PLoS ONE最新文献

We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a contemporary Android environment and a balanced dataset of 124,000 applications. Our findings show that feature-based approaches can still achieve detection accuracies beyond 98%, despite a considerable increase in the size of the underlying Android feature sets. We observe that features derived through dynamic analysis yield only a small benefit over those derived from static analysis, and that simpler models often out-perform more complex models. We also find that API calls and opcodes are the most productive static features within our evaluation context, network traffic is the most predictive dynamic feature, and that ensemble models provide an efficient means of combining models trained on static and dynamic features. Together, these findings suggest that simple, fast machine learning approaches can still be an effective basis for malware detection, despite the increasing focus on slower, more expensive machine learning models in the literature.

In acute stroke, dysregulated cytokine interactions drive secondary injury, yet bidirectional feedback mechanisms between pro-inflammatory mediators (TNF-α, IL-6) and the anti-inflammatory mediator IL-10 remain poorly quantified. We developed a systems biology model using nonlinear ordinary differential equations (ODEs) to resolve these dynamics, incorporating Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB)-mediated cross-activation, delayed IL-10 induction via a Hill function, and empirical parameterization from stroke data. Mathematical analysis revealed bistable inflammatory states via bifurcation theory, mechanistically explaining divergent inflammatory trajectory. Steady-state and stability analyses identified a critical IL-10 suppression threshold ([Formula: see text] hr ⁻ ¹·nM ⁻ ¹) governing transitions between pro-inflammatory dominance and resolution phases. The model replicated experimentally observed cytokine dynamics, including TNF-α/IL-6 peaks (6-24 hours) and delayed IL-10 elevation (48 hours). Global sensitivity analysis highlighted IL-10 production ([Formula: see text]) and TNF-α suppression ([Formula: see text]) as key control parameters. Simulations predicted that IL-10 augmentation accelerates resolution, while TNF-α inhibition attenuates IL-10 induction, potentially compromising long-term recovery. By integrating dynamical systems theory with translational immunology, this model provides a mechanistic basis for optimizing immunomodulatory therapies in stroke and related inflammatory pathologies.

[This corrects the article DOI: 10.1371/journal.pone.0306178.].

Intratumoral Staphylococcus xylosus enhances the ability of breast cancer cells to survive mechanical shear stress, a critical barrier encountered during hematogenous metastasis. However, the bacterial determinants underlying this effect remain unclear. Here, we identify the bacterial molecular chaperone DnaK as a key factor enabling S. xylosus to promote shear-stress tolerance in a human breast cancer cell line. Deletion of dnaK did not affect bacterial adhesion to or invasion of MDA-MB-231 cells but significantly reduced sustained intracellular survival. Under oxidative and acidic stress conditions, the ΔdnaK mutant showed reduced survival compared with the wild-type strain, and its ability to enhance tumor-cell viability under shear stress was markedly impaired. Using a breast cancer-on-a-chip microfluidic model, we demonstrate that infection with wild-type or complemented Staphylococcus xylosus confers increased tumor-cell viability under laminar shear stress in a time-dependent manner, whereas cells infected with the ΔdnaK mutant fail to acquire shear-stress resistance and resemble uninfected controls. Together, these findings establish DnaK-dependent intracellular persistence of S. xylosus as a critical determinant of tumor-cell survival under mechanical stress, linking a conserved bacterial stress-response protein to cancer cell biomechanics in a metastasis-relevant context.

Cholangiocarcinoma (CCA) is a lethal cancer associated with chronic inflammation caused by Opisthorchis viverrini infection, with high prevalence in Thailand. Secretory leukocyte protease inhibitor (SLPI), a serine protease inhibitor involved in inflammation, has recently been identified as an oncogenic factor in several malignancies. However, its role in CCA remains unclear. Here, we demonstrate that SLPI is significantly upregulated during CCA development in both human and hamster-induced tissues. Higher SLPI expression was correlated with poor patient survival based on bioinformatic analyses. SLPI was elevated in highly metastatic CCA cell lines and further inducible by IL-6 stimulation. Overexpression of SLPI enhanced tumorigenic properties, including proliferation, migration, invasion, and in vivo tumor growth. SLPI also increased the activity of matrix metalloproteinases (MMP-2 and MMP-9), promoting metastatic potential. While conditioned media from SLPI-overexpressing cells did not affect angiogenesis, these cells promoted vasculogenic mimicry, with increased expression of VEGFA and VE-cadherin, and decreased N-cadherin. These findings suggest that SLPI promotes cholangiocarcinoma progression through inflammation-associated and vasculogenic mechanisms, highlighting its potential as a candidate molecular target for therapeutic intervention.

Background: This study evaluated the clinical features, management, and outcomes of pregnant women with generalized tonic-clonic seizures presenting to the emergency department (ED). The aim was to demonstrate how patients' clinical features contribute to patient management and prognosis.

Methods: In this retrospective study, pregnant women over the age of 18 who presented to the ED with generalized tonic-clonic seizures were included. The patients' demographic characteristics, clinical findings, treatments administered in the ED, and outcomes were recorded. Descriptive statistics, the chi-square tests or Fisher's exact tests for categorical variables and the Mann-Whitney U or t-test for continuous variables were used in the statistical analysis.

Results: The study included 48 patients, most of whom were in their third trimester. Thirty-three (69%) patients had a history of epilepsy, and 28 (58.3%) were using antiseizure medications (ASMs). The most commonly used ASM was levetiracetam. Seven (14.6%) patients had suspected eclampsia, and seizure control was achieved in four of them by administering ASMs in addition to magnesium sulfate treatment. Two (4.2%) patients developed status epilepticus (SE). A significant relationship was observed between gestational age and hospitalization (p = 0.005).

Conclusion: The diagnostic complexity of epileptic seizures in pregnancy complicates treatment choices, and ASM use may also be beneficial in managing eclampsia.

Predicting the steering angle of robots is a core challenge in autonomous navigation. This paper proposes a novel end-to-end prediction network that integrates non-local attention and lane line guidance mechanisms to significantly reduce computational time and improve prediction accuracy. Built upon the ResNet architecture, the network incorporates a Non Local Block mechanism to enhance global context modeling and a Ghost Module to reduce parameter count and improve feature extraction efficiency. To further optimize training, a ReduceLROnPlateau learning rate scheduler is employed to adaptively adjust the learning rate, effectively mitigating overfitting. Additionally, a lane line annotation method, which combines Canny edge detection with the Hough transform, is used to semantically guide the input images. This enhances the representational power and generalizability of the training data. Experimental results demonstrate that the proposed network outperforms the baseline across multiple evaluation metrics. Under identical experimental conditions, the proposed model achieves a 54.88% increase in inference speed and reduces the mean absolute error (MAE) and root mean square error (RMSE) by 8.47% and 18.23% respectively. Ablation studies further confirm that the combination of the Non-Local Block and Ghost Module significantly improves both expressive capacity and computational efficiency of the model. These findings suggest that the proposed method offers a high-precision, efficient, and low-latency visual perception solution for real-time autonomous navigation of wheeled robots in complex environments.

Rehabilitation after radical gastrectomy or esophagectomy for upper gastrointestinal cancer can improve physical function and quality of life; however, objective day-to-day measures of psychological change are lacking. We aimed to test whether facial emotion analysis can quantitatively evaluate patients' emotional responses before and after each rehabilitation session and whether these changes relate to conventional subjective/physiological stress markers and discharge physical outcomes. We conducted a single-center prospective observational study of 32 patients who underwent radical gastrectomy or esophagectomy between August 2024 and February 2025. Immediately before and after each rehabilitation session, 30 s iPad video interviews (median, six per patient) were recorded and analyzed using MAL Face Emotion software to obtain normalized scores (0%-100%) for Neutral, Happy, Sad, Angry, and Surprised emotions. Subjective stress (0-100 mm visual analog scale) and salivary α-amylase activity were collected concurrently; discharge physical function was assessed using the 6 min walk distance and Five Times Sit-to-Stand tests. Pre- and post-session values were compared using Wilcoxon signed-rank tests, and associations were examined with age-adjusted regression and Spearman correlation. Thirty-one patients completed the study without adverse events. After rehabilitation, the Happy score increased (median +3.5%, p = 0.013) and stress decreased (-1.5 mm, p = 0.025), whereas salivary α-amylase and other emotions were unchanged. Changes in the Happy score (p = 0.21) and stress (p = 0.19) did not predict discharge physical function, whereas changes in the Sad score correlated moderately with changes in salivary α-amylase (ρ = 0.45). The findings of this single-center study provide preliminary evidence supporting the feasibility of facial emotion analysis as a non-invasive, quantitative tool for real-time psychological monitoring during postoperative rehabilitation. Furthermore, our results demonstrate its potential to support a more personalized delivery of cancer rehabilitation.

Background: Abdominal aortic aneurysm (AAA) refers to a lasting enlargement of the abdominal aorta. Senescence, a major risk factor of AAA, demonstrate positive connection with both the formation and rupture of aneurysms. Therefore, investigating the underlying pathogenic mechanisms of senescence in AAA and exploring relevant diagnostic and therapeutic targets is crucial.

Methods: Three transcriptomic datasets related to AAA were obtained from the GEO database, and collection of genes associated with cellular senescence was obtained from MSigDB. Overlapping genes of differentially expressed genes (DEGs), module genes associated with AAA, and senescence-related gene sets were identified as senescence-related DEGs of AAA and subjected to further functional enrichment analysis. Distinct machine learning algorithms were subsequently utilized to screen for senescence-associated biomarkers and develop a diagnostic nomogram. In addition, the interaction between these biomarkers and immune components in the aneurysmal environment were revealed. Consensus clustering was subsequently applied to classify AAA into distinct subtypes. Finally, validation was performed using an AAA murine model.

Results: A total of 11 senescence-related DEGs in AAA were identified, which mainly involved with oxidative stress, inflammatory responses, and vascular smooth muscle cell activity. Following rigorous screening, IL6, ETS1, TDO2, and TBX2 were identified as diagnostic biomarkers for senescence-related DEGs of AAA. The nomogram constructed from these biomarkers demonstrated high discriminatory ability in the training cohort (AUC = 1), though this requires further validation in larger cohorts due to potential overfitting. Immune cell infiltration and single-cell analyses indicated that the expression of the diagnostic biomarkers is linked to various immune cell types. Consensus clustering identified two AAA subtypes, which exhibiting distinct expression patterns of senescence-related biomarkers. Finally, validation in an AAA murine model confirmed the expression changes of these senescence-related biomarkers in AAA.

Conclusion: This study identified senescence-related biomarkers associated with AAA through transcriptomic public databases, revealing their potential functional mechanisms, relationships with immune cells, and associations with AAA subtypes. These results could offer novel candidate targets for both diagnostic and therapeutic strategies in AAA.