Hydrogels for Local and Sustained Delivery of Bacteriophages to Treat Multidrug-Resistant Wound Infections.

Bacteriophages (phages), viruses that specifically target and kill bacteria, represent a promising strategy to combat multidrug-resistant (MDR) pathogens such as Pseudomonas aeruginosa (Pa). However, delivering sufficient concentrations of active phages directly to the infection site remains challenging, with current methods having variable success. Here we present "HydroPhage", an innovative hydrogel system for the sustained release of high-titer phages to effectively treat infections caused by MDR pathogens. Our injectable hydrogels, featuring dual-crosslinking of hyaluronic acid and PEG-based hydrogels through static covalent thioether bonds and dynamic covalent hemithioacetal crosslinks (DCC), encapsulate phages at concentration up to 1011 PFU/mL, and achieves controlled release of 109 PFU daily over a week, surpassing levels of current clinical dosages, with more than 60% total phage recovery. In a preclinical mouse model of extended wound infection, compared to intravenous treatment, we demonstrate enhanced bacterial clearance by localized, high-dose, and repeated phage dosing despite the emergence of bacterial resistance to phages. This work advances the development of clinically practical wound dressings tailored for resistant infections.

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.

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.

Phage Therapy for Respiratory Infections: Opportunities and Challenges.

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.

Pf bacteriophages hinder sputum antibiotic diffusion via electrostatic binding.

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.

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators.

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.

Causal association between air pollution and autoimmune diseases: a two-sample Mendelian randomization study.

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.

Evaluating the risk of developing hyperuricemia in patients with type 2 diabetes mellitus using least absolute shrinkage and selection operator regression and machine learning algorithm.

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.

Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods.

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.

A study on the impact of marital status on the survival status of prostate cancer patients based on propensity score matching.

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.

Pf bacteriophages hinder sputum antibiotic diffusion via electrostatic binding.

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.

Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model.

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.

An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus.

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.

Antimicrobial activity of a natural compound and analogs against multi-drug-resistant Gram-positive pathogens.

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.

Rapid assessment of changes in phage bioactivity using dynamic light scattering.

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.

Iodine-doped TiO2 nanotube coatings: a technique for enhancing the antimicrobial properties of titanium surfaces against Staphylococcus aureus.

BACKGROUND: Implant-related infections are a challenging complication of orthopedic surgery, primarily due to the formation of bacterial biofilms on the implant surface. An antibacterial coating for titanium implants was developed to provide novel insights into the prevention and treatment of implant-related infections. METHODS: Titanium plates were coated with TiO2 nanotubes by anodization, and iodine was doped onto the coating via electrophoretic deposition. The obtained plates were characterized using a range of analytical techniques. Subsequently, Staphylococcus aureus was inoculated onto the surfaces of untreated titanium plates (control group), TiO2-nanocoated titanium plates (TiO2 group), and iodine-doped TiO2-nanocoated titanium plates (I-TiO2 group) to compare their antibacterial properties. RESULTS: Twenty-four hour in vitro antimicrobial activity test of the I-TiO2 group against Staphylococcus aureus was superior to those of the other groups, and this difference was statistically significant (P < 0.05). CONCLUSIONS: This coating technology provides a new theoretical basis for the development of anti-infective implants against Staphylococcus aureus in orthopedics.

Synergistic Antimicrobial Effects of Ibuprofen Combined with Standard-of-Care Antibiotics against Cystic Fibrosis Pathogens.

Cystic fibrosis (CF) is a common life-shortening genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Lungs of CF patients are often colonized or infected with microorganisms requiring frequent courses of antibiotics. Antibiotic-resistant bacterial infections have been a growing concern in CF patients. Chronic bacterial infections and concomitant airway inflammation damage the lungs, ultimately leading to respiratory failure. Several clinical trials have demonstrated that high-dose ibuprofen reduces the rate of pulmonary function decline in CF patients. This beneficial effect has been attributed to the anti-inflammatory properties of ibuprofen. Previously, we have confirmed that high-dose ibuprofen demonstrates antimicrobial activity against P. aeruginosa both in vitro and in vivo. However, no study has examined the antimicrobial effect of combining ibuprofen with standard-of-care antimicrobials. Here, we evaluated the possible synergistic activity of combinations of common nonsteroidal anti-inflammatory drugs (NSAIDs), namely, ibuprofen, naproxen, and aspirin, with commonly used antibiotics for CF patients. The drug combinations were screened against different CF clinical isolates. Antibiotics that demonstrated increased efficacy in the presence of ibuprofen were further tested for potential synergistic effects between these NSAIDS and antimicrobials. Finally, a survival analysis of a P. aeruginosa murine infection model was used to demonstrate the efficacy of the most potent combination identified in in vitro screening. Our results suggest that combinations of ibuprofen with commonly used antibiotics demonstrate synergistic antimicrobial activity against drug-resistant, clinical bacterial strains in vitro. The efficacy of the combination of ceftazidime and ibuprofen against resistant P. aeruginosa was demonstrated in an in vivo pneumonia model.

The relationship between sleep quality and daytime dysfunction among college students in China during COVID-19: a cross-sectional study.

Objective: College Students' sleep quality and daytime dysfunction have become worse since the COVID-19 outbreak, the purpose of this study was to explore the relationship between sleep quality and daytime dysfunction among college students during the COVID-19 (Corona Virus Disease 2019) period. Methods: This research adopts the form of cluster random sampling of online questionnaires. From April 5 to 16 in 2022, questionnaires are distributed to college students in various universities in Fujian Province, China and the general information questionnaire and PSQI scale are used for investigation. SPSS26.0 was used to conduct an independent sample t-test and variance analysis on the data, multi-factorial analysis was performed using logistic regression analysis. The main outcome variables are the score of subjective sleep quality and daytime dysfunction. Results: During the COVID-19 period, the average PSQI score of the tested college students was 6.17 ± 3.263, and the sleep disorder rate was 29.6%, the daytime dysfunction rate was 85%. Being female, study liberal art/science/ engineering, irritable (due to limited outdoor), prolong electronic entertainment time were associated with low sleep quality (p < 0.001), and the occurrence of daytime dysfunction was higher than other groups (p < 0.001). Logistics regression analysis showed that sleep quality and daytime dysfunction were associated with gender, profession, irritable (due to limited outdoor), and prolonged electronic entertainment time (p < 0.001). Conclusion: During the COVID-19 epidemic, the sleep quality of college students was affected, and different degrees of daytime dysfunction have appeared, both are in worse condition than before the COVID-19 outbreak. Sleep quality may was inversely associated with daytime dysfunction.

Rapid assessment of changes in phage bioactivity using dynamic light scattering.

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. Here, 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-year-old archival stocks, and evaluate phage samples for use in a phage therapy/wound infection model. We also provide a web-application (Phage-ELF) to facilitate DLS studies of phages. We conclude that DLS provides a rapid, convenient, and non-destructive tool for quality control of phage preparations in academic and commercial settings.

αCGRP deficiency aggravates pulmonary fibrosis by activating the PPARγ signaling pathway.

In order to explore whether αCGRP (Calca) deficiency aggravates pulmonary fibrosis (PF). Clinical data from patients with PF (n = 52) were retrospectively analyzed. Lung tissue from a bleomycin (BLM)-induced rat model was compared with that of Calca-knockout (KO) and wild type (WT) using immunohistochemistry, RNA-seq, and UPLC-MS/MS metabolomic analyses. The results showed that decreased αCGRP expression and activation of the type 2 immune response were detected in patients with PF. In BLM-induced and Calca-KO rats, αCGRP deficiency potentiated apoptosis of AECs and induced M2 macrophages. RNA-seq identified enrichment of pathways involved in nuclear translocation and immune system disorders in Calca-KO rats compared to WT. Mass spectrometry of lung tissue from Calca-KO rats showed abnormal lipid metabolism, including increased levels of LTB4, PDX, 1-HETE. PPAR pathway signaling was significantly induced in both transcriptomic and metabolomic datasets in Calca-KO rats, and immunofluorescence analysis confirmed that the nuclear translocation of PPARγ in BLM-treated and Calca-KO rats was synchronized with STAT6 localization in the cytoplasmic and nuclear fractions. In conclusion, αCGRP is protective against PF, and αCGRP deficiency promotes M2 polarization of macrophages, probably by activating the PPARγ pathway, which leads to activation of the type 2 immune response and accelerates PF development.