Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks.

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

The mother-offspring transfer of chlorothalonil through human breast milk: A multi-city cross-sectional study.

4-Hydroxychlorothalonil (4-OH CHT), the main metabolite of chlorothalonil and the most widely used fungicide, has been frequently detected in human samples during monitoring. 4-OH CHT may exhibit higher toxicity and persistence in the environment compared to its prototype. In this study, a total of 540 paired serum and breast milk samples from pregnant women in three provinces in China were monitored for contaminant residues. 4-OH CHT was analyzed in the samples using ultra high-performance liquid chromatography - high-resolution mass spectrometry with a detection limit of 20 ng/L. The study investigated the effects of demographic factors, such as BMI, region of residence, and education level, on the levels of 4-OH CHT residues in serum and breast milk. Among the three provinces, the highest median concentration of 4-OH CHT in serum samples was observed in Hebei (1.04 × 103 ng/L), while the highest median concentration of 4-OH CHT in breast milk samples was observed in Hubei and Guangdong (491 ng/L). Multiple linear regression was used to investigate the significant positive correlation between 4-OH CHT in serum and breast milk (p = 0.000) after adjusting for personal characteristics. Based on this, the study further explored the influencing factors of transfer efficiencies (TEs) in conjunction with the individual TEs and the personal characteristics of the participants. Our results demonstrated that the age of the volunteers and their exercise habits had an effect on TEs, but further studies are needed to determine whether exercise leads to an increase in TEs.

Machine learning prediction of exposure to acrylamide based on modelling of association between dietary exposure and internal biomarkers.

The ubiquitous occurrence of acrylamide in various thermal processing food products poses a potential health risk for the public. An accurate exposure assessment is crucial to the risk evaluation of acrylamide. Machine learning emerging as a powerful computational tool for prediction was employed to establish the association between internal exposure and dietary exposure to acrylamide among a Chinese cohort of middle-aged and elderly population (n = 1,272). Five machine learning regression models were constructed and compared to predict the daily dietary acrylamide exposure based on urinary biomarkers including N-acetyl-S-(2-carbamoylethyl)-L-cysteine (AAMA), N-acetyl-S-(2-carbamoylethyl)-L-cysteine-sulfoxide (AAMA-sul), N-acetyl-S-(2-carbamoyl-2-hydroxyethyl)-L-cysteine (GAMA), and N-acetyl-S-(1-carbamoyl-2-hydroxyethyl)-L-cysteine (iso-GAMA). Other important covariates such as age, gender, physical activities, and total energy intake were also considered as predictors in the models. Average dietary intake of acrylamide among Chinese elderly participants was 8.9 µg/day, while average urinary contents of AAMA, AAMA-sul, GAMA, and iso-GAMA were 52.2, 19.1, 4.4, and 1.7 nmol/g Ucr (urine creatinine), respectively. Support vector regression (SVR) model showed the best prediction performance with a R of 0.415, followed by light gradient boosting machine (LightGBM) model (R = 0.396), adjusted multiple linear regression (MLR) model (R = 0.378), neural networks (NN) model (R = 0.365), MLR model (R = 0.363), and extreme gradient boosting (XGBoost) model (R = 0.337). The present study firstly correlated dietary exposure with internal exposure to acrylamide among Chinese elderly population, providing an innovative perspective for the exposure assessment of acrylamide.