Explore the vast range of titles available.

Curcumin is known to exhibit anticancer effects on various cancers with selective cytotoxicity in tumor cells. In the present study, the effects of curcumin-induced multiple PCDs on human non-small cell lung cancer (NSCLC) cells and the potential molecular mechanisms of apoptosis and autophagy triggered by curcumin via the PI3K/Akt/mTOR signaling pathway were explored, further confirmed by co-culture of curcumin with mTOR blocker rapamycin and PI3K/Akt inhibitor LY294002. The anti-proliferation effect of different stimulus was measured by MTT assay. Apoptosis was detected by flow cytometry. Autophagy induction was detected by MDC labeling and western blotting of Beclin1, LC3, and p62 expression. The mRNA and protein expression levels of Akt and mTOR were assayed by real-time fluorescence quantitative (qRT-PCR) technique and western blotting. Our results showed that curcumin inhibited the viability of A549 cells time- and dose-dependently. In addition, a dosage-dependent A549 cell apoptosis-induction phenomena was observed by the curcumin intervention. Moreover, obvious autophagy was induced after curcumin-treatment, characterized by the formation of fluorescent particles [autophagic vesicles (AVs)] and significant increase in ratio of LC3-II/LC3-I and Beclin1 as well as decreased p62 expression. Furthermore, the effect of curcumin on a substantial downregulation of phosphatidylinositol 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) pathway was observed. It is worth noting that the inhibition of mTOR by rapamycin or of PI3K/Akt by LY294002 augmented curcumin-induced apoptosis and autophagy, leading to significant inhibition of cell proliferation. From these findings, it can be speculated that curcumin potently inhibit the cell growth of NSCLC A549 cells through inducing both apoptosis and autophagy by inhibition of the PI3K/Akt/mTOR pathway. These results support the potential use of curcumin as a novel candidate in treatment of human lung cancer.

In this paper, we study a backward problem for a fractional Rayleigh–Stokes equation by using a quasi-boundary value method. This problem is ill-posed; i.e., the solution (if it exists) does not depend continuously on the data. To overcome its instability, a regularization method is employed, and convergence rate estimates are derived under both a priori and a posteriori criteria for selecting the regularization parameter. The theoretical results demonstrate the effectiveness of the proposed method in deriving stable and accurate solutions.

Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining in recent years. Finally, we summarize the content of the review and look into the future of some research directions for the next step.

The prevalence of type 2 diabetes has increased in both young and old people. We examined age-specific associations and population attributable fractions (PAFs) of risk factors for all-cause and cause-specific mortality in people with type 2 diabetes. We analysed data from 360,202 Chinese with type 2 diabetes who participated in a territory-wide diabetes complication screening programme in Hong Kong between January 2000 and December 2019. We compared the hazard ratios and PAFs of eight risk factors, including three major comorbidities (cardiovascular disease [CVD], chronic kidney disease [CKD], all-site cancer) and five modifiable risk factors (suboptimal HbA1c, suboptimal blood pressure, suboptimal low-density lipoprotein cholesterol, smoking, and suboptimal weight), for mortality across four age groups (18 to 54, 55 to 64, 65 to 74, and ≥75 years). During a median 6.0 years of follow-up, 44,396 people died, with cancer, CVD, and pneumonia being the leading causes of death. Despite a higher absolute mortality risk in older people (crude all-cause mortality rate: 59.7 versus 596.2 per 10,000 person-years in people aged 18 to 54 years versus those aged ≥75 years), the relative risk of all-cause and cause-specific mortality associated with most risk factors was higher in younger than older people, after mutually adjusting for the eight risk factors and other potential confounders including sex, diabetes duration, lipid profile, and medication use. The eight risk factors explained a larger proportion of mortality events in the youngest (PAF: 51.6%, 95% confidence interval [CI] [39.1%, 64.0%], p < 0.001) than the oldest (PAF: 35.3%, 95% CI [27.2%, 43.4%], p < 0.001) age group. Suboptimal blood pressure (PAF: 16.9%, 95% CI [14.7%, 19.1%], p < 0.001) was the leading attributable risk factor for all-cause mortality in the youngest age group, while CKD (PAF: 15.2%, 95% CI [14.0%, 16.4%], p < 0.001) and CVD (PAF: 9.2%, 95% CI [8.3%, 10.1%], p < 0.001) were the leading attributable risk factors in the oldest age group. The analysis was restricted to Chinese, which might affect the generalisability to the global population with differences in risk profiles. Furthermore, PAFs were estimated under the assumption of a causal relationship between risk factors and mortality. However, reliable causality was difficult to establish in the observational study. Major comorbidities and modifiable risk factors were associated with a greater relative risk for mortality in younger than older people with type 2 diabetes and their associations with population mortality burden varied substantially by age. These findings highlight the importance of early control of blood pressure, which could reduce premature mortality in young people with type 2 diabetes and prevent the onset of later CKD and related mortality at older ages.

Mathematical modeling can help the medical community to more fully understand and explore the physiological and pathological processes within the human body and can provide more accurate and reliable medical predictions and diagnoses. Neural network models, machine learning models, and statistical models, among others, have become important tools. The paper details the applications of mathematical modeling in the medical field: by building differential equations to simulate the patient’s cardiovascular system, physicians can gain a deeper understanding of the pathogenesis and treatment of heart disease. With machine learning algorithms, medical images can be better quantified and analyzed, thus improving the precision and accuracy of diagnosis and treatment. In the drug development process, network models can help researchers more quickly screen for potentially active compounds and optimize them for eventual drug launch and application. By mining and analyzing a large number of medical data, more accurate and comprehensive disease risk assessment and prediction results can be obtained, providing the medical community with a more scientific and accurate basis for decision-making. In conclusion, research on medical problems based on mathematical models has become an important part of modern medical research, and great progress has been made in different fields.

In the study of blast furnace coke ratio, existing methods can only predict coke ratio of daily. At the same time, the data under abnormal furnace conditions are excluded, and the model’s robustness needs to be improved. In order to improve the prediction accuracy and time precision of the blast furnace mathematical simulation model, a blast furnace coke ratio prediction model based on fuzzy C-means (FCM) clustering and grid search optimization support vector regression (SVR) is proposed to achieve accurate prediction of coke ratio. First, preprocess the blast furnace sensor data and steel plant production data. Then, the FCM algorithm is used to cluster the data under different furnace conditions. Finally, the SVR model optimized by grid search is used to predict the coke ratio under different blast furnace conditions. The average absolute error of the improved model is 1.7721 kg/t, the hit rate within 0.5% error is 81.19%, the coefficient of determination R2 is 0.9474, and the prediction performance is better than ridge regression and decision tree regression. Experiments show that the model can predict the coke ratio of molten iron in each batch when the blast furnace conditions are going forward and fluctuating, and it has high time accuracy and stability. It objectively describes the changing trend of blast furnace conditions, and provides new research ideas for the practical application of blast furnace mathematical models.

Metals have been proved to be one of risk factors for chronic kidney disease (CKD) and diabetes, but the effect of mixed metal co-exposure and potential interaction between metals are still unclear. We assessed the urine and whole blood levels of cadmium (Cd), manganese (Mn), lead (Pb), mercury (Hg), and renal function in 3080 adults from National Health and Nutrition Survey (NHANES) (2011–2018) to explore the effect of mixed metal exposure on CKD especially in people with type 2 diabetes mellitus (T2DM). Weighted quantile sum regression model and Bayesian Kernel Machine Regression model were used to evaluate the overall exposure impact of metal mixture and potential interaction between metals. The results showed that the exposure to mixed metals was significantly associated with an increased risk of CKD in blood glucose stratification, with the risk of CKD being 1.58 (1.26,1.99) times in urine and 1.67 (1.19,2.34) times in whole blood higher in individuals exposed to high concentrations of the metal mixture compared to those exposed to low concentrations. The effect of urine metal mixture was elevated magnitude in stratified analysis. There were interactions between urine Pb and Cd, Pb and Mn, Pb and Hg, Cd and Mn, Cd and Hg, and blood Pb and Hg, Mn and Cd, Mn and Pb, Mn and Hg on the risk of CKD in patients with T2DM and no significant interaction between metals was observed in non-diabetics. In summary, mixed metal exposure increased the risk of CKD in patients with T2DM, and there were complex interactions between metals. More in-depth studies are needed to explore the mechanism and demonstrate the causal relationship.

Resveratrol (RV) is a natural component of red wine and grapes that has been shown to be a potential chemopreventive and anticancer agent. However, the molecular mechanisms underlying RV's anticancer and chemopreventive effects are incompletely understood. Here we show that RV treatment inhibits the clonogenic growth of non-small cell lung cancer (NSCLC) cells in a dose-dependent manner. Interestingly, the tumor-suppressive effect of low dose RV was not associated with any significant changes in the expression of cleaved PARP and activated caspase-3, suggesting that low dose RV treatment may suppress tumor cell growth via an apoptosis-independent mechanism. Subsequent studies reveal that low dose RV treatment induces a significant increase in senescence-associated β-galactosidase (SA-β-gal) staining and elevated expression of p53 and p21 in NSCLC cells. Furthermore, we show that RV-induced suppression of lung cancer cell growth is associated with a decrease in the expression of EF1A. These results suggest that RV may exert its anticancer and chemopreventive effects through the induction of premature senescence. Mechanistically, RV-induced premature senescence correlates with increased DNA double strand breaks (DSBs) and reactive oxygen species (ROS) production in lung cancer cells. Inhibition of ROS production by N-acetylcysteine (NAC) attenuates RV-induced DNA DSBs and premature senescence. Furthermore, we show that RV treatment markedly induces NAPDH oxidase-5 (Nox5) expression in both A549 and H460 cells, suggesting that RV may increase ROS generation in lung cancer cells through upregulating Nox5 expression. Together, these findings demonstrate that low dose RV treatment inhibits lung cancer cell growth via a previously unappreciated mechanism, namely the induction of premature senescence through ROS-mediated DNA damage.

This paper aimed to analyze the relationship between baseline prolactin (PRL) levels and live birth rates (LBRs) in patients undergoing embryo transfer who did not have polycystic ovarian syndrome (PCOS) using a retrospective design. Patient(s): A total of 20,877 patients who had undergone IVF/intracytoplasmic sperm injection (ICSI) between December 2014 and December 2019. We examined the association between PRL concentrations and LBRs using multivariate regression analysis. In addition, a model for nonlinear relationships based on a two-part linear regression was developed. Following adjustment for confounding factors, multivariate regression analysis confirmed a statistically significant correlation between serum PRL and LBR. Particularly, when blood PRL content was less than 14.8 ng/mL, there exists a positive relation between serum PRL and LBRs. In contrast, once PRL concentrations surpassed the inflection point at 14.8 ng/mL, a meaningful relationship could no longer be inferred between serum PRL and LBR. Basal serum PRL levels were segmentally connected with LBRs.

Type 2 diabetes affects multiple systems. We aimed to compare age- and sex-specific rates of all-cause and cause-specific hospital bed-days between people with and without type 2 diabetes. Data were provided by the Hong Kong Hospital Authority. We included 1,516,508 one-to-one matched people with incident type 2 diabetes (n = 758,254) and those without diabetes during the entire follow-up period (n = 758,254) between 2002 and 2018, followed until 2019. People with type 2 diabetes and controls were matched for age at index date (±2 years), sex, and index year (±2 years). We defined hospital bed-day rate as total inpatient bed-days divided by follow-up time. We constructed negative binominal regression models to estimate hospital bed-day rate ratios (RRs) by age at diabetes diagnosis and sex. All RRs were stratified by sex and adjusted for age and index year. During a median of 7.8 years of follow-up, 60.5% (n = 459,440) of people with type 2 diabetes and 56.5% (n = 428,296) of controls had a hospital admission for any cause, with a hospital bed-day rate of 3,359 bed-days and 2,350 bed-days per 1,000 person-years, respectively. All-cause hospital bed-day rate increased with increasing age in controls, but showed a J-shaped relationship with age in people with type 2 diabetes, with 38.4% of bed-days in those diagnosed <40 years caused by mental health disorders. Type 2 diabetes was associated with increased risks for a wide range of medical conditions, with an RR of 1.75 (95% CI [confidence interval] [1.73, 1.76]; p < 0.001) for all-cause hospital bed-days in men and 1.87 (95% CI [1.85, 1.89]; p < 0.001) in women. The RRs were greater in people with diabetes diagnosed at a younger than older age and varied by sex according to medical conditions. Sex differences were most notable for a higher RR for urinary tract infection and peptic ulcer, and a lower RR for chronic kidney disease and pancreatic disease in women than men. The main limitation of the study was that young people without diabetes in the database were unlikely to be representative of those in the Hong Kong general population with potential selection bias due to inclusion of individuals in need of medical care. In this study, we observed that type 2 diabetes was associated with increased risks of hospital bed-days for a wide range of medical conditions, with an excess burden of mental health disorders in people diagnosed at a young age. Age and sex differences should be considered in planning preventive and therapeutic strategies for type 2 diabetes. Effective control of risk factors with a focus on mental health disorders are urgently needed in young people with type 2 diabetes. Healthcare systems and policymakers should consider allocating adequate resources and developing strategies to meet the mental health needs of young people with type 2 diabetes, including integrating mental health services into diabetes care.