Global, regional, and national time trends in incidence for tuberculosis, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease 2019 study.

BACKGROUND: Tuberculosis (TB) represents a significant global health concern, being the leading cause of mortality from a single infectious agent worldwide. The investigation of TB incidence and epidemiological trends is critical for evaluating the effectiveness of control strategies and identifying ongoing challenges. OBJECTIVES: This study presents the trend in TB incidence across 204 countries and regions over a 30-year period. METHODS: The study utilises data sourced from the Global Burden of Disease (GBD) database. The age cohort model and gender subgroup analysis were employed to estimate the net drift (overall annual percentage change), local drift (age annual percentage change), longitudinal age curve (expected age ratio), and cycle and cohort effect (relative risk of cycle and birth cohort) of TB incidence from 1990 to 2019. This approach facilitates the examination and differentiation of age, period, and cohort effects in TB incidence trends, potentially identifying disparities in TB prevention across different countries. RESULTS: Over the past three decades, a general downward trend in TB incidence has been observed in most countries. However, in 15 of the 204 countries, the overall incidence rate is still on the rise (net drift ≥0.0 %) or stagnant decline (≥-0.5 %). From 1990 to 2019, the net drift of tuberculosis mortality ranged from -2.2 % [95 % confidence interval (CI): -2.33, -2.05] in high Socio-demographic Index (SDI) countries to -1.7 % [95 % CI: -1.81, -1.62] in low SDI countries. In some below-average SDI countries,men in the birth cohort are at a disadvantage and at risk of deterioration, necessitating comprehensive TB prevention and treatment. CONCLUSIONS: While the global incidence of TB has declined, adverse period and cohort effects have been identified in numerous countries, raising questions about the adequacy of TB healthcare provision across all age groups. Furthermore, this study reveals gender disparities in TB incidence.

Recent Progress in Phage-Based Nanoplatforms for Tumor Therapy.

Nanodrug delivery systems have demonstrated a great potential for tumor therapy with the development of nanotechnology. Nonetheless, traditional drug delivery systems are faced with issues such as complex synthetic procedures, low reproducibility, nonspecific distribution, impenetrability of biological barrier, systemic toxicity, etc. In recent years, phage-based nanoplatforms have attracted increasing attention in tumor treatment for their regular structure, fantastic carrying property, high transduction efficiency and biosafety. Notably, therapeutic or targeting peptides can be expressed on the surface of the phages through phage display technology, enabling the phage vectors to possess multifunctions. As a result, the drug delivery efficiency on tumor will be vastly improved, thereby enhancing the therapeutic efficacy while reducing the side effects on normal tissues. Moreover, phages can overcome the hindrance of biofilm barrier to elicit antitumor effects, which exhibit great advantages compared with traditional synthetic drug delivery systems. Herein, this review not only summarizes the structure and biology of the phages, but also presents their potential as prominent nanoplatforms against tumor in different pathways to inspire the development of effective nanomedicine.

Machine learning algorithm to construct cuproptosis- and immune-related prognosis prediction model for colon cancer.

BACKGROUND: Over the past few years, research into the pathogenesis of colon cancer has progressed rapidly, and cuproptosis is an emerging mode of cellular apoptosis. Exploring the relationship between colon cancer and cuproptosis benefits in identifying novel biomarkers and even improving the outcome of the disease. AIM: To look at the prognostic relationship between colon cancer and the genes associated with cuproptosis and the immune system in patients. The main purpose was to assess whether reasonable induction of these biomarkers reduces mortality among patients with colon cancers. METHOD: Data obtained from The Cancer Genome Atlas and Gene Expression Omnibus and the Genotype-Tissue Expression were used in differential analysis to explore differential expression genes associated with cuproptosis and immune activation. The least absolute shrinkage and selection operator and Cox regression algorithm was applied to build a cuproptosis- and immune-related combination model, and the model was utilized for principal component analysis and survival analysis to observe the survival and prognosis of the patients. A series of statistically meaningful transcriptional analysis results demonstrated an intrinsic relationship between cuproptosis and the micro-environment of colon cancer. RESULTS: Once prognostic characteristics were obtained, the CDKN2A and DLAT genes related to cuproptosis were strongly linked to colon cancer: The first was a risk factor, whereas the second was a protective factor. The finding of the validation analysis showed that the comprehensive model associated with cuproptosis and immunity was statistically significant. Within the component expressions, the expressions of HSPA1A, CDKN2A, and UCN3 differed markedly. Transcription analysis primarily reflects the differential activation of related immune cells and pathways. Furthermore, genes linked to immune checkpoint inhibitors were expressed differently between the subgroups, which may reveal the mechanism of worse prognosis and the different sensitivities of chemotherapy. CONCLUSION: The prognosis of the high-risk group evaluated in the combined model was poorer, and cuproptosis was highly correlated with the prognosis of colon cancer. It is possible that we may be able to improve patients' prognosis by regulating the gene expression to intervene the risk score.