Analyzing the correlation between gastroesophageal reflux disease and anxiety and depression based on ordered logistic regression.

Numerous studies have indicated a connection between psychiatric symptoms, specifically anxiety and depression, and gastroesophageal reflux. However, the precise nature of the link between the severity of gastroesophageal reflux disease and the severity of anxiety and depression remains uncertain. Here, we gathered 24-h pH monitoring data and baseline patient information from a cohort of 518 individuals. Additionally, we evaluated their psychological well-being using the Hospital Anxiety and Depression Scale. The relationship between baseline characteristics and varying degrees of anxiety, depression, and gastroesophageal reflux disease (GERD) was assessed using R software version 4.1.3 and logistic regression models. The findings indicate a statistically significant variation in anxiety levels based on gender, as well as a significant disparity in depression groups when considering age and literacy levels. Kruskal-Wallis test analysis revealed a significant positive correlation between the severity of anxiety and depression and the 24-h pH monitoring results in our patient cohort. As the anxiety and depression levels increased, the rank mean for each examination result also increased. Logistic regression modeling analysis showed that a higher anxiety level was associated with a higher level of GERD. In the presence of mild anxiety, there is a statistically significant association with a higher incidence of GERD with an odds ratio (OR) of 2.64 (95% CI 1.50, 4.64). Similarly, the moderately severe anxiety group also exhibits a causal relationship with an increased GERD incidence, with an OR of 6.84 (95% CI 3.92, 12.17). Additionally, moderate to severe depression is associated with a higher incidence of GERD, with an OR of 2.32 (95% CI 1.23, 4.37). The prevalence of GERD was greater among males compared to females (OR 2.29, 95% CI 1.51-3.49). Additionally, an elevated body mass index (BMI) demonstrated a positive correlation with the susceptibility to GERD (OR 1.07, 95% CI 1.01-1.14). Increasing age may promote the occurrence of GERD in patients. These findings may help to provide a better basis for psychological or pharmacological interventions for GERD patients with psychosomatic symptoms in the future, and provide a reference basis for clinical treatment of the disease.

Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study.

BACKGROUND: Ovarian cancer (OV) is a prevalent and deadly disease with high mortality rates. The development of accurate prognostic tools and personalized therapeutic strategies is crucial for improving patient outcomes. METHODS: A graph-based deep learning model, the Ovarian Cancer Digital Pathology Index (OCDPI), was introduced to predict prognosis and response to adjuvant therapy using hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The OCDPI was developed using formalin-fixed, paraffin-embedded (FFPE) WSIs from the TCGA-OV cohort, and was externally validated in two independent cohorts from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and Harbin Medical University Cancer Hospital (HMUCH). RESULTS: The OCDPI showed prognostic ability for overall survival prediction in the PLCO (HR, 1.916; 95% CI, 1.380-2.660; log-rank test, P < 0.001) and HMUCH (HR, 2.796; 95% CI, 1.404-5.568; log-rank test, P = 0.0022) cohorts. Patients with low OCDPI experienced better survival benefits and lower recurrence rates following adjuvant therapy compared to those with high OCDPI. Multivariable analyses, adjusting for clinicopathological factors, consistently identified OCDPI as an independent prognostic factor across all cohorts (all P < 0.05). Furthermore, OCDPI performed well in patients with low-grade tumors or fresh-frozen slides, and could differentiate between HRD-deficient or HRD-intact patients with and without sensitivity to adjuvant therapy. CONCLUSION: The results from this multicenter cohort study indicate that the OCDPI may serve as a valuable and labor-saving tool to improve prognostic and predictive clinical decision-making in patients with OV.

Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell-free DNA.

Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson's r = 0.64, P = 0.006 for the TCGA cohort and r = 0.51, P = 0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and nonmalignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75 to 0.99, and prediction accuracy of 0.89 and ranging from 0.66 to 0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61 to 1.00, and prediction accuracy of 0.82 and ranging from 0.63 to 0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.

miR-708 and miR-335-3p Inhibit the Apoptosis of Retinal Ganglion Cells Through Suppressing Autophagy.

This study aimed to clarify the regulation role of miR-708 and miR-335-3p in retinal ganglion cell (RGC) autophagy and apoptosis in glaucoma. Chronic glaucoma mice were established by laser photocoagulation. RGCs were isolated and transfected with a series of plasmids and the cultured in 60 mmHg pressure. miR-335-3p, miR-708, and ATG3 mRNA expressions were detected by qRT-PCR. Protein levels of ATG3, autophagy-related protein LC3, and p62 were detected by Western blot. The apoptosis of RGCs was detected by flow cytometry. The regulation role of miR-335-3p/miR-708 in ATG3 was confirmed by the dual-luciferase reporter gene. The expressions of several miRNAs were measured in retinal tissues from chronic glaucoma mice and RGCs under pressure conditions, and results showed that both miR-335-3p and miR-708 were down-regulated. Besides, the inhibition of miR-708 and miR-335-3p induced the apoptosis of RGCs through promoting autophagy. Also, miR-708 and miR-335-3p could bind to ATG3 and targeted regulated ATG3. Furthermore, the interference with miR-708/miR-335-3p induced RGC apoptosis by up-regulating ATG3 to promote autophagy. In general, the down-regulation of miR-708 and miR-335-3p contributed to the apoptosis of RGCs through promoting autophagy in glaucoma.

Large population-based study using the SEER database: is endoscopic resection appropriate for early gastric cancer patients in the United States?

AIM: To investigate whether endoscopic treatment is applicable to American patients and explores the predictors of lymph node metastasis (LNM) in early gastric cancer (EGC). METHODS: Patients with EGC confined to either mucosa (T1a, n = 1799) and submucosa (T1b, n = 1689) were identified from the Surveillance, Epidemiology, and End Result database. Multivariate logistic regression, Kaplan-Meier method, and univariate/multivariate Cox regression were used to assess the correlation between invasion depth and LNM or prognosis. A nomogram for predicting LNM was constructed and internally validated. RESULTS: EGC limited to T1a exhibited a 2.4% incidence of LNM, which increased to 11.1% when the depth invaded T1b.LNM was present at 1.4%, 5.2%, and 5.0% for sizes ≤2, 2-5, and >5cm of low-grade T1a EGC, respectively, (p = .019) and at 4.8%, 12.4%, and 28.6% of T1b EGC, respectively (p < .001).The multivariate logistic model revealed that older age, T1b invasion, larger tumor size, and high-grade lesions were associated with a higher risk of LNM. Moreover, the T1a EGC patients had better cancer-specific survival (OS) and overall survival(CSS) compared with the T1b EGC patients (5-year OS: 77.2% versus 67.4%, p < .001; 5-year CSS: 90.6% versus 81.4%, p < .001). The discrimination of the prediction model was 0.745. CONCLUSIONS: Endoscopic treatment may only be suitable for patients in the US population who have low-grade T1a lesions of less than 2 cm in size. Patients with T1a lesions of greater than 2 cm in size, lesions with high-grade, and all T1b lesions may benefit from radical surgical resection with lymphadenectomy.

Abnormal serum carbohydrate antigen 19-9 levels in a patient with splenic retiform haemangioendothelioma concomitant with hepatic amyloidosis: A case report.

BACKGROUND: Carbohydrate antigen 19-9 (CA 19-9) is a glycoprotein that is used as a reliable tool for monitoring pancreatic cancer. Serum CA 19-9 levels are increased in patients suffering from liver, lung, and other non-malignant diseases. Haemangioendothelioma is a vascular neoplasm with a borderline biological behaviour. However, no case of haemangioendothelioma has yet been reported to be associated with CA 19-9. CASE SUMMARY: A 54-year-old Chinese man was referred to our hospital for discontinuous fatigue and unintentional weight loss for over one year. Laboratory investigations revealed an elevated serum CA 19-9 concentration of 39 IU/mL (reference interval, 0-37 IU/mL) over one year before admission. Afterwards, coagulopathy appeared, and the patient's serum CA 19-9 concentration increased continuously. At the time of admission, abdominal pain and haemorrhagic shock burst occurred, and emergency medical operation was performed. Laboratory investigations conducted upon admission showed a serum CA19-9 concentration of 392.56 IU/mL. Surgical resection of the spleen was undertaken, and pathological examination showed retiform haemangioendothelioma. The patient developed jaundice ten days after surgical excision of the spleen. Pathological examination of needle biopsy samples of the liver yielded a diagnosis of hepatic amyloidosis. CONCLUSION: We describe a rare case of splenic retiform haemangioenthelioma concomitant with hepatic amyloidosis. Physicians should note abnormal serum CA 19-9 levels with early symptoms of fatigue and unintentional weight loss.

The antibiotic jinggangmycin increases brown planthopper (BPH) fecundity by enhancing rice plant sugar concentrations and BPH insulin-like signaling.

The brown planthopper (BPH), Nilaparvata lugens, is a resurgent pest with an unexpected response to jinggangmycin (JGM), a broadly applied antibiotic used to control rice sheath blight disease. JGM stimulates BPH fecundity, but the underlining molecular mechanisms remain unclear. Here we report that JGM sprays led to increased glucose concentrations, photosynthesis and gene expression, specifically Rubsico, sucrose phosphate synthase, invertase 2 (INV2) and INV3 in rice plants. JGM sprays led to high-glucose rice plants. Feeding BPH on these plants led to increased insulin-like signaling and vitellogenin synthesis. Treating BPH with metformin, a gluconeogenesis inhibitor, reversed the influence of feeding on high-glucose rice, which was rescued by glucose injections. Silencing insulin-like peptide 2 using per os dsRNA led to reduction in juvenile hormone (JH) III titers and other fecundity parameters, which were reversed by topical applications of the JH analog, methoprene. We infer that JGM acts via two broad mechanisms, one through increasing rice plant sugar concentrations and a second by upregulating BPH insulin-like signaling.

MicroRNA-145 regulates immune cytokines via targeting FSCN1 in Staphylococcus aureus-induced mastitis in dairy cows.

Dairy cow mastitis is a detrimental factor in milk quality and food safety. Mastitis generally refers to inflammation caused by infection by pathogenic microorganisms. Our studies in recent years have revealed the role of miRNA regulation in Staphylococcus aureus-induced mastitis. In the present study, we overexpressed and suppressed miR-145 to investigate the function of miR-145 in Mac-T cells. Flow cytometry, ELISA and EdU staining were used to detect changes in the secretion of several Mac-T cytokines and in cell proliferation. We found that overexpression of miR-145 in Mac-T cells significantly reduced the secretion of IL-12 and TNF-α, but increased the secretion of IFN-γ; the proliferation of bovine mammary epithelial cells was also inhibited. Using quantitative real-time PCR (qRT-PCR), Western blotting and luciferase multiplex verification techniques, we found that miR-145 targeted and regulated FSCN1. Knock-down of FSCN1 significantly increased the secretion of IL-12, while the secretion of TNF-α was significantly downregulated in Mac-T cells. Upon S. aureus infection of mammary gland tissue, the body initiated inflammatory responses; Bta-miR-145 expression was downregulated, which reduced the inhibitory effect on the FSCN1 gene; and upregulation of FSCN1 expression promoted mammary epithelial cell proliferation to allow the recovery of damaged tissue. The results of the present study will aid in understanding the immune mechanism opposing S. aureus infection in dairy cows and will provide a laboratory research basis for the prevention and treatment of mastitis.

Enhanced community structure detection in complex networks with partial background information.

Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. However, there is still not a clear and widely-accepted definition of community structure, and in practice, different models may give very different results of communities, making it hard to explain the results. In this paper, different from the traditional methodologies, we design an enhanced semi-supervised learning framework for community detection, which can effectively incorporate the available prior information to guide the detection process and can make the results more explainable. By logical inference, the prior information is more fully utilized. The experiments on both the synthetic and the real-world networks confirm the effectiveness of the framework.