BackgroundCachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance‐based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model.MethodsEighty‐four cancer cachexia patients, 33 pre‐cachectic patients, 105 weight‐stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model.ResultsForty‐six cancer cachexia patients, 22 pre‐cachectic patients, 68 weight‐stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre‐cachectic patients, 37 weight‐stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age‐matched and sex‐matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = −400.53 – 481.88 × log(Carnosine) −239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set.ConclusionsThis metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at‐risk populations through the detection of serum metabolites.

Treating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

The first validated tacrolimus PPK model in patients with PSLE is presented.

Neonatal sepsis (NS) remains a major cause of morbidity and mortality in neonates, but data on the etiology and antibiotic susceptibility patterns of pathogens are limited. The aim of this study was to analyze the clinical characteristics, risk factors, and the antibiotic susceptibility patterns of pathogenic microbes associated with NS at a tertiary children's hospital in Shanghai, China. Episodes of blood culture-proven sepsis in the neonatal intensive care unit (NICU) of Children's Hospital of Fudan University from January 2013 to August 2017 were retrospectively reviewed. Collected data included demographics, perinatal risk factors, clinical symptoms, laboratory values, microbiology results and their antimicrobial susceptibility. Data for early-onset neonatal sepsis (EONS) and late-onset neonatal sepsis (LONS) were compared. The 341 of 976 culture-positive cases were selected, including 161 EONS cases (47.21% of 341) and 180 LONS cases (52.79% of 341). 635 incomplete cases were excluded. There was significant difference in risk factors between the EONS group and LONS group including birth weight, gestational age, 1-minute Apgar score, respiratory support, and the use of peripherally insertion central catheter (PICC). Clinical symptoms such as fever, feeding intolerance, abdominal distension, and neonatal jaundice, and laboratory results such as hemoglobin and lymphocyte counts also showed between-group differences. Staphylococcus epidermidis (22.87%) , Escherichia coli (9.68%), Alcaligenes xylosoxidans (9.38%) and Klebsiella pneumoniae (9.09%) remain the principal organisms responsible for neonatal sepsis. Most isolates of Gram-positive bacteria were sensitive to vancomycin, linezolid, minocycline and tigecycline, of which more than 90% were resistant to penicillin. Most isolates of Gram-negative bacteria were sensitive to amikacin and imipenem and resistant to ampicillin. Fungus was sensitive to antifungal agents. Better medical decisions, especially early detection and appropriate initial antimicrobial therapy can be made after understanding the different clinical features and pathogens of EONS and LONS.

BACKGROUND AND PURPOSE Spinal reactive oxygen species (ROS) are critically involved in chronic pain. d‐Amino acid oxidase (DAAO) oxidizes d‐amino acids such as d‐serine to form the byproduct hydrogen peroxide without producing other ROS. DAAO inhibitors are specifically analgesic in tonic pain, neuropathic pain and cancer pain. This study examined the role of spinal hydrogen peroxide in pain and the mechanism of the analgesic effects of DAAO inhibitors. EXPERIMENTAL APPROACH Formalin‐induced pain behaviours and spinal hydrogen peroxide levels were measured in rodents. KEY RESULTS Formalin injected into the paw increased spinal hydrogen peroxide synchronously with enhanced tonic pain; both were effectively prevented by i.t. fluorocitrate, a selective astrocyte metabolic inhibitor. Given systemically, the potent DAAO inhibitor CBIO (5‐chloro‐benzo[d]isoxazol‐3‐ol) blocked spinal DAAO enzymatic activity and specifically prevented formalin‐induced tonic pain in a dose‐dependent manner. Although CBIO maximally inhibited tonic pain by 62%, it completely prevented the increase in spinal hydrogen peroxide. I.t. catalase, an enzyme specific for decomposition of hydrogen peroxide, completely depleted spinal hydrogen peroxide and prevented formalin‐induced tonic pain by 65%. Given systemically, the ROS scavenger PBN (phenyl‐N‐tert‐butylnitrone) also inhibited formalin‐induced tonic pain and increase in spinal hydrogen peroxide. Formalin‐induced tonic pain was potentiated by i.t. exogenous hydrogen peroxide. CBIO did not increase spinal d‐serine level, and i.t. d‐serine did not alter either formalin‐induced tonic pain or CBIO's analgesic effect. CONCLUSIONS AND IMPLICATIONS Spinal hydrogen peroxide is specifically and largely responsible for formalin‐induced pain, and DAAO inhibitors produce analgesia by blocking spinal hydrogen peroxide production rather than interacting with spinal d‐serine.

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Cancer cachexia is a multifactorial syndrome affecting the skeletal muscle. Previous clinical trials showed that treatment with MEK inhibitor selumetinib resulted in skeletal muscle anabolism. However, it is conflicting that MAPK/ERK pathway controls the mass of the skeletal muscle. The current study investigated the therapeutic effect and mechanisms of selumetinib in amelioration of cancer cachexia. The classical cancer cachexia model was established via transplantation of CT26 colon adenocarcinoma cells into BALB/c mice. The effect of selumetinib on body weight, tumor growth, skeletal muscle, food intake, serum proinflammatory cytokines, E3 ligases, and MEK/ERK-related pathways was analyzed. Two independent experiments showed that 30 mg/kg/d selumetinib prevented the loss of body weight in murine cachexia mice. Muscle wasting was attenuated and the expression of E3 ligases, MuRF1 and Fbx32, was inhibited following selumetinib treatment of the gastrocnemius muscle. Furthermore, selumetinib efficiently reduced tumor burden without influencing the cancer cell proliferation, cumulative food intake, and serum cytokines. These results indicated that the role of selumetinib in attenuating muscle wasting was independent of cancer burden. Detailed analysis of the mechanism revealed AKT and mTOR were activated, while ERK, FoxO3a, and GSK3b were inhibited in the selumetinib -treated cachexia group. These indicated that selumetinib effectively prevented skeletal muscle wasting in cancer cachexia model through ERK inhibition and AKT activation in gastrocnemius muscle via cross-inhibition. The study not only elucidated the mechanism of MEK/ERK inhibition in skeletal muscle anabolism, but also validated selumetinib therapy as an effective intervention against cancer cachexia.

The PK parameters for vancomycin in Chinese infants younger than 2 months of age were estimated using the model developed herein. This model has been used to predict individualized dosing regimens in this vulnerable population in our hospital. A large external evaluation of our model will be conducted in future studies.