Epidemiological features and temporal trends of the co-infection between HIV and tuberculosis, 1990-2021: findings from the Global Burden of Disease Study 2021.

BACKGROUND: The co-infection of human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) and tuberculosis (TB) poses a significant clinical challenge and is a major global public health issue. This study aims to elucidate the disease burden of HIV-TB co-infection in global, regions and countries, providing critical information for policy decisions to curb the HIV-TB epidemic. METHODS: The ecological time-series study used data from the Global Burden of Disease (GBD) Study 2021. The data encompass the numbers of incidence, prevalence, mortality, and disability-adjusted life year (DALY), as well as age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and DALY rate for HIV-infected drug-susceptible tuberculosis (HIV-DS-TB), HIV-infected multidrug-resistant tuberculosis (HIV-MDR-TB), and HIV-infected extensively drug-resistant tuberculosis (HIV-XDR-TB) from 1990 to 2021. from 1990 to 2021. The estimated annual percentage change (EAPC) of rates, with 95% confidence intervals (CIs), was calculated. RESULTS: In 2021, the global ASIR for HIV-DS-TB was 11.59 per 100,000 population (95% UI: 0.37-13.05 per 100,000 population), 0.55 per 100,000 population (95% UI: 0.38-0.81 per 100,000 population), for HIV-MDR-TB, and 0.02 per 100,000 population (95% UI: 0.01-0.03 per 100,000 population) for HIV-XDR-TB. The EAPC for the ASIR of HIV-MDR-TB and HIV-XDR-TB from 1990 to 2021 were 4.71 (95% CI: 1.92-7.59) and 13.63 (95% CI: 9.44-18.01), respectively. The global ASMR for HIV-DS-TB was 2.22 per 100,000 population (95% UI: 1.73-2.74 per 100,000 population), 0.21 per 100,000 population (95% UI: 0.09-0.39 per 100,000 population) for HIV-MDR-TB, and 0.01 per 100,000 population (95% UI: 0.00-0.03 per 100,000 population) for HIV-XDR-TB in 2021. The EAPC for the ASMR of HIV-MDR-TB and HIV-XDR-TB from 1990 to 2021 were 4.78 (95% CI: 1.32-8.32) and 10.00 (95% CI: 6.09-14.05), respectively. CONCLUSIONS: The findings indicate that enhancing diagnostic and treatment strategies, strengthening healthcare infrastructure, increasing access to quality medical care, and improving public health education are essential to combat HIV-TB co-infection.

Infection and biogeographical characteristics of Paragonimus westermani and P. skrjabini in humans and animal hosts in China: A systematic review and meta-analysis.

BACKGROUND: Paragonimiasis, primarily caused by Paragonimus westermani and P. skrjabini in China, is a common food-borne parasitic zoonosis. However, the national distribution of Paragonimus spp. infection and its associated environmental determinants remain poorly understood. In this paper, we summarize the infection of P. westermani and P. skrjabini and describe key biogeographical characteristics of the endemic areas in China. METHODS: Data on Paragonimus infection in humans and animal hosts were extracted from eight electronic databases, including CNKI, CWFD, Chongqing VIP, SinoMed, Medline, Embase, PubMed, and Web of Science. A random-effects meta-analysis model was used to estimate the pooled prevalence. All survey locations were georeferenced and plotted on China map, and scatter plots were used to illustrate the biogeographical characteristics of regions reporting Paragonimus infection. RESULTS: A total of 28,948 cases of human paragonimiasis have been documented, with 2,401 cases reported after 2010. Among the 11,443 cases with reported ages, 88.05% were children or adolescents. The pooled prevalence of P. skrjabini is 0.45% (95% CI: 0.27-0.66%) in snails, 31.10% (95% CI: 24.77-37.80%) in the second intermediate host, and 20.31% (95% CI: 9.69-33.38%) in animal reservoirs. For P. westermani, the pooled prevalence is 0.06% (95% CI: 0.01-0.13%) in snails, 52.07% (95% CI: 43.56-60.52%) in the second intermediate host, and 21.40% (95% CI: 7.82-38.99%) in animal reservoirs. Paragonimus are primarily distributed in regions with low altitude, high temperature, and high precipitation. In northeastern China, only P. westermani infections have been documented, while in more southern areas, infections of both P. westermani and P. skrjabini have been reported. CONCLUSIONS: Paragonimiasis remains prevalent in China, particularly among children and adolescents. Variations exist in the intermediate hosts and geographical distribution of P. westermani and P. skrjabini. Additionally, altitude, temperature, and precipitation may influence the distribution of Paragonimus.

Loratadine Derivative Lo-7: A Weapon against Drug-Resistant Enterococcus and Streptococcal Infections.

The primary obstacles in the management of Enterococcus and Streptococcal infections are drug resistance and biofilm formation. Our study revealed that loratadine at a concentration of ≥25 µM exhibited significant inhibitory effects on biofilm formation in 167 clinical strains of Enterococcus faecalis and 15 clinical isolates of Streptococcus agalactiae, Streptococcus pyogenes, and Streptococcus pneumoniae. Additionally, the antibiofilm activity against E. faecalis and Streptococcal was demonstrated by several loratadine derivatives with altered side-chain carbamate moieties. This study investigated the antibacterial activity of the loratadine derivative Lo-7 against clinical strains of S. agalactiae and S. pyogenes, with minimum inhibitory concentrations ranging from 12.5 to 25 µM. The findings revealed that a low concentration of loratadine derivative Lo-7 (3.125 µM) significantly augmented the bactericidal efficacy of vancomycin against multidrug-resistant (MDR) S. agalactiae, both in vitro and in vivo. The loratadine derivative Lo-7, even at low concentrations, demonstrated significant efficacy in eliminating intracellular MDR S. agalactiae within macrophages, potentially indicating a unique advantage over vancomycin, linezolid, and loratadine. Mechanistically, exposure to the loratadine derivative Lo-7 resulted in membrane depolarization without affecting membrane permeability in S. agalactiae. The potential targeting of the SecG subunit of the SecYEG membrane-embedded channel by the loratadine derivative Lo-7 in S. agalactiae was identified through quantitative proteomics, a drug affinity responsive target stability assay, and molecular docking.

Synergistic integration of climate change and zoonotic diseases by artificial intelligence: a holistic approach for sustainable solutions.

Artificial intelligence (AI) is a rapidly evolving field that can impel research in communicable diseases with respect to climate projections, ecological indicators and environmental impact, at the same time revealing new, previously overlooked events. A number of zoonotic and vector-borne diseases already show signs of expanding their northern geographical ranges and appropriate risk assessment and decision support are urgently needed. The deployment of AI-enabled monitoring systems tracking animal populations and environmental changes is of immense potential in the study of transmission under different climate scenarios. In addition, AI's capability to identify new treatments should not only accelerate drug and vaccine discovery but also help predicting their effectiveness, while its contribution to genetic pathogen speciation would assist the evaluation of spillover risks with regard to viral infections from animals to human. Close collaboration between AI experts, epidemiologists and other stakeholders is not only crucial for responding to challenges interconnected with a variety of variables effectively, but also necessary to warrant responsible AI use. Despite its wider successful implementation in many fields, AI should be seen as a complement to, rather than a replacement of, traditional public health measures.

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.

The global burden of disease attributable to preterm birth and low birth weight in 204 countries and territories from 1990 to 2019: An analysis of the Global Burden of Disease Study.

Background: Preterm birth and low birth weight (PBLBW), recognised globally as primary contributors to infant mortality in children under five, have not been sufficiently investigated in terms of their worldwide impact. In this study we aimed to thoroughly evaluate the contemporary trends in disease burden attributable to PBLBW. Methods: We analysed data from 204 countries and territories between 1990-2019, as sourced from the 2019 Global Burden of Disease Study. We analysed the global incidence of mortality and disability-adjusted life years (DALYs) associated with PBLBW, stratified by age, gender, year, and geographic location, alongside the socio-demographic index (SDI). We calculated the annual percentage changes to evaluate the dynamic trends over time. We employed a generalised linear model and scrutinised the relationship between the SDI and the disease burden attributed to PBLBW. Results: In 2019, the global age-standardised rate of deaths and DALYs related to PBLBW showed significant declines. Over the period 1990-2019, both death and DALY rates displayed substantial downward trends, with similar change trends observed for both females and males. Age-specific ratios revealed a decrease in PBLBW-related deaths and DALYs with increasing age, primarily during the neonatal stages (zero to 27 days). The leading three causes of PBLBW-related DALYs in 2019 were neonatal disorders, lower respiratory infections, and sudden infant death syndrome. Furthermore, the association between SDI and PBLBW-related DALYs indicated that the age-standardised DALY rates in 204 countries and territories worldwide were negatively correlated with SDI in 2019. From 1990 to 2019, the age-standardised DALY rates decreased linearly in most regions, except sub-Saharan Africa. Conclusions: The persistent global burden of disease associated with PBLBW is particularly pronounced in neonates aged less than 28 days and in regions with low SDI. In this study, we highlighted the critical need for tailored interventions aimed at mitigating the detrimental effects of PBLBW to attain specific sustainable development goals, particularly those centred on enhancing child survival and overall well-being.

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.

Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection.

BACKGROUND: Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. METHODS: From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). RESULTS: This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. CONCLUSION: Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection.

Baohuoside I inhibits virulence of multidrug-resistant Staphylococcus aureus by targeting the transcription Staphylococcus accessory regulator factor SarZ.

BACKGROUND: Staphylococcus aureus is a versatile pathogen that can cause a wide range of infections in humans. Biofilms play a crucial role in the pathogenicity of S. aureus and contribute to its ability to cause persistent and chronic infections. Baohuoside I has garnered increasing recognition as a natural flavonol glycoside with a wide spectrum of health-related activities. PURPOSE: The antibacterial and anti-biofilm properties of Baohuoside I have not been extensively investigated. Our study aimed to assess its inhibitory effects and the underlying mechanisms on biofilm formation and hemolytic capacity in S. aureus. STUDY DESIGN/METHODS: The impact of Baohuoside I on the biofilm and virulence of S. aureus was evaluated through in vitro experiments and Galleria mellonella as an in vivo infection model. The mechanisms were explored by Drug affinity responsive target stability (DARTS) and validated in genetic knockout strain and through molecular biological experiments using DARTS, molecular docking, electrophoretic mobility shift assay (EMSA), and bio-layer interferometry (BLI). RESULTS: Baohuoside I significantly inhibits the formation of S. aureus biofilms and hemolytic activity at 6.25 µM. Proteomics analysis revealed that treatment with Baohuoside I led to a reduction in the expression of quorum-sensing system agr-regulated genes. DARTS analysis identified Staphylococcus accessory regulator factor (SarZ), a key regulator involved in the expression of virulence factors in S. aureus by acting as activator of the agr quorum-sensing system, was the direct target of Baohuoside I. Molecular docking, DARTS, BLI and EMSA assays collectively confirmed the direct binding of Baohuoside I to SarZ, inhibiting its binding to downstream promoters. Furthermore, it is found through site-directed protein mutagenesis that the Tyr27 and Phe117 residues are key for Baohuoside I binding to SarZ. Additionally, the knockout of SarZ significantly diminished the hemolytic ability of S. aureus, underscoring its crucial role as a pivotal regulator of virulence. Lastly, in vivo tests utilizing the G. mellonella infection model demonstrated the efficacy of Baohuoside I. CONCLUSION: This study provides valuable insights into the mechanism by which Baohuoside I inhibits the virulence of S. aureus through its interaction with SarZ. These findings highlight the significance of SarZ as an effective target against the virulence of S. aureus.

Prediction for post-ERCP pancreatitis in non-elderly patients with common bile duct stones: a cross-sectional study at a major Chinese tertiary hospital (2015-2023).

BACKGROUND: Post-ERCP pancreatitis is one of the most common adverse events in ERCP-related procedures. The purpose of this study is to construct an online model to predict the risk of post-ERCP pancreatitis in non-elderly patients with common bile duct stones through screening of relevant clinical parameters. METHODS: A total of 919 cases were selected from 7154 cases from a major Chinese tertiary hospital. Multivariable logistic regression model was fitted using the variables selected by the LASSO regression from 28 potential predictor variables. The internal and external validation was assessed by evaluating the receiver operating characteristic curve and the area under curve. Restricted cubic spline modelling was used to explore non-linear associations. The interactive Web application developed for risk prediction was built using the R "shiny" package. RESULTS: The incidence of post-ERCP pancreatitis was 5.22% (48/919) and significantly higher in non-elderly patients with female, high blood pressure, the history of pancreatitis, difficult intubation, endoscopic sphincterotomy, lower alkaline phosphatase and smaller diameter of common bile duct. The predictive performance in the test and external validation set was 0.915 (95% CI, 0.858-0.972) and 0.838 (95% CI, 0.689-0.986), respectively. The multivariate restricted cubic spline results showed that the incidence of pancreatitis was increased at 33-50 years old, neutrophil percentage > 58.90%, hemoglobin > 131 g/L, platelet < 203.04 or > 241.40 × 109/L, total bilirubin > 18.39 umol / L, aspartate amino transferase < 36.56 IU / L, alkaline phosphatase < 124.92 IU / L, Albumin < 42.21 g / L and common bile duct diameter between 7.25 and 10.02 mm. In addition, a web server was developed that supports query for immediate PEP risk. CONCLUSION: The visualized networked version of the above model is able to most accurately predict the risk of PEP in non-elderly patients with choledocholithiasis and allows clinicians to assess the risk of PEP in real time and provide preventive treatment measures as early as possible.

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.

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.

AMXT-1501 targets membrane phospholipids against Gram-positive and -negative multidrug-resistant bacteria.

The rapid proliferation of multidrug-resistant (MDR) bacterial pathogens poses a serious threat to healthcare worldwide. Carbapenem-resistant (CR) Enterobacteriaceae, which have near-universal resistance to available antimicrobials, represent a particularly concerning issue. Herein, we report the identification of AMXT-1501, a polyamine transport system inhibitor with antibacterial activity against Gram-positive and -negative MDR bacteria. We observed minimum inhibitory concentration (MIC)50/MIC90 values for AMXT-1501 in the range of 3.13-12.5 µM (2.24-8.93 µg /mL), including for methicillin-resistant Staphylococcus aureus (MRSA), CR Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. AMXT-1501 was more effective against MRSA and CR E. coli than vancomycin and tigecycline, respectively. Subinhibitory concentrations of AMXT-1501 reduced the biofilm formation of S. aureus and Enterococcus faecalis. Mechanistically, AMXT-1501 exposure damaged microbial membranes and increased membrane permeability and membrane potential by binding to cardiolipin (CL) and phosphatidylglycerol (PG). Importantly, AMXT-1501 pressure did not induce resistance readily in the tested pathogens.

Impact of varied immunosuppressive agents and post-transplant diabetes mellitus on prognosis among diverse transplant recipients (experimental studies).

The success of solid organ transplantation (SOT) and the use of immunosuppressive agents offer hope to patients with end-stage diseases. However, the impact of post-transplant diabetes mellitus (PTDM) on SOT patients has become increasingly evident. In our study, we utilized the Scientific Registry of Transplant Recipients (SRTR) database to investigate the association between PTDM and patient survival in various types of organ transplantations, including liver, kidney, intestinal, heart, lung, and combined heart-lung transplantations (all P <0.001). Our findings revealed a negative effect of PTDM on the survival of these patients. Furthermore, we examined the effects of both generic and innovator immunosuppressive agents on the development of PTDM and the overall survival of different SOT populations. Interestingly, the results were inconsistent, indicating that the impact of these agents may vary depending on the specific type of transplantation and patient population. Overall, our study provides a comprehensive and systematic assessment of the effects of different immunosuppressive agents on prognosis, as well as the impact of PTDM on the survival of patients undergoing various types of SOT. These findings emphasize the need for further research and highlight the importance of optimizing immunosuppressive regimens and managing PTDM in SOT patients to improve their long-term outcomes.

The burden of depression, anxiety and schizophrenia among the older population in ageing and aged countries: an analysis of the Global Burden of Disease Study 2019.

Background: Depression, anxiety and schizophrenia among older persons have become global public health challenges. However, the burden of these disorders in ageing and aged countries has not been analysed. Aims: To investigate the burden of depression, anxiety and schizophrenia among older adults in ageing and aged countries. Methods: Using data from the Global Burden of Disease Study 2019, we calculated the estimated annual percentage change (EAPC) in the age-standardised incidence rates (ASIR) and age-standardised disability-adjusted life years (DALYs) rates (ASDR) for depression, anxiety and schizophrenia of older people in ageing countries (China, India, Indonesia) and aged countries (Japan, Italy, Portugal) between 1990 and 2019. Trends in incidence and DALYs were analysed by gender and age. Results: In 2019, the highest incidence of depression, anxiety and schizophrenia in the older population in aged countries was in Japan (927 271.3 (752 552.3-1 125 796.5), 51 498.2 (37 625.7-70 487.3) and 126.0 (61.0-223.2), respectively), while the highest incidence in ageing countries was in China (5 797 556.9 (4 599 403.4-7 133 006.5), 330 256.1 (246 448.9-445 987.4) and 1067.7 (556.2-1775.9), respectively). DALYs for these disorders were similar, with the highest in Japan and China. From 1990 to 2019, the ASIR for depressive disorders decreased in aged countries but increased in ageing countries; the ASIR for anxiety disorders and schizophrenia declined in both ageing and aged countries. The ASDR for depressive disorders was consistent with the ASIR but not for anxiety disorders and schizophrenia. The ASIR for depressive disorders was higher in older women, while the opposite was observed in anxiety disorders and schizophrenia. Notably, the conditions of burden of depressive disorders, anxiety disorders and schizophrenia in the 65-70-year-old age group were the most burdensome. Conclusions: The incidence and DALYs of these three mental disorders increased while exhibiting differences between ageing and aged countries. Raising awareness about formulating health policies for preventing and treating mental disorders in the older population is necessary to reduce the future burden posed by the ageing challenge.

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.

Dual-Fuel Propelled Nanomotors with Two-Stage Permeation for Deep Bacterial Infection in the Treatment of Pulpitis.

Bacterial infection-induced inflammatory response could cause irreversible death of pulp tissue in the absence of timely and effective therapy. Given that, the narrow structure of root canal limits the therapeutic effects of passive diffusion-drugs, considerable attention has been drawn to the development of nanomotors, which have high tissue penetration abilities but generally face the problem of insufficient fuel concentration. To address this drawback, dual-fuel propelled nanomotors (DPNMs) by encapsulating L-arginine (L-Arg), calcium peroxide (CaO2 ) in metal-organic framework is developed. Under pathological environment, L-Arg could release nitric oxide (NO) by reacting with reactive oxygen species (ROS) to provide the driving force for movement. Remarkably, the depleted ROS could be supplemented through the reaction between CaO2 with acids abundant in the inflammatory microenvironment. Owing to high diffusivity, NO achieves further tissue penetration based on the first-stage propulsion of nanomotors, thereby removing deep-seated bacterial infection. Results indicate that the nanomotors effectively eliminate bacterial infection based on antibacterial activity of NO, thereby blocking inflammatory response and oxidative damage, forming reparative dentine layer to avoid further exposure and infection. Thus, this work provides a propagable strategy to overcome fuel shortage and facilitates the therapy of deep lesions.

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.

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.