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21 pages, 6630 KiB  
Review
Application of Single Cell Type-Derived Spheroids Generated by Using a Hanging Drop Culture Technique in Various In Vitro Disease Models: A Narrow Review
by Hiroshi Ohguro, Megumi Watanabe, Tatsuya Sato, Nami Nishikiori, Araya Umetsu, Megumi Higashide, Toshiyuki Yano, Hiromu Suzuki, Akihiro Miyazaki, Kohichi Takada, Hisashi Uhara, Masato Furuhashi and Fumihito Hikage
Cells 2024, 13(18), 1549; https://doi.org/10.3390/cells13181549 - 14 Sep 2024
Viewed by 264
Abstract
Cell culture methods are indispensable strategies for studies in biological sciences and for drug discovery and testing. Most cell cultures have been developed using two-dimensional (2D) culture methods, but three-dimensional (3D) culture techniques enable the establishment of in vitro models that replicate various [...] Read more.
Cell culture methods are indispensable strategies for studies in biological sciences and for drug discovery and testing. Most cell cultures have been developed using two-dimensional (2D) culture methods, but three-dimensional (3D) culture techniques enable the establishment of in vitro models that replicate various pathogenic conditions and they provide valuable insights into the pathophysiology of various diseases as well as more precise results in tests for drug efficacy. However, one difficulty in the use of 3D cultures is selection of the appropriate 3D cell culture technique for the study purpose among the various techniques ranging from the simplest single cell type-derived spheroid culture to the more sophisticated organoid cultures. In the simplest single cell type-derived spheroid cultures, there are also various scaffold-assisted methods such as hydrogel-assisted cultures, biofilm-assisted cultures, particle-assisted cultures, and magnet particle-assisted cultures, as well as non-assisted methods, such as static suspension cultures, floating cultures, and hanging drop cultures. Since each method can be differently influenced by various factors such as gravity force, buoyant force, centrifugal force, and magnetic force, in addition to non-physiological scaffolds, each method has its own advantages and disadvantages, and the methods have different suitable applications. We have been focusing on the use of a hanging drop culture method for modeling various non-cancerous and cancerous diseases because this technique is affected only by gravity force and buoyant force and is thus the simplest method among the various single cell type-derived spheroid culture methods. We have found that the biological natures of spheroids generated even by the simplest method of hanging drop cultures are completely different from those of 2D cultured cells. In this review, we focus on the biological aspects of single cell type-derived spheroid culture and its applications in in vitro models for various diseases. Full article
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Figure 1

Figure 1
<p>Method for preparation of 3D spheroids by a hanging drop culture plate. Cells obtained by conventional 2D planar culture (<b>A</b>) formed spheroid cultures by using a 384-hanging drop array culture plate ((<b>B</b>): design drawing). As shown in the upward view (<b>C</b>), a drop of culture medium containing cells hangs down from each well. Representative images of a spheroid of mouse orbital fibroblasts with adipogenesis stained by BODIPY (red), phalloidin (green), and DAPI (blue) (<b>D</b>) and a spheroid of human orbital fibroblasts with adipogenesis stained by anti-hyaluronic acid (pink), anti-COL6 (green), and DAPI (blue) (<b>E</b>).</p>
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<p>Measurement of stiffness of spheroids. A living spheroid (3D sph) was initially held between a pedestal and a pressing plate ((<b>A</b>), D: diameter) and was gradually pressed by a press bar until the initial diameter was halved (D/2) over a period of 20 s (<b>B</b>).</p>
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<p>Representative maturation process of H9c2 spheroids. Representative phase contrast (PC) images of a H9c2 spheroid in a well of a hanging drop culture plate during different culture periods at 0.5 h (<b>A</b>), 5 h (<b>B</b>), 7.5 h (<b>C</b>), 14.5 h (<b>D</b>), and 24 h (<b>E</b>). Panel F shows a representative PC image of a H9c2 spheroid taken out from the culture plate after 24 h culture period (<b>F</b>). Scale bar: 100 μm.</p>
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<p>Trypsin digestion of 2D H9c2 cells and 3D H9c2 spheroid. Representative phase contrast images of 2D and 3D cultured H9c2 cells treated by 0.05% trypsin for different time periods are shown. Scale bar: 100 μm.</p>
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<p>Estimation of total number of cells in a single 3D spheroid.</p>
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<p>Representative images of spheroids obtained from various non-cancerous cells. HTM: human trabecular meshwork cell, HconF: human conjunctival fibroblast, G-OF: Graves-related human orbital fibroblast, N-OF: non-Graves-related human orbital fibroblast, 3T3-L1: 3T3-L1 mouse preadipocyte, HSSF: human scleral stromal fibroblast, H9c2: H9c2 rat cardiomyoblast. Scale bar: 100 μm.</p>
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<p>Representative images of 3D spheroid obtained from various non-cancerous cells. MCF7: human breast cancer cell line, HTB26: breast adenocarcinoma cell line, LHK2: lung adenocarcinoma cell line, SK-mel-24: malignant melanoma cell line, MP-1000: oral squamous cell carcinoma cell line, CAFS1: cancer-associated fibroblast cell line. Scale bar: 100 μm.</p>
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4 pages, 819 KiB  
Proceeding Paper
Modelling the Dynamics of P. aeruginosa in the Formation of Biofilms
by Dinesh Singh Bhandari, Dominic Quinn, Erifyli Tsagkari, Katherine Fish, Frances Pick, Joby Boxall, Cindy Smith, Siming You and William Sloan
Eng. Proc. 2024, 69(1), 141; https://doi.org/10.3390/engproc2024069141 - 14 Sep 2024
Viewed by 75
Abstract
The accumulation, growth, and re-mobilization of pathogens on the pipe walls in drinking water distribution systems are processes that affect the risk of exposure at the tap. We present a model that uses the Buckingham Pi theory to embody the physics of Pseudomonas [...] Read more.
The accumulation, growth, and re-mobilization of pathogens on the pipe walls in drinking water distribution systems are processes that affect the risk of exposure at the tap. We present a model that uses the Buckingham Pi theory to embody the physics of Pseudomonas aeruginosa accumulation and move within the system. We apply it to model experimental data from a biofilm annular reactor operated in conditions that are commensurate with the flow in DWDS. By calibrating the model for this benchtop system, we intend to identify the most important physical parameters for use in a simpler, more prudent model, for application in large-scale DWDS. Full article
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Figure 1
<p>Schematic of the <span class="html-italic">P. aeruginosa</span> in the annular reactor: (<b>a</b>) the dynamic transmission of <span class="html-italic">P. aeruginosa</span> in the tap water and attachment on the coupon. Coupons provide a substrate for the initial attachment of the <span class="html-italic">P. aeruginosa</span>, allowing them to colonize and form biofilms; (<b>b</b>) top view to illustrate the generic phenomena of the two-phase bacteria model.</p>
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<p>The two-phase bacteria balance model for the dynamic transmission of <span class="html-italic">P. aeruginosa</span> with time illustrates (<b>a</b>) the number of accumulated and mobilized bacteria in the system; (<b>b</b>) a comparison of theoretical and experimental data.</p>
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20 pages, 1336 KiB  
Article
Diversification of Pseudomonas aeruginosa Biofilm Populations under Repeated Phage Exposures Decreases the Efficacy of the Treatment
by Mark Grevsen Martinet, Mara Lohde, Doaa Higazy, Christian Brandt, Mathias W. Pletz, Mathias Middelboe, Oliwia Makarewicz and Oana Ciofu
Microorganisms 2024, 12(9), 1880; https://doi.org/10.3390/microorganisms12091880 - 12 Sep 2024
Viewed by 305
Abstract
Phage therapy has been proposed as a therapeutic alternative to antibiotics for the treatment of chronic, biofilm-related P. aeruginosa infections. To gain a deeper insight into the complex biofilm–phage interactions, we investigated in the present study the effect of three successive exposures to [...] Read more.
Phage therapy has been proposed as a therapeutic alternative to antibiotics for the treatment of chronic, biofilm-related P. aeruginosa infections. To gain a deeper insight into the complex biofilm–phage interactions, we investigated in the present study the effect of three successive exposures to lytic phages of biofilms formed by the reference strains PAO1 and PA14 as well as of two sequential clinical P. aeruginosa isolates from the sputum of a patient with cystic fibrosis (CF). The Calgary device was employed as a biofilm model and the efficacy of phage treatment was evaluated by measurements of the biomass stained with crystal violet (CV) and of the cell density of the biofilm bacterial population (CFU/mL) after each of the three phage exposures. The genetic alterations of P. aeruginosa isolates from biofilms exposed to phages were investigated by whole-genome sequencing. We show here that the anti-biofilm efficacy of the phage treatment decreased rapidly with repeated applications of lytic phages on P. aeruginosa strains with different genetic backgrounds. Although we observed the maintenance of a small subpopulation of sensitive cells after repeated phage treatments, a fast recruitment of mechanisms involved in the persistence of biofilms to the phage attack occurred, mainly by mutations causing alterations of the phage receptors. However, mutations causing phage-tolerant phenotypes such as alginate-hyperproducing mutants were also observed. In conclusion, a decreased anti-biofilm effect occurred after repeated exposure to lytic phages of P. aeruginosa biofilms due to the recruitment of different resistance and tolerance mechanisms. Full article
(This article belongs to the Special Issue Biotechnological Applications of Bacteriophages and Enteric Viruses)
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<p>Reduction in the biofilms of <span class="html-italic">P. aeruginosa</span> PAO1 and PA14 depends on NP3 and NP1 phage concentrations. (<b>A</b>) The viable cells were determined as CFU/mL in four independent experiments, and the reduction was calculated as a percentage of the viable bacteria after treatment in relation to the untreated controls; means and standard error of means (SEM) are shown. (<b>B</b>) The reduction in biomass was determined via crystal violet staining of the phage-treated biofilms and untreated biofilms (indicated here as the horizontal lines). The measurements were performed 8 times, and means and standard deviations (SD) are shown.</p>
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<p>Effect of NP3 phage on PAO1 and PA14 biofilms after repeated treatments compared to the corresponding control biofilms. (<b>A</b>) PAO1 biomass is measured as CV absorbance. (<b>B</b>) Viable bacteria fraction of the resolved PAO1 biofilms as CFU/mL. (<b>C</b>) PA14 biomass measured as CV absorbance. (<b>D</b>) Viable bacteria fraction of the resolved PA14 biofilms as CFU/mL. In (<b>A</b>,<b>C</b>) mean and standard deviation (SD) and in (<b>B</b>,<b>D</b>) box blots with the 5–95%c percentile (whiskers), the 25–75% quadrille (box) with the median (line within the box) and mean (+ within the box) and outliers (grey dots) of four biological and four technical replicates are presented. Significance was assumed for <span class="html-italic">p</span>-values below or equal to 0.05, indicated as follows: * &lt; 0.05, ** &lt; 0.01, *** &lt; 0.001, **** &lt; 0.0001.</p>
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<p>Effect of phages M32 on PAO1 and NP1 on PA14 biofilms after repeated treatments compared to the corresponding control biofilms. (<b>A</b>) PAO1 biomass is measured as CV absorbance. (<b>B</b>) Viable bacteria fraction of the resolved PAO1 biofilms as CFU/mL. (<b>C</b>) PA14 biomass measured as CV absorbance. (<b>D</b>) Viable bacteria fraction of the resolved PA14 biofilms as CFU/mL. In (<b>A</b>,<b>C</b>) mean and standard deviation (SD) and in (<b>B</b>,<b>D</b>) box blots with the 5–95%c percentile (whiskers), the 25–75% quadrille (box) with the median (line within the box) and mean (+ within the box) and outliers (grey dots) of four biological and four technical replicates are presented. Significance was assumed for <span class="html-italic">p</span>-values below or equal to 0.05, indicated as follows: * &lt; 0.05, ** &lt; 0.01, *** &lt; 0.001, **** &lt; 0.0001.</p>
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<p>Effect of NP3 phage on biofilms of clinical isolates CF341_06 and CF341_08 after repeated treatments compared to the corresponding control biofilms. (<b>A</b>) CF341_06 biomass measured as CV absorbance. (<b>B</b>) Viable bacteria fraction of the resolved CF341_08 biofilms as CFU/mL. (<b>C</b>) CF341_08 biomass measured as CV absorbance. (<b>D</b>) Viable bacteria fraction of the resolved CF341_06 biofilms as CFU/mL. In (<b>A</b>,<b>C</b>) mean and standard deviation (SD) and in (<b>B</b>,<b>D</b>) box blots with the 5–95%c percentile (whiskers), the 25–75% quadrille (box) with the median (line within the box) and mean (+ within the box) and outliers (grey dots) of four biological and four technical replicates are presented. Significance was assumed for <span class="html-italic">p</span>-values below or equal to 0.05, indicated as follows: * &lt; 0.05, ** &lt; 0.01, *** &lt; 0.001, **** &lt; 0.0001.</p>
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<p>Altered genes were identified in the clones of the different strains treated with the respective phages as indicated in each diagram in (<b>A</b>–<b>E</b>). The colors in (<b>A</b>–<b>E</b>) correspond to different pathways (see legend (<b>F</b>)). The number of clones with altered genes in the controls (untreated) and treated clones exhibiting phage-resistant and -sensitive or-tolerant phenotypes. The phage-resistant clones are marked in grey. WP_0031… is the coding side WP_003138482.1 corresponding to glycosyltransferase family 4 protein.</p>
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18 pages, 18776 KiB  
Article
Optimization of Helicobacter pylori Biofilm Formation in In Vitro Conditions Mimicking Stomach
by Paweł Krzyżek, Paweł Migdał, Barbara Krzyżanowska and Anna Duda-Madej
Int. J. Mol. Sci. 2024, 25(18), 9839; https://doi.org/10.3390/ijms25189839 - 11 Sep 2024
Viewed by 289
Abstract
Helicobacter pylori is one of the most common bacterial pathogens worldwide and the main etiological agent of numerous gastric diseases. The frequency of multidrug resistance of H. pylori is growing and the leading factor related to this phenomenon is its ability to form [...] Read more.
Helicobacter pylori is one of the most common bacterial pathogens worldwide and the main etiological agent of numerous gastric diseases. The frequency of multidrug resistance of H. pylori is growing and the leading factor related to this phenomenon is its ability to form biofilm. Therefore, the establishment of a proper model to study this structure is of critical need. In response to this, the aim of this original article is to validate conditions of the optimal biofilm development of H. pylori in monoculture and co-culture with a gastric cell line in media simulating human fluids. Using a set of culture-based and microscopic techniques, we proved that simulated transcellular fluid and simulated gastric fluid, when applied in appropriate concentrations, stimulate autoaggregation and biofilm formation of H. pylori. Additionally, using a co-culture system on semi-permeable membranes in media imitating the stomach environment, we were able to obtain a monolayer of a gastric cell line with H. pylori biofilm on its surface. We believe that the current model for H. pylori biofilm formation in monoculture and co-culture with gastric cells in media containing host-mimicking fluids will constitute a platform for the intensification of research on H. pylori biofilms in in vitro conditions that simulate the human body. Full article
(This article belongs to the Special Issue Pathogenicity and Antibiotic Resistance of Helicobacter pylori)
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<p>Assessment of the impact of a one-week culture on the physiological parameters of <span class="html-italic">H. pylori</span> 2CML in BHI + 5% FCS and the concentration gradient of STF (0–10%, with 1% intervals). An increasing concentration of STF is marked by the intensified gray of bars. (<b>A</b>) Autoaggregation measured microscopically by estimating the degree of the observation field coverage, <span class="html-italic">n</span> = 3. (<b>B</b>) Biofilm formation determined by a crystal violet staining method and spectrophotometric measurements, <span class="html-italic">n</span> = 9. (<b>C</b>) Viability of bacterial cells determined by fluorescent staining with a LIVE/DEAD kit and a ratio of green/red fluorescence, <span class="html-italic">n</span> = 3. (<b>D</b>) Density of planktonic cells assessed by spectrophotometry, <span class="html-italic">n</span> = 9. (<b>E</b>) A set of images showing the growth of <span class="html-italic">H. pylori</span> 2CML under the most optimal conditions of the current model—BHI + 5% FCS + 5% STF (marked with red bars). Scale bars, 20 µm. * indicates statistical difference (<span class="html-italic">p</span> &lt; 0.05) with the control—0%.</p>
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<p>Assessment of the impact of a one-week culture on the physiological parameters of <span class="html-italic">H. pylori</span> 2CML in STF and the concentration gradient of FCS (0–10%, with 1% intervals). An increasing concentration of FCS is marked by the intensified gray of bars. (<b>A</b>) Autoaggregation measured microscopically by estimating the degree of the observation field coverage, <span class="html-italic">n</span> = 3. (<b>B</b>) Biofilm formation determined by a crystal violet staining method and spectrophotometric measurements, <span class="html-italic">n</span> = 9. (<b>C</b>) Viability of bacterial cells determined by fluorescent staining with a LIVE/DEAD kit and the ratio of green/red fluorescence, <span class="html-italic">n</span> = 3. (<b>D</b>) Density of planktonic cells assessed by spectrophotometry, <span class="html-italic">n</span> = 9. (<b>E</b>) A set of images showing growth of <span class="html-italic">H. pylori</span> 2CML under the most optimal conditions of the current model—STF + 8% FCS (marked with red bars). Scale bars, 20 µm. * indicates statistical difference (<span class="html-italic">p</span> &lt; 0.05) with the control—0%. Because of the low values for all the physiological parameters during 1- and 2-day cultures, these data were not recorded.</p>
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<p>Assessment of the impact of a one-week culture on the physiological parameters of <span class="html-italic">H. pylori</span> 2CML in BHI + 5% FCS and the concentration gradient of SGF (0–2%, with 0.2% intervals). An increasing concentration of SGF is marked by the intensified gray of bars. (<b>A</b>) Autoaggregation measured microscopically by estimating the degree of the observation field coverage, <span class="html-italic">n</span> = 3. (<b>B</b>) Biofilm formation determined by a crystal violet staining method and spectrophotometric measurements, <span class="html-italic">n</span> = 9. (<b>C</b>) Viability of bacterial cells determined by fluorescent staining with a LIVE/DEAD kit and a ratio of green/red fluorescence, <span class="html-italic">n</span> = 3. (<b>D</b>) Density of planktonic cells assessed by spectrophotometry, <span class="html-italic">n</span> = 9. (<b>E</b>) A set of images showing the growth of <span class="html-italic">H. pylori</span> 2CML under the most optimal conditions of the current model—BHI + 5% FCS + 1% SGF (marked with red bars). Scale bars, 20 µm. * indicates statistical difference (<span class="html-italic">p</span> &lt; 0.05) with the control—0%.</p>
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<p>Assessment of the impact of a one-week culture on the physiological parameters of <span class="html-italic">H. pylori</span> 2CML in STF and the concentration gradient of SGF (0–2%, with 0.2% intervals). An increasing concentration of SGF is marked by the intensified gray of bars. (<b>A</b>) Autoaggregation measured microscopically by estimating the degree of the observation field coverage, <span class="html-italic">n</span> = 3. (<b>B</b>) Biofilm formation determined by a crystal violet staining method and spectrophotometric measurements, <span class="html-italic">n</span> = 9. (<b>C</b>) Viability of bacterial cells determined by fluorescent staining with a LIVE/DEAD kit and a ratio of green/red fluorescence, <span class="html-italic">n</span> = 3. (<b>D</b>) Density of planktonic cells assessed by spectrophotometry, <span class="html-italic">n</span> = 9. (<b>E</b>) A set of images showing the growth of <span class="html-italic">H. pylori</span> 2CML under the most optimal conditions of the current model—STF + 1% SGF (marked with red bars). Scale bars, 20 µm. * indicates statistical difference (<span class="html-italic">p</span> &lt; 0.05) with the control—0%. Because of the low values for all the physiological parameters during 1- and 2-day cultures, these data were not recorded.</p>
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<p>Representative images showing a co-culture between <span class="html-italic">H. pylori</span> 2CML and KATO III cells growing on semi-permeable membranes in host-mimicking fluids. (<b>A</b>) A set of images obtained by fluorescence microscopy with different components of the co-culture model being stained (FM 1–43 to visualize bacterial biomass (biofilm), DAPI to show cell nuclei of eukaryotic cells, and WGA-conjugated Texas Red to indicate the presence of mucus glycoproteins (mucins)). Scale bars, 20 µm. (<b>B</b>) A set of images obtained by scanning electron microscopy. On the top, a control constituting non-infected KATO III cells; on the bottom, a co-infection of KATO III with <span class="html-italic">H. pylori</span> 2CML, where red arrows indicate areas of biofilm development. Scale bars, 20 µm.</p>
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<p>Representative images showing a co-culture between <span class="html-italic">H. pylori</span> 2CML and KATO III cells growing in microfluidic conditions in host-mimicking fluids. (<b>A</b>) Time-dependent development of <span class="html-italic">H. pylori</span> biofilm attached to KATO III cells. (<b>B</b>) An image showing a one-day co-culture with a marking of specific components of this model, where red circles indicate KATO III cells and yellow dashed lines highlight areas of the development of <span class="html-italic">H. pylori</span> biofilm, being in close contact with these cells. Scale bars, 20 µm. To see stacked time-lapse sequences from the above experiments, please see <a href="#app1-ijms-25-09839" class="html-app">Videos S1 and S2</a>.</p>
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31 pages, 10904 KiB  
Article
Managing Fear and Anxiety in Patients Undergoing Dental Hygiene Visits with Guided Biofilm Therapy: A Conceptual Model
by Marta Leśna, Krystyna Górna and Jakub Kwiatek
Appl. Sci. 2024, 14(18), 8159; https://doi.org/10.3390/app14188159 - 11 Sep 2024
Viewed by 345
Abstract
Fear and anxiety during dental visits are common issues that can lead to avoidance of appointments and deterioration of oral health. Effectively managing patients’ emotions during dental treatments is crucial to improving their experiences, increasing adherence to regular visits, and achieving better treatment [...] Read more.
Fear and anxiety during dental visits are common issues that can lead to avoidance of appointments and deterioration of oral health. Effectively managing patients’ emotions during dental treatments is crucial to improving their experiences, increasing adherence to regular visits, and achieving better treatment outcomes. This study aimed to assess the levels of fear and anxiety in patients undergoing hygiene treatments utilizing Guided Biofilm Therapy (GBT) and identify factors that could reduce these negative emotions. A total of 247 patients were evaluated using standardized questionnaires (MDAS, STAI X1, STAI X2, and Gatchel), custom questions, and heart rate monitoring as a physiological stress indicator. Clinical factors, including dental status confirmed by AI-based radiographic analysis (Diagnocat system), as well as sociodemographic influences, were analyzed. Results indicated significant reductions in fear and anxiety after the procedure, as shown by both heart rate and questionnaire scores. Factors such as pain, the presence of caries, and implants were linked to higher anxiety, while strategies like avoiding visible needles and postprocedure interaction with staff were associated with lower stress levels. The findings underscore the importance of personalized care and emotional support to enhance patient experiences. Full article
(This article belongs to the Special Issue State-of-the-Art of Operative Dentistry)
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<p>Hygiene in the GBT protocol and the AirFlow Prophylaxis Master device.</p>
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<p>The 8 steps of GBT (source: <a href="https://www.ems-dental.com/en" target="_blank">https://www.ems-dental.com/en</a> (accessed on 15 August 2024)).</p>
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<p>Bacterial biofilm disclosed in the Guided Biofilm Therapy protocol.</p>
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<p>Flowchart depicting the number of patients qualified for the study.</p>
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<p>The Diagnocat program.</p>
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<p>(<b>A</b>) The AirFlow nozzle of the AirFlow Prophylaxis Master device. (<b>B</b>) The Piezon handpiece of the AirFlow Prophylaxis Master.</p>
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<p>Author-developed question about the experience of fear before the hygiene visit.</p>
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<p>Characteristics of the study sample.</p>
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<p>Level of dental anxiety in men and women (red line—trend line).</p>
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<p>Factors influencing dental anxiety (MDAS) and strategies for its reduction (black arrows: positive correlation; red arrows: negative correlation).</p>
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<p>Change in state anxiety levels (STAI X1) before and after the hygiene procedure (red line—trend line).</p>
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<p>Factors influencing state anxiety and trait anxiety (black and green arrows: positive correlation; red arrows: negative correlation).</p>
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<p>Change in the level of dental fear (Gatchel’s scale) before and after the hygiene procedure (red line—trend line).</p>
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<p>Factors influencing dental fear (Gatchel’s scale) before the hygiene procedure and strategies for its reduction (black arrows: positive correlation).</p>
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<p>Factors influencing dental fear (Gatchel’s scale) after the hygiene procedure and strategies for its reduction (black arrows: positive correlation; red arrows: negative correlation).</p>
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<p>Change in the level of fear related to hygiene before and after the procedure (red line—trend line).</p>
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<p>Factors influencing fear related to hygiene assessed before the procedure and strategies for its reduction (black arrows: positive correlation; red arrows: negative correlation).</p>
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<p>Factors influencing fear related to hygiene assessed after the procedure and strategies for its reduction (black arrows: positive correlation; red arrows: negative correlation).</p>
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<p>Change in heart rate before, during, and after the hygiene procedure (red line—trend line).</p>
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<p>Factors influencing heart rate before, during, and after hygiene procedures and strategies for its reduction (black arrows: positive correlation; red arrows: negative correlation).</p>
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<p>Change in dental fear levels (Gatchel’s scale) before and after the procedure depending on the first-time hygiene visit (red line—trend line for first-time hygiene visits; blue line—trend line for subsequent hygiene visits).</p>
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<p>Change in the level of fear related to hygiene procedures before and after the treatment depending on the first-time hygiene visit (red line—trend line for first-time hygiene visits; blue line—trend line for subsequent hygiene visits).</p>
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<p>A conceptual model of the impact of sociodemographic factors, clinical factors, and patient experiences on the levels of fear and anxiety during a hygiene visit, along with strategies for their reduction (black arrows: positive correlation; red arrows: negative correlation).</p>
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<p>Factors influencing dental anxiety.</p>
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29 pages, 2527 KiB  
Review
Advanced Nanotechnological Approaches for Biofilm Prevention and Control
by Maria Pia Ferraz
Appl. Sci. 2024, 14(18), 8137; https://doi.org/10.3390/app14188137 - 10 Sep 2024
Viewed by 337
Abstract
Biofilm-associated infections present a significant challenge in modern medicine, primarily due to their resilience and resistance to conventional treatments. These infections occur when bacteria form biofilms, protective layers formed by bacterial communities, which are notoriously resistant to traditional antibiotics on surfaces such as [...] Read more.
Biofilm-associated infections present a significant challenge in modern medicine, primarily due to their resilience and resistance to conventional treatments. These infections occur when bacteria form biofilms, protective layers formed by bacterial communities, which are notoriously resistant to traditional antibiotics on surfaces such as medical implants and biological surfaces, making eradication with standard antibiotics difficult. This resilience leads to persistent infections, imposing a substantial economic burden on healthcare systems. The urgency to find alternative treatments is critical as current methods are insufficient and costly. Innovative approaches, such as nanotechnology-based therapies, offer promising alternatives by targeting biofilms more effectively and reducing the need for invasive procedures. Nanocarriers hold significant promise in the fight against biofilm-associated infections. Nanocarriers can penetrate biofilms more effectively than conventional treatments, delivering higher concentrations of antibiotics or other antimicrobial agents precisely where they are needed. This targeted approach not only enhances the efficacy of treatments but also minimizes potential side effects. The development of nanocarrier-based therapies is crucial for overcoming the limitations of current treatments and ultimately improving patient outcomes and reducing the economic burden of biofilm-associated infections on healthcare systems. In this review, nanotechnology-based systems, their characteristics, limitations, and potential benefits are explored to address biofilms-related infections. Additionally, biofilm evaluation models and the tests necessary for the preclinical validation of these nanosystems to facilitate their clinical application are addressed. Full article
(This article belongs to the Section Materials Science and Engineering)
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<p>Schematic illustration of the review structure.</p>
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<p>Representative structure of various lipid-based nanocarriers. (<b>A</b>) Liposomes, (<b>B</b>) quatsomes, and (<b>C</b>) solid lipid nanoparticles.</p>
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<p>Representative structure of various polymeric-based nanocarriers. (<b>A</b>) Polymeric nanoparticles, (<b>B</b>) polymeric micelles, and (<b>C</b>) dendrimers.</p>
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4 pages, 567 KiB  
Proceeding Paper
Uncertainty Sources in the Mechanistic Modeling of Legionella within Building Water Systems
by Catalina Ortiz, Fatemeh Hatam and Michèle Prévost
Eng. Proc. 2024, 69(1), 84; https://doi.org/10.3390/engproc2024069084 - 9 Sep 2024
Viewed by 131
Abstract
Predicting Legionella concentrations reaching users through building water systems requires a comprehensive water quality modeling approach. We integrate various frameworks and data to test the effect of nutrient availability, temperature, chlorine, and biofilm interactions in modeling Legionella. We show that neglecting biofilm [...] Read more.
Predicting Legionella concentrations reaching users through building water systems requires a comprehensive water quality modeling approach. We integrate various frameworks and data to test the effect of nutrient availability, temperature, chlorine, and biofilm interactions in modeling Legionella. We show that neglecting biofilm detachment underestimates concentrations up to 5.5 logs, while including it increases estimates by 4.2 logs. This study identifies critical factors and uncertainty sources for characterizing the Legionella fate and transport phenomena within buildings. Full article
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<p>Measured, calibrated [<a href="#B2-engproc-69-00084" class="html-bibr">2</a>], and simulated <span class="html-italic">Legionella</span> spp. through sequential inclusion of nutrient limitation, temperature effect, chlorine effect, and biofilm detachment at (<b>a</b>) bathroom sink (hot system) and (<b>b</b>) bathroom shower (cold system).</p>
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4 pages, 1106 KiB  
Proceeding Paper
Water Quality Modelling in Water Distribution Systems: Pilot-Scale Measurements and Simulation
by Csaba Hős, Dániel Medve, Andrea Taczman-Brückner and Gabriella Kiskó
Eng. Proc. 2024, 69(1), 83; https://doi.org/10.3390/engproc2024069083 - 8 Sep 2024
Viewed by 105
Abstract
We present the results of water quality measurements in a pilot-scale, continuously circulated test rig consisting of HDPE pipe segments, where pH, conductivity, turbidity, salinity, temperature, and dissolved oxygen were measured daily. Microbiological measurements (CFU) on the pipe wall and in the bulk [...] Read more.
We present the results of water quality measurements in a pilot-scale, continuously circulated test rig consisting of HDPE pipe segments, where pH, conductivity, turbidity, salinity, temperature, and dissolved oxygen were measured daily. Microbiological measurements (CFU) on the pipe wall and in the bulk water were measured at least once every week. The measurement campaign lasted for 18 weeks. In the first part of the paper, we provide an overview of the results and our experiences. In particular, the time histories of the measured quantities are presented and assessed. Additionally, the flow velocity was increased in six steps from 0.4 to 1.1 m/s to study biofilm detachment once every week. In the second part of the paper, we attempt to use these measurement results for the parameter identification of standard biofilm models. In particular, we search for indirect connections between our measurement results and model parameters (e.g., yield and growth-limiting parameters) via optimising, where the objective is to recover the measured CFU concentration results as closely as possible. Finally, we present preliminary results on the critical wall shear stress resulting in biofilm detachment. Full article
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<p>Schematic figure of the test rig.</p>
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<p>Optimisation result (solid and dashed lines: simulation; markers: measurement).</p>
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22 pages, 2197 KiB  
Review
Artificial Intelligence to Close the Gap between Pharmacokinetic/Pharmacodynamic Targets and Clinical Outcomes in Critically Ill Patients: A Narrative Review on Beta Lactams
by João Gonçalves Pereira, Joana Fernandes, Tânia Mendes, Filipe André Gonzalez and Susana M. Fernandes
Antibiotics 2024, 13(9), 853; https://doi.org/10.3390/antibiotics13090853 - 6 Sep 2024
Viewed by 842
Abstract
Antimicrobial dosing can be a complex challenge. Although a solid rationale exists for a link between antibiotic exposure and outcome, conflicting data suggest a poor correlation between pharmacokinetic/pharmacodynamic targets and infection control. Different reasons may lead to this discrepancy: poor tissue penetration by [...] Read more.
Antimicrobial dosing can be a complex challenge. Although a solid rationale exists for a link between antibiotic exposure and outcome, conflicting data suggest a poor correlation between pharmacokinetic/pharmacodynamic targets and infection control. Different reasons may lead to this discrepancy: poor tissue penetration by β-lactams due to inflammation and inadequate tissue perfusion; different bacterial response to antibiotics and biofilms; heterogeneity of the host’s immune response and drug metabolism; bacterial tolerance and acquisition of resistance during therapy. Consequently, either a fixed dose of antibiotics or a fixed target concentration may be doomed to fail. The role of biomarkers in understanding and monitoring host response to infection is also incompletely defined. Nowadays, with the ever-growing stream of data collected in hospitals, utilizing the most efficient analytical tools may lead to better personalization of therapy. The rise of artificial intelligence and machine learning has allowed large amounts of data to be rapidly accessed and analyzed. These unsupervised learning models can apprehend the data structure and identify homogeneous subgroups, facilitating the individualization of medical interventions. This review aims to discuss the challenges of β-lactam dosing, focusing on its pharmacodynamics and the new challenges and opportunities arising from integrating machine learning algorithms to personalize patient treatment. Full article
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<p>A practical algorithm based on β-lactam dose optimization by Therapeutic Drug Monitoring (adapted from references [<a href="#B12-antibiotics-13-00853" class="html-bibr">12</a>,<a href="#B15-antibiotics-13-00853" class="html-bibr">15</a>]). PK–Pharmacokinetics; C—plasma concentration; MIC—minimum inhibitory concentration; RRT—renal replacement therapy; AKI—acute kidney injury.</p>
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<p>Maximum concentration in different tissues. Bars depict means and SD. In critically ill patients, the plasma concentrations are lower than in volunteers. Also, the ratios of tissue to plasma concentrations are reduced. Differences between volunteers and patients <span class="html-italic">p</span> &lt; 0.005; muscle and subcutis tissue: differences between volunteers and patients <span class="html-italic">p</span> &lt; 0.05. Data from reference [<a href="#B42-antibiotics-13-00853" class="html-bibr">42</a>].</p>
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<p>Persister cells. The antibiotic target is blocked and not functioning in a small proportion of bacteria. This leads to antibiotic failure since the antibiotic can no longer reach its receptor. This is different from resistance. After removal of the blockage, bacteria restart their normal functions and are again susceptible to antibiotics.</p>
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<p>Acquisition of bacterial resistance through various mechanisms: genetic mutation, horizontal gene transfer, and selective pressure exerted by antibiotic use. Created in <a href="http://BioRender.com" target="_blank">BioRender.com</a> with permission.</p>
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<p>Dose adjustment should be a dynamic process, revised daily, including data from the host and biomarkers. Artificial intelligence and microdialysis may help to improve antibiotic prescription. CRP–C-reactive protein; PCT–Procalcitonin; IL 6-Interleukin 6. Created in <a href="http://BioRender.com" target="_blank">BioRender.com</a> with permission.</p>
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<p>Different supervised, unsupervised, and reinforcement AI learning models/tools. Adapted from reference [<a href="#B104-antibiotics-13-00853" class="html-bibr">104</a>]. Abbreviations: SVM, sector vector machine; KNN, k-nearest neighbor; CNN, convolutional neural network; RNN: recurrent neural network; PCA, principal component analysis; tSNE, t-distributed stochastic neighbor embedding; NMF, non-negative matrix factorization; MDP, Markov decision process.</p>
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17 pages, 5387 KiB  
Article
Investigation of Chlorhexidine and Chitosan Gel-Based Coatings for the Prevention of Intravascular Catheter-Associated Infections Following Quality by Design Approach
by S P Yamini Kanti, Mahwash Mukhtar, Martin Cseh, László Orosz, Katalin Burián, Rita Ambrus, Orsolya Jójárt-Laczkovich and Ildikó Csóka
Biomedicines 2024, 12(9), 2032; https://doi.org/10.3390/biomedicines12092032 - 5 Sep 2024
Viewed by 409
Abstract
Intravascular catheter-associated infections pose a significant threat to the health of patients because of biofilm formation. Hence, it is imperative to exploit cost-effective approaches to improve patient compliance. With this aim, our present study reported the potential of an antimicrobial polymeric gel coating [...] Read more.
Intravascular catheter-associated infections pose a significant threat to the health of patients because of biofilm formation. Hence, it is imperative to exploit cost-effective approaches to improve patient compliance. With this aim, our present study reported the potential of an antimicrobial polymeric gel coating of chitosan (CS) and chlorhexidine (CHX) on the marketed urinary catheters to minimize the risk of biofilm formation. The study involved the implementation of the Quality by Design (QbD) approach by identifying the critical parameters that can affect the coating of the catheter’s surface in any possible way. Later, design of experiments (DoE) analysis affirmed the lack of linearity in the model for the studied responses in a holistic manner. Moreover, in vitro studies were conducted for the evaluation of various parameters followed by the antibiofilm study. The coating exhibited promising release of CHX in the artificial urinary media together with retention of the coating on the catheter’s surface. Therefore, this study aims to emphasize the importance of a systematic and quality-focused approach by contributing to the development of a safe, effective, and reliable catheter coating to enhance intravascular catheter safety. Full article
(This article belongs to the Section Drug Discovery, Development and Delivery)
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<p>Schematic representation of implementation of QbD design [<a href="#B30-biomedicines-12-02032" class="html-bibr">30</a>,<a href="#B31-biomedicines-12-02032" class="html-bibr">31</a>].</p>
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<p>Ishikawa diagram of the target product (polymeric coating on catheters) and related factors [<a href="#B41-biomedicines-12-02032" class="html-bibr">41</a>,<a href="#B42-biomedicines-12-02032" class="html-bibr">42</a>].</p>
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<p>(<b>a</b>) Risk estimation matrix/interdependence rating between QTPP and CQAs, and (<b>b</b>) risk estimation matrix/interdependence rating between CPP/CMAs and CQAs.</p>
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<p>(<b>a</b>) Pareto chart presenting the severity scores based on probability rating of CQA and (<b>b</b>) Pareto chart presenting the severity scores based on the probability rating of CPPs/CMAs.</p>
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<p>SEM micrographs; (<b>a</b>,<b>b</b>) are for the uncoated catheter samples at a scale of 500 and 100 microns, respectively, and (<b>c</b>,<b>d</b>) show the polymeric coated samples at a scale of 500 and 100 microns, respectively.</p>
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<p>Release profile of CHX from different formulations C2 and C8.</p>
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<p>No. of germ count at 0 h and at 72 h for all three bacterial strains with control (chitosan coated), blank (uncoated), and coated (CHX-XS coated) catheter samples.</p>
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<p>Biofilm formation on the surface of urinary catheter pieces with <span class="html-italic">E. coli</span> strain (<b>a</b>), <span class="html-italic">S. aureus</span> (<b>b</b>), and <span class="html-italic">P. aeruginosa</span> (<b>c</b>), where control represents catheter with chitosan, blank represents uncoated catheter, and coated represents catheter coated with CHX-CS gel.</p>
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16 pages, 3069 KiB  
Article
An Antibacterial-Loaded PLA 3D-Printed Model for Temporary Prosthesis in Arthroplasty Infections: Evaluation of the Impact of Layer Thickness on the Mechanical Strength of a Construct and Drug Release
by Carlos Tamarit-Martínez, Lucía Bernat-Just, Carlos Bueno-López, Adrián M. Alambiaga-Caravaca, Virginia Merino, Alicia López-Castellano and Vicent Rodilla
Pharmaceutics 2024, 16(9), 1151; https://doi.org/10.3390/pharmaceutics16091151 - 30 Aug 2024
Viewed by 353
Abstract
Infections are one of the main complications in arthroplasties. These infections are difficult to treat because the bacteria responsible for them settle in the prosthesis and form a biofilm that does not allow antimicrobials to reach the infected area. This study is part [...] Read more.
Infections are one of the main complications in arthroplasties. These infections are difficult to treat because the bacteria responsible for them settle in the prosthesis and form a biofilm that does not allow antimicrobials to reach the infected area. This study is part of a research project aimed at developing 3D-printed spacers (temporary prostheses) capable of incorporating antibacterials for the personalized treatment of arthroplasty infections. The main objective of this research was to analyze the impact of the layer thickness of 3D-printed constructs based on polylactic acid (PLA) for improved treatment of infections in arthroplasty. The focus is on the following parameters: resistance, morphology, drug release, and the effect of antibacterials incorporated in the printed temporary prostheses. The resistance studies revealed that the design and layer thickness of a printed spacer have an influence on its resistance properties. The thickness of the layer used in printing affects the amount of methylene blue (used as a model drug) that is released. Increasing layer thickness leads to a greater release of the drug from the spacer, probably as a result of higher porosity. To evaluate antibacterial release, cloxacillin and vancomycin were incorporated into the constructs. When incorporated into the 3D construct, both antibacterials were released, as evidenced by the growth inhibition of Staphylococcus aureus. In conclusion, preliminary results indicate that the layer thickness during the three-dimensional (3D) printing process of the spacer plays a significant role in drug release. Full article
(This article belongs to the Special Issue Pharmaceutical Applications of 3D Printing)
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<p>Digitalized femur (<b>A</b>) from human femur measurements, spacer (<b>B</b>) intended to be placed in the knee during PJI, and designed test construct (<b>C</b>) intended to contain antibacterials and spacer fill pattern of cross-linked overlapping beams (<b>D</b>,<b>E</b>) using “Rhinoceros 3D” software [<a href="#B36-pharmaceutics-16-01151" class="html-bibr">36</a>].</p>
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<p>Representation of stress–strain for the 23.12 cm<sup>3</sup> spacers in horizontal (<b>A</b>) and vertical (<b>B</b>) position for each layer thickness (<span class="html-italic">n</span> = 6) using the Zwick/Roell Z005 dynamometer.</p>
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<p>Representation of stress–strain for the 1.20 cm<sup>3</sup> spacers in horizontal (<b>A</b>) and vertical (<b>B</b>) position for each layer thickness (<span class="html-italic">n</span> = 6) using the Zwick/Roell Z005 dynamometer.</p>
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<p>Optical microscope images of the (<b>A</b>) 0.2, (<b>B</b>) 0.3 and (<b>C</b>) 0.4 mm layer thickness at 25× magnification.</p>
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<p>Electron microscope images of (<b>A</b>) 0.2 mm layer at 150× magnification and (<b>B</b>) 0.3 and (<b>C</b>) 0.4 mm layer at 300× magnification to show how each layer of filament superimposes over the next to create pores.</p>
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<p>Percentage of MB released from the 1.20 cm<sup>3</sup> 3D constructs printed with a layer thickness of 0.2 mm, 0.3 mm, and 0.4 mm for up to 54 h. The regression lines are fitted using the Korsmeyer–Peppas model.</p>
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<p>0.5 McFarland standard 1/10 dilution of <span class="html-italic">Staphylococcus aureus</span> test tubes after 24 h of incubation. (<b>A</b>) negative control (<span class="html-italic">S. aureus</span> and CLOX in solution); (<b>B</b>) positive control (<span class="html-italic">S. aureus</span>, no antibacterial); (<b>C</b>) 3D constructs loaded with CLOX; (<b>D</b>) 3D constructs without CLOX. The 3D constructs in this image have been printed with a layer thickness of 0.2 mm.</p>
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<p>Absorbance for each 3D construct printed with different layer thicknesses (0.2, 0.3, and 0.4 mm) loaded with the antibacterials VAN and CLOX and constructs without loaded antibacterials (no antibacterials) after being incubated for 24 h to allow bacterial growth. * Indicates the statistical differences between the three layer thicknesses (<span class="html-italic">p</span> = 0.01); # indicates the differences between 0.2 and 0.4 mm (<span class="html-italic">p</span> &lt; 0.05).</p>
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22 pages, 18003 KiB  
Review
Characteristics of Metallic Nanoparticles (Especially Silver Nanoparticles) as Anti-Biofilm Agents
by Hongze Li, Zhihe Yang, Sadaf Aiman Khan, Laurence J. Walsh, Chaminda Jayampath Seneviratne and Zyta M. Ziora
Antibiotics 2024, 13(9), 819; https://doi.org/10.3390/antibiotics13090819 - 28 Aug 2024
Viewed by 652
Abstract
Biofilm-associated infections account for a large proportion of chronic diseases and pose a major health challenge. Metal nanoparticles offer a new way to address this problem, by impairing microbial growth and biofilm formation and by causing degradation of existing biofilms. This review of [...] Read more.
Biofilm-associated infections account for a large proportion of chronic diseases and pose a major health challenge. Metal nanoparticles offer a new way to address this problem, by impairing microbial growth and biofilm formation and by causing degradation of existing biofilms. This review of metal nanoparticles with antimicrobial actions included an analysis of 20 years of journal papers and patent applications, highlighting the progress over that time. A network analysis of relevant publications showed a major focus on the eradication of single-species biofilms formed under laboratory conditions, while a bibliometric analysis showed growing interest in combining different types of metal nanoparticles with one another or with antibiotics. The analysis of patent applications showed considerable growth over time, but with relatively few patents progressing to be granted. Overall, this profile shows that intense interest in metal nanoparticles as anti-biofilm agents is progressing beyond the confines of simple laboratory biofilm models and coming closer to clinical application. Looking to the future, metal nanoparticles may provide a sustainable approach to combatting biofilms of drug-resistant bacteria. Full article
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<p>The main stages of biofilm formation. Figure created using ©2024 Biorender.</p>
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<p>Selected antibacterial actions of metallic nanoparticles. Image created using ©2024 Biorender.</p>
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<p>Two main strategies for the synthesis of metallic nanoparticles. Biological methods such as green synthesis follow the bottom-up approach and reduce metal ions to metal nanoparticles. Image created using ©2024 Biorender.</p>
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<p>Entry of AgNPs into the human body (by ingestion, inhalation, and topical application) and some major sites where silver can accumulate. Image created using ©2024 Biorender.</p>
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<p>PRISMA flow diagram for the included studies from 2004 to 2024.</p>
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<p>Publications per year on single metallic nanoparticles and bimetallic nanoparticles over 2004–2024. This graph was prepared using Prism 10 software. Note that for 2024, only the period from January to June 2024 is included.</p>
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<p>The yearly and cumulative patent trend in last two decades. Data is obtained from searching the Espacenet database.</p>
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<p>Metallic nanoparticle patent numbers by jurisdiction. This figure was prepared using Prism 10 software.</p>
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<p>Patent applications (“reports”) and granted patents per year from 2004 to 2024 for metallic nanoparticles used for antimicrobial treatments. This figure was prepared using Prism 10 software.</p>
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<p>Network analysis for silver nanoparticles, showing nodes for nanoparticle characterization (red) and for antimicrobial potency aspects (blue). This figure was prepared using VOS Viewer.</p>
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<p>Network analysis for zinc nanoparticles showing characterization (red) and performance (blue). This figure was prepared using VOS Viewer software.</p>
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<p>Network analysis for copper nanoparticles, showing characterization (red) and performance (blue). This figure was prepared using VOS Viewer software.</p>
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<p>Network analysis for gold nanoparticles, showing characterization (red) and performance (blue). This figure was prepared using VOS Viewer software.</p>
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14 pages, 1405 KiB  
Article
Contrasting Dynamics of Intracellular and Extracellular Antibiotic Resistance Genes in Response to Nutrient Variations in Aquatic Environments
by Lele Liu, Xinyi Zou, Yuan Cheng, Huihui Li, Xueying Zhang and Qingbin Yuan
Antibiotics 2024, 13(9), 817; https://doi.org/10.3390/antibiotics13090817 - 28 Aug 2024
Viewed by 639
Abstract
The propagation of antibiotic resistance in environments, particularly aquatic environments that serve as primary pathways for antibiotic resistance genes (ARGs), poses significant health risks. The impact of nutrients, as key determinants of bacterial growth and metabolism, on the propagation of ARGs, particularly extracellular [...] Read more.
The propagation of antibiotic resistance in environments, particularly aquatic environments that serve as primary pathways for antibiotic resistance genes (ARGs), poses significant health risks. The impact of nutrients, as key determinants of bacterial growth and metabolism, on the propagation of ARGs, particularly extracellular ARGs (eARGs), remains poorly understood. In this study, we collected microorganisms from the Yangtze River and established a series of microcosms to investigate how variations in nutrient levels and delivery frequency affect the relative abundance of intracellular ARGs (iARGs) and eARGs in bacterial communities. Our results show that the relative abundance of 7 out of 11 representative eARGs in water exceeds that of iARGs, while 8 iARGs dominate in biofilms. Notably, iARGs and eARGs consistently exhibited opposite responses to nutrient variation. When nutrient levels increased, iARGs in the water also increased, with the polluted group (COD = 333.3 mg/L, COD:N:P = 100:3:0.6, m/m) and the eutrophic group (COD = 100 mg/L, COD:N:P = 100:25:5, m/m) showing 1.2 and 3.2 times higher levels than the normal group (COD = 100 mg/L, COD:N:P = 100:10:2, m/m), respectively. In contrast, eARGs decreased by 6.7% and 8.4% in these groups. On the other hand, in biofilms, higher nutrient levels led to an increase in eARGs by 1.5 and 1.7 times, while iARGs decreased by 17.5% and 50.1% in the polluted and eutrophic groups compared to the normal group. Moreover, while increasing the frequency of nutrient delivery (from 1 time/10 d to 20 times/10 d) generally did not favor iARGs in either water or biofilm, it selectively enhanced eARGs in both. To further understand these dynamics, we developed an ARGs-nutrient model by integrating the Lotka–Volterra and Monod equations. The results highlight the complex interplay of bacterial growth, nutrient availability, and mechanisms such as horizontal gene transfer and secretion influencing ARGs’ propagation, driving the opposite trend between these two forms of ARGs. This contrasting response between iARGs and eARGs contributes to a dynamic balance that stabilizes bacterial resistance levels amid nutrient fluctuations. This study offers helpful implications regarding the persistence of bacterial resistance in the environment. Full article
(This article belongs to the Special Issue Antibiotic Resistance in Wastewater Treatment Plants)
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<p><b>eARGs are more prevalent in water while iARGs are more prevalent in biofilm.</b> This figure describes the distribution (relative abundance) of iARGs and eARGs in water (<b>A</b>) and biofilm (<b>B</b>).</p>
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<p><b>Differential response of iARGs and eARGs to the variation of nutrient level.</b> This figure describes changes in the relative abundance of iARGs in water (<b>A</b>), eARGs in water (<b>B</b>), iARGs in biofilm (<b>C</b>), and eARGs in biofilm (<b>D</b>) under various nutrient conditions (normal, eutrophic, and contaminated groups) in microcosms. The relative abundance of ARGs in each treatment was normalized to a blank control without nutrient injection. The plots indicate the normalized relative abundance of each single ARG.</p>
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<p><b>Simulation of ARG propagation in response to nutrient variations</b>. This figure illustrates the results from our ARGs-nutrient model, simulating the propagation of ARGs over a 10-day period under varying concentrations of carbon (100–500 mg/L) and nitrogen (5–25 mg/L). The simulations align with our microcosm experiments, demonstrating changes in the relative abundance of iARGs and eARGs. (<b>A</b>,<b>E</b>) The trends of iARGs in water; (<b>B</b>,<b>F</b>) the trends of eARGs in water; (<b>C</b>,<b>G</b>) the trends of iARGs in biofilm; (<b>D</b>,<b>H</b>) the trends of eARGs in biofilm.</p>
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<p><b>Differential response of iARGs and eARGs to the frequency of nutrient delivery.</b> This figure describes changes in the relative abundance of iARGs in water (<b>A</b>), eARGs in water (<b>B</b>), iARGs in biofilm (<b>C</b>), and eARGs in biofilm (<b>D</b>) under various frequencies of nutrient delivery. The relative abundance of ARGs in each treatment was normalized to a blank control without nutrient injection. The plots indicate the normalized relative abundance of each single ARG.</p>
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<p><b>Simulation of ARG propagation in response to the frequency of nutrient delivery</b>. This figure illustrates the results from our ARGs-nutrient model, simulating the propagation of ARGs over low medium, and high frequency of nutrient delivery. (<b>A</b>) The trends of iARGs in water; (<b>B</b>) the trends of eARGs in water; (<b>C</b>) the trends of iARGs in biofilm; (<b>D</b>) the trends of eARGs in biofilm.</p>
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31 pages, 2559 KiB  
Review
Origami of KR-12 Designed Antimicrobial Peptides and Their Potential Applications
by Jayaram Lakshmaiah Narayana, Abraham Fikru Mechesso, Imran Ibni Gani Rather, D. Zarena, Jinghui Luo, Jingwei Xie and Guangshun Wang
Antibiotics 2024, 13(9), 816; https://doi.org/10.3390/antibiotics13090816 - 28 Aug 2024
Viewed by 1035
Abstract
This review describes the discovery, structure, activity, engineered constructs, and applications of KR-12, the smallest antibacterial peptide of human cathelicidin LL-37, the production of which can be induced under sunlight or by vitamin D. It is a moonlighting peptide that shows both antimicrobial [...] Read more.
This review describes the discovery, structure, activity, engineered constructs, and applications of KR-12, the smallest antibacterial peptide of human cathelicidin LL-37, the production of which can be induced under sunlight or by vitamin D. It is a moonlighting peptide that shows both antimicrobial and immune-regulatory effects. Compared to LL-37, KR-12 is extremely appealing due to its small size, lack of toxicity, and narrow-spectrum antimicrobial activity. Consequently, various KR-12 peptides have been engineered to tune peptide activity and stability via amino acid substitution, end capping, hybridization, conjugation, sidechain stapling, and backbone macrocyclization. We also mention recently discovered peptides KR-8 and RIK-10 that are shorter than KR-12. Nano-formulation provides an avenue to targeted delivery, controlled release, and increased bioavailability. In addition, KR-12 has been covalently immobilized on biomaterials/medical implants to prevent biofilm formation. These constructs with enhanced potency and stability are demonstrated to eradicate drug-resistant pathogens, disrupt preformed biofilms, neutralize endotoxins, and regulate host immune responses. Also highlighted are the safety and efficacy of these peptides in various topical and systemic animal models. Finaly, we summarize the achievements and discuss future developments of KR-12 peptides as cosmetic preservatives, novel antibiotics, anti-inflammatory peptides, and microbiota-restoring agents. Full article
(This article belongs to the Special Issue Insights into Natural Antimicrobial Peptides)
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<p>Properties and functions of human cathelicidin LL-37 discovered in different cells. Depicted in the center is the 3D structure of membrane-bound LL-37 (PDB ID: 2K6O) determined by 3D triple-resonance heteronuclear multidimensional nuclear magnetic resonance (NMR) spectroscopy. When targeting bacterial membranes, the C-terminal tail of LL-37 is not folded and remains highly flexible as confirmed by heteronuclear <sup>15</sup>N backbone dynamics on the ps-ns time scale [<a href="#B32-antibiotics-13-00816" class="html-bibr">32</a>]. The C-terminal tail is disordered in complex with SDS, D8PG, and LPS (abbreviations in the text). Direct interactions of LL-37 with anionic bacterial phosphatidylglycerols (PGs) and LPS as demonstrated by NMR provide basis for antimicrobial and anti-inflammatory effects. NET: neutrophil extracellular traps; PBMC: peripheral blood mononuclear cells; DCs: dendritic cells; MSC: mesenchymal stem cell.</p>
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<p>Therapeutic strategies based on human cathelicidin LL-37. (<b>A</b>) Humans can use sunlight or vitamin D and its analog to switch on the expression of LL-37 to boost innate defense against infection. Likewise, recombinant DNA technology can be used to express LL-37 to achieve the same production. (<b>B</b>) Human LL-37 can function synergistically with other human AMPs such as defensin or lysozyme to better control pathogens. Similarly, human LL-37 can work synergistically with bacteriocins from commensal bacteria to better control invading pathogens. Using the same strategy, AMPs can be used with existing antibiotics to overcome resistance. (<b>C</b>) LL-37 can be engineered into novel antimicrobial agents based on different fragments (IG-24, GF-17, and KR-12) discovered from (1) peptide library, (2) structure-based design, (3) combined (1) and (2), and (4) feature-based mimicking (reviewed in ref. [<a href="#B26-antibiotics-13-00816" class="html-bibr">26</a>]). This review focuses on a variety of the engineered constructs based on KR-12, the smallest antibacterial fragment of LL-37.</p>
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<p>The discovery path of KR-12 through structural studies. (<b>A</b>) Amino acid sequences and nomenclature of LL-37 and its fragments. The original peptide names for these fragments are given on the right of the peptide sequences, while the shortened names are provided on the left. While LL-37 has a carboxylic acid at the C-terminus, the C-termini of shorter sequences, including FK-16/GF-17, FK-13, and KR-12, are all amidated to increase the net charge by +1. (<b>B</b>) Backbone structures of LL-37, IG-25, GF-17, FK-13, and KR-12 determined by 2D and 3D NMR spectroscopy [<a href="#B32-antibiotics-13-00816" class="html-bibr">32</a>,<a href="#B39-antibiotics-13-00816" class="html-bibr">39</a>,<a href="#B60-antibiotics-13-00816" class="html-bibr">60</a>]. Except for LL-37 and IG-25, GF-17, FK-13, and KR-12 are C-terminally amidated. (<b>C</b>,<b>D</b>) Horizontal and vertical views of the NMR structure of KR-12 in complex with anionic D8PG [<a href="#B32-antibiotics-13-00816" class="html-bibr">32</a>].</p>
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<p>Helical wheel plots for LL-37 (<b>A</b>), KR-12 (<b>B</b>) and its selective derivatives (<b>C</b>–<b>L</b>). The helical wheel was generated using the NetWheel program (<a href="http://lbqp.unb.br/NetWheels/" target="_blank">http://lbqp.unb.br/NetWheels/</a>, accessed on 31 July 2024). In this program, amino acids are classified into four groups: (1) polar/basic (red square): RHK, (2) polar/acidic (blue triangle): DE, (3) polar/uncharged (green diamond): STNQC, and (4) nonpolar (yellow circle): AGVILMFYWP. Although included in the plot, it is evident that the HIV TAT sequence is not amphipathic (<b>J</b>). However, the amphipathic helical structure can still be seen in the presence of additional sequence from the Trp cage (<b>K</b>). Finally, some symmetry can be seen in the helical wheel plot of cyclic KR-12 dimer (<b>L</b>).</p>
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<p>Various design strategies that transform human LL-37-derived KR-12 to new constructs: (1) amino acid changes, terminal capping, peptide hybridization, and peptide conjugates (<b>top</b>), (2) sidechain stapling and backbone cyclization (<b>right</b>), (3) surface immobilization (<b>left</b>), and (4) peptide formulation (<b>bottom</b>). See the text for further details. These strategies can be applied to other linear antimicrobial peptides as well.</p>
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<p>2D array of the lengths of the KR-12 peptide with 4 to 12 amino acids (aa4 to aa12) and fatty acids (c6 to C14) for antibacterial activity (<b>A</b>) and hemolytic toxicity (<b>B</b>) uncovered a zone for designing selective lipopeptides. On the left, the closer the curves to the green plane, the more potent the peptides are. In contrast, the farther away from the red plane on the right, the less hemolytic the peptides are.</p>
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<p>Like LL-37, KR-12 peptides also possess numerous desired properties such as antimicrobial, antibiofilm, and LPS neutralization. KR-12, as well as LL-37, has been covalently immobilized onto titanium (Ti) implants [<a href="#B123-antibiotics-13-00816" class="html-bibr">123</a>,<a href="#B124-antibiotics-13-00816" class="html-bibr">124</a>].</p>
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<p>In vivo safety and efficacy of KR-12 constructs. (<b>A</b>) Systemic toxicity of nanobiotics Myr-KR-12N and Myr-KR-12C via intravenous administration in mice. (<b>B</b>) Ototoxicity of a KR-12-a2 by applying the solution topically into the middle ears of guinea pigs. (<b>C</b>) Identification of the non-toxic dose of C10-KR8d via the intraperitoneal route in mice. (<b>D</b>) Efficacy of Ti-C10-KR8d implant on catheter-associated MRSA biofilm in mice. (<b>E</b>) LPS neutralization and bone restoration efficacy of KR-12-a2 in mice. (<b>F</b>) Cryogel-HA/TA/KR12 topical application in a mouse wound model. (<b>G</b>) PEEK-PDA-KR-12 coating on implants shows both antibacterial and osteointegration potential in mice. (<b>H</b>) KR-12 has anti-colitis ability against chemical induced colitis in mice. (<b>I</b>) Myr-KR-12N and Myr-KR-12C protection from LPS sepsis in mice. (<b>J</b>) C10-KR8d showcased anti-MRSA efficacy in a neutropenic murine infection model.</p>
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Article
Evidence for the Presence of Borrelia burgdorferi Biofilm in Infected Mouse Heart Tissues
by Sahaja Thippani, Niraj Jatin Patel, Jasmine Jathan, Kate Filush, Kayla M. Socarras, Jessica DiLorenzo, Kunthavai Balasubramanian, Khusali Gupta, Geneve Ortiz Aleman, Jay M. Pandya, Venkata V. Kavitapu, Daina Zeng, Jennifer C. Miller and Eva Sapi
Microorganisms 2024, 12(9), 1766; https://doi.org/10.3390/microorganisms12091766 - 26 Aug 2024
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Abstract
Borrelia burgdorferi, the bacterium responsible for Lyme disease, has been shown to form antimicrobial-tolerant biofilms, which protect it from unfavorable conditions. Bacterial biofilms are known to significantly contribute to severe inflammation, such as carditis, a common manifestation of Lyme disease. However, the [...] Read more.
Borrelia burgdorferi, the bacterium responsible for Lyme disease, has been shown to form antimicrobial-tolerant biofilms, which protect it from unfavorable conditions. Bacterial biofilms are known to significantly contribute to severe inflammation, such as carditis, a common manifestation of Lyme disease. However, the role of B. burgdorferi biofilms in the development of Lyme carditis has not been thoroughly investigated due to the absence of an appropriate model system. In this study, we examined heart tissues from mice infected with B. burgdorferi for the presence of biofilms and inflammatory markers using immunohistochemistry (IHC), combined fluorescence in situ hybridization FISH/IHC, 3D microscopy, and atomic force microscopy techniques. Our results reveal that B. burgdorferi spirochetes form aggregates with a known biofilm marker (alginate) in mouse heart tissues. Furthermore, these biofilms induce inflammation, as indicated by elevated levels of murine C-reactive protein near the biofilms. This research provides evidence that B. burgdorferi can form biofilms in mouse heart tissue and trigger inflammatory processes, suggesting that the mouse model is a valuable tool for future studies on B. burgdorferi biofilms. Full article
(This article belongs to the Special Issue New Strategies for Pathogenic Biofilms)
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Figure 1

Figure 1
<p>Summary of the design of the experimental infection of C3H/HeN mice with <span class="html-italic">B. burgdorferi</span> and the collection of different samples (upper panel). The lower left panel shows the results of ELISA for IgG serum levels in the two infected groups (20 mice/group) and one uninfected group (4 mice). The lower middle panel shows results of the ankle measurements in the infected and uninfected groups, while the lower right panel summarizes the results of the culture and qPCR experiments on infected mice.</p>
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<p>Immunohistochemical (IHC) detection of <span class="html-italic">B. burgdorferi</span> spirochetes and biofilm in heart tissue sections of C3H/HeN infected and uninfected mice. Panels (<b>A</b>,<b>F</b>,<b>K</b>,<b>P</b>) show IHC results using a FITC-labeled anti-borrelia antibody. Green arrows depict <span class="html-italic">B. burgdorferi</span> aggregates; green arrowheads point to small spirochetes. Panels (<b>B</b>,<b>G</b>,<b>L)</b> show IHC results using an anti-alginate antibody (red staining). Red arrows depict the presence of alginate on <span class="html-italic">B. burgdorferi</span> aggregates. Panels (<b>C</b>,<b>H</b>,<b>M</b>,<b>R</b>) show IHC results with non-specific IgG antibody (negative control). Panels (<b>D</b>,<b>I</b>,<b>N,S</b>) show DAPI stain for nuclear DNA. Panels (<b>E</b>,<b>J</b>,<b>O</b>,<b>T</b>) show the structure of the tissues by DIC microscopy. Panels (<b>P</b>,<b>Q</b>) show uninfected mouse heart sections that were stained with the same <span class="html-italic">B. burgdorferi</span> and alginate antibodies. Images were taken at 400×. Bar: 200 μm.</p>
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<p>Representative images of <span class="html-italic">B. burgdorferi and</span> alginate-specific combined FISH/IHC on infected mouse heart tissue. Panel (<b>A</b>) shows <span class="html-italic">B. burgdorferi</span> stained with fluorescently labeled 16S rDNA probe (green staining with a green arrow). Panel (<b>B</b>) shows IHC results using an anti-alginate antibody on subsequent section (red staining with a red arrow). Panel (<b>C</b>) shows the structure of the tissue (DIC). Panels (<b>D</b>–<b>F</b>) show negative controls for the FISH experiments: competing oligo, DNase 1, and random probes respectively as described in Methods. Images were taken at 400×. Bar: 200 μm.</p>
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<p>Representative IHC images of <span class="html-italic">B. burgdorferi</span> infected mouse heart tissue section demonstrating the 3D spatial arrangement (Panel (<b>A</b>)) with the corresponding Z-stack (Panel (<b>B</b>)). Green staining: <span class="html-italic">B. burgdorferi</span>, red staining: alginate and blue staining: DAPI. Bar: 20 μm.</p>
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<p>Three-dimensional analysis of Borrelia aggregates stained positive for <span class="html-italic">B. burgdorferi</span> and alginate via atomic force microscopy (AFM) in infected mouse heart tissue sections. Panel (<b>A</b>) shows the AFM topographical scans of <span class="html-italic">B. burgdorferi</span> biofilm (white arrowhead). Panel (<b>B</b>) shows results using a FITC-labeled anti-<span class="html-italic">Borrelia</span> antibody (green staining, white arrowhead). Panel (<b>C</b>) shows results using an anti-alginate antibody (red staining, white arrowhead). Panel (<b>D</b>) shows the DIC image of the infected mouse tissue section (white arrowhead), which was used for the AFM study. Scale bar: 50 μm.</p>
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<p>Representative IHC images of <span class="html-italic">B. burgdorferi</span> and C-reactive protein (CRP) staining in infected and uninfected C3H/HeN mouse heart sections. Panels (<b>B</b>,<b>F</b>) show results using a <span class="html-italic">B. burgdorferi</span>-specific antibody (green)<b>.</b> Panels (<b>C</b>,<b>G</b>) show results using a CRP antibody (red). Panels (<b>A</b>,<b>E</b>) show DAPI stain for nuclear DNA. Panels (<b>F</b>,<b>G</b>) show negative control, uninfected mouse heart sections that were stained with the same <span class="html-italic">B. burgdorferi</span> and CRP antibodies. Panels (<b>D</b>,<b>H</b>) show the structure of the tissues by DIC microscopy. Green arrows indicate two small <span class="html-italic">B. burgdorferi</span> aggregates. Images were taken at 200×. Bar: 200 μm.</p>
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