Background: Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods: We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results: Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions: A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

Nucleotide-binding and oligomerization domain (NOD)-like receptors (NLRs) are intracellular proteins with a central role in innate and adaptive immunity. As a member of pattern recognition receptors (PRRs), NLRs sense specific pathogen-associated molecular patterns, trigger numerous signaling pathways and lead to the secretion of various cytokines. In recent years, cumulative studies have revealed the significant impacts of NLRs in gastrointestinal (GI) inflammatory diseases and cancers. Deciphering the role and molecular mechanism of the NLR signaling pathways may provide new opportunities for the development of therapeutic strategies related to GI inflammatory diseases and GI cancers. This review presents the structures and signaling pathways of NLRs, summarizes the recent advances regarding NLR signaling in GI inflammatory diseases and GI cancers and describes comprehensive therapeutic strategies based on this signaling pathway.

Background: The detection and management of intracranial aneurysms (IAs) are vital to prevent life-threatening complications like subarachnoid hemorrhage (SAH). Artificial Intelligence (AI) can analyze medical images, like CTA or MRA, spotting nuances possibly overlooked by humans. Early detection facilitates timely interventions and improved outcomes. Moreover, AI algorithms offer quantitative data on aneurysm attributes, aiding in long-term monitoring and assessing rupture risks. Methods: We screened four databases (PubMed, Web of Science, IEEE and Scopus) for studies using artificial intelligence algorithms to identify IA. Based on algorithmic methodologies, we categorized them into classification, segmentation, detection and combined, and then their merits and shortcomings are compared. Subsequently, we elucidate potential challenges that contemporary algorithms might encounter within real-world clinical diagnostic contexts. Then we outline prospective research trajectories and underscore key concerns in this evolving field. Results: Forty-seven studies of IA recognition based on AI were included based on search and screening criteria. The retrospective results represent that current studies can identify IA in different modal images and predict their risk of rupture and blockage. In clinical diagnosis, AI can effectively improve the diagnostic accuracy of IA and reduce missed detection and false positives. Conclusions: The AI algorithm can detect unobtrusive IA more accurately in communicating arteries and cavernous sinus arteries to avoid further expansion. In addition, analyzing aneurysm rupture and blockage before and after surgery can help doctors plan treatment and reduce the uncertainties in the treatment process.

Pancreatic cancer is the deadliest malignancy with a poor response to chemotherapy but is potentially indicated for ferroptosis therapy. Here we identified that cytoplasmic polyadenylation element binding protein 1 (CPEB1) regulates NRF2 proteostasis and susceptibility to ferroptosis in pancreatic ductal adenocarcinoma (PDAC). We found that CPEB1 deficiency in cancer cells promotes the translation of p62/SQSTM1 by facilitating mRNA polyadenylation. Consequently, upregulated p62 enhances NRF2 stability by sequestering KEAP1, an E3 ligase for proteasomal degradation of NRF2, leading to the transcriptional activation of anti-ferroptosis genes. In support of the critical role of this signaling cascade in cancer therapy, CPEB1-deficient pancreatic cancer cells display higher resistance to ferroptosis-inducing agents than their CPEB1-normal counterparts in vitro and in vivo. Furthermore, based on the pathological evaluation of tissue specimens from 90 PDAC patients, we established that CPEB1 is an independent prognosticator whose expression level is closely associated with clinical therapeutic outcomes in PDAC. These findings identify the role of CPEB1 as a key ferroptosis regulator and a potential prognosticator in pancreatic cancer.

Multiple morphological abnormalities of the sperm flagella (MMAF) is a specific form of severe flagellar or ciliary deficiency syndrome. MMAF is characterized by primary infertility with abnormal morphology in the flagella of spermatozoa, presenting with short, absent, bent, coiled, and irregular flagella. As a rare disease first named in 2014, studies in recent years have shed light on the molecular defects of MMAF that comprise the structure and biological function of the sperm flagella. Understanding the molecular genetics of MMAF may provide opportunities for the development of diagnostic and therapeutic strategies for this rare disease. This review aims to summarize current studies regarding the molecular pathogenesis of MMAF and describe strategies of genetic counseling, clinical diagnosis, and therapy for MMAF.

Background: The study is aimed to provide prediction for fertility risk in the setting of assisted reproduction for a woman with complex chromosomal rearrangements (CCRs). Methods: We implemented a robust approach, which combined whole-genome low-coverage mate-pair sequencing (WGL-MPS), junction-spanning PCR and preimplantation genetic testing for aneuploidy (PGT-A) method to provide accurate chromosome breakpoint junctional sequences in the embryo selection process in the setting of assisted reproduction for a couple with recurrent abortions due to CCRs. Result: WGL-MPS was applied to a female carrying CCRs which consisted of 9 breakpoints and 1 cryptic deletion related to fertility risks. Sequencing data provided crucial information for designing junction-spanning PCR and PGT-A process, which was performed on the 11 embryos cultivated. One embryo was considered qualified for transplanting, which carried the exact same CCRs as the female carrier, whose phenotype was normal. The amniotic fluid was also investigated by WGL-MPS and karyotyping at 19 weeks’ gestation, which verified the results that the baby carried the same CCRs. A healthy baby was born at 39 weeks’ gestation by vaginal delivery. Conclusion(s): Our study illustrates the WGL-MPS approach combining with junction-spanning PCR and PGT-A is a powerful and practical method in the setting of assisted reproduction for couples with recurrent miscarriage due to chromosomal abnormalities, especially CCRs carriers.