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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]

Diagnosis of tuberculosis (TB) in children remains challenging due to unspecific clinical presentation and low bacillary load. In low TB incidence countries, most cases are diagnosed by a contact screening strategy after exposure to an index TB case. Due to the severity of TB in young children, the priority is to determine whether a child is infected or not, whereas differential diagnosis between active TB (aTB) and latent TB constitutes a second step. In Belgium, a low TB incidence country, we prospectively included 47 children with a defined M. tuberculosis infection status (12 children with aTB, 18 with latent TB, and 17 uninfected) (exploratory cohort), and determined the optimal combinations of cytokines secreted by their peripheral blood mononuclear cells in response to a 5-days in vitro stimulation with four different mycobacterial antigens, in an attempt to classify the children according to their infectious status. Correct identification of all infected children was obtained by several combinations of two purified protein derivative (PPD)-induced cytokines (IFN-γ and either GM-CSF, MIP-1α, sCD40L or TNF-α), or by combining PPD-induced IFN-γ with culture-filtrate protein-10 (CFP-10)-induced TNF-α. Alternatively, combining CFP-10-induced TNF-α and IP-10 with heparin-binding haemagglutinin (HBHA)-induced-IFN-γ was more effective in testing recently BCG-vaccinated children or those suspected to be infected with non-tuberculous mycobacteria, providing a correct classification of 97% of the M. tuberculosis-infected children. This combination also correctly classified 98% of the children from a validation cohort comprising 40 M. tuberculosis infected children and 20 non-infected children. Further differentiation between aTB and children with latent TB was more difficult. Combining ESAT-6-induced MIP1-α and IP-10, CFP-10-induced MIG, and HBHA-induced MIG provided a correct classification of 77% of the children from the exploratory cohort but only of 57.5% of those from the validation cohort. We conclude that combining the measurement of 2–4 cytokines induced by three different mycobacterial antigens allows an excellent identification of M. tuberculosis-infected children, whereas differentiating children with aTB from those with latent TB remains far from perfect.

Introduction: Belgium is a country with low incidence of tuberculosis (TB) and a very low number of TB cases in children. Children in contact with an adult smear-positive TB case are at high risk of transmission. Early diagnosis is important as young children have a significant predisposition of developing TB disease. In this paper, we describe two outbreaks after exposure to, respectively, two teachers with smear-positive pulmonary TB: one in a primary school, a nursery teacher, and another in a private language school.Methods: An exposure investigation was carried out in both index cases household and school, according to the stone-in-the-pond principle. The tuberculin skin test (TST) was used a screening tool. The time elapsed between TB diagnosis in the index case and contact investigation was, respectively, 1 and 3 weeks. If this initial test was negative, it was repeated after a “window period” of ≥8 weeks.Results: Index cases showed a transmission rate of, respectively, 13 and 40% in their classes at school, defined as casual contacts. The proximity of contact increased the risk of infection. TB disease was observed in, respectively, 4 and 11% of all the casual contacts; all of them were children younger than 5 years old. TB-infected and children with active TB disease had good compliance with recommended treatment. Uptake of chemoprophylaxis during the “window period” was poor, respectively, only 32–42%, in children under 5 years with an initially negative TST.Discussion: The World Health Organization recommends to screen all young children (

Background: Improved diagnostic tests are needed for the early identification of Mycobacterium tuberculosis-infected young children exposed to an active TB (aTB) index case. We aimed to compare the diagnostic accuracy of new blood-based tests to that of the tuberculin skin test (TST) for the identification of all infected children and for a potential differentiation between aTB and latent TB infection (LTBI).Methods: 144 children exposed to a patient with aTB were included, and those who met all inclusion criteria (130/144) were classified in three groups based on results from classical investigations: non-infected (NI: n = 69, 53%, median age 10 months), LTBI (n = 28, 22%, median age 96 months), aTB disease (n = 33, 25%, median age 24 months). The first whole blood assay consisted of a 7-days in vitro stimulation of blood with four different mycobacterial antigens (40 μl/condition), followed by flow cytometric measurement of the proportions of blast cells appearing among lymphocytes as a result of their specific activation. Thresholds of positivity were determined by Receiver Operating Characteristic (ROC) curve analysis (results of NI children vs. children with LTBI/aTB) in order to identify infected children in a first stage. Other cut-offs were determined to discriminate subgroups of infected children in a second step (results from children with aTB/LTBI). Analysis of blood monocytes and dendritic cell subsets was performed on 100 μl of blood for 25 of these children as a second test in a pilot study.Results: Combining the results of the blast-induced CD3+ T lymphocytes by Heparin-Binding Haemagglutinin and by Culture Filtrate Protein-10 identified all but one infected children (sensitivity 98.2% and specificity 86.9%, compared to 93.4 and 100% for the TST). Further identification among infected children of those with aTB was best achieved by the results of blast-induced CD8+ T lymphocytes by purified protein derivative (sensitivity for localized aTB: 61.9%, specificity 96.3%), whereas high proportions of blood type 2 myeloid dendritic cells (mDC) were a hallmark of LTBI.Conclusions: New blood-based tests requiring a very small volume allow the accurate identification of M. tuberculosis-infected young children among exposed children and are promising to guide the clinical classification of children with aTB or LTBI.