International Journal of General Medicine最新文献

Purpose: To evaluate the use of contrast enhanced mammography (CEM) in suspicious microcalcifications and to discuss strategies to cope with its diagnostic limitations.

Methods: We retrospectively evaluated patients with suspicious calcifications who underwent CEM at our institution. We collected and analyzed morphological findings, enhancement patterns and pathological findings of suspicious microcalcifications on CEM. A small proportion of these cases underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The enhancement patterns of CEM in this study were classified into three categories: enhancement, no enhancement, and indeterminate. CEM imaging was independently analyzed by two breast imaging specialists.

Results: A total of 44 patients with 46 lesions were collected from January 2022 to July 2024. Overall, 20 cases (43.5%) microcalcifications showed enhancement on CEM; 23 (50.0%) showed no enhancement; and 3 (6.5%) were indeterminate. Of the 20 enhancement cases, mass enhancement was seen in 9 (45%), and non-mass enhancement (NME) in 11 (55%). DCE-MRI was performed in 13 cases. One case of invasive ductal carcinoma (IDC) showed enhancement on MRI but was indeterminate on CEM due to the masking effect of background parenchymal enhancement (BPE), and one case of ductal carcinoma in situ (DCIS) lacked enhancement on CEM but had significant enhancement on MRI.

Conclusion: CEM provides additional information on the enhancement associated with breast suspicious microcalcifications. It is not perfect for diagnosis and strategies are needed to cope with its limitations.

[This corrects the article DOI: 10.2147/IJGM.S461352.].

Introduction: With the incorporation of artificial intelligence (AI), significant advancements have occurred in the field of fetal medicine, holding the potential to transform prenatal care and diagnostics, promising to revolutionize prenatal care and diagnostics. This scoping review aims to explore the recent updates in the prospective application of AI in fetal medicine, evaluating its current uses, potential benefits, and limitations.

Methods: Compiling literature concerning the utilization of AI in fetal medicine does not appear to modify the subject or provide an exhaustive exploration of electronic databases. Relevant studies, reviews, and articles published in recent years were incorporated to ensure up-to-date data. The selected works were analyzed for common themes, AI methodologies applied, and the scope of AI's integration into fetal medicine practice.

Results: The review identified several key areas where AI applications are making strides in fetal medicine, including prenatal screening, diagnosis of congenital anomalies, and predicting pregnancy complications. AI-driven algorithms have been developed to analyze complex fetal ultrasound data, enhancing image quality and interpretative accuracy. The integration of AI in fetal monitoring has also been explored, with systems designed to identify patterns indicative of fetal distress. Despite these advancements, challenges related to the ethical use of AI, data privacy, and the need for extensive validation of AI tools in diverse populations were noted.

Conclusion: The potential benefits of AI in fetal medicine are immense, offering a brighter future for our field. AI equips us with tools for enhanced diagnosis, monitoring, and prognostic capabilities, promising to revolutionize the way we approach prenatal care and diagnostics. This optimistic outlook underscores the need for further research and interdisciplinary partnerships to fully leverage AI's potential in driving forward the practice of fetal medicine.

Background: Acute ischemic stroke, especially hemorrhage cerebral infarction (HCI), resulted in the leading causes of mortality and long-term disability across populations. However, fewer researches have focused on the risk factors of first admission and recurrence of HCI.

Methods: The study included 1857 patients who underwent cerebral infarction with or without hemorrhagic transformation. Clinical characteristics were collected, and univariate and multivariate analysis were performed to explore the risk factors. The subgroup analysis of cerebral infarction recurrence was performed. ROC analysis was utilized, and AUCs were showed the diagnostic values of the risk factors.

Results: Compared to the patients with non-hemorrhage cerebral infarction, the patients with hemorrhage cerebral infarction were older and had higher Neutrophil infiltration, AST expression, globulin and BUN, while had lower ALT expression, triglyceride, PT, APTT, homocysteine, d-dimer, CRP and glycosylated hemoglobin. Utilizing univariate and multivariate analysis, age, thrombolytic, Hb, AST and glycosylated hemoglobin were the risk factors between the patients with hemorrhagic cerebral infarction and non-hemorrhagic cerebral infarction. ROC analysis was performed to demonstrate that glycosylated hemoglobin was a diagnostic biomarker for the patients with hemorrhagic cerebral infarction and non-hemorrhagic cerebral infarction (AUC = 0.808). Utilizing univariate and multivariate analysis, age, hypertension history, LDL and MRS Score on admission were the risk factors between non-hemorrhagic cerebral infarction patients with first admission or the cerebral infarction recurrence. ROC analysis was performed to demonstrate MRS Score on admission was a diagnostic biomarker for recurrence of cerebral infarction in patients with non-hemorrhagic cerebral infarction (AUC = 0.708). Utilizing univariate and multivariate analysis, only hypertension history was the risk factors between hemorrhagic cerebral infarction patients with first admission or the cerebral infarction recurrence.

Conclusion: In conclusion, age, hypertension history, LDL and MRS Score on admission were the risk factors between cerebral infarction patients with first admission or the cerebral infarction recurrence.

Background: Aggressive biological behavior leads to unfavorable survival of colorectal cancer (CRC) patients. Dysregulation of TXNIP has been reported to be associated with the occurrence, proliferation and metastasis of malignancies such as liver cancer, lung cancer, kidney cancer, gastric cancer, and pancreatic cancer. MiR-424-5p has been reported as a negative regulator of TXNIP involved in lipopolysaccharide-induced acute kidney injury. And disordered Hippo pathway and YAP/TAZ-TEAD activity are related to tumor progression. The study was designed to clarify the function of miR-424-5p and thioredoxin interacting protein (TXNIP) in the progression of CRC.

Methods: The expression pattern of TXNIP and miR-424-5p was detected by immunohistochemistry, qRT-PCR and/or Western blotting. CCK-8 assays and transwell assays were applied to investigate the effect of TXNIP and miR-424-5p on cell proliferation, invasion and migration. Luciferase reporter assays were used to verify the transcriptional regulation among TXNIP, miR-424-5p and Hippo signaling pathway.

Results: TXNIP was poorly expressed whereas miR-424-5p was highly expressed in CRC tissues and cells. TXNIP overexpression suppressed proliferation, invasion and migration of CRC cells. It also suppressed the malignant behavior of the CRC cells promoted by miR-424-5p. Mechanically, TXNIP overexpression significantly inhibited YAP/TAZ transcriptional activity, and the highly expressed miR-424-5p in CRC targeted TXNIP mRNA.

Conclusion: The study clarify a novel miR-424-5p/TXNIP/Hippo signaling pathway that facilitated CRC cells proliferation, migration and invasion. The above findings suggested that miR-424-5p and TXNIP might serve as the potential therapeutic targets for CRC patients.

Purpose: People with fatty liver are at high risk for pulmonary nodules, but the underlying mechanism is unclear. This study aimed to investigate the occurrence of lung nodules in fatty liver patients and explore influencing factors.

Patients and methods: We retrospectively analyzed 57,119 individuals who underwent health checkups at the People's Hospital of Guangxi from May 2020 to May 2024. Patients with fatty liver were divided into pulmonary nodule and no pulmonary nodule groups. Univariate and multifactorial analyses were conducted using physical examination data, laboratory test indexes, and imaging information. Logistic regression analysis was used to identify independent predictors of pulmonary nodules in fatty liver patients.

Results: A total of 20,042 patients with fatty liver were included in the study, with 12,334 (61.5%) in the lung nodule group and 7708 (38.5%) in the non-lung nodule group. Age, gender, systolic and diastolic blood pressure were significantly higher in the pulmonary nodule group, while body weight, waist circumference, hemoglobin, uric acid, and glutamyltransferase were lower. Multifactorial logistic regression analysis showed that male gender, body weight, age, and diastolic blood pressure were significant factors influencing lung nodule development in fatty liver patients.

Conclusion: Fatty liver disease is independently associated with an increased incidence of pulmonary nodules, highlighting its importance in lung cancer screening and prevention.

Purpose: Acute coronary syndrome (ACS), comprising unstable angina and acute myocardial infarction, is the most dangerous and fatal form of coronary heart disease. This study evaluates serum bile acids (BAs) and amino acids (AAs) as potential predictors of AMI in UA patients.

Patients and methods: A total of 72 Non-Coronary Artery Disease (NCAD) patients, 157 UA patients, and 79 AMI patients were analyzed. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) measured 15 bile acids and 19 amino acids. The data was split into training and validation sets (7:3). Univariate and multivariate analyses were performed. Diagnostic value and clinical benefits were assessed using receiver operating characteristic (ROC) curves, decision curve analysis, and metrics such as the area under the curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

Results: Orthogonal partial least squares discriminant analysis (OPLS-DA) of serum BAs and AAs effectively differentiated NCAD, UA, and AMI groups. The differences in serum BA and AA profiles between UA and AMI patients were primarily driven by four metabolites: deoxycholic acid (DCA), histidine (His), lysine (Lys), and phenylalanine (Phe). Together, they had an AUC of 0.830 (0.768 in the validation cohort) for predicting AMI in UA patients. After adjusting for multiple confounding factors, DCA, His, Lys, and Phe were independent predictors distinguishing UA from AMI. The results of AUC, IDI, and NRI showed that adding these four biomarkers to a model with clinical variables significantly improved predictive value, which was confirmed in the validation cohort.

Conclusion: These findings highlight the association of DCA, His, Lys, and Phe with AMI, suggesting their potential role in AMI pathogenesis.

Background: This study analyzed the expression and diagnostic value of hsa_circ_101209 in plasma of pregnant women with in deep vein thrombosis (DVT).

Methods: By circRNA microarray detection and GO/KEGG analysis, hsa_circ_14797 targeting miRNA-mRNA network was predicted. Sixty women with DVT were selected as the DVT group, and 60 women without DVT as the non-DVT group. hsa_circ_14797 in plasma was detected, as well as D-dimer (D-D) concentration and P-selectin expression. Target genes that may be regulated by hsa_circ_14797 were predicted, and GO analysis and KEGG pathway enrichment analysis were performed.

Results: hsa_circ_14797 was highly expressed in DVT. hsa_circ_14797 in plasma of DVT patients was positively correlated with D-D (r = 0.358, P = 0.001). The AUC of plasma hsa_circ_14797, D-D, and P-selectin for maternal DVT diagnosis were 0.787 (95% CI: 0.710-0.864), 0.882 (95% CI: 0.821-0.943), and 0.825 (95% CI: 0.754-0.895), respectively. The AUC of hsa_circ_14797 combined with D-D was 0.886 (95% CI: 0.828-0.944). The AUC of hsa_circ_14797 combined with P-selectin was 0.904 (95% CI: 0.853-0.954). The AUC of P-selectin combined with D-D was 0.935 (95% CI: 0.893-0.978). The AUC of hsa_circ_14797 combined with D-D and P-selectin was 0.953 (95% CI: 0.920-0.986). The functions of hsa_circ_14797 included the biological processes of angiogenesis, vascular development, and vascular morphology. The enrichment pathways included PI3K-Akt pathway, TGF-β pathway, and cytokine-cytokine receptor interaction.

Conclusion: hsa_circ_14797, D-D, and P-selectin in plasma of DVT pregnant patients are increased, and hsa_circ_14797 in plasma is positively correlated with D-D. hsa_circ_14797 combined with D-D and P-selectin can improve the accuracy of diagnosis of DVT and contribute to the early diagnosis of DVT.

Objective: This study aimed to analyze the changes in insomnia characteristics among the general population and explore associated factors during the COVID-19 pandemic and post-pandemic periods.

Methods: A cross-sectional study was conducted using an anonymous online survey. Questionnaires were administered at two-time points (T1: March 1-31, 2022; T2: March 1-31, 2023), which included an Insomnia Severity Index (ISI) and questions related to sleep risk factors, including the COVID-19 pandemic, familial influences, work and study conditions, social activities, physical health, use of electronic devices before sleep, sleep environment, food intake and exercise before sleep, etc. Insomnia characteristics were compared at two points, with logistic regression testing associations with sociodemographic covariates and risk factors. Six machine learning models were employed to develop a predictive model for insomnia, namely logistic regression, random forest, neural network, support vector machine, CatBoost, and gradient boosting decision tree.

Results: The study obtained 2769 and 1161 valid responses in T1 and T2, respectively. The prevalence of insomnia increased from 23.4% in T1 to 34.83% in T2. Univariate analyses indicated the factors of the COVID-19 pandemic, familial influences, social activity, physical health, food intake, and exercise before sleep significantly differed in T1 (p<0.05) between insomnia and non-insomnia groups. In T2, significant differences (p<0.05) were observed between the two groups, including the factors of the COVID-19 pandemic, family structure, work and study conditions, social activity, and physical health status. The random forest model had the highest prediction accuracy (90.92% correct and 86.59% correct in T1 and T2, respectively), while the pandemic was the most critical variable at both time points.

Conclusion: The prevalence and severity of insomnia have worsened in the post-pandemic period, highlighting an urgent need for effective interventions. Notably, the COVID-19 pandemic and physical health status were identified as significant risk factors for insomnia.