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We are entering the post-antibiotic era. Antimicrobial resistance (AMR) is a critical problem in chronic lung infections resulting in progressive respiratory failure and increased mortality. In the absence of emerging novel antibiotics to counter AMR infections, bacteriophages (phages), viruses that infect bacteria, have become a promising option for chronic respiratory infections. However, while personalized phage therapy is associated with improved outcomes in individual cases, clinical trials demonstrating treatment efficacy are lacking, limiting the therapeutic potential of this approach for respiratory infections. In this review, we address the current state of phage therapy for managing chronic respiratory diseases. We then discuss how phage therapy may address major microbiologic obstacles which hinder disease resolution of chronic lung infections with current antibiotic-based treatment practices. Finally, we highlight the challenges that must be addressed for successful phage therapy clinical trials. Through this discussion, we hope to expand on the potential of phages as an adjuvant therapy in chronic lung infections, as well as the microbiologic challenges that need to be addressed for phage therapy to expand beyond personalized salvage therapy.
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Terapia por Fagos , Infecções Respiratórias , Humanos , Terapia por Fagos/métodos , Infecções Respiratórias/terapia , Infecções Respiratórias/microbiologia , Infecções Respiratórias/virologia , Bacteriófagos , Doença Crônica , Antibacterianos/uso terapêuticoRESUMO
BACKGROUND: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. METHOD: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. RESULTS: The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. CONCLUSION: The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.
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Surtos de Doenças , Previsões , Influenza Humana , Humanos , China/epidemiologia , Influenza Humana/epidemiologia , Modelos Estatísticos , Estações do AnoRESUMO
As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.
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Background: Hyperuricemia is a common complication of type 2 diabetes mellitus and can lead to serious consequences such as gout and kidney disease. Methods: Patients with type 2 diabetes mellitus from six different communities in Fuzhou were recruited from June to December 2022. Questionnaires, physical examinations, and laboratory tests were conducted to collect data on various variables. Variable screening steps were performed using univariate and multivariate stepwise regression, least absolute shrinkage and selection operator (LASSO) regression, and Boruta feature selection. The dataset was divided into a training-testing set (80%) and an independent validation set (20%). Six machine learning models were built and validated. Results: A total of 8243 patients with type 2 diabetes mellitus were included in this study. According to Occam's razor method, the LASSO regression algorithm was determined to be the optimal risk factors selection method, and nine variables were identified as parameters for the risk assessment model. The absence of diabetes medication and elevated fasting blood glucose levels exhibited a negative correlation with the risk of hyperuricemia. Conversely, seven other variables demonstrated a positive association with the risk of hyperuricemia among patients diagnosed with type 2 diabetes mellitus. Among the six machine learning models, the artificial neural network (ANN) model demonstrated the highest performance. It achieved an areas under curve of 0.736, accuracy of 68.3%, sensitivity of 65.0%, specificity of 72.2%, precision of 73.6% and F1-score of 69.0%. Conclusions: We developed an ANN model to better evaluate the risk of hyperuricemia in the type 2 diabetes population. In the type 2 diabetes population, women should pay particular attention to their uric acid levels, and type 2 diabetics should not neglect their obesity level, blood pressure, kidney function and lipid profile during their regular medical check-ups, in order to do their best to avoid the risks associated with the combination of type 2 diabetes and hyperuricemia.
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Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical foundation when predicting patient survival. This study aimed to analyze the impact of marital status on survival outcomes of PDAC patients using propensity score matching and machine learning. The goal was to develop a prognosis prediction model specific to married patients with PDAC. We extracted a total of 206,968 patient records of pancreatic cancer from the SEER database. To ensure the baseline characteristics of married and unmarried individuals were balanced, we used a 1:1 propensity matching score. We then conducted Kaplan-Meier analysis and Cox proportional-hazards regression to examine the impact of marital status on PDAC survival before and after matching. Additionally, we developed machine learning models to predict 5-year CSS and OS for married patients with PDAC specifically. In total, 24,044 PDAC patients were included in this study. After 1:1 propensity matching, 8043 married patients and 8,043 unmarried patients were successfully enrolled. Multivariate analysis and the Kaplan-Meier curves demonstrated that unmarried individuals had a poorer survival rate than their married counterparts. Among the algorithms tested, the random forest performed the best, with 0.734 5-year CSS and 0.795 5-year OS AUC. This study found a significant association between marital status and survival in PDAC patients. Married patients had the best prognosis, while widowed patients had the worst. The random forest is a reliable model for predicting survival in married patients with PDAC.
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Carcinoma Ductal Pancreático , Neoplasias Pancreáticas , Humanos , Carcinoma Ductal Pancreático/diagnóstico , Estado Civil , Casamento , Neoplasias Pancreáticas/diagnóstico , Aprendizado de MáquinaRESUMO
Objective: Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening. Methods: The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were "Gaussian Naive Bayes (GNB)", "Support Vector Machine (SVM)", "K Neighbors Classifier (KNN)", and "Logistic Regression (LR)". Finally, the optimal algorithm was selected to construct the final prediction model. Results: Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively. Conclusion: We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.
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Background: In recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NOx) and ADs. Methods: We utilized genome-wide association study (GWAS) data on PM and NOx from the UK Biobank in European and East Asian populations. We also extracted integrated GWAS data from the Finnish consortium and the Japanese Biobank for two-sample MR analysis. We employed inverse variance weighted (IVW) analysis to assess the causal relationship between PM and NOx exposure and ADs. Additionally, we conducted supplementary analyses using four methods, including IVW (fixed effects), weighted median, weighted mode, and simple mode, to further investigate this relationship. Results: In the European population, the results of MR analysis suggested a statistically significant association between PM2.5 and psoriasis only (OR = 3.86; 95% CI: 1.89-7.88; PIVW < 0.00625), while a potential association exists between PM2.5-10 and vitiligo (OR = 7.42; 95% CI: 1.02-53.94; PIVW < 0.05), as well as between PM2.5 and systemic lupus erythematosus (OR = 68.17; 95% CI: 2.17-2.1e+03; PIVW < 0.05). In East Asian populations, no causal relationship was found between air pollutants and the risk of systemic lupus erythematosus and rheumatoid arthritis (PIVW > 0.025). There was no pleiotropy in the results. Conclusion: Our results suggest a causal association between PM2.5 and psoriasis in European populations. With the help of air pollution prevention and control, the harmful progression of psoriasis may be slowed.
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Poluição do Ar , Doenças Autoimunes , Lúpus Eritematoso Sistêmico , Psoríase , Humanos , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Doenças Autoimunes/etiologia , Doenças Autoimunes/genética , Poluição do Ar/efeitos adversos , Material Particulado/efeitos adversos , Psoríase/etiologia , Psoríase/genéticaRESUMO
Marital status is an independent prognostic factor for survival in many types of cancers, but its prognostic impact on patients with prostate cancer (PCa) has not been established. The aim of this study was to explore the independent prognostic factors of PCa and to investigate the effect of marital status on survival outcomes in patients with different stratified by PCa. Using the surveillance, epidemiology, and end results (SEER) database, we collected data on 584,655 PCa patients diagnosed between 1975 and 2019. Marital status was classified as married, divorced, widowed, and single. We used the Kaplan-Meier analysis and single multivariate Cox proportional hazards regression analysis to determine the effect of marital status on overall survival (OS) and cancer-specific survival (CSS). In addition, we performed subgroup analyses for different ages, Gleason score and PSA values, and performed a 1:1 propensity score matching (PSM) to reduce the impact of confounding factors to obtain more accurate matching results. According to our findings, marital status was an independent prognostic factor for the survival of PCa patients and a better prognosis of married patients. Moreover, we also found that factors such as age, TNM stage, Gleason score, and PSA concentration were also considered as important predictors for the prognosis of PCa. The above findings can facilitate early detection and treatment of high-risk PCa patients, prolong their life and reduce family burden.
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Antígeno Prostático Específico , Neoplasias da Próstata , Masculino , Humanos , Pontuação de Propensão , Programa de SEER , Estado Civil , PrognósticoRESUMO
Small cell lung cancer (SCLC) patients exhibit significant heterogeneity in tumor burden, physical condition, and responses to initial treatment. This diversity in treatment responses can result in varying treatment outcomes. The primary objective of this study was to explore the patient demographics associated with improved survival outcomes through radiotherapy. Based on the SEER database, we identified 42,824 SCLC patients enrolled between 2004 and 2015. These patients were stratified into radiotherapy (n = 20,360) and non-radiotherapy groups (n = 22,464). We controlled for confounding factors using propensity score matching (PSM) analysis. Subsequently, Kaplan-Meier (KM) analysis was employed to evaluate the impact of radiotherapy on patients' overall survival (OS) and cancer-specific survival (CSS). Cancer-specific mortality was further analyzed using competitive risk models. Cox analysis was also conducted to examine additional variables potentially affecting the survival of SCLC patients. We identified a total of 42,824 eligible patients, and following PSM, 13,329 patients were successfully matched in both the radiotherapy and non-radiotherapy groups. The KM analysis showed that the median OS was 9 months in the radiotherapy group and 6 months in the non-radiotherapy group. The median CSS was 10 months in the radiotherapy group and 7 months in the non-radiotherapy group. The 5-year OS and 10-year OS rates were 6.2% versus 1.6% in the radiotherapy group and 2.6% versus 0.8% in the non-radiotherapy group (P < 0.001). Competitive risk analysis showed that cancer-specific mortality was significantly higher in the non-radiotherapy group than in the radiotherapy group (P < 0.001). Multivariate Cox analysis showed that the radiotherapy group (relative non-radiotherapy group) showed a significant positive effect on survival outcomes (OS: HR 0.658 95% CI [0.642, 0.675] P < 0.001; CSS: HR 0.662 95% CI [0.645, 0.679], P < 0.001). In addition, age, gender, race, primary tumor site, T stage, N stage, M stage, chemotherapy, and surgery were also considered as important predictors of SCLC outcome. The results of the subgroup analysis showed that the radiotherapy group showed a significant survival advantage regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery (P < 0.001). Radiotherapy may improve both OS and CSS in SCLC patients. Patients with SCLC may benefit from radiotherapy regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery.
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Neoplasias Pulmonares , Programa de SEER , Carcinoma de Pequenas Células do Pulmão , Humanos , Carcinoma de Pequenas Células do Pulmão/radioterapia , Carcinoma de Pequenas Células do Pulmão/mortalidade , Carcinoma de Pequenas Células do Pulmão/patologia , Masculino , Feminino , Neoplasias Pulmonares/radioterapia , Neoplasias Pulmonares/mortalidade , Neoplasias Pulmonares/patologia , Idoso , Pessoa de Meia-Idade , Estudos Retrospectivos , Resultado do Tratamento , Estimativa de Kaplan-Meier , Adulto , Idoso de 80 Anos ou mais , Modelos de Riscos ProporcionaisRESUMO
Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza.
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Poluentes Atmosféricos , Poluição do Ar , Influenza Humana , Humanos , Influenza Humana/epidemiologia , Poluição do Ar/efeitos adversos , Poluição do Ar/análise , Poluentes Atmosféricos/efeitos adversos , Poluentes Atmosféricos/análise , Material Particulado/efeitos adversos , Material Particulado/análise , Monitoramento Ambiental , China/epidemiologiaRESUMO
Background: Prior research has demonstrated that programmed cell death (PCD) and mitochondria assume pivotal roles in controlling cellular metabolism and maintaining bone cell equilibrium. Nonetheless, the comprehensive elucidation of their mode of operation in osteoporosis (OP) warrants further investigation. Therefore, this study aimed at analyzing the role of genes associated with PCD (PCD-RGs) and mitochondria (mortality factor-related genes; MRGs) in OP. Methods: Differentially expressed genes (DEGs) were identified by subjecting the GSE56815 dataset obtained from the Gene Expression Omnibus database to differential expression analysis and comparing OP patients with healthy individuals. The genes of interest were ascertained through the intersection of DEGs, MRGs, and PCD-RGs; these genes were filtered using machine learning methodologies to discover potential biomarkers. The prospective biomarkers displaying uniform patterns and statistically meaningful variances were identified by evaluating their levels in the GSE56815 dataset and conducting quantitative real-time polymerase chain reaction-based assessments. Moreover, the functional mechanisms of these biomarkers were further delineated by constructing a nomogram, which conducted gene set enrichment analysis, explored immune infiltration, generated regulatory networks, predicted drug responses, and performed molecular docking analyses. Results: Eighteen candidate genes were documented contingent upon the intersection between 2,354 DEGs, 1,136 MRGs, and 1,548 PCD-RGs. The biomarkers DAP3, BIK, and ACAA2 were upregulated in OP and were linked to oxidative phosphorylation. Furthermore, the predictive ability of the nomogram designed based on the OP biomarkers exhibited a certain degree of accuracy. Correlation analysis revealed a strong positive correlation between CD56dim natural killer cells and ACAA2 and a significant negative correlation between central memory CD4+ T cells and DAP3. DAP3, BIK, and ACAA2 were regulated by multiple factors; specifically, SETDB1 and ZNF281 modulated ACAA2 and DAP3, whereas TP63 and TFAP2C governed DAP3 and BIK. Additionally, a stable binding force was observed between the drugs (estradiol, valproic acid, and CGP52608) and the biomarkers. Conclusion: This investigation evidenced that the biomarkers DAP3, BIK, and ACAA2 are associated with PCD and mitochondria in OP, potentially facilitate the diagnosis of OP in clinical settings.
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Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.
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Diabetes Mellitus Tipo 2 , Hiperuricemia , Humanos , Fatores de Risco , HDL-Colesterol , Redes Neurais de ComputaçãoRESUMO
The increasing prevalence of methicillin-resistant Staphylococcus aureus (MRSA) has sparked global concern due to the dwindling availability of effective antibiotics. To increase our treatment options, researchers have investigated naturally occurring antimicrobial compounds and have identified MC21-A (C58), which has potent antimicrobial activity against MRSA. Recently, we have devised total synthesis schemes for C58 and its chloro-analog, C59. Here, we report that both compounds eradicate 90% of the 39 MRSA isolates tested [MIC90 and minimum bactericidal concentration (MBC90)] at lower or comparable concentrations compared to several standard-of-care (SoC) antimicrobials including daptomycin, vancomycin, and linezolid. Furthermore, a stable, water-soluble sodium salt of C59, C59Na, demonstrates antimicrobial activity comparable to C59. C59, unlike vancomycin, kills stationary-phase MRSA in a dose-dependent manner and completely eradicates MRSA biofilms. In contrast to vancomycin, exposing MRSA to sub-MIC concentrations of C59 does not result in the emergence of spontaneous resistance. Similarly, in a multi-step study, C59 demonstrates a low propensity of resistance acquisition when compared to SoC antimicrobials, such as linezolid and clindamycin. Our findings suggest C58, C59, and C59Na are non-toxic to mammalian cells at concentrations that exert antimicrobial activity; the lethal dose at median cell viability (LD50) is at least fivefold higher than the MBC90 in the two mammalian cell lines tested. A morphological examination of the effects of C59 on a MRSA isolate suggests the inhibition of the cell division process as a mechanism of action. Our results demonstrate the potential of this naturally occurring compound and its analogs as non-toxic next-generation antimicrobials to combat MRSA infections. IMPORTANCE: The rapid emergence of methicillin-resistant Staphylococcus aureus (MRSA) isolates has precipitated a critical need for novel antibiotics. We have developed a one-pot synthesis method for naturally occurring compounds such as MC21-A (C58) and its chloro-analog, C59. Our findings demonstrate that these compounds kill MRSA isolates at lower or comparable concentrations to standard-of-care (SoC) antimicrobials. C59 eradicates MRSA cells in biofilms, which are notoriously difficult to treat with SoC antibiotics. Additionally, the lack of resistance development observed with C59 treatment is a significant advantage when compared to currently available antibiotics. Furthermore, these compounds are non-toxic to mammalian cell lines at effective concentrations. Our findings indicate the potential of these compounds to treat MRSA infections and underscore the importance of exploring natural products for novel antibiotics. Further investigation will be essential to fully realize the therapeutic potential of these next-generation antimicrobials to address the critical issue of antimicrobial resistance.
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Staphylococcus aureus Resistente à Meticilina , Bifenil Polibromatos , Infecções Estafilocócicas , Humanos , Vancomicina/farmacologia , Linezolida/farmacologia , Testes de Sensibilidade Microbiana , Antibacterianos/farmacologia , Infecções Estafilocócicas/epidemiologiaRESUMO
Despite great progress in the field, chronic Pseudomonas aeruginosa (Pa) infections remain a major cause of mortality in patients with cystic fibrosis (pwCF), necessitating treatment with antibiotics. Pf is a filamentous bacteriophage produced by Pa and acts as a structural element in Pa biofilms. Pf presence has been associated with antibiotic resistance and poor outcomes in pwCF, although the underlying mechanisms are unclear. We have investigated how Pf and sputum biopolymers impede antibiotic diffusion using pwCF sputum and fluorescent recovery after photobleaching. We demonstrate that tobramycin interacts with Pf and sputum polymers through electrostatic interactions. We also developed a set of mathematical models to analyze the complex observations. Our analysis suggests that Pf in sputum reduces the diffusion of charged antibiotics due to a greater binding constant associated with organized liquid crystalline structures formed between Pf and sputum polymers. This study provides insights into antibiotic tolerance mechanisms in chronic Pa infections and may offer potential strategies for novel therapeutic approaches.
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Antibacterianos , Pseudomonas aeruginosa , Escarro , Eletricidade Estática , Escarro/microbiologia , Antibacterianos/farmacologia , Pseudomonas aeruginosa/efeitos dos fármacos , Pseudomonas aeruginosa/virologia , Humanos , Fibrose Cística/metabolismo , Infecções por Pseudomonas/tratamento farmacológico , Infecções por Pseudomonas/microbiologia , Tobramicina/farmacologia , Difusão , Biofilmes/efeitos dos fármacos , BacteriófagosRESUMO
Despite great progress in the field, chronic Pseudomonas aeruginosa (Pa) infections remain a major cause of morbidity and mortality in patients with cystic fibrosis, necessitating treatment with inhaled antibiotics. Pf phage is a filamentous bacteriophage produced by Pa that has been reported to act as a structural element in Pa biofilms. Pf presence has been associated with resistance to antibiotics and poor outcomes in cystic fibrosis, though the underlying mechanisms are unclear. Here, we have investigated how Pf phages and sputum biopolymers impede antibiotic diffusion using human sputum samples and fluorescent recovery after photobleaching. We demonstrate that tobramycin interacts with Pf phages and sputum polymers through electrostatic interactions. We also developed a set of mathematical models to analyze the complex observations. Our analysis suggests that Pf phages in sputum reduce the diffusion of charged antibiotics due to a greater binding constant associated with organized liquid crystalline structures formed between Pf phages and sputum polymers. This study provides insights into antibiotic tolerance mechanisms in chronic Pa infections and may offer potential strategies for novel therapeutic approaches.
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Bacteriophages (phages) are viruses that specifically target and kill bacteria, serving as a promising therapeutic to combat multidrug-resistant (MDR) pathogens such as Pseudomonas aeruginosa (Pa). However, delivering adequate concentrations of active phages directly to the infection site over sufficient times to eradicate infections remains an outstanding challenge to phage therapy (PT). Here we present "HydroPhage", a biocompatible hydrogel system for the sustained release of high-titre phages to effectively treat infections caused by MDR pathogens. We develop injectable hydrogels comprised of hyaluronic acid (HA) and polyethylene glycol (PEG) crosslinked through static covalent thioether bonds and hemithioacetal-based dynamic covalent crosslinks (DCC), which encapsulate phages at concentration up to 1011 PFU per mL gel, and achieve sustained release over a week with more than 60% total phage recovery. In a preclinical mouse model of extended wound infection, we demonstrate enhanced bacterial clearance compared to intravenous treatment. Thus, using hydrogels for local and sustained delivery of phage may represent an effective approach to eradicating MDR infections broadly.
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Extensive efforts are underway to develop bacteriophages as therapies against antibiotic-resistant bacteria. However, these efforts are confounded by the instability of phage preparations and a lack of suitable tools to assess active phage concentrations over time. In this study, we use dynamic light scattering (DLS) to measure changes in phage physical state in response to environmental factors and time, finding that phages tend to decay and form aggregates and that the degree of aggregation can be used to predict phage bioactivity. We then use DLS to optimize phage storage conditions for phages from human clinical trials, predict bioactivity in 50-y-old archival stocks, and evaluate phage samples for use in a phage therapy/wound infection model. We also provide a web application (Phage-Estimator of Lytic Function) to facilitate DLS studies of phages. We conclude that DLS provides a rapid, convenient, and nondestructive tool for quality control of phage preparations in academic and commercial settings.
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BACKGROUND: Maize (Zea mays L.) is a widely cultivated cereal and has been used as an optimum heavy metal phytoremediation crop. Metallothionein (MT) proteins are small, cysteine-rich, proteins that play important roles in plant growth and development, and the regulation of stress response to heavy metals. However, the MT genes for maize have not been fully analyzed so far. METHODS: The putative ZmMT genes were identified by HMMER. The heat map of ZmMT genes spatial expression analysis was generated by using R with the log2 (FPKM + 1). The expression profiles of ZmMT genes under three kinds of heavy metal stresses were quantified by using qRT-PCR. The metallothionein proteins was aligned using MAFFT and phylogenetic analysis were constructed by ClustalX 2.1. The protein theoretical molecular weight and pI, subcellular localization, TFs binding sites, were predicted using ProtParam, PSORT, PlantTFDB, respectively. RESULTS: A total of 9 ZmMT genes were identified in the whole genome of maize. The results showed that eight of the nine ZmMT proteins contained one highly conserved metallothio_2 domain, while ZmMT4 contained a Metallothio_PEC domain. All the ZmMT proteins could be classified into three major groups and located on five chromosomes. The ZmMT promoters contain a large number of hormone regulatory elements and hormone-related transcription factor binding sites. The ZmMT genes exhibited spatiotemporal specific expression patterns in 23 tissues of maize development stages and showed the different expression patterns in response to Cu, Cd, and Pb heavy metal stresses. CONCLUSIONS: We identified the 9 ZmMT genes, and explored their conserved motif, tissue expression patterns, evolutionary relationship. The expression profiles of ZmMT genes under three kinds of heavy metal stresses (Cu, Cd, Pb) were analyzed. In summary, the expression of ZmMTs have poteintial to be regulated by hormones. The specific expression of ZmMTs in different tissues of maize and the response to different heavy metal stresses are revealed that the role of MT in plant growth and development, and stress resistance to heavy metals.