[Clinical phenotype and genetic analysis of a child featuring short stature and multiple skeletal dysplasia].

OBJECTIVE: To analyze the clinical phenotype and genetic basis for a child featuring familial short stature. METHODS: A child who was admitted to Huzhou Maternal and Child Health Care Hospital on October 7, 2021 for growth retardation and pectus carinatum was selected as the study subject. Physical exam and medical imaging was performed. The child was subjected to whole exome sequencing, and candidate variants were verified by Sanger sequencing and bioinformatic analysis. RESULTS: The child, a 1-year-old male, had manifested with slightly short stature (Z = -2.03), midfacial dysplasia, and multiple skeletal dysplasia such as pectus carinatum, irregular vertebral morphology, and defect of lumbar anterior bones. His mother, maternal grandmother and great-maternal grandfather also had short stature. WES revealed that the child has harbored a heterozygous c.2858dupA (p.Asp953GlufsTer476) frameshifting variant of the ACAN gene, which was inherited from his mother. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the c.2858dup (p.Sp953Glufster476) variant was classified as likely pathogenic (PVS1+PM2_Supporting). The patient has shown marked improved height after receiving 11 months of treatment with human recombinant growth hormone (supplemental dose) starting from 20 months of age. CONCLUSION: The ACAN: c.2858dup (p.Asp953GlufsTer476) variant probably underlay the pathogenesis of short stature in this child.

Fecal microbial biomarkers combined with multi-target stool DNA test improve diagnostic accuracy for colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a major global health burden. The current diagnostic tests have shortcomings of being invasive and low accuracy. AIM: To explore the combination of intestinal microbiome composition and multi-target stool DNA (MT-sDNA) test in the diagnosis of CRC. METHODS: We assessed the performance of the MT-sDNA test based on a hospital clinical trial. The intestinal microbiota was tested using 16S rRNA gene sequencing. This case-control study enrolled 54 CRC patients and 51 healthy controls. We identified biomarkers of bacterial structure, analyzed the relationship between different tumor markers and the relative abundance of related flora components, and distinguished CRC patients from healthy subjects by the linear discriminant analysis effect size, redundancy analysis, and random forest analysis. RESULTS: MT-sDNA was associated with Bacteroides. MT-sDNA and carcinoembryonic antigen (CEA) were positively correlated with the existence of Parabacteroides, and alpha-fetoprotein (AFP) was positively associated with Faecalibacterium and Megamonas. In the random forest model, the existence of Streptococcus, Escherichia, Chitinophaga, Parasutterella, Lachnospira, and Romboutsia can distinguish CRC from health controls. The diagnostic accuracy of MT-sDNA combined with the six genera and CEA in the diagnosis of CRC was 97.1%, with a sensitivity and specificity of 98.1% and 92.3%, respectively. CONCLUSION: There is a positive correlation of MT-sDNA, CEA, and AFP with intestinal microbiome. Eight biomarkers including six genera of gut microbiota, MT-sDNA, and CEA showed a prominent sensitivity and specificity for CRC prediction, which could be used as a non-invasive method for improving the diagnostic accuracy for this malignancy.

Identification of iron metabolism-related genes in the circulation and myocardium of patients with sepsis via applied bioinformatics analysis.

Background: Early diagnosis of septic cardiomyopathy is essential to reduce the mortality rate of sepsis. Previous studies indicated that iron metabolism plays a vital role in sepsis-induced cardiomyopathy. Here, we aimed to identify shared iron metabolism-related genes (IMRGs) in the myocardium and blood monocytes of patients with sepsis and to determine their prognostic signature. Methods: First, an applied bioinformatics-based analysis was conducted to identify shared IMRGs differentially expressed in the myocardium and peripheral blood monocytes of patients with sepsis. Second, Cytoscape was used to construct a protein-protein interaction network, and immune infiltration of the septic myocardium was assessed using single-sample gene set enrichment analysis. In addition, a prognostic prediction model for IMRGs was established by Cox regression analysis. Finally, the expression of key mRNAs in the myocardium of mice with sepsis was verified using quantitative polymerase chain reaction analysis. Results: We screened common differentially expressed genes in septic myocardium and blood monocytes and identified 14 that were related to iron metabolism. We found that HBB, SLC25A37, SLC11A1, and HMOX1 strongly correlated with monocytes and neutrophils, whereas HMOX1 and SLC11A1 strongly correlated with macrophages. We then established a prognostic model (HIF1A and SLC25A37) using the common differentially expressed IMRGs. The prognostic model we established was expected to better aid in diagnosing septic cardiomyopathy. Moreover, we verified these genes using datasets and experiments and found a significant difference between the sepsis and control groups. Conclusion: Common differential expression of IMRGs was identified in blood monocytes and myocardium between sepsis and control groups, among which HIF1A and SLC25A37 might predict prognosis in septic cardiomyopathy. The study may help us deeply understand the molecular mechanisms of iron metabolism and aid in the diagnosis and treatment of septic cardiomyopathy.

Diagnostic accuracy of the multi-target stool DNA test in detecting colorectal cancer: A hospital-based study.

BACKGROUND: The multi-target stool DNA test (MT-sDNA) has potential utility in the detection of colorectal cancer (CRC), but validation of its clinical accuracy has been limited in China. AIM: To evaluate the diagnostic performance of MT-sDNA and investigate the combined diagnostic value of alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) with MT-sDNA in CRC and adenomas. METHODS: We evaluated the performance of the MT-sDNA kit based on a hospital clinical trial. In this case-control study, 135 participants from the Affiliated Hospital of Medical School of Ningbo University, including 51 CRC patients, 23 patients with adenomas, and 61 healthy controls were enrolled. We used a risk scoring system to determine the positivity of tests with histological diagnosis or colonoscopy as the reference standard. RESULTS: The main indices of sensitivity, specificity and accuracy were evaluated. The sensitivity and specificity for CRC detection were 90.2% and 83.3%, respectively, with an accuracy of 89.8%. For adenoma, the sensitivity and specificity were 56.5% and 68.9%, respectively, with an accuracy of 73.1%. The sensitivity and specificity of MT-sDNA combined with CEA in the diagnosis of adenoma were 78.3% and 60.7%, respectively. CONCLUSION: The MT-sDNA test showed better performance in the detection of CRC, which was superior to AFP, CEA, and CA199 separately, but not for predicting adenomas. The combination of MT-sDNA with CEA further improved the sensitivity for adenoma diagnosis.

Molecular Dynamics Simulation of Drug Solubilization Behavior in Surfactant and Cosolvent Injections.

Surfactants and cosolvents are often combined to solubilize insoluble drugs in commercially available intravenous formulations to achieve better solubilization. In this study, six marketed parenteral formulations with surfactants and cosolvents were investigated on the aggregation processes of micelles, the structural characterization of micelles, and the properties of solvent using molecular dynamics simulations. The addition of cosolvents resulted in better hydration of the core and palisade regions of micelles and an increase in both radius of gyration (Rg) and the solvent accessible surface area (SASA), causing a rise in critical micelle concentration (CMC), which hindered the phase separation of micelles. At the same time, the presence of cosolvents disrupted the hydrogen bonding structure of water in solution, increasing the solubility of insoluble medicines. Therefore, the solubilization mechanism of the cosolvent and surfactant mixtures was successfully analyzed by molecular dynamics simulation, which will benefit future formulation development for drug delivery.

Traditional Chinese medicine injections: where we are after 80-year development.

Traditional Chinese medicine (TCM) injection is the combination of modern pharmaceutical technology and traditional Chinese prescription, which was born in 1941 and played a great role in the backward medical conditions at that time. However, the debate over TCM injections has never stopped due to adverse drug reactions (ADRs). The regulation on TCM injections has been further strengthened since 2017, which has prompted many TCM injections to carry out re-evaluations on quality, safety, efficiency as well as pharmacoeconomics, which made significant changes and progress. This review presented an up-to-date analysis of the types, amounts, and ADRs of TCM injections based on the published data and literature. This review also summarized the potential reasons for the ADRs and re-evaluation strategies. This review will provide some useful clues for TCM injections and their clinical use.

Study on the effects of combined tea drinking and mental activity after dinner on smokers in China.

INTRODUCTION: Cigarette and tobacco use is a leading cause of chronic obstructive pulmonary disease, lung cancer, and other malignant tumors. In China, people prefer to engage in mental activities (gambling, overtime work, playing video games, or other mental activities) on the weekends or during spare time, especially in the evening before they prepare for bed. In China, smokers frequently consume tea while smoking. The relationship between smokers who consume tea, engage in mental activities after dinner, or both (drinking tea and engaging in cognitive activities after dinner together), and daily cigarette smoking or nicotine addiction must be clarified. METHODS: A total of 438 smokers were included in the study. Age, gender, body mass index (BMI), smoking habits, Fagerström test for nicotine dependence scores, and behaviors, were recorded. The study excluded smokers with a Fagerström score <1 or with a mental disorder diagnosis. The smokers were divided into four groups based on their behaviors: those who did not drink tea, did not engage in mental activities after dinner, those who drank tea only, those who engaged in mental activities only, and those who engaged in both. RESULTS: Only drinking tea or doing mental activities after dinner cannot increase cigarettes per day (22.20 ± 10.143 vs 23.49 ± 11.966, p=0.362; 22.20 ± 10.143 vs 22.66 ± 1.192, p=0.750) or FTND scores [6.0 (4.0; 7.0) vs 6.0 (4.0; 7.75), p=0.941; 6.0 (4.0; 7.0) vs 6.0 (4.25; 7.75), p=0.980]. People who drink tea and engage in mental activities after dinner smoke more (22.20 ± 10.143 vs 30.75 ± 17.264, p<0.0001) and have higher nicotine dependence levels [6.0 (4.0; 7.0) vs 7.0 (5.0; 8.0), p=0.015]. CONCLUSIONS: The consumption of tea or a mental activity after dinner is not associated with daily smoking or nicotine dependence. There is an association between the combined behaviors (tea drinking and mental activity after dinner) and the daily consumption of cigarettes, and the degree of nicotine dependence.

Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm.

Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time. Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines. Firstly, 325 data samples of mRNA vaccine LNP formulations with IgG titer were collected. The machine learning algorithm, lightGBM, was used to build a prediction model with good performance (R 2 > 0.87). More importantly, the critical substructures of ionizable lipids in LNPs were identified by the algorithm, which well agreed with published results. The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model prediction. Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment. The result showed that the lipid molecules aggregated to form LNPs, and mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vaccines was first developed, validated by experiments, and further integrated with molecular modeling. The prediction model can be used for virtual screening of LNP formulations in the future.

Controlled synthesis of tungsten trioxide with globular clusters constructed of nanoplates by rapid breakdown anodization.

Herein, electrochemical synthesis of tungsten trioxide (WO3) with globular clusters constructed of nanoplates is demonstrated. Under a breakdown anodization potential of 25 V at 50 °C, tungsten foil anode was efficiently electro-oxidized into WO3nanoplates-aggragated globular clusters powder, rather than a thin film structure as conventional anodization occurs. The WO3globular clusters were characterized by SEM, TEM, and XRD. Effects of electrolyte composition on the breakdown anodization of the W substrate has been discussed. It is suggested that the growth of the WO3nanoplates is initiated by localized anodic dielectric breakdown, and followed by an effective crystal growth in the electrolyte at high breakdown field.

In silico formulation prediction of drug/cyclodextrin/polymer ternary complexes by machine learning and molecular modeling techniques.

Ternary cyclodextrin (CD) complexes (drug/CD/polymer) can effectively improve the solubility of water-insoluble drugs with large size than binary CD formulations. However, ternary formulations are screened by a trial-and-error approach, which is laborious and material-wasting. Current research aims to develop a prediction model for ternary CD formulations by combined machine learning and molecular modeling. 596 ternary formulations data were collected to build a prediction model by machine learning. The random forest model achieved good performance with R2 = 0.887 in ST prediction and R2 = 0.815 in ST/SB prediction. Two ternary formulations (Hydrocortisone/ß-CD/HPMC and dovitinib/γ-CD/CMC) were used to validate the prediction model. Molecular modeling results showed that HPMC not only warped around hydrocortisone but also prevented CD molecules from self-aggregation to increase solubility. In conclusion, a prediction model for the ternary CD formulations was successfully developed, which will significantly accelerate the formulation screening process to benefit the formulation development of water-insoluble drugs.

Do nicotine dependence influencing and non-influencing behaviors have an association with high nicotine dependence in smokers?

INTRODUCTION: Nicotine dependence (ND) is a maladaptive pattern of tobacco smoking with withdrawal symptoms similar to other drug addictive disorders. It is very common in clinical practice that smokers always have different degrees of nicotine dependence with the same amount of tobacco consumption. Behaviors may influence daily cigarette consumption or smoking status. Hence it is critical to ascertain the association between concurrent behaviors and high nicotine dependence among smokers. METHODS: A total of 343 patients who attended a clinic for smoking cessation were recruited, and the information on concurrent behaviors were recorded. Factors associated and not associated with nicotine dependence were recorded. Nicotine dependence was determined by Fagerström test for nicotine dependence (FTND). RESULTS: High ND patients (FTND >5) showed significant behaviors distribution compared with mild and moderate ND patients (FTND ≤5). There is no single behavior that was significantly different between high ND and mild and moderate ND smokers. However, the combined effects of nicotine dependence influencing behaviors of caffeine drinking and mental activities after dinner have an association with high ND (OR=1.939; 95% CI: 1.154-3.258, p=0.012). In addition, the combined effects of inadequate sleep time (<8 hours), caffeine drinking and mental activities after dinner significantly distinguished patients of high ND from those of low ND (OR=2.208; 95% CI: 1.032-4.737, p=0.042). CONCLUSIONS: Interaction effects of mental activities after dinner and caffeine drinking have an association with high nicotine dependence. Sleep of less than 8 hours with behaviors of mental activities after dinner and caffeine drinking have the same effect.

Computational pharmaceutics - A new paradigm of drug delivery.

In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.

PharmSD: A novel AI-based computational platform for solid dispersion formulation design.

Solid dispersion is an effective way to improve the dissolution and oral bioavailability of water-insoluble drugs. To obtain an effective solid dispersion formulation, researchers need to evaluate a series of important properties of the designed formulation, including in vitro dissolution and physical stability of solid dispersion. It is usually time-consuming and labor-intensive to explore these properties by traditional experimental methods. However, the development of machine learning technology provides a powerful way to solve such problems. By using advanced machine learning algorithms, we established a series of robust models and finally formed a systematic strategy to assist the formulation design. Based on these works, we developed a new formulation prediction platform of solid dispersion: PharmSD. This platform provides efficient functionalities for the prediction of physical stability, dissolution type and dissolution rate of solid dispersion independently. Then, a virtual screening pipeline can be produced by considering those prediction results as a whole, which enables users to filter different kinds of drug-polymer combinations in various experimental situations and figure out which combination could form the best formulation. Moreover, it also provides two tools that enable researchers to evaluate the application domain of models and calculate the similarity of dissolution curves. PharmSD is expected to be the first freely available web-based platform that is fully designed for the formulation design of solid dispersion driven by machine learning. We hope this platform could provide a powerful solution to assist the formulation design in the related research area. It is available at: http://pharmsd.computpharm.org.

The important herbal pair for the treatment of COVID-19 and its possible mechanisms.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is an unprecedented disaster for people around the world. Many studies have shown that traditional Chinese medicine (TCM) are effective in treating COVID-19. However, it is difficult to find the most effective combination herbal pair among numerous herbs, as well as identifying its potential mechanisms. Herbal pair is the main form of a combination of TCM herbs, which is widely used for the treatment of diseases. It can also help us to better understand the compatibility of TCM prescriptions, thus improving the curative effects. The purpose of this article is to explore the compatibility of TCM prescriptions and identify the most important herbal pair for the treatment of COVID-19, and then analyze the active components and potential mechanisms of this herbal pair. METHODS: We first systematically sorted the TCM prescriptions recommended by the leading experts for treating COVID-19, and the specific herbs contained in these prescriptions across different stages of the disease. Next, the association rule approach was employed to examine the distribution and compatibility among these TCM prescriptions, and then identify the most important herbal pair. On this basis, we further investigated the active ingredients and potential targets in the selected herbal pair by a network pharmacology approach, and analyzed the potential mechanisms against COVID-19. Finally, the main active compounds in the herbal pair were selected for molecular docking with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) 3CLpro and angiotensin converting enzyme II (ACE2) for further verification. RESULT: We obtained 32 association rules for the herbal combinations in the selection of TCM treatment for COVID-19. The results showed that the combination of Amygdalus Communis Vas (ACV) and Ephedra sinica Stapf (ESS) had the highest confidence degree and lift value, as well as high support degree, which can be used in almost all the stages of COVID-19, so ACV and ESS (AE) were selected as the most important herbal pair. There were 26 active ingredients and 44 potential targets, which might be related to the herbal pair of AE against COVID-19. The main active ingredients of AE against COVID-19 were quercetin, kaempferol, luteolin, while the potential targets were Interleukin 6 (IL-6), Mitogen-activated Protein Kinase 1 (MAPK)1, MAPK8, Interleukin-1ß (IL-1ß), and Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) p65 subunit (RELA). The protein-protein interaction (PPI) cluster demonstrated that IL-6 was the seed in the cluster, which plays an important role in connecting other nodes in the PPI network. The potential pathways mainly involved tumor necrosis factor (TNF), Toll-like receptor (TLR), hypoxia-inducible factor-1 (HIF-1), and nucleotide-binding oligomerization domain (NOD)-like receptor (NLRs). The molecular docking results showed that the main active ingredients of AE have good affinity with SARS-COV-2 3CLpro and ACE2, which are consistent with the above analysis. CONCLUSIONS: There were 32 association rules in the TCM prescriptions recommended by experts for COVID-19. The combination of ACV and EAS was the most important herbal pair for the treatment of COVID-19. AE might have therapeutic effects against COVID-19 by affecting the inflammatory and immune responses, cell apoptosis, hypoxia damage and other pathological processes through multiple components, targets and pathways.

Using Acetone/Water Binary Solvent to Enhance the Stability and Bioavailability of Spray Dried Enzalutamide/HPMC-AS Solid Dispersions.

We demonstrated a facile approach, by adjusting the solvent ratio of water/acetone binary mixture, to alter the intermolecular interactions between Enzalutamide (ENZ) and hydroxypropyl methylcellulose acetate succinate (HPMC-AS) for spray drying process, which can be readily implemented to produce spray-dried dispersions (SDD) with enhanced stability and bioavailability. The prepared SDD of ENZ/HPMC-AS were examined systematically in terms of particle size, morphology, dissolution, solubility, stability, and bioavailability. Our results show that the introduction of water (up to 30% volume fraction) can effectively reduce the hydrodynamic diameter of HPMC-AS from approximately 220 nm to 160 nm (a reduction of c.a. 20%), which increases the miscibility of the drug and polymer, delaying or inhibiting the crystallization of ENZ during the spray drying process, resulting in a homogeneous amorphous phase. The benefits of using acetone/water binary mixture were subsequently evidenced by an increased specific surface area, improved dissolution profile and relative bioavailability, enhanced stability, and elevated drug release rate. This fundamental finding underpins the great potential of using binary mixture for spray drying process to process active pharmaceutical ingredients (APIs) that are otherwise challenging to handle.

Hereditary and breastfeeding factors are positively associated with the aetiology of mammary gland hyperplasia: a case-control study.

BACKGROUND: Hyperplasia of mammary gland (HMG) has become a common disorder in women. A family history of breast cancer and female reproductive factors may work together to increase the risk of HMG. However, this specific relationship has not been fully characterized. METHODS: A total of 1881 newly diagnosed HMG cases and 1900 controls were recruited from 2012 to 2017. Demographic characteristics including female reproductive factors and a family history of breast cancer were collected. A multi-analytic strategy combining unconditional logistic regression, multifactor dimensionality reduction (MDR) and crossover approaches were applied to systematically identify the interaction effect of family history of breast cancer and reproductive factors on HMG susceptibility. RESULTS: In MDR analysis, high-order interactions among higher-level education, shorter breastfeeding duration and family history of breast cancer were identified (odds ratio [OR] 7.07 [95% confidence interval {CI} 6.08 to 8.22]). Similarly, in crossover analysis, HMG risk increased significantly for those with higher-level education (OR 36.39 [95% CI 11.47 to 115.45]), shorter duration of breastfeeding (OR 27.70 [95% CI 3.73 to 205.70]) and a family history of breast cancer. CONCLUSION: Higher-level education, shorter breastfeeding duration and a family history of breast cancer may synergistically increase the risk of HMG.

An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design.

Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a higher bioavailability than the crystal form. However, current development of SD relies on a random large-scale formulation screening method with low efficiency. Current research aims to integrate various computational tools, including machine learning (ML), molecular dynamic (MD) simulation and physiologically based pharmacokinetic (PBPK) modeling, to accelerate the development of SD formulations. Firstly, based on a dataset consisting of 674 dissolution profiles of SD, the random forest algorithm was used to construct a classification model to distinguish two types of dissolution profiles: "spring-and-parachute" and "maintain supersaturation", and a regression model to predict the time-dependent dissolution profiles. Both of the two prediction models showed good prediction performance. Moreover, feature importance was performed to help understand the key information that contributes to the model. After that, the vemurafenib (VEM) SD formulation in previous report was used as an example to validate the models. MD simulation was used to investigate the dissolution behavior of two SD formulations with two polymers (HPMCAS and Eudragit) at the molecular level. The results showed that the HPMCAS-based formulation resulted in faster dissolution than the Eudragit formulation, which agreed with the reported experimental results. Finally, a PBPK model was constructed to accurately predict the human pharmacokinetic profile of the VEM-HPMCAS SD formulation. In conclusion, combined computational tools have been developed to in silico predict formulation composition, in vitro release and in vivo absorption behavior of SD formulations. The integrated computational methodology will significantly facilitate pharmaceutical formulation development than the traditional trial-and-error approach in the laboratory.

The novel platinum(IV) prodrug with self-assembly property and structure-transformable character against triple-negative breast cancer.

Herein, a small library of Pt(IV) prodrugs based on cisplatin and chemosensitizer adjudin (ADD) were explored for efficient cisplatin resistant triple-negative breast cancer (TNBC) treatment. We further elucidated the detail relationship of chemical structure, alkyl chain length (ethyl to dodecyl) and ADD substituted degree, with respect to the self-assembly ability and cytotoxic effect of prodrugs. It demonstrated that all prodrugs could self-assemble into nanomedicine, which was in consist with the molecule structure building and self-assembly simulation. All nanomedicines possessed small particle size, uniform morphology and ultra-high drug loading content (84.0%-86.5%). Moreover, the length of alkyl chain was of great importance for the structure-transformable character and cytotoxicity of nanomedicines. Interestingly, ADD monosubstituted with butyl or hexyl contralateral substituted prodrug (C4-Pt-ADD or C6-Pt-ADD) assembled nanomedicine could convert to wire or sheet structure. These transformable nanoparticles showed great potential in improving the sensitivity of cisplatin to TNBC with up to 266-fold lower IC50 value and significantly enhanced in vivo tumor growth inhibition. Therefore, the self-assembled nanomedicine based on Pt(IV)-ADD could be a promising strategy for TNBC therapy.

Methylation Status of Transcriptional Modulatory Genes Associated with Colorectal Cancer in Northeast China.

BACKGROUND/AIMS: Methylation status plays a causal role in carcinogenesis in targeted tissues. However, the relationship between the DNA methylation status of multiple genes in blood leukocytes and colorectal cancer (CRC) susceptibility as well as interactions between dietary factors and CRC risks are unclear. METHODS: We performed a case-control study with 466 CRC patients and 507 cancer-free controls to investigate the association among the methylation status of individual genes, multiple CpG site methylation (MCSM), multiple CpG site heterogeneous methylation and CRC susceptibility. Peripheral blood DNA methylation levels were detected by performing methylation-sensitive high-resolution melting. RESULTS: Total heterogeneous methylation of CA10 and WT1 conferred a significantly higher risk of CRC (adjusted odds ratio [ORadjusted], 5.445; 95% confidence interval [CI], 3.075 to 9.643; ORadjusted, 1.831; 95% CI, 1.100 to 3.047; respectively). Subjects with high-level MCSM (MCSM-H) status demonstrated a higher risk of CRC (ORadjusted, 4.318; 95% CI, 1.529 to 12.197). Additionally, interactions between the high-level intake of fruit and CRH, WT1, and MCSM on CRC were statistically significant. CONCLUSIONS: The gene methylation status of blood leukocytes may be associated with CRC risk. MCSM-H of blood leukocytes was associated with CRC, especially in younger people. Some dietary factors may affect hypermethylation status and influence susceptibility to CRC.

The Threshold Temperature and Lag Effects on Daily Excess Mortality in Harbin, China: A Time Series Analysis.

BACKGROUND: A large number of studies have reported the relationship between ambient temperature and mortality. However, few studies have focused on the effects of high temperatures on cardio-cerebrovascular diseases mortality (CCVDM) and their acute events (ACCVDM). OBJECTIVE: To assess the threshold temperature and time lag effects on daily excess mortality in Harbin, China. METHODS: A generalized additive model (GAM) with a Poisson distribution was used to investigate the relative risk of mortality for each 1 °C increase above the threshold temperature and their time lag effects in Harbin, China. RESULTS: High temperature threshold was 26 °C in Harbin. Heat effects were immediate and lasted for 0-6 and 0-4 days for CCVDM and ACCVDM, respectively. The acute cardiovascular disease mortality (ACVDM) seemed to be more sensitive to temperature than cardiovascular disease mortality (CVDM) with higher death risk and shorter time lag effects. The lag effects lasted longer for cerebrovascular disease mortality (CBDM) than CVDM; so did ACBDM compared to ACVDM. CONCLUSION: Hot temperatures increased CCVDM and ACCVDM in Harbin, China. Public health intervention strategies for hot temperatures adaptation should be concerned.