Tag: nevin manimala

Med Phys. 2025 Mar 20. doi: 10.1002/mp.17763. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate segmentation of diffuse large B-cell lymphoma (DLBCL) lesions is challenging due to their complex patterns in medical imaging. Traditional methods often struggle to delineate these lesions accurately.

OBJECTIVE: This study aims to develop a precise segmentation method for DLBCL using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and computed tomography (CT) images.

METHODS: We propose a 3D segmentation method based on an encoder-decoder architecture. The encoder incorporates a dual-branch design based on the shifted window transformer to extract features from both PET and CT modalities. To enhance feature integration, we introduce a multi-scale information fusion (MSIF) module that performs multi-scale feature fusion using cross-attention mechanisms with a shifted window framework. A gated neural network within the MSIF module dynamically adjusts feature weights to balance the contributions from each modality. The model is optimized using the dice similarity coefficient (DSC) loss function, minimizing discrepancies between the model prediction and ground truth. Additionally, we computed the total metabolic tumor volume (TMTV) and performed statistical analyses on the results.

RESULTS: The model was trained and validated on a private dataset of 165 DLBCL patients and a publicly available dataset (autoPET) containing 145 PET/CT scans of lymphoma patients. Both datasets were analyzed using five-fold cross-validation. On the private dataset, our model achieved a DSC of 0.7512, sensitivity of 0.7548, precision of 0.7611, an average surface distance (ASD) of 3.61 mm, and a Hausdorff distance at the 95th percentile (HD95) of 15.25 mm. On the autoPET dataset, the model achieved a DSC of 0.7441, sensitivity of 0.7573, precision of 0.7427, ASD of 5.83 mm, and HD95 of 21.27 mm, outperforming state-of-the-art methods (p < 0.05, t-test). For TMTV quantification, Pearson correlation coefficients of 0.91 (private dataset) and 0.86 (autoPET) were observed, with R² values of 0.89 and 0.75, respectively. Extensive ablation studies demonstrated the MSIF module’s contribution to enhanced segmentation accuracy.

CONCLUSION: This study presents an effective automatic segmentation method for DLBCL that leverages the complementary strengths of PET and CT imaging. The method demonstrates robust performance on both private and publicly available datasets, ensuring its reliability and generalizability. Our method provides clinicians with more precise tumor delineation, which can improve the accuracy of diagnostic interpretations and assist in treatment planning for DLBCL patients. The code for the proposed method is available at https://github.com/chenzhao2023/lymphoma_seg.

PMID:40111352 | DOI:10.1002/mp.17763

Med Phys. 2025 Mar 20. doi: 10.1002/mp.17752. Online ahead of print.

ABSTRACT

BACKGROUND: Deep learning (DL)-based systems have not yet been broadly implemented in clinical practice, in part due to unknown robustness across multiple imaging protocols.

PURPOSE: To this end, we aim to evaluate the performance of several previously developed DL-based models, which were trained to distinguish idiopathic pulmonary fibrosis (IPF) from non-IPF among interstitial lung disease (ILD) patients, under standardized reference CT imaging protocols. In this study, we utilized CT scans from non-IPF ILD subjects, acquired using various imaging protocols, to assess the model performance.

METHODS: Three DL-based models, including one 2D and two 3D models, have been previously developed to classify ILD patients into IPF or non-IPF based on chest CT scans. These models were trained on CT image data from 389 IPF and 700 non-IPF ILD patients, retrospectively, obtained from five multicenter studies. For some patients, multiple CT scans were acquired (e.g., one at inhalation and one at exhalation) and/or reconstructed (e.g., thin slice and/or thick slice). Thus, for each patient, one CT image dataset was selected to be used in the construction of the classification model, so the parameters of that data set serve as the reference conditions. In one non-IPF ILD study, due to its specific study protocol, many patients had multiple CT image data sets that were acquired under both prone and supine positions and/or reconstructed under different imaging parameters. Therefore, to assess the robustness of the previously developed models under different (e.g., non-reference) imaging protocols, we identified 343 subjects from this study who had CT data from both the reference condition (used in model construction) and non-reference conditions (e.g., evaluation conditions), which we used in this model evaluation analysis. We reported the specificities from three model under the non-reference conditions. Generalized linear mixed effects model (GLMM) was utilized to identify the significant CT technical and clinical parameters that were associated with getting inconsistent diagnostic results between reference and evaluation conditions. Selected parameters include effective tube current-time product (known as “effective mAs”), reconstruction kernels, slice thickness, patient orientation (prone or supine), CT scanner model, and clinical diagnosis. Limitations include the retrospective nature of this study.

RESULTS: For all three DL models, the overall specificity of the previously trained IPF diagnosis model decreased (p < 0.05 for two out of three models). GLMM further suggests that for at least one out of three models, mean effective mAs across the scan is the key factor that leads to the decrease in model predictive performance (p < 0.001); the difference of mean effective mAs between the reference and evaluation conditions (p = 0.03) and slice thickness (3 mm; p = 0.03) are flagged as significant factors for one out of three models; other factors are not statistically significant (p > 0.05).

CONCLUSION: Preliminary findings demonstrated the lack of robustness of IPF diagnosis model when the DL-based model is applied to CT series collected under different imaging protocols, which indicated that care should be taken as to the acquisition and reconstruction conditions used when developing and deploying DL models into clinical practice.

PMID:40111345 | DOI:10.1002/mp.17752

Global Spine J. 2025 Mar 20:21925682251328285. doi: 10.1177/21925682251328285. Online ahead of print.

ABSTRACT

Study DesignRetrospective Cohort Study.ObjectivesTo assess long-term alignment descriptors correlating with mechanical complications.MethodsThe study included adult spinal deformity cases older than 18, with a minimum of four instrumented levels and a 5-year follow-up. Exclusions: previous spinal fusion, neuromuscular/rheumatic diseases, active infections, tumors, or incomplete radiographic exams. Collected data: demographic, surgical, pre- and post-operative spinopelvic parameters, and post-operative complications. The GAP score, original Roussouly type restoration, Schwab’s criteria, and Odontoid to hip axis angle were evaluated using machine learning and logistic regression. Complications were evaluated with a Kaplan-Meier curve.ResultsTwo hundred and twelve patients fulfilled the inclusion and exclusion criteria and were enrolled in the study. The observed rate of revision surgery for mechanical complications was 40.6% (86 out of 212 patients). Higher post-operative GAP scores were associated with increased risks of revision for junctional failure (AUC = 0.72 [IC 95%] 0.62-0.80). The inability to restore the original Roussouly spinal shape was statistically associated with higher mechanical failure rates. A machine-learning approach and subsequent logistic regression found that the GAP score and original Roussouly type restoration are the most important predictors for mechanical failure, and GAP score lordosis distribution index and relative pelvic version are the most important factors to predict the risk of mechanical failure.ConclusionsIn our series, a proper post-operative GAP Score and the restoration of the original Roussouly type significantly minimize mechanical complication rates. We observed that junctional failure tends to occur earlier among complications, while implant failure occurs later in the follow-up.

PMID:40111340 | DOI:10.1177/21925682251328285

Rev Med Suisse. 2025 Mar 19;21(910):557-562. doi: 10.53738/REVMED.2025.21.910.557.

ABSTRACT

In elderly patients prone to multiple falls with atrial fibrillation, anticoagulants are often discontinued, primarily due to fear of bleeding. However, even in the presence of repeated falls, the increased risk of stroke associated with discontinuation of anticoagulation significantly outweighs the hemorrhagic risk. The management of fall-prone patients with atrial fibrillation relies on oral anticoagulation along with a systematic assessment of risk factors for bleeding to identify and treat modifiable risk factors. Left atrial appendage closure represents an alternative to anticoagulation that may be considered in cases of irreversible cause of intracerebral hemorrhage and non-modifiable risk factors.

PMID:40111301 | DOI:10.53738/REVMED.2025.21.910.557

Integr Environ Assess Manag. 2025 Mar 20:vjaf036. doi: 10.1093/inteam/vjaf036. Online ahead of print.

ABSTRACT

Air pollution from coal-based power plants poses significant health and environmental risks. This study aimed to evaluate the effectiveness of abatement technologies, specifically flue gas desulfurization (FGD) wet scrubbers and selective catalytic reduction (SCR) systems, in reducing air pollution from power plants in Israel. We analyzed air quality data from eight monitoring stations near the Hadera Power Plant, comparing pollutant concentrations before (2015) and after (2019) the installation of abatement systems. Hourly averages of NOx, NO2, and SO2 concentrations were computed and analyzed using Wilcoxon’s paired test and linear regression models. Results showed significant decreases in overall pollutant concentrations following the installation of abatement systems. Total average NOx concentrations decreased from 11.68 to 6.88 ppb in summer and from 9.78 to 7.38 ppb in winter. Similar reductions were observed for NO2 and SO2. Monitoring Stations data -specific analysis revealed statistically significant decreases in 86.7% of all comparisons. Furthermore, in 21 out of 22 linear regression models, the variable indicating the installation of the abatement systems was negatively associated with the pollutants’ concentrations. These findings demonstrate the effectiveness of abatement technologies in reducing air pollution from power plants, supporting their implementation as a viable strategy for improving air quality and protecting public health in areas near coal-fired power plants.

PMID:40111263 | DOI:10.1093/inteam/vjaf036

Pharmacogenomics. 2025 Mar 20:1-13. doi: 10.1080/14622416.2025.2481016. Online ahead of print.

ABSTRACT

AIM: To assess patient perspectives following evaluation in a multidisciplinary pharmacogenomics clinic run by a clinical pharmacist, genetic counselor, and physician.

METHODS: A survey was distributed to 187 adults seen in the Brigham and Women’s Hospital Pharmacogenomics Clinic. Participants who completed the survey were invited to complete a semi-structured interview. Interview subjects were selected based on order of responses, scheduling availability, and range of participant experiences with testing and the clinic process. Surveys were analyzed with descriptive statistics, and interview transcripts were analyzed with thematic analysis.

RESULTS: Forty-two survey responses were received; 13 participants were interviewed. Quantitative data demonstrated high satisfaction with the multidisciplinary clinic model and belief that pharmacogenomic testing has value. Qualitative analysis identified four themes: 1) Self-Advocacy as a Patient Responsibility in the Utilization of Pharmacogenomic Results, 2) High Satisfaction with Multidisciplinary Pharmacogenomics Clinic Model and Team, 3) Utility of Pharmacogenomics, and 4) Desire for Pharmacogenomics Resources.

CONCLUSION: Patients value the care provided by a multidisciplinary pharmacogenomics clinic team, but they need to advocate for the use of their results with other healthcare professionals.

PMID:40111244 | DOI:10.1080/14622416.2025.2481016

Tree Physiol. 2025 Mar 8;45(3):tpaf014. doi: 10.1093/treephys/tpaf014.

ABSTRACT

Seasonal variation of nutrient concentrations in different organs is an essential strategy for temperate trees to maintain growth and function. The seasonal variations and variability (i.e., seasonality) of leaf nutrient concentrations have been well documented, while the trends and magnitudes of such seasonal variations in other tree organs (e.g., twigs) and their associations with leaf nutrients remain poorly understood. We measured the concentrations of 10 nutrients (nitrogen, N; phosphorus, P; potassium, K; calcium, Ca; magnesium, Mg; iron, Fe; manganese, Mn; copper, Cu; zinc, Zn; boron, B) in twigs and leaves of four temperate tree species (i.e., Pinus tabuliformis, Ginkgo biloba, Cotinus coggygria, and Sophora japonica) to explore their seasonal variations and seasonality. Our results showed that macronutrient concentrations (N, P, K, Ca, and Mg) were significantly higher in leaves and micronutrient concentrations (Fe, Mn, Cu, and Zn) were significantly higher in twigs. Concentrations of P and K both showed a negative seasonal covariation between twigs and leaves, while Ca, Fe, Mn, Cu, Zn, and B showed an opposite relationship. Compared with mobile nutrients, nonmobile nutrients exhibited significantly greater seasonality in the leaves but there were no such differences in twigs. The seasonality of nutrient concentrations in twigs was significantly stronger than in leaves and they were positively correlated. Additionally, nutrients with higher physiological requirements in leaves showed weaker seasonality, confirming the hypothesis of seasonal stability of high-demand nutrients, while such relationships were not statistically significant for twigs. This study demonstrates distinct seasonality of nutrients in twigs and leaves of temperate woody plants. These findings highlight that high-demand nutrients show stronger seasonal stability in leaves but not in twigs and uncover the seasonal coordination between twigs and leaves as a nutrient conservation strategy.

PMID:40111226 | DOI:10.1093/treephys/tpaf014

Exp Physiol. 2025 Mar 20. doi: 10.1113/EP092337. Online ahead of print.

ABSTRACT

Peak oxygen uptake ( ${\dot{V}}_{O_2\mathrm{peak}}$ ) and ${\dot{V}}_{O_2}$ on/off-kinetics are key indicators of exercise capacity and health outcomes, but their assessment often requires separate laboratory visits, which limits feasibility. This cross-sectional study aimed to develop a single cardiopulmonary exercise test (CPET) for both assessments. We designed a single-session combined CPET protocol using an upright cycle ergometer in healthy volunteers (n = 20). ${\dot{V}}_{O_2\mathrm{peak}}$ was first estimated using an a priori formula. The constant work rate (CWR) part of the test (on-kinetics) was set to an intensity of 30% ${\dot{V}}_{O_2\mathrm{reserve}}$ . After an incremental test to measure ${\dot{V}}_{O_2\mathrm{peak}}$ , a 10-min recovery period was used to evaluate off-kinetics. Twenty volunteers (9 females and 11 males), 28.0 ± 8.1 years completed the protocol. No significant differences were found between predicted and measured ${\dot{V}}_{O_2\mathrm{peak}}$ (P = 0.47). A strong correlation (r = 0.88) and good agreement (Bland-Altman bias = -0.82 mL kg^-1 min^-1) were found between the calculated/actual individuals’ 30% ${\dot{V}}_{O_2\mathrm{reserve}}$ (mL kg^-1 min^-1) and the measured steady-state ${\dot{V}}_{O_2}$ at CWR. The measured exercise intensity at CWR closely matched the target of 30%, with no statistical differences, with an average difference of 0.2 percentage points. Small-medium Cohen’s d (0.16) indicated high similarity between predicted and measured ${\dot{V}}_{O_2\mathrm{peak}}$ . ${\dot{V}}_{O_2}$ on- and off-kinetics analyses were also performed for all participants with mono-exponential fittings. A single-session protocol for the combined assessment of ${\dot{V}}_{O_2\mathrm{peak}}$ and ${\dot{V}}_{O_2}$ on/off-kinetics was developed. This protocol will enable greater recruitment and participation in research and enhanced detail for clinical CPET use. Future research should evaluate intra- and inter-participant reproducibility over repeated sessions.

PMID:40111206 | DOI:10.1113/EP092337

Cell Reprogram. 2025 Mar 20. doi: 10.1089/cell.2024.0066. Online ahead of print.

ABSTRACT

Spermatogonial stem cells (SSCs) play an essential role in the transfer of genetic information through generations, making studying their cellular and molecular mechanisms critical. However, since SSCs are few in mice, directly studying them is limited, requiring specialized in vitro cultivation. Feeder layers such as mouse embryonic fibroblasts (MEFs), SNL, neonate, and adult mouse testicular stromal feeder cells (TSCs) support in vitro survival and growth. To understand the effectiveness of these feeder layers on SSC proliferation, we compared MEF, SNL, neonatal, and adult TSCs. Furthermore, we identified hub genes and potential pathways in spermatogenesis. Two populations of differentiated and undifferentiated SSCs were compared for mouse SSC colony formation and proliferation effectiveness. Additionally, Cytoscape and STRING databases were employed for protein-protein interaction networks and functional gene enrichment. The expression of three hub genes, including Dazl, Zbtb16, and Stra8, was analyzed using dynamic array chips (Fluidigm) followed by statistical analysis. Our results indicated that undifferentiated SSCs favored MEF feeders, while differentiated SSCs thrived on SNL and primary TSC feeders for long-term culture. Functional enrichment results demonstrated hub genes involvement in cell differentiation, meiosis, regulation of meiotic nuclear division, cell development, and spermatogenesis. Furthermore, mRNA expression levels of Stra8, Zbtb16, and Dazl genes show different patterns among feeder layers and SSC differentiation phases.

PMID:40111152 | DOI:10.1089/cell.2024.0066

Comput Inform Nurs. 2025 Mar 20. doi: 10.1097/CIN.0000000000001277. Online ahead of print.

ABSTRACT

This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing diagnosis of chronic pain and explore the potential of artificial intelligence (AI) in enhancing clinical decision-making in Italian healthcare settings. Three machine learning algorithms-XGBoost, gradient boosting, and BERT-were optimized through a grid search approach to identify the most suitable hyperparameters for each model. Therefore, the performance of the algorithms was evaluated and compared using Cohen’s κ coefficient. This statistical measure assesses the level of agreement between the predicted classifications and the actual data labels. Results demonstrated XGBoost’s superior performance, whereas BERT showed potential in handling complex Italian language structures despite data volume and domain specificity limitations. The study highlights the importance of algorithm selection in clinical applications and the potential of machine learning in healthcare, specifically addressing the challenges of Italian medical language processing. This work contributes to the growing field of artificial intelligence in nursing, offering insights into the challenges and opportunities of implementing machine learning in Italian clinical practice. Future research could explore integrating multimodal data, combining text analysis with physiological signals and imaging data, to create more comprehensive and accurate chronic pain classification models tailored to the Italian healthcare system.

PMID:40111146 | DOI:10.1097/CIN.0000000000001277