Analyzing the Activity of Micafungin Combined with Tobramycin Against Pseudomonas Aeruginosa Biofilm.

BACKGROUND: This study assessed the potential effect of combining micafungin and tobramycin in vitro against biofilms of clinical Pseudomonas aeruginosa isolates. METHODS: Nine biofilm-positive clinical isolates of P. aeruginosa were used in this study. The minimum inhibitory concentrations (MICs) of micafungin and tobramycin for planktonic bacteria were determined using the agar dilution method. The planktonic bacterial growth curve was plotted for micafungin treatment. Biofilms of these nine strains were treated with different concentrations of micafungin and combined with tobramycin in microtiter plates. Biofilm biomass was detected by crystal violet staining and spectrophotometry. Phenotypic reduction in biofilm formation and the eradication of mature biofilm were significant based on average optical density (p < 0.05). The kinetics of micafungin combined with tobramycin to eradicate mature biofilms was investigated in vitro using the time-kill method. RESULTS: Micafungin exhibited no antibacterial effect on P. aeruginosa, and tobramycin minimum inhibitory concentrations (MICs) did not change in the presence of micafungin. Micafungin alone inhibited biofilm formation and eradicated established biofilms of all isolates in a dose-dependent manner, but the required minimum concentration varied. An increase in micafungin concentration resulted in an observed inhibition rate of 64.9% - 72.3% and achieved an eradication rate of 59.2% - 64.5%. Its combination with tobramycin exhibited synergistic effects, including inhibiting the biofilm formation of PA02, PA05, PA23, PA24, and PA52 isolates above 1/4 × MIC or 1/2 × MIC and eradicating mature biofilms of PA02, PA04, PA23, PA24, and PA52 above 32 × MIC, 2 × MIC, 16 × MIC, 32 × MIC, and 1 × MIC, respectively. Micafungin addition could eradicate biofilm-embedded bacterial cells more rapidly; at 32 mg/L, the biofilm eradication time lowered from 24 hours to 12 hours for the inoculum groups with 106 CFU/mL, and from 12 hours to 8 hours for 105 CFU/mL. Whereas at 128 mg/L, the time was lowered from 12 hours to 8 hours for the inoculum groups with 106 CFU/mL, and from 8 hours to 4 hours for 105 CFU/mL. CONCLUSIONS: Micafungin showed good anti-biofilm activity at low concentrations. The combination of micafungin with tobramycin displayed a synergistic effect in controlling P. aeruginosa biofilm.

CRL4Cdt2 ubiquitin ligase regulates Dna2 and Rad16 (XPF) nucleases by targeting Pxd1 for degradation.

Structure-specific endonucleases (SSEs) play key roles in DNA replication, recombination, and repair. SSEs must be tightly regulated to ensure genome stability but their regulatory mechanisms remain incompletely understood. Here, we show that in the fission yeast Schizosaccharomyces pombe, the activities of two SSEs, Dna2 and Rad16 (ortholog of human XPF), are temporally controlled during the cell cycle by the CRL4Cdt2 ubiquitin ligase. CRL4Cdt2 targets Pxd1, an inhibitor of Dna2 and an activator of Rad16, for degradation in S phase. The ubiquitination and degradation of Pxd1 is dependent on CRL4Cdt2, PCNA, and a PCNA-binding degron motif on Pxd1. CRL4Cdt2-mediated Pxd1 degradation prevents Pxd1 from interfering with the normal S-phase functions of Dna2. Moreover, Pxd1 degradation leads to a reduction of Rad16 nuclease activity in S phase, and restrains Rad16-mediated single-strand annealing, a hazardous pathway of repairing double-strand breaks. These results demonstrate a new role of the CRL4Cdt2 ubiquitin ligase in genome stability maintenance and shed new light on how SSE activities are regulated during the cell cycle.

In vitro evaluation of the antibacterial activities of radezolid and linezolid for Streptococcus agalactiae.

PURPOSE: This study aims to evaluate the antimicrobial activities of linezolid and radezolid against Streptococcus agalactiae in vitro and compared for genetic resistance factors. METHOD: Nonduplicate S. agalactiae clinical isolates (n = 136) were collected and the minimal inhibitory concentrations of antimicrobials were determined by agar dilution methodology. The linezolid-resistant mechanism in the clinical linezolid-non-susceptible S. agalactiae isolates and that induced by linezolid pressure in vitro were analyzed by PCR and sequence alignment. Antimicrobial activities and resistance mechanism distinctions between linezolid and radezolid were further investigated in the clinical linezolid-non-susceptible S. agalactiae isolates and that induced by linezolid pressure in vitro. RESULTS: Our data indicated that 17 (13%) of the 136 clinical S. agalactiae isolates were not susceptible to linezolid. For individual S. agalactiae isolates, including linezolid-nonsusceptible isolates with 23S rRNA V domain mutations, radezolid MIC90 values were generally one-half to one-quarter of the linezolid MIC90 values. Radezolid MICs remained low relative to linezolid MICs among linezolid-resistant S. agalactiae isolates, but exhibited the synchronous increases with the increasing copy numbers of 23S rRNA V domain mutations. Overall, 13 optrA-carrying clinical S. agalactiae isolates were found in this study and their MICs all remained sensitive to both linezolid and radezolid. Clinical S. agalactiae isolates with high radezolid MICs showed clonality clustering to sequence type (ST)10. CONCLUSION: Radezolid exhibits stronger potency against S. agalactiae than linezolid and there is a concerning presence of linezolid-nonsusceptible S. agalactiae in clinical samples.

Eravacycline activity against clinical S. aureus isolates from China: in vitro activity, MLST profiles and heteroresistance.

BACKGROUND: Mortality rates for patients with Staphylococcus aureus (S. aureus) infections have improved only modestly in recent decades and S. aureus infections remain a major clinical challenge This study investigated the in vitro antimicrobial activity of erevacycline (erava) against clinical S. aureus isolates from China, as well as the heteroresistance frequency of erava and sequence types (STs) represented in the sample. RESULTS: A sample of 328 non-duplicate clinical S. aureus isolates, including 138 methecillin-resistant (MRSA) and 190 methecillin-sensitive (MSSA) isolates, were collected retrospectively in China. Erava exhibited excellent in vitro activity (MIC50 ≤ 0.25 mg/L) against MRSA and MSSA, including isolates harboring Tet specific resistance genes. The frequency of erava heteroresistance in MSSA with erava MICs = 0.5 mg/L was 13.79% (4/29); no MRSA with erava MICs ≤0.5 mg/L exhibited heteroresistance. Heteroresistance- derived clones had no 30S ribosome subunit mutations, but their erava MICs (range, 1-4 mg/L) were suppressed dramatically in the presence of efflux protein inhibitors. CONCLUSIONS: Conclusively, erava exhibited excellent in vitro activity against S. aureus, however hints of erava heteroresistance risk and MIC creep were detected, particularly among MSSA with MICs of 0.5 mg/L.

Biofilm formation in erythromycin-resistant Staphylococcus aureus and the relationship with antimicrobial susceptibility and molecular characteristics.

PURPOSE: In this study, we aimed to investigate biofilm formation characteristics in clinical Staphylococcus aureus (S. aureus) isolates with erythromycin (ERY) resistance from China and further analyze their correlations with antimicrobial susceptibility and molecular characteristics. METHODOLOGY: A total of 276 clinical isolates of ERY-resistant S. aureus, including 142 methicillin-resistant S. aureus (MRSA) strains and 134 methicillin-susceptible S. aureus (MSSA) strains, were retrospectively collected in China. Biofilms were determined by crystal violet staining and ERY resistance genes (ermA, ermB and ermC) were detected by polymerase chain reaction. Inducible clindamycin resistance was examined by D test and multilocus sequence typing, and clonal complexes (CCs) based on housekeeping genes were further determined. RESULTS: The frequency of biofilm formation among ERY-resistant S. aureus was 40.9% (113/276) in total and no significant difference was found for the frequency of biofilm formation between ERY-resistant MRSA and ERY-resistant MSSA (44.4% vs 37.3%, P > 0.05). In ERY-resistant MRSA isolates, the frequency of biofilm formation in ermA-positive, gentamicin-resistant and ciprofloxacin-resistant isolates was higher than that in ermA-negative, gentamicin-sensitive and ciprofloxacin-sensitive isolates, respectively (63.9% vs 23.6%, P < 0.01; 60.3% vs 27.5%, P < 0.01; 65.2% vs 26.3%, P < 0.01). In addition, tetracycline resistance facilitated biofilm formation in both ERY-resistant MRSA and MSSA and the frequency of biofilm formation in CC239- or CC7S. aureus isolates with ERY resistance was significantly higher compared with that in CC59S. aureus (both P < 0.01). CONCLUSION: The ermA gene, and gentamicin, ciprofloxacin and tetracycline resistance facilitate biofilm formation in ERY-resistant MRSA isolates and, moreover, ERY-resistant S. aureus isolates with positive biofilm formation exhibited clonality clustering regarding CC239 and CC7.

Inhibition of Adhesion of Enteropathogenic Escherichia coli to HEp-2 Cells by Binding of a Novel Peptide to EspB Protein.

Enteropathogenic Escherichia coli (EPEC) is a major cause of infantile diarrhea in developing countries. The translocator EspB is a key virulence factor in the process of the attaching and effacing effect of EPEC and plays a critical role in the pathogenesis of the bacteria. In this study, we aimed to select the peptides binding to EspB protein by phage display library and further investigate whether these peptides can decrease the extent of invasion and virulence of EPEC on host cells by targeting to EspB protein. The expression and purification of EspB protein from E. coli was demonstrated by Western blotting. The Ph.D. 12-mer peptide phage display library was used to screen the candidate peptides binding specifically to EspB protein. Furthermore, the affinity of these candidate peptides bound to EspB was identified by enzyme-linked immunosorbent assay (ELISA). Moreover, we investigated whether these screened peptides could decrease the adherence ratio of EPEC to HEp-2 cells with increasing concentration. Successful purification of EspB protein from pET21b-EspB-transformed E. coli was identified by Western blotting. Then, the candidate peptides including phages 6, 7, 8, and 12 were screened by the Ph.D. 12-mer peptide phage display library and ELISA test demonstrated that their affinity binding to EspB protein was high compared with the control. Functional analysis indicated that synthetic peptide-6 (YFPYSHTSPRQP) significantly decreased the adherence ratio of EPEC to HEp-2 cells with increasing concentration (P < 0.01). Peptide-6 (100 µg/mL) could lead to a 40 % decrease in the adherence ratio of EPEC to HEp-2 cells compared with control (P < 0.01). However, the other three peptides at different concentrations showed only a slight ability to block the adherence of EPEC to host cells. Our data provided a potential strategy to inhibit the adhesion of EPEC to epithelial cells by a candidate peptide targeted toward EspB protein.

Identification of Infantile Diarrhea Caused by Breast Milk-Transmitted Staphylococcus aureus Infection.

Staphylococcus aureus is a well-known organism which is responsible for a variety of human infectious diseases including skin infections, pneumonia, bacteremia, and endocarditis. Few of the microorganisms can be transmitted from mother to the newborn or infant by milk breastfeeding. This study aims to identify transmission of S. aureus from healthy, lactating mothers to their infants by breastfeeding. Stool specimens of diarrheal infants and breast milk of their mother (totally three pairs) were collected and six Staphylococcus aureus isolates were cultured positively. Homology and molecular characters of isolated strains were tested using pulsed-field gel electrophoresis (PFGE), spa typing, and multilocus sequence typing. Furthermore, toxin genes detection was also performed. Each pair of isolates has the same PFGE type and spa type. Four Sequence types (STs) were found among all the isolates; they are ST15, ST188, and ST59, respectively. Among the strains, seb, sec, and tst genes were found, and all were negative for pvl gene. The homology of the S. aureus strains isolated from the infants' stool and the mothers' milk was genetically demonstrated, which indicated that breastfeeding may be important in the transmission of S. aureus infection, and the character of S. aureus needed to be further evaluated.

Interpretable machine learning for predicting chronic kidney disease progression risk.

Objective: Chronic kidney disease (CKD) poses a major global health burden. Early CKD risk prediction enables timely interventions, but conventional models have limited accuracy. Machine learning (ML) enhances prediction, but interpretability is needed to support clinical usage with both in diagnostic and decision-making. Methods: A cohort of 491 patients with clinical data was collected for this study. The dataset was randomly split into an 80% training set and a 20% testing set. To achieve the first objective, we developed four ML algorithms (logistic regression, random forests, neural networks, and eXtreme Gradient Boosting (XGBoost)) to classify patients into two classes-those who progressed to CKD stages 3-5 during follow-up (positive class) and those who did not (negative class). For the classification task, the area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance in discriminating between the two classes. For survival analysis, Cox proportional hazards regression (COX) and random survival forests (RSFs) were employed to predict CKD progression, and the concordance index (C-index) and integrated Brier score were used for model evaluation. Furthermore, variable importance, partial dependence plots, and restrict cubic splines were used to interpret the models' results. Results: XGBOOST demonstrated the best predictive performance for CKD progression in the classification task, with an AUC-ROC of 0.867 (95% confidence interval (CI): 0.728-0.100), outperforming the other ML algorithms. In survival analysis, RSF showed slightly better discrimination and calibration on the test set compared to COX, indicating better generalization to new data. Variable importance analysis identified estimated glomerular filtration rate, age, and creatinine as the most important predictors for CKD survival analysis. Further analysis revealed non-linear associations between age and CKD progression, suggesting higher risks in patients aged 52-55 and 65-66 years. The association between cholesterol levels and CKD progression was also non-linear, with lower risks observed when cholesterol levels were in the range of 5.8-6.4 mmol/L. Conclusions: Our study demonstrated the effectiveness of interpretable ML models for predicting CKD progression. The comparison between COX and RSF highlighted the advantages of ML in survival analysis, particularly in handling non-linearity and high-dimensional data. By leveraging interpretable ML for unraveling risk factor relationships, contrasting predictive techniques, and exposing non-linear associations, this study significantly advances CKD risk prediction to enable enhanced clinical decision-making.

Natural variables separate the endemic areas of Clonorchis sinensis and Opisthorchis viverrini along a continuous, straight zone in Southeast Asia.

BACKGROUND: Clonorchiasis and opisthorchiasis, caused by the liver flukes Clonorchis sinensis and Opisthorchis viverrini respectively, represent significant neglected tropical diseases (NTDs) in Asia. The co-existence of these pathogens in overlapping regions complicates effective disease control strategies. This study aimed to clarify the distribution and interaction of these diseases within Southeast Asia. METHODS: We systematically collated occurrence records of human clonorchiasis (n = 1809) and opisthorchiasis (n = 731) across the Southeast Asia countries. Utilizing species distribution models incorporating environmental and climatic data, coupled machine learning algorithms with boosted regression trees, we predicted and distinguished endemic areas for each fluke species. Machine learning techniques, including geospatial analysis, were employed to delineate the boundaries between these flukes. RESULTS: Our analysis revealed that the endemic range of C. sinensis and O. viverrini in Southeast Asia primarily spans across part of China, Vietnam, Thailand, Laos, and Cambodia. During the period from 2000 to 2018, we identified C. sinensis infections in 84 distinct locations, predominantly in southern China (Guangxi Zhuang Autonomous Region) and northern Vietnam. In a stark contrast, O. viverrini was more widely distributed, with infections documented in 721 locations across Thailand, Laos, Cambodia, and Vietnam. Critical environmental determinants were quantitatively analyzed, revealing annual mean temperatures ranging between 14 and 20 °C in clonorchiasis-endemic areas and 24-30 °C in opisthorchiasis regions (P < 0.05). The machine learning model effectively mapped a distinct demarcation zone, demonstrating a clear separation between the endemic areas of these two liver flukes with AUC from 0.9 to1. The study in Vietnam delineates the coexistence and geographical boundaries of C. sinensis and O. viverrini, revealing distinct endemic zones and a transitional area where both liver fluke species overlap. CONCLUSIONS: Our findings highlight the critical role of specific climatic and environmental factors in influencing the geographical distribution of C. sinensis and O. viverrini. This spatial delineation offers valuable insights for integrated surveillance and control strategies, particularly in regions with sympatric transmission. The results underscore the need for tailored interventions, considering regional epidemiological variations. Future collaborations integrating eco-epidemiology, molecular epidemiology, and parasitology are essential to further elucidate the complex interplay of liver fluke distributions in Asia.

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data.

OBJECTIVE: This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. METHOD: Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) were employed. RESULTS: From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and R2 = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. CONCLUSION: In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue's complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.

Comprehensive meta-analysis of severe fever with thrombocytopenia syndrome virus infections in humans, vertebrate hosts and questing ticks.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonosis caused by the SFTS virus (SFTSV). Understanding the prevalence of SFTSV RNA in humans, vertebrate hosts and ticks is crucial for SFTS control. METHODS: A systematic review and meta-analysis were conducted to determine the prevalence of SFTSV RNA in humans, vertebrate hosts and questing ticks. Nine electronic databases were searched for relevant publications, and data on SFTSV RNA prevalence were extracted. Pooled prevalence was estimated using a random effects model. Subgroup analysis and multivariable meta-regression were performed to investigate sources of heterogeneity. RESULTS: The pooled prevalence of SFTSV RNA in humans was 5.59% (95% confidence interval [CI] 2.78-9.15%) in those in close contact (close contacts) with infected individuals (infected cases) and 0.05% (95% CI 0.00-0.65%) in healthy individuals in endemic areas. The SFTSV infection rates in artiodactyls (5.60%; 95% CI 2.95-8.96%) and carnivores (6.34%; 95% CI 3.27-10.23%) were higher than those in rodents (0.45%; 95% CI 0.00-1.50%). Other animals, such as rabbits, hedgehogs and birds, also played significant roles in SFTSV transmission. The genus Haemaphysalis was the primary transmission vector, with members of Ixodes, Dermacentor, and Amblyomma also identified as potential vectors. The highest pooled prevalence was observed in adult ticks (1.03%; 95% CI 0.35-1.96%), followed by nymphs (0.66%; 95% CI 0.11-1.50%) and larvae (0.01%; 95% CI 0.00-0.46%). The pooled prevalence in ticks collected from endemic areas (1.86%; 95% CI 0.86-3.14%) was higher than that in ticks collected in other regions (0.41%; 95% CI 0.12-0.81%). CONCLUSIONS: Latent SFTSV infections are present in healthy individuals residing in endemic areas, and close contacts with SFTS cases are at a significantly higher risk of infection. The type of animal is linked to infection rates in vertebrate hosts, while infection rates in ticks are associated with the developmental stage. Further research is needed to investigate the impact of various environmental factors on SFTSV prevalence in vertebrate hosts and ticks.

Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning.

Zimbabwe, located in Southern Africa, faces a significant public health challenge due to schistosomiasis. We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population. To this end, we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public sources. We studied the population requiring praziquantel with regard to whether or not mass drug administration (MDA) had been regularly applied. Three machine-learning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error (MAE), the root mean squared error (RMSE) and the coefficient of determination (R2). The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between Schistosoma haematobium and S. mansoni infections. We found that the top-five correlation factors, such as the past (rather than current) time, unsettled MDA implementation, constrained economy, high rainfall during the warmest season, and high annual precipitation were closely associated with higher S. haematobium prevalence, while lower elevation, high rainfall during the warmest season, steeper slope, past (rather than current) time, and higher minimum temperature in the coldest month were rather related to higher S. mansoni prevalence. The random forest (RF) algorithm was considered as the formal best model construction method, with MAE = 0.108; RMSE = 0.143; and R2 = 0.517 for S. haematobium, and with the corresponding figures for S. mansoni being 0.053; 0.082; and 0.458. Based on this optimal model, the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%, with that of S. haematobium at 13.8% and that of S. mansoni at 7.1%, requiring annual MDA based on a population of 3,003,928. Without MDA, the current total schistosomiasis prevalence would be 23.2%, that of S. haematobium 17.1% and that of S. mansoni prevalence at 7.4%, requiring annual MDA based on a population of 3,521,466. The study reveals that MDA alone is insufficient for schistosomiasis elimination, especially that due to S. mansoni. This study predicts a moderate prevalence of schistosomiasis in Zimbabwe, with its elimination requiring comprehensive control measures beyond the currently used strategies, including health education, snail control, population surveillance and environmental management.

Global burden and risk factors of musculoskeletal disorders among adolescents and young adults in 204 countries and territories, 1990-2019.

BACKGROUND: Current studies on musculoskeletal (MSK) disorders mainly focus on the elderly, while adolescents and young adults (AYAs) are often neglected despite their unique epidemiology, healthcare needs and societal implications. To bridge this gap, we evaluated the global burden and temporal trends of MSK disorders among AYAs from 1990 to 2019, as well as their common categories and main risk factors. METHODS: Data on the global burden and risk factors of MSK disorders were obtained from the Global Burden of Diseases study 2019. Age standardized rates for incidence, prevalence and disability-adjusted life-years (DALYs) were calculated using the world population age standard, and their temporal trends were evaluated by estimated annual percentage changes (EAPC). Locally estimated scatterplot smoothing (LOESS) regression was used to explore the association between two variables. RESULTS: Over the past 30 years, MSK disorders have become the third leading cause of global DALYs among AYAs, with 36.2%, 39.3%, and 21.2% of increases in incident cases, prevalent cases and DALYs, respectively. In 2019, age standardized incidence, prevalence and DALY rates for MSK disorders were positivity associated with socio-demographic index (SDI) among AYAs in 204 countries and territories. The global age-standardized prevalence and DALY rates of MSK disorders began to increases among AYAs since 2000. In the last decade, countries with high SDI not only presented the only increase in age-standardized incidence rate across all SDI quintiles (EAPC = 0.40, 0.15 to 0.65), but also displayed the most rapid increases in age-standardized prevalence and DALY rates (EAPC = 0.41, 0.24 to 0.57; 0.39, 0.19 to 0.58, respectively). Low back pain (LBP) and neck pain (NP) were the most common MSK disorders among AYAs, accounting for 47.2% and 15.4% of global DALYs of MSK disorders in this population, respectively. Rheumatoid arthritis (RA), osteoarthritis (OA), and gout exhibited increasing trends in global age-standardized incidence, prevalence, and DALY rates among AYAs over the past 30 years (all EAPC >0), whereas LBP and NP showed declining trends (all EAPC <0). Occupational ergonomic factors, smoking and high BMI accounted for 13.9%, 4.3%, and 2.7% of global DALYs for MSK disorders among AYAs, respectively. The proportion of DALYs attributable to occupational ergonomic factors was negatively associated with SDI, whereas the proportions attributable to smoking and high BMI increased with SDI. Over the last 30 years, both the proportions of DALYs attributable to occupational ergonomic factors and smoking have consistently decreased globally and across all SDI quintiles, while the proportion attributable to high BMI has increased. CONCLUSIONS: MSK disorders have emerged as the third leading cause of global DALYs among AYAs over the past three decades. Countries with high SDI should make more efforts to tackle the dual challenges posed by the high levels and rapid increases in age standardized incidence, prevalence, and DALY rates in the last decade.

An interpretable machine-learning model for predicting the efficacy of nonsteroidal anti-inflammatory drugs for closing hemodynamically significant patent ductus arteriosus in preterm infants.

Background: Nonsteroidal anti-inflammatory drugs (NSAIDs) have been widely used in the closure of ductus arteriosus in premature infants. We aimed to develop and validate an interpretable machine-learning model for predicting the efficacy of NSAIDs for closing hemodynamically significant patent ductus arteriosus (hsPDA) in preterm infants. Methods: We assessed 182 preterm infants ≤ 30 weeks of gestational age first treated with NSAIDs to close hsPDA. According to the treatment outcome, patients were divided into a "success" group and "failure" group. Variables for analysis were demographic features, clinical features, as well as laboratory and echocardiographic parameters within 72 h before medication use. We developed the machine-learning model using random forests. Model performance was assessed by the area under the receiver operating characteristic curve (AUC). Variable-importance and marginal-effect plots were constructed to explain the predictive model. The model was validated using an external cohort of two preterm infants who received ibuprofen (p.o.) to treat hsPDA. Results: Eighty-three cases (45.6%) were in the success group and 99 (54.4%) in the failure group. Infants in the success group were associated with maternal chorioamnionitis (p = 0.002), multiple births (p = 0.007), gestational age at birth (p = 0.020), use of indometacin (p = 0.007), use of inotropic agents (p < 0.001), noninvasive ventilation (p = 0.001), plasma albumin level (p < 0.001), PDA size (p = 0.038) and Vmax (p = 0.013). Multivariable binary logistic regression analysis showed that maternal chorioamnionitis, multiple births, use of indomethacin, use of inotropic agents, plasma albumin level, and PDA size were independent risk factors influencing the efficacy of NSAIDs (p < 0.05). The AUC of the random forest model was 0.792. The top-three features contributing most to the model in the variable-importance plot were the plasma albumin level and platelet count 72 h before treatment and 24-h urine volume before treatment. In the external cohort, treatment succeeded in one case and failed in the other. The probabilities of success and failure predicted by the random forest model were 60.2% and 48.4%, respectively. Conclusion: Based on clinical, laboratory, and echocardiographic features before first-time NSAIDs treatment, we constructed an interpretable machine-learning model, which has a certain reference value for predicting the closure of hsPDA in premature infants under 30 weeks of gestational age.

Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling.

BACKGROUND: Poverty contributes to the transmission of schistosomiasis via multiple pathways, with the insufficiency of appropriate interventions being a crucial factor. The aim of this article is to provide more economical and feasible intervention measures for endemic areas with varying levels of poverty. METHODS: We collected and analyzed the prevalence patterns along with the cost of control measures in 11 counties over the last 20 years in China. Seven machine learning models, including XGBoost, support vector machine, generalized linear model, regression tree, random forest, gradient boosting machine and neural network, were used for developing model and calculate marginal benefits. RESULTS: The XGBoost model had the highest prediction accuracy with an R2 of 0.7308. Results showed that risk surveillance, snail control with molluscicides and treatment were the most effective interventions in controlling schistosomiasis prevalence. The best combination of interventions was interlacing seven interventions, including risk surveillance, treatment, toilet construction, health education, snail control with molluscicides, cattle slaughter and animal chemotherapy. The marginal benefit of risk surveillance is the most effective intervention among nine interventions, which was influenced by the prevalence of schistosomiasis and cost. CONCLUSIONS: In the elimination phase of the national schistosomiasis program, emphasizing risk surveillance holds significant importance in terms of cost-saving.

Association of TNF-α, IGF-1, and IGFBP-1 levels with the severity of osteopenia in mice with nonalcoholic fatty liver disease.

BACKGROUNDS: Nonalcoholic fatty liver disease (NAFLD) exhibits a close association with osteoporosis. This work aims to assess the potential effects of NAFLD on the progression of osteopenia in animal models. METHODS: Forty-eight C57BL/6 female mice were randomly divided to wild-type (WT) group and high-fat diet (HFD) group. The corresponding detections were performed after sacrifice at 16, 24 and 32 weeks, respectively. RESULTS: At 16 weeks, an remarkable increase in body weight and lipid aggregation in the hepatocytes of HFD group was observed compared to the WT group, while the bone structure parameters showed no significant difference. At 24 weeks, the levels of TNF-α and IL-6 in NAFLD mice were significantly increased, while the level of osteoprotegerin mRNA in bone tissue was decreased, and the level of receptor activator of nuclear factor Kappa-B ligand mRNA was increased. Meanwhile, the function of osteoclasts was increased, and the bone microstructure parameters showed significant changes. At 32 weeks, in the HFD mice, the mRNA levels of insulin-like growth factor-1 (IGF-1), runt-related transcription factor 2, and osterix mRNA were reduced, while the insulin-like growth factor binding protein-1 (IGFBP-1) level was increased. Simultaneously, the osteoblast function was decreased, and the differences of bone structure parameters were more significant, showing obvious osteoporosis. CONCLUSIONS: The bone loss in HFD mice is pronounced as NAFLD progresses, and the changes of the TNF-α, IL-6, IGF-1, and IGFBP-1 levels may play critical roles at the different stages of NAFLD in HFD.

Soil-transmitted helminthiasis in mainland China from 2016 to 2020: a population-based study.

Background: Soil-transmitted helminthiasis is epidemic in China and many other countries of the world, and has caused substantial burdens to human health. We conducted successive national monitoring in China from 2016 to 2020 to analyze the prevalence, changing trends, and factors influencing soil-transmitted helminthiasis, which provided a reference for future control strategies. Methods: Soil-transmitted helminth monitoring was carried out in 31 provinces (autonomous regions or municipalities, herein after referred to as "provinces") throughout China. Each province determined the number and location of monitoring sites (counties), and a unified sampling method was employed. At least 1,000 subjects were investigated in each monitoring county. Stool samples were collected and the modified Kato-Katz thick smear method was employed for stool examination. Infection data and the details of factors influencing soil-transmitted helminthiasis from 2016 to 2020 were collected from national monitoring sites. Additional influencing factors such as environment, climate and human activities were obtained from authoritative websites. Prevalence of soil-transmitted helminths was presented by species, province, sex, and age group. ArcGIS software was used to conduct spatial autocorrelation and hotspot analysis on the infection data. A Poisson distribution model and SaTScan software were used to analyze the infection data with retrospective spatiotemporal scan statistics. A database was built by matching village-level infection rate data with influencing factors. Subsequently, machine learning methods, including a Linear Regression (LR), a Random Forest (RF), a Gradient Boosted Machine (GBM), and an Extreme gradient boosting (XGBOOST) model was applied to construct a model to analyze the main influencing factors of soil-transmitted helminthiasis. Findings: The infection rates of soil-transmitted helminths at national monitoring sites from 2016 to 2020 were 2.46% (6,456/262,380), 1.78% (5,293/297,078), 1.29% (4,200/326,207), 1.40% (5,959/424,766), and 0.84% (3,485/415,672), respectively. The infection rate of soil-transmitted helminths in 2020 decreased by 65.85% compared to that in 2016. From 2016 to 2020, the infection rate of soil-transmitted helminthiasis was relatively high in southern and southwestern China, including Hainan, Yunnan, Sichuan, Guizhou, and Chongqing. In general, the infection rate was higher in females than in males, with the highest rate in the population aged 60 years and above, and the lowest in children aged 0-6 years. Global autocorrelation and hotspot analyses revealed spatial aggregation in both the national and local distribution of soil-transmitted helminthiasis in China from 2016 to 2020. The hotspots were concentrated in southwestern China. The spatiotemporal scanning analysis revealed aggregation years from 2016 to 2017 located in southwestern China, including Yunnan, Sichuan, Chongqing, Guizhou and Guangxi. The RF model was the best fit model for the infection rate of soil-transmitted helminths in China. The top six influencing factors of this disease in the model were landform, barefoot farming, isothermality, temperature seasonality, year, and the coverage of sanitary toilets. Interpretation: The overall infection rate of soil-transmitted helminths in China showed a decreasing trend from 2016-2020 due to the implementation of control measures and the economic boom in China. However, there are still areas with high infection rates and the distribution of such areas exhibit spatiotemporal aggregation. As a strategic next step, control measures should be adjusted to local conditions based on the main influencing factors and the prevalence of different sites to aid in the control and elimination of soil-transmitted helminthiasis. Funding: This research was funded by the National Key Research and Development Program of China (Grant Nos. 2021YFC2300800 and 2021YFC2300804) and the National Natural Science Foundation of China (Grant No. 32161143036).

Ubiquitination-mediated Golgi-to-endosome sorting determines the toxin-antidote duality of fission yeast wtf meiotic drivers.

Killer meiotic drivers (KMDs) skew allele transmission in their favor by killing meiotic progeny not inheriting the driver allele. Despite their widespread presence in eukaryotes, the molecular mechanisms behind their selfish behavior are poorly understood. In several fission yeast species, single-gene KMDs belonging to the wtf gene family exert selfish killing by expressing a toxin and an antidote through alternative transcription initiation. Here we investigate how the toxin and antidote products of a wtf-family KMD gene can act antagonistically. Both the toxin and the antidote are multi-transmembrane proteins, differing only in their N-terminal cytosolic tails. We find that the antidote employs PY motifs (Leu/Pro-Pro-X-Tyr) in its N-terminal cytosolic tail to bind Rsp5/NEDD4 family ubiquitin ligases, which ubiquitinate the antidote. Mutating PY motifs or attaching a deubiquitinating enzyme transforms the antidote into a toxic protein. Ubiquitination promotes the transport of the antidote from the trans-Golgi network to the endosome, thereby preventing it from causing toxicity. A physical interaction between the antidote and the toxin enables the ubiquitinated antidote to translocate the toxin to the endosome and neutralize its toxicity. We propose that post-translational modification-mediated protein localization and/or activity changes may be a common mechanism governing the antagonistic duality of single-gene KMDs.

Assessing the association between the circulating levels of inflammatory cytokines and the risk of tuberculosis: A bidirectional two-sample mendelian randomization study.

BACKGROUND: Numerous observational studies have previously reported an association between inflammatory cytokines and tuberculosis (TB). However, the causal relationship between these factors remains unclear. Consequently, we conducted two-sample Mendelian randomization (MR) analyses to ascertain the causal link between levels of inflammatory cytokines and the risk of TB. METHODS: Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene. SNP was obtained from genome-wide association studies (GWAS) of 8293 individuals of Finnish. TB data was obtained from the UK Biobank, which included 46,293 individuals of European ancestry (comprising 2277 TB cases and 46,056 controls). Two-sample, bi-directional MR analyses using inverse-variance weighted (IVW) method as the primary analysis. Followed by comprehensive sensitivity analyses to validate the robustness of results. RESULT: The study showed that the causal relationship between circulating levels of interleukin (IL)-7 and risk of TB (odds ratio [OR] = 1.001, 95% confidence intervals [CIs]: 1.000, 1.003. p = 0.047). No causal associations were observed between other influencing factors and the occurrence of TB. Furthermore, the analysis revealed that TB infection exhibited negative causal associations with macrophage inflammatory protein 1 alpha ([MIP-1α], OR = 0.007, 95% CI: 0.000, 0.192. p = 0.004), IL-2 (OR = 0.014, 95% CI: 0.010, 0.427. p = 0.014), interleukin-2 receptor alpha chain([IL-2rα], OR = 0.019, 95% CI: 0.001, 0.525. p = 0.019) and basic fibroblast growth factor ([bFGF], OR = 0.066, 95% CI: 0.006, 0.700. p = 0.024). CONCLUSION: The study has illuminated the causal link between inflammatory cytokines and TB, thereby enhancing our comprehension of the potential mechanisms underlying TB pathogenesis. This discovery offers promising avenues for the identification of novel therapeutic targets in TB treatment. These insights may ultimately pave the way for more effective treatment approaches, thereby improving patient outcomes.

Impact factors of Blastocystis hominis infection in persons living with human immunodeficiency virus: a large-scale, multi-center observational study from China.

BACKGROUND: Blastocystis hominis (Bh) is zoonotic parasitic pathogen with a high prevalent globally, causing opportunistic infections and diarrhea disease. Human immunodeficiency virus (HIV) infection disrupts the immune system by depleting CD4+ T lymphocyte (CD4+ T) cell counts, thereby increasing Bh infection risk among persons living with HIV (PLWH). However, the precise association between Bh infection risk and HIV-related biological markers and treatment processes remains poorly understood. Hence, the purpose of the study was to explore the association between Bh infection risk and CD4+ T cell counts, HIV viral load (VL), and duration of interruption in antiviral therapy among PLWH. METHODS: A large-scale multi-center cross-sectional study was conducted in China from June 2020 to December 2022. The genetic presence of Bh in fecal samples was detected by real-time fluorescence quantitative polymerase chain reaction, the CD4+ T cell counts in venous blood was measured using flowcytometry, and the HIV VL in serum was quantified using fluorescence-based instruments. Restricted cubic spline (RCS) was applied to assess the non-linear association between Bh infection risk and CD4+ T cell counts, HIV VL, and duration of interruption in highly active antiretroviral therapy (HARRT). RESULTS: A total of 1245 PLWH were enrolled in the study, the average age of PLWH was 43 years [interquartile range (IQR): 33, 52], with 452 (36.3%) being female, 50.4% (n = 628) had no immunosuppression (CD4+ T cell counts > 500 cells/µl), and 78.1% (n = 972) achieved full virological suppression (HIV VL < 50 copies/ml). Approximately 10.5% (n = 131) of PLWH had interruption. The prevalence of Bh was found to be 4.9% [95% confidence interval (CI): 3.8-6.4%] among PLWH. Significant nonlinear associations were observed between the Bh infection risk and CD4+ T cell counts (Pfor nonlinearity < 0.001, L-shaped), HIV VL (Pfor nonlinearity < 0.001, inverted U-shaped), and duration of interruption in HARRT (Pfor nonlinearity < 0.001, inverted U-shaped). CONCLUSIONS: The study revealed that VL was a better predictor of Bh infection than CD4+ T cell counts. It is crucial to consider the simultaneous surveillance of HIV VL and CD4+ T cell counts in PLWH in the regions with high level of socioeconomic development. The integrated approach can offer more comprehensive and accurate understanding in the aspects of Bh infection and other opportunistic infections, the efficacy of therapeutic drugs, and the assessment of preventive and control strategies.