For decades, common chemotherapeutic drugs have been established to trigger apoptosis, the preferred immunologically "silent" form of cell death. The primary objective of this review was to show that various FDA-approved chemotherapeutic drugs, including cisplatin, cyclosporine, doxorubicin, etoposide, 5-fluorouracil, gemcitabine, paclitaxel, or vinblastine can trigger necroptosis and pyroptosis. We aimed to provide the advantages and disadvantages of the induction of the given type of cell death by chemotherapeutical agents. Moreover, we give a short overview of the molecular mechanism of each type of cell death and indicate the existing crosstalks between cell death types. Finally, we provide a comparison of cell death types to facilitate the exploration of cell death types induced by other chemotherapeutical agents. Understanding the cell death pathway induced by a drug can lessen side effects and assist the discovery of new combinations with synergistic effects and low systemic toxicity. 

Apoptosis, necroptosis, and pyroptosis as alternative cell death pathways induced by chemotherapeutic agents?

Femur fracture is a type of fracture with high disability and mortality. There is no comprehensive analysis and prediction of the global distribution of femur fractures, so we conducted this study. Age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), and years living with disability (YLDs) of femur fractures (excluding femoral neck) were downloaded from the Global burden of disease database. Trend analysis was performed, and 6 time-series machine learning algorithms were applied to predict the global ASIR, ASPR, and YLDs. ASPR for femur fracture had been increasing in most countries worldwide from 1990 to 2019, with the highest in East Asia (AAPC = 1.25 95%Confidence Interval (1.2, 1.3)) and lowest in Central Latin America (AAPC = −0.74 95%CI (−0.81, −0.67)). However, ASIR showed a significant downward trend worldwide, with East Saharan Africa decreasing the most (AAPC = −4.04 95%CI (−5.56, −2.47)), and East Asia elevating the most (AAPC = 1.11 95%CI (0.87, 1.42)). YLDs were increasing over the world, with East Asia still elevating the most AAPC= (3.9 95%CI (3.85, 3.95)), with the only region of decrease being Eastern Europe (AAPC = −0.28 95%CI (−0.3, −0.26)). Both ASPR and ASIR were higher in women than in men in the >75 year group, whereas YLDs was lower in women than in men in the >60 year group. Globally, the ARIMA model was optimal in the prediction of ASPR, the PROPHET model effected in the prediction of ASIR, and the PROPHET WITH XGBOOST model was the best in the prediction of YLDs. The projections showed increase in both ASPR and YLDs, except for ASIR decreasing by 2030. Our study found a rise in femur fracture ASPR and ASIR from 1990 to 2019 in war conflict areas and East Asia, meanwhile, the YLDs of femur fracture increased in populous countries. In both 1990 and 2019, both ASPR and ASIR were higher in women over 75 years than that in men, but YLDs was higher in men over 60 years than that in women. In 2020–2030, while global femur fracture ASIR might decline, both ASPR and YLDs might rise. Femur fracture is a high-energy injury due to direct violence, and in war, conflicting and underdeveloped regions such as East Asia. Accidental injuries may occur due to the rapid development of industry and the frequent traffic accidents. This study suggests that we should focus on elderly women (≥75 years) in the above regions in the future. For older men (>60 years old), more attention should be paid to post-fracture functional rehabilitation and early reintegration into society to reduce the disability rate and lower the socio-economic burden. 

Global, regional, national trends of femur fracture and machine learning prediction: Comprehensive findings and questions from global burden of disease 1990–2019

The incidence of pediatric Crohn’s disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study’s objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD.Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences.Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes.In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.

Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning

PANoptosis is a newly identified inflammatory programmed cell death (PCD) that involves the interplay of apoptosis, necrosis, and pyroptosis. However, its overall biological effects cannot be attributed to any one type of PCD alone. PANoptosis is regulated by a signaling cascade triggered by the recognition of pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) by various sensors. This triggers the assembly of the PANoptosome, which integrates key components from other PCD pathways via adapters and ultimately activates downstream execution molecules, resulting in cell death with necrotic, apoptotic, and pyroptotic features. Autoimmune diseases are characterized by reduced immune tolerance to self-antigens, leading to abnormal immune responses, often accompanied by systemic chronic inflammation. Consequently, PANoptosis, as a unique innate immune-inflammatory PCD pathway, has significant pathophysiological relevance to inflammation and autoimmunity. However, most previous research on PANoptosis has focused on tumors and infectious diseases, leaving its activation and role in autoimmune diseases unclear. This review briefly outlines the characteristics of PANoptosis and summarizes several newly identified PANoptosome complexes, their activation mechanisms, and key components. We also explored the dual role of PANoptosis in diseases and potential therapeutic approaches targeting PANoptosis. Additionally, we review the existing evidence for PANoptosis in several autoimmune diseases and explore the potential regulatory mechanisms involved. 

PANoptosis in autoimmune diseases interplay between apoptosis, necrosis, and pyroptosis

The roles of pyroptosis in the pathogenesis of autoimmune diseases

Background Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets. Method We downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software “limma” package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines. Result CHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A. Conclusion In summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.

/pdf/significance-of-pyroptosis-related-gene-in-the-diagnosis-and-1zbec2i1.pdf

Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis

In order to improve the accuracy of dust storm prediction and reduce the damage and losses by a dust storm event, it is necessary to conduct an in-depth study on the same. The data of the national air quality stations, backward trajectories generated by the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), and the lidar network data from Wuwei, Baiyin, and Dingxi were used to study a large-scale dust storm event that occurred from 12 to 14 May 2019. This study explores the cause of the dust storm and physical characteristics of dust aerosols in three-dimensional space, as well as its impact on air quality. Results show that the dust storm was caused by the eastward movement of the East Asian trough and affected most cities in northern China from northwest to southeast. Consequently, the air quality deteriorated seriously, especially over the Hexi Corridor. The hourly peak concentrations of PM10 in Wuwei and Baiyin were close to 3,000 μg·m−3. The observations from the lidar network show that the dust intensities were similar at different cities, and their extinction coefficients were close. However, the depolarization ratio varied with sources and the physical characteristics of dust particles. According to the simulation results of extinction coefficients and particle concentrations, due to the impact of dust transported at high altitudes, the concentration of particles in Dingxi did not decrease with the increase in altitude. The particle concentrations from ground-based monitoring were lower than those of Baiyin and Wuwei, while particle concentrations above 0.3 km were higher than those of Baiyin and Wuwei.

/pdf/comprehensive-analysis-of-a-dust-storm-by-a-lidar-network-2o8fmkha.pdf

Comprehensive Analysis of a Dust Storm by a Lidar Network Combined With Multiple Data

Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning. Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method. Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9. Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.

Typing characteristics of metabolism-related genes in osteoporosis

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Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis

Comprehensive Analysis of a Dust Storm by a Lidar Network Combined With Multiple Data

Typing characteristics of metabolism-related genes in osteoporosis