Amino acids analysis reveals serum methionine contributes to diagnosis of the Kawasaki disease in mice and children.

BACKGROUND: Kawasaki disease (KD) patients often lack early and definitive diagnosis due to insufficient clinical criteria, whereas biomarkers might accelerate the diagnostic process and treatment. METHODS: The KD mouse models were established and thirteen amino acids were determined. A total of 551 serum samples were collected including KD patients (n = 134), HCs (n = 223) and KD patients after intravascular immunoglobulin therapy (IVIG, n = 194). A paired analysis of pre- and post-IVIG was employed in 10 KD patients. RESULTS: The pathological alterations of the aorta, myocardial interstitium and coronary artery vessel were observed in KD mice; the serum levels of methionine in KD mice (n = 40) were markedly altered and negatively correlated with the C-reactive protein levels. Consistent with the mouse model, serum methionine were significantly decreased in KD children, with the relative variation ratio of KD with HCs above 30% and AUROC value of 0.845. Serum methionine were correlated with Z-Score and significantly restored to the normal ranges after KD patient IVIG treatment. Another case-control study with 10 KD patients with IVIG sensitivity and 20 healthy controls validated serum methionine as a biomarker for KD patients with AUROC of 0.86. Elevation of serum DNMT1 activities, but no differences of DNMT3a and DNMT3b, were observed in KD patients when comparing with those in the HCs. CONCLUSIONS: Our study validated that serum methionine was a potential biomarker for KD, the alteration of which is associated with the activation of DNMT1 in KD patients.

Molecular Epidemiologic and Geo-Spatial Characterization of Staphylococcus aureus Cultured from Skin and Soft Tissue Infections from United States-Born and Immigrant Patients Living in New York City.

(1) Background: With increasing international travel and mass population displacement due to war, famine, climate change, and immigration, pathogens, such as Staphylococcus aureus (S. aureus), can also spread across borders. Methicillin-resistant S. aureus (MRSA) most commonly causes skin and soft tissue infections (SSTIs), as well as more invasive infections. One clonal strain, S. aureus USA300, originating in the United States, has spread worldwide. We hypothesized that S. aureus USA300 would still be the leading clonal strain among US-born compared to non-US-born residents, even though risk factors for SSTIs may be similar in these two populations (2) Methods: In this study, 421 participants presenting with SSTIs were enrolled from six community health centers (CHCs) in New York City. The prevalence, risk factors, and molecular characteristics for MRSA and specifically clonal strain USA300 were examined in relation to the patients' self-identified country of birth. (3) Results: Patients born in the US were more likely to have S. aureus SSTIs identified as MRSA USA300. While being male and sharing hygiene products with others were also significant risks for MRSA SSTI, we found exposure to animals, such as owning a pet or working at an animal facility, was specifically associated with risk for SSTIs caused by MRSA USA300. Latin American USA300 variant (LV USA300) was most common in participants born in Latin America. Spatial analysis showed that MRSA USA300 SSTI cases were more clustered together compared to other clonal types either from MRSA or methicillin-sensitive S. aureus (MSSA) SSTI cases. (4) Conclusions: Immigrants with S. aureus infections have unique risk factors and S. aureus molecular characteristics that may differ from US-born patients. Hence, it is important to identify birthplace in MRSA surveillance and monitoring. Spatial analysis may also capture additional information for surveillance that other methods do not.

Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States.

Staphylococcus aureus (S. aureus) is known to cause human infections and since the late 1990s, community-onset antibiotic resistant infections (methicillin resistant S. aureus (MRSA)) continue to cause significant infections in the United States. Skin and soft tissue infections (SSTIs) still account for the majority of these in the outpatient setting. Machine learning can predict the location-based risks for community-level S. aureus infections. Multi-year (2002-2016) electronic health records of children <19 years old with S. aureus infections were queried for patient level data for demographic, clinical, and laboratory information. Area level data (Block group) was abstracted from U.S. Census data. A machine learning ecological niche model, maximum entropy (MaxEnt), was applied to assess model performance of specific place-based factors (determined a priori) associated with S. aureus infections; analyses were structured to compare methicillin resistant (MRSA) against methicillin sensitive S. aureus (MSSA) infections. Differences in rates of MRSA and MSSA infections were determined by comparing those which occurred in the early phase (2002-2005) and those in the later phase (2006-2016). Multi-level modeling was applied to identify risks factors for S. aureus infections. Among 16,124 unique patients with community-onset MRSA and MSSA, majority occurred in the most densely populated neighborhoods of Atlanta's metropolitan area. MaxEnt model performance showed the training AUC ranged from 0.771 to 0.824, while the testing AUC ranged from 0.769 to 0.839. Population density was the area variable which contributed the most in predicting S. aureus disease (stratified by CO-MRSA and CO-MSSA) across early and late periods. Race contributed more to CO-MRSA prediction models during the early and late periods than for CO-MSSA. Machine learning accurately predicts which densely populated areas are at highest and lowest risk for community-onset S. aureus infections over a 14-year time span.

Space-time trends of community-onset Staphylococcus aureus infections in children: a group-based trajectory modeling approach.

PURPOSE: Staphylococcus aureus (S. aureus) remains a serious cause of infections in the United States and worldwide. In the United States, methicillin-resistant S. aureus (MRSA) is the leading cause of skin and soft tissue infections. This study identifies 'best' to 'worst' infection trends from 2002 to 2016, using group-based trajectory modeling approach. METHODS: Electronic health records of children living in the southeastern United States with S. aureus infections from 2002 to 2016 were retrospectively studied, by applying a group-based trajectory model to estimate infection trends (low, high, very high), and then assess spatial significance of these trends at the census tract level; we focused on community-onset infections and not those considered healthcare acquired. RESULTS: Three methicillin-susceptible S. aureus (MSSA) infection trends (low, high, very high) and three MRSA trends (low, high, very high) were identified from 2002 to 2016. Among census tracts with community-onset S. aureus cases, 29% of tracts belonged to the best trend (low infection) for both methicillin-resistant S. aureus and methicillin-susceptible S. aureus; higher proportions occurring in the less densely populated areas. Race disparities were seen with the worst methicillin-resistant S. aureus infection trends and were more often in urban areas. CONCLUSIONS: Group-based trajectory modeling identified unique trends of S. aureus infection rates over time and space, giving insight into the associated population characteristics which reflect these trends of community-onset infection.

Preparation and evaluation of alendronate sodium solid lipid nanoparticles with high oral bioavailability in rats.

Low oral bioavailability of alendronate sodium (ALE) significantly limits its clinical application. However, few studies focus on preparing ALE solid lipid nanoparticles (ALE-SLNs) and investigating its oral bioavailability in vivo due to highly hydrophilic property of ALE. In this study, ALE-SLNs were prepared through high-speed shearing combined with ultrasonic treatment method. ALE-SLNs were evaluated by average particle size, electric potential, encapsulation efficiency (EE), and drug loading (DL). Our results showed that the average EE and DL reached 62.56±0.94% and 6.26±0.09% (n=3), respectively. 120.27±1.17nm, 0.29±0.13 and -19.1±0.27 mV (n=3) were obtained in the average particle size, polydispersity index and zeta potential, respectively. The stability test showed that ALE-SLNs remained stable for more than 2 months at 4°C. After oral administration of ALE-SLNs (4.5mg/kg), the bioavailability was 2.17 times higher than that of ALE solution (86.82±3.6 vs 40.1±1.3µg) in rats. Our results indicate that high-speed shearing combined with the ultrasound method is simple and rapid to prepare ALE-SLNs. SLNs can improve the oral delivery of ALE in rats, which may exert beneficial effects in clinical applications.

Increased Risk of Sub-Clinical Blood Lead Levels in the 20-County Metro Atlanta, Georgia Area-A Laboratory Surveillance-Based Study.

Lead (Pb) is a naturally occurring, highly toxic metal that has adverse effects on children across a range of exposure levels. Limited screening programs leave many children at risk for chronic low-level lead exposure and there is little understanding of what factors may be used to identify children at risk. We characterize the distribution of blood lead levels (BLLs) in children aged 0-72 months and their associations with sociodemographic and area-level variables. Data from the Georgia Department of Public Health's Healthy Homes for Lead Prevention Program surveillance database was used to describe the distribution of BLLs in children living in the metro Atlanta area from 2010 to 2018. Residential addresses were geocoded, and "Hotspot" analyses were performed to determine if BLLs were spatially clustered. Multilevel regression models were used to identify factors associated with clinical BBLs (≥5 µg/dL) and sub-clinical BLLs (2 to <5 µg/dL). From 2010 to 2018, geographically defined hotspots for both clinical and sub-clinical BLLs diffused from the city-central area of Atlanta into suburban areas. Multilevel regression analysis revealed non-Medicaid insurance, the proportion of renters in a given geographical area, and proportion of individuals with a GED/high school diploma as predictors that distinguish children with BLLs 2 to <5 µg/dL from those with lower (<2 µg/dL) or higher (≥5 µg/dL) BLLs. Over half of the study children had BLLs between 2 and 5 µg/dL, a range that does not currently trigger public health measures but that could result in adverse developmental outcomes if ignored.

Identification of potential serum biomarkers for simultaneously classifying lung adenocarcinoma, squamous cell carcinoma and small cell carcinoma.

BACKGROUND: Histological subtypes of lung cancer are crucial for making treatment decisions. However, multi-subtype classifications including adenocarcinoma (AC), squamous cell carcinoma (SqCC) and small cell carcinoma (SCLC) were rare in the previous studies. This study aimed at identifying and screening potential serum biomarkers for the simultaneous classification of AC, SqCC and SCLC. PATIENTS AND METHODS: A total of 143 serum samples of AC, SqCC and SCLC were analyzed by 1HNMR and UPLC-MS/MS. The stepwise discriminant analysis (DA) and multilayer perceptron (MLP) were employed to screen the most efficient combinations of markers for classification. RESULTS: The results of non-targeted metabolomics analysis showed that the changes of metabolites of choline, lipid or amino acid might contribute to the classification of lung cancer subtypes. 17 metabolites in those pathways were further quantified by UPLC-MS/MS. DA screened out that serum xanthine, S-adenosyl methionine (SAM), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCC) contributed significantly to the classification of AC, SqCC and SCLC. The average accuracy of 92.3% and the area under the receiver operating characteristic curve of 0.97 would be achieved by MLP model when a combination of those five variables as input parameters. CONCLUSION: Our findings suggested that metabolomics was helpful in screening potential serum markers for lung cancer classification. The MLP model established can be used for the simultaneous diagnosis of AC, SqCC and SCLC with high accuracy, which is worthy of further study.

Exploring serum metabolic markers for the discrimination of ccRCC from renal angiomyolipoma by metabolomics.

Aim: The discrimination of renal cell carcinoma from renal angiomyolipoma (RAML) is crucial for the effective treatment of each. Materials & methods: Serum samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and a number of metabolites were further quantified by HPLC-UV. Results: Clear-cell renal carcinoma (ccRCC) was characterized by drastic disruptions in energy, amino acids, creatinine and uric acid metabolic pathways. A logistic model for the differential diagnosis of RAML from ccRCC was established using the combination of serum levels of uric acid, the ratio of uric acid to hypoxanthine and the ratio of hypoxanthine to creatinine as variables with area under the curve of the receiver operating characteristic curve value of 0.907. Conclusion: Alterations in serum purine metabolites may be used as potential metabolic markers for the differential diagnosis of ccRCC and RAML.

Assessing Feasibility of an Early Childhood Intervention Using Mobile Phones Among Low-Income Mothers of Newborns: Qualitative Interview Study.

BACKGROUND: Many children aged younger than 5 years living in low- and middle-income countries are at risk for poor development. Early child development (ECD) programs are cost-effective strategies to reduce poverty, crime, school dropouts, and socioeconomic inequality. With the spread of low-cost mobile phones and internet access in low- and middle-income countries, new service delivery models such as mobile phone-aided interventions have a great potential to improve early childhood development. OBJECTIVE: This study aimed to identify the beliefs on importance of ECD, feasibility of a proposed intervention using mobile phones and factors that may affect the usability of the intervention among mothers of newborns in a poverty-stricken area in southwestern China. METHODS: We conducted an in-depth, semistructured interview study of 25 low-income mothers of newborns recruited from two county hospitals in Yunnan Province. We applied the health belief model and cultural competence theories to identify the facilitators, barriers, and preferences among the target population for parenting knowledge. RESULTS: The results showed that the participants had low health literacy and high perceived needs for learning ECD knowledge. At the same time, they experienced several barriers to learning parenting information and following evidence-based instructions including having limited time, limited financial resources, and different opinions on childcare among family members. Many participants preferred to receive personalized messages tailored to their specific needs and preferred videos or graphics to text only in the messages. Many favored a separate module to support postpartum mental health. CONCLUSIONS: The study assessed the acceptability of an early childhood intervention using mobile phones to meet the needs of the target population based on their beliefs, traits, and preferences and provided suggestions to refine the intervention to improve its usability.