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Introduction ChatGPT, a state-of-the-art large language model, has shown potential in analyzing images and providing accurate information. This study aimed to explore ChatGPT-4 as a tool for identifying commonly prescribed nephrology medications across different versions and testing dates.Methods 25 nephrology medications were obtained from an institutional pharmacy. High-quality images of each medication were captured using an iPhone 13 Pro Max and uploaded to ChatGPT-4 with the query, ‘What is this medication?’ The accuracy of ChatGPT-4’s responses was assessed for medication name, dosage, and imprint. The process was repeated after 2 weeks to evaluate consistency across different versions, including GPT-4, GPT-4 Legacy, and GPT-4.Ø.Results ChatGPT-4 correctly identified 22 out of 25 (88%) medications across all versions. However, it misidentified Hydrochlorothiazide, Nifedipine, and Spironolactone due to misreading imprints. For instance, Nifedipine ER 90 mg was mistaken for Metformin Hydrochloride ER 500 mg because ‘NF 06’ was misread as ‘NF 05’. Hydrochlorothiazide 50 mg was confused with the 25 mg version due to imprint errors, and Spironolactone 25 mg was misidentified as Naproxen Sodium or Diclofenac Sodium. Despite these errors, ChatGPT-4 showed 100% consistency when retested, correcting misidentifications after receiving feedback on the correct imprints.Conclusion ChatGPT-4 shows strong potential in identifying nephrology medications from self-captured images, though challenges with difficult-to-read imprints remain. Providing feedback improved accuracy, suggesting ChatGPT-4 could be a valuable tool in digital health for medication identification. Future research should enhance the model’s ability to distinguish similar imprints and explore broader integration into digital health platforms.

X-linked recessive deficiency of TLR7, a MyD88- and IRAK-4-dependent endosomal ssRNA sensor, impairs SARS-CoV-2 recognition and type I IFN production in plasmacytoid dendritic cells (pDCs), thereby underlying hypoxemic COVID-19 pneumonia with high penetrance. We report 22 unvaccinated patients with autosomal recessive MyD88 or IRAK-4 deficiency infected with SARS-CoV-2 (mean age: 10.9 yr; 2 mo to 24 yr), originating from 17 kindreds from eight countries on three continents. 16 patients were hospitalized: six with moderate, four with severe, and six with critical pneumonia, one of whom died. The risk of hypoxemic pneumonia increased with age. The risk of invasive mechanical ventilation was also much greater than in age-matched controls from the general population (OR: 74.7, 95% CI: 26.8-207.8, P < 0.001). The patients susceptibility to SARS-CoV-2 can be attributed to impaired TLR7-dependent type I IFN production by pDCs, which do not sense SARS-CoV-2 correctly. Patients with inherited MyD88 or IRAK-4 deficiency were long thought to be selectively vulnerable to pyogenic bacteria, but also have a high risk of hypoxemic COVID-19 pneumonia.

Background This study investigated the efficacy of ChatGPT-3.5 and ChatGPT-4 in assessing drug safety for patients with kidney diseases, comparing their performance to Micromedex, a well-established drug information source. Despite the perception of non-prescription medications and supplements as safe, risks exist, especially for those with kidney issues. The study's goal was to evaluate ChatGPT's versions for their potential in clinical decision-making regarding kidney disease patients. Method The research involved analyzing 124 common non-prescription medications and supplements using ChatGPT-3.5 and ChatGPT-4 with queries about their safety for people with kidney disease. The AI responses were categorized as “generally safe,” “potentially harmful,” or “unknown toxicity.” Simultaneously, these medications and supplements were assessed in Micromedex using similar categories, allowing for a comparison of the concordance between the two resources. Results Micromedex identified 85 (68.5%) medications as generally safe, 35 (28.2%) as potentially harmful, and 4 (3.2%) of unknown toxicity. ChatGPT-3.5 identified 89 (71.8%) as generally safe, 11 (8.9%) as potentially harmful, and 24 (19.3%) of unknown toxicity. GPT-4 identified 82 (66.1%) as generally safe, 29 (23.4%) as potentially harmful, and 13 (10.5%) of unknown toxicity. The overall agreement between Micromedex and ChatGPT-3.5 was 64.5% and ChatGPT-4 demonstrated a higher agreement at 81.4%. Notably, ChatGPT-3.5's suboptimal performance was primarily influenced by a lower concordance rate among supplements, standing at 60.3%. This discrepancy could be attributed to the limited data on supplements within ChatGPT-3.5, with supplements constituting 80% of medications identified as unknown. Conclusion ChatGPT's capabilities in evaluating the safety of non-prescription drugs and supplements for kidney disease patients are modest compared to established drug information resources. Neither ChatGPT-3.5 nor ChatGPT-4 can be currently recommended as reliable drug information sources for this demographic. The results highlight the need for further improvements in the model's accuracy and reliability in the medical domain.

As leptospirosis is re-emerging, a seroprevalence study was conducted, assessing the prevalence of anti-Leptospira IgG antibodies and infection-associated risk factors among forestry workers (FWs) in Lower Saxony, Germany, to develop targeted public health measures. Sera of 877 FWs, sampled in 2016, were tested for anti-Leptospira seropositivity by commercial IgG-ELISA. Data on demographics and Leptospira-specific exposures, knowledge, sources of information, and preventive measures were collected by standardized, self-administered questionnaire. A subset of 244 sera was retested via in-house IgG-ELISA. Risk factors were assessed from the subset using multivariable logistic regression analysis. The commercial IgG-ELISA revealed a seroprevalence of 4.8% (95% confidence interval CI95 = 3.5–6.4). Of the 601 FWs who completed the questionnaire, 67.9% had been informed about leptospirosis and Leptospira spp., mainly by employers (55.2%) and peers (38.9%). Positive associations with seropositivity were observed for canoeing (adjusted odds ratio (aOR) = 2.35, p = 0.044), touching rodents (aOR = 2.4, p = 0.021), and living close to beech trees (aOR = 2.18, p = 0.075). Frequently cleaning animal stables was negatively associated (aOR = 0.20, p = 0.002). The unexpected positive association with wearing gloves when handling plants and soil (aOR = 2.16, p = 0.011) needs further discussion. Overall, seroprevalence was in the range of other studies in Germany. The identified factors will be used to develop targeted information reaching out to at-risk groups tapping various communication channels.

ChatGPT, a state-of-the-art large language model, has shown potential in analyzing images and providing accurate information. This study aimed to explore ChatGPT-4 as a tool for identifying commonly prescribed nephrology medications across different versions and testing dates. 25 nephrology medications were obtained from an institutional pharmacy. High-quality images of each medication were captured using an iPhone 13 Pro Max and uploaded to ChatGPT-4 with the query, ‘What is this medication?’ The accuracy of ChatGPT-4’s responses was assessed for medication name, dosage, and imprint. The process was repeated after 2 weeks to evaluate consistency across different versions, including GPT-4, GPT-4 Legacy, and GPT-4.Ø. ChatGPT-4 correctly identified 22 out of 25 (88%) medications across all versions. However, it misidentified Hydrochlorothiazide, Nifedipine, and Spironolactone due to misreading imprints. For instance, Nifedipine ER 90 mg was mistaken for Metformin Hydrochloride ER 500 mg because ‘NF 06’ was misread as ‘NF 05’. Hydrochlorothiazide 50 mg was confused with the 25 mg version due to imprint errors, and Spironolactone 25 mg was misidentified as Naproxen Sodium or Diclofenac Sodium. Despite these errors, ChatGPT-4 showed 100% consistency when retested, correcting misidentifications after receiving feedback on the correct imprints. ChatGPT-4 shows strong potential in identifying nephrology medications from self-captured images, though challenges with difficult-to-read imprints remain. Providing feedback improved accuracy, suggesting ChatGPT-4 could be a valuable tool in digital health for medication identification. Future research should enhance the model’s ability to distinguish similar imprints and explore broader integration into digital health platforms. [ABSTRACT FROM AUTHOR]

Seoul orthohantavirus (SEOV) is a rat-associated zoonotic pathogen with an almost worldwide distribution. In 2019, the first autochthonous human case of SEOV-induced hemorrhagic fever with renal syndrome was reported in Germany, and a pet rat was identified as the source of the zoonotic infection. To further investigate the SEOV reservoir, additional rats from the patient and another owner, all of which were purchased from the same vendor, were tested. SEOV RNA and anti-SEOV antibodies were found in both of the patient’s rats and in two of the three rats belonging to the other owner. The complete coding sequences of the small (S), medium (M), and large (L) segments obtained from one rat per owner exhibited a high sequence similarity to SEOV strains of breeder rat or human origin from the Netherlands, France, the USA, and Great Britain. Serological screening of 490 rats from breeding facilities and 563 wild rats from Germany (2007–2020) as well as 594 wild rats from the Netherlands (2013–2021) revealed 1 and 6 seropositive individuals, respectively. However, SEOV RNA was not detected in any of these animals. Increased surveillance of pet, breeder, and wild rats is needed to identify the origin of the SEOV strain in Europe and to develop measures to prevent transmission to the human population.

As leptospirosis is re-emerging, a seroprevalence study was conducted, assessing the prevalence of anti-Leptospira IgG antibodies and infection-associated risk factors among forestry workers (FWs) in Lower Saxony, Germany, to develop targeted public health measures. Sera of 877 FWs, sampled in 2016, were tested for anti-Leptospira seropositivity by commercial IgG-ELISA. Data on demographics and Leptospira-specific exposures, knowledge, sources of information, and preventive measures were collected by standardized, self-administered questionnaire. A subset of 244 sera was retested via in-house IgG-ELISA. Risk factors were assessed from the subset using multivariable logistic regression analysis. The commercial IgG-ELISA revealed a seroprevalence of 4.8% (95% confidence interval CI95 = 3.5–6.4). Of the 601 FWs who completed the questionnaire, 67.9% had been informed about leptospirosis and Leptospira spp., mainly by employers (55.2%) and peers (38.9%). Positive associations with seropositivity were observed for canoeing (adjusted odds ratio (aOR) = 2.35, p = 0.044), touching rodents (aOR = 2.4, p = 0.021), and living close to beech trees (aOR = 2.18, p = 0.075). Frequently cleaning animal stables was negatively associated (aOR = 0.20, p = 0.002). The unexpected positive association with wearing gloves when handling plants and soil (aOR = 2.16, p = 0.011) needs further discussion. Overall, seroprevalence was in the range of other studies in Germany. The identified factors will be used to develop targeted information reaching out to at-risk groups tapping various communication channels. [ABSTRACT FROM AUTHOR]