Vancomycin is the most frequently used antibiotic, accounting for up to 35% of hospitalized patients with infection, because of its optimal bactericidal effectiveness and relatively low price. Vancomycin-associated AKI (VA-AKI) is a clinically relevant but not yet clearly understood entity in critically ill patients. The current review comprehensively summarizes the pathophysiological mechanisms of, biomarkers for, preventive strategies for, and some crucial issues with VA-AKI. The pathological manifestations of VA-AKI include acute tubular necrosis, acute tubulointerstitial nephritis (ATIN), and intratubular crystal obstruction. The proposed pathological mechanisms of VA-AKI include oxidative stress and allergic reactions induced by vancomycin and vancomycin-associated tubular casts. Concomitant administration with other nephrotoxic antibiotics, such as piperacillin–tazobactam, high vancomycin doses, and intermittent infusion strategies compared to the continuous infusion are associated with a higher risk of VA-AKI. Several biomarkers could be applied to predict and diagnose VA-AKI. To date, no promising therapy is available. Oral steroids could be considered for patients with ATIN, whereas hemodialysis might be applied to remove vancomycin from the patient. In the future, disclosing more promising biomarkers that could precisely identify populations susceptible to VA-AKI and detect VA-AKI occurrence early on, and developing pharmacological agents that could prevent or treat VA-AKI, are the keys to improve the prognoses of patients with severe infection who probably need vancomycin therapy.

Although sepsis and acute kidney injury (AKI) have a bidirectional interplay, the pathophysiological mechanisms between AKI and sepsis are not clarified and worthy of a comprehensive and updated review. The primary pathophysiology of sepsis-associated AKI (SA-AKI) includes inflammatory cascade, macrovascular and microvascular dysfunction, cell cycle arrest, and apoptosis. The pathophysiology of sepsis following AKI contains fluid overload, hyperinflammatory state, immunosuppression, and infection associated with kidney replacement therapy and catheter cannulation. The preventive strategies for SA-AKI are non-specific, mainly focusing on infection control and preventing further kidney insults. On the other hand, the preventive strategies for sepsis following AKI might focus on decreasing some metabolites, cytokines, or molecules harmful to our immunity, supplementing vitamin D3 for its immunomodulation effect, and avoiding fluid overload and unnecessary catheter cannulation. To date, several limitations persistently prohibit the understanding of the bidirectional pathophysiologies. Conducting studies, such as the Kidney Precision Medicine Project, to investigate human kidney tissue and establishing parameters or scores better to determine the occurrence timing of sepsis and AKI and the definition of SA-AKI might be the prospects to unveil the mystery and improve the prognoses of AKI patients.

Background: The application of large language models in clinical decision support (CDS) is an area that warrants further investigation. ChatGPT, a prominent large language models developed by OpenAI, has shown promising performance across various domains. However, there is limited research evaluating its use specifically in pediatric clinical decision-making. This study aimed to assess ChatGPT’s potential as a CDS tool in pediatrics by evCDSaluating its performance on 8 common clinical symptom prompts. Study objectives were to answer the 2 research questions: the ChatGPT’s overall grade in a range from A (high) to E (low) compared to a normal sample and the difference in assessment of ChatGPT between 2 pediatricians. Methods: We compared ChatGPT’s responses to 8 items related to clinical symptoms commonly encountered by pediatricians. Two pediatricians independently assessed the answers provided by ChatGPT in an open-ended format. The scoring system ranged from 0 to 100, which was then transformed into 5 ordinal categories. We simulated 300 virtual students with a normal distribution to provide scores on items based on Rasch rating scale model and their difficulties in a range between −2 to 2.5 logits. Two visual presentations (Wright map and KIDMAP) were generated to answer the 2 research questions outlined in the objectives of the study. Results: The 2 pediatricians’ assessments indicated that ChatGPT’s overall performance corresponded to a grade of C in a range from A to E, with average scores of −0.89 logits and 0.90 logits (=log odds), respectively. The assessments revealed a significant difference in performance between the 2 pediatricians (P < .05), with scores of −0.89 (SE = 0.37) and 0.90 (SE = 0.41) in log odds units (logits in Rasch analysis). Conclusion: This study demonstrates the feasibility of utilizing ChatGPT as a CDS tool for patients presenting with common pediatric symptoms. The findings suggest that ChatGPT has the potential to enhance clinical workflow and aid in responsible clinical decision-making. Further exploration and refinement of ChatGPT’s capabilities in pediatric care can potentially contribute to improved healthcare outcomes and patient management.

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease in diabetes mellitus. Oxidative stress, insulin resistance and pro-inflammatory cytokines have been shown to play an important role in pathogeneses of renal damage on type 2 diabetes mellitus (DM). Inonotus obliquus (IO) is a white rot fungus that belongs to the family Hymenochaetaceae; it has been used as an edible mushroom and exhibits many biological activities including anti-tumor, anti-oxidant, anti-inflammatory and anti-hyperglycemic properties. Especially the water-soluble Inonotus obliquus polysaccharides (IOPs) have been previously reported to significantly inhibit LPS-induced inflammatory cytokines in mice and protect from streptozotocin (STZ)-induced diabetic rats. In order to identify the nephroprotective effects of low molecular weight of IOP fraction (LIOP), from the fruiting bodies of Inonotus obliquus, high-fat diet (HFD) plus STZ-induced type 2-like diabetic nephropathy C57BL/6 mice were investigated in this study. Our data showed that eight weeks of administration of 10–100 kDa, LIOP (300 mg/kg) had progressively increased their sensitivity to glucose (less insulin tolerance), reduced triglyceride levels, elevated the HDL/LDL ratio and decreased urinary albumin/creatinine ratio(ACR) compared to the control group. By pathological and immunohistochemical examinations, it was indicated that LIOP can restore the integrity of the glomerular capsules and increase the numbers of glomerular mesangial cells, associated with decreased expression of TGF-β on renal cortex in mice. Consistently, three days of LIOP (100 μg/mL) incubation also provided protection against STZ + AGEs-induced glucotoxicity in renal tubular cells (LLC-PK1), while the levels of NF-κB and TGF-β expression significantly decreased in a dose-dependent manner. Our findings demonstrate that LIOP treatment could ameliorate glucolipotoxicity-induced renal fibrosis, possibly partly via the inhibition of NF-κB/TGF-β1 signaling pathway in diabetic nephropathy mice.

Background: Many previous papers have investigated most-cited articles or most productive authors in academics, but few have studied most-cited authors. Two challenges are faced in doing so, one of which is that some different authors will have the same name in the bibliometric data, and the second is that coauthors’ contributions are different in the article byline. No study has dealt with the matter of duplicate names in bibliometric data. Although betweenness centrality (BC) is one of the most popular degrees of density in social network analysis (SNA), few have applied the BC algorithm to interpret a network’s characteristics. A quantitative scheme must be used for calculating weighted author credits and then applying the metrics in comparison. Objective: This study aimed to apply the BC algorithm to examine possible identical names in a network and report the most-cited authors for a journal related to international mobile health (mHealth) research. Methods: We obtained 676 abstracts from Medline based on the keywords “JMIR mHealth and uHealth” (Journal) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were then calculated for the following: (1) the most-cited authors displayed on Google Maps; (2) the geographical distribution of countries/areas for the first author; and (3) the keywords dispersed by BC and related to article topics in comparison on citation indices. Pajek software was used to yield the BC for each entity (or node). Bibliometric indices, including h-, g-, and x-indexes, the mean of core articles on g(Ag)=sum (citations on g-core/publications on g-core), and author impact factor (AIF), were applied. Results: We found that the most-cited author was Sherif M Badawy (from the United States), who had published six articles on JMIR mHealth and uHealth with high bibliometric indices (h=3; AIF=8.47; x=4.68; Ag=5.26). We also found that the two countries with the highest BC were the United States and the United Kingdom and that the two keyword clusters of mHealth and telemedicine earned the highest indices in comparison to other counterparts. All visual representations were successfully displayed on Google Maps. Conclusions: The most cited authors were selected using the authorship-weighted scheme (AWS), and the keywords of mHealth and telemedicine were more highly cited than other counterparts. The results on Google Maps are novel and unique as knowledge concept maps for understanding the feature of a journal. The research approaches used in this study (ie, BC and AWS) can be applied to other bibliometric analyses in the future.

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.