Spearman Correlation Analysis Research Articles

Feasibility of an artificial intelligence system for tumor response evaluation

PurposeThe objective of this study was to evaluate the feasibility of using Artificial Intelligence (AI) to measure the long-diameter of tumors for evaluating treatment response.MethodsOur study included 48 patients with lung-specific target lesions and conducted 277 measurements. The radiologists recorded the long-diameter in axial imaging plane of the target lesions for each measurement. Meanwhile, AI software was utilized to measure the long-diameter in both the axial imaging plane and in three dimensions (3D). Statistical analyses including the Bland-Altman plot, Spearman correlation analysis, and paired t-test to ascertain the accuracy and reliability of our findings.ResultsThe Bland-Altman plot showed that the AI measurements had a bias of -0.28 mm and had limits of agreement ranging from − 13.78 to 13.22 mm (P = 0.497), indicating agreement with the manual measurements. However, there was no agreement between the 3D measurements and the manual measurements, with P < 0.001. The paired t-test revealed no statistically significant difference between the manual measurements and AI measurements (P = 0.497), whereas a statistically significant difference was observed between the manual measurements and 3D measurements (P < 0.001).ConclusionsThe application of AI in measuring the long-diameter of tumors had significantly improved efficiency and reduced the incidence of subjective measurement errors. This advancement facilitated more convenient and accurate tumor response evaluation.

Evaluation of water richness in coal seam roof aquifer based on factor optimization and random forest method.

Water richness evaluation of coal seam roofs is a crucial prerequisite for preventing and controlling water hazards in coal seam roofs. For this purpose, Spearman correlation and GeoDetector were employed for factor optimization to investigate the significance of lithological and structural factors and the impact of interactions among different factors on the water richness of coal seam roofs. Water richness evaluation models of coal seam roofs were separately established via the entropy weight method (EWM), coefficient of variation method (CVM), and random forest method (RFM), and the predictive accuracies of these models were compared. Eleven lithological and structural factors were collected. Through Spearman correlation analysis, 6 factors were identified to have significant influences on water richness. By utilizing the interaction detection of GeoDetector, the effects of interfactor interactions on water richness were explored, and 3 combination factors were selected. AICc was used to determine the model's superiority. The evaluation results of the study area based on factor optimization and three methods were further compared and validated via pumping tests, workface water inflow tests, and three-dimensional high-density electrical method. The results indicated that the RFM exhibited higher prediction accuracy than did the entropy weight and coefficient of variation methods. Additionally, within each method, factor optimization led to improved model accuracy.

The spatiotemporal evolution characteristics and influencing factors of habitat quality in the typical region of the Lunan Economic Belt: a case study of Donggang District, Rizhao.

Habitat quality is a key indicator for evaluating the biodiversity of a region. This study aims to assess the current status and characteristic changes of habitat quality in Donggang District, Rizhao while analyzing the potential factors influencing the quality of the habitat. The study employed the Integrated Valuation of Ecosystem Services and Tradeoffs Habitat Quality (InVEST-HQ) mode to assess the spatiotemporal variations in habitat quality in the Donggang District from 2008 to 2022. GeoDetector was utilized to investigate the impact of various factors on the spatial differentiation characteristics of habitat quality. The potential correlation between economic development and habitat quality was further explored using the Spearman correlation coefficient and Grayscale Association Analysis. The results reveal that habitat quality in Donggang District is highest in the east and lowest in the west, with significant correlations to land use and vegetation coverage. The study highlights a decline in habitat quality over the period, linked to rapid economic growth and industrial expansion. These insights are crucial for balancing urban biodiversity with economic development.

Multi-omics insights into the microbiota-gut-brain axis and cognitive improvement post-bariatric surgery

BackgroundAlthough numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA).MethodsDemographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors.ResultsLSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05).ConclusionThese findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed.Trial registrationChinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/.

Lower Free Thyroxine Levels Are Associated with Diabetic Kidney Disease in Males with Type 2 Diabetes Mellitus: An Observational Cross-Sectional Study.

Objectives: We aimed to explore the correlation between thyroid function and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Methods: A total of 7516 T2DM patients were enrolled and grouped according to DKD status. Clinical parameters, including blood glucose parameters, thyroid function, and indicators of renal impairment, were collected and compared between the DKD and Non-DKD groups. Correlation analysis and univariate/multivariate logistic regression analyses were performed. Results: Age, T2DM duration, the use of insulin and lipid-lowering drugs, systolic and diastolic blood pressure, body mass index, and fasting blood glucose levels were greater in the DKD group than in the Non-DKD group (p < 0.001). Notably, compared with those in the Non-DKD group, patients in the DKD group had lower triiodothyronine (T3), thyroxine (T4), free triiodothyronine (FT3), and free thyroxine (FT4) levels and higher thyrotropin levels (p < 0.001). Univariate logistic regression analysis revealed that T3, T4, FT3, and FT4 levels were negatively correlated with the risk of DKD. Spearman correlation analysis confirmed that T3, T4, FT3, and FT4 levels were negatively correlated with blood urea nitrogen levels, blood creatinine levels, and the urinary albumin-to-creatinine ratio (p < 0.05). Multivariate logistic regression analysis revealed that a greater FT4 level was a protective factor against DKD in T2DM patients, especially in males, with a cut-off value of 13.35 pmol/L (area under the curve = 0.604). Conclusions: Thyroid hormone levels, especially FT4 levels, were significantly negatively correlated with DKD in T2DM patients.

Public Health Nurses' Competence Related to Long-Term Breastfeeding in the Context of Maternity and Child Health Clinics.

To explore public health nurses' competence (namely knowledge, skills, and attitudes) in relation to long-term breastfeeding and their experience of the need for additional training on the subject. The study design was quantitative, descriptive, and cross-sectional. Public health nurses (n = 270). Data were collected with the Long-Term Breastfeeding Competence Scale (LBCS) online survey. Data analysis was done with Spearman's correlation analysis and binary logistic regression analysis. Slightly more than half of the respondents had a good level of knowledge and skills. The majority had a baseline positive attitude toward long-term breastfeeding, but the attitude became more negative as the age of the breastfed child increased. Better competence was associated with younger age, parenthood, an additional degree in midwifery, and breastfeeding specialist certification. Knowledge and skills, and attitudes revealed a high correlation: the higher the knowledge and skills level, the more positive attitudes. Respondents with better knowledge and skills experienced more often the need for additional training on the subject. This study addresses that public health nurses lack competence in relation to long-term breastfeeding. This may compromise the quality of breastfeeding guidance for families in healthcare settings.

Blood L-cystine levels positively related to increased risk of hypertension.

Hypertension is one component of metabolic syndrome (MetS). Here, the study evaluated hypertension-associated metabolites in relation to other MetS components. Fasting plasma samples were collected from 22 hypertensive and 63 normotensive subjects for non-targeted metabolomics. Compared with normotensive subjects, hypertensive patients were more diabetic (6.3%vs. 36.4%) and had dyslipidemia (27.0%vs. 63.6%) (both p<.05). By non-targeted metabolomics, 758 metabolites in 22 classes were identified and 56 were differentially regulated between hypertensive and normotensive groups. Amongst these 56 metabolites, receiver operating characteristic analysis showed that 14 had an area under the curve above 0.6. Multivariate-adjusted logistic regression analysis demonstrated that per one-fold increase of L-glutmatic acid, L-cystine, (9S,10E,12Z,15Z)-9-Hydroxy-10,12,15-octadecatrienoic acid, deoxyribose 5-phosphate, and falcarinolone, the odds ratios were 3.64, 4.61, 0.26, 0.26, and 0.37 for having the risk of hypertension, respectively. Of five metabolites, by Spearman's correlation analysis, only L-glutmatic acid and L-cystine levels were positively associated with systolic and diastolic blood pressure (all p<.05). Spearman's correlation analysis further revealed that L-glutmatic acid levels were positively correlated with to body mass index (BMI), fasting blood glucose, and serum triglyceride but negatively associated with HDL-c (all p<.05) whereas L-cystine levels were not related to any of these components (p ≥.13). Multivariate-adjusted linear regression analysis confirmed the positive correlation between L-cystine levels and systolic or diastolic blood pressure (β=2.66 for SBP; β=2.50 for DBP; both p<.05). In conclusion, L-cystine could be a potent metabolite for increased risk of hypertension.

Diagnostic value of baseline 18F-FDG PET/CT and peripheral blood inflammatory markers for aggressive lymphoma in non-Hodgkin's lymphoma.

This study aims to investigate the diagnostic value of baseline F18 fluorodeoxyglucose (FDG) PET/computed tomography (CT) parameters and peripheral blood inflammatory markers in aggressive lymphoma of non-Hodgkin lymphoma (NHL) and the correlation between peripheral blood inflammatory markers and maximum standardized uptake value (SUVmax). We conducted a retrospective analysis including 121 patients with NHL. Patients were divided into aggressive lymphoma group and indolent lymphoma group. Mann-Whitney U test, chi-square test and multivariate stepwise logistic regression were used to analyse. Subsequently, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance. Additionally, Spearman correlation analysis was utilized to explore the correlation between peripheral blood inflammatory markers and SUVmax. Leptin mass criterion uptake value (SUL)max, SUVmax, SUVavg, SUVpeak, focal SUVmax/liver SUVmax, focal SUVmax/ mediastinal SUVmax, SULavg, SULpeak, systemic immune-inflammation, neutrophil ratio, total lesion glycolysis, neutrophils versus lymphocyte ratio, platelet-to-lymphocyte ratio, hemoglobin-to-white blood cell ratio, lactate dehydrogenase and lymphocyte ratio between two groups were statistically significant (P < 0.05). SUVmax was an independent influencing factor, and the area under the ROC curve was 0.862. There was a positive correlation between the platelet-to-lymphocyte ratio and SUVmax (r = 0.239; P = 0.008). PET/CT parameters and peripheral blood inflammatory markers have certain value in the diagnosis of aggressive lymphoma in NHL, among which SUVmax is an independent influencing marker and is positively correlated with PLR.

Association between plasma osteopontin level and mild cognitive impairment in patients with type 2 diabetes mellitus

Objective: To analyze the influencing factors of type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI), and to explore the association between plasma osteopontin (OPN) levels and MCI. Methods: A retrospective analysis was conducted on the clinical data of 254 patients with T2DM admitted to Zhongda Hospital Affiliated to Southeast University from October 2021 to May 2023. The patients were divided into MCI group (n=106) and normal cognitive function control group (n=148) according to whether they had MCI. Clinical data were collected, cognitive function was assessed using neurological scales and plasma OPN levels were measured by enzyme linked immunosorbent assay. A multivariate logistic regression model was applied to analyze the influencing factors of MCI in T2DM patients. Interaction terms between gender, age, body mass index (BMI), and OPN were established to verify their significance levels. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive value of OPN for MCI in T2DM patients. The mediation model of OPN-FPG-montreal cognitive assessment(MoCA) was constructed with fasting plasma glucose (FPG) as the mediating variable to test the mediating effect, and the mediating effect percentage was calculated. Results: A total of 254 patients were included, including 162 males and 92 females, with an average age of (61.5±7.5) years old. Compared with the control group, the patients in MCI group were older[63.0(59.0, 69.0) years vs 60.0(54.2, 66.8) years], had a greater proportion of females [(43.4%(46/106) vs 31.1%(46/148)], shorter years of education[12(9, 12) years vs 12(9,15) years], longer duration of diabetes[15.0(8.0, 20.0) years vs 10.0(5.0, 15.0) years], and higher levels of FPG[7.78(6.07, 10.23) mmol/L vs 6.86(5.36, 8.59) mmol/L], insulin resistance index[2.93(2.47, 3.98) vs 2.79(2.27, 3.25)], glycated hemoglobin (HbA1c) [9.24%(7.89%, 10.96%) vs 7.97%(7.00%, 9.45%)], total cholesterol(TC)[(4.51±1.17) mmol/L vs (4.19±0.99) mmol/L], and OPN [11.30(8.68, 12.84) ng/ml vs 9.69(7.82, 11.74) ng/ml] (all P<0.05). The scores of various neuropsychological tests in MCI patients were lower than those in control group with normal cognitive function (all P<0.05). Spearman correlation analysis showed that age(r=-0.212), duration of diabetes mellitus(r=-0.156), duration of hypertension(r=-0.132), FPG(r=-0.207), insulin resistance index(r=-0.171), HbA1c(r=-0.271), OPN(r=-0.238), and total cholesterol (r=-0.125) were negatively correlated with MoCA scores, whereas years of education(r=0.285) were positively correlated with MoCA scores(all P<0.05). Multifactorial logistic regression analysis showed that age, years of education, duration of diabetes mellitus, HbA1c, TC and OPN levels were the influencing factors of T2DM patients with MCI, and the risk of MCI increased by 15% for every 1 ng/ml increase in OPN (OR=1.15, 95%CI: 1.021-1.295, P=0.021), and the relationship was not affected by age, gender and BMI(The interaction effects are all P>0.05). The area under the curve (AUC) of the working curve of subjects with OPN predicting combined MCI in patients with T2DM was 0.612 (95%CI: 0.541-0.682), and the AUC was 0.702 (95%CI: 0.638-0.767) after the combination of HbA1c and OPN. The results of the mediated effect model showed that FPG partially mediated the correlation between OPN and MoCA in T2DM patients, and the mediated effect accounted for 11.34% of the total effect. Conclusions: Plasma OPN level is associated with MCI in patients with T2DM,and the higher the OPN level, the higher the risk of T2DM patients developing MCI.

Unraveling the brain dynamics of Depersonalization-Derealization Disorder: a dynamic functional network connectivity analysis

BackgroundDepersonalization-Derealization Disorder (DPD), a prevalent psychiatric disorder, fundamentally disrupts self-consciousness and could significantly impact the quality of life of those affected. While existing research has provided foundational insights for this disorder, the limited exploration of brain dynamics in DPD hinders a deeper understanding of its mechanisms. It restricts the advancement of diagnosis and treatment strategies. To address this, our study aimed to explore the brain dynamics of DPD.MethodsIn our study, we recruited 84 right-handed DPD patients and 67 healthy controls (HCs), assessing them using the Cambridge Depersonalization Scale and a subliminal self-face recognition task. We also conducted a Transcranial Direct Current Stimulation (tDCS) intervention to understand its effect on brain dynamics, evidenced by Functional Magnetic Resonance Imaging (fMRI) scans. Our data preprocessing and analysis employed techniques such as Independent Component Analysis (ICA) and Dynamic Functional Network Connectivity (dFNC) to establish a comprehensive disease atlas for DPD. We compared the brain's dynamic states between DPDs and HCs using ANACOVA tests, assessed correlations with patient experiences and symptomatology through Spearman correlation analysis, and examined the tDCS effect via paired t-tests.ResultsWe identified distinct brain networks corresponding to the Frontoparietal Network (FPN), the Sensorimotor Network (SMN), and the Default Mode Network (DMN) in DPD using group Independent Component Analysis (ICA). Additionally, we discovered four distinct dFNC states, with State-1 displaying significant differences between DPD and HC groups (F = 4.10, P = 0.045). Correlation analysis revealed negative associations between the dwell time of State-2 and various clinical assessment factors. Post-tDCS analysis showed a significant change in the mean dwell time for State-2 in responders (t-statistic = 4.506, P = 0.046), consistent with previous clinical assessments.ConclusionsOur study suggests the brain dynamics of DPD could be a potential biomarker for diagnosis and symptom analysis, which potentially leads to more personalized and effective treatment strategies for DPD patients.Trial registrationsThe trial was registered at the Chinese Clinical Trial Registry on 03/01/2021 (Registration number: ChiCTR2100041741, https://www.chictr.org.cn/showproj.html?proj=66731) before the trial.

Effect of different genetic backgrounds on rumen microbiota and serum metabolic phenotypes in beef cattle

Species with different genetic backgrounds exhibit distinct metabolic traits. Nine beef cattle were selected for the experiment to study changes in serum metabolic phenotypes, rumen microbiota diversity, and composition in beef cattle from different genetic backgrounds. Three groups were Chinese Simmental (S group), Simmental×Chinese Holstein (SH group), and Simmental × Mongolian (SM group) cattle. We used ELISA to detect serum biochemical indicators. The Short-chain fatty acids (SCFAs) in the rumen were examined, and a significant difference was observed in the acetic acid content of the three experimental groups (p < 0.01). The propionic acid content in the rumen of the S group was significantly higher than that of the SH and SM groups (p < 0.05). The A/P ratios of both the S and SM groups were significantly higher than that of the SH group (p < 0.05). We analyzed rumen microbiota composition and diversity in each group of cattle using 16 S rRNA sequencing and found that their composition was generally similar in the three groups of crossbred fattening cattle; however, the f_Bacteroidales_RF16_group and g_norank_f_Bacteroidales_RF16_group were significantly enriched in the SH group, whereas Treponema and Spirochaetia were significantly enriched in the SM group. Spirochaetia was significantly enriched in the SM group. Differences in rumen bacterial enrichment indicated that starch, protein, and cellulolytic abilities differed among the S, SH, and SM groups. The results of Spearman correlation analysis confirmed the correlation between rumen genera and serum biochemical indices. Overall, differences in rumen microflora play an important role in influencing the serum metabolic phenotype.

Efficient Method for Photovoltaic Power Generation Forecasting Based on State Space Modeling and BiTCN

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.

Bacterial and clinical metabolic signatures and their interactions in obese patients post-bariatric surgery

BackgroundObesity is a growing health concern in China, closely linked to metabolic disorders such as type 2 diabetes. Laparoscopic Sleeve Gastrectomy (LSG) is effective in promoting weight loss and improving metabolic outcomes. Emerging evidence highlights the role of gut microbiota in metabolic regulation, yet the specific alterations in gut microbiota and their association with metabolic changes post-surgery in Chinese patients remain unclear. Understanding these shifts could provide key insights into optimizing treatment strategies for metabolic improvement following bariatric surgery.MethodsStool samples and clinical data were collected from 30 obese patients before and 6 months after surgery. The composition of the gut microbiota was analyzed through 16S rRNA sequencing, and Spearman correlation analysis was used to determine the association between gut microbiota and clinical indicators.ResultsThe analysis of 30 patients showed a significant decrease in Body Mass Index (BMI) (36.75 ± 4.09 kg/m2 vs 26.37 ± 3.47 kg/m2, p < 0.0001). Glucose metabolism, including Hemoglobin A1C levels, improved significantly (6.05 ± 0.96 vs 5.05 ± 0.25, p < 0.0001), and liver function as well as serum lipid levels were also notably improved. LSG increased the richness and composition of gut microbiota in obese patients post-surgery. These changes in gut microbiota were closely associated with improved clinical metabolic parameters.ConclusionLSG not only significantly reduces body weight while also alleviating metabolic syndrome and comorbidities by altering gut microbiota.

Aggregatibacter is inversely associated with inflammatory mediators in sputa of patients with chronic airway diseases and reduces inflammation in vitro

BackgroundChronic airway disease (CAD) is characterized by chronic airway inflammation and colonization of the lungs by pro-inflammatory pathogens. However, while various other bacterial species are present in the lower airways, it is not fully understood how they influence inflammation. We aimed to identify novel anti-inflammatory species present in lower airway samples of patients with CAD.MethodsPaired sputum microbiome and inflammatory marker data of adults with CAD across three separate cohorts (Australian asthma and bronchiectasis, Scottish bronchiectasis) was analyzed using Linear discriminant analysis Effect Size (LEfSE) and Spearman correlation analysis to identify species associated with a low inflammatory profile in patients.ResultsWe identified the genus Aggregatibacter as more abundant in patients with lower levels of airway inflammatory markers in two CAD cohorts (Australian asthma and bronchiectasis). In addition, the relative abundance of Aggregatibacter was inversely correlated with sputum IL-8 (Australian bronchiectasis) and IL-1β levels (Australian asthma and bronchiectasis). Subsequent in vitro testing, using a physiologically relevant three-dimensional lung epithelial cell model, revealed that Aggregatibacter spp. (i.e. A. actinomycetemcomitans, A. aphrophilus) and their cell-free supernatant exerted anti-inflammatory activity without influencing host cell viability.ConclusionsThese findings suggest that Aggregatibacter spp. might act to reduce airway inflammation in CAD patients.

Quinoa ameliorates polycystic ovary syndrome via regulating gut microbiota through PI3K/AKT/mTOR pathway and autophagy

BackgroundPolycystic ovary syndrome (PCOS) is a unity of endocrine and metabolic disorders, associated with PI3K/AKT/mTOR, autophagy, and gut microbiota. Quinoa is a valuable food source, which contains rich minerals, unsaturated fatty acids, and has a positive modulating effect on metabolic diseases. However, its effects and potential mechanisms on PCOS have not been reported yet. Therefore, the purpose of this study is to investigate the effect of quinoa on PCOS rats by regulating PI3K/AKT/mTOR, autophagy, and gut microbiota.MethodsTen–week-old female Sprague-Dawley (SD) rats have received letrozole for 24 days for induction of PCOS and subsequently were treated with a quinoa diet for 8 weeks. Vaginal smears were used to analyze the estrous cycle of rats. Hormone and biochemical indexes were analyzed by kit assays and glucometer. The pathological changes of ovary, pancreas, duodenum and colon were observed by HE staining. PI3K, AKT, mTOR and autophagy-related proteins in the ovary and colon were measured by western blot and immunohistochemistry staining. Tight junction proteins in colon were measured by immunohistochemistry staining. 16 s rDNA sequencing was used to detect the changes of intestinal microbiota in rats. Network pharmacology and molecular docking were used to study the possible targets and mechanisms of quinoa on PCOS. Spearman correlation analysis was used to study the relationship between intestinal microbial abundance and hormone levels of PCOS rats at the phylum and genus level.ResultsQuinoa significantly improved estrous cycle and biochemical parameters of PCOS-like rats, and the pathological state of ovary, pancreas, duodenum and colon tissues. Especially, quinoa significantly regulated the expression of PI3K, AKT, mTOR and autophagy-related proteins in the ovary. Quinoa may repair the intestinal barrier by upregulating the expression of tight junction proteins in the colon, and regulate autophagy-related factors in colon. Additionally, quinoa increased the abundance of Lactobacillu, Bacteroides and Oscillospira, and decreased the Firmicutes/Bacteroidetes ratio and the Blautia, and Prevotella, reversing the dysregulation of the gut microbiota. Correlation analysis showed that there is a strong correlation between gut microbiota with significant changes in abundance and hormone related to PCOS.ConclusionOur result indicated that effect of quinoa on PCOS maybe associated with activation of the PI3K/AKT/mTOR signaling pathway, inhibition of autophagy, and regulation of intestinal flora.

FOXN3-AS1: A Candidate Prognostic Marker and Epigenetic Target with Immunotherapeutic Implications in Acute Myeloid Leukemia.

We focused on the FOXN3 gene and selected its antisense transcripts (FOXN3-AS1) to investigate its potential involvement in acute myeloid leukemia (AML). Several integrated multi-omics datasets have expanded the horizons of the cancer landscape. With the emergence of new high-throughput technologies, a large number of non-coding RNAs have been confirmed to be involved in the pathogenesis of different types of hematological malignancies. We conducted experimental validation using quantitative polymerase chain reaction (qPCR) with bone marrow specimens from AML patients. Then, Kaplan-Meier (KM) and Receiver Operating Characteristic (ROC) curves were used to substantiate the prognostic association between FOXN3-AS1 and AML patients within the TCGA database. Correlation between FOXN3-AS1 expression and gene mutation, immune, and immune function using Spearman correlation analysis. To explore the physical and functional interaction between FOXN3-AS1 and the DNMT1 protein, we utilized the RPISeq web tool from Iowa State University. Subsequently, we performed qPCR experiments to test the effect of 5AzaC (DNMT1 inhibitor) on FOXN3-AS1 expression AML cell lines (THP1 and OCI-AML3). We leveraged the "OncoPredict" R package in conjunction with the Genomics of Drug Sensitivity (GDSC) database to predict drug response in AML patients expressing FOXN3-AS1. We observed a significant upregulation of FOXN3-AS1 expression in AML patients compared to healthy controls using clinical samples. The TCGA database revealed an association between high FOXN3-AS1 expression and adverse prognosis. In our subsequent analysis, genes with poor prognostic implications in AML patients were exclusively identified in the FOXN3-AS1 high-expression group, further corroborating this relationship. AML patients with higher FOXN3-AS1 expression levels may respond less optimally to immunotherapy than patients with lower levels. Besides, we computationally predicted the interaction of FOXN3- AS1 and DNMT1 protein and experimentally confirmed that DNMT1i (GSK-3484862) affects the expression level of FOXN3-AS1. We also found that the chemotherapy drugs (5-Fluorouralic, Cisplatin, Dactolisib, Sapitinib, Temozolomide, Ulixertinib, Vinorelbine, Ruxolitinib, Osimertinib and Cisplatin) showed favorable responses in AML patients with high FOXN3-AS1 expression levels. Our candidate approach identifies FOXN3-AS1 as a prognostic indicator of survival in AML with a potential immune-related role. The preliminary observations we made on FOXN3-AS1/DNMT1 crosstalk warrant more in-depth invested immunotherapeutic approaches in AML.

Beyond the Surface: Investigating Clinical and Magnetic Resonance Imaging Dynamics in Knee Injuries: A Cross-sectional Study

Abstract Context: Orthopedic surgeons frequently encounter knee soft tissue injuries such as meniscal and ligament injuries. This study seeks to explore the diagnostic efficacy of clinical examination, magnetic resonance imaging (MRI), and arthroscopy in identifying knee injuries. Subjects and Methods: This prospective observational study enrolled 120 patients with knee injuries slated for arthroscopic surgery based on clinical and MRI assessments. The study assessed and compared the diagnostic accuracy of clinical and MRI findings. Statistical Analysis Used: Statistical analysis was performed using SPSS software (SPSS Inc., Chicago, IL, USA) for the Windows program (21.0 version). The Spearman correlation analysis was done to assess the relationship between the various parameters. The level of agreement was assessed using Cohen’s kappa statistics. Interpretation of kappa statistics is as follows: 0 to 0.20-Slight agreement; 0.1 to 0.39-Fair; 0.40 to 0.59-Moderate; 0.60 to 0.79- Substantial and 0.80 to 0.90-Almost perfect agreement. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated and observed by using 2 × 2 contingency table. P &lt; 0.05 was considered statistically significant. Results: When comparing MRI and arthroscopy findings, the anterior cruciate ligament (ACL) tear exhibited the highest sensitivity and specificity at 100.00% and 91.84%, respectively, while the medial meniscus tear had the lowest sensitivity and specificity at 80.23% and 61.76%. Regarding the comparison between clinical examination and arthroscopy, the Lachman test displayed the highest sensitivity at 95.92%, whereas McMurray’s test (Lateral Meniscus) had the lowest sensitivity at 54.55%, and the posterior Drawer’s test showed the lowest specificity at 60.00%. Lachman also demonstrated the highest diagnostic accuracy at 98.33%, while the posterior Drawer’s test had the lowest diagnostic accuracy at 70.00%. Conclusions: Despite a few limitations, clinical examination and MRI currently stand out as the most precise non-invasive methods for detecting abnormalities in the menisci and the ACL. These diagnostic approaches provide valuable insights into the structural integrity of these knee components, allowing for a comprehensive assessment without the need for invasive procedures.

Prospective cohort study on the clinical significance of interferon-γ, D-dimer, LDH, and CRP tests in children with severe mycoplasma pneumonia.

Mycoplasma pneumoniae is a significant cause of respiratory infections in children, often leading to severe pneumonia. This study aimed to assess the clinical relevance of interferon-gamma (interferon-γ), D-dimer, lactate dehydrogenase (LDH), and C-reactive protein (CRP) as biomarkers in the severity of mycoplasma pneumonia in pediatric patients. In this prospective study, 203 pediatric patients with mycoplasma pneumonia were classified into mild (123 patients) and severe (80 patients) groups. Biomarkers including interferon-γ, D-dimer, LDH, and CRP were measured and analyzed. Statistical methods employed included Pearson and Spearman correlation analyses, logistic regression, and receiver operating characteristic curve analysis. The severe group exhibited significantly higher median and interquartile ranges for interferon-γ, D-dimer, LDH, and CRP compared to the mild group. Logistic regression identified IL-10, IL-6, interferon-γ, tumor necrosis factor-alpha, D-dimer, and LDH as independent predictors of severity, with the model achieving 92% accuracy. Receiver operating characteristic curve analysis showed optimal diagnostic efficacy for interferon-γ, D-dimer, and LDH, with the best threshold values being 8.11, 0.64, and 379, respectively. A significant positive correlation was observed between IL-6 and LDH, as well as between interferon-γ and D-dimer. This study showed that interferon-γ >8.11, D-dimer >0.64, and LDH >379 have an important role in the assessment of severe mycoplasma pneumonia.

Scale-Dependent Habitat Nestedness and Its Implications for Anuran Conservation in the Chengdu Region: A Multi-Extent Analysis.

Nestedness in community ecology predicts that species in a species-poor site should be a subset of species of a species-rich site. A variety of ecological mechanisms have been offered to explain community nestedness; however, few studies have systematically discussed the issue of scale dependence when interpreting community nestedness. This study conducted surveys of anuran species data in the vicinity of Chengdu, Sichuan, in the summers of 2019-2020, using the transect method. The study area was divided into 23 sampling sites and 8 regions to explore the relationship between environmental factors and the nested distribution pattern of anuran communities under different sampling extents (with sampling buffers set at 1 km, 2 km, and 5 km). The WNODF (weighted-nestedness metric based on overlap and decreasing fill) results indicated that anurans exhibited a strong nested pattern at both the sampling sites scale and the regional scale. The habitat matrix test results suggested that a small-scale study area requires a correspondingly small habitat-sampling extent to effectively test for habitat nestedness. As the study area expands, the habitat-sampling range can be appropriately increased. The nested pattern of anurans in the vicinity of Chengdu can only be explained by habitat nestedness, as a Spearman's correlation analysis showed that other environmental factors (area size, connectivity index, concentration index, proximity index, and distance to the city center) were not significantly correlated with the nested sequences of sampling points and regions. Therefore, regarding the conservation strategies for anurans in the vicinity of Chengdu, we recommend prioritizing the protection of areas with higher habitat diversity.

LncRNA LINC00261 associates with chemoresistance and clinical prognosis in patients with epithelial ovarian cancer.

The purpose of this experiment is to explore the role of long intergenic noncoding RNA 261 (LINC00261) gene in the chemoresistance and clinical prognosis of epithelial ovarian cancer (EOC). We used matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry to detect the methylation levels of the LINC00261 promoter region in EOC patient specimens. The expression levels of LINC00261, miR-545-3p, and MT1M in EOC patients were evaluated by quantitative real-time reverse transcriptase PCR (RT-qPCR). Spearman's correlation analysis was used for relevance analyses and clinical prognosis was counted by Kaplan-Meier analysis. Stable overexpressed LINC00261 SKOV3 cells were established to test the influence of LINC00261 on proliferation, platinum sensitivity, migration, and invasion. The promoter region methylation level of the LINC00261 was hypermethylated and LINC00261 was significantly downregulated in platinum-resistant EOC tissues. The methylation level of LINC00261was significantly negative correlated with its RNA expression in EOC. Moreover, hypermethylation and lower expression of LINC00261 in EOC patients were related to shorter progression-free survival (PFS) and overall survival (OS). Furthermore, Spearman's correlation analysis showed that the expression of miR-545-3p had a negative relevance with LINC00261. According to the website prediction, MT1M might be the downstream target gene of LINC00261. Expression of MT1M was negatively correlated with miR-545-3p and positively with LINC00261 in EOC tissues. And lower MT1M mRNA expression was correlated with chemotherapy resistance and worse prognosis. In vitro, overexpression of LINC00261 could inhibit cisplatin resistance, proliferation, and suppression of migration and invasion in SKOV3 cells. This research indicates that the aberrant hypermethylation and low expression of LINC00261 were associated with platinum resistance and adverse outcomes in EOC patients.