T-cell exhaustion-related genes in Graves' disease: a comprehensive genome mapping analysis.

Background: T-cell exhaustion (Tex) can be beneficial in autoimmune diseases, but its role in Graves' disease (GD), an autoimmune disorder of the thyroid, remains unknown. This study investigated Tex-related gene expression in GD patients to discern the potential contributions of these genes to GD pathogenesis and immune regulation. Methods: Through gene landscape analysis, a protein-protein interaction network of 40 Tex-related genes was constructed. mRNA expression levels were compared between GD patients and healthy control (HCs). Unsupervised clustering categorized GD cases into subtypes, revealing distinctions in gene expression, immune cell infiltration, and immune responses. Weighted gene co-expression network analysis and differential gene expression profiling identified potential therapeutic targets. RT-qPCR validation of candidate gene expression was performed using blood samples from 112 GD patients. Correlations between Tex-related gene expression and clinical indicators were analyzed. Results: Extensive Tex-related gene interactions were observed, with six genes displaying aberrant expression in GD patients. This was associated with atypical immune cell infiltration and regulation. Cluster analysis delineated two GD subtypes, revealing notable variations in gene expression and immune responses. Screening efforts identified diverse drug candidates for GD treatment. The Tex-related gene CBL was identified for further validation and showed reduced mRNA expression in GD patients, especially in cases of relapse. CBL mRNA expression was significantly lower in patients with moderate-to-severe thyroid enlargement than in those without such enlargement. Additionally, CBL mRNA expression was negatively correlated with the disease-specific indicator thyrotropin receptor antibodies. Conclusion: Tex-related genes modulate GD pathogenesis, and their grouping aids subtype differentiation and exploration of therapeutic targets. CBL represents a potential marker for GD recurrence.

Triiodothyronine (T3) promotes browning of white adipose through inhibition of the PI3K/AKT signalling pathway.

Obesity arises from an imbalance between energy consumption and energy expenditure, and thyroid hormone levels serve as a determinant of energy expenditure. We conducted experiments at the animal and cellular levels and combined those findings with clinical data to elucidate the role of triiodothyronine (T3) in facilitating the browning of white adipose tissue (WAT) and its underlying mechanism. The results showed (i) the impaired metabolic function of local WAT and the compensatory elevation of systemic thermogenesis in obesity; (ii) T3 treatment of white adipocytes in vitro and local WAT in vivo induced a shift towards a morphologically "brown" phenotype, accompanied by upregulation of mRNA and protein expression of browning-related and mitochondrial function markers, which suggest that T3 intervention promotes the browning of WAT; and (iii) the aforementioned processes could be modulated through inhibition of the PI3K/AKT signalling pathway; however, whether T3 affects the PI3K/AKT signalling pathway by affecting insulin signalling remains to be studied and clarified. The results of our study indicate that T3 treatment promotes browning of WAT through inhibition of the PI3K/AKT signalling pathway; these findings offer novel perspectives regarding the potential of localised therapies for addressing WAT volume in individuals with obesity.

Identification and functionalization of thyrotropin receptor antibodies with different antigenic epitopes.

One of the sensitive markers for autoimmune thyroid disease (AITD) clinical identification is thyroid-stimulating hormone receptor antibodies (TRAbs). To quickly distinguish TRAb with distinct antigenic epitopes, a straightforward and uncomplicated technique has not yet been created. The objective of this study is to search for molecular diagnostic targets for different types of AITD {Graves' disease (GD), Graves' orbitopathy (GO), GD with third-degree goiter [GD(3)], hypothyroidism combined with positive TRAb [HT(TRAb+)]} as molecular diagnostic targets. Following action on thyroid cells, differential genes (DEGs) generated by TRAb with distinct antigenic epitopes were detected and identified by RNA sequencing (RNA-Seq), bioinformatics analysis, and quantitative reverse transcription-polymerase chain reaction (RT-qPCR) in the serum of patients with AITD. Using the 5-ethynyl-2'-deoxyuridine (EdU) assay, the effect of coculturing thyroid cells with different antigenic TRAb epitopes on the cells' capacity to proliferate was investigated. Bioinformatics analysis and RT-qPCR validation identified one GD key gene alpha 2-HS glycoprotein (AHSG), two GO key genes [adrenoceptor alpha 1D (ADRA1D) and H2B clustered histone 18 (H2BC18)], two GD(3) key genes [suppressor of cytokine signaling 1 (SOCS1) and cytochrome b-245 beta (CYBB)], and one HT(TRAb+) key gene (MASP2). Correlation analysis and ROC curves showed that the abovementioned genes could be used as molecular diagnostic targets for different types of AITD. Finally, EdU results showed that TRAb inhibited thyroid cell proliferation in the HT(TRAb+) group compared with the normal control group, whereas the remaining three groups promoted thyroid cell proliferation, with a statistically significant difference (P < 0.05). We identified six key genes for different types of AITD, which have diagnostic value for different types of AITD. Meanwhile, we found that TRAbs with different antigenic epitopes in AITD have different biological functions.NEW & NOTEWORTHY We identified six molecular targets of different types of AITD [GD, GO, GD(3), and HT(TRAb+)], which have diagnostic value for different types of AITD. Meanwhile, we found that TRAb with different antigenic epitopes extracted from the sera of patients with AITD had different biological functions, which also provided a new idea for further research on the mechanism of action of TRAb with different antigenic epitopes in AITD.

The correlation between blood lipids and intrahepatic cholestasis syndrome during pregnancy.

BACKGROUND: To analyse changes in lipid levels during the development of intrahepatic cholestasis of pregnancy (ICP) and identify new biomarkers for predicting ICP. METHODS: A retrospective case-control study was conducted to analyse 473 pregnant women who underwent regular prenatal examinations and delivered at the Women and Children's Hospital, School of Medicine, Xiamen University, between June 2020 and June 2023, including 269 normal pregnancy controls and 204 pregnant women with cholestasis. RESULTS: Patients with ICP with gestational diabetes mellitus (GDM) have lower high-density lipoprotein (HDL) levels than in those without GDM. Total bile acid (TBA) levels were significantly higher in pregnant women with GDM than those without. The apolipoprotein A (APOA) level was lower in patients with ICP and hypothyroidism than those without hypothyroidism. TBA levels were significantly higher in pregnant women with hypothyroidism than those without. Triglyceride (TG) levels were significantly higher in patients with preeclampsia (PE) than those without. HDL and APOA levels were lower in women with ICP complicated by preterm delivery than those with normal delivery. The AUC (area under the curve) of the differential diagnosis of cholestasis of pregnancy for the APOA/APOB (apolipoprotein B) ratio was 0.727, with a sensitivity of 85.9% and specificity of 47.5%. CONCLUSIONS: The results suggested that dyslipidaemia is associated with an increased risk of ICP and its complications. The timely detection of blood lipid and bile acid levels can assist in the diagnosis of ICP and effectively prevent ICP and other complications.

Intrahepatic cholestasis of pregnancy (ICP) is recognized as one of the most severe complications during pregnancy. Currently, elevated fasting serum total bile acid (TBA) levels are commonly used as diagnostic markers for ICP. However, it has been observed that women diagnosed with ICP often do not exhibit elevated TBA levels. Additionally, other medical conditions can also lead to increased TBA levels. Our study has revealed a potential correlation between abnormal lipid metabolism and the occurrence and progression of ICP and its associated complications. Specifically, we found that patients with ICP who have higher serum bile acid levels tend to have more disrupted lipid metabolism, as well as a higher risk of complications and adverse pregnancy outcomes. This manuscript is the first to investigate the link between dyslipidemia and ICP, as well as other pregnancy complications. As a result, our findings offer a foundation for the clinical diagnosis and treatment of ICP and its comorbidities during pregnancy, while also highlighting the need for further research in this area.

Circular RNA circPHF16 enhances IL-17A expression and secretion by sequestering miR-378a-3p to activate the IL6ST axis in Graves' disease.

Interleukin-17A (IL-17A) plays a pivotal role in the pathogenesis of Graves' disease (GD), an autoimmune disorder affecting thyroid function, but the detailed regulatory mechanisms remain elusive. Circular RNAs (circRNAs) have emerged as key regulators of IL-17A expression and secretion in autoimmune diseases, yet their specific role in GD, especially within CD4 + T lymphocytes, are not well understood. In this study, a circRNA, circPHF16 (hsa_circ_0090364) was found to be highly expressed in the peripheral blood mononuclear cells and serum of GD patients. In vitro experiments in Jurkat T cells revealed that silencing of circPHF16 suppressed IL-17A expression and secretion, while overexpression of circPHF16 had the opposite effect. Furthermore, bioinformatics analysis demonstrated a circPHF16/miR-378a-3p/IL6ST pathway, in which circPHF16 regulates IL6ST expression, which, in turn, influences IL-17A expression and secretion by interacting with miR-378a-3p. In vivo studies in a mouse model of GD showed similar trends in molecular expression levels, consistent with competitive endogenous RNA interactions. Together the results of the study identify circPHF16 as a potential target in the development of new strategies for GD diagnosis and treatment, and thus, offer a theoretical foundation for clinical therapeutic approaches in GD.

Management of refined and personalized newborn blood specimen collection.

Background: Iatrogenic blood loss is an important cause of neonatal anemia. In this study, a spreadsheet tool was developed to reduce blood collection, providing a new idea for the prevention of iatrogenic blood loss in newborns. Methods: Based on hematocrit, minimum test volume and dead volume, a new tool was to calculate the minimum blood collection volume and the number of containers required for the test portfolio. We collected data from October 2022 to October 2023 from Xiamen Maternal and Child Health Hospital for analysis and validation. Results: During this year, there were 16,434 patients and 13,696 plasma/serological samples in the neonatology department. Among them, there were 8 test combinations of greater than 1%, and 9490 samples in total. According to the hospital manual, the recommended amount of blood collection is 27,534 ml and 9490 containers. Through the analysis of this tool, total blood collection was 8864.77 ml, marked qnantity of upward containers (closest level to the calculated blood collection volume) was 10301 ml, and the amount of containers was 8835, which decreased by 67.8%, 62.58% and 6.9% respectively. Besides, if the hematocrit information cannot be obtained in advance and the high hematocrit is calculated as 0.8, the recommended amount of blood collection is 14334.3 ml, and the marked amount of the upward container markering is 17340 ml, decreasing by 47.9% and 37.02% respectively. Conclusion: We have developed an auxiliary tool that can manage neonatal blood specimen collection in a fine and personalized way and can be applied among different laboratory instruments by parameters modification.

Survival benefit of postoperative radioiodine therapy among patients with intermediate-risk differentiated thyroid carcinoma.

BACKGROUND: The 2015 American Thyroid Association (ATA) guidelines proposed the use of the ATA Risk Stratification System and American Joint Committee on Cancer Tumor-Node-Metastasis (AJCC/TNM) Staging System for postoperative radioiodine decision-making. However, the management of patients with intermediate-risk differentiated thyroid carcinoma (DTC) is not well defined. In this study, we aimed to evaluate the therapeutic efficacy of radioactive iodine therapy (RAIT) among various subgroups of patients with intermediate-risk DTC after surgery. METHODS: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015). The DTC patients with intermediate risk of recurrence were divided into two groups (treated or not treated with radioactive iodine (RAI)). As the treatment was not randomly assigned, stabilized inverse probability treatment weighting (sIPTW) was used to reduce selection bias. We used the Kaplan-Meier method and log-rank test to analyze overall survival (OS) and cancer-specific survival (CSS). RESULTS: Kaplan-Meier analysis after sIPTW found a significant difference in OS and CSS between no RAIT and RAIT (log-rank test, P < 0.0001; P = 0.0019, respectively). The Kaplan-Meier curves of CSS in age cutoff of 55 years showed a significant association between no RAIT and RAIT (log-rank test, P = 0.0045). Univariate and multivariate Cox regression showed RAIT was associated with a reduced risk of mortality compared with no RAIT (hazard ratio [HR] 0.59, 95% confidence interval [95% CI 0.44-0.80]). Age (≥ 55) years showed a worse CSS regardless of whether or not a patient was treated or not treated with RAI ([HR] 8.91, 95% confidence interval [95% CI 6.19-12.84]). CONCLUSIONS: RAIT improves OS and CSS in patients with intermediate-risk DTC after surgery. 55 years is a more appropriate prognostic age cutoff for the relevant classification systems and is a crucial consideration in RAI decision-making. Therefore, we need individualized treatment plans.

Serum vitamin D is substantially reduced and predicts flares in diabetic retinopathy patients.

AIMS/INTRODUCTION: Research has suggested that vitamin D deficiency is associated with diabetic retinopathy (DR). Our study aimed to determine whether vitamin D deficiency is the cause of diabetic retinopathy or if diabetic retinopathy reduces vitamin D levels. MATERIALS AND METHODS: Participants with type-2 diabetes were recruited for this prospective observational clinical study and were divided into a diabetic group without retinopathy and a diabetic group with retinopathy, with additional healthy volunteers serving as a control group. The differences in clinical characteristics among the three groups were also compared. Patients without retinopathy were then followed for 1 year to monitor the incidence of diabetic retinopathy. After follow-up, participants were divided into subgroups based on whether diabetic retinopathy occurred. The baseline data of the subgroups were compared, and the independent risk factors were analyzed. RESULTS: Vitamin D levels were generally low. Participants with diabetic retinopathy had significantly lower vitamin D levels than did those without retinopathy (P < 0.01). A comparison of the two subgroups revealed lower baseline vitamin D concentrations in the new-DR subgroup than in the non-DR subgroup (P < 0.01). Vitamin D deficiency and elevated HbA1c levels were found to be independent risk factors for diabetic retinopathy (OR = 0.935, 95% CI: 0.867-0.981, P = 0.006; OR = 2.208, 95% CI: 1.764-2.764, P < 0.01). The limit of vitamin D intake according to the receiver-operating characteristic (ROC) curve was 26.01 ng/mL, and the area under the ROC curve was 0.603 (95% CI: 0.559-0.706, P = 0.002). CONCLUSIONS: Vitamin D levels were significantly lower in patients diagnosed with diabetic retinopathy. More importantly, vitamin D deficiency may accelerate the onset of diabetic retinopathy.

Modulation of adipose tissue metabolism by exosomes in obesity.

Obesity and its related metabolic complications represent a significant global health challenge. Central to this is the dysregulation of glucolipid metabolism, with a predominant focus on glucose metabolic dysfunction in the current research, whereas adipose metabolism impairment garners less attention. Exosomes (EXs), small extracellular vesicles (EVs) secreted by various cells, have emerged as important mediators of intercellular communication and have the potential to be biomarkers, targets, and therapeutic tools for diverse diseases. In particular, EXs have been found to play a role in adipose metabolism by transporting cargoes such as noncoding RNAs (ncRNA), proteins, and other factors. This review article summarizes the current understanding of the role of EXs in mediating adipose metabolism disorders in obesity. It highlights their roles in adipogenesis (encompassing adipogenic differentiation and lipid synthesis), lipid catabolism, lipid transport, and white adipose browning. The insights provided by this review offer new avenues for developing exosome-based therapies to treat obesity and its associated comorbidities.

Comparison of in-patient glucose team based management with conventional blood glucose management- a retrospective study from China.

BACKGROUND: Glycemic control for patients with diabetes in the surgical department is often unsatisfactory. Compounding this issue is the fact that conventional glucose management models are often inefficient and difficult to monitor over time. OBJECTIVE: To investigate the impact of inpatient glucose team-based management on glycemic control and hospital days in surgical patients with diabetes. METHODS: A retrospective analysis was conducted on 4156 patients with diabetes in the surgical department who received inpatient management of diabetes at a tertiary medical center from June 2020 to May 2022. Based on whether they received inpatient glucose team-based management, the surgical patients with diabetes were divided into two groups: the inpatient glucose team-based management (GM group, consisting of 1698 participants) and the conventional blood glucose management group (control group, consisting of 2458 participants). We compared the two groups in terms of glycemic control, hospital days, and health-care costs. Multiple logistic regression analysis was performed to build the hospital days prediction model and nomogram. Finally, the performance of the model was evaluated. RESULTS: The rate of glucose detection was higher in the GM group at 2 h postprandial (P < 0.01). The incidence of hypoglycemia and severe hyperglycemia, blood glucose attainment time, pre-operative preparation days, hospital days, and health-care costs were lower in the GM group than in the control group (P < 0.01). The linear regression model revealed that blood glucose attainment time, incidence of hypoglycemia (< 3.9mmol/L), preoperative preparation days, perioperative complications, and health-care costs were the factors influencing the hospital days (Total Point 83.4 points, mean hospital days 9.37 days). Receiver operating characteristic (ROC) curve analysis demonstrated that the nomogram had good accuracy for predicting hospital days (area under the ROC curve 0.83, 95% confidence interval [CI], 0.74 to 0.92). CONCLUSION: Inpatient glucose team-based management demonstrated significant improvements in glycemic control among surgical patients with diabetes, resulting in reduced hospital days and associated costs. The developed nomogram also exhibited promising potential in predicting hospital days, offering valuable clinical applications.

Elevated serum CA199 levels in patients suffering type 2 diabetes vs. various types of cancer.

AIMS: Carbohydrate antigen 199 (CA199) is a standard tumor marker, and recent studies have found elevated in CA199 levels in patients with diabetes. However, there is no systematic measurement and comparison of serum CA199 levels in patients with diabetes and cancer. Here, a detailed description of the changes in serum CA199 levels in patients with type 2 diabetes and various cancers was explored. METHODS: A total of 5,641 participants were screened for clinical laboratory test results of serum CA199 levels over the past three years (2020-2023). This study included 2,464 healthy controls, 688 patients with type 2 diabetes, and 2,489 patients with 16 different types of cancer. Each type of cancer had more than 30 independent serum CA199 level test results. The serum CA199 levels were compared between cancer groups, type 2 diabetes patients, and healthy controls. Additionally, the CA199 levels of cancer patients were compared with those of patients with type 2 diabetes. RESULTS: The serum CA199 levels of esophagus cancer, lung cancer, pancreatic cancer, ovarian cancer, breast cancer, rectum cancer, prostate cancer, bladder cancer, liver cancer, gastric cancer, cervical cancer, colon cancer, lymphoma, thyroid cancer, intracranial tumors, and nasopharyngeal laryngeal cancer were found to be elevated compared to healthy controls (P < 0.01). In addition, the serum CA199 levels of patients with type 2 diabetes were also significantly elevated compared to healthy controls (P < 0.01). Moreover, the degree of elevation in serum CA199 levels in patients with type 2 diabetes was not significantly different from that observed in some types of cancer, such as esophagus cancer (P = 0.163), breast cancer (P = 0.927), prostate cancer (P = 1.000), bladder cancer (P = 0.406), Lymphoma (P = 0.975), thyroid cancer (P = 1.000), intracranial tumors (P = 0.161), nasopharyngeal and laryngeal cancer (P = 1.000). CONCLUSIONS: Serum CA199 levels also increase in type 2 diabetes, and the magnitude of the increase is similar to that seen in some cancers.

Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort.

BACKGROUND: The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model may suffer from overfitting or poor interpretability. METHODS: This study employed nine commonly used machine learning methods to construct overweight risk models. The general community are the target of this study, and a total of 10,905 Chinese subjects from Ningde City in Fujian province, southeast China, participated. The best model was selected through appropriate verification and validation and was suitably explained. RESULTS: The overweight risk models employing machine learning exhibited good performance. It was concluded that CatBoost, which is used in the construction of clinical risk models, may surpass previous machine learning methods. The visual display of the Shapley additive explanation value for the machine model variables accurately represented the influence of each variable in the model. CONCLUSIONS: The construction of an overweight risk model using machine learning may currently be the best approach. Moreover, CatBoost may be the best machine learning method. Furthermore, combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Diagnostic biomarker for type 2 diabetic peripheral neuropathy via comprehensive bioinformatics analysis.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common complication of Type 2 diabetes mellitus (T2DM), which frequently results in disabling neuropathic pain and lower-limb amputation. The identification of noninvasive biomarkers for DPN may help early detection and individualized treatment of DPN. METHODS: In this study, we identified differentially expressed genes (DEGs) between DPN and the control based on blood-source (GSE95849) and tissue-source gene expression profiles (GSE143979) from the Gene Expression Omnibus (GEO) database using limma, edgeR, and DESeq2 approaches. KEGGG and GO functional enrichments were performed. Hub genes and their correlation with infiltrating immune cells were analyzed. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to quantify hub gene expression. RESULTS: In total, 144 DEGs between DPN and the control were identified. Functional enrichment revealed that the DEGs were mainly enriched in immune-related pathways like the Fc epsilon receptor Ig signaling pathway. By protein-protein interaction (PPI) network analysis, FCER1G, SYK, ITGA4, F13A1, MS4A2, and PTK2B were screened as hub genes with higher expression in DPN patients, among which half were immune genes (FCER1G, PTK2B, and SYK). RT-qPCR demonstrated that mRNA expression of FCER1G, PTK2B, and SYK was significantly increased in patients with DPN compared with both diabetic nonperipheral neuropathy (DNN) and normal subjects. The area under the receiver operating characteristic (ROC) curve of FCER1G, PTK2B, and SYK was 0.84, 0.81, and 0.73, respectively, suggesting their great advantages as diagnostic biomarkers to predict the progression of neuropathy in T2DM. Further analysis indicated that the expression of FCER1G, PTK2B, and SYK was negatively correlated with the cell proportion of significantly altered resting natural killer cells, T follicular helper cells, and activated mast cells, but positively correlated with monocytes. CONCLUSIONS: Our findings demonstrated FCER1G, PTK2B, and SYK are potential diagnostic biomarkers and therapeutic targets for DPN, which provides new insight into DPN pathogenesis and therapies.

Predicting risk of obesity in overweight adults using interpretable machine learning algorithms.

Objective: To screen for predictive obesity factors in overweight populations using an optimal and interpretable machine learning algorithm. Methods: This cross-sectional study was conducted between June 2011 and January 2012. The participants were randomly selected using a simple random sampling technique. Seven commonly used machine learning methods were employed to construct obesity risk prediction models. A total of 5,236 Chinese participants from Ningde City, Fujian Province, Southeast China, participated in this study. The best model was selected through appropriate verification and validation and suitably explained. Subsequently, a minimal set of significant predictors was identified. The Shapley additive explanation force plot was used to illustrate the model at the individual level. Results: Machine learning models for predicting obesity have demonstrated strong performance, with CatBoost emerging as the most effective in both model validity and net clinical benefit. Specifically, the CatBoost algorithm yielded the highest scores, registering 0.91 in the training set and an impressive 0.83 in the test set. This was further corroborated by the area under the curve (AUC) metrics, where CatBoost achieved 0.95 for the training set and 0.87 for the test set. In a rigorous five-fold cross-validation, the AUC for the CatBoost model ranged between 0.84 and 0.91, with an average AUC of ROC at 0.87 ± 0.022. Key predictors identified within these models included waist circumference, hip circumference, female gender, and systolic blood pressure. Conclusion: CatBoost may be the best machine learning method for prediction. Combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Association of altered fibrinogen indexes levels as a potential biomarker in determining the possible onset of diabetic retinopathy.

Research suggests that fibrinogen was related to diabetic retinopathy (DR). Then, the relationship between functional indices of fibrinogen and detailed staging of DR has not been explored. Type 2 diabetic and healthy control subjects (n = 960) were recruited in a cross-sectional study. Participants with type 2 diabetes mellitus were categorized into five stages according to their fundus lesions, and fibrinogen (Fib) and its functional indices (angle α and k value) were measured. The angle α levels increased in diabetic subjects with retinopathy compared with those without, and it was significantly elevated early in retinopathy. In contrast, the k value levels slightly decreased. Despite observing an increase in angle α levels and a decrease in k value levels during the later stages of retinopathy compared to the earlier stages, there was no statistically significant difference in the later stages. The association of the angle α and k value with DR was independent of the hyperglycaemic state and other potential confounders (OR = 1.672, 95% CI 1.489-1.876, P < 0.01; OR = 0.013, 95% CI 0.004-0.041, P < 0.01). The angle α levels and k value levels were closely correlated with retinopathy (r = 0.593, P < 0.00; r = - 0.646, P < 0.01). The ROC curve indicated that the diagnostic value of angle α and k value were (AUC = 0.897, P < 0.001; AUC = 0.859, P < 0.001). Fibrinogen function indexes, such as angle α and k value, may be valuable for the early diagnosis of DR but do not directly assess the severity of DR.

Long noncoding RNAs as potential diagnostic biomarkers for diabetes mellitus and complications: A systematic review and meta-analysis.

AIMS: Long noncoding RNAs (lncRNAs) may be associated with the development of type 2 diabetes mellitus and its complications; however, the findings remain controversial. We aimed to synthesize the available data to assess the diagnostic utility of lncRNAs for identification of type 2 diabetes mellitus and its consequences. MATERIALS AND METHODS: We performed a systematic review and meta-analysis, searching PubMed, Embase, and Web of Science for articles published from September 11, 2015 to December 27, 2022. We evaluated human case-control or cohort studies on differential lncRNA expression in type 2 diabetes mellitus or its associated comorbidities. We excluded studies if they were non-peer reviewed or published in languages other than English. From 2387 identified studies, we included 17 (4685 participants). RESULTS: Analysis of the pooled data showed that lncRNAs had a diagnostic area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87), with a sensitivity of 0.79 (95% CI: 0.74-0.83) and a specificity of 0.75 (95% CI: 0.69-0.80). LncRNAs had an AUC of 0.65 for the diagnosis of prediabetes, with 82% sensitivity and 65% specificity. CONCLUSIONS: LncRNAs may be promising diagnostic markers for type 2 diabetes mellitus and its complications.

Anti-Inflammatory Phomalones from the Deep-Sea-Derived Fungus Trichobotrys effuse FS522.

Four new phomalones A-D (1-4), together with five known analogues (5-9) were isolated from the deep-sea-derived fungus Trichobotrys effuse FS522. Their structures of the new compounds established by analysis of their NMR and HR-ESI-MS spectroscopic data, and the absolute configurations of 2 was determined by electronic circular dichroism (ECD) calculations. compounds 4, 6 and 8 substantially inhibited the production of nitric oxide (NO) with IC50 values of 4.64, 13.90, and 34.07 µM.

TSHR-based chimeric antigen receptor T cell specifically deplete auto-reactive B lymphocytes for treatment of autoimmune thyroid disease.

Graves' disease (GD) is a prominent antibody-mediated autoimmune disorder characterized by stimulating antibodies (TRAb) that target the thyroid-stimulating hormone receptor (TSHR). Targeting and eliminating TRAb-producing B lymphocytes hold substantial therapeutic potential for GD. In this study, we engineered a novel chimeric antigen receptor T cell (CAR-T) therapy termed TSHR-CAR-T. This CAR-T construct incorporates the extracellular domain of the TSH receptor fused with the CD8 transmembrane and intracellular signal domain (4-1BB). TSHR-CAR-T cells demonstrated the ability to recognize and effectively eliminate TRAb-producing B lymphocytes both in vitro and in vivo. Leveraging this autoantigen-based chimeric receptor, our findings suggest that TSHR-CAR-T cells offer a promising and innovative immunotherapeutic approach for the treatment of antibody-mediated autoimmune diseases, including GD.

A novel type of Brassica napus with higher stearic acid in seeds developed through genome editing of BnaSAD2 family.

KEY MESSAGE: Modifications of multiple copies of the BnaSAD2 gene family with genomic editing technology result in higher stearic acid content in the seed of polyploidy rapeseed. Solid fats from vegetable oils are widely used in food processing industry. Accumulating data showed that stearic acid is more favorite as the major composite among the saturate fatty acids in solid fats in considerations of its effects on human health. Rapeseed is the third largest oil crop worldwide, and has potential to be manipulated to produce higher saturated fatty acids as raw materials of solid fats. Toward that end, we identified four SAD2 gene family members in B. napus genome and established spatiotemporal expression pattern of the BnaSAD2 members. Genomic editing technology was applied to mutate all the copies of BnaSAD2 in this allopolyploid species and mutants at multiple alleles were generated and characterized to understand the effect of each BnaSAD2 member on blocking desaturation of stearic acid. Mutations occurred at BnaSAD2.A3 resulted in more dramatic changes of fatty acid profile than ones on BnaSAD2.C3, BnaSAD2.A5 and BnaSAD2.C4. The content of stearic acid in mutant seeds with single locus increased dramatically with a range of 3.1-8.2%. Furthermore, combination of different mutated alleles of BnaSAD2 resulted in more dramatic changes in fatty acid profiles and the double mutant at BnaSAD2.A3 and BnaSAD2.C3 showed the most dramatic phenotypic changes compared with its single mutants and other double mutants, leading to 11.1% of stearic acid in the seeds. Our results demonstrated that the members of BnaSAD2 have differentiated in their efficacy as a Δ9-Stearoyl-ACP-Desaturase and provided valuable rapeseed germplasm for breeding high stearic rapeseed oil.