Red Cell Distribution Width is Related to Mild Cognitive Impairment: A Cross-Sectional Study of Community Residents.

BACKGROUND AND OBJECTIVE: Previous literature has reported that red cell distribution width (RDW) correlated with Alzheimer's disease (AD), but the correlation with mild cognitive impairment (MCI) was not clear. This study aimed to investigate MCI in the residents aged ≥65 living in the suburban of Shanghai, China. MATERIALS AND METHODS: A total of 550 participants were recruited as MCI (MCI group, 226) and normal (NC group, 284) groups and received blood examination voluntarily. Blood routine indexes were tested by blood tests using Sysmex XT-4000i (Japan). The Chi-square test, t-test, and linear regression analysis were used to find the statistical difference and correlation of data, respectively. RESULTS: Each cognition domain of MCI was found to be impaired, the weight of which, however, was different in integral damage. Most MCI people had impairment of attention among cognitive domains (235, 88.3%). According to the results of the binary logistic regression, the highest weight among impaired cognitive domains was for attention in MCI, and the Wald value of attention was higher than those of others (Wald = 51.83). Additionally, RDW had the greatest negative correlation with attention score (P < 0.05). CONCLUSIONS: Increased RDW may be considered as a biomarker of MCI.

SPDEF drives pancreatic adenocarcinoma progression via transcriptional upregulation of S100A16 and activation of the PI3K/AKT signaling pathway.

Pancreatic adenocarcinoma (PAAD) is a notably aggressive malignancy with limited treatment options and an unfavorable prognosis for patients. We aimed to investigate molecular mechanisms by which Sam's pointed domain-containing ETS transcription factor (SPDEF) exerts effects on PAAD progression. We analyzed differentially expressed genes (DEGs) and their integration with ETS family members using the The Cancer Genome Atlas (TCGA) database, hence identifying SPDEF as a core gene in PAAD. Kaplan-Meier survival analysis confirmed SPDEF's prognostic potential. In vitro experiments validated the association with cell proliferation and apoptosis, affecting pancreatic cancer cell dynamics. We detected increased SPDEF expression in PAAD tumor samples. Our in vitro studies revealed that SPDEF regulates mRNA and protein expression levels, and significantly affects cell proliferation. Moreover, SPDEF was associated with reduced apoptosis and enhanced cell migration and invasion. In-depth analysis of SPDEF-targeted genes revealed four crucial genes for advanced prognostic model, among which S100A16 was significantly correlated with SPDEF. Mechanistic analysis showed that SPDEF enhances the transcription of S100A16, which in turn enhances PAAD cell migration, proliferation, and invasion by activating the PI3K/AKT signaling pathway. Our study revealed the critical role of SPDEF in promoting PAAD by upregulating S100A16 transcription and stimulating the PI3K/AKT signaling pathway. This knowledge deepened our understanding of pancreatic cancer's molecular progression and unveiled potential therapeutic strategies targeting SPDEF-driven pathways.

The mitochondrial quality control system: a new target for exercise therapeutic intervention in the treatment of brain insulin resistance-induced neurodegeneration in obesity.

Obesity is a major global health concern because of its strong association with metabolic and neurodegenerative diseases such as diabetes, dementia, and Alzheimer's disease. Unfortunately, brain insulin resistance in obesity is likely to lead to neuroplasticity deficits. Since the evidence shows that insulin resistance in brain regions abundant in insulin receptors significantly alters mitochondrial efficiency and function, strategies targeting the mitochondrial quality control system may be of therapeutic and practical value in obesity-induced cognitive decline. Exercise is considered as a powerful stimulant of mitochondria that improves insulin sensitivity and enhances neuroplasticity. It has great potential as a non-pharmacological intervention against the onset and progression of obesity associated neurodegeneration. Here, we integrate the current knowledge of the mechanisms of neurodegenration in obesity and focus on brain insulin resistance to explain the relationship between the impairment of neuronal plasticity and mitochondrial dysfunction. This knowledge was synthesised to explore the exercise paradigm as a feasible intervention for obese neurodegenration in terms of improving brain insulin signals and regulating the mitochondrial quality control system.

Metabolites of intestinal fora can be used as diagnostic and progressive markers for mild cognitive impairment.

Purpose: The aim of the work was to analyze the metabolites of the intestinal microbiota from the patients with mild cognitive impairment (MCI) and progressive MCI due to Alzheimer's disease (AD). Method: Two cohorts were established. The first one included 87 subjects with 30 healthy controls (NC), 22 patients with MCI due to AD, and 35 patients with AD. The second cohort included 87 patients with MCI due to AD, who were followed up for 2 years and finally were divided into progressive MCI due to AD group (P-G) and unprogressive MCI due to AD group (U-G) according their cognitive levels. Fecal samples were collected to all patients at the baseline time point. Differential metabolites were subjected to pathway analysis by MetaboAnalyst. Results: In the first cohort, we found 21 different metabolites among the three groups (AD, MCI, and NC). In the second cohort, we identified 19 differential metabolites between the P-G and U-G groups. By machine learning analysis, we found that seven characteristic metabolites [Erythrodiol, alpha-Curcumene, Synephrine, o-Hydroxylaminobenzoate, 3-Amino-4-hydroxybenzoic acid, 2-Deoxystreptamine, and 9(S] were of characteristic significance for the diagnosis of MCI due to AD, and six metabolites (Indolelactate, Indole-3-acetaldehyde, L-Proline, Perillyl, Mesaconate, and Sphingosine) were the characteristic metabolites of early warning for the progression of MCI due to AD. D-Glucuronic acid was negatively correlated with Apolipoprotein E4 (APOE4). Perillyl alcohol was negatively correlated with all of the five biomarkers [P-tau181, Neurofilament light chain (NF-light), Aß1-42, Aß1-40, and glial fibrillary acidic protein (GFAP)], but Indoleacetaldehyde was positively correlated with three biomarkers (P-tau181, Aß1-42, and GFAP). Three characteristic metabolites (3-Amino-4-hydroxybenzoate, 2-Deoxystreptamine, and p-Synephrine) were positively correlated with Aß1-42. 2-Deoxystreptamine, 9(S)-HPOT, and Indoleacetaldehyde were positively correlated with GFAP. L-Proline and Indoleacetaldehyde were positively correlated with NF-light. Conclusion: Specific metabolites of intestinal fora can be used as diagnostic and progressive markers for MCI.

Dysregulation of Ruminococcaceae and Megamonas could be predictive markers for rapid progression of mild cognitive impairment.

BACKGROUND: Intestinal flora dysregulation may affect the development of Alzheimer's disease (AD). However, the correlation between intestinal flora and rapid progression of mild cognitive impairment (MCI) is rarely reported. Our aim was to investigate the features of the intestinal flora in patients with rapidly progressive MCI. METHOD: A total of 1013 participants were screened, in which 87 patients with MCI were followed up for two years. At the baseline time point, fecal samples of the patients were sequenced via the microbial diversity high-throughput 16 s-rDNA. RESULTS: After a two-year follow-up, 30 patients with MCI presented rapidly progressive cognitive impairment, whereas the 57 patients remained unprogressive. Analyses of their fecal samples showed that the abundance of 11 intestinal microflora were significantly different between the two groups at the baseline time point. Further analyses revealed that the decrease of Ruminococcaceae abundance and the increase of Megamonas abundance were significantly correlated with the progression of MCI. Also, the decreased Ruminococcaceae was independently associated with several factors such as P-tau181, and the increased Megamonas was independently associated with diabetes, low-density lipoprotein, median cell count. CONCLUSION: The decrease of Ruminococcaceae and the increase of Megamonas could act as predictive markers for the rapidly progressive MCI.

Dynamic nanoassemblies derived from small-molecule homodimeric prodrugs for in situ drug activation and safe osteosarcoma treatment.

Supramolecular prodrug self-assembly is a cost-effective and powerful approach for creating injectable anticancer nanoassemblies. Herein, we describe the self-assembly of small-molecule prodrug nanotherapeutics for tumor-restricted pharmacology that can be self-activated and independent of the exogenous stimuli. Covalent dimerization of the anticancer agent cabazitaxel via reactive oxygen species (ROS)- and esterase-activatable linkages produced the homodimeric prodrug diCTX, which was further coassembled with an ROS generator, dimeric dihydroartemisinin (diDHA). The coassembled nanoparticles were further refined in an amphiphilic matrix, making them suitable for in vivo administration. The ROS obtained from the coassembled diDHA synergized with intracellular esterase to activate the neighboring diCTX, which in turn induced potent cytotoxicity. In a preclinical orthotopic model of human osteosarcomas, nanoparticle administration exhibited durable antitumor efficacy. Furthermore, this smart, dual-responsive nanotherapeutic exhibited lower toxicity in animals than those of free drug combinations. We predict that this platform has great potential for further clinical translation.

The combined analysis of urine and blood metabolomics profiles provides an accurate prediction of the training and competitive status of Chinese professional swimmers.

Objective: The purpose of this study was to employ metabolomics for the analysis of urine metabolites in swimmers, with the aim of establishing models for assessing their athletic status and competitive potential. Furthermore, the study sought to compare the identification efficacy of multi-component (urine and blood) model versus single-component (urine or blood) models, in order to determine the optimal approach for evaluating training and competitive status. Methods: A total of 187 Chinese professional swimmers, comprising 103 elite and 84 sub-elite level athletes, were selected as subjects for this study. Urine samples were obtained from each participant and subjected to nuclear magnetic resonance (NMR) metabolomics analysis. Significant urine metabolites were screened through multivariable logistic regression analysis, and an identification model was established. Based on the previously established model of blood metabolites, this study compared the discriminative and predictive performance of three models: either urine or blood metabolites model and urine + blood metabolites model. Results: Among 39 urine metabolites, 10 were found to be significantly associated with the athletic status of swimmers (p < 0.05). Of these, levels of 2-KC, cis-aconitate, formate, and LAC were higher in elite swimmers compared to sub-elite athletes, while levels of 3-HIV, creatinine, 3-HIB, hippurate, pseudouridine, and trigonelline were lower in elite swimmers. Notably, 2-KC and 3-HIB exhibited the most substantial differences. An identification model was developed to estimate physical performance and athletic level of swimmers while adjusting for different covariates and including 2-KC and 3-HIB. The urine metabolites model showed an area under the curve (AUC) of 0.852 (95% CI: 0.793-0.912) for discrimination. Among the three identification models tested, the combination of urine and blood metabolites showed the highest performance than either urine or blood metabolites, with an AUC of 0.925 (95% CI: 0.888-0.963). Conclusion: The two urine metabolites, 2-KC and 3-HIV, can serve as significant urine metabolic markers to establish a discrimination model for identifying the athletic status and competitive potential of Chinese elite swimmers. Combining two screened urine metabolites with four metabolites reported exhibiting significant differences in blood resulted in improved predictive performance compared to using urine metabolites alone. These findings indicate that combining blood and urine metabolites has a greater potential for identifying and predicting the athletic status and competitive potential of Chinese professional swimmers.

Development and validation of a newly developed nomogram for predicting the risk of deep vein thrombosis after surgery for lower limb fractures in elderly patients.

Background: Prevention of deep vein thrombosis (DVT) is indispensable in the treatment of lower limb fractures during the perioperative period. This study aimed to develop and validate a novel model for predicting the risk of DVT in elderly patients after orthopedic surgeries for lower limb fractures. Methods: This observational study included 576 elderly patients with lower limb fractures who were surgically treated from January 2016 to December 2018. Eleven items affecting DVT were optimized by least absolute shrinkage and selection operator regression analysis. Multivariable logistic regression analysis was performed to construct a predictive model incorporating the selected features. C-index was applied to evaluate the discrimination. Decision curve analysis was employed to determine the clinical effectiveness of this model and calibration plot was applied to evaluate the calibration of this nomogram. The internal validation of this model was assessed by bootstrapping validation. Results: Predictive factors that affected the rate of DVT in this model included smoking, time from injury to surgery, operation time, blood transfusion, hip replacement arthroplasty, and D-dimer level after operation. The nomogram showed significant discrimination with a C-index of 0.919 (95% confidence interval: 0.893-0.946) and good calibration. Acceptable C-index value could still be reached in the interval validation. Decision curve analysis indicated that the DVT risk nomogram was useful within all possibility threshold. Conclusion: This newly developed nomogram could be used to predict the risk of DVT in elderly patients with lower limb fractures during the perioperative period.

Oral liposomal delivery of an activatable budesonide prodrug reduces colitis in experimental mice.

Inflammatory bowel disease (IBD) is one of the most common intestinal disorders, with increasing global incidence and prevalence. Numerous therapeutic drugs are available but require intravenous administration and are associated with high toxicity and insufficient patient compliance. Here, an oral liposome that entraps the activatable corticosteroid anti-inflammatory budesonide was developed for efficacious and safe IBD therapy. The prodrug was produced via the ligation of budesonide with linoleic acid linked by a hydrolytic ester bond, which was further constrained into lipid constituents to form colloidal stable nanoliposomes (termed budsomes). Chemical modification with linoleic acid augmented the compatibility and miscibility of the resulting prodrug in lipid bilayers to provide protection from the harsh environment of the gastrointestinal tract, while liposomal nanoformulation enables preferential accumulation to inflamed vasculature. Hence, when delivered orally, budsomes exhibited high stability with low drug release in the stomach in the presence of ultra-acidic pH but released active budesonide after accumulation in inflamed intestinal tissues. Notably, oral administration of budsomes demonstrated favorable anti-colitis effect with only ∼7% mouse body weight loss, whereas at least ∼16% weight loss was observed in other treatment groups. Overall, budsomes exhibited higher therapeutic efficiency than free budesonide treatment and potently induced remission of acute colitis without any adverse side effects. These data suggest a new and reliable approach for improving the efficacy of budesonide. Our in vivo preclinical data demonstrate the safety and increased efficacy of the budsome platform for IBD treatment, further supporting clinical evaluation of this orally efficacious budesonide therapeutic.

A new immune signature for survival prediction and immune checkpoint molecules in non-small cell lung cancer.

Background: Immune checkpoint blockade (ICB) therapy has brought remarkable clinical benefits to patients with advanced non-small cell lung carcinoma (NSCLC). However, the prognosis remains largely variable. Methods: The profiles of immune-related genes for patients with NSCLC were extracted from TCGA database, ImmPort dataset, and IMGT/GENE-DB database. Coexpression modules were constructed using WGCNA and 4 modules were identified. The hub genes of the module with the highest correlations with tumor samples were identified. Then integrative bioinformatics analyses were performed to unveil the hub genes participating in tumor progression and cancer-associated immunology of NSCLC. Cox regression and Lasso regression analyses were conducted to screen prognostic signature and to develop a risk model. Results: Functional analysis showed that immune-related hub genes were involved in the migration, activation, response, and cytokine-cytokine receptor interaction of immune cells. Most of the hub genes had a high frequency of gene amplifications. MASP1 and SEMA5A presented the highest mutation rate. The ratio of M2 macrophages and naïve B cells revealed a strong negative association while the ratio of CD8 T cells and activated CD4 memory T cells showed a strong positive association. Resting mast cells predicted superior overall survival. Interactions including protein-protein, lncRNA and transcription factor interactions were analyzed and 9 genes were selected by LASSO regression analysis to construct and verify a prognostic signature. Unsupervised hub genes clustering resulted in 2 distinct NSCLC subgroups. The TIDE score and the drug sensitivity of gemcitabine, cisplatin, docetaxel, erlotinib and paclitaxel were significantly different between the 2 immune-related hub gene subgroups. Conclusions: These findings suggested that our immune-related genes can provide clinical guidance for the diagnosis and prognosis of different immunophenotypes and facilitate the management of immunotherapy in NSCLC.

Predicting the Rapid Progression of Mild Cognitive Impairment by Intestinal Flora and Blood Indicators through Machine Learning Method.

INTRODUCTION: The aim of the work was to establish a prediction model of mild cognitive impairment (MCI) progression based on intestinal flora by machine learning method. METHOD: A total of 1,013 patients were recruited, in which 87 patients with MCI finished a two-year follow-up. To establish a prediction model, 61 patients were randomly divided into a training set and 26 patients were divided into a testing set. A total of 121 features including demographic characteristics, hematological indicators, and intestinal flora abundance were analyzed. RESULTS: Of the 87 patients who finished a two-year follow-up, 44 presented rapid progression. Model 1 was established based on 121 features with the accuracy 85%, sensitivity 85%, and specificity 83%. Model 2 was based on the first fifteen features of model 1 (triglyceride, uric acid, alanine transaminase, F-Clostridiaceae, G-Megamonas, S-Megamonas, G-Shigella, G-Shigella, S-Shigella, average hemoglobin concentration, G-Alistipes, S-Collinsella, median cell count, average hemoglobin volume, low-density lipoprotein), with the accuracy 97%, sensitivity 92%, and specificity 100%. Model 3 was based on the first ten features of model 1, with the accuracy 97%, sensitivity 86%, and specificity 100%. Other models based on the demographic characteristics, hematological indicators, or intestinal flora abundance features presented lower sensitivity and specificity. CONCLUSION: The 15 features (including intestinal flora abundance) could establish an effective model for predicting rapid MCI progression.

Machine-Based Learning Shifting to Prediction Model of Deteriorative MCI Due to Alzheimer's Disease - A Two-Year Follow-Up Investigation.

OBJECTIVE: The aim of the present work was to investigate the features of the elderly population aged ≥65 yrs and with deteriorative mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to establish a prediction model. METHODS: A total of 105 patients aged ≥65 yrs and with MCI were followed up, with a collection of 357 features, which were derived from the demographic characteristics, hematological indicators (serum Aß1-40, Aß1-42, P-tau and MCP-1 levels, APOE gene), and multimodal brain Magnetic Resonance Imaging (MRI) imaging indicators of 116 brain regions (ADC, FA and CBF values). Cognitive function was followed up for 2 yrs. Based on the Python platform Anaconda, 105 patients were randomly divided into a training set (70%) and a test set (30%) by analyzing all features through a random forest algorithm, and a prediction model was established for the form of rapidly deteriorating MCI. RESULTS: Of the 105 patients enrolled, 41 deteriorated, and 64 did not come within 2 yrs. Model 1 was established based on demographic characteristics, hematological indicators and multi-modal MRI image features, the accuracy of the training set being 100%, the accuracy of the test set 64%, sensitivity 50%, specificity 67%, and AUC 0.72. Model 2 was based on the first five features (APOE4 gene, FA value of left fusiform gyrus, FA value of left inferior temporal gyrus, FA value of left parahippocampal gyrus, ADC value of right calcarine fissure as surrounding cortex), the accuracy of the training set being 100%, the accuracy of the test set 85%, sensitivity 91%, specificity 80% and AUC 0.96. Model 3 was based on the first four features of Model 1, the accuracy of the training set is 100%, the accuracy of the test set 97%, sensitivity100%, specificity 95% and AUC 0.99. Model 4 was based on the first three characteristics of Model 1, the accuracy of the training set being 100%, the accuracy of the test set 94%, sensitivity 92%, specificity 94% and AUC 0.96. Model 5 was based on the hematological characteristics, the accuracy of the training set is 100%, the accuracy of the test set 91%, sensitivity 100%, specificity 88% and AUC 0.97. The models based on the demographic characteristics, imaging characteristics FA, CBF and ADC values had lower sensitivity and specificity. CONCLUSION: Model 3, which has four important predictive characteristics, can predict the rapidly deteriorating MCI due to AD in the community.

Single-Atom Nanozymes: Fabrication, Characterization, Surface Modification and Applications of ROS Scavenging and Antibacterial.

Nanozymes are nanomaterials with intrinsic natural enzyme-like catalytic properties. They have received extensive attention and have the potential to be an alternative to natural enzymes. Increasing the atom utilization rate of active centers in nanozymes has gradually become a concern of scientists. As the limit of designing nanozymes at the atomic level, single-atom nanozymes (SAzymes) have become the research frontier of the biomedical field recently because of their high atom utilization, well-defined active centers, and good natural enzyme mimicry. In this review, we first introduce the preparation of SAzymes through pyrolysis and defect engineering with regulated activity, then the characterization and surface modification methods of SAzymes are introduced. The possible influences of surface modification on the activity of SAzymes are discussed. Furthermore, we summarize the applications of SAzymes in the biomedical fields, especially in those of reactive oxygen species (ROS) scavenging and antibacterial. Finally, the challenges and opportunities of SAzymes are summarized and prospected.

Lactate Is Answerable for Brain Function and Treating Brain Diseases: Energy Substrates and Signal Molecule.

Research to date has provided novel insights into lactate's positive role in multiple brain functions and several brain diseases. Although notable controversies and discrepancies remain, the neurobiological role and the metabolic mechanisms of brain lactate have now been described. A theoretical framework on the relevance between lactate and brain function and brain diseases is presented. This review begins with the source and route of lactate formation in the brain and food; goes on to uncover the regulatory effect of lactate on brain function; and progresses to gathering the application and concentration variation of lactate in several brain diseases (diabetic encephalopathy, Alzheimer's disease, stroke, traumatic brain injury, and epilepsy) treatment. Finally, the dual role of lactate in the brain is discussed. This review highlights the biological effect of lactate, especially L-lactate, in brain function and disease studies and amplifies our understanding of past research.

lncRNA TM4SF1-AS1 predicts dismal outcomes and promotes cholangiocarcinoma progression via modulating miR-744-3p.

BACKGROUND: Cholangiocarcinoma (CCA) is of great malignancy and high mortality. Identification of effective biomarkers could improve the monitoring of CCA development and attenuate patients' outcomes. OBJECTIVE: The potential of lncRNA TM4SF1-AS1 (TM4SF1-AS1) serving biomarker of CCA was estimated and the underlying mechanism was also investigated. METHODS: A total of 107 pairs of tumor and paracancer tissues were collected from CCA patients. The expression levels of TM4SF1-AS1 and miR-744-3p were analyzed in CCA by PCR, and their clinical significance was estimated by a series of statistical analyses. CCK8 and Transwell assays were used to assess the development-related cellular processes of CCA. The interaction between TM4SF1-AS1 and miR-774-3p was evaluated by cell transfection and dual-luciferase reporter assay. RESULTS: The elevated expression of TM4SF1-AS1 and the declined expression of miR-744-3p were observed in CCA. Both TM4SF1-AS1 and miR-744-3p were found to possess a close association with the malignant progression and poor prognosis of CCA patients. TM4SF1-AS1 was suggested to act as a tumor promoter of CCA, where miR-744-3p was found to mediate the function of TM4SF1-AS1. CONCLUSION: Both TM4SF1-AS1 and miR-744-3p were identified as prognostic biomarkers of CCA. TM4SF1-AS1 served as tumor promoter of CCA via modulating miR-744-3p.

Combinatorial nanococktails via self-assembling lipid prodrugs for synergistically overcoming drug resistance and effective cancer therapy.

BACKGROUND: Combinatorial systemic chemotherapy is a powerful treatment paradigm against cancer, but it is fraught with problems due to the emergence of chemoresistance and additive systemic toxicity. In addition, coadministration of individual drugs suffers from uncontrollable pharmacokinetics and biodistribution, resulting in suboptimal combination synergy. METHODS: Toward the goal of addressing these unmet medical issues, we describe a unique strategy to integrate multiple structurally disparate drugs into a self-assembling nanococktail platform. Conjugation of a polyunsaturated fatty acid (e.g., linoleic acid) with two chemotherapies generated prodrug entities that were miscible with tunable drug ratios for aqueous self-assembly. In vitro and in vivo assays were performed to investigate the mechanism of combinatorial nanococktails in mitigating chemoresistance and the efficacy of nanotherapy. RESULTS: The coassembled nanoparticle cocktails were feasibly fabricated and further refined with an amphiphilic matrix to form a systemically injectable and PEGylated nanomedicine with minimal excipients. The drug ratio incorporated into the nanococktails was optimized and carefully examined in lung cancer cells to maximize therapeutic synergy. Mechanistically, subjugated resistance by nanococktail therapy was achieved through the altered cellular uptake pathway and compromised DNA repair via the ATM/Chk2/p53 cascade. In mice harboring cisplatin-resistant lung tumor xenografts, administration of the nanococktail outperformed free drug combinations in terms of antitumor efficacy and drug tolerability. CONCLUSION: Overall, our study provides a facile and cost-effective approach for the generation of cytotoxic nanoparticles to synergistically treat chemoresistant cancers.

Understanding the Contribution of Lactate Metabolism in Cancer Progress: A Perspective from Isomers.

Lactate mediates multiple cell-intrinsic effects in cancer metabolism in terms of development, maintenance, and metastasis and is often correlated with poor prognosis. Its functions are undertaken as an energy source for neighboring carcinoma cells and serve as a lactormone for oncogenic signaling pathways. Indeed, two isomers of lactate are produced in the Warburg effect: L-lactate and D-lactate. L-lactate is the main end-production of glycolytic fermentation which catalyzes glucose, and tiny D-lactate is fabricated through the glyoxalase system. Their production inevitably affects cancer development and therapy. Here, we systematically review the mechanisms of lactate isomers production, and highlight emerging evidence of the carcinogenic biological effects of lactate and its isomers in cancer. Accordingly, therapy that targets lactate and its metabolism is a promising approach for anticancer treatment.

Prediction and Identification of Power Performance Using Polygenic Models of Three Single-Nucleotide Polymorphisms in Chinese Elite Athletes.

Objective: The manuscript aims to explore the relationship between power performance and SNPs of Chinese elite athletes and to create polygenic models. Methods: One hundred three Chinese elite athletes were divided into the power group (n = 60) and endurance group (n = 43) by their sports event. Best standing long jump (SLJ) and standing vertical jump (SVJ) were collected. Twenty SNPs were genotyped by SNaPshot. Genotype distribution and allele frequency were compared between groups. Additional genotype data of 125 Chinese elite athletes were used to verify the screened SNPs. Predictive and identifying models were established by multivariate logistic regression analysis. Results: ACTN3 (rs1815739), ADRB3 (rs4994), CNTFR (rs2070802), and PPARGC1A (rs8192678) were significantly different in genotype distribution or allele frequency between groups (p < 0.05). The predictive model consisted of ACTN3 (rs1815739), ADRB3 (rs4994), and PPARGC1A (rs8192678), the area under curve (AUC) of which was 0.736. The identifying model consisted of body mass index (BMI), standing vertical jump (SVJ), ACTN3, ADRB3, and PPARGC1A, the area under curve (AUC) of which was 0.854. Based on the two models, nomograms were created to visualize the results. Conclusion: Two models can be used for talent identification in Chinese athletes, among which the predictive model can be used in adolescent athletes to predict development potential of power performance and the identifying one can be used in elite athletes to evaluate power athletic status. These can be applied quickly and visually by using nomograms. When the score is more than the 130 or 148 cutoff, it suggests that the athlete has a good development potential or a high level for power performance.

Nomogram for Predicting Bone Development State of Female Children and Adolescents-A Fast Screening Approach Based on Pubes Stages for Growth and Development.

Objective: To develop a nomogram for predicting bone development state (BDS) of female children and adolescents in a large scale. Methods: Four hundred forty-seven female students were designated as the training cohort to develop the predictive model, whereas 196 female students were used as the validation cohort to verify the established model. Bone age, height, body mass, body fat percentage, and secondary sexual characteristics were recorded, and BDS was determined with the chronological age and bone age. Multivariate logistic regression was conducted to determine the factors, and nomogram was developed and validated with the training and validation cohorts, respectively. Results: One hundred forty-seven female students were identified as BDS abnormal in the training cohort (32.9%), and 104 were determined in the validation cohort (53.1%). Age, height, weight, and pubes stage were selected for the predictive model. A nomogram was developed and showed a good estimation, with a C-index of 0.78 and a good calibration in the training cohort. Application of the nomogram to the validation cohort showed a similar C-index of 0.75 and a good calibration. Conclusion: A nomogram for predicting bone development was developed, which can provide a relatively good estimation of BDS for female children and adolescents in Chinese metropolis.

Anlotinib suppresses MLL-rearranged acute myeloid leukemia cell growth by inhibiting SETD1A/AKT-mediated DNA damage response.

Leukemias driven by chromosomal translocation of the mixed-lineage leukemia (MLL) gene are highly prevalent in hematological malignancy. The poor survival rate and lack of effective targeted therapy for patients with MLL-rearranged (MLL-r) leukemias emphasize an urgent need for improved knowledge and novel therapeutic approaches for these malignancies. The present study aimed to investigate the potential effectiveness and mechanism of Anlotinib, a novel receptor tyrosine kinase inhibitor, in MLL-r acute myeloid leukemia (AML). The findings revealed that Anlotinib significantly inhibited the growth of MLL-r AML cells in both in vivo and a murine xenograft model. RNA sequencing identified that multiple genes involved in DNA damage response were responsible for Anlotinib activity. To further elucidate the correlation between the DNA damage response induced by Anlotinib and MLL fusion, Gene Expression Profiling Interactive Analysis (GEPIA) was conducted. It revealed that Anlotinib impaired DNA damage response via inhibiting SETD1A and AKT. In conclusion, Anlotinib exerts anti-leukemia function by inhibiting SETD1A/AKT-mediated DNA damage response and highlights a novel mechanism underlying Anlotinib in the treatment of MLL-r AML.