This paper presents an extended mathematical model for tumor angiogenesis incorporating oxygen dynamics as a main regulator. We enhance a five-component PDE system describing endothelial cells, proteases, inhibitors, extracellular matrix, and oxygen concentration, with a focus on their spatiotemporal interactions. We establish existence, uniqueness, and boundedness of solutions through a mathematical analysis. A numerical scheme using method of lines and fourth-order Runge-Kutta methods is developed, with proven stability constraints and convergence properties. Numerical experiments demonstrate biologically plausible vascular formation with oxygen-mediated regulation.

Multiple myeloma (MM) is a hematologic cancer marked by clonal expansion of plasma cells in the bone marrow. Although its genomic landscape has been extensively characterized, the transcriptional mechanisms that govern malignant progression and long-term tumor cell survival remain incompletely understood.

We integrated single-cell RNA sequencing (scRNA-seq) data from healthy donors (HD), monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and newly diagnosed multiple myeloma (NDMM) patients, retrieved from GEO datasets GSE124310 and GSE271107, to construct a comprehensive transcriptional landscape of plasma cell differentiation. Pseudotime and enrichment analyses identified PDIA4 as a terminal-state-associated gene. The prognostic significance of PDIA4 was validated using the multiple myeloma research foundation (MMRF) CoMMpass cohort. Functional analyses were performed in vitro and in vivo to validate the role of PDIA4 in MM cell survival and therapeutic response.

Pseudotime trajectory analysis revealed progressive upregulation of genes involved in protein processing in the endoplasmic reticulum (ER), with PDIA4 identified as a top candidate in terminal-stage plasma cells. Survival analysis in the MMRF CoMMpass cohort further demonstrated that high PDIA4 expression correlated with poor overall survival. In RPMI-8226 cells, PDIA4 knockout activated the IRE1α/XBP1s branch of the unfolded protein response (UPR), impaired proliferation, and induced G1-phase arrest. PDIA4 depletion also sensitized cells to bortezomib. In vivo, sg-PDIA4 suppressed tumor growth in RPMI-8226 xenografts.

PDIA4 is a key regulator of the unfolded protein response and MM cell survival. Targeting PDIA4 may enhance the efficacy of proteasome inhibitors and offers a potential strategy to overcome therapeutic resistance in multiple myeloma.

Increasing evidence suggests associations between gut microbiota composition and colorectal cancer (CRC) or hepatocellular carcinoma. However, whether gut microbiota influences metastatic liver cancer (MLC) originating from CRC remains unclear. We performed a bidirectional 2-sample Mendelian randomization (MR) analysis using summary statistics from genome-wide association studies. Gut microbiota data (N = 18,340) from MiBioGen served as exposures. MLC (N = 463,010) and CRC (N = 399,920) datasets were sourced from IEU OpenGWAS. The correlation analysis was primarily conducted using the inverse variance weighted method, which demonstrated reliability as confirmed through sensitivity analysis. Inverse variance weighted estimates indicated that class_Actinobacteria showed an inverse association with MLC (odds ratio [OR] = 0.997, 95% confidence interval [CI]: 0.995-0.999, P = .003), while class_Melainabacteria exhibited a positive association (OR = 1.001, 95% CI: 1.000-1.002, P = .011). For CRC, both class_Actinobacteria (OR = 0.992, 95% CI: 0.986-0.997, P = .005) and order_Bifidobacteriales (OR = 0.991, 95% CI: 0.986-0.997, P = .003) demonstrated inverse associations. Notably, MR estimates revealed that class_Actinobacteria had consistently inverse associations with both MLC and CRC. Reverse MR analysis suggested CRC may increase abundance of family_BacteroidalesS24.7group (OR = 4.178, 95% CI: 3.233-6.304, P = .002), but no significant associations were observed for MLC. This study provides novel evidence supporting potential causal associations between specific gut microbial taxa and the risk of MLC, suggesting a possible protective role of Actinobacteria in the pathogenesis of both MLC and CRC. Further large-scale observational and mechanistic studies are warranted to clarify these relationships.

Cases of acute myeloid leukemia occurring simultaneously with multiple myelomas are rare. To date, only 31 cases have been reported worldwide. The prognosis of these patients is very poor, and it is very important for them to be diagnosed promptly and treated effectively.

A 68-year-old male patient was admitted due to the presence of intermittent nosebleeds accompanied by fever for 10 days, in addition to the development of a right neck mass over 3 days.

Diagnosis of acute monocytic leukemia (high-risk group) was based on morphology, immunophenotyping, histochemical staining, and chromosomal and genetic test results. The patient's chromosomes were found to be normal, yet next-generation sequencing revealed a TP53 mutation, thus classifying the risk stratification as high risk. The diagnosis of multiple myeloma was diagnosed based on the presence of > 10% myeloma cells and bone marrow biopsy findings suggestive of multiple myeloma.

Azacitidine 100 mg subcutaneous injection on days 1 to 7, in conjunction with hydrocortisone 50 mg every 12 hours, to treat acute myeloid leukemia in conjunction with multiple myeloma. During this course of treatment, the patient was administered anti-infectious therapy.

The patient developed a brain abscess during treatment and passed away 2 months after hospitalization.

The patient's disease was severe and rapidly progressive, and comorbid severe infections and consistent comorbid severe pancytopenia posed challenges in managing this disease. It is hoped that more effective targeted therapies can be explored for such patients.

To culturally adapt Helping Her Heal (HHH), a nurse-delivered educational counseling intervention for spouse caregivers of women with breast cancer, for applicability in Ghana, Africa.

Four spouses of women with breast cancer and two nurses in breast clinics were interviewed to review HHH for adaptability.

The Ecological Validity Model guided the cultural adaptation of HHH to review the intervention, have Ghanaian nurses review the HHH manuals, modify the original HHH to the targeted demographic, verify the adaptation, and review for acceptability.

The intervention manuals needed minor modifications in three of the eight dimensions of the Ecological Validity Model: language, content, and metaphor. The structure and focus of each intervention session did not need changes.

Findings from this study have laid the groundwork for the cultural adaption of studies. This study is the first step in the process of adapting an intervention for spouse caregivers that will be used by breast cancer nurses in the delivery of care.

Non-Hodgkin lymphoma (NHL) is the most common hematologic cancer in the US. Validated projections of NHL cases are important for various stakeholders. The study aimed to identify and characterize methods forecasting NHL incidence, prevalence, and number of treatment eligible patients with NHL by line of therapy (LoT). In addition, methods evaluating the performance of cancer forecasting methods were also identified and utilized in selecting the most robust projection method applicable to NHL disease setting.

A comprehensive search was conducted in MEDLINE and EMBASE databases, covering January 2002 to April 2024 for English-language studies reporting methods evaluating cancer count estimation and NHL projection methods. Study characteristics were extracted and described. Criteria was developed to identify the most appropriate methods for evaluating projection methods. The identified methods of evaluation were then adopted to measure the accuracy of NHL projection methods.

Twenty-nine articles met the inclusion criteria for methods of evaluation, with 58.6% evaluating projection methods through calculating relative difference between observed and predicted case numbers. The most appropriate methods found for evaluating cancer incidence and prevalence projection were the average absolute relative deviation (AARD) and percent variation (VAR%), respectively. These methods were applied to projection methods identified through literature review to determine the robust method to project incidence and prevalence. Among twenty-six articles met the inclusion criteria for NHL projection methods, the joinpoint regression model was determined as the most robust method for projecting NHL incidence in the US, with the lowest AARD (1.6%). The projection method with assumptions of a 52.8% cure rate, a cure beginning ten years post-diagnosis, and all surviving patients cured after 20 years was identified as the most robust method for projecting NHL prevalence, with the lowest VAR% (8.3%). Unfortunately, due to the limited number and quality of studies, no robust method was identified for projecting the number of treatment-eligible NHL patients by LoT.

This review identified the most appropriate method of evaluating projection methods, and identified methods for projecting NHL incidence and prevalence in the US. Nevertheless, further research is needed to validate and project the number of treatment-eligible NHL patients by LoT.

Immunization is an essential intervention that protects millions of children against vaccine-preventable diseases across the world. Although vaccination coverage has improved over the years, ensuring timely administration continues to be a major challenge affecting child health in India. The COVID-19 pandemic further aggravated this situation. A cross-sectional study using the Kids Recode dataset of the National Family Health Survey 5 was conducted, which inlcuded 232920 children under 5 y of age. Timeliness was considered as 4 d earlier and up to 28 d after the standard date in comparison with the National Immunization Schedule. Regression analysis was used on Survey Data to assess the impact of factors related to children, mothers, households, communities, and the COVID-19 pandemic on timeliness by deriving adjusted Prevalence Ratios. Timeliness at birth was highest (94.6%), however, it declined for subsequent doses at 6 weeks (74.2%), and 14 weeks (67.5%). Untimeliness at 14 weeks was mainly due to Polio 3 (60.2%) and Rotavirus 3 (58.0%) vaccines. Factors associated with untimeliness included - low birth weight, fewer antenatal visits, non-institutional delivery, no maternal education, rural residency and belonging to lower socioeconomic strata. Children residing in the Central and Northeast zones had the highest prevalence of untimeliness in receiving vaccines. Despite the COVID-19 pandemic disruptions, the timeliness of vaccination was comparable between the pre- and post-pandemic periods. This study shows that the untimeliness of vaccination is influenced by factors related to the child, mother, household and community. Comprehensive interventions involving various stakeholders are required to improve the timeliness of vaccination.

The aim of this study was to obtain additional data in the definition of phenotypes of Obstructive Sleep Apnea Syndrome (OSAS) by comparing the body mass index (BMI) and neck and waist circumference( NC and WC) measurements with polysomnography (PSG) findings in patients with OSAS.

This retrospective study included 150 patients diagnosed with OSAS. The sociodemoographic data, comorbidities, PSG data (apnea-hypopnea index [AHI], obstructive apnea, central apnea, mixed apnea, obstructive hypopnea, central hypopnea, mixed hypopnea index, rapid eye movement [REM] AHI, supine AHI, minimum oxygen saturation), Epworth Sleepiness Scale (ESS), and Mallampati types were analyzed. The PSG data were compared in males and females according to BMI and NC and WC measurements.

In female patients with NC > 40 cm, the highest obstructive apnea index and AHI values (p = 0.009; p < 0.01, p = 0.030; p < 0.05) and the lowest O2 saturation values (p = 0.049; p < 0.05) were seen to be statistically significant. In females with BMI of 25-30, Mallampati 3 was seen at the highest rate, and the highest rates of central apnea and mixed apnea index values were determined in this group (p = 0.018; p < 0.05, p = 0.038; p < 0.05). In males with NC > 43 cm, the AHI, obstructive apnea-hypopnea index, and REM-supine AHI values were highest (p < 0.01 for all), and the lowest O2 saturation values were seen in this group (p = 0.009; p < 0.01). In males with WC > 102 cm, the highest AHI, obstructive apnea-hypopnea, and supine AHI values were determined (p = 0.042; p < 0.05; p = 0.042; p < 0.05; p = 0.002; p < 0.01; p = 0.016; p < 0.05), and the lowest O2 saturation values (p = 0.001; p < 0.01) at levels of statistical significance.

The NC, WC, and BMI values were determined to be correlated with AHI in males but not in females. In females with BMI of 25-30, elevated central apnea and mixed apnea index values were a noticeable finding. The airway in these patients was determined to be narrower, and NC in females was related to supine AHI. It is thought that these findings could be helpful in the identification of OSAS phenotypes according to anthropometric measurements.

We aimed to explore the association of serum brominated flame retardant (BFR) metabolites and mixture profiles with asthma risk among US adults. Data were sourced from the National Health and Nutrition Examination Survey (NHANES), 1999-2023. Four machine learning methods (light gradient boosting machine, eXtreme gradient boosting [XGBoost], random forest, and neural network) annexed with SHapley Additive exPlanations (SHAP) and one traditional logistic regression were used to develop and validate an explainable asthma prediction model. This study included 9,948 US adults. XGBoost outperformed other models with the highest area under the curve (AUC) at 0.814. Sixteen features-family history, BMI, PBDE47, PBDE28, PBDE154, age, race/ethnicity, smoking, second-hand smoking, sex, education, PIR, marriage, drinking, PBDE153, and PBB153-identified by at least two of applied methods were ultimately entered into machine learning models. According to the SHAP-quantified contribution to asthma risk, five key BFRs in predicting asthma were identified: PBDE47, PBDE28, PBDE154, PBDE153, and PBB153. Our findings indicated that XGBoost model proved most effective in predicting adulthood asthma based on serum BFRs. This machine learning-based model holds substantial promise for the early prevention, risk stratification, and clinical management of asthma.

Acute kidney injury (AKI) is a frequent and severe complication in intensive care unit (ICU) patients with respiratory failure, associated with high mortality, prolonged hospitalization, and substantial healthcare burden. Conventional risk scores, such as SOFA and APACHE II, are not optimized for AKI prediction in this heterogeneous population. This study aimed to develop and validate an early AKI prediction model using machine learning. We analyzed 10,780 adult ICU patients with unspecified respiratory failure from the MIMIC-IV database, of whom 53.96% developed AKI according to KDIGO criteria. Ten supervised learning algorithms were trained using predictors from the first 48 h of ICU admission, with each model independently selecting its 15 most informative features via recursive feature elimination. Extreme gradient boosting (XGBoost) achieved the best performance (AUC 0.9023; accuracy 0.8247; sensitivity 0.8077; specificity 0.8386; precision 0.8040; negative predictive value 0.8419; F1-score 0.8058; Brier score 0.108). SHAP analysis identified creatinine_max, length of hospital stay, BUN_max, preexisting renal disease, and urine output as the most influential predictors. Leveraging routinely available early ICU data, this model enables accurate and interpretable AKI risk stratification. With external validation and integration into electronic health records, it could support proactive prevention and individualized management of AKI in critically ill respiratory failure patients.