High-throughput identification of regulatory elements and functional assays to uncover susceptibility genes for nasopharyngeal carcinoma.

Large-scale genetic association studies have identified multiple susceptibility loci for nasopharyngeal carcinoma (NPC), but the underlying biological mechanisms remain to be explored. To gain insights into the genetic etiology of NPC, we conducted a follow-up study encompassing 6,907 cases and 10,472 controls and identified two additional NPC susceptibility loci, 9q22.33 (rs1867277; OR = 0.74, 95% CI = 0.68-0.81, p = 3.08 × 10-11) and 17q12 (rs226241; OR = 1.42, 95% CI = 1.26-1.60, p = 1.62 × 10-8). The two additional loci, together with two previously reported genome-wide significant loci, 5p15.33 and 9p21.3, were investigated by high-throughput sequencing for chromatin accessibility, histone modification, and promoter capture Hi-C (PCHi-C) profiling. Using luciferase reporter assays and CRISPR interference (CRISPRi) to validate the functional profiling, we identified PHF2 at locus 9q22.33 as a susceptibility gene. PHF2 encodes a histone demethylase and acts as a tumor suppressor. The risk alleles of the functional SNPs reduced the expression of the target gene PHF2 by inhibiting the enhancer activity of its long-range (4.3 Mb) cis-regulatory element, which promoted proliferation of NPC cells. In addition, we identified CDKN2B-AS1 as a susceptibility gene at locus 9p21.3, and the NPC risk allele of the functional SNP rs2069418 promoted the expression of CDKN2B-AS1 by increasing its enhancer activity. The overexpression of CDKN2B-AS1 facilitated proliferation of NPC cells. In summary, we identified functional SNPs and NPC susceptibility genes, which provides additional explanations for the genetic association signals and helps to uncover the underlying genetic etiology of NPC development.

Defining the separation landscape of topological domains for decoding consensus domain organization of the 3D genome.

Topologically associating domains (TADs) have emerged as basic structural and functional units of genome organization and have been determined by many computational methods from Hi-C contact maps. However, the TADs obtained by different methods vary greatly, which makes the accurate determination of TADs a challenging issue and hinders subsequent biological analyses about their organization and functions. Obvious inconsistencies among the TADs identified by different methods indeed make the statistical and biological properties of TADs overly depend on the chosen method rather than on the data. To this end, we use the consensus structural information captured by these methods to define the TAD separation landscape for decoding the consensus domain organization of the 3D genome. We show that the TAD separation landscape could be used to compare domain boundaries across multiple cell types for discovering conserved and divergent topological structures, decipher three types of boundary regions with diverse biological features, and identify consensus TADs (ConsTADs). We illustrate that these analyses could deepen our understanding of the relationships between the topological domains and chromatin states, gene expression, and DNA replication timing.

Visualizing Dynamics of Cell Signaling In Vivo with a Phase Separation-Based Kinase Reporter.

Visualizing dynamics of kinase activity in living animals is essential for mechanistic understanding of cell and developmental biology. We describe GFP-based kinase reporters that phase-separate upon kinase activation via multivalent protein-protein interactions, forming intensively fluorescent droplets. Called SPARK (separation of phases-based activity reporter of kinase), these reporters have large dynamic range (fluorescence change), high brightness, fast kinetics, and are reversible. The SPARK-based protein kinase A (PKA) reporter reveals oscillatory dynamics of PKA activities upon G protein-coupled receptor activation. The SPARK-based extracellular signal-regulated kinase (ERK) reporter unveils transient dynamics of ERK activity during tracheal metamorphosis in live Drosophila. Because of intensive brightness and simple signal pattern, SPARKs allow easy examination of kinase signaling in living animals in a qualitative way. The modular design of SPARK will facilitate development of reporters of other kinases.

Historic dog Furs Unravel the Origin and Artificial Selection of Modern Nordic Lapphund and Elkhound dog Breeds.

The origins and extreme morphological evolution of the modern dog breeds are poorly studied because the founder populations are extinct. Here, we analyse eight 100 to 200 years old dog fur samples obtained from traditional North Swedish clothing, to explore the origin and artificial selection of the modern Nordic Lapphund and Elkhound dog breeds. Population genomic analysis confirmed the Lapphund and Elkhound breeds to originate from the local dog population, and showed a distinct decrease in genetic diversity in agreement with intense breeding. We identified eleven genes under positive selection during the breed development. In particular, the MSRB3 gene, associated with breed-related ear morphology, was selected in all Lapphund and Elkhound breeds, and functional assays showed that a SNP mutation in the 3'UTR region suppresses its expression through miRNA regulation. Our findings demonstrate analysis of near-modern dog artifacts as an effective tool for interpreting the origin and artificial selection of the modern dog breeds.

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer.

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes.

Identifying cancer driver genes plays a curial role in the development of precision oncology and cancer therapeutics. Although a plethora of methods have been developed to tackle this problem, the complex cancer mechanisms and intricate interactions between genes still make the identification of cancer driver genes challenging. In this work, we propose a novel machine learning method of heterophilic graph diffusion convolutional networks (called HGDCs) to boost cancer-driver gene identification. Specifically, HGDC first introduces graph diffusion to generate an auxiliary network for capturing the structurally similar nodes in a biomolecular network. Then, HGDC designs an improved message aggregation and propagation scheme to adapt to the heterophilic setting of biomolecular networks, alleviating the problem of driver gene features being smoothed by its neighboring dissimilar genes. Finally, HGDC uses a layer-wise attention classifier to predict the probability of one gene being a cancer driver gene. In the comparison experiments with other existing state-of-the-art methods, our HGDC achieves outstanding performance in identifying cancer driver genes. The experimental results demonstrate that HGDC not only effectively identifies well-known driver genes on different networks but also novel candidate cancer genes. Moreover, HGDC can effectively prioritize cancer driver genes for individual patients. Particularly, HGDC can identify patient-specific additional driver genes, which work together with the well-known driver genes to cooperatively promote tumorigenesis.

A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions.

Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug-drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs. To address the above issues, we first leverage Balance theory and Status theory in social networks to reveal the topological patterns among directed pharmacological DDIs, which are modeled as a signed and directed network. Then, we design a novel graph representation learning model named SGRL-DDI (social theory-enhanced graph representation learning for DDI) to realize the multitask prediction of DDIs. SGRL-DDI model can capture the task-joint information by integrating relation graph convolutional networks with Balance and Status patterns. Moreover, we utilize task-specific deep neural networks to perform two tasks, including the prediction of enhancive/depressive DDIs and the prediction of directed DDIs. Based on DDI entries collected from DrugBank, the superiority of our model is demonstrated by the comparison with other state-of-the-art methods. Furthermore, the ablation study verifies that Balance and Status patterns help characterize directed pharmacological DDIs, and that the joint of two tasks provides better DDI representations than individual tasks. Last, we demonstrate the practical effectiveness of our model by a version-dependent test, where 88.47 and 81.38% DDI out of newly added entries provided by the latest release of DrugBank are validated in two predicting tasks respectively.

m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model.

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.

Self S-RNase inhibits ABF-LRX signaling to arrest pollen tube growth to achieve self-incompatibility in pear.

Gametophytic self-incompatibility (GSI) has been widely studied in flowering plants, but studies of the mechanisms underlying pollen tube growth arrest by self S-RNase in GSI species are limited. In the present study, two leucine-rich repeat extensin genes in pear (Pyrus bretschneideri), PbLRXA2.1 and PbLRXA2.2, were identified based on transcriptome and quantitative real-time PCR analyses. The expression levels of these two LRX genes were significantly higher in the pollen grains and pollen tubes of the self-compatible cultivar 'Jinzhui' (harboring a spontaneous bud mutation) than in those of the self-incompatible cultivar 'Yali'. Both PbLRXA2.1 and PbLRXA2.2 stimulated pollen tube growth and attenuated the inhibitory effects of self S-RNase on pollen tube growth by stabilizing the actin cytoskeleton and enhancing cell wall integrity. These results indicate that abnormal expression of PbLRXA2.1 and PbLRXA2.2 is involved in the loss of self-incompatibility in 'Jinzhui'. The PbLRXA2.1 and PbLRXA2.2 promoters were directly bound by the ABRE-binding factor PbABF.D.2. Knockdown of PbABF.D.2 decreased PbLRXA2.1 and PbLRXA2.2 expression and inhibited pollen tube growth. Notably, the expression of PbLRXA2.1, PbLRXA2.2, and PbABF.D.2 was repressed by self S-RNase, suggesting that self S-RNase can arrest pollen tube growth by restricting the PbABF.D.2-PbLRXA2.1/PbLRXA2.2 signal cascade. These results provide novel insight into pollen tube growth arrest by self S-RNase.

Autoantibody repertoire profiling in tissue and blood identifies colorectal cancer-specific biomarkers.

Autoantibodies (AAbs) in the blood of colorectal cancer (CRC) patients have been evaluated for tumor detection. However, it remains uncertain whether these AAbs are specific to tumor-associated antigens. In this study, we explored the IgG and IgM autoantibody repertoires in both the in situ tissue microenvironment and peripheral blood as potential tumor-specific biomarkers. We applied high-density protein arrays to profile AAbs in the tumor-infiltrating lymphocyte supernatants and corresponding serum from four patients with CRC, as well as in the serum of three noncancer controls. Our findings revealed that there were more reactive IgM AAbs than IgG in both the cell supernatant and corresponding serum, with a difference of approximately 3-5 times. Immunoglobulin G was predominant in the serum, while IgM was more abundant in the cell supernatant. We identified a range of AAbs present in both the supernatant and the corresponding serum, numbering between 432 and 780, with an average of 53.3% shared. Only 4.7% (n = 23) and 0.2% (n = 2) of reactive antigens for IgG and IgM AAbs, respectively, were specific to CRC. Ultimately, we compiled a list of 19 IgG AAb targets as potential tumor-specific AAb candidates. Autoantibodies against one of the top candidates, p15INK4b-related sequence/regulation of nuclear pre-mRNA domain-containing protein 1A (RPRD1A), were significantly elevated in 53 CRC patients compared to 119 controls (p < 0.0001). The project revealed that tissue-derived IgG AAbs, rather than IgM, are the primary source of tumor-specific AAbs in peripheral blood. It also identified potential tumor-specific AAbs that could be applied for noninvasive screening of CRC.

m 6 Aexpress-BHM: predicting m6A regulation of gene expression in multiple-groups context by a Bayesian hierarchical mixture model.

As the most abundant RNA modification, N6-methyladenosine (m6A) plays an important role in various RNA activities including gene expression and translation. With the rapid application of MeRIP-seq technology, samples of multiple groups, such as the involved multiple viral/ bacterial infection or distinct cell differentiation stages, are extracted from same experimental unit. However, our current knowledge about how the dynamic m6A regulating gene expression and the role in certain biological processes (e.g. immune response in this complex context) is largely elusive due to lack of effective tools. To address this issue, we proposed a Bayesian hierarchical mixture model (called m6Aexpress-BHM) to predict m6A regulation of gene expression (m6A-reg-exp) in multiple groups of MeRIP-seq experiment with limited samples. Comprehensive evaluations of m6Aexpress-BHM on the simulated data demonstrate its high predicting precision and robustness. Applying m6Aexpress-BHM on three real-world datasets (i.e. Flaviviridae infection, infected time-points of bacteria and differentiation stages of dendritic cells), we predicted more m6A-reg-exp genes with positive regulatory mode that significantly participate in innate immune or adaptive immune pathways, revealing the underlying mechanism of the regulatory function of m6A during immune response. In addition, we also found that m6A may influence the expression of PD-1/PD-L1 via regulating its interacted genes. These results demonstrate the power of m6Aexpress-BHM, helping us understand the m6A regulatory function in immune system.

Asymptomatic norovirus infection during outbreaks in China: A systematic review and meta-analysis.

Acute gastroenteritis outbreaks may be caused by the excretion of norovirus (NoV) from asymptomatic individuals. Despite numerous studies involving asymptomatic NoV infection during outbreaks in China, a comprehensive assessment of its role has not been conducted, which is critical for emergency management. Our objective was to assess the prevalence of asymptomatic NoV infection during outbreaks in China. We conducted a comprehensive search of multiple databases, including PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure, China Wanfang, and China Weipu, between January 1, 1997 and June 19, 2023. The retrieved articles and their references underwent screening, which utilized polymerase chain reaction-based assays for the detection of NoV in asymptomatic individuals during outbreaks that occurred in China. The primary summary data were the prevalence of asymptomatic NoV infection in outbreaks. We generated pooled estimates of asymptomatic prevalence in the population as a whole and in subgroups by using random-effect models. Of the 97 articles included, the pooled asymptomatic prevalence of NoV among 5117 individuals in outbreaks was 17.6% (95% confidence interval [CI]: 14.1-21.3). The asymptomatic prevalence of NoV GII (17.1%, 95% CI: 12.9-21.5) was similar to that of NoV GI (22.0%, 95% CI: 12.8-32.4). However, the proportion of asymptomatic individuals involved in NoV GII (57.44%) was significantly higher than that of NoV GI (5.12%), and NoV GII (75.26%) was reported much more frequently than NoV GI (14.43%) in the included articles. Meta-regression analysis of 11 possible influencing factors (geographic region, setting, season, sample type, genotype, transmission route, occupation, age, per capita income, study quality, and cases definition) showed that the source of heterogeneity might be related to the outbreak settings, per capita income, and study quality (p = 0.037, 0.058, and 0.026, respectively). Of particular note was the asymptomatic prevalence peaked in preschoolers (27.8%), afterward, it fell into trough in elementary and junior school children (10.5%), before the second peak located in adults (17.8%), and the elderly (25.2%). Prevalent genotypes reported include GII.4, followed by GII.17, GII.2, GII.3, GII.6, and so forth. The estimated asymptomatic prevalence of NoV during outbreaks in China was as high as 17.6%, with NoV GII dominating. In addition, genetic subtypes of NoV in outbreaks should be detected whenever possible. The role of asymptomatic individuals in NoV outbreaks cannot be ignored. This knowledge will help governments develop public health policies and emergency response strategies for outbreaks, assess the burden, and develop vaccines.

Characterization the prognosis role and effects of snoRNAs in melanoma patients.

Melanoma is a melanocyte-derived malignant cancer and is known for its early metastasis and high mortality rates. It is a highly cutaneous tumour disease that could be related to the abnormal immune microenvironment, and the identification of reliable diagnostic and prognostic markers is crucial for improving patient outcomes. In the search for biomarkers, various types of RNAs have been discovered and recognized as reliable prognostic markers. Among these, small nucleolar RNAs (snoRNAs) have emerged as a promising avenue for studying early diagnosis and prognostic markers in tumours due to their widespread presence in tissues, tumour specificity and stability. In our study, we analysed snoRNAs data from melanoma samples in the TCGA-SKCM cohort and developed a prognostic model comprising 12 snoRNAs (SNORD9, SNORA31, SNORD14E, SNORA14A, SNORA5A, SNORD83A, SNORA75, AL096855, AC007684, SNORD14A, SNORA65 and AC004839). This model exhibited unique prognostic accuracy and demonstrated a significant correlation with the immune infiltration tumour microenvironment. Additionally, analysis of the GSE213145 dataset, which explored the sensitivity and resistance of immune checkpoint inhibitors, further supported the potential of snoRNAs as prognostic markers for immunotherapy. Overall, our study contributes reliable prognostic and immune-related biomarkers for melanoma patients. These findings can offer valuable insights for the future discovery of novel melanoma treatment strategies and hold promise for improving clinical outcomes in melanoma patients.

Cardiovascular health metrics defined by Life's Essential 8 scores and subsequent macrovascular and microvascular complications in individuals with type 2 diabetes: A prospective cohort study.

AIM: To investigate the association between cardiovascular health metrics defined by Life's Essential 8 (LE8) scores and vascular complications among individuals with type 2 diabetes (T2D). MATERIALS AND METHODS: This prospective study included 11 033 participants with T2D, all devoid of macrovascular diseases (including cardiovascular and peripheral artery disease) and microvascular complications (e.g. diabetic retinopathy, neuropathy and nephropathy) at baseline from the UK Biobank. The LE8 score comprised eight metrics: smoking, body mass index, physical activity, non-high-density lipoprotein cholesterol, blood pressure, glycated haemoglobin, diet and sleep duration. Cox proportional hazards models were established to assess the associations of LE8 scores with incident macrovascular and microvascular complications. RESULTS: During a median follow-up of 12.1 years, we identified 1975 cases of incident macrovascular diseases and 1797 cases of incident microvascular complications. After adjusting for potential confounders, each 10-point increase in the LE8 score was associated with an 18% lower risk of macrovascular diseases and a 15% lower risk of microvascular complications. Comparing individuals in the highest and lowest quartiles of LE8 scores revealed hazard ratios of 0.55 (95% confidence interval 0.47-0.62) for incident macrovascular diseases, and 0.61 (95% confidence interval 0.53-0.70) for incident microvascular complications. This association remained robust across a series of sensitivity analyses and nearly all subgroups. CONCLUSION: Higher LE8 scores were associated with a lower risk of incident macrovascular and microvascular complications among individuals with T2D. These findings underscore the significance of adopting fundamental strategies to maintain optimal cardiovascular health and curtail the risk of developing diabetic vascular complications.

Upregulated LncRNA H19 Sponges MiR-106a-5p and Contributes to Aldosterone-Induced Vascular Calcification via Activating the Runx2-Dependent Pathway.

BACKGROUND: Excess aldosterone is implicated in vascular calcification (VC), but the mechanism by which aldosterone-MR (mineralocorticoid receptor) complex promotes VC is unclear. Emerging evidence indicates that long-noncoding RNA H19 (H19) plays a critical role in VC. We examined whether aldosterone-induced osteogenic differentiation of vascular smooth muscle cells (VSMCs) through H19 epigenetic modification of Runx2 (runt-related transcription factor-2) in a MR-dependent manner. METHODS: We induced in vivo rat model of chronic kidney disease using a high adenine and phosphate diet to explore the relationship among aldosterone, MR, H19, and VC. We also cultured human aortic VSMCs to explore the roles of H19 in aldosterone-MR complex-induced osteogenic differentiation and calcification of VSMCs. RESULTS: H19 and Runx2 were significantly increased in aldosterone-induced VSMC osteogenic differentiation and VC, both in vitro and in vivo, which were significantly blocked by the MR antagonist spironolactone. Mechanistically, our findings reveal that the aldosterone-activated MR bound to H19 promoter and increased its transcriptional activity, as determined by chromatin immunoprecipitation, electrophoretic mobility shift assay, and luciferase reporter assay. Silencing H19 increased microRNA-106a-5p (miR-106a-5p) expression, which subsequently inhibited aldosterone-induced Runx2 expression at the posttranscriptional level. Importantly, we observed a direct interaction between H19 and miR-106a-5p, and downregulation of miR-106a-5p efficiently reversed the suppression of Runx2 induced by H19 silencing. CONCLUSIONS: Our study clarifies a novel mechanism by which upregulation of H19 contributes to aldosterone-MR complex-promoted Runx2-dependent VSMC osteogenic differentiation and VC through sponging miR-106a-5p. These findings highlight a potential therapeutic target for aldosterone-induced VC.

Current development and structure-activity relationship study of berberine derivatives.

Berberine is a quaternary ammonium isoquinoline alkaloid derived from traditional Chinese medicines Coptis chinensis and Phellodendron chinense. It has many pharmacological activities such as hypoglycemic, hypolipidemic, anti-tumor, antimicrobial and anti-inflammatory. Through structural modifications at various sites of berberine, the introduction of different groups can change berberine's physical and chemical properties, thereby improving the biological activity and clinical efficacy, and expanding the scope of application. This paper reviews the research progress and structure-activity relationships of berberine in recent years, aiming to provide valuable insights for the exploration of novel berberine derivatives.

Identifying cooperating cancer driver genes in individual patients through hypergraph random walk.

OBJECTIVE: Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS: Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS: The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION: We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.

Causal Association of Golgi Protein 73 With Coronary Artery Disease: Evidence from Proteomics and Mendelian Randomization.

Background: Identification of the unknown pathogenic factor driving atherosclerosis not only enhances the development of disease biomarkers but also facilitates the discovery of new therapeutic targets, thus contributing to the improved management of coronary artery disease (CAD). We aimed to identify causative protein biomarkers in CAD etiology based on proteomics and 2-sample Mendelian randomization (MR) design. Methods: Serum samples from 33 first-onset CAD patients and 31 non-CAD controls were collected and detected using protein array. Differentially expressed analyses were used to identify candidate proteins for causal inference. We used 2-sample MR to detect the causal associations between the candidate proteins and CAD. Network MR was performed to explore whether metabolic risk factors for CAD mediated the risk of identified protein. Vascular expression of candidate protein in situ was also detected. Results: Among the differentially expressed proteins identified utilizing proteomics, we found that circulating Golgi protein 73 (GP73) was causally associated with incident CAD and other atherosclerotic events sharing similar etiology. Network MR approach showed low-density lipoprotein cholesterol and glycated hemoglobin serve as mediators in the causal pathway, transmitting 42.1% and 8.7% effects from GP73 to CAD, respectively. Apart from the circulating form of GP73, both mouse model and human specimens imply that vascular GP73 expression was also upregulated in atherosclerotic lesions and concomitant with markers of macrophage and phenotypic switching of vascular smooth muscle cells (VSMCs). Conclusions: Our study supported GP73 as a biomarker and causative for CAD. GP73 may involve in CAD pathogenesis mainly via dyslipidemia and hyperglycemia, which may enrich the etiological information and suggest future research direction on CAD.

Navigating appendicitis care during the Covid-19 pandemic: a retrospective cohort study in China.

BACKGROUND: The emergence of the COVID-19 pandemic in December 2019 initiated a global transformation in healthcare practices, particularly with respect to hospital management. PCR testing mandates for medical treatment seekers were introduced to mitigate virus transmission. AIMS: This study examines the impact of these changes on the management of patients with appendicitis. METHODS: We conducted a retrospective analysis of medical records for 748 patients diagnosed with appendicitis who underwent surgery at a tertiary care hospital during two distinct periods, the pre-pandemic year 2019 and the post-pandemic year 2021. Patient demographics, clinical characteristics, laboratory data, surgical outcomes, and hospital stay duration were assessed. RESULTS: While no significant differences were observed in the general characteristics of patients between the two groups, the time from hospital visit to operation increased significantly during the pandemic. Unexpectedly, delayed surgical intervention was associated with shorter hospital stays but did not directly impact complication rates. There was no discernible variation in the type of surgery or surgical timing based on symptom onset. The pandemic also prompted an increase in appendicitis cases, potentially related to coronavirus protein expression within the appendix. CONCLUSIONS: The COVID-19 pandemic has reshaped the landscape of appendicitis management. This study underscores the complex interplay of factors, including changes in hospital protocols, patient concerns, and surgical timing. Further research is needed to explore the potential link between COVID-19 and appendicitis. These insights are valuable for informing healthcare practices during and beyond the pandemic.

Report on Pseudoaneurysm Caused by Injury of Internal Carotid Artery During Endoscopic Pituitary Surgery and Rebleeding After Treatment With Willis Covered Stent.

OBJECTIVE: Report on a case of pseudoaneurysm which was caused by injury of the internal carotid artery (ICA) during endoscopic endonasal surgery (EES), which was followed by rebleeding after treatment with a Willis covered stent. METHODS: A woman, aged 68, underwent EES for the treatment of a pituitary adenoma. During the surgery, the right ICA was injured, and successfully hemostasis by packed with cottonoid and gelatin sponge. Besides, cerebral angiography was performed in the interventional operating room for the purpose of discovering the formation of a pseudoaneurysm in the cavernous sinus segment of ICA, which was treated with a covered stent. After successfully placing the covered stent, the patient was promptly transferred to the general operating room for the removal of the cottonoid and to address the bleeding once again. The authors employ crushed muscles and cottonoid to locally compress and stop bleeding. Owing to concerns about the risk of rebleeding in the patient, after stent implantation, the patient did not utilize antiplatelet drugs. After the surgery, the patient developed occlusion of the right ICA and massive cerebral infarction in the right hemisphere. Dehydration, anti-infection, rehabilitation, hyperbaric oxygen, as well as related treatments, were given. The cottonoid was removed in EES 2 months postsurgery, and no instances of bleeding were observed. Six months after surgery, the patient had clear consciousness and hemiplegia in the left limb, with a Glasgow Outcome Scale score of 4. RESULTS: The ICA was injured during EES, which resulted in the formation of a pseudoaneurysm, the Willis stent was adopted for treatment, and there was a risk of rebleeding after the nasal packing (cottonoid, crushed muscles) was removed immediately. CONCLUSIONS: The ICA was injured during EES after bleeding was controlled by packing with cottonoid, crushed muscles, etc, subsequently, the patient was given intravascular treatment, it is advised to make thorough preparations and, after a suitable period, remove nasal packing in the hybrid operating room to address unexpected situations and unforeseen circumstances.