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Chemotherapy has shown promising potential for overcoming various tumors in clinic, however, the chemotherapeutic strategy suffers from the lower therapeutic efficiency and boring side-effect. Self-delivery drug nanomedicine with high drug loading, precise controlled release, low excipients-trigged adverse reaction and high efficiency in treatment have been as one very promising strategy for promoting chemotherapy. Herein, we reasonably design a reductive stimuli-triggered molecular prodrug containing a camptothecin (CPT) and near-infrared hemicyanine dye (CyOH), which can be formed nanoassemblies (denoted as CyCPT) via nano-precipitated method for enhanced chemotherapy. Peculiarly, CyCPT not only improves the water-solubility of hydrophobic CPT but also has outstanding cellular internalization and tumor permeability, showing a remarkable kill-ability for various tumor cells. Meanwhile, the CyCPT shows excellent in-vivo biosafety. Overall, the self-delivery nanomedicine may present a potential appealing nanoplatform for tumor eradication. [Display omitted] • A tumor intracellular reduction-triggered self-delivery molecular prodrug nanomedicine is prepared. • The obtained prodrug nanoparticles show excellent ability of tumor infiltration. • The self-delivery prodrug system has good in-vivo biosafety. [ABSTRACT FROM AUTHOR]

Background: Infectious meningitis/encephalitis (IM) is a severe neurological disease that can be caused by bacterial, viral, and fungal pathogens. IM suffers high morbidity, mortality, and sequelae in childhood. Metagenomic next-generation sequencing (mNGS) can potentially improve IM outcomes by sequencing both pathogen and host responses and increasing the diagnosis accuracy. Methods: Here we developed an optimized mNGS pipeline named comprehensive mNGS (c-mNGS) to monitor DNA/RNA pathogens and host responses simultaneously and applied it to 142 cerebrospinal fluid samples. According to retrospective diagnosis, these samples were classified into three categories: confirmed infectious meningitis/encephalitis (CIM), suspected infectious meningitis/encephalitis (SIM), and noninfectious controls (CTRL). Results: Our pipeline outperformed conventional methods and identified RNA viruses such as Echovirus E30 and etiologic pathogens such as HHV-7, which would not be clinically identified via conventional methods. Based on the results of the c-mNGS pipeline, we successfully detected antibiotic resistance genes related to common antibiotics for treating Escherichia coli, Acinetobacter baumannii, and Group B Streptococcus. Further, we identified differentially expressed genes in hosts of bacterial meningitis (BM) and viral meningitis/encephalitis (VM). We used these genes to build a machine-learning model to pinpoint sample contaminations. Similarly, we also built a model to predict poor prognosis in BM. Conclusions: This study developed an mNGS-based pipeline for IM which measures both DNA/RNA pathogens and host gene expression in a single assay. The pipeline allows detecting more viruses, predicting antibiotic resistance, pinpointing contaminations, and evaluating prognosis. Given the comparable cost to conventional mNGS, our pipeline can become a routine test for IM. [ABSTRACT FROM AUTHOR]

Sepsis is an inflammatory disease with exacerbated inflammation at early stages. Inflammatory cytokines play critical roles in the pathophysiology of sepsis. Ubiquitin-specific peptidase 18 (USP18), a deubiquitinating enzyme, has been shown to modulate transforming growth factor-β-activated kinase 1 (TAK1) activity. However, the precise role of USP18 in sepsis is not clear. Here, we investigated the potential effect of USP18 on inflammation in sepsis. We generated mice with USP18 or/and TAK1 deficiency in macrophages (USP18MKO mice, TAK1MKO mice and USP18MKO-TAK1MKO mice) and established a lipopolysaccharide (LPS)-induced sepsis model in mice. Bone marrow-derived macrophages were isolated from wild-type (WT), USP18MKO or TAK1MKO mice and treated with LPS or CpG, and the expression of cytokines including IL-6, IL-10, IL-1β and tumor necrosis factor α (TNF-α) was measured. The activation of NF-κB, ERK and p38 signaling pathways and ubiquitination of TAK1 were detected. We induced sepsis in WT, USP18MKO, TAK1MKO or USP18MKO-TAK1MKO mice and evaluated the survival rate, lung pathology and inflammatory cytokine levels in serum. Macrophages deficient in USP18 produced significantly increased IL-6, IL-1β and TNF-α post-LPS or -CpG stimulation. Macrophages deficient in USP18 had promoted activation of NF-κB, p38 and ERK, and increased ubiquitination of TAK1. Mice with TAK1 deficiency in macrophages had increased survival rates, decreased immune cell infiltration in lung and decreased pro-inflammatory cytokines in serum. In contrast, mice with USP18 deficiency in macrophages had decreased survival rates, increased cell infiltration in lung and increased pro-inflammatory cytokines in serum. USP18 alleviated LPS-induced sepsis by inhibiting TAK1 activity. [ABSTRACT FROM AUTHOR]

Background: Acute meningitis and encephalitis syndromes (AMES) is a severe neurological infection which causes high case fatality and severe sequelae in children. To determine the etiology of childhood AMES in Shenzhen, a hospital-based study was undertaken.Methods: A total of 240 cerebrospinal fluid (CSF) samples from 171 children meeting the case definition were included and screened for 12 common causative organisms. The clinical data and conventional testing results were collected and analyzed. Whole genome sequencing was performed on a Neisseria meningitidis isolate.Results: A pathogen was found in 85 (49.7%) cases; Group B Streptococcus (GBS) was detected in 17 cases, Escherichia coli in 15, Streptococcus pneumoniae in 14, enterovirus (EV) in 13, herpes simplex virus (HSV) in 3, N. meningitidis in 1, Haemophilus influenzae in 1, and others in 23. Notably, HSV was found after 43 days of treatment. Twelve GBS and 6 E. coli meningitis were found in neonates aged less than 1 month; 13 pneumococcal meningitis in children aged > 3 months; and 12 EV infections in children aged > 1 year old. The multilocus sequence typing of serogroup B N. meningitidis isolate was ST-3200/CC4821. High resistance rate to tetracycline (75%), penicillin (75%), and trimethoprim/sulfamethoxazole (75%) was found in 4 of S. pneumoniae isolates; clindamycin (100%) and tetracycline (100%) in 9 of GBS; and ampicillin (75%) and trimethoprim/sulfamethoxazole (67%) in 12 of E. coli.Conclusions: The prevalence of N. meningitidis and JEV was very low and the cases of childhood AMES were mainly caused by other pathogens. GBS and E. coli were the main causative organisms in neonates, while S. pneumoniae and EV were mainly found in older children. HSV could be persistently found in the CSF samples despite of the treatment. A better prevention strategy for GBS, the introduction of pneumococcal vaccine, and incorporation of PCR methods were recommended. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to evaluate different pretreatment, extraction, amplification, and library generation methods for metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) and to develop an efficient procedure for the simultaneous detection of DNA and RNA pathogens.Methods: We generated thirteen mock CSF samples with four representative pathogens of encephalitis. Each sample was subjected to ten different methods by varying sample pretreatment/nucleic acid extraction (microbial DNA, total DNA, total NA, total RNA, Whole Transcriptome Amplification (WTA)) and library generation (Illumina or NEB). Negative extraction controls (NECs) were used for each method variation.Results: We found that the quality of mNGS sequencing reads was higher from the NEB kit for library generation. Microbial DNA and total RNA increased microbial deposition by depleting the host DNA. Methods total NA and total RNA can detect gram-positive, gram-negative, RNA and DNA pathogens. We applied mNGS, including total NA and NEB library generation, to CSF samples from five patients diagnosed with infectious encephalitis and correctly determined all pathogens identified in clinical etiological tests.Conclusions: Our findings suggested that total nucleic acid extraction combined with NEB library generation is the most effective mNGS procedure in CSF pathogen detection. The optimization of positive criteria and databases can improve the specificity and sensitivity of mNGS diagnosis.Trial Registration: Chinese Clinical Trial Registry, ChiCTR1800015425 (29/03/2018), https://www.chictr.org.cn/edit.aspx?pid=26292&htm=4 . [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed through the longitudinal electronic medical record (EMR) analysis. Method: We applied the transition-based network entropy methodology which previously identified a dynamic driver network (DDN) underlying the critical T2DM transition at the tissue molecular biological level. To profile pre-disease phenotypical changes that indicated a critical transition state, a cohort of 7,334 patients was assembled from the Maine State Health Information Exchange (HIE). These patients all had their first confirmative diagnosis of T2DM between January 1, 2013 and June 30, 2013. The cohort’s EMRs from the 24 months preceding their date of first T2DM diagnosis were extracted. Results: Analysis of these patients’ pre-disease clinical history identified a dynamic driver network (DDN) and an associated critical transition state six months prior to their first confirmative T2DM state. Conclusions: This 6-month window before the disease state provides an early warning of the impending T2DM, warranting an opportunity to apply proactive interventions to prevent or delay the new onset of T2DM. [ABSTRACT FROM AUTHOR]

Background: In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-efficient identification of CHF patients.Methods and Results: We set to identify CHF cases from both EMR codified and natural language processing (NLP) found cases. Using narrative clinical notes from all Maine Health Information Exchange (HIE) patients, the NLP case finding algorithm was retrospectively (July 1, 2012-June 30, 2013) developed with a random subset of HIE associated facilities, and blind-tested with the remaining facilities. The NLP based method was integrated into a live HIE population exploration system and validated prospectively (July 1, 2013-June 30, 2014). Total of 18,295 codified CHF patients were included in Maine HIE. Among the 253,803 subjects without CHF codings, our case finding algorithm prospectively identified 2411 uncodified CHF cases. The positive predictive value (PPV) is 0.914, and 70.1% of these 2411 cases were found to be with CHF histories in the clinical notes.Conclusions: A CHF case finding algorithm was developed, tested and prospectively validated. The successful integration of the CHF case findings algorithm into the Maine HIE live system is expected to improve the Maine CHF care. [ABSTRACT FROM AUTHOR]

Objectives: Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups. Methods: Our objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients. Results: A risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates. Conclusions: The risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans. [ABSTRACT FROM AUTHOR]

Background: Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization. Methods and Findings: A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns. Conclusions: Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes. [ABSTRACT FROM AUTHOR]

Objective: Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls.Methods: The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort. The data were collected from electronic health records (EHR) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age).Results: This model attained a validated C-statistic of 0.807, where 50 % of the identified high-risk true positives were confirmed to fall during the first 94 days of next year. The model also captured in advance 58.01 % and 54.93 % of falls that happened within the first 30 and 30-60 days of next year. The identified high-risk patients of fall showed conditions of severe disease comorbidities, an enrichment of fall-increasing cardiovascular and mental medication prescriptions and increased historical clinical utilization, revealing the complexity of the underlying fall etiology. The XGBoost algorithm captured 157 impactful predictors into the final predictive model, where cognitive disorders, abnormalities of gait and balance, Parkinson's disease, fall history and osteoporosis were identified as the top-5 strongest predictors of the future fall event.Conclusions: By using the EHR data, this risk assessment tool attained an improved discriminative ability and can be immediately deployed in the health system to provide automatic early warnings to older adults with increased fall risk and identify their personalized risk factors to facilitate customized fall interventions. [ABSTRACT FROM AUTHOR]

Background: Although the prognosis of chronic myeloid leukemia (CML) has dramatically improved, the pathogenesis of CML remains elusive. Studies have shown that sustained phosphorylation of AKT1 plays a crucial role in the proliferation of CML cells. Evidence indicates that in tongue cancer cells, FAM168A, also known as tongue cancer resistance-associated protein (TCRP1), can directly bind to AKT1 and regulate AKT1/NFκB signaling pathways. This study aimed to investigate the role of FAM168A in regulation of AKT1/NFκB signaling pathway and cell cycle in CML.Methods: FAM168A interference was performed, and the expression and phosphorylation of FAM168A downstream proteins were measured in K562 CML cell line. The possible roles of FAM168A in the proliferation of CML cells were investigated using in vitro cell culture, in vivo animal models and clinical specimens.Results: We found that the expression of FAM168A significantly increased in the peripheral blood mononuclear cells of CML patients, compared with normal healthy controls. FAM168A interference did not change AKT1 protein expression, but significantly decreased AKT1 phosphorylation, significantly increased IκB-α protein level, and significantly reduced nuclear NFκB protein level. Moreover, there was a significant increase of G2/M phase cells and Cyclin B1 level. Immunoprecipitation results showed that FAM168A interacts with breakpoint cluster region (BCR) -Abelson murine leukemia (ABL1) fusion protein and AKT1, respectively. Animal experiments confirmed that FAM168A interference prolonged the survival and reduced the tumor formation in mice inoculated with K562 cells. The results of clinical specimens showed that FAM168A expression and AKT1 phosphorylation were significantly elevated in CML patients.Conclusion: This study demonstrates that FAM168A may act as a linker protein that binds to BCR-ABL1 and AKT1, which further mediates the downstream signaling pathways in CML. Our findings demonstrate that FAM168A may be involved in the regulation of AKT1/NFκB signaling pathway and cell cycle in CML. [ABSTRACT FROM AUTHOR]