We report a case of accidental Emamectin Benzoate poisoning in a six‐year‐old child resulting in nausea, vomiting, abdominal pain, and confusion. We did vigorous gastric lavage with saline, activated charcoal, and coconut oil. The other supportive treatment improved the outcome of the patient with complete resolution of symptoms. We report a case of accidental Emamectin Benzoate poisoning in a six‐year‐old child resulting in nausea, vomiting, abdominal pain, and confusion. We did vigorous gastric lavage with saline, activated charcoal, and coconut oil. The other supportive treatment improved the outcome of the patient with complete resolution of symptoms. It was accompanied by her parents who had noted her of being alert and playful after returning from school on the same day. The mother while doing her household chores noticed her playing and consuming “JALJEERA” (cumin powder used to fortify beverages) sachets with a glass of water after a regular evening snack. Following to which she started developing repeated bouts of nausea, vomiting, and abdominal pain in next half an hour to an hour and was then brought to ED by her parents. The thirst for the perplexed and not so concluding history of presenting illness got quenched with the multiple probing and discussions with the family members, making them reveal of having several empty sachets at her home. One of the sachets was discovered to be of “LURA” instead of “JALJEERA”. The “LURA” comes as a brand name of a sachet of “Emamectin Benzoate 5% SG” (soluble granules). The packaging of “LURA” was similar to a packet of “JALJEERA” that easily led the child to consume the sachet of “Emamectin Benzoate 5% SG”. A provisional diagnosis of Emamectin Benzoate Poisoning was made.A six‐year‐old girl was brought to the Emergency Department (ED) of Kalaiya District Hospital, Bara, Nepal, with complaints of nausea, vomiting, and abdominal pain at 3:30 Oriented to time, place, and person on examination, she was awake and alert but mildly confused and irritable. Her Glassgow Coma Scale (GCS) score was 13/15 (E3V4M6). Her pulse rate was 94 beats per minute and regular. Her respiratory rate, axillary body temperature, and saturation were 22 per min., 98.4֯F, and 98% respectively. Pupils were isochoric with normal light reflex. The chest was clear on auscultation. Auscultation of the precordium did not reveal a murmur or any other added sound. The abdomen was soft, flat, and without tenderness or rebound tenderness.3), platelet 170,000 cells/mm3 , Na+ 136 mEq/L (reference range: 135–145 mEq/L), K+ 4.2 mEq/L (reference range: 3.3–5.3 mEq/L), serum blood‐urea‐nitrogen 12.3 mg/dL (reference range: 6–20 mg/dL), creatinine 0.9 mg/dL (reference range: 0.6–1.2 mg/dL), and random blood sugar 82 mg/dL (reference range: 60–100 mg/dL). The urinalysis was normal without any casts or crystals. The chest X‐ray was normal without any infiltrates or adenopathy. The ECG (electrocardiogram) showed normal sinus rhythm. The arterial‐blood‐gas analysis showed pH 7.38 (reference range: 7.35–7.45), PaCO2 42 mm Hg (reference range: 35–45 mm Hg), HCO3 25 mEq/L (reference range: 22–26 mEq/L), and serum lactate 0.8 mmol/L (reference range: 0.7–2.5 mmol/L).The laboratory investigations revealed hemoglobin 13.8 mg/dL (reference range: 12–16 mg/dL), white blood cells count 7700 cells/mm3  injection of pantoprazole (@ 20 mg IV stat), ketorolac (@ 15 mg slow IV stat), ondansetron (@ 2 mg IV stat), and hydrocortisone (@ 50 mg slow IV stat) for symptoms management. 30 min following gastric lavage and emergency observation for 6 h, she was shifted to the general medical ward on the same day.In the medical ward, the nasogastric tube was continued in situ for 24 h. With IV maintenance fluids, she was kept nil per oral (NPO) for next 24 h. The medications pantoprazole (@ 20 mg IV once a day), and ondansetron (@ 2 mg IV three times a day) were continued on the first day withholding the hydrocortisone. The ketorolac (@ 50 mg IV) was planned to give when needed. Vitals  and systemic examinations  were assessed four‐hourly in the ward and were noted to be normal.The next day of inpatient admission, her symptoms like nausea, vomiting, and abdominal pain improved, and she was more alert and playful. The nasogastric tube was removed, and IV fluids and medications were stopped. Regarding the diet, the oral sips were allowed, followed by liquid, which was gradually advanced to a semisolid and solid diet. Also, the oral medications  were continued.On the third day of admission, she was discharged with proper counseling on the same oral medications for 3 more days, and ORS, and advised to follow‐up after 1 week in the clinic.In follow‐up, after 1 week, she did not have any presenting complaints. Vitals and systemic examinations were within the normal limit. Neuropsychiatric evaluation was done which was normal.3LURA” , that is, 250 mg/kg of EB. Clinical manifestations were limited to a mild disturbance of consciousness and gastrointestinal distress.In this case, the patient  ingested a packet of “The clinical presentations of EB poisoning involve central nervous system dysfunction and gastrointestinal upset. However, mammalian species are less sensitive to it due to lower GABA affinity and relative impermeability of the blood–brain barrier.As EB has no specific antidote, the management of this poisoning is always supportive and symptomatic. As it possesses GABA mimetic activity, the drugs that enhance GABA activity like benzodiazepines, barbiturates, and valproic acid should be avoided.There is paucity of literature regarding the complete spectrum of manifestations of EB poisoning in human. Similarly, there are no consensus recommendations for the management of EB poisoning. The EB is available as 5% SG. In view of its water solubility nature, we used both coconut oil and activated charcoal for gastric lavage. We assumed that the coconut oil might coat the gastric mucosa to delay the absorption of EB and may additionally prevent damage to the raw mucosa because of anti‐ulcer nature of coconut oil in some studies.In this case, apart from the gastric lavage, symptomatic and supportive management was provided. This was a mild case of poisoning in which the patient improved quickly. It is important to label and store chemicals like this safely to minimize harm by accidental ingestion.4In the case of Emamectin Benzoate poisoning, vigorous gastric lavage with both activated charcoal and coconut oil can improve the patient's outcome. The rest of the treatment is symptomatic and supportive. Unfortunately, there is no specific antidote for this poisoning.GKY was involved in management of patient and in the conception of the report, literature review, initial manuscript preparation, editing and submission. DKR, KM, BP, and ARD were involved in the manuscript critique and review, and final manuscript preparation. All authors have read and approved the final manuscript.This study did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors.The authors declare no conflicts of interest.Not applicable.Written informed consent was obtained from the parent for publication of this case report. A copy of the written consent is available for review by the Editor‐in‐Chief of this journal on request.


The predictive value of NT‐proBNP,d‐dimer, and LAD in identifying the occurrence of IS in NVAF was determined by plotting the receiver operating characteristic (ROC) curves.We conducted a retrospective study of 445 patients with NVAF in the First Affiliated Hospital of Nanchang University. They were divided into the NVAF (309 cases) and NVAF with stroke (136 cases) groups according to whether acute ischemic stroke (AIS) occurred at admission. Multivariate logistic regression was used to analyze the odds ratio (OR) of NT‐proBNP, d‐dimer, and LAD levels were significantly higher in the NVAF with stroke group than in the NVAF group (p < .05). NT‐ProBNP,d‐dimer, and LAD were independently associated with IS in NVAF patients . The optimal cutoff points for NT‐ProBNP,d‐dimer, and LAD levels to distinguish the NVAF group from the NVAF with stroke group were 715.0 pg/ml, 0.515 ng/ml, and 38.5 mm, respectively, with the area under the curve (AUC) being . The combined score of NT‐proBNP,d‐dimer, and LAD improved the predictive efficacy of the single index, with an AUC of 0.846 , sensitivity of 77.2%, and specificity of 76.4%.NT‐proBNP, d‐dimer, and the echocardiographic parameter LAD have outstanding value in predicting the risk of IS in patients with NVAF.NT‐proBNP,  PAULIN et al.d‐dimer, with the general clinical characteristics of patients, and identified the potential value of hematological parameters in predicting the occurrence of IS in AF. Due to the complex process of thromboembolism in patients with NVAF, current research focuses on different markers. However, in view of their extensive clinical use, easy access, and promotion, they are still highly beneficial in guiding clinical work.Previous studiesd‐dimer, and LAD in the early prediction of NVAF‐related IS. This provides a reliable basis for early prevention and treatment of NVAF‐related IS.This study aimed to investigate the risk factors for NVAF‐related IS and analyze the potential value of NT‐proBNP, 22.1This retrospective study enrolled 445 patients  diagnosed with NVAF at the First Affiliated Hospital of Nanchang University between June 2020 and July 2022, who met the inclusion criteria. According to whether AIS occurred at admission, there were 309 and 136 cases in the NVAF and NVAF with stroke groups.The inclusion criteria were as follows: 1) AF determined by past medical history, admission with 12‑lead electrocardiography (ECG) or 24‐h Holter monitoring, and nonvalvular diagnosis detected by cardiac ultrasound; (2) IS, including the current onset or previous history of IS, with a definite diagnosis of “AIS” by magnetic resonance imaging (MRI) on admission, but with other causes of IS excluded. The exclusion criteria were as follows: (1) cerebral hemorrhage, history of trauma surgery, or acute myocardial infarction combined with other types of arrhythmias within the last 3 months; (2) patent foramen ovale, valvular AF, rheumatic mitral valve disease, dilated cardiomyopathy, hypertrophic cardiac disease, and so forth; and (3) severe infection, severe liver and kidney dysfunction, thyroid disease, and malignant tumors. A flowchart of the entry process is shown in Figure  AF deter2.2d‐dimer, fibrinogen, HsT, lipid quadruple, creatinine, uric acid, homocysteine, C‐reactive protein (CRP), and all test results obtained from the first venous blood test after admission and before treatment; (4) echocardiographic parameters, such as LAD, left ventricular end‐diastolic dimension (LVDd), and left ventricular ejection fraction (LVEF); and (5) CHA2DS2‐VASc score of all patients admitted.Patients who met the inclusion and exclusion criteria during hospitalization were analyzed by reviewing their medical records and collecting corresponding materials, including (1) basic demographic characteristics, such as sex, age, and body mass index (BMI); (2) medical or drug history, such as smoking, hypertension, diabetes mellitus, coronary heart disease (CHD), previous IS, antiplatelets, and anticoagulants; (3) serological indicators, such as NT‐proBNP, 2.32). Smoking is described as a total of ≥100 cigarettes in their lifetime, regardless of whether they have quit smoking now.NVAF was defined as AF without mechanical valves or moderate‐to‐severe mitral stenosis . Patients with NVAF included all three types of AF  with arbitrary durations.2.4The CHA2DS2‐VASc score was calculated as congestive heart failure, hypertension, diabetes mellitus, vascular disease, age 65–74 years, and female sex; each of the above was scored as 1 point. A history of stroke, transient ischemic stroke, or age ≥75 years was scored as 2 points2.5t‐test for independent samples was used to compare two continuous variables with a normal distribution, and the Mann–Whitney U test was used to compare two continuous variables with a non‐normal distribution. Univariate and multivariate logistic regression analyses were used to explore independent risk factors for the development of IS in patients with NVAF. Receiver operating characteristic (ROC) curves were used to determine the predictive ability of CHA2DS2‐VASc, NT‐proBNP, d‐dimer, and LAD levels in patients with NVAF. Their optimal cutoff values, sensitivities, and specificities were calculated. Statistical significance was set at a two‐tailed p‐value < .05. Statistical analyses were performed using SPSS software .Continuous variables with a normal distribution are described as mean ± standard deviation, while continuous variables with a non‐normal distribution are represented by quartiles. Categorical variables are summarized as frequencies and percentages. Categorical variables were compared using the Chi‐squared test or Fisher's exact test. The Student's 33.1d‐dimer, fibrinogen, CRP, NT‐proBNP, HsT, LAD, and LVDd were all higher in the NVAF with stroke group (p < .05). In the CHA2DS2‐VASc score ≥ 2 subgroup segment, the within‐group percentage was higher in the NVAF with stroke group . In particular, the levels of NT‐proBNP, d‐dimer, and LAD in the NVAF with stroke group were significantly higher than those in the NVAF group . Patients in the NVAF with stroke group had lower LVEF  and less anticoagulant use (19.9% vs. 31.1%) than those in the NVAF group.We analyzed the baseline data of 445 patients with NVAF, of which 247 (55.5%) were males and with a mean age of all patients of 71.3 ± 10.7 years. A total of 309 patients were included in the NVAF group and 136 patients were included in the NVAF with stroke group. The clinical characteristics of the two groups are shown in Table 3.2d‐dimer, and echocardiographic parameter LAD were included in the logistic regression analysis .Transformed NT‐proBNP (100‐fold increments), is Table . After a3.3d‐dimer, LAD, and CHA2DS2‐VASc scores had a high diagnostic value for the development of IS in patients with NVAF . The optimal cutoff value for NT‐ProBNP was 715 pg/ml, with a sensitivity of 72.1% and specificity of 25.9%. The optimal cutoff values for d‐dimer, LAD, and CHA2DS2‐VASc scores were 0.515 ng/ml, 38.5 mm, and 3.5 points, respectively, with sensitivities of 82.4%, 90.4%, and 69.1% and specificities of 61.5%, 55.3%, and 61.2%, respectively. NT‐ProBNP and d‐dimer levels combined with LAD improved the predictive value of a single indicator  with a sensitivity of 77.2% and a specificity of 76.4%. Compared with the CHA2DS2‐VASc score, the AUC, sensitivity, and specificity of the combined index were higher .ROC analysis was used to evaluate the predictive value of the risk factors for the occurrence of IS events in patients with NVAF. As shown in Figure d‐dimer, and LAD  were older, had a higher incidence of CHA2DS2‐VASc score ≥ 2 points , and had a significantly higher risk of IS .In addition, the included patients were divided into two groups according to the optimal cutoff values of NT‐proBNP, D Tables  and S3. 4d‐dimer, and the echocardiographic parameter LAD for the development of IS in NVAF patients who were at a high risk for cardiogenic stroke. The results showed that NT‐proBNP, d‐dimer, and LAD levels were significantly higher in NVAF patients with stroke than in those without stroke. Increased levels of NT‐proBNP, d‐dimer, and LAD are of great value in confirming the diagnosis of IS and can be used as potential predictors of IS in NVAF patients.Our research investigated the potential predictive value of NT‐proBNP, BNP is produced primarily by cardiomyocytes in response to increased end‐diastolic pressure and/or volume expansion and then enzymatically cleaved to NT‐proBNP.d‐dimer has been extensively studied as an indicator of thrombosis.d‐dimer are elevated compared with matched controls in sinus rhythm and appeared to remain elevated despite successful cardioversion, demonstrating a correlation between d‐dimer and NVAF.d‐dimer levels also had a higher risk of IS, transient ischemic attack, and arterial thrombotic events. Numerous studiesd‐dimer level is elevated during acute stroke, is associated with stroke subtypes and volume, and is significantly elevated in cardioembolic ischemic stroke. In recent years, prospective studiesd‐dimer levels were correlated to acute stroke, and can be a valuable and independent short‐term prognostic marker for acute stroke. Therefore, assessment of d‐dimer levels is of great value in the early prevention of IS events in patients with NVAF. The incidence of stroke was also significantly higher in the d‐dimer level ≥ 0.515 ng/ml group in our study .Among the coagulation indicators in patients with AF, plasma A meta‐analysis of 22 clinical studies by Njoku et al.d‐dimer), which will likely contribute to the refinement of the CHA2DS2‐VASc score and be useful for the early benefit of NVAF patients. The present study also has some limitations. First, this was a single‐center retrospective study with limited sample size; a large multicenter study is required to reduce this bias. Second, considering that this is an observational research, further prospective studies are needed to measure the true value of these indicators.Despite widespread clinical use, there are limitations in the ability of the CHA2DS2‐VASc score to predict the risk of IS in patients with NVAF. Evidence5d‐dimer, and LAD are reliable biomarkers for detecting the occurrence of IS and can be used as independent predictors of IS in patients with NVAF. NT‐proBNP and d‐dimer levels combined with LAD had higher sensitivity and specificity in predicting IS in NVAF than the CHA2DS2‐VASc score. The results of this study will help further screen for NVAF patients at a higher risk of IS and help clinicians administer anticoagulation therapy as soon as possible.This study indicates that NT‐proBNP, Zican Shen and Dong Chen analyzed the data, drafted the manuscript, and were the major contributors in writing this manuscript. Hao Cheng and Feng Tan collected data. Wei Fang, Sunan Wang, Jianwei Yan, and Haiming Deng assisted with the research and analysis. Jianbing Zhu conceived and developed the study, provided financial support, and performed analytical revisions and review of the article.The authors declare no conflicts of interest.Supporting information.Click here for additional data file.


Like most 3D cultures, organoids lack sufficient oxygen supply and therefore experience cellular stress. These negative effects are particularly prominent in complex models, such as brain organoids, and can affect lineage commitment. Here, we analyze brain organoid and fetal single‐cell RNA sequencing (scRNAseq) data from published and new datasets, totaling about 190,000 cells. We identify a unique stress signature in the data from all organoid samples, but not in fetal samples. We demonstrate that cell stress is limited to a defined subpopulation of cells that is unique to organoids and does not affect neuronal specification or maturation. We have developed a computational algorithm, Gruffi, which uses granular functional filtering to identify and remove stressed cells from any organoid scRNAseq dataset in an unbiased manner. We validated our method using six additional datasets from different organoid protocols and early brains, and show its usefulness to other organoid systems including retinal organoids. Our data show that the adverse effects of cell stress can be corrected by bioinformatic analysis for improved delineation of developmental trajectories and resemblance to in vivo data.Organoids enable  A unique stress signature found in in brain organoid samples but not in fetal samples can be quantified and removed from scRNAseq datasets using a new algorithm. Gruffi can clarify developmental trajectories and increase similarity of in vitro datasets to the fetal brain.We found stressed cells in all organoid samples, forming a distinct subpopulation. Beyond stress pathway activity, stressed cells showed widespread transcriptional changes that we refer to as the “stressed‐state.” We do not find this stressed state et al, et al, et al, et al, et al, We reanalyzed recent, landmark single‐cell transcriptomics studies and performed new experiments , XBP1, DDIT4, P4HB , and hypoxia: PDK1, PHD, GLUT1  map Fig . We highet al, et al, et al, et al, While the initial clustering‐based approach identified cells with stress signatures, it had three major limitations. First, while some clusters are too large and comprise mixed populations  and stressed cells identified on single‐cell scores (scSC). We contrasted cells only identified by either, both or neither of the approaches, thus validating whether top stress markers alone is superior in filtering stressed cells. scSC were evenly scattered across all clusters, while most gSC were close to the previously identified stress clusters and the cells identified by both methods .As all samples had stress‐identity cells, we searched for their defining features and their consequences on the whole organoid. Stressed cells fell in two distinct clusters with either a more glial or a more neuronal character Fig . We therP.adj < 1e‐100 in all cases). At the same time, the groups scored similarly for “autophagy” , and as for mitophagy, found that stressed cells scored positively, while normal cells scored negatively Fig . AltogetZ‐score normalized activity to each pathway  and a progenitor population II, Fig . To testet al, in vitro artifact, and there is minimal to no stress‐identity in vivo.Our analyses focused on mature organoids, in which hypoxia is a well‐known problem. To test whether similar stress is indeed not present in the human brain, we additionally investigated published samples of early brain development , the cells can be identified, studied and removed. Here, we applied Gruffi to remove stressed cells from brain organoid datasets, but we think that there are many other applications possible, such as selecting cells from a lineage or cells responding to a treatment. Currently, brain organoids are the largest and longest‐cultured 3D organoid systems and are therefore particularly affected by stress. As 3D tissue models and investigations become ever more sophisticated, cell culture‐induced artifacts are more important to account for. Therefore, we expect that our approach will find many applications beyond its original scope.et al, HipSci guidelines. We also grew organoids from the feeder‐free human ES cell line . The two iPSC Lines SCCF – 177  and SCCF – 178  were generated at the IMBA Stem Cell Core Facility and are part of the IPSC Biobank. The study was approved by the local ethics committee of the Medical University of Vienna (MUV). After informed consent, a skin biopsy was taken from three healthy donors, and fibroblasts were isolated for iPSC reprogramming. iPSC lines were generated using the Sendai virus  carrying the Yamanaka reprogramming factors OCT3/4, SOX2, c‐MYC and KLF4 factors. All cell lines were used within 10 passages from last STR profiling and tested regularly for mycoplasma contamination. We additionally used the above cell lines (177 and 178) for the media comparison experiments  line from the HipSci catalog  with 1% N2 Supplement (Thermo Fisher Scientific), 1% MEM‐NEAA (Sigma Aldrich), 1% Glutamax (Thermo Fisher Scientific) and 1 μg/ml Heparin. Imp‐A: of 50% DMEM/F12 (Thermo Fisher Scientific), 50% Neurobasal (Thermo Fisher Scientific), 0.5% N2 Supplement (Thermo Fisher Scientific), 2% B27—Vitamin A (Thermo Fisher Scientific), 1% Glutamax (Thermo Fisher Scientific), 0.5% MEM‐NEAA (Sigma Aldrich), 50 μM 2‐ME solution, 1% Penicillin/Streptomycin (Sigma Aldrich) and 0.025% Insulin solution (Sigma Aldrich). Imp + A (HN): Imp‐A with 2.5 mM Ascorbic Acid, 2 g/l Bicarbonate (Sigma Aldrich). BP (LN): BrainPhys Neuronal Medium (Stem Cell Technologies), 2% B27 + A , 1% N2 supplement (Thermo Fisher Scientific), 200 nM Ascorbic Acid (Sigma Aldrich), 0.2% CD Lipid Concentrate (Thermo Fisher Scientific), 7.4% glucose, and 1% Penicillin/Streptomycin.Organoids were cultured to 120 days, then washed in PBS and dissociated using the gentleMACS Dissociator (Miltenyi Biotec) in program NTDK1 using the enzyme mix: Trypsin (Sigma Aldrich)/Accutase  containing 10 U/ml DNaseI (Thermo Fisher Scientific). The washed cell suspension was passed through a 70 μm cell strainer.et al, et al, In the newly generated datasets, we pooled cells from samples from four different genotypes and were combined in a lane (other cell lines used for other purposes). We additionally used sample barcoding using lipid‐anchor barcoding following instructions as in  and processed through the Chromium controller to generate single‐cell GEMs (Gel Beads in Emulsion). scRNA‐seq libraries were prepared with the Chromium Single Cell 3′ Library & Gel Bead Kit v.3 . Ready 10× libraries were sequenced paired end  on NovaSeq (Illumina).et al, et al, ENA PRJEB33917  using pre‐mRNA gene models and default parameters to produce the cell‐by‐gene, unique molecular identifier (UMI) count matrix. UMI counts were then analyzed in R, using the Seurat v4. We filtered for high‐quality cells based on the number of genes detected (> 500). Thereafter, expression matrices of high‐quality cells were normalized  and scaled to a total expression of 10 K UMI for each cell. Regression of variables at this step did not improve clustering results; hence, no variables were regressed or removed.et al, Before integration datasets were checked for quality, as certain IPS lines are prone to mis‐differentiation. As a consequence, multiple datasets included non‐telencephalic cells that would interfere with the CCA integration; henceforth, we applied initial filtering for CNS cells . We found that these cells correspond to one cluster coming from one dataset . Next, we aligned and merged sequencing libraries by Seurat's canonical correlation analysis or CCA  graph of cells in PCA‐space (dimensions:50). Based on Jaccard similarity scores on the knn graph, the shared nearest neighbor (snn) graph is computed. Louvain clustering on the snn graph identified clusters of cells. Differentially expressed genes were identified by Wilcoxon‐test, and filtered for We found a group of 1,304 interneurons that formed a separate cluster on the very top of the UMAP  codung gene count. Where “expected” is the number of proteins with a given annotation that is expected in a random gene set of the same size.GO‐term enrichment was performed on coding, differentially expressed genes, using the STRING toolkit , and normal bins. Cells corresponding to each class were visualized on the umap and contrasted to stressed cells. The same analysis was repeated for ribosomal transcripts (RPL* and RPS* genes). Cells with very‐high (> 30%) ribosomal content was visualized on the UMAP of the unfiltered integrated object. This cutoff was used to generate  ribosomal content was visualized on the UMAP of . Gruffi functions encompass the following major steps as in Fig : (1–3) Aet al, et al, AddModuleScore). Briefly, in the AddModuleScore function, the following steps have been implemented as in the original: (i) Take a target set of genes; (ii) Calculate their average expression; and (iii) Create control sets of genes. The control gene‐sets are used to control for the cell‐to‐cell variability in quality and depth. To create the control sets, first all genes are binned by expression levels (25 bins), then for each gene in the target set, randomly select 100 genes from its expression bin, and finally (iv) subtract the average of control from each target gene. The expression binning is important, because expression levels affect the variability of gene expression  that results in a median of 100–200 cells per granule, which we call the optimal resolution, as clusters contain a statistically robust number of cells. All these parameters are also manually customizable. Next, clustering is performed at the determined resolution, resulting in cells separated into 100's of granules, depending on the size of the dataset. Finally, all cells in granules with < 30 cells are reassigned to the nearest cluster center in the 3D UMAP space (Euclidean distance) using reassign.small.clusters.Gene detection in single cells is noisy. To overcome this noise, we grouped cells into small clusters  by high‐resolution snn‐clustering . Gruffi's Shiny.GO.thresh. For this, we refer to cell number normalized, mean GO scores per granule. In the following this will be referred to as granule score. We assume that: (b1) The granules consist of a statistically sufficient number of cells. (b2) GO scores of non‐stressed cells independently follow the same unknown distribution. (b3) GO scores of stressed cells are significantly higher than GO scores of non‐stressed cells. (b4) The dataset consists of more non‐stressed than stressed cells. Assumptions (b1) and (b2) together with cell number normalization allow us to use the central limit theorem and hence fit a normal distribution to the granule scores of non‐stressed granules. Based on (b3) and (b4) we conclude that respective non‐stressed GO‐scores are small, and the mean of the normal distribution can be estimated by the median of granule scores. The standard variation of the normal distribution is now estimated only w.r.t. to GO scores smaller than the median of granule scores. Now, the theoretical 99% quantile of the fitted normal distribution can be computed and used as an upper threshold.Finally, the average GO‐scores for each granule were calculated, and stress level per granule was evaluated. We propose two possible methods to estimate an upper threshold for the assignment of stressed granules for one GO term. (a) Determining manually, based on the expression of stress genes and stress score values on UMAP. Based on these, an empirical quantile (90% if observing 10% stressed cells) as threshold can be assigned. (b) Automatic stress threshold estimation by Assumption (b1) is fulfilled by the automatic clustering resolution search and the reassignment of cell granules with less than 30 cells. Although we based our analysis on thresholds retrieved by this method, as we cannot assure that assumptions (b2) to (b4) hold true, we highly recommend further inspections and refinements of suggested thresholds in any case. To do so, we propose to visually monitor further manual adjustments via the implemented Shiny App interface.When considering a combination of GO terms, for example, response to endoplasmic reticulum stress and glycolytic process, we combine the respective thresholds such that a cell is assigned as stressed if either upper threshold is crossed. In case one additionally wants to include a GO term for non‐stressed cells, for example, gliogenesis, the above threshold method can be applied, too, but in this case, granules with a score higher than the threshold are assigned as non‐stressed. Finally, based on the thresholds on each score, stressed cells are annotated and can be excluded from the dataset.et al, permalink: bxso1NJafq8R).We selected all genes enriched in either stress clusters, and jointly ranked them by descending log2FC. We selected the top 150 coding genes, and visualized the “high‐confidence” connected component of the protein–protein interaction network using the STRING database  were provided for ShinyGO v0.741  were then analyzed in STRINGdb as before .We obtained the Seurat R object of , which would physiologically be an intermediate state.As previously (Bhaduri find_gene_modules function. A module for upper layer and deep layer neurons was selected for each mature dataset (Dataset x) versus the expression of the respective score (y).For analysis of the “dorsal lineage maturation,” datasets of pairwise H‐ and L‐medium organoids were integrated as outlined above. The datasets were then transferred to monocle3 and UMAP was calculated with three dimensions. The trajectory graph was constructed on the three‐dimensional dataset from progenitors to mature neurons. To compare the maturation of gene expression modules of co‐regulated genes were calculated with the  Dataset . To plotÁbel Vértesy: Conceptualization; data curation; software; formal analysis; supervision; funding acquisition; validation; investigation; visualization; methodology; writing – original draft; project administration; writing – review and editing. Oliver L Eichmüller: Conceptualization; resources; data curation; formal analysis; validation; investigation; visualization; methodology; writing – original draft; writing – review and editing. Julia Naas: Data curation; software; formal analysis; validation; investigation; visualization; methodology; writing – review and editing. Maria Novatchkova: Data curation; formal analysis; validation; investigation; methodology. Christopher Esk: Resources; supervision; funding acquisition; investigation. Meritxell Balmaña: Resources; investigation. Sabrina Ladstaetter: Validation; investigation. Christoph Bock: Resources; supervision; funding acquisition; writing – review and editing. Resources; supervision; funding acquisition; writing – review and editing. Juergen A Knoblich: Conceptualization; resources; supervision; funding acquisition; project administration; writing – review and editing.CRediT author contributions listed above, the contributions in detail are:In addition to the A.V., O.L.E., and J.A.K. conceived, and J.A.K. and A.H. supervised the project. A.V. and J.N. developed the stress identification algorithm. Data analysis was performed by A.V., O.L.E., J.N., and M.N. Organoids were grown by C.E. and M.B.E., and sequencing libraries were prepared by A.V. and S.L. A.V. and O.L.E. wrote the manuscript with input and editing from J.A.K. and J.N. Funding acquisition: J.A.K., A.v.H., C.B., and A.V.aheadbio.com). J.A.K. is an inventor on several patents relating to cerebral organoids. The authors declare that they have no conflict of interest.J.A.K. is on the supervisory and scientific advisory board of a:head bio AG (Appendix S1Click here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.


Muribaculaceae was increased, whereas that of Lachnospiraceae, a family of butyrate–producing bacteria, was decreased. The combined serum metabonomics and gut microbiome datasets illustrated the involvement of butanoic acid and energy metabolism in the regulatory changes of the gut–liver axis associated with MXF–induced liver injury. The regulation of endogenous small molecules and intestinal flora during drug–induced liver injury was first described from the perspective of the gut–liver axis, providing a research basis for the mechanism of clinical drug–induced liver injury.Cases of unpredictable, idiosyncratic liver damage of moxifloxacin (MXF) have been occasionally reported. However, the health effects of MXF exposure remain controversial. The current study examined the metabolic phenotypes and intestinal flora characteristics of hepatotoxicity induced by MXF. Rats were administered moxifloxacin hydrochloride tablets at doses of 36, 72, and 108 mg/kg body weight/day for 21 days. The levels of tricarboxylic acid cycle intermediates were decreased, whereas those of lipids  were increased, reflecting disorders of energy–related and lipid metabolism. Enrichment analysis of the differential metabolites suggested that butanoate metabolism was associated with MXF–induced liver injury. 16S rRNA sequencing uncovered that the diversity of gut intestinal was decreased in MXF–treated rats. Specifically, the abundance of  Liver injury induced by both prescription and over–the–counter drugs is a growing public health problem. Antibiotics represent a relatively frequent cause of acute noninfectious liver injury in the general population . FluoroqThe application of moxifloxacin (MXF), a fluoroquinolone used to treat respiratory, reproductive tract, and cutaneous infections, has increased in recent years , 5. An AMetabonomics provides a method platform and basis by which multiple environmental toxicants might interact with a host organism to produce their overall phenotype . NeverthEscherichia coli colonization [Broad–spectrum antibiotics have a profound and long–lasting impact on the composition of the microbiota, including losses of diversity and shifts in community composition , 19. In nization . HoweverOscillospira and Ruminococcus counts [Lactobacillus, Bacteroides, and Enterobacteriaceae were more sensitive to tacrine–induced hepatotoxicity, which provided insights for personalized medicine initiatives [Combining metabonomics and other omics provides a promising strategy for obtaining mechanistic insights into various impacts on the microbial community and, in the longer term, on host outcomes. Ishii et al. explored the effects of the American diet on the intestinal environment in mice using an integrated metabonomics and microbiome analysis approach and described positive correlations among increased levels of butyrate, the relative abundance of butyryl–CoA/acetate–CoA transferase, and s counts . An intetiatives . The exiWe speculated that orally administered MXF might affect the gut–liver axis in rats and thereby induce hepatotoxicity as indicated by metabolic disorders and gut microbe dislocation. In the present study, an integrated metabonomics and intestinal microflora analysis approach were developed for comprehensive metabolite profiles and the structure and abundance of the gut microbiota in rats treated with MXF using liquid chromatography–high resolution mass spectrometry (LC–HRMS) and 16S rRNA sequencing analysis. Our study first verified the potential phenotypes of metabolites and intestinal microbes involved in the gut–liver axis and examined hepatotoxicity induced by moxifloxacin hydrochloride tablets.4OAc) were purchased from Merck . Ultrapure water was provided by Wa–Haha Corporation . Moxifloxacin hydrochloride tablets were produced by Bayer Pharma AG . Anticleaved caspase–3 antibody was obtained from Abcam (ab2302).Analytical–grade methanol (MeOH), acetonitrile (ACN), isopropanol (IPA), and methyl tertiary butyl ether (MTBE) were purchased from Fisher Scientific Co., Ltd . Formic acid (FA) and ammonium acetate  were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (China). The experimentation on the animal was approved by the Animal Ethics Committee of Beijing Chao–Yang Hospital affiliated with Capital Medical University (China). The animals were maintained in an air–conditioned room at a temperature of 24 ± 2°C, relative humidity of 50 ± 5%, and a 12 h/12 h light/dark cycle. Water and food were available ad libitum during the entire experiment. After 1 week of adaptation to laboratory conditions, the rats were randomly divided into four experimental groups (24 rats/group). Moxifloxacin hydrochloride tablets were prepared as suspensions in distilled water. According to the commonly used clinical dose, suspensions of moxifloxacin  were administered to rats via oral gavage daily. The flow chart of the experimental research framework is presented in g and 4°C for 5 min and immediately used for biochemical analysis. Fecal samples were carefully removed from the colon, collected in cryogenic vials, and snap–frozen in liquid nitrogen at once. Then, the serum collected for metabonomics and colonic content collected for 16S rRNA gene sequencing were stored at -80°C. Liver tissues were fixed in 4% paraformaldehyde for pathological examination. The liver tissues were trimmed, embedded in paraffin wax, and then sectioned. The slices were stained with hematoxylin and eosin (H&E) for histopathological examination.Male Sprague–Dawley rats , aspartate aminotransferase (AST), alkaline phosphatase (ALP), total bilirubin (TBIL), superoxide dismutase (SOD), and malondialdehyde (MDA). Serum biochemical analyses were performed using a clinical biochemistry analyzer  at Beijing De–Yi Biotechnology Co., Ltd. (China). Liver oxidative stress biomarkers were examined using visible spectrophotometers .Tissue cell apoptosis was detected using a TUNEL fluorescence assay kit. Liver sections were treated with 0.3% Triton X–100, and after washing, the TUNEL detection solution was added for 1 h. Nuclei were counterstained with DAPI. Antifluorescence quenching sealing agents were added. The samples were visualized by confocal microscopy.μm and dewaxed in xylene and rehydrated in descending concentrations of ethanol. Then, sections were processed for antigen retrieval by incubation in 10 mM sodium citrate buffer (pH = 6.0) containing 0.05% Tween 20 at 95°C for 10 min, washed twice with 0.1% Triton X–100 in PBS, and blocked for 45 min in 3% donkey serum in 0.1% Triton X–100 in PBS. Tissue slices were incubated overnight at 4°C with a primary antibody targeting cleaved caspase–3, followed by incubation with HRP labeled goat anti–rabbit secondary antibody for 1 h and washed three times with PBS. Finally, sections were incubated with CY3–TSA in the dark for 30 min and washed three times with TBST, then bathed in antigen repair solution (pH = 8.0) in a microwave to remove the primary and secondary antibodies bound to the tissue. After immunostaining, sections were counterstained with 5 μg/mL DAPI for 10 min in the dark at room temperature and mounted with antifade mounting medium . HistoQuest image analysis software was used to differentiate cleaved caspase–3 and DAPI–positive cells. Tissue virtual microscopy analysis was then performed using the soft system.IF–TSA  staining was used for the simultaneous labeling of proteins. Paraffin–embedded liver tissue was cut to a thickness of 5 μL aliquot of serum, 750 μL of MeOH and 750 μL of MTBE were added to precipitate proteins and extract the metabolites. The mixture was vortexed for 5 min and then centrifuged at 15,000 rpm . The supernatant was transferred and added to 600 μL of H2O and 750 μL of MTBE. The upper organic portion and lower aqueous portion were separated via vortexing and centrifuged at 15,000 rpm . The polar and nonpolar metabolites were derived from the aqueous and organic phases, respectively. The supernatant was transferred and evaporated to dryness. The polar and nonpolar residues were redissolved in 50 μL of ACN/H2O  and 50 μL of MeOH/CHCl3 , respectively, vortexed for 5 min, and then centrifuged at 15,000 rpm . The supernatant was collected for further analysis.To each 50 3 column , and the temperature was set at 40°C. The mobile phases for polar metabolite detection consisted of 0.1% FA in water (mobile phase A) and ACN (mobile phase B). The mobile phases for nonpolar metabolite detection consisted of 0.1% FA and 2 mmol NH4OAc in water (mobile phase A) and 0.1% FA and 2 mmol NH4OAc in ACN/IPA . The flow rate and autosampler were set at 0.25 mL/min and 4°C, respectively. The injection volumes of the positive and negative electrospray ionization (ESI) modes were set as 5 μL. The gradient conditions were as follows: 0–3 min, linear gradient of 2%–20% B; 3–10 min, linear gradient of 60% B; 10–15 min, 60%–100% B; and 15–20 min, 100% B. The column was equilibrated for 8 min before injection.Untargeted metabonomic analysis relying on a Dionex UltiMate 3000 HPLC system  coupled with a Q–Orbitrap mass spectrometer  via a heated electrospray source. Chromatographic separation was performed on a reversed–phase ACQUITY UPLC HSS Tm/z 66.6–1000.0; spray voltage, 3.5 kV; capillary temperature, 350°C; auxiliary gas heater temperature, 220°C; sheath gas flow rate, 40 psi; auxiliary gas flow rate, 11 Arb; and sweep gas flow rate, 0. Atomized gas and collision gas used for high–energy collision dissociation is high–purity nitrogen (N2). The data–dependent MS/MS (dd–MS2) method was followed by full–scan MS. The operational parameters for targeted MS/MS were as follows: resolution, 17,500; scan range, m/z 66.6–1000.0; automatic gain control target, 1e−6; max injection time, 100 ms; and isolation window, 3 m/z. The collision energies were set at 15, 30, and 45 eV.The detailed parameters for MS were as follows: scan mode, full scan; resolution, 70,000; scan range, m/z format using Mass Matrix MS Data File Conversion software (http://www.massmatrix.net). Then, using XCMS software, peak recognition, filtering, alignment, and scaling were conducted, and a two–dimensional data matrix including m/z, the retention time, and the peak area was acquired. For polar extracts, Mummichog [http://www.metaboanalyst.ca/). MATLAB was further used to analyze the ratio of directly related metabolites in the metabolic network generated by the aggregation of metabolic pathways for which gamma p < 0.05. For nonpolar extracts, mass spectra data were imported into SIMCA–P for orthogonal partial least squares discriminant analysis (OPLS–DA) analysis and intergroup differential metabolite screening . The metabolites obtained after screening were identified using the Lipidmaps database (https://www.lipidmaps.org/) according to the precise mass number.Raw LC–HRMS data were converted to the ummichog , 29 was μL of template DNA (10–50 ng/μL), 12.5 μL of KAPA HiFiHotStart Ready Mix , and 25 μM of each primer  in a 25 μL reaction volume. The first PCR program setup consisted of the following steps: 3 min at 95°C; 25 cycles of 30 s at 95°C, 30 s at 55°C, and 72°C for 30 s; and then 5 min at 72°C. The conditions for the second PCR were the same as that of the first PCR, and the sequencing adaptors and primers were attached to the amplicon library with eight cycles. Purification of the amplicon products was performed using of Agencourt AMPure XP Beads . The amplicon products were quantified by densitometry using Quantity One software  and mixed in equimolar amounts. The DNA library was purified by gel extraction using a Qiagen gel extraction kit. The concentration and length distribution of the DNA library were checked using a Qubit Fluorometer  and Qseq100 . The V4 region of bacterial 16S rRNA genes was sequenced using the Miseq PE300 platform as mentioned above. The aforementioned experimental operations were performed by Shanghai OE Biotechnology Co., Ltd. (China).Genomic DNA was extracted from colonic content using a QIAamp 96 PowerFecal QIAcube HT Kit  in accordance with the operation instruction manual. All operations were performed in a sterile environment. The DNA concentration was checked using a NanoDrop spectrophotometer (Thermo Scientific), and DNA quality was tested using 1% agarose gel electrophoresis. The 16S rRNA gene hypervariable V3–V4 region was amplified by PCR for 25 cycles using 1 Raw sequencing data were in FASTQ format. Paired–end reads were then preprocessed using Trimmomatic software to detect and remove ambiguous bases (N) . The sofThe changes in transaminase levels and the histopathology of hepatic injury induced by MXF are presented in Furthermore, we performed TUNEL analysis to evaluate the apoptosis in vivo  and immup < 0.05) and seven relevant metabolic pathways were revealed from polar extract data. Using the SMICA–P software combined with the Lipidmaps database, we uncovered nine lipid metabolites from the nonpolar extract data with significantly different levels between the control and MXF groups (p < 0.05) that were associated with MXF–induced liver injury . In addition, the levels of two metabolites increased first and then decreased, and those of four metabolites decreased and then increased. The level of each metabolite in the treatment groups was compared with that in the control group and expressed as a fold change. Changes were observed in the levels of amino acids, organic acids, carbohydrates, complex lipids, fatty acids and related compounds, hormones, alkaloids, biogenic amines, and miscellaneous compounds. The levels of L–valine and 3,4–dihydroxy–L–phenylalanine (levodopa) were decreased at all time points. The abundance of 3–methyl–2–oxobutanoic acid increased gradually over time, and that of 3–hydroxybutyric acid sharply decreased after 7 days of administration. The levels of carbohydrates and related compounds, including inosinic acid, D–mannose, glycerone, and glucose, declined by varying degrees at most time points. Among complex lipids and fatty acids, arachidonic acid, hexadecanoic acid, and linoleic acid levels were changed by 2–fold following MXF exposure for different times. The relative abundance of sn–glycero–3–phosphoethanolamine decreased by 9–fold in the MXF group compared with the control level, but substantial variation was noted among the four–time points. Acetaldehyde and methylglyoxal levels also increased over time.As revealed in Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria as indicated by α–diversity, a measure of within–sample diversity that is calculated using the richness and evenness of bacterial taxa within a single population. The diversity indices of MXF–treated rats were significantly lower than those of control rats, which indicated that the species richness of the intestinal flora in rats was dropped off by MXF administration. The β–diversity, which refers to between–sample diversity, measures the distance between pairs of samples. Nonmetric multidimensional scaling (NMDS) analysis based on OUT information was performed to demonstrate the repeatability of each group (stress = 0.094). On days 7 and 14 of administration, the consistency of each dose group was good, and the differences between different groups were marked.The composition and structure of the gut microbiota at the phylum level (top 15) are presented in Bacteroidetes and Firmicutes were retained after incorporating and retaining the lowest taxonomic level of duplicated results. They were Firmicutes–Clostridia–Clostridiales–Lachnospiraceae, Bacteroidetes–Bacteroidia–Bacteroidales–Muribaculaceae–Muribaculum, and Bacteroidetes–Bacteroidia–Bacteroidales–Prevotellaceae–Prevotella_9. Compared with the control findings, the abundance of Lachnospiraceae was significantly decreased, whereas that of Muribaculaceae was significantly increased.Linear discriminate analysis (LDA) coupled with effect size measurements was used to identify strains with differences in abundance among the groups. The branching evolutionary map with the different strains labeled and the primitive data histogram of the relative abundance of different strains are presented in The gut–liver axis is the focus of drug–induced liver injury. The gut microbiome in chemical–induced hepatotoxicity is well established based on pharmacological studies utilizing a variety of chemical–induced metabolic and toxicological disease models . The gutMXF, as a broad–spectrum antimicrobial drug, has been reported to alter the growth and composition of gut flora. However, whether MXF–induced liver injury is mediated by the metabolism of endogenous metabolites and the intestinal flora in the gut–liver axis has not been studied. The current experiment thus explored the potential small molecules and intestinal microbes affecting hepatotoxicity in the gut–liver axis of MXF–treated rats. Based on changes in biochemical and pathological indices, including AST, ALT, and TBIL, a rat model of MXF–induced liver injury was successfully established, and biological samples were collected. MXF–induced liver injury in this study appeared to be independent of the dosage, and the changes of pathology and serum transaminase levels within 7 days were consistent with rules of idiosyncratic drug–induced liver injury in earlier reports .p < 0.05) and seven relevant metabolic pathways were revealed from polar and nonpolar extract data. The serum levels of metabolites involved in the TCA cycle and oxidative phosphorylation including citrate, oxaloacetate, succinate, and isocitrate were altered by MXF treatment. Oxaloacetate is a key rate–limiting substrate affecting the speed of the TCA cycle. Previous research revealed that ATP production and respiratory fluxes were enhanced after oxaloacetate treatment in SHSY5Y neuronal cells [β–cell dysfunction, creating a vicious cycle between glycation and hyperglycemia [LC–HRMS combined with a multivariate data analysis method including Mummichog and OPLS–DA analysis displayed the advantages of network analysis for pathway enrichment, the prediction of functional activity, and putative metabolite identification. Forty unique marker metabolites . In this study, we found that serum valine levels were significantly decreased and isoleucine levels were increased by MXF treatment. These findings implied that MXF administration might hamper the utilization of circulating BCAAs. Previous studies reported the conclusive effects of BCAAs in the treatment of patients with liver diseases, and BCAA levels can be used to diagnose liver dysfunction , 48. IntMuribaculaceae counts were significantly increased, and those of Lachnospiraceae were significantly decreased. It has been reported that the abundance of Muribaculaceae consistently positively covaries with the inner mucus layer (IML) barrier function [Muribaculaceae in association with resistance to fat [Muribaculaceae may be related to the immune function of the intestinal mucosa and the absorption and utilization of fat. However, little is known about the relationships between Muribaculaceae and liver diseases at present. Lachnospiraceae is a member of the phylum Firmicutes, and it is linked to short–chain fatty acids (SCFA) production [Lachnospiraceae, leading to the impaired intestinal mucosal mechanical barrier and immune barrier function (16S rRNA gene sequencing was performed to profile the composition of the gut microbiota of rats before and after MXF dosing. The results indicated that the structure and composition of the intestinal flora were significantly influenced by MXF. When liver damage was most severe, function , and thee to fat . Therefooduction . The decoduction . The conoduction , 55. Butoduction , 57. Confunction . A largeTo the best of our knowledge, this is the first study to report metabolic alternations and microbiota composition differences in a model of MXF–induced hepatotoxicity. We proposed the use of metabonomics combined with intestinal microflora analysis of the gut–liver axis to investigate MXF–induced liver injury. MXF induced dose–independent hepatotoxicity after oral exposure. The energy–related metabolic disorders caused by butyric acid deficiency and intestinal microecological disorders are primarily responsible for liver damage based on the changes in serum metabolite levels and intestinal bacterial species abundance. The underlying relationship of the gut–liver axis associated with the gut flora and liver metabolism may act as a critical part of the underlying pathogenesis. Our results provide new insights into the pathogenesis of hepatotoxicity caused by MXF and improve our knowledge of the drug–induced liver injury.


The purpose of this study is to compare serum pancreatic polypeptide (PP), insulin, C‐peptide, and glucagon in different glucose tolerance stages; analyze the influencing factors of PP secretion; and further explore the role of PP in the pathogenesis of diabetes mellitus.pp (AUC of PP) was used as the dependent variable and the potentially influencing factors were used as the independent variable for multiple linear regression analysis.Data were collected from 100 subjects from hospital. According to the results of oral glucose tolerance test (OGTT), the subjects were divided into a normal glucose tolerance (NGT) group, an impaired glucose regulation (IGR) group, and a newly diagnosed type 2 diabetes mellitus (T2DM) group. PP and the related parameters were measured, and the area under the curve (AUC) 120 min after OGTT was calculated. AUCp < 0.05). AUCpp was significantly higher in the IGR group  and newly diagnosed T2DM group  than in the NGT group. AUCpp was negatively correlated with body mass index (BMI)  and positively correlated with postprandial 60 min blood glucose  and AUCbg (AUC of blood glucose) . Multiple linear regression analysis indicated that there was a linear correlation between BMI, AUCbg, and AUCpp , and the regression equation was calculated as: AUCpp = 6592.272 + 86.275 × AUCbg‐95.291 × BMI .Postprandial 60 min PP in the IGR group was higher than those in the NGT group  patients have an enhanced postprandial pancreatic polypeptide (PP) secretion.In patients with T2DM, the secretion of PP is mainly affected by body mass index and blood glucose.There may exist a potential protective effect of PP on the function of isletcells. This study aims to explore the secretion changes and influencing factors of PP in different stages of glucose metabolism, and further analyze the role of PP in the pathogenesis of T2DM, which will not only help to clarify the pathogenesis of diabetes but also provide new ideas for the prevention, diagnosis, and treatment of diabetes in the future.22.1According to the 1999 World Health Organization diabetes diagnostic criteria, a total of 100 subjects, including 34 patients with impaired glucose regulation (IGR), 29 patients with T2DM, and 37 healthy subjects, were selected from the Department of Endocrinology of the Affiliated Hospital of Qingdao University and the Physical Examination Center during the same period from June 2019 to December 2019; none of the subjects had been previously diagnosed with diabetes or had received glucose‐lowering therapy. The inclusion criteria for normal glucose tolerance (NGT) group were fasting venous blood glucose (FBG) <6.1 mmol/L and 2‐hour venous BG (2hBG) <7.8 mmol/L during an oral glucose tolerance test (OGTT). IGR included impaired fasting BG (IFG) and impaired glucose tolerance (IGT), which can exist separately or simultaneously. The inclusion criterion for IFG was 6.1 mmol/L ≤ FBG<7.0 mmol/L, and the inclusion criterion for IGT was FBG<6.1 mmol/L, 7.8mmo/L ≤ 2hBG during an OGTT<11.1 mmol/L. The inclusion criterion for diabetes was FBG ≥7.0 mmol/L, or 2hBG during an OGTT ≥11.1 mmol/L, or a random venous BG ≥11.1 mmol/L with typical symptoms of diabetes.The exclusion criteria were as follows: type 1 diabetes mellitus (T1DM), gestational diabetes, special type diabetes, secondary diabetes, and other non‐T2DM; recently taken glucocorticoid, thyroid hormone, and other drugs that can cause significantly influence on blood sugar; insulin, secretagogues, glucagon‐like peptide 1 (GLP‐1) agonist, or dipeptidyl peptidase‐IV (DPP‐4) inhibitor drugs were used in the first week of the experiment; diabetic acute complications; complicated with severe cardiovascular, liver, or kidney disease, etc; severe infection, stress, or trauma in the recent past; combined with Cushing disease, polycystic ovary syndrome, acromegaly, hyperthyroidism, and other endocrine and autoimmune system diseases; and complicated with acute or chronic pancreatitis, pancreatic malignancy, PP cell tumor, gastrointestinal ulcer, gastric cancer, liver cirrhosis, or depression and other diseases affecting PP secretion.http://www.chictr.org.cn/ under number ChiCTR2100047486.The protocol was designed in accordance with the Declaration of Helsinki and was approved by the ethics committee of the Affiliated Hospital of Qingdao University, and all participants provided written informed consent. The study was registered on 2.22) = weight (kg) / height (m)2 was calculated. Through laboratory examination, we measured the following indicators: glycosylated hemoglobin (HbA1c), FBG, INS, C‐peptide (C‐P), GC, GLP‐1, urinary albumin to creatinine ratio (UACR), blood lipids , free fatty acid (FFA), triglycerides (TG), total cholesterol (TC)], and liver function , glutamyltranspeptidase, renal function , and uric acid.The general clinical data collection of the selected subjects included gender, age, past medical history, current medical history, smoking history, alcohol consumption history, and drug history. Clinical examinations were conducted by a trained staff group according to a standard guideline. The subjects took off their shoes, hats, and coats, and their body weight (kg), height (cm), and waist circumference (cm) were measured by a specific person under the premise of empty urine and fasting. Systolic blood pressure and diastolic blood pressure of the right upper limb in each subject were measured after resting for 15 min, and the mean of the two measurements was taken. The circumferential diameter of the midpoint between the costal margin and the anterior superior iliac spine was used as the measurement method of waist circumference. Body mass index (BMI) . The LDL‐C, HDL‐C, TG, TC, and FFA were determined by enzymatic method . The levels of INS and C‐P at each time point were determined by radioimmunoassay . The concentrations of PP and GC in the samples were determined by double antibody sandwich enzyme‐linked immunosorbent assay (ELISA). The detection kit was purchased from Shanghai Enzyme‐linked Biotechnology Co., Ltd.OGTT was performed on the subjects selected from the First Outpatient Clinic and Physical Examination Center of the Affiliated Hospital of Qingdao University from June 2019 to December 2019: After the fasting blood of the subjects was extracted, 75 g glucose powder was dissolved in 250‐300 mL water, mixed well, and then the drink was finished within 10 min. According to the results of OGTT, the subjects were divided into the NGT group, the IGR group, and the newly diagnosed T2DM group. Hard physical work or extra food intake was not allowed during the experiment. The elbow venous blood was extracted at 60 min and 120 min after glucose loading to detect the BG, INS, and C‐P at each time point, and 2 mL of elbow venous blood was stored in EDTA anticoagulant tube pre‐added with DPP‐4 inhibitor and then centrifuged in batches in a laboratory centrifuge at 3000 r/min for 10 min. The upper serum was taken for the determination of PP and GC. After the collection is completed, it should be stored in a refrigerator at −80°C in the laboratory for unified determination. During the storage process, attention should be paid to avoid repeated freezing and thawing. After fasting for at least 8 h to 12 h, the blood samples were obtained at about 6:00 The homeostasis model was used to evaluate insulin resistance index (homeostatic model assessment of insulin resistance [HOMA‐IR]) to assess insulin sensitivity, and the calculation formula was fasting glucose × fasting insulin / 22.5. The secretory function of islet β cells was assessed by homeostasis model (homeostatic model assessment of beta cell function [HOMA‐β]), which was calculated as 20 × fasting insulin/(fasting glucose −3.5). The area under the curve (AUC) of OGTT 120 min PP, GC, INS, C‐P, BG:bg=0.5 × BG0 + BG60 + 0.5 × BG120. Note: PP0, PP60, PP120, GC0, GC60, GC120, INS0, INS60, INS120, C‐P0, C‐P60, C‐P120, BG0, BG60, BG120 represent the PP, GC, INS, C‐P, and BG concentrations corresponding to fasting, 60 min, and 120 min after glucose administration, respectively.AUC2.3pp curve. When p < 0.05, the difference between groups was considered statistically significant.SPSS software version 22.0  was used to carry out statistical analyses. The measurement data with normal distribution were expressed as mean ± SD; those with the nonnormal distribution were expressed as the median ; classified data were described by rate or composition ratio. The comparison of means between different glucose tolerance groups conformed to normal distribution and homogeneity of variance using analysis of variance, including single factor, multivariate and repeated measures analysis of variance. One‐way analysis of variance (ANOVA) was used for the comparison of general indexes and hormone secretion indexes of the three groups. Two‐factor repeated measure ANOVA was used for the overall analysis of PP, GC, INS, and C‐P groups and time at each time point, and multivariate ANOVA was used for pair comparison (least significant difference method was used for multiple comparisons). Pearson correlation analysis was used for normal distribution data, and Spearman correlation analysis was used for nonnormal distribution data. Multivariate stepwise regression analysis was used to build a regression model for the AUC3The general clinical characteristics and biochemical indexes of the study population in NGT, IGR, and newly diagnosed T2DM groups are shown in Table p < 0.001), and the differences in interaction between different glucose tolerance groups, between groups, and time did not reach statistical significance (p > 0.05). After the oral administration of 75 g glucose, the concentration of PP gradually increased and reached the peak of secretion 60 min after the administration of glucose, which was about 1.5 to 2 times of the basal level. After that, the level of PP gradually decreased and dropped to the basal level 120 min after the administration of glucose. Multiple comparison between groups showed that fasting PP was in the newly diagnosed T2DM group > IGR group > NGT group, and there was no significant difference between groups (p > 0.05); PP at 60 min after OGTT was in IGR group > T2DM group > NGT group, and the difference between IGR group and NGT group was significant ; PP at 120 min after OGTT was in the newly diagnosed T2DM group > IGR group > NGT group, and there was no significant difference between groups (p > 0.05). AUCpp in the newly diagnosed T2DM group and IGR group was higher than that in the NGT group; the differences were significant , but there was no significant difference between the newly diagnosed T2DM group and the IGR group (p > 0.05). These results suggested that the secretion of PP varies greatly at different time points of OGTT, and PP secretion was increased under abnormal glucose metabolism, but the change trend over time was not markedly affected by glucose metabolism status.The comparison of PP trend between different glucose tolerance groups is shown in Table bg , and was negatively correlated with 60 min postprandial C‐P, 60 min postprandial insulin, and fasting GLP‐1 . At 60 min after OGTT, PP was positively correlated with 60 min postprandial BG . At 120 min after OGTT, PP was negatively correlated with UACR . AUCpp was positively correlated with 60 min postprandial BG and AUCbg  and negatively correlated with BMI . The basis of PP and the secretion after glucose stimulation were not significantly correlated with the subject's age, waist circumference, SBP, DBP, HbA1c, FBG, 120 min postprandial BG, fasting C‐P, 120 min postprandial C‐P, fasting INS, 120 min postprandial INS, GC, LDL‐c, HDL‐c, TC, TG, FFA, UA, BUN, estimated glomerular filtration rate, ALT, AST, HOMA‐IR, HOMA‐β, AUCc−p, AUCgc, and AUCins.As seen in Table pp was used as the dependent variable and the potentially influencing factors such as BMI and AUCbg were used as the independent variable for stepwise fitting. After adjusting for TC, TG, LDL‐C, HDL‐C, ALT, AST, SBP, DBP, and age, multiple linear regression analysis showed that there was a linear correlation between BMI, AUCbg, and AUCpp , and the regression equation was calculated as AUCpp = 6592.272 + 86.275 × AUCbg‐95.291 × BMI .AUC4This study explored the differences of PP in different glucose metabolism states and analyzed the influencing factors of PP secretion by measuring the serum concentration of PP, GC, INS, and C‐P in people with different glucose tolerance states before and after glucose loading. The results showed that, compared with NGT, the postprandial secretory response of PP to oral glucose stimulation was enhanced in IGR (including IFG and IGT) and newly diagnosed T2DM subjects. The secretion of PP was mainly affected by BMI and BG level, which was negatively correlated with BMI and positively correlated with BG level. We hypothesized that the disorder of PP secretion after glucose stimulation in people with abnormal glucose metabolism might be related to the disorder of autonomic (parasympathetic) nerve system, and the specific mechanism remains to be further studied in the future.PP is secreted mainly by pancreatic PP cells,In this study, we found an enhanced secretion response of PP to oral glucose stimulation in patients with IGR and T2DM compared with those with NGT. This result is consistent with previous findings that diabetes patients with poor glycemic control have enhanced PP reactive secretion.p > 0.05) between C‐P and PP concentration at 60 min after glucose load is further consistent with this point.In this study, the correlation factor analysis of PP showed that there was a significant negative correlation between C‐P and PP concentration in basal conditions. Previously, Weyer et alThe development of diabetes is not only because of the secretory dysfunction of pancreatic endocrine cells but also the structural change of pancreatic islet and the disorder of communication signals between pancreatic endocrine cells.The correlation factor analysis of PP in this study showed that there was a significant negative correlation between GLP‐1 and PP in the basal state. Recent studies have shown that exogenous GLP‐1 administration significantly weakens the effects of postprandial INS, GC, and PP secretion after vagal nerve transection.In conclusion, compared with NGT subjects, IGR and T2DM patients have an enhanced postprandial PP secretion, which tends to be more significant in IGR subjects. In T2DMs, the secretion of PP is mainly affected by BMI and BG. There may exist a potential protective effect of PP on the function of islet cells. Although further prospective cohort studies and mechanism exploration are still needed to figure out the causal relationship in the future, our findings provide novel insights into the potential roles of PP in the pathogenesis of established diabetes.The authors declare no conflict of interest.


Hidden renal cell carcinoma (RCC) is not evident during severe inflammation such as acute pyelonephritis. We presented a 62‐year‐old male presenting with features of obstructive pyelonephritis managed with simple nephrectomy. However, the histopathology findings suggested unilateral papillary RCC. Our case showed that RCC might present with features of obstructive pyelonephritis. Severe inflammation, such as obstructive pyelonephritis, may masquerade RCC, which may delay the cancer diagnosis and lead to improper management. The pain was of moderate severity, dragging type and radiating to the loin. Also, he had increased frequency and urgency of micturition of the same duration. The patient did not have a fever. His medical, family and personal history were unremarkable. The patient was hemodynamically stable with a temperature of 98.4°F. Physical examination revealed left costovertebral angle tenderness with no palpable mass over the abdominal region. The rest of the systemic examinations were normal.3, platelet count of 4 lacs/mm3, serum urea of 25 mg/dL, and serum creatinine of 1 mg/dL. And, urinalysis revealed +1 for glucose and protein, with pus cells 10‐20/HPF. Contrast‐enhanced computed tomography (CECT) of the abdomen and pelvis (Figure Laboratory investigations showed hemoglobin of 9.8 g/dL, total leukocyte count of 14,800 cells/mmFollowing this, we did a simple nephrectomy under general anesthesia. While keeping the patient in the right lateral position, we gave a cutting incision on the left 11th rib. Then we dissected the external oblique muscle, transected the internal oblique muscle, and cut the transversus abdominis muscle. After that, we opened the renal fascia and did the blunt dissection of perirenal fat and psoas muscle. Then the kidney was identified and separated from the surrounding Gerota's fascia Figure . Also, t3Papillary RCC is a malignant renal parenchymal tumor showing a 5:1 male predilection with the usual incidence in the sixth to seventh decades.4Our case illustrates that a surgeon should be vigilant while treating a case of obstructive pyelonephritis and always consider a nuclear scan or renal biopsy to look for a possible masquerading renal cell carcinoma.Mohan Nayak Guguloth: Writing – original draft; writing – review and editing. Juhi Manzoor: Writing – original draft; writing – review and editing. Sai Kiran Kuchana: Writing – original draft; writing – review and editing. Tarun Kumar Suvvari: Writing – original draft; writing – review and editing. Anthony Reddy Gopu: Writing – original draft; writing – review and editing. Vikram Das Kunden: Writing – original draft; writing – review and editing. Sashank Bhattarai: Writing – original draft; writing – review and editing. Ayush Anand: Conceptualization; project administration; supervision; visualization; writing – original draft; writing – review and editing.The authors did not receive any funding for the manuscript.The authors have no conflict of interest to declare.SKK is the guarantor of the manuscript.A written informed consent was obtained from the patient based on the journal's policies.


Incarcerated people are at a disproportionate risk of contracting HIV. We estimated the prevalence and correlates of HIV testing among incarcerated people with a history of HIV-related high-risk behaviours in Iran.Data for this analysis were obtained from three consecutive nationwide bio-behavioural surveillance surveys of a random sample of incarcerated people in 2009 (n = 5953), 2013 (n = 5490), and 2017 (n = 5785). History of testing for HIV in the last 12 months was the primary outcome variable. HIV testing was examined among those with a history of HIV-related high-risk behaviours . The outcome variable was divided into three categories: Never tested for HIV, ever tested for HIV inside the prison in the last 12 months, and ever tested for HIV outside the prison in the last 12 months. We used multivariable multinomial logistic regression models to examine factors associated with HIV testing.Overall, 8,553 participants with a history of HIV-related high-risk behaviors with valid responses to the HIV testing question were included in the analysis. Although HIV testing inside prison has increased , the prevalence of HIV testing outside prison has decreased  over time. Our multivariable multinomial regression model showed older age , history of the previous incarceration , currently receiving methadone maintenance therapy inside prison , having access to condoms inside prison  and sufficient HIV knowledge  were significantly associated with an increased probability of having an HIV test in the last 12 months inside prison.HIV testing among high-risk Iranian prisoners has increased from 2009 to 2017. However, HIV testing remains considerably low, and half of the incarcerated people with a history of HIV-related high-risk behaviours had never tested for HIV inside prison. Evidence-based programs are needed to optimize HIV testing inside and outside prisons and identify those at greater risk of HIV.The online version contains supplementary material available at 10.1186/s12879-022-07897-z. Incarcerated people are at a higher risk of contracting or transmitting HIV than the general population, primarily due to their engagement in HIV-related high-risk behaviours  and PWID –25. HarmThree national BBSS were conducted among Iranian incarcerated people in 2009, 2013, and 2017. The design, sampling, and data collection approaches were harmonized across the three surveys and have been described elsewhere . A multiThe eligibility criteria across all surveys were being incarcerated for at least one week, having not participated in a similar study in the previous two months, and providing verbal informed consent . Gender-“having completed an HIV test in the last 12 months”. The primary outcome had three options (never tested vs. tested inside a prison in the last 12 months vs. tested outside a prison in the last 12 months). Incarcerated people were questioned: “Have you ever tested for HIV?” If no, they were coded as “never tested.” If yes, they were asked, “When did you last test for HIV?” and “Where did you last test for HIV?”. Participants who had a test during the last 12 months and inside the prison were considered to be “tested inside a prison in the last 12 months”. Participants who had a test during the last 12 months and outside the prison were considered to “test outside a prison in the last 12 months”.As routine HIV testing is recommended for people at a high risk of HIV, we restricted the analysis to those with a history of HIV-related high-risk behaviours, including a history of injection drug use, multiple-sex partnerships (two or more partners at the same time), and having had a tattoo (ever). The primary outcome variable in this study was Socio-demographic factors of interest were sex , age at interview ≤ 29 vs. >29) , 29, cur vs. >29 Data were also recorded on drug use and sexual risk behaviours, history of previous incarcerations (yes vs. no), lifetime history of alcohol consumption (yes vs. no), lifetime history of illicit drug use (yes vs. no), current receipt of MMT (yes vs. no), having access to sterile syringe inside prison (yes vs. no), age at first sex (< 18 vs. ≥18 years), and having access to condom inside prison (yes vs. no). We also measured HIV knowledge  and self-perceived risk of HIV (yes vs. no).Descriptive statistics, including frequencies, percentages, and 95% confidence intervals (95% CI), were reported for HIV testing inside and outside of prison in the last 12 months. People living with HIV who knew their HIV status were excluded from the analysis. Bivariable and multivariable multinomial logistic regression models were built to compare the probability of having an HIV test in the last 12 months among different subgroups of incarcerated people after merging the data from three rounds of surveys and adjusting for the year of data collection. Variables with a p-value less than < 0.2 in the bivariable multinomial logistic regression model were entered into the multivariable multinomial logistic regression model. The final model was chosen through the backward elimination method using Wald statistics. Survey analysis using the Svy package in Stata /SE 14.0  was used to analyze the data. Analyses were adjusted for prison’s sample sizes by applying appropriate sampling weights. A sensitivity analysis was conducted to assess correlates of HIV testing across different surveys in 2009, 2013, and 2017 through separate regression analyses  participants had a history of HIV-related high-risk behaviours . Additionally, of the 5490 participants in 2013, 3,449 , and out of 5785 participants in 2017, 3,353  had a history of HIV-related high-risk behaviours and were included in this analysis. Among the 2009 survey participants, 97.6% (n = 2811) were male, 52.1% (n = 1500) were > 29, 45.7% (n = 1309) were single, and 55.5% (n = 1598) had secondary or high school education. In the 2013 survey, 98.8% (n = 2770) were male, 69.7% (n = 1945) were in > 29 years, 46.2% (n = 1294) were married, 51.9% (n = 1451) were secondary or high school education. In the 2017 survey, 96.1% (n = 2756) were male, 72.9% (n = 2086) were in > 29 years, 41.3% (n = 1185) were single, 61.8% (n = 1770) had secondary or high school education  participants had tested for HIV inside the prison in the last 12 months. Also, 223  participants had tested for HIV outside the prison in the last 12 months. Of the 2,804 included participants in 2013, 603  had a history of HIV test inside the prison in the last 12 months, and 211  had a history of HIV test outside the prison in the last 12 months. In 2017, of the 2,869 included participants, 1,442 participants had a history of HIV test inside the prison in the last 12 months  and 119  participants with an HIV test outside the prison in last 12 months : 1.24, 95% CI: 1.05, 1.47), history of previous incarceration , current receipt of MMT inside prison , having access to condoms inside prison , and sufficient HIV knowledge  were significantly associated with an increased probability of having an HIV test in the last 12 months inside prison. No covariate was significantly associated with the probability of having an HIV test in the last 12 months outside prison  , 32, andWe also found that incarcerated people with a history of previous incarceration, currently receiving MMT inside prison, access to condoms inside prison, and sufficient knowledge about HIV transmission had a higher probability of HIV testing inside the prison. This finding highlights that harm reduction services inside prisons might have been impactful in encouraging incarcerated people with a higher risk of drug use and sexual behaviour profiles to engage in HIV testing; as shown by our findings in 2009, the prevalence of HIV testing among the incarcerated people has remained low . HoweverWe acknowledge the limitations of our study. First, the cross-sectional nature of our surveys limited the drawing of any causal inference in the observed associations. Second, the self-reported nature of our data makes it prone to social desirability and recall biases. As some of the behaviours asked in the interview were sensitive , some people may not have disclosed their behaviors. Third, we did not measure the availability of antiretroviral treatment after HIV testing for people living with HIV, which could be explored in future studies. Lastly, we did not measure the barriers to HIV testing inside and outside the prisons. We recommend future quantitative and qualitative studies to examine the obstacles to HIV testing among incarcerated people.While the increasing prevalence of HIV testing inside prisons in Iran is encouraging, our findings indicate that incarcerated people’s HIV testing practices are still considerably low both inside and outside prisons in Iran. Our findings highlight the need to revisit and re-evaluate existing HIV testing policies and services inside and outside prison settings to help improve HIV testing uptake among this subpopulation. Scaling up rapid tests and routine opt-out HIV testing services could help encourage incarcerated people to further use the available harm reduction facilities inside and outside prisons.Additional file 1. Multivariable Multinomial logistic regression on HIV testing in thelast 12 months among incarcerated in 2009, 2013, and 2017.


Older people have increasingly complex healthcare needs, often requiring appropriate access to diagnostic imaging, an essential component of their health and disease management planning. Ultrasound is a safe imaging tool used to diagnose several conditions commonly experienced by older people such as deep vein thrombosis.To evaluate the utilisation of major ultrasound services by Australians ≥ 65 years old between 2009- and 2019.This population-based and yearly cross-sectional study of ultrasound utilisation per 1,000 Australians ≥ 65 years old was conducted using publicly available data sources. Overall, examination site and age- and sex-specific incidence rate (IR) of ultrasound per 1,000 people, adjusted incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were calculated using negative binomial regression models.Over the study period, the crude utilisation of ultrasound increased by 83% in older Australians. Most ultrasound examinations were conducted on extremities (39%) and the chest (21%), with 25% of all ultrasounds investigating the vascular system. More men than women use ultrasounds of the chest , particularly echocardiograms , and abdomen , especially in those ≥ 85 years old. Hip and pelvic ultrasound were used more by women than men . There were increases in vascular abdominal  and extremeties  ultrasounds over the study period, particularly in ≥ 75 years old men.Ultrasound is a common and increasingly used diagnostic tool for conditions commonly experienced by older Australians.The online version contains supplementary material available at 10.1186/s12877-023-03771-y. Part of the essential care required for older people is the timely and appropriate delivery of diagnostic examinations, including diagnostic imaging. In Australia in 2005, 25% of diagnostic imaging encounters were attributed to older people (≥ 65 years old) despite them representing 13% of the overall population, with X-ray being the most utilised diagnostic imaging method followed by ultrasound . RecentlOur study aimed to assess the utilisation of ultrasound by older Australians by 1) estimating the total usage of ultrasound services related to the examination of the chest, the abdomen and extremities, 2) identifying trends in ultrasound utilisation between 2009–10 and 2018–19, and 3) highlighting differences in service usage between age-sex groups.st of July of a given year and end on the 30st of June the following year). The study population corresponds to Australians aged 65 years old or older during the study period.We used publicly available data from 2009–10 to 2018–19 from the Medicare statistics website and the Australian Bureau of Statistics (ABS) to conduct a population based epidemiological and yearly cross-sectional study , 12. AllThe present analysis includes ultrasound services covered by the Australian Government Medicare Benefits Schedule (MBS), which subsidises health services to citizens and permanent residents. This system covers, at least partially, diagnostic imaging evaluations ordered by a range of medical practitioners  –15. MBS Ultrasound services were grouped by examination site . Overall, age and sex specific crude and adjusted utilisation rates (incidence rate) and 95% confidence intervals (CIs) of ultrasound per 1,000 people were calculated. Changes in utilisation over time were evaluated with overall and age- and sex-adjusted incidence rate ratios (IRRs) and 95%CIs, estimated using negative binomial regression (to accommodate overdispersion in the data). A p value of < 0.05 was considered statistically significant. Statistics analyses were performed using R version 4.0.3 .The Australian population aged ≥ 65 years old increased from 2,914,336 in 2009–10 to 4,038,179 people in 2018–19  was of 30/1,000 people ≥ 65 years old in 2009–10 compared to 49/1,000 people in 2018–19, with a statistically significant increase  . In each age-sex group, heart ultrasound usage was higher in men compared to women Fig. , Table 1The overall utilisation of ultrasound of abdominal vessels was 19/1,000 people ≥ 65 years old in 2009–10 and 32/1,000 people ≥ 65 years old in 2018–19, with a statistically significant yearly increase over the study period  Table a. The utThe overall utilisation of ultrasound of vessels in extremities was of 70/1,000 people ≥ 65 years old in 2009–10 and 103/1,000 people ≥ 65 years old in 2018–19, with a statistically significant yearly increase over the study period  Table b. The utn = 5,900,818 and 3,058,677, respectively), demonstrating considerable uptake of this diagnostic method [Over the study period, crude ultrasound ultilisation by older Australians increased by 83%, while the corresponding population increased by 38%. Similar trends were observed in Canada and the USA, with ultrasound utilisation by older people doubling between 2000 and 2016 . The cruThe increase in ultrasound utilisation between 2009 and 2019 in Australia was driven by the number of ultrasound examinations for the extremities and chest. This contrasts with prior studies reporting that, amongst conditions relevant to older Australians (i.e. omission of pregnancy and fertility-related ultrasound), the leading indication for ultrasound examination in the general population was abdominal pain , followed by sprains/strains (3.5%), shoulder syndrome (3.0%), breast mass investigation (2.9%), cholecystitis (2.8%), bursitis (2.6%) and phlebitis (2.5%) [Time trends in ultrasound utilisation varied by examination site, with a doubling in the sex-adjusted utilisation of chest ultrasound over the observation period for men ≥ 85 years old. These trends underpinned the utilisation of ultrasound of the heart, which is exacerbated in older men (i.e. ≥ 85 years old). These trends are consistent with the substantial increase in transthoracic and transoesophagal heart ultrasound between 2002 and 2013 for the overall Australian population . FonsecaThe utilisation of abdominal ultrasound increased in most age-sex groups examined between 2009–10 and 2018–19 and was underpinned by the increase in ultrasound of abdominal vessels, which represented 54% of this class of ultrasound services. In the overall population, abdominal pain remains the main indication for ordering an abdominal ultrasound, both in public and private settings , 23. In The utilisation of ultrasound of the extremities and hip joint increased during the study period, especially in older women. This increase was driven by an increase in ultrasound of veins and arteries of the extremities. In the general Australian population, ultrasound of extremities are mainly used to investigate injuries related to sprains/strains , followed by shoulder syndrome (3%), bursitis/tendonitis 2.6%) and phlebitis/thrombophlebitis (2.5%) .6% and p–34. The Our study relied on publicly available data and therefore limited our ability to examine the appropriateness of service access, the indication for services, and other important clinical characteristics that may influence the utilisation of these services . Our stuThe crude utilisation of ultrasound by older Australians has increased by 83% between 2009–10 and 2018–19. The changes in ultrasound utilisation observed during the study period, varied by age group, sex and examination site, with ultrasound of the chest and extremities being the most common in older people. Vascular ultrasound represented a major part of all services conducted on the abdomen and the extremities, being a leading cause for the increase. These results demonstrate that ultrasound is increasingly used to address the diagnostic needs of older Australians.Additional file 1:Table S1. MBS codes investigated.Additional file 2:Supplementary Table 2. Number of Australians ≥65 years old between 2010 and 2019 by age-sex group as provided by the Australian Bureau of Statistics.