To improve the endo-lysosomal escape of siRNA for effective cytosolic siRNA delivery, a pH-triggered CO2 gas-generating nanoplatform was developed using the guanidine group of Met. It can react reversibly with CO2 to form Met-CO2 for the pH-dependent capture/release of CO2. The introduction of Met, a conventional anti-diabetic drug, promotes programmed death-ligand 1 (PD-L1) degradation by activating adenosine monophosphate-activated protein kinase, subsequently blocking the inhibitory signals of PD-L1. As a result, siFGL1 delivery by the camouflaged nanoparticles of the hybrid biomimetic membrane can effectively silence the FGL1 gene, promoting T-cell-mediated immune responses and enhancing antitumor immunity. We found that a combination of PD-L1/programmed death 1 signaling blockade and FGL1 gene silencing exhibited high synergistic therapeutic efficacy against breast cancer in vitro and in vivo. Additionally, Met alleviated tumor hypoxia by reducing oxygen consumption and inducing M1-type differentiation of tumor-related macrophages, which improved the tumor immunosuppressive microenvironment. Our results indicate the potential of hybrid biomimetic membrane-camouflaged nanoparticles and combined Met-FGL1 blockade in breast cancer immunotherapy.The combination of an immuno-metabolic adjuvant and immune checkpoint inhibitors holds great promise for effective suppression of tumor growth and invasion. In this study, a pH-responsive co-delivery platform was developed for metformin (Met), a known immuno-metabolic modulator, and short interfering RNA (siRNA) targeting fibrinogen-like protein 1 mRNA (siFGL1), using a hybrid biomimetic membrane (from macrophages and cancer cells)-camouflaged poly (lactic- Immunotherapy is a groundbreaking therapeutic approach that provides unparalleled opportunities to significantly improve the treatment of various diseases, including cancer –3. DistiProgrammed death ligand-1 (PD-L1) is a well-studied immune checkpoint inhibitor of programmed death 1 (PD-1), a T-cell negative regulator, and is upregulated on the surface of solid tumors, including breast cancers , 9. PD-LLymphocyte-activation gene 3 (LAG3) is regarded as one of the main inhibitory receptors associated with depleted T lymphocytes, especially in tumors . Fibrinoco-glycolic acid) (PLGA)-based nanocarrier, as previously reported  × 100%, where Wt is the final mean tumor weight of the tested groups, Wi is the initial mean tumor weight of the tested groups, and Ws is the final mean tumor weight of the saline group.To investigate the in vivo antitumor effect of MC-PLGA@Met-COIFN-γ and interleukin-2 (IL-2) in tumor samples obtained from the mice after treatment using an enzyme-linked immunosorbent assay (ELISA), following the manufacturer’s protocol. Furthermore, the tumor tissue sections were stained with Ki-67 and hematoxylin and eosin (H&E) for histopathologic analysis.Tumor specimens were cut into small pieces with a scissor. After lysis of red blood cells using red blood cell lysis buffer, the resulting cells were filtered through nylon-mesh filters (70 μm). The single-cell suspensions were re-suspended in PBS containing 2% fetal bovine serum. The cell suspension was stained with anti-CD3-FITC, anti-CD4-PE, and anti-CD8-APC antibodies, following the manufacturer’s protocols to analyze the T cells. The cells were then analyzed using flow cytometry. We also analyzed the levels of tumor-associated 2/siFGL1 NPs on the polarization of tumor-associated macrophages (TAMs), rabbit polyclonal FITC-anti-macrophage mannose receptor 1 antibody (dilution 1:50) and rabbit polyclonal PE-CD86 antibody (dilution 1:50) were used to determine the distribution of macrophages with M1 or M2 phenotype in tumor tissues, respectively. CD8 positive cells were stained with anti-mouse CD8 antibody and detected by Cyanine 3 (Cy3)-labeled secondary antibody. Nuclei were stained with DAPI. Finally, the sections were observed under CLSM (Zeiss LSM710) and all images were digitally acquired using LSM soiware (ZEN2010).Tumor tissues collected from sacrificed mice were fixed in 4% paraformaldehyde solution and embedded in paraffin for immunofluorescence staining. We assessed the hypoxic area of the tumor tissues in each group using Hypoxyprobe™-1 Plus Kits  as a hypoxia probe. To observe the effect of MC-PLGA@Met-COHistological examination of the major organs  and tumor tissues was performed using H&E staining. At the end of the treatment, the major organs and tumor tissues were collected and immediately fixed in 4% paraformaldehyde for one day, embedded in paraffin, and then sectioned. The major organs were stained with H&E. Moreover, the tumor issue sections were immunohistochemically stained for PD-L1, FGL1, Thr172, and AMPK-α histopathologic analysis.p-value < 0.05 was considered statistically significant.Data are expressed as the mean ± standard deviation (SD). Analysis of variance (ANOVA) was used to evaluate the differences among groups. A 2 was produced by bubbling an aqueous solution of Met for 1 h with CO2. As shown in Fig. 2 exhibited maximum absorbance at about 233 nm; accordingly, the concentrations of both Met and Met-CO2 were determined using a UV-spectrophotometer at 233 nm. The formation of Met-CO2 was verified using FTIR spectra and 13C-NMR. The formation of bicarbonate of Met by CO2 are shown by the FTIR transmittance patterns of Met-CO2 (red curve) along with Met (black curve) , indicating the formation of bicarbonate of Met by CO2. However, the FTIR spectrum of Met-CO2 was similar to that of Met. Both Met-CO2 and Met displayed peaks at 3446 cm−1, 3387 cm−1, 3303 cm−1, and 3061 cm−1, corresponding to the amide groups (amide stretching vibration); at 1671 cm−1, corresponding to the imine (C=NH) bond of the Met-CO2 or Met; and at 1484 cm−1 and 1411 cm−1, which belonged to the stretching vibration mode of the two methyl groups attached to one nitrogen atom in Met-CO2 or Met. The C-N stretching vibration mode in Met-CO2 or Met was located at 1052 cm−1. Figure 13C-NMR  spectra of Met-CO2. A peak centered at 158.46 ppm emerged, consistent with the formation of carbonate [2.Met-COarbonate . The spe2/siFGL1 NPs, and MC-PLGA@Met-CO2/siFGL1 NPs  are shown in Fig. 2/siFGL1 NPs displayed characteristic core–shell-like bilayer membrane structures. The hybrid membrane-coated NPs were stable at neutral pH of 7.4  was not significantly affected at a pH of 6.0 or 5.0 .We further investigated the uptake of FAM-siFGL1 mediated by hybrid membrane-coated NPs using flow cytometry. As shown in Fig. 2/FAM-siFGL1 NPs than for PLGA@Met-CO2/FAM-siFGL1 NPs, while hardly any green fluorescence was observed for free FAM-siFGL1. These results reflected the results obtained from flow cytometry.CLSM was conducted to observe the intracellular distribution of FAM-siFGL1 mediated by hybrid membrane-coated NPs in 4T1 cells. As shown in Fig. 2) or MC-PLGA@Met-CO2/FAM-siFGL1 NPs (with CO2). We labeled intracellular acidic lysosomes with LysoTracker Red, which presents with red fluorescence. Additionally, the location of nuclei is indicated by blue fluorescence, green fluorescence corresponds to FAM-siFGL1, and yellow fluorescence corresponds to the overlap between green and red fluorescence, representing FAM-siFGL1 trapped within the endosome. As shown in Fig. 2) was trapped in the lysosome at both 2 h and 4 h, while FAM-siFGL1 mediated by MC-PLGA@Met-CO2/FAM-siFGL1 NPs (with CO2) was always distributed outside the lysosome after 2 h or 4 h. We further investigated the intracellular distribution of FAM-siFGL1 mediated by MC-PLGA@Met-CO2/FAM-siFGL1 NPs (with CO2) using 3D CLSM images. As shown by the merged images in Fig. 2 facilitates the escape of FAM-siFGL1 from the lysosomes.To investigate intracellular trafficking, FAM-siFGL1 was encapsulated in MC-PLGA@Met/FAM-siFGL1 NPs  Fig. a. In the2 NPs, there was a non-obvious upsurge in the accumulation of G1 phase cells among 4T1 cells with MC-PLGA@Met-CO2/siFGL1 NPs. This indicated that siFGL1 had no obvious cell cycle arresting effect on 4T1 cells.Flow cytometry was used to further investigate the effect of Met on the cell cycle. The effects of Met formulations on cell cycle arrest in 4T1 cells are shown in Fig. We investigated the toxicity of the blank nanomaterials (MC-PLGA NPs) Fig. c. It was2 and MC-PLGA@Met-CO2 NPs. MC-PLGA@Met-CO2 NPs induced cell apoptosis by 50.21%, which was approximately 1.41-fold higher than that for Met-CO2. MC-PLGA@Met-CO2/siFGL1 NPs induced 4T1 cell apoptosis more effectively than MC-PLGA@Met-CO2 NPs, by approximately 1.51-fold. Additionally, siFGL1 failed to induce cell killing efficacy when compared with the control (apoptosis rate: 13.76% vs. 11.21%), which might be attributed to its instability and easy degradation in serum, and limited membrane permeability [2/siFGL1 NPs caused a high rate of apoptosis in 4T1 cells (approximately 75.71%) (p < 0.01 vs. MC-PLGA@Met-CO2 NPs and p < 0.01 vs. MC-PLGA@siFGL1 NPs), indicating that the delivery of Met-CO2 NPs and siFGL1 mediated by hybrid membrane-coated NPs had a synergistic antitumor immune killing effect, causing a higher level of tumor cell apoptosis.To further investigate the antitumor response in the co-culture system, flow cytometry was used to analyze cell apoptosis after incubation with the Annexin V-FITC/PI kit. As shown in Fig. eability . MC-PLGA2/siFGL1 NPs significantly inhibited the expression of PD-L1 in 4T1 cells, with the highest effect observed at a concentration of 10 mM of Met. Simultaneously, the phosphorylation levels of AMPK-α in 4T1 cells were significantly enhanced than that for the control, with the highest effect observed at a concentration of 2.5 mM of Met . The overall inhibitory rate by tumor volume was 2.66%, 56.1%, and 97.3% in the siFGL1-treated, MC-PLGA@siFGL1 NP-treated, and MC-PLGA@Met-CO2/siFGL1 NP-treated groups, respectively, which illustrates the strong synergism due to the MC hybrid biomimetic membrane-camouflaged PLGA NP-mediated delivery. These in vivo results are consistent with the in vitro results. As shown in Fig. 2-treated and siFGL1-treated groups was 28 days and 27 days, respectively, vs. 39 days in the MC-PLGA@Met-CO2/siFGL1 NP-treated group (p < 0.01), indicating that MC-PLGA@Met-CO2/siFGL1 NPs effectively inhibited tumor growth and prolonged the survival of mice. Treatment with MC-PLGA@Met-CO2 NPs and MC-PLGA@siFGL1 NPs showed slightly improved survival rates, whereas co-loaded MC-PLGA@Met-CO2/siFGL1 NPs enhanced the survival period by nearly 40 days (p < 0.05).After showing excellent activity in vitro, different nano-formulations were examined using BALB/c mice bearing 4T1 mammary tumors. The tumor volumes and mouse weights were monitored at the indicated time points  .The immune response was also investigated by analyzing intratumoral cytokine levels using ELISA. The production of cytokines, including 2/siFGL1 NP-treated group, extensive nuclear pyknosis and cancer necrosis were observed on the tumor, indicating severe cancer necrosis . As indicated in Fig. 2 NPs group and MC-PLGA@Met-CO2/siFGL1 NPs group than with the control (p < 0.01).Hypoxyprobe™-1, a substituted 2-nitroimidazole with a chemical name of pimonidazole hydrochloride, is widely used as a specific hypoxic cell marker –47. The + T cells in tumor cryosections was conducted. As indicated in Fig. 2 NP-treated and MC-PLGA@siFGL1 NP-treated groups, the percentage of intratumoral CD8+ T cells was significantly upregulated relative to the total number of cells in tumors in the MC-PLGA@Met-CO2/siFGL1 NP-treated group (p < 0.01), which is consistent with the results of the flow cytometry analysis.To further evaluate the local immune response, an immunofluorescence staining analysis of tumor-infiltrating CD82 NP-treated and MC-PLGA@Met-CO2/siFGL1 NP-treated groups showed lower PD-L1 levels owing to the reduction of PD- L1 by Met-activated AMPK. AMPK activation was indirectly evaluated by immunohistochemistry analysis of AMPK phosphorylation at Thr172, critical for AMPK activity [2 NP-treated and MC-PLGA@Met-CO2/siFGL1 NP-treated groups compared to that in the other groups. The reduction in FGL1 expression in the MC-PLGA@siFGL1 NP-treated and MC-PLGA@Met-CO2/siFGL1 NP-treated groups suppressed the expression of FGL1 in vivo  showed no obvious abnormalities in any treatment group, similar to the control group Fig. a. The hiactivity . As indiivo Fig. b.Fig. 6B2 gas-generating hybrid membrane-biomimetic nanoplatform to deliver Met and siFGL1 (MC-PLGA@Met-CO2/siFGL1 NPs).ICIs have emerged as promising candidates for cancer immunotherapy by reversing the immunosuppressive mechanisms employed by tumors to reinvigorate anti-cancer immunity. In the tumor microenvironment, PD-L1 and its receptor, PD-1, are two typical immune checkpoints. The interaction between PD-1 and PD-L1 can promote tumor immune escape and tumor survival. Thus, blocking the PD-L1/PD-1 axis is considered to be an effective target for cancer immunotherapy. However, the durable response rate following anti-PD-1/PD-L1 immunotherapy remains relatively low in most cases , 50. Som2/siFGL1 NPs showed that under low pH conditions, CO2 generated from Met-CO2 encapsulated in the PLGA NPs could cause the membrane to expand or break open, resulting in a nanobomb effect. In addition, the drug release results indicated that Met released from MC-PLGA@Met-CO2/siFGL1 NPs exhibited a typical sustained and low-pH-triggered release. This pH-responsive release might be attributed to CO2 generation in the acidic environment, which could avoid drug leakage during circulation, and induce a faster release with low-pH triggering in the cellular environment. Furthermore, the results of intracellular lysosome escape assay showed that MC-PLGA@Met-CO2/FAM-siFGL1 NPs (with CO2) exhibit extraordinary endosome escape ability owing to the presence of CO2 produced under acidic conditions. This study is consistent with previous reports, in which CO2 effectively achieved endosomal escape [2 showed a slight G1 phase arrest by AMPK activation, which agrees with the results of previous studies [2 NPs triggered the highest level of cell cycle arrest in 4T1 cells, indicating that the delivery of Met-CO2, mediated by hybrid membrane-coated NPs, increased the efficacy of cell cycle arrest by Met, which is consistent with the data from the cellular uptake assay. Further, tumor cell killing assay conducted by co-culture has shown an increase in the percentage of apoptosis in Met-CO2 and MC-PLGA@Met-CO2 NPs-treated groups. This result might be due to the ability of Met-CO2 to regulate the immunosuppressive tumor microenvironment by reducing the stability and membrane localization of PD-L1 [In the present study, the guanidine-modified Met could expand or break open the hybrid membrane-coated nanoparticles in a pH-dependent manner. The membrane coated NPs were designed to improve the condensation and endosomal/lysosomal escape of the encapsulated siRNA. Some reports have suggested that electrostatic interactions could enhance the encapsulation efficiency of gene therapeutic agents in NPs , 55–57. l escape . In the  studies , 62. MC-of PD-L1 .+ T lymphocytes and CD4+ T cells and intratumoral cytokine levels at the end of the antitumor treatment in vivo. Our study showed that MC-PLGA@Met-CO2/siFGL1 NPs significantly increased the ratio of CD8+ T cells to CD4+ T cells and the cytokine levels (IFN-γ and IL-2). which suggests that the combination therapy can induce an effective antitumor immune response.FGL1 is a major ligand of LAG3, which diminishes the function of T cells and immune evasion, and is upregulated in several human cancers; its expression is limited in most of the normal tissues and is associated with poor treatment outcomes . Met canIn the tumor microenvironment, the hypoxic regions commonly exist in many solid tumors because of hypermetabolism and rapid cell proliferation , 64. Met2/siFGL1 NPs have the potential to become a promising strategy to optimize cancer immunotherapy using combination therapy with multiple ICIs.We developed a pH-responsive hybrid biomimetic membrane-camouflaged PLGA nanoparticle system based on multiple tumor targets and low-pH-activated endosomal/lysosomal escape to co-deliver the FGL1/LAG3 blockade molecule, siFGL1, and immuno-metabolic adjuvant Met. This hybrid biomimetic system, designed for co-delivery of Met and siFGL1, exerted a synergistic immunotherapeutic effect both in vitro and in vivo by blocking the inhibitory signals of PD-L1 and FGL1. Additionally, in vivo antitumor results showed that the Met-loaded hybrid biomimetic membrane-camouflaged PLGA NP delivery system could effectively alleviate tumor hypoxia and induce M1-type differentiation of tumor-related macrophages, thereby improving the tumor immunosuppressive microenvironment. Overall, our results indicated that MC-PLGA@Met-CO


The clinical utility of serum immunoglobulin free light chains (sFLC) in IgG4-related disease (IgG4-RD) is unknown. Herein we evaluated their association with clinical phenotypes, serology and activity in patients with IgG4-RD. Cross-sectional study that included 45 patients with IgG4-RD, and as controls 25 with Sjögren’s syndrome (SS) and 15 with sarcoidosis. IgG4-RD patients were classified in clinical phenotypes: pancreato-hepato-biliary, retroperitoneum/aorta, head/neck-limited and Mikulicz/systemic; as well as proliferative vs. fibrotic phenotypes. We assessed the IgG4-RD Responder Index (IgG4-RD RI) at recruitment and measured IgG1, IgG4, κ and λ sFLC serum levels by turbidometry. sFLC levels were similar among IgG4-RD, SS and sarcoidosis groups. Regarding the IgG4-RD patients, the mean age was 49 years, 24 (53.3%) were men and 55.5% had activity. Eight (17.7%) belonged to pancreato-hepato-biliary, 6 (13.3%) to retroperitoneum/aorta, 14 (31.1%) to head/neck-limited, 16 (35.5%) to Mikulicz/systemic phenotypes, whereas 36 (80%) to proliferative and 9 (20%) to fibrotic phenotypes. High κ sFLC, λ sFLC and κ/λ ratio were present in 29 (64.4%), 13 (28.9%) and 13 (28.9%) of IgG4-RD patients, respectively. There were no differences in sFLC among IgG4-RD phenotypes. κ sFLC and κ/λ ratio correlated positively with the number of involved organs and IgG4-RD RI. Patients with renal involvement had higher κ sFLC and λ sFLC. The AUC for κ sFLC and λ sFLC, for renal involvement was 0.78 and 0.72, respectively. Active IgG4-RD had higher levels of κ sFLC and more frequently a high κ/λ ratio. The AUC for κ sFLC and κ/λ ratio for predicting active IgG4-RD was 0.67 and 0.70, respectively. sFLC correlated positively with IgG1 and IgG4 levels. sFLC may be useful as a biomarker of disease activity as well as multiorgan and renal involvement. In particular, a high κ/λ ratio may identify patients with active disease. During pathological conditions such as monoclonal gammopathies, the measurement of sFLC is clinically useful and the detection of an excess of one light chain over another using the κ/λ ratio is accepted as a surrogate for monoclonality2. Thus, assessment of sFLC is of paramount importance in the diagnosis and monitoring of multiple myeloma, AL amyloidosis, or monoclonal gammopathy of undetermined significance3.Physiologically during immunoglobulin synthesis more light chains than heavy chains are produced. Consequently, the unbound light chains can be detected in serum as free light chains (sFLC), either κ or λ4. Other studies in patients with SLE, rheumatoid arthritis (RA) and systemic sclerosis (SSc) have reported abnormal levels of sFLC and a potential utility as biomarkers of disease activity as well7.Systemic autoimmune diseases such as Sjögren’s syndrome (SS) or systemic lupus erythematosus (SLE) are accompanied by over activation of B cells and plasma cells, and consequently by polyclonal hypergammaglobulinemia. Thus, the potential utility of measuring sFLC in these disorders has been investigated. For instance, a recent study including 45 patients with SS disclosed that 64% had abnormal sFLC levels and 11% had a high κ/λ ratio; furthermore, sFLC levels correlated with systemic activity and their levels were modified by treatment with rituximab and abatacept, showing a sensitivity to change8. Serum IgG4 levels and oligoclonal plasmablasts are the two main biomarkers for diagnosis and/or prognosis accepted to date10. However, both tools had their limitations: IgG4 levels may be normal in up to a third of patients with IgG4-RD12 and could be elevated in other conditions that are typically part of the differential diagnosis with IgG4-RD such as pancreatic malignancies, Erdheim-Chester disease and systemic vasculitides16; whereas, oligoclonal plasmablasts had a better specificity and positive predictive value, but their determination by flow cytometry is not easily accessible. Due to these shortcomings, different biomarkers have been proposed for IgG4-RD such as serum soluble IL-2 receptor, cc-chemokine ligand 18 and B cell-activating factor of the tumor necrosis factor family19.IgG4-related disease (IgG4-RD) is a recently described immune-mediated condition characterized by tumefactive fibrosclerosing lesions, high serum IgG4 levels and tissue infiltration by IgG4 positive plasma cells, accompanied often by polyclonal hypergammaglobulinemiaThe clinical utility of sFLC assessment in IgG4-RD is not known. Thus, in this study we aimed to determine the levels of κ and λ sFLC and the κ/λ ratio in patients with IgG4-RD, their utility to distinguish IgG4-RD from mimickers and their association with clinical phenotypes, serology and activity.2 attributable to kidney and retroperitoneal involvement, age and comorbidities. Forty-five patients with IgG4-RD were included in the analysis with a mean age of 49.4 ± 15.3 years; twenty four (53.3%) were male. Age among IgG4-RD patients and controls was similar  and SS patients were less frequently male .We included 52 patients with IgG4-RD and 40 controls (25 with SS and 15 with sarcoidosis). Seven patients with IgG4-RD were excluded due to eGFR < 60 mL/min/1.73 mSeven 15.5%) patients had single organ, 7 (15.5%) 2 organs and 31 (68.8%) 3 ≥ organs involved during the course of the disease. Most frequently involved organs were: submandibular glands in 26 (57.8%), pancreas in 20 (44.4%), lymph nodes in 20 (44.4%), lacrimal glands in 17 (37.8%), parotid glands in 14 (31.3%), biliary tract in 14 (31.1%), paranasal sinus in 13 (28.9%) and kidney in 11 (24.4%). Phenotyping classification and other clinical features are given in Table 5.5% patiIgG4 serum levels were elevated in 30 out of 44 (68.2%) patients ever tested during their disease course. At recruitment, serum IgG1 levels were elevated in 17 (37.7%) patients and serum IgG4 levels in 16 (35.5%), 10 with active IgG4-RD. Thirty five (77.7%) patients had biopsy proven disease, of which 26 had IgG4 + plasma cell count available.Twenty five (55.5%) patients were under immunosuppressive treatment at recruitment. Type of immunosuppressive treatment is given in Table Twenty nine (64.4%) IgG4-RD patients had high κ sFLC levels with a median of 24.37 mg/L (1.61–287.96), 13 (28.9%) had high λ sFLC levels with a median of 19.19 mg/L (4.57–130.46) and 13 (28.9%) had an elevated κ/λ ratio with a median of 1.33 (0.35–3.92). Only one patient with inactive IgG4-RD had diminished κ and λ sFLC levels. There were no IgG4-RD patients with low κ/λ ratio.There were no differences in the proportion of IgG4-RD patients with high κ sFLC  and elevated κ/λ ratio  when compared with SS and sarcoidosis patients. On the contrary, patients with IgG4-RD had less frequently high levels of λ sFLC than SS patients and more frequently than sarcoidosis patients . Furthermore, the serum levels of κ sFLC, λ sFLC and κ/λ ratio were not significantly different among IgG4-RD patients and control groups , p = 0.56), λ sFLC (1 [12.5%] vs. 3 [50%] vs. 3 [21.4%] vs. 5 (31.3%), p = 0.42) and κ/λ ratio (2 [25%] vs. 2 [33.3%] vs. 4 [28.6%] vs. 5 (31.3%), p = 0.98) among pancreato-hepato-biliary, retroperitoneum and aorta, head and neck-limited and Mikulicz and systemic phenotypes of IgG4-RD. Levels of κ sFLC, λ sFLC and κ/λ ratio were not significantly different among the four IgG4-RD clinical phenotypes , λ sFLC  and κ/λ ratio  between proliferative and fibrotic phenotypes of IgG4-RD. Levels of κ sFLC, λ sFLC and κ/λ ratio were not significantly different among these clinical phenotypes  and the κ/λ ratio , but not with λ sFLC   had higher levels of κ sFLC  and λ sFLC  compared to patients without renal involvement  and the rest had normal kidney function; eight had active disease. At the linear regression analysis, eGFR did not correlate with κ  and λ sFLC levels  nor the κ/λ ratio .Only 2 patients with renal involvement had mild kidney dysfunction  had higher levels of κ sFLC  and a trend for higher levels λ sFLC  compare to patients without lymph node involvement. We did not find any association with other organs.Twenty-five of the IgG4-RD patients had active disease, of whom 14 (56%) had de novo, nine (36%) relapsing and two (8%) persistent active disease.Patients with active IgG4-RD had more frequently high κ sFLC compared to patients with inactive IgG4-RD, although without reaching a statistical significance ; whereas the proportion of patients with high λ sFLC was similar . Patients with active IgG4-RD had more frequently an elevated κ/λ ratio than patients with inactive disease . The concentrations of κ sFLC  and the κ/λ ratio  were higher in active than in inactive IgG4-RD  and higher κ/λ ratio . The AUC for κ sFLC and κ/λ ratio for discriminating de novo active from inactive disease with the same cut-off values (33.42 mg/L and 1.61) were 0.75 and 0.74, respectively. On the contrary, when including only the nine patients with relapsing disease, there were no differences compare to the inactive group (data not shown).IgG4-RD patients with damage at the time of recruitment did not have differences in sFLC levels compare to patients without damage (data not shown).There was a significant positive correlation between serum IgG1 levels and κ sFLC , λ sFLC  and the κ/λ ratio   have no differences in the prevalence κ sFLC, λ sFLC and the κ/λ ratio and their levels compare to patients without biopsy proven disease (n = 10) (data not shown). The number of IgG4 + plasma cells in tissue did not correlate with κ sFLC  λ sFLC  nor the κ/λ ratio .There were no differences in the prevalence and levels of κ sFLC, λ sFLC and the κ/λ ratio between patients with and without immunosuppressive treatment (data not shown).In a sensitivity analysis including the 14 patients with active untreated IgG4-RD, an elevated κ/λ ratio remained more frequent compare to SS and sarcoidosis patients (7 [50%] vs. 4 [16%] vs. 2 (13.3), p = 0.04).In this study we demonstrated that a substantial proportion of patients with IgG4-RD had high levels of sFLC, and although their assessment may not be useful for distinguishing from other conditions, it may be valuable as a biomarker of disease activity and multiorgan and renal involvement. Additionally, a high κ/λ ratio may be valuable for the same purposes.20. In our study, we found that 64.4% and 28.9% of our cohort had elevated κ and λ sFLC respectively, which is lower than the frequencies reported by Grados et al. These differences may be explained by the fact that in the French study patients with severe kidney impairment were also included and it is well established that sFLC correlate negatively with the degree of renal insufficiency21.To date, only one study have assessed sFLC levels in patients with IgG4-RD. Grados et al. determined sFLC levels in 16 French patients with IgG4-RD and found that they were higher compared to a group of healthy subjects; furthermore, they found that 87% and 53% of the patients had elevated κ and λ sFLC respectively. However, due to the small sample size they were not able to assess the relationship of sFLC with clinical characteristics and disease activity5. It was noteworthy that a considerable proportion of our IgG4-RD cohort (28.9%) had an abnormal κ/λ ratio, in contrast to the lower frequencies found in SS and SLE22. Skewing of the κ/λ ratio reflects the excess production of one immunoglobulin light chain over the other and is considered a surrogate laboratory parameter indicating clonality in plasmaproliferative or lymphoproliferative disorders2. This pattern of κ-restricted pseudoclonality has been previously reported in IgG4-RD and in settings where there is polyclonal IgG4 excess. Grados et al. found an elevated κ/λ ratio of sFLC in 44% of 16 patients with IgG4-RD20. Furthermore, two studies have demonstrated that sera from patients with increased serum IgG4 levels evaluated by immunosubstraction, including patients with IgG4-RD, may have a monotypic appearance with a κ-restricted pseudoclonal pattern24. The observed κ-restricted pseudoclonality may be attributable to the intrinsic characteristics of the IgG4 subclass itself. The ratio of κ- to λ-expressing IgG, commonly 2 to 1, depends on the IgG subclass. IgG1, for example, has a κ/λ ratio of 2.4 to 1, whereas IgG4 has a κ/λ ratio of about 8 to 1 with a heavily skewing towards the κ light chain24. This κ-restricted pseudoclonality seen by immunosubstraction and by measurement of sFLC, together with the polyclonal IgG4 serum protein electrophoresis pattern that may mimic a monoclonal gammopathy24, may lead to a presumed diagnosis of clonality. While some cases of IgG4-related ophthalmic disease with κ light chain restriction evaluated by in-situ hybridization have been reported26, if tissue specimens from IgG4-RD patients had the same immunoglobulin light chain pseudo-restriction remains to be determined.The increase levels of sFLC in patients with IgG4-RD is undoubtedly related to the increase synthesis of immunoglobulins by the B cell lineage which also could explain why we found a positive correlation between sFLC and IgG1 and IgG4 levels. This phenomenon is well described in systemic autoimmune diseases in which B cell over-activation is a component of the pathogenesis of the disease such as SS and SLE27. Furthermore, whereas serum IgG4 levels are a valuable tool to disclose multi-systemic disease and risk of relapse, complement levels may predict renal involvement28. However, no single serological feature is by itself specific enough for the diagnosis of IgG4-RD. Data on biomarkers to differentiate IgG4-RD from other conditions have been conflicting. For instance, Akiyama et al. described that serum soluble IL-2 receptor was not useful for the differential diagnosis between IgG4-RD and SS17. On the contrary, a recent study by Umeda et al. found that serum thymus and activation-regulated chemokine could discriminate between IgG4-RD and SS29. In our study we did not find differences in sFLC concentrations among IgG4-RD group and both control groups, however, up to half of the patients with active IgG4-RD had an elevated κ/λ ratio.There is an unmet need regarding biomarkers in IgG4-RD. Up to date, only serum IgG4 and complement levels have a role in diagnosis and/or classification of the disease7, measurement of sFLC may be a useful biomarker to assess systemic activity in IgG4-RD, as demonstrated by our findings of higher levels of κ sFLC and κ/λ ratio in patients with active vs. inactive IgG4-RD. Interestingly, only two patients with inactive disease had an elevated κ/λ ratio. Furthermore, half of the patients with an elevated κ/λ ratio had normal serum IgG4 levels, thus, an elevated κ/λ ratio could point to active disease even in those patients with normal serum IgG4. Interestingly, most of the active patients with an elevated κ/λ ratio had de novo active disease.As has been described for other systemic autoimmune diseases such as SLE, SS, RA and SScIn certain clinical scenarios after treatment of IgG4-RD, it may be difficult to assess whether a clinical or imaging finding is a reflection of ongoing organ inflammation and activity or permanent scarring and damage; this is especially true for clinical manifestations of the fibrotic phenotype such as retroperitoneal fibrosis and some cases of orbital pseudotumor. In other settings, biochemical findings reflecting permanent damage such as persistent elevation of alkaline phosphatase or urine protein may lead to the false reassurance of ongoing biliary tract and kidney activity followed by unnecessary treatment. Thus, a biomarker for disease activity in IgG4-RD may be particularly beneficial in these situations. The ROC curve generated by the results in the present study showed that the cut-off value of 33.42 mg/L for κ sFLC and 1.61 for the κ/λ ratio may discriminate between active and inactive IgG4-RD with a high specificity and positive predictive value, although with a low sensitivity. Interestingly, the AUC for de novo active disease had a better performance, while this was not true for relapsing disease. The latter finding may be due to the smaller number of patients with relapsing disease compare to de novo active disease included in our study.20, as has been reported for multiple myeloma and AL amyloidosis where sFLC levels correlate with bone marrow infiltration by plasma cells and the number of organs involved by amyloid, respectively31.In agreement with the aforementioned findings, we found a positive correlation between the number of organs involved and the IgG4-RD RI with both the κ sFLC and the κ/λ ratio. Furthermore, we observed higher levels of sFLC in patients with lymph node involvement. As suggested by Grados et al. these findings may correlate with the volume of the polyclonal “tumoral” infiltrate of IgG4-RD23. Differences in laboratory parameters among different subsets is well stablished in terms of IgG, IgG4, IgE, eosinophils and complement levels. It was tempting to hypothesize that patients belonging to the Mikulicz and systemic and the proliferative phenotype would display higher sFLC levels, as these phenotypes more frequently present with polyclonal hypergammagobulinemia; however, we did not find differences in sFLC levels among the distinct IgG4-RD phenotypes.Nowadays, classifying patients with IgG4-RD in different phenotypic groups is appealingInterestingly, we observed that concentrations of both κ and λ sFLC were higher in IgG4-RD patients with kidney involvement, although this observation was not driven by a low eGFR. The ROC curves showed that the cut-off value of 33.42 mg/L for κ sFLC had an acceptable sensitivity and specificity, with a negative predictive value of 90%, rendering κ sFLC as an excellent tool to exclude kidney involvement.Our study is not exempt of limitations. First, the study had a cross sectional design and thus we did not measure sFLC prospectively to assess their utility to predict relapses and treatment response. Second, serum and urine electrophoresis and immunofixation was not routinely done in our patients to exclude a monoclonal plasma cell disorder, nevertheless, we have no doubt about the certainty of IgG4-RD diagnosis. Third, although some patients were under immunosuppressive therapy at the time of recruitment, we did not find differences in sFLC levels among patients with vs. without such therapy. Finally, longitudinally studies are needed to better define if sFLC levels are sensitive to change under immunosuppressive therapy and for predicting relapsing disease.Some strengths also need to be mentioned. First, this is the first study to evaluate the usefulness of sFLC in cohort of IgG4-RD according to the clinical phenotype and activity status. Second, we included a control group with other systemic autoimmune diseases that commonly mimic IgG4-RD in order to assess their value as a complementary diagnostic tool. Nevertheless, the performance of sFLC might be further evaluated in other IgG4-RD mimickers such as malignancies or ANCA-associated vasculitides.In summary, a significant proportion of patients with IgG4-RD may present with high levels of sFLC and, even though their assessment may not be useful for distinguishing from SS and sarcoidosis. Measurement of sFLC may be useful as a biomarker of disease activity and multiorgan and renal involvement. In particular, a high κ/λ ratio may identify patients with active disease even in those with normal IgG4 levels.32 who attended a referral center in Mexico City from August 2018 to November 2019. We excluded patients with chronic kidney disease (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 for ≥ 3 months), concomitant diagnosis of another systemic autoimmune/inflammatory disease, active infection or malignancy, including monoclonal gammopathies. As a control group, we included patients with SS according to the 2016 American College of Rheumatology/European League Against Rheumatism classification criteria for SS33 and patients with sarcoidosis according to the American Thoracic Society/European Respiratory Society/World Association of Sarcoidosis and other Granulomatous Disorders diagnostic criteria34. We obtained approval from the Institutional Review Board  and the study complies with the Declaration of Helsinki. Patients and controls gave written informed consent.This was a cross-sectional study. We included consecutive patients with a diagnosis of IgG4-related disease according to the Comprehensive Diagnostic Criteria for IgG4-related diseaseWe retrospectively collected patient information such age at diagnosis, number of organs ever involved, IgG4 serum levels at diagnosis and/or during the disease course, biopsy availability and the use of glucocorticoids and immunosuppressive therapy.11; patients with only mesentery or mediastinal involvement were included in the retroperitoneum and aorta phenotype; and patients who could not be fitted in one of these clinical phenotypes were termed “unclassifiable” phenotype. Patients were also classified according to the clinical phenotypes described by Zhang et al. in proliferative and fibrotic phenotypes35. We assessed the number of involved organs and the IgG4-RD Responder Index (IgG4-RD RI) at recruitment36.Patients were classified according to clinical phenotypes as described by Wallace et al. in pancreato-hepato-biliary, retroperitoneum and aorta, head and neck-limited and Mikulicz and systemic phenotypes36.We defined active disease as the presence of clinical signs and symptoms, laboratory abnormalities or unequivocal radiological findings attributable to IgG4-RD. Patients with active disease included those with de novo active disease (first episode of active IgG4-RD), relapsing disease (active disease after a period of inactive IgG4-RD) and persistent active disease (active disease despite immunosuppressive treatment). We defined inactive disease as the absence of clinical signs and symptoms, laboratory abnormalities and new unequivocal radiological findings attributable to IgG4-RD. Damage was defined as irreversible organ dysfunction or failure caused by IgG4-RD or as a consequence of surgical procedures performed to diagnose or treat IgG4-RDPLUS). The immunoassay consisted of two separate measurements, one for free κ and one for free λ and then the automatic calculation of the κ/λ ratio. Normal values were set according to the manufacturer’s recommendations as follows: normal free κ range: 3.3–19.4 mg/L; normal free λ range: 5.7–26.3 mg/L; and normal κ/λ ratio range: 0.26–1.6537. In patients with IgG4-RD, serum IgG1 and IgG4 levels were determined with turbidimetry analyzer  and normal values were also set according to the manufacturer’s recommendations as follows: IgG1 normal range: 382.4–928.6 mg/dL; IgG4 normal range: 3.9–86.4 mg/dL. eGFR was calculated with the Chronic Kidney Disease Epidemiology Collaboration equation at the time of recruitment.Blood samples from patients and controls were collected. Serum samples were extracted and frozen at − 20 °C until processing. sFLC levels were measured in the same laboratory using latex-enhanced immunoassay  using turbidimetry (SPAWe used descriptive statistics. Dichotomous variables were expressed as absolute frequencies and continuous variables as means and standard deviations (SD) or medians and minimum and maximum range as appropriate. Comparison between means was made with Student’s T-test and between medians with Mann–Whitney U test. Categorical variables were analyzed with Chi square test or Fisher exact test as appropriate. We used Kruskal–Wallis test for comparison among multiple groups. Correlations among variables were evaluated using Spearman’s test.We estimated the sensitivity, specificity, positive predictive value and negative predictive value, as area under the curve (AUC) for predicting active disease and kidney involvement. Finally, we conducted a linear regression analysis to elicit GFR as a predictor of sFLC levels. A two-tailed P < 0.05 was considered statistically significant. All analyses were performed using the SPSS 20.0 and GraphPad Prism 8.3.0 for Windows 20.0.


Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, showed equivalent performance to checkerboard methodology, with the advantage of strongly reduced assay complexity and costs for CombiANT. A synergy screening of 10 antibiotic combinations for 12 E. coli urinary tract infection (UTI) clinical isolates illustrates the need for refined combination treatment strategies. For example, combinations of trimethoprim (TMP) + nitrofurantoin (NIT) and TMP + mecillinam (MEC) showed synergy, but only for certain individual isolates, whereas MEC + NIT combinations showed antagonistic interactions across all tested strains. These data suggest that the CombiANT methodology could allow personalized clinical synergy testing and large-scale screening. We anticipate that CombiANT will greatly facilitate clinical and basic research of antibiotic synergy.Antibiotic combination therapies are important for the efficient treatment of many types of infections, including those caused by antibiotic-resistant pathogens. Combination treatment strategies are typically used under the assumption that synergies are conserved across species and strains, even though recent results show that the combined treatment effect is determined by specific drug–strain interactions that can vary extensively and unpredictably, both between and within bacterial species. To address this problem, we present a new method in which antibiotic synergy is rapidly quantified on a case-by-case basis, allowing for improved combination therapy. The novel CombiANT methodology consists of a 3D-printed agar plate insert that produces defined diffusion landscapes of 3 antibiotics, permitting synergy quantification between all 3 antibiotic pairs with a single test. Automated image analysis yields fractional inhibitory concentration indices (FICis) with high accuracy and precision. A technical validation with 3 major pathogens,  Existing methods for identifying efficient combinations of antibiotics are time-consuming and costly, restricting their use in clinics and research. This study describes the novel CombiANT methodology, which uses defined diffusion landscapes of three antibiotics to permit rapid and low-cost synergy quantification between all antibiotic pairs. Staphylococcus aureus (VRSA) [P. aeruginosa in cystic fibrosis and other bacterial pathogens [Antibiotic therapy increasingly relies on the combined activity of 2 or more agents. Combinations of antibiotics are applied for up to 50% of patient cases in the treatment of severe surgical site infections, bacteremia, pneumonia, or septic shock –4. Antibs (VRSA) , resistas (VRSA) , and hets (VRSA) . Moreoveathogens ,17–19. TThe individual inhibitory effects of antibiotics on the bacterial cell can interact to produce combination effects that are either stronger (synergistic) ,21 or weSynergy and antagonism are not inherent properties of an antibiotic pair but arise from the combination of actions that the antibiotics have on bacterial cells. Thus, the interaction, be it positive, additive, or negative, is the net effect of drug-induced damage and drug-defense responses. As a result, variation in the genetics and physiology of the bacterial population could potentially cause variation in antibiotic interaction type. Indeed, several recent studies have shown that co-treatment with different pairs of clinically relevant antibiotics exhibit interaction patterns that depend both on the bacterial species and the specific strain within a species –26. TogeRefined combination therapy requires a case-by-case examination of antibiotic interactions for each patient isolate. However, such personalized diagnostics is—because of its complexity and high labor cost—not feasible with the current gold-standard methods, broth-based checkerboard assays and time-kill experiments ,28. AgarE. coli urinary tract infections (UTIs). Lastly, we identified conserved and variable antibiotic interactions that have high potential for personalized and refined combination therapy.In this study, we present a new and easy-to-conduct assay for testing antibiotic synergy that is robust and highly quantitative. Our solution, CombiANT, is a diffusion-based assay that provides quantitative information of all pairwise interactions of 3 antibiotics in a single agar plate. The present study shows that CombiANT performs equally well to broth-based checkerboard methodology, but due to its unique design and function, offers much reduced method complexity that is comparable to a disk diffusion test. Similar to checkerboard assays, CombiANT produces fractional inhibitory concentration indices (FICis) but enables higher throughput. The assay can be applied without previous information of the susceptibility of the strain. We demonstrate the potential of CombiANT for antibiotic interaction screening by applying the assay to the antibiotics used for treating The CombiANT assay was designed to fulfil the following criteria: (i) the generation of quantitative information of antibiotic interactions, (ii) a reduction of assay complexity and work hours for assay preparation and analysis, (iii) high throughput, and (iv) easy integration into clinical microbiology laboratory routines. Our solution is a diffusion-based assay that provides quantitative information of the pairwise synergy of 3 different antibiotics in a single agar plate. The assay consists of a custom-designed culture inserts  that canTo run a CombiANT assay, antibiotic-containing agar is loaded into the insert reservoirs by pipetting . For mosTo implement a specific synergy test, the prepared insert is placed into a culture plate and overcast with a final layer of culture agar. This step activates the assay, which is ready for use once the agar solidifies . The finThe insert is designed for easy and precise image analysis. Specifically, the geometry of the insert and its geometric relation to—i.e., its precise and predetermined distance from—the agar surface generates a predetermined and controlled diffusion of the antibiotics. The controlled diffusion is modeled with a finite elements method (FEM) for each antibiotic individually , yieldinAntibiotics differ in their diffusion rate due to their structure and the interaction with the diffusion matrix, in this case agar. The diffusion of an antibiotic is specific to the precise assay conditions  used. Therefore, an experimental diffusion coefficient was pre-calculated for every antibiotic in an assay calibration step. To carry out the calibration of the assay, a reference strain with known MIC is tested with 3 concentrations of the target antibiotic .In an image of a CombiANT agar plate, certain areas of interest can be observed . On the AC and BC are the concentrations of A and B, respectively, in their corresponding CP. ACFICi and BCFICi are calculated similarly. An FICi value of 1 denotes additivity. FICi < 1 indicates synergy, whereas FICi > 1 indicates antagonism. Threshold values for clinically relevant levels of synergy and antagonism are usually set at <0.5, and >2–4, respectively [Inside the interaction imaging area . Similarectively –35. The E. coli K-12 MG1665 and P. aeruginosa PA14) and a gram-positive reference strain (S. aureus ATCC29213) against a panel of 4 antibiotics. The antibiotics, ampicillin (AMP), cefotaxime (CTX), ciprofloxacin (CIP), and gentamicin (GEN), span 3 distinct mechanisms of action, and 2 are commonly used in treatments of bacteremia and sepsis caused by the bacterial species described. FICis were calculated for all pairwise interactions and the 3 strains  using the CombiANT assay protocol . The choice of method on the other hand was not statistically correlated with experimental outcome . Altogether, we concluded that the CombiANT assay has an equal accuracy for the detection of antibiotic interactions as broth-based checkerboard assays.Having shown that the 2 methods are interchangeable, we tested the effect choosing one method over the other by performing a multivariate linear regression analysis. We tested the correlation between 2 factors and the outcome of the experiment in the form of FICi values. The 2 factors were the identity of the antibiotics used, and the method applied to that specific test (either CombiANT or checkerboard). The analysis yielded a correlation coefficient and a corresponding A difference between the CombiANT and checkerboard assays is that, by virtue of diffusion, CombiANT applies a continuous concentration range, while checkerboard assays typically test discrete 2-fold dilutions. Therefore, we tested whether the high precision of the CombiANT assay was a result of the continuous concentration range. To do that, we performed refined checkerboard assays with increased linear concentration ranges. All 18 strain-combination pairs, used in this technical validation, were replicated with this method and quantified using a Bliss independence null model . The BliE. coli UTI clinical isolates and included the E. coli K-12 MG1655 as a reference strain  but was observed to have an additive behavior in all remaining strains. In contrast, NIT-FOF had a slight but significant positive interaction in the strain DA44578 but was additive in all other strains. The medically more interesting results were obtained for the remaining 3 combinations, TMP-NIT, TMP-MEC, and MEC-NIT. Positive synergy for TMP-NIT was detected in only 4 out of 12 UTI isolates . TMP-MEC exhibited positive synergy in 3 out of the 12 isolates . All remaining isolates in both cases displayed no synergy between the antibiotics, indicating genetic variation in the nature of these interactions among UTI isolates. For the clinically relevant combination of TMP-MEC strains DA44560, DA45406, and DA46056 exhibited entirely different synergy profiles, ranging from border line antagonism to positive synergy. We confirmed further the variation in TMP-MEC synergy by performing additional synergy experiments on those strains using a time-kill assay and a growth-rate assay. Both assays reiterated the findings of CombiANT  Figs. FiIn this study, we present and characterize the new CombiANT assay, which enables an efficient determination of antibiotic interactions. Our extensive technical validation indicated high accuracy and precision, and an overall equal performance to the established checkerboard assay. We then implemented CombiANT for a screen of antibiotic synergy among UTI isolates. A consistent antagonistic interaction was discovered as well as 4 interactions with significant strain-to-strain variation, indicating the potential of CombiANT for both individualized clinical diagnostics and basic research.An alternative methodology has been successfully applied for the high-throughput measurement of drug interactions in basic research . In thisTo allow for easy use of CombiANT both by clinics and academic laboratories, we designed 2 different protocols, namely (i) a resistance breakpoint-based protocol for clinical use , and ii. CombiANAn important design principle of CombiANT was that antibiotic synergy is quantified at high, clinically relevant concentrations. Synergy is measured from the edge of inhibition zones, meaning the MIC-equipotency isobole of combination space using the FICi. The FICi method is based on the null model of Loewe additivity of doses . An alteE. coli K-12 MG1655 [P. aeruginosa PA14 strain and the S. aureus ATCC29213 were previously characterized, limiting comparisons to our study. AMP-CIP additivity was previously reported for S. aureus [P. aeruginosa [The results obtained in the datasets in Figs 2 MG1655 ,43. Howe. aureus , and intruginosa , and ComThe screening of 12 UTI clinical isolates presented in As the above synergy screening of 12 clinical UTI isolates iterates, there are cases, such as the interaction between TMP and MEC, in which combining 2 antibiotics seems to have a consistent antagonistic effect across most strains. Cases such as these, with antagonistic behavior across the board, illustrate the need for clear guidance when designing combination therapies, even when carried out empirically. Identifying such combinations of antibiotics that should be avoided will require large-scale systematic synergy screenings. With the current methods that quantify antibiotic synergy, a systematic screening of such a scale is unfeasible. However, CombiANT presents a new, less labor-costly method that is still capable of quantifiable results. Using our new approach to interaction studies makes such large-scale synergy screens attainable. The screening performed here illustrates CombiANT’s scalability. To get all pairwise interactions for the 5 antibiotics that we screened one UTI isolate for and assuming that a range of 8 concentrations is tested for all antibiotics would require 640 tubes for a time-kill experiment or ten 96-well plates for a checkerboard assay. With CombiANT, all synergy data were extracted from only 4 agar plates.The screening for the interactions between TMP-NIT and TMP-MEC revealed an important result, namely that the same 2 antibiotics might not have a consistent synergy profile across different strains of the same species. If antibiotics can be synergistic against one strain but additive or antagonistic for another, then we suggest that synergy screens should become a part of standard testing in clinical microbiology laboratories. Thus, an assay such as CombiANT, which is quantitative and simple, could be part of routine screening in a microbiology laboratory. Translating CombiANT’s in vitro positive synergy findings to in vivo antibiotic synergy is not trivial. Antibiotic absorption as well as kinetics in the human body might disrupt a positive synergy profile. In silica antagonistic behavior, however, represents a compound-to-compound inhibition in its simplest form. Therefore, we think any detection of antibiotic antagonism in vitro could be a helpful guide for clinical decisions in an even more straightforward way than antibiotic synergy.Aside from the clinical applicability, CombiANT has high potential as a tool for basic research in biology. For a majority of observed antibiotic interactions, a mechanistic and evolutionary understanding is currently lacking, and these knowledge gaps may partly be explained by the complexity of current synergy measurement methods. In our limited screen of UTI isolates, the majority of antibiotic interactions were additive. The variable synergy profiles that were observed with TMP-NIT, FOF-CIP, NIT-FOF, and TMP-MEC indicate interesting biological strain-to-strain variation that warrants further investigation. The highly antagonistic interaction between the beta-lactam MEC and NIT may potentially indicate an evolutionary conservation of this drug interaction, implying a functional constraint between cellular functional modules. The antagonism could potentially be explained by an overlap in the cellular drug and stress responses to the component drugs. It has been shown that both beta-lactams and NIT individually induce expression of the cellular SOS response for DNA repair ,46, poteFinally, CombiANT is not restricted to the study of interactions between antibiotics. In principle, the individual and combined activity of any bioactive compound can be studied with CombiANT. We anticipate similar applications in cell biology, as well as environmental microbiology. A potential diversity of applications is supported by the high flexibility of CombiANT, which can be easily implemented with different growth medium and culturing conditions . We anticipate that CombiANT could, e.g., be applied for the characterization of combination effects between chemotherapy agents on hydrogel-embedded growth of spheroids or organoids ,50. ThusThe insert was designed with computer-aided design software  and manufactured by 3D printing  using proprietary formulations for autoclavable or dental resin version 2 . The 3D printing was performed in the U-Print facility of Uppsala University. The designs are available upon request to the authors.E. coli K-12 MG1655 (DA5438), P. aeruginosa PA14 (DA64160), and S. aureus ATCC29213 (DA64485). For the UTI study, we screened 12 clinical isolates of independent origin that were susceptible to the antibiotics CIP, FOF, MEC, NIT, and TMP. Bacteria were cultured on Mueller-Hinton agar and in Mueller-Hinton broth  with incubations at 37°C. Overnight cultures were prepared from single colonies in 1 mL and 190 rpm orbital shaking. For the UTI isolate screening, the agar was supplemented with 25 mg/L glucose-6-phosphate, as this is required for the action of FOF. Antibiotic stocks were prepared according to the manufacturer’s recommendations and stored frozen at −20°C in aliquots for single use: AMP 100 mg/mL in water , CIP 25 mg/mL in 0.1 M HCl , CTX 50 mg/mL in water , GEN 50 mg/mL in water , FOF 50 mg/mL in water , MEC 10 mg/mL in water , NIT 10 mg/mL in 100% DMSO , and TMP 10 mg/mL in 100% DMSO .For the technical validation, we tested the reference strains 5 cells from a dense overnight culture  and incubated without shaking at 37°C for 24 hours, after which wells were mixed by pipetting and growth was measured by optical density at 540 nm . MIC was called at the lowest concentration that yielded a growth signal of uninoculated control wells. Measurements were performed with 2 biological replicates, and their average value was designated MIC. For determination of MIC to FOF, the media were supplemented with 25 mg/L of glucose-6-phosphate, which is required for FOF-mediated inhibition.To calibrate CombiANT, we determined MIC values for antibiotics individually, using standard broth microdilution methodology in agreement with EUCAST guidelines. Two-fold serial dilutions of antibiotics in Mueller Hinton broth were prepared in 96-well microtiter plates. The plates were then inoculated with approximately 3 × 10For the technical calibration of the CombiANT assay, the antibiotic concentrations were determined based on the MIC of the antibiotic against the strains used . In the 5 cells (0.5 McFarland), and optical density was read at 540 nm  after 16 hours of static incubation. For Bliss model synergy quantification, higher-resolution checkerboards (9 × 9 concentrations) were obtained using linear concentrations up to 1 × MIC, and with 3 biological replicates. Treatments positions were fully randomized to avoid bias from edge and gradient effects. Degree of synergy was calculated as previously described [Y1+2 according to a Bliss independence model [Y1 and Y2 obtained in the single-antibiotic treatments. The degree of synergy S of a combination is defined as: S = Y1+2–Yobserved. S = 0 expresses additivity, positive values denote synergy, and negative values denote antagonism.Standard checkerboard assays (8 × 8 concentrations) for FICi determination were performed in single biological replicate and with 2-fold serial dilutions ranging from 4 × MIC to one-quarter MIC, as previously described . Inoculuescribed . Growth ce model  was calcAn FEM was used to model diffusion of reagents from the reservoirs. Diffusion was assumed to follow Fick’s laws of diffusion, and concentration of the agent was calculated by solving the convection-diffusion equation for no advective flux and no net volumetric source. The diffusion coefficients used were experimentally determined for every antibiotic. As per the protocol described in the Supporting Information , each exStatistical analyses were performed using Graph Pad Prism and Matlab. Statistical difference of measured FICi to the additive model (FICi = 1) was assessed using Wilcoxon signed rank tests. In the UTI screen, only interactions that show clinically relevant levels of antibiotic interaction  were tested. Numerical values underlying all analyses are available in E. coli isolates DA44560, DA45406, and DA46056, which showed different interaction profiles for the combination MEC-TMP. In brief, MHB broth was supplemented with TMP, MEC at isolate-adjusted MIC dosage, or the MEC-TMP combination (MIC-MIC), of which 1 mL was inoculated with approximately 3 × 106 cells from a dense overnight culture (1:1000 dilution). The MICs of the isolates were determined before the assay using broth microdilution and were found to vary between 0.1 to 0.4 mg/L for both antibiotics. Cultures were incubated at 37°C with orbital shaking, and survival was measured after 0, 2, 4, 8, and 24 hours by plating diluted samples on well-dried MHA using Miles and Misra spotting [A time-kill experiment was performed with spotting  with 5 μE. coli isolates DA44560, DA45406, and DA46056 were obtained for a 5 × 5 concentration checkerboard of MEC-TMP and measurement of growth using optical density at 600 nm (OD) in a Bioscreen C plate reader . The strains were selected for their variation in synergy profiles for this antibiotic combination. Antibiotics were dosed at 0, 0.025, 0.05, 0.1, and 0.2 mg/L. Treatment conditions were prepared in large volume of which 300-μL portions were aliquoted into honeycomb well plates. To start antibiotic treatments, the wells were inoculated with approximately 3 × 106 cells from a dense overnight culture. Immediately after inoculation, plates were transferred to Bioscreen reader for incubation  and measurements of OD every 4 minutes for a total of 12 hours. The experiment was performed with 3 biological replicates. Data were analysed using the R statistical platform. Raw OD values were background corrected using OD from uninoculated wells. Exponential growth rates were calculated from log-transformed data (ln OD) in the early time window of 60 minutes to 110 minutes during which growth was strictly exponential , in which R0 represents the uninhibited growth of untreated reference wells. The expected inhibition effect E1+2 was calculated by addition of the inhibition effects of the antibiotics when acting alone, E1 and E2. Synergy was then quantified, using degree of synergy S, expressed as S = E1+2–Eobserved. Positive values denote synergy, and negative values denote antagonism. The obtained S-values were then averaged across the checkerboard concentration grid and tested against the null expectation (S = 0) using a one-sample Wilcoxon signed rank test. A parallel analysis was conducted using growth yields after 12 hours, according to the procedures described in the section ‘Checkerboard experiments,’ given earlier.Growth curves of S1 FigE. coli, P. aeruginosa, and S. aureus tested against the antibiotics AMP, CIP, GEN, and CTX. CombiANT MIC predictions  fall within the log2 dilution window of the BMD assay (indicated by the shaded regions), supporting the physics diffusion model. An accurate determination of MIC for P. aeruginosa by BMD was not possible for AMP and CTX due to extensive cell elongation preceding cell death. Numerical values for this figure are available in Comparison of the MIC values obtained with CombiANT as part of the synergy calculations with those obtained from BMD. Data of reference strains (TIF)Click here for additional data file.S2 FigFICi for all self-interactions for the 8 antibiotics AMP, CIP, CTX, GEN, FOF, NIT, MEC, and TMP. Bars indicate average values and SD from 3 biological replicates. The dotted line indicates additivity, i.e., FICi = 1. All interactions were additive in nature and showed no statistically significant difference from FICi = 1 according to one-sample Wilcoxon signed rank test. Numerical values for this figure are available in (TIF)Click here for additional data file.S3 FigE. coli, P. aeruginosa, and S. aureus tested against the antibiotics AMP, CIP, GEN, and CTX. Each antibiotic was tested with 8 equally spaced linearly increasing concentrations (denoted 1–8) between zero and the MIC for the respective species. Data are average growth values of 3 biological replicates relative to untreated controls. (B) Dose-dependent synergy profiles according to a Bliss independence additive model. Numerical values for this figure are available in (A) Antibiotic checkerboard data with linear concentrations ranges for (TIF)Click here for additional data file.S4 FigP < 0.05. Numerical values for this figure are available in FICi for all pairwise combinations of the 5 antibiotics CIP, FOF, NIT, MEC, and TMP. Bars indicate average values and SD from 3 biological replicates. The dotted line indicates additivity, i.e., FICi = 1. Synergy with FICi < 0.5 is indicated in blue, antagonism with FICi > 4 is indicated in red. One-sample Wilcoxon signed rank test against FICi = 1, *(TIF)Click here for additional data file.S5 Fign = 3 biological replicates). Antibiotics were applied at isolate-adjusted MIC dosage. The circle symbols (●) denote the effect of MEC on strain survival, triangles (π) denote the effect of TMP, and open squares (ο) denote the combined effect of the MEC-TMP combination. The red lines represent the Bliss additive interaction model . Increased survival in the combination compared to the additivity line indicates antagonism, whereas the opposite indicates synergy. The dotted line shows the assay’s limit of certainty. Missing data points indicate a colony count of zero . Numerical values for this figure are available in Time-kill assay on the UTI strains DA44560, DA45406, and DA46056. The 3 strains showed contrasting interaction profiles for the MEC-TMP combination in the CombiANT assay, which was additive for DA45406, synergistic for DA46056, and mildly antagonistic for DA44560 . Survival fraction was expressed as CFU/mL relative to time point 0 hours Click here for additional data file.S6 FigS in drug–drug combinations according to a Bliss additive model . Positive values of S indicate synergy, and negative values indicate antagonism. Values are averages of 3 biological replicates. (B) Distribution of coefficient of determination R2 for fitted exponential growth models in the measurement time window of 60 to 110 minutes after start of treatment. (C) Average degree of growth rate synergy across the concentration grid . (D) Growth yields after 12 hours of treatment, and calculated degree of synergy according to a Bliss additive model (relative yields are multiplied). Values are averages of 3 biological replicates. (E) Logarithmic presentation of underlying growth curves. The dotted lines show the measurement window for exponential growth rates in panel A. (F) Average degree of growth yield synergy across the concentration grid . MEC-TMP additivity in DA45406 is replicated by growth rate and growth yield measurements. MEC-TMP synergy in DA46056 is apparent in growth yields, but not growth rates. Mild antagonism of MEC-TMP in DA44560 is replicated in growth rates, but not growth yields. Numerical values for this figure are available in The UTI isolates DA44560, DA45406, and DA46056 were treated with low concentrations of MEC, TMP, or combinations of MEC-TMP, using a 5 × 5 checkerboard setup. The 3 strains showed contrasting interaction profiles for the MEC-TMP combination in the CombiANT assay, which was additive for DA45406, synergistic for DA46056, and mildly antagonistic for DA44560 . Growth was monitored in 4-minute intervals using OD. (A) Exponential growth rates of the checkerboard, and degree of synergy (TIF)Click here for additional data file.S1 File(STL)Click here for additional data file.S1 Code(DOCX)Click here for additional data file.S1 TableE. coli, P. aeruginosa, and S. aureus and the antibiotics AMP, CIP, GEN, and CTX.MIC-based initial concentrations for the technical calibration with reference strains (PNG)Click here for additional data file.S2 TableE. coli UTI isolates.Breakpoint-based initial concentrations, as applied for the synergy screening of the (PNG)Click here for additional data file.S3 TableE. coli K-12 MG1665. Concentrations in every point are expressed as fold of MIC for that specific antibiotic in a broth microdilution.Concentrations of AMP, CIP, and GEN in a CombiANT plate of (PNG)Click here for additional data file.S1 TextThe calibration protocol for the use of new media-antibiotic combination in the CombiANT assay.(DOCX)Click here for additional data file.S2 TextThe execution protocol for the CombiANT assay.(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


Rosai–Dorfman disease (RDD) and IgG4-related (IgG4-RD) disease are both rare diseases, but in some cases, RDD mimics IgG4-RD clinically and pathologically. RDD mimicking IgG4-RDs (RDD mimic IgG4-RD), referring to disease initially diagnosed as IgG4-RD but finally pathologically confirmed to be RDD, is a clinically rare and confusing disease. To summarize the characteristics of this disease, we prospectively analyzed the clinical features, laboratory parameters, pathological characteristics, treatment and prognosis of patients diagnosed with RDD mimic IgG4-RD. Moreover, by analyzing characteristics of RDD mimic IgG4-RD, RDD and IgG4-RD, we further compared the similarities and differences between RDD and IgG4-RD.7 patients with RDD mimic IgG4-RD were included in this study and all of them had extranodal organ involvement, especially the central nervous system, which occurred in 5 patients (71.4%). Although serum IgG4 level was elevated in 6 cases , overall, it was still lower than that in IgG4-RD patients. Furthermore, we found a new cut-off value of serum IgG4 concentration for differentiating RDD and IgG4-RD with higher specificity. Pathological findings of RDD also showed features resembling IgG4-RD: IgG4-positive plasma cell enrichments were observed in all RDD mimic IgG4-RD patients, and the proportion of IgG4/IgG in tissues was 10–40% in 4 patients and more than 40% in 2 patients. However, none of the RDD mimic IgG4-RD patients or RDD patients displayed obliterative phlebitis or storiform fibrosis. Most of the RDD mimic IgG4-RD patients were treated with glucocorticoids combined with immunosuppressants, and a good prognosis was obtained following treatment.RDD has clinical manifestations that mimic IgG4-RD. However, detailed differences in laboratory parameters and pathological characteristics are present between these two diseases. Our study underlines the necessity to rule out RDD while diagnosing IgG4-RD using pathological findings as the identification criteria and provides advice for both differentiating these two diseases and clinical treatment of RDD mimic IgG4-RD. IgG4-related diseases (IgG4-RDs) are a series of diseases characterized by specific clinical, serological and pathological features  displayiThere were 7 patients with RDD mimic IgG4-RD, all of whom were male, with a median age of 56 29–63) years Tables , 2 and a9–63 yearp = 0.0038, Mann–Whitney test between the total RDD group and the IgG4-RD group) , supporting previous results that LSGs are frequently affected in IgG4-RD. A similar pattern was also observed for pancreas involvement, which is an organ commonly affected in IgG4-RDs  but rarely affected in RDD . A typical clinical characteristic of RDD is the enlargement of lymph nodes. However, there was no remarkable difference in lymph node involvement between the total RDD  and IgG4-RD  groups . Frequencies of the involvement of other sites, including the liver, retroperitoneum and skin, were also analyzed in each group, but no statistically significant difference was found.Among the 7 patients with RDD mimic IgG4-RD, predominant disease involvement was found in the nervous system, affecting 5 out of 7 patients. Nevertheless, central nervous system involvement rarely occurred in the IgG4-RD group 1/34, 2.9%)  was found in 6 patients in the RDD mimic IgG4-RD group, ranging from 1360 to 54,100 mg/L  to distinguish RDD mimic IgG4-RD from IgG4-RD, the specificity is 85.71%, while the sensitivity is only 50.00% , while only 29% (2/7) of the RDD mimic IgG4-RD patients showed this serological phenotype   than in the IgG4-RD group (22.7 ± 12.3%) , blood eosinophil count and high-sensitivity C-reactive protein (hs-CRP), were determined in the RDD mimic IgG4-RD and IgG4-RD groups, and no significant differences were found between them. A group of antibodies, such as ANA, anti-ds-DNA Ab, anti-ENA Ab, ACA, anti-β2 glycoprotein Ab, LAC, ANCA, were also tested, and all displayed negative results.Previous immunohistochemical studies in RDD patients revealed strong reactivity in histiocytes using antibodies against S100 protein, and markers of macrophage lineage  showed mid to high expression, while CD1a antigen staining was negative. Another pathological feature of RDD, emperipolesis, which is the engulfment of mature lymphocytes, monocytes, plasma cells, few red blood cells and sporadic neutrophils by histiocytes with abundant cytoplasm, is also frequently observed. Previous reports on RDD have demonstrated lymphocytic aggregation with abundant plasma cells and sclerosis in affected tissues in most RDD cases, both nodal and extranodal. Furthermore, IgG4 expression from plasma cells in extranodal RDD was reported in several cases –9.In our study, pathological analysis was performed on all patients using affected tissues, including the dura mater, endocranium, liver and skin. Lymphoplasmacytic infiltration enriched in IgG4-positive plasma cells (> 10 cells/hpf) was a feature found in all samples from RDD mimic IgG4-RD patients, and 3 patients  showed more than 50 IgG4 + plasma cells/hpf, and another 2 patients  showed more than 100 IgG4 + plasma cells/hpf , although there was a trend toward more IgG4 + plasma cell enrichment in the IgG4-RD group, with a higher percentage of patients  in the IgG4-RD group with more than 50 IgG4 + plasma cells/hpf   when the cut-off value of serum IgG4 concentration is set at > 1350 mg/L. Many studies have reported that serum IgG4 levels could be increased in various diseases, including connective tissue disease, chronic infection, lymphoma, and Castleman disease, making this parameter unreliable as the only criterion for the diagnosis of IgG4-RD. Our results support this notion, showing that 6 out of 7 RDD mimic IgG4-RD patients had elevated serum IgG4 levels. However, we also found that patients with RDD mimic IgG4-RD had a lower level of serum IgG4 levels than IgG4-RD patients. Furthermore, we tried to set a new cut-off value of serum IgG4 levels to distinguish these two diseases. Research indicates that the cut-off value should be adjusted in some situations to improve the diagnosis of IgG4-RDs . Since tRecently, the presence of IgG4 + plasma cells has been pathologically discovered in a considerable number of RDD cases. Research performed by Zhang et al.  demonstrNonetheless, IgG4-RDs and RDD display diverse and specific pathological features. Pathological features, including storiform fibrosis, obliterative phlebitis and eosinophilic infiltration, were found specifically in IgG4-RD patients but absent in RDD patients in our analysis, consistent with findings in previous studies . These cTreatment is different for RDD and IgG4-RD. As most IgG4-RDs show a good response to corticosteroids, the treatment for RDD varies according to the severity of the disease. Some of the RDD cases are self-limited and no treatment is required, and some can be controlled using corticosteroids, while others are lymphoma-like and need chemotherapy. As for RDD mimic IgG4-RD, in these cases, response to corticosteroids is not as good as it is in IgG4-RD, and most of them need treatment of high intensity, such as combining with immunosuppressive drug or using chemotherapy. Therefore, diagnosing RDD from IgG4-RD correctly, especially for those mimicking IgG4-RD clinically and pathologically, can lead to standard treatment with better prognosis.There are some limitations in our study regarding time of follow-up and number of patients. Firstly, the number of patients with RDD mimic IgG4-RD is limited in our study. Because both RDD and IgG4-RD are rare diseases, very few numbers of patients could fulfill our requirements and be enrolled in our research, leaving the problem of not enough samples to be analyzed. A study with more patients is warmly welcomed to give a better cut-off value of serum IgG4 to distinguish these two diseases. Secondly, follow-up time for prognosis is not long enough. In our study, the follow-up time period for each patient post diagnosis is no longer than 1 year, which is adequate to notice the improvement after treatment but not long enough to discover a relapse of the disease. Further study with a longer period of follow-up should be conducted to estimate long-term prognosis.In conclusion, some RDD cases mimic IgG4-RDs in terms of clinical manifestations, laboratory parameters and pathological characteristics. However, since both of these diseases have specific pathological features, we speculate that these two diseases fall along distinct spectra, emphasizing the necessity to rule out RDD when suggesting a definitive diagnosis of IgG4-RD. Special attention should be paid to the level and proportion of serum IgG4 and the number and proportion of IgG4 + plasma cells on histopathology, and above all, the respective pathological characteristics for these two diseases are the key step for diagnosing. Because RDD mimic IgG4-RD tends to involve sites that may result in severe symptoms, aggressive therapy is recommended for these cases.We prospectively enrolled 7 patients who were suspected to have IgG4-RD but were finally diagnosed with RDD (the RDD mimic IgG4-RD group) in the Rheumatology Department, PUMCH from January 2012 to March 2018. Another 10 patients diagnosed with RDD in other departments  during the same period were retrospectively enrolled in our study (the RDD group). Serum IgG4 level was not estimated at the onset of disease for these 10 patients, but immunohistochemical staining of IgG4 and IgG was performed on samples from affected tissues. We classified the 7 patients in the RDD mimic IgG4-RD group and the 10 patients in the RDD group together as the total RDD group, which contained 17 patients. Thirty-four IgG4-RD patients who were matched to the 17 RDD patients by age and sex were randomly selected at a ratio of 2:1 from our IgG4-RD cohort as controls.We analyzed general conditions, clinical symptoms and signs, laboratory parameters, histopathological features, treatment and prognosis of the patients and compared the differences among the groups.Statistical analysis was performed using GraphPad Prism 7 (GraphPad Software v7.1). Continuous normally distributed data were displayed as the mean ± standard deviation and analyzed by parametric tests. Continuous nonnormally distributed data were displayed as the mean ± standard deviation and analyzed by nonparametric tests. Discrete nonnormally distributed data were presented as medians (minima–maxima) and analyzed by nonparametric tests.


The use of antibiotics is the most important modifiable risk factor for the development of microorganism resistance. A cross-sectional study of outpatients receiving antibiotic prescriptions registered in a population database in Colombia was conducted. The characteristics of the consumption in capital cities and small municipalities was studied and the AWaRe classification was used. AWaRe classifies antibiotics into three stewardship groups: Access, Watch and Reserve, to emphasize the importance of their optimal use and potential harms of antimicrobial resistance. A total of 182,397 patients were prescribed an antibiotic; the most common were penicillins (38.6%), cephalosporins (30.2%) and fluoroquinolones (10.9%). ‘Access’ antibiotics (86.4%) were the most frequently prescribed, followed by ‘Watch’ antibiotics (17.0%). Being 18 or older, being male, living in a municipality, having one or more comorbidities and urinary, respiratory or gastrointestinal disorders increased the probability of receiving ‘Watch’ or ‘Reserve’ antibiotics. Penicillin and urinary antiseptic prescriptions predominated in cities, while cephalosporin and fluoroquinolone prescriptions predominated in municipalities. This analysis showed that the goal set by the WHO Access of mainly using Access antibiotics is being met, although the high use of Watch antibiotics in municipalities should be carefully studied to determine if it is necessary to design specific campaigns to improve antibiotics use. The discovery of antibiotics remains one of the most important recent scientific advances in human health as it has increased the life expectancy of the population. However, the proportion of infections caused by resistant bacteria and by those with new patterns of resistance has been increasing for some years ,2. It isE. coli resistant to third-generation cephalosporins, Methicillin-resistant S. aureus, carbapenem-resistant P. aeruginosa, and third-generation cephalosporin-resistant K. pneumoniae [Infections caused by resistant microorganisms are associated with an increased risk of morbidity, complications, mortality, health services use, and increased costs ,7,8. Infeumoniae .Proper prescription of antibiotics is essential to reduce resistance  and ensuHowever, it is unlikely that this problem can be solved solely by the development of new antimicrobial drugs because the threat of resistance will always accompany any new drugs introduced for clinical use. Therefore, it is essential to implement rational drug use programs, improve targeted antibiotic therapy, and establish preventive measures and faster diagnostic tools, among other strategies, as these are the only ways to preserve antibiotics for future generations and ensure a healthy future for the world population .The World Health Organization (WHO) developed an instrument that seeks to improve the quality of antibiotic prescriptions to decrease the spread of resistant microorganisms and reduce adverse reactions and costs. The AWaRe tool classifies antibiotics into three groups: Access , Watch  and Reserve  ,14,15. AThis becomes even more important, when it has been established that the general consumption of antibiotics increased 39% between the years 2000 to 2015, where most of the dispensations occurred in primary care centers . DiffereThere is substantially less information on the patterns of antibiotic resistance in patients in the community than in hospitalized patients, on whom clinical practice recommendations are based ,11, and n = 110,998) were women. The mean age was 37.1 ± 23.0 years (range: 0.08–108.75 years), with the following disbursement across age groups: <18 years , 18–49 years , 50–64 years , 65–79 years  and ≥ 80 years . A total of 60.5%  resided in capital cities, and most of them were in the Caribbean Region , followed by the Bogotá–Cundinamarca Region , Central Region , Pacific Region , Eastern Region , and Amazon–Orinoco Region .A total of 182,397 people distributed throughout 187 different capital cities or municipalities who were prescribed an antibiotic were identified. Of these, 60.9%  of the patients received a single antimicrobial, while 8.8%  received two, and 1.0% (n = 1901) received three or more. A total of 93.5%  of the patients were given antibiotics to be used orally, with tablets or capsules being the most frequently prescribed pharmaceutical dosage form   were prescribed Access antibiotics, 17.0%  received Watch antibiotics, and 0.1% (n = 188) were prescribed Reserve antibiotics.A total of 37 different antibiotics were prescribed; 90.2%  . The mos; 82.1%) . The mos; 82.1%) . In termn = 52,324) of all patients had some chronic pathology  had one to two pathologies, 9.5% (n = 4964) had three to four pathologies, and 1.2% (n = 606) had five or more pathologies. These comorbidities predominated in individuals aged 65 years or older . The 10 most common comorbidities were arterial hypertension , diabetes , hypothyroidism , chronic gastritis , dyslipidemia , acne , benign prostatic hyperplasia , cancer , irritable colon , and osteoarthritis .A total of 28.7% , while the prescription of Watch and Reserve antibiotics increased with patient age (p < 0.001)  .The prescription of two or more antibiotics was found most frequently in the Caribbean Region. Penicillins, followed by cephalosporins, were the most commonly prescribed antibiotic groups in all regions of the country. In the Caribbean Region, the prescription of fluoroquinolones, macrolides and aminoglycosides was more common than in the other geographic regions, while tetracyclines were most often prescribed in Bogotá–Cundinamarca, and urinary antiseptics were most often prescribed in the Central and Pacific regions. The prescription of Access antibiotics predominated in the Bogotá–Cundinamarca Region, while the prescription of Watch antibiotics predominated in the Pacific and Caribbean regions .A binary logistic regression model was conducted using the prescription of antibiotics classified as Watch or Reserve (yes/no) as a dependent variable. The multivariate analysis found that prescriptions written in municipalities; the presence of one or more comorbidities; and the presence of urinary, respiratory or gastrointestinal pathologies increased the probability of receiving antibiotics classified as Watch and/or Reserve, while residing in the Bogotá–Cundinamarca, Central or Eastern regions and having dermatological, rheumatological, cardiovascular or neurological pathologies reduced this risk. Interaction terms were analyzed. There was an interaction between age and male sex; with higher probability of receiving Watch/Reserve antibiotics with increasing age . Other iThe patterns of antibiotic prescription for outpatients of any age and sex were evaluated for nearly one-sixth of the population of Colombia to characterize the differences and similarities in the dispensing of these drugs according to geographic region, capital city or municipality and age group and the variables associated with the use of antibiotics classified as Watch or Reserve. In general, antibiotics were more often prescribed for women, a finding that has also been reported in the United States ,5, EuropPenicillins were the most commonly used pharmacological group (38.6%), which is consistent with the findings of Goossens et al. in a multicenter study conducted in 28 countries in Europe and North America 38.9–45.7%)  and thos.7%  and Amoxicillin was the most commonly prescribed antibiotic 28.1%) in the present study, a finding consistent with results for countries in North America, Europe, Asia, and Africa 20.8–22.5%) ,24,26 an.1% in th% ,24,26 p < 0.001). In addition, the most commonly used antibiotic groups were penicillins and tetracyclines, which predominated in rural areas [p = 0.002) of dispensations from rural and urban pharmacies, with amoxicillin being the best-selling antibiotic  [p < 0.001) [Many studies have shown that the frequency of use and prescription patterns of antibiotics varies according to the geographical area of each country ,22,23,30al areas . A study 0.0001) . In the  0.0001) . However< 0.001)  and also< 0.001) . In our In England, the majority of prescribed antibiotics were classified as Access (68.7%), followed by Watch (18.4%) and Reserve (0.4%) ; and in Watch antibiotics have not been sufficiently characterized in epidemiological studies because this classification was first proposed by the WHO in 2017. In our analysis, this group was mainly represented by fluoroquinolones and macrolides, and the factors that were associated with increased use of this group of antibiotics were male sex, older age and number of comorbidities, probably because these drugs are indicated for prostatitis, sexually-transmitted infections, urinary tract infections, and upper and lower respiratory tract infections . HoweverSome limitations in the interpretation of the results are recognized. There was no access to medical records to identify the clinical indications for the prescription of antibiotics, nor could it be established whether the antibiotics were taken as recommended, including whether they were taken for the minimum time. Likewise, on medical forms, the ICD-10 code corresponding to the infectious disease is not always recorded; thus, the quality of the prescriptions could not be determined, and information about antibiotics purchased outside the health system is unknown.This was a cross-sectional study of the prescription of antibiotics for outpatient use. It compared antibiotics use between capital cities and municipalities in a group of Colombian patients from a population-based drug-dispensing database that collects information on approximately 8.5 million persons affiliated with the Colombian Health System and six insurers. The population covered by the database corresponds to 30.0% of the active population affiliated with the contributory regime and 6.0% with the subsidized regime, which corresponds to 16.3% of the Colombian population.Capital city and municipality of dispensation: See annex 1Geographic areas: The region of residence was categorized by department according to the regions of Colombia and considering the classification of the National Administrative Department of Statistics  of Colombia as follows:Caribbean Region: Atlántico, Bolívar, Cesar, Córdoba, La Guajira, Magdalena, Sucre, San Andrés, Providencia, and Santa Catalina.Central Region: Antioquia, Caldas, Quindío, Risaralda, Caquetá, Huila, Tolima.Bogotá–Cundinamarca Region.Eastern Region: Boyacá, Meta, Norte de Santander, Santander, Arauca, Casanare.Pacific Region: Cauca, Chocó, Nariño, Valle del Cauca.Amazon–Orinoco Region: Amazonas, Guaviare, Guainía, Vaupés, Vichada, Putumayo.Sociodemographic: Sex, age, insurance company, and place of dispensation , and the term municipality was used for all other populations with fewer inhabitants).Cardiovascular: High blood pressure, ischemic heart disease, tachyarrhythmias, heart failure, peripheral arterial disease.Endocrine: Diabetes, hypothyroidism, dyslipidemia, obesity, hyperthyroidism.Rheumatological: Osteoarthrosis, rheumatoid arthritis, osteoporosis, fibromyalgia, systemic lupus erythematosus, systemic sclerosis, ankylosing spondylitis.Renal: Chronic kidney disease.Psychiatric: Depression, anxiety, bipolar affective disorder, sleep disorders, psychosis.Neurological: Peripheral neuropathies, chronic pain, dementia, migraine, epilepsy, Parkinson’s disease, stroke, mental retardation.Digestive: Chronic gastritis, gastroesophageal reflux, constipation, cirrhosis, peptic ulcer, hepatitis, irritable colon.Respiratory: Chronic obstructive pulmonary disease, asthma.Urinary: Benign prostatic hyperplasia, urinary incontinence/overactive bladderSkin: Acne, psoriasis.Chronic comorbidities: Identified from the main and secondary diagnoses reported using ICD-10 codes in the database between October 1, 2019, and January 31, 2020. Chronic comorbidities were grouped into four categories: no comorbidities and one, two and three or more pathologies. The following groups of diseases were considered:Antibiotic groups according to the AWaRe classification (13) see :Pharmaceutical dosage forms: Tablet, capsule, powder for reconstitution to oral solution, suspension, injectable solution, powder for inhalation.Number of antibiotics per patient: Grouped into three categories: One antibiotic, two antibiotics and three or more antibiotics received during the month of January.Patients of any age and sex who were treated at outpatient clinics and received antibiotics for outpatient use between January 1 and 31, 2020 were selected. A database was designed to collect the following groups of variables:The protocol was approved by the Bioethics Committee of Universidad Tecnológica de Pereira in the category of risk-free research . The ethical principles established by the Declaration of Helsinki were respected.p < 0.05.The data were analyzed with the statistical package SPSS Statistics, version 26.0 for Windows . A descriptive analysis was performed with frequencies and proportions for the qualitative variables and measures of central tendency and dispersion for the quantitative variables. Quantitative variables were compared using Student’s t-test or ANOVA; categorical variables  were compared using the X2 test. The Benjamini–Hochberg method was used to adjust for multiple hypotheses. An exploratory binary logistic regression model was fitted using the prescription of antibiotics classified as Watch or Reserve (yes/no). Sex, age groups and variables that were significantly associated in the bivariate analyses were used as covariates. Interaction terms and multicollinearity were also assessed (Variance inflation factor—VIF <10 as limit). The level of statistical significance adopted was The study results indicate that there are differences in the patterns of antibiotic prescription between capital cities and more rural municipalities; in particular, there is a greater proportion of Watch antibiotic use in municipalities than in cities, and there are differences in the prescription of penicillins, cephalosporins, fluoroquinolones, and macrolides. In general, the goals established by the WHO to reduce resistance to antimicrobials are being met, but it is necessary to implement continuing medical education measures to promote more homogeneous prescription patterns between cities and municipalities in different regions of the country. Studies of the use of antibiotics at the patient level in areas that prescribe a higher proportion of Watch antibiotics could provide data on the reasons for their use and whether it complies with treatment guidelines.