpassing the results of previous studies.

PMID:39901056 | DOI:10.1038/s41598-025-88492-5

Sci Rep. 2025 Feb 3;15(1):4058. doi: 10.1038/s41598-025-87824-9.

ABSTRACT

With the rapid advancement of artificial intelligence, questions are increasingly being raised by stakeholders regarding how such technologies can enhance the environmental, social, and governance outcomes of organizations. In this study, challenges related to the organization and retrieval of video content within large, heterogeneous media archives are addressed. Existing methods, often reliant on human intervention or low-complexity algorithms, are observed to struggle with the growing demands of online video quantity and quality. To address these limitations, a novel approach is proposed, where convolutional neural networks and long short-term memory networks are utilized to extract both frame-level and temporal video features. Residual networks 50 (ResNet50) is integrated for enhanced content representation, and two-frame video flow is employed to improve system performance. The framework achieves precision, recall, and F-score of 79.2%, 86.5%, and 83%, respectively, on the YouTube, EPFL, and TVSum datasets. Beyond technological advancements, opportunities for effective content management are highlighted, emphasizing the promotion of sustainable digital practices. By minimizing data duplication and optimizing resource usage, scalable solutions for large media collections are supported by the proposed system.

PMID:39901035 | DOI:10.1038/s41598-025-87824-9

Sci Rep. 2025 Feb 3;15(1):4089. doi: 10.1038/s41598-025-88446-x.

ABSTRACT

One of the most significant, intricate, and little-discussed aspects of pressurized irrigation is cost estimation. This study attempts to model the early-stage cost of the drip irrigation system using a database of 515 projects divided into four sections the cost of the pumping station and central control system (TCP), the cost of on-farm equipment (TCF), the cost of installation and operation on-farm and pumping station (TCI), and the total cost (TCT). First, 39 environmental and management features affecting the cost of the listed sectors were extracted for each of the 515 projects previously mentioned. A database (a matrix of 515 × 43) was created, and the costs of all projects were updated for the baseline year of 2022. Then, several feature selection algorithms, such as WCC, LCA, GA, PSO, ACO, ICA, LA, HTS, FOA, DSOS, and CUK, were employed to choose the most significant features that had the biggest influence on the system cost. The selection of features was carried out for all features (a total of 39 features) as well as for easily available features (those features that existed before the irrigation system's design phase, 18 features). Then, different machine learning models such as Multivariate Linear Regression, Support Vector Regression, Artificial Neural Networks, Gene Expression Programming, Genetic Algorithms, Deep Learning, and Decision Trees, were used to estimate the costs of each of the of the aforementioned sections. Support vector machine (SVM) and optimization algorithms (Wrapper) were found to be the best learner and feature selection techniques, respectively, out of all the available feature selection algorithms. The two LCA and FOA algorithms produced the best estimation, according to the evaluation criteria results. Their RMSE for all features was 0.0020 and 0.0018, respectively, and their R2 was 0.94 and 0.94. For readily available features, these criteria were 0.0006 and 0.95 for both algorithms. In the part of the overall feature, the early-stage cost modeling with selected features revealed that the SVM model (with RBF Kernel) is the best model among the four cost sections discussed. Its evaluation criteria in the training stage are R2 = 0.923, RMSE = 0.008, and VE = 0.082; in the testing stage, they are R2 = 0.893, RMSE = 0.009, and VE = 0.102. The ANN model (MLP) was found to be the best model for a subset of features in the easily available feature part, with R2 = 0.912, RMSE = 0.008, and VE = 0.083 in the training stage and R2 = 0.882, RMSE = 0.009, and VE = 0.103 in the testing stage. The findings of this study can be utilized to highly accurately estimate the cost of local irrigation systems based on the recognized environmental and management parameters and by employing particular models.

PMID:39900997 | DOI:10.1038/s41598-025-88446-x

Sci Rep. 2025 Feb 3;15(1):4057. doi: 10.1038/s41598-025-87519-1.

ABSTRACT

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus, resulting in significant health and socio-economic challenges for the diagnosed individual. Severe cases of DFU can lead to lower limb amputation in diabetic patients, making their diagnosis a complex and costly process that poses challenges for medical professionals. Manual identification of DFU is particularly difficult due to their diverse visual characteristics, leading to multiple cases going undiagnosed. To address this challenge, Deep Learning (DL) methods offer an efficient and automated approach to facilitate timely treatment and improve patient outcomes. This research proposes a novel feature fusion-based model that incorporates two parallel tracks for efficient feature extraction. The first track utilizes the Swin transformer, which captures long-range dependencies by employing shifted windows and self-attention mechanisms. The second track involves the Efficient Multi-Scale Attention-Driven Network (EMADN), which leverages Light-weight Multi-scale Deformable Shuffle (LMDS) and Global Dilated Attention (GDA) blocks to extract local features efficiently. These blocks dynamically adjust kernel sizes and leverage attention modules, enabling effective feature extraction. To the best of our knowledge, this is the first work reporting the findings of a dual track architecture for DFU classification, leveraging Swin transformer and EMADN networks. The obtained feature maps from both the networks are concatenated and subjected to shuffle attention for feature refinement at a reduced computational cost. The proposed work also incorporates Grad-CAM-based Explainable Artificial Intelligence (XAI) to visualize and interpret the decision making of the network. The proposed model demonstrated better performance on the DFUC-2021 dataset, surpassing existing works and pre-trained CNN architectures with an accuracy of 78.79% and a macro F1-score of 80%.

PMID:39900977 | DOI:10.1038/s41598-025-87519-1

Sci Rep. 2025 Feb 3;15(1):4064. doi: 10.1038/s41598-025-88313-9.

ABSTRACT

The World Health Organization predicts that by 2030, depression will be the most common mental disorder, significantly affecting individuals, families, and society. Speech, as a sensitive indicator, reveals noticeable acoustic changes linked to physiological and cognitive variations, making it a crucial behavioral marker for detecting depression. However, existing studies often overlook the separation of speaker-related and emotion-related features in speech when recognizing depression. To tackle this challenge, we propose a Mixture-of-Experts (MoE) method that integrates speaker-related and emotion-related features for depression recognition. Our approach begins with a Time Delay Neural Network to pre-train a speaker-related feature extractor using a large-scale speaker recognition dataset while simultaneously pre-training a speaker's emotion-related feature extractor with a speech emotion dataset. We then apply transfer learning to extract both features from a depression dataset, followed by fusion. A multi-domain adaptation algorithm trains the MoE model for depression recognition. Experimental results demonstrate that our method achieves 74.3% accuracy on a self-built Chinese localized depression dataset and an MAE of 6.32 on the AVEC2014 dataset. Thus, it outperforms state-of-the-art deep learning methods that use speech features. Additionally, our approach shows strong performance across Chinese and English speech datasets, highlighting its effectiveness in addressing cultural variations.

PMID:39900968 | DOI:10.1038/s41598-025-88313-9