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INTRODUCTION This statement revises our earlier “WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications” (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These Recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work, and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop. A chatbot is a tool “[d]riven by [artificial intelligence], automated rules, natural-language processing (NLP), and machine learning (ML)…[to] process data to deliver responses to requests of all kinds.” (1) Artificial intelligence (AI) is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.” (2) “Generative modeling is an artificial intelligence technique that generates synthetic artifacts by analyzing training examples; learning their patterns and distribution; and then creating realistic facsimiles. Generative AI (GAI) uses generative modeling and advances in deep learning (DL) to produce diverse content at scale by utilizing existing media such as text, graphics, audio, and video.” (3, 4) Chatbots are activated by a plain-language instruction, or “prompt,” provided by the user. They generate responses using statistical and probability-based language models. (5)

Introduction This statement revises our earlier “WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications” (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These Recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work, and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop. A chatbot is a tool “[d]riven by [artificial intelligence], automated rules, natural-language processing (NLP), and machine learning (ML)…[to] process data to deliver responses to requests of all kinds.”1 Artificial intelligence (AI) is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”2 “Generative modeling is an artificial intelligence technique that generates synthetic artifacts by analyzing training examples; learning their patterns and distribution; and then creating realistic facsimiles. Generative AI (GAI) uses generative modeling and advances in deep learning (DL) to produce diverse content at scale by utilizing existing media such as text, graphics, audio, and video.”3, 4 Chatbots are activated by a plain-language instruction, or “prompt,” provided by the user. They generate responses using statistical and probability-based language models.5 This output has some characteristic properties. It is usually linguistically accurate and fluent but, to date, it is often compromised in various ways. For example, chatbot output currently carries the risk of including biases, distortions, irrelevancies, misrepresentations, and plagiarism many of which are caused by the algorithms governing its generation and heavily dependent on the contents of the materials used in its training. Consequently, there are concerns about the effects of chatbots on knowledge creation and dissemination – including their potential to spread and amplify mis- and disinformation6 – and their broader impact on jobs and the economy, as well as the health of individuals and populations. New legal issues have also arisen in connection with chatbots and generative AI.7 Chatbots retain the information supplied to them, including content and prompts, and may use this information in future responses. Therefore, scholarly content that is generated or edited using AI would be retained and as a result, could potentially appear in future responses, further increasing the risk of inadvertent plagiarism on the part of the user and any future users of the technology. Anyone who needs to maintain confidentiality of a document, including authors, editors, and reviewers, should be aware of this issue before considering using chatbots to edit or generate work.9 Chatbots and their applications illustrate the powerful possibilities of generative AI, as well as the risks. These Recommendations seek to suggest a workable approach to valid concerns about the use of chatbots in scholarly publishing.

BackgroundAntibody-mediated rejection (ABMR) is the main cause of renal allograft loss. The most common treatment strategy is based on plasmapheresis plus the subsequent administration of intravenous immunoglobulin (IVIG). Unfortunately, no approved long-term therapy is available for ABMR. The current study was designed to analyze the effect of various ABMR treatment approaches on allograft survival and to compare treatment effects in the presence or absence of donor-specific antibodies (DSAs).MethodsThis single-center study retrospectively analyzed 102 renal allograft recipients who had biopsy-proven ABMR after transplant. DSA was detectable in 61 of the 102 patients. Initial standard treatment of ABMR consisted of plasmapheresis (PS) or immunoadsorption (IA), followed by a single course of IVIG. In case of nonresponse or recurrence, additional immunosuppressive medications, such as rituximab, bortezomib, thymoglobulin, or eculizumab, were administered. In a second step, persistent ABMR was treated with increased maintenance immunosuppression, long-term therapy with IVIG (more than 1 year), or both.ResultsOverall graft survival among transplant patients with ABMR was

The GNAS gene encodes the alpha-subunit of the stimulatory G-protein (Gαs) in humans and mice. The single-nucleotide polymorphism of GNAS, c.393C>T, is associated with an elevated production of Gαs and an increased formation of cyclic adenosine monophosphate (cAMP). In the present study, we analyzed the effect of this GNAS polymorphism on a renal allograft outcome. We screened a cohort of 436 renal allograft recipients, who were retrospectively followed up for up to 5 years after transplant. GNAS genotypes were determined with polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) assays. The 393T allele was detected in 319 (73%) recipients (113 recipients with TT and 206 with CT genotype) and the CC genotype in 117 (27%). The CC genotype was associated with a significantly lower frequency of BK viremia (CC, 17 recipients (15%); T 84 (26%)); p = 0.01; TT, 27 vs. CC, 17, p = 0.07; TT, 27 vs. CT, 57, p = 0. 46; CT, 57 vs. CC, 17, p = 0.01) and BKV-associated nephropathy (CC, 3 recipients (3%); T, 27 (8%); p = 0.03; TT,10 vs. CC, 3, p = 0.04; TT, 10 vs. CT,17, p = 0.85; CT, 17 vs. CC,3, p = 0.04) after transplant. BKV-associated nephropathy-free survival was significantly better among CC genotype carriers than among T allele carriers (p = 0.043; TT vs. CC, p = 0.03; CT vs. CC, p = 0.04; TT vs. CT, p = 0.83). Multivariate analysis indicated an independent protective effect of the CC genotype against the development of both BK viremia (relative risk. 0.54; p = 0.04) and BKV-associated nephropathy after renal transplant (relative risk. 0.27; p = 0.036). The GNAS 393 CC genotype seems to protect renal allograft recipients against the development of BK viremia and BKV-associated nephropathy.

The c.825C>T single-nucleotide polymorphism (rs5443) of the guanine nucleotide-binding protein subunit β3 (GNB3) results in increased intracellular signal transduction via G-proteins. The present study investigated the effect of the GNB3 c.825C>T polymorphism on cardiovascular events among renal allograft recipients posttransplant. Our retrospective study involved 436 renal allograft recipients who were followed up for up to 8 years after transplant. The GNB3 c.825C>T polymorphism was detected with restriction fragment length polymorphism (RFLP) polymerase chain reaction (PCR). The GNB3 TT genotype was detected in 43 (10%) of 436 recipients. Death due to an acute cardiovascular event occurred more frequently among recipients with the TT genotype (4 [9%]) than among those with the CC/CT genotypes (7 [2%]; p = 0.003). The rates of myocardial infarction (MI)–free survival (p = 0.003) and acute peripheral artery occlusive disease (PAOD)–free survival (p = 0.004) were significantly lower among T-homozygous patients. A multivariate analysis showed that homozygous GNB3 c.825C>T polymorphism exerted only a mild effect for the occurrence of myocardial infarction (relative risk, 2.2; p = 0.065) or acute PAOD (relative risk, 2.4; p = 0.05) after renal transplant. Our results suggest that the homozygous GNB3 T allele exerts noticeable effects on the risk of MI and acute PAOD only in the presence of additional nonheritable risk factors.

Introduction. Most investigations on autoimmune pancreatitis (AIP) were published on Asian cohorts while those on Caucasians are limited. However, there might be differences related to the origin. Patients and Methods. We analyzed 36 patients and compared type 1 (AIP1) with type 2 (AIP2). Results. The majority of patients suffered from AIP1 (55.6%). AIP1 patients were significantly older than AIP2 patients (54.4 versus 40.8 years). Moreover, 85.0% of AIP1 patients had concurrent autoimmune cholangitis (AIC) while 18.8% of AIP2 patients suffered from overlap to ulcerative colitis (UC). However, AIP1 patients revealed a cholestatic course and had significantly higher immunoglobulin G4 levels (IgG4). When compared to allele frequencies in healthy controls, in patients with AIP1 HLA-B8 reached statistical significance. Response to steroids was excellent in both groups, but we noticed high rates of relapse especially in AIP1 patients. Finally, 3 patients with AIP1 were diagnosed with cholangiocellular carcinoma (CCC). Conclusion. In contrast to Asian studies, we found an almost equal distribution of AIP1 and AIP2 patients in our German cohort. AIP2 patients were younger and mostly of female gender whereas AIP1 patients revealed higher IgG4 levels and involvement of the biliary tract in sense of IgG4-associated cholangitis.