The purpose of this study is to present the nationwide disease burden and survival of nasopharyngeal carcinoma (NPC) in Sweden. The subcohort from the Stockholm-Gotland region was included to investigate the prevalence of Epstein-Barr virus (EBV) in NPC and to describe pattern of relapse.

This population-based nationwide study included patients diagnosed with NPC in Sweden during 2008-2021. The series was retrieved from the Swedish Head and Neck Cancer Register. Age at diagnosis, sex, tumor histopathology, stage, treatment intent, treatment, radiation dose, follow-up time, time to relapse, and site of relapse were recorded. The Stockholm-Gotland region series was used to obtain an updated histopathological analysis including EBV status and to analyze site of relapse.

The nationwide study cohort comprised 399 patients, 33% were female. Mean age at diagnosis did not differ between the sexes: 56.3 years for females, 57.5 years for males. Seventy-one percent presented with Stage III or IV. The 5-year overall survival (OS) was 73.2%. In the regional cohort, 73.9% were EBV positive. In the competing risk analysis, the cumulative incidence of distant metastatic relapse was higher than that of local and/or regional relapse at 5 years (18.7% vs. 12.4%). However, the confidence intervals were wide, and the difference should be interpreted with caution.

The survival outcome in our study seems comparable to previous studies in nonendemic countries. There was a high percentage of EBV-positive tumors compared with the previous studies in nonendemic countries.

The selection of patients with low-grade gliomas for proton therapy (PT) is often based on the comparison of photon and PT plans and demonstrating meaningful dose reduction to the healthy brain or critical structures. The aim of this retrospective study was to identify clinical parameters associated with referral to PT and build a prediction model.

The dataset consisted of patients with isocitrate dehydrogenase (IDH)-mutant grades 2 and 3 glioma and candidates for PT at the Aarhus University Hospital. Clinical (age, diagnosis, clinical target volume [CTV], and treatment) and dosimetric (prescribed dose and mean dose (Dmean) to healthy brain) parameters were collected. Univariate and multivariate logistic regression were used to assess the association with selection for PT. The dataset was split into training (n = 37, period 2019-2022) and test (n = 12, period 2023) cohorts. Prediction models were built using logistic regression algorithms and support vector machines (SVMs) and evaluated using the area under the precision-recall curve (AUC-PR).

Age (p = 0.03) and CTV (p = 0.01) were significantly associated with the selection for PT and were used for model prediction. The logistic regression demonstrated AUC-PR at 0.999 (CI 0.999-1.000) and 0.998 (0.996-1.000) for training and test cohorts, respectively. SVM showed similar results with AUC-PR at 0.993 (0.993-0.994) for training and 0.999 (0.998-1.000) for test cohorts.

Logistic regression and SVM using age and CTV performed equally well and achieved a very high positive predictive value. With the pending external validation in a larger dataset, the prospects of this work suggest more consistent and efficient patient referral for PT.

Inflammatory responses critically influence tumor progression, yet traditional inflammatory biomarkers in neuroblastoma (NB) research lack optimal sensitivity and specificity. This study aimed to evaluate the prognostic value of two pretreatment inflammatory biomarkers: the C-reactive protein-to-lymphocyte ratio (CLR) and hemoglobin-to-red cell distribution width ratio (HRR) in NB patients. A retrospective analysis was conducted on NB patients diagnosed and treated at Shanghai Children's Medical Center (2016-2022). Pretreatment blood parameters (within 1 week before therapy) were utilized to calculate CLR, HRR, and conventional biomarkers. Optimal cutoff values for each inflammatory biomarker were defined separately. Multivariate Cox regression models identified independent prognostic factors, while Kaplan-Meier curves with log-rank tests assessed survival differences. The cohort included 201 NB children (95 males, 106 females; median age, 37 months). CLR and HRR demonstrated clinically significant predictive accuracy (AUC > 0.7) over traditional biomarkers for both progression-free and overall survival. Elevated ferritin (hazard ratio = 0.35; 95% confidence interval, 0.14-0.90; P = 0.030) independently predicted poor short-term outcomes. For long-term survival, high CLR (hazard ratio = 0.20; 95% confidence interval, 0.05-0.86; P = 0.031) and low HRR (hazard ratio = 2.91; 95% confidence interval, 1.19-7.13; P = 0.019) were significant independent predictors. Kaplan-Meier survival curves demonstrated that high CLR and low HRR were associated with poor long-term outcomes in NB patients (P < 0.05). Intergroup comparisons indicated that the high-CLR and low-HRR groups were predominantly composed of high-risk M-stage patients (P < 0.05).

CLR and HRR outperform conventional inflammatory biomarkers as pretreatment prognostic indicators in NB. Elevated CLR and reduced HRR were strongly linked with advanced-stage grouping and adverse long-term outcomes and may serve as effective practical tools for enhancing clinical risk stratification in pediatric NB.

•Neuroblastoma is the most common extracranial solid malignancy in children, and inflammatory biomarkers can effectively predict its prognosis.

•CLR and HRR are cost-effective biomarkers that enhance risk stratification and correlate strongly with adverse long-term prognosis in NB.

We are interested in how patients with hepatitis B progress to liver cirrhosis (an intermediate outcome) and liver cancer (a primary outcome). The separable effect has recently been proposed to study causal effects in the setting of competing risks. In this work, we extend the separable effect approach to semicompeting risks involving a primary and intermediate outcome. We decompose the exposure to hepatitis B virus into two disjoint components: the first component affects liver cancer directly, that is, direct effect, and the other affects liver cancer through liver cirrhosis, that is, indirect effect. Under such an effect separation, the identification formula of counterfactual risk for liver cancer that we derive for semicompeting risks is a function of cause-specific hazards and transition hazards of multistate models. It can be reduced to the formula for competing risks as a special case. We propose nonparametric and semiparametric methods to estimate the causal effects and study their asymptotic properties. The model-free nonparametric method is robust but less efficient for confounder adjustment; the model-based semiparametric method flexibly accommodates confounders by treating them as covariates. We conduct comprehensive simulations to study the performance of the proposed methods. Our data analyses of the hepatitis study show that there exist both direct and indirect effects of hepatitis B infection on the incidence of liver cancer through liver cirrhosis.

Spine MRI is increasingly considered for patients presenting through the emergency department (ED). It was hypothesized that more MRIs are being obtained of the entire spine (relative to localized regions) over time, and there are inconsistencies in this practice.

Data were abstracted from the 2010 to 2021 M151Ortho PearlDiver national, multi-insurance, administrative data set. Patients who received spine MRI within 7 days of presenting through the ED were categorized as having had localized MRI (cervical, thoracic, lumbar, cervical and thoracic, or thoracic and lumbar) or total spine (cervical, thoracic, and lumbar). Patient characteristics were compared between these categories with multivariable analyses. Furthermore, the rate of follow-up MRI within 14 days of initial MRI was assessed and compared.

Of the 275,999 patients identified as undergoing spine MRI, 93.25% were localized MRI and 6.75% were total spine MRI. Over a decade, total spine MRIs increased from 4.85% in 2010 to 12.38% in 2021 (P < 0.0001). Independent predictive factors for receiving a total spine MRI included indication (trauma, infection, neoplasm with odds ratio [OR], 1.30 relative to degenerative), patient factors (younger age [OR, 1.47 per decade], male sex [OR, 1.21], higher comorbidity [OR, 1.38]), and nonclinical factors (region of the country West [OR, 1.32 relative to South] and insurance plan (Medicaid OR, 1.23 and Medicare OR, 1.10 relative to commercial; P < 0.0001 for each except 0.0005 for Medicare). Follow-up MRIs within 14 days were more likely for total spine MRI than for the localized MRI group (7.28% vs. 5.26%, P < 0.0001).

For patients presenting through the ED, total spine MRIs represent a small but growing minority of spine MRI scans obtained. The nonclinical factors associated with this decision and mildly increased need for follow-up scans after total relative to localized MRI suggest room for greater consistency of practice.

Brain tumor detection and classification from multichannel magnetic resonance imaging (MRI) using deep learning techniques for an accurate detection and classification of brain tumors from multichannel MRI are essential for guiding effective treatment strategies and improving patient outcomes. Traditional methods often struggle with handling large volumes of MRI data, leading to limitations in both efficiency and reliability. This study aims to develop a robust approach for brain tumor detection and classification by leveraging computer vision and deep learning techniques, addressing the limitations of conventional methods. The proposed approach utilizes the dual boundary-sensitive transformation (DBST) algorithm for precise tumor edge detection, whereas the scale-invariant feature transform (SIFT) method provides robust and invariant features for classification. Additionally, deep learning models, DarkNet53 and DenseNet201, are employed to enhance classification performance by learning complex patterns from a large dataset of multichannel MRI images. The dataset used in this study is publicly available, ensuring reproducibility and accessibility of the research. The results show a specificity of 98%, indicating the model's strong ability to correctly identify negative cases, and a sensitivity of 99%, demonstrating its effectiveness in identifying positive cases. This performance significantly surpasses traditional methods and is competitive with state-of-the-art (SOTA) techniques in the field. MATLAB is utilized to implement the models, showcasing the potential of deep learning in medical imaging. Future work will explore more advanced deep learning architectures, incorporate additional modalities, and further refine the techniques to improve accuracy and robustness in brain tumor detection and classification.

Oral squamous cell carcinoma (OSCC) is a multifactorial disease influenced by microbial dysbiosis and biofilm-induced chronic inflammation. Streptococcus mutans, a principal pathogen, aggravates OSCC by fostering an immunosuppressive tumor microenvironment via biofilm development and virulence-related metabolic alterations. This work investigated the post-biotic effects of Lactiplantibacillus plantarum in reducing S. mutans-related OSCC by obstructing bacterial adhesion, biofilm integrity, and virulence gene expression. GC-MS research revealed that the cell-free supernatant (CFS) of L. plantarum contains the bioactive metabolite 2,4-di-tert-butylphenol (DTP), which demonstrates significant antibacterial and anti-tumor activities. The new antimicrobial peptide Plpl_18 exhibited substantial biofilm inhibition and reduction of bacterial viability. Transcriptomic research indicated that S. mutans 890 treatment with DTP and Plpl_18 downregulated essential biofilm-associated genes (gtfB, gtfC), disturbed carbohydrate metabolism, and initiated a metabolic transition towards lactose utilization. Molecular docking and molecular dynamics simulations (MDS) validated persistent interactions between DTP and Plpl_18 with bacterial virulence factors and OSCC-related proteins (p38, NF-κB), underscoring their therapeutic potential. This research offers innovative perspectives on probiotic biofilm suppression methods and identifies DTP and Plpl_18 as potential options for targeted treatments against S. mutans-induced OSCC. Subsequent investigations into clinical applications may facilitate the development of novel antibacterial interventions and cancer treatment methodologies.

Breast cancer poses a significant threat to women's health globally, exhibiting the greatest incidence and fatality rates among female cancers. Chemotherapy is frequently employed in the clinical management of breast cancer. While the majority of patients require novel chemotherapeutic agents due to treatment resistance. We sought to examine the impact of ursolic acid (UA) on apoptosis and autophagy levels in breast cancer through Polo-like Kinase 1 (PLK1) via the AKT/mTOR signaling pathway. Tamoxifen (TAM) and adriamycin (ADM) served as positive control agents. In vitro experiments, the MTT assay was utilized to evaluate the viability of MCF-7/MDA-MB-231 cells, flow cytometry and JC-1 staining to analyze apoptosis, electron microscopy and MDC staining to scrutinize autophagy, and Western blot (WB) to measure the expression of pertinent proteins. In vivo research utilized the BALB/c mouse breast cancer model established with 4T1, comparing the volume and weight of transplanted tumors across several groups. Tumor necrosis was identified using HE staining, anti-apoptotic protein Bcl-2 expression was assessed via IHC labeling, and protein expression was evaluated using WB. UA suppressed tumor proliferation in BALB/c mice models of breast cancer. Tumor proliferation was markedly suppressed in the TAM and medium/high-dose UA cohorts. HE staining demonstrated significant necrosis, while IHC/WB analysis validated that UA or UA combined with Volasertib may reduce levels of Bcl-2, PLK1, p-AKT/AKT, and p-mTOR/mTOR, in conjunction with increased LC3 II/I. In conclusion, the study revealed that UA may affect the apoptosis and autophagy of breast cancer through PLK1 via AKT/mTOR signaling pathway.

Ferroptosis, a form of regulated cell death caused by iron-dependent accumulation of lipid peroxides, is recently demonstrated as a vital player in cancer development. Tribbles homolog 3 (TRIB3) is a contributing factor to the malignant progression of several human cancers, including colorectal cancer (CRC). However, its regulatory effect and mechanism in CRC are obscure. qRT-PCR and western blot assays determined the mRNA and protein expression of TRIB3, methyltransferase-like 14 (METTL14), and YT521-B homology domain family 2 (YTHDF2). Cell ferroptosis was evaluated by measuring the levels of intracellular reactive oxygen species (ROS), lipid ROS, malondialdehyde (MDA), and glutathione (GSH). Cell malignant progression were assessed by CCK-8, EdU, transwell, and xenograft assays. The m6A sites of TRIB3 were confirmed using m6A RNA immunoprecipitation (Me-RIP) assay. The binding between TRIB3 and METTL14 or YTHDF2 was validated using RIP or luciferase reporter experiments. We observed higher expression of TRIB3 and lower expression of METTL14 in CRC tissues and cells. Knockdown of TRIB3 increased ferroptosis by promoting the generation of intracellular ROS, lipid ROS, and MDA and inhibiting the production of GSH. Suppressing TRIB3 also decreased tumor growth by increasing ferroptosis in mice. Mechanistically, knockdown of METTL14 reduced the m6A modification of TRIB3 and elevated TRIB3 mRNA expression. Moreover, METTL14-methylated TRIB3 was was recognized by YTHDF2, which resulted in the degradation of TRIB3 mRNA. TRIB3 overexpression reversed METTL14-mediated ferroptosis in CRC cells. Silencing YTHDF2 also abrogated the promotive effect of METTL14 on ferroptosis in CRC cells. Additionally, knockdown of TRIB3 induced ferroptosis by inactivating the SLC7A11/GPX4 signaling. METTL14 suppressed the SLC7A11/GPX4 signaling by targeting TRIB3. METTL14 suppressed CRC cell proliferation, migration, and invasion by downregulating TRIB3. Our findings suggest that METTL14 suppressed TRIB3 expression via an m6A-YTHDF2-dependent manner, thus inducing ferroptosis to inhibit the malignant progression of CRC. TRIB3 is potentially exploited as a molecular target for CRC treatment based on ferroptosis.

The addition of gemtuzumab ozogamicin (GO) to intensive chemotherapy (IC) has become a mainstay in treating patients with core binding factor acute myeloid leukemia (CBF-AML). However, evidence for the efficacy of GO in this particular subgroup is primarily based on meta-analytic data from different trials conducted more than a decade ago. In this registry-based study, we evaluated the impact of adding GO to IC in 265 CBF-AML patients from the SAL, AMLCG, and CELL cooperative study groups. Patients receiving GO had a 2-year overall survival of 90% compared with 80% in those without GO (hazard ratio [HR] 0.45, 95% confidence interval [CI] 0.21-0.95, P = 0.036) and a 2-year event-free survival of 51% versus 36% (HR 0.69, 95% CI 0.48-0.99, P = 0.046). While complete remission rates in GO vs. non-GO patients were comparable (89% vs. 90%, P = 0.81), more GO patients achieved measurable residual disease-negative remission (77% vs. 49%, P < 0.001), resulting in numerically reduced cumulative incidence of relapse (HR 0.67, 95% CI 0.43-1.02, P = 0.06). Despite delayed platelet recovery, high-grade toxicities were not increased in GO-treated patients. These findings support the integration of GO into treatment protocols for IC-eligible patients with CBF-AML.