Circular RNAs in EMT-driven metastasis regulation: modulation of cancer cell plasticity, tumorigenesis and therapy resistance.

The non-coding RNAs comprise a large part of human genome lack of capacity in encoding functional proteins. Among various members of non-coding RNAs, the circular RNAs (circRNAs) have been of importance in the pathogenesis of human diseases, especially cancer. The circRNAs have a unique closed loop structure and due to their stability, they are potential diagnostic and prognostic factors in cancer. The increasing evidences have highlighted the role of circRNAs in the modulation of proliferation and metastasis of cancer cells. On the other hand, metastasis has been responsible for up to 90% of cancer-related deaths in patients, requiring more investigation regarding the underlying mechanisms modulating this mechanism. EMT enhances metastasis and invasion of tumor cells, and can trigger resistance to therapy. The cells demonstrate dynamic changes during EMT including transformation from epithelial phenotype into mesenchymal phenotype and increase in N-cadherin and vimentin levels. The process of EMT is reversible and its reprogramming can disrupt the progression of tumor cells. The aim of current review is to understanding the interaction of circRNAs and EMT in human cancers and such interaction is beyond the regulation of cancer metastasis and can affect the response of tumor cells to chemotherapy and radiotherapy. The onco-suppressor circRNAs inhibit EMT, while the tumor-promoting circRNAs mediate EMT for acceleration of carcinogenesis. Moreover, the EMT-inducing transcription factors can be controlled by circRNAs in different human tumors.

Epidemiology and SARIMA model of deaths in a tertiary comprehensive hospital in Hangzhou from 2015 to 2022.

BACKGROUND: By analysing the deaths of inpatients in a tertiary hospital in Hangzhou, this study aimed to understand the epidemiological distribution characteristics and the composition of the causes of death. Additionally, this study aimed to predict the changing trend in the number of deaths, providing valuable insights for hospitals to formulate relevant strategies and measures aimed at reducing mortality rates. METHODS: In this study, data on inpatient mortality at a tertiary hospital in Hangzhou from 2015 to 2022 were obtained via the population information registration system of the Chinese Center for Disease Control and Prevention. The death data of inpatients were described and analysed through a retrospective study. Excel 2016 was utilized for data sorting, and SPSS 22.0 software was employed for data analysis. The statistical inference of single factor differences was conducted via χ2 tests. The SARIMA model was established via the forecast, aTSA, and tseries software packages (version 4.3.0) to forecast future changes in the number of deaths. RESULTS: A total of 1938 inpatients died at the tertiary hospital in Hangzhou, with the greatest number of deaths occurring in 2022 (262, 13.52%). The sex ratio was 2.22:1, and there were significant differences between sexes in terms of age, marital status, educational level, and place of residence (P < 0.05). The percentage of males in the groups aged of 20 to 29 and 30 to 39 years was significantly greater than that of females (χ2 = 46.905, P < 0.001). More females than males died in the widowed group, and divorced and married males experienced a greater number of deaths than divorced and married females did (χ2 = 61.130, P < 0.001). The proportions of male students with a junior college and senior high school education were significantly greater than that of female students (χ2 = 12.310, P < 0.05). The primary causes of mortality within the hospital setting included circulatory system diseases, injury, poisoning, tumours, and respiratory system diseases. These leading factors accounted for 86.12% of all recorded deaths. Finally, the SARIMA (2, 1, 1) (1, 1, 1)12 model was determined to be the optimal model, with an AIC of 380.23, a BIC of 392.79, and an AICc of 381.81. The MAPE was 14.99%, indicating a satisfactory overall fit of this model. The relative error between the predicted and actual number of deaths in 2022 was 8.02%. Therefore, the SARIMA (2, 1, 1) (1, 1, 1)12 model demonstrates good predictive performance. CONCLUSIONS: Hospitals should enhance the management of sudden cardiac death, acute myocardial infarction, severe craniocerebral injury, lung cancer, and lung infection to reduce the mortality rate. The SARIMA model can be employed for predicting the number of deaths.

Racial/Ethnic Differences in Bone Mineral Density for Osteoporosis.

PURPOSE OF REVIEW: We primarily aim to review differences in bone mineral density (BMD) for osteoporosis among different racial/ethnic groups and to highlight the magnitude of racial/ethnic differences in obesity and diabetes. We also explore the factors contributing to the BMD differences among various subgroups. In addition, we investigate the existing disparities in research, educational initiatives, screening practices, and treatment options for osteoporosis and discuss these findings' clinical and public health implications. RECENT FINDINGS: Racial/ethnic differences in BMD for osteoporosis exist in the USA and other countries. There are disparities regarding osteoporosis screening and treatment. Understanding the factors contributing to these differences can help develop targeted interventions and policies to reduce their impact. Clinicians should consider the racial/ethnic differences in BMD when making treatment decisions and providing preventive care. Future research could contribute to developing effective strategies for preventing osteoporosis among different racial/ethnic groups. This review offered a comprehensive examination of differences in BMD across various racial and ethnic groups, elucidating the influence of genetic, lifestyle, and cultural factors on these differences. This review also highlighted the disparities in osteoporosis screening, treatment options, research on medical effectiveness, and educational outreach tailored to each subgroup. Recognizing the importance of addressing these inequalities, we present this review to advocate for targeted interventions to reduce disparities in osteoporosis and improve bone health for all populations.

Taurine Alleviates LPS-Induced Acute Lung Injury by Suppressing TLR-4/NF-κB Pathway.

Taurine has the function of immune regulation, relieving acute and chronic inflammation caused by various agents, and maintaining cell homeostasis. This investigation focused on the protective functions of taurine targeting acute lung injury (ALI) induced by LPS. Sixty male SD rats aged 6-7 weeks were segregated at random: blank control group (C group), taurine control group (T group), ALI model group (LPS group), and taurine prevention groups (LPST1, LPST, LPST3 Groups). C group and LPS group were given normal drinking water, while T group and LPST group were given 2% taurine in drinking water. LPST1 group was given 1% taurine in drinking water while. LPST3 group was given 3% taurine in drinking water. On the 14th and 28th day, LPS group and LPST1, LPST, and LPST3 groups were subjected to injection of LPS (25 mg/kg) into the trachea of rats. Serum, peripheral blood, lung tissue, and bronchoalveolar lavage fluid (BALF) were collected at 6 h post-LPS injection. The wet/dry ratio (W/D) of lung was measured by hot drying method. The population of white blood cells and the abundance of inflammatory-related cells within peripheral blood were counted by an automatic blood cell analyzer. The population of white blood cells within BALF was counted by a white blood cell counting plate combined with Swiss Giemsa staining, while the proportion of related white blood cells was calculated. BCA reagent was used to determine the protein concentration in BALF. The levels of pro-inflammatory factors (IL-1 ß, IL-6, IL-18, TNF - α), anti-inflammation factors (IL-10, IL-4), and taurine within serum and lung tissue were detected by ELISA. Lung structural tissue alterations were observed through HE staining techniques. Myeloperoxidase (MPO) activities within lung tissue were detected through colorimetry. Protein expression levels of TLR4, MyD88, NF-κ Bp65, NF-κ Bp-p65, MCP-1, together with CD68 within lung tissue, were analyzed by Western blot (WB) and immunohistochemistry (IHC). The taurine pretreatment group contained significantly reduced W/D, MPO activity, and the number of inflammatory cells in BALF induced by LPS. In addition, compared with ALI model group, the taurine pretreatment group contained significantly reduced levels of pro-inflammatory factors in lung tissue, increased levels of anti-inflammatory factors, and decreased expression levels of key proteins in TLR-4/NF-κ B pathway. Taurine can protect rats from ALI by inhibiting the activation of neutrophils, macrophages, and TLR-4/NF-κ B signaling pathway.

Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.

This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS), derived from 1,103 risk single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS), was formulated for 25,772 postmenopausal women from the Women's Health Initiative dataset. We developed four ML models: Support Vector Machine (SVM), Random Forest, XGBoost, and Artificial Neural Network (ANN) for binary fracture outcome and 10-year fracture risk prediction. GRS and FRAX clinical risk factors (CRFs) were used as predictors. Death as a competing risk was accounted for in ML models for time-to-fracture data. ML models were subsequently fine-tuned through Bayesian optimization, which displayed marked superiority over traditional grid search. Evaluation of the models' performance considered an array of metrics such as accuracy, weighted F1 Score, the area under the precision-recall curve (PRAUC), and the area under the receiver operating characteristic curve (AUC) for binary fracture predictions, and the C-index, Brier score, and dynamic mean AUC over a 10-year follow-up period for fracture risk predictions. We found that GRS-integrated XGBoost with Bayesian optimization is the most effective model, with an accuracy of 91.2% (95% CI: 90.4-92.0%) and an AUC of 0.739 (95% CI: 0.731-0.746) in MOF binary predictions. For 10-year fracture risk modeling, the XGBoost model attained a C-index of 0.795 (95% CI: 0.783-0.806) and a mean dynamic AUC of 0.799 (95% CI: 0.788-0.809). Compared to FRAX, the XGBoost model exhibited a categorical net reclassification improvement (NRI) of 22.6% (P = .004). A sensitivity analysis, which included BMD but lacked GRS, reaffirmed these findings. Furthermore, portability tests in diverse non-European groups, including Asians and African Americans, underscored the model's robustness and adaptability. This study accentuates the potential of combining genetic insights and optimized ML in strengthening fracture predictions, heralding new preventive strategies for postmenopausal women.

This study presents a novel method for improving osteoporotic fracture predictions in postmenopausal women. By integrating genetic risk scores from genome wide association studies with established clinical risk factors and employing advanced machine learning techniques like Support Vector Machine, Random Forest, XGBoost, and Artificial Neural Networks, we analyze data from over 25,000 Women's Health Initiative participants. Our findings show that incorporating genetic risk scores significantly boosts predictive accuracy. The XGBoost model, optimized with Bayesian methods, outperforms existing algorithms. This approach underscores the value of genetic data in clinical evaluations and advances the personalized management of osteoporosis. It enables more accurate risk stratification and the development of tailored preventive strategies, likely reducing osteoporotic fractures among postmenopausal women and setting a new standard for future precision-based interventions.

Prognostic relevance and validation of ARPC1A in the progression of low-grade glioma.

Low-grade glioma (LGG) is a grade II-III glioma accompanied by distinct clinical and molecular characteristics and the studies related to its prognosis are still unclear. The objective of this study is to explore the involvement of mitochondrial-related genes SLBP, COMMD7, LSM4, TOMM34, RPP40, FKBP1A, ARPC1A, and TBCA for the prognosis of LGG. We detected differences in the expression of some of the genes by analyzing the bioinformatics dataset and combining it with RT-PCR experiments. Subsequently, a nomogram was constructed and validated for the clinical relevance of risk factors such as age, WHO grade, IDH mutation status, Ch.1p19q co-deletion status, and high and low expression of ARPC1A to predict the 1-, 3-, 5-year overall survival and prognostic relevance of ARPC1A. Gene set enrichment analysis was performed for the relevant datasets pertinent to the expression of ARPC1A to elucidate the cancer-promoting pathways involved in the LGG through KEGG and GO analysis. Transfection assays, CCK-8 assays, and flow cytometry were used to determine the proliferation rate, and apoptosis rate of the HS683 and SW1783 cell lines respectively. Western blotting was used to examine the involvement of the cancer-promoting activity of ARPC1A through MAPK signaling. In this study, the prognostic value of ARPC1A in LGG was found by bioinformatics analysis combined with experimental approach analysis and may be a significant independent risk factor. ARPC1A fosters a higher LGG proliferation rate that may control the MAP kinase signaling and could be a prominent biomarker for LGG. Future studies are warranted to explore its clinical implications.

Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis.

Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87-96, p< 0.01) and specificity (90%; 95% CI: 85-93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90-94, p< 0.01; and 91%; 95% CI: 88-93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77-85, p< 0.01; and 83%; 95% CI: 76-88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90-96, p < 0.01) and specificity (92%; 95% CI: 89-94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).

Chitosan-based hydrogels in cancer therapy: Drug and gene delivery, stimuli-responsive carriers, phototherapy and immunotherapy.

Nanotechnology has transformed the oncology sector by particularly targeting cancer cells and enhancing the efficacy of conventional therapies. Environmentally friendly materials are the top choice for treating cancer. Chitosan, sourced from chitin, is widely used with its derivatives for the extensive synthesis or modification of nanoparticles. Chitosan has been deployed to develop hydrogels, as 3D polymeric networks capable of water absorption. The chitosan hydrogels are biocompatible and biodegradable structures that can deliver drugs, genes or a combination of them in cancer therapy. Increased tumor ablation, reducing off-targeting feature and protection of genes against degradation are benefits of using chitosan hydrogels in cancer therapy. The efficacy of cancer immunotherapy can be improved by chitosan hydrogels to prevent emergence of immune evasion. In addition, chitosan hydrogels facilitate photothermal and photodynamic therapy for tumor destruction. Chitosan hydrogels can synergistically integrate chemotherapy, immunotherapy, and phototherapy in cancer treatment. Additionally, chitosan hydrogels that respond to stimuli, specifically thermo-sensitive hydrogels, have been developed for inhibiting tumors.

Peptide-functionalized, -assembled and -loaded nanoparticles in cancer therapy.

The combination of peptides and nanoparticles in cancer therapy has shown synergistic results. Nanoparticle functionalization with peptides can increase their targeting ability towards tumor cells. In some cases, the peptides can develop self-assembled nanoparticles, in combination with drugs, for targeted cancer therapy. The peptides can be loaded into nanoparticles and can be delivered by other drugs for synergistic cancer removal. Multifunctional types of peptide-based nanoparticles, including pH- and redox-sensitive classes, have been introduced in cancer therapy. The tumor microenvironment remolds, and the acceleration of immunotherapy and vaccines can be provided by peptide nanoparticles. Moreover, the bioimaging and labeling of cancers can be mediated by peptide nanoparticles. Therefore, peptides can functionalize nanoparticles in targeted cancer therapy.

Taurine reduces apoptosis mediated by endoplasmic reticulum stress in islet ß-cells induced by high-fat and -glucose diets.

Poor eating habits, especially high-fat and -glucose diets intake, can lead to endoplasmic reticulum (ER) stress in islet ß-cells, insulin resistance, and islet ß-cell dysfunction and cause islet ß-cell apoptosis, which leads to type 2 diabetes mellitus (T2DM). Taurine is a crucial amino acid in the human body. In this study, we aimed to explore the mechanism through which taurine reduces glycolipid toxicity. INS-1 islet ß-cell lines were cultured with a high concentration of fat and glucose. SD rats were fed a high-fat and -glucose diet. MTS, Transmission electron microscopy, Flow cytometry, Hematoxylin-eosin, TUNEL, Western blotting analysis and other methods were used to detect relevant indicators. The research found that taurine increases the cell activity, reduces the apoptosis rate, alleviates the structural changes of ER under high-fat and -glucose exposure models. In addition, taurine improves blood lipid content and islets pathological changes, regulates the relative protein expression in ER stress and apoptosis, increases the insulin sensitivity index (HOMA-IS), and reduces the insulin resistance index (HOMAC-IR) of SD rats fed with a high-fat and -glucose diet.

Dietary taurine effect on intestinal barrier function, colonic microbiota and metabolites in weanling piglets induced by LPS.

Diarrhea in piglets is one of the most important diseases and a significant cause of death in piglets. Preliminary studies have confirmed that taurine reduces the rate and index of diarrhea in piglets induced by LPS. However, there is still a lack of relevant information on the specific target and mechanism of action of taurine. Therefore, we investigated the effects of taurine on the growth and barrier functions of the intestine, microbiota composition, and metabolite composition of piglets induced by LPS. Eighteen male weaned piglets were randomly divided into the CON group (basal diet + standard saline injection), LPS group (basal diet + LPS-intraperitoneal injection), and TAU + LPS group (basal diet + 0.3% taurine + LPS-intraperitoneal injection). The results show that taurine significantly increased the ADG and decreased the F/G (p < 0.05) compared with the group of CON. The group of TAU + LPS significantly improved colonic villous damage (p < 0.05). The expression of ZO-1, Occludin and Claudin-1 genes and proteins were markedly up-regulated (p < 0.05). Based on 16s rRNA sequencing analysis, the relative abundance of Lactobacilluscae and Firmicutes in the colon was significantly higher in the LPS + TAU group compared to the LPS group (p < 0.05). Four metabolites were significantly higher and one metabolite was significantly lower in the TAU + LPS group compared to the LPS group (p < 0.01). The above results show that LPS disrupts intestinal microorganisms and metabolites in weaned piglets and affects intestinal barrier function. Preventive addition of taurine enhances beneficial microbiota, modulates intestinal metabolites, and strengthens the intestinal mechanical barrier. Therefore, taurine can be used as a feed additive to prevent intestinal damage by regulating intestinal microorganisms and metabolites.

Metalized polyamide heterostructure as a moisture-responsive actuator for multimodal adaptive personal heat management.

Personal thermal management textile/wearable is an effective strategy to expand the indoor temperature setpoint range to reduce a building's energy consumption. Usually, textiles/wearables that were engineered for controlling conduction, convection, radiation, or sweat evaporation have been developed separately. Here, we demonstrate a multimodal adaptive wearable with moisture-responsive flaps composed of a nylon/metal heterostructure, which can simultaneously regulate convection, sweat evaporation, and mid-infrared emission to accomplish large and rapid heat transfer tuning in response to human perspiration vapor. We show that the metal layer not only plays a crucial role in low-emissivity radiative heating but also enhances the bimorph actuation performance. The multimodal adaptive mechanism expands the thermal comfort zone by 30.7 and 20.7% more than traditional static textiles and single-modal adaptive wearables without any electricity and energy input, making it a promising design paradigm for personal heat management.

Role of PML SUMOylation in arsenic trioxide-induced fibrosis in HSCs.

BACKGROUND: Arsenic trioxide (ATO) can bind directly to the human promyelocytic leukemia (PML) protein, leading to modification of PML by SUMOs. UBC9 is the only known E2-conjugating enzyme involved in SUMOylation. PML degradation via RNF4, an E3 ubiquitin ligases family member. PML is key organizer of nuclear bodies (NBs) that regulate many biological processes such as senescence, and DNA damage. ATO can activate the TGFß/Smad signaling pathway, causing liver fibrosis. However, the roles of PML Sumoylation in ATO-induced liver fibrosis remain unclear. OBJECTIVE: This study aimed to investigate the role of PML Sumoylation in the ATO-induced HSCs activation and to improve the mechanism of ATO-induced liver fibrosis. METHODS: Hepatic stellate cells (HSCs) were treated with 2 µmol/L ATO. Cell viability was detected by CCK-8 analysis. Immunoblot analysis and real-time quantitative PCR were used to detect the expression of IL-1ß, TNF-α, TGF-ß1, p-Smad2/3, α-SMA, Collagen I and PML SUMOylation after silencing PML, UBC9, and RNF4, respectively. The formation of PML-NBs was observed by immunofluorescence staining. RESULTS: 2 and 5 µmol/L ATO intervention increased HSCs cell viability. ATO was able to significantly trigger PML SUMOylation and the formation of PML-NBs. Inhibition of SUMOylated PML by silencing UBC9, subsequently preventing the downregulation of HSCs activation indicators induced by ATO (P < 0.05). Conversely, enhancing SUMOylated PML accumulation by silencing RNF4, activating TGFß/Smad signaling pathway, eventually promoting the induction of liver fibrosis. CONCLUSION: These results indicated that PML SUMOylation plays a critical role in the development of liver fibrosis induced by ATO.

Bi-directional regulation of TGF-ß/Smad pathway by arsenic: A systemic review and meta-analysis of in vivo and in vitro studies.

BACKGROUND: Arsenic exposure can cause fibrosis of organs including the liver, heart and lung. It was reported that TGF-ß/Smad pathway played a crucial role in the process of fibrosis. However, the mechanism of arsenic-induced fibrosis through TGF-ß/Smad signaling pathway has remained controversial. OBJECTIVE: A systematic review and meta-analysis was performed to clarify the relationship between arsenic and TGF-ß/Smad pathway, providing a theoretical basis of fibrosis process caused by arsenic. METHODS: A meta-analysis was used to reveal a correlation between arsenic and fibrosis markers of TGF-ß/Smad pathway, including 47 articles of both in vivo and in vitro studies. (Standardized Mean Difference) SMD was employed to compare and analyze the combined effects. When I2 > was 50%, random effect model was selected and subgroup analysis was used to explore the source of heterogeneity. RESULTS: Arsenic exposure up-regulated the expression of TGF-ß1, p-Smad2/3, α-SMA, Collagen1/3 and FN. The dose-response relationship showed that low dose (≤5 µmol/L) arsenic exposure up-regulated the expression of TGF-ß1, whereas high doses had a tendency to down-regulate that of TGF-ß1. Subgroup analysis showed that low or short-term arsenic exposure induced the expression of TGF-ß1 and fibrosis markers. CONCLUSION: The results indicated that arsenic activates the TGF-ß/Smad pathway and induced fibrosis. The mechanism is related to the up-regulation of NADPH oxidase and ROS accumulation. However, high-dose arsenic exposure may inhibit this pathway.