Systemic and local chronic inflammation might enhance the risk of pancreatic ductal adenocarcinoma (PDAC), and PDAC-associated inflammatory infiltrate in the tumor microenvironment concurs in enhancing tumor growth and metastasis. Inflammation is closely correlated with immunity, the same immune cell populations contributing to both inflammation and immune response. In the PDAC microenvironment, the inflammatory cell infiltrate is unbalanced towards an immunosuppressive phenotype, with a prevalence of myeloid derived suppressor cells (MDSC), M2 polarized macrophages, and T_reg, over M1 macrophages, dendritic cells, and effector CD4⁺ and CD8⁺ T lymphocytes. The dynamic and continuously evolving cross-talk between inflammatory and cancer cells might be direct and contact-dependent, but it is mainly mediated by soluble and exosomes-carried cytokines. Among these, tumor necrosis factor alpha (TNFα) plays a relevant role in enhancing cancer risk, cancer growth, and cancer-associated cachexia. In this review, we describe the inflammatory cell types, the cytokines, and the mechanisms underlying PDAC risk, growth, and progression, with particular attention on TNFα, also in the light of the potential risks or benefits associated with anti-TNFα treatments.

Background: Coronavirus disease 2019, abbreviated to COVID-19, represents an emerging health threat worldwide as, after initial reports in China, it has continued to spread rapidly. The clinical spectrum of the disease varies from mild to severe acute respiratory distress syndrome (ARDS). Moreover, many patients can be asymptomatic, thus increasing the uncertainty of the diagnostic work-up. Laboratory tests play a pivotal role in the diagnosis and management of COVID-19, the current gold standard being real-time reverse transcription polymerase chain reaction (rRT-PCR) on respiratory tract specimens. However, the diagnostic accuracy of rRT-PCR depends on many pre-analytical and analytical variables. The measurement of specific COVID-19 antibodies (both IgG and IgM) should serve as an additional, non-invasive tool for disease detection and management. Methods: The imprecision of the MAGLUMI™ 2000 Plus 2019-nCov IgM and IgG assays (Snibe, Shenzhen, China) was assessed by adopting the Clinical and Laboratory Standards Institute (CLSI) EP15-A3 protocol. Linearity of dilution and recovery was evaluated by means of mixes of high-level pools and low-level pools of serum samples. Immunoglobulin time kinetics were evaluated using a series of serum samples, repeatedly collected from COVID-19-positive patients at different times, from <5 days up to 26–30 days. Results: Findings at the analytical validation of the assay carried out according to the CLSI EP15-A3 guideline demonstrated that imprecision and repeatability were acceptable (repeatability was <4% and <6% for IgM and IgG, respectively, whilst intermediate imprecision was <6%). In addition, results of dilution and recovery studies were satisfactory. The kinetics of COVID-19 antibodies confirmed previously reported findings, showing a rapid increase of both IgM and IgG after 6–7 days from the symptom onset. IgG had 100% sensitivity on day 12, whilst 88% was the higher positive rate achieved for IgM after the same time interval. Conclusions: The findings of this study demonstrate the validity of the MAGLUMI 2000 Plus CLIA assay for the measurement of specific IgM and IgG in sera of COVID-19 patients, and for obtaining valuable data on the kinetics of both (IgM and IgG) COVID-19 antibodies. These data represent a pre-requisite for the appropriate utilization of specific antibodies for the diagnosis and management of COVID-19 patients.

Background: In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notable ethical concerns. Content: This study delineates AI’s dual role in scientific publishing: as a co-creator in the writing and review of scientific papers and as an ethical challenge. We first explore the potential of AI as an enhancer of efficiency, efficacy, and quality in creating scientific papers. A critical assessment follows, evaluating the risks vs. rewards for researchers, especially those early in their careers, emphasizing the need to maintain a balance between AI’s capabilities and fostering independent reasoning and creativity. Subsequently, we delve into the ethical dilemmas of AI’s involvement, particularly concerning originality, plagiarism, and preserving the genuine essence of scientific discourse. The evolving dynamics further highlight an overlooked aspect: the inadequate recognition of human reviewers in the academic community. With the increasing volume of scientific literature, tangible metrics and incentives for reviewers are proposed as essential to ensure a balanced academic environment. Summary: AI’s incorporation in scientific publishing is promising yet comes with significant ethical and operational challenges. The role of human reviewers is accentuated, ensuring authenticity in an AI-influenced environment. Outlook: As the scientific community treads the path of AI integration, a balanced symbiosis between AI’s efficiency and human discernment is pivotal. Emphasizing human expertise, while exploit artificial intelligence responsibly, will determine the trajectory of an ethically sound and efficient AI-augmented future in scientific publishing.

Objectives: ChatGPT, a tool based on natural language processing (NLP), is on everyone’s mind, and several potential applications in healthcare have been already proposed. However, since the ability of this tool to interpret laboratory test results has not yet been tested, the EFLM Working group on Artificial Intelligence (WG-AI) has set itself the task of closing this gap with a systematic approach. Methods: WG-AI members generated 10 simulated laboratory reports of common parameters, which were then passed to ChatGPT for interpretation, according to reference intervals (RI) and units, using an optimized prompt. The results were subsequently evaluated independently by all WG-AI members with respect to relevance, correctness, helpfulness and safety. Results: ChatGPT recognized all laboratory tests, it could detect if they deviated from the RI and gave a test-by-test as well as an overall interpretation. The interpretations were rather superficial, not always correct, and, only in some cases, judged coherently. The magnitude of the deviation from the RI seldom plays a role in the interpretation of laboratory tests, and artificial intelligence (AI) did not make any meaningful suggestion regarding follow-up diagnostics or further procedures in general. Conclusions: ChatGPT in its current form, being not specifically trained on medical data or laboratory data in particular, may only be considered a tool capable of interpreting a laboratory report on a test-by-test basis at best, but not on the interpretation of an overall diagnostic picture. Future generations of similar AIs with medical ground truth training data might surely revolutionize current processes in healthcare, despite this implementation is not ready yet.