Background: Despite remarkable progress in contemporary medical technology and enhanced survival outcomes for various cancer types, pancreatic cancer (PC) continues to stand out as a particularly deadly gastrointestinal malignancy. Given a persistent rise in both incidence and the corresponding mortality rates of PC globally, evaluations of PC burden by sex are of great importance. Here, we used the illness‐death multi‐state model (IDM) to forecast the prevalence of PC through the year 2040.Methods: IDM was established based on obtainable data to predict the future prevalence of PC on global, regional, and national scales from 2019 to 2040. Analyses were also performed regarding sex and 95% confidence intervals (CIs) are presented for all estimates.Results: The projected prevalence rate for 2040 is anticipated to be 6.093 ([95% CI 5.47–6.786] per 100,000) worldwide, indicating a significant increase of 31.45% since 1990, and a 12.29% increase since 2019. The estimated average annual increase since 2020 was 0.5%. Considering sex differences, females are expected to have a steeper slope in prevalence rate than males. Intriguingly, when considering the percentage changes between the periods of 2019–2040 and 1990–2019 for both sexes, females exhibited 29% and 11% increase relative to males (2.6‐fold greater increase).Conclusions: By 2040, it is predicted that the prevalence of PC will increase globally, with women being at higher risk of developing the disease. Considering the percentage changes, regions with lower socioeconomic status are anticipated to face a greater risk of experiencing PC compared to other geographical areas.

Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence from treatment-related changes, such as radionecrosis, is a major diagnostic challenge. Machine learning (ML) offers a novel approach, leveraging advanced algorithms to analyze complex imaging data with high precision. A comprehensive search of PubMed, Embase, Scopus, Web of Science, and Google Scholar identified eligible studies. The sensitivity, specificity, accuracy, precision, F1 score, and the (area under the curve) AUC items were extracted from the included studies. After screening 1077 records, seven studies met the eligibility criteria for the systematic review, of which five were included in the meta-analysis. ML algorithm, particularly Support Vector Machines (SVM), demonstrated promising performance. A meta-analysis of five studies revealed a pooled sensitivity of 0.95 (95% CI: 0.84-0.99) and specificity of 0.80 (95% CI: 0.69-0.88). Additionally, the positive diagnostic likelihood ratio (DLR) was 4.75 (95% CI: 2.91-7.76), the negative DLR was 0.06 (95% CI: 0.02-0.21), and the diagnostic odds ratio was 80.97 (95% CI: 17.5-374.61). The diagnostic score was calculated as 4.39 (95% CI: 2.86-5.93), and the AUC was 0.86 (95% CI: 0.83-0.89). Subgroup analyses showed SVM-based models with higher sensitivity (0.98 vs. 0.87) and specificity (0.82 vs. 0.77) than non-SVM (p = 0.13). Comparing glioblastoma and Grade 3 tumors, sensitivities were 94 % vs. 97 %, and specificities were 79 % vs. 83 %, with no significant heterogeneity. These findings suggest that ML models, particularly SVM, offer promising diagnostic performance in distinguishing true tumor recurrence from treatment-related changes.

Pancreatic cancer (PC) is one of the deadliest cancers worldwide with low survival rates and poor outcomes. The treatment landscape for PC is fraught with obstacles, including drug resistance, lack of effective targeted therapies and the immunosuppressive tumor microenvironment (TME). The resistance of PC to existing immunotherapies highlights the need for innovative approaches, with the TME emerging as a promising therapeutic target. The recent advancements in understanding the role of macrophages, this context highlight their significant impact on tumor development and progression. There are two important types of macrophages: M1 and M2, which play critical roles in the TME. Therapeutics strategies including, depletion of tumor-associated macrophages (TAMs), reprogramming TAMs to promote anti-tumor activity, and targeting macrophage recruitment can lead to promising outcomes. Targeting macrophage-related pathways may offer novel strategies for modulating immune responses, inhibiting angiogenesis, and overcoming resistance to chemotherapy in PC treatment.

Background and aim: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a rare multisystem autoimmune disease developed by autoantibody production against human neutrophilic granulocytes, including proteinase-3 (PR3) and myeloperoxidase (MPO). The management of AAV patients is difficult due to the multiorgan involvement, high rate of relapse, and complications of immunosuppressive agents that make it challenging. This study aims to investigate the efficacy and safety of rituximab (RTX) therapy in patients with granulomatosis with polyangiitis (GPA) or microscopic polyangiitis (MPA) subtypes. Method: The PubMed/Medline database was searched for any studies related to RTX therapy in ANCA-associated vasculitis (GPA and MPA subtypes), from inception to 1 August 2022, and proceeded in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Our search resulted in 1082 initial records. After the elimination of review papers, irrelevant studies, and non-English records, 223 articles were included, and the data related to the efficacy and safety of RTX therapy were extracted. Several randomized and non-randomized studies showed that RTX is an effective treatment option for patients with AAV. Most of the studies showed the very effective effect of RTX in controlling disease in AAV patients, including pediatrics, adults, and elderlies, although RTX cannot completely prevent relapse. However, maintenance therapy helps delay the disease’s relapse and causes sustained remission. Not only the licensed dose (375 mg/m² intravenous per week for 4 weeks) could induce disease remission, but studies also showed that a single infusion of RTX could be effective. Although RTX could resolve many rare manifestations in AAV patients, there are few reports showing treatment failure. Additionally, few sudies have reported the unexpeted worsening of the disease after RTX administration. Generally, RTX is relatively safe compared to conventional therapies, but some serious adverse effects, mainly infections, cytopenia, hypogammaglobinemia, malignancy, and hypersensitivity have been reported. Conclusions: RTX is an effective and relatively safe therapeutic option for AAV. Studies on the evaluation of the safety profiles of RTX and the prevention of severe RTX-related side effects in AAV patients are required.